River Pollution Research Progress [1 ed.] 9781607414186, 9781604566437

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Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved. River Pollution Research Progress, Nova Science Publishers, Incorporated, 2008. ProQuest Ebook Central,

Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved. River Pollution Research Progress, Nova Science Publishers, Incorporated, 2008. ProQuest Ebook Central,

RIVER POLLUTION RESEARCH PROGRESS

Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved.

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

River Pollution Research Progress, Nova Science Publishers, Incorporated, 2008. ProQuest Ebook Central,

Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved. River Pollution Research Progress, Nova Science Publishers, Incorporated, 2008. ProQuest Ebook Central,

RIVER POLLUTION RESEARCH PROGRESS

MATTIA N. GALLO AND

MARCO H. FERRARI Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved.

EDITORS

Nova Science Publishers, Inc. New York

River Pollution Research Progress, Nova Science Publishers, Incorporated, 2008. ProQuest Ebook Central,

Copyright © 2009 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA River pollution research progress / Mattia N. Gallo and Marco H. Ferrari, Editor. p. cm. ISBN 978-1-60741-418-6 (E-Book) 1. Water--Pollution--Environmental aspects. I. Gallo, Mattia N. II. Ferrari, Marco H. TD425.R57 2008 628.1'68091693--dc22 2008015167

Published by Nova Science Publishers, Inc.  New York

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CONTENTS

Preface Chapter 1

Chapter 2

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Chapter 3

Chapter 4

Chapter 5

Chapter 6

Chapter 7

vii Simulation of Ecosystem Degradation and Its Application for Effective Policy-Making in Regional Scale Tadanobu Nakayama Radionuclide Distribution in the Lower Yenisey and Pechora Reaches: Landscape Geochemical Signatures and Patterns of Global and Regional Contamination E. M. Korobova, N. G.Ukraintseva, V. V.Surkov, J. E. Brown, W. Standring and A. P.Borisov

1

91

Photochemical Transformation Processes of Organic Pollutants in Surface Waters Davide Vione, Claudio Minero and Valter Maurino

157

Complexity, Nonlinearity and Scaling in Sediment Transport Dynamics Bellie Sivakumar

201

Spatial and Temporal Variation of Dissolved Organic Carbon in the Sacramento and San Joaquin River Watersheds Alex T. Chow, Randy A. Dahlgren and Anthony T. O’Geen

235

Applications of an Urban Diffuse Pollution Model to Support WFD Investment Appraisal G. Mitchell, A. McDonald and M. Clarke

253

Assessing Groundwater Pollution Risk in Sarigkiol Basin, NW Greece K. S. Voudouris

265

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vi Chapter 8

Chapter 9

Chapter 10

Contents What Do People Think about Pollution? Contributions of Human Ecology to the Study of River Pollution with a Focus on Brazil R. A. M. Silvano and A. Begossi

283

Surveillance of River Water Pollution by Use of An Automatic Trace Metal System Øyvind Mikkelsen and Silje M. Skogvold

297

The Status of River Water Chemical Pollution in Zimbabwe: A Review J. Nyamangara, G. Nyamadzawo, C. Bangira and A. Senzanje

319

Chapter 11

Ambivalent Role of Microbial Communities in Polluted Rivers Alexandre José Macedo and Wolf-Rainer Abraham

Chapter 12

Assessment of Pathogen Indicator Microorganism Loading from Diffuse Sources Geonha Kim

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Index

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351 373

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PREFACE The size and importance of the world's rivers are measured in terms of discharge and length. A river's importance may also be measured in terms of local and regiional water availability and population. A smalll river flowing through a densely populated, arid region may be extremely important, for example. Rivers flow downhill from their sources to their mouths at the sea. Although the world's major rivers flow through many different types of terrain, they provide similar physical and biological functins. Rivers transport sediments from their basins to the sea through the processes of erosion, transport, and deposition. In a river system's upland areas, erosion is the dominant process. Tectonic processes result in the uplift and formation of major mountain chains, while the world's major river systems help erode those mountains. A river's flow is halted when it reaches the sea, where the river deposits its sediemtns and creates a delta. River deltas commonly assume a triangular pattern that resembles the Greek letter delta (Ä) a letter based on the shape of the Nile River delta in northern Egypt. Fresh water and salt water meet in these deltas, which are some of the world's most biologically productive areas. The world's major river systems are storehouses of biological productivity and diversity. Rivers and their floodplains provide habitat for aquatic and terrestrial species. Many of the world's large rivers experience an annual flooding cycle that is important for spreading water, nutients, and sediment into floodplains as well as providing reproductive cues for fish. This new book presents up-to-date research on this important field. Chapter 1 - Human activity has dramatically changed ecosystem dynamics in East Asian catchments. In particular, the change from rural or wilderness to urban or agricultural uses has greatly affected the ecosystems downstream of the catchments. To facilitate sustainable development, it is necessary to quantify the mechanisms of ecosystem change. Numerical models are very powerful tools in this process. The author has developed the new processbased catchment model, called the NIES Integrated Catchment-based Eco-hydrology (NICE) model (Nakayama, 2007a, 2008a, 2008b, 2008c; Nakayama and Watanabe, 2004, 2006, 2008a, 2008b; Nakayama et al., 2006, 2007), to evaluate ecosystem dynamics in the catchments of East Asia. East Asia encompasses various ecosystems and is undergoing rapid economic development. One example of this change is the Kushiro Mire, the largest mire in Japan, which has been changed by conversion to urban or agricultural uses since 1884. The rivers flowing through the mire have been altered by water works, especially channelization of meandering rivers, which was introduced in the northern part of the mire to smoothly drain runoff and

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Mattia N. Gallo and Marco H. Ferrari

protect farmlands from flooding. Afterwards, runoff containing nutrients from farmland and sediments from short-cut channels has flowed directly into the mire. Sediment deposition in the flood-borne area has changed its topography, lowering the groundwater level and causing some of the soil to dry out. Consequently, alder (Alnus japonica (Thunb.) Steud.), which requires drier conditions and more nutrients than previously existed in the mire, has propagated widely around the mire and has contributed to the mire’s gradual shrinkage, illustrating the effect of human activities on the water/heat/mass cycles in the mire. The NICE model, which simulates the water/heat budget, the mass transport, and vegetation succession processes iteratively, was able to reproduce this drying phenomenon in the mire. The simulation reproduced well the spatial distribution of elevation aggradations by the sediment deposits from the channelized rivers into the mire. Furthermore, the simulation of channelized rivers showed that the recharge rate of the mire decreases greatly. This indicates that channelization also causes an increase of sedimentation/nutrient load and flooding in the downstream area around the mire. The NICE reproduced excellently the invasion of alder in the mire over the last 30 years, which represents a dramatic advance in the understanding of the drying phenomenon associated with alder invasion. The reproducibility of these simulation results suggests that the NICE includes some of the important factors affecting vegetation succession in the mire. The author hopes this publication will help to facilitate further research on the sustainable development of human society in the catchment and, especially, to clarify various kinds of interactions among human activities and water/heat/mass cycles and their effects on environmental degradation. Chapter 2 - A study of landscape transects characterizing the terrace and floodplain landscape at different distances from the river mouth was performed in the lower Yenisey reaches. The study involved the determination of 137Cs in soil, dominating plant species and phytomass to reveal spatial peculiarities of the involvement of technogenic elements in natural processes in areas with low level contamination. This enabled a comparative estimate to be made of the considerable secondary redistribution of 137Сs in soils, sediments and plants. Furthermore the locations of soils and plant species indicative of contamination could be identified. Vertical 137Cs profiles in terrace and floodplain soils were used to identify different sources of contamination and particular contamination periods. A similar approach was applied to study global 137Cs contamination patterns in the lower Pechora area. A comparison of results confirmed a considerable difference in the process of secondary redistribution of 137Cs contamination within natural landscapes. This landscape geochemical approach proved to be a suitable way of undertaking a rapid preliminary evaluation of the contaminant distribution in river basins. This approach may also prove to be important for spatial analysis of the contamination field and for selection of appropriate sites for ecological estimates and monitoring in river basins. Chapter 3 - Photochemical processes are important pathways for the transformation of biologically refractory organic compounds, including harmful pollutants, in surface waters. They include the direct photolysis of sunlight-absorbing molecules, the transformation photosensitised by the triplet states of dissolved organic matter (DOM), and the reaction with photochemically generated radical transients. Differently from the direct photolysis, the other processes can also induce the phototransformation of compounds that do not absorb sunlight. The excited triplet states of DOM, 3DOM*, play a very important role in the phototransformation processes in surface waters. The most important reactive radical species

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in surface waters are the hydroxyl radical •OH, the carbonate radical CO3−•, and various peroxy radicals that can be produced upon degradation of DOM. Additionally, radical species such as •NO2, Cl2−• and Br2−• can be involved in the generation of harmful degradation intermediates such as aromatic nitro-, chloro-, and bromoderivatives. The main photochemical sources of the hydroxyl radical are nitrate, nitrite, and DOM. The hydroxyl radicals are very reactive and undergo scavenging by many dissolved compounds, including DOM, which limits their ability to degrade other molecules. However, reaction with •OH could be a significant sink for compounds that are particularly refractory toward other transformation processes. The carbonate radical can be produced by reaction between •OH and the carbonate and bicarbonate anions. It is less reactive than •OH and would undergo lesser depletion by natural sinks such as DOM. Accordingly, CO3−• would reach higher steady-state concentration than •OH in surface waters and could be a significant sink for electron-rich aromatics and sulphur-containing molecules that react quickly with it. Nitrogen dioxide is mainly produced by nitrate photolysis and nitrite photooxidation and is involved in the nitration of aromatic molecules, including phenol derivatives, yielding toxic nitrophenols. The radical Cl2−• is involved in oxidation and chlorination processes. It can be formed upon chloride oxidation by •OH in acidic solution and chloride photooxidation by Fe(III) oxide colloids at neutral to basic pH. The radical Br2−•, which would mostly be produced upon oxidation of bromide by •OH, is mainly a brominating agent. The radicals Cl2−• and Br2−• could produce harmful and environmentally persistent halogenated compounds in brackish waters, which can for instance be found in estuarine areas. Chapter 4 - Adequate knowledge of the dynamic characteristics of sediment transport in rivers (and channels) is important for studies of river morphology, reservoir sedimentation, soil and water conservation planning, water quality modeling, and design of erosion control structures. Although numerous variables contribute to the occurrence and movement of sediment (e.g. water flow, land use, suspended sediment concentration, particle size and shape), their levels of influence are often significantly different. Reliable determination of the dominant variables is, therefore, necessary for modeling and prediction purposes, especially from the viewpoints of required model complexity and data collection. This chapter investigates the utility of a nonlinear dynamic approach for pattern recognition in suspended sediment load transport, with particular emphasis on the ‘extent of complexity’ of the underlying dynamics. The approach involves two steps: (1) representation of the multidimensional complex dynamic system through reconstruction of the available single- (or multi-)variable data series; and (2) determination of the extent of complexity of the system (defined especially in the context of variability of relevant data) using trajectories of its evolution. The effectiveness of the approach is tested through its application to the suspended sediment load and related data observed in the Mississippi River basin at St. Louis, Missouri. The effects of data aggregation (in time scale) on the degree of complexity of the suspended sediment dynamics are also studied. Chapter 5 - The Sacramento and San Joaquin Rivers are the two major rivers supplying drinking water for over 23 million people in California, USA. The combined drainage network of these rivers covers an area of approximately 90,000 km2 with diverse physographic settings including forest, agriculture, urban, grassland, wetland, etc, representing different sources of nutrients and contaminants. The Mediterranean climate in California is characterized by dry summers and cool wet winters. Both climate and land-use

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play an important role in the dissolved organic carbon (DOC) levels in the Sacramento-San Joaquin Watersheds. DOC is of particular interest because it is a precursor of trihalomethanes (THMs), which are carcinogenic and mutagenic disinfection byproducts formed during chlorination in drinking water treatment. Water samples were collected every two weeks for four years at up to 35 locations along the Sacramento and San Joaquin Rivers and selected tributaries and agricultural drains, were analyzed for DOC concentration, ultra-violet absorbance at 254 nm, and THM formation potential, and were characterized into hydrophobic, transphilic, and hydrophilic fractions by XAD-8 and XAD-4 resins. Historical data from California Department of Water Resources reports were also used for comparison. Results showed that DOC concentration was significantly higher at sampling sites that were dominated by agriculture and wetlands. A significantly lower annual average DOC concentration was recorded in the Sacramento River compared to the San Joaquin River (1.9 vs 3.6 mg L-1). Due to greater river discharge in the Sacramento River, it carried a greater annual DOC load (39,000 Mg vs 9,000 Mg). Discharge volume was the controlling factor on DOC loads in both rivers. Regardless of the differences in types of land use/land cover, DOC across the two river basins has similar aromaticity and was dominated by the hydrophobic acid fraction. However, waters from the San Joaquin River (136 g-THMs mg-C) have a higher reactivity in forming THMs than that of the Sacramento River (97 g-THMs mg-C) and central Delta (75 g-THMs mg-C) because of a higher concentration of bromide. No relationship was found between aromaticity of DOC and land use/land cover at the watershed scale. Chapter 6 - The Water Framework Directive (WFD) requires member states to design a programme of measures (PoM) to manage contaminant fluxes so as to achieve good ecological status. A cost effective PoM requires good understanding of pollutant apportionment between source types (rural v urban; point v diffuse), and for diffuse sources, a characterisation of the location of significant diffuse loadings within the source area. Identification of a cost effective PoM for a catchment is currently constrained by the ability to characterise the urban diffuse component. This paper addresses this need, outlining a flexible, cost effective urban screening model that supports WFD requirements via load estimation, ‘hot spot’ mapping, land use change analysis and source apportionment. Integration with sewer network data offers the potential to better apportion the urban diffuse component. This is clearly within the interests of major water companies who will, in the absence of such information, be likely to acquire default responsibility and consequential liability. Chapter 7 - The alluvial aquifer of the Sarigkiol basin extends NW of Greece, covering an area of 60 km2. In a large part of the area irrigated agriculture is practiced. The aquifer is the main source of water supply in the area and is showing signs of contamination due to the existence of pollution sources. Regional assessment of groundwater pollution risk is an useful tool for groundwater resource management and protection. Risk assessment is defined as a combination of hazard and vulnerability. The DRASTIC method was applied to evaluate groundwater vulnerability, including the following seven parameters: Depth, Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone and hydraulic Conductivity. Determination of the DRASTIC index involves multiplying each parameter weight by its site rating and summing the total. Based on DRASTIC index values a groundwater vulnerability map was illustrated, using a Geographical Information System (GIS). The higher index values represent greater potential for groundwater pollution, or greater aquifer vulnerability. In order to evaluate the degree of hazard the land use map was

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used. A groundwater pollution risk map was created by overlaying the vulnerability map with the hazard map. The results provide important information and the pollution risk map could be used from local authorities and decision makers for groundwater resources management and protection zoning. Chapter 8 - River pollution has been reducing water quality for human consumption and affecting ecological integrity and biodiversity. Notwithstanding the biological focus of many studies addressing river pollution, it also has a relevant social dimension: pollution is caused by people and affects people in turn. The research area of human ecology studies the relationships between people and their environment. The main purpose of this chapter is to provide a brief review of studies on human ecology in Brazil, addressing three major approaches involving river pollution. First, studies of fish consumption by local riverine fishers reveal not only those preferred fish, which people regularly eat, but also the food taboos involving fish, or rules that lead people to avoid or to limit the consumption of certain types of fish A broad survey on fish food taboos among riverine fishers in the Brazilian Amazon shows that people tend to avoid the large piscivorous fish, which are top predators more prone to accumulate toxins. According to an independent study on mercury content in fish from an Amazonian river, some of the tabooed fish are also those showing high mercury content. On the other hand, in an urban river located in southeastern Brazil, people avoid eating bottom-dwelling fish due to increased seasonal levels of organic pollution, which is more noticeable. However, these people do not seem to perceive the danger of mercury pollution and its effect on fish. Such studies can provide indirect insights about water quality and the patterns of human consumption of contaminated fish. Second, some studies address the perception that people have about the ecosystem’s integrity, comparing such perception to the literature or biological surveys. One such study shows that local farmers in southeastern Brazil overestimate the water quality and ecological integrity of streams located inside their properties, due to patterns of water use and to the financial opportunity of allowing reforestation on their land. Third, local fishers usually show a detailed knowledge about the behavior and ecology of the exploited fish. Such local knowledge may be a first-hand and invaluable source of information to deal with the biological pollution, or the invasive exotic fish (and other aquatic organisms), which can quickly colonize an aquatic habitat, often with drastic and unknown consequences to the local biological communities. These and other studies including those involning people, can potentially improve our knowledge of river pollution. Chapter 9 - Recent developments and results in the field of remote monitoring of trace metals in polluted river water are reviewed in combination with previous reported research in this field. Some years ago an automatic trace metal system (ATMS) using different modes of voltammetry in combination with innovative solid alloy electrodes was invented. The ATMS device is an integrated system specifically designed for early warning detection and continuously monitoring of rivers, lakes and water resources. By using different solid alloy electrodes like dental amalgam electrode (DAM), silver-bismuth electrodes and gold-bismuth electrodes the system can be used to measure low concentrations of several metals with longtime stability and low maintenance. This system used in combination with other well known methods like ICP-MS, makes a better and more complete analytical procedure for environmental water monitoring by reporting both the labile fraction and the total amount of metals. Concerning the mobility of trace metals, various physical and chemical changes occur, such as dissolution, precipitation, binding to and release from suspended particulate

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matter and removal by sedimentation. These changes have consequences on the interaction of trace metals with aquatic biota. Toxicity and uptake of trace metals by living organisms strongly depend on their availability and therefore their speciation. In view of this, it is important to develop analytical procedures which both reports labile fraction and total amount of metals in the water. By also including results obtained by use of diffusive gradient in thin films (DGT) and comparing it to results found by voltammetry and ICP-MS, it is possible to achieve important information about the composition of the complexes in the matrix. These three methods complement each other and render the ability to get information about the charge of the complexes, and thus important information about the mobility of the complexes. The present paper reviews briefly the ATMS system and includes recent results from continuous measurements of iron, zinc and copper in some selected river systems, and demonstrates how such results obtained from ATMS, DGT, and manual samples analysed by ICP-MS gives valuable and unique new information about trace metal speciation in river courses. Chapter 10 - River water quality has been impacted on severely due to number of factors among them urbanization, industrial activity, agricultural activities, and mining activities in Zimbabwe, and most developing countries. Obsolete infrastructure and lack of enforcement of regulations on industrial and municipal waste disposal has resulted in heavy pollution of sources of potable water for the cities of Harare, Bulawayo, Marondera and Chitungwiza in Zimbabwe. These cities lie within their own catchments and effluent is transported through a network of rivers to dams where potable water is drawn. Runoff from mining operation operations, have also played significant roles on river water pollution. With an increase in mining activities (often uncontrolled), competing sectors for the limited fiscus and increased urbanization, environmental issues will continue to be neglected by most developing countries in sub Saharan Africa. The effects of quality water pollution pose serious concerns on human and ecological health, and environmental quality. This review assesses the current state of river water pollution studies in Zimbabwe, the challenges, possible solutions and future opportunities of river water pollution research. Chapter 11 - Because of their huge metabolic potentials microorganisms have an important role in cleaning pollutions in rivers. From river sediments a number of strains have been isolated and characterized which can grow on highly recalcitrant compounds, e. g. dioxins, polychlorinated biphenyls or hexachloro-cyclohexanes. Furthermore, in the environment microorganisms act not as single entities but as consortia with complex carbon sharing which enables them to metabolize recalcitrant compounds not used by single microorganisms and to detoxify toxic intermediates. Therefore, in recent years the focus from single highly potent bacterial isolates for in-situ bioremediations turned to microbial communities. Especially microbial communities glued by polymers to surfaces, so called biofilms became a focus of interest because of their high resistances against environmental stress factors. Understanding these microbial community interactions is a prerequisite to use intrinsic bioremediations to control river pollution. On the other hand microbes are also known to bear pathogenicity factors which allow them to infect humans. Poor treatments of waste water introduce pathogenic bacteria into the river were they survive for a while. How long these strains survive in the rivers and what the niches for longer survival are is poorly known but essential to control water-borne diseases. Bacteria and other microorganisms that cause infections are remarkably resilient and can develop ways to survive drugs meant to kill or weaken them. The problem becomes more urgent because due to gene and plasmid

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transfers resistance genes against antibiotics can spread in the environment. This resistance is due largely to the growing use of antibiotics and becoming an increasing health problem probably caused by widespread applications of antibiotics in medicine and agriculture. This is also reflected by distinct antibiotic resistances of isolates from polluted rivers of different continents. Antibiotic resistance severely limit the control of infections in the clinic and more and more strains are not susceptible against any of the known antibiotics. To optimize microbial communities in rivers for maximal intrinsic bioremediations and minimal health risks seems to be a big future challenge. Chapter 12 - The pathogens originating from diffuse pollution have raised much concern recently. In many countries, pathogen levels are monitored in surface water by measuring the pathogen indicator organism level, which indicates the concentration of pathogen associated microorganisms to determine contamination. Among indicator organisms, total coliform (TC), and Escherichia coli were selected to be monitored. Monitoring stations include five sampling stations in the Geum River, three small watersheds used for forestry, agricultural land, and urban area were selected in the Geum River basin, Republic of Korea. The coliform concentration of the combined sewer overflow was the highest, followed by the runoff from agricultural land use, and the runoff from forestry land use. By monitoring coliform concentrations of overlying water and sediment at five monitoring points in the downstream of the Geum River, average concentration from spring to summer was higher than those values from fall to spring. Coliform concentrations in the pore water were higher compared to those of overlying water and closely related with flow rate of the river. The load duration curve methodology was developed as a useful protocol to estimate the pollutant loading to a river in exceedance of the probability scale. The technique was applied to estimate the total coliform loading to Gongju, where a monitoring location in the Geum River. A procedure to construct flow and load duration curves for the TC loading for a monitoring station is presented. A standard duration curve reflecting the water quality criteria was constructed to determine water quality compliance. A comparison of the load duration curve with the standard duration curve for Gongju revealed that the water quality did not meet the desired water quality for about 47% of the exceedance probability. From the analysis of the TC load duration curve and standard load duration curve, it could be inferred that diffuse pollution was mainly responsible for the water quality degradation at Gongju.

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In: River Pollution Research Progress Editors: Mattia N. Gallo and Marco H. Ferrari

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Chapter 1

SIMULATION OF ECOSYSTEM DEGRADATION AND ITS APPLICATION FOR EFFECTIVE POLICY-MAKING IN REGIONAL SCALE Tadanobu Nakayama∗ National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan

ABSTRACT

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Human activity has dramatically changed ecosystem dynamics in East Asian catchments. In particular, the change from rural or wilderness to urban or agricultural uses has greatly affected the ecosystems downstream of the catchments. To facilitate sustainable development, it is necessary to quantify the mechanisms of ecosystem change. Numerical models are very powerful tools in this process. The author has developed the new process-based catchment model, called the NIES Integrated Catchment-based Eco-hydrology (NICE) model (Nakayama, 2007a, 2008a, 2008b, 2008c; Nakayama and Watanabe, 2004, 2006, 2008a, 2008b; Nakayama et al., 2006, 2007), to evaluate ecosystem dynamics in the catchments of East Asia. East Asia encompasses various ecosystems and is undergoing rapid economic development. One example of this change is the Kushiro Mire, the largest mire in Japan, which has been changed by conversion to urban or agricultural uses since 1884. The rivers flowing through the mire have been altered by water works, especially channelization of meandering rivers, which was introduced in the northern part of the mire to smoothly drain runoff and protect farmlands from flooding. Afterwards, runoff containing nutrients from farmland and sediments from short-cut channels has flowed directly into the mire. Sediment deposition in the flood-borne area has changed its topography, lowering the groundwater level and causing some of the soil to dry out. Consequently, alder (Alnus japonica (Thunb.) Steud.), which requires drier conditions and more nutrients than previously existed in the mire, has propagated widely around the mire and has

∗

Tel.: +81-29-850-2564; fax: +81-29-850-2584. E-mail address: [email protected] (T. Nakayama)

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Tadanobu Nakayama contributed to the mire’s gradual shrinkage, illustrating the effect of human activities on the water/heat/mass cycles in the mire. The NICE model, which simulates the water/heat budget, the mass transport, and vegetation succession processes iteratively, was able to reproduce this drying phenomenon in the mire. The simulation reproduced well the spatial distribution of elevation aggradations by the sediment deposits from the channelized rivers into the mire. Furthermore, the simulation of channelized rivers showed that the recharge rate of the mire decreases greatly. This indicates that channelization also causes an increase of sedimentation/nutrient load and flooding in the downstream area around the mire. The NICE reproduced excellently the invasion of alder in the mire over the last 30 years, which represents a dramatic advance in the understanding of the drying phenomenon associated with alder invasion. The reproducibility of these simulation results suggests that the NICE includes some of the important factors affecting vegetation succession in the mire. The author hopes this publication will help to facilitate further research on the sustainable development of human society in the catchment and, especially, to clarify various kinds of interactions among human activities and water/heat/mass cycles and their effects on environmental degradation.

