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Simulating the impacts of climate change on hydrology and crop production in the Northern High Plains of Texas using an improved SWAT model

Author

Listed:
  • Chen, Yong
  • Marek, Gary W.
  • Marek, Thomas H.
  • Moorhead, Jerry E.
  • Heflin, Kevin R.
  • Brauer, David K.
  • Gowda, Prasanna H.
  • Srinivasan, Raghavan

Abstract

Modeling the effects of climate change on hydrology and crop yield provides opportunities for choosing appropriate crops for adapting to climate change. In this study, climate change impacts on irrigated corn and sorghum, dryland (rainfed) sorghum, and continuous fallow in the Northern High Plains of Texas were evaluated using an improved Soil and Water Assessment Tool (SWAT) model equipped with management allowed depletion (MAD) irrigation scheduling. Projected climate data (2020–2099) from the Coupled Model Intercomparison Project Phase 5 (CMIP 5) of 19 General Circulation Models (GCMs) were used. Climate data were divided into four 20-year periods of near future (2020–2039), middle (2040–2059), late (2060–2079), and end (2080–2099) of the 21st century under two Representative Concentration Pathway (RCP) emission scenarios (RCP 4.5 and RCP 8.5). For irrigated corn, median annual crop evapotranspiration (ET) and irrigation decreased by 8%–25% and 15%–42%, respectively, under the climate change scenarios compared to the historical period (2001–2010). The median yield was reduced by 3%–22% with exponentially decreases in the latter half of the 21st century. For sorghum, the reduction of median annual crop ET ranged from 6%–27%. However, the decline in the median annual irrigation was within 15%, except for the 2060–2079 and 2080–2099 periods under RCP 8.5 scenarios with 30% and 49% reductions in median annual irrigation. The median irrigated sorghum yield declined by 6%–42%. The median annual crop ET of dryland sorghum decreased by 10%–16%. The reduction in median yield was within 10% of the historical dryland sorghum yield. The decrease in median annual evaporation varied from 15%–23% under future continuous fallow conditions. The elevated CO2 level of future climate scenarios was the primary factor for the decrease in the ET and irrigation. The reduction in future crop yield was mainly attributed to the shortening of the maturity period caused by increased future temperature.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:agiwat:v:221:y:2019:i:c:p:13-24
    DOI: 10.1016/j.agwat.2019.04.021
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    3. King, Darran A. & Meyer, Wayne S. & Connor, Jeffery D., 2019. "Interactive land use strategic assessment: An assessment tool for irrigation profitability under climate uncertainty," Agricultural Water Management, Elsevier, vol. 224(C), pages 1-1.
    4. Wen, Na & Marek, Gary W. & Srinivasan, Raghavan & Brauer, David K. & Qi, Junyu & Wang, Nan & Han, Yiwen & Zhang, Xueliang & Feng, Puyu & Liu, De Li & Chen, Yong, 2024. "Assessing the impacts of long-term climate change on hydrology and yields of diversified crops in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 302(C).
    5. Marcinkowski, Paweł & Piniewski, Mikołaj, 2024. "Future changes in crop yield over Poland driven by climate change, increasing atmospheric CO2 and nitrogen stress," Agricultural Systems, Elsevier, vol. 213(C).
    6. 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.
    7. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Malik, Anurag & Maroufpoor, Saman, 2020. "Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 241(C).

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