Access the full text.
Sign up today, get DeepDyve free for 14 days.
A. Nadiri, Keyvan Naderi, R. Khatibi, Maryam Gharekhani (2019)
Modelling groundwater level variations by learning from multiple models using fuzzy logicHydrological Sciences Journal, 64
L. Nyembo, I. Larbi, M. Mwabumba, J. Selemani, S. Dotse, A. Limantol, E. Bessah (2021)
Impact of climate change on groundwater recharge in the lake Manyara catchment, TanzaniaScientific African
I. Zektser, H. Loáiciga (1993)
Groundwater fluxes in the global hydrologic cycle: past, present and futureJournal of Hydrology, 144
Ali Azizpor, M. Izadbakhsh, S. Shabanlou, F. Yosefvand, A. Rajabi (2021)
Estimation of water level fluctuations in groundwater through a Hybrid Learning MachineGroundwater for Sustainable Development
M. Malekzadeh, S. Kardar, K. Saeb, S. Shabanlou, L. Taghavi (2019)
A Novel Approach for Prediction of Monthly Ground Water Level Using a Hybrid Wavelet and Non-Tuned Self-Adaptive Machine Learning ModelWater Resources Management, 33
(2015)
Simulating the impact of climate change onthe groundwater resources of the Magdalen Islands
Diogo Costa, Helen. Zhang, J. Levison (2021)
Impacts of climate change on groundwater in the Great Lakes Basin: A reviewJournal of Great Lakes Research
K. Taheri, F. Gutiérrez, H. Mohseni, E. Raeisi, Milad Taheri (2015)
Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude–frequency relationships: A case study in Hamadan province, IranGeomorphology, 234
Zahra Goorani, S. Shabanlou (2021)
Multi-objective optimization of quantitative-qualitative operation of water resources systems with approach of supplying environmental demands of Shadegan Wetland.Journal of environmental management, 292
A. Azari, Mohammad Zeynoddin, Isa Ebtehaj, A. Sattar, Bahram Gharabaghi, H. Bonakdari (2021)
Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecastingActa Geophysica, 69
Vida Kamkar, A. Azari, Seyed Fatemi (2021)
Estimation of recharge and flow exchange between river and aquifer based on coupled surface water-groundwater model
G. Khanlari, M. Heidari, A. Momeni, M. Ahmadi, A. Beydokhti (2012)
The effect of groundwater overexploitation on land subsidence and sinkhole occurrences, western Iran, 45
CP Kumar, S Singh (2015)
Climate change effects on groundwater resourcesOcta J Environ Res, 3
S Ansari, A Massah Bavani, A Roozbahani (2016)
Effects of climate change on groundwater recharge (case study: sefid dasht plain)Water Soil, 30
S. Shrestha, T. Bach, V. Pandey (2016)
Climate change impacts on groundwater resources in Mekong Delta under representative concentration pathways (RCPs) scenariosEnvironmental Science & Policy, 61
M. New, M. Hulme (2000)
Representing uncertainty in climate change scenarios: a Monte-Carlo approachIntegrated Assessment, 1
(2011)
Evaluation of the impact of climate change on groundwater resources of Rafsanjan. In: 4th Iranian conference of water resources management, Tehran, Amirkabir University, May 3th and 4th
H. Karimi, K. Taheri (2010)
Hazards and mechanism of sinkholes on Kabudar Ahang and Famenin plains of Hamadan, IranNatural Hazards, 55
Masoomeh Zeinali, A. Azari, Mohammad Heidari (2020)
Multiobjective Optimization for Water Resource Management in Low-Flow Areas Based on a Coupled Surface Water–Groundwater ModelJournal of Water Resources Planning and Management, 146
R. Taylor, B. Scanlon, P. Döll, M. Rodell, R. Beek, Y. Wada, L. Longuevergne, M. Leblanc, J. Famiglietti, M. Edmunds, L. Konikow, T. Green, Jianyao Chen, M. Taniguchi, M. Bierkens, A. MacDonald, Ying Fan, R. Maxwell, Y. Yechieli, J. Gurdak, D. Allen, M. Shamsudduha, K. Hiscock, P. Yeh, I. Holman, H. Treidel (2013)
Ground water and climate changeNature Climate Change, 3
F. Yosefvand, S. Shabanlou (2020)
Forecasting of Groundwater Level Using Ensemble Hybrid Wavelet–Self-adaptive Extreme Learning Machine-Based ModelsNatural Resources Research
M. Jyrkama, Jon Sykes (2006)
The Impact of Climate Change on Groundwater
M. Gulacha, Deogratias Mulungu (2017)
Generation of climate change scenarios for precipitation and temperature at local scales using SDSM in Wami-Ruvu River Basin TanzaniaPhysics and Chemistry of The Earth, 100
A. Alizadeh, A. Rajabi, S. Shabanlou, B. Yaghoubi, F. Yosefvand (2021)
Modeling long-term rainfall-runoff time series through wavelet-weighted regularization extreme learning machineEarth Science Informatics, 14
H. Ansari, M. Khadivi, N. Salehnia, I. Babaeian (2015)
EVALUATION OF UNCERTAINTY LARS MODEL UNDER SCENARIOS A1B, A2 AND B1 IN PRECIPITATION AND TEMPERATURE FORECAST (CASE STUDY: MASHHAD SYNOPTIC STATIONS), 8
K. Taheri, H. Shahabi, K. Chapi, A. Shirzadi, F. Gutiérrez, K. Khosravi (2019)
Sinkhole susceptibility mapping: A comparison between Bayes‐based machine learning algorithmsLand Degradation & Development, 30
R. Crosbie, B. Scanlon, F. Mpelasoka, R. Reedy, J. Gates, Lu Zhang (2013)
Potential climate change effects on groundwater recharge in the High Plains Aquifer, USAWater Resources Research, 49
A. Kamal, A. Massahbavani (2011)
THE UNCERTAINTY ASSESSMENT OF AOGCM & HYDROLOGICAL MODELS FOR ESTIMATING GHARESU BASIN TEMPERATURE, PRECIPITATION, AND RUNOFF UNDER CLIMATE CHANGE IMPACT, 5
Mojtaba Poursaeid, R. Mastouri, S. Shabanlou, M. Najarchi (2020)
Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networksWater and Environment Journal, 35
M. Malekzadeh, S. Kardar, S. Shabanlou (2019)
Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine modelsGroundwater for Sustainable Development
Masoomeh Zeinali, A. Azari, Mohammad Heidari (2020)
Simulating Unsaturated Zone of Soil for Estimating the Recharge Rate and Flow Exchange Between a River and an AquiferWater Resources Management, 34
(2014)
2014: Impacts, of adaptation, and vulnerability. Part a: global and sectoral aspect
P. Ashofteh, O. Hadad (2014)
A NEW PROBABILISTIC APPROACH FOR EVALUATION OF THE EFFECTS OF CLIMATE CHANGE ON WATER RESOURCES
Sandra Guzmán, J. Paz, M. Tagert, A. Mercer (2018)
Evaluation of Seasonally Classified Inputs for the Prediction of Daily Groundwater Levels: NARX Networks Vs Support Vector MachinesEnvironmental Modeling & Assessment, 24
R. Wilby, I. Harris (2006)
A framework for assessing uncertainties in climate change impacts: Low‐flow scenarios for the River Thames, UKWater Resources Research, 42
H Ansari (2014)
664Iran J Irrigat Drain, 8
M. Ruíz-Ramos, M. Minguez (2010)
Evaluating uncertainty in climate change impacts on crop productivity in the Iberian PeninsulaClimate Research, 44
Mojtaba Poursaeid, R. Mastouri, S. Shabanlou, M. Najarchi (2020)
Estimation of total dissolved solids, electrical conductivity, salinity and groundwater levels using novel learning machinesEnvironmental Earth Sciences, 79
S. Changnon, F. Huff, C. Hsu (1988)
Relations between precipitation and shallow groundwater in IllinoisJournal of Climate, 1
A. Azizpour, M. Izadbakhsh, S. Shabanlou, F. Yosefvand, A. Rajabi (2022)
Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy modelEnvironmental Science and Pollution Research, 29
Achiransu Acharyya (2014)
Groundwater, Climate Change and Sustainable Well Being of the Poor: Policy Options for South Asia, China and AfricaProcedia - Social and Behavioral Sciences, 157
J. Epting, A. Michel, A. Affolter, P. Huggenberger (2021)
Climate change effects on groundwater recharge and temperatures in Swiss alluvial aquifersJournal of Hydrology X
M. Jalali (2009)
Geochemistry characterization of groundwater in an agricultural area of Razan, Hamadan, IranEnvironmental Geology, 56
S Ansari (2016)
416Water Soil, 30
Mojtaba Poursaeid, Amir Poursaeid, S. Shabanlou (2022)
A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level PredictionWater Resources Management, 36
Hosseinikhah Mansoor, Zeinivand Hossein, Haghizadeh Ali, T. Naser (2015)
