IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v274y2022ics0378377422005261.html
   My bibliography  Save this article

Improved reference evapotranspiration methods for regional irrigation water demand estimation

Author

Listed:
  • Su, Qiong
  • Singh, Vijay P.
  • Karthikeyan, Raghupathy

Abstract

Temperature- and radiation-based methods are widely used for reference evapotranspiration (ETo) estimates and in regional irrigation water demand studies, especially in areas with limited weather data. A major limitation is that without local calibration, the constant parameters used in these methods usually cannot ensure the same reliability in regional analysis. On the other hand, the controlling variables of ETo under different climatic conditions and how they are related to the structures of these methods have not been adequately addressed yet. Herein, we evaluated the performance of three commonly used temperature-based methods, i.e., Thornthwaite (Th_T), Blaney-Criddle (BC_T), and Hargreaves and Samani (HS_T), and five radiation-based methods, i.e., Makkink (Ma_R), Priestley-Taylor (PT_R), Jensen and Haise (JH_R), Turc (Tu_R), and Abtew (Ab_R), for regional ETo estimates and calibrated them using the Penman-Monteith method. Monthly meteorological data (1961–2010) from 15 weather stations in Texas, United States, covering humid, subhumid, semiarid, and arid climates, were used. The HS_T method is recommended for regional analysis if reliable wind speed and relative humidity data are not available. The radiative component was the driving factor of ETo, accounting for 72− 84% of its variation in the warm season in all climates. Therefore, temperature- and radiation-based methods performed well and showed little variation in the warm season. However, we demonstrated that the vapor pressure deficit impact on ETo variations could be as high as 43–49% in the cool season in subhumid, semiarid, and arid climates, and wind speed impact on ETo variations was 4–12% and 10–16% in humid, semiarid, and arid climates, respectively. To accurately reflect these impacts, we adjusted the HS_T and PT_R methods by multiplying a calibration-free coefficient. By doing so, the accuracy of ETo under cool, dry, and windy conditions is considerably improved compared with the locally calibrated methods. The adjusted HS_T and PT_R methods can significantly improve the efficiency and accuracy of applying the less data-intensive ETo methods to predict regional or global irrigation water demand.

