以北京市延庆区妫水河为例,使用SWAT(Soil and Water Assessment Tool)模型对妫水河流域进行月尺度水文模拟,使用SUFI-2(Sequential Uncertainty Fitting)算法分析参数的敏感性,依据SWAT-CUP自动率定得到P因子和R因子分析模型的不确定性...以北京市延庆区妫水河为例,使用SWAT(Soil and Water Assessment Tool)模型对妫水河流域进行月尺度水文模拟,使用SUFI-2(Sequential Uncertainty Fitting)算法分析参数的敏感性,依据SWAT-CUP自动率定得到P因子和R因子分析模型的不确定性,从而完成本流域分布式水文模型的构建。率定结果显示,率定期确定系数R 2为0.65,效率系数NSE为0.61;验证期确定系数R 2为0.89,效率系数NSE为0.88;不确定性分析结果中P-factor均大于0.5,R-factor均小于0.3。通过以上分析可得该模型对妫水河流域的水文模拟有良好的效果。展开更多
The Ganga River, the longest river in India, is stressed by extreme anthropogenic activity and climate change, particularly in the Varanasi region. Anticipated climate changes and an expanding populace are expected to...The Ganga River, the longest river in India, is stressed by extreme anthropogenic activity and climate change, particularly in the Varanasi region. Anticipated climate changes and an expanding populace are expected to further impede the efficient use of water. In this study, hydrological modeling was applied to Soil and Water Assessment Tool （SWAT） modeling in the Ganga catchment, over a region of 15 621.612 km2 in the southern part of Uttar Pradesh. The primary goals of this study are： ① To test the execution and applicability of the SWAT model in anticipating runoff and sediment yield; and ② to compare and determine the best calibration algorithm among three popular algorithms-sequential uncertainty fitting version 2 （SUFI-2）, the generalized likelihood uncertainty estimation （GLUE）, and par-allel solution （ParaSol）. The input data used in the SWAT were the Shuttle Radar Topography Mission （SRTM） digital elevation model （DEM）, Landsat-8 satellite imagery, soil data, and daily meteorological data. The watershed of the study area was delineated into 46 sub-watersheds, and a land use/land cover （LULC） map and soil map were used to create hydrological response units （HRUs）. Models utilizing SUFI- 2, GLUE, and ParaSol methods were constructed, and these algorithms were compared based on five cat-egories： their objective functions, the concepts used, their performances, the values of P-factors, and the values of R-factors. As a result, it was observed that SUFI-2 is a better performer than the other two algo-rithms for use in calibrating Indian watersheds, as this method requires fewer runs for a computational model and yields the best results among the three algorithms. ParaSol is the worst performer among the three algorithms. After calibrating using SUFI-2, five parameters including the effective channel hydraulic conductivity （CH_K2）, the universal soil-loss equation （USLE） support parameter （USLE_P）, Manning＇s n value for the main channel （CH_N2）, the sur展开更多
缺资料流域水文模型的参数率定是水文学科的一个研究重点.除传统的参数区域化方法以外,考虑到在部分缺资料流域可能存在少量短时间序列或零星不连续径流量观测数据的实际情况,近年来采用少量不连续径流量观测数据率定模型参数的方法逐...缺资料流域水文模型的参数率定是水文学科的一个研究重点.除传统的参数区域化方法以外,考虑到在部分缺资料流域可能存在少量短时间序列或零星不连续径流量观测数据的实际情况,近年来采用少量不连续径流量观测数据率定模型参数的方法逐渐引起关注.本研究以我国西北内陆的黑河流域上游为研究区域,选取分布式水文模型SWAT(Soil and Water Assessment Tool)为研究工具,使用SUFI-2方法作为参数自动率定与不确定性分析工具,分析不同径流量观测数据数量对模型参数率定结果的影响.研究结果显示,4组不同径流量数据在验证期的Nash效率系数均为0.67,表明使用1年的径流量数据进行参数率定时所获得的模拟效果可以达到使用多年径流数据率定参数的水平;比较各组模拟结果获得的P因子和R因子,表明使用1年径流量数据进行模型率定的不确定性会更大.展开更多
The Soil and Water Assessment Tool （SWAT） was implemented in a small forested watershed of the Soan River Basin innorthern Pakistan through application of the sequential uncertainty fitting （SUFI-2） method to inve...The Soil and Water Assessment Tool （SWAT） was implemented in a small forested watershed of the Soan River Basin innorthern Pakistan through application of the sequential uncertainty fitting （SUFI-2） method to investigate the associateduncertainty in runoff and sediment load estimation. The model was calibrated for a 10-year period （1991–2000） with aninitial 4-year warm-up period （1987–1990）, and was validated for the subsequent 10-year period （2001–2010）. Themodel evaluation indices R2 （the coefficient of determination）, NS （the Nash-Sutcliffe efficiency）, and PBIAS （percentbias） for stream flows simulation indicated that there was a good agreement between