1. INTRODUCTION

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1.1. Background Human activity has dramatically changed ecosystem dynamics in East Asian catchments. In particular, the change from rural or wilderness to urban or agricultural uses has greatly affected the ecosystems downstream of the catchments. To facilitate sustainable development, it is necessary to quantify the mechanisms of ecosystem change. Numerical models are very powerful tools in this process. One example of this change is the Kushiro Mire (the largest mire in Japan), the largest mire in Japan (Figure 1), has been changed by conversion to urban or agricultural uses since 1884 (Figure 2 and Figure 3). The rivers flowing through the mire have been altered by water works, especially channelization of meandering rivers, which was introduced in the 1980s in the northern part of the mire to smoothly drain runoff and protect farmlands from flooding. Owing to channelization, runoff containing nutrients from farmland and sediments from short-cut channels flows directly into the mire and deposits flood-borne sediment. Sediment deposition in the mire has changed its topography, lowering the groundwater level and causing some of the soil to dry out. Consequently, alder (Alnus japonica (Thunb.) Steud.), a deciduous tree approximately 15 m tall, has invaded the mire in the lower Kucyoro River and has increased its distribution. Alders, which require drier conditions and more nutrients than previously existed in the Kushiro Mire, have propagated widely there since channelization, mainly because of the lowering of the groundwater level and the increased nutrient input, resulting in the gradual shrinking of the mire. This change illustrates the effect of human activities on the water cycle in the Kushiro Mire and thus on vegetative succession. The water dynamics in the Kushiro River catchment, including the Kushiro Mire, are changing spatially. Deep groundwater levels in forested areas and shallow groundwater levels in the mire, for example, change because of the sudden topographic fluctuations over short distances and seasonal variations in vegetation. When a hydrological model is applied to this area, it should take into account surface runoff, unsaturated–

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Simulation of Ecosystem Degradation and Its Application…

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saturated-water processes, and land-surface processes assimilated with satellite data to describe temporal variations in vegetative growth and phenology (Nakayama, 2007a, 2008a, 2008b, 2008c; Nakayama and Watanabe, 2004, 2006, 2008a, 2008b; Nakayama et al., 2006, 2007).

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Figure 1. Land cover and observation points of study area in this research (Kushiro River catchment). Three meteorological stations were established in vegetation typical of the Kushiro River catchment (mire: 43°06′05″N, 144°20′29″E, mean elevation 8 m; grassland: 43°31′08″N, 144°28′10″E, mean elevation 187 m; forest: 43°20′18″N, 144°38′55″E, mean elevation 127 m).

a

b

Figure 2. Study area for drying phenomena shown in Figure 1 in the downstream of Kucyoro River, a tributary of Kushiro River, (a) Topography before channelization in 1965 and after channelization in 1995, (b) Temporal change in the distribution of Alnus japonica before channelization in 1977 and after channelization in 1993. In (b), arrow shows the downstream of Kucyoro River into the Kushiro Mire, and red area shows the invasion of Alnus japonica.

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Figure 3. Land cover and observation points of study area in the Kucyoro River catchment, a tributary of Kushiro River catchment (Figure 1), at (a) 1976, and (b) 1997 (Digital National Land Information GIS Data of Japan). Dark-blue line is Kucyoro River, and light-blue is Kushiro main river and other tributary of Kushiro River catchment. Black line is border of study area for vegetation succession simulation at the downstream of Kucyoro River catchment. In (a), river has been channelized at the downstream of Kucyoro River.

The author combines the grid-based numerical model, ground-truth observation data, and satellite data such as MODIS (Figure 4) to study the ecosystem dynamics in the catchments. This research area is representative of areas in which the environment has been negatively affected by human activity. In Japan, regulations promoting natural restoration took effect on 1 January 2005, including various measures to promote symbiotic relationships between humans and ecosystems, including wildlife. The development of a process-based model and the simulation of ecosystem dynamics in these catchments are therefore very important and effective for quantitative evaluation of the catchments. This publication puts together the previous researches about the Kushiro Mire the author has done so far (Nakayama, 2007a, 2008a, 2008b; Nakayama and Watanabe, 2004, 2006). The author hopes that this publication will help to facilitate further research on the sustainable development of human society in the catchment and, especially, to clarify various kinds of interactions among human activities and water/heat/mass cycles and their effects on environmental degradation.

1.2. Modeling Approach in the Previous Researches Process-based distributed models can satisfy these requirements. Many process-based models are available, such as TOPMODEL (TOPography-based hydrological MODEL) (Beven and Kirkby, 1979), SHE (Système Hydrologique Européen) (Abbott et al., 1986),

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MIKE-SHE (Refsgaard and Storm, 1995), TOPOG (O’Loughlin, 1986), IHDM (Institute of Hydrology Distributed Model) (Calver and Wood, 1989), MMS/PRMS (Modular Modeling System / Precipitation Runoff Modeling System) (Leavesly et al., 1996), TOPLATS (TOPMODEL-based Land–Atmosphere Transfer Scheme) (Famiglietti and Wood, 1994), SWATMOD (SWAT + MODFLOW) (Sophocleous et al., 1999), HMS-MM5 (Hydrologic Model System and Pennsylvania State National Center for Atmospheric Research Mesoscale Meteorological Model) (Yu et al., 2001), and WEC-C (Water and Environmental Consultants Catchment) model (Croton and Barry, 2001). Only SHE, SWATMOD, and WEC-C combine surface runoff with groundwater flow. SHE utilizes the major hydrological processes of water movement to describe overland and channel flow, unsaturated and saturated subsurface flow, canopy interception, evapotranspiration, and snowmelt. However, in this model the modeled land-surface process of vegetation phenology is treated as constant, and the unsaturated– saturated water processes are quasi-lumped as a constant. SWATMOD, consisting of SWAT (Soil and Water Assessment Tool) (Arnold et al., 1993) and MODFLOW (three-dimensional groundwater flow model) (Sophocleous et al., 1999), is a semi-conceptual model, which includes unsaturated–saturated water processes. WEC-C is a distributed, deterministic, catchment-scale model of water flow and solute transport with a rectangular grid of uniform cell size in the lateral plane combined with a system of soil layers in the vertical direction. However, the land-surface process is treated as constant and does not reflect seasonal changes. Therefore, it is necessary to develop process-based distributed models including these two critical requirements, which are necessary to clarify the relationship between the water cycle and various indices, such as soil moisture, groundwater level, surface temperature, and evapotranspiration, caused by the spatial variability in vegetation type, soil texture, and topography. Furthermore, vegetation phenology and water cycle are closely related. There are only a few studies which have simulated both soil moisture and groundwater levels in a larger catchment and assimilated them with, for example, MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data from storm events and over an annual time scale. A very powerful tool for simulating precise land-surface processes and for predicting a time series of root zone soil moisture content is the assimilation of remote sensing measurements of surface soil moisture into land surface models, for example, SVAT (Soil Vegetation Atmosphere Transfer) or a simple biosphere model (Ragab, 1995; Li and Islam, 1999; Wigneron, et al., 1999; Montaldo, et al., 2001). However, the studies do not consistently use higher-order products such as fraction of photosynthetically active radiation (FPAR) or leaf area index (LAI), important parameters for evaluating vegetation growth and phenology (Christopher et al., 1998). Previous studies of the frost/thaw process have firstly focused on the mechanism of granular structure formation through freezing and thawing of soil material by the observed fabrics of cross section view of core (Pawluk, 1988), the relationship between the infiltration, hydraulic conductivity, the snowmelt water equivalent, and the premelt soil moisture (Chamberlain and Gow, 1979; Benoit et al., 1988; Hayhoe et al., 1993), and the heat and water dynamics of the soil column by using one-dimensional model simulation such as SOIL (Lundin, 1990; Johnsson and Jansson, 1991), SHAW (Simultaneous Heat And Water) (Flerchinger and Saxton, 1989a,b), and SVAT (Soil–Vegetation–Atmosphere Transfer) (Stahli and Jansson, 1998). However, these studies are applied to the point scale and not applied to the catchment scale in the snowmelt period due to the difficulty of setting

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measurement systems, the complexities and variety of gathering data, and deviations from the original objectives of collecting the meteorological data.

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Figure 4. Combination of grid-based model, observation, and MODIS satellite data.

Some researches by using remote sensing data such as NOAA-AVHRR (Advanced Very High Resolution Radiometer), RADARSAT, ERS-SAR (European Remote Sensing Satellite – Synthetic Aperture Radar), and Landsat TM data (Swamy and Brivio, 1997; Mitchell and DeWalle, 1998; Schaper et al., 1999; Nagler et al., 2000) and by using point measurements (Mamiya and Chiba, 1985; Sharratt et al., 1999) show that the effect of micro-topography on snow depth and frost/thaw processes needs to be included to the physically based model in order to evaluate and forecast both qualitatively and quantitatively the spring snowmelt runoff, although some researches show the effect of localities of snow and frost depths on the runoff discharge disappears at the downstream of catchment (Flerchinger and Saxton, 1989a, b; Lundin, 1990; Johnsson and Jansson, 1991; Hayhoe et al., 1993; Semadeni-Davies, 1997; Kennedy and Sharratt, 1998; Stahli and Jansson, 1998; Boggild et al., 1999; Shanley and Chalmers, 1999; Stahli et al., 2001). Many models of snowmelt runoff have been developed around the world: SSARR (Streamflow Synthesis and Reservoir Regulation) (USACE, 1991), HEC (Hydrologic Engineering Center) (Hydrologic Engineering Center, 1997), NWSRFS (National Weather Service River Forecast System) (Anderson, 1968, 1973), PRMS (Precipitation Runoff Modelling System) (Leavesley et al., 1983), SRM (Snowmelt Runoff Model) (Martinec and Rango, 1986), and GAWSER (Guelph All-Weather Storm-Event Runoff) (Ghate and Whitely, 1977). These semi-physical surface runoff models can predict generally well the spring peaks and recessions, but cannot evaluate quantitatively both the snow and frozen effects on spring runoff because of the dependence on various empirical relations (Semadeni-Davies, 1997). Some recent researches used the physically based model including the effects of topography on discharge generation both in large and small scales, for

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example, SAC (Burnash et al., 1973), Mosaic (Koster and Suarez, 1996), Noah (Ek et al., 2003), RHESSys (Regional Hydro-Ecologic Simulation System) (Hartman et al., 1999), and VIC (Variable Infiltration Capacity) (Liang et al., 1994; Cherkauer and Lettenmaier, 1999). Some researches show the predominance of snow effect (Shanley and Chalmers, 1999) and the importance of frozen effect (Stahli et al., 2001) in runoff response. These models simulate and evaluate the land surface and runoff processes, and the subsurface flow is only simulated by the water budget and there is no verification about the groundwater flow. By the way, the spring snowmelt runoff is closely related to the groundwater level change in addition to snow depth, soil temperature, and soil moisture (Daniel and Staricka, 2000; Nyberg et al., 2001), which is considered important around the Kushiro Mire of this study. It is necessary to develop the process-based hydrology model, which includes snowmelt runoff process and local vegetation-surface-unsaturated–saturated water/heat process. When reaches are shortened and slopes are steepened at the meander cut-offs, increased sedimentation and flooding occur downstream (Talbot and Lapointe, 2002), causing drying of the Kushiro Mire (Nakamura et al., 1997; Nakayama and Watanabe, 2004). In order to arrest sediment-load influx and allow recovery of the Kushiro Mire, the Japanese government started a new project in 2002, the Kushiro Mire Convervation Plan, to re-meander the channelized rivers in the catchment (Ministry of Environment, 2002), which is a kind of river restoration and the assessment of ecological integrity (Jungwirth et al., 2002). Therefore, it is very important to assess the characteristic of sediment load into the mire. In order to better understand the behavior of sediment transport and accumulation in the mire, it is essential to study sediment flux, particularly during the flood seasons of both the snowmelt and typhoon periods in the study area. The sediment-rating curve, an effective method to express the close correlation between discharge and sediment load, or sediment concentration (Sc), is also effective to evaluate the suspended sediment concentration and flux, and sediment erosion rate (Asselman, 2000; Syvitski et al., 2000; Fuller et al., 2003; Horowitz, 2003) in addition to the numerical model. The vegetation succession from the reed to the alder is most predominant in the mire, which is closely related to sediment aggradations and nutrient loading, and vice versa. It is very important to estimate the primary factor on the vegetation change in the mire. There are some previous researches about the environmental factors and primary succession of alder, mainly through field observations, field experiments, sampling and analysis; (i) effect of environmental factors (soil texture, soil depth, litterfall, accumulation rate, root density, organic content, moisture, and nutrients) on primary succession (Chapin et al., 1994); (ii) relative growth rates under different relative addition rates and nitrogen uptake/fixation in seedlings by the experiment (Burgess and Peterson, 1986); (iii) biomass increase about 1.2/1.7 times in the presence of increased phosphorus/alkalinity (Ministry of Environment, 2004); (iv) growth condition in submerged water level, water level variations, pH, EC (electrical conductivity), and DO (dissolved oxygen) by using Principal Component Analysis (PCA) (Yabe and Onimaru, 1997; Hotes et al., 2001); (v) oxygen uptake and nitrogen fixing based on measurements both in the chamber and in the field (Hendrickson et al., 1990; Grosse et al. 1993); (vi) environmental factors related to reed and mire vegetation (Glaser et al., 1990; Wassen et al., 1990, 1995; Yabe and Onimaru, 1997), et al. Because these previous studies were effective for the classification of characteristics of alder and mire vegetation, it is powerful to combine these results with a numerical simulation in order to reproduce the drying phenomenon of the mire, to evaluate the relationships among water,

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heat, nutrient, sediment and vegetation, and to forecast the influence of river channelization/meandering and land-cover change on vegetation change downstream in the mire.

1.3. Research Objective

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The objective of the current research is to simulate the water cycle change and drying phenomena in the Kushiro Mire due to the effects of vegetation changes (Figure 1 and Figure 3). The author has developed the process-based NICE (NIES Integrated Catchment-based Eco-hydrology) model (Nakayama, 2007a, 2008a, 2008b, 2008c; Nakayama and Watanabe, 2004, 2006, 2008a, 2008b; Nakayama et al., 2006, 2007), which includes surfaceunsaturated–saturated water processes and assimilates land-surface processes describing the variation in phenology with MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data (Figure 5). The NICE was extended to include the slope and shading characteristics of microtopography and the phase change transitions in soil moisture (NICESNOW; Nakayama and Watanabe, 2006). The author assessed the quantitative goodness-offit and parameter sensitivity in relation to changes in soil structure, soil temperature, soil moisture, groundwater level, and river discharge. The author evaluated the effect of the snow layer and the frost/thaw soil layer on spring snowmelt runoff with much greater time-to-peak and slower decrease of discharge, and carried out a long-term (annual) simulation not only at the snow-free periods but also at snowmelt periods in the upstream regions of shrinking Kushiro Mire in the invasion of alder (Figure 2 and Figure 3).

Figure 5. Description of NIES Integrated Catchment-based Eco-hydrology (NICE) model (Nakayama, 2007a, 2008a, 2008b, 2008c; Nakayama and Watanabe, 2004, 2006, 2008a, 2008b; Nakayama et al., 2006, 2007).

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Furthermore, the author quantified the influence of hydrologic and geomorphic changes on shrinking mire. Firstly, the characteristic of suspended sediment load in the snowmelt period was compared with that in the snow-free period. The NICE was expanded to include the mass transport process in the Kushiro River catchment during the entire year (NICEMASS; Nakayama, 2007a). The NICE-MASS evaluated and clarified the influence of vegetation distribution on solute and sediment runoff processes. Secondly, the primary source of the vegetation succession about the invasion of alder in the mire was evaluated by simulating the elevation aggradations by the sediment deposits from the inflowing rivers. Thirdly, the NICE-MASS simulated the hydrologic changes of groundwater degradation by channelized rivers in the past. It is assumed that these hydrologic and geomorphic changes are closely related to the alder invasion in the mire. Finally, the limiting factor about submergeddepth was evaluated by combining the simulation results, the GIS-databases, and the previous researches, which is very important to simulate/forecast the vegetation successions for the policy making to re-meandering of channelized rivers in the future. Finally, the author estimated the influence of river channelization/meandering and landcover changes on vegetation change downstream in Kushiro Mire by adding the interrelationships among water, heat, nutrients, sediment, and vegetation. The author expanded the NICE to include mass transport and vegetation succession processes including competition between two species in the Kucyoro River catchment, Japan (NICE-VEG; Nakayama, 2008a, 2008b). The NICE-VEG simulated the water/heat budget, the mass transport, and vegetation succession processes iteratively by adding the environmental limiting factor of controlling vegetation succession in relation to submerged depth, which is the newly developed part of this study. The author conducted a simulation to reproduce the alder invasion that has occurred up to now, and evaluated the influence of river channelization and land-cover change on the vegetation changes that have occurred downstream in Kushiro Mire. The results showed that the new nature restoration project launched in this area would be very effective for recovery of the mire vegetation, which is now very seriously affected by the drying phenomenon associated with vegetation change caused by the invasion of alder. The author conducted a simulation to forecast that the new nature restoration project launched in this area (Ministry of Environment 2002) as described in the following section would be effective for recovery of the mire vegetation in the future. The details are presented in this paper.

2. STUDY AREA The Kushiro Mire (the largest mire in Japan) and the Kushiro River catchment (area: 2204.7 km2 located in northern Japan) (Figure 1) (Digital National Land Information GIS Data of Japan, 1976, 1997) have been changed by conversion to urban or agricultural uses since 1884. The annual mean temperature is about 5–6 °C, making it one of the coldest regions in Japan. Mean annual precipitation is about 1100 mm. In summer, the mean temperature is 17–19 °C, and fog is common. The water dynamics in the Kushiro River catchment, including the Kushiro Mire, are changing spatially. Deep groundwater levels in forested areas and shallow groundwater levels in the Mire, for example, change on account of the sudden topography fluctuations over short distances and seasonal variations in vegetation.

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Kushiro Mire, located in the downstream region of the Kucyoro River, has been protected under the Ramsar Convention since 1980 and was declared a national park in 1987. Nevertheless, the water cycle has recently changed and a drying phenomenon has occurred in the mire. Owing to the channelization of meandering rivers in the northern part of Kushiro Mire in the 1970s-80s in order to smoothly drain runoff and protect farmlands from flooding, runoff containing nutrients from farmland and sediments from short-cut channels have flowed directly into the mire and deposited flood-borne sediment (Nakamura et al., 1997; Nakayama and Watanabe, 2004). The dominant species in this mire include alder (Alnus japonica), reed (Phragmites australis), moss (Polytrichum spp., Sphagnum spp.), sedge (Eriophorum vaginatum), willow (Salix spp.), Japanese ash (Fraxinus mandshurica var. japonica), and meadow sweet (Spiraea salicifolia) (Tanaka, 1963; Shinsho, 1982). Alder, a deciduous tree forming swamp forests with a height of approximately 15 m, has propagated widely around the Kushiro Mire after channelization, due to various reasons; decrease of recharge rate, lowering of the groundwater level, increase of surface runoff, sedimentation, and increased nutrient input, which have resulted in gradual shrinkage of the mire, as shown in Table 1 (Ministry of Environment, 2004).

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Table 1. Vegetation change in the Kushiro Mire (Ministry of Environment, 2004). 1947 (ha)

1977 (ha)

1996 (ha)

1947→1977 (ha/year)

1977→1996 (ha/year)

Mire (A)

22,476

19,586

12,303

-96.3

-383.3

Alder (B)

2,097

2,941

7,127

28.1

220.3

Willow

712

873

976

5.4

5.4

Forest

11,218

8,976

10,479

-74.7

79.1

Meadow

1,452

2,263

552

27.0

-90.1

Farm

2,165

3,789

6,024

54.1

117.6

Residential

33

780

1,655

24.9

46.1

Road

498

822

1,061

10.8

12.6

Bare

0

676

432

22.5

-12.8

River and Lake

1,742

1,684

1,782

-1.9

5.2

Mire＋Alder (C)

24,573

22,527

19,430

-68.2

-163.0

A/C

0.91

0.87

0.63

B/C

0.09

0.13

0.37

These phenomena are closely related to river flooding, not only in the typhoon season but also during spring snowmelt (Nakayama and Watanabe, 2006). The spring snowmelt runoff has a greater effect on the water balance in the northern region of Japan because the spring flood continues in longer time than that in typhoon seasons from August to October (Figure 6). This runoff also affects sediment/nutrient transports and vegetation change in the downstream region (Ministry of Environment, 2002; Nakayama and Watanabe, 2004). At the

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end of the 1990s, the proportion of pioneer species of the mire such as reed and sedge was about 63 % and that of alder was about 37 %.

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Figure 6. Observation data of (a) total phosphorus, (b) total nitrogen, (c) suspended load, (d) river discharge (Shibecya observation point; Hokkaido Regional Development Bureau) and (e) precipitation (M-4 in Table 6) (Japan Meteorological Business Support Center) around the Kushiro Mire during 2001-2003.

This drying phenomenon shows that human activities have changed the water cycle, and thus vegetation succession, in the mire (Nakamura et al., 1997; Nakamura, 2003; Nakayama and Watanabe, 2004, 2006). When a hydrological model is applied to this area, it is recommended to take into account surface runoff and unsaturated–saturated water processes, and land-surface processes assimilated with satellite data to describe temporal variation in vegetation growth and phenology. Some previous studies have investigated the responses of a gravel bed river to meander straightening and rectification (Brookes, 1985; Schilling and Wolter, 2000; Talbot and Lapointe, 2002). When reaches are shortened and slopes are steepened at meander cut-offs, increased sedimentation and flooding occur downstream (Talbot and Lapointe, 2002), causing drying of Kushiro Mire (Nakamura et al., 1997; Nakayama and Watanabe, 2004). In order to arrest sediment-load influx and allow recovery of the mire, the Japanese government started a new project in 2002, the Kushiro Mire Conservation Plan, to re-meander the channelized rivers in the catchment (Ministry of Environment, 2002), which included river restoration and assessment of ecological integrity (Jungwirth et al., 2002). Therefore, it is very important to assess the effect of river channelization on Kushiro Mire, in order to clarify the factors controlling species competition between alder, reeds, and willows, and to reproduce the alder invasion in the mire so far, in order to prevent sediment loading from riparian forests and to recover the mire in the future.

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3. MODEL DESCRIPTION 3.1. General Structure of NICE Model (Nakayama and Watanabe, 2004)

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The NICE model consists of SiB2 (Sellers et al., 1996) for soil moisture and heat flux, the USGS MODFLOW model of three-dimensional groundwater flow (McDonalds and Harbaugh, 1988), and a grid-based hydrology model (Figure 5 and Figure 7).

Figure 7. Flow diagram of NICE model series.

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MODIS satellite data with a 1-km mesh were inputted into the model to describe the spatial and temporal changes of vegetation phenology. The water flux between recharge layer and groundwater layer was calculated in order to combine the soil moisture model and the groundwater flow model in each time step. The effective precipitation and the seepage between river and groundwater are included in the model. Therefore, the model can reproduce long-term components of river flow discharge due to recharge rates in addition to short-term components.

