Skip to main content
SpringerLink
Log in

Coupling uncertain patterns of climatic variables in estimating evaporation from open water bodies

  • ORIGINAL PAPER
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Since the variables involved in changing evaporation rate interact with each other, the result of first-order Sensitivity Analysis (SA1) is imperfect to reveal the dynamics of this phenomenon. The main goal of this study was to reveal coupling patterns of climatic agents in forming evaporation. The Partial Deviations (PaD) method, based on Back-Propagation Artificial Neural Network (BPNN), was used to reveal these patterns. While non-derivative methods survey only first-order sensitivity values, PaD can investigate higher-order sensitivity values. In this way, Summing Squares of partial Derivations (SSD) revealed magnitude of interactions. Also, gradient surfaces statistically reflected information on evaporation changes. To achieve better results, Wavelet-based denoising method was used to remove high-frequency component of inputs. The new method was tested at two neighboring sites (Ahvaz and Isfahan) in Iran. By feeding denoised input to BPNN, the uncertainty of first/ second order PaD values was reduced highly at Ahvaz station by 47.76 and 28.68%. At Ahvaz station coupling between one day-lagged evaporation with air temperature and humidity with magnitudes of 26.37 and 25.21%, respectively, had a major effect on the evaporation gradient. Similarly, the major effects on evaporation rate at Isfahan station belonged to coupling one day-lagged evaporation with air temperature and wind speed with magnitudes of 36.97 and 18.98%, respectively. The interaction patterns showed that rate of evaporation reversed for both stations in warm seasons because of an increase in atmospheric humidity. It seems cities near sea, despite having warm climate, show complex patterns of evaporation. Temperature, one-day-lagged evaporation, wind speed, and radiation aroused effect of other variables. The interaction of these variables caused an inverse rate of evaporation in some cases where the role of one day-lagged evaporation, as the ambient humidity memory, was more prominent than the rest.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig.5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419:224–232

    Article  ADS  CAS  PubMed  Google Scholar 

  • Adamala S, Raghuwanshi NS, Mishra A (2018) Development of generalized higher order neural network-based models for estimating pan evaporation. In Book: Hydrologic Modeling. 55–71.

  • Altman GD, Machin D, Bryant TN, Gardner MJ (2000) Statistics with Confidence. BMJ book, 1–254, ISBN 978 0 72791375 3.

  • Azzahari AD, Yusuf SNF, Selvanathan V, Yahya R (2016) Artificial neural network and response surface methodology modeling in ionic conductivity predictions of Phthaloylchitosan-based gel polymer electrolyte. Polymers 8:22. https://doi.org/10.3390/polym8020022

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Abed M, Imteaz MA, Ahmed AN, Huang YF (2023) A novel application of transformer neural network (TNN) for estimating pan evaporation rate. Water Sci Appl. https://doi.org/10.1007/s13201-022-01834-w

    Article  Google Scholar 

  • Alazard M, Leduc C, Travi Y, Boclet G, Ben Salem A (2015) Estimating evaporation in semi-arid areas facing data scarcity: example of the El Haouareb dam (Merguellil catchment, Central Tunisia). J Hydrol Reg Stud 3:265–284

    Article  Google Scholar 

  • Cajetan MA (2022) Wavelets and wavelet transform systems and their applications - A digital signal processing approach series. Springer Int Publishing. https://doi.org/10.1007/978-3-030-87528-2

    Article  Google Scholar 

  • Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, Book Company, Berlin

    Google Scholar 

  • Campisi-Pinto S, Adamowski J, Oron J (2012) Forecasting urban water demand via wavelet-de-noising and neural network models. Case study: city of Syracuse. Italy Water Resour Manage 26:3539–3558

    Article  Google Scholar 

  • Cavusoglu AH, Chen X, Gentine P, Sahin O (2017) Potential for natural evaporation as a reliable renewable energy resource. Nat Commun 8:1–9

    Article  CAS  Google Scholar 

  • Chen JL, Yang H, Lv MQ, Xiao ZL, Wu SJ (2019) Estimation of monthly pan evaporation using support vector machine in three gorges reservoir area. China Theor Appl Climatol 138:1095–1107

