Acknowledgements This thesis is the result of four years of work whereby I have been accompanied and supported by many people It is a pleasant aspect that I have now the opportunity to express my gratitude for all of them I’d like to sincerely thank my supervisor A/P Xixi Lu for providing me with the opportunity to conduct PhD studies with him Without his involvement and advice in the research, this thesis would have never been ready in the present form My gratitude goes to A/P Shie-Yui Liong from Department of Civil Engineering, NUS, for giving me the confidence and support to work with neural network I would also like to express my sincere appreciation to A/P David Higgitt from Department of Geography, NUS, for his stimulating suggestions and encouragement I am grateful for Miss Pauline Lee in Department of Geography for her administrative assistance I am grateful for Prof Yue Zhou from Kunming University of Science and Technology for his support during the research Thanks Youan Guo, Hongbo Li and Jingping Wang for their kind help in the field work Friendship makes my life in Singapore more enjoyable I thank all graduate students in the Department of Geography Thanks go to Luqiang, Jiangnan, Jianfeng and Hanbing, for sharing the joys and trials with me; Joy, for being my first teacher in remote sensing; Shurong, for her help in statistics; Chih Yuan and Songuang, for being my walking Chinese-English dictionary and for fixing up the printer on the right time A special thank goes to Gu Ming, for making the thesis period less stressful (I miss the time of “BaGua” with her) I feel a deep sense of gratitude for my parents I’d like to thank my sister for talking to me on phones for hours I am very grateful for my husband Liang, for his love and support in everything I Table of Contents Acknowledgements I Table of Contents II Summary VII List of Tables IX List of Figures XII List of Plates XVII Introduction 1.1 Background 1.1.1 Impact of climate and land use change on water discharge and sediment flux 1.1.2 Mathematical modelling of the impact and limitation of current research 1.1.3 Longchuanjiang catchment for impact study 1.2 1.3 Framework of methodology 1.4 Aims and objectives of the study Arrangement and structure of the dissertation 11 Study area 13 2.1 2.2 Climate in the catchment 17 2.3 Social and economic environment 19 2.4 Physical characteristics of the catchment 14 Problem statement of the catchment 20 Hydroclimatic change in the catchment 24 3.1 Data and method 25 3.1.1 Data 25 3.1.2 Mann-Kendall nonparametric trend test 26 3.1.3 Sen’s slope test .29 3.1.4 Pettitt change-point test 30 3.2 Climate change in the catchment 31 3.2.1 Spatial variety of climate change in the catchment 31 3.2.2 Catchment average rainfall change .38 3.2.3 Catchment average temperature change 42 3.3 3.3.1 Change of water discharge at Huangguayuan 43 Annual water discharge 43 II 3.3.2 Seasonal water discharge 47 3.3.3 Maximum monthly and daily water discharge .50 3.3.4 Minimum monthly and daily water discharge 51 3.3.5 Flow duration curve at Huangguayuan 52 3.4 Change of sediment flux at Huangguayuan 56 3.4.1 Annual average sediment flux 56 3.4.2 Seasonal sediment flux 60 3.4.3 Maximum monthly and daily sediment flux .62 3.5 3.6 Water discharge and sediment flux in the upper and lower reaches 62 Possible relations between changes in rainfall, water and sediment 65 Land cover/use change in the catchment 70 4.1 Introduction 70 4.2 Materials and method 70 4.3 Satellite image processing 74 4.3.1 Main characteristics of satellite images used 76 4.3.2 Procedure of land cover/use classification 76 4.3.3 Pre-classification work 79 4.3.4 Supervised classification 81 4.3.5 Post classification 89 4.3.6 Image classification results 95 4.4 4.5 Land cover/use change along the Longchuanjiang River 106 4.6 Land cover/use change at catchment scale 101 Conclusion 112 Modelling water discharge with Artificial Neural Network 114 5.1 Mathematical models for hydrological modelling 114 5.2 Basics of Artificial Neural Network 119 5.2.1 Structure of MLP 120 5.2.2 Information processing in MLP .122 5.2.3 Evaluation