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Stoch Environ Res Risk Assess 27(6):1293–1301. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00477-012-0665-y",{"doi":412},"10.1007\u002Fs00477-012-0665-y",{"id":18,"text":414,"url":18,"identifiers":415},"Zhang DJ, Chen XW, Yao HX, Lin BQ (2015) Improved calibration scheme of SWAT by separating wet and dry seasons. Ecol Model 301:54–61. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolmodel.2015.01.018",{"doi":416},"10.1016\u002Fj.ecolmodel.2015.01.018",{"id":18,"text":418,"url":18,"identifiers":419},"Zhang DM, Guo P (2016) Integrated agriculture water management optimization model for water saving potential analysis. Agric Water Manag 170:5–19. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agwat.2015.11.004",{"doi":420},"10.1016\u002Fj.agwat.2015.11.004",{"id":18,"text":422,"url":18,"identifiers":423},"Zhao M, Huang S, Huang Q, Wang H, Leng G, Liu S, Wang L (2019) Copula-based research on the multi-objective competition mechanism in cascade reservoirs optimal operation. Water 11(5). https:\u002F\u002Fdoi.org\u002F10.3390\u002Fw11050995",{"doi":424},"10.3390\u002Fw11050995",{"id":18,"text":426,"url":18,"identifiers":427},"Zhao XH, Chen X (2015) Auto regressive and ensemble empirical mode decomposition hybrid model for annual runoff forecasting. Water Resour Manag 29(8):2913–2926. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11269-015-0977-z",{"doi":428},"10.1007\u002Fs11269-015-0977-z",{"id":18,"text":430,"url":18,"identifiers":431},"Zhou YL, Guo SL, Hong XJ, Chang FJ (2017) Systematic impact assessment on inter-basin water transfer projects of the Hanjiang River Basin in China. J Hydrol 553:584–595. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2017.08.039",{"doi":432},"10.1016\u002Fj.jhydrol.2017.08.039",{"id":18,"text":434,"url":18,"identifiers":435},"Zhu YP, Zhang HP, Chen L, Zhao JF (2008) Influence of the South-North Water Diversion Project and the mitigation projects on the water quality of Han River. Sci Total Environ 406(1–2):57–68. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.scitotenv.2008.08.008",{"doi":436},"10.1016\u002Fj.scitotenv.2008.08.008",false,{"id":439,"createTime":440,"updateTime":441,"relativeEntities":442,"slug":443,"properties":444,"entityType":135,"verifyStatus":136,"verifyTime":441,"verifyNote":137,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":453,"fullTextUrl":18,"authors":454,"publicationType":233,"publisherRelationship":517,"citationCount":18,"citationInfo":18,"publishDate":550,"publishYear":551,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":437},"3e1cc3af-c84d-4a72-950c-a874f8c93209","2023-12-05T18:42:03.374+00:00","2025-01-03T23:59:26.172+00:00",[],"Adapting-Water-Allocation-to-Irrigation-Demands-to-Constraints-in-Water-Availability-Imposed-by-Climate-Change",{"references":445,"abstract":447,"title":449,"doi":451},{"VOID":446},"Alcamo J, Döll P, Henrichs T et al (2003) Global estimates of water withdrawals and availability under current and future “business-as-usual” conditions. Hydrological 48:339–348\nAlcamo J, Flörke M, Märker M (2007) Future long-term changes in global water resources driven by socio-economic and climatic changes. Hydrol Sci J 37–41\nAndreu J, Capilla J, Sanchis E (1996) AQUATOOL, a generalized decision-support system for water-resources planning and operational management. J Hydrol 177:269–291. doi:10.1016\u002F0022-1694(95)02963-X\nBehera UK, Panigrahi P, Sarangi A (2012) Multiple Water Use Protocols in Integrated Farming System for Enhancing Productivity. Water Resour Manag 26:2605–2623. doi: 10.1007\u002Fs11269-012-0035-z\nBerbel J, Gómez-Limón J (2000) The impact of water-pricing policy in Spain: an analysis of three irrigated areas. Agric Water Manag 43:219–238. doi:10.1016\u002FS0378-3774(99)00056-6\nBerbel J, Martin-Ortega J, Mesa P (2010) A Cost-Effectiveness Analysis of Water-Saving Measures for the Water Framework Directive: the Case of the Guadalquivir River Basin in Southern Spain. Water Resour Manag 25:623–640. doi:10.1007\u002Fs11269-010-9717-6\nBerbel J, Mesa-Jurado MA, Pistón JM (2011) Value of Irrigation Water in Guadalquivir Basin (Spain) by Residual Value Method. Water Resour Manag 25:1565–1579. doi:10.1007\u002Fs11269-010-9761-2\nBhat A, Blomquist W (2004) Policy, politics, and water management in the Guadalquivir River Basin, Spain. Water Resour Res 40:n\u002Fa–n\u002Fa. doi: 10.1029\u002F2003WR002726\nCamacho Poyato E (1995) Análisis de la eficiencia y el ahorro de agua en el regadío de la cuenca del Guadalquivir. Inversiones en la modernización de regadíos. FERAGUA, España. Agricultura 880–886.\nChavez-Jimenez A, De Lama B, Garrote L et al (2013) Characterisation of the sensitivity of water resources systems to climate change. Water Resour Manag 27:4237–4258. doi:10.1007\u002Fs11269-013-0404-2\nChen C, Wang E, Yu Q (2010) Modelling the effects of climate variability and water management on crop water productivity and water balance in the North China Plain. Agric Water Manag 97:1175–1184. doi:10.1016\u002Fj.agwat.2008.11.012\nCheng H, Hu Y (2011) Improving China’s water resources management for better adaptation to climate change. Clim Change 112:253–282. doi:10.1007\u002Fs10584-011-0042-8\nD’Agostino D, Scardigno A, Lamaddalena N, El Chami D (2014) Sensitivity analysis of coupled hydro-economic models: quantifying climate change uncertainty for decision-making. Water Resour Manag 4303–4318. doi: 10.1007\u002Fs11269-014-0748-2\nDworak T, Berglund M, Cornelius L, et al. (2007) Final report EU Water saving potential (Part 1 – Report). ENV.D.2\u002FETU\u002F2007\u002F0001r. 1–247.\nEl Chami D, Scardigno A, Malorgio G (2011) Impacts of Combined Technical and Economic Measures on Water Saving in Agriculture under Water Availability Uncertainty. Water Resour Manag 25:3911–3929. doi:10.1007\u002Fs11269-011-9894-y\nEstrela T, Quintas L (1996) El sistema integrad de modelización precipitación escorrentía (SIMPA). Rev Ing Civ 104:43–52\nFischer G, Tubiello FN, van Velthuizen H, Wiberg DA (2007) Climate change impacts on irrigation water requirements: Effects of mitigation, 1990–2080. Technol Forecast Soc Chang 74:1083–1107. doi:10.1016\u002Fj.techfore.2006.05.021\nGarcía de Jalón S, Iglesias A, Quiroga S, Bardají I (2013) Exploring public support for climate change adaptation policies in the Mediterranean region: A case study in Southern Spain. 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Agric Water Manag 96:1275–1284. doi:10.1016\u002Fj.agwat.2009.04.009\nGeorge B, Malano H, Davidson B et al (2011) An integrated hydro-economic modelling framework to evaluate water allocation strategies I: Model development. Agric Water Manag 98:747–758. doi:10.1016\u002Fj.agwat.2010.12.004\nVan Halsema GE, Vincent L (2012) Efficiency and productivity terms for water management: A matter of contextual relativism versus general absolutism. Agric Water Manag 108:9–15. doi:10.1016\u002Fj.agwat.2011.05.016\nHanks RJ (1974) Model for predicting plant yield as influenced by water use. Agron J 66:660–665. doi:10.2134\u002Fagronj1974.00021962006600050017x\nHowden SM, Soussana J-F, Tubiello FN et al (2007) Adapting agriculture to climate change. Proc Natl Acad Sci U S A 104:19691–6. doi:10.1073\u002Fpnas.0701890104\nIglesias A, Garrote L, Diz A et al (2011a) Re-thinking water policy priorities in the Mediterranean region in view of climate change. Environ Sci Pol. doi:10.1016\u002Fj.envsci.2011.02.007\nIglesias A, Quiroga S, Moneo M, Garrote L (2011b) From climate change impacts to the development of adaptation strategies: Challenges for agriculture in Europe. Clim Chang 112:143–168. doi:10.1007\u002Fs10584-011-0344-x\nIPCC (2013) Climate Change 2013: The Physical Science Basis. I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Contribution of Working Group\nMAGRAMA-CHG (2013) Ministerio de Agricultura, Alimentación y Medio Ambiente-Confederación Hidrográfica del Guadalquivir. Plan hidrológico del Guadalquivir 2009–2015. 484\nMartin-Carrasco F, Garrote L, Iglesias A, Mediero L (2012) Diagnosing Causes of Water Scarcity in Complex Water Resources Systems and Identifying Risk Management Actions. Water Resour Manag. doi:10.1007\u002Fs11269-012-0081-6\nMartin-Ortega J, Giannoccaro G, Berbel J (2011) Environmental and Resource Costs Under Water Scarcity Conditions: An Estimation in the Context of the European Water Framework Directive. Water Resour Manag 25:1615–1633. doi:10.1007\u002Fs11269-010-9764-z\nMehta VK, Haden VR, Joyce BA et al (2013) Irrigation demand and supply, given projections of climate and land-use change, in Yolo County, California. Agric Water Manag 117:70–82. doi:10.1016\u002Fj.agwat.2012.10.021\nMolden D, Oweis T, Steduto P et al (2010) Improving agricultural water productivity: Between optimism and caution. Agric Water Manag 97:528–535. doi:10.1016\u002Fj.agwat.2009.03.023\nMontesinos P, Camacho E, Campos B, Rodríguez-Díaz JA (2011) Analysis of Virtual Irrigation Water. Application to Water Resources Management in a Mediterranean River Basin. Water Resour Manag 25:1635–1651. doi:10.1007\u002Fs11269-010-9765-y\nPahl-Wostl C (2007) Transitions towards adaptive management of water facing climate and global change. Water Resour Manag 21:49–62. doi:10.1007\u002Fs11269-006-9040-4\nPlayán E, Mateos L (2006) Modernization and optimization of irrigation systems to increase water productivity. Agric Water Manag 80:100–116. doi:10.1016\u002Fj.agwat.2005.07.007\nPRUDENCE (2007) Prediction of regional scenarios and uncertainties for defining European climate change risks and effects. Project EKV2-CT2001-00132 in the EU 5th Framework program for energy, environment and sustainable development.