Assessing the Performance of WRF Model in Prediction of Evapotranspiration in Paddy Fields
Subject Areas : Farm water management with the aim of improving irrigation management indicatorsEbrahim Asadi Oskouei 1 , Mohammadreza Mohammadpour Penchah 2 , Leila Goodarzi 3 , Mojtaba Shokouhi 4
1 - Assistant professor in Atmospheric Science and Meteorology Research Center,Tehran, Iran.
2 - Research expert in Atmospheric Science and Meteorology Research Center,Tehran, Iran.
3 - Research expert in Atmospheric Science and Meteorology Research Center,Tehran, Iran.
4 - Research expert in Atmospheric Science and Meteorology Research Center,Tehran, Iran.
Keywords: Water requirements of paddy fields, lysimeter, WRF model, Prediction of evapotranspiration, FAO-Penman-Monteith,
Abstract :
Background and Aim: Evapotranspiration as one of the main components of the hydrological cycle, has a significant role in proper irrigation planning and water resources management. In this case, estimating evapotranspiration is limited due to a lack of data and a deficiency of meteorological stations. Therefore, today numerical models such as WRF are a powerful tool for generating and predicting meteorological quantities (wind speed, humidity, etc.) that are needed to estimate evapotranspiration. So far, no research has been conducted to investigate the effect of different schemes of the WRF model on the estimate of rice evapotranspiration. The purpose of this study is to evaluate the efficiency of the WRF model and obtain the result for estimating evaporation for rice plant in the central plain of Guilan.Method: Evapotranspiration rates vary from 2.7 to 8.5 mm per day. The average ET during three different periods of plant growth, including the initial, middle, and final periods, is estimated to be 4.63, 5.97, and 5.98 mm per day, respectively. The three configurations 1, 2, and 4 are mainly overestimated in predicting evapotranspiration of rice plants, and the computational values are estimated to be higher than the values measured by the lysimeter. The results show that the highest amount of RMSE occurred in configuration No. 4 at 8.47 and the lowest rate occurred in configuration No. 3 at 1.26. Summary of results shows that configuration No. 3 in all four criteria mentioned has performed better than other configurations to predict daily evapotranspiration of rice. The results showed that the non-local schema used in the model, simulates better than the local schemas for the daily evapotranspiration of the rice plant. Findings show that in the local YSU schema, the accuracy of predictions is significantly increased and is only 0.64 mm on average less than the estimated lysimetric data.Results: The results showed that using appropriate schemas in the surface layer and boundary layer of the WRF model, affects on accuracy of evapotranspiration predictions. The results of this study showed that, this model by using the YSU non-local boundary layer scheme can accurately predict the evapotranspiration rates of the rice plant for the next day and this is due to the higher ability of this schema in predicting the parameters affecting evapotranspiration (including temperature and wind). Therefore, the WRF model can be implemented by using GFS forecast data for the next few days and by applying the FAO-Penman-Monteith equations to the model outputs, the values of potential evapotranspiration for different regions of the country can be calculated. Since evapotranspiration is directly related to atmospheric thermodynamic processes, so using other different atmospheric physics schemas (not considered in this study) can produce different results.
Alkaeed, O., Flores, C., Jinno, K. and Tsutsumi, A. 2006. Comparison of several reference evapotranspiration methods for Itoshima Peninsula area, Fukuoka, Japan. Mem. Fac. Eng. Kyushu Univ. 66: 1–14.
Allen, R.G., Pereira, L.S., Raes, D. and Smith, M. 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300: D05109.
Cai, X., Wang, D. and Laurent, R. 2009. Impact of climate change on crop yield: A case study of rainfed corn in central Illinois. J. Appl. Meteorol. Climatol, 48: 1868–1881.
Carvalho, D., Rocha, A., Gómez-Gesteira, M. and Santos, C.S. 2014. Comparison of reanalyzed, analyzed, satellite-retrieved and NWP modelled winds with buoy data along the Iberian Peninsula coast. Remote Sens. Environ, 152: 480–492.