of MLP performance 124 5.3 Review on the application of ANN in hydrological modelling 126 5.3.1 Ability of ANN in rainfall-runoff modelling 131 5.3.2 Architecture of ANNs .132 5.3.3 Conclusion related to the selection of input variables 133 5.3.4 Data set partitioning for calibration and validation .134 5.3.5 Improvement to conventional ANN 136 5.4 Modelling water discharge of the Longchuanjiang River with ANN 139 III 5.4.1 Introduction 139 5.4.2 Materials and method 141 5.4.3 Result and discussion 149 5.5 Conclusion 154 Modelling suspended sediment flux with Artificial Neural Network 156 6.1 Introduction 156 6.2 Materials and data 163 6.3 Methodology 166 6.3.1 Data processing .166 6.3.2 Artificial Neural Networks 167 6.3.3 Multiple linear regression (MLR) and power relation (PR) models .169 6.4 Application and results 170 6.4.1 Results from ANNs 170 6.4.2 Results from the MLR and PR models 174 6.5 6.6 Discussion 174 Conclusion 181 Anthropogenic impact on sediment—qualitative analysis 183 7.1 7.2 Statistical evidence of the anthropogenic impact 190 7.3 Impact of deforestation and reforestation 195 7.4 Impact of agriculture intensification 202 7.5 Impact of engineering projects 207 7.6 Impact of dams and reservoirs 211 7.7 Introduction 183 Conclusion 218 Anthropogenic impact on sediment—ANN modelling 222 8.1 Introduction 222 8.2 Differentiating influences from climate change and human activity 228 8.2.1 Data 228 8.2.2 Double mass curve method 229 8.2.3 Linear regression method 232 8.2.4 ANN method 233 8.2.5 Discussion 238 8.3 Differentiating influences from individual human activity 240 8.3.1 Method 240 8.3.2 Model application and result discussion .250 8.4 Conclusion 254 IV Sensitivy of water discharge and sediment flux to climate change 256 9.1 Introduction 256 9.2 Methodology 260 9.2.1 Climate scenarios 260 9.2.2 Data and method 262 9.3 Performance of ANNs 264 9.4 Sensitivity of water discharge to climate change 265 9.4.1 Overall changes in water discharge 265 9.4.2 Seasonal changes in water discharge 267 9.5 Sensitivity of sediment flux to climate change 269 9.5.1 Results of ANN prediction 269 9.5.2 Sensitivity of sediment flux to rainfall and temperature 273 9.5.3 Sediment flux under possible future climate in the Longchuanjiang catchment 277 9.6 Sensitivity of water and sediment to climate change under changing human activity 278 9.6.1 Introduction 278 9.6.2 Sensitivity of water discharge under Level I and Level II human activity 280 9.6.3 Sensitivity of sediment flux under Level I and Level II human activity 282 9.7 Conclusion 285 10 Conclusions .289 10.1 A brief overview of the study 289 10.2 Main findings of the study and their implication 290 10.2.1 Land use, climate and hydrological change in the catchment 290 10.2.2 Anthropogenic impact on sediment—qualitative analysis .292 10.2.3 Anthropogenic impact on sediment—ANN modelling .294 10.2.4 Sensitivity of water and sediment to climate change 295 10.3 Application of ANN in hydrological modelling 297 10.4 Limitation of the study 299 10.4.1 Causal variables considered 299 10.4.2 Land use/cover data 300 10.4.3 Applicability of the study results to other catchments .301 10.5 Prospects and future work 301 10.5.1 Examination of hydrological change on shorter temporal scale .301 10.5.2 Influence of reservoir and road construction 302 V 10.5.3 Modification to ANN 302 Appendix I Data collection and quality control at gauging station in the Longchuanjiang catchment 303 Bibliography .306 VI Summary Climate change coupled with intensified human activity could significantly affect the hydrological processes and have posed a serious threat to the sustainable management of the river system This research aimed to investigate the impact of climate change and human activities on river water discharge and, particularly, suspended sediment flux with a case study in the Longchuanjiang catchment, the Upper Yangtze River, China Non-updating artificial neural network (ANN) was used as a modelling tool to assess the influence from human activities and to project the response of water discharge and sediment flux under hypothetical climate