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Climate change projections for the Texas High Plains and Rolling Plains


Potential changes in future climate in the Texas Plains region were investigated in the context of agriculture by analyzing three climate model projections under the A2 climate scenario (medium–high emission scenario). Spatially downscaled historic (1971–2000) and future (2041–2070) climate datasets (rainfall and temperature) were downloaded from the North American Regional Climate Change Assessment Program (NARCCAP). Climate variables predicted by three regional climate models (RCMs) namely the Regional Climate Model Version3–Geophysical Fluid Dynamics Laboratory (RCM3-GFDL), Regional Climate Model Version3–Third Generation Coupled Global Climate Model (RCM3-CGCM3), and Canadian Regional Climate Model–Community Climate System Model (CRCM-CCSM) were evaluated in this study. Gaussian and Gamma distribution mapping techniques were employed to remove the bias in temperature and rainfall data, respectively. Both the minimum and maximum temperatures across the study region in the future showed an upward trend, with the temperatures increasing in the range of 1.9 to 2.9 °C and 2.0 to 3.2 °C, respectively. All three climate models predicted a decline in rainfall within a range of 30 to 127 mm in majority of counties across the study region. In addition, they predicted an increase in the intensity of extreme rainfall events in the future. The frost-free season as predicted by the three models showed an increase by 2.6–3.4 weeks across the region, and the number of frost days declined by 17.9 to 30 %. Overall, these projections indicate considerable changes to the climate in the Texas Plains region in the future, and these changes could potentially impact agriculture in this region.

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  • Adhikari P, Ale S, Bordovsky JP, Thorp KR, Modala NR, Rajan N, Barnes EM (2016) Simulating future climate change impacts on seed cotton yield in the Texas High Plains using the CSM-CROPGRO-Cotton model. Agric Water Manag 164:317–330

  • Adusumilli NC, Rister ME, Lacewell RD (2011) Estimation of irrigation water demand: a case study for the Texas High Plains. In Selected Paper presented at the Southern Agricultural Economics Association Annual Meeting. Corpus Christi, Texas

    Google Scholar 

  • Allen VG, Brown CP, Segarra E, Green CJ, Wheeler TA, Acosta-Martinez V, Zobeck TM (2008) In search of sustainable agricultural systems for the Llano Estacado of the U.S. Southern High Plains. Agric Ecosyst Environ 124(1):3–12

    Article  Google Scholar 

  • Block PJ, Souza Filho FA, Sun L, Kwon HH (2009) A streamflow forecasting framework using multiple climate and hydrological models. JAWRA J Am Water Resour Assoc 45:828–843

    Article  Google Scholar 

  • Caya D, Laprise R, Giguère M, Bergeron G, Blanchet JP, Stocks BJ, Boer GJ, McFarlane NA (1995) Description of the Canadian regional climate model. Boreal Forests and Global Change, Springer Netherlands 477–482

  • Cayan DR, Maurer EP, Dettinger MD, Tyree M, Hayhoe K (2008) Climate change scenarios for the California region. Clim Chang 87(1):21–42

    Article  Google Scholar 

  • Chang HH, Zhou J, Fuentes M (2010) Impact of climate change on ambient ozone level and mortality in South-eastern United States. Int J Environ Res Public Health 7(7):2866–2880

    Article  Google Scholar 

  • Chang HH, Hao H, Sarnat SE (2014) A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change. Atmos Environ 89:290–297

    Article  Google Scholar 

  • Chen J, Brissette FP, Chaumont D, Braun M (2013) Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resour Res 49(7):4187–4205

    Article  Google Scholar 

  • Colaizzi PD, Gowda PH, Marek TH, Porter DO (2009) Irrigation in the Texas High Plains: a brief history and potential reductions in demand. Irrig Drain 58(3):257–274

    Article  Google Scholar 

  • Collins WD, Bitz CM, Blackmon ML, Bonan GB, Bretherton CS, Carton JA, Chang P, Doney SC, Hack JJ, Henderson TB, Kiehl JT, Large WG, McKenna DS, Santer BD, Smith RD (2006) The Community Climate System Model version 3 (CCSM3). J Clim 19(11):2122–2143