3.1.1. Biophysical and Soil Moisture Models Since a detailed description of SiB2 can be found in a previous study (Sellers et al., 1996), only a brief description of heat and water transfer is given here. SiB2 divides canopy into two layers (canopy layer and ground surface), and soil into three layers (upper layer, intermediate layer, and lower layer) in the vertical dimension. The governing equations for SiB2 prognostic variables consists of temperatures, interception stores, soil moisture stores, and canopy conductance to water vapor. (a) Canopy, ground surface, and deep soil temperatures

∂Tc = Rnc − Hc − λ Ec − ξcs ∂t ∂Tg 2πCd Cg = Rng − Hg − λ Eg − (Tg − Td ) − ξgs ∂t τd Cc

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Cd

∂Td 1 = ( Rng − Hg − λ Eg ) ∂t 2 365π

(1) (2) (3)

The subscript c refers to the canopy, g to the soil surface, and d to the deep soil. Tc, Tg, and Td (K) are canopy, ground surface, and deep soil temperatures; Rnc and Rng (W/m2) are absorbed net radiation of canopy and ground; Hc and Hg (W/m2) are sensible heat flux; Ec and Eg (kg/m2/s) are evapotranspiration rates; Cc, Cg, and Cd (J/m2/K) are effective heat capacities; λ (J/kg) is latent heat of vaporization; τd (s) is daylength; and ξcs and ξgs (W/m2) are energy transfer due to phase changes in Mc and Mg (described next), respectively. (b) Interception stores

∂M c = P − Dd − Dc − Eci / ρ w ∂t ∂M g = Dd + Dc − Egi / ρ w ∂t

(4) (5)

Mc and Mg (m) are water or snow/ice stored on the canopy and on the ground; P (m/s) is precipitation rate; Dd (m/s) is canopy throughfall rate; Dc (m/s) is canopy drainage rate; Eci and Egi (kg/m2/s) are interception loss of canopy and ground; and ρw (kg/m3) is density of water.

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(c) Soil moisture stores

1 1 ∂W1 = [Pw1 − Q1,2 − E ] ∂t θ s D1 ρ w gs

(6)

1 1 ∂W2 = [Q1,2 − Q2,3 − E ] ∂t θ s D2 ρw ct

(7)

1 ∂W3 = [Q − Q3 ] ∂t θ s D3 2,3

(8)

Wi is the soil moisture fraction of the i-th layer (=θi/θs); θi (m3/m3) is volumetric soil moisture in the i-th layer; θs (m3/m3) is the value of θ at saturation; Di (m) is the thickness of the soil layer; Qi,j (m/s) is the flow between layers i and j; Q3 (m/s) is gravitational drainage from recharge soil moisture store; Ect (kg/m2/s) is canopy transpiration; Egs (kg/m2/s) is ground evaporation; and Pw1 (m/s) is infiltration of precipitation into the upper soil moisture store. (d) Canopy conductance to water vapor

∂g c = −k g ( g c − g c, inf ) ∂t

(9)

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gc (m/s) is canopy conductance, kg (1/s) is a time constant, and gc,inf (m/s) is the estimated value of gc at t → ∞. In addition to the above equations, SiB2 models the radiative transfer, aerodynamic resistance, turbulent transfer, and canopy photosynthesis.

3.1.2. Groundwater Model A partial-differential equation of three-dimensional groundwater flow is expressed in the following equation (McDonalds and Harbaugh, 1988):

∂hg ⎞ ∂ ⎛ ∂hg ⎞ ∂ ⎛ ∂hg ⎞ ∂hg ∂ ⎛ ⎜ Kxx ⎟ + ⎜ K yy ⎟ + ⎜ Kzz ⎟ + W = Ss ∂x ⎝ ∂x ⎠ ∂y ⎝ ∂y ⎠ ∂z ⎝ ∂z ⎠ ∂t

(10)

Kxx, Kyy, and Kzz (m/s) are values of hydraulic conductivity along the x, y, and z coordinate axes; hg (m) is the potentiometric head; W (1/s) is the volumetric flux per unit volume representing sources and/or sinks of water; and Ss (1/m) is the specific storage. Equation (10) is discretized by using the finite-difference method on the assumption that the variables between two cells change linearly:

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(

)

(

CRi, j− 12 ,k hi,mj−1, k − hi,mj, k + CRi, j+ 12 , k hi,mj+1,k − hi,mj,k

( (h

) )+ CV

( (h

)

+CCi, j− 12 ,k hi,mj−1,k − hi,mj, k + CCi, j+ 12 , k hi,mj+1, k − hi,mj,k +CVi, j− 12 ,k

m i, j−1,k

+ Pi, j,k hi,mj, k

− hi,mj, k

i, j+ 12 , k

m i, j+1, k

15

− hi,mj,k

) )

(11)

hi,mj, k − hi,m−1 j, k

+ Qi, j, k = SS i, j,k (DELR j × DELCi × THICKi, j,k )

t m − t m −1

him, j ,k is head at cell i,j,k at time step m; CV, CR, and CC are hydraulic conductances or branch conductances between node i,j,k and a neighboring node in the horizontal, lateral, and vertical directions; Pi,j,k is the sum of coefficients of head from source and sink terms; Qi,j,k is the sum of constants from source and sink terms, where Qi,j,k < 0.0 for flow out of the groundwater system and Qi,j,k > 0.0 for flow in; DELRj is the cell width of column j in all rows; DELCi is the cell width of row i in all columns; THICKi,j,k is the vertical thickness of cell i,j,k; and tm is the time at time step m. For steady-state stress periods, the right-hand side of equation (11), the storage term, is set to zero. For solution by computer, equation (11) is modified into the following form at time step m:

CVi , j ,k − 1 hi , j ,k −1 + CC i − 1 , j ,k hi −1, j ,k + CRi , j − 1 ,k hi , j −1,k

(

2

2

2

)

+ − CVi , j ,k − 1 − CC i − 1 , j ,k − CRi , j − 1 ,k − CRi , j + 1 ,k − CC i + 1 , j ,k HCOFi , j ,k hi , j ,k 2

2

2

2

2

(12)

+ CRi , j + 1 ,k hi , j +1,k + CC i + 1 , j ,k hi , j +1,k + CVi , j ,k + 1 hi , j ,k +1 = RHS i , j ,k Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved.

2

2

2

In equation (12), the time superscript is removed for simplicity. HCOFi,j,k contains Pi,j,k and the negative of the part of the storage term, which includes the head in the current time step m (the negative sign comes from moving the term to the left-hand side). RHS includes – Q (the negative sign comes from moving Q to the right-hand side) and the part of the storage term that is multiplied by the head at time step (m – 1). The horizontal conductance between cells i,j,k and i,j+1,k is given by using the equivalent conductance in a set of conductances arranged in series as follows:

1

CRi, j+ 12 ,k

=

1 1 + TRi, j,k DELCi TRi, j+1, k DELCi

(1 2 )DELR (1 2 )DELR j

(13)

j+1

where TRi,j,k is transmissivity in the row direction at cell i,j,k; DELRj is the grid width of column j; and DELCi is the grid width of row i. In the quasi-three-dimensional approach (McDonalds and Harbaugh, 1988), the semiconfining unit makes no measurable contribution to the horizontal conductance or the storage capacity of either model layer; the only effect of the confining bed is to restrict vertical flow between the model cells. Under these assumptions, the impact of the

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Tadanobu Nakayama

semiconfining unit can be simulated without using a separate layer in the finite-difference grid. Because three intervals (the lower half of the upper aquifer, the semiconfining unit, and the upper half of the lower aquifer) must be represented in the summation of conductance between the nodes, the vertical conductance can be expressed as follows:

1 CVi, j, k + 1

=

1 1 1 + + DELR j DELCiVK i, j, k DELR j DELCiVKCBi, j, k DELR j DELCiVKi, j,k +1 THICKCB 1 THICK i, j, k 1 THICKi, j,k +1 2 2

( )

2

( )

(14) where VKi,j,k is vertical hydraulic conductivity of cell i,j,k; VKCBi,j,k is the hydraulic conductivity of the semiconfining unit between cells i,j,k and i,j,k+1; and THICKCB is the thickness of the semiconfining unit.

3.1.3. Surface Hydrology Model The surface hydrology model consists of a hillslope hydrology model based on a kinematic wave theory and a distributed stream network model based on both kinematic and dynamic wave theories. The hillslope hydrology model consists of a water and thermal energy budget model and a surface runoff model. The kinematic wave model for distributed surface runoff can be expressed by the following equations (Takasao and Shiiba, 1988):

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1 ∂ ∂ha + {q b(x)}= r( x,t) cosθ ( x) ∂t b(x) ∂x k sin θ (x ) q= ha , (0 < ha < d ) , (16a) q=

γ sin θ (x) n

(ha − d ) m +

k sinθ (x )

γ

h a,

(15)

(ha ≥ d )

(16b)

where q (m2/s) is the discharge of unit width; r(x,t) (m/s) is the effective rainfall intensity at position x and time t; b(x) (m) is the width of the flow; θ(x) is the riverbed gradient; k (m/s) is the hydraulic conductivity in the “A-layer” with a depth of D (m) near the ground surface; n (m-s) is the Manning coefficient; and m = 5/3. When H (m) is defined as the depth of the rainwater flow in the A-layer, ha (m) as the apparent flow depth (= γH), and γ as the porosity of the A-layer, and d = γD, then the true flow depth is given by ha/γ for ha < d, and by d/γ + ha – d for ha > d. The basic equation of one-dimensional unsteady flow is expressed by the stream network model in the following continuity equation (17) and momentum equation (18):

∂A ∂Q + = ql ∂t ∂x

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Simulation of Ecosystem Degradation and Its Application…

17

∂Q ∂ ⎛ Q2 ⎞ ∂hr + + gA(I f − i ) = 0 ⎜ ⎟ + gA ∂t ∂x ⎝ A ⎠ ∂x

(18)

where A (m2) is the cross-sectional area; Q (m3/s) is the discharge; ql (m2/s) is the lateral inflow q entering along the side of the river channel simulated by a hillslope model; hr (m) is the flow depth in the river; g (m/s2) is the gravitational acceleration; If is the friction slope; and i is the bed slope. The first term on the left-hand side of equation (18) is the local acceleration term, the second is the convective acceleration term, the third is the pressure force term, the fourth is the friction force term, and the fifth is the gravity force term. The local and convective acceleration terms represent the effect of inertial forces on the flow. The alternative distributed flow routing models are produced by using the full continuity equation while eliminating some terms of the equation (18). The simplest distributed model is the kinematic wave model, which neglects the local acceleration, convective acceleration, and pressure terms in equation (18); that is, it assumes If = i and the friction and gravity forces balance each other. The diffusion wave model neglects the local and convective acceleration terms but incorporates the pressure term. The dynamic wave model takes in all the acceleration and pressure terms in equation (18). Equations (17) and (18) are discretized by using Preissmann’s implicit scheme in the following non-linear simultaneous equations (19) and (20):

{

}

{

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{

}

1 1 ( Ain++11 + Ain+1 ) − ( Ain+1 + Ain ) + θ (Qin++11 − Qin+1 ) + (1 − θ ) (Qin+1 − Qin ) 2Δt Δx 1 − θq n +1 + (1 − θ )q n = 0 Δx 1 (Qin++11 + Qin +1 ) − (Qin+1 + Qin ) 2Δt n+1 n +1 n ⎤ ⎡ ⎧ ⎧⎛ 2 ⎞ n ⎛ Q2 ⎞ ⎫⎪ ⎛ Q2 ⎞ ⎫⎪ ⎪ Q 1 ⎢ ⎪⎛ Q 2 ⎞ θ ⎨⎜ ⎟ − ⎜ ⎟ ⎬ + (1 − θ ) ⎨⎜ ⎟ − ⎜ ⎟ ⎬⎥ + ⎢ Δx ⎪⎝ A ⎠ ⎝ A ⎠ i ⎪⎭ ⎪⎩⎝ A ⎠ i+1 ⎝ A ⎠ i ⎪⎭⎥⎦ i+1 ⎣ ⎩ g + θ ( Ain++11 + Ain +1 ) + (1 − θ ) ( Ain+1 + Ain ) 2

{

}

}

{

{

(19)

}

} {

}

⎡1 ⎤ 1 n+1 n n n θ (hi+1 − hin+1 ) + (1 − θ )(hi+1 − hin ) + θ (I fn,+1i+1 + I nf +1 ⎢ ,i ) + (1 − θ ) (I f , i+1 + I f ,i ) − i ⎥ = 0 2 ⎣ Δx ⎦

(20) where the superscript n refers to the time step, the subscript i to the number of simulation section, and θ to the weighting parameter (0.5 < θ < 1.0). When equations (19) and (20) are applied to a single channel component with N simulation section, the equations consist of 2N – 2 non-linear simultaneous equations, and the unknown variables are the discharge and flow depth (i = 0, 1, …, N – 1) at time t = (n + 1)Δt, which account for 2N. Then two equations for the unknown variables at both ends of a single channel (ΔQ0, Δh0, ΔQN–1, ΔhN–1) are produced by applying the Taylor expansion to the first-order term around the unknown approximations

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Tadanobu Nakayama

and eliminating the unknown variables except the ends. Because there are the continuity conditions on discharge and flow depth at the confluent/effluent points of several channels in a grid box, the simultaneous equations consist of only the unknown variables at the intersections between the river channel and the grid boundary in the grid box. The simultaneous equations for the unknown variables for discharge and flow depth in the whole catchment are solved for the continuity conditions at each grid boundary and for the boundary conditions at the furthest upstream and downstream ends by using the Gauss method. This process was repeated by using the Newton–Raphson iteration method for convergence and correcting the approximate solution until the variables (ΔQi, Δhi) become small enough. Finally, the discharge and flow depth at each point are given.

3.1.4. Integration of Models In a region where the gradient of an elevation or a wetting front is much smaller than 1, the vector of hydraulic gradient is approximately downward. So the flow in an unsaturated layer can be estimated as vertically one-dimensional. The transfer of water between adjacent soil layers in equations (6), (7), and (8) is given by

⎡ ψ − ψ i+1 ⎤ Qi, i+1 = K ⎢2 i + 1⎥ , ⎣ Di + Di+1 ⎦ D K + Di +1K i +1 K= i i Di + Di +1

for i = 1,2

(21) (22)

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where Qi,i+1 (m/s) is downward flow from soil layer i to soil layer i + 1; Di (m) is thickness of the soil layer; Ki (m/s) is hydraulic conductivity for i-th layer (= KsWi2B+3); K (m/s) is the estimated effective hydraulic conductivity between layers; ψi is matric potential for the i-th layer (= ψsWi-B); Ks (m/s) is hydraulic conductivity at saturation; ψs (m) is soil moisture potential at saturation; and B is an empirical constant. To combine unsaturated flow and saturated flow, the water flux qf is expressed by using the gradient of hydraulic potentials between the deepest layer of unsaturated flow and the groundwater level in the expansion of the drainage in the original SiB2 model (Sellers et al., 1996) in the following equation:

⎛ ⎞ Ψg − Ψ3 ⎟ ⎜ ΔΨ ψ3 q f = − K ∇Ψ = − K = −K = K⎜ +1 D3 + (D −h ) D3 + (D − h ) ⎟⎟ Δz ⎜ g g g g ⎠ ⎝ 2 2

(23)

where K (m/s) is the estimated effective hydraulic conductivity between unsaturated and saturated layers; Ψg (= hg) and Ψ3 (= ψ3 + Dg + D3/2) (m) are hydraulic potentials at the groundwater surface and the lower layer of unsaturated flow; Dg (m) is the distance between the top of the second layer and the bottom of the 20th layer in the groundwater model; and hg (m) is the hydraulic head simulated by the groundwater model. When the groundwater level rises and enters into the soil moisture layer, the partial pressure is set at the bottom of

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unsaturated layer (Ψ3 = ψp) to simulate soil moisture. After the water flux qf is calculated in each time step, the flows between each unsaturated soil layer Qi,j are simulated in equations (21) and (22) by using an improved backward-implicit scheme in order to simulate soil moisture θi in the i-th layer in equations (6) to (8). Furthermore, this flux is input into the groundwater flow model as recharge rate at the highest active cell as the upper boundary condition, and the groundwater flow model is simulated. For the treatment of effective rainfall intensity r in equation (15) of the surface hydrology model, the effective precipitation can be calculated from the precipitation rate P (m/s), the infiltration of precipitation into the upper soil moisture store Pw1 (m/s), and the evapotranspiration rates (Ec+Eg) (kg /m2 /s) by the following equation:

r = P − Pw1 −

1

ρw

( Ec + E g )

(24)

when the volumetric soil moisture θi takes greater a value than that of saturation θs, the surplus of each value is added to the right-hand side of equation (24) as return flow to the surface. The seepage between river and groundwater depends on the interaction between flow depth at river and groundwater level of each cell. A volumetric flux between them is calculated by means of Darcy’s Law (McDonalds and Harbaugh, 1988):

Qs = k b Ab , (hg ≤ H b ),

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Qs = k b

hg − H b bb

Ab , (hg > H b )

(25)

where Qs (m3/s) is the volumetric flux of seepage between river and groundwater (= ql l; l is the channel length of each component); kb (m/s) is the hydraulic conductivity of the riverbed; Ab (m2) is the cross-sectional area of the groundwater section; bb (m) is riverbed thickness; hg (m) is groundwater head; and Hb (m) is the hydraulic potential of the river. In a recharge situation (hg < Hb), the volumetric flux does not exceed the total volume of river flow. When the convergence procedure is conducted in a groundwater flow simulation, the flow depth of the river in the grid-based distributed runoff model is fixed. In this way, various models from the ground to the surface were connected by considering the flux, allowing groundwater level and soil moisture to be calculated only from the meteorological data, vegetation class, and soil texture, as shown in Figure 5. The ambiguity of effective precipitation seen in most of the previous catchment models is avoided because the model can simulate the change of infiltration flux in equation (24) at every time step. Furthermore, both short-term and long-term (base flow) components of river runoff can be simulated correctly owing to the effects of return flow and seepage in equations (24) and (25) by combining the land-surface process model, grid-based surface runoff model, and groundwater flow model.

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Tadanobu Nakayama

3.2. Model Description of NICE-SNOW (Nakayama and Watanabe, 2006) 3.2.1. Effect of Micro-Topography and Meteorology on Snow and Frost Depth NICE model can simulate the snow process and soil temperatures in three unsaturated layers by using a full energy balance only in a flat plain (Nakayama and Watanabe, 2004). But it is difficult to simulate the local snow/frost depths and the phase changes in the soil. In order to simulate the local snow/frost depths, it is necessary to calculate the downward radiation perpendicular to the ground surface from the total solar radiation including the effect of solar angle and ground-slope angle because the micro-topography and meteorology affect greatly the local snow/frost depths (Figure 7). Total solar radiation perpendicular to the ground surface, Wb (W/m2), can be adjusted by the observed total solar radiation, Wa (W/m2):

Wb = Wa [cos ω cos θ + sin ω sin θ cos(γ a − λ )]

(26)

cosω =sin φ sin δ + cosφ cosδ cos ha

(27)

sin γ a =

cos δ sin ha sin ω

(28)

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where ω is the solar zenith angle, θ is the slope angle, γa is the solar azimuthal angle, λ is the slope azimuthal angle, ha (rad) is the hour angle, φ is the latitude (‘+’ means north and ‘–’ means south), and δ is the solar declination. The value of δ can be empirically approximated as in equations (29) and (30):

δ =sin −1 (0.398 × sin c) c =4.841+η+ 0.033sinη ⎛ 2π ⎞ I η= ⎜ ⎝ 365⎠

(29) (30) (31)

where I is the day of the year. The solar radiation on the south slope becomes much greater and the value on the northern slope decreases and approaches zero as the slope angle increases. Furthermore, the frost and thaw depths in the soil were evaluated by the phase change simulation and the Stefan solution as written in detail in the sections 3.2.2 and 3.2.3.

3.2.2. Modeling of Phase Changes in Unsaturated Layer During the winter season, the depth of frost/thaw layer change temporarily depending on the frost/thaw processes of soil structure (Daniel and Staricka, 2000). In the coldest part of winter, the infiltration rate is greatly reduced in the freezing layer because the water in the soil pores freezes, filling them with ice as the soil frost layer develops. The NICE-SNOW extends the original NICE model (section 3.1) by the partial differentiation of the left sides of the equations (6)-(8), and calculates the phase changes of ice and liquid fractions of soil moisture (Figure 7);

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∂θ l ,1

ρ i ∂θ i ,1 1 1 = [ Pw1 − q1, 2 − E gs ] ∂t D1 ρ l ∂t ρl ∂θ l , 2 ρ i ∂θ i , 2 1 1 + = [q1, 2 − q 2,3 − E ct ] ρ l ∂t ρl ∂t D2 ∂θ l ,3 ρ i ∂θ i ,3 1 = [q 2,3 − q3 ] + D3 ∂t ρ l ∂t +

21

(32) (33) (34)

θl,j (m3/m3) and θi,j (m3/m3) (j=1,2,3) are the liquid water content and the ice content of each soil layer; ρl (kg/m3) and ρi (kg/m3) are densities of the liquid water and the ice, respectively. Furthermore, the fraction of unfrozen water is calculated by the following equation (Flerchinger and Saxton, 1989a, b; Cherkauer and Lettenmaier, 1999): Lf T ⎤ ⎡ + cR(T + 273.16) ⎥ ⎢ 273 . 16 T + θl = θ s ⎢ ⎥ gψ s ⎥ ⎢ ⎦⎥ ⎣⎢

−

1 B

(35)

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ψs (m) is the soil matric potential at saturation; θs (m3/m3) is the value of θ at saturation; Lf (J/kg) is the latent heat of fusion; T (C) is the soil temperature; B is the empirical constant; c (moles/kg) is the solute concentration in the soil solution (=0); R is the universal gas constant (=8.3143 J/mole/K). The above equations (32)-(35) include the phase changes in the unsaturated layer, and can simulate the penetration of the frost front in the section 3.2.3. These processes also include the vertical and temporal changes of hydraulic conductivity during the winter season, which finally returns to the original value in snow-free period.

3.2.3. Estimation of Frost and Thaw Depth by the Stefan Solution The Stefan solution provides a useful method for predicting the frost depth Z in soils:

Z=β F,

(36)

where β is an empirical constant, and F is a time–temperature integral usually calculated by summing mean daily temperatures below 0 °C. Though the equation (36) is very useful where little site-specific information is available (Nelson et al., 1997), only the peak value of the frost depth can be calculated and the effect of micro-topography on the frost depth depends on the value of coefficient β. In the NICE-SNOW, the frost and thaw depth is defined in a similar equation to the approximation of the Stefan solution by the following (The Institute of Geocryology, 1974): t

ξf =

2κ f τ h ∑ (T f − Tg ) i =1

L f ρ lθ

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22

Tadanobu Nakayama t

ξt =

2κ tτ h ∑ (Tg − T f ) i =1

L f ρ iθ

(38)

κf and κt (W/m/K) are the thermal conductivity of frost and thawed soils; τh (s) is the time length; Tf (K) is the freezing point of water.

3.2.4. Two-Layer Surface Runoff Model Including Frost/Thaw Processes The author supposes that the soil A-layer (Takasao and Shiiba, 1980) is divided into two layers during the winter season (Figure 7): the frost/thaw processes do not affect the deeper layer in winter, and the upper layer is temporarily changed. The author call the deeper layer an “A0-layer”, and the upper layer an “A1-layer”. So, the kinematic wave model for surface runoff with one layer in the equations (15) and (16) can be extended into the following twolayer model:

∂h 1 ∂ {q b( x)} = r ' ( x, t ) cos θ ( x) + ∂t b( x) ∂x

q=

k 0 sin θ ( x)

h , (0 < h < d 0 )

γ0 k sin θ ( x ) k sin θ ( x) q= 1 (h − d 0 ) + 0 h, ( d 0 ≤ h ≤ d 0 + d1 ) γ1 γ0

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q=

sin θ ( x) k sin θ ( x) k sin θ ( x) ( h − d 0 − d1 ) m + 1 (h − d 0 ) + 0 h, n γ1 γ0

(39) (40a) (40b)

(h ≥ d 0 + d1 ) (40c)

where q (m2/s) is the discharge of unit width; r’(x,t) (m/s) is the effective rainfall intensity including the snowmelt volume at position x and time t; b(x) (m) is the width of the flow; θ(x) is the riverbed gradient; k (m/s) is the hydraulic conductivity in the A-layer with a depth of D (m) near the ground surface; n (m-s) is the Manning coefficient; and m = 5/3. When H (m) is defined as the depth of the rainwater flow in the A-layer, h (m) as the apparent flow depth (= γH), γ as the porosity of the A-layer and d = γD. When the subscripts 0 and 1 are defined as the values at the A0-layer and the A1-layer respectively, the hydraulic conductivity k and the porosity γ are (k0, γ0) at the A0-layer and (k1, γ1) at the A1-layer, and then d0 = (1 – α)γ0D, d1 = αγ1D. α is the depth ratio of the A1-layer to the A-layer, and can be determined from the simulation result of phase changes as written in the above sections 3.2.2 and 3.2.3. The hydraulic conductivity in the frozen layer is calculated in the following (Lundin, 1990; Johnsson and Lundin, 1991; Stahli et al., 1999): − Ei⋅ε

k f = 10

k,

(41)

where kf is the hydraulic conductivity of frost soil, ε is the thermal quality, Ei is the impedance parameter, and k is the hydraulic conductivity in the unfrozen layer. In the spring River Pollution Research Progress, Nova Science Publishers, Incorporated, 2008. ProQuest Ebook Central,

Simulation of Ecosystem Degradation and Its Application…

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snowmelt period, the frost/thaw processes increase the soil porosity and hydraulic conductivity (Benoit et al., 1988; Pawluk, 1988). So, the author supposed that the hydraulic conductivity increases temporarily in the thawing layer due to the macropores and desiccation cracks of the soil in the previous research at the end of winter (Chamberlain and Gow, 1979; Benoit et al., 1988).

