Abstract
Evapotranspiration is sensitive to climate change. The main objective of this study was to examine the response of reference evapotranspiration (ET0) under various climate change scenarios using artificial neural networks and the Canadian Earth System Model Second Generation (CanESM2). The Hargreaves method was used to calculate ET0 for western, central, and eastern parts of Prince Edward Island using their two input parameters: daily maximum temperature (Tmax), and daily minimum temperature (Tmin). The Tmax and Tmin were downscaled with the help of statistical downscaling model (SDSM) for three future periods 2020s (2011-2040), 2050s (2041-2070), and 2080s (2071-2100) under three representative concentration pathways (RCP’s) including RCP 2.6, RCP P4.5, and RCP 8.5. Temporally, there were major changes in Tmax, Tmin, and ET0 for the 2080s under RCP8.5. The temporal variations in ET0 for all RCPs matched the reports in the literature for other similar locations. For RCP8.5, it ranged from 1.63 (2020s) to 2.29 mm/day (2080s). As a next step, a one-dimensional convolutional neural network (1D-CNN), long-short term memory (LSTM), and multilayer perceptron (MLP) were used for estimating ET0. High coefficient of correlation (r > 0.95) values for both calibration and validation periods showed the potential of the artificial neural networks in ET0 estimation. The results of this study will help decision makers and water resource managers in future quantification of the availability of water for the island and to optimize the use of island water resources on a sustainable basis.
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References
Abbas F, Sarwar N, Ibrahim M, Adrees M, Ali S, Saleem F, Hammad HM (2018) Patterns of climate extremes in the coastal and highland regions of Balochistan, Pakistan. Earth Interact 22:1–23. https://doi.org/10.1175/EI-D-16-0028.1
Afzaal H, Farooque AA, Abbas F, Acharya B, Esau T (2020a) Computation of evapotranspiration with artificial intelligence for precision water resource management. Appl Sci 10:1621. https://doi.org/10.3390/app10051621
Afzaal H, Farooque AA, Abbas F, Acharya B, Esau T (2020b) Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning. Water (Switzerland) 12:5. https://doi.org/10.3390/w12010005
Allen RG, Pereira LS, Raes D (1998) Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome
Birara H, Pandey RP, Mishra SK (2020) Projections of future rainfall and temperature using statistical downscaling techniques in Tana Basin, Ethiopia. Sustain Water Resour Manag 6:77
Chipanshi AC, Maphanyane JG (1997) Nature of rainfal variability in Botswana over the 1961-1990 period. JSTOR J Afr Res Dev 299–317
Dau QV, Kuntiyawichai K, Adeloye AJ (2020) Future changes in water availability due to climate change projections for Huong Basin, Vietnam. Environ Process 81(8):77–98. https://doi.org/10.1007/S40710-020-00475-Y
Ferreira LB, da Cunha FF (2020) Multi-step ahead forecasting of daily reference evapotranspiration using deep learning. Comput Electron Agric 178:105728. https://doi.org/10.1016/j.compag.2020.105728
Government of Canada (2019) CanESM2 predictors: CMIP5 experiments. https://climate-scenarios.canada.ca/?page=pred-canesm2. Accessed 11 Apr 2020
Government of Canada (2017) Station results - historical data. https://climate.weather.gc.ca/historical_data/search_historic_data_stations_e.html?searchType=stnProv&timeframe=1&lstProvince=PE&optLimit=yearRange&StartYear=1840&EndYear=2020&Year=2020&Month=11&Day=3&selRowPerPage=25. Accessed 11 Apr 2020
Hafeez M, Chatha ZA, Khan AA, Bakhsh A, Basit A, Tahira F, Khan G (2020) Estimating reference evapotranspiration by hargreaves and blaney-criddle methods in humid subtropical conditions. Curr Res Agric Sci 7:15–22. https://doi.org/10.18488/journal.68.2020.71.15.22
Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99. https://doi.org/10.13031/2013.26773
Hashemi M, Sepaskhah AR (2020) Evaluation of artificial neural network and Penman Monteith equation for the prediction of barley standard evapotranspiration in a semi-arid region. Theor Appl Climatol 139:275–285. https://doi.org/10.1007/s00704-019-02966-x