\nPulido-Calvo I, Gutiérrez-Estrada JC, Savic D (2012) Heuristic Modelling of the Water Resources Management in the Guadalquivir River Basin, Southern Spain. Water Resour Manag 26:185–209. doi:10.1007\u002Fs11269-011-9912-0\nPurkey DR, Joyce B, Vicuna S et al (2007) Robust analysis of future climate change impacts on water for agriculture and other sectors: a case study in the Sacramento Valley. Clim Chang 87:109–122. doi:10.1007\u002Fs10584-007-9375-8\nRodríguez Díaz J, Weatherhead E, Knox J, Camacho E (2007) Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain. Reg Environ Chang 7:149–159. doi:10.1007\u002Fs10113-007-0035-3\nRodríguez N, Sánchez MT, López J (2008) Un análisis de la eficiencia socioeconómica del agua en el regadío andaluz. Rev Española Estud Agrosociales y Pesq 217:183–208\nShao W, Yang D, Hu H, Sanbongi K (2008) Water Resources Allocation Considering the Water Use Flexible Limit to Water Shortage—A Case Study in the Yellow River Basin of China. Water Resour Manag 23:869–880. doi:10.1007\u002Fs11269-008-9304-2\nStrosser P, Roussard J, Grandmougin B, Kossida M, Kyriazopoulou I, Berbel J, Kolberg S, Rodríguez-Díaz J, Montesinos P, Joyce J, Dworak T, Berglund M, Laaser C (2007) Final report EU water saving potential (Part 2 – Case Studies). ENV.D.2\u002FETU\u002F2007\u002F0001r. Berlin\nTarjuelo JM, De-Juan JA, Moreno MA, Ortega JF (2010) Review. Water resources deficit and water engineering. Spanish J Agric Res 8(S2):S102–S121\nWheida E, Verhoeven R (2007) An alternative solution of the water shortage problem in Libya. Water Resour Manag 21:961–982. doi:10.1007\u002Fs11269-006-9067-6\nYilmaz B, Yurdusev MA, Harmancioglu NB (2008) The Assessment of Irrigation Efficiency in Buyuk Menderes Basin. Water Resour Manag 23:1081–1095. doi:10.1007\u002Fs11269-008-9316-y",{"EN":448},"Climate change projections predict a rise in temperatures which may result in a reduction in water resource availability. Irrigation is both the most demanding water use and that which is the lowest priority. Consequently, adaptation measures regarding irrigation demands are required in coping with such a resource decrease. As improvement in water efficiency use could not be enough to counteract strong stream flow reductions, management actions regarding demands may be implemented. This paper proposes a methodology for identifying the required reductions and sequence in which water allocation is to be reduced in order to meet satisfactory system behaviour. Such a methodology could help basin managers in decision making in meeting irrigation demands which, accordingly, could offer better performance in terms of both reliability and productivity. The methodology is applied at the Guadalquivir Basin in Spain, under eight hydrological projections which represent future climate change scenarios. The results show that it is possible to reduce future water scarcity problems and, hence, improve system performance. In addition to this, it is found that optimal reduction sequence is not only affected by water productivity, but also by the system topology which influences reliability. In the case study, the most sensitive demands are those located at the river head. As such demands have no alternative sources, they typically offer the lowest degree of reliability.",{"EN":450},"Adapting Water Allocation to Irrigation Demands to Constraints in Water Availability Imposed by Climate Change",{"VOID":452},"10.1007\u002Fs11269-014-0882-x","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11269-014-0882-x",[455,471,483,495],{"id":456,"sortIndex":182,"researcher":18,"roles":457,"affiliations":459,"properties":468},"33cea58f-6270-4e4b-9eb8-f0e600339d6a",[458],"AUTHOR",[460],{"id":18,"sortIndex":19,"affiliation":461,"properties":18},{"id":462,"createTime":463,"updateTime":463,"relativeEntities":464,"slug":18,"properties":465,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"bd30c5c0-9ab8-4496-a6be-fc53c463cc54","2023-12-05T18:42:03.381+00:00",[],{"title":466},{"VI":467},"Department of Civil Engineering: Hydraulic and Energy Engineering, Technical University of 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N, Khayat S, Natsheh B (2013) Multivariate data analysis to identify the groundwater pollution sources in Tulkarm area\u002FPalestine. Sci Technol 3(4):99–104\nAkrami SA, El-Shafie A, Jaafar O (2013) Improving rainfall forecasting efficiency using modified adaptive neuro-fuzzy inference system (MANFIS). Water Resour Manag 27(9):3507–3523\nAnand A, Suganthi L (2017) Forecasting of electricity demand by hybrid ANN-PSO models. Int J of Ene Opt and Eng (IJEOE) 6(4):66–83\nAyers RS, Westcot DW (1994) Water quality for agriculture. FAO irrig and drain paper. 29 Rev 1\nAzad A, Karami H, Farzin S, Saeedian A, Kashi H, Sayyahi F (2018a) Prediction of water quality parameters using ANFIS optimized by intelligence algorithms (case study: Gorganrood River). KSCE J Civ Eng 22(7):2206–2213\nAzad A, Kashi H, Farzin S, Singh VP, Kisi O, Karami H, Sanikhani H (2018b) Novel approaches for air temperature prediction: comparison of four hybrid evolutionary fuzzy models. Meteorol Appl. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fmet.1736\nAzadi Moghaddam M, Golmezerji R, Kolahan F (2017) Simultaneous optimization of joint edge geometry and process parameters in gas metal arc welding using integrated ANN-PSO approach. Scientia Iranica 24(1):260–273\nBedekar PP, Bhide SR (2011) Optimum coordination of overcurrent relay timing using continuous genetic algorithm. Expert Syst Appl 38(9):11286–11292\nChau KW (2007) Application of a PSO-based neural network in analysis of outcomes of construction claims. Autom Constr 16(5):642–646\nChen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. CATENA 157:310–324\nDorigo M (1992) Optimization, learning and natural algorithms Ph. D. Thesis, Politecnico di Milano, Italy\nEl-Shafie A, Jaafer O, Akrami SA (2011) Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River. Malaysia Int J of Phys Sci 6(12):2875–2888\nHaznedar B, Kalinli A (2016) Training ANFIS using genetic algorithm for dynamic systems dentification. Adv Tech and Sci (IJIASE) 4:44–47\nHe Z, Wen X, Liu H, Du J (2014) A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. J Hydrol 509:379–386\nHolland JH (1975) Adaption in natural and artificial systems. The University of Michigan Press, Ann Arbor\nJalalkamali A (2015) Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters. Earth Sci Inf 8(4):885–894\nJang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst, Man Cybern 23(3):665–685\nKennedy J, Eberhart RC (1995) Particle swarm optimization. Proce of IEEE int conf on neutral net, Perth, Australia 1942–1948\nKheradpisheh Z, Talebi A, Rafati L, Ghaneian MT, Ehrampoush MH (2015) Groundwater quality assessment using artificial neural network: a case study of Bahabad plain, Yazd, Iran. Desert 20(1):65–71\nKisi O, Keshavarzi A, Shiri J, Zounemat-Kermani M, Omran ESE (2017) Groundwater quality modeling using neuro-particle swarm optimization and neuro-differential evolution techniques. Hydrol Res. https:\u002F\u002Fdoi.org\u002F10.2166\u002Fnh.2017.206\nMekanik F, Imteaz MA, Talei A (2016) Seasonal rainfall forecasting by adaptive network-based fuzzy inference system (ANFIS) using large scale climate signals. Clim Dyn 46(9–10):3097–3111\nMirrashid M (2014) Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm. Nat Hazards 74(3):1577–1593\nMousavi SF, Amiri MJ (2012) Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system. Soil Water Resour 7(2):73–83\nNajah A, El-Shafie A, Karim OA, El-Shafie AH (2014) Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring. Environ Sci Pollut Res 21(3):1658–1670\nPeyghami MR, Khanduzi R (2013) Novel MLP neural network with hybrid tabu search algorithm. Neural Network World 3(13):255–270\nRezakazemi M, Dashti A, Asghari M, Shirazian S (2017) H2-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS. Int J Hydrog Energy 42(22):15211–15225\nRuben GB, Zhang K, Bao H, Ma X (2018) Application and sensitivity analysis of artificial neural network for prediction of chemical oxygen demand. Water Resour Manag 32(1):273–283\nSelvi V, Umarani DR (2010) Comparative analysis of ant colony and particle swarm optimization techniques. Int J Comput Appl 5(4):0975–8887\nSocha K, Dorigo M (2008) Ant colony optimization for continuous domains. Eur J Oper Res 185(3):1155–1173\nStorn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359\nTabari MMR (2016) Prediction of river runoff using fuzzy theory and direct search optimization. Arab J Sci Eng 41(10):4039–4051\nZadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353",{"EN":562},"In this study, the application of four evolutionary algorithms, continuous genetic algorithm (CGA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR), and differential evolution (DE) were considered for training and optimization of adaptive neuro-fuzzy inference system (ANFIS) to model groundwater quality variables. At first, using correlation and sensitivity analysis, the best inputs were selected to estimate electrical conductivity (EC), sodium adsorption ratio (SAR) and total hardness (TH). After that, the quality variables were modeled by simple ANFIS and the ANFIS trained by evolutionary algorithms. Finally, the models’ performances were evaluated using determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) and sensitivity analysis. Results indicated that: 1) All the suggested algorithms improved the ANFIS performance in the modeling of EC and TH. Also, in SAR, CGA and PSO had a better performance than existing algorithms of ANFIS. 2) CGA with the most appropriate results, was the best algorithm in improving ANFIS performance for modeling the groundwater quality variables such that the amounts of R2, RMSE, and MAPE were improved by 0.14, 35.4, and 0.59 for TH, by 0.13, 226 (μmho Cm−1), 2.16 for EC, and by 0.15, 690, and 19.04 for SAR, respectively. 