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Article

Integrated GIS-Based Multi-Criteria Analysis for Groundwater Potential Mapping in the Euphrates’s Sub-Basin, Harran Basin, Turkey

1
Construction Technology, Hilvan Vocational School, Hilvan Harran University, 63900 Şanlıurfa, Turkey
2
Civil Engineering Department, Engineering Faculty, Dicle University, 21280 Diyarbakır, Turkey
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(13), 7375; https://doi.org/10.3390/su13137375
Submission received: 30 March 2021 / Revised: 22 June 2021 / Accepted: 25 June 2021 / Published: 1 July 2021
(This article belongs to the Special Issue Sustainable Groundwater Resource Development for Agriculture)

Abstract

:
The Harran Basin is an important region where agricultural activities have been conducted for millennia. The agricultural water needs of the basin are largely met with surface irrigation through the GAP (South-Eastern Anatolian Project), while groundwater is used in some regions as potable water, tap water, and irrigation water. In this study, the groundwater potential of the Harran Basin was determined through the “GIS-based Multi-Criteria Decision Making (MCDM)” Method. Ten criteria were considered: Land Use, Soil, Geomorphology, Geology, Aquifer, Drainage Density, Rainfall, Slope, Lineament Density, and Terrain Class. The weights of these criteria were determined through the AHP method, and the operations were performed in the ArcGIS environment. As the results of this study, the Groundwater Potential Index (GWPI) values were obtained, and five regions were determined based on their Groundwater Potential Zone (GWPZ) classifications: very poor GWPI: 0.10% (5.14 km2); poor GWPI: 29.20%; moderate GWPI: 38.20%; good GWPI: 30.90%; and very good GWPI: 1.50%). We determined that the region is a plain with a low slope and geologically consists of limestone. Notably, areas with poor water potential are mountainous and hilly, and the slopes over these areas are high.

1. Introduction

The Harran Basin hosted some of the oldest agricultural activities in history. Settlement in the basin began approximately 11,000 years ago [1], and there has been a continuous settlement in the basin since the Neolithic period [2]. In this timeframe, the basin has witnessed regular agriculture and stockbreeding activities. Here, the existence of ample water resources is as important for human life and agricultural activities as appropriate climatic conditions and a physical environment with fertile lands [3,4]. Since agriculture accounts for 19% of the gross national income and 9% of exports and provides employment opportunities for 51% of the population, agriculture plays an important social and economic role in the lives of the people in Turkey. Here, irrigated farming has a higher value due to the geographical location of Turkey, its demographic structure, and its economic determination. Today, the Harran basin is one of the most important regions that hosts irrigated farming in Turkey [5]. While the majority of the Harran Basin has the opportunity to engage in surface irrigation under the GAP [6], due to unconscious irrigation, agricultural activities are conducted with groundwater in the south–southeast sections of the basin. However, groundwater provides important water resources as an alternative for a possible surface water shortage. Studies on groundwater and drought indicate the importance of properly determining the groundwater potential [7,8,9]. On the other hand, groundwater is being used as potable water and tap water in some of the rural settlements in the basin. Therefore, it is important to determine the groundwater potential of the basin. Since the 1950s, groundwater levels in agricultural areas in several regions have fallen by as many as 50 m [10], and groundwater-dependent rivers, wetlands, and ecosystems have been compromised. The increase in the use of unplanned groundwater destabilizes the balance of natural recharge [11,12]. In general, the excessive use of aquifers leads to serious impacts on the environment and ecology, such as aquifer depletion, low-quality water migration, land subsidence, and the destabilization of the economic balance of the region [13,14,15].
The Groundwater Potential Index (GWPI) is a coefficient that indicates the groundwater potential of a region. This coefficient is obtained from the weights of the criteria comprising the groundwater potential through the “GIS-Based Multi-Criteria Decision Making (MCDM)” method [16,17,18]. GWPI is used to describe the possibility of obtaining groundwater resources in a region. The GWPI of a region varies according to the characteristics of the groundwater potential and its weighted importance coefficient. GWPIs provide remote sensing data and a possible assessment of the groundwater resources based on the integration of the criteria which impact on groundwater potential occurrence under the GIS environment. GWPI can also be defined through a sufficient number of water driller logs analyzed with hydrological and geophysical surface explorations [19,20,21]. However, these methods are time-consuming and expensive. Therefore, for the appropriate estimation of groundwater potential in a region, besides a limited number of field observations, Remote Sensing (RS) views and/or GIS data and software can be used [22,23,24]. In a multi-criteria decision-making process, Geographical Information System (GIS) can effectively produce a qualitative estimate of the groundwater resources [25,26,27,28,29].
Previous studies conducted on the groundwater potential in the Harran Basin are either too old [30] or were conducted locally with limited data [31]. More broadly, we found no study in the literature that analyzed groundwater potential on a basin basis. In the basin, groundwater potential studies were instead conducted on contamination based on agricultural activities [32,33,34] and the quality of groundwater [35]. The smaller number of studies conducted on groundwater potential is a vital deficiency in the Harran Basin, which is one of the most fertile basins in the Middle East. One of the objectives of this study is to fill this gap.
To obtain groundwater potential maps of the Harran Basin, a multi-criteria decision-making process was used together with a hydrological model. In defining the groundwater potential, the ArcGIS 10.2.2 program and GIS tools such as Spatial analysis and Arc Hydro were used in this study to create an AHP (Analytical Hierarchy Process). Influencing the groundwater resource flow, ten criteria were taken into consideration: Land Use, Soil, Geomorphology, Geology, Aquifer, Drainage Density, Rainfall, Slope, Lineament Density, and Terrain Class. The relative weights of each criterion were determined through the AHP–MCDM method, and thematic maps of the Harran Basin groundwater potential were obtained through the Spatial Analysis Overlay method based on these weight values. The obtained results were then compared with the data of the 18 water-wells previously established in the region and were confirmed to a large extent.

