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Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
GROUNDWATER VULNERABILITY GIS MODELS IN THE
CARPATHIAN MOUNTAINS UNDER CLIMATE AND LAND
COVER CHANGES
NISTOR, M. M.1* – NICULA, A. S.2,3 – CERVI, F.4 – MAN, T. C.2 – IRIMUŞ, I. A.2 – SURDU, I.3
1School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
2Faculty of Geography, University of Babeş-Bolyai, Cluj-Napoca, Romania
3Centre of Mountain Economy of the National Institute for Economic Research “Costin C.
Kiriţescu”, Romanian Academy, Bucharest, Romania
4Department of Civil, Chemical, Environmental and Materials Engineering, University of
Bologna, Bologna, Italy
*Corresponding author
e-mail: renddel@yahoo.com
(Received 16th May 2018; accepted 31st Jul 2018)
Abstract. Water resources are facing nowadays with two main problems: climate change and land cover
variation. Their influences on environment and water resources have been evidenced worldwide. In this
work, we have applied a complex methodology based on Geographical Information System (GIS) to
combine the spatial information of several parameters that allow to obtain the groundwater vulnerability
under climate and land cover modifications. The spatial analysis performed in this paper includes the
aquifers, water availability, load pollution index, and infiltration map raster grids data of Carpathian
Mountains area, from Central Europe. The analysis presented in this study follow three periods, which
include 30 years climate data models of 1961-1990 (1990s), 2011-2040 (2020s), and 2041-2070 (2050s).
Land cover projections forecast future changes in artificial areas, agriculture areas, and forest areas for
2020s and 2050s. For both periods (2020s and 2050s), the very low vulnerability class area is reduced
while the high class appears on a large area. The worst scenario is forecasted for 2050s (high vulnerability
class increase up to 2.41% of the whole study area) and is mainly due to agriculture. These findings
evidence the negative impact of land cover and climate changes on the groundwater resources in the
Carpathians Mountains area. The original maps carried out in this work together with the concise
methodology integrated in GIS may be a useful tool for the water resources management and future
strategies plans of this region.
Keywords: climatic models, aquifers, pollution load index, infiltration map, water availability, spatial
analysis
Abbreviations: GIS – Geographical Information Systems; Kc – crop coefficients; ETc – crop
evapotranspiration; ET0 – potential evapotranspiration; AETc – actual crop evapotranspiration; PLI –
pollution load index; PIC – potential infiltration coefficient; DEM – digital elevation model; asl – above
sea level; Dfb – fully humid climate and warm summers; Dfc – fully humid precipitation conditions and
cool summers; AOGCM – Atmosphere Ocean Global Climate Model; CMIP5 – Coupled Model
Intercomparison Project phase 5; CanESM2 – Canadian Earth System Model 2; CCSM4 – Community
Climate System Model version 4; INM-CM4 – Institute of Numerical Mathematics Climate Model
version 4; ACCESS1.0 – Australian Community Climate and Earth System Simulator; HadGEM2-ES –
Hadley Centre Global Environment Model version 2; MRI-CGCM3 – Meteorological Research Institute
Coupled Global Climate Model version 3; IPSL-CM5A-MR Institut Pierre Simon Laplace Climate Model
5A Medium resolution; CNRM-CM5 – Centre National de Recherches Météorologiques Climate Model
version 5; MIROC-ESM – Model for Interdisciplinary Research on Climate Earth System Models;
MIROC5 – Model for Interdisciplinary Research on Climate version 5; CSIRO Mk 3.6 – Commonwealth
Scientific and Industrial Research Organisation model version 3.6; CESM1-CAM5 – Community Earth
System Model version 1 Community Atmosphere Model version 5; MPI-ESM-LR – Max-Planck-Institut
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
für Meteorologie Earth System Model on Low Resolution; GFDL-CM3 – Geophysical Fluid Dynamics
Laboratory Climate Model version 3; GISS-E2R – Goddard Institute for Space Studies ModelE 2 Russell
ocean model
Introduction
Groundwater represents the most important freshwater resources for human
purposes. Several authors highlighted the influence of climate and land cover changes
occurred in the last decades on groundwater, both in term of quality and quantity. In
particular, severe impact took place in Mediterranean countries and South-central
Europe. Galleani et al. (2011), Čenčur Curk et al. (2014), Nistor et al. (2015) signalled
the negative effect of climate change on groundwater. Kløve et al. (2014) illustrated the
negative impact of climate changes on the related ecosystems. The recent climate
warming (Haeberli et al., 1999) indicates negative effects on the natural and semi-
natural places (Nistor and Mîndrescu, 2017). Due to climate change, in the next decades
the mean annual temperatures are expected to increase (IPCC, 2001) and several models
