The impact of climate change human activity on water sediment artificial neutral network modelling in the longchuanjiang catchment, upper yangtze river

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 ca

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

Table of Contents

Acknowledgements I Table of Contents II Summary VII List of Tables IX List of Figures XII List of Plates XVII

1 Introduction 1

1.1 Background 1

1.1.1 Impact of climate and land use change on water discharge and sediment flux 1

1.1.2 Mathematical modelling of the impact and limitation of current research 3

1.1.3 Longchuanjiang catchment for impact study 6

1.2 Aims and objectives of the study 8

1.3 Framework of methodology 9

1.4 Arrangement and structure of the dissertation 11

2 Study area 13

2.1 Physical characteristics of the catchment 14

2.2 Climate in the catchment 17

2.3 Social and economic environment 19

2.4 Problem statement of the catchment 20

3 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 Change of water discharge at Huangguayuan 43

3.3.1 Annual water discharge 43

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 Water discharge and sediment flux in the upper and lower reaches 62

3.6 Possible relations between changes in rainfall, water and sediment 65

4 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 Land cover/use change at catchment scale 101

4.5 Land cover/use change along the Longchuanjiang River 106

4.6 Conclusion 112

5 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

5.4.1 Introduction 139

5.4.2 Materials and method 141

5.4.3 Result and discussion 149

5.5 Conclusion 154

6 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 Discussion 174

6.6 Conclusion 181

7 Anthropogenic impact on sediment—qualitative analysis 183

7.1 Introduction 183

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 Conclusion 218

8 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

9 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

10.5.3 Modification to ANN 302

Appendix I Data collection and quality control at gauging station in the Longchuanjiang catchment 303 Bibliography 306

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

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

List of Tables

Table 2.1 Annual water and sediment discharge at Xiaohekou and Huangguayuan

(1957-2001) 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

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 Characteristics of methods reviews of sediment flux change related impact assessment 227

Table 8.2 Statistical parameters of hydro-climatic data in pre- and post-1990 period 229

Table 8.3 Annual sediment flux under background, only climate variation and both climate/human activity change — double mass curve method 231

Table 8.4 Annual sediment flux under background, only climate variation and both

climate/human activity change — linear regression method 233

Table 8.5 Performances of ANNs 235

Table 8.6 Comparison of the performance of S60-90 and ANN_6 235

Table 8.7 Annual sediment flux under background, only climate variation and both climate and human activity changes — ANN method (ANN used: S60-90) 238

Table 8.8 Possible variables representing human influence on sediment flux 242

Table 8.9 Input combination tested 244

Table 8.10 Performance of ANNs tested in identifying dominant human activities 245

Table 8.11 Actual sediment flux, estimated sediment without road construction and estimated sediment without reforestation 253

Table 9.1 Predicted climate changes by GCMs and historical data 261

Table 9.2 Performances of ANNs for the Longchuanjiang catchment 265

Table 9.3 Seasonal runoff changes (%) with changes in rainfall and temperature 268

Table 9.4 Change of sediment discharge with changes in rainfall and temperature 271

Table 9.5 Sensitivity of water discharge to climate scenarios under Level I and Level II human influence 281

Table 9.6 Sensitivity of sediment flux to climate scenarios under Level I and Level II level human influence 284

List of Figures

Figure 1.1 Framework of methodology 10

Figure 2.1 Location of the Longchuanjiang Catchment 13

Figure 2.2 Topography of the Longchuanjiang catchment (unit: meter) 14

Figure 2.3 Spatial variation of rainfall (mm) and temperature (ºC) in the catchment 18

Figure 2.4 Time series of annual average suspended sediment load and water discharge 22

Figure 3.1 Thiessen polygons of the weather stations in the Longchuanjiang catchment 26

Figure 3.2 Time series of annual rainfall (a) and temperature (b) at individual weather stations (straight line indicates the linear regression trend for time series with significant changes) 34

Figure 3.3 Time series of maximum monthly (a) and daily (b) rainfall for the year at individual weather stations (strait line indicates the linear regression trend for time series with significant changes) 35

Figure 3.4 Time series of annual potential evaporation (a) and annual average humidity (b) at individual weather stations (straight line indicates the linear regression trend for time series with significant changes) 37

Figure 3.5 Time series of annual catchment rainfall in the Longchuanjiang catchment 39

Figure 3.6 Change-point in annual rainfall time series of the Longchuanjiang catchment (Dashed line indicates the change-point) 39

Figure 3.7 Time series of maximum monthly and daily rainfall in the Longchuanjiang catchment 41

Figure 3.8 Long term monthly average rainfall 42

Figure 3.9 Time series of annual average temperature in the Longchuanjiang catchment 43

Figure 3.10 Time series of annual water discharge at Huangguayuan 44

Figure 3.11 Change-point in annual water discharge time series at Huangguayuan (Dash line indicates the change-point) 45

Figure 3.12 Long term monthly average water discharge and standard deviation 47

Figure 3.13 Time series of maximum monthly and daily water discharge at Huangguayuan 50 Figure 3.14 Time series of minimum monthly and daily water discharge at Huangguayuan 51

Figure 3.15 Flow duration curve at Huangguayuan (1960-1974) 53

Figure 3.16 Flow duration curve at Huangguayuan (1975-1989) 54

Figure 3.17 Flow duration curve at Huangguayuan (1990-2001) 55

Figure 3.18 Time series of annual sediment flux at Huangguayuan 57

Figure 3.19 Change-point in annual sediment flux time series at Huangguayuan (Dashed line indicates the change-point) 58