2. Materials and Methods

2.1. Study Area

The Harran Basin is part of the Upper Euphrates Basin and is located in the Southeast Anatolia Region of Turkey, to the south of Sanliurfa province. Stretching from north to south toward the Syrian border, the research area is located at a Latitude of 37°20′ and a Longitude of 39°30′ E, 38°30′ W (Figure 1).
The average elevation is 500 m in the north, which decreases to 350 m in the south on the Turkey–Syria border. The basin is separated from the Ceylanpınar Basin with the Tektek Mountains in the east and from the Suruç Basin with the Urfa Mountains. The northern part of the region is quite uneven and hilly. However, there is a limitation in the east–west direction. The Tektek Mountains to the east rise up to 600–700 m, while the altitude increases to 800 m in the Urfa Mountains to the west. As in the North, hills reaching an altitude of 850 m surround the plain. The region has a continental semi-arid climate, and the 40-year rainfall average of the Harran Basin is approximately 332.3 mm. The research area comprises approximately 5144.4 km2 of the basin drainage area, which includes the Şanlıurfa province, Harran, and the Akcakale districts. In the basin, grain and cotton growing are the primary agricultural activities, and the economy in the region is based on agriculture and stockbreeding.
As a north–south graben, the Harran basin is surrounded with Eocene epoch limestones to the east, west, and north. The Akcakale graben is one of the last productions of the severe tectonism that occurred during and after the Miocene period in Southeast Anatolia. Although the initial products of compressive motions were stretched in an east–west direction, the fault systems and structural axis comprising this graben are oriented approximately in a north–south direction [36]. The limestones surrounding the east and west of the basin stretch toward the plain with a high slope due to faulting. The slopes to the north, northeast, and northwest are oriented toward the plain, with an average gradient of 15–25%. The slope decreases toward the south and becomes too small to observe toward the Turkey–Syria border. The mostras are covered by upper red clay, and the baseline of the basin is formed by the topography of these limestones [30].