indicate rise of temperature during the 21st century (IPCC Assessment Report 5, 2013;
Stocks et al., 1998; Shaver et al., 2000; Stavig et al., 2005; The Canadian Centre for
Climate Modelling, 2014). Aguilera and Murillo (2009), Jiménez Cisneros et al. (2014),
Kløve et al. (2014) signalled several problems that appeared in the biodiversity and
surface waters depletion related to climate change. In the temperate zone of South
Europe and Turkey, the precipitation amount will reduce (Čenčur Curk et al., 2014;
Cheval et al., 2017; Nistor and Mîndrescu, 2017). The dynamics of the land cover and
the human activities at continental scale are going to affect the water resources, which
are sensitive to urbanization, agricultural lands’ exploitations, and improper forest
management, e.g. deforestation, chaotic building in the forest proximity. With respect to
the decrease of precipitation, the groundwater recharge is influenced from quantity
point of view (Nistor et al., 2015). On other hand, the possibility of rainfall intensity
increase will affect directly the infiltration rate on short-term period.
The temperature pattern together with the land cover contribute at the
evapotranspiration processes taking place on the recharge areas. In the central and
eastern Europe, the Carpathian Mountains area represents a precious reservoir of water
resources, especially for many establishments of the region, but also for the large cities
located out of the Carpathians, such as Bucharest and Cluj-Napoca. Moreover, the
studies show that the crop evapotranspiration (ETc) increase from the past period to
2050s (Nistor et al., 2016) and the deforestation of the several sectors in the Carpathian
Mountain area could damage the regime of watershed basins.
The goal of this work is to define the most sensitive layers for groundwater at climate
change and cover modifications on long-term and to generate the spatial analysis for
groundwater vulnerability in the Carpathian Mountains area. We used high-resolution
climate models of mean annual air temperature, mean annual precipitation, and annual
ET0 for 1990s, 2020s, and 2050s periods and the CORINE land cover dated from 2010
(World Land Cover 30m) and three projections for 2050s. In addition, the aquifers layer
of the study area and the digital elevation model (DEM) were integrated in the
methodology of the paper. Thus, the determination of groundwater vulnerability in the
study area is based on a spatial analysis by weights using ArcGIS environment.
Following the method of Nistor et al. (2015), tested in a small area of the Beliș
district from Apuseni Mountains, our approach and obtained results are reliable at large
scale, being important instruments for the panning of this large mountain area.
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
Materials and methods
Study area
The Carpathian Mountains area extends from 44°28’ to 49°51’ N and from 18°00’ to
26°46’ E (Fig. 1). The geographical position in the central part of Europe and the large
natural sectors of these mountains makes the Carpathians to be the most important
ecosystem from this side of the continent (Nistor et al., 2016). Here, we simply defined
the Western Carpathians in the northwestern side of the study area, the eastern
Carpathians mainly in Romania and Ukraine, and Southern Carparthians. In addition,
we highlighted the Curvatures Carpathians and Apuseni Mountains as two units used in
the paper for descriptions and toponymy points of view. Moreover, the analysed area
represents a significant source of drinking water with valuable aquifers (e.g. highly
productivity fissured aquifers, highly productive porous aquifers). In base of the
geomorphology present in the region, the orography of the Carpathians is much
diversified and could be reflected in the runoff pattern and infiltration processes. The
elevations ranges mainly from 300 m in depression and valleys to 2642 m in
Gerlachovský Peak from Slovakia. The geology of the territory is complex and could be
easier reflected into the aquifers composition. The main geological formations are
represented by gneisses, mica schists and amphibolites in the Southern Carpathians and
by marlstones, limestones, clays, marls, sandstones and sands in the Curvatures
Carpathians and Eastern Carpathians. Pyroclastic rocks, volcanic rocks, marlstones,
dolomitic limestones, gravels and clays are presented in the Western Carpathians and
Apuseni Mountains. Figure A1 in the Appendix (BGR and UNESCO, 2013) shows the
geological formations of the Carpathian Mountains area. In base of the BGR and
UNESCO (2013) map, the aquifers types are divided in six categories of productivity:
highly productive fissured aquifers, highly productive porous aquifers, low and
moderately productive fissured aquifers, low and moderately productive porous
aquifers, locally aquiferous rocks - porous or fissured, practically non-aquiferous -
porous or fissured. Table 1 indicates the aquifers types in the Carpathian region area.