3) Sensitivity analysis showed that the results obtained by correlation analysis was dependable and could be used as a primary step in choosing the best input data for prediction of groundwater quality variables.",{"EN":564},"Modeling Groundwater Quality Parameters Using Hybrid Neuro-Fuzzy Methods",{"VOID":566},"10.1007\u002Fs11269-018-2147-6","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11269-018-2147-6",[569,584,599,616,631,643],{"id":570,"sortIndex":19,"researcher":18,"roles":571,"affiliations":572,"properties":581},"f0803c32-f5f4-4a9f-b14e-cb0ac22f0872",[458],[573],{"id":18,"sortIndex":19,"affiliation":574,"properties":18},{"id":575,"createTime":576,"updateTime":576,"relativeEntities":577,"slug":18,"properties":578,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"86e16dfc-99ba-4d40-b872-e883058f153e","2023-12-28T03:42:36.240+00:00",[],{"title":579},{"VI":580},"School of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia",{"title":582},{"VI":583},"Ozgur Kisi",{"id":585,"sortIndex":182,"researcher":18,"roles":586,"affiliations":587,"properties":596},"7267e1da-ef63-4c7f-9d5f-8d1a275c0f6c",[458],[588],{"id":18,"sortIndex":19,"affiliation":589,"properties":18},{"id":590,"createTime":591,"updateTime":591,"relativeEntities":592,"slug":18,"properties":593,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"67f68168-447c-420a-8b00-4b566fd10e0b","2023-12-28T03:42:36.256+00:00",[],{"title":594},{"VI":595},"Depatment of Plant Science, Technology University of Munich, Munich, Germany",{"title":597},{"VI":598},"Hamed Kashi",{"id":600,"sortIndex":165,"researcher":18,"roles":601,"affiliations":602,"properties":613},"ee3439df-a5d4-491a-a0a9-226a2a040083",[458],[603],{"id":18,"sortIndex":19,"affiliation":604,"properties":18},{"id":605,"createTime":606,"updateTime":607,"relativeEntities":608,"slug":609,"properties":610,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"00df10b9-15d4-4183-ad77-53d6ee2c9f67","2024-04-12T03:22:00.239+00:00","2025-06-11T14:21:28.258+00:00",[],"Department-of-Civil-Engineering-Semnan-University-Semnan-Iran",{"title":611},{"EN":612},"Department of Civil Engineering, Semnan University, Semnan, Iran",{"title":614},{"VI":615},"Amir Saeedian",{"id":617,"sortIndex":106,"researcher":18,"roles":618,"affiliations":619,"properties":628},"6b7ffe6f-ae47-4cfc-834f-1a0ab3520d6e",[458],[620],{"id":18,"sortIndex":19,"affiliation":621,"properties":18},{"id":622,"createTime":623,"updateTime":623,"relativeEntities":624,"slug":18,"properties":625,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"65bb8846-d8f4-4808-9f3d-4b8d1a828968","2023-12-28T03:42:36.272+00:00",[],{"title":626},{"VI":627},"Agriculture Research and Education Organization - Natural Resources Research Center of Semnan Province, Semnan, Iran",{"title":629},{"VI":630},"Seyed Ali Asghar Hashemi",{"id":632,"sortIndex":103,"researcher":18,"roles":633,"affiliations":634,"properties":640},"03cfe877-b6f9-4627-87b3-8f46cbd542eb",[458],[635],{"id":18,"sortIndex":19,"affiliation":636,"properties":18},{"id":605,"createTime":606,"updateTime":607,"relativeEntities":637,"slug":609,"properties":638,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":639},{"EN":612},{"title":641},{"VI":642},"Salar Ghorbani",{"id":644,"sortIndex":105,"researcher":18,"roles":645,"affiliations":646,"properties":652},"e3584fd1-3911-43ea-8adf-a87a7272c1bc",[458],[647],{"id":18,"sortIndex":19,"affiliation":648,"properties":18},{"id":605,"createTime":606,"updateTime":607,"relativeEntities":649,"slug":609,"properties":650,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":651},{"EN":612},{"title":653},{"VI":654},"Armin Azad",{"url":567,"publisher":656,"properties":683},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":657,"slug":10,"properties":658,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":661,"manageAffiliations":662,"indexDatabases":663,"url":18,"thumbnailPath":18,"statistic":678,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":659,"title":660},{"VOID":13},{"EN":15},[],[],[664,671],{"id":82,"indexDatabase":665,"url":95,"indexYears":96,"academicFieldIds":670,"indexDatabaseRanking":100},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":666,"label":667,"description":668,"key":92,"publicationTags":669,"standard":18},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99],{"id":62,"indexDatabase":672,"url":77,"indexYears":18,"academicFieldIds":677,"indexDatabaseRanking":18},{"id":64,"createTime":65,"updateTime":66,"relativeEntities":673,"label":674,"description":675,"key":73,"publicationTags":676,"standard":18},[],{"EN":69,"VI":69},{"VI":71,"EN":72},[75,76],[79,80],{"impactFactor":19,"impactFactorByYear":679,"i10Index":103,"i10IndexLast5Year":19,"totalPublication":103,"totalPublicationByYear":680,"totalCitation":107,"totalCitationByYear":681,"totalCitationPerPublication":111,"totalCitationPerPublicationByYear":682,"hindexLast5Year":103,"hindex":103},{},{"2003":105,"2004":106},{"2003":109,"2004":110},{"2003":109,"2004":113},{"volume":684,"pages":686},{"VOID":685},"33",{"VOID":687},"847-861","2018-12-17",2018,{"id":691,"createTime":692,"updateTime":693,"relativeEntities":694,"slug":695,"properties":696,"entityType":135,"verifyStatus":136,"verifyTime":693,"verifyNote":137,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":705,"fullTextUrl":18,"authors":706,"publicationType":233,"publisherRelationship":787,"citationCount":18,"citationInfo":18,"publishDate":820,"publishYear":821,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":437},"8752bc8b-1ce3-4f17-9c1e-4c60f8d2c406","2024-02-15T13:05:41.836+00:00","2025-01-15T23:58:02.759+00:00",[],"Evolutionary-Game-Analysis-of-Tripartite-Cooperation-Strategy-under-Mixed-Development-Environment-of-Cascade-Hydropower-Stations",{"references":697,"abstract":699,"title":701,"doi":703},{"VOID":698},"Buchholz M, Holst G, Musshoff O (2016) Irrigation water policy analysis using a business simulation game. 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J Hydrol 336(3–4):269–281. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2007.01.003\nLi F, Pan B, Wu Y, Shan L (2017) Application of game model for stakeholder management in construction of ecological corridors: a case study on Yangtze River basin in China. Habitat Int 63:113–121. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.habitatint.2017.03.011\nLiu L, Sun LX (2013) Benefit compensation of construction diversion in upstream flood discharge control based on improved shapley method, pp 2202–2206, Manchester, ENGLAND\nLiu L, Feng C, Zhang H, Zhang X (2015) Game analysis and simulation of the river basin sustainable development strategy integrating water emission trading. Sustainability 7(5):4952–4972. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu7054952\nLu SB, Shang YZ, Li W, Peng Y, Wu XH (2018) Economic benefit analysis of joint operation of cascaded reservoirs. J Clean Prod 179:731–737. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jclepro.2017.08.140\nLyapunov AM (1992) The general problem of the stability of motion. Int J Control 55(3):531–773. https:\u002F\u002Fdoi.org\u002F10.1080\u002F00207179208934253\nMadani K, Hooshyar M (2014) A game theory-reinforcement learning (GT-RL) method to develop optimal operation policies for multi-operator reservoir systems. J Hydrol 519:732–742. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2014.07.061\nMoridi A, Yazdi J (2017) Optimal allocation of flood control capacity for multi-reservoir systems using multi-objective optimization approach. Water Resour Manag 31(14):4521–4538. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11269-017-1763-x\nMosquera-Lopez S, Uribe JM, Manotas-Duque DF (2018) Effect of stopping hydroelectric power generation on the dynamics of electricity prices: An event study approach. Renew Sustain Energy Rev 94:456–467. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rser.2018.06.021\nNeedham JT, Watkins DW, Lund JR, Nanda SK (2000) Linear programming for flood control in the Iowa and Des Moines rivers. J Water Resour Plan Manag 126(3):118–127. https:\u002F\u002Fdoi.org\u002F10.1061\u002F(asce)0733-9496(2000)126:3(118)\nParsapour-Moghaddam P, Abed-Elmdoust A, Kerachian R (2015) A heuristic evolutionary game theoretic methodology for conjunctive use of surface and groundwater resources. Water Resour Manag 29(11):3905–3918. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11269-015-1035-6\nRichter BD, Thomas GA (2007) Restoring environmental flows by modifying dam operations. Ecol Soc 12(1):12\nShang YZ, Lu SB, Ye YT, Liu RH, Shang L, Liu CN, Meng XY, Li XF, Fan QX (2018) China' energy-water nexus: hydropower generation potential of joint operation of the three gorges and Qingjiang cascade reservoirs. Energy 142:14–32. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.energy.2017.09.131\nShen JJ, Cheng CT, Zhang XF, Zhou BB (2018) Coordinated operations of multiple-reservoir cascaded hydropower plants with cooperation benefit allocation. Energy 153:509–518. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.energy.2018.04.056\nSheng J, Webber M (2017) Incentive-compatible payments for watershed services along the eastern route of China's south-north water transfer project. Ecosyst Serv 25:213–226. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecoser.2017.04.006\nSmith JM (1974) The theory of games and the evolution of animal conflicts. J Theor Biol 47(1):209–221\nSong XH, Shu MD, Wei YM, Liu JP (2018) A study on the multi-agent based comprehensive benefits simulation analysis and synergistic optimization strategy of distributed energy in China. Energies 11(12). https:\u002F\u002Fdoi.org\u002F10.3390\u002Fen11123260\nvan Vliet MTH, Wiberg D, Leduc S, Riahi K (2016) Power-generation system vulnerability and adaptation to changes in climate and water resources. Nat Clim Chang 6(4):375–380. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnclimate2903\nWan YH, Huang SY, Marino MA (1989) Optimal sequencing of development for hydropower stations in cascade. J Water Resour Plan Manag 115(3):379–395. https:\u002F\u002Fdoi.org\u002F10.1061\u002F(asce)0733-9496(1989)115:3(379)\nWang YK, Zhang N, Wang D, Wu JC, Zhang X (2018) Investigating the impacts of cascade hydropower development on the natural flow regime in the Yangtze River, China. Sci Total Environ 624:1187–1194. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.scitotenv.2017.12.212\nWasimi SA, Kitanidis PK (1983) Real-time forecasting and daily operation of a multireservoir system during floods by linear quadratic gaussian control. 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Water Resour Manag 32(14):4625–4642. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11269-018-2075-5\nYu B, Xu LY (2016) Review of ecological compensation in hydropower development. Renew Sustain Energy Rev 55:729–738. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rser.2015.10.038\nZeng Y, Li JB, Cai YP, Tan Q, Dai C (2019) A hybrid game theory and mathematical programming model for solving trans-boundary water conflicts. J Hydrol 570:666–681. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2018.12.053\nZhang HM, Xu ZD, Zhou DQ, Cao J (2017) Waste cooking oil-to-energy under incomplete information: identifying policy options through an evolutionary game. Appl Energy 185:547–555. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.apenergy.2016.10.133\nZhou YL, Guo SL, Chang FJ, Liu P, Chen AB (2018) Methodology that improves water utilization and hydropower generation without increasing flood risk in mega cascade reservoirs. Energy 143:785–796. