Abstract:Accurate soil moisture and streamflow data are an aspirational need of
many hydrologically-relevant fields. Model simulated soil moisture and
streamflow hold promise but numerical models require calibration prior
to application to ensure sufficient model performance. Manual or
automated calibration methods require iterative model runs and hence are
computationally expensive. In this study, we leverage the Soil Survey
Geographic (SSURGO) database and the probability mapping of SSURGO
(POLARIS) to help constrain… Show more
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