Abstract
This study analyses the variability in climate change projections under different Shared Socio-economic Pathway (SSP) scenarios to understand the trends of crop water requirements using the reference evapotranspiration (ETo) as a baseline indicator in the Cauvery Basin, India. A novel approach to ensemble different Global Circulation Models (GCMs) using deep learning techniques was developed to obtain the best representative climate projections. The current progression of climate aligns with the SSP5 scenario, indicating a potential climate distress in the future. Ensembled climate projections were used to estimate the daily ETo using Hargreaves radiation method. Comprehensive insights into the trends of ETo under different techniques indicate a downward trend over 80% of the basin. The rate of change in trend is severe towards the mountainous western parts of the basin compared to the urbanized eastern region, which experiences a much gentler rate of change. The critical change in the increasing trend over the eastern region coincides with the north-east monsoon, while the decreasing trend occurring predominantly in the western region coincides with the south-west monsoon. SSP3-7.0 scenario was noted to produce the highest impact in the increase of average ETo to about 6.45% the current trend, while SSP2-4.5 scenario resulted in a rise of only 0.79%. It was found that ETo is highly correlated with the difference between maximum and minimum temperatures scoring an R value around 0.97 in all scenarios. While the highest temperatures (42.17 °C vs 41.03 °C) were observed in SSP5 scenario, the higher average temperature (36.49 °C vs 33.27 °C) in SSP3 scenario has a much higher impact on the ETo across the basin.
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Open source data sets have been used in the study. Adequate data has been incorporated into the manuscript for clear understanding of the manuscript.
References
Aguilar C, Polo MJ (2011) Generating reference evapotranspiration surfaces from the Hargreaves equation at watershed scale. Hydrol Earth Syst Sci 15:2495–2508. https://doi.org/10.5194/hess-15-2495-2011
Ajjur SB, Al-Ghamdi SG (2021) Evapotranspiration and water availability response to climate change in the Middle East and North Africa. Clim Change 166:28. https://doi.org/10.1007/s10584-021-03122-z
Althoff D, Santos RAD, Bazame HC, Cunha FFD, Filgueiras R (2019) Improvement of hargreaves-samani reference evapotranspiration estimates with local calibration. Water 11:2272. https://doi.org/10.3390/w11112272
Aschale TM, Peres DJ, Gullotta A, Sciuto G, Cancelliere A (2023) Trend analysis and identification of the meteorological factors influencing reference evapotranspiration. Water 15:470. https://doi.org/10.3390/w15030470
Aydın Y (2021) Assessing of evapotranspiration models using limited climatic data in Southeast Anatolian Project Region of Turkey. PeerJ 9:e11571. https://doi.org/10.7717/peerj.11571
Berti A, Tardivo G, Chiaudani A, Rech F, Borin M (2014) Assessing reference evapotranspiration by the Hargreaves method in north-eastern Italy. Agric Water Manag 140:20–25. https://doi.org/10.1016/j.agwat.2014.03.015
Bhave AG, Conway D, Dessai S, Stainforth DA (2018) Water resource planning under future climate and socioeconomic uncertainty in the Cauvery River Basin in Karnataka, India. Water Resour Res 54:708–728. https://doi.org/10.1002/2017WR020970
Blaney HF (1959) Monthly consumptive use requirements for irrigated crops. J Irrig Drain Div 85:1–12. https://doi.org/10.1061/JRCEA4.0000084
Bottazzi M, Bancheri M, Mobilia M, Bertoldi G, Longobardi A, Rigon R (2021) Comparing evapotranspiration estimates from the GEOframe-Prospero model with Penman-Monteith and Priestley-Taylor approaches under different climate conditions. Water 13:1221. https://doi.org/10.3390/w13091221
Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: A seasonal-trend decomposition procedure based on loess. J Off Stat 6:3–73
Crawford J, Venkataraman K, Booth J (2019) Developing climate model ensembles: a comparative case study. J Hydrol 568:160–173. https://doi.org/10.1016/j.jhydrol.2018.10.054
