Customizing large-scale hydrological models: Harnessing the open data realm for impactful local applications

Lake Hume in Australia and Harsha Lake in USA. Large-scale hydrological models (LSHMs), though important for both scientific and societal reasons, require the representation of many unknown features that influence river system response. However, current model identification practices in catchment modelling cannot lead to robust LSHMs for local decision-making. To address this, it is necessary to customise the models by integrating local data and knowledge from various sources (e.g. in-situ and earth observations) and fluxes. We present a framework to customize LSHMs for impactful local applications and showcase this using the global WWH hydrological model as the reference LSHM. We present significant improvements in modelling streamflow and actual and potential evapotranspiration, following WWH refinements to include local lakes and reservoir management. Local streamflow measurements and earth observations from NASA MODIS evapotranspiration products were used to re-calibrate the locally adapted model, leading to spatial consistency in performance. Combining multiple variables and metrics during model identification improved streamflow performance and robustness, with combination sets reducing variability and enhancing representation of diverse hydrological processes, highlighting the need for tailored metric and variable selection. This underpins the importance of including informative data in customized multi-objective modelling chains. Finally, incorporating reservoir management improved simulation of a regulated system, with local insights informing reservoir parameterization in LSHMs and bridging the gap to global-scale applications. • Traditional data used in LSHMs lack sufficient information for local parameter identification. • Parameters need multi-objective, multi-basin, multi-period, multi-variable setups. • Earth observations bridge the gap where local in-situ hydrological data are unavailable. • Customized LSHMs enable reliable and accurate simulations at the local scale. • Local data integration enriches the information content of customized LSHMs.