Lotfi M, Kamali GA, Meshkatee AH, Varshavian V (2020) Study on the impact of climate change on evapotranspiration in west of Iran. Arab J Geosci 13:1–11. https://doi.org/10.1007/s12517-020-05715-x
Mahmood R, Babel MS (2013) Evaluation of SDSM developed by annual and monthly sub-models for downscaling temperature and precipitation in the Jhelum basin. Pakistan and India. Theor Appl Climatol 113:27–44. https://doi.org/10.1007/s00704-012-0765-0
Majhi B, Naidu D, Mishra AP, Satapathy SC (2020) Improved prediction of daily pan evaporation using Deep-LSTM model. Neural Comput Appl 32:7823–7838. https://doi.org/10.1007/s00521-019-04127-7
Maqsood J, Farooque AA, Wang X, Abbas F, Acharya B, Afzaal H (2020) Contribution of climate extremes to variation in potato tuber yield in Prince Edward Island. Sustain 12:4937. https://doi.org/10.3390/SU12124937
Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ (2007) Climate models and their evaluation. In Climate Change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC (FAR). Cambridge University Press 589–662
Richards W, Daigle R (2011) Scenarios and guidance for adaptation to climate change and sea level rise – NS and PEI municipalities. Atlantic climate adaptation solutions association
Roy DK (2021) Long short-term memory networks to predict one-step ahead reference evapotranspiration in a subtropical climatic zone. Environ Process 82:911–941. https://doi.org/10.1007/S40710-021-00512-4
Shi L, Feng P, Wang B, Li Liu D, Cleverly J, Fang Q, Yu Q (2020) Projecting potential evapotranspiration change and quantifying its uncertainty under future climate scenarios: A case study in southeastern Australia. J Hydrol 584:124756. https://doi.org/10.1016/j.jhydrol.2020.124756
Tabari H, Talaee PH (2013) Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Comput Appl 23:341–348. https://doi.org/10.1007/s00521-012-0904-7
Wilby RL, Dawson CW, Barrow EM (2002) SDSM - A decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17:145–157. https://doi.org/10.1016/s1364-8152(01)00060-3
Zanetti SS, Sousa EF, Oliveira VPS, Almeida FT, Bernardo S (2007) Estimating evapotranspiration using artificial neural network and minimum climatological data. Irrig Drain Syst 133:83–89. https://doi.org/10.1061/ASCE0733-94372007133:283
Zhai Y, Huang G, Wang X, Zhou X, Lu C, Li Z (2019) Future projections of temperature changes in Ottawa, Canada through stepwise clustered downscaling of multiple GCMs under RCPs. Clim Dyn 52:3455–3470. https://doi.org/10.1007/s00382-018-4340-y
Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol 561:918–929. https://doi.org/10.1016/j.jhydrol.2018.04.065
Zhang LEI, Xu Y, Meng C, Li X, Liu H, Wang C (2020) Comparison of statistical and dynamic downscaling techniques in generating high-resolution temperatures in China from CMIP5 GCMs. J Appl Meteorol Climatol 59:207–235. https://doi.org/10.1175/JAMC-D-19-0048.1
Zhu S, Xu Z, Luo X, Wang C, Zhang H (2019) Quantifying the contributions of climate change and human activities to drought extremes, using an improved evaluation framework. Water Resour Manag 3315:5051–5065. https://doi.org/10.1007/S11269-019-02413-6
Acknowledgments
We would like to acknowledge the assistance and cooperation of the Precision Agriculture Team of the University of Prince Edward Island during this study.
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Natural Science and Engineering Research Council of Canada.
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Conceptualization: J.M, A.A.F, F.A; Methodology: J.M, F.A; Formal analysis and investigation: J.M, F.A, H.A; Data curation: J.M, T.E, B.A, H.A; Validation: A.A.F, X.W, T.E; Writing - original draft preparation: J.M, A.A.F, F.A; Writing – review and editing: X.W, B.A; Project administration: A.A.F, F.A; Funding acquisition: A.A.F; Supervision: A.A.F.
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Maqsood, J., Farooque, A.A., Abbas, F. et al. Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios. Water Resour Manage 36, 835–851 (2022). https://doi.org/10.1007/s11269-021-02997-y
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DOI: https://doi.org/10.1007/s11269-021-02997-y