    Article  ADS  Google Scholar 

  • Donoho DL, Johnstone IM (1994) Ideal spatial adaptation by wavelet shrinkage. Biometrika 81:425–455

    Article  MathSciNet  Google Scholar 

  • Duan Z, Bastiaanssen WGM (2017) Evaluation of three energy balance-based evaporation models for estimating monthly evaporation for five lakes using derived heat storage changes from a hysteresis model. Environ Res Lett. https://doi.org/10.1088/1748-9326/aa568e

    Article  Google Scholar 

  • Dimopoulos Y, Bourret P, Lek S (1995) Use of some sensitivity criteria for choosing networks with good generalization ability. Neural Process Lett 2:1–4. https://doi.org/10.1007/BF02309007

    Article  ADS  Google Scholar 

  • Doust AM, Rahimi M, Feyzi M (2015) Effects of solvent addition and ultrasound waves on viscosity reduction of residue fuel oil. Chem Eng Process 95:353–361

    Article  CAS  Google Scholar 

  • Donoho DL, Johnstone IM, Kerkyacharian G, Picard D (1995) Wavelet shrinkage: asymptopia. J Roy Stat Soc: Ser 57:301–369

  • Dimopoulos I, Chronopoulos J, Chronopoulou-Sereli A, Lek S (1999) Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece). Ecol Model 120:157–165. https://doi.org/10.1016/S0304-3800(99)00099-X

    Article  CAS  Google Scholar 

  • Elbeltagi A, Al-Mukhtar M, Kushwaha NL, Al-Ansari N, Vishwakarma DK (2023) Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment. Water Sci Appl. https://doi.org/10.1007/s13201-022-01846-6

    Article  Google Scholar 

  • Finch WJ, Hall RL (2001) Estimation of open water evaporation: a review of methods, Environment Agency, ISBN: 1 85705 604 3.

  • Finch J, Calver A (2008) Methods for the quantification of evaporation from lakes; World Meteorological Organization’s Commission for Hydrology: Oxfordshire, UK.

  • Feng Y, Jia Y, Zhang Q, Gong D, Cui N (2018) National-scale assessment of pan evaporation models across different climatic zones of China. J Hydrol 564:314–328

    Article  Google Scholar 

  • Gedeon TD (1997) Data mining of inputs: analysing magnitude and functional measures. Int J Neural Syst 8:209–218

    Article  CAS  PubMed  Google Scholar 

  • Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model 160:249–264

    Article  Google Scholar 

  • Gevrey M, Dimopoulos I, Lek S (2006) Two-way interaction of input variables in the sensitivity analysis of neural network models. Ecol Model 195:43–50

    Article  Google Scholar 

  • Goyal MK, Bharti B, Quilty J, Adamowski H, Pandey A (2014) Modeling of daily pan evaporation in subtropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Syst Appl 41:5267–5276

    Article  Google Scholar 

  • Harbeck GE (1962) A practical field technique for measuring reservoir evaporation utilizing mass-transfer theory. Surv Prof Pap Geol. https://doi.org/10.3133/pp272E

    Article  Google Scholar 

  • Hashem S (1992) Sensitivity analysis for feed forward artificial neural networks with differentiable activity functions. IJCNN. https://doi.org/10.1109/ijcnn.1992.287175

    Article  Google Scholar 

  • Howard KWF, Loyd JW (1979) The sensitivity of parameters in the Penman evaporation equations and direct recharge balance. J Hydrol 41:329–344

    Article  Google Scholar 

  • Huang X, Cao H, Jia B (2023) Optimization of Levenberg Marquardt algorithm applied to nonlinear systems. Processes. https://doi.org/10.3390/pr11061794

    Article  Google Scholar 

  • Hadjisolomou E, Stefanidis K, Papatheodorou G, Papastergiadou E (2016) Assessing the contribution of the environmental parameters to eutrophication with the use of the “PaD” and “PaD2” methods in a Hypereutrophic Lake. Int J Environ Res Public Health 13:764. https://doi.org/10.3390/ijerph13080764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Johnson F, Sharma A (2010) A Comparison of Australian open water body evaporation trends for current and future climates estimated from class a evaporation pans and general circulation models. J Hydrometeorol (JHM) 11:105–121. https://doi.org/10.1175/2009JHM1158.1

