Abstract
Abstract. Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P, 14 % to 26 % for ET, 28 % to 82 % for R, 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R, 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.
Highlights
Along-term goal of the atmospheric and hydrologic scientific communities has been to produce accurate estimates of the hydrologic cycle across continental and global domains (Archfield et al, 2015; Beven, 2006; Freeze and Harlan, 1969)
Uncertainties between annual estimates of states and fluxes of the hydrologic cycle are substantial when averaged over the conterminous United States (CONUS) for each studied component (Fig. 2)
The actual magnitude of model estimate differences averaged by water budget component, measured as σ, is similar for P, ET, and R (Fig. 2a) and low (6.6 mm per year) for snow water equivalent (SWE)
Summary
Along-term goal of the atmospheric and hydrologic scientific communities has been to produce accurate estimates of the hydrologic cycle across continental and global domains (Archfield et al, 2015; Beven, 2006; Freeze and Harlan, 1969). Many of the resulting estimates are made publicly available by an assortment of scientific entities at both continental and global extents across a wide range of spatiotemporal resolutions. These data sets accelerate progress in the atmospheric and hydrologic sciences by filling knowledge gaps in data-sparse regions, reducing the need for time-consuming and computationally expensive modeling, and by providing numerous estimates to apply within ensemble analyses. Available modeled estimates have been applied to work on water budget analyses (Gao et al, 2010; Pan et al, 2012; Rodell et al, 2015; Smith and Kummerow, 2013; Velpuri et al, 2019; Zhang et al, 2018), the effects of climate change
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