Data-driven modelling framework for streamflow prediction in a physio-climatically heterogeneous river basin

The prediction of streamflows is essential for efficient water resources management at basin scale. The present study examines the performance of model tree (MT) data-driven technique in predicting streamflows for an intermittent and a perennial river in a physio-climatically heterogeneous river basin. The correlation and mutual information analyses of predictor (hydrometeorological) variables are performed to determine the model input structure. Overall, seventy-two model configurations are formulated for each stream gauging station based on the combination of input variables, MT variants and variable lengths of calibration and validation datasets. The model simulation results are analysed by estimating a suite of statistical performance indices for each model configuration. The influence of parameter sensitivity on model performance is also assessed. The results indicate that selection of input variables play a governing role in capturing the interplay of hydrological processes in a catchment. The model outputs displayed more sensitivity to pruning than smoothing in MT, and minimal sensitivity towards data portioning, since the datasets were homoscedastic. The study also proposes a procedure for model evaluation considering multiple criteria, such as forecasting error, efficiency, predictability and false alarms, and enabling multi-model comparisons for better decision making. The proposed procedure was successfully applied for selection of best-fit model to predict one-day ahead streamflows at each stream gauging station.