Figure 3.20 Long term monthly average sediment flux and standard deviation 60

Figure 3.21 Time series of maximum monthly and daily sediment flux at Huangguayuan 62 Figure 3.22 Sen’s slope of changes in annual average and extreme water discharge/sediment

flux at Xiaohekou and Huangguayuan (1970-2001) (Q: water discharge; Qs:

Figure 3.23 Sen’s slope of change in seasonal water discharge 64

Figure 3.24 Sen’s slope of change in seasonal sediment flux 64

Figure 3.25 Cumulative rainfall, water discharge and sediment flux from 1960 to 2001 (straight line indicates trendline based on data from 1960-1990) 65

Figure 3.26 Scatter plot of rainfall-water discharge and rainfall-sediment flux (Huangguayuan)(Grey line indicates the linear relation between water/sediment and rainfall in 1960-1990; dark line for 1991-2001 Notice the higher sediment-rainfall ratio in 1991-2001) 66

Figure 3.27 Scatter plots of cumulative water discharge against water discharge (a), rainfall against water discharge (b), rainfall against sediment flux (c)(straight line indicates trendline based on data from 1960-1990) 67

Figure 3.28 Sen’s slope of rainfall, water discharge and sediment flux (Huangguayuan, 1970-2001) 67

Figure 4.1 Flow chart for land cover/use data retrieving and analysis 72

Figure 4.2 Landsat MSS, TM and ETM+ false color images covering the Longchuanjiang catchment 78

Figure 4.3 Procedure of land use/cover classification 79

Figure 4.4 Ground reference data collected in field survey in 2004 81

Figure 4.5 DN values of the training sets for each land cover type in the Longchuanjiang catchment (image TM 1989) 83

Figure 4.6 Sample histograms for data points included in the training areas for cover type “forest” in the Longchuanjiang catchment (TM 1989) 85

Figure 4.7 Two-dimensional scatter plot for separability assessment (TM 1989) 86

Figure 4.8 Land cover/use of Longchuanjiang catchment in 1974 97

Figure 4.9 Land cover/use of Longchuanjiang catchment in 1989 98

Figure 4.10 Land cover/use of Longchuanjiang catchment in 1999 99

Figure 4.11 Percentage of land cover/use type in the Longchuanjiang catchment: (a) 1974, (b) 1999 and (c) 1989 100

Figure 4.12 Land cover/use within 1,000m of the Longchuanjiang River (1999) 108

Figure 4.13 Land cover along the river system: (a) 1974, (b) 1989 and (c) 1999 111

Figure 5.1 Structure of Multilayer Perceptrons (MLP) 121

Figure 5.2 Structure of individual neuron 121

Figure 5.3 Framework of network construction 142

Figure 5.4 Thiessen polygons of the weather stations in the Longchuanjiang catchment 143

Figure 5.5 Observed and predicted monthly runoff by model W4—validation period 153

Figure 5.6 Observed and predicted monthly runoff by model W8—validation period 153

Figure 5.7 Observed and predicted monthly runoff by model W9—validation period 153

Figure 6.1 Architecture of the MLP used and the schematic representation of a neuron 168

Figure 6.2 Comparison between the observed and predicted sediment fluxes based on validation data (a) ANN_2, (b) ANN_6, (c) ANN_9 and (d) ANN_14 173

Figure 6.3 Comparison between the observed and predicted sediment fluxes based on

validation data (a) MLR_A, (b) MLR_B, (c) MLR_C, and (d) PR 175

Figure 6.4 Scatter plots of the observed and predicted sediment fluxes by the best performing ANN in each group and the MLR/PR models, based on validation data (a) ANN_2, (b) ANN_6, (c) ANN_9 and (d) ANN_14, (e) MLR_A, (f) MLR_B, (g) MLR_C, and (h) PR 176

Figure 6.5 Comparison between observed and predicted cumulative suspended sediment load values by the best performing ANN of each group and MLR/PR models based on validation data (a) MLR_A and ANN_2, (b) MLR_B and ANN_6, (c) MLR_C and ANN_9, (d) PR and ANN_14 177

Figure 6.6 Difference between observed and predicted sediment fluxes by selected ANNs (a) ANN_2, (b) ANN_6, (c) ANN_9 181

Figure 7.1 Time series of annual rainfall, water discharge and suspended sediment flux (a) annual rainfall, (b) annual water discharge and (c) annual suspended sediment flux (straight line represents average during the corresponding time period) 191

Figure 7.2 Changing relationship between water discharge and sediment flux (a) scatter plot of annual water discharge and sediment flux, and (b) relationship between cumulative water discharge and sediment flux 192

Figure 7.3 Relationship between cumulative rainfall-cumulative water discharge and cumulative rainfall-cumulative sediment flux from 1960 to 2001 (straight line indicates trendline based on data from 1960-1990) 193

Figure 7.4 Scatter plot of observed and predicted sediment flux by ANN_6 and ANN_spatial 195

Figure 7.5 Difference between observed and predicted sediment flux by ANN_6 and ANN_spatial 195

Figure 7.6 Location of medium-sized reservoirs and change of forest between 1989 and 1999 201

