Abstract
Evaporation from surface water plays a crucial role in water accounting of basins, water resource management, and irrigation systems management. As such, the simulation of evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. The best overall relationship obtained in the first method was used as the basic relationship in the second method, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation, were drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.
Highlights
Evaporation is one of the main components of hydrology, and accurate estimation of evaporation plays an essential role in estimating the water balance of basins, designing and managing irrigation systems, and managing water resources [1,2,3,4,5].There are two types of methods, direct and indirect, for estimating evaporation [6,7]
Pan evaporation is used for determining crop water requirements, irrigation scheduling, rainfall–runoff modeling, and computation of water balance components [5]
Multilayer perceptron neural network (MLP-NN) has become one of the most useful artificial intelligence tools in simulating pan evaporation, and its ability for simulation of pan evaporation has been verified in many studies e.g., Alsumaiei [29]; Pandey et al [28]; Ashrafzadeh et al [34]; Patle et al [35], as listed in Table S3 in Supplementary Material (SM)
Summary
Evaporation is one of the main components of hydrology, and accurate estimation of evaporation plays an essential role in estimating the water balance of basins, designing and managing irrigation systems, and managing water resources [1,2,3,4,5].There are two types of methods, direct and indirect, for estimating evaporation [6,7]. Various studies have sought to identify linear experimental relationships [11,12,13] and non-linear experimental relationships [14,15,16,17,18,19,20] as indirect methods to simulate evaporation from free water surfaces. Multilayer perceptron neural network (MLP-NN) has become one of the most useful artificial intelligence tools in simulating pan evaporation, and its ability for simulation of pan evaporation has been verified in many studies e.g., Alsumaiei [29]; Pandey et al [28]; Ashrafzadeh et al [34]; Patle et al [35], as listed in Table S3 in SM
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