Chen, D., Gao, G., Xu, C.-Y., Guo, J. and Ren, G. 2005. Comparison of the Thornthwaite method and pan data with the standard Penman-Monteith estimates of reference evapotranspiration in China. Clim. Res. 28: 123–132.
Djaman, K., Balde, A.B., Sow, A., Muller, B., Irmak, S., N’Diaye, M.K., Manneh, B., Moukoumbi, Y.D., Futakuchi, K. and Saito, K. 2015. Evaluation of sixteen reference evapotranspiration methods under sahelian conditions in the Senegal River Valley. J. Hydrol. Reg. Stud. 3: 139–159.
Falk, M., Pyles, R.D., Ustin, S.L., Paw U, K.T., Xu, L., Whiting, M.L., Sanden, B.L. and Brown, P.H. 2014. Evaluated crop evapotranspiration over a region of irrigated orchards with the improved ACASA–WRF model. J. Hydrometeorol, 15: 744–758.
Gharahdaghi, M. H., Homaee, M., Mirlatifi, M., & Noroozi, A. (2020). Using Forecasts of WRF Regional Model to Improve the Accuracy of Reference Evapotranspiration Estimation. Iranian Journal of Soil and Water Research, 51(1), 165–177. https://doi.org/10.22059/IJSWR.2019.285920.668274. [in Persian]
Gholami Sefid Kouhi, M., & Mirlatifi, S., & Mohammadi, K., & Ali Mohammadi, A. (2010). Estimating Crop Coefficient and Actual Evapotranspiration of Wheat by Remote Sensing: A Case Study, Gorgan Rood Command Area, Golestan, Iran. Iranian Journal of Irrigation and Drainage,4(2),222-231. https://www.sid.ir/en/journal/ViewPaper.aspx?id= 87570. [in Persian]
Hamon, W.R. 1963. Estimating potential evapotranspiration. Trans. Am. Soc. Civ. Eng, 128: 324–338.
Ishak, A.M., Bray, M., Remesan, R. and Han, D. 2010. Estimating reference evapotranspiration using numerical weather modelling. Hydrol. Process, 24: 3490–3509.
Jiménez-Esteve, B., Udina, M., Soler, M.R., Pepin, N. and Miró, J.R. 2018. Land use and topography influence in a complex terrain area: A high resolution mesoscale modelling study over the Eastern Pyrenees using the WRF model. Atmos. Res, 202: 49–62.
Kamasi, F., Ali Akbari Beidakhti, A. And steadfast, s. (2016). Evaluation of different boundary layer schemas in WRF model (Tehran case study). 17th Iranian Geophysical Conference [Conference presentation]. [in Persian]
Kar, S.K., Nema, A.K., Singh, A., Sinha, B.L. and Mishra, C.D. 2016. Comparative study of reference evapotranspiration estimation methods including Artificial Neural Network for dry sub-humid agro-ecological region. J. Soil Water Conserv, 15: 233–241.
Kwak, J., Kim, S., Kim, G., Singh, V.P., Hong, S. and Kim, H.S. 2015. Scrub typhus incidence modeling with meteorological factors in South Korea. Int. J. Environ. Res. Public Health, 12: 7254–7273.
Lang, D., Zheng, J., Shi, J., Liao, F., Ma, X., Wang, W., Chen, X. and Zhang, M. 2017. A comparative study of potential evapotranspiration estimation by eight methods with FAO Penman–Monteith method in southwestern China. Water 9: 734-744.
Lin, P., Rajib, M.A., Yang, Z., Somos‐Valenzuela, M., Merwade, V., Maidment, D.R., Wang, Y. and Chen, L. 2018. Spatiotemporal evaluation of simulated evapotranspiration and streamflow over Texas using the WRF‐Hydro‐RAPID modeling framework. JAWRA J. Am. Water Resour. Assoc, 54: 40–54.
