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The Impact of Noah-MP Physical Parameterizations on Modeling Water Availability during Droughts in the Texas–Gulf Region

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  • 1 a Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas
  • | 2 b Research Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado
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Abstract

Texas is subject to severe droughts, including the record-breaking one in 2011. To investigate the critical hydrometeorological processes during drought, we use a land surface model, Noah-MP, to simulate water availability and investigate the causes of the record drought. We conduct a series of experiments with runoff schemes, vegetation phenology, and plant rooting depth. Observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare with results from the model. Overall, the results suggest that using different parameterizations can influence the modeled water availability, especially during drought. The drought-induced vegetation responses not only interact with water availability but also affect the ground temperature. Our evaluation shows that Noah-MP with a groundwater scheme produces a better temporal relationship in terrestrial water storage compared with observations. Leaf area index from dynamic vegetation is better simulated in wet years than dry years. Reduction of positive biases in runoff and reduction of negative biases in evapotranspiration are found in simulations with groundwater, dynamic vegetation, and deeper rooting zone depth. Multiparameterization experiments show the uncertainties of drought monitoring and provide a mechanistic understanding of disparities in dry anomalies.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0189.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zong-Liang Yang, liang@jsg.utexas.edu

This article is included in the Progress in Advancing Drought Monitoring and Prediction Special Collection.

Abstract

Texas is subject to severe droughts, including the record-breaking one in 2011. To investigate the critical hydrometeorological processes during drought, we use a land surface model, Noah-MP, to simulate water availability and investigate the causes of the record drought. We conduct a series of experiments with runoff schemes, vegetation phenology, and plant rooting depth. Observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare with results from the model. Overall, the results suggest that using different parameterizations can influence the modeled water availability, especially during drought. The drought-induced vegetation responses not only interact with water availability but also affect the ground temperature. Our evaluation shows that Noah-MP with a groundwater scheme produces a better temporal relationship in terrestrial water storage compared with observations. Leaf area index from dynamic vegetation is better simulated in wet years than dry years. Reduction of positive biases in runoff and reduction of negative biases in evapotranspiration are found in simulations with groundwater, dynamic vegetation, and deeper rooting zone depth. Multiparameterization experiments show the uncertainties of drought monitoring and provide a mechanistic understanding of disparities in dry anomalies.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JHM-D-20-0189.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Zong-Liang Yang, liang@jsg.utexas.edu

This article is included in the Progress in Advancing Drought Monitoring and Prediction Special Collection.

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