    Article  ADS  Google Scholar 

  • Jin J, Li M, Jin L (2015) Data normalization to accelerate training for linear neural net to predict tropical cyclone tracks. Math Probl Eng. https://doi.org/10.1155/2015/931629

    Article  Google Scholar 

  • Kisi O, Heddam S (2019) Evaporation modelling by heuristic regression approaches using only temperature data. Hydrol Sci J 64:653–672

    Article  CAS  Google Scholar 

  • Kisi O, Genc O, Dinc S, Zounemat-Kermani M (2016) Daily pan evaporation modeling using chi-squared automatic interaction detector, neural networks, classification and regression tree. Comput Electron Agric 122:112–117

    Article  Google Scholar 

  • Kushwaha NL, Rajput J, Elbeltagi A, Elnaggar AY, Sena DR, Vishwakarma DK, Mani I, Hussein EE (2021) Data intelligence model and meta-heuristic algorithms-based pan evaporation modelling in two different agro-climatic zones: a case study from northern India. Atmosphere. https://doi.org/10.3390/atmos12121654

    Article  Google Scholar 

  • Lu M, Abourizk SM, Hermann UH (2001) Sensitivity analysis of neural networks in spool fabrication productivity studies. J Comput Civ Eng 15:299–308

    Article  Google Scholar 

  • Li Z, Pan N, He Y, Zhang Q (2016) Evaluating the best evaporation estimate model for free water surface evaporation in hyper-arid regions: a case study in the Ejina basin, northwest China. Environ Earth Sci. https://doi.org/10.1007/s12665-015-5090-3

    Article  Google Scholar 

  • Li Z, Chu R, Shen S, Md AR, Islam T (2018) Dynamic analysis of pan evaporation variations in the Huai River Basin, a climate transition zone in eastern China. Sci Total Environ 625:496–509

    Article  ADS  CAS  PubMed  Google Scholar 

  • Legates D, McCabe G Jr (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation: Water Resour. Res 35:233–241. https://doi.org/10.1029/1998WR900018

    Article  Google Scholar 

  • NASA Science, 2023. URL: https://science.nasa.gov/earth-science/oceanography/ocean-earth-system/ocean-water-cycle#:~:text=Besides%20affecting%20the%20amount%20of,%2C%20cloud%2Dfree%20subtropical%20seas.

  • Nasrollahi M, Zolfaghari AA, Yazdani MR (2021) Investigation of pan evaporation paradox and climatic parameters affecting it in half-west and center of Iran. JSWC 11:61–76 ((In Persian))

    Google Scholar 

  • Nourani V, Sayyah-Fard M (2012) Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Adv Eng Softw 47:127–146

    Article  Google Scholar 

  • Nourani V, Baghanam AH, Adamowski J, Kisi O (2014) Applications of hybrid wavelet–artificial intelligence models in hydrology: A review. J Hydrol 514:358–377

    Article  Google Scholar 

  • Nourani V, Sayyah-Fard M, Alami MT, Shargi E (2020a) Data pre-processing effect on ANN-based prediction intervals construction of the evaporation process at different climate regions in Iran. J Hydrol 588:1–15

    Article  Google Scholar 

  • Nourani V, Gökçekuş H, Umar IK, Najafi H (2020) An emotional artificial neural network for prediction of vehicular traffic noise. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2019.136134

    Article  PubMed  Google Scholar 

  • Nourani V, Sharghi E, Behfar N, Zhang Y (2022) Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data. Appl Energy 315:119069

    Article  Google Scholar 

  • Oki T, Kanae S (2006) Global hydrological cycles and world water resources. Science 313:1068–1072

    Article  ADS  CAS  PubMed  Google Scholar 

  • Shirgure PS (2013) Evaporation modeling with artificial neural network: a review. Sci J Rev 2:73–84

    Google Scholar 

  • Sundararajan, D., 2015. Discretewavelet transform: A signal processing approach. John Wiley & Sons, ISBN: 978–1–119–04606–6.

