Abstract
This study represents a new strategy for assessing how climate change has impacted urban water demand per capita in Neyshabur, Iran. Future rainfall depths and temperature variations are projected using several general circulation models (GCMs) for two representative concentration pathway (RCP) (i.e., RCP45 and RCP85) scenarios using LARS-WG software. A simulator model is developed using the genetic programming (GP) model to predict future water demand based on projected climate variables of rainfall depth and maximum temperature. The period of 1996–2016 is selected as the base period. Three future periods, namely the near-future (2021–2040), middle future (2041–2060), and far future (2061–2080), are also employed to assess climate change impact on water demand. Results indicate significant increases in annual projected rainfall depth (14~53%), maximum temperature (0.04~4.21 °C), and minimum temperature (1.01~4.71 °C). The projected monthly patterns of rainfall depth and temperature are predicted to cause a 1-month shift in the water demand peak (i.e., it will occur in April instead of May) for all future periods. Furthermore, the annual water demand per capita is projected to increase by 0.5~1.2%, 1.5~3.2%, and (2.2~7.1%), during the near-, middle-, and far-future periods, respectively. The uncertainty associated with water demand is also projected to increase over time for RCP45. The mathematical expression of urban water demand based on climatic variables is vital to managing the water resources of Neyshabur. The methodology proposed in the present study represents a robust approach to assessing how climate change might affect urban water demand in cities other than Neyshabur and provides crucial information for decision-makers.
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References
Abdelwares M, Lelieveld J, Zittis G, Haggag M, Wagdy A (2020) A comparison of gridded datasets of precipitation and temperature over the Eastern Nile Basin region. Euro-Mediterranean J Environ Integr. https://doi.org/10.1007/s41207-019-0140-y
Abraham A, Nedjah N, de Macedo ML (2006) Evolutionary computation: from genetic algorithms to genetic programming. Genetic systems programming. Springer, In, pp 1–20
Abrams B, Kumaradevan S, Sarafidis V, Spaninks F (2012) An econometric assessment of pricing Sydney’s residential water use. Econ Rec 88:89–105
Adamowski J, Karapataki C (2010) Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. J Hydrol Eng 15:729–743
Adamowski J, Fung Chan H, Prasher SO, et al (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48
Arbués F, Villanúa I, Barberán R (2010) Household size and residential water demand: an empirical approach. Aust J Agric Resour Econ 54:61–80
Ashoori N, Dzombak DA, Small MJ (2016) Modeling the effects of conservation, demographics, price, and climate on urban water demand in Los Angeles, California. Water Resour Manag 30:5247–5262
Atsalakis G, Minoudaki C, Markatos N, et al (2007) Daily irrigation water demand prediction using adaptive neuro-fuzzy inferences systems (anfis). In: Proc. 3rd IASME/WSEAS International Conference on Energy, Environment, Ecosystems and Sustainable Development
Babel MS, Shinde VR (2011) Identifying prominent explanatory variables for water demand prediction using artificial neural networks: a case study of Bangkok. Water Resour Manag 25:1653–1676
Bakker M, Van Duist H, Van Schagen K et al (2014) Improving the performance of water demand forecasting models by using weather input. In: Procedia Engineering. In: CCWI 2013: 12th International Conference on Computing and Control for the Water Industry, vol 70. Elsevier
Bata MH, Carriveau R, Ting DS-K (2020) Short-term water demand forecasting using nonlinear autoregressive artificial neural networks. J Water Resour Plan Manag 146:4020008
Behboudian S, Tabesh M, Falahnezhad M, Ghavanini FA (2014) A long-term prediction of domestic water demand using preprocessing in artificial neural network. J Water Supply Res Technol 63:31–42
Bougadis J, Adamowski K, Diduch R (2005) Short-term municipal water demand forecasting. Hydrol Process An Int J 19:137–148
Brentan BM, Luvizotto E Jr, Herrera M et al (2017) Hybrid regression model for near real-time urban water demand forecasting. J Comput Appl Math 309:532–541
Candelieri A, Giordani I, Archetti F, Barkalov K, Meyerov I, Polovinkin A, Sysoyev A, Zolotykh N (2019) Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization. Comput Oper Res 106:202–209