Suggested Citation

  • Su, Qiong & Singh, Vijay P. & Karthikeyan, Raghupathy, 2022. "Improved reference evapotranspiration methods for regional irrigation water demand estimation," Agricultural Water Management, Elsevier, vol. 274(C).
  • Handle: RePEc:eee:agiwat:v:274:y:2022:i:c:s0378377422005261
    DOI: 10.1016/j.agwat.2022.107979
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422005261
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2022.107979?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Sheng & Lian, Jinjiao & Peng, Yuzhong & Hu, Baoqing & Chen, Hongsong, 2019. "Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China," Agricultural Water Management, Elsevier, vol. 221(C), pages 220-230.
    2. Hu, Xuhua & Chen, Mengting & Liu, Dong & Li, Dan & Jin, Li & Liu, Shaohui & Cui, Yuanlai & Dong, Bin & Khan, Shahbaz & Luo, Yufeng, 2021. "Reference evapotranspiration change in Heilongjiang Province, China from 1951 to 2018: The role of climate change and rice area expansion," Agricultural Water Management, Elsevier, vol. 253(C).
    3. C.-Y. Xu & V. Singh, 2002. "Cross Comparison of Empirical Equations for Calculating Potential Evapotranspiration with Data from Switzerland," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 16(3), pages 197-219, June.
    4. Chia, Min Yan & Huang, Yuk Feng & Koo, Chai Hoon, 2021. "Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine," Agricultural Water Management, Elsevier, vol. 243(C).
    5. Martina Flörke & Christof Schneider & Robert I. McDonald, 2018. "Water competition between cities and agriculture driven by climate change and urban growth," Nature Sustainability, Nature, vol. 1(1), pages 51-58, January.
    6. Yang, Yong & Chen, Rensheng & Han, Chuntan & Liu, Zhangwen, 2021. "Evaluation of 18 models for calculating potential evapotranspiration in different climatic zones of China," Agricultural Water Management, Elsevier, vol. 244(C).
    7. Liu, Xiaoying & Xu, Chunying & Zhong, Xiuli & Li, Yuzhong & Yuan, Xiaohuan & Cao, Jingfeng, 2017. "Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement," Agricultural Water Management, Elsevier, vol. 184(C), pages 145-155.
    8. Hossein Tabari, 2010. "Evaluation of Reference Crop Evapotranspiration Equations in Various Climates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2311-2337, August.
    9. Valipour, Mohammad & Gholami Sefidkouhi, Mohammad Ali & Raeini−Sarjaz, Mahmoud, 2017. "Selecting the best model to estimate potential evapotranspiration with respect to climate change and magnitudes of extreme events," Agricultural Water Management, Elsevier, vol. 180(PA), pages 50-60.
    10. Ahmadi, Farshad & Mehdizadeh, Saeid & Mohammadi, Babak & Pham, Quoc Bao & DOAN, Thi Ngoc Canh & Vo, Ngoc Duong, 2021. "Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation," Agricultural Water Management, Elsevier, vol. 244(C).
    11. Lai, Chengguang & Chen, Xiaohong & Zhong, Ruida & Wang, Zhaoli, 2022. "Implication of climate variable selections on the uncertainty of reference crop evapotranspiration projections propagated from climate variables projections under climate change," Agricultural Water Management, Elsevier, vol. 259(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Qiong Su & Raghupathy Karthikeyan, 2023. "Regional Water Stress Forecasting: Effects of Climate Change, Socioeconomic Development, and Irrigated Agriculture—A Texas Case Study," Sustainability, MDPI, vol. 15(12), pages 1-14, June.
    2. Kim, Ho-Jun & Chandrasekara, Sewwandhi & Kwon, Hyun-Han & Lima, Carlos & Kim, Tae-woong, 2023. "A novel multi-scale parameter estimation approach to the Hargreaves-Samani equation for estimation of Penman-Monteith reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 275(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Malik, Anurag & Jamei, Mehdi & Ali, Mumtaz & Prasad, Ramendra & Karbasi, Masoud & Yaseen, Zaher Mundher, 2022. "Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection," Agricultural Water Management, Elsevier, vol. 272(C).
    2. Xiang, Keyu & Li, Yi & Horton, Robert & Feng, Hao, 2020. "Similarity and difference of potential evapotranspiration and reference crop evapotranspiration – a review," Agricultural Water Management, Elsevier, vol. 232(C).
    3. Roy, Dilip Kumar & Lal, Alvin & Sarker, Khokan Kumer & Saha, Kowshik Kumar & Datta, Bithin, 2021. "Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system," Agricultural Water Management, Elsevier, vol. 255(C).