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.energy.2017.11.035",{"EN":700},"Joint operation of cascade hydropower stations maximizes the utilization rate of water resources of a river basin and the benefit of the entire river system. However, under mixed development environment of cascade hydropower stations, i.e. simultaneous existence of operating and under-construction hydropower stations, the difficulty of the joint operation is increased. Moreover, this difficulty is further enhanced due to the cooperation among multiple stakeholders and uncertain evolutionary characteristic of stakeholder’s strategy. To handle these problems, this paper takes two upstream operating hydropower stations and one downstream hydropower station under construction as research objects, where one of upstream hydropower station locates in a tributary. First, all possible strategy combinations among these three stakeholders are comprehensively analyzed, and the benefit of each stakeholder strategy under each strategy combination is respectively calculated. A tripartite evolutionary game model is then established. It aims at exploring directions and conditions of cooperative and non-cooperative strategies evolving into stable states. Finally, the exploration results find that the strategy evolution of a stakeholder relies on its partners’ behaviors and net benefit of self-behavior; the tripartite cooperation will eventually form four stable states; the conditions for cooperation between upstream and downstream hydropower stations are that the compensation paid by downstream hydropower station is greater than the loss of upstream power generation and downstream project benefit is greater than the sum of compensation expenditure and risk benefit.",{"EN":702},"Evolutionary Game Analysis of Tripartite Cooperation Strategy under Mixed Development Environment of Cascade Hydropower Stations",{"VOID":704},"10.1007\u002Fs11269-020-02537-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11269-020-02537-0",[707,734,749,768],{"id":708,"sortIndex":105,"researcher":18,"roles":709,"affiliations":710,"properties":731},"36fcc0c2-e4fa-4a3a-aabf-8f0fd6e94db4",[458],[711,721],{"id":712,"sortIndex":105,"affiliation":713,"properties":720},"e0d1be2f-657b-426c-a67d-db4070beaef9",{"id":714,"createTime":715,"updateTime":715,"relativeEntities":716,"slug":18,"properties":717,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"a06bcc49-1a33-4a65-a097-b35f1daaaf00","2023-12-25T07:46:10.608+00:00",[],{"title":718},{"VI":719},"School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan, China",{},{"id":18,"sortIndex":19,"affiliation":722,"properties":18},{"id":723,"createTime":724,"updateTime":725,"relativeEntities":726,"slug":727,"properties":728,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"19c43464-63d1-45a7-960e-2e15aac6f4f6","2024-01-02T02:58:34.435+00:00","2025-02-10T16:43:11.016+00:00",[],"State-Key-Laboratory-of-Water-Resources-and-Hydropower-Engineering-Science-Wuhan-University-Wuhan-China",{"title":729},{"VI":730},"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China",{"title":732},{"VI":733},"Zhigen Hu",{"id":735,"sortIndex":165,"researcher":18,"roles":736,"affiliations":737,"properties":746},"c473cdc4-c8ea-4e17-9dc0-20223ca43e5e",[458],[738],{"id":18,"sortIndex":19,"affiliation":739,"properties":18},{"id":740,"createTime":741,"updateTime":741,"relativeEntities":742,"slug":18,"properties":743,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"0f687790-f8fd-46b8-82b6-d956b2e91a66","2023-12-01T22:25:30.144+00:00",[],{"title":744},{"VI":745},"College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, China",{"title":747},{"VI":748},"Shu Chen",{"id":750,"sortIndex":19,"researcher":18,"roles":751,"affiliations":752,"properties":765},"23bf01ef-3b74-4036-abb0-46cc52a50b1c",[458],[753,760],{"id":754,"sortIndex":105,"affiliation":755,"properties":759},"fdf92e26-7469-42e9-9687-4a3bd53d9acd",{"id":714,"createTime":715,"updateTime":715,"relativeEntities":756,"slug":18,"properties":757,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":758},{"VI":719},{},{"id":18,"sortIndex":19,"affiliation":761,"properties":18},{"id":723,"createTime":724,"updateTime":725,"relativeEntities":762,"slug":727,"properties":763,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":764},{"VI":730},{"title":766},{"VI":767},"Yun 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Liu",{"url":705,"publisher":788,"properties":815},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":789,"slug":10,"properties":790,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":793,"manageAffiliations":794,"indexDatabases":795,"url":18,"thumbnailPath":18,"statistic":810,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":791,"title":792},{"VOID":13},{"EN":15},[],[],[796,803],{"id":82,"indexDatabase":797,"url":95,"indexYears":96,"academicFieldIds":802,"indexDatabaseRanking":100},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":798,"label":799,"description":800,"key":92,"publicationTags":801,"standard":18},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99],{"id":62,"indexDatabase":804,"url":77,"indexYears":18,"academicFieldIds":809,"indexDatabaseRanking":18},{"id":64,"createTime":65,"updateTime":66,"relativeEntities":805,"label":806,"description":807,"key":73,"publicationTags":808,"standard":18},[],{"EN":69,"VI":69},{"VI":71,"EN":72},[75,76],[79,80],{"impactFactor":19,"impactFactorByYear":811,"i10Index":103,"i10IndexLast5Year":19,"totalPublication":103,"totalPublicationByYear":812,"totalCitation":107,"totalCitationByYear":813,"totalCitationPerPublication":111,"totalCitationPerPublicationByYear":814,"hindexLast5Year":103,"hindex":103},{},{"2003":105,"2004":106},{"2003":109,"2004":110},{"2003":109,"2004":113},{"volume":816,"pages":818},{"VOID":817},"34",{"VOID":819},"1951-1970","2020-04-29",2020,{"id":823,"createTime":824,"updateTime":825,"relativeEntities":826,"slug":827,"properties":828,"entityType":135,"verifyStatus":136,"verifyTime":825,"verifyNote":137,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":837,"fullTextUrl":18,"authors":838,"publicationType":233,"publisherRelationship":893,"citationCount":18,"citationInfo":18,"publishDate":926,"publishYear":689,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":437},"47986507-7184-4848-8bd0-521a68be3ddf","2023-12-09T01:42:00.052+00:00","2024-12-19T23:57:51.818+00:00",[],"Analysis-of-Effective-Environmental-Flow-Release-Strategies-for-Lake-Urmia-Restoration",{"references":829,"abstract":831,"title":833,"doi":835},{"VOID":830},"Abbaspour M, Nazaridoust A (2007) Determination of environmental water requirements of Lake Urmia, Iran: An ecological approach. Int J Environ Stud 64:161–169\nAbbaspour M, Javid AH, Mirbagheri SA, Givi FA, Moghimi P (2012) Investigation of lake drying attributed to climate change. Int J Environ Sci Technol 9:257–266\nFathian F, Morid S, Kahya E (2014) Identification of trends in hydrological and climatic variables in Urmia Lake basin, Iran. Theor Appl Climatol 119:443–464\nFazel N, Berndtsson R, Uvo CB, Madani K, Kløve B (2017a) Regionalization of precipitation characteristics in Iran’s Lake Urmia basin. Theor Appl Climatol:1–11\nFazel N, Torabi Haghighi A, Kløve B (2017b) Analysis of land use and climate change impacts by comparing river flow records for headwaters and lowland reaches. Glob Planet Chang 158:47–56\nHassanzadeh E, Zarghami M, Hassanzadeh Y (2012) Determining the main factors in declining the Urmia Lake level by using system dynamics modeling. Water Resour Manag 26:129–145\nKakahaji H, Banadaki HD, Kakahaji A, Kakahaji A (2013) Prediction of Urmia Lake water-level fluctuations by using analytical, linear statistic and intelligent methods. Water Resour Manag 27:4469–4492\nKarbassi A, Bidhendi GN, Pejman A, Bidhendi ME (2010) Environmental impacts of desalination on the ecology of Lake Urmia. J Great Lakes Res 36:419–424\nKohler MA, Nordenson TJ, Fox WE (1955) Evaporation from pans and lakes. Research paper\u002FU.S. Department of Commerce, Weather Bureau, Washington, DC, pp 1–16\nLytle DA, Poff NL (2004) Adaptation to natural flow regimes. Trends Ecol Evol 19:94–100\nMadani K (2014) Water management in Iran: what is causing the looming crisis? J Environ Stud Sci 4:315–328\nMenberu MW, Torabi Haghighi A, Ronkanen A, Kværner J, Kløve B (2014) Runoff curve numbers for peat-dominated watersheds. J Hydrol Eng 040:4058-1-04014058-10\nNouri H, Mason RJ, Moradi N (2017) Land suitability evaluation for changing spatial organization in Urmia County towards conservation of Urmia Lake. Appl Geogr 81:1–12\nOWWMP (2011a) Iran’s comprehensive water resources plan. Agricultural water use (Lake Urmia Watershed) report. Iran Ministry of Energy’s Office for Water and Wastewater Macro-Planning\nOWWMP (2011b) Iran’s comprehensive water resources plan. Meteorological report. Iran Ministry of Energy’s Office for Water and Wastewater Macro-Planning\nOWWMP (2011c) Iran’s comprehensive water resources plan. Groundwater studies (Lake Urmia Watershed) report. Iran Ministry of Energy’s Office for Water and Wastewater Macro-Planning\nPoff NL, Allan JD, Bain MB, Karr JR, Prestegaard KL, Richter BD, Sparks RE, Stromberg JC (1997) The natural flow regime: a paradigm for river conservation and restoration. Bioscience 47:769–784\nSima S, Tajrishy M (2013) Using satellite data to extract volume–area–elevation relationships for Urmia Lake, Iran. J Great Lakes Res 39:90–99\nTennant DL (1976) Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries 1:1–10\nTharme RE (2003) A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River Res Appl 19:397–441\nTorabi Haghighi A, Kløve B (2015) A sensitivity analysis of lake water level response to changes in climate and river regimes. Limnologica 51:118–130\nTorabi Haghighi A, Kløve B (2017) Design of environmental flow regimes to maintain lakes and wetlands in regions with high seasonal irrigation demand. Ecol Eng 100:120–129\nTorabi Haghighi A, Menberu MW, Aminnezhad M, Marttila H, Kløve B (2016) Can lake sensitivity to desiccation be predicted from lake geometry? J Hydrol 539:599–610\nTourian MJ, Elmi O, Chen Q, Devaraju B, Roohi S, Sneeuw N (2015) A spaceborne multisensor approach to monitor the desiccation of Lake Urmia in Iran. Remote Sens Environ 156:349–360\nUNEP, GEAS (2012) The drying of Iran's Lake Urmia and its environmental consequences. Environmental Development 2:128–137\nWebb EK (1966) A pan-lake evaporation relationship. J Hydrol 4:1–11\nZarghami M (2011) Effective watershed management; case study of Urmia Lake, Iran. Lake Reserv Manage 27:87–94",{"EN":832},"Saline lakes have diminished