The climate of the study area has oceanic influences in the West and North-West,
Baltic influence in the North and Mediterranean influence in the South-West. According
to the Köppen–Geiger climate classification, the Carpathian Mountains area has Dfb
climate in the main part pf territory, with fully humid climate and warm summers
(Kottek et al., 2006). In the eastern sectors of the Romanian and Ukrainian Carpathians,
in the central parts of the Slovakian Carpathians and in South Poland, the study area
have a Dfc climate type, characterized by fully humid precipitation conditions and cool
summers (Kottek et al., 2006). During the 1990s, the mean annual temperature in the
study area ranged from –3.1 to 11.4 °C (Fig. 2). The precipitation pattern shows values
between 546 mm to 1695 mm year–1 during 1990s (Fig. 2b). The annual ET0 in the
Carpathian Mountains area ranged from 279 mm up to 548 mm during 1990s (Fig. 2c).
The land cover of the Carpathian Mountains area is composed at high elevations
(over 1000 m asl) by forest and pasture (coniferous, mixed and broad-leaved forests),
the valleys and depressions are covered by agricultural cultivated lands, villages and
also cities. The cities and rural localities compose the artificial areas in the Carpathian
mountain region. For groundwater vulnerability determination, the land cover has a
quantitative role with respect to the evapotranspiration and water availability and a
qualitatively role due to the phosphorous and load pollution present in each type of land
cover.
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
Figure 1. Location of the Carpathian Mountains region on the South-central map and the main
aquifers productivity type in the region
Figure 2. Spatial distribution of temperature, precipitation, and ET0 in the Carpathian
Mountains region. (a) The average of mean annual air temperature between 1961 and 1990
(1990s). (b) The average of mean annual precipitation between 1961 and 1990 (1990s). (c) The
average of mean annual precipitation between 2011 and 2040 (2020s)
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
Table 1. Summary of the climate models used in the application of groundwater vulnerability
assessment for the Carpathian region. (Sources: Scherrer, 2011; Watanabe et al, 2011;
Hamann et al., 2013; IPCC Assessment Report 5, 2013; Knutti et al., 2013; Ohgaito et al.,
2013; Wang et al., 2016)
CMIP5 multi-model dataset which compose the ensemble average of 15 AOGCMs
CanESM2
CCSM4
INM-CM4
ACCESS1.0
HadGEM2-ES
MRI-CGCM3
IPSL-CM5A-MR
CNRM-CM5
MIROC-ESM
MIROC5
CSIRO Mk 3.6
CESM1-CAM5
MPI-ESM-LR
GFDL-CM3
GISS-E2R
Climate data
High spatial-resolution data (1 km2) in three periods (past – 1961-1990, present –
2011-2040, and future – 2041-2070) of monthly mean precipitation, monthly ET0, and
annual ET0 were used in this study to carry out the water availability. The climate
models of temperature and precipitation were developed and validated for whole Europe
(Hamann et al., 2013).
Regarding the source of base dataset and type of meteorological stations, as well as
the number of stations included in the normal period (1961-1990), we would like to
mention that the data used in this paper is online available (http://www.cru.uea.ac.uk/)
and it belong to the CRU TS 2.1 project (Mitchell and Jones, 2005). The dataset
contains mean monthly precipitation and mean, minimum, and maximum monthly
temperature and it were quality controlled and checked for inhomogeneities (New et al.,
1998). For Europe, gridded climatological data, at finer spatial resolution of 0.5° lat X
0.5° long, were constructed using surface observations from about 1644 stations for
temperature (344 for mean monthly temperature, 647 for minimum monthly
temperature, and 653 for maximum monthly temperature). For the precipitation gridded
data were used about 1333 meteorological stations.
The models performed by Hamann et al. (2013) were done in base of Parameter
Regression of Independent Slopes Model (PRISM) for precipitation and ANUSplin for
temperature, considering the Representative Concentration Pathway (RCP) of 4.5 for
emission, which means a moderate climate changes projection, based on a globally
predicts of +1.4 °C (±0.5). The ANUSplin interpolation procedure is in line with
Mitchell and Jones (2005). In the climate models creation ClimateEU v4.63 software
was used, available on the website (http://tinyurl.com/ClimateEU). The base-dataset is
in principle described by Wang et al. (2016) in their study about historical and future
climate in North America
(https://sites.ualberta.ca/~ahamann/publications/pdfs/Wang_et_al_2016.pdf).
