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The present research is an attempt to find out the groundwater potential zones within an arid region of India supported by the scientific investigation of lithology, geomorphology, geohydrological characterization of geological formations and their interrelationship. Thematic layers of drainage, lithology, geomorphology, lineaments, slope, soil, digital elevation model, rainfall, landuse/land cover and well inventory have been generated by using ancillary data, digital satellite image, water level data of 90 observation wells for last 11 years (2000–2010), litholog data along with ground truthing. The groundwater potential zones have been classified into five categories like very poor, poor, moderate, good and excellent. The potential zones were obtained by weighted overlay combination using the spatial analyst tool in ArcGIS 9.2. During weighted overlay analysis, the ranking was given for each individual parameter of each thematic map and weights were assigned according to their influence such as lithology (20 %), geomorphology (15 %), lineament density (15 %), drainage density (15 %), soil (10 %), slope (10 %), rainfall (5 %), land use and land cover (5 %) and digital elevation model (DEM) (5 %) and it was found that the potential zones in terms of very poor, poor, moderate, good and excellent zones covered 13.7 %, 42.8 %, 27.3 %, 10.8% and 5.4% respectively of the total area. The result also has been validated by yield data collected from existing sources and it confirms that the higher yield categories are falling within excellent groundwater potential zones where yield ranges from 23 to 40.3 l/s and lower values ranging from 8.1 to 10.6 l/s are falling within poor groundwater potential zones.

Fluoride-enriched water has become a major public health issue in India. The present study tries to evaluate the geochemical mechanism of fluoride enrichment in groundwater of western India. Total 100 groundwater samples were collected for the study spreading across the entire study area. The results of the analyzed parameters formed the attribute database for geographical information system (GIS) analysis and final output maps. A preliminary field survey was conducted and fluoride testing was done using Hach make field kits. The fluoride concentration ranges from 0.08 to 6.6 mg/L (mean 2.4 mg/L), with 63 % of the samples containing fluoride concentrations that exceed the World Health Organization (WHO) drinking water guideline value of 1.5 mg/L and 85 % samples exceeding the Bureau of Indian Standards (BIS) guidelines of 1 mg/L. The study also reveals high concentration of nitrate that is found to be above WHO standrads. The dominant geochemical facies present in water are Na-Cl-HCO₃(26 samples), Na-Ca-Cl-HCO₃(20 samples), Na-Cl (14 samples), and Na-Ca-Mg-Cl-HCO₃(11 samples); however, sodium and bicarbonate being the major component in all the water types of 100 samples, which in fact has a tendency to increase fluoride concentration in water by dissolving fluoride from fluorite. The thermodynamic considerations between the activities of calcium, fluoride, and bicarbonate suggest that fluoride concentration is being governed by activity of calcium ion. X-ray diffraction analysis of sediments reveals calcite and fluorite are the main solubility-control minerals controlling the aqueous geochemistry of high fluoride groundwater. The results indicate that the fluoride concentration in groundwater is mainly governed by geochemical composition of rocks, such as metamorphic granites and sedimentary rocks, alkaline hydrogeological environment, climatic conditions, high temperature and lesser rainfall, and geochemical processes such as weathering, evaporation, dissolution, and ion exchange.

The dependency of people on groundwater has increased in the past few decades due to tremendous increase in crop production, population and industrialization. Groundwater is the main source of irrigation in Shiwaliks of Punjab. In the present study the samples were collected from predetermined location as was located on satellite image on basis of spectral reflectance. Global positioning system was used to collect samples from specific locations. Principal components analysis (PCA) together with other factor analysis procedures consolidate a large number of observed variables into a smaller number of factors that can be more readily interpreted. In the present study, concentrations of different constituents were correlated based on underlying physical and chemical processes such as dissociation, ion exchange, weathering or carbonate equilibrium reactions. The PCA produced six significant components that explained 78% of the cumulative variance. The concentration of the few trace metals was found to be much higher indicating recharge due to precipitation as main transport mechanism of transport of heavy metals in groundwater which is also confirmed by PCA. Piper and other graphical methods were used to identify geochemical facies of groundwater samples and geochemical processes occurring in study area. The water in the study area has temporary hardness and is mainly of Ca–Mg–HCO 3 type.

