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Spatio-Temporal Analysis of Historical and Future Climate Data in the Texas High Plains

Author

Listed:
  • Yong Chen

    (Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77843, USA)

  • Gary W. Marek

    (USDA-ARS Conservation and Production Research Laboratory, Bushland, TX 79012, USA)

  • Thomas H. Marek

    (Texas A&M AgriLife Research and Extension Center at Amarillo, Amarillo, TX 79106, USA)

  • Dana O. Porter

    (Texas A&M AgriLife Research and Extension Center at Lubbock, Lubbock, TX 79403, USA)

  • Jerry E. Moorhead

    (USDA-ARS Conservation and Production Research Laboratory, Bushland, TX 79012, USA)

  • Qingyu Wang

    (Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA)

  • Kevin R. Heflin

    (Texas A&M AgriLife Research and Extension Center at Amarillo, Amarillo, TX 79106, USA)

  • David K. Brauer

    (USDA-ARS Conservation and Production Research Laboratory, Bushland, TX 79012, USA)

Abstract

Agricultural production in the Texas High Plains (THP) relies heavily on irrigation and is susceptible to drought due to the declining availability of groundwater and climate change. Therefore, it is meaningful to perform an overview of possible climate change scenarios to provide appropriate strategies for climate change adaptation in the THP. In this study, spatio-temporal variations of climate data were mapped in the THP during 2000–2009, 2050–2059, and 2090–2099 periods using 14 research-grade meteorological stations and 19 bias-corrected General Circulation Models (GCMs) under representative concentration pathway (RCP) scenarios RCP 4.5 and 8.5. Results indicated different bias correction methods were needed for different climatic parameters and study purposes. For example, using high-quality data from the meteorological stations, the linear scaling method was selected to alter the projected precipitation while air temperatures were bias corrected using the quantile mapping method. At the end of the 21st century (2090–2099) under the severe CO 2 emission scenario (RCP 8.5), the maximum and minimum air temperatures could increase from 3.9 to 10.0 °C and 2.8 to 8.4 °C across the entire THP, respectively, while precipitation could decrease by ~7.5% relative to the historical (2000–2009) observed data. However, large uncertainties were found according to 19 GCM projections.

Suggested Citation

  • Yong Chen & Gary W. Marek & Thomas H. Marek & Dana O. Porter & Jerry E. Moorhead & Qingyu Wang & Kevin R. Heflin & David K. Brauer, 2020. "Spatio-Temporal Analysis of Historical and Future Climate Data in the Texas High Plains," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6036-:d:390621
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    References listed on IDEAS

    as
    1. Henderson, Benjamin & Cacho, Oscar & Thornton, Philip & van Wijk, Mark & Herrero, Mario, 2018. "The economic potential of residue management and fertilizer use to address climate change impacts on mixed smallholder farmers in Burkina Faso," Agricultural Systems, Elsevier, vol. 167(C), pages 195-205.
    2. Chen, Yong & Marek, Gary W. & Marek, Thomas H. & Moorhead, Jerry E. & Heflin, Kevin R. & Brauer, David K. & Gowda, Prasanna H. & Srinivasan, Raghavan, 2019. "Simulating the impacts of climate change on hydrology and crop production in the Northern High Plains of Texas using an improved SWAT model," Agricultural Water Management, Elsevier, vol. 221(C), pages 13-24.
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    4. Mingzhi Yang & Weihua Xiao & Yong Zhao & Xudong Li & Ya Huang & Fan Lu & Baodeng Hou & Baoqi Li, 2018. "Assessment of Potential Climate Change Effects on the Rice Yield and Water Footprint in the Nanliujiang Catchment, China," Sustainability, MDPI, vol. 10(2), pages 1-19, January.
    5. repec:aen:journl:2006se_weyant-a19 is not listed on IDEAS
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