Hossein Emami; Ali Salajegh; Alireza Moghaddamnia; Shahram Khalighi; Abolhassan Fathbabadi
Abstract
Precipitation is of the most important inputs of runoff modeling. The availability of precipitation data with appropriate temporal and spatial accuracy is very important and necessary for watersheds with small and scattered rainfall stations. Nowadays, climatic satellites are practical and widely-used ...
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Precipitation is of the most important inputs of runoff modeling. The availability of precipitation data with appropriate temporal and spatial accuracy is very important and necessary for watersheds with small and scattered rainfall stations. Nowadays, climatic satellites are practical and widely-used tools in precipitation estimations. In this study, first the efficiency of TRMM satellite precipitation data in the monthly time series of Chehelchai Watershed was evaluated using R2, RMSE, NSE and Bias statistical indices by comparing the precipitation data of rain gauge stations (observed) and the values of these statistical indices were 0.54, 22.70, 0.44 and -14.86, respectively. Considering the value of the coefficient of determination (R2), it can be concluded that the TRMM satellite was able to estimate the 0.54 of observed precipitation. In the next step, three base data models including MLP, ANFIS and SVR were used to estimate the monthly runoff. Two different input scenarios were selected :1) observed precipitation data in t and t-1 time steps and runoff in t-1 time step and 2) satellite precipitation data in t and t-1 time steps and runoff in t-1 time step. To compare the accuracy and error of the models, R2 and RMSE of the validation stage were used. The ANFIS model with the values of R2 and RMSE were 0.80 and 0.97 for the first type input combination and 0.78 and 1.02 for the second type input combination, respectively, as the suitable single model for estimating runoff in the study area were selected. Then weighted-mean method was used in the data fusion approach to provide a data driven combination model for each combination of inputs into the model in the studied watershed. This data fusion approach data-driven model improved the values (R2=0.81) and (Bias=-4.85) for the first type input combination and also improved the value (R2=0.79) for the second type input combination.
Mohammad Rostami Khalaj; Ali Salajeghe
Abstract
Rainfall-runoff modeling is on of runoff estimation techniques and an appropriate tool for studying hydrological processes, water resources evaluation and watershed management. But the complexity and the non-linear nature of rainfall-runoff process and not being known factors affecting it and generally ...
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Rainfall-runoff modeling is on of runoff estimation techniques and an appropriate tool for studying hydrological processes, water resources evaluation and watershed management. But the complexity and the non-linear nature of rainfall-runoff process and not being known factors affecting it and generally on discharge at watershed outlet, modeling has become more difficult. Therefore, using the methods that have additionally dynamic, development capability, conceptual structure and user friendly is essential. Therefore, in this study for rainfall-runoff modeling were used system dynamics methods in the Kardeh dam basin of Mashhad. The proposed model includes 6 reservoir including snow storage, canopy storage, impervious storage, surface soil storage, subsurface storage and groundwater storage. The input data required includes average daily precipitation and temperature. To calibrate the model daily discharge data from basin outlet in the period from 1998 to 2008 and for evaluation the discharge from 2009 to 2012 were used. The results of sensitivity analysis showed temperature parameters are more sensitive and have considerable impact on discharge and peak flow. Also the results indicate that the simulated stream flow pattern is quite similar to that observed and Nash-Sutcliffe coefficient obtained in the evaluation period between 0.57-0.67 which represents that the system dynamics methods is high ability to rainfall-runoff modeling.
