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Prediction Model for Hydrological Responses of Watershed using SWAT and ANN under Climate Change Scenarios

초록/요약

Objectives: Absolutely, climate change is a significant concern impacting our planet in the 21st century. Prediction and understanding how climate change affects the earth planet especially water resources help in planning and managing them effectively. Therefore, in this research an attempt has been made to survey the influence of this issue on climatic and hydrological parameters in Saghez watershed in Iran. Methods: In this research to determine the trend and the slope of changes Sen’s slope estimator and Mann-Kendall tests were applied. Besides, for checking precipitation, temperature, relative humidity, wind speed, and solar radiation the Coupled Model Intercomparison Project phase 6 (CMIP6), and for investigation of runoff the Artificial Neural Network (ANN), and Soil and Water Assessment Tool (SWAT) models under the Shared Socio-economic Pathway scenarios (SSPs) used for the future period (2021-2050) compared to the base period (1985-2014). In addition, the Linear Scaling Bias Correction (LSBC) applied to downscale the CMIP6 data. Results: According to findings of Mann-Kendall and Sen’s slope estimator tests precipitation and relative humidity had the falling trend. This falling trend was significant only in minimum relative humidity at the level of 5%. The temperature trend also showed that minimum, average and maximum temperatures had a rising trend, and these rising trends were not significant in any of the mentioned parameters. The trend of changes in wind speed and solar radiation has also risen, and this rising trend was significant in wind speed with the Mann-Kendall's coefficient equals to 3.4 at the level of 1%. The process of changes in runoff in this area also showed that because of the changes in land use and vegetation, watershed's runoff had an increasing trend, but this increasing trend was not impressive in any of the hydrometric stations. The results of predicting the precipitation will have a decrease of 6.1%, while minimum and maximum temperatures will have an rise of 1.6 Cº and 1.4 Cº, respectively. The findings from SWAT model demonstrated that the surface runoff based on SSP5-8.5, SSP3-7.0, and SSP1-2.6 scenarios will have a decrease of 22.8% on average. In addition, investigating using ANN, precipitation is the most effective parameter in changing the runoff. Conclusion: Based on the rise in temperature, drop in soil moisture and happening the flash floods in the study area due to the climate change, the suitable management for this area is crucial. It is recommended to implement the sufficient watershed management operations such as structural and biological operations in the area in order to enhance the vegetation and mitigate the floods due to the climate change.

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목차

I. INTRODUCTION 1
A. Description and Expression of The Research Problem 2
B. The Importance of The Research 5
C. The Research Questions 6
D. The Objectives of This Research 6
E. Research Method in Terms of Nature 7
F. Methods and Tools of Data Collection 7
G. Statistical Society and Number of Samples 8
H. Sampling Method 8
Ⅱ. THEORETICAL FOUNDATIONS AND RESEARCH BACKGROUND 9
A. Introduction 9
B. Theoretical Foundations 9
1. Climate and climatology 9
(A) Climate change 10
(B) Climate oscillation 11
(C) Effective factors on climate change 12
(D) Consequences of climate change 12
(E) The climate change scenarios 13
(1) Non-climatic scenarios 14
a. IS92 scenarios 14
b. The Special Report on Emission Scenarios (SRES) scenarios 16
c. Representative Concentration Pathway (RCP) scenarios 20
d. SSP scenarios 22
(2) Climatic scenarios 26
(F) Climate prediction models 26
(G) The Global climate models (GCMs) 27
(1) Atmospheric-Oceanic General Circulation Models (AOGCM) 28
(H) Downscaling 30
C. Literature Review 31
III. METHODOLOGY 38
A. Study Area 38
1. Climatic conditions 39
(A) Precipitation 41
(B) Temperature 43
(C) Humidity 44
(D) Wind 45
2. The topography and hydrographic of the study watershed 47
3. The land use status 50
4. The geological status 50
B. Research Methodology 51
1. Nonparametric Mann-Kendall 52
2. The sen's slope estimator 54
3. GCM models 55
4. The study scenarios 56
5. Downscaling 57
6. Performance criteria 58
7. Artificial Neural Networks (ANN) 59
8. Soil & Water Assessment Tool (SWAT) 64
(A) Simulation of watershed hydrologic cycle 64
(1) Inputs and outputs 67
(B) Simulation of hydrological features 68
(1) The analysis of uncertainty 68
(2) The sequential Uncertainty Fitting (SUFI-2) algorithm 69
(3) Model performance evaluation 69
IV. RESULTS AND DISCUSSION 71
A. Introduction 71
B. Investigating The Process of Changes in Climatic Parameters 71
C. Assessment of The Cmip6 Models' Performance Based on LSBC 73
D. Future Climate Change Forecasting 84
1. Precipitation 84
2. Maximum temperature 85
3. Minimum temperature 86
4. Average temperature 87
5. Relative humidity 88
6. Wind speed 89
E. Assessment of climatic parameters impacting runoff using ANN 91
F. Simulating runoff using SWAT 94
1. The model's first implementation 94
2. The analysis of sensitivity, calibration and validation 95
(A) The analysis of sensitivity 95
(B) Implementing of calibration and validation 95
3. The evaluation of model performance 96
4. Modeling the impact of climate and land use change 98
(A) Land use change's effect modeling 99
(B) Watershed's runoff 101
V. DISCUSSION AND CONCLUSION 105
A. Introduction 105
B. Discussion and Conclusion 105
C. Research problems and limitations 108
D. Suggestions 109
REFERENCES 111

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