the measured and simulated flows.To assess the uncertainty in the model outputs, p-factor （a 95% prediction uncertainty, 95PPU） and r-factors （averagewideness width of the 95PPU band divided by the standard deviation of the observed values） were taken into account.The 95PPU band bracketed 72% of the observed data during the calibration and 67% during the validation. The r-factorwas 0.81 during the calibration and 0.68 during the validation. For monthly sediment yield, the model evaluation coefficients（R2 and NS） for the calibration were computed as 0.81 and 0.79, respectively; for validation, they were 0.78and 0.74, respectively. Meanwhile, the 95PPU covered more than 60% of the observed sediment data during calibrationand validation. Moreover, improved model prediction and parameter estimation were observed with the increasednumber of iterations. However, the model performance became worse after the fourth iterations due to an unreasonableparameter estimation. Overall results indicated the applicability of the SWAT model with moderate levels of uncertaintyduring the calibration and high levels during the validation. Thus, this calibrated SWAT model can be used for assessmentof water balance components, climate change studies, and land use management practices.展开更多
The city of Bouaké, the second biggest city of Côte d’Ivoire, experienced a water shortage in 2018 that lasted four months due to the drying up of the Loka reservoir, which supplies two-thirds of the c...The city of Bouaké, the second biggest city of Côte d’Ivoire, experienced a water shortage in 2018 that lasted four months due to the drying up of the Loka reservoir, which supplies two-thirds of the city. The challenge of the Loka reservoir is that it is located in an ungauged basin where very few hydrological studies have been carried out, despite the recurrent problems of access to drinking water. In the purpose to better understand the phenomena that caused this temporary drying of the dam, the methodology implemented was based on agro-hydrological modeling with SWAT using a regionalization technique of a nearby watershed. The model performance was assessed using three statistical indices (the Nash-Sutcliffe coefficient (NS), the coefficient of determination (R<sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">) and the percentage of bias (PBIAS)) and the visual appreciation of hydrographs for monthly series. The statistical indices appear satisfactory with a NS and R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> ≥ 0.6 both for calibration and validation, and a PBIAS of </span><span style="font-family:;" "=""><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">11.2 and </span><span style="font-family:Verdana;">-</span><span><span style="font-family:Verdana;">3.8 respectively for calibration and validation. The hydrological modeling of Loka basin has shown the impact of climate change already reported by some authors as well as anthropization. Thus, while the reservoir records a decrease in its water volume estimated at 384,604 m</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> each year, the water demand undergoes an increase of 122,033 m</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> per year.</span></span></span>展开更多
The semi-distributed SWAT (Soil and Water Assessment Tools) model was used in this study to model the sediment yield in the watershed of the Aghien lagoon with an area of 365 km<sup><span style="font-fam...The semi-distributed SWAT (Soil and Water Assessment Tools) model was used in this study to model the sediment yield in the watershed of the Aghien lagoon with an area of 365 km<sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">, located in the north of the district of Abidjan (South-East from</span><span style="font-family:Verdana;"> C<span style="white-space:nowrap;">?