Figure 7.7 Changes of total arable land and cropping index in the catchment 203

Figure 7.8 Land use/cover in the dry-hot valley in 1974 205

Figure 7.9 Land use/cover in the dry-hot valley in 1989 206

Figure 7.10 Land use/cover in the dry-hot valley in 1999 206

Figure 7.11 Land use/cover change in the dry-hot valley and the whole catchment (a) 1974, (b) 1989 and (c), 1999 207

Figure 7.12 Change of road length and road intensity in the Longchuanjiang catchment 208

Figure 7.13 Location of Xiaohekou, Huangguayuan and medium size reservoirs 212

Figure 7.14 Sediment yield at different scale (a), sediment yield at plots, reservoirs and rivers in the Longchuanjiang catchment (note the decreasing sediment yield to the larger scale); (b), comparison of sediment yield in the Longchuanjiang catchment with other catchments in the Upper Yangtze (after Lu, 2005) 216

Figure 7.15 Increase of medium-sized reservoir storage capacity in the Longchuanjiang catchment 217 Figure 8.1 Mass curve of sediment flux in the Longchuanjiang catchment (Black dots are

observed sediment flux in the pre-1990 period; grey dots are observed sediment flux

in the post-1990 period; the straight line is the trend line of the sediment flux in the

pre-1990 period; the equation is for the trend line) Note the difference d is the

Figure 8.2 Double mass curve of sediment flux in the Longchuanjiang catchment (Black dots

are observed sediment-water relationship in the pre-1990 period; grey dots are observed sediment-water relationship in the post-1990 period; the straight line is the trend line of this relationship the pre-1990 period; the equation is for the trend line)

Note the difference d is the change of sediment flux due to the intensified human

activity 231

Figure 8.3 Trend in sediment flux by linear regression (a), sediment flux–water discharge relationship (blue dots represent data in the post-1990 period; dark dots represent data in pre-1990 period and trend line is based on data in this period.) , (b) residuals of the sediment flux estimation 232

Figure 8.4 Observed and estimated sediment flux during 1960-1990 (a), observed and estimated sediment flux; (b), scatter plot 235

Figure 8.5 Annual sediment flux under background, under only climate variation and under both climate and human activity change from 1991 to 2001 236

Figure 8.6 Cumulative monthly sediment flux under background, only climate variation and under both climate and human activity change from 1991 to 2001 236

Figure 8.7 Comparison of the estimated sediment flux under only climate variation by ANN, double mass curve and linear regression methods 239

Figure 8.8 Comparison of the estimated influence of climate variation by double mass curve, linear regression method and ANN 239

Figure 8.9 Comparison of the estimated influence of human activity by double mass curve, linear regression method and ANN 239

Figure 8.10 Change of variables representing human activities in the Longchuanjiang catchment 242

Figure 8.11 RMSE of calibration, testing and validation data set by ANNs 245

Figure 8.12 CE of calibration, testing, validation and the whole data set by ANNs 246

Figure 8.13 Observed and estimated sediment flux by H1 248

Figure 8.14 Observed and estimated sediment flux by H8 248

Figure 8.15 Scatter plots of H1 and H8 249

Figure 8.16 Prediction errors of ANN_6, H1 and H8 249

Figure 8.17 Actual sediment flux, estimated sediment without road construction and estimated sediment without reforestation (by H8) 251

Figure 8.18 Actual sediment flux and estimated flux without road construction (by H1) 252

Figure 9.1 Time series of annual rainfall, suspended sediment load and water discharge (a) double mass plot of cumulative water discharge and suspended sediment load; (b) annual rainfall; (c) annual water discharge and (d) annual suspended sediment load 263

Figure 9.2 Observed and predicted water discharge and sediment flux 265

Figure 9.3 Predicted water discharge under scenario T0&R0, T-1&R+20 and T+3&R-20 266

Figure 9.4 Sensitivity of runoff to the rainfall and temperature 267

Figure 9.5 Sensitivity of runoff to rainfall and temperature—in February and August 269

Figure 9.6 Predicted sediment flux under scenario T0&R0, T-1&R+20 and T+3&R-20 270

Figure 9.7 Sensitivities of sediment flux and water discharge to climate scenarios (a) under

T-1 scenarios; (b) under T0 scenarios; (c) under T+T-1 scenarios; (d) under T+2

scenarios, and (e) under T+3 scenarios 272

Figure 9.8 Sensitivity of sediment flux to climate scenarios (a) in November and (b) in August 272

Figure 9.9 Cumulative rainfall (R 25 and R 50) of 25mm- and 50mm-or-more rain days under climate scenarios 274

Figure 9.10 Change of sediment concentration under climate scenarios 277

Figure 9.11 Possible range of sediment flux change till the 2050s 278

Figure 9.12 Relationship between rainfall and (a) water discharge or (b)sediment flux 280

Figure 9.13 Sensitivity of water discharge to climate scenarios under Level I and Level II human influence 282

Figure 9.14 Sensitivity of water discharge to climate scenarios under Level I and Level II human influence 285