The climate models represent an ensemble average of 15 AOGCMs, consider the
CMIP5 multi-model dataset accordingly IPCC Assessment Report 5 (2013). To
represent the main clusters of similar AOGCMs, the individual models were selected
(Knutti et al., 2013). The chosen of these models was based on the high validation
statistics in the CMIP3 equivalents (Scherrer, 2011). The models which compose the
ensemble average of AOGCMs are reported in the Table 1.
The bilinear interpolation was used to correct the artefacts in the AOGCM grid cells,
for the adjacent areas. A Change Factor (CF) method for the GCM results have been
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
applied instead the bias correction. The methodology of the climate models is in line
with Daly (2006). Moreover, the models were validated and described by Hamann and
Wang (2005), Mbogga et al. (2009), Wang et al. (2016), Dezsi et al. (2018). At the
continental and regional scales, the evapotranspiration studies were completed by Nistor
(2018a, b) and Nistor et al. (2018).
Aquifers data
The Carpathian Mountains area has a diversified geology, composed by clays,
conglomerates, metamorphic, limestones, carbonated, and plutonic rocks (Fig. A1).
Accordingly to the geological compositions, the International Hydrogeological Map of
Europe (IHME), dating from 2013 at 1:1,500,000 scale (BGR and UNESCO, 2013)
reported six types of aquifer’s productivity (Fig. 1). In base of the productivity of
aquifers, the vulnerability factor for each aquifer was assigned into the GIS database
(Table 2) and further at spatial scale of Carpathian Mountains region (Fig. 3). For each
media that compose the aquifer, the potential infiltration coefficient (PIC) was assigned
accordingly to the hydrogeological specific literature (Civita, 2005). The importance of
infiltration coefficients for groundwater highlights the aquifers sensitivity in term of
quality (Čenčur Curk et al., 2014). Figure A2 in the Appendix shows the PIC spatial
distribution in the Carpathian Mountains area.
Table 2. Aquifers productivity in Carpathian Mountains region. (Source: IHME, 2013;
Čenčur Curk et al, 2014)
Aquifers type
Vulnerability factor
Highly productive fissured aquifers (including karstified rocks)
0.8
Low and moderately productive fissured aquifers (including karstified rocks)
0.4
Highly productive porous aquifers
0.7
Low and moderately productive porous aquifers
0.3
Locally aquiferous rocks, porous or fissured
0.1
Practically non-aquiferous rocks, porous or fissured
0.05
Snow field/ice field
0
Figure 3. Vulnerability factor distribution at spatial scale of the Carpathian Mountains region
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
Terrain data and infiltration map
The infiltration process is mainly controlled by the terrain configuration and
lithology. In base of the Nistor et al. (2015) approach, the infiltration map is calculated
as the ratio between PIC and slope angle. Thus, the digital elevation model (DEM) of
the Carpathians region have been used to generate the slope angle and from the
geological map it was processed the PIC. This reasoning considers that where the PIC is
higher and the slope angle is lower, the infiltration values will be higher. The
calculations were performed in ArcGIS software using normalized values (0 to 1) of the
PIC and slope angle raster grid data. Figure 4 depicts the infiltration map of the
Carpathian Mountains area used in the groundwater vulnerability mapping.
Figure 4. Infiltration map of the Carpathian Mountains region
Land cover data
The vegetation layer, bare soil, water bodies, and artificial areas over Carpathian
Mountains area were collected from the World Land Cover database elaborated by
China in collaboration with the United Nations. This land cover is 30 × 30 m in spatial
resolution and cover entire study area indicating 10 classes of cover type. The future
projections of the land cover were done accordingly to Nistor et al. (2015) only for the
artificial, agricultural, and forest areas. All transformations have been processed on the
vector layer of and cover, applying a linear buffer to the vector feature of artificial areas
by 23 m, to agriculture by 41 m, and to forest by 80 m. Thus, the resulted surfaces were
clipped and integrated in the original land cover vector layer for present and future
(Fig. A3). Table 3 reports the values of pollution load index (PLI) used in the present
study. The resolution of the PLI at spatial scale (Fig. 5) were set at 1 km2, to be in line
with the climatological data.
