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A comparative study of remote sensing and gene expression programming for estimation of evapotranspiration in four distinctive climates

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Abstract

An accurate estimation of Evapotranspiration (ET) is an important issue in hydrology, water resources management and irrigation scheduling. There are a wide range of methods for estimation of ET, among which, machine learning techniques and remote sensing-based approaches demonstrated more reasonable results. Accordingly, this study attempts to compare the capability of two developed models of Gene-Expression Programming (GEP) and Surface Energy Balance Algorithm for Land (SEBAL) in estimation of ET, at four different climate types of Temperate-Warm (T-W), Wet-Warm (W-W), Arid-Cold (A-C), and Arid-Warm (A-W). In this way, a-two year of daily records as weather variables (i.e., maximum and minimum temperature, dew-point temperature, vapor pressure, saturated vapor pressure, relative humidity, 24-h rainfall, sunshine hours, and wind speed) were considered as input variables, whereas ET values were computed as output variable (observed ET) by using FAO Penman–Monteith-56 method. After development of two predictive models, the statistical results were compared with well-known Hargreaves–Samani method. The results showed that while Hargreaves–Samani equation could not yield remarkable results in any of the climates, GEP and SEBAL demonstrated accurate predictions. In this way, GEP was the superior model in T-W (R2 = 0.902 and RMSE = 0.713 mm/day) and A-W (R2 = 0.951 and RMSE = 0.634 mm/day) climates but it dropped a bit in two other climates. However, SEBAL not only had the best performance in both climates of W-W (R2 = 0.967 and RMSE = 0.515 mm/day) and A-C (R2 = 0.990 and RMSE = 0.720 mm/day), but also demonstrated good predictions in T-W and A-W climates. Therefore, SEBAL is recommended as the best model for estimation of ET in all climate types.

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References

  • Allen RG, Tasumi M, Trezza R, Waters R, Bastiaanssen W (2002) Sebal (surface energy balance algorithms for land). Adv Train Users Man Idaho Implement Version 1:97

    Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Fao Rome 300:D05109

    Google Scholar 

  • Bastiaanssen WGM (1995) Regionalization of surface flux densities and moisture indicators in composite terrain: a remote sensing approach under clear skies in Mediterranean climates (SC-DLO)

  • Bastiaanssen WGM (2000) SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. J Hydrol 229:87–100

    Google Scholar 

  • Bastiaanssen WGM, Ahmad MD, Chemin Y (2002) Satellite surveillance of evaporative depletion across the Indus Basin. Water Resour Res 38:1–9

    Google Scholar 

  • Bastiaanssen WGM, Menenti M, Feddes RA, Holtslag AAM (1998a) A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J Hydrol 212:198–212

    Google Scholar 

  • Bastiaanssen WGM, Pelgrum H, Wang J, Ma Y, Moreno JF, Roerink GJ, der Wal TV (1998b) A remote sensing surface energy balance algorithm for land (SEBAL): part 2: validation. J Hydrol 212:213–229

    Google Scholar 

  • Bezerra BG, da Silva BB, dos Santos CA, Bezerra JR (2015) Actual evapotranspiration estimation using remote sensing: comparison of SEBAL and SSEB approaches. Adv Remote Sens 4(03):234

    Google Scholar 

  • Bhattarai N, Quackenbush LJ, Im J, Shaw SB (2017a) A new optimized algorithm for automating endmember pixel selection in the SEBAL and METRIC models. Remote Sens Environ 196:178–192

    Google Scholar 

  • Bhattarai N, Wagle P, Gowda PH, Kakani VG (2017b) Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions. ISPRS J Photogramm Remote Sens 133:128–141

    Google Scholar 

  • Deshaine LM (2014) Decision support for complex planning challenges. Ph. D. Dissertation, Chalmers University of Technology, Göteborg, Sweden, 233p

  • Domingo F, Villagarcıa L, Boer MM, Alados-Arboledas L, Puigdefábregas J (2001) Evaluating the long-term water balance of arid zone stream bed vegetation using evapotranspiration modelling and hillslope runoff measurements. J Hydrol 243:17–30

    Google Scholar 

  • Ebtehaj I, Bonakdari H, Zaji AH, Azimi H, Sharifi A (2015) Gene expression programming to predict the discharge coefficient in rectangular side weirs. Appl Soft Comput 35:618–628

    Google Scholar 

  • Evett SR, Schwartz RC, TAHowell, RL Baumhardt, KS Copeland. (2012) Can weighing lysimeter ET represent surrounding field ET well enough to test flux station measurements of daily and sub-daily ET? Adv Water Resour 50:79–90