2.2. Method

Determining the groundwater potential using MCDM with the CBS software has become a commonly used method in recent years [37,38,39,40,41]. Groundwater potential is based on numerous parameters such as rainfall, geology, type of soil, use of land, and slope [42,43,44,45,46]. In this study, ten criteria were considered to determine the GWPI: use of land, land structure, slope, geology, hydrogeology, geomorphology, soil map, drainage density, fault density, and rainfall parameters. The flowchart shown in Figure 2 summarizes the transactions. Initially, the feature maps of all the criteria were converted to the raster format, and the thematic maps were subsequently re-classified according to their weighted values, determined through the AHP method.
The methods for obtaining these parameters are summarized in Table 1. The use of land (Figure 3a) was obtained from the “Global Land Cover Facility” site in the Erdas image format and converted according to the CORRINE method. Moreover, the soil type characteristics (Figure 3b) were appropriately obtained after processing data from the Ministry of Agriculture’s official website. The DEM maps were obtained from the Turkey N43 and N441/100,000 topography maps. These maps were digitized in a 10 × 10 m resolution. We produced Geomorphology (Figure 3c) from DEM maps. Geology (Figure 3d) and active Fault maps (Figure 3h) were digitized in the .kml format using the online data system on the MTA website; these maps were subsequently converted via the ArcGIS Data Interoperability program to the .shp format. Using these DEM maps and the Spatial Analysis and Arc-Hydro modules in the ArcGIS 10.2.2 software, Drainage Density Maps (Figure 3f) and Slope (Figure 3j). These two maps were initially converted to raster maps with ArcGIS and subsequently re-classified according to the impacts of the basement layers on the formation of groundwater potential. Annual rainfall values between 1971 and 2017 were obtained from the official website of the State Meteorological Services department, and the rainfall maps of all the regions were obtained with these data through the ArcGIS “Inverse Distance Weight” (IDW) method (Figure 3g).
Ultimately, the Groundwater Potential Index (GWPI) was shaped with the Overlay Sum using the relative weighted values of each parameter. To define the regions with groundwater potential, the standard index approach was used.

GIS-Based AHP Method

The analytical hierarchy process is a quantitative method that involves sorting and selecting decision alternatives according to multiple criteria [47]. This method was developed by Saaty [48,49] and is based on three principles: sortation, relative decision, and a combination of preferences [48]. Analytic Hierarchy Process (AHP) can be applied to estimate the weights (W) of all parameters influencing groundwater potential through the MCDM method. The 1–9 scale of the AHP (1: extremely insignificant, 2: very insignificant, 3: insignificant, 4: reasonably insignificant, 5: equally significant, 6: reasonably significant, 7: more significant, 8: very significant, and 9: extremely significant) was used to shape the decision matrices [50].
Subsequently, the Decision Making Matrix was shaped by paired comparisons (Table 2). Then, the relative weights (W) of the criteria were calculated (Table 3).
The application of the GIS technique and multicriteria decision analysis provides more flexible solutions for the prediction of groundwater-potential zones.
In this study, the weighting of various criteria was carried out through field analysis and a literature review. The basic steps for determining the system’s normalized weight and consistency ratio (CR) were as follows:
Step 1. Establishment of judgment matrices (p) by pairwise comparison:
p = p 11 p 12 p 1 n p 21 p 22 p 2 n p n 1 p n 2 p n n
where pn displays the n-th indicator unit, and pnn is the judgment matrix element.
Step 2. Calculation of the normalized weight:
W n = G M n / n = 1 N G M n
where W is the weight vector (column), and GMn is the geometric mean of the i-th row of the judgment.
Step 3. CR calculation to verify the coherence of the judgements:
C R = C I / R C I .
The Consistency Index (CI) is denoted as follows:
C I = λ max N N 1
where λmax is the eigenvalue of the judgment matrix, which is calculated as follows:
λ = i = 1 n P i .   W n N .   W
The Random Consistency Index (RCI) was then obtained from standard tables [51]. To be accepted, the CR value was required to be about 0.10 or less.

2.3. Groundwater Potential Index (GWPI)

GWPI is the size that demonstrated the groundwater potential in a certain region. It is calculated by considering the weight of each criterion that constitutes the GWPI. Thus, determinations can be made about the groundwater potential of various parts of a region [52]. Therefore, a general assessment can be made by classifying the GWPI value ranges as poor–normal–good–very good. This map is the conclusion map of the study, and it is demonstrated in Figure 4.
It is calculated according to the AHP method as indicated in Equation (6);
GWPI = LUr.LUw + STr.STw+ GMr.GMw + Gr.Gw+ Ar.Aw+ DDr.DDw + Rr.Rw +Sr.Sw
+ LDr.LDw + Tr.Tw
where GWPI is the Groundwater Potential Index, LU represents the Land Use, ST is the Soil Type, GM is Geomorphology, G is Geology, A is Aquifer, DD is the Drainage Density, R is the Rainfall, S is the Slope, LD is the Lineament Density, and T is the Terrain Class. In addition, the subscripts “r” and “w” refer to the rating and weight of the parameter, respectively.