scenarios The study area had experienced a sharp increase in suspended sediment flux in the post-1990 period The research indicated that compared with the background condition (1960-1990), the intensification of human activity had lead to an increase of 2.76 million t yr-1 in years from 1991 to 2001 Of the total change in sediment flux this period, the contribution of the intensified human activity exceeded that of the increased rainfall, with the former accounting for 66~75% and the latter for 25~34% Among the various human activities, road construction was the dominant variable for the increase of the sediment During the period from 1991 to 2001, road construction was estimated to have result in an increase of 30.01 million t in the total sediment flux But meanwhile, conversion of barren land to range land in areas along the channel resulted in a reduction of 9.88 million t in sediment Reservoir was another factor that contributed to reduce the sediment in the river The trapping efficiency of the reservoirs in was estimated to be approximately 90% The change of forest in the catchment was failed to be related to sediment in the river due to various reason like the immaturity of the trees, the lack of the undergrowth and the location of the reforested area Climate change will affect water and sediment in a river The sensitivities of water discharge and sediment flux to 25 hypothetical climate changes were predicted by ANNs Under the possible future climate change in the catchment till 2050, the change of sediment flux was estimated to be between -0.7%~13.7% In addition, sediment under intensified human activity was found to be more sensitive to the climate change ANN provides a competitive alternative to the physical and conventional empirical models in hydrological modelling, especially in sediment modelling The current study indicated that VII ANN is capable of modelling the monthly water discharge and sediment flux with fairly good accuracy when proper input variables representing drivers and their lag effect are included One significant feature of the ANN in the current study is that it relates sediment directly to the drivers that have physical influence on it Such ANN can be used to investigate the physical relationship between the drivers and the water/sediment and it permits the assessment of hydrological responses to climate change and human activity The current research demonstrated a method for use in studying the impact of climate change and human activity on water discharge and sediment flux The conclusion drawn may provide information for understanding the complicated hydrological system and its response to the changing climate and human activity Further research on the influences from variables such as gully erosion, sediment re-transportation, location of road and reservoir retention may help to elucidates hydrological change in the catchment VIII List of Tables Table 2.1 Annual water and sediment discharge at Xiaohekou and Huangguayuan (19572001) 16 Table 2.2 Forest areas in the catchment 17 Table 2.3 Summary of some climate indicators in the Longchuanjiang catchment (1960-2001) 18 Table 2.4 Reservoirs in the Longchuanjiang catchment 20 Table 2.5 Change of road and railway length in the catchment 20 Table 2.6 Soil erosion affected area in the catchment 21 Table 3.1Estimated coefficients of linear regression equations for annual rainfall and annual average temperature 32 Table 3.2 Estimated coefficients of linear regression equations for maximum monthly and daily rainfall 33 Table 3.3 Estimated coefficients of linear regression equation for annual potential evaporation and annual average humidity 36 Table 3.4 Trend in rainfall and temperature time series (Mann-Kendall test and Sen’s test) 40 Table 3.5 Change-point in rainfall and temperature time series in the Longchuanjiang catchment (with Pettitt test) 40 Table 3.6 Trend in seasonal rainfall time series (Mann-Kendall test) 42 Table 3.7 Estimated coefficients of linear regression equations for annual, maximum monthly/daily and minimum monthly/daily water discharge 44 Table 3.8 Trend in water discharge time