List of Plates

Plate 2.1 Soil forest in Yuanmou—result of severe soil erosion 21 Plate 7.1 Reforested area (a and b) and original forest (c) in the catchment 199 Plate 7.2 Barren slope along the main channel in the middle reach (gully developed on the

slope) 202 Plate 7.3 The highly dissected area in the lower reach — dry-hot valley 202 Plate 7.4 Construction material excavation site in the Longchuanjiang catchment 209 Plate 7.5 Chuda highway along the Longchuanjiang River (the exposed cutting slope seven

years after completion) 209 Plate 7.6 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 River over the Chengkun

railway 211

1 Introduction

1.1 Background

Hydrological regimes in a catchment can be described by a series of processes, such

as precipitation, interception, evapotranspiration, depression storage, overland flow, infiltration, interflow and channel flow Four interrelated sets of factors, including catchment physical attributes, climate, land use and resource management system, determine the hydrological processes in the catchment (Arnell, 1996, pp.7-60) Changes in these factors could significantly influence water discharge and sediment flux in the river

1.1.1 Impact of climate and land use change on water discharge and sediment flux

Climate is the most important driver of the hydrological cycle During the 20th century, the average global surface temperature increased by 0.6℃; it was predicted

that temperature would rise by about 1.4~5.8℃ by the year 2100 (Folland et al., 2001

pp.99-182) Meanwhile, there was an increase in global precipitation over the 20thcentury Instrumental records of land-surface precipitation continue to show an increase of 0.5 to 1% per decade in much of the northern hemisphere mid- and high latitudes In contrast, over much of the sub-tropical land areas rainfall had decreased during the 20th century (by -0.3% per decade), although this trend has weakened in

recent decades (Folland et al., 2001 pp.99-182)

The warming in the last 50 years in China is more rapid than the average values of the

world and the Northern Hemisphere (Folland et al., 2001 pp.99-182) Mean annual

surface air temperature across China had increased by about 1.1℃ for the last 50

years (Ren et al., 2004b) Some of the effects of increasing temperature would be the

changes in the rainfall and in the magnitude and frequency of extreme weather events

A significant drying trend was observed in the Yellow River Basin and North China

(Ren et al., 2004a) The annual precipitation in the Middle and Lower Yangtze

showed an increasing but insignificant trend from 1951 to 2002, whereas it showed

significant decreasing trend in the upper Yangtze (Liu, 2003; Su et al., 2004)

Temperature in the middle and lower Yangtze was dominated by a decreasing trend, while in the upper Yangtze it decreased from 1955-1994 and increased thereafter

(Zhang et al., 2005) Researches show that climatic changes will alter basic

components of hydrological cycle such as evaporation, soil moisture and groundwater availability, and thus influence the magnitude and timing of water discharge and

sediment flux (Chiew et al., 1995; Menzel and Burger, 2002; Xu, 2005; Zhang and

the coastal ocean (Milliman et al., 1987) Syvitski et al (2005) estimated that

compared with the pre-human condition, humans have increased the river transport of sediment through soil erosion activities by 2.3 ± 0.6 billion tons per year at global

scale, but reduced the sediment flux to the global coastal ocean by 1.43 ± 0.3 billion tons because of retention within reservoirs

Many researches on the impact of climate and human activity on water discharge and sediment flux have been conducted and some conclusions have been drawn However,

an explicit understanding of the hydrological system is still not available because the complexity of the hydrological system has made the analysis of the hydrological response and its driving forces problematic Hydrological system is a complex system with a large number of interrelated factors A change in one factor might induce changes in other factors and feedbacks are also expected The magnitude as well as the spatial and temporal distribution of the effective variables will also influence the

hydrological processes in the watershed (Vanacker et al., 2003) It is even more

difficult in differentiating the influences of different types of human activities from climate change, considering the overlapping of the influence and 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

on hydrological processes (Bultot et al., 1990; Boorman and Sefton, 1997; Coulthard

et al., 2000; Fohrer et al., 2001; Brath et al., 2002; Bobrovitskaya et al., 2003;

Nearing et al., 2005) Quantifying hydrological change arising from modifications to

the climate, land use and management of environmental systems has been declared by the British Hydrological Society as one of the three major themes to be addressed by hydrologists Mathematical models are among the best tools available for analysing this kind of impact and improving our understanding of the system These models are put into categories like empirical, conceptual and physically-based models according

to the degree to which the hydrological processes are represented (Viessman and Lewis, 1996)

Physically-based models attempt to represent the spatial heterogeneity of variables by dividing the catchment into grids, and describe the processes of the water and sediment transport from grid to grid with simplified partial differential equations

(Lorup et al., 1998; Andersen et al., 2001; Muzik, 2002; Vanacker et al., 2003;

Eckhardt and Ulbrich, 2003) For sediment flux, the widely used conceptual or physically-based models include SWAT (Hanratty and Stefan, 1998; Boorman, 2003), WEPP and its modified version WEEP-CO2 (Nearing et al., 2005; O'Neal et al., 2005),

CREAMS and ICECREAMS (Boorman, 2003), and MEFIDIS (Jetten et al., 2003; Nunes et al., 2005) Their distributed structure allows the evaluation of the influence

of land management measures on soil erosion However, inadequate scientific basis and intensive data requirment may be the major constraints on its application to larger scale catchment (Refsgaard and Abbott, 1996; Kisi, 2004) Their application is limited

to small and heavily instrumented catchments (usually less than 100 km2) (Beven, 1993) For example, WEPP was applied to catchment up to approximately 4 km2 in size (Flanagan and Nearing, 1995; Nearing et al., 2005) and MEFIDIS was tested from 0.05 to 120 km2 (Nearing et al., 2005)