3. Results and Discussion

In this study Land Use, Soil, Geomorphology, Geology, Aquifer, Drainage Density, Rainfall, Slope, Lineament Density, and Terrain Class were taken into consideration. Ten thematic maps were defined to determine the GWPIs. All thematic maps (Figure 3) produced for this study relate to groundwater potential. The general details of these maps are discussed in the following sub-sections. The abstract data are summarized in Table 4.

3.1. Land Use

ArcGIS was used to determine the models of the research area. The details of the classifications of use of land are given in Table 4 and illustrated in Figure 3a. High weight values were determined for the perennially irrigated lands, forested lands, forages, and sandy areas. For the wetted areas and cultivated areas, good weight values were determined. Since the settled areas and the settlements impede rainfall infiltration, they prevent water from reaching the underground reservoirs [53]; therefore, lower weight values were given to the settlement areas, cultivated areas, and fallow lands. Besides, in irrigated cropland occurs more uniform infiltration. The low-stem water clutch in fallow fields during wet winters results in a higher recharge flow through the reservoirs, which flushes salt deposits from the vadose zones [54].

3.2. Soil Type

Soil plays an important role in mapping the areas with groundwater potential. For example, soil types with thick layers are generally permeable, while fine-textured soils are less permeable. The soil types with higher permeability allow for a higher infiltration rate—in this case, most of the rainfall waters can reach the groundwater layer faster [55]. The soil map of the research area was obtained from the Administration of Disaster and Emergency Management, Şanlıurfa, Turkey. The soil map of the research area was classified using the following categories: reddish brown soils, brown soils, other areas, and basalt soils (Figure 3). The soil types and their percentages by area are given in Table 4.
The majority of the research area is composed of reddish-brown soil (4497 km2), brown soil (516 km2), and basalt soil (174 km2).

3.3. Geomorphology

Geomorphology, which describes the formation process of a region, uses maps that provide information about the geographical formations resulting from internal and external forces [56]. The geomorphology map is classified into four sections. The majority of the region is composed of plains—particularly to the north, toward Şanlıurfa—and smooth lands to the south, ending with partial hills in the southeast (Figure 3). Since the slope is lower on the plains, underground infiltration is more significant. In the mountainous and hilly regions, the runoff is even greater. Therefore, the scoring of the smooth and plain lands was high, while the scoring of the mountainous lands was low.

3.4. Geology

The Harran Plain experienced some faulting and subsidence events resulting from the Karacadağ volcano following the Eocene and Miocene periods. While units of large particles of silt, sand, and pebble materials are present on the borders of the basin, materials with a high clay content can be found toward the center of the basin. Within the aggregation of class at the center of the plain, there are lens-shaped permeable units (silt, sand, and pebbles) formed by various causes [30]. These permeable units appear as separate and independent lens-shaped units throughout the plain, rather than as a unified whole (Figure 3). The ratings of areas with geological characteristics of sand and pebbles were higher, while ratings of the clayey layers were lower.

3.5. Aquifers

The formation and mobility of the groundwater is controlled through porosity, permeability, the structure of the aquifers, aquifer distribution, feeding areas, and the use of aquifers [57]. Eocene-period limestone provides the bore holes for the main aquifers. The depth maps of the aquifers were obtained from the difference between the static level and the height of the land. The ratings were higher for areas with lower aquifer depths. The greater the depth, the lower the scores.

3.6. Drainage Density

The drainage densities of the basins cause the majority of rainfall to become runoff [58]. Lower drainage-density values are more appropriate for high groundwater potential and weights. Moreover, a lower drainage density indicates a higher infiltration of rainfall. The drainage density was obtained by dividing the basin area by the unit length:
D D = D L / D A
where DD is Drainage Density, DL is Drainage Length, and DA is the Drainage Unit Area. In general, the groundwater potential increases from the south of the research area to the north due to the lower drainage density (Figure 3).