VALIDATION OF GLOBAL CLIMATE MODELS (GCMS) FOR TEMPERATURE AND RAINFALL SIMULATION IN KERMANSHAH, RAVANSAR AND WEST ISLAMABAD STATIONS, 1
In this research, the impact of the human factors and climate change on groundwater level fluctuations affected by uncertainty within 27-year upcoming period (2018–2045) in the Razan Plain is examined. To simulate the aquifer performance, the GMS model is calibrated and verified for two 18-month periods, respectively. To forecast climate variables changes in the future time-frame, six CMIP5 models with three scenarios Rcp 2.6, Rcp 4.5 and Rcp 8.5 are utilized. To study the prediction uncertainty of the climate change models, the method of probabilistic levels of precipitation and temperature changes were used. In this technique, by combining 6 climate change models and 3 mentioned scenarios for each month, 18 prediction values for ∆T and ∆P in upcoming years were approximated. After that, by implementing appropriate distribution for each month, next values of ∆T and ∆P in the probabilistic levels of 50% and 90% are estimated. Finally, in two probabilistic levels of 50% and 90% considering the uncertainty of general circulation models, the climate variables of precipitation and temperature were forecasted. Eventually, based on the probabilistic level technique and using the GMS model, the influence of the human factors and climate change on the groundwater level variations under these scenarios are determined. Results showed that climatic factors have a lesser contribution in reducing the groundwater level in the plain, and the largest con- tribution is related to human factors and excessive withdrawal from the aquifer. The contribution of climate change in the reduction of the groundwater level in probability scenarios of 0.9 and 0.5 and emission scenarios Rcp8.5, Rcp4.5 and Rcp2.6 is about 40.8, 24.3, 32.3, 27.6 and 22.2 percent respectively. Based on these results, the first priority for aquifer planning and management should be focused on human activities and controlling the amount of withdrawal from the aquifer. These results clearly show that the main cause of creating sinkholes and the sharp reduction of the groundwater level in the region is the excessive extraction of groundwater resources as a result of human activities, including agriculture and industrial demands, and not climate change. Keywords Climate change · Probabilistic level method · Groundwater level variations · Uncertainty · GMS model Introduction the destruction and depletion of natural resources and the unsustainable use of water and other resources. Population growth and the rapid increase in human activi- One of the negative effects of indiscriminate extraction ties, including urbanization, industrial growth, and other of groundwater resources is the creation of subsidence and agricultural and economic activities, especially in underde- sinkholes in critical aquifers, in addition to a sharp decrease veloped and developing countries, have led to a reduction in the groundwater level. Many studies have been done on in water resources and other land resources. This causes the causes and the structure of aquifer subsidence, as well as significant damage to the physical environment, including the structure of sinkholes (Karimi and Taheri 2010; Khan- lari et al. 2012; Taheri et al. 2015, 2019). The drop in the groundwater level in some aquifers has changed the hydro- chemical parameters of the groundwater resources (Jalali * Saeid Shabanlou 2009). saeid.shabanlou@gmail.com Also, the impact of climate change on the ecosystem and Department of Water Engineering, Kermanshah Branch, rising temperatures has accelerated the reduction of existing Islamic Azad University, Kermanshah, Iran Vol.:(0123456789) 1 3 143 Page 2 of 16 Applied Water Science (2023) 13:143 water resources (Acharyya 2014). The adverse impact of in the Rafsanjan plain using the LARS-WG and PMWIN this phenomenon on water resources in arid and semi-arid models. Ansari et al. (2016) investigated the effect of climate regions, which are mainly from developing countries, is par- change on groundwater recharge in the Sefid Plain based on ticularly significant (Kumar and Singh 2015). Groundwa- the HADCM3 model under two scenarios A2 and B1. Cros- ter is the largest supplier of freshwater in the world, which bie et al. (2013) have investigated the potential effects of cli- plays an important role in preserving the ecosystem. The mate change on groundwater recharge in the highland aquifer strategic importance of groundwater for global food and of the United States. In this study, 16 global climate models water security is not hidden from anyone, and the effect (GCM) and three scenarios were used to examine changes in of climate change with the intensification of droughts and groundwater recharge rates in 2050 compared to 1990. The floods, as well as changes in rainfall, soil moisture and sur - results included increased recharge in the northern high plains face water has led to increasing discharge and depletion of (% + 8), a slight decrease in the central high plain (% − 3) and groundwater. The effect of climate change on groundwater a greater decrease in the southern high plains (%− 10). resources through natural and man-made processes as well Lemieuxet al. (2015) in the Magdalen Islands of Quebec, as groundwater-based feedback on the climate system can be Canada, examined the effects of climate change on water evaluated (Taylor et al. 2012). Changes in temperature and resources. The simulation results show an increase in the precipitation in the future will affect the aquifer recharge. sea level, a decrease in groundwater level and an increase The response of unconfined aquifers to changes in tempera- in coastal erosion. Over a period of 28 years, the combina- ture and precipitation parameters is in the form of changes tion of these effects will cause the intrusion of saline sea- in groundwater level (Zektser and Loaiciga 1993; Chang- water towards groundwater. Shrestha et al. (2016) based on non et al. 1988). Mathematical models are used to study the the CMIP5 models, studied the effects of climate change on fluctuations of groundwater resources, balance changes and groundwater resources in the Mekong Delta in Vietnam. The management of aquifer operation (Kersic 1997). results showed that the average annual temperature under the Mathematical models GMS and MODFLOW are the RCP4.5 scenario would increase by 1.5 °C and in the RCP8.5 most complete models that have been used in many new scenario by 4.5 °C. Also, the amount of rainfall would increase researches to predict the temporal and spatial fluctuations in wet seasons and decrease in dry seasons, leading to reduce of the groundwater level (Zeinali et al. 2020a, b; Kamkar the groundwater level. Gulacha et al. (2017) used the SDSM et al. 2021; Malekzadeh et al. 2019a, b; Poursaeid et al. model for statistical microscaling and conversion of atmos- 2020, 2021, 2022 Azizpour et al. 2021, 2022; Yosefvand pheric general circulation (GCM) models to local scale in the and Shabanlou 2020; Alizadeh et al. 2021; Goorani and Wami-Ruvu River Basin, Tanzania. Finally, their research Shabanlou 2021). Forecasting the groundwater level without showed that in the Wami-Ruvu River Basin, the potential for using mathematical models is usually a averages series of floods and droughts is very high in climate change conditions. groundwater level and does not provide a distribution map The conducted research shows the undeniable effect of for the plain (Guzman et al. 2019; Nadiri et al. 2019; Azari climate change on groundwater resources and the effect of et al. 2021). choosing a climate model and the proposed emission sce- The best tool for studying and generating climate sce- nario on the results. Therefore, the purpose of this