López-Díaz, F., Conde, C. and Sánchez, O. 2013. Analysis of indices of extreme temperature events at Apizaco, Tlaxcala, Mexico, Atmósfera, 26: 349–358.
Mall, R.K. and Gupta, B.R.D. 2002. Comparison of evapotranspiration models. Mausam, 53: 119–126.
McCabe, G.J., Hay, L.E., Bock, A., Markstrom, S.L. and Atkinson, R.D. 2015. Inter-annual and spatial variability of Hamon potential evapotranspiration model coefficients. J. Hydrol, 521: 389–394.
Nag, A., Adamala, S., Raghuwanshi, N.S., Singh, R. and Bandyopadhyay, A. 2014. Estimation and ranking of reference evapotranspiration for different spatial scales in India. J. Indian Water Resour. Soc, 34, 35.
Noble, E., Druyan, L.M. and Fulakeza, M. 2017. The sensitivity of WRF daily summertime simulations over West Africa to alternative parameterizations. Part II: precipitation. Mon. Weather Rev, 145: 215–233.
Oskouei Asadi, E. (2017). Partitioning of transpiration and evaporation in different irrigation management of rice in Guilan province [Doctoral dissertation, Ferdowsi University of Mashhad]. [in Persian]
Pandey, P.K., Dabral, P.P. and Pandey, V. 2016. Evaluation of reference evapotranspiration methods for the northeastern region of India. Int. Soil Water Conserv. Res, 4: 52–63.
Penchah, M.M., Malakooti and H., Satkin, M. 2017. Evaluation of planetary boundary layer simulations for wind resource study in east of Iran. Renew. Energy, 111:1-10.
Ries, H. and Schlünzen, K.H. 2009. Evaluation of a mesoscale model with different surface parameterizations and vertical resolutions for the Bay of Valencia. Mon. Weather Rev, 137: 2646–2661.
Rosegrant, M.W., Ringler, C., McKinney, D.C., Cai, X., Keller, A. and Donoso, G. 2000. Integrated economic‐hydrologic water modeling at the basin scale: The Maipo River basin. Agric. Econ, 24: 33–46.
Scripca, A.-S., Strapazan, C. and Holobâca, I.H. 2016. Regional Aspects Of The Variability Of Atmospheric Precipitations In W inter And Summer Seasons In Europe During 2001-2090. Aerul si Apa. Compon. ale Mediu, 143-153.
Silva, D., Meza, F.J. and Varas, E. 2010. Estimating reference evapotranspiration (ETo) using numerical weather forecast data in central Chile. J. Hydrol, 382: 64–71.
Song, X., Lu, F., Xiao, W., Zhu, K., Zhou, Y. and Xie, Z., 2019. Performance of 12 reference evapotranspiration estimation methods compared with the Penman–Monteith method and the potential influences in northeast China. Meteorol. Appl, Climatol, 26: 83–96.
Srivastava, P.K., Han, D., Islam, T., Petropoulos, G.P., Gupta, M. and Dai, Q. 2016. Seasonal evaluation of evapotranspiration fluxes from MODIS satellite and mesoscale model downscaled global reanalysis datasets. Theor. Appl. Climatol, 124: 461–473.
Srivastava, P.K., Han, D., Rico Ramirez, M.A. and Islam, T. 2013. Comparative assessment of evapotranspiration derived from NCEP and ECMWF global datasets through Weather Research and Forecasting model. Atmos. Sci. Lett, 14: 118–125.
Thornthwaite, C.W. 1948. Una aproximación para una clasificación racional del clima. Geogr. Rev, 38: 85–94.
Xie, B., Fung, J.C.H., Chan, A. and Lau, A. 2012. Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model. J. Geophys. Res. Atmos, 117-127.
Zittis, G., Hadjinicolaou, P. and Lelieveld, J. 2014. Comparison of WRF model physics parameterizations over the MENA-CORDEX domain. Am. J. Clim. Chang. 3: 490-500.
_||_