Potential evapotranspiration (ET0)
Thornthwaite (1948) method (Eq. 1) was adopted for the long-term calculations of
monthly ET0 during the three set time periods: 1961-1990 (1990s), 2011-2040 (2020s),
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
and 2041-2070 (2050s). Even if old, this approach is still used in the regional studies of
climate, hydrology, and agriculture (Zhao et al., 2013). The monthly and annual ET0
and water availability have been computed for whole Europe by Dezsi et al. (2018) and
the gridded data are available through open access web site
(https://doi.org/10.5281/zenodo.1044306). In base of monthly ET0 we have computed
the annual ET0 and further annual ETc and AETc raster maps.
(Eq.1)
where:
ET0 – monthly potential evapotranspiration [mm]
Ti – average monthly temperature [°C], ET0 = 0 if mean temperature < 0
I – heat index (Eq. 2)
α – complex function of heat index (Eq. 3)
(Eq.2)
where:
Ti – monthly air temperature
(Eq.3)
where:
I – annual heat index
Table 3. Corine Land Cover classes and relative pollution load index applied the
Carpathian region. (Source: Wochna, 2011; Čenčur Curk et al., 2014; Nistor et al., 2015)
CLC code 2012
CLC description
Pollution load index
Normalized values
133
Artificial surface
7
0.35
211
Non-irrigated arable land
12
0.80
313
Forest
2.8
0.18
321
Grasslands
2.5
0.16
324
Shrubland
2.6
0.17
332
Bareland
5
0.33
335
Permanent snow and ice
0.1
0.007
411
Wetland
2.3
0.15
512
Water bodies
3
0.20
ETc, AETc, and water availability
In base of crop coefficients (Kc) assigned to each land cover type we have carried
out the ETc. Allen et al. (1998) present the standard Kc for various types of crops.
Moreover, Grimmond and Oke (1999) calculated the Kc in the urban areas indicating
the specific Kc for several cities in the United States. In South Europe, Nistor and
Porumb-Ghiurco (2015) set a methodology for the ETc mapping at spatial scale.
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
Figure 5. Pollution load index calculated in base of land cover in the Carpathian Mountains
region. (a) Pollution load index pattern related to land cover 2010. (b) Pollution load index
pattern related to artificial area projection. (c) Pollution load index pattern related to forest
area projection. (d) Pollution load index pattern related to agricultural area projection
The calculation of ETc is the product of the ET0 and Kc (Eq. 4). We prefer to
incorporate the land cover in the evapotranspiration calculation because the aquifers
recharge is in base of effective precipitations, which are not included in the water
demand for crops. In this work, we have used the standard annual Kc values (Nistor and
Mîndrescu, 2017) together with the annual ET0 to determinate the annual ETc for the
past, present, and future. The annual Kc values, for the land cover classes related to
Carpathian Mountains are presented in the Table 4.
Budyko approach (1974) was adopted to carry out the AETc calculation. This
method is often used in the hydrology and related studies which required the water
balance determination (Gerrits et al., 2009).
(Eq.4)
From the difference between precipitation and AETc, we have found the water
availability. Budyko (1974) formula is expressed in Equation 5. The water availability
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
(Eq. 7) for 1990s, 2020s, and 2050s was subtracted from the annual AETc and annual
precipitation. All raster grids operations were performed in ArcGIS environment.
(Eq.5)
where:
AETc – actual land cover evapotranspiration [mm]
PP – total annual precipitation [mm]
Φ – aridity index (Eq. 6)
(Eq.6)
(Eq.7)
Table 4. Corine Land Cover classes and appropriate annual Kc used for ETc in Carpathian
Mountains region. (Source: From Allen et al., 1998; Nistor and Mîndrescu, 2017)
Corine land cover
Kc annual
CLC code 2012
CLC description
Kc
Ks
Ku
Kw
Kclc
133
Artificial surface
-
-
0.26
-
0.26
211
Non-irrigated arable land
1.14
-
-
-
1.14
313
Forest
1.35
-
-
-
1.35
321
Grasslands
0.95
-
-
-
0.95
324
Shrubland
0.9
-
-
-
0.9
332
Bareland
-
0.2
-
-
0.2
335
Permanent snow and ice
-
-
-
0.51
0.51
411
Wetland
-
-
-
0.45
0.45
512
Water bodies
-
-
-
0.64
0.64
Kc – crop coefficient for plants, Ks – evaporation coefficient for bare soils, Ku – crop coefficient for
urban areas, Kw – evaporation coefficient for open water, Kclc – crop coefficient for land cover
Groundwater vulnerability assessment using Spatial Analyst Tools
The groundwater vulnerability mapping was determinate by multi-layers analysis
using Spatial Analyst Tools from ArcGIS. The method adopted here is based on the
methodology applied by Nistor et al. (2015). In European regions, Čenčur Curk et al.
