IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v275y2023ics0378377422005856.html
   My bibliography  Save this article

A novel multi-scale parameter estimation approach to the Hargreaves-Samani equation for estimation of Penman-Monteith reference evapotranspiration

Author

Listed:
  • Kim, Ho-Jun
  • Chandrasekara, Sewwandhi
  • Kwon, Hyun-Han
  • Lima, Carlos
  • Kim, Tae-woong

Abstract

The main focus of this study is to develop a multi-scale surrogate model for the FAO-56 Penman-Monteith (PM) evapotranspiration (ETo) using Hargreaves-Samani (HS) equation, which uses only temperature as a hydrometeorological variable to estimate ET. This feature is particularly useful for scarce data regions and climate change impact assessment studies, where the direct estimation of ETo from the PM equation can be problematic. As the parameters of the HS equation may vary across space, a Bayesian approach was adopted to estimate (or recalibrate) them rather than relying on the fixed values as suggested in the traditional model. The Bayesian approach allows a sound development of our model in a multi-scale temporal framework, where the ETo at daily, monthly and annual scales are jointly used to estimate the HS equation parameters. The proposed and reference models are applied and tested using meteorological data from 17 stations located across the Han river basin in South Korea. The results indicate that the traditional HS equation with fixed parameters and without recalibration tends to overestimate the reference ET for all stations. The locally recalibrated approach to the HS equation at a daily temporal scale can effectively reduce the systematic bias associated with the use of the traditional HS equation but fails to reproduce the underlying distribution of ETo at different temporal scales (e.g., monthly and annual). This leads to a large systematic bias in ETo at these scales. In contrast, the proposed multi-scale surrogate model offers a more precise estimation of the reference ET at a daily timescale as well as at the aggregated monthly and annual temporal scales. This is particularly useful to minimize the systematic bias often observed when using traditional surrogate models for the reference ET in hydrological studies such as rainfall-runoff modeling and assessment of climate change impact on water resources.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:agiwat:v:275:y:2023:i:c:s0378377422005856
    DOI: 10.1016/j.agwat.2022.108038
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377422005856
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2022.108038?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. Falamarzi, Yashar & Palizdan, Narges & Huang, Yuk Feng & Lee, Teang Shui, 2014. "Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs)," Agricultural Water Management, Elsevier, vol. 140(C), pages 26-36.
    2. Martinez-Cob, A. & Tejero-Juste, M., 2004. "A wind-based qualitative calibration of the Hargreaves ET0 estimation equation in semiarid regions," Agricultural Water Management, Elsevier, vol. 64(3), pages 251-264, February.
    3. 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).
    4. W. R. Gilks & N. G. Best & K. K. C. Tan, 1995. "Adaptive Rejection Metropolis Sampling Within Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 455-472, December.
    5. Kang, Yan & Chen, Peiru & Cheng, Xiao & Zhang, Shuo & Song, Songbai, 2022. "Novel hybrid machine learning framework with decomposition–transformation and identification of key modes for estimating reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 273(C).
    6. Raziei, Tayeb & Pereira, Luis S., 2013. "Estimation of ETo with Hargreaves–Samani and FAO-PM temperature methods for a wide range of climates in Iran," Agricultural Water Management, Elsevier, vol. 121(C), pages 1-18.
    7. 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).
    8. Gavilan, P. & Lorite, I.J. & Tornero, S. & Berengena, J., 2006. "Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment," Agricultural Water Management, Elsevier, vol. 81(3), pages 257-281, March.
    9. Traore, Seydou & Luo, Yufeng & Fipps, Guy, 2016. "Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages," Agricultural Water Management, Elsevier, vol. 163(C), pages 363-379.
    10. Shirmohammadi-Aliakbarkhani, Zahra & Saberali, Seyed Farhad, 2020. "Evaluating of eight evapotranspiration estimation methods in arid regions of Iran," Agricultural Water Management, Elsevier, vol. 239(C).
    11. 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).
    12. Feng, Yu & Jia, Yue & Cui, Ningbo & Zhao, Lu & Li, Chen & Gong, Daozhi, 2017. "Calibration of Hargreaves model for reference evapotranspiration estimation in Sichuan basin of southwest China," Agricultural Water Management, Elsevier, vol. 181(C), pages 1-9.
    Full references (including those not matched with items on IDEAS)