considerably due to large-scale irrigation projects throughout the world. Environmental flow (EF) release from upstream reservoirs could help conserve and restore these lakes. However, experiences from regions lacking environmental legislation or with insufficient water resources management show that, despite EF allocation, farmers tend to use all available water for agriculture. In this study, we employed a new method for designing environmental flow release strategies to restore desiccated terminal lakes in arid and semi-arid regions with intensive cultivation within the catchment. The novelty of the method is that it takes into account farmers’ water use behavior and the natural flow regime in upstream systems to design an optimum monthly EF release strategy for reservoirs. We applied the method to the water resource system of Lake Urmia, once the largest saline lake in the Middle East and now one of the most endangered saline lakes in the world. The analysis showed that the EF released is exploited by lowland farmers before reaching Lake Urmia and that inflow to the lake from some rivers has decreased by up to 80%. We propose a new EF release strategy that requires a considerable change in practice whereby water is released in the shortest possible time (according to reservoir outlet capacity) during the period of lowest irrigation demand in winter. Restoring the lake to minimum ecological level would require 2.4–3.4 km3 EF allocation by different methods of release based on the recent condition (2002–2011) of the lake.",{"EN":834},"Analysis of Effective Environmental Flow Release Strategies for Lake Urmia Restoration",{"VOID":836},"10.1007\u002Fs11269-018-2008-3","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs11269-018-2008-3",[839,854,869,881],{"id":840,"sortIndex":182,"researcher":18,"roles":841,"affiliations":842,"properties":851},"263de114-81f4-4a51-a271-a2c81a7a67cb",[458],[843],{"id":18,"sortIndex":19,"affiliation":844,"properties":18},{"id":845,"createTime":846,"updateTime":846,"relativeEntities":847,"slug":18,"properties":848,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"7d2a88ef-6e93-4fa9-94af-c3b0748145ec","2024-01-08T18:07:24.621+00:00",[],{"title":849},{"VI":850},"Department of Civil and Environmental Engineering, Shiraz University of Technology, Shiraz, Iran",{"title":852},{"VI":853},"Ali Akbar Hekmatzadeh",{"id":855,"sortIndex":105,"researcher":18,"roles":856,"affiliations":857,"properties":866},"17e03818-451a-4027-aebd-32dcf84c1a44",[458],[858],{"id":18,"sortIndex":19,"affiliation":859,"properties":18},{"id":860,"createTime":861,"updateTime":861,"relativeEntities":862,"slug":18,"properties":863,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"14df7180-0e5e-4941-a24c-3dec775d6556","2023-12-09T01:42:00.060+00:00",[],{"title":864},{"VI":865},"Water Resources and Environmental Engineering Research Unit, Faculty of Technology, University of Oulu, Oulu, Finland",{"title":867},{"VI":868},"Nasim Fazel",{"id":870,"sortIndex":19,"researcher":18,"roles":871,"affiliations":872,"properties":878},"0ba44daf-fa8a-470b-964e-1c8eb77b855b",[458],[873],{"id":18,"sortIndex":19,"affiliation":874,"properties":18},{"id":860,"createTime":861,"updateTime":861,"relativeEntities":875,"slug":18,"properties":876,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":877},{"VI":865},{"title":879},{"VI":880},"Ali Torabi Haghighi",{"id":882,"sortIndex":165,"researcher":18,"roles":883,"affiliations":884,"properties":890},"138e64ef-eb41-4cc9-b302-ae83278706c8",[458],[885],{"id":18,"sortIndex":19,"affiliation":886,"properties":18},{"id":860,"createTime":861,"updateTime":861,"relativeEntities":887,"slug":18,"properties":888,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":889},{"VI":865},{"title":891},{"VI":892},"Björn Klöve",{"url":837,"publisher":894,"properties":921},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":895,"slug":10,"properties":896,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":899,"manageAffiliations":900,"indexDatabases":901,"url":18,"thumbnailPath":18,"statistic":916,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":897,"title":898},{"VOID":13},{"EN":15},[],[],[902,909],{"id":82,"indexDatabase":903,"url":95,"indexYears":96,"academicFieldIds":908,"indexDatabaseRanking":100},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":904,"label":905,"description":906,"key":92,"publicationTags":907,"standard":18},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99],{"id":62,"indexDatabase":910,"url":77,"indexYears":18,"academicFieldIds":915,"indexDatabaseRanking":18},{"id":64,"createTime":65,"updateTime":66,"relativeEntities":911,"label":912,"description":913,"key":73,"publicationTags":914,"standard":18},[],{"EN":69,"VI":69},{"VI":71,"EN":72},[75,76],[79,80],{"impactFactor":19,"impactFactorByYear":917,"i10Index":103,"i10IndexLast5Year":19,"totalPublication":103,"totalPublicationByYear":918,"totalCitation":107,"totalCitationByYear":919,"totalCitationPerPublication":111,"totalCitationPerPublicationByYear":920,"hindexLast5Year":103,"hindex":103},{},{"2003":105,"2004":106},{"2003":109,"2004":110},{"2003":109,"2004":113},{"volume":922,"pages":924},{"VOID":923},"32",{"VOID":925},"3595-3609","2018-05-13",{"id":928,"createTime":929,"updateTime":930,"relativeEntities":931,"slug":932,"properties":933,"entityType":135,"verifyStatus":136,"verifyTime":930,"verifyNote":137,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":942,"fullTextUrl":18,"authors":943,"publicationType":233,"publisherRelationship":971,"citationCount":18,"citationInfo":18,"publishDate":1004,"publishYear":1005,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":437},"198d16f2-a7f6-4714-8298-e90dbd200658","2023-12-28T02:33:09.484+00:00","2024-10-17T23:57:51.139+00:00",[],"Two-step-dynamic-programming-approach-for-optimal-irrigation-water-allocation",{"references":934,"abstract":936,"title":938,"doi":940},{"VOID":935},"DoorenbosJ. and KassamA. H., 1979, Yield response to water, Irrigation Drainage Paper 33, Food and Agriculture Organization of the United Nations, Rome.\nDudleyN. J., 1972, Irrigation planning 4. Optimal intraseasonal water allocation, Water Resour. Res. 8, 586–594.\nDudleyN. J., HowellD. T., and MusgraveW. F., 1971a, Optimal intreaseasonal irrigation water allocation, Water Resour. Res. 7, 770–788.\nDudleyN. J., HowellD. T., and MusgraveW. F., 1971b, Irrigation planning 2: Choosing optimal acreages within a season, Water Resour. Res. 7, 1051–1063.\nHallW. A. and ButcherW. S., 1968, Optimal timing of irrigation, J. Irrig. Drain. Div., Am. Soc. Civ. Eng. 94, 267–275.\nHanksR. J., 1974, Model for predicting plant yield as influenced by water use, Agron. J. 66, 660–665.\nHowellT. A. and HilerE. A., 1975, Optimization of water use efficiency under high frequency irrigation I — Evapotranspiration and yield relationship, Trans. ASAE 18, 873–878.\nJensenM. E., 1968, Water consumption by agricultural plants, in T. T.Kozlowskie (ed.), Water Deficit and Plant Growth, Vol. II, Academic Press, New York.\nJonesJ. W. and SmajstrlaA. G., 1980, Application of modeling to irrigation management of soybean, in F. T.Corbin (ed.), World Soybean Research Conference II, Westview Press, Colorado, pp. 571–599.\nKennedyJ. O. S., 1988, Dynamic Programming — Applications to Agriculture and Natural Resources, Elsevier, New York.\nMartinD. L., WattsD. G., and GilleyJ. R., 1984, Model and production function for irrigation management, J. Irrig. Drain. Div. Am. Soc. Civ. Eng. 110, 149–164.\nMinhasB. S., ParikhK. S., and SrinivasanT. N., 1974, Toward the structure of a production function for wheat yields with dated inputs of irrigation water, Water Resour. Res. 10, 383–393.\nNairiziS. and RydzewskiJ. R., 1977, Effects of dated soil moisture stress on crop yields, Exp. Agric. 13, 51–59.\nNagelF. W., 1974, Water yield relation in wheat-growing in Negev (Israel), International Comm. Irrig. Drain. Memoirs 2, 264–309.\nRaoN. H., SarmaP. B. S., and ChanderS., 1988a, A simple dated water production function for use in irrigated agriculture, Agric. Water Manag. 13, 25–342.\nRaoN. H., SarmaP. B. S., and ChanderS., 1988b, Irrigation scheduling under a limited water supply, Agric. Water Manag. 15, 165–175.\nRhenalsA. E. and BrasR. L., 1981, The irrigation scheduling problem and evapotranspiration uncertainty, Water Resour. Res. 17, 1328–1338.\nYaronD. and DinarA., 1982, Optimal allocation of farm irrigation water during peak seasons, Amer. J. Agric. Econ. 64, 681–689.",{"EN":937},"A two-step (deterministic and stochastic) dynamic programming approach has been introduced in this study to solve the complex problem of optimal water allocation in a run-of-the-river-type irrigation project. The complexity of a real-world situation is represented by incorporating in the optimization model the stochasticity of water supply and the nonlinearity of crop production functions. A nonlinear, dated, and multiplicative production function is transformed into a sequentially additive type to replace the usual method of creating an additional ‘state of the plant variable’ which only increases the dimension of the problem. As compared to the explicit stochastic dynamic programming which necessitates, along with its use, an enormous computational complexity due to the so-called ‘curse of dimensionality’, the present model can approximate the theoretical global optimum, at least for the present case study, with a dramatic reduction in computer processing time. It also eliminates the rigidity of the policy derived by the explicit approach, since it provides irrigation planners with alternative decision policies which incorporate intangibles and other nonengineering factors. The traditional method of fixing the cropping pattern based on deterministic estimates of a dependable water supply can likewise be evaluated by the use of the present model. The results of the model's application appear to be practically acceptable.",{"EN":939},"Two-step dynamic programming approach for optimal irrigation water allocation",{"VOID":941},"10.1007\u002FBF00431143","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF00431143",[944,959],{"id":945,"sortIndex":19,"researcher":18,"roles":946,"affiliations":947,"properties":956},"a5e373d4-67f0-47bc-b11d-25a96d2dd9fa",[458],[948],{"id":18,"sortIndex":19,"affiliation":949,"properties":18},{"id":950,"createTime":951,"updateTime":951,"relativeEntities":952,"slug":18,"properties":953,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"1405e03b-f973-4839-8b3e-0de96d3ca446","2023-12-28T02:33:09.504+00:00",[],{"title":954},{"VI":955},"Agricultural Land and Water Development Program, Asian Institute of Technology, Bangkok, Thailand",{"title":957},{"VI":958},"Guna N. 