2022
DOI: 10.5194/essd-2022-133
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An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015

Abstract: Abstract. Terrestrial water budget (TWB) data over large domains are of high interest for various hydrological applications. Spatiotemporally continuous and physically consistent estimations of TWB rely on land surface models (LSMs). As an augmentation of the operational North American Land Data Assimilation System Phase 2 (NLDAS‑2) four-LSM ensemble, this study presents a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP). The 48 Noah‑MP physics configuratio… Show more

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Cited by 1 publication
(2 citation statements)
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“…The dataset variables include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The dataset is available at https://doi.org/10.5281/zenodo.7109816 (Zheng et al, 2022). Evaluations carried out in this study and previous investigations show that the ensemble performs well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff.…”
mentioning
confidence: 69%
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“…The dataset variables include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The dataset is available at https://doi.org/10.5281/zenodo.7109816 (Zheng et al, 2022). Evaluations carried out in this study and previous investigations show that the ensemble performs well in reproducing the observed terrestrial water storage, snow water equivalent, soil moisture, and runoff.…”
mentioning
confidence: 69%
“…The dataset is freely available for download from the Zenodo online repository at https://doi.org/10.5281/zenodo.7109816 (Zheng et al, 2022). The dataset (along with datasets on which it is based) is subject to a Creative Commons BY (attribution) license agreement (https://creativecommons.org/ licenses, last access: 16 August 2021).…”
Section: Code and Data Availabilitymentioning
confidence: 99%