Curceac S, Atkinson PM, Milne A, Wu L, Harris P (2020) Adjusting for conditional bias in process model simulations of hydrological extremes: an experiment using the north wyke farm platform. Front Artif Intell 3:565859. https://doi.org/10.3389/frai.2020.565859
Debnath M, Sarma AK, Mahanta C (2022) Multimodel climate change projections of temperature for evapotranspiration scenarios and potential impact on the cropping system in Jamuna Command Area, Assam, Northeast India (preprint). In Review. https://doi.org/10.21203/rs.3.rs-1878009/v1
Doorenbos J, Pruitt W (1977) Crop water requirements. FAO
Duan H, Zhao H, Li Q, Xu H, Han C (2023) Estimation of evapotranspiration based on a modified Penman–Monteith–Leuning model using surface and root zone soil moisture. Water 15:1418. https://doi.org/10.3390/w15071418
FAO (1998) Crop evapotranspiration: guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56
Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J Hydrol 377:80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
Habeeb R, Zhang X, Hussain I, Hashmi MZ, Elashkar EE, Khader JA, Soudagar SS, Shoukry AM, Ali Z, Al-Deek FF (2021) Statistical analysis of modified Hargreaves equation for precise estimation of reference evapotranspiration. Tellus Dyn Meteorol Oceanogr 73:1966869. https://doi.org/10.1080/16000870.2021.1966869
Hafeez M, Khan AA (2019) Assessment of hargreaves and blaney-criddle methods to estimate reference evapotranspiration under coastal conditions. Am J Sci
Hamed KH (2008) Trend detection in hydrologic data: the Mann-Kendall trend test under the scaling hypothesis. J Hydrol 349:350–363. https://doi.org/10.1016/j.jhydrol.2007.11.009
Hamed KH, RamachandraRao A (1998) A modified Mann-Kendall trend test for autocorrelated data. J Hydrol 204:182–196. https://doi.org/10.1016/S0022-1694(97)00125-X
Hargreaves GH, Allen RG (2003) History and evaluation of Hargreaves evapotranspiration equation. J Irrig Drain Eng 129:53–63. https://doi.org/10.1061/(ASCE)0733-9437(2003)129:1(53)
Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from ambient air temperature. https://api.semanticscholar.org/CorpusID:183987608
India: climate change impacts (2013) https://www.worldbank.org/en/news/feature/2013/06/19/india-climate-change-impacts
IPCC (ed) (2000) Emissions scenarios: summary for policymakers;a special report of IPCC Working Group III$Intergovernmental Panel on Climate Change, IPCC special report. Intergovernmental Panel on Climate Change
IPCC (2022) Framing and context. In: Global warming of 1.5°C: IPCC special report on impacts of global warming of 1.5°C above pre-industrial levels in context of strengthening response to climate change, sustainable development, and efforts to eradicate poverty. Cambridge University Press. pp 49–92. https://doi.org/10.1017/9781009157940.003
Islam S, Alam AKMR (2021) Performance evaluation of FAO Penman-Monteith and best alternative models for estimating reference evapotranspiration in Bangladesh. Heliyon 7:e07487. https://doi.org/10.1016/j.heliyon.2021.e07487
Jagosz BL, Rolbiecki S, Figas A, Ptach W, Rolbiecki R, Stachowski P, Kasperska-Wołowicz W, Grybauskiene V, Klimek A, Dobosz K (2019) Water needs of Sambucus nigra L. grown in the reclaimed areas in Poland. Folia Hortic 31:269–276. https://doi.org/10.2478/fhort-2019-0021
Jung C-G, Lee D-R, Moon J-W (2016) Comparison of the Penman-Monteith method and regional calibration of the Hargreaves equation for actual evapotranspiration using SWAT-simulated results in the Seolma-cheon basin, South Korea. Hydrol Sci J 61:793–800. https://doi.org/10.1080/02626667.2014.943231
Liu H, Song D, Kong J, Mu Z, Zhang Q, Wang X (2022) Spatiotemporal variation in actual evapotranspiration and the influencing factors in Ningxia from 2001 to 2020. Int J Environ Res Public Health 19:12693. https://doi.org/10.3390/ijerph191912693
Machado FDS, Campos TR, Duarte TP, Arrieta FRP, Júnior PAAM (2018) Experimental determination of the convective coefficient of heat transfer using the global capacitance method. Int J Adv Eng Res Sci 5:241–245. https://doi.org/10.22161/ijaers.5.6.38