Compared with physically-based model, empirical models are more widely used on sediment flux prediction in large-scale catchments due to their relatively simple structure and mathematical methods involved, and their ability to work with limited

input data (Flaxman, 1972; Walling, 1983; Prosser et al., 2001; Verstraeten and Poesen, 2001; Zhou et al., 2002) Empirical models relate the hydrological response

to the driving forces, such as rainfall, temperature and land use, by statistical

relationship between them, without considering the actual physical processes involved Although they are unable to represent the spatial variability of hydrologic processes and catchment parameters, it can provide simulations as good as those from complex physically-based models when the interest in on e response on the entire water system (Beven, 2000) In fact, the soil erosion modules in some of the so-called physically-based models remain empirically-based For example, the erosion models for CREAMS and WEPP are based on USLE and MUSLE, respectively However, conventional linear or nonlinear regression models can only simulate the highly nonlinear suspended sediment flux with limited accuracy, due to their simple model structure and mathematical methods employed

Artificial Neural Network (ANN) is a type of empirical model, which is based on

concepts derived from the research on the nature of human brains (Müller et al., 1995)

With its ability to approximate highly non-linear system without any priori assumption of processes involved and the ability to give a good solution even when input data are incomplete or ambiguous, ANN provides a promising alternative to the conventional empirical and physical models in water discharge and sediment flux

modelling (Clair and Ehrman, 1998; Liong et al., 2000; ASCE, 2000a; ASCE, 2000b; Rajurkar et al., 2004; Cigizoglu and Alp, 2006) There are, however, not many reports

on the application of ANN in sediment studies The research conducted by e.g

Abrahart and White (2001), Jain (2001), Tayfur (2002), Kisi (2004) and Agarwal et al

(2005) may be deemed as pathfinder experiments in this area These studies demonstrated that the modelling of sediment, including its concentration in a river or flux from a slope or a watershed, is possible through the use of ANN Most commonly, they predict sediment flux by relating it to water level/discharge and

as input may increase the accuracy of the simulation However, ANNs established by this method, also called updating ANNs, are unable to explain the contribution from climatic variables Also, they are insufficient to predict sediment flux if water and sediment data for previous time periods are not available The current attempt to establish a non-updating ANN by relate the suspended sediment flux to original driving forces, i.e., climatic variables such as rainfall, temperature, and rainfall intensity instead of water and suspended sediment flux at previous time steps as inputs Such ANN could be used to explore the relationships between the climate inputs and sediment responses In addition, it would have a potential of filling missing data in a suspended sediment flux time series and predicting the influence of climatic change on suspended sediment flux

1.1.3 Longchuanjiang catchment for impact study

The Longchuanjiang catchment, drains an area of 5560 km2, is located in the Upper Yangtze River The lower reach of the catchment is a typical dry-hot valley Dry-hot valley is a special environmental type in Southwest China They are widely found along the main streams and their tributaries in this region, notably along the upper Yangtze (Jinsha), Dadu, Yalong, Min, Lancang (Mekong), Nu (Salween), and Yuan (Red) and their tributaries They usually refer to the valleys under the elevation of 1300m (northern slope of the mountain) ~ 1600m (southern slope) and are characterized by a hotter and dryer climate, compared with their neighboring areas For example, the annual average temperature in the dry-hot valleys along Jinsha River (upper part of Yangtze River) is 20~27ºC, the annual total precipitation is only 600~800mm, and the annual evaporation is 3~6 times of the precipitation Furthermore, the precipitation in dry season ( December to May of next year) only

accounts for 10% to 22.2% of the total annual precipitation, which results in a arid index as high as 10~20 in the dry season Despite the fragile ecosystem in the dry hot valleys, they are among highly populated areas because of the relatively even landscape and the abundant solar radiation and heat for agriculture industry Due to the harsh natural environment and the increasing pressure from human activities, most

of the dry hot valleys in Southwest China have the problem of degradation The most common one is soil erosion

The Longchuanjiang catchment is in nature vulnerable to soil erosion due to several physical factors such as intensive rainfall in rain season, fragmented topography and surface soil which is susceptible to water erosion In 1989, soil erosion affected area accounted for more than 50% of its total area (Yunnan Bureau of Water Resources & Hydropower, 1999) Soil erosion has caused many problems in this area For example, more than 85% of the farmland in the catchment is of medium to low productivity due

to the loss of surface soil It also results in sedimentation downstream which reduces the capacity of rivers and drainage ditches, blocks irrigation canals and shortens the design life of reservoirs According to a survey of 48 reservoirs built in the late 1950s

in Chuxiong, the total storage capacity of these reservoirs reduced by 9.88% in 1982, due to deposition

As population increases from 0.62 million in 1949 to 1.37 million in 2001, the Longchuanjiang catchment had experienced changes in a wide range of human activities, including deforestation/reforestation, intensification of agricultural activity, engineering construction and reservoir building, as in most catchments in China Meanwhile, it had also experienced a climate variation characterized by higher rainfall in the 1990s Affected by changes in human activity and the variation in

climate, sediment flux showed a sharp rise since the 1990’s Given all these processes

in the catchment, it is well suited to be a laboratory for studying the impact of simultaneous landuse and climate change on water discharge and suspended sediment flux