study is narios and the impact of greenhouse gases on the Earth's to investigate the effect of uncertainty of climate models atmosphere on a regional scale is the use of the General and emission scenarios on the prediction of groundwater Atmospheric Circulation Model (AOGCM) (Wilby and Har- level under the influence of climate change. Another goal of ris 2006). In climate change studies, various uncertainties this research is to evaluate the efficiency of the developed affect the final results and by ignoring them, the validity of method using probability levels to apply the uncertainty of the results is reduced (IPCC 2014). To reduce the uncer- climate scenarios. Using the method of probability levels tainty of models in climate change studies, a general cir- instead of the weighting method of climate change models culation model should not be enough. We should try to use to check the uncertainty of these models and also to separate the results of several models and scenarios to create a wide the contribution of the effect of human factors and climate range for analysis and minimize uncertainty in the produc- change on the groundwater level is one of the innovations tion of future climate data (New and Hulme 2000; Ansari of this research. et al. 2014). Climate change and its consequences are one of the major problems in the management of surface and groundwater resources, and an accurate estimation of it in the future is nec- essary. Many studies on this subject have been done in recent years. Karamouz et al. (2011) evaluated the effect of climate change and meteorological elements on groundwater resources 1 3 Applied Water Science (2023) 13:143 Page 3 of 16 143 field are of special importance. The position of this plain is Materials and methods shown in Fig. 1. Study area Establishing models and scenarios The study area is the Razan plain with an area of 1553 square General circulation models and scenarios to investigate kilometers located in Hamadan province. This plain is the effects of climate change located in the area between the cities of Famenin, Razan and Hamedan. The number of extraction wells in this plain Statistics and data of the Famenin synoptic station were used is about 1817. Rivers and streams of the region flow north to extract rainfall data, minimum and maximum tempera- to south in this plain. The Razan plain has faced a drop in tures and sundials. To extract rainfall and temperature data groundwater level in recent years, and climate change will in climate change conditions, the CMIP5 series models of exacerbate the crisis in the region. Due to sinkholes that has the Fifth Report of the International Board (AR5) were used. occurred in this plain and its surroundings, studies in this The CMIP5 model series includes 39 models from the Fifth Report of the International Climate Change Board (AR5), Fig. 1 Location of the study area, meteorological stations and rivers 1 3 143 Page 4 of 16 Applied Water Science (2023) 13:143 which is available via the database at: https://esgf- node. llnl. month in the present climate (P ), thus 12 change factors or gov/ search/ cmip5/ delta is obtained for the grade in which the station is located. They can be selected and downloaded. These models have In this case, Eq. (1) is used to obtain rainfall in each of the the ability to produce precipitation, minimum temperature, climatic scenarios. maximum temperature and the average temperature in the P = C × P f f h (1) form of historical data from 1950 to 2005 and future pre- diction data in the form of the emission scenarios RCP2.6, In the case of temperature data, the microscaling method RCP4.5, RCP6.0, RCP8. 5 from 2006 to 2100. Also, the using the change factor or delta method is similar to pre- spatial separation capability of the CMIP5 series compared cipitation, except that Eq. (2) is used to predict the tempera- to the CMIP4 series from the fourth report and the CMIP3 ture under climatic scenarios. To calculate the value of the series from the third report has been enhanced from about change factor or delta related to the temperature of each of 2.5 by 2.5 degrees to about 0.5 by 0.5 degrees, which is a the 12 months of the year, the average temperature of each great improvement (IPCC 2014). In this study, six models future climate month must be subtracted from the average BCC-CSM1-1, CCSM4, GFDL-CM3, IPSL-CM5A-LR, temperature of the same month in the present climate. In this MIROC-ESM and HadGEM2-ES, which have complete way, 12 change factors or delta are obtained for the desired information of three scenarios Rcp 2.6, Rcp 4.5, Rcp 8.5 grade or station. are chosen to extract climate change data. T = T + C f h f (2) Given that the output of atmospheric circulation models Reference scenario can produce precipitation, average temperature, minimum and maximum temperatures as historical data from 1950 to In order to separate the effects of human activities and cli- 2005 and future forecast data in the form of the emission mate change on groundwater drawdown, another scenario scenarios RCP2.6, RCP4.5, RCP8 5 from 2006 to 2100, in was defined as the reference scenario. The reference scenario this study, to calculate the change factor change or delta, was developed assuming the continuation of the existing the historical period of 30 years leading to 2005 is used and well operation conditions and no change in climatic condi- future data on precipitation, average temperature, maximum tions in the coming years (from 2018 to 2045). Therefore, and minimum are utilized to predict and analyze the future this scenario examines exactly the effect of human factors status of the study area in two stages. Then, the groundwater without changing the climatic conditions. In this scenario, level simulation is performed based on climate change data it is assumed that the withdrawal pattern from the wells will related to the periods 2018 to 2045 and 2045 to 2072. not change in the next 27 years and will be similar to the past 27 years (1991 to 2018) and the climate parameters such as temperature and precipitation will also be similar to the past. Calculation of probabilities So it is assumed that the amount of withdrawal from the wells and the changes in rainfall and temperature are similar In order to reduce the inter-model turbulences of the to the last 27 years. Other climate change scenarios AOGCM model in calculations and to increase the accu- described in the previous section (Rcp 2.6، Rcp 4.5، Rcp racy of the existing climate change rates, the average period 8.5) examine the combined effect of human factors and cli- of these data is usually used instead of the direct use of the mate change (temperature and precipitation) in comparison AOGCM data in the climate change calculations. To calcu- to the reference scenario. Finally, the results of these sce- late the climate change scenario in each AOGCM model, the narios were compared with the reference scenario and the values of the temperature difference and the ratio of rainfall effects of climate change on groundwater drawdown were between the average annual long-term temperature in the separated. future periods (2018–2045), (2045–2072) and the simulated base period (1991–2018) are computed by the same model for each cell of the computational network as follows (Wilby Delta microscaling method or change factor and Harris 2006; Sadat Ashofte and Bozorg Hadad 2014): The Delta method or Change Factor method is used for sta- ΔTi = TAOGCM, fut − TAOGCM, base (3) i i tistical microscaling. To calculate the value of the change factor or delta related to precipitation in each of the 12 months of the year, the ΔPi =(PAOGCM, fut ∕PAOGCM, base ) (4) i i average precipitation of each future climate month (P ) must be divided by the historical average