    4. Yang, Yong & Chen, Rensheng & Han, Chuntan & Liu, Zhangwen, 2021. "Evaluation of 18 models for calculating potential evapotranspiration in different climatic zones of China," Agricultural Water Management, Elsevier, vol. 244(C).
    5. Shiri, Jalal, 2017. "Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran," Agricultural Water Management, Elsevier, vol. 188(C), pages 101-114.
    6. Xing, Liwen & Zhao, Lu & Cui, Ningbo & Liu, Chunwei & Guo, Li & Du, Taisheng & Wu, Zongjun & Gong, Daozhi & Jiang, Shouzheng, 2023. "Apple tree transpiration estimated using the Penman-Monteith model integrated with optimized jarvis model," Agricultural Water Management, Elsevier, vol. 276(C).
    7. Yan, Shicheng & Wu, Lifeng & Fan, Junliang & Zhang, Fucang & Zou, Yufeng & Wu, You, 2021. "A novel hybrid WOA-XGB model for estimating daily reference evapotranspiration using local and external meteorological data: Applications in arid and humid regions of China," Agricultural Water Management, Elsevier, vol. 244(C).
    8. Dimitrios Samaras & Albert Reif & Konstantinos Theodoropoulos, 2014. "Evaluation of Radiation-Based Reference Evapotranspiration Models Under Different Mediterranean Climates in Central Greece," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(1), pages 207-225, January.
    9. Malakshahi, Amir- Ashkan & Darzi- Naftchali, Abdullah & Mohseni, Behrooz, 2020. "Analyzing water table depth fluctuation response to evapotranspiration involving DRAINMOD model," Agricultural Water Management, Elsevier, vol. 234(C).
    10. Mohamed A. Mattar & A. A. Alazba & Bander Alblewi & Bahram Gharabaghi & Mohamed A. Yassin, 2016. "Evaluating and Calibrating Reference Evapotranspiration Models Using Water Balance under Hyper-Arid Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3745-3767, September.
    11. Singh Rawat, Kishan & Kumar Singh, Sudhir & Bala, Anju & Szabó, Szilárd, 2019. "Estimation of crop evapotranspiration through spatial distributed crop coefficient in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 213(C), pages 922-933.
    12. Valipour, Mohammad & Khoshkam, Helaleh & Bateni, Sayed M. & Jun, Changhyun & Band, Shahab S., 2023. "Hybrid machine learning and deep learning models for multi-step-ahead daily reference evapotranspiration forecasting in different climate regions across the contiguous United States," Agricultural Water Management, Elsevier, vol. 283(C).
    13. Ali Sabziparvar & Roya Mousavi & Safar Marofi & Niaz Ebrahimipak & Majid Heidari, 2013. "An Improved Estimation of the Angstrom–Prescott Radiation Coefficients for the FAO56 Penman–Monteith Evapotranspiration Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2839-2854, June.
    14. Tapsuwan, Sorada & Peña-Arancibia, Jorge L. & Lazarow, Neil & Albisetti, Melisa & Zheng, Hongxing & Rojas, Rodrigo & Torres-Alferez, Vianney & Chiew, Francis H.S. & Hopkins, Richard & Penton, David J., 2022. "A benefit cost analysis of strategic and operational management options for water management in hyper-arid southern Peru," Agricultural Water Management, Elsevier, vol. 265(C).
    15. Mohammad Akrami & Alaa H. Salah & Akbar A. Javadi & Hassan E.S. Fath & Matthew J. Hassanein & Raziyeh Farmani & Mahdieh Dibaj & Abdelazim Negm, 2020. "Towards a Sustainable Greenhouse: Review of Trends and Emerging Practices in Analysing Greenhouse Ventilation Requirements to Sustain Maximum Agricultural Yield," Sustainability, MDPI, vol. 12(7), pages 1-18, April.
    16. Xueping Zhu & Chi Zhang & Guangtao Fu & Yu Li & Wei Ding, 2017. "Bi-Level Optimization for Determining Operating Strategies for Inter-Basin Water Transfer-Supply Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4415-4432, November.
    17. Vasileios A. Tzanakakis & Andrea G. Capodaglio & Andreas N. Angelakis, 2023. "Insights into Global Water Reuse Opportunities," Sustainability, MDPI, vol. 15(17), pages 1-30, August.
    18. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    19. Entezari, A. & Wang, R.Z. & Zhao, S. & Mahdinia, E. & Wang, J.Y. & Tu, Y.D. & Huang, D.F., 2019. "Sustainable agriculture for water-stressed regions by air-water-energy management," Energy, Elsevier, vol. 181(C), pages 1121-1128.
    20. Dongying Sun & Jiarong Gu & Junyu Chen & Xilin Xia & Zhisong Chen, 2022. "Spatiotemporal differentiation and influencing factors of urban water supply system resilience in the Yangtze River Delta urban agglomeration," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(1), pages 101-126, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:274:y:2022:i:c:s0378377422005261. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.