1.2 Aims and objectives of the study

To improve understanding of the relationship of climate/human and water discharge/sediment flux at catchment scale is crucial for both academic study and decision making on economic and technical development The aim of this research is

to investigate the impact of climate and human activity on water discharge and sediment flux in a meso-scale catchment, Longchuanjiang catchment, China, with a focus on sediment This can be broken down into more specific objectives:

• To investigate the relationship between climate and water discharge/sediment flux with Artificial Neural Network (ANN);

• To identify the specific causal variables, including climate and human activity, and estimate their contributions to the change of water discharge and sediment flux in the catchment;

• To investigate the sensitivity of water discharge and sediment flux to possible climate changes, and

• To investigate the application of ANN, in terms of its advantage and disadvantage, in the impact assessment study

1.3 Framework of methodology

This research consists of five stages, including background information collection and data retrieval, time series analysis, ANN construction, impact assessment and conclusion The framework of this research is shown in Fig 1.1 The first stage involves the collection and compilation of data required for the study The second stage analyses the characteristics and changes of climate, water/sediment and human activity in the catchment and briefly discusses their possible relationship In the third stage, ANNs that can simulate the change of water discharge and sediment flux are constructed through steps such as input variables selection, ANN 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 Conclusion of the research is made in the fifth stage Detailed method used in each stage is given in the corresponding chapters

Figure 1.1 Framework of methodology

Fifth stage Fourth stage

Third stage

Second stage

Background information collection

and data retrieval

First stage

Data processing stage 1

Temporary scale and value ANN selection

Network Type(MLP) Training Algorithms(BP)

Dataset

Training Network evaluation

Conclusion Impact assessment

Predictors/predictants

Time series analysis Water/sediment time series Climate change time series

Land use time series

Documents Local maps Field survey

Convertion to digital format

Digital image processing Remote sensing data

(DEM; LANDSAT) GIS analysis and interpretation

Database for research

Climate scenarios Sensitivity analysis ANNs pre-1990

ANNs post-1990

Anthropogenic Impact assessment

1.4 Arrangement and structure of the dissertation

The structure of this thesis and the main content of each chapter are briefly described

as follows It is important to note that introductions and literature reviews about specific topics are given in the corresponding chapters

• Chapter 2 provides descriptions of physical and social characteristics of the study area, the problems in the catchment and the reason for choosing

it as the study area

• Chapter 3 analyses the basic characteristics and changes of rainfall, temperature, water discharge and sediment flux The spatial variations of these variables are also discussed

• Chapter 4 examines the land use/cover change in the catchment based on information from satellite image classification of three periods

• Chapter 5 and 6 establish ANNs to predict water discharge and sediment flux, respectively, from climate inputs only

• Chapter 7 discusses the changes of human activities that may have an influence on sediment flux in the catchment and provides a semi-quantitative estimation about their contribution

• Chapter 8 quantitatively estimates the impact of human activities and climate variation on sediment flux in the post-1990 period with ANNs

• Chapter 9 assesses the sensitivity of water discharge and sediment flux to

25 climate scenarios and investigates the sensitivity of water and sediment under changing human activities

• Chapter 10 summarizes the findings about the hydrological responses to climate/human activity and the capacity of ANNs in hydrological modelling

2 Study area

The Longchuanjiang catchment, which drains an area of 5560 km2 (before Huangguayuan station), is a tributary of the Jinsha River (the Upper Yangtze River) (Fig 2.1) The catchment is located between 24º45’~26º15’N and 100º56’~102º02’E

in southwest China The physical characteristics, climate and social economic environment of the catchment are described in the following sections

Figure 2.1 Location of the Longchuanjiang Catchment

2.1 Physical characteristics of the catchment

Topography, geology and soil

The Longchuanjiang catchment is on the Yunnan-Guizhou Plateau Fig 2.2 shows the topography of the Longchuanjiang catchment (above the Huangguayuan station) The catchment is characterized by rugged relief, steep slopes and fragmented landforms The elevation in the catchment varies from 1,028 m to 2,852 m, with an average of approximately 1,940m Most of the areas are between 1,700 and 2,200 m Surrounded

by the high mountains, Yuanmou valley in the lower reach has the lowest elevations

Figure 2.2 Topography of the Longchuanjiang catchment (unit: meter)

The catchment is dominated by Triassic shale and sandstone with small proportion of granite, limestone and Quaternary deposits Purple shale weathers rapidly in subtropical climatic conditions yielding “purplish soil” (belonging to skeletal primitive soils in the classification by China National Soil Survey (China national soil survey, 1992), which is particularly susceptible to erosion The area covered by purple soil accounts for 65.16% of the catchment

River system

The Longchuanjiang River is a tributary which directly flows into the Jinsha River (the upper reach of Yangtze River) The main channel originates in Tianzimiao, Nanhua County, and flows east and north through Chuxiong, Mouding, Lufeng and Yuanmou till it reaches the Jinsha River at Jiangbian, Yuanmou County The length

of the main channel is 231.2 km There are two large tributaries, the Qinglinhe and the Mengganghe The Qinglinhe originates in Yaoan County and flows into the main channel at Xinjiang, Yuanmou County The Mengganghe originates in Yanan, flows through Mouding County and joins the main channel at Longshan, Yuanmou County The river system is shown in Fig 2.1

There are two gauging stations along the river, Xiaohekou in the upper reach and Huangguayuan in the lower reach The drainage sizes of Xiaohekou and Huangguayuan are 1,788.0 km2 and 5,557.6 km2 respectively A reservoir, Dahaibo, was built on the main channel of Longchuanjiang River between Xiaohekou and Huangguayuan in 1959 It is estimated that most of the sediment that passed through