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Hydrol Earth Syst Sci 16:1817–1831. https:\u002F\u002Fdoi.org\u002F10.5194\u002Fhess-16-1817-2012\nAlkhaier F, Schotting RJ, Su Z (2009) A qualitative description of shallow groundwater effect on surface temperature of bare soil. Hydrol Earth Syst Sci 13:1749–1756. https:\u002F\u002Fdoi.org\u002F10.5194\u002Fhess-13-1749-2009\nAllen RG, Pereira LS, Smith M, Raes D, Wright JL (2005) FAO-56 Dual Crop Coefficient Method for Estimating Evaporation from Soil and Application Extensions. J Irrig Drain Eng 131:2–13. https:\u002F\u002Fdoi.org\u002F10.1061\u002F(ASCE)0733-9437(2005)131:1(2)\nAmerican Public Health Association (APHA) (2017) Standard Methods for the Examination of Water and Wastewater. 23rd edition. American Public Health Association, American Water Works Association, and Water Environment Federation, Washington DC (1268 pp. ISBN: 978-0-87553-287-5)\nAssouline S, Narkis K, Gherabli R, Lefort P, Prat M (2014) Analysis of the impact of surface layer properties on evaporation from porous systems using column experiments and modified definition of characteristic length. Water Resour Res 50:3933–3955. https:\u002F\u002Fdoi.org\u002F10.1002\u002F2013WR014489\nAydin M, Yano T, Evrendilek F, Uygur V (2008) Implications of climate change for evaporation from bare soils in a Mediterranean environment. Environ Monit Assess 140:123–130. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-007-9854-4\nBalugani E, Lubczynski MW, Reyes-Acosta L, van der Tol C, Francés AP, Metselaar K (2017) Groundwater and unsaturated zone evaporation and transpiration in a semi-arid open woodland. J Hydrol 547:54–66. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2017.01.042\nBittelli M, Ventura F, Campbell GS, Snyder RL, Gallegati F, Pisa PR (2008) Coupling of heat, water vapor, and liquid water fluxes to compute evaporation in bare soils. J Hydrol 362:191–205. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2008.08.014\nBouwer H, Rice RC (1976) A slug test for determining hydraulic conductivity of unconfined aquifers with completely or partially penetrating wells. Water Resour Res 12:423–428. https:\u002F\u002Fdoi.org\u002F10.1029\u002FWR012i003p00423\nButler JJ, Healey JM (1998) Relationship Between Pumping-Test and Slug-Test Parameters: Scale Effect or Artifact? Ground Water 36:305–312. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1745-6584.1998.tb01096.x\nCarrier WD (2003) Goodbye, Hazen; Hello, Kozeny-Carman. J Geotech Geoenviron Eng 129:1054–1056. https:\u002F\u002Fdoi.org\u002F10.1061\u002F(ASCE)1090-0241(2003)129:11(1054)\nColombani N, Giambastiani BMS, Mastrocicco M (2016) Use of shallow groundwater temperature profiles to infer climate and land use change: interpretation and measurement challenges. Hydrol Process 30:2512–2524. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fhyp.10805\nDoble RC, Crosbie RS (2017) Review: Current and emerging methods for catchment-scale modelling of recharge and evapotranspiration from shallow groundwater. Hydrogeol J 25:3–23. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10040-016-1470-3\nDoherty J (2010) PEST - Model-independent parameter estimation. Version 12. Watermark Computing. Australia. Downloaded from http:\u002F\u002Fwww.pesthomepage.org\u002F\nFetter CW (2001) Applied hydrogeology, 4th edn. Waveland Press Inc., Long Grove, IL\nFlammini A, Corradini C, Morbidelli R, Saltalippi C, Picciafuoco T, Giráldez JV (2018) Experimental analyses of the evaporation dynamics in bare soils under natural conditions. Water Resour Manage 32(3):1153–1166. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11269-017-1860-x\nGiambastiani BMS, Colombani N, Mastrocicco M (2013) Limitation of using heat as a groundwater tracer to define aquifer properties: experiment in a large tank model. Environ Earth Sci 70:719–728. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-012-2157-2\nGong C, Wang W, Zhang Z, Wang H, Luo J, Brunner P (2020) Comparison of field methods for estimating evaporation from bare soil using lysimeters in a semi-arid area. J Hydrol 590:125334. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2020.125334\nHarbaugh AW (2005) MODFLOW-2005, the U.S. Geological Survey modular groundwater model – the Ground-Water Flow Process: U.S. Geol Surv Tech Method 6–A16. https:\u002F\u002Fdoi.org\u002F10.3133\u002Ftm6A16\nHarwell GR (2012) Estimation of evaporation from open water—a review of selected studies. USGS Sci Investig Rep 2012–5202. https:\u002F\u002Fpubs.er.usgs.gov\u002F\nHilhorst MA (2000) A Pore Water Conductivity Sensor. Soil Sci Soc Am J 64:1922–1925. https:\u002F\u002Fdoi.org\u002F10.2136\u002Fsssaj2000.6461922x\nHingerl L, Kunstmann H, Wagner S, Mauder M, Bliefernicht J, Rigon R (2016) Spatio-temporal variability of water and energy fluxes - a case study for a mesoscale catchment in pre-alpine environment. Hydrol Process 30:3804–3823. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fhyp.10893\nJensen ME, Allen RG (Eds.) (2016) Evaporation, Evapotranspiration, and Irrigation Water Requirements. Am Soc Civil Eng Reston VA. https:\u002F\u002Fdoi.org\u002F10.1061\u002F9780784414057\nJin J, Wang Q, Wang J, Otieno D (2019) Tracing water and energy fluxes and reflectance in an arid ecosystem using the integrated model SCOPE. J Environ Manag 231:1082–1090. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jenvman.2018.10.090\nKollet SJ, Maxwell RM (2008) Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model. Water Resour Res 44. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2007WR006004\nKurylyk BL, Irvine DJ, Bense VF (2019) Theory, tools, and multidisciplinary applications for tracing groundwater fluxes from temperature profiles. Wiley Interdiscip. Rev Water 6:e1329. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fwat2.1329\nLangevin CD, Thorne Jr DT, Dausman AM, Sukop MC, Guo W (2008) SEAWAT version 4: a computer program for simulation of multi-species solute and heat transport. Tech Method Book 6 Chap A22 USGS. https:\u002F\u002Fdoi.org\u002F10.3133\u002Ftm6A22\nLarsen MAD, Refsgaard JC, Jensen KH, Butts MB, Stisen S, Mollerup M (2016) Calibration of a distributed hydrology and land surface model using energy flux measurements. Agric For Meteorol 217:74–88. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agrformet.2015.11.012\nLehmann P, Assouline S, Or D (2008) Characteristic lengths affecting evaporative drying of porous media. Phys Rev E 77:056309. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevE.77.056309\nLehmann P, Merlin O, Gentine P, Or D (2018) Soil Texture Effects on Surface Resistance to Bare-Soil Evaporation. Geophys. Res Lett 45:10398–10405. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2018GL078803\nMansell MG, Hussey SW (2005) An investigation of flows and losses within the alluvial sands of ephemeral rivers in Zimbabwe. J Hydrol 314:192–203. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2005.03.015\nMartens B, Miralles DG, Lievens H, van der Schalie R, de Jeu RAM, Fernández-Prieto D, Beck HE, Dorigo WA, Verhoest NEC (2017) GLEAM v3: satellite-based land evaporation and root-zone soil moisture. Geosci Model Dev 10:1903–1925. https:\u002F\u002Fdoi.org\u002F10.5194\u002Fgmd-10-1903-2017\nMastrocicco M, Busico G, Colombani N, Vigliotti M, Ruberti D (2019) Modelling actual and future seawater intrusion in the Variconi coastal wetland (Italy) due to climate and landscape changes. Water 11:1502. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fw11071502\nMcHugh TE, Newell CJ, Landazuri RC, Molofsky LJ, Adamson DT (2012) The influence of seasonal vertical temperature gradients on no-purge sampling of wells. Remediat J 22:21–36. https:\u002F\u002Fdoi.org\u002F10.1002\u002Frem.21328\nMcMillan LA, Rivett MO, Tellam JH, Dumble P, Sharp H (2014) Influence of vertical flows in wells on groundwater sampling. J Contam Hydrol 169:50–61. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jconhyd.2014.05.005\nMoriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans ASABE 50:885–900. https:\u002F\u002Fdoi.org\u002F10.13031\u002F2013.23153\nNeal I (2012) The potential of sand dam road crossings. Dams Reserv 22:129–143. https:\u002F\u002Fdoi.org\u002F10.1680\u002Fdare.13.00004\nParadis D, Lefebvre R, Gloaguen E, Giroux B (2016) Comparison of slug and pumping tests for hydraulic tomography experiments: a practical perspective. Environ Earth Sci 75:1159. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-016-5935-4\nQuinn R, Parker A, Rushton K (2018) Evaporation from bare soil: Lysimeter experiments in sand dams interpreted using conceptual and numerical models. J Hydrol 564:909–915. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2018.07.011\nShah N, Nachabe M, Ross M (2007) Extinction depth and evapotranspiration from ground water under selected land covers. Groundwater 45(3):329–338. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1745-6584.2007.00302.x\nShokri N, Lehmann P, Or D (2010) Evaporation from layered porous media. J Geophys Res 115:B06204. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2009JB006743\nTanji KK (2002) Salinity in the Soil Environment, in: Salinity: Environment - Plants - Molecules. Kluwer Academic Publishers, Dordrecht 21–51. https:\u002F\u002Fdoi.org\u002F10.1007\u002F0-306-48155-3_2\nTodd R (2000) The Bowen ratio-energy balance method for estimating latent heat flux of irrigated alfalfa evaluated in a semi-arid, advective environment. Agric For Meteorol 103:335–348. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0168-1923(00)00139-8\nTrautz AC, Illangasekare TH, Howington S (2018) Experimental testing scale considerations for the investigation of bare-soil evaporation dynamics in the presence of sustained above-ground airflow. Water Resour Res 54(11):8963–8982. https:\u002F\u002Fdoi.org\u002F10.1029\u002F2018WR023102\nTrevisan A, Venema V, Kollet S, Rahman M (2020) The topographic control on land surface energy fluxes: A statistical approach to bias correction. J Hydrol 584:124669. