Evaluation of FAO WaPOR product and PYSEBAL algorithm in estimating The amount of water consumed

Document Type : Research Paper

Authors

1 Department of Water Engineering, Faculty of Agricultural and Natural Resources, Imam Khomeini International University, Qazvin, Iran

2 Water Eng. and Science Dept., Imam Khomeini International University

Abstract

Evapotranspiration is an essential component of the hydrological cycle and a key element of water resources management, especially in arid and semi-arid regions. Today, remote sensing technology has made it possible to estimate evapotranspiration on a large scale, so using this technology can be considered as a promising method to reduce the spatial variation of evapotranspiration. In this study, the daily, monthly and seasonal changes of actual evapotranspiration were investigated using the global model of WaPOR product and PYSEBAL algorithm in Qazvin plain during the period from 2015 to 2021. The results obtained in this study were compared with Hargreaves and Samani evapotranspiration data, which is an accurate empirical method in the region. The mean deviation of the estimated and observed data in the daily scale in both models shows that the PYSEBAL algorithm has a more negligible difference and, in most cases, is close to the data obtained from the empirical method. While in WaPOR product, it was associated with underestimation compared to the empirical method during the almost entire period. WaPOR product was able to show the amount of seasonal evapotranspiration changes to a good extent, but it did not have good accuracy. This product had acceptable accuracy in spring and summer, but in autumn and winter, its accuracy decreases. The results showed that the WaPOR product can provide water needs for each time period, so it can play an important role in managing water resources, determining the required water consumption. PYSEBAL algorithm presented more accurate results than WaPOR product, so that the correlation coefficient values in PYSEBAL algorithm and WaPOR model are equal to 1.51 and 2.86 mm / day and the correlation coefficient values were 0.87 and 0.64, respectively. Therefore, if the studies on estimation of the amount of evapotranspiration for large areas (such as basins) and long periods are considered, the use of WaPOR products due to the lack of missing data can play a suitable role in managing water resources and water needs for the region or basin.