3.3. Model Description of NICE-MASS (Nakayama, 2007a) 3.3.1. Expansion of the NICE Model To Mass Transport Process Two-dimensional diffusion model including sedimentation in the following equation (42) simulates the mass transport in hillslope after the simulation of hillslope runoff model including snowmelt runoff in the equations (39) and (40) (Nakayama and Watanabe, 2006).

∂ {D ⋅ C} + ∂(M ⋅ C ) + ∂(N ⋅ C ) ∂t ∂x ∂y

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∂ ⎧ ∂C ⎫ ∂ ⎧ ∂C ⎫ = ⎨ Kx ⋅ D ⋅ 　 ⎬ + ⎨ Ky ⋅ D ⋅ ⎬ + D ⋅ L(C ) + qsu − w f ⋅ cb ∂x ⎩ ∂x ⎭ ∂y ⎩ ∂y ⎭

(42)

where M and N are discharges per unit width along the x and y coordinate axes; H (m) is the elevation of the hillslope bottom; S (m) is the hydraulic head; D (m) is the flow depth (=S-H); C (mg/l) is the mass concentration; Kx and Ky (m/s) are kinematic eddy diffusivities along the x and y coordinate axes; L(C) is concentration; qsu (m/s) is the resuspension rate from the hillslope bed; wf (m/s) is sedimentation rate (settling velocity) of suspended load; and cb (mg/l) is the standard concentration, respectively. The suspended load per unit width in two-dimensional uniform flow is expressed in the following equation (Itakura, 1984). h

qs = ∫ c( z ) u ( z ) dz b

(43)

h (m) is flow depth; c(z) is concentration at z above the riverbed; u(z) is the flow velocity at z above the riverbed; and b (m) is the riverbed elevation. Though the concentration would greatly depend on the value of b, the author used the semi-empirical relation (b=0.05h) in the following sections. When the riverbed is on equilibrium condition and the particle size distribution is uniform, the resuspension rate from the riverbed is equal to the sedimentation volume. cb and qsu are expressed by using a theory based on an energy equation of solidliquid two-phase flow and the Monin-Obukhov length in the following equations (44)-(48) (Itakura, 1984).

⎡ ρ − ρ gd ⎤ ・ ・Ω − 1⎥ c b = K ⎢α * s ρs u* w f ⎦⎥ ⎣⎢

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24

Tadanobu Nakayama

⎡ wf ⎤ ρ Ω = K ⎢α * ・ − ⎥ s ′gd s ′gd ⎥⎦ ⎢⎣ ρ s τ * ∞ 1 ξ exp − ξ 2 dξ ∫ ′ a τ τ π Ω = *・ + * −1 ∞ 1 B* B*η 0 exp − ξ 2 dξ ∫ q su

[

a′

∫

∞

1

a′

π

ξ

[

]

[

π

]

exp − ξ 2 dξ =

(45)

(46)

]

1 2π

exp(−a' 2 )

(47)

1

∞ 1 exp ⎡− ξ 2 ⎤ dξ ≅ ∫ a′ξ ⎢⎣ ⎥⎦ π

exp(−a ′ 2 ・ ) (α T + α T 2 + α T 3 ) 1 2 3 2π

(48)

where a’ (=B*/τ* - 1/η0), T (= 1/(1+0.33267 2 a’)), η0 (=0.5), B* (=0.143), α* (=0.14), K (=0.008), α1 (=0.4361836), α2 (= -0.1201676), and α3 (=0.937298) are constant values; ρ (kg/m3) and ρs (kg/m3) are density of water and fine sand; g (m/s2) is the gravitational acceleration; s’ (=ρs /ρ -1) is relative gravity of fine sand in the water; and d (m) is particle size. The concentration c is expressed in the equation (49) (Shimizu and Arai, 1988), and the sedimentation rate wf uses the Rubey’s formula in the equation (50).

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c=

cb

β

wf =

(1 − e − β ) , β =

wf h

2 ( s − 1) gd + α 2 − α 3

(49)

ε , α=

6ν d

, ε=

ρs ρ

(50)

One-dimensional diffusion model including sedimentation in the following equation (51) simulates the mass transport in river channel after the simulation of stream network model in the equations (17) and (18) (Nakayama and Watanabe, 2004).

∂ ( A ⋅ C ) + ∂( A ⋅ U ⋅ C ) = ∂ ⎧⎨ A ⋅ Kx ⋅ ∂C ⎫⎬ + AL(C ) + qsu − w f ⋅ cb ∂t ∂x ∂x ⎩ ∂x ⎭

(51)

A (m2) is the cross-section area of river channel. When the cross-section is rectangular, the above equation (51) is equal to the equation (42). The lateral inflow of mass was inputted to the equation (51) supposing that the lateral inflow simulated by a hillslope model enters along the side of the river channel. The bed material load is expressed in the Einstein’s formula (Einstein, 1950) in the equation (52).

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qB * =

(ρ s

qB

ρ − 1)gd 3

, qB = (8τ * − 0.047 )

3/2

, τ* =

u*2 (ρ s ρ − 1)gd

25 (52)

where d (m) is particle diameter of bed material load; τ* is non-dimensional tractive force; and u* (m/s) is friction velocity, respectively. The continuity equation of riverbed sediment is expressed by using the suspended load qsu and the bed material load qB in the following;

∂zB ∂q ⎞ 1 ⎛ =− ⎜ qsu − w f cb + B ⎟ ∂t ∂x ⎠ 1− λ ⎝

(53)

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where zB (m) is bed elevation; and λ is porosity of bed material (void ratio). For the hillslope runoff, the kinematic wave theory was applied to each cell of the 50-m mesh. Then kinematic wave theory was applied to a stream network of 317 rivers by inputting the discharges at the upper river channels and the lateral flows into the hillslope model. For the model simulating hillslope hydrology, the flow depth and the discharge at the uppermost ridges of the mountains were set as zero throughout the simulation (Nakayama and Watanabe, 2004). The author supposed that a little water stays at the cell in order to keep dissolving the mass flux even in the case that the flow depth is almost zero, which is necessary to simulate the flash-out of mass process at the higher precipitation. Furthermore, the author supposed that all the water and mass mix completely in the vertical direction when the flow depth rises up to the upper cell in the next time step (Figure 7). This model can be applicable to the surface runoff combined by the surface and intermediate flows.

3.3.2. Theory of Estimating Suspended Sediment (SS) Loads in Rivers The sediment-rating curve, an effective method to express the close correlation between discharge and sediment load, or sediment concentration (Sc), is also effective to evaluate the suspended sediment concentration and flux (Asselman, 2000; Syvitski et al., 2000; Fuller et al., 2003; Horowitz, 2003). From the relationship of sediment concentration with discharge data, the following typical statistical equation can be built by correlation analysis on the assumption that the log of sediment flux is assumed to have a linear relationship with the log of discharge;

log(Sc × Q) = a ⋅ log(Q) + b

(54)

where Sc (g/m3) is suspended sediment concentration; Q (m3/s) is discharge; L ( = Sc x Q) (g/s) is sediment flux; and a and b are the constant values (can be derived from the log(L) vs. Log(Q) plot), respectively. This relationship is generally used because a bivariate log-normal distribution often best describes both discharge and concentration. Given the logarithms of the equation (54), the equation can be written in the following correlation;

Sc = A ⋅ Q B

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26

Tadanobu Nakayama

where A (= 10b) and B (= a – 1) are the constant value, respectively. After the relationship between sediment flux and river discharge is established, it is very easy to estimate the sediment flux or the concentration from discharge.

3.4. Model description of NICE-VEG (Nakayama, 2008a, 2008b) 3.4.1. Vegetation Succession Model The forest model used in this study is ZELIG (Urban et al., 1991, 1993; Cumming and Burton, 1994), a recent reformation of the basic JABOWA (Bonan, 1989) and FORET (Shugart and West, 1977) models. Although gap models have been applied to a variety of forests and differ in some details, they all share the same basic structure and logic (Urban and Shugart, 1992), simulating the establishment, annual diameter growth, and mortality of each tree. Studies comparing the simulation results of some models have shown that ZELIG is more accurate and suitable, despite having some limitations (Busing and Solomon, 2004). ZELIG simulates the establishment, annual diameter growth, and mortality of each tree on an array of model plots (Figure 7). The primary zone of influence of a single canopydominant tree defines plot size. The plot is considered homogeneous horizontally, but vertical heterogeneity (canopy height and height to base of crown) is simulated in some detail. Adjacent cells interact through light interception at low sun angles. Seedling establishment, mortality, and regeneration are solved stochastically by using Monte-Carlo simulations, while the growth stage is largely deterministic. The competitive environment of the plot is defined by the height, leaf area, and woody biomass of each individual tree determined by allometric relationships with diameter as follows (Urban and Shugart, 1992);

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H = 137 + b2 D − b3 D 2

, where b2 = 2

H max − 137 Dmax

and

b3 =

H max − 137 (56) 2 Dmax

where D (cm) is diameter at breast height (DBH) and H (cm) is height. The crown diameter CD (m) is derived in equation (57) (Garman, 2003).

C D = exp[ln( D)b0 + b1 ]

(57)

The species coefficients were set as b0=0.5212634 and b1=0.6300169 for red alder (Garman, 2003). CD was used to calculate the simulated predominant species at each mesh because vegetation cover of GIS data (Figure 3), which was compared with the simulated value in the results of this study, was basically categorized from aerial photographs (Digital National Land Information GIS Data of Japan). The tree diameter growth is given in equation (58), assuming a logistic curve (JABOWA-derived gap model) and L=cD2 (Prentice and Leemans, 1990) where L is leaf area and c is a parameter (=0.160694):

dD GD(1 − DH / D max H max ) = dt (274 + 3b2 D − 4b3 D 2 )

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Simulation of Ecosystem Degradation and Its Application…

27

where G is a growth rate scalar. The growth rate is calculated as a function of several growth reduction factors as described below. Several studies have pointed out that the multiplicative approach in ZELIG in the same way as that in JABOWA causes rapid convergence to zero and results in unrealistically low growth rates when many growth factors are considered (Bugmann, 1996; Bugmann et al., 1996; Yaussy, 2000; Bugmann, 2001). Though some studies have applied “Liebig’s Law of the Minimum” (Kienast, 1987; Bugmann, 1996; Bugmann, 2001) to use only the smallest of all the growth factors in view of these limitations, this approach is based on the unrealistic assumption that only the smallest factor limits tree growth and that one single environmental factor explains all the variability of tree growth during any given year. In this study, the author applied a stepwise procedure by Bugmann (1996) in order to satisfy the above two requirement for the environmental limiting factors r for light Qh, nutrients F, soil moisture M, temperature T, and submerged depth SD are given as follows (Urban and Shugart, 1992);

G = G max [r (Q h )r ( F )r ( M )r (T )r ( S D )]

1 3

(59)

r (Q h ) = c1 {1 − exp[− c 2 (Q h − c 3 )]} , where Q h = Q0 exp[− kL(h')]

(60)

r (F ) = c 4 + c5 F − c6 F

(61)

2

1/ 2

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⎡ M * −M ⎤ r (M ) = ⎢ ⎥ ⎣ M* ⎦ 4(T − Tmin )(Tmax − T ) r (T ) = (Tmax − Tmin ) 2

(62) (63)

where Gmax is the maximum growth rate, Qh is the incident radiation at height h (1=full sun), h’ is the height greater than h, k is a light extinction coefficient (=0.4), F is relative soil fertility (dimensionless on 0-1), M is soil moisture index (on 0-1), M* is maximum tolerable for a species, T is degree-day index (5.56 base), Tmim and Tmax are minimum and maximum degree-day limits for species (Tmim=1420, Tmax=3084) used in this study, and SD (m) is submerged depth of vegetation from the ground surface. The available light Qh falls off negative-exponentially within the canopy according to the Beer-Lambert law (Urban and Shugart, 1992). The parameters ci (i=1-6) are fitted constants dependent on shade-stress and nutrient-stress tolerance (c1=1.11, c2=2.52, c3=0.07, c4=0.2133, c5=1.789, c6=-1.014). The author added the effect of submerged depth as a limiting factor r(SD) into equation (59) because this effect is very different between growths of reed and alder (Hotes et al., 2001; Ohmi Environment Preservation Foundation, 2001). In this study the author added the limiting factor by using previous data for reed (Ohmi Environment Preservation Foundation, 2001) and alder (Hotes et al., 2001):

rr ( S D ) = 0.01

for

= 0.01 + 4.95S D = 1.0

S D < 0.0 or S D ≥ 0.8 for 0.0 ≤ S D < 0.2

for 0.2 ≤ S D < 0.7

= 7.93 − 9.9S D

for 0.7 ≤ S D < 0.8

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28

Tadanobu Nakayama

ra ( S D ) = 1.0

for

S D < 0.06

= 2.98 − 33.0S D = 0.01

for

for 0.06 ≤ S D < 0.09 (= 0.06 + 2σ )

(65)

S D ≥ 0.09

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The subscripts r and a in the above equations (64)-(65) show reed and alder, respectively. Although the above equation (59) lacks a mechanistic basis, this approach yields intuitively reasonable results, probably superior to both the multiplicative approach and Liebig’s Law (Bugmann, 1996).

3.4.2. Integration of Models Because the NICE-VEG simulates water/heat budget, mass transport (Itakura, 1984; Shimizu and Arai, 1988; Kushiro Branch Office, 2002; Toda et al., 2002), and vegetation succession processes iteratively (Figure 7), it is possible to estimate the influence of river channelization/meandering and land-cover change on vegetation change downstream in Kushiro Mire by adding the relationships between water, heat, nutrients, sediment, and vegetation. It is very powerful to simulate the spatial and temporal variations of environmental factors controlling vegetation change, such as soil moisture, submerged depth, nutrient loading, and sediment accumulation, and to input the dynamic changes of these variables into the vegetation succession model as the boundary of model expansion (Figure 7). In the first step, the model simulates water/heat values such as evapotranspiration, river flow discharge and depth, soil moisture, soil temperature, and groundwater level in each grid at each time step by inputting the meteorological forcing data. In the second step, the model simulates the vegetation succession processes by inputting the simulated results of the first step and the meteorological data. These simulations do not include feedback from vegetation change to climatic change because this study area is not so large, and therefore this process can be considered negligible.

4. DATA AND BOUNDARY CONDITIONS FOR SIMULATION 4.1. Input Data The hourly observation data of downward short- and long-wave radiation, precipitation, atmospheric pressure, air temperature, air humidity, and wind speed at a reference level were calculated from 11 points of AMeDAS (Automated Meteorological Data Acquisition System) data in the catchment collected by the Japan Meteorological Business Support Center and from three meteorological stations data of NIES. The friction velocity is estimated by the bulk transfer formula. Furthermore, the downward radiation perpendicular to the ground surface was calculated from the total solar radiation by the equation (26) in order to include the effect of solar angle and ground-slope angle because the micro-topography and meteorology affect greatly the local snow/frost depths. When the vapor pressure is unknown, this value was estimated from the Tetens formula by using the dew point temperature.

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The mean elevation of each 500-m grid-cell was calculated by using the spatial average of a digital elevation model (DEM) of 50-m mesh (Geographical Survey Institute of Japan, 1999) throughout the Kushiro River catchment (Figure 1). The vertical dimension was divided into 20 layers with a weighting factor of 1.1 (finer at the upper layers). The upper layer was set at 2 m depth, and the 20th layer was defined as an elevation of –250 m from the sea surface. Furthermore, two vegetation characteristics, FPAR and LAI, obtained from MODIS satellite data (1-km mesh), were input every 8 days after validation and verification of the MODIS data by ground-truth data collected at the Tomakomai Flux Tower (42°44′13.1″N, 141°31′7.1″E, mean elevation 115–140 m), Hokkaido. For the hillslope runoff, the kinematic wave theory was applied to each cell of the 50-m mesh. Then both kinematic and dynamic wave theories were applied to a stream network of 317 rivers by inputting the discharges at the upper river channels and the lateral flows into the hillslope model. The dynamic wave theory was applied to a large flat area around Kushiro Mire, where stream slopes generally do not exceed 3/1000 (Samuels and Skeels, 1990; Meselhe and Holly, 1997). Constant head values were used at the upstream boundaries (Lake Kussharo: annual mean elevation 121 m; Lake Mashu: annual mean elevation 351 m; northern edge of simulation area) for the groundwater flow model. About the other upstream boundaries where there are no lakes or observation points, reflecting condition on hydraulic head was used supposing that there is no inflow from the mountains in the opposite direction. At the sea boundary, constant head was set at 0 m (southern part of simulation area). Groundwater levels at about 150 sampling points obtained by Ohara et al. (1975) were used in well cells. The hydraulic head values parallel to the ground level were inputted as the initial condition for the groundwater flow model. For the model simulating hillslope hydrology, the flow depth and the discharge at the uppermost ridges of the mountains were set as zero throughout the simulation. In river cells, outflows from the riverbeds of –1 m mean elevation from the ground level were considered. Table 2. Constant mass-fluxes per unit area in the catchment Legend/Case

Unit

Case1

Case2

Case3

Appendix

Paddy Field

kgN/ha/year

19.00

19.00

19.00

Toda et al (2002)

Farm

kgN/ha/year

0.32*(fertiliza tion)+9.6

0.32*(fertiliza tion)+9.6

9.60

fertilization=100

Forest

kgN/ha/year

3.90

3.90

39.00

Toda et al (2002)

Urban

kgN/ha/year

12.20

12.20

12.20

Toda et al (2002)

0.00

0.29(kgN/head/day) (Toda et al 2002), milk cattle=125190, beef cattle=32800 (Kushiro Branch Office 2002), area=867.54km2

Other Land

kgN/ha/day

0.00

0.29*(125190 +32800) /86754

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Tadanobu Nakayama

The values of non-point source about the sediment and nutrient are very important for simulating the mass transport in the catchment. In the mass transport simulation, various land covers in Figure 3 have the corresponding constant mass-fluxes per unit area in the catchment (Table 2). The author simulated the three cases changing constant mass-fluxes in farm, forest, and stockbreeding by using the unit flux data in the previous researches (Kushiro Branch Office, 2002; Toda et al., 2002). Important data for alder and reed in this study input to the NICE-VEG were summarized from previous studies (Burges and Peterson, 1986; Chapin et al., 1994; Hotes et al., 2001; Ohmi Environment Preservation Foundation, 2001; Ministry of Environment, 2004) (Table 3). Because previous research has shown that the nutrient load to the mire has increased about 20% during the past 30 years (Ministry of Environment, 2004), the author increased the environmental limiting factor for soil fertility in equation (61) from 0.5 to 1.0 linearly by considering the relative growth rates (Burges and Peterson, 1986). Table 3. Summarized inputted data to NICE-VEG about alder and reed from the previous researches. Alder

Reed

References

Soil texture (% of total) 74.6±4. 3 15.8±2. 8

Sand

64.8±1.9

Chapin et al (1994)

Silt

24.7±1.5

Clay

10.5±0.7

9.6±1.8

Chapin et al (1994)

Litter

1.7±0.2

0

Chapin et al (1994)

Organic horizon

2.8±0.8

0

Chapin et al (1994)

A+B horizons

8.8±0.2

5.2±0.5

Chapin et al (1994)

Fine litter

203-260

1.3-1.8

Chapin et al (1994)

Wood

45-47

0

Chapin et al (1994)

Total litter

248-307

1.3-1.8

Chapin et al (1994)

Chapin et al (1994)

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Soil depth (cm/horizon)

2

Litterfall (g/m /yr)

Biomass for limiting factor of seedlings growth (mg/plant) Control

298

Chapin et al (1994)

N added

823

Chapin et al (1994)

P added

1110

Chapin et al (1994)

Survivorship of seedlings (% of total)

81

Chapin et al (1994)

Height growth (cm)

3.28-5.45

Chapin et al (1994)

Total biomass (g/plant)

3.2-3.8

Chapin et al (1994)

Aboveground production (g/plant/yr)

0.3-0.8

Chapin et al (1994)

Relative growth rate (g/g/yr)

0.3-0.8

Chapin et al (1994)

Seedling growth rate

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Simulation of Ecosystem Degradation and Its Application… Height increment after 3 year (cm)

2.1-9.2

Chapin et al (1994)

5% N added

3.8-4.9

Burges and Peterson (1986)

10% N added

9.2-10.1

Burges and Peterson (1986)

15% N added

15.1-17

Burges and Peterson (1986)

31

(Noninoculated) Relative growth rates

Alder

Reed

References

(Inoculated) Relative growth rates 0% N added

5.2-8.0

Burges and Peterson (1986)

5% N added

6.2-7.6

Burges and Peterson (1986)

10% N added

10.3-13.2

Burges and Peterson (1986)

15% N added

14.3-16.8

Burges and Peterson (1986)

Submerged depth for living （cm）

Eq.(65)

Diameter at breast height (m)

2-16（Peak:4-6)

Eq.(64)

Hotes et al (2001), Ohmi Environment Preservation Foundation (2001) Ministry of Environment(2004)

Effect of P on living Monitoring site（Total drying weight; g)

0.05

P added（Total drying weight; g)

0.067

Ministry of Environment(2004) Ministry of Environment(2004)

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4.2. Observation for Calibration and Validation Three meteorological stations, 30 groundwater level meters, and 13 flow depth meters were set throughout the overall catchment for the calibration and validation of numerical results. Three meteorological stations were established in vegetation typical of the Kushiro River catchment (mire: mean elevation 8 m; grassland: mean elevation 187 m; forest: mean elevation 127 m) (Figure 1). Meteorological variables were automatically recorded hourly at each station. The data were collected for two years from 1 January 2001 to 31 December 2002. The variables measured were air temperature (Kona-System, KDC-S2), humidity (Kona-System, KDC-S2), wind speed (Makino-Keiki, AC750), net radiation (Eikou-Seiki, CN-11), albedo (Eikou-Seiki, MR-22), precipitation (Ikeda-Keiki, RH-5), soil temperature (Chino, platinum), soil moisture (Delta-T, ML2x, and PR1/6), and groundwater level (KonaSystem, Kadec-Mizu-II). Furthermore, snow depth was measured every one - two weeks by using snow depth meters during winter and spring. At the same time, frozen tubes were used to measure and monitor the extent of the frost soil layer. To measure soil water content at each station, soil samples were taken at three depths (0.1, 0.2, and 0.3 m) to calibrate the values measured by water content reflectometers (ML2x) and profile probes (PR1/6) on the basis of time domain reflectometry. Because the correlation coefficient between soil water content measured by reflectometer and profile probe was better in grassland (Rr = 0.956), the measured data were calibrated against sampled data. In contrast, the correlation was not so good in forest (Rr = 0.600), because of the large amount of bamboo and macropores, which created difficulties in taking measurements. Furthermore, the snow depth data at Tomakomai

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Tadanobu Nakayama

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(42°37′24″N, 141°35′06″E, mean elevation 6 m) and Noboribetsu (42°27′24″N, 141°07′18″E, mean elevation 197 m; see Figure 1) outside the Kushiro River catchment in Hokkaido and the frost depth data at Noboribetsu from 1 January 1984 to 31 December 1985 (Mamiya and Chiba, 1985) were used in order to evaluate the effect of micro-topography, local meteorology and land cover on the snow and frost depths (Figure 7). The forcing meteorology data for 1984-1985 were the AMeDAS data in the same as the simulation for 2001-2002. Changes in groundwater levels at 30 sites and river flow-depths at 13 sites were measured every hour during the same period. The water level (Kadec-Mizu-II) was automatically recorded in data-loggers (LS-3000PtV) every hour. H–Q curves (usually with almost a 1:1 relationship between water level and stream discharge) were used to convert water level into river discharge, for flow-depth measurements in rivers. The relationship was poor in the mire because beds and cross-sections were frequently variable in rivers their, each river flowing into the mire has its own of runoff hydrography, and bed slopes are very small and are thus affected by downstream flow depths and floods. To validate MODIS data against groundtruth data, measurements were provided by the Center for Global Environment Research (CGER), NIES, for surface temperature (Minolta, R505) at 15 m above the ground surface, FPAR (Li-Cor, LI-190s) at 25 and 40 m, and net radiation (Eikou-Seiki, MR-40) at 25 m, recorded at the Tomakomai Flux Tower, which stands in a coniferous forest with a height of about 15–20 m, mainly larch (Larix). The water turbidity concentration of suspended sediment (SS) and water temperature (CTI, C105) were automatically recorded to data-loggers at 10-minute intervals from July 2002 to June 2003. Furthermore, samples of the turbid water below the water surface were automatically taken using sample bottles (ISCO, Model-3700) at hourly intervals six times during periods of high precipitation (July, August, October 2002, and May, June, July 2003) in order to calibrate the water turbidity concentration (Figure 8).

