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Abstract The main objective of this study was to use deep learning, and convolutional neural networks (CNN), integrated with field geology to identify distinct lithological units. The Samadia-Tunduba region of the South Eastern Desert of Egypt was mapped geologically for the first time thanks to the use of processed developed CNN algorithms using Landsat 9 OLI-2, which were further enhanced by geological fieldwork, spectral measurements of field samples, and petrographic examination. According to previously published papers, a significant difference was observed in the distribution of rocks and their boundaries, as well as the previously published geological maps that were not accurately compatible with the nature of the area. The many lithologic units in the region are refined using principal component analysis, color ratio composites, and false-color composites. These techniques demonstrated the ability to distinguish between various igneous and metamorphic rock types, especially metavolcanics, metasediments, granodiorite, and biotite monzogranite. The Key structural trends, lithological units, and wadis affecting the area under study are improved by the principal component analysis approach (PC 3, 2, 1), (PC 2, 3, 4), (PC 4, 3, 2), (PC 5, 4, 3), and (PC 6, 5, 4) in RGB, respectively. The best band ratios recorded in the area are recorded the good discrimination (6/5, 4/3, and 2/1), (4/2, 6/7, and 5/6), and (3/2, 5/6, and 4/6) for RGB. The classification map achieved an overall accuracy of 95.27%, and these results from Landsat-9 data were validated by field geology and petrographical studies. The results of this survey can make a significant difference to detailed geological studies. A detailed map of the new district has been prepared through a combination of deep learning and fieldwork.

Abstract Evapotranspiration plays a pivotal role in the hydrological cycle. It is essential to develop an accurate computational model for predicting reference evapotranspiration (RET) for agricultural and hydrological applications, especially for the management of irrigation systems, allocation of water resources, assessments of utilization and demand and water use allocations in rural and urban areas. The limitation of climatic data to estimate RET restricted the use of standard Penman–Monteith method recommended by food and agriculture organization (FAO-PM56). Therefore, the current study used climatic data such as minimum, maximum and mean air temperature (T max, Tmin, T mean), mean relative humidity (RHmean), wind speed (U) and sunshine hours (N) to predict RET using gene expression programming (GEP) technique. In this study, a total of 17 different input meteorological combinations were used to develop RET models. The obtained results of each GEP model are compared with FAO-PM56 to evaluate its performance in both training and testing periods. The GEP-13 model (T max, T min, RHmean, U) showed the lowest errors (RMSE, MAE) and highest efficiencies (R 2, NSE) in semi-arid (Faisalabad and Peshawar) and humid (Skardu) conditions while GEP-11 and GEP-12 perform best in arid (Multan, Jacobabad) conditions during training period. However, GEP-11 in Multan and Jacobabad, GEP-7 in Faisalabad, GEP-1 in Peshawar, GEP-13 in Islamabad and Skardu outperformed in testing period. In testing phase, the GEP models R 2 values reach 0.99, RMSE values ranged from 0.27 to 2.65, MAE values from 0.21 to 1.85 and NSE values from 0.18 to 0.99. The study findings indicate that GEP is effective in predicting RET when there are minimal climatic data. Additionally, the mean relative humidity was identified as the most relevant factor across all climatic conditions. The findings of this study may be used to the planning and management of water resources in practical situations, as they demonstrate the impact of input variables on the RET associated with different climatic conditions.

Globally, oral infections and inflammatory lesions persist as substantial public health concerns, necessitating the introduction of novel oral treatment protocols. Oral diseases are linked to various causative factors, with dental plaque/biofilm resulting from inadequate hygiene practices playing a predominant role. The strategic implementation of novel topical therapies holds promise for effectively controlling the biofilms, addressing oral infections and promoting enhanced oral wound healing. This review aims to providing a comprehensive overview of the available evidence pertaining to the potential efficacy of topical oxygen and lactoferrin-releasing biomaterials, exemplified by the blue®m formula, as novel oral care interventions within the scope of contemporary implantology, oral surgery and periodontology.

Abstract Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes.

