Soil Conservation Service (SCS) model, developed by U. S. Soil Conservation Service in 1972, has been widely applied in the estimation of runoff from an small watershed. In this paper, based on the remote sensing geo-...Soil Conservation Service (SCS) model, developed by U. S. Soil Conservation Service in 1972, has been widely applied in the estimation of runoff from an small watershed. In this paper, based on the remote sensing geo-information data of land use and soil classification all obtained from Landsat images in 1996 and 1997 and con-ventional data of hydrology and meteorology, the SCS model was investigated for simulating the surface runoff for single rainstorm in Wangdonggou watershed, a typical small watershed in the Loess Plateau, located in Changwu County of Shaanxi Province of China. Wangdonggou watershed was compartmentalized into 28 sub-units according to natural draining division,and the table of curve number (CN) values fitting for Wangdonggou watershed was also presented. During the flood period from 1996 to 1997, the hydrograph of calculated runoff process using the SCS model and the hydrograph of observed runoff process coincided very well in height as well as shape, and the model was of high precision above 75%. It is indicated that the SCS model is legitimate and can be successfully used to simulate the runoff generation and the runoff process of typical small watershed based on the remote sensing geo-information in the Loess Plateau.展开更多
In recent years,there has been a growing interest in using artificial intelligence(AI)for rainfall-runoff modelling,as it has shown promising adaptability in this context.The current study involved the use of six dist...In recent years,there has been a growing interest in using artificial intelligence(AI)for rainfall-runoff modelling,as it has shown promising adaptability in this context.The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed,India.These models included the artificial neural network(ANN),k-nearest neighbour regression model(KNN),extreme gradient boosting(XGBoost)regression model,random forest regression model(RF),convolutional neural network(CNN),and CNN-RNN(convolutional recurrent neural network).The years 2003-2007 are classified as the calibration or training period,while the years 2008-2009 are classified as the validation or testing period for the span of time 2003 to 2009.The available rainfall,maximum and minimum temperatures,and discharge data were collected and utilized in the models.To compare the performance of the models,five criteria were employed:R^(2),NSE,MAE,RMSE,and PBIAS.The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods(training:R^(2) is 0.99,NSE is 0.99,MAE is 1.76,RMSE is 3.11,and PBIAS is1.45;testing:R^(2) is 0.97,NSE is 0.97,MAE is 2.05,RMSE is 3.60,and PBIAS is3.94).These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study.The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management,flood control,and environmental planning.展开更多
基金Under the auspices of National Natural Science Foundation of China (No 40101005)
文摘Soil Conservation Service (SCS) model, developed by U. S. Soil Conservation Service in 1972, has been widely applied in the estimation of runoff from an small watershed. In this paper, based on the remote sensing geo-information data of land use and soil classification all obtained from Landsat images in 1996 and 1997 and con-ventional data of hydrology and meteorology, the SCS model was investigated for simulating the surface runoff for single rainstorm in Wangdonggou watershed, a typical small watershed in the Loess Plateau, located in Changwu County of Shaanxi Province of China. Wangdonggou watershed was compartmentalized into 28 sub-units according to natural draining division,and the table of curve number (CN) values fitting for Wangdonggou watershed was also presented. During the flood period from 1996 to 1997, the hydrograph of calculated runoff process using the SCS model and the hydrograph of observed runoff process coincided very well in height as well as shape, and the model was of high precision above 75%. It is indicated that the SCS model is legitimate and can be successfully used to simulate the runoff generation and the runoff process of typical small watershed based on the remote sensing geo-information in the Loess Plateau.
文摘In recent years,there has been a growing interest in using artificial intelligence(AI)for rainfall-runoff modelling,as it has shown promising adaptability in this context.The current study involved the use of six distinct AI models to simulate monthly rainfall-runoff modelling in the Bardha watershed,India.These models included the artificial neural network(ANN),k-nearest neighbour regression model(KNN),extreme gradient boosting(XGBoost)regression model,random forest regression model(RF),convolutional neural network(CNN),and CNN-RNN(convolutional recurrent neural network).The years 2003-2007 are classified as the calibration or training period,while the years 2008-2009 are classified as the validation or testing period for the span of time 2003 to 2009.The available rainfall,maximum and minimum temperatures,and discharge data were collected and utilized in the models.To compare the performance of the models,five criteria were employed:R^(2),NSE,MAE,RMSE,and PBIAS.The CNN-RNN model simulates the rainfall-runoff model in the Bardha watershed best in both the training and testing periods(training:R^(2) is 0.99,NSE is 0.99,MAE is 1.76,RMSE is 3.11,and PBIAS is1.45;testing:R^(2) is 0.97,NSE is 0.97,MAE is 2.05,RMSE is 3.60,and PBIAS is3.94).These results demonstrate the superior performance of the CNN-RNN model in simulating monthly rainfall-runoff modelling when compared to the other models used in the study.The findings suggest that the CNN-RNN model could be a valuable tool for various applications related to sustainable water resource management,flood control,and environmental planning.