Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

Yan, Hongxiang; Sun, Ning; Eldardiry, Hisham; Thurber, Travis B.; Reed, Patrick M.; Malek, Keyvan; Gupta, Rohini; Kennedy, Daniel; Swenson, Sean C.; Wang, Linying; Li, Dan; Vernon, Chris R.; Burleyson, Casey D.; Rice, Jennie S.

doi:10.1038/s41597-023-02049-7

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

Journal Article · Thu Apr 06 00:00:00 EDT 2023 · Scientific Data

DOI:https://doi.org/10.1038/s41597-023-02049-7· OSTI ID:1968639

; ; Eldardiry, Hisham; Thurber, Travis B.; Reed, Patrick M.; Malek, Keyvan; Gupta, Rohini; Kennedy, Daniel; Swenson, Sean C.; ; Li, Dan; ; ;

Abstract

Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes.

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Sponsoring Organization:: USDOE

OSTI ID:: 1968639

Alternate ID(s):: OSTI ID: 1970457

Journal Information:: Scientific Data, Journal Name: Scientific Data Journal Issue: 1 Vol. 10; ISSN 2052-4463

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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