Towards parameter estimation in global hydrological models

Abstract Global hydrological models (GHMs) supply key information for stakeholders and policymakers simulating past, present future water cycles. Inaccuracy in GHM simulations, i.e., simulation results that poorly match observations, leads to uncertainty hinders valuable decision support. Improved parameter estimation is one more accurate simulations of global models. Here, we introduce an efficient transparent way understand the control GHMs advance using sensitivity analysis (GSA). In our analysis, use WaterGAP3 find most influential parameters 50% 347 basins worldwide are model have traditionally not been included when calibrating this model. Parameter importance varies space between metrics. For example, a controls groundwater flow velocity on signatures related duration curve but traditional statistical Parameters linked evapotranspiration high flows exhibit unexpected behaviour, defining potential influences than other would expected be relevant. This behaviour suggests structure could improved. We also basin attributes explain spatial variability better Köppen-Geiger climate zones. Overall, demonstrate GSA can effectively inform guide improvement structure. Thus, supports cycle robust policymakers.