3.7. Rainfall

We used district-level average annual rainfall values (latitude/longitude) obtained from the Şanlıurfa Regional Directorate of Meteorology based on the meteorological data between 1929 and 2017. Since the station-location data of the districts are present in the geographical coordinate system, the rainfall data of the stations were used in the ArcGIS environment. We obtained contour rainfall maps with these data by using Arc Map, Spatial Analysis, and IDW methods. These vectorial maps were initially classified into raster maps and subsequently reclassified according to their impact scores (Figure 3). Since the region is located in a semi-arid climate zone, the region does not experience significant rainfall. The rainfall values vary between 264 and 365 mm/year. Regions with an average of 330–365 mm/year scored 6 points, regions with an average rainfall of 300–330 mm/year scored 5 points, and regions with an average rainfall below 300 mm/year (Table 4) scored 4 points. Overall, rainfall is scarce in this region. However, we considered nine criteria in the area where Rainfall has the maximum score, although the recharge of the aquifer will be slow, and the possibility of overexploitation will be greater.

3.8. Slope

Rainfall-based infiltration is an important hydrological parameter for determining groundwater potential [59]. Infiltration depends on characteristics such as the type of the soil, vegetation, and slope. When there is a high slope, no ponds occur on the soil. The slope map was obtained from the DEM through the CBS method. Ultimately, since the research area is smooth, the slope is not great. The research area is flat, which indicates a high potential for the formation of groundwater. Figure 3 illustrated the slope maps classified by research area. The scoring of these maps is shown in Table 4.

3.9. Fault Density

The fault line of the planet is used here as the wide linear underground characteristic, which increases the direct porosity and is used as a diffraction line [60]. The lines are the manifestations of linear features that can play important roles in determining the appropriate areas for groundwater feeding [12]. Lineaments facilitate the mobility of the groundwater. The fault density maps were drawn by means of DEM maps and fault lines. The fault density maps of the research area are shown in Figure 3 and were classified based on five categories. The fault density is highest towards the east and west of the research area, as indicated in red. However, the fault density was found to be low in other regions. The basin has a level of density that increases the groundwater porosity and the permeability of the area. Thus, the weights are higher, indicating groundwater potential.

3.10. Terrain Structure

A change in the land cover influences the runoffs [61], the water intake speed [62], and the vaporization from the soil surface [63]. As shown in Figure 3, the structure of the land was classified into six groups. The majority of the land (36%) is VIIth class land, which has the highest scores for groundwater feeding and infiltration. The second-highest score belongs to the VIth class land, which is distributed in 8% of the area. Thirty-two percent of the land is Ist class land, with a rating of 5 (moderate). Six percent of the area is IInd class land, and 11% is IIIrd class land, which has the lowest ratings.

3.11. The Distribution of the Groundwater Potential Regional Map

The GWPI value was obtained through multi-assessment transactions of the Multi-Criteria Decision System based on ten parameters: Land Structure, Use of Land, Geology, Soil, Geomorphology, Rainfall, Fault density, Drainage density, Slope, and Aquifer criteria. The Groundwater Potential Zone (GWPZ) was determined by classifying its value (Figure 4).
In this thematic map, the GWPI value varies between 370 and 617. Table 5 outlines the classification ranges and the total classification ratings on a basin-basis according to the GWPZ values. As shown in Figure 4, in the central Harran Basin (and partially to the south), the groundwater potential is at a good level. However, particularly in the northern areas, the groundwater potential is at a moderate level.