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2020.124669\nWhite WN (1932) Method of estimating groundwater supplies based on discharge by plants and evaporation from soil – Results of investigation in Escalante valley. Tech Rep Utah US Geol Surv Water Supply Paper 659–A\nZheng C, Wang PP (1999) MT3DMS: A modular three-dimensional multispecies model for simulation of advection, dispersion and chemical reactions of contaminants in groundwater systems; Documentation and Users Guide, Contract Report SERDP-99-1, U.S. Army Eng Res Dev Center Vicksburg MS",{"EN":1016},"A large tank (1.4 m x 4.0 m x 1.3 m) filled with medium-coarse sand was employed to measure evaporation rates from shallow groundwater at controlled laboratory conditions, to determine drivers and mechanisms. To monitor the groundwater level drawdown 12 piezometers were installed in a semi regular grid and equipped with high precision water level, temperature, and electrical conductivity (EC) probes. In each piezometer, 6 micro sampling ports were installed every 10 cm to capture vertical salinity gradients. Moreover, the soil water content, temperature and EC were measured in the unsaturated zone using TDR probes placed at 5, 20 and 40 cm depth. The monitoring started in February 2020 and lasted for 4 months until the groundwater drawdown became residual. To model the groundwater heads, temperature, and salinity variations SEAWAT 4.0 was employed. The calibrated model was then used to obtain the unknown parameters, such as: maximum evaporation rates (1.5-4.4 mm\u002Fd), extinction depth (0.90 m), mineral dissolution (5.0e-9 g\u002Fd) and evaporation concentration (0.35 g\u002FL). Despite the drawdown was uniformly distributed, the increase of groundwater salinity was rather uneven, while the temperature increase mimicked the atmospheric temperature increase. The initial groundwater salinity and the small changes in the evaporation rate controlled the evapoconcentration process in groundwater, while the effective porosity was the most sensitive parameter. This study demonstrates that shallow groundwater evaporation from sandy soils can produce homogeneous water table drawdown but appreciable differences in the distribution of groundwater salinity.",{"EN":1018},"Modelling Shallow Groundwater Evaporation Rates from a Large Tank 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FR\u002FR0012, Foundation for Water Research Allen House, Liston Road, Marlow",{},{"id":1489,"createTime":1490,"updateTime":1491,"relativeEntities":1492,"slug":1493,"properties":1494,"entityType":135,"verifyStatus":136,"verifyTime":1503,"verifyNote":137,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1504,"fullTextUrl":18,"authors":1505,"publicationType":233,"publisherRelationship":1594,"citationCount":18,"citationInfo":18,"publishDate":1626,"publishYear":689,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":437},"4ff5755b-ab66-4377-99c3-361c7b2dc39b","2024-01-20T17:42:11.494+00:00","2025-01-11T23:56:53.623+00:00",[],"Learning-from-Multiple-Models-Using-Artificial-Intelligence-to-Improve-Model-Prediction-Accuracies-Application-to-River-Flows",{"references":1495,"abstract":1497,"title":1499,"doi":1501},{"VOID":1496},"Al-Shammari ET, Mohammadi K, Keivani A et al (2016) Prediction of daily dewpoint temperature using a model combining the support vector machine with firefly algorithm. J Irrig Drain Eng. https:\u002F\u002Fdoi.org\u002F10.1061\u002F(ASCE)IR.1943-4774.0001015\nCh S, Anand N, Panigrahi BK, Mathur S (2013) Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101:18–23. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neucom.2012.07.017\nCh S, Sohani SK, Kumar D, et al (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279–288. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.neucom.2013.09.030\nClemen RT (1989) Combining forecasts: a review and annotated bibliography. Int J Forecast 5(4):559–583\nCollobert R, Williamson RC (2001) SVM torch: support vector Machines for Large-Scale Regression Problems. J Mach Learn Res 1:143–160. https:\u002F\u002Fdoi.org\u002F10.1162\u002F15324430152733142\nFahimi F, Yaseen ZM, El-shafie A (2016) Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theor Appl Climatol:1–29. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-016-1735-8\nGhorbani MA, Khatibi R, Goel A et al (2016a) Modeling river discharge time series using support vector machine and artificial neural networks. Environ Earth Sci 75:685. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-016-5435-6\nGhorbani MA, Zadeh HA, Isazadeh M, Terzi O (2016b) A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction. Environ Earth Sci 75:476. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-015-5096-x\nGhorbani MA, Shamshirband S, Zare Haghi D et al (2017) Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Tillage Res 172:32–38. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.still.2017.04.009\nKadkhodaie-Ilkhchi A, Rezaee MR, Rahimpour-Bonab H, Chehrazi A (2009) Petro physical data prediction from seismic attributes using committee fuzzy interference system. Comput Geosci 35:2314–2330\nKarush W (1939) Minima of Functions of Several Variables with Inequalities as Side Conditions. Masters Thesis, University of Chicago\nKhatibi R, Ghorbani MA, Kashani MH, Kisi O (2011) Comparison of three artificial intelligence techniques for discharge routing. J Hydrol 403:201–212. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2011.03.007\nKhatibi R, Sivakumar B, Ghorbani MA, et al (2012) Investigating chaos in river stage and discharge time series. J Hydrol 414–415:108–117. doi: https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2011.10.026\nKhatibi R, Ghorbani MA, Akhoni Pourhosseini F (2017) Stream flow predictions using nature-inspired firefly algorithms and a multiple model strategy – directions of innovation towards next generation practices. Adv Eng Inform 34:80–89. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aei.2017.10.002\nKuhn HW, Tucker AW (1951) Nonlinear Programming. In Proceedings of the 2nd Berkley Symposium. University of California Press pp. 481–492\nNadiri AA, Fijani E, Tsai FTC, Asgharimoghaddam A (2013) Supervised committee machine with artificial intelligence for prediction of fluoride concentration. J Hydroinf 15(4):1474–1490\nNadiri A, Hassan MM, Asadi S (2015) Supervised intelligence committee machine to evaluate field performance of photocatalytic asphalt pavement for ambient air purification. Transportation Research Record: Trans Res B 2528:96–105\nNadiri AA, Gharekhani M, Khatibi R et al (2016) Groundwater vulnerability indices conditioned by supervised intelligence committee machine (SICM). Sci Total Environ 574:691–706. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.scitotenv.2016.09.093\nNajah A, El-shafie A, Karim OA et al (2011) An application of different artificial intelligences techniques for water quality prediction. Int J Phys Sci 6:5298–5308. https:\u002F\u002Fdoi.org\u002F10.5897\u002FIJPS11.1180\nRaheli B, Aalami MT, El-Shafie M et al (2017) Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River. Environ Earth Sci 76:503. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12665-017-6842-z\nRubio G, Pomares H, Rojas I, Herrera LJ (2011) A heuristic method for parameter selection in LS-SVM: application to time series prediction. Int J Forecast 27:725–739. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ijforecast.2010.02.007\nShamshirband S, Mohammadi K, Tong CW et al (2016) A hybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theor Appl Climatol 125:53–65. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-015-1482-2\nTayfur G, Nadiri AA, Asgharimoghaddam A (2014) Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resour Manag 28(4):1173–1184\nVapnik VN (2000) The Nature of Statistical Learning Theory. Springer New York\nWang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2009.06.019\nWillmott CJ (1981) On the validation of models. Phys Geogr 2:184–194. https:\u002F\u002Fdoi.org\u002F10.1080\u002F02723646.1981.10642213\nXiong T, Bao Y, Hu Z (2014) Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowledge-Based Syst 55:87–100. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.knosys.2013.10.012\nYang X-S (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio-inspired Comput 2(2):78–84. https:\u002F\u002Fdoi.org\u002F10.1504\u002FIJBIC.2010.032124\nYu X, Liong S, Babovic V (2004) EC-SVM approach for real-time hydrologic forecasting. J Hydroinf 3:209–223",{"EN":1498},"An investigation is presented in this paper to study the performance of Artificial Intelligence running Multiple Models (AIMM) using time series of river flows. This is a modelling strategy, which is formed by first running two Artificial Intelligence (AI) models: Support Vector Machine (SVM) and its hybrid with the Fire-Fly Algorithm (FFA) and they both form supervised learning at Level 1. The outputs of Level 1 models serve as inputs to another AI Model at Level 2. The AIMM strategy at Level 2 is run by Artificial Neural Network (MM-ANN) and this is compared with the Simple Averaging (MM-SA) of both inputs. The study of the performances of these models (SVM, SVM-FFA, MM-SA and MM-ANN) in the paper shows that the ability of SVM-FFA in matching observed values is significantly better than that of SVM and that of MM-ANN is considerably better than each SVM and\u002For SVM-FFA but the performances are deteriorated by using the MM-SA strategy. The results also show that the residuals of MM-ANN are less noisy than those shown by the models at  Level 1 and those at Level 2 do not display any trend.",{"EN":1500},"Learning from Multiple Models Using Artificial Intelligence to Improve Model Prediction Accuracies: Application to River Flows",{"VOID":1502},"10.1007\u002Fs11269-018-2038-x","2025-01-11T23:56:53.622+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11269-018-2038-x",[1506,1532,1544,1560,1577],{"id":1507,"sortIndex":19,"researcher":18,"roles":1508,"affiliations":1509,"properties":1529},"abb1ad22-9021-410b-9d5e-2eb178079374",[458],[1510,1519],{"id":18,"sortIndex":19,"affiliation":1511,"properties":18},{"id":1512,"createTime":1513,"updateTime":1513,"relativeEntities":1514,"slug":1515,"properties":1516,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"1694d628-1b63-48cd-a4d5-6c8a36463759","2024-04-19T01:42:49.427+00:00",[],"Department-of-Water-Engineering-University-of-Tabriz-Tabriz-Iran",{"title":1517},{"EN":1518},"Department of Water Engineering, University of