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

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.

Full text

Vol.:(0123456789)
Water Resources Management
https://doi.org/10.1007/s11269-022-03204-2
1 3
Groundwater Level Fluctuations inCoastal Aquifer: Using
Artificial Neural Networks toPredict theImpacts ofClimatical
CMIP6 Scenarios
AdibRoshani1 · MehdiHamidi1
Received: 3 March 2022 / Accepted: 25 May 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022
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 ground-
water 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 poli-
cymakers to identify adaptation strategies more precisely for basins with similar climates.
Keywords CMIP6· Groundwater level· Artificial neural network· LARS-WG· Sari-Neka
1 Introduction
According to studies by the Intergovernmental Panel on Climate Change (IPCC), the short-
age of water resources is expected to become a major challenge in many regions of Asia,
as the demand for water is increasing due to the rise in population and standards of living
(IPCC 2014). Given the strong dependence of the Asian economy on agriculture, about 80%
of groundwater in Asia is used for this purpose. The groundwater level in coastal aquifers
* Mehdi Hamidi
hamidi@nit.ac.ir
1 Department ofCivil Engineering, Babol Noshirvani University ofTechnology, Babol, Iran

A.Roshani, M.Hamidi
1 3
can be influenced by population growth (Alimohammadi etal. 2020), High quality of life
(Mirdashtvan etal. 2021), tides, rising sea level, increased salinity, uncontrolled withdrawal,
and reduced recharge (Hamidi and Sabbagh-Yazdi 2008; Natarajan and Sudheer 2020;
Nasiri etal. 2021). As an arid and semi-arid country in Western Asia, Iran has a shoreline of
750km on the Caspian Sea and about 2250km on the Persian Gulf and the Gulf of Oman,
and numerous islands and estuaries. As a result, it has earned the title of a coastal country.
As of the latest census, about 22% of Iran’s population inhabit coastal lines. These regions
play a significant role in the economic growth of a developing society, in terms of both agri-
culture and industry. Moreover, they can greatly contribute to the gross domestic product of
a country. The critical role played by groundwater in this economic growth necessitates the
awareness and management of changes in groundwater levels. Researchers have employed
theoretical and observation models to investigate prospective climate conditions. Models of
global climate change (GCMs) are primary tools for predicting the trend of changing climate
change through different scenarios of greenhouse gas emissions (Ouhamdouch and Bahir
2017). General circulation models (GCMs) are some of the most authoritative theoretical
models that are based on physics (O’Neill etal. 2017). However, they have certain disadvan-
tages, such as their large scale. Nonetheless, they can be converted to small scales through
techniques termed “downscaling methods”, (Hewitson and Crane 1996; Wilby and Wigley
1997; Guo and Wang 2016; Theodossiou 2016). One of the most well-known and useful
downscaling models is the Long Ashton Research Station-Weather Generator (LARS-WG)
developed by Semenov and Barrow (Semenov etal. 1998). Its input data include minimum
temperature, maximum temperature, precipitation, and sunshine hours or solar radiation on
a daily scale. The model is capable of simulating climatic parameters for future decades
based on various scenarios and GCMs. The models in Coupled Model Intercomparison Pro-
ject Phase 6 (CMIP6) have produced the latest simulations of the past, present, and future
climates for a better understanding of climate change. These new models boast considerable
improvements over those used in CMIP5, including a higher resolution and better physics
(Priestley etal. 2020; Xin etal. 2020). Various researchers have simulated climatic param-
eters using the LARS-WG model and confirmed the results (Hassan etal. 2014; Sha etal.
2019; Bayatvarkeshi etal. 2020). Most previous research has used CMIP3 or CMIP5 out-
puts for this purpose. In this regard, it is necessary to simulate and evaluate the impact of
the latest versions of CMIPs on climatic parameters and natural resources. In this study, the
impact of updating CMIPs was investigated on groundwater levels.
In recent years, international climatologists and economists have created a wide range
of new pathway scenarios. They have described changes in climates and international com-
munities in terms of population, economy, and greenhouse gas (GHG) emissions. The lat-
est is named Shared Socio-economic Pathways (SSP), divided into five categories, from
SSP1 to SSP5. These scenarios are displayed in the form of SSPx-y, where x represents
SSP, and y denotes the radiative forcing (w/m2) in 2100 (O’Neill etal. 2017; Gupta etal.
2020).
The artificial neural network (ANN) is known as an estimator whose effectiveness has
been verified in previous research, being utilized in numerous scientific fields in recent
years (Yoon et al. 2011; Shen 2018; Rajaee et al. 2019). The applications of ANN in
hydrology include the prediction of nonlinear phenomena, such as rainfall-runoff, evapo-
transpiration, dam volume evaluation, streamlines, and water and rain quality modeling
(Taormina etal. 2012; Zhao et al. 2020; Roy et al. 2020; Di Nunno and Granata 2020;
Chen etal. 2020; Derbela and Nouiri 2020). One of the most important challenges in fore-
casting groundwater levels using ANN has been the selection of input data. Daily, weekly,
or monthly temperature and precipitation have been the major driving factors in the impact

