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Water Resources Management
Published in: Water Resources Management 11/2022

07-06-2022

Groundwater Level Fluctuations in Coastal Aquifer: Using Artificial Neural Networks to Predict the Impacts of Climatical CMIP6 Scenarios

Authors: Adib Roshani, Mehdi Hamidi

Published in: Water Resources Management | Issue 11/2022

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Abstract

Groundwater resources play a crucial role in supplying water for domestic, industrial, and agricultural use. In this study ACCESS-CM2, HadGEM3-GC31-LL, and NESM3 were selected for validation from Coupled Model Intercomparison Project Phase 6 (CMIP6). In the following, the feedforward neural network was employed to predict monthly groundwater level (GWL) based on the emission scenarios of the sixth IPCC report (SSP2-4.5 and SSp5-8.5) for the next two decades (2021–2040) in the Sari-Neka coastal aquifer near the Caspian Sea, Iran. In this regard, the monthly maximum and minimum temperature, precipitation, and water table of previous month from four piezometers from 2000 to 2019 were used as input variables to forecast GWL. The evaluation of the three GCM models demonstrated that the ACCESS-CM2 provided the best values of the R2 and RMSE with observation parameters. The results of r, R2, RMSE, and MAE were evaluated for the model and indicated good performance of the model. The results also illustrated that under such mentioned scenarios, the mean monthly temperature would rise approximately from 0.1–1.2 °C. In addition, the mean monthly precipitation is likely to witness changes from -10% to 78% in the next two decades. As a result, this seems to lead to improvement and recharge of groundwater level for the near future. The results can help managers and policymakers to identify adaptation strategies more precisely for basins with similar climates.
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Literature
Abiodun BJ, Adegoke J, Abatan AA, Ibe CA, Egbebiyi TS, Engelbrecht F, Pinto I (2017) Potential impacts of climate change on extreme precipitation over four African coastal cities. Clim Change 143(3):399–413CrossRef
Adnan RM, Liang Z, Trajkovic S, Zounemat-Kermani M, Li B, Kisi O (2019) Daily streamflow prediction using optimally pruned extreme learning machine. J Hydrol 577:123981CrossRef
Alimohammadi H, Massah Bavani AR, Roozbahani A (2020) Mitigating the impacts of climate change on the performance of multi-purpose reservoirs by changing the operation policy from SOP to MLDR. Water Resour Manage 34(4):1495–1516CrossRef
Anjum MN, Ding Y, Shangguan D (2019) Simulation of the projected climate change impacts on the river flow regimes under CMIP5 RCP scenarios in the westerlies dominated belt, northern Pakistan. Atmos Res 227:233–248CrossRef
Araya-Osses D, Casanueva A, Román-Figueroa C, Uribe JM, Paneque M (2020) Climate change projections of temperature and precipitation in Chile based on statistical downscaling. Clim Dyn 54(9):4309–4330CrossRef
Bayatvarkeshi M, Zhang B, Fasihi R, Adnan RM, Kisi O, Yuan X (2020) Investigation into the effects of climate change on reference evapotranspiration using the HadCM3 and LARS-WG. Water 12(3):666CrossRef
Bodman RW, Karoly DJ, Dix MR, Harman IN, Srbinovsky J, Dobrohotoff PB, Mackallah C (2020) Evaluation of CMIP6 AMIP climate simulations with the ACCESS-AM2 model. J Southern Hemisphere Earth Syst Sci 70(1):166–179CrossRef
Boudiaf B, Dabanli I, Boutaghane H, Şen Z (2020) Temperature and precipitation risk assessment under climate change effect in northeast Algeria. Earth Syst Environ 4(1):1–14CrossRef
Brusco MJ, Steinley D (2007) A comparison of heuristic procedures for minimum within-cluster sums of squares partitioning. Psychometrika 72(4):583–600CrossRef
Chang J, Wang G, Mao T (2015) Simulation and prediction of suprapermafrost groundwater level variation in response to climate change using a neural network model. J Hydrol 529:1211–1220CrossRef
Chen Y, Song L, Liu Y, Yang L, Li D (2020) A review of the artificial neural network models for water quality prediction. Appl Sci 10(17):5776CrossRef
Chitsazan M, Rahmani G, Neyamadpour A (2015) Forecasting groundwater level by artificial neural networks as an alternative approach to groundwater modeling. J Geol Soc India 85(1):98–106CrossRef
Coppola EA Jr, Rana AJ, Poulton MM, Szidarovszky F, Uhl VW (2005) A neural network model for predicting aquifer water level elevations. Groundwater 43(2):231–241CrossRef
Daliakopoulos IN, Coulibaly P, Tsanis IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309(1–4):229–240CrossRef
Derbela M, Nouiri I (2020) Intelligent approach to predict future groundwater level based on artificial neural networks (ANN). Euro-Mediterranean J Environ Integration 5(3):1–11CrossRef