</span>te d’Ivoire). A sensitivity and uncertainty analysis, as well as calibration of the SWAT model, was conducted using the Sequential Uncertainty Adjustment Procedure (SUFI-2) which is one of the programs interfaced with SWAT in the SWAT-Cup package (SWAT-Calibration-Uncertainty</span><span style="font-family:Verdana;"> Programs). Five parameters of the SWAT model were found to be more sensitive to sediment fluxes. These have been modified (calibration) sparingly in order to improve the reproduction of observed sediments data. Two measures were used to assess the uncertainty analysis of the model: P-factor and R-factor. The R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> and Nash-Sutcliffe (NS) coefficients of determination were used to assess the quality of the calibration. The P-factor obtained is 0.58 and the R-factor is 2.28. The NS and R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> coefficients in calibration over the period from June 2014 to January 2015 are 0.51 and 0.86 respectively. These values </span><span style="font-family:Verdana;">indicate correct consideration of uncertainties by the model and satisfactory calibration</span><span style="font-family:Verdana;"> of the SWAT model for solid fluxes. Then, the model was used to simulate the sediment fluxes at the horizons 2040 (2035-2056), 2060 (2057-2078</span><span style="font-family:Verdana;">)</span><span style="font-family:Verdana;"> and 2080 (2079-2100) in order to assess the impact of climate change on sediments in the watershed of the Ag展开更多
文摘以北京市延庆区妫水河为例,使用SWAT(Soil and Water Assessment Tool)模型对妫水河流域进行月尺度水文模拟,使用SUFI-2(Sequential Uncertainty Fitting)算法分析参数的敏感性,依据SWAT-CUP自动率定得到P因子和R因子分析模型的不确定性,从而完成本流域分布式水文模型的构建。率定结果显示,率定期确定系数R 2为0.65,效率系数NSE为0.61;验证期确定系数R 2为0.89,效率系数NSE为0.88;不确定性分析结果中P-factor均大于0.5,R-factor均小于0.3。通过以上分析可得该模型对妫水河流域的水文模拟有良好的效果。
文摘The Ganga River, the longest river in India, is stressed by extreme anthropogenic activity and climate change, particularly in the Varanasi region. Anticipated climate changes and an expanding populace are expected to further impede the efficient use of water. In this study, hydrological modeling was applied to Soil and Water Assessment Tool （SWAT） modeling in the Ganga catchment, over a region of 15 621.612 km2 in the southern part of Uttar Pradesh. The primary goals of this study are： ① To test the execution and applicability of the SWAT model in anticipating runoff and sediment yield; and ② to compare and determine the best calibration algorithm among three popular algorithms-sequential uncertainty fitting version 2 （SUFI-2）, the generalized likelihood uncertainty estimation （GLUE）, and par-allel solution （ParaSol）. The input data used in the SWAT were the Shuttle Radar Topography Mission （SRTM） digital elevation model （DEM）, Landsat-8 satellite imagery, soil data, and daily meteorological data. The watershed of the study area was delineated into 46 sub-watersheds, and a land use/land cover （LULC） map and soil map were used to create hydrological response units （HRUs）. Models utilizing SUFI- 2, GLUE, and ParaSol methods were constructed, and these algorithms were compared based on five cat-egories： their objective functions, the concepts used, their performances, the values of P-factors, and the values of R-factors. As a result, it was observed that SUFI-2 is a better performer than the other two algo-rithms for use in calibrating Indian watersheds, as this method requires fewer runs for a computational model and yields the best results among the three algorithms. ParaSol is the worst performer among the three algorithms. After calibrating using SUFI-2, five parameters including the effective channel hydraulic conductivity （CH_K2）, the universal soil-loss equation （USLE） support parameter （USLE_P）, Manning＇s n value for the main channel （CH_N2）, the sur
文摘缺资料流域水文模型的参数率定是水文学科的一个研究重点.除传统的参数区域化方法以外,考虑到在部分缺资料流域可能存在少量短时间序列或零星不连续径流量观测数据的实际情况,近年来采用少量不连续径流量观测数据率定模型参数的方法逐渐引起关注.本研究以我国西北内陆的黑河流域上游为研究区域,选取分布式水文模型SWAT(Soil and Water Assessment Tool)为研究工具,使用SUFI-2方法作为参数自动率定与不确定性分析工具,分析不同径流量观测数据数量对模型参数率定结果的影响.研究结果显示,4组不同径流量数据在验证期的Nash效率系数均为0.67,表明使用1年的径流量数据进行参数率定时所获得的模拟效果可以达到使用多年径流数据率定参数的水平;比较各组模拟结果获得的P因子和R因子,表明使用1年径流量数据进行模型率定的不确定性会更大.
