Advancing the identification and evaluation of distributed rainfall‐runoff models using global sensitivity analysis

[1] This study provides a step-wise analysis of a conceptual grid-based distributed rainfall-runoff model, the United States National Weather Service (US NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM). It evaluates model parameter sensitivities for annual, monthly, and event time periods with the intent of elucidating the key parameters impacting the distributed model's forecasts. This study demonstrates a methodology that balances the computational constraints posed by global sensitivity analysis with the need to fully characterize the HL-RDHM's sensitivities. The HL-RDHM's sensitivities were assessed for annual and monthly periods using distributed forcing and identical model parameters for all grid cells at 24-hour and 1-hour model time steps respectively for two case study watersheds within the Juniata River Basin in central Pennsylvania. This study also provides detailed spatial analysis of the HL-RDHM's sensitivities for two flood events based on 1-hour model time steps selected to demonstrate how strongly the spatial heterogeneity of forcing influences the model's spatial sensitivities. Our verification analysis of the sensitivity analysis method demonstrates that the method provides robust sensitivity rankings and that these rankings could be used to significantly reduce the number of parameters that should be considered when calibrating the HL-RDHM. Overall, the sensitivity analysis results reveal that storage variation, spatial trends in forcing, and cell proximity to the gauged watershed outlet are the three primary factors that control the HL-RDHM's behavior.

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