Di Luca A, Pitman AJ, de Elía R (2020) Decomposing temperature extremes errors in CMIP5 and CMIP6 models. Geophys Res Lett 47(14):e2020GL088031
Di Nunno F, Granata F (2020) Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network. Environ Res 190:110062CrossRef
Emamgholizadeh S, Moslemi K, Karami G (2014) Prediction the groundwater level of bastam plain (Iran) by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Water Resour Manage 28(15):5433–5446CrossRef
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci Model Devel 9(5):1937–1958CrossRef
Ghazi B, Jeihouni E, Kalantari Z (2021) Predicting groundwater level fluctuations under climate change scenarios for Tasuj plain. Iran Arabian J Geosci 14(2):1–12
Guo D, Wang H (2016) Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China. Adv Atmos Sci 33(5):559–570CrossRef
Gupta V, Singh V, Jain MK (2020) Assessment of precipitation extremes in India during the 21st century under SSP1–1.9 mitigation scenarios of CMIP6 GCMs. J Hydrol 590:125422
Hamidi M, Sabbagh-Yazdi SR (2008) Modeling of 2D density-dependent flow and transport in porous media using finite volume method. Comput Fluids 37(8):1047–1055CrossRef
Hasda R, Rahaman MF, Jahan CS, Molla KI, Mazumder QH (2020) Climatic data analysis for groundwater level simulation in drought prone Barind Tract, Bangladesh: Modelling approach using artificial neural network. Groundw Sustain Dev 10:100361CrossRef
Hassan Z, Shamsudin S, Harun S (2014) Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theoret Appl Climatol 116(1):243–257CrossRef
Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Climate Res 7(2):85–95CrossRef
IPCC (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: Core Writing Team, Pachauri RK, Meyer LA (eds). IPCC, Geneva, Switzerland, p 151
Konapala G, Mishra AK, Wada Y, Mann ME (2020) Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat Commun 11(1):1–10CrossRef
Li SY, Miao LJ, Jiang ZH, Wang GJ, Gnyawali KR, Zhang J, Zhang H, Fang K, He Y, Li C (2020) Projected drought conditions in Northwest China with CMIP6 models under combined SSPs and RCPs for 2015–2099. Adv Clim Chang Res 11(3):210–217CrossRef
Ligotin VA, Savichev OG, Makushin JV (2010) The long-term changes of seasonal and annual levels and temperature of ground waters of the top hydro dynamical zone in Tomsk area. Geoecology 13(1):23–29 (in Russian)
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. Proc Fifth Berkeley Symp Math Stat Probab 1(14):281–297
Maharjan M, Aryal A, Talchabhadel R, Thapa BR (2021) Impact of Climate Change on the Streamflow Modulated by Changes in Precipitation and Temperature in the North Latitude Watershed of Nepal. Hydrology 8(3):117CrossRef
Maier HR, Dandy GC (1997) Determining inputs for neural network models of multivariate time series. Comput Aided Civil Infrastruct Eng 12(5):353–368CrossRef
Mirdashtvan M, Najafinejad A, Malekian A, Sa’doddin A (2021) Sustainable water supply and demand management in semi-arid regions: optimizing water resources allocation based on RCPs scenarios. Water Resour Manage 35(15):5307–5324CrossRef
Moghaddam HK, Moghaddam HK, Kivi ZR, Bahreinimotlagh M, Alizadeh MJ (2019) Developing comparative mathematic models, BN and ANN for forecasting of groundwater levels. Groundw Sustain Dev 9:100237CrossRef
Mohanty S, Jha MK, Kumar A, Sudheer KP (2010) Artificial neural network modeling for groundwater level forecasting in a river island of eastern India. Water Resour Manage 24(9):1845–1865CrossRef
Msowoya K, Madani K, Davtalab R, Mirchi A, Lund JR (2016) Climate change impacts on maize production in the warm heart of Africa. Water Resour Manage 30(14):5299–5312CrossRef
Nasiri M, Moghaddam HK, Hamidi M (2021) Development of multi-criteria decision making methods for reduction of seawater intrusion in coastal aquifers using SEAWAT code. J Contam Hydrol 242:103848CrossRef
Natarajan N, Sudheer C (2020) Groundwater level forecasting using soft computing techniques. Neural Comput Appl 32(12):7691–7708CrossRef
Nie S, Fu S, Cao W, Jia X (2020) Comparison of monthly air and land surface temperature extremes simulated using CMIP5 and CMIP6 versions of the Beijing Climate Center climate model. Theoret Appl Climatol 140(1):487–502CrossRef
Nover DM, Witt JW, Butcher JB, Johnson TE, Weaver CP (2016) The effects of downscaling method on the variability of simulated watershed response to climate change in five US basins. Earth Interact 20(11):1–27CrossRef
O’Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K, Rothman DS, van Ruijven BJ, van Vuuren DP, Birkmann J, Kok K, Levy M (2017) The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob Environ Chang 42:169–180CrossRef