    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. Yang, Yang & Luo, Yufeng & Wu, Conglin & Zheng, Hezhen & Zhang, Lei & Cui, Yuanlai & Sun, Ningning & Wang, Li, 2019. "Evaluation of six equations for daily reference evapotranspiration estimating using public weather forecast message for different climate regions across China," Agricultural Water Management, Elsevier, vol. 222(C), pages 386-399.
    2. Paredes, P. & Pereira, L.S. & Almorox, J. & Darouich, H., 2020. "Reference grass evapotranspiration with reduced data sets: Parameterization of the FAO Penman-Monteith temperature approach and the Hargeaves-Samani equation using local climatic variables," Agricultural Water Management, Elsevier, vol. 240(C).
    3. Nouri, Milad & Homaee, Mehdi, 2022. "Reference crop evapotranspiration for data-sparse regions using reanalysis products," Agricultural Water Management, Elsevier, vol. 262(C).
    4. Gavilán, P. & Castillo-Llanque, F., 2009. "Estimating reference evapotranspiration with atmometers in a semiarid environment," Agricultural Water Management, Elsevier, vol. 96(3), pages 465-472, March.
    5. Wu, Lifeng & Peng, Youwen & Fan, Junliang & Wang, Yicheng & Huang, Guomin, 2021. "A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation," Agricultural Water Management, Elsevier, vol. 245(C).
    6. Paredes, Paula & Trigo, Isabel & de Bruin, Henk & Simões, Nuno & Pereira, Luis S., 2021. "Daily grass reference evapotranspiration with Meteosat Second Generation shortwave radiation and reference ET products," Agricultural Water Management, Elsevier, vol. 248(C).
    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).
    8. Landeras, Gorka & Ortiz-Barredo, Amaia & López, Jose Javier, 2008. "Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)," Agricultural Water Management, Elsevier, vol. 95(5), pages 553-565, May.
    9. 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).
    10. Bellido-Jiménez, Juan Antonio & Estévez, Javier & García-Marín, Amanda Penélope, 2021. "New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain," Agricultural Water Management, Elsevier, vol. 245(C).
    11. Cruz-Blanco, M. & Lorite, I.J. & Santos, C., 2014. "An innovative remote sensing based reference evapotranspiration method to support irrigation water management under semi-arid conditions," Agricultural Water Management, Elsevier, vol. 131(C), pages 135-145.
    12. Paredes, Paula & Martins, Diogo S. & Pereira, Luis Santos & Cadima, Jorge & Pires, Carlos, 2018. "Accuracy of daily estimation of grass reference evapotranspiration using ERA-Interim reanalysis products with assessment of alternative bias correction schemes," Agricultural Water Management, Elsevier, vol. 210(C), pages 340-353.
    13. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.
    14. 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.
    15. Jabloun, M. & Sahli, A., 2008. "Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data: Application to Tunisia," Agricultural Water Management, Elsevier, vol. 95(6), pages 707-715, June.
    16. Feng, Yu & Gong, Daozhi & Mei, Xurong & Hao, Weiping & Tang, Dahua & Cui, Ningbo, 2017. "Energy balance and partitioning in partial plastic mulched and non-mulched maize fields on the Loess Plateau of China," Agricultural Water Management, Elsevier, vol. 191(C), pages 193-206.
    17. England, Peter, 2002. "Addendum to "Analytic and bootstrap estimates of prediction errors in claims reserving"," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 461-466, December.
    18. Estévez, J. & García-Marín, A.P & Morábito, J.A & Cavagnaro, M., 2016. "Quality assurance procedures for validating meteorological input variables of reference evapotranspiration in mendoza province (Argentina)," Agricultural Water Management, Elsevier, vol. 172(C), pages 96-109.
    19. Santos, Reginaldo Ferreira & Bassegio, Doglas & de Almeida Silva, Marcelo, 2017. "Productivity and production components of safflower genotypes affected by irrigation at phenological stages," Agricultural Water Management, Elsevier, vol. 186(C), pages 66-74.
    20. Cunha, Angélica Carvalho & Filho, Luís Roberto Almeida Gabriel & Tanaka, Adriana Aki & Goes, Bruno Cesar & Putti, Fernando Ferrari, 2021. "Influence Of The Estimated Global Solar Radiation On The Reference Evapotranspiration Obtained Through The Penman-Monteith Fao 56 Method," Agricultural Water Management, Elsevier, vol. 243(C).

    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:275:y:2023:i:c:s0378377422005856. 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.