Groundwater Level Fluctuations inCoastal Aquifer: Using…
1 3
of climate change on water resources in the literature. (Yoon etal. 2011; Maharjan et al.
2021). Moghaddam etal. (2019) used monthly evaporation, average temperature, aquifer
discharge and recharge, and aquifer level to evaluate the performance of ANN, Bayes-
ian network (BN), and MODFLOW in the simulation of a 12-year period work. Hasda
etal. (2020) employed the neural network (NN) to forecast weekly levels of groundwater,
52weeks in advance. The studies by Chang etal. 2015; Ghazi etal. 2021; and Sharma etal.
2021 investigated the impact of climate change using the output of CMIP3 and CMIP5 cli-
mate models and various scenarios on variations in groundwater levels.
Various organizations have cooperated to use groups of climate model outputs to stand-
ardize the design of GCMs and the distribution of simulated models. These models have
recently become a crucial element in guiding global research on climates (Thorne etal.
2017). The rapid growth in population and industry has had diverse effects on the manner
of GHG emissions. Since new emission scenarios are introduced every couple of years, it
seems necessary to be up-to-date on the latest trends in climate change. The most common
aim of climate studies is to determine the changes in the weather of the region under study.
To this end, the present study attempted to evaluate the best available models (CMIP6)
for the specified basin. The main objective of the CMIP6 is to determine how the struc-
ture of the earth responds to different forces in relation to the origins and consequences of
organized models, climate change quantification, and scenario uncertainty. The number of
vertical layers in all CMIP6 models has increased compared to those in CMIP5 models.
An advantage of this increase is a more accurate simulation in the stratosphere, in addition
to a significant increase in the number of investigated prospective scenarios. The new sce-
narios added to CMIP6 are SSP1-1.9, SSP4-3.4, and SSP3-7.0. In addition, the 4 scenarios
SSP1-2.6, SSP2-4.5, SSP4-6.0, and SSP5-8.5 have updated RCP2.6, RCP4.5, RCP6.0, and
RCP8.5 scenarios in CMIP5, respectively (Eyring etal. 2016; Li etal. 2020; Gupta etal.
2020).
The new CMIP6 models are enhanced in terms of horizontal resolution and better rep-
resentation of synoptic processes (Di Luca et al. 2020; Nie et al. 2020; Srivastava etal.
2020), which will lead to more reasonable results in climate studies. As a result, CMIP6
simulations of climates are more reliable than before, and this makes investigation to better
understand future climates necessary. In this regard, the main aim of this study was to: (1)
determine the performance of three CMIP6 GCMs and select the best ones; (2) assess the
impact of climate change and forecast monthly groundwater depths of the coastal Sari-Neka
aquifer in Iran by FNN under the latest scenarios codified by the Intergovernmental Panel on
Climate Change (IPCC) (SSP2-4.5, SSP5-8.5) for near future (2021–2040) decades.
2 Materials andMethods
For the purpose of assessing the potential impact of climate change on groundwater lev-
els in the Sari-Neka aquifer, GCM models were used. Firstly, three CMIP6 GCM models
(ACCESS-CM2, HadGEM3-GC31-LL, and NESM3) were selected and compared with
observation data. In the following, temperature and precipitation were simulated under
two scenarios (SSP2-4.5 and SSP5-8.5) for the near future (2021 to 2040) by LARS-WG.
Monthly datasets, maximum and minimum temperature, precipitation, and groundwater
level in the previous month were used for the input of the ANN model, with the groundwa-
ter level being simulated for two next decades (2021–2040). How the research was carried
out is shown in Fig.1.