precipitation in the same 1 3 Applied Water Science (2023) 13:143 Page 5 of 16 143 In the above relations, ∆Ti and ∆ Pi respectively repre- scenarios. As an example, the values of ∆P for different sent the climate change scenario related to temperature and months in the period 2045–2018 are given in the Fig. 2. rainfall for the long-term average of each month (12 ≤ i ≤ 1), T̅AOGCM, futi and P̅AOGCM, futi, respectively denote Performance index and evaluation of models average long-term temperature and precipitation simulated by AOGM in the next period for each month, T̅AOGCM, To validate and evaluate the prediction accuracy of general basei and P̅AOGCM, basei are the average long-term tem- circulation models and data fitting, goodness-of-fit tests peratures and precipitation simulated by AOGCM in the including root mean square error (RMSE), mean absolute period similar with the observed period for each month. error (MAE) and Nash–Sutcliffe coefficient (NS) are used. It is simply not possible to consider all sources of uncer- Using these indices, the prediction accuracy of the models tainty in climate change studies (Ruiz-Ramos and Minguez can be evaluated (Hosseinikhah et al. 2014; Sadat Ashofte 2010). Therefore, in this study, the most important source and Bozorg Hadad 2014), which are presented in formulas of uncertainty, i.e. uncertainty of the AOGCM models, is (5) to (7). investigated. To produce monthly climate and rainfall sce- N 2 narios, considering the uncertainty of the AOGCM models, O − C i i (5) RMSE = the values of ∆T and ∆P (Eqs. 3 and 4) are computed for i=1 each AOGCM model and each scenario including RCP2.6, RCP4.5, RCP8.5 are calculated for each month. In other words, to produce a probabilistic climate scenario in each � � O − C � i i� i=1 (6) MAE = future period, for each month, from 6 AOGCM models and 3 climate scenarios, a total of 18 ∆T and ∆P are calculated. Then, using the Easy Fit software, the best distribution func- (O −C ) i i i=1 tion (Beta distribution function) is fitted to the values of ∆T NS = 1 − (7) ∑ 2 and ∆P, and for each month, a beta distribution function is (O − O) i=1 obtained for ∆T and ∆P of the same month. Then, the proba- bilistic cumulative distribution function (CDF) of ∆T and In the above equations, O represents the observed value, ∆Ps for each month is determined from the corresponding Ō is the mean value of observed data, C is the value calcu- beta distribution function. Finally, ∆T and ∆P values are lated by the models, and N is the number of observed data. extracted from the respective CDF at 4 probability levels of The best predictions occur once the RMSE and MAE 0.30, 0.50, 0.70 and 0.90 under three scenarios (including quantities are their lowest state and the Nash – Sutcliffe RCP2.6, RCP4.5 and RCP8.5). Using the extracted ∆T and coefficient is close to 1 (Kamal and Massahbavani 2012). ∆Ps selected for two levels of probability of 0.5 and 0.9, Table 1 shows that the MIROC model with coefficients monthly temperature and precipitation scenarios are gener- of 0.54, 0.41 and 0.7 for precipitation and the HadGEM ated for the next period using Eqs. 3 and 4. In the next step, model with coefficients of 1.7, 1.47 and 0.96 for tempera- time series for temperature and precipitation are generated ture, respectively have the lowest rate in RMSE and MAE, at the probability levels of 0.9 and 0.5, and temperature and and highest rate in NS compared to other models and have precipitation values are predicted for these probabilistic Fig. 2 ∆ P values for different months in the period 2045–2018 1 3 143 Page 6 of 16 Applied Water Science (2023) 13:143 Table 1 Coefficients of Precipitation Temperature Abbreviation Model performance indices of AOGCM models compared to RMSE MAE NS RMSE MAE NS the observed period for climatic 0.54 0.41 0.7 5.62 5.54 0.56 MIROC MIROC-ESM parameters of temperature and precipitation 0.58 0.43 0.67 3.2 2.82 0.86 BCC BCC-CSM1-1 1.24 1.13 − 0.5 7.65 6.44 − 0.33 CCSM CCSM4 1.06 0.81 − 0.09 1.7 1.47 0.96 HadGEM HadGEM2-ES 0.76 0.57 0.45 2.65 2.22 0.9 GFDL GFDL-CM3 0.89 0.67 0.23 2.82 2.17 0.87 IPSL IPSL-CM5A-LR 30.0 25.0 20.0 15.0 10.0 5.0 0.0 -5.0 OctNov DecJan FebMar AprMay JunJul AugSep Rcp2.6 14.32 8.25 2.22 -1.28 -1.55 4.39 9.29 14.60 19.99 23.89 23.56 20.31 Rcp4.5 14.42 8.35 2.28 -1.24 -1.55 4.41 9.32 14.66 20.00 23.92 23.62 20.41 Rcp 8.5 14.70 8.50 2.29 -1.30 -1.56 4.44 9.41 14.88 20.40 24.33 23.99 20.74 50% 14.36 8.04 2.16 -1.55 -1.78 4.47 9.26 14.58 20.15 23.66 23.64 20.32 90% 14.15 7.55 1.80 -1.40 -1.42 4.34 9.06 14.35 19.79 23.68 23.57 20.11 OBS 13.86 7.6 1.99 -1.35 -1.6 4.18.9 13.9 19.5 23.2 22.9 19.5 Fig. 3 Long-term average temperature prediction using Rcp 2.6, Rcp 4.5, Rcp 8.5 scenarios and 90 and 50% probability levels in the period 2018–2045 compared to the base period of 1991–2018 30.0 25.0 20.0 15.0 10.0 5.0 0.0 -5.0 OctNov DecJan FebMar AprMay JunJul AugSep Rcp2.6 15.14 8.71 2.47 -1.66 -1.81 4.76 9.93 15.50 20.92 24.78 24.40 21.20 Rcp4.5 15.80 9.59 2.76 -1.63 -1.91 5.00 10.42 16.18 21.76 25.66 25.13 21.93 Rcp 8.5 15.81 9.63 2.76 -1.74 -1.87 4.95 10.35 16.03 21.81 25.85 25.46 22.25 50% 15.62 9.04 2.75 -2.22 -2.19 5.03 9.91 15.87 21.71 27.51 25.17 21.79 90% 15.07 7.97 2.27 -1.90 -1.92 4.79 9.50 15.30 20.62 24.39 24.88 21.09 OBS 13.86 7.6 1.99 -1.35 -1.6 4.1 8.9 13.9 19.5 23.2 22.9 19.5 Fig. 4 Long-term average temperature prediction using Rcp 2.6, Rcp 4.5, Rcp 8.5 scenarios and 90 and 50% probability levels in 2046–2072 compared to the base period of 1991–1998 the highest accuracy and efficiency for predicting precipi- Prediction of temperature and precipitation tation and temperature quantities (Figs. 3, 4). parameters in future periods As mentioned, first, the microscaling data of the BCC- CSM1-1, CCSM4, GFDL-CM3, IPSL-CM5A-LR, MIROC- ESM and HadGEM2-ES models that have complete infor- mation of three scenarios Rcp 2.6, Rcp 4.5, Rcp 8.5 are 1 3 Applied Water Science (2023) 13:143 Page 7 of 16 143 generated using the Delta method. The results of fore- and + 2.3 °C and changes in long-term mean precipitation by casting climatic variables for the scenarios Rcp 2.6, Rcp -17, -23.7, -18.3, -46 and 13.8% during the statistical period 4.5, Rcp 8.5 and two levels of probability of 90 and 50%, of 2046–2072. respectively, changes for the long-term mean temperature As can be seen in the above Figs. 5 and 6, the highest of + 0.65, + 0.653, + 0.653, /04 -0 and + 6.6° C and changes increase in temperature for the period 2018–2045 occurs in the long-term average rainfall of − 0.15, − 0.6, + 2.25, in September and May and for the period 2046–2072 in − 30.2 and − 0.095 percent during the period 2018–2045 July and the highest decrease in temperature for the period In the same way, for long-term mean temperature changes 2018–2045 and the lowest increase in temperature in the of + 2, + 2.2, + 1.55, + 0.98 and + 3.3 °C and changes in long- period 2046–2072 are obtained in January and February term mean precipitation by − 17.7 They show − 23, − 18.3, (Figs. 7, 8). − 46 and 13.8% during the statistical period of 2046–2072. As can be seen in Figs. 9 and 10, the highest increase The results of forecasting climatic variables for the sce- in precipitation occurs in September and October for the narios of Rcp 2.6, Rcp 4.5, Rcp 8.5 and two levels of prob- scenarios Rcp 2.6, Rcp 4.5, Rcp 8.5 in both periods and the ability of 90 and 50%, respectively, show changes for the highest decrease in temperature the period 2018–2045 and long-ter m mean temperature of + 0.65, + 0.653, + 0.653, the lowest temperature increase in the period 2046–2072 -0.04 and + 0.6 °C and changes in the long-term average occur in January and February. Winter has the highest share rainfall of − 0.15, − 0.6, + 2.25, − 30.2 and − 0.095 percent of rainfall among other seasons and summer's share of rain- during the period 2018–2045 and in the same way, for long- fall is very small, so changes in the percentage of rainfall term mean temperature changes of + 2, + 2.2, + 1.55, + 0.98 in this season have little effect on annual rainfall variation. 