IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v180y2017ipap50-60.html
   My bibliography  Save this article

Selecting the best model to estimate potential evapotranspiration with respect to climate change and magnitudes of extreme events

Author

Listed:
  • Valipour, Mohammad
  • Gholami Sefidkouhi, Mohammad Ali
  • Raeini−Sarjaz, Mahmoud

Abstract

There are a lot of investigations to select the best model to estimate potential evapotranspiration (ETo) in a certain climate or region. In this paper, the types of climate include arid, semiarid, Mediterranean, and very humid. A spatial and temporal study of the ETo is the aim of this paper, according to the peak and low events (extreme events) and climate change alarms. For this purpose, 50 years (1961–2010) monthly meteorological data of 18 regions in Iran, with various climates, were collected. For estimating the ETo, 5 temperature−based, 5 radiation−based, and 5 mass transfer−based models, were selected with respect to better performance of them in different climates on the basis of past investigations. The results will especially be useful in the regions where the monthly (rather than daily) meteorological data are available. The results appear that the Blaney−Criddle (BC) (root mean square error (RMSE)=1.32mmday−1) and Abtew (Ab) (RMSE=0.83mmday−1) are the best models for estimating the ETo in the arid and semiarid regions, respectively. While, modified Hargreaves−Samani 2 (MHS2) represents the best performance in the Mediterranean and very humid regions (RMSE=0.30mmday−1 & 0.68mmday−1, respectively). In addition, radiation—and mass transfer−based models are proper tools to estimate the ETo in warm and cold seasons on the basis of improving values of evaluation indices in 40% and 70% of the study area, respectively. Increasing air temperature and decreasing minimum relative humidity for best performance of most models alarms a climate change in most regions of Iran. As a result, the radiation−based models were adapted with climate change better than the temperature−based and particularly mass transfer−based models. Finally, a step by step flowchart was presented for selecting the best model to estimate the ETo in each climate.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:agiwat:v:180:y:2017:i:pa:p:50-60
    DOI: 10.1016/j.agwat.2016.08.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377416303195
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2016.08.025?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. Ahmed El-Shafie & Ali Najah & Humod Alsulami & Heerbod Jahanbani, 2014. "Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 947-967, March.
    2. Mohammad Valipour, 2014. "Use of average data of 181 synoptic stations for estimation of reference crop evapotranspiration by temperature-based methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4237-4255, September.
    3. Abdoulkarim Esmaeili, 2009. "Environmental change and fishery management in the northern Persian Gulf," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 52(8), pages 1071-1081.
    4. Ahmed El-Shafie & Alaa Abdin & Aboelmagd Noureldin & Mohd Taha, 2009. "Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2289-2315, September.
    5. Abolfazl Mosaedi & Hamid Zare Abyaneh & Mohammad Ghabaei Sough & S. Samadi, 2015. "Quantifying Changes in Reconnaissance Drought Index using Equiprobability Transformation Function," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2451-2469, June.
    6. Mohammad Valipour, 2016. "How Much Meteorological Information Is Necessary to Achieve Reliable Accuracy for Rainfall Estimations?," Agriculture, MDPI, vol. 6(4), pages 1-9, October.
    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. Xiao, Chunan & Cai, Jiabing & Zhang, Baozhong & Chang, Hongfang & Wei, Zheng, 2023. "Evaluation and verification of two evapotranspiration models based on precision screening and partitioning of field temperature data," Agricultural Water Management, Elsevier, vol. 278(C).
    2. 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.
    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. Zhao, Xiancong & Bai, Hao & Shi, Qi & Lu, Xin & Zhang, Zhihui, 2017. "Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing," Applied Energy, Elsevier, vol. 195(C), pages 100-113.
    5. 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.
    6. 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).
    7. 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).
    8. Shiva Moslemi & Mohammad Hossein Zavvar Sabegh & Abolfazl Mirzazadeh & Yucel Ozturkoglu & Eric Maass, 2017. "A multi-objective model for multi-production and multi-echelon closed-loop pharmaceutical supply chain considering quality concepts: NSGAII approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1717-1733, November.
    9. Granata, Francesco, 2019. "Evapotranspiration evaluation models based on machine learning algorithms—A comparative study," Agricultural Water Management, Elsevier, vol. 217(C), pages 303-315.
    10. Amaresh Sarkar & Mrinmoy Majumder, 2019. "Real-time monitoring of water requirement in protected farms by using polynomial neural networks and image processing," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1451-1483, June.
    11. 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).
    12. Kostić, Srđan & Vasović, Nebojša & Todorović, Kristina & Franović, Igor, 2018. "Nonlinear dynamics behind the seismic cycle: One-dimensional phenomenological modeling," Chaos, Solitons & Fractals, Elsevier, vol. 106(C), pages 310-316.
    13. Lei Tian & Jiming Jin & Pute Wu & Guo-yue Niu, 2018. "Assessment of the Effects of Climate Change on Evapotranspiration with an Improved Elasticity Method in a Nonhumid Area," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    14. Peddinti, Srinivasa Rao & Kambhammettu, BVN P, 2019. "Dynamics of crop coefficients for citrus orchards of central India using water balance and eddy covariance flux partition techniques," Agricultural Water Management, Elsevier, vol. 212(C), pages 68-77.
    15. Abdelkader Zitouni & Abderrahmane Ouadi & Hamid Bentarzi & Mahfoud Chafai, 2019. "Distance relay reliability enhancement using false trip root cause analysis," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 475-483, August.