Abstract This study primarily investigates the natural radioactivity level in alkaline rocks collected from the Wadi El-Dib ring complex (WDRC) in North Eastern Desert of Egypt, and assesses potential health risks associated with their use as decorative building materials. The work was accomplished using a high-purity germanium detector as well as ICP-MS and ICP-AES techniques. The WDRC composed essentially of trachyte, quartz syenite, granite and syenite. Geochemically, these rocks contain high SiO2 and alkalis with metaluminous to slightly peraluminous features. All rocks contain high concentrations of rare earth elements (∑REEs = 109–1075 ppm), with clear enrichment in light REEs compared to heavy REEs [(La/Yb)N = 8.3–25.3. Radiometrically, the concentrations of the natural radioisotopes (238U, 232Th, and 40K) in the studied rock types surpassed the worldwide average values assigned for building materials by UNSCEAR. This elevation of the radioisotope concentration values is due to the presence of supplement minerals such as monazite, zircon, allanite, and rutile. Granites exhibit the highest mean concentrations of 238U (av. 164.24 ± 14.76 Bq/kg) and 232Th (av. 214.37 ± 23.33 Bq/kg), while trachytes demonstrate the highest 40K (av. 1352.56 ± 65.56 Bq/kg) concentrations. In contrast, syenites exhibite the lowest mean concentrations for 238U (av. 54.51 ± 6.81 Bq/kg) and 232Th (av. 56.76 ± 6.25 Bq/kg), while quartz syenites display the lowest mean concentration of 40K (av. 1144.78 ± 96.19 Bq/kg). The radiogenic heat production (RHP) associated with U, Th, and K range between 1.41 to 9.33 μW/m3, exceeding the typical crustal mean value of 0.8 to 1.2 μW/m3. The radiological parameters and indices evaluating risks of the outdoor and indoor radiation doses due to the investigated rocks were assessed. The results indicated that these rocks meet globally accepted values and safety standards (approved by UNSCEAR, ICRP, and EC) for surface building materials, as well as they underscore the importance of adhering to safety protocols to safeguard workers from radiation exposure within the WDRC area. Ultimately, the data herein provide a valuable database for assessing the compatibility of geochemical data and natural radioactivity level in WDRC rocks. Additionally, it reveals that from the radiological perspective, the investigated rocks are considered safe for use as decorative construction materials.

This research uses both three-dimensional (3D) modeling and geologic well control to piece back together the architectural parts of the Late Cretaceous formations. The goal is to figure out the sizes, directions, locations, and controls of the layers of the fluvial sandstone reservoirs. Sequence stratigraphy is essential for 3D reservoir modeling and petroleum geology understanding in the Bahga oilfield. The purpose of this work is to create a static model that shows the layers and facies distribution in the reservoir interval. We will use data from nine well logs and 22 seismic lines calibrated by the Abu Roash G Member reservoir core intervals to accomplish this. The petrophysical study discovered three parts in the Abu Roash G Member reservoir rock: channel fill that is affected by tides, channel fill that is dominated by tides (intertidal sands), and channel top with lenticular bedded sandstone. The model's findings point to the existence of an NNW-oriented sand body, which could be a prime location to produce hydrocarbons. The original oil in place (OOIP) is about 3,438,279 Stock Tank Barrels (STB), and the oil reserve reaches up to 1,031,484 (STB). Sequence stratigraphic analysis using seismic and well log information (SB) reveals that the Upper Cretaceous AR/G reservoir of the Bahga field is characterized by third- and fourth-order stratigraphic sequences, which are constrained by three Maximum Flooding Surfaces (MFS) and two Sequence Boundaries. The integration of the derived geological model and sequence stratigraphic results can lower future extraction risk by identifying the locations and trends of the geologic facies with the necessary petrophysical properties for the hydrocarbon accumulations.

