Identification of uncertainty sources in quasi-global discharge and inundation simulations using satellite-based precipitation products

Abstract

Predicting river discharge and inundation is crucial for water resources management and flood hazard reduction; however, it is still unclear to what extent their variabilities can be captured on global scale. This study evaluates uncertainty sources in the quasi-global river discharge and inundation simulations using the Variable Infiltration Capacity (VIC) macroscale hydrologic model and the Catchment-based Macroscale Floodplain (CaMa-Flood) hydrodynamic model, forced with five high-resolution satellite precipitation datasets. The simulated discharge is first evaluated against more than 2852 sites selected from the Global Streamflow Indices and Metadata Archive (GSIM) dataset, and then the simulated inundation is compared with complementary multiple satellite observations. Globally, about 38% – 43% of the stations produce reasonable discharge simulations with positive Kling-Gupta Efficiency (KGE) on monthly time scale. The simulations show good agreement for flood fractions with mean correlations ranging from 0.47 to 0.62 for satellite detected events. The potential uncertainties sources of discharge and inundation simulation related to physics setting and forcing datasets, such as precipitation, land surface model, routing model, and observation from site and satellite are discussed, as well as future directions for improving large-scale model applications. By using default model settings, we hope our study can offer valuable insights into the applicability of flood simulations and provide guides for model development.

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Identifiers

ORCID for Justin Sheffield: orcid.org/0000-0003-2400-0630

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