    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. Mohammed Seyam & Faridah Othman & Ahmed El-Shafie, 2017. "RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 187-204, January.
    2. 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.
    3. Mohammed Falah Allawi & Ahmed El-Shafie, 2016. "Utilizing RBF-NN and ANFIS Methods for Multi-Lead ahead Prediction Model of Evaporation from Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4773-4788, October.
    4. Mohammad Ghabaei Sough & Hamid Zare Abyaneh & Abolfazl Mosaedi, 2018. "Assessing a Multivariate Approach Based on Scalogram Analysis for Agricultural Drought Monitoring," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3423-3440, August.
    5. Zhiqiang Jiang & Zhengyang Tang & Yi Liu & Yuyun Chen & Zhongkai Feng & Yang Xu & Hairong Zhang, 2019. "Area Moment and Error Based Forecasting Difficulty and its Application in Inflow Forecasting Level Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4553-4568, October.
    6. Jian Yin & Chesheng Zhan & Wen Ye, 2016. "An Experimental Study on Evapotranspiration Data Assimilation Based on the Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5263-5279, November.
    7. Girón, I.F. & Corell, M. & Martín-Palomo, M.J. & Galindo, A. & Torrecillas, A. & Moreno, F. & Moriana, A., 2015. "Feasibility of trunk diameter fluctuations in the scheduling of regulated deficit irrigation for table olive trees without reference trees," Agricultural Water Management, Elsevier, vol. 161(C), pages 114-126.
    8. Qiu, Jing & Zhao, Junhua & Yang, Hongming & Wang, Dongxiao & Dong, Zhao Yang, 2018. "Planning of solar photovoltaics, battery energy storage system and gas micro turbine for coupled micro energy grids," Applied Energy, Elsevier, vol. 219(C), pages 361-369.
    9. Ahmed El-Shafie & Ali Najah & Humod Alsulami & Heerbod Jahanbani, 2014. "Optimized Neural Network Prediction Model for Potential Evapotranspiration Utilizing Ensemble Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 947-967, March.
    10. Desalegn Edossa & Mukand Babel, 2011. "Application of ANN-Based Streamflow Forecasting Model for Agricultural Water Management in the Awash River Basin, Ethiopia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1759-1773, April.
    11. Kichul Jung & Deg-Hyo Bae & Myoung-Jin Um & Siyeon Kim & Seol Jeon & Daeryong Park, 2020. "Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    12. Mohammad Valipour, 2014. "Use of average data of 181 synoptic stations for estimation of reference crop evapotranspiration by temperature-based methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4237-4255, September.
    13. Gebdang B. Ruben & Ke Zhang & Hongjun Bao & Xirong Ma, 2018. "Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 273-283, January.
    14. Zhao, Xiancong & Bai, Hao & Shi, Qi & Lu, Xin & Zhang, Zhihui, 2017. "Optimal scheduling of a byproduct gas system in a steel plant considering time-of-use electricity pricing," Applied Energy, Elsevier, vol. 195(C), pages 100-113.
    15. Andres Ticlavilca & Mac McKee, 2011. "Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 523-543, January.
    16. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    17. Amaresh Sarkar & Mrinmoy Majumder, 2019. "Real-time monitoring of water requirement in protected farms by using polynomial neural networks and image processing," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1451-1483, June.
    18. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    19. Mahmood Akbari & Peter Overloop & Abbas Afshar, 2011. "Clustered K Nearest Neighbor Algorithm for Daily Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(5), pages 1341-1357, March.
    20. 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).

    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:180:y:2017:i:pa:p:50-60. 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.