IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v174y2016icp39-51.html
   My bibliography  Save this article

Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT—Application in data-scarce rural Tunisia

Author

Listed:
  • Aouissi, Jalel
  • Benabdallah, Sihem
  • Lili Chabaâne, Zohra
  • Cudennec, Christophe

Abstract

Potential evapotranspiration (PET) is an important factor used in hydrological models as well as in management of irrigation projects and water-balance estimations. At the catchment level, hydrological models first calculate PET and then actual evapotranspiration (ET) by considering soil moisture and land use. In this study, we used the SWAT model to estimate PET, actual ET and streamflow. SWAT provides three methods for computing PET: (i) Penman-Monteith (PM), (ii) Hargreaves (HA) and (iii) Priestly-Taylor (PT). Due to missing weather parameters for the PM method, a statistical weather generator embedded in SWAT, WXGEN was used in several studies to generate missing weather data and to fill in gaps in measured records. The goals of this work were to evaluate the PM method’s accuracy in calculating PET using generated and measured meteorological data and further to compare the three embedded methods in SWAT to predict PET. The model was applied to the Joumine basin, covering an area of 418km2, located in northern Tunisia. For each run, simulated streamflow was compared with measured data by calculating Nash-Sutcliffe efficiency, root mean square error and coefficient of determination. The PM method predicted PET well with generated data. The method used to calculate PET did not considerably affect stream flow predictions; however, significant differences were found among them. Model predictions of streamflow were close to observed values, with a Nash-Sutcliffe efficiency of 0.90 and R2 value of 0.92after monthly calibration using HA method. During the validation period, SWAT predictions were nearly as accurate, with Nash-Sutcliffe efficiency and R2 values of 0.89 and 0.92, respectively.

Suggested Citation

  • Aouissi, Jalel & Benabdallah, Sihem & Lili Chabaâne, Zohra & Cudennec, Christophe, 2016. "Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT—Application in data-scarce rural Tunisia," Agricultural Water Management, Elsevier, vol. 174(C), pages 39-51.
  • Handle: RePEc:eee:agiwat:v:174:y:2016:i:c:p:39-51
    DOI: 10.1016/j.agwat.2016.03.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377416300804
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.agwat.2016.03.004?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. 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.
    2. Sentelhas, Paulo C. & Gillespie, Terry J. & Santos, Eduardo A., 2010. "Evaluation of FAO Penman-Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada," Agricultural Water Management, Elsevier, vol. 97(5), pages 635-644, May.
    3. 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.
    4. Harmsen, Eric W. & Miller, Norman L. & Schlegel, Nicole J. & Gonzalez, J.E., 2009. "Seasonal climate change impacts on evapotranspiration, precipitation deficit and crop yield in Puerto Rico," Agricultural Water Management, Elsevier, vol. 96(7), pages 1085-1095, July.
    5. Er-Raki, S. & Chehbouni, A. & Guemouria, N. & Duchemin, B. & Ezzahar, J. & Hadria, R., 2007. "Combining FAO-56 model and ground-based remote sensing to estimate water consumptions of wheat crops in a semi-arid region," Agricultural Water Management, Elsevier, vol. 87(1), pages 41-54, January.
    6. Schuol, J. & Abbaspour, K.C., 2007. "Using monthly weather statistics to generate daily data in a SWAT model application to West Africa," Ecological Modelling, Elsevier, vol. 201(3), pages 301-311.
    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. Tadesse, Haile K. & Moriasi, Daniel N. & Gowda, Prasanna H. & Marek, Gary & Steiner, Jean L. & Brauer, David & Talebizadeh, Mansour & Nelson, Amanda & Starks, Patrick, 2018. "Evaluating evapotranspiration estimation methods in APEX model for dryland cropping systems in a semi-arid region," Agricultural Water Management, Elsevier, vol. 206(C), pages 217-228.
    2. Tewekel Melese Gemechu & Hongling Zhao & Shanshan Bao & Cidan Yangzong & Yingying Liu & Fengping Li & Hongyan Li, 2021. "Estimation of Hydrological Components under Current and Future Climate Scenarios in Guder Catchment, Upper Abbay Basin, Ethiopia, Using the SWAT," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
    3. Sangchul Lee & Junyu Qi & Hyunglok Kim & Gregory W. McCarty & Glenn E. Moglen & Martha Anderson & Xuesong Zhang & Ling Du, 2021. "Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    4. 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).