Xiaohekou was deposited in the Dahaibo reservoir (Wen et al., 2000) Hence, since

1960, the sediment at Huangguayuan is mainly from the area between Xiaohekou and

Huangguayuan Annual water and sediment discharge at the two gauging stations are given in Table 2.1

Table 2.1 Annual water and sediment discharge at Xiaohekou and Huangguayuan (1957-2001)

Station size(kmDrainage 2 )

Annual water discharge(106m3)

Water yield(mm/yr)

Annual sediment flux(106t)

Sediment yield(t/(km2·a)

Vegetation and land use

The vegetation in the study area is mainly subtropical evergreen broadleaf forest and Pinus Yunnanensis Due to the influence of topography and local climate, the distribution of the vegetation shows vertical zonality For example, in the areas with elevation between 900~1,350 m in Yuanmou county, the original vegetation was sparse grassland But now, most of the area is barren land due to human influence In the area between 1,350~1,600 m, the original subtropical evergreen broadleaf forest has degenerated into shrubbery At the highland above 1,600m, the original evergreen broadleaf forest was destroyed and replaced by secondary evergreen broadleaf forest and Pinus Yunnanensis forest In terms of spatial distribution, most of the forest is in the high elevation mountain area The dominant land use/cover types in the river valley are barren land and agricultural land (detailed description in Chapter 4)

The forest area in the catchment was estimated to be 36.9% in 1949 It decreased to 16.5% in 1960 but went on an upward trend to reach 39.4% in 2000 (Table 2.2) However, the increase is believed to be partly a result of the different forest classification standard used In the two surveys in the 1960s, areas with canopy coverage > 40% would be classified as forest In the 1973 and 1985 surveys, the standard was lowered to 30% and further to 20% in the last two surveys According to

the interview made by the author during the field survey, forest area showed a decreasing trend till the 1980s when a series of ecological projects involving reforestation in the catchment was launched In the dry-hot valley, the forest was only 0.06% in areas under 1,350 m in 1999 due to the harsh environment and intensive human activity (Forestry Administration of Chuxiong, 2000)

Table 2.2 Forest areas in the catchment

Year 1949 1960 1963 1973 1985 1993 2000

Data source: (Forestry Administration of Chuxiong, 1987; Forestry Administration of Chuxiong, 2000)

According to a survey in 1985, the biggest land use in the catchment was forest land, including matured forest, young forest, sparse forest and bush land, accounting for 66.95% of the catchment The second was barren land, 18.77% Agricultural and urban lands were 7.93% and 1.21%, respectively Water surface accounted for only 0.87% of the catchment

2.2 Climate in the catchment

The Longchuanjiang catchment lies in the subtropical zone and is dominated by the subtropical monsoon climate The annual average temperature is 17.2℃ with the hottest month and coldest month experiencing average temperature of 21.4ºC and 7.4ºC, respectively The maximum and minimum temperature is 42ºC and -8.4ºC while the long-term annual rainfall is 793.7 mm Because of the dominant monsoon climate, more than 80% of the rainfall occurs in the wet season, from May to October Annual potential evaporation is 2545 mm, approximately three times that of annual rainfall

In terms of spatial variation, rainfall is higher in the upper and middle reach than in

the lower reach, with Mouding having the highest annual rainfall (Fig 2.3 and Table

2.3) On the contrary, temperature is lower in the upper and middle reach than in the

lower reach The river valley in Yuanmou County has a dry-hot climate characterized

by lower rainfall (620 mm), higher temperature (21.9ºC) and higher evaporation and

high arid index due to the enclosure of the surrounding mountains Summary of some

of the climate indicators in the Longchuanjiang catchment is listed in Table 2.3

Figure 2.3 Spatial variation of rainfall (mm) and temperature (ºC) in the catchment

Table 2.3 Summary of some climate indicators in the Longchuanjiang catchment (1960-2001)

County

Annual temperature (ºC)

Annual rainfall (mm)

Percentage of rainfall in wet season (%)

Annual evaporation (mm)

Aridity index

2.3 Social and economic environment

The Longchuanjiang River flows through eight counties of Chuxiong Prefecture, namely Nanhua, Chuxiong, Mouding, Yaoan, Lufeng, Dayao, Yongren and Yuanmou The Longchuanjiang catchment accounts for 1/3 of Chuxiong Prefecture in area Chuxiong Prefecture is one of the two Yi nationality autonomous prefectures in China Besides Han nationality, there are 25 other ethnic groups in Chuxiong Prefecture, such as Yi, Miao, Dai, Bai, Hani, Lisu etc The population in Chuxiong Prefecture was approximately 1.36 million in 2000

The GDP in 2000 was 58.9% higher than that in 1995, with 9.7% increase per year The main industries in the catchment are tobacco plantation, medicine, metallurgy, mining and travelling Agriculture and livestock husbandry are conventional industries in the catchment Most of the arable lands are in the valley But some mountain slopes have been reclaimed due to the pressure of an increasing population The agricultural land in the valley is usually used for double crops, with paddy and maize in the monsoon season and wheat and broad bean as winter crop In areas with higher elevation, crops are planted only in spring because of the lower temperature In winter, the farmlands have lain fallow

