Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Two Comprehensive and Practical Methods for Simulating Pan Evaporation Under Different Climatic Conditions in Iran

Version 1 : Received: 29 August 2021 / Approved: 31 August 2021 / Online: 31 August 2021 (11:20:33 CEST)

How to cite: Dehghanipour, M.H.; Karami, H.; Ghazvinian, H.; Kalantari, Z.; Dehghanipour, A.H. Two Comprehensive and Practical Methods for Simulating Pan Evaporation Under Different Climatic Conditions in Iran. Preprints 2021, 2021080563 (doi: 10.20944/preprints202108.0563.v1). Dehghanipour, M.H.; Karami, H.; Ghazvinian, H.; Kalantari, Z.; Dehghanipour, A.H. Two Comprehensive and Practical Methods for Simulating Pan Evaporation Under Different Climatic Conditions in Iran. Preprints 2021, 2021080563 (doi: 10.20944/preprints202108.0563.v1).

Abstract

Evaporation from surface water plays a key role in water accounting of basins, water resources management, and irrigation systems management, so simulating 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. In the second method, the best overall relationship obtained in the first method was used as the basic relationship, 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 was 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.

Keywords

Iran; Pan Evaporation; Genetic Algorithm; MLP Neural Network; Experimental Relationship

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