Uncertainty Hotspots in Global Hydrologic Modeling: The Impact of Precipitation and Temperature Forcings

Abstract Quantifying hydrologic modeling uncertainties originating from meteorological uncertainties is crucial yet unexplored on the global scale. This study integrates a novel ensemble meteorological dataset with process-based hydrologic models, assessing the impact of precipitation and temperature uncertainties across approximately three million subbasins globally. We introduce two metrics to identify uncertainty hotspots: one tracing the uncertainty propagation from inputs to model outputs and the other measuring the uncertainty magnitude relative to hydrologic climatology (i.e., ratio between uncertainty and climate average). Our findings reveal different uncertainty responses across hydrologic variables to the combined precipitation and temperature uncertainties. For routed river streamflow, uncertainty propagation is strong in tropical rain forests and Europe (except the Scandinavian Peninsula) but weak in the deserts, which is partly attributed to the regional differences in baseflow ratios. In contrast, both uncertainty metrics indicate low streamflow uncertainties in cryosphere areas and downstream areas of major rivers. The substantial modeling uncertainties observed, particularly in the Southern Hemisphere and less developed regions, underscore the need to improve global spatial meteorological datasets. Significance Statement Understanding how uncertainties in precipitation and temperature data affect hydrologic modeling is essential for making informed decisions. Yet, there is a gap in our global understanding of these impacts. This study tackles this challenge by using a novel ensemble meteorological dataset to drive a process-based hydrologic model across ∼3 million subbasins. We quantify the global impact of real-world precipitation and air temperature uncertainties on snow water equivalent, total soil water, total runoff, and evapotranspiration. Our findings indicate significant variations in these effects. For instance, for river streamflow, uncertainty propagation is strong in tropical rain forests and Europe (except the Scandinavian Peninsula) but weak in the deserts. Low streamflow uncertainty magnitude is observed in cryosphere areas and downstream areas of major rivers. Our research uncovers significant, diverse uncertainties in hydrological simulations across the globe, which underscores the need to improve global spatial meteorological datasets.

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