    Google Scholar 

  • Falamarzi Y, Palizdan N, Huang YF, Lee TS (2014) Estimating evapotranspiration from temperature and wind speed data using artificial and wavelet neural networks (WNNs). Agric Water Manag 140:26–36

    Google Scholar 

  • Faridatul MI, Wu B, Zhu X, Wang S (2020) Improving remote sensing based evapotranspiration modelling in a heterogeneous urban environment. J Hydrol 581:124405

    Google Scholar 

  • Farzanpour H, Shiri J, Sadraddini AA, Trajkovic S (2018) Global comparison of 20 reference evapotranspiration equations in a semi-arid region of Iran. Hydrol Res 50:282–300

    Google Scholar 

  • Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. arXiv preprint cs/0102027

  • Granata F (2019) Evapotranspiration evaluation models based on machine learning algorithms—a comparative study. Agric Water Manag 217:303–315

    Google Scholar 

  • Gandomi AH, Alavi AH, Ting TO, Yang X-S (2013) Intelligent modeling and prediction of elastic modulus of concrete strength via gene expression programming. In: International conference in swarm intelligence, pp 564–571. Springer

  • Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99

    Google Scholar 

  • Jaafar HH, Ahmad FA (2020) Time series trends of Landsat-based ET using automated calibration in METRIC and SEBAL: the Bekaa Valley, Lebanon. Remote Sens Environ 238:111034

    Google Scholar 

  • Jaber HS, Mansor S, Pradhan B, Ahmad N (2016) Evaluation of SEBAL model for evapotranspiration mapping in Iraq using remote sensing and GIS. Int J Appl Eng Res 11:3950–3955

    Google Scholar 

  • Jovic S, Nedeljkovic B, Golubovic Z, Kostic N (2018) Evolutionary algorithm for reference evapotranspiration analysis. Comput Electron Agric 150:1–4

    Google Scholar 

  • Kiafar H, Babazadeh H, Marti P, Kisi O, Landeras G, Karimi S, Shiri J (2016) Evaluating the generalizability of GEP models for estimating reference evapotranspiration in distant humid and arid locations. Theor Appl Climatol 130:377–389

    Google Scholar 

  • Kazemi MH, Majnooni-Heris A, Kisi O, Shiri J (2020) Generalized gene expression programming models for estimating reference evapotranspiration through cross-station assessment and exogenous data supply. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-020-10916-8

    Article  Google Scholar 

  • Karimi S, Shiri J, Martic P (2020) Supplanting missing climatic inputs in classical and random forest models for estimating reference evapotranspiration in humid coastal areas of Iran. Comput Electron Agric 176:105633

    Google Scholar 

  • Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351:299–317

    Google Scholar 

  • Kisi O (2016) Modeling reference evapotranspiration using three different heuristic regression approaches. Agric Water Manag 169:162–172

    Google Scholar 

  • Kisi O, Guven A (2010) Evapotranspiration modeling using linear genetic programming technique. J Irrig Drain Eng 136:715–723

    Google Scholar 

  • Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128:224–233

    Google Scholar 

  • Kumar V, Khan S, Shakti B, Rahul AK (2018) Review of evapotranspiration methodologies. Am J Earth Sci Eng 1:72–84

    Google Scholar 

  • Majumdar A, Mitra A, Banerjee D, Majumdar PK (2010) Soft computing applications in fabrics and clothing: a comprehensive review. Res J Text Appar 14:1

    Google Scholar 

  • Mattar MA (2018) Using gene expression programming in monthly reference evapotranspiration modeling: a case study in Egypt. Agric Water Manag 198:28–38

    Google Scholar 

  • Mhawej M, Elias G, Nasrallah A, Faour G (2020) Dynamic calibration for better SEBALI ET estimations: validations and recommendations. Agric Water Manag 230:105955

    Google Scholar 

  • Mollahasani A, Alavi AH, Gandomi AH (2011) Empirical modeling of plate load test moduli of soil via gene expression programming. Comput Geotech 38:281–286

    Google Scholar 

  • Oberg JW, Melesss AM (2006) Evapotranspiration dynamics at an ecohydrological restoration site: an energy balance and remote sensing approach 1. JAWRA J Am Water Resour Assoc 42:565–582

    Google Scholar 

  • Odi-Lara M, Campos I, Neale CMU, Ortega-Farías S, Poblete-Echeverría C, Balbontín C, Calera A (2016) Estimating evapotranspiration of an apple orchard using a remote sensing-based soil water balance. Remote Sens 8:253

    Google Scholar 

  • Shiri J (2017) Evaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran. Agric Water Manag 188:101–114

    Google Scholar 

  • Shiri J, Kişi Ö, Landeras G, López JJ, Nazemi AH, Stuyt LCPM (2012) Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). J Hydrol 414:302–316