1.2 0.8 Rcp2.6 0.6 0.4 Rcp4.5 0.2 Rcp8.5 50% -0.2 -0.4 90% -0.6 -0.8 OctNov DecJan FebMar AprMay JunJul AugSep Fig. 5 Long-term temperature difference in various months for Rcp 2.6, Rcp 4.5, Rcp 8.5 scenarios and 90 and 50% probability levels in the period 2018–2045 compared to the base period of 1991–2018 Rcp2.6 Rcp4.5 Rcp8.5 2 50% 90% OctNov DecJan FebMar AprMay JunJul AugSep Fig. 6 Long-term temperature difference in various months for Rcp 2.6, Rcp 4.5, Rcp 8.5 scenarios and 90 and 50% probability levels in the period 2046–2072 compared to the base period of 1991–1998 1 3 T (°c) T (°c) 143 Page 8 of 16 Applied Water Science (2023) 13:143 Rcp2.6 Rcp4.5 Rcp8.5 50% 90% OBS OctNov DecJan Feb Mar Apr May JunJul AugSep Fig. 7 Long-term average rainfall prediction using Rcp 2.6, Rcp 4.5, Rcp 8.5 scenarios and 90 and 50% probability levels in the period 2018– 2045 compared to the base period of 1991–2018 Rcp2.6 Rcp4.5 Rcp8.5 50% 90% OBS OctNov DecJan Feb Mar AprMay JunJul AugSep Fig. 8 Prediction of long-term average rainfall using Rcp 2.6, Rcp 4.5, Rcp 8.5 scenarios and 90 and 50% probability levels in the period 2046– 2072 compared to the base period of 1991–1998 GMS groundwater model h h h h k h + k h + k h = S ± w x y z y x x y y z z t In this research, the GMS (Groundwater Modeling System) (8) model is used to simulate the aquifer behavior in climate where K , K and K are hydraulic conductivity in different x y z change conditions. The GMS numerical model is based on directions, w denotes the recharge or discharge of ground- solving three-dimensional equations governing groundwater water, h represents the potential head (hydraulic head), S flow, which is presented in both steady and transient state represents the specific yield and t is time. Equation (8) is conditions according to the flow conditions. Due to the fact solved by applying the initial and boundary conditions and that the Razan plain aquifer is of free type, the equation gov- based on the finite difference method. erning the groundwater flow, which is known as the Bouss- The structure of the conceptual model of the Razan plain inesq nonlinear equation, is defined as follows. aquifer includes modeling and initial distribution of the hydrogeological parameters (hydraulic conductivity and 1 3 P (mm) P (mm) Applied Water Science (2023) 13:143 Page 9 of 16 143 Rcp2.6 -50 Rcp4.5 -100 Rcp8.5 -150 0.5 OctNov DecJan FebMar AprMay JunJul AugSep 0.9 Rcp2.6 100 -13.11 -1.23 9.48 -4.7 4.17 6.04 -46.56 50.53 -73.31 -78.83 45.82 Rcp4.5 88.44 -14.94 -9.07 4.45 -9.53 1.81 4.63 -50.69 5.67 -83.34 -44.86 49.95 Rcp8.5 74.7-14.84 -7.15 7.52 -3.51 6.16.7 -51.12 -0.24-85.62 -49.26 41.99 0.5 4.13 3.69 3.55 0.47 -10.12 -4.74-7.71 -6.26 26.81 6.63 17.79-15.49 0.9 -59.4 -25-29.01 -30.6 -35.49 -29.52 -25.25 -39.12 -72.84 -88.87 -89.15 -91.62 Fig. 9 Percentage of precipitation variations in different months, the scenarios Rcp 2.6, Rcp 4.5, Rcp 8.5 and probability levels of 90 and 50% in the period 2018–2045 compared to the base period of 1991–1998 -50 -100 -150 OctNov DecJan FebMar AprMay JunJul AugSep Rcp2.6 60.28-31.45 -19.01-18.23-31.13-18.81-20.74-49.52-51.74-57.53 12.99 -17.25 Rcp4.5 44.75-34.55 -23.52-22.77-35.12-21.58-30.63-59.24-70.29-59.14 15.74 -19.85 Rcp8.5 86 -28.36 -19.21-20.54-30.71-16.4 -23.79 -58.01 -61.2-59.14 37.51 34.11 0.5 -4.65 -5.84 -0.7 -19.67-30.97-18.48-15.34-26.38-2.59 -59.35 -36.07 -23.55 0.9 -39.78 -32.8 -36.27-68.68-48.99-38.4 -57.3-47.79-72.61-99.78-103.36 -71.15 Fig. 10 Percentage of precipitation changes in different months, the scenarios Rcp 2.6, Rcp 4.5, Rcp 8.5 and probability levels of 90 and 50% in the period 2046–2072 compared to the base period of 1991–1998 specific yield), discharge of extraction wells and their return of the region (level map of the month before the start of the water, observation wells, water exchange between river and simulation period), the digital elevation map of the region aquifer, recharge rate from the surface to the aquifer and the (DEM) and existing drilling points are utilezd, respectively. boundary conditions of the aquifer. In this research, mod- The inflows to the aquifer are also calculated by the values of eling has been done for a single-layer groundwater system the head in the General Head Boundary (GHB) cells based and the modeling domain corresponds to the groundwater on the topography map. The initial values of hydraulic con- water budget. In order to estimate the initial hydraulic load ductivity and specific yield are considered according to the and topography of the plain surface and bedrock, the inter- grain size of the saturation layer (logs of wells) and their polated groundwater level map obtained from piezometers final values are obtained after calibration and validation of 1 3 precipitaon (%) precipitaon (%) 143 Page 10 of 16 Applied Water Science (2023) 13:143 the model. To prepare the initial map of the recharge from Results and discussion the surface, soil maps, land use as well as rainfall status in the area are implemented. Also, the flow rates of the rivers in Calibration and validation of groundwater model the tributaries are estimated from the hydrometric stations. All data required for aquifer modeling were obtained from To adapt and proper performance of the model simulation, Hamedan regional water company. the model calibration is conducted for an 18-month period Considering the depth of the groundwater level in the (October 2008 to March 2009). During the calibration phase, region, which is more than 4 m, evaporation does not play a the input parameters of the model, including hydraulic and role in the groundwater balance in the region. Therefore, in hydrodynamic data, are adjusted to an acceptable agreement the stages of preparing the conceptual model as well as the between the observed groundwater level in the piezometers numerical model, the evaporation package is not considered. and the groundwater level calculated by the model. The To clarify the research steps, a flowchart that briefly model is calibrated in two states comprising steady and tran- shows the research steps is shown in Fig. 11. sient. In the steady state, one month with a steady groundwa- ter level is selected and the model is calibrated in this state (Fig. 12). The inflows to the aquifer are calculated via the Fig. 11 Flowchart of the research steps 1 3 Applied Water Science (2023) 13:143 Page 11 of 16 143 yield parameter (Sy) of the aquifer are entered into the model to in the form of zoning for by manual trial and error method to calibrate the model. In the transient state, the monthly variations of the aquifer are examined and the out- put of the model in the steady state (hydraulic conductivity) is set as the basis of the transient state. The specific yield and recharge parameters are calibrated at 103 piezometers for 18 months and the final simulation error values are obtained at the location of each piezometer. For both steady and tran- sient states, the error rate of the RMSE and MAE indices is obtained. Figures 13 and 14 show the acceptable adaptation of the level simulation results in the groundwater model to the field data in the calibration and validation steps. The error rate in the steady state after the calibration of the model is in the acceptable range. For the transient state, the error values in different time steps (18 months) after the calibration of the model are seen in Fig. 13 and are in the acceptable range in all months, which indicates the proper