Toward Improved Regional Hydrological Model Performance Using State-Of-The-Science Data-Informed Soil Parameters

Li, Chuxuan; Yu, Guo; Wang, Jiali; Horton, Daniel E.

doi:10.1029/2023wr034431

Title: Toward Improved Regional Hydrological Model Performance Using State-Of-The-Science Data-Informed Soil Parameters

Journal Article · 14 August 2023 · Water Resources Research

DOI: https://doi.org/10.1029/2023wr034431 · OSTI ID:2376134

^[1]; Yu, Guo ^[2];

^[3];

^[1]

Northwestern Univ., Evanston, IL (United States)
Desert Research Institute (DRI), Las Vegas, NV (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well-established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 2376134

Journal Information:: Water Resources Research, Journal Name: Water Resources Research Journal Issue: 9 Vol. 59; ISSN 0043-1397

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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NLDAS VIC Land Surface Model L4 Hourly 0.125 x 0.125 degree, Version 002 Xia, Y.; (2012), Et Al. NASA Goddard Earth Sciences Data and Information Services Center https://doi.org/10.5067/ELBDAPAKNGJ9	dataset	January 2014
NLDAS Mosaic Land Surface Model L4 Hourly 0.125 x 0.125 degree, Version 002 Xia, Y.; (2012), Et Al. NASA Goddard Earth Sciences Data and Information Services Center https://doi.org/10.5067/EN4MBWTCENE5	dataset	January 2009
SMAP L4 Global 3-hourly 9 km EASE-Grid Surface and Root Zone Soil Moisture??Analysis Update, Version 7?? Reichle, Rolf; De Lannoy, Gabrielle; Koster, Randal NASA National Snow and Ice Data Center Distributed Active Archive Center https://doi.org/10.5067/LWJ6TF5SZRG3	dataset	January 2022
A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: a case study over the midwestern United States Wang, Jiali; Wang, Cheng; Rao, Vishwas Geoscientific Model Development, Vol. 12, Issue 8 https://doi.org/10.5194/gmd-12-3523-2019	journal	January 2019
Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept Matgen, P.; Montanari, M.; Hostache, R. Hydrology and Earth System Sciences, Vol. 14, Issue 9 https://doi.org/10.5194/hess-14-1773-2010	journal	January 2010
On the utility of land surface models for agricultural drought monitoring Crow, W. T.; Kumar, S. V.; Bolten, J. D. Hydrology and Earth System Sciences, Vol. 16, Issue 9 https://doi.org/10.5194/hess-16-3451-2012	journal	January 2012
Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models Brunner, Manuela I.; Melsen, Lieke A.; Wood, Andrew W. Hydrology and Earth System Sciences, Vol. 25, Issue 1 https://doi.org/10.5194/hess-25-105-2021	journal	January 2021
Analysis of an extreme weather event in a hyper-arid region using WRF-Hydro coupling, station, and satellite data Wehbe, Youssef; Temimi, Marouane; Weston, Michael Natural Hazards and Earth System Sciences, Vol. 19, Issue 6 https://doi.org/10.5194/nhess-19-1129-2019	journal	June 2019
Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars Li, Chuxuan; Handwerger, Alexander L.; Wang, Jiali Natural Hazards and Earth System Sciences, Vol. 22, Issue 7 https://doi.org/10.5194/nhess-22-2317-2022	journal	July 2022
data-informed calibration of WRF-Hydro data1 Li, Chuxuan Zenodo https://doi.org/10.5281/zenodo.7487179	dataset	January 2022