  • Shiri J (2018) Evaluation of a neuro-fuzzy technique in estimating pan evaporation values in low-altitude locations. Meteorol Appl 26:204–212

    Article  Google Scholar 

  • Said KO, Onifade M, LawalGithiria AIJM (2020) An artificial intelligence-based model for the prediction of spontaneous combustion liability of coal based on its proximate analysis. Combust Sci Technol 193:2350–2367

    Article  Google Scholar 

  • Sharghi E, Nourani V, Molajou A, Najafi H (2019) Conjunction of emotional ANN (EANN) and wavelet transform for rainfall-runoff modeling. J Hydroinformatics 21:136–152

    Article  Google Scholar 

  • Shabani S, Samadianfard S, Sattari MT, Mosavi A, Shamshirband S, Kmet T, Várkonyi-Kóczy AR (2020) Modeling pan evaporation using gaussian process regression K-nearest neighbors random forest and support vector machines; comparative analysis. Atmosphere. https://doi.org/10.3390/atmos11010066

    Article  Google Scholar 

  • Sit M, Demiray ZB, Xiang J, Ewing G, Sermet Y, Demir I (2020) A comprehensive review of deep learning applications in hydrology and water resources. Water Sci Technol 82:2635–2670

    Article  PubMed  Google Scholar 

  • Stephens GL, Li J, Wild M, Clayson CA, Loeb N, Kato S, L’EcuyerPWS LebsockAndrews TMT Jr (2012) An update on Earth’s energy balance in light of the latest global observations. Nat Geosci 5:691–696

    Article  ADS  CAS  Google Scholar 

  • Tabari H, Marofi S, Sabziparvar AA (2010) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28:399–406

    Article  Google Scholar 

  • Tunkiel AT, Sui D, Wiktorski T (2020) Data-driven sensitivity analysis of complex machine learning models: a case study of directional drilling. J Pet Sci Eng 195:107630

    Article  CAS  Google Scholar 

  • Vanzyl WH, De Jager JM, Maree CJ (1989) The relationship between daylight evaporation from short vegetation and the USWB Class A pan. Agric for Meteorol 46:107–118

    Article  ADS  Google Scholar 

  • Wang T, Zhang J, Sun F, Liu W (2017) Pan evaporation paradox and evaporative demand from the past to the future over China: a review. WIREs Water. https://doi.org/10.1002/wat2.1207

    Article  Google Scholar 

  • Wu Q, Wang Z, Qin Y, Yang W (2023) Intelligent model for dynamic shear modulus and damping ratio of undisturbed marine clay based on back-propagation neural network. J Mar Sci Eng. https://doi.org/10.3390/jmse11020249

    Article  Google Scholar 

  • Wang H, Yan H, Zeng W, Lei G, Ao C, Zha Y (2020) A novel nonlinear Arps decline model with salp swarm algorithm for predicting pan evaporation in the arid and semi-arid regions of China. J Hydrol 582:124545

    Article  Google Scholar 

  • Wu L, Huang G, Fan G, Ma X, Zhou H, Zeng W (2019) Hybrid extreme learning machine with meta-heuristic algorithms for monthly pan evaporation prediction. Comput Electron Agric. https://doi.org/10.1016/j.compag.2019.105115

    Article  Google Scholar 

  • Xiao K, Griffs TJ, Baker JM, Bolstad PV, Erickson MD, Lee X, Wood JD, Hu C, Nieber JL (2018) Evaporation from a temperate closed-basin lake and its impact on present, past, and future water level. J Hydrol 561:59–75

    Article  Google Scholar 

  • Antonopoulos Z, GianniouAntonopoulos A VKSV (2016) Artificial neural networks and empirical equations to estimate daily evaporation: application to Lake Vegoritis. Greece Hydrol Sci J 61:2590–2599

    Article  Google Scholar 

  • Zhang Y, Liu C, Tang Y, Yang Y (2007) Trends in pan evaporation and reference and actual evapotranspiration across the Tibetan Plateau. J Geophys Res. https://doi.org/10.1029/2006JD008161

    Article  Google Scholar 

  • Zheng ZY, Xie G, Li L, Liu WL (2020) The joint effect of ultrasound and magnetic Fe3O4 nanoparticles on the yield of 2,6-dimethoxy-ρ-benzoquinone from fermented wheat germ: comparison of evolutionary algorithms and interactive analysis of paired-factors. Food Chem. https://doi.org/10.1016/j.foodchem.2019.125275