3.12. Validity

To validate the groundwater area map, we used data from the 18 water observation wells (Table 6) within the borders of the basin. The GWPZ map in Figure 4 illustrates the groundwater research area map together with the locations of the water wells. The groundwater potential areas of almost all the existing pumping wells for irrigation were evaluated according to the following categories: very good, good, moderate, poor, and very poor. Based on this classification, the reference data for only 2 of the 18 wells were determined to be partially compatible. Among these wells, 16 references were found to be completely compatible with the study classification.
In the basin, an area of 5.14 km2 had very poor groundwater potential (0.1%), an area of 1501 km2 had poor potential (29%), an area of 1963 km2 had moderate potential (38%), an area of 1589.5 km2 had good potential (31%), and an area of 77.12 km2 had very good potential. The 18 observation wells yielded performance and locations in the research area that were related to the groundwater potential area map and presented good compliance, with 88.9% accuracy. Similar studies using similar methods in various regions achieved varying results. Ghosh et al. studied the upper Kangsabati river basin; the result of the overall accuracy assessment was 79.77%, which supports the validity of the study. The authors also claimed that slope was most dominant factor among the seven selected hydro-geological factors that influence the occurrence of groundwater [64]. Zhu and Abdelkareem (2021) determined that the groundwater potential zones of East Indian regions contain nearly 40% land with very high potential. The downstream areas and Wadi Garara were, moreover, shown to have very high recharge and storage potential. This study also indicated that about 86.17% of the observation wells could be matched with very good to moderate potential zones under this method [65]. Mukherjee and Singh applied this method with an accuracy of 80.48% in their study on the arid regions of Western India [66]. Zaidi et al. (2015) focused on identifying the potential zones of Artificial Groundwater Recharge (AGR) in northwestern Saudi Arabia. The results showed that 17.90% of the total studied area was suitable for AGR [28].

4. Conclusions

The Mesopotamian Basin is a region where agricultural activities have been carried out since the beginning of civilization. The Harran Plain is a sub-basin of the Euphrates, where the wheat was harvested for the first time in ancient Mesopotamia. Including irrigation in an area’s agricultural activities increases crop yields, and utilizing surface water via dams could be one of the most useful irrigation methods. However, in places where this method is unfeasible, irrigation via groundwater resources is becoming widespread. One possible method of observation uses wells drilled into narrow and tiny areas to determine the potential of the groundwater. However, this method is inappropriate for large-scale plains because it has long-lasting effects and is economically unsustainable. Instead of this method, it is possible to produce a map of groundwater potential by modeling, in wider areas, the impact rates of the factors forming the groundwater. A parametric approach utilizing the techniques of RS, GIS, and AHP could reduce the time, labor, and costs to their minimum levels, thereby enabling faster decisions for the productive management of water resources. Despite the limitations inherent to multi-criteria analysis, this type of analysis represents a valuable and practical tool for areas and regions (especially in developing states) that suffer from challenges in determining water solutions due to data scarcity (both in terms of quality and quantity).
The present study outlined a methodology using RS, GIS, and MCDM techniques to identify the charge regions and determine the potential charge areas from the Harran Plain sub-basin to the Euphrates Basin, located in the south-eastern part of Turkey. To prepare the thematic layers of permeability, we used Land Use, Geology, Geomorphology, Drainage Density, Lineament Density, Slope, Soil, Aquifer, and Terrain data. Finally, we determined appropriate charge areas by overlapping the artificial charge region map, second- and third-degree drainage maps, and graphical maps. According to the charge region map, the middle and southern areas of the plain are, respectively, suitable (31%) and moderately suitable (37%). We determined that the region is a plain with a low slope and geologically consists of limestone. Notably, the areas with poor water potential were found to be mountainous and hilly, and the slopes over these areas are high. Therefore, the features of topography, slopes, and aquifers are more active parameters than the other parameters of groundwater potential. The groundwater potential maps can be effectively used to manage the aquifer in a sustainable way and drill new wells in the high-potential maps. The well-yield performance and locations in the research area are related to the groundwater potential area map and show good compliance. The groundwater potential area map obtained in this study would also be appropriate for future sustainable groundwater development plans.

