A pareto multi-objective calibration of a hydrological model integrating streamflow and snow cover area

Abstract Accurate hydrological modeling in high-mountainous snow-dominated basins is essential for effective water resource management, in particular for climate change-sensitive regions. To better understand the processes that govern hydrological responses, model calibration against multiple variables offers a valuable approach to reduce parameter uncertainty and model equifinality. In data-scarce environments, simple lumped-parameter hydrological models that account for snow accumulation and melt processes are particularly useful. In this study, we used the Témez lumped hydrological model enhanced by the integration of a new semi-distributed snow module to simulate key snow-related processes. We performed a novel sensitivity analyses of efficiency of the models depending on the adopted multiobjective functions within an automatic procedure to calibrate and validate the models. We evaluated three calibration approaches by varying the weight of the snow cover objective \(\:{w}S\). The first procedure consists of single-objective calibration against streamflow alone. The other procedures applied multi-objective calibration against streamflow and snow cover, which differed in the performance metric used for the snow component: Nash-Sutcliffe efficiency and Kling-Gupta efficiency. The results demonstrated that incorporating snow cover data into the calibration process clearly improved snow cover simulation without significantly compromising streamflow efficiency, except when the streamflow weight \(\:{w}Q\) is reduced to zero. Notably, the KGE-based approach yielded a better-defined Pareto front with more robust snow cover efficiency and reduced bias. Our findings also revealed that snow-related parameters are highly sensitive to the inclusion of snow cover data. Key parameters exhibited substantial changes and a reduction in variability of around 30%.