Coastal cities-wide estimation of daily class A pan evaporation from few hydrometeorological variables

ABSTRACT This study investigates the accuracy of the experimental models in two standard and optimized cases using data from meteorological stations located on the northern and southern coasts of Iran. The results show that the spatial distribution / different hydrometeorological conditions are quite effective in the accuracy of the models. However, KNF and Papadakis lead to the most accurate estimation among the standard models. The optimization significantly increases all models' accuracy except Papadakis, indicating its remarkable robustness in coastal cities. Comparison between all models demonstrates that the Linacre-1994 optimal model has the best accuracy. Findings reveal that wind speed is the second variable affecting pan evaporation in coastal cities after the vapor pressure deficit. In addition, the form of wind speed inclusion in the Trabert model significantly affects its inaccuracy. Elevation and latitude variables do not affect estimating pan evaporation in coastal cities considering optimization findings.

[1]  Mohammed Abdallah,et al.  Reference evapotranspiration estimation in hyper-arid regions via D-vine copula based-quantile regression and comparison with empirical approaches and machine learning models , 2022, Journal of Hydrology: Regional Studies.

[2]  H. Citakoglu,et al.  Modeling monthly reference evapotranspiration process in Turkey: application of machine learning methods , 2022, Environmental Monitoring and Assessment.

[3]  Pouya Aghelpour,et al.  Evaluating Three Supervised Machine Learning Algorithms (LM, BR, and SCG) for Daily Pan Evaporation Estimation in a Semi-Arid Region , 2022, Water.

[4]  E. Uncuoğlu,et al.  Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems , 2022, Appl. Soft Comput..

[5]  Vahdettin Demir,et al.  Developing numerical equality to regional intensity–duration–frequency curves using evolutionary algorithms and multi-gene genetic programming , 2022, Acta Geophysica.

[6]  M. Salarijazi,et al.  Estimation of flood hydrographs in the ungauged mountainous watershed with Gray synthetic unit hydrograph model , 2022, Arabian Journal of Geosciences.

[7]  K. Ghorbani,et al.  Estimation of Non-Stationary Behavior in Annual and Seasonal Surface Freshwater Volume Discharged into the Gorgan Bay, Iran , 2022, Natural Resources Research.

[8]  M. Salarijazi,et al.  Improving the estimation of sedimentation in multi-purpose dam reservoirs, considering hydrography and time scale classification of sediment rating curve (case study: Dez Dam) , 2022, Arabian Journal of Geosciences.

[9]  S. Allen,et al.  Spatial Variations and Long-Term Trends (1901–2013) of Rainfall Across Uttarakhand Himalaya, India , 2021, Mountain Landscapes in Transition.

[10]  I. Ahmadianfar,et al.  Robust Diversity-based Sine-Cosine Algorithm for Optimizing Hydropower Multi-reservoir Systems , 2021, Water Resources Management.

[11]  K. Ghorbani,et al.  Aquifer-wide estimation of longitudinal dispersivity by the combination of empirical equations, inverse solution, and aquifer zoning methods , 2021, Applied Water Science.

[12]  Parnian Hashempour Bakhtiari,et al.  Risk-based Stochastic Optimization of Evaporation Ponds as a Cost-Effective and Environmentally-Friendly Solution for the Disposal of Oil-Produced Water , 2020 .

[13]  Junhua Li,et al.  Spatial variations and long-term trends of potential evaporation in Canada , 2020, Scientific Reports.

[14]  V. P. Singh,et al.  An ANN model for estimation of potential evaporation , 2020 .

[15]  H. Loáiciga,et al.  Comparison of methods for estimating loss from water storage by evaporation and impacts on reservoir management , 2020, Water and Environment Journal.

[16]  Khaled S. Balkhair,et al.  A novel approach for predicting daily pan evaporation in the coastal regions of Iran using support vector regression coupled with krill herd algorithm model , 2020, Theoretical and Applied Climatology.

[17]  D. Jhajharia,et al.  Monthly pan evaporation modelling using multiple linear regression and artificial neural network techniques , 2020, Water Supply.

[18]  Babita Majhi,et al.  Pan evaporation modeling in different agroclimatic zones using functional link artificial neural network , 2020 .

[19]  Demetrius David da Silva,et al.  Impacts of climate change on the evaporation and availability of water in small reservoirs in the Brazilian savannah , 2020, Climatic Change.

[20]  H. Haider,et al.  Identification of Parameters of Evaporation Equations Using an Optimization Technique Based on Pan Evaporation , 2020 .

[21]  K. Ghorbani,et al.  Spatial estimation of aquifer’s hydraulic parameters by a combination of borehole data and inverse solution , 2019, Bulletin of Engineering Geology and the Environment.

[22]  Y. Sheng,et al.  Impact of amplified evaporation due to lake expansion on the water budget across the inner Tibetan Plateau , 2019, International Journal of Climatology.

[23]  O. Kisi,et al.  Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs , 2019, Arabian Journal of Geosciences.

[24]  Shengjun Wu,et al.  Estimation of monthly pan evaporation using support vector machine in Three Gorges Reservoir Area, China , 2019, Theoretical and Applied Climatology.