Understanding the origins of potential source rocks and unraveling the intricate connections between reservoir oils and their source formations in the Siwa Basin (Western Desert, Egypt) necessitate a thorough oil-source correlation investigation. This objective is achieved through a meticulous analysis of well-log responses, Rock-Eval pyrolysis, and biomarker data. The analysis of Total Organic Carbon across 31 samples representing Paleozoic formations in the Siwa A-1X well reveals a spectrum of organic richness ranging from 0.17 wt% to 2.04 wt%, thereby highlighting diverse levels of organic content and the presence of both Type II and Type III kerogen. Examination of the fingerprint characteristics of eight samples from the well suggests that the Dhiffah Formation comprises a blend of terrestrial and marine organic matter. Notably, a significant contribution from more oxidized residual organic matter and gas-prone Type III kerogen is observed. Contrarily, the Desouky and Zeitoun formations exhibit mixed organic matter indicative of a transitional environment, and thus featuring a pronounced marine influence within a more reducing setting, which is associated with Type II kerogen. Through analysis of five oil samples from different wells—SIWA L-1X, SIWA R-3X, SIWA D-1X, PTAH 5X, and PTAH 6X, it is evident that terrestrial organic matter, augmented by considerable marine input, was deposited in an oxidizing environment, and contains Type III kerogen. Geochemical scrutiny confirms the coexistence of mixed terrestrial organic matter within varying redox environments. Noteworthy is the uniformity of identified kerogen Types II and III across all samples, known to have potential for hydrocarbon generation. The discovery presented in this paper unveils captivating prospects concerning the genesis of oil in the Jurassic Safa reservoir, suggesting potential links to Paleozoic sources or even originating from the Safa Member itself. These revelations mark a substantial advancement in understanding source rock dynamics and their intricate relationship with reservoir oils within the Siwa Basin. By illuminating the processes of hydrocarbon genesis in the region, this study significantly enriches our knowledge base.

Abstract Among the various natural disasters that take place around the world, flood is considered to be the most extensive. There have been several floods in Buzău river basin, and as a result of this, the area has been chosen as the study area. For the purpose of this research, we applied deep learning and machine learning benchmarks in order to prepare flood potential maps at the basin scale. In this regard 12 flood predictors, 205 flood and 205 non-flood locations were used as input data into the following 3 complex models: Deep Learning Neural Network-Harris Hawk Optimization-Index of Entropy (DLNN-HHO-IOE), Multilayer Perceptron-Harris Hawk Optimization-Index of Entropy (MLP-HHO-IOE) and Stacking ensemble-Harris Hawk Optimization-Index of Entropy (Stacking-HHO-IOE). The flood sample was divided into training (70%) and validating (30%) sample, meanwhile the prediction ability of flood conditioning factors was tested through the Correlation-based Feature Selection method. ROC Curve and statistical metrics were involved in the results validation. The modeling process through the stated algorithms showed that the most important flood predictors are represented by: slope (importance ≈ 20%), distance from river (importance ≈ 17.5%), land use (importance ≈ 12%) and TPI (importance ≈ 10%). The importance values were used to compute the flood susceptibility, while Natural Breaks method was used to classify the results. The high and very high flood susceptibility is spread on approximately 35–40% of the study zone. The ROC Curve, in terms of Success, Rate shows that the highest performance was achieved FPIDLNN-HHO-IOE (AUC = 0.97), followed by FPIStacking-HHO-IOE (AUC = 0.966) and FPIMLP-HHO-IOE (AUC = 0.953), while the Prediction Rate indicates the FPIStacking-HHO-IOE as being the most performant model with an AUC of 0.977, followed by FPIDLNN-HHO-IOE (AUC = 0.97) and FPIMLP-HHO-IOE (AUC = 0.924).

AbstractThis research uses a Classification and Regression Tree (CART) model with Google Earth Engine (GEE) to assess the winter season’s land cover and change detection mapping impact on the evapotranspiration (crop water requirement) parameters. Winter seasons, crucial for agricultural planning, and irrigation water requirement challenges in accurately mapping land cover and detecting changes due to the dynamic nature of farming practices during this period. In this study, Landsat-8 OLI images have been combined to map Land use and Land cover (LULC) and other change detection mapping in Akola Block, Maharashtra, India, during the 2018–2022 winter season. As an discoverer researcher that found detailed information of LULC classes during last 2018 to 2022 winter seasons, the use of the CART model in combination with a cloud-computing GEE demonstrates to be a practical approach for accurate land cover classification and change detection maps to create a pixel-based winter seasons information of study area. The novelty of this study lies in its innovative use of GEE, a powerful platform for remote sensing and geospatial analysis, to create LULC maps with remarkable accuracy. Achieving a 100% training accuracy across the four years under consideration is an exceptional feat, highlighting the reliability and stability of the methodology. Furthermore, the validation accuracy values, ranging from 89 to 94% for the winter seasons of 2018 to 2022, underscore the robustness of this approach. Such consistently high accuracy in mapping LULC over time is a groundbreaking achievement and offers a new dimension to the field of hydrology. For the hydrological community, the implications of this study are profound. Accurate LULC mapping and change detection provide critical data for modeling and analyzing the effects of land use changes on water resources, watershed management, and water quality. The User, Kappa, and Producer accuracy metrics used in this research highlight the model’s performance and its suitability for hydrological applications. These accurate LULC maps can aid in the development of hydrological models, forecasting, and decision-making processes, ultimately contributing to more effective water resource management and environmental conservation. In summary, this study’s innovative use of GEE, its remarkable accuracy in LULC mapping, and its relevance to the hydrological community demonstrate the potential for advanced remote sensing and geospatial tools to significantly improve our understanding of land use changes and their implications for water resources and environmental management.

