Data-driven models for streamflow regionalization in Krishna River Basin, India

Predicting streamflow in ungauged basins remains a significant challenge in hydrological studies. In recent years, data-driven models have been shown to outperform traditional physics-based models in streamflow prediction for ungauged catchments. However, few studies have examined the potential of such models for predicting streamflow in ungauged basins within India. This study aims to evaluate the performance of two machine learning models, namely Support Vector Regression (SVR) and Random Forest (RF), alongside two deep learning models, Long Short-Term Memory (LSTM) and Bi-LSTM, in the context of streamflow regionalization within the Krishna River Basin in India. Each prediction model is trained using meteorological variables as input features, with streamflow as the output variable. K-means clustering is employed to group selected catchments (based on data availability) into an optimum number of clusters based on spatial proximity and physical similarity. It is assumed that catchments within a cluster share homogeneous characteristics. Regionalization is achieved by sharing model parameters across catchments within the same cluster. For each cluster, one gauged catchment is designated as the donor catchment, while the others are treated as pseudo-ungauged. Each proposed model is trained and tested using the meteorological inputs and streamflow data available at the gauged donor catchment. The trained model for each cluster is then transferred to the remaining receptor catchments within the cluster, where the meteorological variables corresponding to each ungauged catchment are used as inputs. The performance of the models in ungauged catchments is rigorously evaluated by comparing the simulated streamflow against observed streamflow using metrics such as Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Coefficient of Determination (R²), and Percentage Bias (PBIAS). This study highlights the advantages of utilizing data-driven methods for streamflow prediction in both gauged and ungauged basins, particularly due to their ability to capture complex, non-linear relationships between meteorological inputs and streamflow generation. The findings of this study are expected to be instrumental in water resources planning and management, flood assessment, and the design of hydraulic structures in the Krishna River Basin.