series (Mann-Kendall test and Sen’s test) 46 Table 3.9 Change-point in water discharge time series at Huangguayuan (Pettitt’s test) 47 Table 3.10 Trend in seasonal water discharge time series (Mann-Kendall test) 49 Table 3.11 Statistics of flow duration curves at Huanguayuan in 1960-1974, 1975-1990 and 1991-2001 56 Table 3.12 Estimated coefficients of linear regression equations for annual, maximum monthly/daily sediment flux 58 Table 3.13 Trend in sediment flux time series (Mann-Kendall test and Sen’s test) 59 Table 3.14 Change-point in sediment flux time series at Huangguayuan 59 Table 3.15 Trend in seasonal sediment flux time series (Mann-Kendall test and Sen’s test) 61 Table 4.1 Data sources for land use/cover time series analysis 71 Table 4.2 Main characteristics of Landsat MSS, TM and ETM+ images 77 Table 4.3 Land use/cover classification scheme 80 Table 4.4 Jeffries-Matusita and Transformed Divergence between land cover pairs 87 Table 4.5 Confusion matrix for image MSS 1974 of the Longchuanjiang catchment 92 Table 4.6 Confusion matrix for image TM 1989 of the Longchuanjiang catchment 93 Table 4.7 Confusion matrix for image ETM+ 1999 of the Longchuanjiang catchment 94 IX Table 4.8 Land cover/use of the Longchuanjiang catchment in 1974, 1989 and 1999 96 Table 4.9 Medium sized reservoirs in the Longchuanjiang catchment 102 Table 4.10 Arable land in the Longchuanjiang catchment (from statistical year book) 103 Table 4.11 Forest land in the catchment—from document and satellite image classification105 Table 4.12 Changes between forest, range land and barren land 107 Table 4.13 Land cover/use along the Longchuanjiang River 109 Table 5.1 Details of hydrologic models reviewed 117 Table 5.2 Review of papers on the application of ANN in hydrological modelling 127 Table 5.3 Cross-correlation (r) between climate variables and water discharge (average interval: month) 145 Table 5.4 Inputs combinations for water discharge modeling (average interval: month) 146 Table 5.5 Statistical characteristics of the calibration, testing and validation data sets (average interval: month) 148 Table 5.6 Performance of ANN for water discharge modelling in the Longchuanjiang basin (average interval: month) 151 Table 6.1 Characteristics of previous works on modelling sediment discharge with ANN 160 Table 6.2 Statistical parameters of hydro-climatic data for the Longchuanjiang catchment 165 Table 6.3 Correlation coefficients (r) of the hydro-climatic data for the Longchuanjiang catchment 166 Table 6.4 Performances of ANNs, MLR and PR models for sediment flux modelling in the Longchuanjiang basin (average interval: month) 171 Table 6.5 Estimated MLR and PR models for sediment flux modelling in the Longchuanjiang basin (average interval: month) 174 Table 7.1 P values for linear regression of annual rainfall, water discharge and sediment flux in 1960-1990 and 1991-2001 periods 192 Table 7.2 Input combination and performance of ANN_spatial 194 Table 7.3 Ecological projects in the Longchuanjiang catchment 197 Table 7.4 Change of soil erosion area between 1987 and 1999 197 Table 7.5 Land use/cover change in the dry-hot valley 205 Table 7.6 Sediment deposition in reservoirs 213 Table 7.7 Trapping efficiency of medium-sized reservoirs in the Longchuanjiang catchment 215 Table 7.8 Soil erosion rate estimated from plot (after Yunnan Hydraulic Bureau, 1987) 216 Table 7.9 Change of human activity in the pre- and post-1990 periods and it hydrological effect 220 Table 8.1 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Gullies on the cutting slope of the road 209 Plate 7.7 Construction waste dumped into the main channel of the Longchuanjiang River 211 Plate 7.8 Plate canal conducting the mud into the Longchuanjiang. .. the lag effect of the driving forces 1.1.2 Mathematical modelling of the impact and limitation of current research There is a growing concern about the impact of climate change and land use change. .. selection, network training and network evaluation The fourth stage focuses on the implementation of the ANNs established to estimate the impact of climate and human activity on water and sediment