Evaluating Hydrological Model Structures and Parameter Transfer across the Indian Subcontinent

Hydrological simulations in the Indian subcontinent are impacted by substantial uncertainty contributed by the selection of model structure and parameterization. Nevertheless, most studies in the Indian subcontinent have relied on a single model structure to simulate streamflow, without evaluating how such a choice might impact simulations  in ungauged basins. In this study, we comprehensively evaluate the 277 gauged basins spanning across the diverse hydro-climatic regions of the Indian subcontinent using the Framework for Understanding Structural Errors (FUSE). FUSE allows testing different model structures within a controlled experimental framework, enabling the systematic evaluation of the impact of different model structures on streamflow simulations. For each gauged basin, we calibrate 78 FUSE structures and evaluate their performance with respect to the basic benchmarking models using the HydroBM python package and simulations from Noah-Multiparameterization Land Surface Model (Noah-MP LSM).

For regionalization, we generate ensembles of 500 parameter sets for each selected decision structure using Latin Hypercube Sampling and propagated these through FUSE to characterize predictive uncertainty in ungauged basin simulations. These simulations are subsequently used to train surrogate emulators of the performance surface response, enabling efficient transfer of parameters from gauge to ungauged basins.Results indicate strong variability in predictive skill, uncertainty, and regionalization performance across model structures, highlighting that structures identified as optimal in calibrated basins do not necessarily generalize under parameter transfer. These findings underscore the need to consider structural benchmarking, uncertainty reliability, and regionalization performance when developing hydrological modelling frameworks for data-scarce regions such as the Indian subcontinent.