    Google Scholar 

  • Shiri J, Marti P, Nazemi AH, Sadraddini AA, Kisi O, Landeras G, Fard AF (2014a) Local vs external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through k-fold testing. Hydrol Res 46:72–88

    Google Scholar 

  • Shiri J, Nazemi AH, Sadraddini AA, Landeras G, Kisi O, Fard AF, Marti P (2014b) Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran. Comput Electron Agric 108:230–241

    Google Scholar 

  • Shiri J, Sadraddini AA, Nazemi AH, Kisi O, Landeras G, Fard AF, Marti P (2014c) Generalizability of gene expression programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran. J Hydrol 508:1–11

    Google Scholar 

  • Shiri J, Zounemat-Kermani M, Kisi O, Mohsenzadeh-Karimi S (2020) Comprehensive assessment of 12 soft computing approaches for modelling reference evapotranspiration in humid locations. Meteorol Appl 27(1):1–15

    Google Scholar 

  • Shiri J, Nazemi AH, Sadraddini AA, Marti P, Fard AF, Kisi O, Landeras G (2019) Alternative heuristics equations to the Priestley-Taylor approach: assessing reference evapotranspiration estimation. Theor Appl Climatol 138:831–848

    Google Scholar 

  • Shiri J (2019) Modeling reference evapotranspiration in island environments: assessing the practical implications. J Hydrol 570:265–280

    Google Scholar 

  • Silva AM, da Silva RM, Santos CAG (2019) Automated surface energy balance algorithm for land (ASEBAL) based on automating endmember pixel selection for evapotranspiration calculation in MODIS orbital images. Int J Appl Earth Obs Geoinf 79:1–11

    CAS  Google Scholar 

  • Tabari H, Kisi O, Ezani A, Talaee PH (2012) SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. J Hydrol 444:78–89

    Google Scholar 

  • Tang R, Li Z-L, Chen K-S, Jia Y, Li C, Sun X (2013) Spatial-scale effect on the SEBAL model for evapotranspiration estimation using remote sensing data. Agric For Meteorol 174:28–42

    Google Scholar 

  • Themistocleous K, Hadjimitsis D (2008) The importance of considering atmospheric correction in the pre-processing of satellite remote sensing data intended for the management and detection of cultural sites: a case study of the Cyprus area. In: Paper presented at the Conference Paper (PDF Available): 14th International Conference on Virtual Systems and Multimedia (dedicated to culture heritage). VSMM, Limmasol, Turkey

  • Traore S, Guven A (2013) New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa. Irrig Sci 31:1–10

    Google Scholar 

  • Wang C, Yang J, Myint SW, Wang Z-H, Tong B (2016) Empirical modeling and spatio-temporal patterns of urban evapotranspiration for the Phoenix metropolitan area, Arizona. GISci Remote Sens 53:778–792

    Google Scholar 

  • Wang S, Fu Z, Chen H, Nie Y, Wang K (2015) Modeling daily reference ET in the karst area of northwest Guangxi (China) using gene expression programming (GEP) and artificial neural network (ANN). Theor Appl Climatol 126:493–504

    Google Scholar 

  • Wang S, Lian J, Peng Y, Hu B, Chen H (2019) Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China. Agric Water Manag 221:220–230

    Google Scholar 

  • Waters R, Allen R, Bastiaanssen W, Tasumi M, Trezza R (2002) 'SEBAL', surface energy balance algorithms for land. Idaho implementation. Advanced Training and Users Manual, Idaho, USA

  • Wu H (2016) Evapotranspiration estimation of Platycladus orientalis in Northern China based on various models. J For Res 27:871–878

    CAS  Google Scholar 

  • Wu W, Hall CAS, Scatena FN, Quackenbush LJ (2006) Spatial modelling of evapotranspiration in the Luquillo experimental forest of Puerto Rico using remotely-sensed data. J Hydrol 328:733–752

    Google Scholar 

  • Zhang K, Kimball JS, Running SW (2016) A review of remote sensing based actual evapotranspiration estimation. Wiley Interdiscip Rev Water 3:834–853

    Google Scholar 

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran

    Ali Barzkar & Sajad Shahabi

  2. Department of Surveying Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran

    Saeid Niazmradi

  3. Department of Water Engineering, Faculty of Agriculture, University of Jiroft, Jiroft, Iran

    Mohamad Reza Madadi

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  1. Ali Barzkar
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Correspondence to Sajad Shahabi.

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Barzkar, A., Shahabi, S., Niazmradi, S. et al. A comparative study of remote sensing and gene expression programming for estimation of evapotranspiration in four distinctive climates. Stoch Environ Res Risk Assess 35, 1437–1452 (2021). https://doi.org/10.1007/s00477-020-01956-0

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