Mishra V, Udit Bhatia, Tiwari AD (2020) Bias corrected climate projections from CMIP6 models for Indian sub-continental river basins. https://doi.org/10.5281/ZENODO.3874046
Moeletsi ME, Walker S, Hamandawana H (2013) Comparison of the Hargreaves and Samani equation and the Thornthwaite equation for estimating dekadal evapotranspiration in the Free State Province, South Africa. Phys Chem Earth Parts ABC 66:4–15. https://doi.org/10.1016/j.pce.2013.08.003
Mondal SK, Tao H, Huang J, Wang Y, Su B, Zhai J, Jing C, Wen S, Jiang S, Chen Z, Jiang T (2021) Projected changes in temperature, precipitation and potential evapotranspiration across Indus River Basin at 1.5–3.0 °C warming levels using CMIP6-GCMs. Sci Total Environ 789:147867. https://doi.org/10.1016/j.scitotenv.2021.147867
Moratiel R, Bravo R, Saa A, Tarquis AM, Almorox J (2019) Estimation of evapotranspiration by FAO Penman–Monteith Temperature and Hargreaves–Samani models under temporal and spatial criteria. A case study in Duero Basin (Spain) (preprint). Hydrol Hazards. https://doi.org/10.5194/nhess-2019-250
Ndiaye PM, Bodian A, Diop L, Dezetter A, Guilpart E, Deme A, Ogilvie A (2021) Future trend and sensitivity analysis of evapotranspiration in the Senegal River Basin. J Hydrol Reg Stud 35:100820. https://doi.org/10.1016/j.ejrh.2021.100820
Nikolaou G, Neocleous D, Kitta E, Katsoulas N (2023) Assessment of the Priestley-Taylor coefficient and a modified potential evapotranspiration model. Smart Agric Technol 3:100075. https://doi.org/10.1016/j.atech.2022.100075
NóiaJúnior RDS, Fraisse CW, Cerbaro VA, Karrei MAZ, Guindin N (2019) Evaluation of the Hargreaves-Samani method for estimating reference evapotranspiration with ground and gridded weather data sources. Appl Eng Agric 35:823–835. https://doi.org/10.13031/aea.13363
Nooni IK, Hagan DFT, Wang G, Ullah W, Lu J, Li S, Dzakpasu M, Prempeh NA, Slim Kam Sian KTC (2021) Future changes in simulated evapotranspiration across continental Africa based on CMIP6 CNRM-CM6. Int J Environ Res Public Health 18:6760. https://doi.org/10.3390/ijerph18136760
Nusantara D, Nadiar F (2020) Using ANN to evaluate the climate data that high affect on calculate daily potential evapotranspiration with Modified-Penman method in the tropical regions. J Phys Conf Ser 1569:042028. https://doi.org/10.1088/1742-6596/1569/4/042028
Pachauri RK, Mayer L, Intergovernmental Panel on Climate Change (eds) (2015) Climate change 2014: synthesis report. Intergovernmental Panel on Climate Change, Geneva, Switzerland
Pettitt AN (1979) A non-parametric approach to the change-point problem. Appl Stat 28:126. https://doi.org/10.2307/2346729
Riahi K, van Vuuren DP, Kriegler E, Edmonds J, O’Neill BC, Fujimori S, Bauer N, Calvin K, Dellink R, Fricko O, Lutz W, Popp A, Cuaresma JC, Kc S, Leimbach M, Jiang L, Kram T, Rao S, Emmerling J, Ebi K, Hasegawa T, Havlik P, Humpenöder F, Da Silva LA, Smith S, Stehfest E, Bosetti V, Eom J, Gernaat D, Masui T, Rogelj J, Strefler J, Drouet L, Krey V, Luderer G, Harmsen M, Takahashi K, Baumstark L, Doelman JC, Kainuma M, Klimont Z, Marangoni G, Lotze-Campen H, Obersteiner M, Tabeau A, Tavoni M (2017) The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob Environ Change 42:153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009
Rodrigues GC, Braga RP (2021) Estimation of reference evapotranspiration during the irrigation season using nine temperature-based methods in a hot-summer Mediterranean climate. Agriculture 11:124. https://doi.org/10.3390/agriculture11020124
Sammis T (2011) The transition of the Blaney-Criddle formula to the Penman-Monteith equation in the western United States. J Appl Serv Climatol 2011. https://doi.org/10.46275/JoASC.2011.02.001
Shiogama H, Fujimori S, Hasegawa T, Hayashi M, Hirabayashi Y, Ogura T, Iizumi T, Takahashi K, Takemura T (2023) Important distinctiveness of SSP3–7.0 for use in impact assessments. Nat Clim Change 13:1276–1278. https://doi.org/10.1038/s41558-023-01883-2
