Hydrological models help scientists understand water movement and predict changes in water resources. However, their increasing complexity makes them difficult to use. This study developed an open-source framework to simplify the setup and application of the Variable Infiltration Capacity model. Tests in multiple basins showed that the framework improves efficiency, provides reliable simulations, and supports wider use of hydrological models.

Evapotranspiration changes rapidly within a day, and its plant transpiration component remains difficult to measure. We developed a machine-learning framework that estimates hourly total evapotranspiration and plant transpiration together while keeping transpiration physically bounded. This provides a physically consistent reference for plant water use at sites where only total evapotranspiration is observed.

This study presents an experimental framework (EF-SPM) to disentangle and evaluate the benefits of spatially explicit parameterization and multi-gauge calibration in distributed hydrological modelling. Experiments in a nested catchment show that both strategies consistently improve streamflow simulations across sub-basins, jointly alleviating multi-objective competition and the trade-off between spatial complexity and parameter identifiability.

Water budget non-closure is a widespread phenomenon among multisource datasets which undermines the robustness of hydrological inferences. This study proposes a Multisource Dataset Correction Framework grounded in Physical Hydrological Process Modelling to enhance water budget closure, termed PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.