performance of the model. To confirm the performance of the model, the model is validated for a period of 18 months (September 2011-April 2010), and the values of the obtained RMSE and MAE indices (Fig. 14) indicate the reasonable accuracy of the simulation model. Error values in different time steps (18 months) during the validation of the model are seen in Fig. 14 and are in the acceptable range in all months, indicating the appropriate adaptation of the simulated model to the natural conditions of the aquifer. Fig. 12 Position of calibrated piezometers in the steady state Groundwater change trends Human factors of declining groundwater level can be values of the head in the boundary cells (GHB) based on the divided into two parts. The first part includes the produc- topography map and imported to the model. tion and increase of greenhouse gases and the consequences In the steady state, the hydraulic conductivity (K) and of climate change such as changes in temperature and rain- recharge, and in addition, in the transient state, the specific fall and the second part includes increasing groundwater Fig. 13 Values of ME, MAE and RMSE indices in the 18-month calibration period 1 3 143 Page 12 of 16 Applied Water Science (2023) 13:143 Fig. 14 Values of ME, MAE and RMSE indices in the 18-month validation period 1713 120 1708 60 1703 0 0102030405060708090 100 110 120 130 140 150 Month Number Fig. 15 Unit hydrograph of Razan plain, the trend of groundwater level change and monthly rainfall changes (mm) during the historical observa- tional period extraction such as increasing the area under cultivation on groundwater drawdown. The reference scenario was (increasing water demand), increasing withdrawal from developed assuming the continuation of the existing well pumping wells, etc. The effects of these changes can also operation conditions and no change in climatic conditions in be extended to the groundwater of the Razan plain, which the coming years (from 2018 to 2045). This scenario exam- in this study the effect of climate change on fluctuations in ines exactly the effect of human factors without changing groundwater resources of this plain are evaluated. The trend the climatic conditions. of changes in the groundwater level of the Razan plain, as Considering the fact that the hydrodynamic coefficients seen in Fig. 15, shows a decrease of 4.5 m during the histori- of the aquifer change with the change of the aquifer con- cal observational period. Also, in Fig. 15, the bar graph of ditions in the far future (2045 to 2072), therefore, it is monthly rainfall changes in millimeters was shown, which a little difficult to generalize the aquifer conditions for indicates a decreasing trend of monthly rainfall, especially the future period of 2045 to 2072. Therefore, this period in recent years. was excluded from the comparisons and only the ground- water drawdown in the future period (2018 to 2045) was The effects of climatic variables on the groundwater compared under the reference scenario and different cli- level of the region in future periods mate change scenarios so that the results are more realis- tic. Based on this, after simulating the system using the As mentioned, the reference scenario was implemented to GMS model, a three-dimensional map of the groundwater separate the effects of human activities and climate change level was drawn in the reference scenario at the end of the selected period (Sep, 2045), which is shown in Fig. 16. 1 3 Groundwater Elevation (m) Rainfall (mm) Applied Water Science (2023) 13:143 Page 13 of 16 143 Fig. 16 The three-dimensional map of the groundwater level for Sep 2045 in the reference scenario According to Fig. 16, the average drop of the ground- For this purpose, using the climate change scenarios water level in the northern, central and southern parts of Rcp2.6, Rcp 4.5, Rcp 8.5 and two probability levels of 90 the plain is 3.5, 15.7 and 5.5 m, respectively. Therefore, and 50%, the aquifer is evaluated and its fluctuations are the largest amount of drawdown is in the central areas of calculated. To this end, after applying changes in various the plain and near the sinkholes. In some central areas and parameters that have been affected by rainfall and tempera- in the vicinity of sinkholes, the maximum groundwater ture, including aquifer recharge, river flow rate and extrac- drawdown reaches 56 m. Considering the entire area of tion from aquifer by wells, the conceptual model is re-run the plain, the average drawdown in the whole plain in the and the simulation was conducted for upcoming period. reference scenario was 8.5 m. The output of the results indicates that the fluctuations In the next step, the future status of the aquifer for of the groundwater level caused by climate change under future climate change scenarios in the next period, i.e. the scenarios. 2018–2045 was forecasted using the GMS model. The groundwater level under the climatic scenarios Rcp2.6, Rcp4.5, Rcp8.5 and two probability levels of 90 and 0.0 -0.9 -1.8 -2.7 -3.6 -4.5 OctNov DecJan Feb Mar AprMay JunJul AugSep Rcp2.6 -0.73 -0.77 -0.58 -0.44 -0.57-0.90 -1.71 -1.86 -1.70 -1.90 -1.93 -1.79 Rcp4.5 -0.83 -0.88 -0.68 -0.52 -0.66-1.04 -1.94 -2.15 -1.97 -2.18 -2.21 -2.08 Rcp8.5 -0.98 -1.03 -0.79 -0.60 -0.76-1.20 -2.25 -2.51 -2.30 -2.54 -2.58 -2.44 50% -1.41 -1.38 -1.05 -0.86 -1.14-1.79 -3.24 -3.60 -3.30 -3.59 -3.77 -3.71 90% -1.55 -1.50 -1.22 -1.02 -1.28-1.92 -3.35 -3.77 -3.54 -3.87 -4.04 -3.98 Fig. 17 Groundwater level fluctuations in climate change scenarios in different months for the period 2018–2045 compared to the reference sce- nario 1 3 143 Page 14 of 16 Applied Water Science (2023) 13:143 50% in different months for the period 2018–2045 compared sharp decrease of rainfall in the future period (2045–2018) to the reference scenario (Fig. 17). in this scenario. After that, in the Rcp8.5 scenario, which is As shown in Fig. 17, in the six months of October to considered a pessimistically scenario, the contribution of March (autumn and winter) the groundwater level drop has climate change in lowering the groundwater level is finally a soft decreasing trend and the reason is less change in rain- 32%, and human factors and improper management of the fall in future periods and even increased rainfall for three aquifer are about 68% effective. Based on these results, the scenarios Rcp 2.6, Rcp 4.5, Rcp 8.5. So, there is a slight r fi st priority for aquifer planning and management should be increase in temperature in these six months and even in focused on human activities and controlling the amount of some scenarios, a decrease in temperature is observed. The withdrawal from the aquifer. These results clearly show that groundwater level in the six months of April to September the main cause of creating sinkholes and the sharp reduc- (spring and summer) have a greater decrease than the previ- tion of the groundwater level in the region is the excessive ous six months (autumn and winter). Rising temperatures extraction of groundwater resources as a result of human and declining rainfall are the main reasons for the increase activities, including agriculture and industrial demands, and of groundwater level drop in this months. The highest drop not climate change. in groundwater occurs in August and the lowest amount in January. As mentioned in the definition of scenarios section, the Conclusion reference scenario examines the effect of human factors assuming no change in climate conditions in the future. The combined role of human factors and climate change in Other climate change scenarios including emission scenarios intensifying on the groundwater drawdown and separating (Rcp 2.6, Rcp 4.5, Rcp 8.5) as well as scenarios with prob- the role of each on the reduction of