Many reservoirs and ponds have been constructed since the 1950s In 1949, the total storage capacity of reservoirs and ponds in the Longchuanjiang catchment was 27.8 million m3 This was increased to 763.4 million m3 in 1990 and further to 876.3 million m3 in 2001 (Table 2.4) Of the 18 medium-sized reservoirs in the catchment,

12 are located before the Huangguayuan station Zhang et al (2002a) suggested that

the increase in reservoirs and irrigated farmland could have contributed to the increase

Roads and railways have also been constructed in the catchment since 1950, including the Chengdu-Kunming railway, Kunming-Dali railway, Kunming-Dali expressway, Panzhihua-Kunming highway and other county level roads Road and railway length

in the catchment increased significantly, especially in the 1990s It was only 63.4 km

in 1949, but reached 1288.8 in 1990 and 2542.4 in 2000

Table 2.4 Reservoirs in the Longchuanjiang catchment

a : data from Hydraulic Chronicles of Chuxiong (Hydraulic and Hydropower Bureau of Chuxiong,

1992) ; b : data from Statistical Year Book in 2003 (Statistical Bureau of Chuxiong Prefecture, 2003); c:

storage capacity between 10 to 100 million m 3 ; d : storage capacity between 1 to 10 million m 3 ; e :

storage capacity between 0.1 to 1 million m 3

Table 2.5 Change of road and railway length in the catchment

Length of road and railways (km) 63.4 407.3 512.4 1045.6 1288.8 2542.4

2.4 Problem statement of the catchment

The Longchuanjiang catchment is vulnerable to soil erosion due to its physical environment First, more than 60% of the catchment is covered by “purplish soil”, which is very susceptible to water erosion Second, more than 80% of the rainfall occurs in the wet season from May to October In addition, the undulated and fragmented topography also contribute to severe soil erosion According to the results

of a survey in 1999, the area suffering from soil erosion in Chuxiong Prefecture was 13588.43km2 or 47.44% of its total area (Yunnan Bureau of Water Resources &

Hydropower, 1999) In the Longchuanjiang catchment, covering mainly Nanhua, Chuxiong, Lufeng and Yuanmou counties, the soil erosion area accounted for more

than 50% of its total area (Table 2.6) The ‘soil forest’ in Yuanmou in Plate 2.1 is the

result of severe erosion (ironically, it is now a tourist attraction)

Table 2.6 Soil erosion affected area in the catchment

Area under different erosion intensity*

Land

area

Erosion affected area +

County

Nanhua 2263.61 1172.03 51.78 602.01 51.36 545.72 46.56 24.3 2.07 Chuxiong 4424.59 2254.01 50.94 1178.88 52.30 939.89 41.70 135.24 6.00

Mouding 1441.56 512.16 35.53 302.54 59.07 200.49 39.15 9.13 1.78 Yuanmou 2026.33 942.67 46.52 551.02 58.45 363.99 38.61 27.66 2.93

+ : Data provided by Yunnan Bureau of Water Resources and Hydropower, 1999 *: soil erosion

intensity standard of China The sediment yield 500-2500 t km -2 yr -1 for light erosion, 2500-5000 t km -2

yr -1 for medium erosion and 5000-8000 t km -2 yr -1 for high intensity erosion

Plate 2.1 Soil forest in Yuanmou—result of severe soil erosion

Soil erosion has caused many problems in this area Severe soil erosion results in the

loss of soil from a field, the breakdown of soil structure, the decline in organic matter

and nutrient, a reduction of cultivable soil depth and a decline in soil fertility and a

than 85% of the farmland is of medium to low productivity suffering from soil erosion Soil erosion also results in sedimentation downstream which reduces the capacity of rivers and drainage ditches, blocks irrigation canals and shortens the design life of reservoirs According to a survey of 48 reservoirs built in the late 1950s in Chuxiong, the total storage capacity of these reservoirs reduced by 9.88% in 1982, due to deposition

In the past 50 years, the catchment has undergone changes in both climate and human activity An increase in temperature and a slight increase in precipitation were observed in the upper reach (Chuxiong) while temperature decreased by 1.11℃ and rainfall increased by 131.8 mm from the 1950s to the 1990s in the lower reach (Yuanmou) Meanwhile, the catchment has experienced a wide range of human activities, which are typical in China, such as deforestation and reforestation, road construction, reservoirs and canals building, and intensification of agriculture activity Influenced by climate variation and changes in human activities, both water discharge and suspended sediment flux in the catchment (at Huangguayuan) showed a rising trend (Fig 2.4) This enhances the risk of flooding in the catchment For example, floods from 1980 to 1990 were 8.4 times more than from 1950 to 1979

0 2 4 6 8 10 12 14 16

Figure 2.4 Time series of annual average suspended sediment load and water discharge

Although it is obvious that the change of water and sediment is a result of climate variation and changes in human activities, it is still not clear what the exact causal variables are and to what degree they influence the water and sediment For example,

it is noticed that sediment flux had a sharp increase in the 1990s, at a much higher rate than that of water discharge (more statistical evidence will be provided in Chapter 3) This disproportional change in water and sediment could be a result of their nonlinear relationship and/or to other factors such as land surface disturbance influencing the generation and transportation of sediment A study of the causal variables not only contribute to the understanding of the hydrological system, but also provide information to the local government for future ecological engineering projects, its water management and even social-economic development policy