Spatiotemporal variability of modeled watershed scale surface-depression storage and runoff for the conterminous United States

Journal of the American Water Resources Association
By: , and 

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Abstract

This study uses the explores the viability of a proxy model calibration strategy through assessment of the spatiotemporal variability of surface-depression storage and runoff generated with the U.S. Geological Survey’s National Hydrologic Model (NHM) infrastructure for hydrologic response units (HRUs; n=109,951) across the conterminous United States (CONUS). Simulated values for each HRU of daily surface-depression storage (treated as a decimal fraction of total possible volume) and monthly normalized runoff (0 to 1) values were calculated using Spearman’s rho at monthly and annual aggregations. Locations where values are correlated show where previously-developed proxy calibration strategies are likely to be effective. In addition, differences in the correlation for monthly and annual time scale aggregations show which time scale drives surface-depression storage processes in the NHM. Results show overall long-term (annual) correlation is more common than short-term (monthly) correlation over the CONUS; however, summary statistics for eighty-six ecoregions show five with higher ranges of monthly relative to annual Spearman’s rank coefficient values. This landscape-scale analysis shows simulations aggregated to an annual time scale are generally more dominant for the CONUS; however, simulations aggregated to monthly, short-term time scales are more dominant in focused areas where surface-depression storage processes are investigated.

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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Spatiotemporal variability of modeled watershed scale surface-depression storage and runoff for the conterminous United States
Series title Journal of the American Water Resources Association
DOI 10.1111/1752-1688.12826
Volume 56
Issue 1
Year Published 2020
Language English
Publisher Wiley
Contributing office(s) Geosciences and Environmental Change Science Center, WMA - Integrated Modeling and Prediction Division
Description 14 p.
First page 16
Last page 29
Country United States
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