Land surface temperature (LST) can fully reflect the water–heat exchange cycle of the earth surface that is important for the study of environmental change. There is little research on LST in the semi-arid region of Abha-Khamis-Mushyet, which has a complex topography. The study used LST data, retrieved from ASTER data in semi-arid mountain areas and discussed its relationship with land use/land cover (LULC), topography and the normalized difference vegetation index (NDVI). The results showed that the LST was significantly influenced by altitude and corresponding LULC type. In the study area, during the summer season, extreme high-temperature zones were observed, possibly due to dense concrete surfaces. LST among different types of land use differed significantly, being the highest in exposed rocky areas and built-up land, and the lowest in dense vegetation. NDVI and LST spatial distributions showed opposite trends. The LST–NDVI feature space showed a unique ABC obtuse-angled triangle shape and showed an overall negative linear correlation. In brief, the LST could be retrieved well by the emissivity derived NDVI TES method, which relied on upwelling, downwelling, and transmittance. In addition, the LST of the semi-arid mountain areas was influenced by elevation, slope zenith angle, aspect and LULC, among which vegetation and elevation played a key role in the overall LST. This research provides a roadmap for land-use planning and environmental conservation in mountainous urban areas.

Groundwater is a valuable resource because it is widely used for drinking, and for domestic, agricultural, and industrial purposes. Globally, Saudi Arabia is known to be one of the driest regions with scarce water resources. The shallow groundwater near the major cities in the Kingdom of Saudi Arabia is becoming polluted because of industrial effluent discharge, use of fertilizers in agriculture and domestic sewerage in the region. This review tries to focus on groundwater quality problems due to anthropogenic or geogenic sources in the region of Saudi Arabia. In this paper, we focus on different water-quality variables, for groundwater quality evaluation and aquifer vulnerability assessment due to pollutants/contaminants present in groundwater. The current study gives a holistic understanding of different groundwater quality problems and therefore identifies the gaps of the previous studies and identifies the viewpoints of the future research dimensions. We describe the different groundwater quality problems related to toxicities of the fluoride, nitrate, and heavy metals and radionuclides in Saudi Arabia. A majority of the groundwater pollutants are of natural origin, but there is significant wastewater effluent discharge in the region that is also responsible for contamination of aquifers with heavy metals.

Water is a fluctuating resource making it difficult to measure in time and in space. To demonstrate the efficiency of the geographic information system (GIS) for groundwater studies, information on the parameters controlling groundwater such as lithology, geomorphology and lineament analysis were analyzed. LISS-III and Landsat satellite image of the area was used to infer information on the geologic lineaments and geomorphology. To delineate linear features enhancement and direction, filtering was performed on single bands of Landsat images. Thematic maps for geology, slope, geomorphology and lineament were prepared and integrated in GIS by assigning the weights and ranking to various parameters controlling the occurrence of groundwater to generate the groundwater potential map for the study area. The results indicate that the floodplain of river and its adjoining areas have very good groundwater potential, whereas the steeply sloping area in the northern part having high relief and slope possesses poor groundwater potential.