A.Roshani, M.Hamidi
1 3
2.1 Study Area
Sari-Neka region, a basin which is located by the Caspian Sea between 35° 56′ 36° 52’ N
and 52° 43′ 54° 44’ E, was selected as a case study in Mazandaran province (Fig.2). It is
divided into mountainous parts, hills, and flat plains, mainly covered by alluvial sediments
from a geomorphological view. The climate there is regarded as generally Mediterranean
and semi-humid by the DeMartonne’s Method. This region covers an area of about 6938.5
km2, of which an area of 977.87 km2 is devoted to plains, while the rest is highland (5877.8
km2). The highest elevation in the area is 3836m, with the lowest point being -27m (Nasiri
Modeling
Downscaling &
Simula n
Temperature & Precipitan
in dailyscale
InputLARS-WG
Calibrateby Q-test
Select climatemodel
Select SSPscenarios
SSP2-4.5 & SSP5-8.5
Simulate Temperature &
precipitanfor future
period
Clustering
observaon wells
Defineinput and
target parameters
Selecnofnetwork
structure
Change of parameters fortraining
1. Number of hidden layers
2. Number of neurons in hidden layer
3. Transfer func n
Training andtes ng ANN
Error
acceptable
No
Yes
Foreseegroundwater
depthfor future period
Fig. 1 flowchart of the current study

Groundwater Level Fluctuations inCoastal Aquifer: Using…
1 3
etal. 2021). Annual average rainfall fluctuated between 400–1000mm from 2000 to 2019.
The most humid months there are October and November, and the annual mean maximum
and minimum temperatures are 23 and 13 Celsius, with January and August being the cold-
est and the warmest months respectively. On plains, the groundwater flows from the south
to the north. Most of the study area consists of limestone structures, which are the princi-
pal sources of groundwater supply through groundwater fronts. East and south portions of
the basin predominantly contain geological Jurassic formations that provide surface and
groundwater recharge, as well as snow accumulation in the basin (Nasiri etal. 2021). Land
types are diverse in the study region, including farmland (rice fields and citrus gardens),
residential areas, rangeland, forests, and water bodies. Piezometric wells, which are 0 to
31.1m deep, are situated within this aquifer (Sahour etal. 2020).
Fig. 2 Location of the study area in the map of Iran and the location of piezometer wells in the aquifer

A.Roshani, M.Hamidi
1 3
2.2 Date Collection
Different datasets were prepared for this study, identified as:
1- Maximum temperature, minimum temperature, and precipitation data of historical and
future period for three models ACCESS-CM2, HadGEM3-GC31-LL, and NESM3 under
CMIP6 report from IPCC based on SSP2-4.5 and also SSP5-8.5 scenarios were received
for downscaling from https:// clima te4im pact. eu.
2- Maximum and minimum temperatures, and precipitation for the observation period
(2000–2019) were provided from the meteorological organization of Mazandaran prov-
ince on a daily scale.
3- Data on groundwater level fluctuations in 68 piezometers (2000–2019) were provided
from the regional water organization of Mazandaran province on a monthly scale.
2.3 Climate Models andEmissions Scenarios
In this study, the outputs of the three climate models from CMIP6 were received from the
mentioned databases, and the data of the region were extracted using ArcGis10.8. To this
end, the ACCESS-CM2, HadGEM3-GC31-LL, and NESM3 models were used. The pre-
cipitation, maximum temperature, and minimum temperature of these models are shown
in Table1. The SSP2-4.5 and SSP5-8.5 scenarios used in this study were called the mid-
dle of the road and fossil-fueled development—taking the highway, respectively. Their
specifications are summarized in Table2. These scenarios were used due to the following
reasons: (1) the SSP2-4.5 and SSP-5–8.5 were utilized for the vulnerabilities to climate
change and its consequences (Warnatzsch and Reay 2019); (2) since SSP1-2.6, which is an
update of RCP2.6, is absent in some models, a comparison between the models becomes
problematic.
2.4 Downscaling
Presently, GCM outputs are not directly employed in hydrological models due to their reso-
lution inability and lack of sufficient spatial and temporal certainty (Semenov and Barrow
1997). The model employed in this research was LARS-WG6, the initial version of which
was introduced by Racsko et al. (1991), to address the issues of the Markov chain, which
was frequently used to model precipitation, and was later upgraded by Semenov and Barrow.
As a generator, LARS-WG produces climatic parameters, such as maximum temperature,
minimum temperature, precipitation, and solar radiation on a daily basis for any period of
time according to a set of semi-empirical distributions (Roshan etal. 2013). Even though this
model is not a weather forecast tool, it is a means to generate an artificial weather time series
that statistically resembles observation data.
The delta change factor (DCF) technique was utilized to generate the Atmosphere–Ocean
General Circulation Model (AOGCM) climate change scenario. This method calculates
the maximum and minimum temperature difference and the precipitation ratio of the pro-
spective and base periods in the studied region’s model according to Eqs.1, 2, and 3. The

