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Estimation of Future Reference Evapotranspiration using Artificial Neural Network and Climate Change Scenario

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010  1009967.(doi:10.13031/2013.30023)
Authors:   Eun Jeong Lee, Moon Seong Kang, Seung Woo Park, Hak Kwan Kim
Keywords:   Artificial Neural Networks, climate change, evapotranspiration, FAO Penman-Monteith, LARS-WG

Evapotranspiration (ET) is the basis for estimating crop evapotranspiration and planning crop agricultural water requirements. In this study, artificial neural network (ANN) models for reference evapotranspiration (ET0) estimation were developed on a monthly basis (May~October). The models were trained and validated for Suwon, Korea. Four climate factors, daily maximum temperature (Tmax), minimum temperature (Tmin), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.72 mm/yr in baseline period (1973-2008), 558.08 mm/yr in 2011-2030, 593.03 mm/yr in 2046-2065, and 641.07 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

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