    Article  PubMed  Google Scholar 

  • Zhang Y, Leuning R, B Hutley L, BeringerMcHughWalker JIPJ (2010) Using long-term water balances to parameterize surface conductances and calculate evaporation at 0.05 spatial resolution. Water Resour Res 46:1–14. https://doi.org/10.1029/2009WR008716

    Article  Google Scholar 

  • Zhang Y, Peña AJ, McVicar T, Chiew F, Vaze J, Liu C, Pan M, Lu X, Zheng H, Wang Y, Liu Y, Miralles D, Pan M (2016) Multi-decadal trends in global terrestrial evapotranspiration and its components. Sci Rep 6:19124. https://doi.org/10.1038/srep19124

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang Y, HS Chiew F, Peña-Arancibia J, Sun F, Li H, Leuning R (2017) Global variation of transpiration and soil evaporation and the role of their major climate drivers. J Geophys Res Atmos 122:6868–6881

    Article  ADS  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Key R&D Program of China (Grant No. 2022YFC3002804), CAS Pioneer Talents Program and CAS-PIFI professorial fellowship (Grant No. 2022VMA00).

Funding

National Key R&D Program of China (Grant No. 2022YFC3002804), CAS Pioneer Talents Program and CAS-PIFI professorial fellowship (Grant No. 2022VMA00).

Author information

Authors and Affiliations

  1. Center of Excellence in Hydroinformatics, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Vahid Nourani & Mina Sayyah-Fard

  2. Faculty of Civil and Environmental Engineering, Near East University, Lefkosa, Turkey

    Vahid Nourani

  3. Water Resources Research Center, Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan

    Vahid Nourani

  4. Institute of Geographic Sciences and Natural Resources Research, The Chinese Academy of Sciences, Beijing, 100101, China

    Yongqiang Zhang

Authors
  1. Vahid Nourani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mina Sayyah-Fard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

VN: Conceptualization, Supervision, Methodology, Writing—review & editing. MS-F: Project administration, Formal analysis, Methodology, Investigation, Resources, Data curation. YZ: Supervision, Formal analysis, Methodology, Writing—original draft.

Corresponding author

Correspondence to Vahid Nourani.

Ethics declarations

Conflict of interest

There is not any conflict of the interest.

Data availability statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 3897 kb)

Appendix A: Survey the noise affection on the quality of SA

Appendix A: Survey the noise affection on the quality of SA

Radar / Spider charts of Fig. A1 illustrate the width of CIs for the mean SSD values in the multidimensional space of noisy and denoised variables and examples at the Ahvaz station. From Fig. A1, the results show that denoised input vectors led to a decrease of CI width for both PaD1 and PaD2 mean values (also, led to enhance the model's performance as Figs. S4-S5) because of the reduction of the riot in the PaD values. It should be noted that the internal instability of ANNs because of random initial weights is also the inability to find the most optimal structure for it (here for Et modeling) in terms of the number of neurons are other reasons for the disturbance of results. An example of noise affection on coupling patterns was reported as Fig. A2 for the interaction of paired variables of Et-1 with T and Rh at Ahvaz station. It can be seen that a high-frequency component in the input vector amplifies disturbances and hinders the interpretation of the coupling patterns.

See Figs.

Fig. 10
figure 10

The noise affection on the quality of first- and second-order Partial Deviations (PaD1, 2) values regarding uncertainty reduction, for example at Ahvaz station a PaD1, b PaD2

Full size image

10 and

Fig. 11
figure 11

The noise affection on the quality of coupling patterns for example at Ahvaz station, a interaction of one-day lagged evaporation (Et-1) and temperature (T), b interaction of Et-1 and relative humidity (Rh)

Full size image

11

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nourani, V., Sayyah-Fard, M. & Zhang, Y. Coupling uncertain patterns of climatic variables in estimating evaporation from open water bodies. Stoch Environ Res Risk Assess 38, 383–405 (2024). https://doi.org/10.1007/s00477-023-02549-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-023-02549-3

Keywords

Search

Navigation