Dehghan H, Alizadeh A, Haghayeghi SA (2011) Water balance components estimating in farm scale using simulation model SWAP. Neyshabur Region, Case Study
Donkor EA, Mazzuchi TA, Soyer R, Alan Roberson J (2014) Urban water demand forecasting: review of methods and models. J Water Resour Plan Manag 140:146–159
Evans J, Karvonen A, Luque-Ayala A, et al (2019) Smart and sustainable cities? Pipedreams, practicalities and possibilities
Felfelani F, Kerachian R (2016) Municipal water demand forecasting under peculiar fluctuations in population: a case study of Mashhad, a tourist city. Hydrol Sci J 61:1524–1534
Flörke M, Schneider C, McDonald RI (2018) Water competition between cities and agriculture driven by climate change and urban growth. Nat Sustain 1:51–58
Gato S, Jayasuriya N, Roberts P (2007) Temperature and rainfall thresholds for base use urban water demand modelling. J Hydrol 337:364–376
Ghiassi M, Zimbra DK, Saidane H (2008) Urban water demand forecasting with a dynamic artificial neural network model. J Water Resour Plan Manag 134:138–146
Giorgetta MA, Jungclaus J, Reick CH, Legutke S, Bader J, Böttinger M, Brovkin V, Crueger T, Esch M, Fieg K, Glushak K, Gayler V, Haak H, Hollweg HD, Ilyina T, Kinne S, Kornblueh L, Matei D, Mauritsen T, Mikolajewicz U, Mueller W, Notz D, Pithan F, Raddatz T, Rast S, Redler R, Roeckner E, Schmidt H, Schnur R, Segschneider J, Six KD, Stockhause M, Timmreck C, Wegner J, Widmann H, Wieners KH, Claussen M, Marotzke J, Stevens B (2013) Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J Adv Model Earth Syst 5:572–597
Guo W, Liu T, Dai F, Xu P (2020) An improved whale optimization algorithm for forecasting water resources demand. Appl Soft Comput 86:105925
Hai T, Sharafati A, Mohammed A, Salih SQ, Deo RC, al-Ansari N, Yaseen ZM (2020) Global solar radiation estimation and climatic variability analysis using extreme learning machine based predictive model. IEEE Access 8:12026–12042
Haque MM, Rahman A, Hagare D, Kibria G (2014) Probabilistic water demand forecasting using projected climatic data for Blue Mountains water supply system in Australia. Water Resour Manag 28:1959–1971
Haque MM, Rahman A, Hagare D, Chowdhury RK (2018) A comparative assessment of variable selection methods in urban water demand forecasting. Water 10:419
Harlan SL, Ruddell DM (2011) Climate change and health in cities: impacts of heat and air pollution and potential co-benefits from mitigation and adaptation. Curr Opin Environ Sustain 3:126–134
Hashmi MZ, Shamseldin AY, Melville BW (2011) Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed. Stoch Env Res Risk A 25:475–484. https://doi.org/10.1007/s00477-010-0416-x
Haughton G, Hunter C (2004) Sustainable cities. Routledge
Hazeleger W, Severijns C, Semmler T, Ştefănescu S, Yang S, Wang X, Wyser K, Dutra E, Baldasano JM, Bintanja R, Bougeault P, Caballero R, Ekman AML, Christensen JH, van den Hurk B, Jimenez P, Jones C, Kållberg P, Koenigk T, McGrath R, Miranda P, van Noije T, Palmer T, Parodi JA, Schmith T, Selten F, Storelvmo T, Sterl A, Tapamo H, Vancoppenolle M, Viterbo P, Willén U (2010) EC-Earth: a seamless earth-system prediction approach in action. Bull Am Meteorol Soc 91:1357–1364
Herrera M, Torgo L, Izquierdo J, Pérez-García R (2010) Predictive models for forecasting hourly urban water demand. J Hydrol 387:141–150
House-Peters L, Pratt B, Chang H (2010) Effects of urban spatial structure, sociodemographics, and climate on residential water consumption in Hillsboro, Oregon 1. JAWRA J Am Water Resour Assoc 46:461–472
Hughes JP, Guttorp P, Charles SP (1999) A non-homogeneous hidden Markov model for precipitation occurrence. J R Stat Soc Ser C Applied Stat 48:15–30
Jayarathna L, Rajapaksa D, Managi S, Athukorala W, Torgler B, Garcia-Valiñas MA, Gifford R, Wilson C (2017) A GIS based spatial decision support system for analysing residential water demand: a case study in Australia. Sustain Cities Soc 32:67–77
Jones C, Hughes JK, Bellouin N et al (2011) The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci Model Dev 4:543–570
Keath NA, Brown RR (2009) Extreme events: being prepared for the pitfalls with progressing sustainable urban water management. Water Sci Technol 59:1271–1280
Keith MJ, Martin MC (1994) Genetic programming in C++: implementation issues. Adv Genet Program 1:285–310
Khan MS, Coulibaly P, Dibike Y (2006) Uncertainty analysis of statistical downscaling methods. J Hydrol 319:357–382