Ouhamdouch S, Bahir M (2017) Climate change impact on future rainfall and temperature in semi-arid areas (Essaouira Basin, Morocco). Environ Process 4(4):975–990CrossRef
Priestley MD, Ackerley D, Catto JL, Hodges KI, McDonald RE, Lee RW (2020) An Overview of the Extratropical Storm Tracks in CMIP6 Historical Simulations. J Clim 33(15):6315–6343CrossRef
Racsko P, Szeidl L, Semenov M (1991) A serial approach to local stochastic weather models. Ecol Model 57(1–2):27–41CrossRef
Rajaee T, Ebrahimi H, Nourani V (2019) A review of the artificial intelligence methods in groundwater level modeling. J Hydrol 572:336–351CrossRef
Roshan G, Ghanghermeh A, Nasrabadi T, Meimandi JB (2013) Effect of global warming on intensity and frequency curves of precipitation, case study of Northwestern Iran. Water Resour Manage 27(5):1563–1579CrossRef
Roy DK, Barzegar R, Quilty J, Adamowski J (2020) Using ensembles of adaptive neuro-fuzzy inference system and optimization algorithms to predict reference evapotranspiration in subtropical climatic zones. J Hydrol 591:125509CrossRef
Sahour H, Gholami V, Vazifedan M (2020) A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer. J Hydrol 591:125321CrossRef
Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Change 35(4):397–414CrossRef
Sha J, Li X, Wang ZL (2019) Estimation of future climate change in cold weather areas with the LARS-WG model under CMIP5 scenarios. Theoret Appl Climatol 137(3):3027–3039CrossRef
Sharma P, Madane D, Bhakar SR (2021) Monthly streamflow forecasting using artificial intelligence approach: a case study in a semi-arid region of India. Arab J Geosci 14(22):1–10CrossRef
Shen C (2018) A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resour Res 54(11):8558–8593CrossRef
Srivastava A, Grotjahn R, Ullrich PA (2020) Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions. Weather Clim Extrem 29:100268CrossRef
Szidarovszky F, Coppola EA Jr, Long J, Hall AD, Poulton MM (2007) A hybrid artificial neural network-numerical model for ground water problems. Groundwater 45(5):590–600CrossRef
Tabari H (2020) Climate change impact on flood and extreme precipitation increases with water availability. Sci Rep 10(1):1–10
Tan ML, Yusop Z, Chua VP, Chan NW (2017) Climate change impacts under CMIP5 RCP scenarios on water resources of the Kelantan River Basin, Malaysia. Atmos Res 189:1–10CrossRef
Taormina R, Chau KW, Sethi R (2012) Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon. Eng Appl Artif Intell 25(8):1670–1676CrossRef
Theodossiou N (2016) Assessing the impacts of climate change on the sustainability of groundwater aquifers. Application in Moudania Aquifer in N. Greece. Environ Process 3(4):1045–1061
Thorne KM, Elliott-Fisk DL, Freeman CM, Bui TV, Powelson KW, Janousek CN, Buffington KJ, Takekawa JY (2017) Are coastal managers ready for climate change? A case study from estuaries along the Pacific coast of the United States. Ocean Coast Manag 143:38–50CrossRef
Warnatzsch EA, Reay DS (2019) Temperature and precipitation change in Malawi: Evaluation of CORDEX-Africa climate simulations for climate change impact assessments and adaptation planning. Sci Total Environ 654:378–392CrossRef
Wilby RL, Wigley TM (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21(4):530–548CrossRef
Williams KD, Copsey D, Blockley EW, Bodas‐Salcedo A, Calvert D, Comer R, Davis P, Graham T, Hewitt HT, Hill R, Hyder P (2018) The Met Office global coupled model 3.0 and 3.1 (GC3. 0 and GC3. 1) configurations. J Adv Model Earth Syst 10(2):357–380
Xin X, Wu T, Zhang J, Yao J, Fang Y (2020) Comparison of CMIP6 and CMIP5 simulations of precipitation in China and the East Asian summer monsoon. Int J Climatol 40(15):6423–6440CrossRef
Yan T, Bai J, Arsenio T, Liu J, Shen Z (2019) Future climate change impacts on streamflow and nitrogen exports based on CMIP5 projection in the Miyun Reservoir Basin. China Ecohydrol Hydrobiol 19(2):266–278CrossRef
Yoon H, Jun SC, Hyun Y, Bae GO, Lee KK (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396(1–2):128–138CrossRef
Zhang Y, You Q, Chen C, Ge J (2016) Impacts of climate change on streamflows under RCP scenarios: A case study in Xin River Basin, China. Atmos Res 178:521–534CrossRef
Zhao L, Dai T, Qiao Z, Sun P, Hao J, Yang Y (2020) Application of artificial intelligence to wastewater treatment: a bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Process Saf Environ Prot 133:169–182CrossRef
Metadata
Title
Groundwater Level Fluctuations in Coastal Aquifer: Using Artificial Neural Networks to Predict the Impacts of Climatical CMIP6 Scenarios
Authors
Adib Roshani
Mehdi Hamidi
Publication date
07-06-2022
Publisher
Springer Netherlands
Published in
Water Resources Management / Issue 11/2022
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-022-03204-2

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