groundwater reserves is ability levels of 50% and 90% examine the combined effect one of the basic issues in water resources management and of human factors and climate change (temperature and pre- its estimation is very necessary in the proper management of cipitation) compared to the reference scenario. In these sce- the aquifer in the future. In this study to predict temperature narios, the changes in the groundwater level were simulated and precipitation in the future, a general circulation model using the GMS model for the period of 2018–2045, and then called " AOGCM" was utilized. To validate and evaluate the by comparing with the reference scenario, the contribution accuracy of general circulation models and data fitting, the of climate change in reducing the groundwater level during RMSE, MAE and NS indices were used. In the uncertainty this period was separated. The results of the separation of the evaluation, one model is not enough to validate and increase contribution of climate change and human factors in reduc- the accuracy of the prediction results. So six general circula- ing the groundwater level are shown in Table 2. tion models including BCC-CSM1-1, CCSM4, GFDL-CM3, This table shows that in all scenarios, climatic factors IPSL-CM5A-LR and MIROC ESM and HadGEM2-ES were have a lesser contribution in reducing the groundwater level used for the emission scenarios Rcp2.6, Rcp 4.5, Rcp 8.5 in the plain, and the largest contribution is related to human and two probability levels of 90 and 50% of the output of factors and excessive withdrawal from the aquifer. According six models and three scenarios. Also, in order to separate to Table 2, the contribution of climate change in the reduc- the effects of human activities and climate change on the tion of the groundwater level in probability scenarios of 0.9 amount of groundwater drawdown, the reference scenario and 0.5 and emission scenarios Rcp8.5, Rcp4.5 and Rcp2.6 was developed, assuming the continuation of the existing is about 40.8, 24.3, 32.3, 27.6 and 22.2 percent respectively. conditions of the exploitation of wells and without changing In the probability scenario of 0.9, which is considered the the climatic conditions in the coming years (from 2018 to upper limit of probability, the contribution of climate change 2045). The contribution of climate change in the reduction in reducing the water level is significant, which is due to the of the groundwater level in probability scenarios of 0.9 and Table 2 Separating the contribution of climate change and human factors in average groundwater drawdown (m) in the emission scenarios com- pared to the reference scenario (2018–2045) The parameter affecting the drawdown The drawdown caused by human factors and climate change (m) for each sce- nario Rcp2.6 Rcp4.5 Rcp8.5 Probability 0.5 Probability 0.9 The effect of both human factors and climate change (m) − 10.8 − 11.6 − 12.4 − 11.1 − 14.2 Contribution of climate change (m) − 2.4 − 3.2 − 4 − 2.7 − 5.8 Contribution of climate change on the drawdown (%) 22.2 27.6 32.3 24.3 40.8 1 3 Applied Water Science (2023) 13:143 Page 15 of 16 143 included in the article's Creative Commons licence, unless indicated 0.5 and emission scenarios Rcp8.5, Rcp4.5 and Rcp2.6 is otherwise in a credit line to the material. If material is not included in about 40.8%, 24.3%, 32.3%, 27.6% and 22.2% respectively. the article's Creative Commons licence and your intended use is not Based on these results, the first priority for aquifer plan- permitted by statutory regulation or exceeds the permitted use, you will ning and management should be focused on human activities need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://cr eativ ecommons. or g/licen ses/ b y/4.0/ . and controlling the amount of withdrawal from the aquifer. These results clearly show that the main cause of creat- ing sinkholes and the sharp reduction of the groundwater References level in the region is the excessive extraction of ground- water resources as a result of human activities, including Acharyya A (2014) Groundwater, climate change and sustainable agriculture and industrial demands, and not climate change. well being of the poor: policy options for South Asia, China and In order to provide useful solutions, these results should be Africa. Procedia Soc Behav Sci 157:226–235 taken into consideration by planners and managers. Consid- Alizadeh A, Rajabi A, Shabanlou S, Yaghoubi B, Yosefvand F (2021) Modeling long-term rainfall-runoff time series through ering these changes, by using proper management of water wavelet-weighted regularization extreme learning machine. resources and considering all agriculture, drinking, industry Earth Sci Inform 14:1047–1063. https:// doi. or g/ 10. 1007/ and environmental aspects, the adverse effects of human fac- s12145- 021- 00603-8 tors and climate change on water resources of the region can Ansari H, Khadivi M, Salehnia N, Babaeian I (2014) Evaluation of uncertainty LARS model under scenarios A1B, A2 and B1 in pre- be reduced. In recent years, many researches have investi- cipitation and temperature forecast (case study: mashhad synoptic gated the effect of climate change on water level drawdown stations). Iran J Irrigat Drain 8(4):664–672 ((In Farsi)) and groundwater recharge based on the fifth climate change Ansari S, Massah Bavani A, Roozbahani A (2016) Effects of climate report that indicate the undeniable effect of climate change change on groundwater recharge (case study: sefid dasht plain). Water Soil 30(2):416–431 ((In Farsi)) on groundwater resources and the effect of choosing a cli- Azari A, Zeynoddin M, Ebtehaj I, Sattar A, Gharabaghi B, Bonak- mate model and emission scenario on the work (Epting et al. dari H (2021) Integrated preprocessing techniques with linear 2021; Costa et al. 2021; Nyembo et al. 2022). But in these stochastic approaches in groundwater level forecasting. Acta researches, the uncertainty of climate change models has not Geophys 69(4):1395–1411 Azizpor A, Izadbakhsh MA, Shabanlou S, Yosefvand F, Rajabi A been investigated by defining probability levels. Also, the (2021) Estimation of water level fluctuations in groundwater separation of the contribution of human factors and climate through a hybrid learning machine. Groundw Sustain Dev change on the drop of the groundwater level was a prominent 15:100687 case that was discussed in this research. This makes manag- Azizpour A, Izadbakhsh MA, Shabanlou SY, F Rajabi (2022) A simulation of time-series groundwater parameters using a ers and planners have a correct understanding of the aquifer hybrid metaheuristic neuro-fuzzy model. Environ Sci Pollut conditions and provide more realistic solutions to solve the Res 29:28414–28430 aquifer problem or to improve and restore lost groundwater Changnon SA, Huff FA, Hsu CF (1988) Relations between precipita - reserves. tion and shallow groundwater in Illinois. J Clim 1:1239–1250 Costa D, Zhang H, Levison J (2021) Impacts of climate change on Acknowledgements The authors of this paper would like to express groundwater in the Great Lakes Basin: a review. J Great Lakes their sincerest gratitude to the regional Water Company of Hamedan Res 47(6):1613–1625. https:// doi. org/ 10. 1016/j. jglr. 2021. 10. Province, Iran who made this research possible. Crosbie RS, Scanlon BR, Mpelasoka FS, Reedy RC, Gates JB, Author contributions All authors contributed to the study conception Zhang L (2013) Potential climate change effects on groundwa- and design. Material preparation, data collection and analysis were per- ter recharge in the High Plains Aquifer, USA. Water Resour Res formed by All authors. The first draft of the manuscript was written by 49(7):3936–3951 Saeid Shabanlou. All authors read and approved the final manuscript. Epting J, Michel A, Affolter A, Huggenberger P (2021) Climate change effects on groundwater recharge and temperatures in Swiss allu- Funding No funding was received for this study. vial aquifers. J Hydrol X 11(3):100071. https://doi. or g/10. 