(2014) performed the groundwater vulnerability in the South East Europe using
weights. In the works of Stempvoort et al. (1993), Daly et al. (2002) and Dixon (2005),
the appropriate weights for each layer were found.
We consider that the water availability represents one of the most important layers
related to the water recharge quantity, which is driven by climate. Secondly, the land
cover variability and implication for PLI and ecosystems play also an important role of
the groundwater quality. Due to these reasons, we agree that water availability and
ecosystems may have the same weights of 30%. For the aquifers vulnerability factor
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
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APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
and infiltration map were set the equal weights of 20%. All layers were pondered using
weights like in Equation 8. These weights represent the relative importance of the
parameter in the group. The water availability and ecosystems have a balancing factor
that shows the relative importance to the maximal deviations of the respective layer and
its limitation to substitute another layer.
All layers were normalized under the standard method between 0 and 1. Final
vulnerability map was divided in classes as follow: 0–0.2 for very low, 0.21–0.4 for
low, 0.41–0.6 for medium, 0.61–0.8 for high, and 0.81–1 for very high. The
groundwater vulnerability map was divided in four classes of vulnerability: very low,
low, medium, and high.
GW V = (1-WA)1.5 X 0.3 + AV X 0.2 + IM X 0.2 + ES1.5 X 0.3 (Eq.8)
where:
GW V = Groundwater Vulnerability
WA = Water Availability
AV = Aquifer Vulnerability
IM = Infiltration Map
ES = Ecosystem Services
Results and discussion
The climate change in Europe and in the Carpathian Mountains area indicates
significant variation in the mean air temperature and evapotranspiration values for
2020s and 2050s, but also slightly modifications in the precipitation extremes. In base
of the climate models observations, the mean annual temperature is expected to increase
from –0.8 to 13.5 °C during the 2020s (Fig. 6a) in the Carpathian Mountains area. The
future projections indicate values of the mean air temperature from 0.2 to 14.4 °C
during the 2050s period (Fig. 6b) in the study area. The precipitation pattern shows
values between 460 mm and 1667 mm year-1 for the 2020s (Fig. 6c) and between 478
mm to 1730 mm year-1 for 2050s (Fig. 6d). The annual ET0 in the Carpathian
Mountains area is expected to range from 312 mm to 674 mm during 2020s and from
327 mm to 713 mm during 2050s (Fig. 6e and f).
Climate change effects on the Carpathian Mountains are reflected into the annual
ETc. During 1990s, the annual ETc ranges 56 mm to 740 mm while in the future
periods the ETc is expected to increase up to 910 mm (2020s), respective 963 mm
(2050s). Figure A4 depicts the spatial variation of annual ETc in the study area,
highlighting the high values (over 700 mm) in the Apuseni ad Eastern Carpathians,
especially during the 2020s and 2050s periods. The large area with high values is
represented by the forest projection scenario (Fig. A3d) while in the artificial and
agricultural scenarios for 2050s the ETc pattern do not differ too much (Fig. A3c and d).
The implication of the annual ETc (Fig. A4) in the groundwater vulnerability is
reflected through AETc and water availability. Thus, the annual AETc varies in the past
period from 55 mm to 525 mm showing the maximum values in the Western
Carpathians. During 2020s period, the annual AETc ranges from 63 mm to 575 mm
indicating high values (over 500 mm) in the Western and Southern Carpathians. For the
2050s period, the annual AETc ranges from 66 mm to 596 mm, depicting the high
values (over 500 mm) in western, southern, and eastern sides of the study area. In the
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DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
future years of 2050s, a larger area with high values of the annual AETc occupies the
territory in comparison with the 1990s and 2020s. Figure A5 illustrates the spatial
distribution of annual AETc in Carpathian Mountains area.
