Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Estimation of Future Reference Crop Evapotranspiration using Artificial Neural Networks

인공신경망 기법을 이용한 장래 잠재증발산량 산정

  • 이은정 (서울대학교 농업생명과학연구원) ;
  • 강문성 (서울대학교 조경.지역시스템공학부, 농업생명과학연구원) ;
  • 박정안 (서울대학교 생태조경.지역시스템공학부) ;
  • 최진영 (서울대학교 생태조경.지역시스템공학부) ;
  • 박승우 (서울대학교 조경.지역시스템공학부, 농업생명과학연구원)
  • Received : 2010.04.13
  • Accepted : 2010.07.21
  • Published : 2010.09.30
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Abstract

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), 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 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

Keywords

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