Koga I, Ono K (2018) Effective Pre-processing of genetic programming for solving symbolic regression in equation extraction. International Workshop on Information Search, Integration, and Personalization. Springer, In, pp 89–103
Koza JR (1994) Genetic programming as a means for programming computers by natural selection. Stat Comput 4:87–112
Leichenko R (2011) Climate change and urban resilience. Curr Opin Environ Sustain 3:164–168
Lobell DB, Ortiz-Monasterio JI, Asner GP, Matson PA, Naylor RL, Falcon WP (2005) Analysis of wheat yield and climatic trends in Mexico. F Crop Res 94:250–256
Malik A, Kumar A, Kim S et al (2020) Modeling monthly pan evaporation process over the Indian central Himalayas: application of multiple learning artificial intelligence model. Eng Appl Comput Fluid Mech 14:323–338
Mehr AD, Nourani V (2018) Season algorithm-multigene genetic programming: a new approach for rainfall-runoff modelling. Water Resour Manag 32:2665–2679
Merabtene T, Kawamura A, Jinno K, Olsson J (2002) Risk assessment for optimal drought management of an integrated water resources system using a genetic algorithm. Hydrol Process 16:2189–2208
Meza FJ, Silva D, Vigil H (2008) Climate change impacts on irrigated maize in Mediterranean climates: evaluation of double cropping as an emerging adaptation alternative. Agric Syst 98:21–30
Mohammadi AA, Zarei A, Esmaeilzadeh M et al (2019) Assessment of heavy metal pollution and human health risks assessment in soils around an industrial zone in Neyshabur. Iran Biol Trace Elem Res:1–10
Moriasi DN, Arnold JG, Van Liew MW et al (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900
Narsimlu B, Gosain AK, Chahar BR (2013) Assessment of future climate change impacts on water resources of Upper Sind River Basin, India using SWAT model. Water Resour Manag 27:3647–3662
Nasseri M, Asghari K, Abedini MJ (2008) Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Syst Appl 35:1415–1421
Nasseri M, Moeini A, Tabesh M (2011) Forecasting monthly urban water demand using extended Kalman filter and genetic programming. Expert Syst Appl 38:7387–7395
Nazif S, Tavakolifar H, Eslamian S (2017) Climate change impact on urban water deficit. Handbook of Drought and Water Scarcity. CRC Press, In, pp 81–106
Noiva K, Fernández JE, Wescoat JL Jr (2016) Cluster analysis of urban water supply and demand: toward large-scale comparative sustainability planning. Sustain Cities Soc 27:484–496
Özerol G, Dolman N, Bormann H, Bressers H, Lulofs K, Böge M (2020) Urban water management and climate change adaptation: a self-assessment study by seven midsize cities in the North Sea Region. Sustain Cities Soc 55:102066
Parandvash GH, Chang H (2016) Analysis of long-term climate change on per capita water demand in urban versus suburban areas in the Portland metropolitan area, USA. J Hydrol 538:574–586
Perea RG, Poyato EC, Montesinos P, Díaz JAR (2019) Optimisation of water demand forecasting by artificial intelligence with short data sets. Biosyst Eng 177:59–66
Rahimi J, Malekian A, Khalili A (2019) Climate change impacts in Iran: assessing our current knowledge. Theor Appl Climatol 135:545–564
Rasifaghihi N, Li SS, Haghighat F (2020) Forecast of urban water consumption under the impact of climate change. Sustain Cities Soc 52:101848
Richardson CW, Wright DA (1984) WGEN: A model for generating daily weather variables. ARS
Ruth M, Bernier C, Jollands N, Golubiewski N (2007) Adaptation of urban water supply infrastructure to impacts from climate and socioeconomic changes: the case of Hamilton, New Zealand. Water Resour Manag 21:1031–1045
Salimi M, Al-Ghamdi SG (2020) Climate change impacts on critical urban infrastructure and urban resiliency strategies for the Middle East. Sustain Cities Soc 54:101948
Satterthwaite D (1997) Sustainable cities or cities that contribute to sustainable development? Urban Stud 34:1667–1691
Sedki A, Ouazar D, El Mazoudi E (2009) Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting. Expert Syst Appl 36:4523–4527
Semenov M, Brooks R, Barrow E, Richardson C (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:95–107. https://doi.org/10.3354/cr010095
Shabani S, Candelieri A, Archetti F, Naser G (2018) Gene expression programming coupled with unsupervised learning: a two-stage learning process in multi-scale, short-term water demand forecasts. Water 10:142