基金supported by the Centre of Excellence in Water Resources Engineering, University of Engineering and Technology Lahore, and local authorities in Pakistan
文摘The Soil and Water Assessment Tool （SWAT） was implemented in a small forested watershed of the Soan River Basin innorthern Pakistan through application of the sequential uncertainty fitting （SUFI-2） method to investigate the associateduncertainty in runoff and sediment load estimation. The model was calibrated for a 10-year period （1991–2000） with aninitial 4-year warm-up period （1987–1990）, and was validated for the subsequent 10-year period （2001–2010）. Themodel evaluation indices R2 （the coefficient of determination）, NS （the Nash-Sutcliffe efficiency）, and PBIAS （percentbias） for stream flows simulation indicated that there was a good agreement between the measured and simulated flows.To assess the uncertainty in the model outputs, p-factor （a 95% prediction uncertainty, 95PPU） and r-factors （averagewideness width of the 95PPU band divided by the standard deviation of the observed values） were taken into account.The 95PPU band bracketed 72% of the observed data during the calibration and 67% during the validation. The r-factorwas 0.81 during the calibration and 0.68 during the validation. For monthly sediment yield, the model evaluation coefficients（R2 and NS） for the calibration were computed as 0.81 and 0.79, respectively; for validation, they were 0.78and 0.74, respectively. Meanwhile, the 95PPU covered more than 60% of the observed sediment data during calibrationand validation. Moreover, improved model prediction and parameter estimation were observed with the increasednumber of iterations. However, the model performance became worse after the fourth iterations due to an unreasonableparameter estimation. Overall results indicated the applicability of the SWAT model with moderate levels of uncertaintyduring the calibration and high levels during the validation. Thus, this calibrated SWAT model can be used for assessmentof water balance components, climate change studies, and land use management practices.
文摘The city of Bouaké, the second biggest city of Côte d’Ivoire, experienced a water shortage in 2018 that lasted four months due to the drying up of the Loka reservoir, which supplies two-thirds of the city. The challenge of the Loka reservoir is that it is located in an ungauged basin where very few hydrological studies have been carried out, despite the recurrent problems of access to drinking water. In the purpose to better understand the phenomena that caused this temporary drying of the dam, the methodology implemented was based on agro-hydrological modeling with SWAT using a regionalization technique of a nearby watershed. The model performance was assessed using three statistical indices (the Nash-Sutcliffe coefficient (NS), the coefficient of determination (R<sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">) and the percentage of bias (PBIAS)) and the visual appreciation of hydrographs for monthly series. The statistical indices appear satisfactory with a NS and R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> ≥ 0.6 both for calibration and validation, and a PBIAS of </span><span style="font-family:;" "=""><span style="font-family:Verdana;">-</span><span style="font-family:Verdana;">11.2 and </span><span style="font-family:Verdana;">-</span><span><span style="font-family:Verdana;">3.8 respectively for calibration and validation. The hydrological modeling of Loka basin has shown the impact of climate change already reported by some authors as well as anthropization. Thus, while the reservoir records a decrease in its water volume estimated at 384,604 m</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> each year, the water demand undergoes an increase of 122,033 m</span><sup><span style="font-family:Verdana;">3</span></sup><span style="font-family:Verdana;"> per year.</span></span></span>
文摘The semi-distributed SWAT (Soil and Water Assessment Tools) model was used in this study to model the sediment yield in the watershed of the Aghien lagoon with an area of 365 km<sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;">, located in the north of the district of Abidjan (South-East from</span><span style="font-family:Verdana;"> C<span style="white-space:nowrap;">?</span>te d’Ivoire). A sensitivity and uncertainty analysis, as well as calibration of the SWAT model, was conducted using the Sequential Uncertainty Adjustment Procedure (SUFI-2) which is one of the programs interfaced with SWAT in the SWAT-Cup package (SWAT-Calibration-Uncertainty</span><span style="font-family:Verdana;"> Programs). Five parameters of the SWAT model were found to be more sensitive to sediment fluxes. These have been modified (calibration) sparingly in order to improve the reproduction of observed sediments data. Two measures were used to assess the uncertainty analysis of the model: P-factor and R-factor. The R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> and Nash-Sutcliffe (NS) coefficients of determination were used to assess the quality of the calibration. The P-factor obtained is 0.58 and the R-factor is 2.28. The NS and R</span><sup><span style="font-family:Verdana;">2</span></sup><span style="font-family:Verdana;"> coefficients in calibration over the period from June 2014 to January 2015 are 0.51 and 0.86 respectively. These values </span><span style="font-family:Verdana;">indicate correct consideration of uncertainties by the model and satisfactory calibration</span><span style="font-family:Verdana;"> of the SWAT model for solid fluxes. Then, the model was used to simulate the sediment fluxes at the horizons 2040 (2035-2056), 2060 (2057-2078</span><span style="font-family:Verdana;">)</span><span style="font-family:Verdana;"> and 2080 (2079-2100) in order to assess the impact of climate change on sediments in the watershed of the Ag