[25]  Meysam Salarijazi,et al.  Improvement of the simple regression model for river’ EC estimation , 2019, Arabian Journal of Geosciences.

[26]  S. H. Ahmadi,et al.  Calibration and evaluation of the FAO56-Penman-Monteith, FAO24-radiation, and Priestly-Taylor reference evapotranspiration models using the spatially measured solar radiation across a large arid and semi-arid area in southern Iran , 2019, Theoretical and Applied Climatology.

[27]  B. Majhi,et al.  Improved prediction of daily pan evaporation using Deep-LSTM model , 2019, Neural Computing and Applications.

[28]  S. Heddam,et al.  Predicting Daily Pan Evaporation (Epan) from Dam Reservoirs in the Mediterranean Regions of Algeria: OPELM vs OSELM , 2019, Environmental Processes.

[29]  H. Loáiciga,et al.  Comparison of methods to calculate evaporation from reservoirs , 2018, International Journal of River Basin Management.

[30]  Maisa’a W. Shammout,et al.  Improving Water Use Efficiency under Deficit Irrigation in the Jordan Valley , 2018, Sustainability.

[31]  Lifeng Wu,et al.  Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models , 2018, Journal of Hydrology.

[32]  T. McVicar,et al.  Revisiting Pan Evaporation Trends in Australia a Decade on , 2018, Geophysical Research Letters.

[33]  O. Kisi,et al.  Daily Pan Evaporation Estimation Using Heuristic Methods with Gamma Test , 2018, Journal of Irrigation and Drainage Engineering.

[34]  Ozgur Kisi,et al.  Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation , 2018 .

[35]  A. Sabziparvar,et al.  Long-term comparison of the climate extremes variability in different climate types located in coastal and inland regions of Iran , 2018, Theoretical and Applied Climatology.

[36]  J. Giráldez,et al.  Experimental Analyses of the Evaporation Dynamics in Bare Soils under Natural Conditions , 2018, Water Resources Management.

[37]  S. Wullschleger,et al.  Evaporation dominates evapotranspiration on Alaska’s Arctic Coastal Plain , 2018 .

[38]  F. Jones.  Evaporation of Water With Emphasis on Applications and Measurements , 2017 .

[39]  M. Falvey,et al.  A solar radiation database for Chile , 2017, Scientific Reports.

[40]  Yongling Li,et al.  Optimal control system for pressurizer in nuclear power plant based on NMGSA , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).

[41]  T. McMahon,et al.  Historical developments of models for estimating evaporation using standard meteorological data , 2016 .

[42]  Ali Dag,et al.  A Novel Study for the Modeling of Monthly Evaporation Using K-Nearest Neighbor Algorithms for a Semi-Arid Continental Climate , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[43]  C. Vallet-Coulomb,et al.  Deuterium excess in the atmospheric water vapour of a Mediterranean coastal wetland: regional vs. local signatures , 2015 .

[44]  Hans-Christian Steen-Larsen,et al.  Deuterium excess in marine water vapor: Dependency on relative humidity and surface wind speed during evaporation , 2014 .

[45]  T. McMahon,et al.  Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis , 2013 .

[46]  Arun Goel,et al.  Application of SVMs Algorithms for Prediction of Evaporation in Reservoirs , 2009 .

[47]  Nicole M. Hesch,et al.  Trends in evaporation for the Canadian Prairies , 2007 .

[48]  John D. Valiantzas,et al.  Simplified versions for the Penman evaporation equation using routine weather data , 2006 .

[49]  M. Roderick,et al.  A simple pan‐evaporation model for analysis of climate simulations: Evaluation over Australia , 2006 .

[50]  Vijay P. Singh,et al.  EVALUATION AND GENERALIZATION OF 13 MASS‐TRANSFER EQUATIONS FOR DETERMINING FREE WATER EVAPORATION , 1997 .

[51]  E. Linacre.  Estimating U.S. Class A Pan Evaporation from Few Climate Data , 1994 .

[52]  de Bruin,et al.  A Simple Model for Shallow Lake Evaporation , 1978 .

[53]  Edward T. Linacre,et al.  A simple formula for estimating evaporation rates in various climates, using temperature data alone , 1977 .

[54]  Robert B. Stewart,et al.  A simple method for determining the evaporation from shallow lakes and ponds , 1976 .

[55]  J. Griffiths.  Another evaporation formula , 1966 .

[56]  Abdul Mounem Mouazen,et al.  A novel spectral index for estimation of relative chlorophyll content of sugar beet , 2021, Comput. Electron. Agric..

[57]  K. C. Khare,et al.  Estimation of pan evaporation using soft computing tools , 2012 .

[58]  A COMPARISON OF PROCEDURES FOR COMPUTING EVAPORATION AND EVAPOTRANSPIRATION (i) , 2007 .

[59]  Xiao Zhang,et al.  Impacts of Climate Change , 2007 .

[60]  Madan Bahadur Basnyat.  Estimation of daily Class A pan evaporation from meteorological data , 1987 .

[61]  J. E. Christiansen,et al.  Estimating Evaporation and Evapotranspiration From Climatic Data , 1966 .

[62]  Juan Papadakis,et al.  Climatic Tables for the World , 1961 .