    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. 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.
    2. 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).
    3. Berti, Antonio & Tardivo, Gianmarco & Chiaudani, Alessandro & Rech, Francesco & Borin, Maurizio, 2014. "Assessing reference evapotranspiration by the Hargreaves method in north-eastern Italy," Agricultural Water Management, Elsevier, vol. 140(C), pages 20-25.
    4. Valle Júnior, Luiz C.G. & Ventura, Thiago M. & Gomes, Raphael S.R. & de S. Nogueira, José & de A. Lobo, Francisco & Vourlitis, George L. & Rodrigues, Thiago R., 2020. "Comparative assessment of modelled and empirical reference evapotranspiration methods for a brazilian savanna," Agricultural Water Management, Elsevier, vol. 232(C).
    5. 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.
    6. 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).
    7. de Oliveira, Renan G. & Valle Júnior, Luiz Claudio G. & da Silva, Jonh Billy & Espíndola, Duani A.L.F. & Lopes, Rute D. & Nogueira, José S. & Curado, Leone F.A. & Rodrigues, Thiago R., 2021. "Temporal trend changes in reference evapotranspiration contrasting different land uses in southern Amazon basin," Agricultural Water Management, Elsevier, vol. 250(C).
    8. Laishram Kanta Singh & Madan K. Jha & Mohita Pandey, 2018. "Framework for Standardizing Less Data-Intensive Methods of Reference Evapotranspiration Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4159-4175, October.
    9. Paweł Bogawski & Ewa Bednorz, 2014. "Comparison and Validation of Selected Evapotranspiration Models for Conditions in Poland (Central Europe)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(14), pages 5021-5038, November.
    10. 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.
    11. 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.
    12. Vásquez, Cristina & Célleri, Rolando & Córdova, Mario & Carrillo-Rojas, Galo, 2022. "Improving reference evapotranspiration (ETo) calculation under limited data conditions in the high Tropical Andes," Agricultural Water Management, Elsevier, vol. 262(C).
    13. Gonçalves, Ivo Zution & Mekonnen, Mesfin M. & Neale, Christopher M.U. & Campos, Isidro & Neale, Michael R., 2020. "Temporal and spatial variations of irrigation water use for commercial corn fields in Central Nebraska," Agricultural Water Management, Elsevier, vol. 228(C).
    14. El-Naggar, A.G. & Hedley, C.B. & Horne, D. & Roudier, P. & Clothier, B.E., 2020. "Soil sensing technology improves application of irrigation water," Agricultural Water Management, Elsevier, vol. 228(C).
    15. 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).
    16. Zhang, Xiying & Chen, Suying & Sun, Hongyong & Shao, Liwei & Wang, Yanzhe, 2011. "Changes in evapotranspiration over irrigated winter wheat and maize in North China Plain over three decades," Agricultural Water Management, Elsevier, vol. 98(6), pages 1097-1104, April.
    17. Gonçalo C. Rodrigues & Ricardo P. Braga, 2021. "Estimation of Reference Evapotranspiration during the Irrigation Season Using Nine Temperature-Based Methods in a Hot-Summer Mediterranean Climate," Agriculture, MDPI, vol. 11(2), pages 1-13, February.
    18. Ying Guo & Rui Wang & Zhijun Tong & Xingpeng Liu & Jiquan Zhang, 2019. "Dynamic Evaluation and Regionalization of Maize Drought Vulnerability in the Midwest of Jilin Province," Sustainability, MDPI, vol. 11(15), pages 1-21, August.
    19. Choudhury, B.U. & Singh, Anil Kumar & Pradhan, S., 2013. "Estimation of crop coefficients of dry-seeded irrigated rice–wheat rotation on raised beds by field water balance method in the Indo-Gangetic plains, India," Agricultural Water Management, Elsevier, vol. 123(C), pages 20-31.
    20. Elfarkh, Jamal & Simonneaux, Vincent & Jarlan, Lionel & Ezzahar, Jamal & Boulet, Gilles & Chakir, Adnane & Er-Raki, Salah, 2022. "Evapotranspiration estimates in a traditional irrigated area in semi-arid Mediterranean. Comparison of four remote sensing-based models," Agricultural Water Management, Elsevier, vol. 270(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:174:y:2016:i:c:p:39-51. 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.