  • Cramér H (1999) Mathematical methods of statistics. 9, Princeton University press

  • Delworth T (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19(5):643–674

    Article  Google Scholar 

  • Flato GM (2005) The third generation coupled global climate model (CGCM3). http://www. Accessed 3 March 2014

  • Glotter M, Elliott J, McInerney D, Best N, Foster I, Moyer EJ (2014) Evaluating the utility of dynamical downscaling in agricultural impacts projections. Proc Natl Acad Sci 111(24):8776–8781

    Article  Google Scholar 

  • Hayhoe K, Cayan D, Field CB, Frumhoff PC, Maurer EP, Miller NL, Verville JH (2004) Emissions pathways, climate change, and impacts on California. Proc Natl Acad Sci U S A 101(34):12422–12427

    Article  Google Scholar 

  • Hayhoe K, Stoner A, Gelca R (2013) Climate change projections and indicators for Delaware . Accessed 28 May 2015

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978

  • Ines AV, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138(1):44–53

    Article  Google Scholar 

  • IPCC-SRES, Intergovernmental Panel on Climate Change—Special Report on Emission Scenarios (2000). Accessed 2 June 2013

  • Jakob Themeßl M, Gobiet A, Leuprecht A (2011) Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int J Climatol 31(10):1530–1544

    Article  Google Scholar 

  • Jensen R (2004) Ogallala aquifer: using improved irrigation technology and water conservation to meet future needs. Texas Water Resource Institute. Accessed 23 February 2012

  • Kunkel KE, Easterling DR, Hubbard K, Redmond K (2004) Temporal variations in frost-free season in the United States. Geophys Res Lett 31(3):1895–2000

  • Lafon T, Dadson S, Buys G, Prudhomme C (2013) Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods. Int J Climatol 33(6):1367–1381

    Article  Google Scholar 

  • Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. Journal of Geophysical Research: Atmospheres (1984–2012) 115(D10).

  • Maurer EP, Adam JC, Wood AW (2009) Climate model based consensus on the hydrologic impacts of climate change to the Rio Lempa basin of Central America. Hydrol Earth Syst Sci 13(2):183–194

  • Mearns LO, Gutowski W, Jones R, Leung R, McGinnis S, Nunes A, Qian Y (2009) A regional climate change assessment program for North America. Trans Am Geophys Union 90(36):311–311

    Article  Google Scholar 

  • Mearns LO, McGinnis S, Arritt R, Biner S, Duffy P, Gutowski W, Zoellick C (2007) The North American Regional Climate Change Assessment Program dataset, National Center for Atmospheric Research Earth System Grid data portal, Boulder, CO. Data downloaded 2013–02-21

  • Miller GO (2013) Landscaping with native plants of Texas. Voyageur Press

  • Modala NR (2014) Assessing the impacts of climate change on cotton production in the Texas High Plains and Rolling Plains (PhD Dissertation). Texas A&M University, College Station, TX

    Google Scholar 

  • Modala NR, Ale S, Rajan N, Thorp KR, Munster C (2015) Simulating the impacts of future climate variability and change on cotton production in the Texas Rolling Plains. Presented at the Beltwide Cotton Conferences. 5–7 January, 2015, San Antonio, TX

  • Nakicenvoic (2000) Special report on emissions scenarios. A special report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p. 599

    Google Scholar 

  • Nielsen-Gammon J (2011) The changing climate of Texas. The impact of global warming on Texas. University of Texas Press, Austin, pp. 39–68

    Google Scholar 

  • Overpeck JT, Meehl GA, Bony S, Easterling DR (2011) Climate data challenges in the 21st century. Science 331(6018):700–702

  • Piani C, Haerter JO, Coppola E (2010) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99(1–2):187–192

    Article  Google Scholar 

  • Pryor SC, Barthelmie RJ (2013) Assessing the vulnerability of wind energy to climate change and extreme events. Clim Chang 121(1):79–91