Figure 6. Models projections of temperature, precipitation, and ET0 in the Carpathian
Mountains region. (a) The average of mean annual air temperature between 2011 and 2040
(2020s). (b) The average of mean annual air temperature between 2041 and 2070 (2050s). (c)
The average of mean annual precipitation between 2011 and 2040 (2020s). (d) The average of
mean annual precipitation between 2041 and 2070 (2050s). (e) The average of annual ET0
between 2011 and 2040 (2020s). (f) The average of annual ET0 between 2041 and 2070 (2050s)
Considering the annual AETc and precipitation patterns, the annual water availability
was carried out for the 1990s, 2020s, and 2050s periods. In the Carpathian Mountains
area, the water availability ranges during 1990s from 129 mm to 1635 mm (Fig. 6a),
indicating the lower values (below 200 mm) in the South of Apuseni Mountains, in the
western sides of the Curvatures Carpathians, eastern side of the Eastern Carpathains.
The high values (over 800 mm) of the water availability spread in the Southern
Carpathians and Western Carpathians, but also in the North of Eastern Carpathians.
During the 2020s period, the water availability ranges from 66 mm to 1603 mm
illustrating a large area with low water availability (below 200 mm) in the Southern and
Eastern Carpathians. The annual water availability ranges in the 2050s from 69 mm to
1663 mm depicting low values of water availability in the Apuseni Mountains, Southern
and Eastern Carpathians. Figure 7 shows the water availability variation in the
Carpathian Mountains area at spatial scale.
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2018, ALÖKI Kft., Budapest, Hungary
Figure 7. Spatial distribution of water availability (WA) in the Carpathian Mountains region.
(a) WA related to the past period (1990s). (b) WA related to the present period (2020s). (c) WA
related to the future period (2050s) artificial area projection. (d) WA related to the future
period (2050s) forest area projection. (e) WA related to the future period (2050s) agricultural
area projection
Groundwater vulnerability at spatial scale in the Carpathian Mountains area was
calculated using spatial analysis by weights in the ArcGIS environment. A complex
methodology was applied using climate models, geological data, DEM, and land cover
of the study area. Figure 7a depicts the groundwater vulnerability map of Carpathian
Mountains area during 1990s. The groundwater vulnerability in the 1990s shows major
part of the territory as low vulnerable at climate change and land cover. The very low
class was depicted in the Western and Southern Carpathians only in the elevated areas
(above 2000 m). The high vulnerability areas were found in the Curvatures Carpathians
and South of Apuseni Mountains. During the 2020s years the groundwater vulnerability
map (Fig. 8b) indicates a large area of high vulnerability class, especially in the
Curvatures Carpathians, while the very low vulnerability reduced at spatial scale. The
future scenarios (Fig. 8c–e) show decreasing in area of very low vulnerability class and
increase of the high vulnerability class. The forest projection indicate the maps of worst
scenario with 2.41% from the total area with high vulnerability, followed by the
artificial area projection (Table 5).
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DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
Figure 8. Groundwater vulnerability map of the Carpathian Mountains region. (a)
Groundwater vulnerability map related to the past period (1990s). (b) Groundwater
vulnerability map related to the present period (2020s). (c) Groundwater vulnerability map
related to the future period (2050s) artificial area projection. (d) Groundwater vulnerability
map related to the future period (2050s) forest area projection. (e) Groundwater vulnerability
map related to the future period (2050s) agricultural area projection
The main aim of this work was to calculate the groundwater vulnerability at spatial
scale of Carpathian region area. The analysis of multi-layers data in ArcGIS was
focused on the climate models and land cover projections. Under the climate change,
the southern areas of Carpathians are negatively affected in terms of groundwater
vulnerability increases. These modifications are related to the increase of territory with
low water availability (below 200 mm) for the 2020s and 2050s, in all scenarios. In this
sense, the groundwater vulnerability increase from the quantitative point of view
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DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
because the recharge of aquifers might a reduction. The quality of the groundwater was
mainly controlled in our analysis by aquifers potential infiltration coefficient (PIC)
(Fig. A2) and the pollution load index (PLI) (Fig. 5). As a first driver of groundwater
quality, the higher PIC varies in the Carpathian Mountains region overlapping with the
existing aquifers in limestones, sandstones, sandy, and gravels. These aquifers are
defined as highly productive porous aquifers located mainly in the intermountain
depressions. In these types of aquifers the hydraulic conductivity in relatively higher
than in the aquifers formed by schists and gneisses or plutonic rocks. Secondly, the PLI
factor follow the land cover distribution (Fig. A3). Thus, the artificial areas and
agricultural lands influence the increases of groundwater vulnerability, while the forest
and grass form the ecosystems, which provide an advantageous media for groundwater
quality. For this reason, the low vulnerability extends on larger area (around 77.73%) in
the forest projection model.