Groundwater Level Fluctuations inCoastal Aquifer: Using…
1 3
Table 1 List of CMIP6 models that have been used in this study (Priestley etal. 2020)
Model name Institution Resolution Processing Simulated scenarios
ACCESS-CM2 CSIRO-ARCCSS; Commonwealth Scientific and Industrial Research
Organization, and Bureau of Meteorology, Australia
192*145 r1i1p1f1 SSP2-4.5
SSP5-8.5
HadGEM3-GC31-LL MOHC; Met Office Hadley Center, United Kingdom 192*144 r1i1p1f1 SSP2-4.5
SSP5-8.5
NESM3 NUIST; Nanjing University of Information Science and Technology, China 192*96 r1i1p1f1 SSP2-4.5
SSP5-8.5

A.Roshani, M.Hamidi
1 3
Table 2 Summary of assumptions regarding demographic and human development elements of SSP2 and SSP5 (O’Neill etal. 2017)
SSP element SSP2 SSP5
Technology
Development Medium, uneven Rapid
Carbon intensity Medium High
Energy tech change Some investment in renewables but
continued reliance on fossil fuels
Directed toward fossil fuels; alternative sources not actively pursued
Economy & lifestyle
Growth (per capita) Medium, uneven High
Globalization Semi-open globalized economy Strongly globalized, increasingly connected
Consumption & Diet Material-intensive
consumption, medium meat consumption
Materialism, status consumption, tourism,
mobility, meat-rich diets
Policies & institutions
International
Cooperation
Relatively weak Effective in pursuit of development goals, more limited for envt. goals
Environmental Policy Concern for local pollutants but only
moderate success in implementation
Focus on local environment with obvious benefits to
well-being, little concern with global problems
Policy orientation Weak focus on sustainability Toward development, free markets, human capita

Groundwater Level Fluctuations inCoastal Aquifer: Using…
1 3
base and prospective periods were assumed to be 2000–2019 and 2021–2040, respectively
(Semenov and Barrow 1997).
In the above equations,
ΔPi
,
ΔT
i,
Min
, and
ΔT
i,
Max
represent the monthly climate change sce-
narios of precipitation, minimum temperature, and maximum temperature, respectively.
P
GCM,FUT ,
i
denotes the 20-year precipitation average simulated by the AOGCM models for
the prospective period, and
P
GCM
,
Base
,i
represents the same for the base period (2000–2019
in this study). the explanations provided for the minimum and maximum temperature are
correct. Two files were created to generate climatic data in the LARS-WG model and to
downscale the GCM data for future periods. The first file describes past climatic behavior,
while the other contains climate change scenarios. The model was calibrated in the first
step and then verified using statistical tests and a comparison of the graphs.
2.5 Clustering ofObservation Well
Clustering is an unsupervised learning technique in which samples are categorized into
similar groups with identical features. A common clustering method is the K-means, intro-
duced by MacQueen in 1967 (MacQueen 1967), in which the number of clusters is pre-
determined. The number of clusters was validated using the common Elbow index, deter-
mined by Eq. (4) (Brusco and Steinley 2007). Since there were 68 piezometers in this
basin, clustering was performed to avoid over-complication of the model. The geographical
coordinates and groundwater levels of the piezometers were used for clustering.
where
Ck
is the set of observations in the Kth and
xvk
is the mean of variable
v
in cluster
k
. In this technique, the number of clusters is directly related to the Within-Cluster Sum
of Square (WCSS), which is the sum of the squared distance between each point and the
centroid in a cluster. The vertical axis represents WCSS, with the horizontal axis showing
the number of clusters. In this index, the number of clusters, K, begins from 1 and grows
to where the value of WCSS remains almost constant, being the largest in the first cluster.
2.6 Artificial Neural Network (ANN)
Artificial neural networks (ANN) are typically based on human nervous systems. In hydro-
logical contexts, these heuristics are particularly appropriate for predicting and forecasting
variables because they are capable of modeling nonlinear, nonstationary, and nongaussian
processes. NNs are trained to recognize data patterns until a minimum acceptable error is
found between the ANN predicted data and the observation data. NNs are mostly divided
(1)
Δ
Pi=PGCM,FUT ,i∕PGCM,Base,
i
(2)
Δ
Ti
,
Min =TMin
(
GCM
,
FUT
,
i
)−
TMin
(
GCM
,
Base
,
i
)
(3)
Δ
Ti
,
Max =TMax
(
GCM
,
FUT
,
i
)−
TMax
(
GCM
,
Base
,
i
)
(4)
WCSS
=
K
∑
k=1
∑
i∈C
k
V
∑
v=1
(
xiv −xvk
)2

