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

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.

Hydrogeochemical Facies Investigation of Surface and Groundwater Resources at West Luxor Area, Egypt using Spatial and Statistical Techniques

“…Deterministic interpolation comprises global polynomial (GPI), local polynomial (inverse distance weighted (IDW)), planar spline, and local polynomial interpolation (LPI) (Zandi et al, 2011;Ahmadian and Chavoshian, 2012). The posterior method can be used to correct the local errors without reducing the accuracy, the precision of the method is close to the Kriging techniques (Gunarathna, et al, 2016;Al-Omran et al, 2017;Aslan and Çelik, 2021). A considerable amount of literature has been published on water quality in Egypt, including many topics such as water quality assessment and groundwater pollution (Sabry, et al, 2019) but there were no reported researches on the investigation and verification of hydrogeochemical processes of water resources using statistical techniques.…”

Determination of groundwater potential zones using geographic information systems and remote sensing in Lupane District, Zimbabwe

“…The AHP technique, which was used to determine the ranking of individual themes and the class weights, is most often used in multicriteria decision analysis for natural resource management, site selection, suitability analysis, etc. (Arunbose et al, 2021; Aslan & Çelik, 2021; Doke et al, 2021; Roy et al, 2022; Verma & Patel, 2021).…”

An Approach to Delineate Potential Groundwater Zones in Kilinochchi District, Sri Lanka, Using GIS Techniques

“…The World Bank estimated in 2014 that Sri Lanka's annual freshwater withdrawals were 13 billion cubic meters [15]. Water shortage is the foremost issue in the country's dry zone [16][17][18][19], where there is a significant demand for water to irrigate 85% of the land to boost agricultural productivity [20,21]. Although Sri Lanka has six types of aquifers and a wide range of geological and hydrogeological settings in its 65,610 km 2 territory, groundwater extraction for agricultural purposes has long been restricted to the districts in the Northern Province (Jaffna, Kilinochchi, Mullaitivu, Vavuniya, and Mannar), that lack a perennial surface water resource [22,23].…”

“…Incorporating RS and GIS into groundwater potentiality mapping allows for data storage, manipulation, and analysis [44,47]. Integrated GIS and RS with AHP prove that it is a handy tool for delineating potential groundwater zones by reducing time and cost [4,17,32,37,41,44,45,[48][49][50]. Researchers adopt various techniques for delineation potential groundwater zones: statistical method [51], influence factor (IMF) [36,38,40,52], groundwater modelling, the combination of GIS, RS with AHP [37,41,45,49], and GIS-based machine learning [39,53,54].…”

“…As groundwater is dynamic, RS data integration into the GIS is convenient for identifying the potential groundwater zones (GWPZ). [41] x x x x x x x x Karimi-Rizvandi et al, [39] x x x x x x x x x x Aslan and Çelik, [17] x x x x x x x x x Jhariya et al, [40] x x x x x x x x Sarwar et al, [52] x x x x x x x Nasir et al, [36] x x x x x x x Kumar et al, [37] x x x x x x x x x x Dilekoglu and Aslan, [49] x x x x x x x x x Khan et al, [38] x x x x x x x x x Abijith et al, [44] x x x x x x x x Achu et al, [32] x x x x x x x Ajay Kumar et al, [50] x x x x x x x Ahmad et al, [47] x x x x x x x x Arefin, [31] x x x x x x x x x x Arya et al, [56] x x x x x x x x x x Bhunia, [57] x x x x x x x Qadir et al, [58] x x x x x x Lentswe and Molwalefhe, [48] x x x x x Sl-Slope, Lu-Land use, Ge-Geology, Li-Lithology, Gm-Geomorphology, Rf-Rainfall, SPi-Stream Power Index, El-Elevation, So-Soil, Ld-lineament density, Rd-River density, If-Infiltration TWi-Topographic wetness index, STi-Sediment transport index, Cu-Curvature, Dd-Drainage density, Al-Altitude, Dr-Distance from the river, Aq-Aquifer, Dg-Depth to groundwater.…”