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.

Background: Intravenous contrast media (IVCM) in radiology are extremely important for accurate diagnosis and are used daily all over the globe. These contract media are very safe if administered correctly. However, adverse events have also been reported. This study was undertaken to determine the understanding of the patients towards the usage of IVCM in radiology. Methods: This is a cross-sectional study conducted on patients recommended for IVCM-mediated radiological examinations. The data were collected by face-to-face interactions using the self-administered questionnaire before IVCM administration. The questionnaire was all about patient’s information for IVCM usage, their associated benefits and risks. Results: Out of 225 patients who participated, a majority (72%) had insufficient knowledge of IVCM-associated risks and benefits. Both male and female patients showed a similar understanding of IVCM (p>0.05). Educated patients showed better understanding than uneducated patients (p

Abstract This study was undertaken to evaluate the influence of changes in the gingival display of the maxillary teeth on smile attractiveness assessed by Saudi Arabian dental professionals and laypeople. A total of 138 dental professional and 182 laypeople rated the attractiveness of male and female smiles in a computerized survey. A smiling photograph of a male and a female dental students were selected and digitally manipulated to create changes the amount of gingival display from 4 mm of gingival display to 4mm of gingival covered by the upper lip in 1 mm increments. Each photo was accompanied by a visual analog scale (VAS) for rating. Among dental professionals, 61% rated the female photo with a 1-mm low lip line as the most attractive smile (VAS score ± SE, 7.3 ± 3.18), while 52.7% of laypeople considered the smile with a 2-mm low lip line as the most attractive (6.7 ± 3.4). Regarding male smile photos, 61.6% of dental professionals found the 1-mm low lip line the most attractive (7.3 ± 3.18). The same rating was given by 48.3% of laypeople (6.1 ± 3.6) (p ≤ 0.009). The least attractive smile photo was the smile showing 4 mm of gingiva for male and female smiles. More than half of the laypeople believed that an attractive smile highly affects social life and communication. The Saudi Arabian population appears to be sensitive to the amount of gingival display. The difference in female smile assessment between dental professionals and laypeople highlights the importance of dentist-patient consensus regarding decisions for esthetic treatments. Esthetic treatment is of a major concern for both dentist and patient. The careful assessment of smile pillars including gingival display must be tailored to each patient.

AbstractChoosing appropriate locations for built-up growth while maintaining natural balance has become necessary for sustainable urban planning. To this end, two multi-criteria decision-making (MCDM) approaches—the multi-influencing factor (MIF) and the analytical hierarchy process (AHP)—have been applied in this work. The MIF model results show that 23.546% of the study region was very highly suitable for newly built-up areas, and 18.786% was restricted for urban settlement, whereas, in the AHP model, 28.907% of the area was highly suitable, 17.619% was restricted for urban settlement. Finally, the dependability and efficiency of the two models used for the study area were assessed through the receiver operating curve (ROC) and area under curve (AUC) analysis. The comparative analysis demonstrates that the MIF approach (AUC 0.817) is more suitable and reliable than the AHP (AUC 0.783). The present work provides new insights to integrate GIS tools with MCDM for practical urban land suitability assessment.

This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as 'good' and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA's factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.