A.Roshani, M.Hamidi
1 3
into three general layers. The input layer is responsible for receiving data, the output layer
contains forecast information, and the middle layer performs the necessary calculations
(Maier and Dandy 1997; Daliakopoulos etal. 2005; Moghaddam etal. 2019). The inputs are
multiplied by synaptic weights and delivered to the first hidden layer. In the hidden units, the
weighted sum of inputs is transformed by a nonlinear activation function (Taormina etal.
2012).
ANNs are either feedforward or recurrent. The present research utilized feedforward
NNs and the sigmoid activation function. The Levenberg–Marquardt algorithm was
employed to train the NN (Daliakopoulos etal. 2005; Derbela and Nouiri 2020). In feed-
forward networks, the input enters from the left, and the output exits from the right. The
number of input and output neurons are determined by the number of parameters in the
network and is typically determined by the nature of the problem (Emamgholizadeh etal.
2014). and those of the hidden layer are determined via trial and error. The hidden layer is
tasked with linking the input and output layers. Using this layer, the NN can extract nonlin-
ear relationships from the data input to the model.
Training is aimed at reaching a state where the network is capable of correctly respond-
ing to training data in addition to similar non-training data. NN learning is either super-
vised or unsupervised, with the former being used in the present study. In this approach,
training is mostly performed using sample vectors of pairs, such that a specific output
vector is assigned to each input vector. As these vectors are presented to the network, the
weights are corrected according to the learning algorithm. The present research minimum
and maximum temperature, precipitation in the current month, and groundwater level in
the previous month were used as the input data. In addition, groundwater level in the cur-
rent month was employed as the output data (Coppola etal. 2005; Chitsazan etal. 2015;
Moghaddam etal. 2019).
2.7 Performance Criteria
Statistical methods for assessing the error between observated and predicted data were
used in terms of correlation coefficients (r), coefficients of determination (R2), Mean Abso-
lute Error (MAE), and Root Mean Squared Error (RMSE).
(5)
r
=
n
n
i=1OiPi

−
n
i=1Oi

.
n
i=1Pi


n

n
i=1Oi
2
−

n
i=1Oi

2

.

n

n
i=1Pi
2
−

n
i=1Pi

2

(6)
R2=1−
n
i=1(Oi−Pi)
2

n
i=1

Oi−Oi

2
(7)
RMSE
=
�∑
n
i=1(Pi−Oi)2
n
(8)
MAE
=
∑n
t=1
�
�
Pi−Oi
�
�
n

Groundwater Level Fluctuations inCoastal Aquifer: Using…
1 3
where n is the total number of measured data,
Pi
&
Oi
are the predicted and observed value,
respectively, and
Oi
&
Pi
are the average value of the measured data (Natarajan and Sudheer
2020).
3 Result andDiscussion
3.1 CMIP6 Models Validation
In the first step, the performances of the NESM3, HadGEM3-GC31-LL, and ACCESS-
CM2 of the CMIP6 models with respect to the observation data were evaluated in order to
determine the best model for studying climate change in the area. To this end, the
R2
coef-
ficient of determination and the root-mean-square error (RSME) were used for the min-
imum and maximum temperature and precipitation parameters. According to the results
presented in Table3, the ACCESS-CM2 model showed the smallest RMSE at the mini-
mum and maximum temperatures and the best precipitation performance in both statistical
tests. This indicated the superiority of this model over the other two in simulating the area
under study. Therefore, it was selected for investigating climate change in the research step.
This step involved a forecast of the meteorological data by the ACCESS-CM2 model
under the climate change scenarios SSP2-4.5 and SSP5-8.5. Additionally, the 2000–2019
climatic data were utilized for the LARS-WG model’s base period, and forecast was made
for the subsequent two decades (2021–2040). Using the Site Analysis functionality of the
LARS-WG model, calibration and verification were performed simultaneously using two
datasets including daily observation data and the geographic information of the research
station. The performance of LARS-WG was evaluated by the K-S, t, and F statistical tests.
The K-S test was conducted to test the equality of the seasonal distributions of the wet and
dry series, the daily distribution of precipitation, and the daily distribution of minimum and
maximum temperature. The F-test was performed to test the equality in the standard devia-
tion of monthly precipitation. The t-test was aimed at testing the equality of the monthly
average of precipitation and the monthly average of the maximum and minimum daily
Table 3 Statistical comparison of AOGCM models to select the best model
Statistical test Models AOGCM
Maximum temperature
ACCESS-CM2 HadGEM3-GC31-LL NESM3
R2
0.97 0.96 0.98
RMSE (°C) 4.09 6.32 5.19
Minimum temperature
ACCESS-CM2 HadGEM3-GC31-LL NESM3
R2
0.98 0.95 0.98
RMSE (°C) 5.55 8.38 5.75
Precipitation
ACCESS-CM2 HadGEM3-GC31-LL NESM3
R2
0.95 0.94 0.94
RMSE (mm) 2.29 2.51 2.70

