An assessment of differences in gridded precipitation datasets in complex terrain

TitleAn assessment of differences in gridded precipitation datasets in complex terrain
Publication TypeJournal Article
Year of Publication2018
AuthorsHenn B., Newman A.J, Livneh B., Daly C., Lundquist J.D
JournalJournal of Hydrology
Volume556
Pagination1205-1219
Date Published2018/01
Type of ArticleArticle
ISBN Number0022-1694
Accession NumberWOS:000423641300093
Keywordsclimate trends; Engineering; enhancement; fluxes; gauge observations; Geology; gridded forcing data; hydroclimatology; hydrologically based dataset; land-surface; north-america; orographic; precipitation; sierra-nevada; snow; temperature; uncertainty; variability; Water resources; western united-states; winter precipitation
Abstract

Hydrologic modeling and other geophysical applications are sensitive to precipitation forcing data quality, and there are known challenges in spatially distributing gauge-based precipitation over complex terrain. We conduct a comparison of six high-resolution, daily and monthly gridded precipitation datasets over the Western United States. We compare the long-term average spatial patterns, and interannual variability of water-year total precipitation, as well as multi-year trends in precipitation across the data sets. We find that the greatest absolute differences among datasets occur in high-elevation areas and in the maritime mountain ranges of the Western United States, while the greatest percent differences among datasets relative to annual total precipitation occur in arid and rain-shadowed areas. Differences between datasets in some high-elevation areas exceed 200 mm yr(-1) on average, and relative differences range from 5 to 60% across the Western United States. In areas of high topographic relief, true uncertainties and biases are likely higher than the differences among the datasets; we present evidence of this based on streamflow observations. Precipitation trends in the datasets differ in magnitude and sign at smaller scales, and are sensitive to how temporal inhomogeneities in the underlying precipitation gauge data are handled. (C) 2017 Elsevier B.V. All rights reserved.

DOI10.1016/j.jhydrol.2017.03.008
Short TitleJ. Hydrol.
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