Background During early 2020, because of the COVID-19 pandemic and related lockdown, most education systems—including universities—shifted from face-to-face classes to online learning. In Saudi Arabia, this might have contributed to a decreased level of physical activity (PA) and a concurrent increase in sedentary behaviour among young adults. This study aimed to investigate the impact of online learning on PA during the COVID-19 lockdown period among female undergraduate students in Saudi Arabia. Methods Data were collected through an online survey administered to participants. It consisted of three sections including demographic information, participants’ perception towards online learning and PA, and PA level using the self-reported active-questionnaire survey tool. The association between online learning and PA was measured using linear regression. The statistical significance was set at P 30 hours/week had lower PA (r = − 363.24; 95% confidence interval (CI) −593.97, −132.50), followed by those attending 25–30 hours/week (r = − 277.66; 95% CI −484.65, −70.66). Conclusion Online learning has negatively affected the PA level of female undergraduate students in Saudi Arabia during the COVID-19 lockdown period, in a dose-dependent manner. Moreover, this might affect their phycological status. Nevertheless, future studies are warranted to further investigate the relationship between PA level and psychological status.

The Water Editorial Office retracts the article “Exploring Groundwater Quality Assessment: A Geostatistical and Integrated Water Quality Indices Perspective” [...]

Objectives: The current study was designed to capture comprehensive imaging of acute ischemic stroke (AIS) using multidetector computed tomography (MDCT). The study also evaluated the accessibility and potential use of computed tomography angiography (CTA) and sought to determine stroke subtypes in patients with AIS. Methods: The presence of ischemic lesions was detected by the MDCT, transesophageal echocardiography (TEE), and non-contrast flat detector computed tomography (FDCT). The study selected the AIS patients with hyperacute stroke for less than 6 hours from July 2021 to August 2022. The CTA of cervical arteries was performed in order to determine stroke subtypes. Results: The MDCT-based detection of ischemic lesions in 110 studied ischemic stroke patients showed 34% had extensive intracranial artery damage, 86% detected a partial territorial infarct, while 52% patients had detected artery-to-artery embolization. These percentages of detected ischemic lesions overlapped in all studied AIS patients. The findings showed significant susceptible plaques and arterial stenosis. Conclusion: The findings of the study clearly indicated that AIS patients admitted within less than 6 hours of a hyperacute stroke showed multiple types of ischemic lesions. The findings also concluded that the MDCT technique is highly useful for the detection of multiple patterns of brain infarction and for the determination of the causative mechanisms in patients with acute ischemic stroke.

Water scarcity is becoming a growing problem in the Middle East due to urbanization, industrialization, and population growth. Saudi Arabia is one of the region’s largest consumers of water, so it is important to take immediate action to address this issue. This study used data from the Gravity Recovery and Climate Experiment (GRACE) to assess changes in groundwater storage in Wadi Fatimah and its surrounding areas. The results showed that the average annual rainfall (AAR) in Wadi Fatimah was 131 mm, while the AAR for the entire Makah province was 99.3 mm. The AAR in Makah province can be divided into three climatic periods: Period I (April 2002-December 2011): AAR of 92.8 mm; Period II (January 2012-December 2016): AAR of 101.8 mm and Period III (January 2017-December 2021): AAR of 116.4 mm. The GRACE-derived ΔTWS (time-variable gravity) variations were −0.18 ± 0.023 cm/yr in Wadi Fatimah and −0.38 ± 0.018 cm/yr in the entire Makah Province. The soil moisture storage (ΔSMS) variations were +0.039 ± 0.025 mm/yr in Wadi Fatimah and −0.008 ± 0.002 mm/yr in the entire Makah Province. The average groundwater storage (ΔGWS) variation in Wadi Fatimah was −0.18 ± 0.022 cm/yr, which indicates a slight decrease. The ΔGWS variation in the entire Makah region was −0.38 ± 0.017 cm/yr, which indicates a negative trend. The study also found that surface runoff from rainfall in the eastern section of Wadi Fatimah flows westward to join other streams that flow into the Wadi’s central and downstream areas. This runoff replenishes the shallow alluvium deposits and aquifers. Wadi Fatimah is able to partially compensate for the impact of its groundwater extraction with a recharge rate of +0.22 ± 0.22 mm/yr. The integrated method used in this study is a helpful and economical way to evaluate groundwater resource variability over Wadi Fatimah region and its surrounding province.

