Estimating the Hourly Reference Evapotranspiration with Fuzzy Inference Systems and Limited Weather Data

Document Type: Research Paper

Authors

1 Assistant Professor of Irrigation and Drainage, Water Engineering Department, Jiroft University, Iran

2 Ph.D Student of Irrigation and Drainage, Water Engineering Department, Ferdowsi University of Mashhad, Iran

3 Assistant Professor of Irrigation and Drainage, Water Engineering Department, Ferdowsi University of Mashhad, Iran

Abstract

Evapotranspiration is the most important part of the hydrological cycle, which plays
a key role in water resource management, crop yield simulation, and irrigation
scheduling. Therefore, developing a cost-effective and precise model is essential for
estimating hourly grass crop reference evapotranspiration (ETo). In this study the
potential of the fuzzy inference system (FIS) is investigated as a simple technique for
modeling hourly ETo obtained using the FAO-56 Penman-Monteith and ASCE
equations. Then, combinations of efficient hourly climatic data namely temperature,
wind speed, relative humidity and solar radiation were used as inputs to the fuzzy
model. Four fuzzy models were developed based on different combinations of inputs.
Common statistics such as Mean square error, average absolute relative error and
determination coefficient and two more statistics of Jacovides (t) and R2/t are used as
comparison criteria for evaluation of the model performance. Here, Training and
testing fuzzy models were done with Fariman meteorological data – an arid region in
the northeast of Iran. The fuzzy model whose inputs are solar radiation, air
temperature, relative humidity and wind speed, yield the highest correlation and
compatibility to reference models of FAO-56 PM and ASCE, based on common
statistics. Whereas, the fuzzy model whose inputs are solar radiation, air temperature
and relative humidity, are selected as the best model based on combination of
common and additional statistics. The fuzzy model with two inputs namely solar
radiation and relative humidity has acceptable results, too. The results show that solar
radiation is the most effective parameter on hourly reference evapotranspiration and
temperature, relative humidity and wind speed were other effective parameters,
respectively. These results for training and testing phase are alike. It was found that
the developed fuzzy models could be successfully employed in estimating the hourly
ETo with a limited weather data.

Keywords