1016/j. hydroa. 2020. 100071 Data availability statement All data, and models used during the study Goorani Z, Shabanlou S (2021) Multi-objective optimization of are available from the corresponding author by request. quantitative-qualitative operation of water resources systems with approach of supplying environmental demands of Shadegan Wetland. J Environ Manage 292:112769. https://doi. or g/10. 1016/j. Declarations jenvm an. 2021. 112769 Gulacha MM, Mulungu DMM (2017) Generation of climate change Conflict of interest The authors declare that they have no conflict of scenarios for precipitation and temperature at local scales using interest. SDSM in Wami-Ruvu River Basin Tanzania. Phys Chem Earth 100:62–72 Open Access This article is licensed under a Creative Commons Attri- Guzman SM, Paz JO, Tagert MLM, Mercer AE (2019) Evaluation of bution 4.0 International License, which permits use, sharing, adapta- seasonally classified inputs for the prediction of daily groundwa- tion, distribution and reproduction in any medium or format, as long ter levels: NARX networks vs support vector machines. Environ as you give appropriate credit to the original author(s) and the source, Model Assess 24(2):223–234 provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are 1 3 143 Page 16 of 16 Applied Water Science (2023) 13:143 Hosseinikhah M, Zeinivand H, Haghizadeh A, Tahmasebipour N recharge in the lake Manyara catchment, Tanzania. Sci Afr (2014) Validation of global climate models (GCMS) temperature 15(10):e01072. https:// doi. org/ 10. 1016/j. sciaf. 2021. e01072 and rainfall simulation in kermanshah, ravansar and west islama- Poursaeid M, Mastouri R, Shabanlou S et al (2020) Estimation of total bad stations. Iran J Ecohydrol 1(3):195–206 ((In Farsi)) dissolved solids, electrical conductivity, salinity and groundwater IPCC (2014) Summary for policmarkers. In: Climate Change. 2014: levels using novel learning machines. Environ Earth Sci 79:453 Impacts, of adaptation, and vulnerability. Part a: global and sec- Poursaeid M, Mastouri R, Shabanlou S, Najarchi M (2021) Model- toral aspect. Contribution working group II to the Fifth Assess- ling qualitative and quantitative parameters of groundwater using ment Report of the Intergovernmental Panel on Climate Change a new wavelet conjunction heuristic method: wavelet extreme camberidge University Press, Cambridge, United Kingdom and learning machine versus wavelet neural networks. Water Environ New York, NY, USA, pp 1–132 J 35:67–83 Jalali M (2009) Geochemistry characterization of groundwater in Poursaeid M, Poursaeid AH, Shabanlou S (2022) A comparative an agricultural area of Razan, Hamadan. Iran Environ Geol study of artificial intelligence models and a statistical method for 56(7):1479–1488 groundwater level prediction. Water Resour Manag 36:1499–1519 Kamal A, Massahbavani A (2012) The uncertainty assessment of Ruiz-Ramos M, Minguez MI (2010) Evaluating uncertainty in climate AOGCM and hydrological models for estimating gharesu basin change impacts on crop productivity in the Iberian Peninsula. temperature, priciitation, and runoff under climate change impact. Clim Res 44:69–82 Iran Water Res J 5(9):39–49 ((In Farsi)) Sadat Ashofte P, Bozorg Hadad O (2014) A New Probabilistic Kamkar V, Azari A, Fatemi SE (2021) Estimation of recharge and Approach for Evaluation of the Effects of Climate Change on flow exchange between river and aquifer based on coupled surface Water Resources. Water Resources Engineering 6(19):51–66 ((In water-groundwater model. Iran J Soil Water Res 52(7):1779–1793 Farsi)) ((In Farsi)) Shrestha S, Bach TV, Pandey VP (2016) Climate change impacts on Karamouz M, Abolpour A, Nazif S (2011) Evaluation of the impact groundwater resources in Mekong Delta under representative con- of climate change on groundwater resources of Rafsanjan. In: centration pathways (RCPs) scenarios. Environ Sci Policy 61:1–13 4th Iranian conference of water resources management, Tehran, Taheri K, Gutiérrez F, Mohseni H, Raeisi E, Taheri M (2015) Sink- Amirkabir University, May 3th and 4th. (In Farsi) hole susceptibility mapping using the analytical hierarchy process Karimi H, Taheri K (2010) Hazards and mechanism of sinkholes on (AHP) and magnitude-frequency relationships: a case study in Kabudar Ahang and Famenin plains of Hamadan. Iran Nat Haz- Hamadan province. Iran Geomorphol 234:64–79 ards 55(2):481–499 Taheri K, Shahabi H, Chapi K, Shirzadi A, Gutiérrez F, Khosravi K Kersic N (1997) Quantitative solution in hydrology and groundwater (2019) Sinkhole susceptibility mapping: a comparison between modeling. Lewis Publishers, New York Bayes-based machine learning algorithms. Land Degrad Dev Khanlari G, Heidari M, Momeni AA, Ahmadi M, Beydokhti AT (2012) 30(7):730–745 The effect of groundwater overexploitation on land subsidence Taylor RG et al (2012) Ground water and climate change. Nat Clim and sinkhole occurrences, western Iran. Q J Eng GeolHydrogeol Change 3:322–329 45(4):447–456 Wilby R, Harris I (2006) A framework for assessing uncertainties in Kumar CP, Singh S (2015) Climate change effects on groundwater climate change impacts: low flow scenarios for the River Thames resources. Octa J Environ Res 3(4):264–271 UK. Water Resour Res 42(2):1–10 Lemieux J, Hassaoui J, Molson J, Therrien R, Therrien P, Chouteau Yosefvand F, Shabanlou S (2020) Forecasting of groundwater level M, Ouellet M (2015) Simulating the impact of climate change using ensemble hybrid wavelet–self-adaptive extreme learning onthe groundwater resources of the Magdalen Islands. J Hydrol machine-based models. Nat Resour Res 29:3215–3232 3:400–423 Zeinali M, Azari A, Heidari M (2020a) Simulating unsaturated zone of Malekzadeh M, Kardar S, Saeb K, Shabanlou S, Taghavi L (2019a) A soil for estimating the recharge rate and flow exchange between a novel approach for prediction of monthly ground water level using river and an aquifer. Water Resour Manag 34:425–443 a hybrid wavelet and non-tuned self-adaptive machine learning Zeinali M, Azari A, Heidari M (2020b) Multiobjective optimization for model. Water Resour Manag 33:1609–1628. https:// doi. org/ 10. water resource management in low-flow areas based on a coupled 1007/ s11269- 019- 2193-8 surface water-groundwater model. J Water Resour Plan Manag Malekzadeh M, Kardar S, Shabanlou S, (2019b). Simulation of ground- ASCE 146(5):04020020 water level using MODFLOW, extreme learning machine and Zektser IS, Loaiciga HA (1993) Groundwater fluxes in the global Wavelet-Extreme Learning Machine models. Groundwater for hydrologic cycle: past, present, and future. J Hydrol 144:405–427 Sustainable Development, 9. Nadiri AA, Naderi K, Khatibi R, Gharekhani M (2019) Modelling Publisher's Note Springer Nature remains neutral with regard to groundwater level variations by learning from multiple models jurisdictional claims in published maps and institutional affiliations. using fuzzy logic. Hydrol Sci J 64(2):210–226 New M, Hulme M (2000) Representing uncertainty in climate change scenarios: a Monte-Carlo approach. Integr Assess 1:203–213 Nyembo LO, Larbi I, Mwabumba M, Selemani JR, Dotse SQ, Limantol AM, Bessah E (2022) Impact of climate change on groundwater 1 3
Applied Water Science – Springer Journals
Published: Jun 1, 2023
Keywords: Climate change; Probabilistic level method; Groundwater level variations; Uncertainty; GMS model
You can share this free article with as many people as you like with the url below! We hope you enjoy this feature!
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.