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

Yong Chen, Gary W. Marek, Thomas H. Marek, Dana O. Porter, Jerry E. Moorhead, Qingyu Wang, Kevin R. Heflin and David K. Brauer
Additional contact information
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

Sustainability, 2020, vol. 12, issue 15, 1-19

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.

Keywords: climate change; meteorological data; precipitation; maximum air temperature; minimum air temperature; General Circulation Models; bias correction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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