Climate Program Office News

Modeling water availability during droughts in the Texas-Gulf region

  • 29 March 2021

Occurring frequently over the Southern Plains, droughts are the second most costly U.S. weather and climate disaster. In 2011, a devastating drought impacted Texas, Oklahoma, and nearby states, causing damage estimates of $14 billion and was responsible for 96 deaths. Evidence shows the 2011 Texas drought was accompanied by decreased terrestrial water storage and suppressed vegetation activity. Many efforts have been made to advance drought monitoring. However, modeling water availability during drought remains challenging as numerous physical processes control soil moisture variability. Therefore, it is important to examine the physical process that controls water availability during drought to understand the mechanisms causing the disparities between models.

In a new Journal of Hydrometeorology, authors Wen-Ying Wu, Zong-Liang Yang, and Michael Barlage investigate the critical hydrometeorological processes during drought using the land surface model Noah-MP to simulate water availability and investigate the causes of the record 2011 drought. Using a series of experiments with runoff schemes, vegetation life cycles, and plant rooting depth, observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare the results from the model.

Overall, it was found that drought-induced vegetation responses interact with both water availability and affect the ground temperature. In addition, using a groundwater scheme with the Noah-MP model produced a better temporal relationship in terrestrial water storage compared with observations. Results show that the Noah-MP model tends to overestimate runoff and underestimate evapotranspiration in the Texas-Gulf region, especially during drought. The authors note that more effort could be made to improve vegetation dynamics and representing the rooting depth variability in a realistic manner is one of the potential approaches. Authors note the insights gained from this study could help identify the directions of model development, and that besides drought monitoring, this study may have implications for weather, subseasonal-to-seasonal, and streamflow predictions.

Read the full study here.

This study was funded by NIDIS in partnership with the MAPP program.

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About NIDIS
The National Integrated Drought Information System (NIDIS) program was authorized by Congress in 2006 (Public Law 109-430) with an interagency mandate to coordinate and integrate drought research, building upon existing federal, tribal, state, and local partnerships in support of creating a national drought early warning information system. For more information, please visit https://www.drought.gov/drought/.


About MAPP
The Modeling, Analysis, Predictions, and Projections (MAPP) Program is a competitive research program in NOAA Research's Climate Program Office. MAPP's mission is to enhance the Nation's and NOAA's capability to understand, predict, and project variability and long-term changes in Earth's system and mitigate human and economic impacts. To achieve its mission, MAPP supports foundational research, transition of research to applications, and engagement across other parts of NOAA, among partner agencies, and with the external research community. MAPP plays a crucial role in enabling national preparedness for extreme events like drought and longer-term climate changes. For more information, please visit www.cpo.noaa.gov/MAPP.

 

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About the Climate Program Office

The Climate Program Office (CPO) manages competitive research programs in which NOAA funds high-priority climate science, assessments, decision support research, outreach, education, and capacity-building activities designed to advance our understanding of Earth’s climate system, and to foster the application of this knowledge in risk management and adaptation efforts.  CPO-supported research is conducted in regions across the United States, at national and international scales, and globally.  Learn more...

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