Abstract
The process of evapotranspiration (ET) is a vital part of the water cycle. Exact estimation of the value of ET is necessary for designing irrigation systems and water resources management. Accurate estimation of ET is essential in agriculture, its over-estimation leads to cause the waste of valuable water resources and its underestimation leads to the plant moisture stress and decrease in the crop yield. The well known Penman-Monteith (PM) equation always performs the highest accuracy results of estimating reference Evapotranspiration (ET0) among the existing methods is without any discussion. However, the equation requires climatic data that are not always available particularly for a developing country. ET0 is a complex process which is depending on a number of interacting meteorological factors, such as temperature, humidity, wind speed, and radiation. The lack of physical understanding of ET0 process and unavailability of all appropriate data results in imprecise estimation of ET0. Over the past two decades, artificial neural networks (ANNs) have been increasingly applied in modeling of hydrological processes because of their ability in mapping the input–output relationship without any understanding of physical process. This paper investigates for the first time in the semiarid environment of Junagadh, the potential of an artificial neural network (ANN) for estimating ET0 with limited climatic data set.
Highlights
In semi arid regions, water resources management is a crucial requirement for increasing agricultural production because food insecurity is becoming a main concern
ET is one of the hydrologic cycle components and the precise estimation of ET is very important for the researches such as water balance, irrigation design and management, crop yield modelling, and water resources planning and management reported by Kumar et al.5 (2002)
The goal of this study is to develop the artificial neural networks (ANNs) based models which perform close to FAO 56 PM estimates and required less meteorological data because in un-gauged basins the meteorological information is generally unavailable
Summary
Water resources management is a crucial requirement for increasing agricultural production because food insecurity is becoming a main concern. Large number of climatic parameters that are not always available for many locations Several models such as Hargreaves and Blaney-Criddle and other models have been proposed to predict ET0, but Traore et al. (2008) reported that, these models do not have universal consensus for different climatic conditions. Over the past two decades, artificial neural networks (ANNs) have been used more and more in modeling of hydrological processes because of it has ability in mapping the input–output relationship without any understanding of physical process. ANNs are capable of modeling complex nonlinear processes effectively extracting the relation between the inputs and outputs of a process without the physics being explicitly provided to them and they identify the underlying rule even if the data is noisy and contaminated with errors, suggested by ASCE3 (2000a) and ASCE4 (2000b)
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