    Article  Google Scholar 

  • Rajsekhar D, Singh VP, Mishra AK (2015) Integrated drought causality, hazard, and vulnerability assessment for future socioeconomic scenarios: an information theory perspective. J Geophys Res Atmos 120(13):6346–6378

    Article  Google Scholar 

  • Stewart BA (2003) Aquifers, Ogallala. Encyclopedia of Water Science, pp 43–44

  • Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies. Geography Compass 4(7):834–860

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456:12–29

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2013) Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? Hydrol Earth Syst Sci 17(12):5061–5077

    Article  Google Scholar 

  • Thom HCS (1952) Seasonal degree-day statistics for the United States 1. Mon Weather Rev 80(9):143–147

    Article  Google Scholar 

  • Thom HCS (1958) A note on the gamma distribution. Mon Weather Rev 86(4):117–122

    Article  Google Scholar 

  • Vashisht BB, Nigon T, Mulla DJ, Rosen C, Xu H, Twine T, Jalota SK (2015) Adaptation of water and nitrogen management to future climates for sustaining potato yield in Minnesota: field and simulation study. Agric Water Manag 152:198–206

    Article  Google Scholar 

  • Walsh J, Wuebbles D, Hayhoe K, Kossin J, Kunkel K, Stephens G, Thorne P, Vose R, Wehner M, Willis J, Anderson D, Doney S, Feely R, Hennon P, Kharin V, Knutson T, Landerer F, Lenton T, Kennedy J, Somerville R (2014) Ch. 2: our changing climate. Climate change impacts in the United States: the Third National Climate Assessment, Melillo JM, Terese TC Richmond, Yohe GW, Eds., U.S. Global Change Research Program, pp 19–67. doi:10.7930/J0KW5CXT

  • Wang T, Hamann A, Spittlehouse DL, Murdock TQ (2012) Climate WNAHigh-resolution spatial climate data for Western North America. J Appl Meteorol Climatol 51(1):16–29

  • Webb WP (1931) The Great Plains. Ginn and Co., New York, NY

  • Weeks JB (1986) High plains regional aquifer study. In: Sun, RJ (Ed) Regional Aquifer-System Analysis Program of the US Geological Survey of Projects, 1978–1984. US Geological Survey Circular 1002. US Government Printing Office, Washington DC

  • Weeks JB, Gutentag E (1984) The High Plains regional aquifer—geohydrology. In: Whetstone (Ed) Proceedings of the Ogallala Aquifer Symposium II. Water Resources Center. Texas Tech University, Lubbock, Texas, 1984

  • Wilby RL, Wigley TML (2002) Future changes in the distribution of daily precipitation totals across North America. Geophys Res Lett 29(7):39–31

    Article  Google Scholar 

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang 62(1–3):189–216

    Article  Google Scholar 

  • Yang Y, Wilson LT, Wang J (2010) Development of an automated climatic data scraping, filtering and display system. Comput Electron Agric 71(1):77–87

    Article  Google Scholar 

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We thank the Texas A&M AgriLife Research for funding this study through the Cropping Systems Initiative. We also thank Seth McGinnis, Associate Scientist at the National Center for Atmospheric Research–University Cooperation for Atmospheric Research (NCAR-UCAR), for providing assistance with NARCCAP climate datasets and bias correction. We apprecaite the valuable support provided by Sai Murali Krishna and Pradeep Garigipati of Computer Science department at Texas A&M University in computer programing. We also appreciate the valuable feedback provided by Karen Davis, Florence Davis, and Nancy Vazquez of Texas A&M University Writing Center on earlier version of this paper.

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Correspondence to Srinivasulu Ale.

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Modala, N.R., Ale, S., Goldberg, D.W. et al. Climate change projections for the Texas High Plains and Rolling Plains. Theor Appl Climatol 129, 263–280 (2017).

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  • Rainfall Event
  • Bias Correction
  • Extreme Rainfall Event
  • Cumulative Distribution Frequency
  • Bias Correction Method