The design of water resource structures needs long-term runoff data which is always a problem in developing countries due to the involvement of huge cost of operation and maintenance of gauge discharge sites. Hydrological modelling provides a solution to this problem by developing relationship between different hydrological processes. In the past, several models have been propagated to model runoff using simple empirical relationships between rainfall and runoff to complex physical model using spatially distributed information and time series data of climatic variables. In the present study, an attempt has been made to compare two conceptual models including TANK and Australian water balance model (AWBM) and a physically distributed but lumped on HRUs scale SWAT model for Tandula basin of Chhattisgarh (India). The daily data of reservoirs levels, evaporation, seepage and releases were used in a water balance model to compute runoff from the catchment for the period of 24 years from 1991 to 2014. The rainfall runoff library (RRL) tool was used to set up TANK model and AWBM using auto and genetic algorithm, respectively, and SWAT model with SWATCUP application using sequential uncertainty fitting as optimization techniques. Several tests for goodness of fit have been applied to compare the performance of conceptual and semi-distributed physical models. The analysis suggested that TANK model of RRL performed most appropriately among all the models applied in the analysis; however, SWAT model having spatial and climatic data can be used for impact assessment of change due to climate and land use in the basin.
In this study, the performance evaluation of five machine learning models, namely, ANNLM, ANNSCG, least square-support vector regression (LS-SVR), reduced error pruning tree (REPTree) and M5, was carried out for predicting runoff and sediment in the Pokhariya watershed, India using hydro-meteorological variables as input. The input variables were selected using the trial-and-error procedure which represents the hydrological process in the watershed. The seven input variables to all the models comprised a combination of rainfall, average temperature, relative humidity, pan evaporation, sunshine duration, solar radiation and wind speed. The monthly runoff and sediment yield data were used to calibrate and validate all models for the years 2000 to 2008. Evaluation of models' performances were carried out using four statistical indices, i.e., Nash–Sutcliffe coefficient (NSE), coefficient of determination (R2), percent bias (PBIAS) and RMSE-observations standard deviation ratio (RSR). Comparative analysis showed that the ANNLM model marginally outperformed the LS-SVR model and all the other models investigated during calibration and validation for runoff modelling whereas the LS-SVR model surpassed the artificial neural networks (ANN) model and other models for sediment yield modelling. Moreover, M5 model tree is better in simulating sediment yield and runoff than its near counterpart, the REPTree model, and marginally inferior when compared to LS-SVR and ANN models.
The current study used satellite imagery datasets to extract various morphometric parameters in a geospatial environment to prioritize the problematic areas in the Rarhu watershed of Ranchi district, Jharkhand, India. Two decision-making methods, AHP and VIKOR, were integrated for prioritizing different sub-watershed. The Rarhu watershed has an area of 630 km2 with an elevation ranging from 824 to 210 m. NASADEM was used to extract drainage networks which were verified from Survey of India (SOI) toposheets. To prioritize 21 sub-watersheds using the MCDM method, 11 morphometric parameters were selected from linear, areal, and relief parameters. The VIKOR method prioritized sub-watersheds using AHP criteria weights, which are classified into four priority levels ranging from very high to low. In addition, performing sensitivity analysis validated the robustness of the decision-making model. As per the analysis, Rarhu watershed has an elongated shape and the highest 6th order stream with a dendritic pattern of streams. Nearly 36.17% of the area is more vulnerable with very high priority. Using the results of the study, policymakers, watershed planners, watershed development programme, and soil and water conservation programme can identify vulnerable sub-watersheds that require urgent adaptation of soil and water management control measures.
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