Author Contributions

Supervision: R.Ç.; Investigation: V.A. Both authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Harran Basin Research Area Location Map (Şanlıurfa), Turkey.
Figure 1. Harran Basin Research Area Location Map (Şanlıurfa), Turkey.
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Figure 2. Hierarchical flowchart for the mapping of the Harran Basin’s groundwater potential.
Figure 2. Hierarchical flowchart for the mapping of the Harran Basin’s groundwater potential.
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Figure 3. Main criteria affecting the reclassified Groundwater Potential Index (GWPI) raster maps ((a): Land Use, (b): Soil, (c): Geomorphology, (d): Geology, (e): Aquifer, (f): Drainage Density, (g): Rainfall, (h): Fault (Lineament) Density, (i): Terrain Class, and (j): Slope).
Figure 3. Main criteria affecting the reclassified Groundwater Potential Index (GWPI) raster maps ((a): Land Use, (b): Soil, (c): Geomorphology, (d): Geology, (e): Aquifer, (f): Drainage Density, (g): Rainfall, (h): Fault (Lineament) Density, (i): Terrain Class, and (j): Slope).
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Figure 4. Groundwater Potential Zone (GWPZ) Distribution Map.
Figure 4. Groundwater Potential Zone (GWPZ) Distribution Map.
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Table 1. Data Sources.
Table 1. Data Sources.
ParameterData SourceMode of Processing
Slope (S)Topographic maps (N43, N44 layouts): 1/100,000), DEMDigitalization/3D analyst ArcGIS
Lineament DensityGeological map,mta.gov.tr online mapsOnline digitalization in .kml format, converted to .shp file via the Data Interoperability ext.
GeologyGeological maps; map,mta.gov.tr online mapsOnline digitalization in .kml format, converted to .shp file via the Data Interoperability ext.
GeomorphologyDEM maps, topographic mapsDigitalization/3D analyst ArcGIS/classification
Land Use (LU)Landsat&imageryClassification in ENVI
Soil Type(https://www.tarim.gov.tr (accessed on 1 December 2020))Digitalization/classification in ArcGIS
Rainfall (R)Turkish State Meteorological Service (https://mgm.gov.tr (accessed on 12 October 2020))Interpolation/classification
Drainage DensityDEM maps, topographic mapsStream generation with Arc Hydro Tools/density
line calculation/validation
Table 2. Pairwise Comparison Matrix with Analytic Hierarchy Process (AHP).
Table 2. Pairwise Comparison Matrix with Analytic Hierarchy Process (AHP).
LUSGMGADDRSLLDTC
Land Use1.000.860.750.750.751.000.670.751.501.00
Soil1.171.000.880.880.881.170.780.881.751.17
Geomorphology1.331.141.001.001.001.330.891.002.001.33
Geology1.331.141.001.001.001.330.891.002.001.33
Aquifer1.331.141.001.001.001.330.891.002.001.33
Drainage Density1.000.860.750.750.751.000.670.751.501.00
Rainfall1.501.291.131.131.131.501.001.132.251.50
Slope1.331.141.001.001.001.330.891.002.001.33
Lineament Density0.670.570.500.500.500.670.440.501.000.67
Terrain class1.000.860.750.750.751.000.670.751.501.00
LU: Land Use, S: Soil, GM: Geomorphology, G: Geology, A: Aquifer, DD: Drainage Density, R: Rainfall, SL: Slope, LD: Lineament Density, TC: Terrain Class.
Table 3. Normalized Criteria Matrix with AHP.
Table 3. Normalized Criteria Matrix with AHP.
Land UseSoilGMGeologyAquiferDDRainfallSlopeLDTerrain ClassW
Land Use0.090.090.090.090.090.090.090.090.090.090.09
Soil0.100.100.100.100.100.100.100.100.100.100.10
Geomorphology0.110.110.110.110.110.110.110.110.110.110.11
Geology0.110.110.110.110.110.110.110.110.110.110.11
Aquifer0.110.110.110.110.110.110.110.110.110.110.11
Drainage Density0.090.090.090.090.090.090.090.090.090.090.09
Rainfall0.130.130.130.130.130.130.130.130.130.130.13
Slope0.110.110.110.110.110.110.110.110.110.110.11
Lineament Density0.060.060.060.060.060.060.060.060.060.060.06
Terrain Class0.090.090.090.090.090.090.090.090.090.090.09
λmax = 8.12, CI = 0.02, RCI = 1.41, CR = 0.011 < 0.1: acceptable.
Table 4. Details of the layers of the research area.