Figure 8. Observation of water turbidity and nutrient concentrations by using water sampler and turbidity sensor; (a) setting of instruments box beside the river, (b) details of instruments such as datalogger and water sampler (ISCO, Model-3700), and (c) sensors for water turbidity concentration and water temperature (CTI, C105).

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The subsamples used for analysis of SS were filtered through precombusted GF/F glass fiber membrane filter (Whatman Japan Ltd., Tokyo, Japan). Furthermore, the particle size distributions for SS were examined by the laser particle size analyzer (CILAS 920/1064). The averaged diameter of SS examined by this analyzer was 30 μm. The calibrated suspended sediment was used for comparing with the sediment-rating curve and calibrating/validating the simulation result of NICE-MASS. Furthermore, in order to simulation the elevation change by sediment aggradations in the Kushiro Mire, the observed discharge and estimated sediment-flux data at the 12-inflow points (7 points by Hokkaido Regional Development Bureau; 10 points by Nakayama and Watanabe, 2004) and 1-outflow point (Hokkaido Regional Development Burea) tributaries from 1970s to 2003 were inputted to the twodimensional depth-averaged simulation (Figure 3).

4.3. Estimation of Heat Flux Budgets from Meteorological Data The heat flux budgets were evaluated from the measured meteorological data at the grassland station. Equation (66) describes the relationship between the heat flux at the ground surface (with vegetation) based on net radiation NR (W/m2), latent heat flux LH (W/m2), sensible heat flux SH (W/m2), and ground-transfer heat flux GH (W/m2):

NR + LH + SH + GH = 0

(66)

In equation (66), the heat flux transferred to the ground surface is set at a positive value. NR (short-wave radiation + long-wave radiation) is defined as follows:

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4 4 ⎛ ⎞ NR = (1 − α )SR + εR − εσTg = (1− α )SR − ε ⎝ σTg − R ⎠

(67)

where α is surface albedo; SR (W/m2) is solar radiation; R (W/m2) is atmospheric radiation; σ (= 5.56 × 10–8 W/m2/K4) is the Stefan-Boltzmann constant; and ε (≈1) is emissivity. NR was directly measured by net radiometer (Eikou-Seiki, CN-11). GH is estimated as follows:

GH = − Qg − ρ sCs

ΔTm z Δt s

(68)

where ρs (kg/m3) is the density of soil; Cs (J/kg/K) is the specific heat of soil; Tm is the mean soil temperature; zs (m) is the depth of the heat flow plate; and Qg is the heat flow on the soil measured by heat flow plate. The second term on the right-hand side of equation (68) is the temporal variation of heat storage between the ground surface and depth zs, estimated from the temporal gradient of soil temperature. Both LH and SH are estimated from the vertical gradients of measured values at two points by using a gradient method (Oke, 1990):

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Tadanobu Nakayama

SH = ρ aCaκ

LH = ρ aλκ

2

2

ΔTa ⋅ ΔWa

(Δ ln za )

2

Δqa ⋅ ΔWa

(Δ ln za )

2

−1

(Φ M Φ H )

−1

(Φ M Φ E )

(69)

(70)

where κ is Karman constant (≈ 0.41); ρa (kg/m3) is the density of air; Ca (J/kg/K) is the specific heat of air; Ta (K) is air temperature; Wa (m/s) is wind speed; qa is specific humidity of air; and za (m) is the measurement height of Ta, Wa, and qa from the ground. Φ is a nondimensional function displaying the degree of aerodynamic stability (stable, neutral, or unstable), and the subscripts M, H, and E refer to momentum, sensible heat, and latent heat, respectively. There is an empirical correlation between (ΦMΦX)–1 in equations (69) and (70) when the Richardson number Ri is used as follows (X = H or E):

(Φ M Φ X ) = (1− 5Ri ) , (Ri > 0) −1 0.75 (Φ M Φ X ) = (1−16Ri ) , ( Ri < 0) −1

Ri =

2

(71)

g(ΔTa / Δza ) Ta (ΔWa / Δza )2

where Ta (K) is the mean temperature of two points. If Ri = 0 (neutral condition), (ΦMΦX)–1 Copyright © 2008. Nova Science Publishers, Incorporated. All rights reserved.

= 1.

4.4. Vegetation and Soil Properties About 50 vegetation and soil parameters were calculated in each cell on the basis of vegetation class and soil texture obtained from the Digital National Land Information GIS data of Japan for 1993 (Clapp and Hornberger, 1978; Rawls et al., 1982). The major parameters include vegetation cover, green fraction, albedo, surface roughness length and zero displacement height, soil conductivity and soil water potential at saturation, and some parameters of stomatal resistance that relate to environmental factors. Soil texture data were digitized and categorized from a soil map of arable land in Hokkaido (Hokkaido National Agricultural Experiment Station, 1985) into 7 types for SiB2, and were converted to a 1-km mesh (Figure 9). The red square shows the study area for drying phenomena, identical to Figure 1. The soil of Kushiro Mire is mainly peat. Vegetation class data categorized into 11 types, identified by the Environment Agency of Japan (1993), were also converted to a 1-km mesh (data not shown). There is natural vegetation where vegetation class takes higher value. Soil samples were taken at two depths (0.1 and 1.0 m) to identify soil hydraulic permeability, geological structure, and water content at eight sites (Figure 1 and Table 4).

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Table 4. Results of soil property and field permeability test Horizontal Hydraulic Conductivity Kh (m/hour)

Vertical Hydraulic Conductivity Kv (m/hour)

Specific Storage Ss (1/m)

Specific Yield Sy

Type

Soil Type

1

1.0E+01

1.0E-05

2.0E-01

2

Gravel 5.0E+01 Fine to Medium Sand 5.0E+00

1.0E+00

1.0E-04

2.0E-01

3

Finer than Silt

5.0E-01

1.0E-01

1.0E-03

1.0E-01

4

Rigid Base

5.0E-01

1.0E-01

1.0E-03

5.0E-02

Table 5. Geological parameters used in numerical simulation

Point

Depth (m)

Soil Type

Hydraulic Conductivity (cm/sec)

K-2

0.1

Organic Soil

4.43E-05

1.0

Pumice Stone

2.56E-06

0.1

Organic Soil & Volcanic Ash Sand

6.41E-04

1.0

Organic Soil & Volcanic Ash Sand

1.12E-05

0.1

Organic Soil

9.18E-05

1.0

Volcanic Ash Sand

3.70E-06

0.1

Organic Soil

1.83E-05

1.0

Volcanic Ash Sand

5.49E-06

0.1

Organic Soil

4.26E-05

1.0

Volcanic Ash Sand

2.42E-05

0.1

Gravel & Sand

4.31E-04

1.0

Volcanic Ash Sand

1.53E-05

0.1

Peat

1.59E-04

1.0

Peat

5.71E-06

0.1

Volcanic Ash Sand

3.13E-04

1.0

Organic Soil

6.75E-05

T-1

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I-1 O-1 Tsu-1 H-1 Ku-1 Ku-2

By using these soil samples and about 150 sample data points (Explanatory text of hydrogeological maps of Hokkaido, Ohara et al., 1975), geological structure was divided into four types on the basis of hydraulic conductivity (Kh and Kv), the specific storage of porous material (Ss), and specific yield (Sy) after the calibration for fitting simulated hydraulic heads

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to the observed heads, while keeping these values in the initial estimated known range (Table 5). Kushiro Mire consists largely of soils finer than silt, mainly peat.

Figure 9. Example of boundary conditions for simulation at 1 km mesh in Kushiro River catchment, soil texture (1981).

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4.5. MODIS Data The MODIS-LST (land surface temperature) data were converted to surface temperatures T0 by using the NASA MOD11-ATBD (algorithm theoretical basis documents) equation (Wan, 1999). FPAR and LAI were calculated by using MOD15-ATBD from the surface reflectance product (MOD09) and the land cover type product (MOD12): T0 = 0.02 × DN1, FPAR = 0.01 × DN2, LAI = 0.1 × DN3, where T0 is surface temperature (°C), FPAR (µmol/m2/s) is fraction of photosynthetically active radiation, LAI is leaf area index, and DNi (i = 1, 2, 3) is the digital number of MODIS data for T0, FPAR, and LAI, respectively. Because MODIS has more data channels than previous satellites, such as Landsat, it is possible to analyze higher-order products, such as LAI and FPAR, which are important parameters for evaluating vegetation growth (Christopher et al., 1998). Because the MODIS data are recorded at about 10:30 h every 8 days, the author averaged the Tomakomai Flux Tower data at 10:00, 10:30, and 11:00 h for comparison with MODIS data. The MODIS data were averaged over each 3 × 3 squares of pixels because there is transformation error from sinusoidal to universal transverse Mercator coordinate system after noise and null values due to bad weather were eliminated. MODIS data were compared with the synchronized groundtruth data at the Tomakomai Flux Tower for surface temperature (°C) and FPAR (µmol/m2/s). The estimate of surface temperature is highly accurate (Rr = 0.992). Although the MODIS

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FPAR value underestimates the observed value in winter (mainly because of higher cloud cover and falling snow), the correlation is still good (Rr = 0.788) (data not shown). MODIS LAI and FPAR (µmol/m2/s) images of the Kushiro River catchment were obtained every 8 days in the entire 2001 (1-km mesh; image areas) for input into the model after noise was removed and moving average was applied (Figs 10a, b). Seasonal changes in LAI and FPAR, particularly in the mire, imply that the vegetation phenology and the water cycle are closely related in the mire. Both parameters take their maximum values (green) from early summer to fall while vegetation is growing, and decrease toward winter, and both variables are highly correlated (Rr = 0.891). In winter, both parameters are almost zero in the lower elevation areas because the snow mostly covers the comparatively shorter vegetations. LAI and FPAR values can integrate the net assimilation rate (subtracting leaf respiration rate from leaf photosynthetic rate) for leaves at the top of the canopy into the total assimilation rate over the depth-profile of leaf multiplied by Π (Sellers et al., 1996). Π is calculates as follows:

Π=

g r (1− e − k LAI ) k

≈

FPAR

(72)

k

where k is temporal-mean (radiation-weighted) extinction coefficient for PAR and gr is the greenness of canopy. Both LAI and FPAR values were input into the model by improving the original SiB2 model developed by Sellers et al. (1996). The greenness of canopy was calculated by using equations (73) and (74) and input into equation (72), whereas previous research used a greenness of a constant value.

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k=−

a log(1 − FPAR) LAI

(73) 2

ω v − [α1,2 + δ1,2 ] gr = [α1,1 + δ1,1]− [α1,2 + δ1,2 ]

⎛ k ⎞ 1 − ⎜ ⎟ − α1,2 + δ1,2 ⎝ Kd ⎠ = α 1,1 + δ1,1 − α1,2 + δ1,2

[

[ ][

] ]

(74)

ωv is leaf scattering coefficient in the visible wavelength interval; Kd is optical depth of direct beam per unit leaf area; αi,j is the leaf reflectance; and δi,j is the leaf transmittance. In αi,j and δi,j, i = 1 and 2 denote visible and near infrared, and j = 1 and 2 denote live and dead, respectively. Consistent canopy conductance can be calculated because SiB2 combines a photosynthetic model with a leaf stomatal conductance model, and calculates the canopy transpiration rate and surface energy balance (Sellers, et al., 1996).

4.6. Running the Simulation The simulation area is 50 km wide by 80 km long, covering the whole Kushiro River catchment (Figure 1). This area is discretized into a grid of 100 × 160 blocks, with a grid

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Tadanobu Nakayama

spacing of 500 m. The simulation was conducted on an NEC SX-6 supercomputer from 1 January 2001 to 31 December 2002 by using the interpolated forcing data at each grid from the observed data and the AMeDAS data (Table 6).

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Table 6. Lists of observation and AMeDAS (Automated Meteorological Data Acquisition System) data points. These locations are plotted in Figure 1 No.

Point Name

Type

Lat.

Lon.

Elev.(m)

A-1

Onnenai (Mire)

Meteorology

43°06'05″

144°20'29″

8.00

A-2

Teshikaga (Grassland)

Meteorology

43°31'08″

144°28'10″

187.00

A-3

Shibecya (Forest)

Meteorology

43°20'18″

144°38'55″

127.00

M-1

Kawayu

Meteorology

43°38'18″

144°27'24″

133.00

M-2

Teshikaga

Meteorology

43°30'54″

144°28'48″

198.00

M-3

Akanko

Meteorology

43°26'00″

144°05'36″

430.00

M-4

Shibecha

Meteorology

43°18'24″

144°36'18″

32.00

M-5

Tsurui

Meteorology

43°13'48″

144°19'30″

42.00

M-6

Nakateshibetsu

Meteorology

43°11'48″

144°08'48″

80.00

M-7

Sakakimachi

Meteorology

43°07'06″

145°06'54″

2.00

M-8

Ohta

Meteorology

43°05'24″

144°46'54″

85.00

M-9

Shiranuka

Meteorology

42°58'06″

144°03'54″

9.00

M-10

Kushiro

Meteorology

42°58'30″

144°23'30″

32.00

M-11

Chippomanai

Meteorology

42°56'06″

144°44'12″

145.00

R-1

Onnenai (Onnenai River)

43°06'51″

144°19'55″

6.73

R-2

Setsuri (Hororo River)

43°09'17″

144°19'48″

7.39

R-3

Otowa (Setsuri River)

43°11'06″

144°20'19″

8.80

R-4

Tsuruhashinai (Tsuruhashinai River)

43°11'49″

144°23'10″

15.78

R-5

Meikyo (Kucyoro River)

43°09'50″

144°27'05″

9.85

R-6

Daini (Kottaro River)

43°14'20″

144°27'52″

12.71

R-7

Numaoro (Numaoro River)

43°15'40″

144°29'53″

16.92

R-8

Shimoosobetsu (Osobetsu River)

43°16'05″

144°32'49″

14.04

R-9

Tohmi (Shirarutoroetoro River)

43°12'02″

144°31'26″

7.82

R-10

Tohro (Arekinai River)

43°09'11″

144°30'18″

6.56

River Discharge River Discharge River Discharge River Discharge River Discharge River Discharge River Discharge River Discharge River Discharge River Discharge

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No.

Point Name

Type

R-11

Isobunnai (Isobunnai River)

R-12

Mansui (Kushiro River)

R-13

Shimotoubetsu (Toubetsu River)

River Discharge River Discharge River Discharge

No.

Point Name

Type

G-1

K-1 (Kucyoro River)

G-2

K-2 (Kucyoro River)

G-3

K-4 (Kucyoro River)

G-4

K-7 (Kucyoro River)

G-5

K-9 (Kucyoro River)

G-6

T-1 (Toubetsu River)

G-7

T-2 (Toubetsu River)

G-8

T-3 (Toubetsu River)

G-9

T-5 (Toubetsu River)

G-10

I-1 (Isobunnai River)

G-11

I-2 (Isobunnai River)

G-12

I-5 (Isobunnai River)

G-13

O-1 (Osobetsu River)

G-14

O-2 (Osobetsu River)

G-15

N-1 (Numaoro River)

G-16

Ko-1 (Kottaro River)

G-17

Tsu-1 (Setsuri River)

G-18

Tsu-2 (Setsuri River)

G-19

H-1 (Hororo River)

G-20

Ta-1 (Tawa River)

Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level

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Lat.

Lon.

Elev.(m)

43°23'00″

144°32'54″

48.44

43°28'55″

144°28'22″

91.43

43°28'49″

144°28'12″

92.19

Lat.

Lon.

Elev.(m)

43°13'04″

144°26'10″

18.14

43°14'53″

144°25'19″

39.27

43°16'49″

144°22'46″

73.48

43°20'46″

144°20'48″

139.55

43°21'56″

144°18'07″

200.00

43°28'22″

144°27'03″

111.23

43°27'56″

144°24'00″

142.59

43°29'18″

144°22'35″

221.89

43°26'15″

144°20'36″

234.00

43°23'29″

144°33'35″

63.59

43°25'48″

144°33'56″

103.57

43°28'19″

144°36'10″

196.62

43°19'56″

144°28'53″

51.58

43°23'50″

144°24'59″

116.33

43°17'45″

144°27'55″

49.98

43°16'28″

144°26'23″

47.27

43°15'30″

144°19'58″

58.48

43°16'36″

144°15'42″

108.02

43°12'44″

144°16'09″

65.77

43°22'33″

144°37'26″

58.37

40

Tadanobu Nakayama

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Table 6. (Continued) No.

Point Name

G-21

C-1 (Tsuruhashinai River)

G-22

Ku-1 (Kushiro River)

G-23

Ku-2 (Kushiro River)

G-24

Ku-3 (Kushiro River)

G-25

W-1 (Kushiro Mire)

G-26

W-2 (Kushiro Mire)

G-27

W-3 (Kushiro Mire)

G-28

W-4 (Kushiro Mire)

G-29

W-5 (Kushiro Mire)

G-30

A-1 (Akita River)

Type Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level Groundwater Level

Lat.

Lon.

Elev.(m)

43°11'34″

144°23'35″

20.63

43°18'56″

144°36'21″

30.13

43°14'04″

144°32'55″

18.40

43°21'01″

144°34'03″

39.34

43°07'31″

144°25'51″

73.48

43°08'02″

144°26'41″

3.71

43°08'12″

144°26'32″

3.73

43°08'57″

144°27'17″

5.35

43°08'53″

144°26'48″

8.42

43°26'45″

144°30'23″

8.16

Figure 10. Seasonal variation in MODIS LAI and FPAR images at 1km mesh in 2001 (image areas: 43 °00’N - 43°45’N, 144°00’E - 144°45’E), (a) LAI-Image from MODIS-Data, (b) FPAR-Image from MODIS-Data. Blue line is border of catchment, and pink line is that of Kushiro Mire.

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The first 6 months were used as a warm-up period until equilibrium conditions were reached, and parameters were estimated by comparison of simulated steady-state values in steady-state condition with the observed values published in the literature. A time step of Δt = 1 hour was used. Furthermore, one-dimensional simulation at Tomakomai and Noboribetsu outside the catchment was conducted from 1 January 1984 to 31 December 1985 (Mamiya and Chiba, 1985) by using the AMeDAS data in order to validate the snow and frost depth. In particular, the phase changes and the frozen/thaw front are calculated as follows; Firstly, three soil layers in unsaturated zone are subdivided into frost and thaw layers after determining the position of the frozen/thaw front. Secondly, the water/heat balance equations are solved by considering the phase changes. Thirdly, the hydraulic conductivity is updated by considering the effect of frost/thaw layer. Finally, the soil thermal properties for the next time step are calculated by the updated ice and liquid water contents. The simulation area for water/heat budget and mass transport was 22 km wide by 46 km long, covering the whole Kucyoro River catchment, a tributary of Kushiro River (Figure 3). This area is discretized into a grid of 226 × 460 blocks with a grid spacing of 100 m, which is sufficiently fine to describe the shape of meandered channels. Each stream channel was divided into 20 sections. The time-step in the water/heat budget simulation was Δt = 30 min in order to facilitate numerical stability. The surface runoff submodel of the NICE model including snowmelt runoff (Nakayama and Watanabe, 2006) was applied to the catchment in each cell of a 50-m mesh by using a digital elevation model of 50-m mesh (Geographical Survey Institute of Japan, 1999). Only kinematic wave theory was applied to the stream network submodel because dynamic wave effect is negligible in the Kucyoro River catchment (Nakayama and Watanabe, 2004). The simulation was conducted on an NEC SX-6 supercomputer from 1970 (meandering channel, Figure 3a) to 2003 (present channelized river, Figure 3b) by using the same data for soil texture, vegetation class, geological structure, and the different shapes of the river channels. The first 6 months were used as a warm-up period until equilibrium conditions were reached, and parameters were estimated by comparison of simulated results using the observed values published in the literature. After simulation results of water/heat budget such as river discharge, soil moisture, groundwater level, and soil temperature, were calibrated and validated by the observed data in 2001-2002 (Nakayama and Watanabe, 2004, 2006), the simulation could back-cast the water/heat budget during 1970-2000. In the simulation during 1970-2002, soil texture and geological structure were fixed at constant, and only meteorological forcing data, land cover (Figure 3), and vegetation (Table 1) were changed on the assumption that predominant changes in the underground structure had not occurred as had changes in land cover and vegetation from 1970 to 2001. Then, the mass transport was simulated at the hillslope and the river by using the equations (42) and (53) and by using the unit flux data in the previous researches (Itakura, 1984; Shimizu and Arai, 1988; Kushiro Branch Office, 2002; Toda et al., 2002). The timesteps in the hillslope and stream network models were Δt = 50 and 10 s in order to facilitate numerical stability. Kinematic eddy diffusivity at each layer was set in order to satisfy the stability condition of diffusion term and to set the time step as large as possible. The simulated results of nutrient and sediment loads were validated by the observed data in 20022003. Around the Kushiro Mire, the two-dimensional depth-averaged simulations for shallow water theory and advection-diffusion equation were conducted by inputting the observed discharge and estimated sediment-flux at the inflow (12 points) and outflow (1 point)

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tributaries (Nakayama and Watanabe, 2004; Kushiro Development and Construction Department, Hokkaido Regional Development Bureau) (Figure 1) in order to simulate the elevation change by sediment aggradations from 1970 to 2003. The time-step in the sediment transport simulation around the mire was Δt = 1 s. Simulated results of river discharge, groundwater level, and elevation change by sediment aggradation, were validated by the NIES observation data (Nakayama and Watanabe, 2004, 2006) and the previous researches (Ministry of Environment, 2004; Kushiro Development and Construction Department, Hokkaido Regional Development Bureau). The simulation area for vegetation succession was 5 km wide by 13 km long in the downstream section of the Kucyoro River in the mire (Figure 3). Mesh size was 10 m x 10 m, which was sufficient to represent a vegetation colony of plot size (Shinsho et al., 1988; Shinsho and Tsujii, 1996). Simulated results for soil moisture, groundwater level, submerged depth, nutrient loading, and sediment accumulation averaged at every month were input to the vegetation succession model in order to simulate vegetation growth for every year after a warm-up simulation of 300 years (Figure 7). The simulated tree height was calibrated using values obtained in previous studies. Finally, the simulation of vegetation succession was conducted for the period 1970-2003, and the results were compared with the vegetation distribution of GIS data (Figure 3).