A.Roshani, M.Hamidi
1 3
temperature. Based on the P-value calculated in all tests, there was no significant difference
between the simulated and observation values at a significance level of 5%. According to
Table4, the LARS-WG model exhibited acceptable consistency between the simulated and
observation series of monthly climate data (Nover etal. 2016; Adnan etal. 2019). How-
ever, the same results were erroneous and unreliable on a daily scale. In addition, the larg-
est error corresponded to the simulated precipitation in January. The largest precipitation
error might be due to the large variation in precipitation (Hassan etal. 2014) and compared
to changes in temperature, changes in precipitation are more uncertain (Msowoya etal.
2016). Given the results of the model, the maximum temperature was simulated better than
the other two parameters, providing higher accuracy (Bayatvarkeshi etal. 2020). Although
there has been no complete correlation between the models and observations to date, the
lack of such correlation is not a barrier to using climate models. Climate models are not
used to predict specific weather events, but to predict a trend in climate change over time
(Warnatzsch and Reay 2019). Based on simulated and observational values of climatic
parameters, Table4 presents a statistical comparison. As can be seen, the overall statistical
evaluation of the ACCESS-CM2 model provided an acceptable result.
3.2 Future Climate Modeling
Figure 3 displays a comparison of the average minimum and maximum temperatures of
the base period and the average monthly temperature for both SSP2-4.5 and SSP5-8.5 sce-
narios during the 2021–2040 period. As shown, the minimum and maximum temperatures
increased over the months due to climate change. Therefore, minimum and maximum tem-
peratures are expected to rise by 0.20°C to 1.3°C over the next two decades. The largest
increase in minimum temperature corresponded to SSP5-8.5 with a 1.3°C in November,
and the smallest increase was in February (0.2 °C), corresponding to SSP5-8.5. For the
maximum temperature, a similar method was followed, and the results indicated that the
Table 4 K-S test daily distribution of observation and simulated data
Month Minimum temperature Maximum temperature Precipitation
Evaluation K-S P-value Evaluation K-S P-value Evaluation K-S P-value
January * 0.105 0.999 * 0.053 1.000 * 0.138 0.971
February * 0.053 1.000 * 0.106 0.999 * 0.060 1.000
march * 0.053 1.000 * 0.053 1.000 * 0.048 1.000
April * 0.053 1.000 * 0.010 1.000 * 0.054 1.000
May * 0.053 1.000 * 0.053 1.000 * 0.105 0.999
June * 0.053 1.000 * 0.106 0.999 * 0.078 1.000
July * 0.053 1.000 * 0.053 1.000 * 0.062 1.000
August * 0.106 0.999 * 0.053 1.000 * 0.107 0.999
September * 0.053 1.000 * 0.053 1.000 * 0.089 1.000
October * 0.053 1.000 * 0.053 1.000 * 0.081 1.000
November * 0.053 1.000 * 0.053 1.000 * 0.067 1.000
December * 0.053 1.000 * 0.053 1.000 * 0.067 1.000
*not statistically significant

Groundwater Level Fluctuations inCoastal Aquifer: Using…
1 3
most significant changes in the base period would occur in April under SSP5-8.5, while the
smallest changes would occur in October under SSP2-4.5. The results of the present study
are in good agreement with previous research by (Al-Maktoumi etal. 2018; Shahvari etal.
2019; Maghsood etal. 2019). In general, it can be said that the average air temperature in
this basin is on the rise. The rise in temperature could be caused by the unstable and rapid
industrial development that is taking place in this area.
The average monthly precipitation during the base period and the forecast period
(2021–2040) for both scenarios is displayed in Fig.3. Except for February, May, June,
Fig. 3 Comparison of Minimum and maximum temperature average and precipitation of base (2000–2019)
and future (2021–2040) under 2 scenarios SSP2-4.5 and SSP5-8.5: (a) minimum temperature, (b) maxi-
mum temperature, (c) precipitation

