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Effects of Human Activities on Evapotranspiration and Its Components in Arid Areas

Author

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  • Yunfei Liu

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele 848300, China
    College of Resources and Environment, University of Chinese Academy of Sciences, College of Resources and Environment, Beijing 100049, China)

  • Dongwei Gui

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele 848300, China)

  • Changjun Yin

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele 848300, China
    College of Resources and Environment, University of Chinese Academy of Sciences, College of Resources and Environment, Beijing 100049, China)

  • Lei Zhang

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele 848300, China
    College of Resources and Environment, University of Chinese Academy of Sciences, College of Resources and Environment, Beijing 100049, China)

  • Dongping Xue

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele 848300, China
    College of Resources and Environment, University of Chinese Academy of Sciences, College of Resources and Environment, Beijing 100049, China)

  • Yi Liu

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele 848300, China)

  • Zeeshan Ahmed

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele 848300, China)

  • Fanjiang Zeng

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele 848300, China)

Abstract
With the increasing impact of human activities on the environment, evapotranspiration (ET) has changed in arid areas, which further affects the water resources availability in the region. Therefore, understanding the impact of human activities on ET and its components is helpful to the management of water resources in arid areas. This study verified the accuracy of Fisher’s model (PT-JPL model) for ET estimation in southern Xinjiang, China by using the evaporation complementarity theory dataset (AET dataset). The ET and the evapotranspiration components (T:E) of six land-use types were estimated in southern Xinjiang from 1982 to 2015, and the impact of human activities on ET was analyzed. In addition, the impact of four environmental factors (temperature (Temp), net radiation (Rn), relative humidity (RH), and NDVI) on ET were evaluated. The results showed that the calculated ET values of the PT-JPL model were close to the ET values of the AET dataset. The correlation coefficient (R 2 ) was more than 0.8, and the NSE was close to 1. In grassland, water area, urban industrial and mining land, forest land, and cultivated land, the ET values were high, and in unused land types, the ET values were the lowest. The T:E values varied greatly in urban industrial and mining land, forest land, and cultivated land, which was due to the intensification of human activities, and the values were close to 1 in summer in recent years. Among the four environmental factors, temperature largely influenced the monthly ET. These findings suggest that human activities have significantly reduced soil evaporation and improved water use efficiency. The impact of human activities on environmental factors has caused changes in ET and its components, and appropriate oasis expansion is more conducive to regional sustainable development.

Suggested Citation

  • Yunfei Liu & Dongwei Gui & Changjun Yin & Lei Zhang & Dongping Xue & Yi Liu & Zeeshan Ahmed & Fanjiang Zeng, 2023. "Effects of Human Activities on Evapotranspiration and Its Components in Arid Areas," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:2795-:d:1058131
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    References listed on IDEAS

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    1. Wenfeng Li & Shuzhi Wang & Rehemanjiang Wufuer & Jia Duo & Xiangliang Pan, 2022. "Microplastic Contamination in Urban, Farmland and Desert Environments along a Highway in Southern Xinjiang, China," IJERPH, MDPI, vol. 19(15), pages 1-12, July.
    2. Kodur, S., 2017. "Improving the prediction of soil evaporation for different soil types under dryland cropping," Agricultural Water Management, Elsevier, vol. 193(C), pages 131-141.
    3. Ferreira, Lucas Borges & da Cunha, Fernando França, 2020. "New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning," Agricultural Water Management, Elsevier, vol. 234(C).
    4. Gong, Daozhi & Mei, Xurong & Hao, Weiping & Wang, Hanbo & Caylor, Kelly K., 2017. "Comparison of ET partitioning and crop coefficients between partial plastic mulched and non-mulched maize fields," Agricultural Water Management, Elsevier, vol. 181(C), pages 23-34.
    5. M. Majidi & A. Alizadeh & A. Farid & M. Vazifedoust, 2015. "Estimating Evaporation from Lakes and Reservoirs under Limited Data Condition in a Semi-Arid Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3711-3733, August.
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