Comparative Evaluation of ERA5-Land and ISIMIP3 Runoff Forcing for Global River Streamflow Simulation

Abstract. Flooding is among the most widespread natural hazards worldwide, yet many high-risk regions lack the observational data needed for effective flood planning. In these data-sparse regions, global flood models remain essential tools for estimating flood hazard, although their performance is strongly influenced by the choice of runoff forcing data. Two widely used global runoff products are the reanalysis-based ERA5-Land dataset and the ISIMIP3a multi-model hydrological ensemble. Their selection involves an inherent trade-off between high-resolution reanalysis runoff and runoff simulated by hydrological models driven by bias-corrected meteorological inputs, the latter also providing an explicit representation of uncertainty through ensemble spread. This study presents a comparative evaluation of these two products by routing both through a consistent global hydrodynamic framework (CaMa-Flood). Model performance was assessed across IPCC SREX regions against observations from 5,071 gauging stations using the Kling-Gupta Efficiency and its components, while long-term trends in low, mean, and high streamflow were evaluated from a subset of 3,135 stations with sufficient temporal coverage. Simulations forced by ERA5-Land show superior skill in reproducing observed daily streamflow, with consistently higher correlation and stronger agreement in the spatial pattern of regional streamflow trends. However, systematic biases in streamflow magnitude and a tendency to exaggerate drying trends, particularly for low streamflow, are also evident. In contrast, the ISIMIP3a ensemble shows lower skill in reproducing observed daily streamflow metrics but provides more conservative and observation-consistent estimates of long-term trends. Ensemble averaging further improves robustness, with simulated trend ranges more frequently overlapping observational uncertainty bounds, albeit at the expense of dampened variability and extremes. Differences between native and spatially aggregated ERA5-Land runoff were negligible within the present modelling framework. Overall, the results demonstrate that no single runoff product is universally optimum: ERA5-Land is well suited for reproducing historical streamflow dynamics, whereas ISIMIP3a is particularly valuable for robust assessments of long-term hydrological change and uncertainty.