Table 5. Percentage area of groundwater vulnerability calculated in Carpathian Mountains
area
Vulnerability
classes
GW
vulnerability
1990s
(area %)
GW
vulnerability
2020s
(area %)
GW vulnerability
2050s, artificial
projection
(area %)
GW vulnerability
2050s, forest
projection
(area %)
GW vulnerability
2050s, agriculture
projection
(area %)
Very low
6.19
3.50
2.75
2.49
2.67
Low
70.45
71.73
72.29
77.73
68.66
Medium
22.16
22.64
22.83
17.87
26.25
High
1.19
2.13
2.13
1.91
2.41
The methodology of groundwater calculation and land cover projection applied in
this survey is close to the procedure followed by Nistor et al. (2015). They used the
appropriate data to determine the groundwater vulnerability on a district from Apuseni
Mountains, in Romanian Carpathians. Here, we extrapolated the methodology to entire
area of Carpathians Mountains using spatial analysis in ArcGIS. With few changes
regarding the utilization of high-resolution climate models and geology implication, our
results indicate appropriate pattern of groundwater vulnerability with the studies carried
out by Čenčur Curk et al. (2014), Nistor et al. (2015). The limitations of this study may
be improved using in-situ monitoring of spring’s groundwater to observe the climate
change effects on seasonal periods. In this study, we have focused on long-term period
analysis, so the runoff and water availability variables on short-term period were
neglected. Using additional methods, such as VESPA index (Galleani et al., 2011),) or
spring variability in base of hydrographs, the groundwater vulnerability could be
performed in specific locations and further to create various models at spatial scale. At
this level, GIS techniques were the best solutions in sense of long-term period, land
cover projections, and layer analysis.
Conclusions
Spatial analysis by weights in ArcGIS environment contributed to determine the
groundwater vulnerability in the Carpathian Mountains area. A complex methodology
including the climate models in three periods, aquifers and geology, morphometric
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DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
terrain data, and the dynamic land use layers indicates a quarter part of the Carpathian
Mountains area territory suffers high and very high groundwater vulnerability under
climate change. Moreover, the future scenarios highlight almost a third part of the
region in the high and very high vulnerability class. These results evidence that the
territory of the Carpathian Mountains area is faced with the actual global threats that
may occur also at regional scale. These problems are mainly related to both a reduction
of precipitation amount and an increase in the mean air temperature. These findings can
help the policymakers and the administrative staff of the region to take the
counterbalance measures against the natural changes that are expected during the 21st
century. The maps carried out in this study may represent strategic tools for the
landscaping and delineating the areas with more restrictive actions.
We presented a long-term analysis which is based on spatial data and empirical
formulas. Considering the complexity of the data used here, the errors may be neglected
for the long period. However, the methodology was applied for other study cases from
Europe and we assume that our results are valid. To be more accurate, the future work
could be conducted at site scale to assess the groundwater vulnerability in situ (i.e.
springs, check quality of groundwater in wells). The groundwater quality in the
agricultural areas and different flow models of groundwater may improve the results at
local scale.
Acknowledgements. The authors would like to thank Andreas Hamann from Alberta University for the
climate models data and support during the revision process.
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2018, ALÖKI Kft., Budapest, Hungary
APPENDIX
Figure A1. Geological formations of the Carpathian Mountains region
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
- 5115 -
APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
Figure A2. Potential infiltration coefficient assigned to each type of aquifer in the Carpathian
Mountains region
Figure A3. Land cover of the Carpathian Mountains region in 2010 and future projections
Nistor et al.: Groundwater vulnerability GIS models in the Carpathian Mountains under climate and land cover changes
- 5116 -
APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 16(4):5095-5116.
http://www.aloki.hu ● ISSN 1589 1623 (Print) ● ISSN 1785 0037 (Online)
DOI: http://dx.doi.org/10.15666/aeer/1604_50955116
2018, ALÖKI Kft., Budapest, Hungary
Figure A4. Spatial distribution of annual crop evapotranspiration (ETc) in the Carpathian
Mountains region and future projection under land cover and climate models
Figure A5. Spatial distribution of annual actual crop evapotranspiration (AETc) in the
Carpathian Mountains region and future projection under land cover and climate models