Shweta S, Chand SK, Nayak SK, Chand S (2023) Climate change and its impact. In: Rai PK (ed) Advances in water resource planning and sustainability. Springer Nature Singapore, Singapore. pp 153–168. https://doi.org/10.1007/978-981-99-3660-1_9
Singh D, Vardhan M, Sahu R, Chatterjee D, Chauhan P, Liu S (2023) Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data. Hydrol Earth Syst Sci 27:1047–1075. https://doi.org/10.5194/hess-27-1047-2023
Soltani K, Masoompour Samakosh J, Mojarrad F, Hadi Pour S, Jalilian A (2023) Spatial changes of seasonal reference evapotranspiration in Iran based on CMIP6 models. فیزیک زمین و فضا. https://doi.org/10.22059/jesphys.2023.364373.1007556
Srivastava AK, Rajeevan M, Kshirsagar SR (2009) Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos Sci Lett n/a-n/a. https://doi.org/10.1002/asl.232
Steduto P (2012) Coping with water scarcity: an action framework for agriculture and food security, FAO water reports. FAO, Rome
Tabari H, Talaee PH (2011) Local calibration of the Hargreaves and Priestley-Taylor equations for estimating reference evapotranspiration in arid and cold climates of Iran based on the Penman-Monteith model. J Hydrol Eng 16:837–845. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000366
Ulloa A, van Maanen N, Vora S, Yashodha Y (2022) Review editors: Blanca Elena Jimenez Cisneros (France/Mexico), Zbigniew Kundzewicz (Poland)
Wang X (2014) Climate change trend and its effects on reference evapotranspiration at Linhe Station, Hetao Irrigation District 7
Watanabe S (2023) Tree-structured parzen estimator: understanding its algorithm components and their roles for better empirical performance. rXiv. http://arxiv.org/abs/2304.11127
Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194. https://doi.org/10.1080/02723646.1981.10642213
World Bank (2021) Climate risk country profile: India. World Bank
Wu H, Zhu W, Huang B (2021) Seasonal variation of evapotranspiration, Priestley-Taylor coefficient and crop coefficient in diverse landscapes. Geogr Sustain 2:224–233. https://doi.org/10.1016/j.geosus.2021.09.002
Yahaya I, Li Z, Zhou J, Jiang S, Su B, Huang J, Xu R, Havea PH, Jiang T (2024) Estimations of potential evapotranspiration from CMIP6 multi-model ensemble over Africa. Atmospheric Res 300:107255. https://doi.org/10.1016/j.atmosres.2024.107255
Yeh H-F (2017) Comparison of evapotranspiration methods under limited data. In: Bucur D (ed) Current perspective to predict actual evapotranspiration. InTech. https://doi.org/10.5772/intechopen.68495
Yong SLS, Ng JL, Huang YF, Ang CK (2021) Trend analysis of potential evapotranspiration in peninsular Malaysia. IOP Conf Ser Mater Sci Eng 1101:012008. https://doi.org/10.1088/1757-899X/1101/1/012008
Zhang P, Lu J, Chen X (2022) Machine-learning ensembled CMIP6 projection reveals socio-economic pathways will aggravate global warming and precipitation extreme (preprint). Hydrometeorology/remote Sensing and GIS. https://doi.org/10.5194/hess-2022-235
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A.J performed the analysis, created the visualziations and wrote the manuscript. A.E supervised the work, and edited the paper. Both authors were involved in the conceptualization, analysis and inferring the results and review of the manuscript.
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Highlights
• Ensembled climate model datasets were created using deep learning techniques to best represent observed conditions of the study area.
• Comprehensive qualitative and quantitative trend analyses of ETo were carried out.
• A correlation between temperature difference and reference evapotranspiration was found.
• SSP3-7.0 was found to be the most extreme case climate scenario.
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J, A.K., E, A. Assessing climate change impacts in the Cauvery Basin using evapotranspiration projections and its implications on water management. Theor Appl Climatol (2024). https://doi.org/10.1007/s00704-024-04998-4
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DOI: https://doi.org/10.1007/s00704-024-04998-4