5. RESULTS

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5.1. Hydrologic Budget in Snow-Free Periods 5.1.1. Soil Moisture in Various Land Covers Simulated results of soil moisture for different land covers (mire, grassland, and forest) were compared with observed values and the precipitation distribution from 1 August to 31 October 2001 (Figures 11a–c). The higher precipitation around 10 September was due to typhoons. Soil moisture fluctuation in response to changes in precipitation, evaporation, and redistribution decreases in deeper layers, indicating that the influence of meteorological forcing is smoothed and the response times become longer with depth (Li and Islam, 1999; Yu et al., 2001). In grassland (Figure 11b), where vegetation and soil structures are simpler, the simulation accurately reproduces the observed data at soil depths of 10 cm and 1.0 m. In forested areas (Figure 11c), the porosity changes greatly in the vertical direction owing to a more complex root distribution, which suppresses the response of the observed soil moisture at 1.0 m. The simulated value excellently reproduces the observed data at 10 cm. However, it does not reproduce it at 1.0 m with higher precipitation, because porosity and hydraulic conductivity are constant in the vertical direction in this model despite the change in soil texture with depth (Yu et al., 2001). The surface soil moisture content in forest is higher than in grassland, indicating that the forest land cover retains more water in soil and vegetation than grassland does, and that grassland tends to quickly lose infiltrated water to the groundwater system and evapotranspiration. Furthermore, the differences in land cover become less marked with depth. In the mire (Figure 11a), the simulation takes on an almost constant saturated value (≈ 1.0) owing to the shallow groundwater level calculated by MODFLOW. This simulated value

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agrees well with the observed value of approximately 0.99. This high value is characteristic of a mire because of the poor drainage at lower elevations, the almost flat surface, the peaty soil texture, and the soil’s elasticity (Kellner and Halldin, 2002). The simulations of soil moisture reproduce very well the measured values in the mire, grassland, and forest.

Figure 11. Time-series of precipitation and soil moisture at three meteorological stations from 1 August to 31 October 2001. Soil moisture at (a) mire, (b) grassland, and (c) forest, respectively. Open circle and open triangle show observed values at soil depths of 10cm and 1.0m, respectively. Solid line and dashed line show calculated values at soil depths of 10cm and 1.0m, respectively.

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5.1.2. Soil Temperature and Heat Flux Budget The simulated values of soil temperature in mire, grassland, and forest from 1 September to 30 September 2001 at a depth of 30 cm agree strongly with the measured values (Figures 12a–c). The simulated value at a depth of 10 cm in grassland does not reproduce the observed value around 15 September due to the occurrence of a typhoon, because rapid infiltration flow increases the effective heat capacity of soils despite the inclusion of soil moisture and soil porosity effects in SiB2 (Sellers et al., 1996). To strongly reproduce the soil temperature, it is necessary to solve the simultaneous heat-transfer equations for both the water and soil layers with a finer mesh. In September, the temperature in the grassland is highest because the forest trees prevent the sunshine from warming the ground and the mire is almost saturated with a higher effective heat capacity.

Figure 12. Soil temperature at three meteorological stations from 1 September to 30 September 2001. Soil temperature at (a) mire, (b) grassland, and (c) forest, respectively. Open circle and open triangle show observed values at soil depths of 10cm and 30cm, respectively. Solid line and bold line show calculated values at soil depths of 10cm and 30cm, respectively.

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Figure 13. Time-series of heat-flux components of (a) observation and (b) simulation at grassland meteorological station from 1 August to 11 August 2001. NR (net radiation); dashed line, LH (latent heat flux); dash-dotted line, SH (sensible heat flux); solid line, and GH (ground-transfer heat flux); bold line.

Globally, the simulated values of the heat flux budgets (W/m2) in grassland excellently reproduce the observed values from 1 August to 11 August 2001, despite small discrepancies in sensible heat flux (SH) and latent heat flux (LH) (Figures 13a-b). Therefore, the soil and vegetation parameters were correctly selected in the simulation and the simulation model accurately described the vegetation and soil structures (Figures 11–13).

5.1.3. Groundwater Levels Simulated hydraulic heads in steady-state average for the overall Kushiro River catchment were plotted against the measured values (Ohara et al., 1975). Hydraulic head becomes smaller as the riverbed hydraulic conductance kr (m2/h) becomes larger (Figure 14). The simulated groundwater level for kr = 300 m2/h agrees closely with the measured value, and therefore this kr value was used in the following simulations. In the vicinity of the Kushiro Mire (well number h-60 to 120), groundwater almost saturates the surrounding soil, which can be reproduced very well by the numerical simulation. Generally speaking, because a three-dimensional groundwater model is used in the current study, the simulation values of groundwater fluctuations agree strongly with the measured values from 1 May to 31 October 2001, which depend not only on precipitation but also on local topography (Figure 15).

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Figure 14. Distribution of hydraulic heads in Kushiro River catchment. Open circles; measured values by Ohata et al. (1975), dashed line; simulated value without river, dash-dotted line; simulated value of kr=30m2/h, and solid line; simulated value of kr=300m2/h.

Figure 15. (a) Validation points of ground-water fluctuations, and (b) temporal variation of groundwater fluctuations from 1 May to 31 October 2001. Open circles are observed values and solid lines are simulated values.

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The large amount of precipitation due to typhoons in September 2001 greatly affected the groundwater fluctuations in all areas of the catchment. In mountainous areas away from Kushiro Mire (O-1 and I-1), the groundwater level decreases gradually from spring to summer after snowmelt. However, around the mire (Ku-2 and W-5), the groundwater level is almost constant from spring through the typhoon season, as can also be seen with soil moisture in Figure 11a, which shows the high soil water capacity in the mire (Winter, 1988; Kellner and Halldin, 2002). The simulated groundwater levels reproduce actual levels very accurately, both in the mountainous areas and near the mire, owing to the contribution of recharge rates in the soil moisture model in the upper layer. The overall variations in both soil water content and groundwater levels are very similar, namely a rapid increase before precipitation and a gradual decrease after precipitation (Figures 11a–c and 15); similar fluctuation patterns were observed by Chen et al. (2002) and Eltahir and Yeh (1999). However, the groundwater level shows its peak only on 10 September with small fluctuations (Figure 15), whereas soil moisture fluctuates sharply in response to precipitation (Figures 11a–c). The groundwater acts as a long-term reservoir storing excessive soil water. Furthermore, the simulations do not consider short-term outflow rigidly, causing a poor response to short-term groundwater fluctuations (W-5), which implies that it is necessary to improve the numerical models to include the effect of short-term outflow. The short-term groundwater fluctuations are due to the locations of groundwater gauges near river flows.

5.1.4. River Discharge The simulated discharges of both the kinematic wave (dashed line) and dynamic wave (solid line) methods from stream network modeling are compared with observed values (open circles) at several points in the Kushiro River catchment from May 1 to October 31 2001 (Figures 16a, b). In both figures, the observed discharge decreases gradually in the same way as the ground-water levels in the mountainous areas from the spring to the early summer (Ohara et al., 1975). In the late summer and fall, the discharge fluctuates greatly, depending on volume of precipitation from typhoons. The simulated river discharge at the Kucyoro water-flow survey station in the Kucyoro River (bed gradient here is about 1/2000), a tributary of the Kushiro River flowing into the Kushiro Mire, reproduces very well the observed value in the typhoon period, and the differences between kinematic wave and dynamic wave models are smaller, showing that the dynamic wave effect is small at this point despite the lower bed gradient (Figure 16a). Furthermore, the base flow can be calculated correctly because the grid-based distributed model used includes recharge rates, seepage, and the return flow from the ground. However, the simulation cannot reproduce the observed data during the melting period, in particular, in early May, and the simulated discharge is smaller than the observed value. The integrated model in this study does not completely include the melting process of snow volume to surface flow, a necessary consideration in the future. At the Gojikkoku water-flow survey station downstream of the main Kushiro River region (bed gradient is about 1/1700), the dynamic effect is important because of the influence from the Kushiro Mire (Figure 16b). The peak discharge comes later and milder (but with a larger volume) in the downstream regions (maximum delay is about 5 days) owing to the lower discharge rate from the mire (Winter, 1988; Kellner and Halldin, 2002), which is 50%–60% of the flow rate in the mire (Ohara et al., 1975).

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Figure 16. Temporal variation of river discharge at Kushiro River catchment from 1 May to 31 October 2001, (a) tributary of Kushiro River flowing into Kushiro mire (Shimo-Kucyoro observation point), and (b) Kushiro River (Gojikkoku observation point). Open circles are observed values and lines are calculated values (dashed line; kinematic wave model, solid line; dynamic wave model).

The simulated results of the kinematic wave model overestimate the observed values, especially during the rainy periods. The dynamic wave model reproduces excellently the peak value of discharge in precipitation and the backwater effect after a flood. Thus, the grid-based distributed model used is highly accurate for different topographies because it includes surface-unsaturated–saturated water processes displaying both short- and long-term components of river discharge, effective precipitation, and the kinematic and dynamic wave effects at various river slopes.

5.1.5. Soil Moisture Changes (Drying) from 1977 to 2001 The size of the simulated area for monthly-averaged groundwater, and soil moisture around the Kushiro Mire in 2001 (the same study area for drying phenomena shown in Figure 2, b) is 14.0 km long by 10.0 km wide (Figures 17a, b). This region is a low-elevation mire surrounded by capes and low mountains, of which about 10–20 km2 floods in very wet weather (Figure 2). In those times, large amounts of sediment and gravel flow into the mire and piles are formed, as evaluated by the WTI (water turbidity index) from a Landsat TM image (Kameyama et al., 2001), showing the higher turbidity area during flood periods. The groundwater levels fluctuate seasonally and spatially (Figure 17a), and the monthlyaveraged levels were greatly affected by the typhoons in September 2001 (Figure 15). In

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particular, the regions of low-level groundwater closely correspond to the regions of higher turbidity at the southern and eastern sides of the Kirakotan Cape (Kameyama et al., 2001).

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Figure 17. Simulated results of (a) monthly-averaged groundwater level, and (b) soil moisture around the Kushiro Mire in the snow-free period of 2001.

Figure 18. Simulated results of 6 month (1 May to 31 October) averaged (a) groundwater level, and (b) soil moisture in the snow-free period of 1977 and 2001 corresponding with Figure 2. Circle areas show the decreasing of groundwater level and soil moisture corresponding with the invasion of Alnus japonica in Figure 2.

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The eastern side of the cape is located downstream of the channelized river (Figure 3); therefore, the coarser sediments are deposited there as the flow velocity decreases. In contrast, on the southern side of the cape, floodwater spreads out to the lowest areas, depositing the finer sediments widely. In these regions, the simulation showed that the outflow of groundwater to the river and the inflow of recharge became smaller because of the lowered groundwater levels. The simulated soil moisture for 2001 (Figure 17b) takes a greater value near the Kushiro Mire, especially from summer to fall, than in the surrounded areas, with a value greater than 0.80. In the simulation, the regions of higher soil moisture in the northeast and southwest correspond closely to those with higher groundwater levels (Figure 17a). Furthermore, the region of lower soil moisture near the mire closely depends on the region of higher turbidity and the invasion of alder (Figure 3). In the simulation of groundwater levels and soil moisture for 1977 (Figs 18a, b), the same data of soil texture and geological structure were used as those for 2001. Only meteorological forcing data and the vegetation classes were changed, on the assumption that predominant changes in the underground structure had not occurred as had changes in vegetation phenology from 1977 to 2001. The simulated 6 month- (1 May to 31 October) averaged groundwater levels (Figure 18a) are greatly affected by the local topography and fluctuate more as the slope becomes less steep, in particular near the Kushiro Mire. In some areas near the Kushiro Mire, the low-lying region is inundated, and has higher soil moisture throughout the year, which is characteristic of mire that is covered mainly by reeds. The groundwater levels in 1977 and 2001 are almost similar, but a modest decrease in groundwater levels can be observed near the alder invasion area (circled area) owing to the deposit of sediment load and the decrease of recharge rate to the groundwater. The simulated annual-averaged soil moisture (Figure 18b) clearly indicates the drying phenomena near the circled area due to the invasion of alder (Figure 3).

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5.2. Hydrologic Budget in Snow Periods 5.2.1. Effect of Micro-Topography and Land Cover on Snow Depth and Soil Frost The simulation values of snow depth were compared with detailed experimental data at Hokkaido (Figures 19a-b). Furthermore, the correlation between observed value and simulated value r2 when the slope of regression line equals to 1 was also plotted. Figure 19a shows a simulation result of snow depth at Tomakomai and Noboribetsu from 1 January 1984 to 31 December 1985, plotted with the observed value (Mamiya and Chiba, 1985). The snow depth at Noboribetsu was much greater than that at Tomakomai, because of the microtopography and weather conditions, in particular, mean elevation. The simulation reproduces 2

2

well the observed values at both places ( rNobo =0.556, rToma =0.727). Effect of different land covers (mire, grassland, and forest; A-1 to –3 in Table 6, Tsurui; M-5 in Table 6) on snow depth from 1 January 2001 to 30 April 2002 in Kushiro River catchment was evaluated 2

2

(Figure 19b). The correlation values in mire and forest ( rA−1 =0.605, rA−3 =0.544) are smaller 2

2

than those in grassland and Tsurui observation point ( rA− 2 =0.846, rM −5 =0.815). The NICESNOW cannot simulate correctly the snow depth affected by the almost saturated groundwater in mire, which is also correlated with the overestimates of soil temperature written in the following section 5.2.2. The snow depth in forest is affected by the locality of

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various trees, which can be reproduced better by using a finer model raster around the canopy and inputting a more correct meteorological data. The snow depth in grassland is largest and in mire smallest, which can be reproduced well by the simulation.

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a

b Figure 19. Comparison of snow depth of (a) effect of local meteorology (from 1 January 1984 to 31 December 1985). Open circle and triangle : observed data (Mamiya and Chiba, 1985); lines and dotted lines : simulated values; and (b) effect of land cover (from 1 January 2001 to 30 April 2002). Open circle, triangle, square, and reverse triangle: observed values at mire (A-1), grassland (A-2), forest (A3), and Tsurui (M-5) observation point; line, dashed line, dotted line, and dash-dotted line : simulated values. The r2 shows the correlation between observed value and simulated value when the slope of regression line equals to 1.

The simulation results of frost depth by using the NICE-SNOW at Noboribetsu were plotted with the values observed by Mamiya and Chiba (1985), together with the correlation value r2 (Figure 20). The coefficient β evaluated by the equation (36) is also plotted in this figure. Although previous research in Hokkaido suggested that the frost penetration is almost zero at a snow depth greater than 20 cm owing to the insulation effect of the snow preventing soil cooling (Ishikawa and Suzuki, 1964), the frost penetration progresses at a greater snow

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depth in this figure, which implies that various topographical and meteorological characteristics besides snow depth also affect frost depth.

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Figure 20. Comparison of frost depth from 1 January 1984 to 31 December 1985. β means the coefficient of the original Stefan solution in the equation (11). Open circle and triangle : observed data (Mamiya and Chiba, 1985); lines and dotted lines : simulated values by NICE-2; filled circle and triangle : calculated values by Stefan solution. The r2 shows the correlation between observed value and simulated value.

Furthermore, β takes different values depending mainly on the slope direction, which suggests that the original Stefan solution in the equation (36) does not include physically the effect of slope direction and can’t reproduce the effect of micro-topography. This is because the Stefan solution regards the air temperature as being the same as the ground-surface temperature. The frost depth simulated by the NICE-SNOW in the equation (37) agrees well 2

2

with the observed value on both northern and southern slopes ( rNorth =0.833, rSouth =0.732), and the NICE-SNOW can reproduce the frost depth on the northern slope is larger. This means that the NICE-SNOW correctly simulates the heat budget dependent on the slope and shading characteristics, and that the amount of sunshine falling on the slope is a major factor in the local difference of frost depth.

5.2.2. Soil Moisture and Groundwater with Phase Changes The observed soil moisture data were compared with the simulated results in grassland and forest from 1 January 2001 to 31 December 2002 for two years, together with the precipitation distribution in the same period (Figures 21; A-2 and –3 in Table 6). Soil moisture is not always recorded in the coldest period, because the measuring systems are vulnerable to the severe weather. The soil moisture takes greater values both in early spring (snowmelt) and fall (typhoon). The surface soil moisture in forest is higher than that in grassland, which indicates that the forest landcover retains more water in soil and vegetation

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than grassland does, and that grassland tends to quickly lose infiltrated water to the groundwater system and evapotranspiration. Furthermore, the differences among land cover become less marked with depth (Nakayama and Watanabe, 2004).

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a

b Figure 21. Time-series of precipitation and soil moisture at two meteorological stations from 1 January 2001 to 31 December 2002 for two years (A-2 and –3 in Table 6). Soil moisture at (a) grassland, and (b) forest, respectively. Open circle and open triangle show observed values at soil depths of 10cm and 1.0m, respectively. Solid line and dashed line show calculated total moisture (ice + liquid) at soil depths of 10cm and 1.0m, respectively. Bold line and bold dashed-line show calculated liquid moisture at soil depths of 10cm and 1.0m, respectively.

Generally, the simulated soil moistures reproduce excellently the observed values during two years. In winter periods, in particular, some of the soil water is frozen and the simulated liquid water agrees well the observed value, which indicates that the NICE-SNOW can simulate correctly the phase changes between the ice and the liquid. The soil temperature quantified this phenomenon, and the NICE-SNOW reproduced excellently the observed soil temperature (Table 7). Some parts of the ground are frozen until mid March, and the snow does not melt except when the temperature temporarily becomes

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much greater than 0 °C. Then, around mid March, the temperature is about 0–2 °C, which is the phase-change temperature between snowfall and rainfall. Table 7. Statistical comparison between observed and simulated values about soil temperature and groundwater level Soil temperature

Soil temperature

A-1

obs.10cm

cal.10cm

A-1

obs.30cm

cal.30cm

MV

10.18

10.68

MV

8.40

10.72

SD

5.55

4.54

SD

4.49

3.55

CV

0.55

0.43

CV

0.53

0.33

A-2

obs.10cm

cal.10cm

A-2

obs.30cm

cal.30cm

MV

11.92

9.54

MV

11.64

9.77

SD

5.80

5.23

SD

4.50

3.91

CV

0.49

0.55

CV

0.39

0.40

A-3

obs.10cm

cal.10cm

A-3

obs.30cm

cal.30cm

MV

10.86

10.44

MV

10.60

10.44

SD

4.79

4.91

SD

3.69

3.45

CV

0.44

0.47

CV

0.35

0.33

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Groundwater level

Groundwater level

G-1

obs.

cal.

G-21

obs.

cal.

MV

-0.95

-0.94

MV

-3.99

-4.19

SD

0.22

0.11

SD

0.20

0.27

CV

-0.23

-0.12

CV

-0.05

-0.06

G-10

obs.

cal.

G-23

obs.

cal.

MV

-2.83

-2.45

MV

-1.59

-1.40

SD

0.31

0.22

SD

0.12

0.08

CV

-0.11

-0.09

CV

-0.08

-0.06

G-13

obs.

cal.

G-29

obs.

cal.

MV

-4.81

-4.31

MV

-0.17

-0.24

SD

0.30

0.13

SD

0.05

0.11

CV

-0.06

-0.03

CV

-0.30

-0.45

MV = Mean Value, SD = Standard Deviation, CV = Coefficient of Variation (= SD/MV)

At that time, the simulated surface soil temperature at a depth of 10 cm is about 0 °C, and the thawing process starts to move down. From late March to early April, the deeper soil River Pollution Research Progress, Nova Science Publishers, Incorporated, 2008. ProQuest Ebook Central,

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temperature rises to 0 °C. Theoretically, most of the frozen soil has melted by this time. In reality, there are a lot of macropores, large fractures, and roots in the soil, and various vegetations on the ground, where the deeper layer remains frozen longer in some places because they form an insulating layer (Nyberg et al., 2001). Though the simulated values at the depth of 10 cm fluctuate more than the observed values due to the daily cycle of temperature fluctuations, the daily-averaged values agree well with the observed values 2

2

2

( rA− 2,10 =0.876, rA−3,10 =0.789, rA−1,10 =0.804). The simulated values at the depth of 30 cm in 2

grassland and forest reproduce excellently the observed values in this period ( rA − 2,30 =0.958,

rA2− 3,30 =0.974). The simulated data at the depth of 30 cm in mire overestimates the observed 2

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data because the NICE-SNOW simulates only the soil temperature ( rA −1,30 =0.834). In mire, there is shallow and almost saturated snowmelt water with comparatively colder at the early spring, and therefore, the soil temperature at mire is lowest. It is necessary to include both soil and water effect in order to simulate the soil temperature in mire. Furthermore, the soil moisture increases till late March owing to the temporary increase in hydraulic conductivity, enhancing the infiltration of meltwater into the ground after the frost layer thaws (Daniel and Staricka, 2000), and then gradually decreases until late spring. The increase in soil moisture till late March is also affected by the accumulation of water in frost soil through the upward movement of water from deeper unfrozen soil (Benoit et al., 1988). When soil moisture reaches a maximum, the value is almost constant from the ground surface to the deeper layers, and the frozen soil layer is almost melted. After this period, meltwater transports silt and clay particles and plugs the newly created voids, consequently returning the soil to a state near the original hydraulic conductivity (Schuh, 1990). The temporal variations in groundwater fluctuations are also quantitatively assessed (Table 7). The groundwater is affected by snowmelt water in the early spring season and flood in the typhoon season in the same way as soil moisture (Figs. 21). Generally speaking, the value takes a rapid increase at typhoon and a gradual decrease until the next typhoon period. The simulation values agree excellently with the measured values all through the two years depending on precipitation and micro-topography both in the mountainous areas 2

2

2

( rG −13 =0.649), around the mire ( rG − 23 =0.719) and in the mire ( rG − 29 =0.408), because the NICE-SNOW includes a three-dimensional groundwater model and simulates the recharge rate in the upper layer and the seepage between river and groundwater. In the mire (G-29), the groundwater level is almost constant from spring to the typhoons in the same way as soil moisture, which shows the high soil water capacity in the mire and that the groundwater acts as a long-term reservoir storing excessive soil water (Nakayama and Watanabe, 2004).

5.2.3. Surface Runoff Process Including Snowmelt Period The simulated river discharges are compared with observed values at a point in the Kucyoro River catchment (R-5 in Table 6) together with the precipitation and the temperature at the Tsurui AMeDAS observation point (M-5 in Table 6) (Figure 22). In this figure, the correlation between observed value and simulated value r2 when the slope of regression line equals to 1 and Nash-Sutcliffe Criterion NS (Nash and Sutcliffe, 1970) were also plotted. The observed precipitation includes both rain and snow by using the heater in the measurements of precipitation in winter.

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Figure 22. Time-series of precipitation, temperature, and river discharge at Kushiro River catchment (Shimo-Kucyoro observation point; R-5 in Table 6); (a) precipitation and temperature; river discharge (b) without snow depth and frost depth, (c) with snow model, (d) soil layer is completely frozen with snow model, (e) soil layer is completely thawed with snow model, (f) two-layer model of frost/thaw layer with snow model; (g) river discharge in case-f from 1 January to 31 December 2001. In Figures 22b-f, line and bold-line show observed and simulated values. In Figure 22g, open-circles are observed values and lines are simulated values. The r2 shows the correlation between observed value and simulated value when the slope of regression line equals to 1, and NS shows Nash-Sutcliffe Criterion NS (Nash and Sutcliffe, 1970).