Saudi Arabia is an arid country with very limited water resources. The absence of surface water bodies along with erratic rainfall renders groundwater as the most reliable source of potable water in arid and semi-arid regions globally. Groundwater quality is determined by aquifer characteristics regional geology and it is extensively influenced by both natural and anthropogenic activities. In the recent past, several methodologies have been adopted to analyze the quality of groundwater and associated hydro-geochemical process i.e., multivariate statistical analysis, geochemical modelling, stable isotopes, a redox indicator, structural equation modelling. In the current study, statistical methods combined with geochemical modelling and conventional plots have been used to investigate groundwater and related geochemical processes in the Aseer region of Saudi Arabia. A total of 62 groundwater samples has been collected and analyzed in laboratory for major cations and anions. Groundwater in the study region is mostly alkaline with electrical conductivity ranging from 285–3796 μS/cm. The hydro-geochemical characteristics of groundwater are highly influenced by extreme evaporation. Climatic conditions combined with low rainfall and high temperature have resulted in a highly alkaline aquifer environment. Principal component analysis (PCA) yielded principal components explaining 79.9% of the variance in the dataset. PCA indicates ion exchange, soil mineralization, dissolution of carbonates and halite are the major processes governing the groundwater geochemistry. Groundwater in this region is oversaturated with calcite and dolomite while undersaturated with gypsum and halite which suggests dissolution of gypsum and halite as major process resulting into high chloride in groundwater. The study concludes that the combined approach of a multivariate statistical technique, conventional plots and geochemical modelling is effective in determining the factors controlling the groundwater quality.

The Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The NDVI–rainfall association, on the other hand, is spatially non-stationary, non-linear, scale-dependent, and influenced by local factors (e.g., soil background). In this study, two statistical methods are used in the modeling, i.e., traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR), to evaluate the NDVI–rainfall relationship. The GWR was implemented annually in the growing seasons of 2000 and 2016, using climate data (Normalized Vegetation Difference Index and rainfall). The NDVI–rainfall relationship in the studied Bisha watershed (an eco-sensitive zone with a complex landscape) was found to have a stable operating scale of around 12 km. The findings support the hypothesis that the OLS model’s average impression could not accurately represent local conditions. By addressing spatial non-stationarity, the GWR approach greatly improves the model’s accuracy and predictive ability. In analyzing the relationship between NDVI patterns and rainfall, our research has shown that GWR outperforms a global OLS model. This superiority stems primarily from the consideration of the relationship’s spatial variance across the study area. Global regression techniques such as OLS can overlook local details, implying that a large portion of the variance in NDVI is unexplained. It appears that rainfall is the most significant factor in deciding the distribution of vegetation in these regions. Furthermore, rainfall had weak relationships with areas predominantly located around wetlands, suggesting the need for additional factors to describe NDVI variations. The GWR method performed better in terms of accuracy, predictive power, and reduced residual autocorrelation. Thus, GWR is recommended as an explanatory and exploratory technique when relations between variables are subject to spatial variability. Since the GWR is a local form of spatial analysis that aligned to local conditions, it has the potential for more accurate prediction; however, a larger amount of data is needed to allow a reliable local fitting.

The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.

In semi-arid/arid regions, groundwater is the major source of irrigation, drinking and industrial requirements, water salinity and shortage are major problems of concern. North Gujarat, India, is one such area where highly saline groundwater is generally ascribed to rapid increase of population, agriculture and industries induced decline in water table by unplanned abstraction of groundwater. However, no effort has been made to discriminate the natural and anthropogenic influences on groundwater salinity. In this brief background, the present study attempts to identify the factors and processes controlling the groundwater salinity in the area, based on ionic ratios in integration with various graphical methods, saturation indices and geographical information system. Na⁺/Ca²⁺ > 1 indicates the deficiency of Ca²⁺ possibly due to CaCO₃ precipitation or ion exchange process. Na⁺/Cl⁻ > 1 and [Formula: see text] suggest salinization is mainly due to wastewater infiltration and/or due to irrigation water return flow. Sea water intrusion in coastal parts, vertical and lateral mixing of water and anthropogenic inputs are also responsible for salinization of groundwater. USSL diagram, Na%, sodium adsorption ratio, residual sodium carbonate and magnesium hazard indicate unsuitability of groundwater for irrigation purposes. To prevent groundwater salinization, appropriate measures need to be taken to control further indiscriminate exploitation of groundwater for irrigation.