Sharafati A, Pezeshki E (2019) A strategy to assess the uncertainty of a climate change impact on extreme hydrological events in the semi-arid Dehbar catchment in Iran. Theor Appl Climatol 1–14
Sharafati A, Zahabiyoun B (2014) Rainfall threshold curves extraction by considering rainfall-runoff model uncertainty. Arab J Sci Eng 39:6835–6849. https://doi.org/10.1007/s13369-014-1246-9
Sharafati A, Pezeshki E, Shahid S, Motta D (2020a) Quantification and uncertainty of the impact of climate change on river discharge and sediment yield in the Dehbar river basin in Iran. J Soils Sediments
Sharafati A, Tafarojnoruz A, Yaseen ZM (2020b) New stochastic modeling strategy on the prediction enhancement of pier scour depth in cohesive bed materials. J Hydroinformatics
Sodiq A, Baloch AAB, Khan SA, et al (2019) Towards modern sustainable cities: review of sustainability principles and trends. J Clean Prod
Song S, Singh VP (2010) Frequency analysis of droughts using the Plackett copula and parameter estimation by genetic algorithm. Stoch Env Res Risk A 24:783–805
Stakhiv EZ (1998) Policy implications of climate change impacts on water resources management. Water Policy 1:159–175
Stone B, Hess JJ, Frumkin H (2010) Urban form and extreme heat events: are sprawling cities more vulnerable to climate change than compact cities? Environ Health Perspect 118:1425–1428
Tiwari MK, Adamowski J (2013) Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resour Res 49:6486–6507
Waha K, Krummenauer L, Adams S et al (2017) Climate change impacts in the Middle East and Northern Africa (MENA) region and their implications for vulnerable population groups. Reg Environ Chang 17:1623–1638
Wahyuni I, Mahmudy WF (2017) Rainfall prediction in Tengger, Indonesia using hybrid Tsukamoto FIS and genetic algorithm method. J ICT Res Appl 11:38–54
Wang X, Zhang J, Shamsuddin S, Oyang RL, Guan TS, Xue JG, Zhang X (2017) Impacts of climate variability and changes on domestic water use in the Yellow River Basin of China. Mitig Adapt Strateg Glob Chang 22:595–608
Wang X-J, Zhang J-Y, Shahid S, Xie W, du CY, Shang XC, Zhang X (2018) Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics. Environ Dev Sustain 20:911–924
Watanabe M, Suzuki T, O’ishi R, Komuro Y, Watanabe S, Emori S, Takemura T, Chikira M, Ogura T, Sekiguchi M, Takata K, Yamazaki D, Yokohata T, Nozawa T, Hasumi H, Tatebe H, Kimoto M (2010) Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J Clim 23:6312–6335
Wilby RL, Dawson CW, Barrow EM (2002) SDSM—a decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17:145–157
Wu CL, Chau KW (2006) A flood forecasting neural network model with genetic algorithm. Int J Environ Pollut 28:261
Xiang Y, Gou L, He L, Xia S, Wang W (2018) A SVR–ANN combined model based on ensemble EMD for rainfall prediction. Appl Soft Comput 73:874–883
Xiao-jun W, Jian-yun Z, Shamsuddin S et al (2015) Potential impact of climate change on future water demand in Yulin city, Northwest China. Mitig Adapt Strateg Glob Chang 20:1–19
Yaghoobzadeh M, Ahmadi M, Seyyed KH, et al (2017) The evaluation of effect of climate change on agricultural drought using ETDI and SPI indexes
Yen Y-S, Chao H-C, Chang R-S, Vasilakos A (2011) Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs. Math Comput Model 53:2238–2250
Zubaidi SL, Gharghan SK, Dooley J, Alkhaddar RM, Abdellatif M (2018) Short-term urban water demand prediction considering weather factors. Water Resour Manag 32:4527–4542
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The authors would like to reveal their gratitude and appreciation to the data providers, Iranian Meteorological Organization and Iran Water Resources Management Company.
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Ahmad Sharafati proposed the topic, participated in coordination, and aided in the interpretation of results and paper editing. Seyed Babak Haji Seyed Asadollah carried out the investigation and participated in drafting the manuscript. Armin Shahbazi carried out the review analysis and modeling and participated in drafting the manuscript. All authors read and approved the final manuscript.
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Sharafati, A., Asadollah, S.B.H.S. & Shahbazi, A. Assessing the impact of climate change on urban water demand and related uncertainties: a case study of Neyshabur, Iran. Theor Appl Climatol 145, 473–487 (2021). https://doi.org/10.1007/s00704-021-03638-5
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DOI: https://doi.org/10.1007/s00704-021-03638-5