This hydrological study investigated a combined rating methodology tested on a 14,090 km2 area in Southwest Florida. The approach applied the Hydrological Simulation Program-Fortran (HSPF) over a 23-year period and was validated by 28 stream gauging stations. The regional hydrological complexity includes natural and agricultural areas, as well as extensive phosphate mining and urbanizing areas. This application is a novel and efficient methodology for generating stage–storage–discharge relationships using a geographic information system (GIS), empirical equations, and spreadsheets for 148,000 isolated and connected alluvial wetlands within the model domain. The validation metrics used to evaluate the applied methodology for populating the stage–storage–discharge relationship demonstrated the model effectiveness in simulating a range of hydrological events across various regions. For discharge prediction, the Nash–Sutcliffe efficiency values surpassed 0.7 at most stations, with an average of 0.67, and the average R squared was 0.74. This methodology, when applied, achieved a root-mean-square error of 4 m3/s for discharge prediction and 0.47 m for stage prediction. However, limitations emerged in simulating baseflow (low flows), highlighting the need for integrated modeling approaches to accurately capture groundwater–surface water interactions. The research provides an improved means for modeling regional water resources and lays the groundwork for enhanced hydrological modeling in watersheds with complex alluvial and isolated wetland systems.

Dissolved arsenic (As) may appear at the tile line level through preferential flow (PF), leading to contamination of shallow water bodies. Limited work on the movement of As forms in field soils urged the need for more research. The PF of arsenate (As(V)) and arsenite (As(III)) compared to chloride (Cl) at constant flow under saturated (10 mm), slightly unsaturated (−10 mm), and unsaturated (−40 mm) pressure heads was evaluated in replicated large field columns varying in subsoil structure. A solute containing As(V), As(III), and Cl was pulsed until the Cl concentration ratio in the drainage samples reached maxima and flushed with solute-free irrigation. HYDRUS-1D software version 4.15 was utilized to fit the breakthroughs of As(V) and As(III) in the dual-porosity physical non-equilibrium model (DP-PNE). The Langmuir equation was used to fit the As(V) and As(III) sorption isotherms, and blue dye staining was used for the marking of flow paths. Dye leaching was observed up to 50 cm or deeper in the soils. Under saturated conditions (+10 mm), Kotli, Guliana, and Mansehra soils showed chemical non-equilibrium (CNE) for As(V) and As(III); however, the extent of CNE was less under unsaturated conditions (−40 mm). These results implied that these well-structured soils had enough large macropores, which cause PF, but at the same time, they were also small enough to retain water and leach solutes under unsaturated conditions (−40 mm). It is concluded that irrigation of contaminated water or dumping solid waste on well-aggregated soil may exhibit PF of dissolved As during and after rains, and additionally As(III), which is more toxic and mobile under reduced conditions, has equal or greater potential for movement.

Water scarcity is developing in the Middle East as a result of the region’s growing population and tremendously advanced agricultural and industrial sectors. Saudi Arabia is the Middle East country with the highest water consumption, so there is an urgent need to take action, and new technology advancements in geophysical measurements allow for the monitoring of groundwater. Wadi Ar-Ramah is one region that has witnessed significant agricultural expansion as well as a serious over-exploitation of the groundwater resources that are available there. Depletion rate in groundwater of the Wadi Ar-Ramah basin at eastern Saudi Arabia was determined for the time interval of 04/2002 to 12/2021 using a combination of gravity data from the Gravity Recovery and Climate Experiment (GRACE) mission and results of land surface models. The findings are: 1) the average yearly rainfall rate was computed at 87.7 mm yr−1 over the Wadi Ar-Ramah; 2) the terrestrial water storage variation (ΔTWS) was computed at −1.216 ± 0.013 cm yr−1; 3) the GLDAS-derived soil moisture (ΔSMS) was minimal at −0.32 ± 0.025 mm yr−1; 4) the GRACE-derievd groundwater decreasing rate was calculated at 1.212 ± 0.012 cm yr−1; 5) the relief of the ground surface is producing northeasterly streams that carry the minimal surface water to the east; 6) our integrated method provides a repeatable and cost-effective approach.

Water demands have increased even more in recent decades because of the high population density. Surface and groundwater resources are insufficient to meet these demands. As a result, governments have turned to the treatment of sewage water. Sewage water contains multiple types of contamination, creating a major health risk. In the research region, 48 water samples were obtained, including 18 samples of surface water and 30 samples of groundwater. The Canadian Council Water Quality Index (CCWQI) program calculates the water quality index to evaluate the water quality for drinking and human use. The World Health Organization (WHO) and the Egyptian Ministry of Health (EMH) determined regulatory limits for drinking water and each value of the investigated parameter connected with them. According to the findings, 79% of the tested water samples are safe to drink and are excellent for human and wildlife use. Due to infiltration or recharging of groundwater with drainage water, as well as the involvement of dissolution, leaching processes, and anthropogenic activities that damage human health, animals, and some plants, these samples are unfit for drinking and domestic consumption. The heavy metal level of Cd and Pb in the examined water samples was found to be above WHO and EMH acceptable limits. Furthermore, due to oral exposures, the examined water samples may cause complex health concerns such as non-carcinogenic and carcinogenic influences for children over adults due to a reduction in children's immunity. As a result, water treatment should be carried out in the examined region to protect the health of the residents.