A.Roshani, M.Hamidi
1 3
and September, the results show an increase in precipitation for the rest of the months. As
compared to the base period, the highest increase corresponded to the SSP5-8.5 scenario
at 41mm in October. On the other hand, the smallest increase corresponded to Septem-
ber at 6.3 mm under SSP2-4.5. In this regard, as predicted, the precipitation in the two
upcoming decades will vary between -12% and 78%, with an overall increase from 2000
to 2040, (Abiodun etal. 2017; Konapala etal. 2020; Tabari 2020). In addition, the results
of Zhang etal. (2016) who studied three different GCMs under the RCP2.6, RCP4.5 and
RCP8.5 scenarios indicated that precipitation in the RCP2.6 and RCP4.5 scenarios would
increase gradually and continuously, but the increase in precipitation in future decades
under RCP8.5 is likely to be remarkable.
The highest-precipitation month during the observation period was November, but it
changed to October due to climate change in both scenarios. On the other hand, August
tends to remain the warmest month in the two coming decades. The results also indicated
the highest rise in precipitation in the winter and fall, while the largest absolute increase
corresponded to July and August during the summer, which agreed with the results of
Sha etal. (2019). Based on the findings, precipitation is also expected to increase in both
scenarios. According to IPCC, a rise in temperature intensifies the groundwater cycle and
increases evaporation. In turn, an increase in evaporation leads to frequent and strong
storms and may further humidify humid regions and dry out dryer regions. In this regard,
Boudiaf etal. (2020) reported a rise in temperature and precipitation in the coastal Medi-
terranean regions. Climate change has contributed to atmospheric warming and resulted in
stronger precipitation. Research carried out by Araya-Osses etal. (2020) indicated that an
increase in precipitation could lead to a higher snow potential in the heights, but a change
in the form of precipitation, for example stronger precipitation was encountered during the
same period due to higher temperatures.
3.3 Clustering
The Elbow index was used in this work to determine the optimal number of clusters. The
outcome of the clustering is displayed in Fig.4. The results indicated a significant differ-
ence in WCSS in the first four clusters. However, this difference became negligible from
K = 4 onward and hence four clusters were considered for the region under study. The loca-
tions of the four clusters and the four piezometers are shown in Fig.5.
Fig. 4 Clustering by Elbow method

Groundwater Level Fluctuations inCoastal Aquifer: Using…
1 3
3.4 Groundwater Modeling
The groundwater level of the Sari-Neka basin under climate change during the 2021–2040
period was predicted in MATLAB using ANN. For this purpose, the data were divided
into training, validation, and testing data, with 75% of them being assigned to training,
and 15% to each of validation and testing. Since an important point in ANN is the num-
ber of neurons in the hidden layer, the trial and error resulted in an optimal number of
eight neurons in the present work. In the first step, the model was trained to simulate the
groundwater level during the observation period. It was then used to predict the prospec-
tive groundwater level. To develop the ANN model, maximum and minimum temperature
and precipitation in the current month and groundwater levels in the previous months were
used. Furthermore, the groundwater level in the current month was used for the output of
the ANN model. The performance assessment of the constructed model was conducted
for each cluster of piezometers over the 2000–2019 period, and the results are shown in
Table5. In all four piezometers, the results indicated that network performance was accept-
able (Chitsazan etal. 2015; Sun etal. 2016; Derbela and Nouiri 2020).
On the other hand, previous research results demonstrated that the three available obser-
vation data inputs (groundwater level, temperature, and precipitation or evaporation) were
sufficient to construct a high-performance ANN (Daliakopoulos etal. 2005; Szidarovszky
etal. 2007; Mohanty etal. 2010; Chang etal. 2015; Ghazi etal. 2021).
In order to better comprehend the results predicted by the ANN, the Taylor diagram
was used, (Fig. 6). The horizontal and vertical axes represent the observation data and
the standard deviation, respectively. The points approach the hypotenuse which indicates
that the standard deviation of the predicted data was close to that of the observation data,
Table 5 Performance of the
artificial neural network based
on Correlation coefficient (r),
coefficient of determination
(R.2), root mean square error
(RMSE) and Mean absolute error
(MAE)
Representative
cluster well
UTMXUTMYr R2RMSE(m) MAE
P1 692,838 4,058,999 0.835 0.697 0.324 0.233
P2 697,350 4,075,550 0.929 0.864 0.360 0.253
P3 686,501 4,048,865 0.860 0.739 0.878 0.639
P4 688,031 4,074,145 0.895 0.802 0.339 0.237
Fig. 5 Clustering and positioning of groundwater observation wells (P1-P4) on the Sari-Neka region