Total precipitation was 311 mm, and the maximum precipitation intensity was 12 mm/hr during this period. Temperature takes 0 °C from the middle March to the early April when the river discharge increases due to the snowmelt runoff. In the case that the simulation does not include the effects of snow or frost depth (Figure 22b), constant values of the parameters in equations (39) and (40) were used in the simulation (thickness of A-layer, d = γD = 20 cm; hydraulic conductivity, k = 0.02 m/s; effective porosity, γ = 0.2; and Manning coefficient, n = 0.5 m-s) and the thickness of snow depth and A1-layer was set at zero. The simulated value agrees well with the observed value after mid May, which implies that the original NICE model reproduces well the discharge in the snow-free period (Nakayama and Watanabe, 2004). However, from March to May, the simulated value does not agree with the observed

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value. It overestimates the observed value before early March, but underestimates it from late March to late May, in particular, in April (r2=0.131, NS =-0.308). The simulated discharge including the infiltration of precipitation into the snow layer and the snowmelt volume reproduces better the observed value (Figure 22c). For the calculation of snow volume, the author set the effective porosity of the snow at 0.7 because the value generally takes a value of 0.4 to 0.9. Before early March, the simulated value becomes smaller than that in Figure 22b and agrees better with the observed value, because the snow depth increases and the precipitation infiltrates the snow layer in this period. In the snowmelt period, the simulated value is improved by adding the snowmelt volume though the simulated value has a smaller time-to-peak and a larger peak value a little than the observed value (r2=0.684, NS =0.236). This indicates that the snow layer causes a much greater time-to-peak of discharge than the discharge in snow-free period (Shanley and Chalmers, 1999), and that the NICE-SNOW can reproduce this swell of long wavelength of hydrograph characteristic of spring snowmelt runoff. Figures 22d–f show the simulated discharge including the frost/thaw processes of soil layer in the snowmelt period. The hydraulic conductivity and the effective porosity for freezing soil were calculated as k1 = 10–3 k = 2.0 × 10–5 m/s and γ1 = 0.02, by substituting ε = 1 and Ei = 3 in equation (41) (Lundin, 1990; Stahli et al., 1999) (Figure 22d). In the coldest part of winter, from early February to early March, the simulated value is better than the simulated value in Figure 22c and agrees well with the observed value (r2=0.755, NS =0.562). This result supports the view that frost promotes a larger and somewhat quicker response of runoff to rainfall than the absence of frost (Shanley and Chalmers, 1999; Stahli et al., 2001). However, the simulation underestimates the observed value after the snowmelt period. Because the hydraulic conductivity, k1, increases temporarily in thawing soil, it was set at k1 = 5k = 0.1 m/s, and γ1 = 0.4 was calculated by using equation (41) (Figure 22e). This case reproduces better the observed value in the thawing period from early March to late April (r2=0.742, NS =0.534). Figure 22f indicates the simulation result with frost/thaw layer and snow model. The depth of A1-layer was regarded as the frost/thaw soil layer calculated by the method described in the sections 3.2.3 and 3.2.4 in each time step. In particular, at the earlier stage of the thawing process, the soil layer consists of a surface layer with larger porosity (higher conductivity) and a deeper layer with smaller porosity (smaller conductivity). In this way, the NICE-SNOW reproduces excellently the characteristics of the spring flood, in particular, the early thawing process from mid April to mid May (r2=0.811, NS =0.702). By using the NICE-SNOW including snow layer and frost/thaw soil layer, the simulation of river discharge was conducted at the entire year of 2001 (Figure 22g). The simulated value reproduces excellently not only at the snow-free periods (Nakayama and Watanabe, 2004) but also at snowmelt periods (r2=772, NS =0.746).

5.3. Geomorphic Changes in the Catchment 5.3.1. Characteristic of Suspended Sediment Load in Different Seasons Coefficients of estimating suspended sediment (SS) are analyzed during 2002-2003 (Table 8). Correlation coefficients r2 are generally high in the entire year. Values of power function B in regression equation (55) take higher values in the snowment seasons (from

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April to June). The higher values continues longer in the main Kushiro River than in the tributary (Kucyoro River), which shows that the snowmelt takes longer time in the main river because of the longer river channel and the greater river catchment.

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Table 8. Coefficients of estimating suspended sediment (SS) Kucyoro R.

a

b

A (=10b)

B (=a-1)

r2

Jul-02 Aug-02 Oct-02 May-03 Jun-03 Jul-03 Average

2.42 2.03 2.27 5.81 4.89 2.31 2.43

0.90 1.07 1.35 -1.24 -0.71 1.12 0.89

7.87E+00 1.18E+01 2.26E+01 5.71E-02 1.95E-01 1.33E+01 7.77E+00

1.42 1.03 1.27 4.81 3.89 1.31 1.43

0.98 0.90 0.98 0.70 0.87 0.99 0.89

Kushiro R.

a

b

A (=10b)

B (=a-1)

r2

Jul-02 Aug-02 Oct-02 Jun-03 Jul-03 Average

3.34 6.93 3.12 6.11 4.64 4.34

-1.78 -7.80 -1.37 -6.21 -4.04 -3.74

1.64E-02 1.60E-08 4.30E-02 6.15E-07 9.16E-05 1.80E-04

2.34 5.93 2.12 5.11 3.64 3.34

0.97 0.98 0.98 0.96 0.93 0.96

Simulated values of river discharge and SS concentration were compared with the observed values at the downstream of Kucyoro River entering the mire (Figure 3) during 2002-2003 (Figure 23). About the river discharge in Figs. 23a and 23b, the correlation between observed value and simulated value r2 and Nash-Sutcliffe Criterion NS (Nash and Sutcliffe, 1970) were also estimated. The simulated discharge by the NICE-MASS agrees well with the observed discharge and the reported value (Ministry of Environment, 2004) not only in the summer season (July 2 –22, 2002) but also in the spring snowmelt season (April 23 – May 13, 2003) (Figs. 23a and 23b) (r2 = 0.957, NS = 0.947) because this model includes the snowmelt process (Nakayama and Watanabe, 2006). The snowmelt runoff takes the greater value of base flow than the summer runoff, which is also reproduced well by the NICE-MASS. Though the correlation (55) using the annual averaged coefficients of sediment-rating curve in Table 8 reproduces well the observed SS concentration in the summer season, it underestimates greatly the observed value in the spring snowmelt season (Figs. 23c and 23d). The NICE-MASS uses the averaged diameter of SS examined by the laser particle size analyzer (= 30 μm). The model also reproduces well the SS value in the entire year because it includes the flash-out of mass process at the higher precipitation as shown in the model description (Figure 6), which indicates that this flash-out process in very important in the spring snowmelt runoff. The estimated annual flux of suspended sediment in Kucyoro River inflowing to the mire is about 3100 m3/year, which is in the range of the previous researches 2440 m3/year (Ministry of Environment, 2004) to 7400 m3/year (Nakamura, 2003).

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Figure 23. River discharge and SS concentration during both the summer period (July 2 –22, 2002) and the spring snowmelt period (April 23 – May 13, 2003) in the Kucyoro River (Shimo-Kucyoro observation point; Figure 1). Figures 23a and 23b are river discharge, and Figure 23c and 23d are SS concentration during both periods, respectively. Open-circles are observed values. Open-triangle and open-square are the observed by Ministry of Environment (2004). Dashed-lines are the SS concentration estimated by the sediment-rating curve in the equation (55). Bold-lines are simulated values by the NICE-MASS. The precipitation data (Tsurui observation station; 43°13′48″N, 144°19′30″E, mean elevation 42 m) are also plotted in Figures 23a-b.

The NICE-MASS also simulates that the annual flux accounts about 3000 m3/year without the effect of spring snowmelt runoff, which is about 3 % smaller than the total annual flux. Though the model generally reproduces well the observed SS concentration in the spring snowmelt season, the simulated value underestimates a little the observed value (Figure 23d). This result indicates that more sediment load is delivered into the mire in the snowmelt season and that it can’t be negligible to account the total flux in the entire year.

5.3.2. Elevation Changes by Sediment Accumulation in Mire The previous studies (Nakayama and Watanabe, 2004; Ministry of Environment, 2004) show that the alder has propagated widely around the Kushiro Mire, owing mainly to the lowering of the groundwater level and the increase of suspended sediment and sedimentassociated nutrients into mire (Digital National Land Information GIS Data of Japan, 1976, 1997) (Figure 3). The increase of alder invasion has been clearly affected by the increases of agricultural and urban areas around the mire in this figure. The elevation changes from 1970s to 2000s were evaluated by using the GIS-database (Kushiro Development and Construction Department, Hokkaido Regional Development Bureau, 1975; Geographical Survey Institute of Japan, 1999) (Figure 24). Because there were no digital elevation data in 1970s, the topography map (1/10,000) (Kushiro Development and Construction Department, Hokkaido Regional Development Bureau, 1975) was scanned and digitized to create the GIS database (Figure 24a).

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Figure 24. Elevation in the same area as Figure 3 at (a) 1975 (Hokkaido Regional Development Bureau, 1975), and (b) 1999 (Geographical Survey Institute of Japan, 1999). Figure 24(a) was scanned and digitized from the topography map (1/10,000) (Kushiro Development and Construction Department, Hokkaido Regional Development Bureau, 1975). In Figs 24(a)-(b), the elevation less than 40 m above sea level was only plotted. The lines are contour of groundwater level scanned and digitized from the observed data of previous research (Ministry of Environment, 2004).

The elevation changes from 1970s to 2000s evaluated by using the GIS-database (Figure 24) was compared with the simulated value (Figure 25). The elevation increases predominantly around the downstream of inflowing rivers into the mire for 30 years (Figure 25a). This indicates that the increase of sediment delivery has caused the morphological changes in the mire (Nakamura et al., 1997; Nakayama and Watanabe, 2004). This result agrees quantitatively the previous in-situ study using Cs-137 fallout concentration analysis that the aggradation takes about 2 m at the downstream end of the channelized reach of Kucyoro River (Nakamura et al., 1997; Mizugaki et al., 2006). This deposition heterogeneity is also related to the particle size distribution of deposited sediments. The coarser materials such as gravel (> 2 mm) are observed in the rivers flowing on alluvial fans, in particular, deposited around the channelized reach. The finer materials such as sand and silt (< 2 mm) are typically observed in alluvial lowlands where natural levees develop. The simulated elevation change by using the two-dimensional diffusion model of NICEMASS (Figure 25b) agrees qualitatively with the GIS-database (Figure 25a) in the Kucyoro River catchment. The aggradation is predominant around the downstream of the river inflowing to the mire, which shows that most of the inflowing sediments are deposited there mainly by the rapid decrease of elevation gradients (Figure 24) and by the increase of drag coefficients in alder propagation (Figure 3). The simulation cannot reproduce completely the local heterogeneity of elevation change because the input data does not include the detailed information about the particle size of suspended sediment and about the differences in zonation and vegetation growth along the river, in the mire, and between the natural river and

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the improved river as described in the previous researches (Shinsho, 1982; Ministry of Environment, 2004).

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Figure 25. Comparison of elevation changes from 1970s to 2000s in the mire around the downstream of Kucyoro River (study area for hydrogeological change simulation in Figure 3); (a) evaluated value by using the GIS-database of Figure 24, and (b) simulation value by NICE-MASS. White cell is the meandering river before channelization at 1976, and black cell is the straightening river after channelization at 1997, as shown in Figure 2.

The elevation change in the simulation overestimates the GIS data at the lower end of the Kucyoro River (lower part of the figure), where sediments are not so deposited because most of coarser materials are deposited around the channelized river as described in the above (Nakamura et al., 1997). The discrepancy is also related to inapplicability of constant massfluxes depending on various land covers in another catchments (Kushiro Branch Office, 2002; Toda et al., 2002). Furthermore, the simulated value overestimates the observed data in the Kottaro River catchment (in the northeastern area in this figure) mainly because of inaccuracy of elevation data in 1970s (Figure 24a).

5.3.3. Relation between Hydrologic/Geomorphic Changes and Alder Invasion The simulated groundwater level was compared and validated by the observed value combining the NIES observation data (Nakayama and Watanabe, 2004, 2006) and the scanned and digitized data from the previous research (Ministry of Environment, 2004) in the mire at the downstream area of the Kucyoro River in 2001-2002 (Figure 26). The relative groundwater level takes a minus value at the downstream of channelized river because sediment accumulation by coarser materials has caused the surface elevation (Figure 25), which agrees with the previous researches (Nakamura et al., 1997; Nakayama and Watanabe, 2004). The groundwater level recovers further downstream of the channelized river because most of the sediment is deposited in the upper area. The annual-averaged simulation result reproduces these characteristics very well in the Kucyoro River catchment though the

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simulated value overestimates the observed data in the Kottaro River catchment (in the northeastern area in this figure).

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Figure 26. Averaged groundwater-level relative to ground surface in 2001-2002; (a) observed value combining the NIES observation data (Nakayama and Watanabe, 2004, 2006) and the scanned and digitized data from the previous research (Ministry of Environment, 2004), and (b) simulated value by NICE-MASS. Red color region shows the higher submerged area. White cell is the meandering river and black cell is the channelized river.

Hydrogeological changes from 1970s to 2000s were evaluated by long-term simulation (Figure 27). The groundwater level above sea level has decreased predominantly around the channelized rivers and the downstream area in the mire (Figure 27a). The simulation result shows that the maximum value of groundwater degradation is more than 1 m. The increase of river discharge caused by channelization results in a decrease of seepage infiltration from the river to the aquifer, which accounts for the decrease in the groundwater level downstream of the channelized rivers. The change in groundwater level relative to the ground surface (Figure 27b) was calculated by summation of both the elevation change (Figure 25) and the absolute groundwater level change (Figure 27a). It was assumed that the alder invasion (Figure 3) and the decrease in groundwater level relative to the ground surface (Figure 27b) have a close correlation. The spatial occupation rate of alder invasion has generally a positive correlation to the hydrologic and geomorphic changes. Some discrepancies attribute the local change of groundwater level change (Nakayama and Watanabe, 2004), the heterogeneity of porosity due to the particle size distributions of deposited sediments (Nakamura et al., 1997), and the other limiting factor (nutrient, soil moisture, light, temperature) (Urban and Shugart, 1992), et al.

5.4. Vegetation Succession Process in the Catchment 5.4.1. Evaluation of Ecohydrological Characteristics in the Mire The NICE simulation shows that the groundwater level has decreased predominantly around the channelized rivers and the downstream area in the mire.

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Figure 27. Hydrogeological changes from 1970s to 2000s; (a) simulated result of groundwater level change (above sea level) by river channelization, and (b) estimated value of groundwater level change (relative to ground surface). White cell is the meandering river and black cell is the channelized river.

This simulated distribution is very similar to that observed in Figure 18 (Nakayama and Watanabe, 2004), which directly input changes in meteorological forcing data and vegetation classes (Figure 3) between 1977 and 2001. The simulated result of elevation change increases predominantly in the downstream areas of rivers flowing into the mire (Kushiro main river and tributaries; Kucyoro River, Chiruwatsunai River, Setsuri River, Ashibetsu River, Hororo River, Onnenai River, etc.) particularly around the downstream area of the Kucyoro River, which indicates that the increase of sediment delivery has caused morphological changes in the mire (Nakamura et al., 1997; Nakayama and Watanabe, 2004). The number of cells of both alder and reed are counted by using GIS data and plotted versus submerged-depth of vegetation (groundwater level relative to ground surface) in the mire around the downstream of Kucyoro River where the alder has predominantly invaded in the past (Figure 3) in Figure 28. There is a simple correlation between these survival rates and the submerged-depth, which agrees well with the limiting factor about submerged-depth in the previous researches for reed (Ohmi Environment Preservation Foundation, 2001) and alder (Yabe and Onimaru, 1997; Hotes et al., 2001). The second peak about reed (submergeddepth is about –1 m) does not agree with the previous research (Ohmi Environment Preservation Foundation, 2001) because of the heterogeneity of reed depending various soil, hydrologic, and nutrient conditions. Anyway, the alder lives at the drier condition than the mire vegetation, which agrees the qualitative correlation between the alder invasion and the drying phenomenon in the mire (Nakamura et al., 1997; Nakayama and Watanabe, 2004). This result indicates that the limiting factor about submerged-depth is very important to simulate/forecast the vegetation successions for the policy making to re-meandering of channelized rivers in the future. The simulated tree height H (cm) was validated by values obtained in previous studies (Shinsho et al., 1988; Shinsho and Tsujii, 1996) (Figure 29).

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Figure 28. Survival rates of both alder and reed versus submerged-depth of vegetation in the mire around the downstream of Kucyoro River. The limiting factors about submerged-depth in the previous researches for reed (Ohmi Environment Preservation Foundation, 2001) and alder (Yabe and Onimaru, 1997; Hotes et al., 2001) are also plotted in the figure.

Figure 29. Comparison between simulated tree height H (cm) and observed value versus diameter at breast height (DBH). Lines are results of NICE-VEG simulation, and dots are the previously observed values (Shinsho et al., 1988; Shinsho and Tsujii, 1996). The Onnenai and Chiruwatsunai Rivers are outside the Kucyoro River catchment, and their data are also plotted as a reference to show the spatial heterogeneity of alder growth.

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The observed data are more scattered because alder thickets show different types of zonation and vegetation growth in natural river areas (with germinated alder thickets showing similar height and DBH) and improved river areas (with alder thickets raised from seed showing different height and DBH) (Shinsho and Tsujii, 1996). The simulated value depends greatly on the maximum tree height Hmax (m), and the following simulation uses Hmax = 9 (m) on the assumption that there are no differences in the types of zonation and vegetation growth of alder thickets in the mire.

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5.4.2. Reproduction of the Drying Phenomenon and Alder Invasion in the Mire The long-term NICE-VEG simulation for the period 1970 to 2002 reproduces qualitatively and quantitatively the invasion of alder in the mire (Figure 30a). The GIS data show that reed was predominant in 1976 (proportion of reed 85.3%; that of alder 14.7%), and that alder spread in 1997 (proportion of reed 52.9 %; that of alder 47.1%) (Figure 3), and this was reproduced well by the NICE-VEG simulation for both 1976 (proportion of reed 84.0%; that of alder 16.0%) and 1997 (67.0% and 33.0%, respectively) (Figure 30a). The model forecasts that reed would be predominant and that alder would not have invaded the mire in 1997 if the river had not been channelized in the 1970s (dotted line in the figure; proportion of reed 85.0%; that of alder 15.0%).

Figure 30. Results of simulation of alder invasion in the mire; (a) proportion of alder and reed during 1970-2000, (b)-(c) spatial distribution of alder and reed in 1976 and 1997, respectively. In Figure 30(a), the lines are actual simulated results for when the rivers were channelized in the 1970s-80s (solid line: alder, bold line: reed), and the area represented by dotted lines represents the simulated results that would have been expected if the rivers had not been channelized in the past (solid dotted-line: alder, bold dotted-line: reed). Circles and triangles represent the proportions of alder and reed calculated from the GIS data (Figure 3). In Figure 30(b)-(c), the dark-blue cell represents the Kucyoro River. In Figure 28(c), the river has been channelized in its downstream area. The simulated results in Figure 30(b)-(c) reproduce well the GIS data shown in Figure 3, respectively.

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The NICE-VEG also simulated the spatial distribution of alder invasion up to the present time (Figure 30b-c). Invasion of alder is very sensitive to submerged depth, nutrient input, and other factors. The simulation result indicated that the degradation of groundwater level relative to ground surface and submerged depth are the most predominant factors associated with mire shrinkage, which is greatly affected by river channelization (Talbot and Lapointe, 2002) (Figures 25 and 27). The increase in elevation agrees quantitatively with a previous in situ study using Cs-137 fallout concentration analysis, which indicated that the aggradation was about 2 m at the downstream end of the channelized reach of the Kucyoro River (Nakamura et al., 1997; Mizugaki et al., 2006). Although the simulation result for alder invasion in 1997 underestimates the GIS data (Figure 3) at the center of the mire, the simulation values reproduce qualitatively the spatial distribution of alder invasion, similar to the GIS data (Figure 3).

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5.5. Future Forecast in the Mire 5.5.1. Effect of Re-Meandering of Channelized Rivers on Hydrologic Budget in Mire The simulation result shows that the re-meandering of channelized river is effective for the decrease of river discharge both in peak value and base flow (maximum decrease is about 25 %) at the downstream of re-meandering area around the mire (point-III in Figure 31b). This shows that the re-meandering will also cause the decreased sedimentation and less flooding downstream (Talbot and Lapointe, 2002). This re-meandering also affects the increases of groundwater level around the mire (Figure 31c) because of the decrease of surface flow as described in Figure 31b, which indicates the recharge rate increases greatly around the re-meandering area. The maximum value of groundwater increase is more than 1 m and this increase affects greatly the mire vegetation (Ministry of Environment, 2004; Nakayama and Watanabe, 2004). The estimated annual flux of suspended sediment in Kucyoro River inflowing to the mire is about 3000 m3/year (Table 9), which is in the range of the previous researches 2440 m3/year (Ministry of Environment, 2004) to 7400 m3/year (Nakamura, 2003). Table 9. Simulated results of annual-averaged discharge and sediment load before and after re-meandering of channelized rivers without changing the land cover in the catchment Present (Channelized)

Future (Re-meandering)

Effect of re-meandering

Q (m3/year)

SS (m3/year)

Q (m3/year)

SS (m3/year)

Q (m3/year)

SS (m3/year)

Upper

5.11E+07

1114

4.67E+07

984

decrease

decrease

Middle

9.63E+07

2231

9.71E+07

2117

increase

decrease

Downstream

1.36E+08

2928

1.32E+08

2753

decrease

decrease

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Figure 31. Forecast of hydrologic changes in the mire by re-meandering of channelized rivers; (a) elevation at the downstream of Kucyoro River, (b) river discharge at three points (I, II, III) in Figure 31a, and (c) annual-averaged groundwater level. In Figs. 31a and 31c, black cell is the present channelized river and white cell is the re-meandering river in the future. In Figure 31a, three points correspond to the simulated value in Figure 31b. In Figure 31b, dashed line and solid line are simulated discharge before re-meandering in the present (2000-2001) and after re-meandering in the future, respectively.

The simulation results without changing the land cover in the catchment also indicate that annual-averaged sediment load into the mire may decrease about 10 % at the downstream of re-meandering area in the future (Table 9), which may slow down slightly the elevation change in the mire by the sediment aggradation (Brookes, 1985; Talbot and Lapointe, 2002). This may also promote the decrease of the groundwater level relative to ground surface and attribute to the relaxation of drying phenomenon in the mire. The simulation result of NICEVEG indicates that the beginning project to re-meander the channelized rivers there are effective for the recovery of groundwater level and mire vegetation and for the river restoration and the assessment of ecological integrity (Jungwirth et al., 2002).

5.5.2. Forecast of Vegetation Change in Mire by Re-Meandering of Channelized Rivers NICE-VEG forecasts the vegetation change in the mire by re-meandering of channelized river up to the 2030. Alder will invade more and more in the mire and the proportion of alder will finally approach a constant value if current situation of channelized rivers will be retained (Figure 32a and 32c). On the other hand, reed will recover if the current channelized rivers will be re-meandered and the sediment/nutrient loads will be reduced about 40 % and 20 % as reported in the previous research (Ministry of Environment, 2004) in the present of 2000s (Figure 32a and 32b). The 40 % of sediment load is much greater than the simulated results of 10 % decrease in Table 9, which indicates that it is necessary to reduce about 30 % of sediment load by taking mitigation measures in addition to land cover change in the catchmnt. This recovery of mire is also related to the simulation results of the hydrologic

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change (Figure 31 and Table 9), which agrees well with the straightening of meandering rivers the previous research (Talbot and Lapointe, 2002).

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Figure 32. Forecast of alder invasion in the mire; (a) proportion of alder and reed during 1970-2030, (b) spatial distribution of alder and reed in the 2030 if the current channelized rivers will re-meandered in the present of 2000s, and (c) spatial distribution of alder and reed in the 2030 if current situation of channelized rivers will be retained, respectively. In Figure 32a, lines area simulated forecasts if the current channelized rivers will be re-meandered in the present of 2000s (solid line: alder, bold line: reed), and dotted-lines are simulated forecasts if current situation of channelized rivers will be retained (solid dotted-line: alder, bold dotted-line: reed). In Figure 32b-c, dark-blue cell is Kucyoro River. In Figure 32b, river is re-meandered at the downstream of Kucyoro River.

The model shows that it will take at least the same periods (about 30 years) to recover to the previous situation of 30 years ago (Figure 32a). However, NICE-VEG does not completely include nitrogen fixation in seedlings of alder, which actually plays an important role in the alder growth (Burgess and Peterson, 1986; Hendrickson et al., 1990), and it may take more time to recover the mire.

6. DISCUSSIONS 6.1. Drying Phenomena and Vegetation Change Caused by Invasion of Alder The NICE model shows extremely high accuracy in simulating river discharge, soil moisture, and groundwater flow over the Kushiro River catchment during the snow-free period of 6 months. The use of very accurate field measurement data, MODIS data, flux tower data, and various parameters categorized by GIS and geological structure support this precision. Seasonal vegetation change, mechanisms of vegetation–water relations, and surface-unsaturated–saturated water processes were included in the model. This model explains water cycle change and drying phenomena in the Kushiro Mire associated with vegetation change.

