ABSTRACT
Due to the increases in agriculture and industry sector as well as high population, lack of water is becoming a major problem in the Middle East especially in arid regions. Saudi Arabia needs more groundwater research and explorations because of its higher water use and no source of freshwater. Assessing groundwater zonation in semi-arid locations is essential due to the significant degree of variation in groundwater depth, aquifer features, topographical characteristics, and insufficient precipitation. Mapping prospective groundwater zones in Al Qunfudhah region of southwestern Saudi Arabia has utilized the capability of the multi-criteria decision approaches (MCDA), and the Geographic information system (GIS). We have used the analytical hierarchy process (AHP) as one of the MCDA that is applied to achieve the objective of the current study by integrating twelve controlling factors. These factors are represented by the thematic layers; slope, precipitation, soil type, land use/cover (LULC), drainage density (DD), normalized difference vegetation index (NDVI), curvature, topographic position index (TPI), Terrain Ruggedness Index (TRI), drainage density (DD), and Lineament Density (LD). These thematic layers are combined with GIS to delineate the zones of groundwater potentialities. All factors were classified and weighted according to their importance and its effect on groundwater zones. Their normalized weights were evaluated using a pairwise comparison matrix. The present study shows that the groundwater potential zones (GWPZs) map is represented by five groups ranging between a very high zone with an area of 23781.06 Km2 that represents 4.04 % of the studied area, and a very poor GWPZ with an area of 182944.4 Km2 that represents 31.09 % of the studied area. The AHP model suggests that lineament density, slope, and drainage density are more important for determining the groundwater potentiality than other physiographic factors. The study's findings will be helpful in developing practical strategies for the region's groundwater supply. This analysis shows how the methodology may be used to study a broad coastal groundwater basin. The current study will give the decision makers to select suitable sites with a high groundwater potential.
ABSTRACT
Soils along the Egyptian coast are vulnerable to environmental degradation and soil salinity problems. The main objective of this study is to identify and rapidly predict salt affected soils using remote sensing data and multivariate statistical analysis. To achieve this objective, the Operational Land Imager 8 (OLI) of Landsat imagery acquired in March 2022 was processed through the Maximum Likelihood classifier to assess Landscape features and to produce Normalized difference salinity index (NDSI) and normalized difference vegetation index (NDVI). Water and soil samples were collected from 13 field sites as ground truth data and to investigate representative physical and chemical properties. Linear regression model was used to predict soil salinity while soil parameters were mapped using Inverse Distance Weight (IDW) in ArcGIS 10.5. In order to explore the soil salinity content using VNIR-SWIR spectra, this work investigated the potential of Partial least squares regression (PLS regression) and SVM (Support vector machine). For simulating salinity in the investigated area, a total number of 65 different sites were identified considering that almost 75 % (50 sites) were used to develop the model and 25 % (15 sites) for validation of the established model. The results indicated that EC levels of water samples are not suitable for irrigation (> 3 mS/cm). Majority of the collected soil samples represent saline-alkaline soils. NDSI ranged from -0.83 to 0.57 with mean of -0.25. Based on the variance of components, 90 % of data were obtained from the first three PCA. The PLS model's R2 score of 0.763 and extremely low p value indicates how well it predicts soil salinity. SVM model R2, on the other hand, is 0.719. Further, Ca++ and Mg++ are the main significant parameters selected in the predicted model. This shows that remote sensing data and multivariate analysis are very important tools to map spatial variation and predict soil salinity. The developed model for salinity considered both the spectral retrieved parameters and lab analyses of cations giving higher accuracy.