Tabriz, Tabriz, Iran",{"id":1520,"sortIndex":105,"affiliation":1521,"properties":1528},"adbc8f13-2ae6-4328-a4b1-53f272d79bb1",{"id":1522,"createTime":1523,"updateTime":1523,"relativeEntities":1524,"slug":18,"properties":1525,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"1e89040b-a710-4b21-a970-d471f9c7ee69","2024-01-20T17:42:11.510+00:00",[],{"title":1526},{"VI":1527},"Engineering Faculty, Near East University, Mersin, Turkey",{},{"title":1530},{"VI":1531},"M. A. Ghorbani",{"id":1533,"sortIndex":182,"researcher":18,"roles":1534,"affiliations":1535,"properties":1541},"35190efb-f6c0-4d81-8356-70718ead64b4",[458],[1536],{"id":18,"sortIndex":19,"affiliation":1537,"properties":18},{"id":1512,"createTime":1513,"updateTime":1513,"relativeEntities":1538,"slug":1515,"properties":1539,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},[],{"title":1540},{"EN":1518},{"title":1542},{"VI":1543},"V. Karimi",{"id":1545,"sortIndex":106,"researcher":18,"roles":1546,"affiliations":1547,"properties":1557},"527f48d8-daed-4f4f-ad92-8b2ea953124c",[458],[1548],{"id":18,"sortIndex":19,"affiliation":1549,"properties":18},{"id":1550,"createTime":1551,"updateTime":1551,"relativeEntities":1552,"slug":1553,"properties":1554,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"8134b15e-3986-4287-bc18-83986e97ebaf","2023-11-29T14:51:21.004+00:00",[],"Department-of-Water-Engineering-Shahid-Bahonar-University-of-Kerman-Kerman-Iran",{"title":1555},{"VI":1556},"Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran",{"title":1558},{"VI":1559},"M. Zounemat-Kermani",{"id":1561,"sortIndex":165,"researcher":18,"roles":1562,"affiliations":1563,"properties":1574},"8f77c8b6-4ca4-4f5c-9329-6506394db653",[458],[1564],{"id":18,"sortIndex":19,"affiliation":1565,"properties":18},{"id":1566,"createTime":1567,"updateTime":1568,"relativeEntities":1569,"slug":1570,"properties":1571,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"6e3b652b-eb6e-4ace-94ce-8fd4b6749449","2024-04-07T07:41:18.771+00:00","2024-12-25T23:15:28.345+00:00",[],"Sustainable-Developments-in-Civil-Engineering-Research-Group-Faculty-of-Civil-Engineering-Ton-Duc-Thang-University-Ho-Chi-Minh-City-Vietnam",{"title":1572},{"VI":1573},"Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam",{"title":1575},{"VI":1576},"Zaher Mundher Yaseen",{"id":1578,"sortIndex":105,"researcher":18,"roles":1579,"affiliations":1580,"properties":1591},"077c0ed7-23b3-4bc8-b5be-b69ac10d2987",[458],[1581],{"id":18,"sortIndex":19,"affiliation":1582,"properties":18},{"id":1583,"createTime":1584,"updateTime":1585,"relativeEntities":1586,"slug":1587,"properties":1588,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"453d507c-d5c3-488a-9c03-25d7647ab560","2024-04-20T01:09:07.381+00:00","2024-10-01T07:47:47.519+00:00",[],"GTEV-ReX-Limited-Swindon-UK",{"title":1589},{"EN":1590},"GTEV-ReX Limited, Swindon, UK",{"title":1592},{"VI":1593},"R. Khatibi",{"url":1504,"publisher":1595,"properties":1622},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1596,"slug":10,"properties":1597,"entityType":16,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"subjectFields":1600,"manageAffiliations":1601,"indexDatabases":1602,"url":18,"thumbnailPath":18,"statistic":1617,"gsStatistic":18,"type":18,"analyzePriority":18},[],{"issn":1598,"title":1599},{"VOID":13},{"EN":15},[],[],[1603,1610],{"id":82,"indexDatabase":1604,"url":95,"indexYears":96,"academicFieldIds":1609,"indexDatabaseRanking":100},{"id":84,"createTime":85,"updateTime":86,"relativeEntities":1605,"label":1606,"description":1607,"key":92,"publicationTags":1608,"standard":18},[],{"EN":89,"VI":89},{"EN":89,"VI":91},[94],[98,99],{"id":62,"indexDatabase":1611,"url":77,"indexYears":18,"academicFieldIds":1616,"indexDatabaseRanking":18},{"id":64,"createTime":65,"updateTime":66,"relativeEntities":1612,"label":1613,"description":1614,"key":73,"publicationTags":1615,"standard":18},[],{"EN":69,"VI":69},{"VI":71,"EN":72},[75,76],[79,80],{"impactFactor":19,"impactFactorByYear":1618,"i10Index":103,"i10IndexLast5Year":19,"totalPublication":103,"totalPublicationByYear":1619,"totalCitation":107,"totalCitationByYear":1620,"totalCitationPerPublication":111,"totalCitationPerPublicationByYear":1621,"hindexLast5Year":103,"hindex":103},{},{"2003":105,"2004":106},{"2003":109,"2004":110},{"2003":109,"2004":113},{"volume":1623,"pages":1624},{"VOID":923},{"VOID":1625},"4201-4215","2018-07-26",{"id":1628,"createTime":1629,"updateTime":1630,"relativeEntities":1631,"slug":1632,"properties":1633,"entityType":135,"verifyStatus":136,"verifyTime":1630,"verifyNote":137,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1642,"fullTextUrl":18,"authors":1643,"publicationType":233,"publisherRelationship":1752,"citationCount":18,"citationInfo":18,"publishDate":1785,"publishYear":1786,"citationAnalyzeStatus":17,"lastCitationAnalyze":18,"indexDatabases":18,"openAccess":18,"references":18,"isForceReanalyzing":437},"e56dcaec-b024-409f-af37-38be1da85876","2024-01-03T18:16:59.155+00:00","2025-02-16T23:56:53.982+00:00",[],"Application-of-Artificial-Neural-Networks-to-Project-Reference-Evapotranspiration-Under-Climate-Change-Scenarios",{"references":1634,"abstract":1636,"title":1638,"doi":1640},{"VOID":1635},"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:\u002F\u002Fdoi.org\u002F10.1175\u002FEI-D-16-0028.1\nAfzaal 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:\u002F\u002Fdoi.org\u002F10.3390\u002Fapp10051621\nAfzaal 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:\u002F\u002Fdoi.org\u002F10.3390\u002Fw12010005\nAllen RG, Pereira LS, Raes D (1998) Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome\nBirara 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\nChipanshi AC, Maphanyane JG (1997) Nature of rainfal variability in Botswana over the 1961-1990 period. JSTOR J Afr Res Dev 299–317\nDau 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:\u002F\u002Fdoi.org\u002F10.1007\u002FS40710-020-00475-Y\nFerreira LB, da Cunha FF (2020) Multi-step ahead forecasting of daily reference evapotranspiration using deep learning. Comput Electron Agric 178:105728. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2020.105728\nGovernment of Canada (2019) CanESM2 predictors: CMIP5 experiments. https:\u002F\u002Fclimate-scenarios.canada.ca\u002F?page=pred-canesm2. Accessed 11 Apr 2020\nGovernment of Canada (2017) Station results - historical data. https:\u002F\u002Fclimate.weather.gc.ca\u002Fhistorical_data\u002Fsearch_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\nHafeez 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:\u002F\u002Fdoi.org\u002F10.18488\u002Fjournal.68.2020.71.15.22\nHargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99. https:\u002F\u002Fdoi.org\u002F10.13031\u002F2013.26773\nHashemi 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:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-019-02966-x\nLotfi 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:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12517-020-05715-x\nMahmood 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:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-012-0765-0\nMajhi 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:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00521-019-04127-7\nMaqsood 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:\u002F\u002Fdoi.org\u002F10.3390\u002FSU12124937\nRandall 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\nRichards 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\nRoy 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:\u002F\u002Fdoi.org\u002F10.1007\u002FS40710-021-00512-4\nShi 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:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2020.124756\nTabari H, Talaee PH (2013) Multilayer perceptron for reference evapotranspiration estimation in a semiarid region. Neural Comput Appl 23:341–348. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00521-012-0904-7\nWilby 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:\u002F\u002Fdoi.org\u002F10.1016\u002Fs1364-8152(01)00060-3\nZanetti 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:\u002F\u002Fdoi.org\u002F10.1061\u002FASCE0733-94372007133:283\nZhai 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:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00382-018-4340-y\nZhang 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:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2018.04.065\nZhang 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:\u002F\u002Fdoi.org\u002F10.1175\u002FJAMC-D-19-0048.1\nZhu 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:\u002F\u002Fdoi.org\u002F10.1007\u002FS11269-019-02413-6",{"EN":1637},"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\u002Fday (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.",{"EN":1639},"Application of Artificial Neural Networks to Project Reference Evapotranspiration Under Climate Change Scenarios",{"VOID":1641},"10.1007\u002Fs11269-021-02997-y","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs11269-021-02997-y",[1644,1673,1685,1701,1713,1725,1737],{"id":1645,"sortIndex":105,"researcher":18,"roles":1646,"affiliations":1647,"properties":1670},"b6a09637-51b6-41a9-8291-8e0982f282c3",[458],[1648,1660],{"id":1649,"sortIndex":105,"affiliation":1650,"properties":1659},"e5cee094-35dd-43b4-ab6e-ad9077845d92",{"id":1651,"createTime":1652,"updateTime":1653,"relativeEntities":1654,"slug":1655,"properties":1656,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"00b5d15e-e585-40ef-9f38-54b978841eaf","2024-01-21T13:28:37.367+00:00","2025-06-11T14:12:13.947+00:00",[],"School-of-Climate-Change-and-Adaptation-University-of-Prince-Edward-Island-Charlottetown-Canada",{"title":1657},{"VI":1658},"School of Climate Change and Adaptation, University of Prince Edward Island, , Charlottetown, Canada",{},{"id":18,"sortIndex":19,"affiliation":1661,"properties":18},{"id":1662,"createTime":1663,"updateTime":1664,"relativeEntities":1665,"slug":1666,"properties":1667,"entityType":46,"verifyStatus":17,"verifyTime":18,"verifyNote":18,"syncStatus":17,"languages":18,"translateLanguages":18,"viewCount":19},"82fa90e0-826d-4957-a6e3-2f5fc019e078","2024-01-29T15:33:18.000+00:00","2024-09-20T17:01:54.570+00:00",[],"Faculty-of-Sustainable-Design-Engineering-University-of-Prince-Edward-Island-Charlottetown-Canada",{"title":1668},{"VI":1669},"Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, Canada",{"title":1671},{"VI":1672},"Aitazaz A. 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