A.Roshani, M.Hamidi
1 3
confirming that the model was performing properly. In addition, the correlation coefficient
was on the main hypotenuse and measured the performance of the model on a 0–1 basis,
where a value close to 1 indicated the good performance of the model.
The observation groundwater level in all of the piezometers, except for the 4th one,
increased, illustrating an improvement in the groundwater depth. Also, the groundwater
depth at all of the piezometers showed a gradual decrease in the coming two decades
due to climate change (Chang etal. 2015). As seen from the observation trend of the
four piezometers in Fig.7, the depth of groundwater in the basin under study exhibited
an increasing trend from about 2012 onward. However, a decreasing trend in depth due
to climate change was observed for the coming two decades, except in the fourth pie-
zometer under SSP2-4.5. As discussed, due to the higher precipitation in SSP5-8.5 than
SSP2-4.5, the groundwater level was expected to be higher in SSP5-8.5. Additionally,
the largest change observed in the depth of groundwater was associated with piezometer
P2 in SSP5-8.5. The results of previous research demonstrated an increase in groundwa-
ter levels under climate change (Ligotin etal. 2010; Chang etal. 2015). Therefore, from
the results, it can be concluded that the groundwater level will improve in the future due
Fig. 6 Performance of the artificial neural network based on Taylor diagram

Groundwater Level Fluctuations inCoastal Aquifer: Using…
1 3
to the influence of climatic parameters, such as temperature and precipitation. Neverthe-
less, other factors, such as industrial and economic developments, population growth,
and migration, may adversely affect the availability of water resources.
4 Conclusions
Awareness of the changes in the groundwater level can make a remarkable contribution to
water resource management. In the present research, three models of the CMIP6 GCMs
were selected, and based on the model with the most adaptation to observation data, the
groundwater level under two scenarios was simulated for the coastal Sari-Neka aquifer.
In the first step, a comparison of the CMIP6 GCMs with observed data was performed in
order to determine how well they would reproduce weather parameters over the basin and
Fig. 7 Groundwater level predicted under climate scenarios (SSP2-4.5 & SSP5-8.5) for 2021–2040

A.Roshani, M.Hamidi
1 3
as a result the best model was selected for the area. Subsequently, the ACCESS-CM2 model
was selected, and the weather parameters (temperature and precipitation) under scenarios
(SSP2-4.5 and SSP5-8.5) were simulated by LARS-WG for the coming two decades. The
results have indicated that the selected model is an improvement over the previous ones,
i.e., ACCESS 1.0 and ACCESS 1.3 (Williams etal. 2018; Bodman etal. 2020). The results
indicated that the precipitation fluctuated between -10% and 78% and average temperature
(0.1–1.2 °C) would increase, which agrees with other research results in this field (Tan
etal. 2017; Anjum etal. 2019; Yan etal. 2019). Variations in precipitation throughout the
months of the year can make events such as floods and droughts more frequent. Therefore,
efficient strategies should be undertaken to address these issues. In the second step, the 68
piezometers were clustered based on geographical coordinates and groundwater level and
evaluated using the Elbow method. This resulted in four piezometer clusters. The Sigmoid
activation function and the Levenberg–Marquardt algorithm were employed to construct
the ANN model. The number of neurons in the hidden layer was determined using trial
and error that resulted in eight neurons. The groundwater depth was predicted under the
two mentioned scenarios (SSP2-4.5 & SSP5-8.5) for future, 2021–2040 period. The results
indicated that the groundwater depth of the Sari-Neka basin is likely to decrease with a
gentle slope.
Author Contribution Adib Roshani: Conceptualization and design of the study, Acquisition of data, Analy-
sis and/or interpretation of data, Methodology, Software, Validation, and Drafting the manuscript. Mehdi
Hamidi: Conceptualization and design of the study, Acquisition of data, Analysis and/or interpretation of
data, Methodology, Software, Validation, Revising the manuscript critically for important intellectual con-
tent, Approval of the version of the manuscript to be published, Project administration, and Supervision.
Funding The authors have not received support from any organization for the submitted work.
Data Availability All GCM models are available online from: https:// clima te4im pact. eu
Code Availability ANN is commercial codes.
Declarations
Ethical Approval Authors agreed to the ethical approval needed to publish this manuscript.
Consent to Participate The authors declare their consent to participate in this work.
Consent to Publish The authors declare their consent of the publication of this manuscript by “Water
Resources Management” journal.
Conflicts of Interest The authors declare that they have no conflict of interest.
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