Table 4. Details of the layers of the research area.
Sequence No.ParametersRankSub-ParametersLand Coverage Area (km2)Groundwater ViewsDegree
1Land Use6Continuous Irrigated Area
Woodland
grassland
Sand Area
Wet area
Agriculture, Planting Area Residential
Agriculture, Fallow Land
1414Very Good9
3Very Good8
1530Very Good7
1530Good7
86Moderate6
86Moderate6
82Moderate5
414Poor (Weak)4
2Soil7Reddish brown territory4497Very Good8
Brown territory516Very Good7
Other areas41Good6
Basalt lands174Moderate5
3Geology8Unspoiled Terrestrial Crumbs1986Very Good8
Basalt3049Good7
Terrestrial Crumbs55Good6
Crumbs and Carbonates27Moderate5
Unassisted Quaternary27Moderate5
4Geomorphology8Flat1768Very Good9
Plain1323Good7
Plateau955Moderate5
Hill1097Poor3
5Aquifer Elevation (m)8810–890150Very Good9
740–8102229Good8
671–740742Good7
601–671535Moderate6
531–601465Moderate5
461–531740Poor4
391–461215Poor3
321–39168Very Poor2
6Drainage Density
(km/km2)
50–142Good7
2–445Moderate5
5–748Poor3
8–125009Very Poor1
7Rainfall (mm/year)9318–3311754Poor3
331–3501964Moderate4
350–3814521Moderate5
8Slope (%)80.00–1.501791Very Good9
1.50–3.002322Very Good8
3.00–4.50945Good7
4.50–6.0066Moderate5
6.00–9.4120Poor3
9Fault Density (km/km2)40.00–15.2942Good7
15.29–3045Moderate5
30–5048Poor3
50–705009Very Poor1
10Terrain Class6I1662Moderate5
II323Moderate4
III581Poor3
IV317Very poor2
VI400Very good8
VII1861Very9
Table 5. Classification of the Harran Basin according to the GWPI values.
Table 5. Classification of the Harran Basin according to the GWPI values.
GWPI ValuesDefinitionRating (%)Area (km2)
307–340Very Poor0.105.14
340–445Poor29.201501.17
445–530Moderate38.201963.86
530–580Good30.901588.57
580–617Very Good1.5077.12
Table 6. Comparison of the data for the wells and the GWPI.
Table 6. Comparison of the data for the wells and the GWPI.
Reference NumberXYZDepthSWLDWLYieldGWPIEvaluationCompliance
0517,0534,126,3966932901752351,5396PoorCompatible
1502,4004,135,099760-1101651,5435PoorCompatible
2486,1644,073,547393110708010568GoodCompatible
3524,9614,129,044725-851321428PoorCompatible
4525,9514,074,60948020516018010574GoodCompatible
5471,7734,094,2286092541201752435PoorCompatible
6468,6804,118,684713180901501,5380PoorCompatible
7489,4244,090,377388200601006576GoodCompatible
8504,3274,093,56338417036410561GoodCompatible
9497,7424,101,4384222204011010577GoodCompatible
10493,3944,142,126798250601801419PoorCompatible
11518,3884,107,470466220501505556GoodCompatible
12476,2154,111,22869525013020010431PoorPartially Compatible
13524,3714,092,1636113302803005462ModerateCompatible
14517,0474,063,7624221601301604509ModerateCompatible
15502,1644,079,18438218014017010561GoodCompatible
16494,0244,122,396661180501009454ModeratePartially Compatible
17503,6074,118,421524150251203435PoorCompatible
Accuracy rate: compatible wells number/all references wells number: 16/18 = 88.9%.
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Aslan, V.; Çelik, R. Integrated GIS-Based Multi-Criteria Analysis for Groundwater Potential Mapping in the Euphrates’s Sub-Basin, Harran Basin, Turkey. Sustainability 2021, 13, 7375. https://doi.org/10.3390/su13137375

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Aslan V, Çelik R. Integrated GIS-Based Multi-Criteria Analysis for Groundwater Potential Mapping in the Euphrates’s Sub-Basin, Harran Basin, Turkey. Sustainability. 2021; 13(13):7375. https://doi.org/10.3390/su13137375

Chicago/Turabian Style

Aslan, Veysel, and Recep Çelik. 2021. "Integrated GIS-Based Multi-Criteria Analysis for Groundwater Potential Mapping in the Euphrates’s Sub-Basin, Harran Basin, Turkey" Sustainability 13, no. 13: 7375. https://doi.org/10.3390/su13137375

APA Style

Aslan, V., & Çelik, R. (2021). Integrated GIS-Based Multi-Criteria Analysis for Groundwater Potential Mapping in the Euphrates’s Sub-Basin, Harran Basin, Turkey. Sustainability, 13(13), 7375. https://doi.org/10.3390/su13137375

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