Keywords

References

Ayenew, T. (2003). Evapotranspiration estimation using thematic mapper spectral satellite data in the Ethiopian rift and adjacent highlands. Journal of Hydrology, 279(1–4), 83–93.
Allen, R.G. Pereira, LS. Raes, D.Smith M. (1998). Crop evapotranspiration. Guidelines for computing crop water requirements. FAO irrigation and drainage. Paper no. 56. FAO, Rome
Bastiaanssen, W. G. M., Cheema, M. J. M., Immerzeel, W. W., Miltenburg, I. J., & Pelgrum, H. (2012). Surface energy balance and actual evapotranspiration of the transboundary Indus Basin estimated from satellite measurements and the ETLook model. Water Resources Research, 48(11).
Blatchford, M. L., Mannaerts, C. M., Njuki, S. M., Nouri, H., Zeng, Y., Pelgrum, H., Wonink, S., & Karimi, P. (2020). Evaluation of WaPOR V2 evapotranspiration products across Africa. Hydrological Processes, 34(15), 3200–3221.
Caiserman, A., Amiraslani, F., & Dumas, D. (2021). Assessment of the agricultural water budget in southern Iran using Sentinel-2 to Landsat-8 datasets. Journal of Arid Environments, 188, 104461.
Fakhar, M. S., & Kaviani, A. (2021). Comparison of the concepts of Potential evapotranspiration (ETp) and reference evapotranspiration (ETo) using lysimetric data in qazvin province. Inviroment and Water Ingineering, 7(1), 1–16. https://doi.org/10.22034/jewe.2021.279059.1535 (In Persian).
Fakhar, M. S., & Kaviani, A. (2022). Estimation of actual evapotranspiration using automatic calibration in PYSEBAL and METRIC algorithms in Qazvin plain. Iranian Journal of Soil and Water Research.(In Persian).
Fakhar, M. S., & Kaviani, A. (2022). Estimation of Actual Evapotranspiration Based on the Latest Modified Version of the Surface Energy Balance Algorithm Using Lysimeter Data. Iranian Irrigation and Drainage. (In Persian).
Geshnigani, F. S., Mirabbasi, R., & Golabi, M. R. (2021). Evaluation of FAO’s WaPOR product in estimating the reference evapotranspiration for stream flow modeling. Theoretical and Applied Climatology, 144(1), 191–201.
Gowda, P. H., Chavez, J. L., Colaizzi, P. D., Evett, S. R., Howell, T. A., & Tolk, J. A. (2008). ET mapping for agricultural water management: present status and challenges. Irrigation Science, 26(3), 223–237.
Hargreaves, G. H., & Samani, Z. A. (1985). Estimating potential evapotranspiration. Journal of the Irrigation and Drainage Division, 108(3), 225–230.
Hessels, T., van Opstal, J., Trambauer, P., Bastiaanssen, W., Faouzi, M., Mohamed, Y., & ErRaji, A. (2017). pySEBAL Version 3.3. 7.
Hu, Y., Modat, M., Gibson, E., Li, W., Ghavami, N., Bonmati, E., Wang, G., Bandula, S., Moore, C. M., & Emberton, M. (2018). Weakly-supervised convolutional neural networks for multimodal image registration. Medical Image Analysis, 49, 1–13.
Kazamias, A. P., & Sapountzis, M. (2017). Spatial and temporal assessment of potential soil erosion over Greece. Water, 59, 315–321.
Kisekka, I., Peddinti, S. R., Kustas, W. P., McElrone, A. J., Bambach-Ortiz, N., McKee, L., & Bastiaanssen, W. (2022). Spatial–temporal modeling of root zone soil moisture dynamics in a vineyard using machine learning and remote sensing. Irrigation Science, 1–17.
Kustas, W. P., & Norman, J. M. (1996). Use of remote sensing for evapotranspiration monitoring over land surfaces. Hydrological Sciences Journal, 41(4), 495–516.
Lee, T. S., Najim, M. M. M., & Aminul, M. H. (2004). Estimating evapotranspiration of irrigated rice at the West Coast of the Peninsular of Malaysia.
Lettenmaier, D. P., & Famiglietti, J. S. (2006). Water from on high. Nature, 444(7119), 562–563.
Mancosu, N., Snyder, R. L., Kyriakakis, G., & Spano, D. (2015). Water scarcity and future challenges for food production. Water, 7(3), 975–992.
Majozi, N. P., Mannaerts, C. M., Ramoelo, A., Mathieu, R., Mudau, A. E., & Verhoef, W. (2017). An intercomparison of satellite-based daily evapotranspiration estimates under different eco-climatic regions in South Africa. Remote Sensing, 9(4), 307.
McCabe, M. F., Ershadi, A., Jimenez, C., Miralles, D. G., Michel, D., & Wood, E. F. (2016). The GEWEX LandFlux project: Evaluation of model evaporation using tower-based and globally gridded forcing data. Geoscientific Model Development, 9(1), 283–305.
Paul, G., Gowda, P. H., Prasad, P. V. V., Howell, T. A., Staggenborg, S. A., & Neale, C. M. U. (2013). Lysimetric evaluation of SEBAL using high resolution airborne imagery from BEAREX08. Advances in Water Resources, 59, 157–168.
Rahimpour, M., Karimi, N., Rouzbahani, R., & Eftekhari, M. (2018). Validation and calibration of FAO WaPOR product (actual evapotranspiration) in Iran using in-situ measurements. Iran-Water Resources Research, 14(2), 254–263.(In Persian)
Sánchez, J. M., Kustas, W. P., Caselles, V., & Anderson, M. C. (2008). Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations. Remote Sensing of Environment, 112(3), 1130–1143.
Senay, G. B., Friedrichs, M., Singh, R. K., & Velpuri, N. M. (2016). Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin. Remote Sensing of Environment, 185, 171–185.
Tang, R., Li, Z.-L., & Tang, B. (2010). An application of the Ts–VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implementation and validation. Remote Sensing of Environment, 114(3), 540–551.
Xue, J., Fulton, A., & Kisekka, I. (2021). Evaluating the role of remote sensing-based energy balance models in improving site-specific irrigation management for young walnut orchards. Agricultural Water Management, 256, 107132.
 
Iranian Journal of Soil and Water Research
Volume 53, Issue 6
August 2022
Pages 1359-1372

Files

Share

How to cite

Statistics