Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-2755-2023
https://doi.org/10.5194/essd-15-2755-2023
Data description paper
 | 
04 Jul 2023
Data description paper |  | 04 Jul 2023

An ensemble of 48 physically perturbed model estimates of the 1∕8° terrestrial water budget over the conterminous United States, 1980–2015

Hui Zheng, Wenli Fei, Zong-Liang Yang, Jiangfeng Wei, Long Zhao, Lingcheng Li, and Shu Wang

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Cited articles

Abolafia-Rosenzweig, R., He, C., Burns, S. P., and Chen, F.: Implementation and Evaluation of a Unified Turbulence Parameterization throughout the Canopy and Roughness Sublayer in Noah-MP Snow Simulations, J. Adv. Model. Earth Sy., 13, e2021MS002665, https://doi.org/10.1029/2021MS002665, 2021. a, b
Ajami, N. K., Duan, Q., and Sorooshian, S.: An Integrated Hydrologic Bayesian Multimodel Combination Framework: Confronting Input, Parameter, and Model Structural Uncertainty in Hydrologic Prediction, Water Resour. Res., 43, W01403, https://doi.org/10.1029/2005WR004745, 2007. a
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903, https://doi.org/10.5194/hess-21-2881-2017, 2017. a, b
Brutsaert, W.: Evaporation into the Atmosphere: Theory, History, and Applications, Springer, Dordrecht, https://doi.org/10.1007/978-94-017-1497-6, 1982. a
Burnash, R. J. C., Ferral, R. L., and McGuire, R. A.: A Generalized Streamflow Simulation System: Conceptual Modeling for Digital Computers, Technical Report, Joint Federal-State River Forecast Center, U.S. National Weather Service and California Department of Water Resources, Sacramento, California, USA, https://searchworks.stanford.edu/view/753303 (last access: 6 February 2016), 1973. a
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Short summary
An ensemble of evapotranspiration, runoff, and water storage is estimated here using the Noah-MP land surface model by perturbing model parameterization schemes. The data could be beneficial for monitoring and understanding the variability of water resources. Model developers could also gain insights by intercomparing the ensemble members.
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