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Although the overall simulated values excellently reproduced the observed values, some discrepancies existed. The land-surface model simulation could not reproduce the rapid change in soil moisture in the vertical direction at higher precipitation (Figs 11a–c). This occurred because the vertical mesh size was rough and porosity/hydraulic conductivity were treated as constants in the vertical direction in the unsaturated layer in this model, even though the soil texture changes with depth (Yu et al., 2001). The rapid change in soil moisture can be reproduced more excellently when Richards’ equation is solved with a finer mesh, the soil structure is evaluated by measuring pF-moisture characteristics, and the vegetation structure including the root depth and density is better parameterized. In the groundwater flow model, the rapid change of groundwater level is difficult to reproduce in the same way as soil moisture, because correct recharge rates from the unsaturated layer are not attainable by observation (Figure 15). For better recharge rate simulation, the unsaturated layers must be more correctly simulated. More groundwater level data are necessary near mountain areas because boundary conditions for groundwater levels are difficult to input correctly except with constant head values at lake or sea level. The effect of the snow layer and the freezing/thawing soil layer during spring snowmelt runoff must be included in the surface hydrology model to simulate the entire year, because the spring surface runoff, which occurs when the ground is still partly frozen during the snowmelt period, can be a significant component of the water balance in the Kushiro River catchment. Simulating the stream network by using the dynamic wave theory requires a lot more computation time than by using kinematic wave theory (Figs 16a, b). In order to stop the sediment-load influx and to recover the Kushiro Mire, a new project started to re-meander the channelized rivers in 2002. In most cases, kinematic wave theory reproduces primarily river discharge, except after a flood when the backwater effect is observed (Figure 16b). However, the dynamic wave theory is very important for the simulation of meandering rivers because the backwater greatly affects sedimentation near the meandering rivers. The water cycle and heat flux processes relate implicitly to the change in vegetation phenology, which is excellently modeled by the model. MODIS data, which have multiple channels and broader areas, are spatially moderate in resolution (1-km mesh) (Figs 10a, b), and it is required to composite 8 days of MODIS data of biological products such as LAI and net primary production (NPP) because there is noise due to cloud, rain, and snow. Despite of these difficulties in MODIS data, the vegetation changes are successfully observed spatially and seasonally around the Kushiro Mire. However, some aspects of the MODIS data need improvement. FPAR and LAI are calculated from MOD09 and MOD12, which are based on only six types of land cover (grasses/cereal crops, shrubs, broadleaf crops, savanna, broadleaf forests, and needle forests), as in MOD12-ATBD (Strahler et al., 1999). Mires and paddy fields were forced to fit into these six categories. In future work, FPAR and LAI values for mires and paddy fields will require the new classification of land cover, and it is necessary to add the growth process of vegetation to the model in order to refine the water cycle mechanism determination for the area of the Kushiro Mire. From the comparison between simulated and observed results in snow-free periods between 1977 and 2001 (Figures 18a, b), the drying phenomena clearly occurred in the area where alder dominated (Figure 3). This shows that alder absorbed more water from the roots and transpired more to the atmosphere than original mire vegetation of reeds, and that the soil moisture decreases dramatically in the alder because the recharge rate to the groundwater decreases. Furthermore, the area of lower soil moisture and lower surface temperature closely

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corresponds to the area covered by alder (Figure 3) (data not shown). The simulation clearly demonstrates that this drying phenomenon is closely related with the increased influx of sediments from the surrounding area, where agricultural development, reclamation, and channelization of the river occurred (Environment Agency of Japan, 1984, 1993). Thus, the positive correlations between soil moisture and groundwater levels indicate that the inflowing sediments during flood periods formed spatially distributed piles, and resistance to water supply from the surroundings was increased. Consequently, the soil was drying and the local groundwater levels were lowering, resulting in a further invasion of alder in this area. The areas of alder promote the deposit of sediments by their overland roots and shed leaves. The topography changes owing to sediment deposits, and the nutrient infiltration processes are important for the long-term simulation in order to clarify the relationships between water, heat, vegetation, sediment, and nutrients, and ultimately to reproduce the invasion of alder in the Kushiro Mire (Figure 3) due to the channelization of rivers. The succession from mire vegetation (reeds, etc.) to alder and finally to willow trees occurs during the mire’s drying process. The extension of the model is very powerful in estimating the effect of river channelization on the Kushiro Mire, in predicting the recovery of groundwater recharge by remeandering the channelized rivers, and in protecting the sediment loads from riparian forests, which are most sensitive for soil moisture and the succession in mire vegetation among alder, reeds and willows, as the author describes in this publication (NICE-MASS and NICE-VEG).

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6.2. Effect of Micro-Topography/Phase-Changes in Soil Layer on Spring Snowmelt Runoff The NICE-SNOW could reproduce well the snowmelt runoff process, for example, the observed values of snow depth, frost depth, soil temperature, soil moisture, groundwater level, and river flow discharge by conducting the quantitative assessment of goodness-of-fit and parameter sensitivity. This model developed a multi-layer surface-runoff submodel including the effect of micro-topography and meteorology, includes the phase change transitions in soil moisture, and also considered the effect of the snow layer and the frost/thaw soil layer on spring snowmelt runoff. The author quantified that the mechanism of spring snowmelt runoff is related to changes in soil structure, soil temperature, soil moisture, and groundwater level, which includes the previous qualitative researches that the frozen soil alters the hydraulic character of soil by restricting the infiltration in the coldest part of winter, increasing the pore size after the thawing process in the soil. The NICE-SNOW could explain how the snowmelt causes the greater time-to-peak of runoff than in snow-free period because some part of meltwater flows as an intermediate flow in the partially-thawed hillslope soil layer. From these results, a conceptual process of the frost/thaw and the relation to snowmelt runoff is constructed (Figure 33). The snowmelt period can be divided into three periods. The first is the coldest period when precipitation infiltrates the snow layer and the soil is almost frozen. If the soil moisture is high and the soil has a fine-grained texture, concrete frost develops, which forms a surface nearly impermeable to runoff and increases overland flow (Stahli et al., 2001). In this period, the infiltration to the ground is seriously reduced by the

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blocking effect of the soil frost. The second period is the beginning of the thawing process when the frozen soil melts from both the surface and the deeper layers.

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Figure 33. Conceptual model of the frost/thaw process and the relation to snowmelt runoff.

The temperature is about 0–2 °C, and the ground surface temperature approaches 0 °C. The snow begins to melt, and this meltwater percolates vertically into the macropores and cracks in the soil, increasing the soil moisture (Daniel and Staricka, 2000). The third period begins when the frozen soil thaws completely. Most of the meltwater flows out directly, adding to the surface runoff, causing a rapid decrease in soil moisture in the soil layer. In the thawing process, a phase delay occurs in the river discharge (Shanley and Chalmers, 1999). In this study, it became clear that the local effect of snow depth and the frost depth disappears in the snowmelt runoff discharge of catchment (Figure 22g) in the same way as some previous researches (Flerchinger and Saxton, 1989a, b; Boggild et al., 1999; Shanley and Chalmers, 1999; Stahli et al., 2001) though they are very important as water resources of catchment (Figures 19 and 20). Because freezing and thawing processes influence the amount of runoff discharge during the early spring, the snowmelt flood continues a longer time than that in the typhoon period (Figure 22). The NICE-SNOW reproduces this phenomenon excellenly because this model in the equations (32) – (35) calculates correctly that some part of meltwater flows as an intermediate flow in the partially-thawed hillslope soil layer. This is also related to the simulation result that more than half of total soil moisture stays unfrozen at some places even in winter periods (Figure 21), which indicates that there is a high degree of spatial heterogeneity of frozen ground. Data assimilation of remote-sensing data, such as snow cover in MOD10-ATBD of MODIS data (Hall et al., 2001) and in NOAAAVHRR/RADARSAT/ERS-SAR/Landsat TM (Swamy and Brivio, 1997; Mitchell and DeWalle, 1998; Schaper et al., 1999; Nagler et al., 2000), and in-situ measurements with the

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NICE-SNOW is very powerful to improve model-based estimates of the water resources of catchment for practical benefits in near future. However, the simulated value has a smaller time-to-peak and a larger peak value a little than the observed value from the mid March to the mid April (Figure 22). Because the NICESNOW can reproduce the time lag of snowmelt in the vertical direction, this discrepancy of runoff is due to an imperfectness of the heat-budget and hillslope surface-runoff submodel of the NICE-SNOW. While the simulation reproduces well the phase changes in the unsaturated layer in winter periods (Figure 21), the simulated soil temperature overestimates the observed value in winter season (Table 7). The NICE-SNOW simulates only the soil temperature, and it is necessary to include the effect of water temperature in the unsaturated layer on the soil temperature and on the soil moisture. Furthermore, the water volume always flows to the downward directions of hillslope in the model after the snowmelt volumes by the heat-flux model are inputted to the surface runoff model. In reality, some of the water volumes are frozen at night and start to be melted during the daytime in the early spring period when the temperature takes about 0 °C. It is far more likely that preferential flowpaths exist in the partially-thawed hillslope soil layers. This frozen/melted cycle in the surface runoff is not completely included in the NICE-SNOW, and the disagreement between the simulated values and the observed values occurs, which also relates whether the kinematic wave theory for the hillslope hydrology is appropriate given the high degree of spatial heterogeneity of frozen ground. Anyway, these results suggest that the spring runoff process is closely related to changes in the phase change and frost/thaw soil structure. Therefore, it is necessary to evaluate the mechanism of transformation of soil structure and water phase and their interaction, because temperature, snow depth, and soil moisture at initial freezing influence soil frost formation, retention of soil moisture during freezing/thawing, and soil water movement. To reproduce this effect more correctly, it is necessary to conduct more observation in the vertical direction of local area and simulate by including the frozen/melted cycle in the surface runoff with finer mesh resolution in the vertical direction. The effect of the spring snowmelt runoff on sediment-load influx and nutrient infiltration is very important (Figure 6) because the spring snowmelt runoff is a significant component of the water balance in the Kushiro River catchment (Figure 22g) and the soil structure changes dramatically during this period. The drying phenomenon in the Kushiro Mire is closely related with the increased influx of sediments from the surrounding area, where agricultural development, reclamation, and channelization of the river occurred (Talbot and Lapointe, 2002; Nakayama and Watanabe, 2004). In order to stop the sediment-load and nutrient influx and to recover the Kushiro Mire, several ministries in Japan started a new project to remeander the channelized rivers in 2002 (Ministry of Environment, 2002). The combination of GIS-data such as slope angle, soil properties (Figure 9), and land cover (Figure 1), with the sediment/nutrient transport model is very powerful in estimating the sediment flux into the mire. The NICE-SNOW can simulate the relation between water and sediment/nutrient influx to the mire in the long-term periods by including the mass-transfer and chemical-reaction processes to this model, which will be very important in protecting the sediment-load and the nutrient influx from riparian forests, and in predicting the recovery of a mire ecosystem by remeandering the channelized rivers. The extension of the model with the vegetation growth (tree regeneration and succession, etc.) model (Glenn-Lewin et al., 1992), which also includes the clarification of the interaction between the water–heat–sediment–nutrient–vegetation by using the method such as the correspondence analysis, is also be very attractive in order to

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evaluate the better environmental conditions for the mire, to reproduce the alder invasion to the mire, and to forecast the possibility of the mire-ecosystem recovery, as the author develops the NICE-MASS and NICE-VEG in this publication.

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6.3. Relation between Hydrologic and Geomorphic Changes Affecting Shrinking Mire The drying phenomenon in Kushiro Mire is closely related with the aggradations by the increased influx of sediments from the surrounding area (Figs. 24 and 25) as pointed out by the previous researches (Nakamura et al., 1997; Nakayama and Watanabe, 2004), where agricultural development, reclamation, and channelization of the river occurred (Figure 3). The NICE-MASS can reproduce well the elevation change of sediment deposit around the mire in the long-term simulation (Figure 25). It is also very interesting and important result that the river channelization in the past has caused the groundwater decrease at the downstream area due to the decrease of seepage infiltration from the river to the aquifer (Figure 27a). These hydrologic and geomorphic changes are also related to the alder invasion in the mire, whose occupation rate has a positive correlation with the groundwater degradation relative to ground surface (Figure 27b). The NICE-MASS could simulate the hydrologic/geomorphic relation between water, sediment, and nutrient influx to the mire in the long-term periods by including the sedimenttransport/deposit processes to this model, which will be very important in protecting the sediment-load and the nutrient influx from riparian forests, and in predicting the recovery of a mire ecosystem by re-meandering the channelized rivers. The extension of the model with the vegetation growth (tree regeneration and succession, etc.) model (Glenn-Lewin et al., 1992), which also includes the clarification of the interaction between the water–heat–sediment– nutrient–vegetation by using the statistical analysis such as canonical correspondence analysis and cluster analysis (Nakamura et al., 2002), will also be very attractive in order to evaluate the better environmental conditions for the mire, to reproduce the alder invasion to the mire, and to forecast the possibility of the mire-ecosystem recovery in the future. The evaluation of limiting factor about submerged-depth in the study (Figure 28) in comparison with the previous research (Yabe and Onimaru, 1997; Hotes et al., 2001; Ohmi Environment Preservation Foundation, 2001) is very important to simulate the vegetation succession from mire vegetation (reeds, etc.) to alder and finally to willow trees during the mire’s drying process. It is further necessary to collect data for various conditions affecting mire species such as alder, reed, moss, sedge, willow, Japanese ash, and meadow sweet. These would include hydrology (water level fluctuations; Hotes et al., 2001), water quality (nitrogen, phosphate, potassium, pH, electrical conductivity, dissolved oxygen; Burgess and Peterson, 1986; Chapin et al.,1994; Yabe and Onimaru, 1997) in Figures 8 and 23, radiation, and temperature, in order to simulate/forecast the competition among mixtures of plant species and to preserve biodiversity (UNEP, 2002) in the mire with high accuracy (Figure 3). The higher nutrient concentration in the groundwater has also affected the vegetation change in the mire (Ministry of Environment, 2004). The alder invasion and the soil conditions (sand and silt, gravel, organic content, peat soil, et al.) are closely related with each other. Fallout concentration analysis such as Cs-137, Ru-103, Ba-140, Be-7, and Th-232 is also attractive to

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estimate the thickness of sediment accumulation (Mizugaki et al., 2006). Alder usually grows above the local mound of the mire, where the groundwater level is almost constant in comparison with the local change of elevation (Nakayama and Watanabe, 2004). Though this phenomenon is in some parts included as the limiting factor about submerged-depth (Figure 26), there are some scatterings from the previous researches (Yabe and Onimaru, 1997; Hotes et al., 2001; Ohmi Environment Preservation Foundation, 2001). Because the spatial scale of this mound is often smaller than the grid spacing of the NICE-MASS simulation (100 m), it is necessary to simulate by using a finer grid in the future. In order to stop the sediment loading and nutrient influx and to recover the Kushiro Mire, several ministries in Japan started a new project to re-meander the channelized rivers, to establish riparian buffer, and to create sediment retention pond, in 2002 (Ministry of Environment, 2002; Nakamura, 2003). This Japanese project is very important to solve the recent hullabaloo about river restoration in the United States (Palmer and Bernhardt, 2006) because the US practitioners rarely consider the vegetation in their restoration efforts. The simulation to re-meander the channelized river is interesting whether the recharge rate in mire may increase, whether the peak value of river discharge may decrease around the remeandering, and whether the inflowing sediment may decrease during the flood periods, as the author described in this publication. The combination of GIS-data such as slope angle, soil properties (Nakayama and Watanabe, 2004), and land cover (Figure 3), with the sediment/nutrient transport model is very powerful in estimating the sediment flux into the mire.

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6.4. Factors Controlling Vegetation Succession in the Mire The drying phenomenon in Kushiro Mire (Figures 2 and 18) is closely related to the increased influx of sediments from the surrounding area, where agricultural development, reclamation, and channelization of the river occurred (Nakayama and Watanabe, 2004). The spatial occupation rate of alder invasion is positively correlated with hydrogeological changes (Figures 25 and 27). Some discrepancies in Figure 30 are attributable to local changes in groundwater level (Nakayama and Watanabe, 2004), heterogeneity of porosity due to the particle size distribution of deposited sediments (Nakamura et al., 1997), and other limiting factors (nutrients, soil moisture, light, temperature) (Urban and Shugart, 1992). The higher nutrient concentration in the groundwater has also affected the vegetation change in the mire (Ministry of Environment, 2004). Furthermore, it was noteworthy that the simulation of river channelization showed that the recharge rate in the mire decreased greatly (Figure 27). This indicates that channelization will also cause an increase of sedimentation/nutrient loading and flooding in the downstream area around the mire (Talbot and Lapointe, 2002). In comparison with the vegetation change derived from GIS data (Figure 3), NICE-VEG reproduced very accurately the invasion of alder in the mire over the last 30 years (Figure 30). This result represents a dramatic advance in our understanding of the drying phenomenon associated with alder invasion. The reproducibility of the simulation result implies that the NICE-VEG includes some of the important factors associated with vegetation succession in the mire. In particular, the model clarified that the change in submerged depth in equations (64) and (65) (Yabe and Onimaru, 1997; Hotes et al., 2001) is one of the crucial factors effecting alder invasion, accompanied by aggradation of sediment flowing in from the

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surrounding catchments due to urban or agricultural land use (Nakamura et al., 1997; Nakayama and Watanabe, 2004). Collecting data for various conditions affecting mire species such as alder, reed, moss, sedge, willow, Japanese ash, and meadow sweet, are important for reproducing more correctly the actual vegetation change. These would include hydrology (water level fluctuations; Hotes et al., 2001), water quality (nitrogen, phosphate, potassium, pH, electrical conductivity, dissolved oxygen; Burgess and Peterson, 1986; Chapin et al., 1994; Yabe and Onimaru, 1997) (Table 3), radiation and temperature, in order to simulate/forecast the competition among mixtures of plant species and to preserve biodiversity (UNEP, 2002) in the mire with high accuracy (Figure 3). It is further necessary to clarify interactions among water, heat, sediment, nutrients and vegetation using statistical approaches such as canonical correspondence analysis and cluster analysis in order to evaluate more favorable environmental conditions for the mire and to reproduce the invasion of alder. Alder is distributed as swamp thickets in the mire, which shows differences in zonation and vegetation growth of alder thickets (tree height, crown diameter, and DBH) along the river, in the mire, and between the natural river and the improved river (Shinsho and Tsujii, 1996; Ministry of Environment, 2004). Germinated alder thickets that consist of trees of similar height and DBH, together with bushes of meadow sweet on the forest floor, are found in natural rivers because of limited sediment supply. An alder thicket raised from seed, which consists of trees of different heights and DBH, together with reed and sedge on the forest floor, is found along improved rivers together with small-scale germinated alder thickets, because the sediment supply is frequent and the soil layer resulting from sediment accumulation is thick (Nakamura et al., 1997; Mizugaki et al., 2006). It is necessary to include these types of heterogeneity in equations (56)-(65) of NICE-VEG as a function of hydrology, geology, and nutrient conditions in order to reproduce more accurately the alder invasion at the center of the mire (Figure 30). The characteristics of alder thickets are closely associated with soil conditions (sand and silt, gravel, organic content, peat soil, etc.). Fallout concentration analysis such as Cs-137, Ru-103, Ba-140, Be-7, and Th-232 is also an attractive approach for estimating the thickness of sediment accumulation (Mizugaki et al., 2006). It is further necessary to clarify the relationship between seedling establishment/mortality/regeneration and the expansion of alder distribution by both airborne and water-borne alder seeds. Though NICE-VEG computes stochastically seedling establishment, mortality, and regeneration depending on the seedling number, it is further necessary to improve these processes deterministically by describing the growth process based on carbon balance (Moore, 1989; Bugmann et al., 1996). This is closely related to not only the above zonation heterogeneity but also the spatial localization of alder growth. Alder usually grows above the local mound of the mire in the shallow groundwater (Figure 26). In some parts, this phenomenon is included as a reduction factor for submerged depth in equations (64)-(65) (Yabe and Onimaru, 1997; Hotes et al., 2001; Ohmi Environment Preservation Foundation, 2001). Because the spatial scale of this mound is often smaller than the grid spacing of the NICE-VEG simulation (100 m), it will be necessary to simulate it by using a finer grid in the future. The NICE-VEG can also include the effect of airborne seedling distribution by using the forcing meteorological data for wind speed or coupling with the atmospheric model. This is also related to the differences in evapotranspiration between alder and other mire vegetation species such as reed. Investigations of the genetic diversity and population structure of alder (Huh, 1999) are very powerful for specifying the source and

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process of alder growth in combination with ground truth data in addition to plant maps, aerial photographs, and satellite data by using GPS (Global Positioning System) and GIS. In order to stop sediment loading and nutrient influx, and to assist the recovery of Kushiro Mire, several ministries in Japan initiated a new project in 2002 to re-meander the channelized rivers, to establish a riparian buffer, and to create a sediment retention pond (Ministry of Environment, 2002). This Japanese project will be very informative for clarifying certain aspects of the controversy associated with river restoration in the United States (Palmer and Bernhardt, 2006), because US practitioners rarely consider vegetation in their restoration efforts. For effective policy-making, it is necessary to forecast the effects of re-meandering of channelized rivers and to assess whether reed will recover if the current channelized rivers are re-meandered and sediment/nutrient loads are reduced by using the NICE-VEG, as currently there is a very serious drying phenomenon associated with the vegetation change caused by alder invasion (Ministry of Environment, 2002).

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6.5. Recovery of Mire Ecosystem by Re-Meandering of Channelized Rivers The drying phenomenon in Kushiro Mire (Figures 2 and 18) is closely related to the increase of sediment loading and nutrient influx from the surrounding area (Nakayama and Watanabe 2004). Several ministries in Japan recently started a new project to re-meander the channelized rivers in 2002 in order to stop the sediment loading and nutrient influx and to recover the Kushiro Mire (Ministry of Environment, 2002). The forecast simulation to remeander the channelized river shows the interesting results that the recharge rate in mire increases greatly and that the peak value of river discharge decreases around the remeandering (Figure 31). This indicates that the re-meandering will also cause the decreased sedimentation/nutrient loads and less flooding downstream for the recovery of mire (Table 9) as described in the previous research (Talbot and Lapointe, 2002). The combination of GISdata such as slope angle, soil properties (Nakayama and Watanabe, 2004), and land cover (Figure 1), with the sediment/nutrient transport model is very powerful in estimating the sediment flux into the mire. NICE-VEG can simulate more correctly the relation between water and sediment/nutrient influx to the mire in the long-term periods by adding the masstransport and chemical-reaction processes to the model, which will be very important to prevent sediment loading and nutrient influx from riparian forests, and to predict the recovery of a mire ecosystem by re-meandering the channelized rivers. The simulation result shows that the re-meandering of channelized river is effective for the decrease of river discharge, the increases of groundwater level, and the decrease of suspended sedimentation at the downstream of re-meandering area around the mire (Figure 31 and Table 9). This result also agrees well the previous research in the points that the elevation change would slow down slightly (Brookes, 1985; Talbot and Lapointe, 2002). It is also very interesting that these changes would affect greatly the mire vegetation (Ministry of Environment, 2004; Nakayama and Watanabe, 2004). Anyway, it is further necessary to evaluate the forecasted results of hydrologic, geomorphic, and vegetation changes with the observed data of the new restoration project to re-meander the channelized rivers in the future. The simulated forecast of NICE-VEG has a possibility to overestimate the alder degradation and the mire recovery up to the 2030 (Figure 32) because the decrease of nutrient influx decreases directly the alder growth in equation (61). Nitrogen fixation in seedlings of

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A. japonica actually plays an important role in the alder growth (Burgess and Peterson, 1986; Hendrickson et al., 1990) and it may take more time to recover the mire. The weak point of gap-phase models is that the best growth of a species occurs in the middle of its range and decreases to the north and south of this middle point. This is related to the accuracy of growth rate parameter in equation (59) based on Bugmann (1996) in comparison with the multiplicative approach (Urban and Shugart, 1992) and Liebig’s Law (Kienast, 1987; Bugmann, 1996; Bugmann, 2001). The geometric mean to get growth rate parameter from maximum growth rate may be also effective to solve this problem (Wunder et al., 2006). It is further necessary to gather the observed data about the relationship between the nitrogen fixation and the alder growth. The disagreement is also related that it is more necessary to validate gap-phase models of ecosystem response to the environmental change for time scales of