In arid and semi-arid regions, assessment of groundwater quality and potentially toxic elements is essential issue for health of the human being. Groundwaters were collected from sixty-eight wells in Harrat Khaybar, Saudi Arabia to evaluate their suitability for drinking and irrigation purposes and to document the potential sources of contamination. Several contamination indices and inverse distance weighted technique were applied for assessing contamination and generate spatial maps for the potentially toxic elements (PTEs). The results showed that the average values of the ions, Cl–, SO42–, HCO3–, NO3–, Na+, Ca2+, Mg2+, and the total dissolved solids (TDS) were greater than the permissible limit for drinking water while the average values of PTEs were less than the permissible limit, with exceeding limits of Cr, Se, As, Zn, and Pb in some individual samples. Piper diagram indicated that 47.10% of the water samples are of Na-K-SO4-Cl type, 23.51% of Ca-Mg-CO3-HCO3 type, 23.51% of Ca-Mg-SO4-Cl type, and 5.88% of Na-K-CO3-HCO3 type. Based on the groundwater quality index (GWQI), 29 of the groundwater wells were categorized as excellent and good water for drinking purposes, while 29 wells fell under poor, very poor water, and unsuitable for drinking. Additionally, results of heavy metal pollution index (HPI) indicated that all waters fell within the low pollution category, while results of the metal index (MI) indicated that 35 wells fell within very pure, pure, and slightly affected categories, while 33 wells fell in the moderately, strongly, and seriously affected categories. Results of sodium adsorption ratio (SAR), sodium percentage (%Na), and magnesium ratio (MR) revealed that 33.82–98.5 % of the water samples are suitable for irrigation depending on the parameter type. Ions exchange reactions and dissolution of rock forming minerals, as well as industrial and domestic effluents and intensive use of fertilizers and pesticides were the natural and anthropogenic factors controlling the groundwater geochemistry in the study area and PTE contamination in some wells.

The Wadi Fatimah area suffers from a lack of freshwater resources, so this study aimed to analyze the groundwater in this region and evaluate its quality for irrigation and drinking purposes. Eleven water quality parameters, including pH, total dissolved solids (TDS), Ca2+, HCO−3, NO−3, F−, Cl−, K+, Mg2+, SO2−4, and Na+, were utilized to evaluate the quality of the water and produce a water quality index (WQI). These parameters were measured at 100 different chosen locations. The spatial distribution map revealed that all parameters are high in the southern part except K+. Using the correlation matrix, a high positive correlation is obtained among TDS, Cl−, Mg2+, and Ca2+ in addition to a high correlation among TDS with Cl− and Na+. From PCA analysis, PCA1, PCA2, and PCA3 represent about 52%, 12%, and 10% of all components along the study area, respectively. PCA1 has low variance than PCA2 and PCA3. The majority of the Southern region’s sites went from having extremely poor to poor water classifications and from poor to unsuitable water. However, the center part possesses exceptionally high-quality groundwater. According to the results from the current study’s water quality index, the presence of nitrate and fluoride in the groundwater samples was primarily responsible for their high WQI values. The statistics showed that a higher percentage of the population had poor drinking water due to direct pollutant release, agricultural effects, and excessive groundwater resource use. The study offers a groundwater quality modeling technique that is both affordable and replicable in other areas.

The El Nino Southern Oscillation (ENSO) phenomenon is devastating as it negatively impacts global climatic conditions, which can cause extreme events, including floods and droughts, which are harmful to the region’s economy. Pakistan is also considered one of the climate change hotspot regions in the world. Therefore, the present study investigates the effect of the ENSO on extreme precipitation events across the Upper Indus Basin. We examined the connections between 11 extreme precipitation indices (EPIs) and two ENSO indicators, the Southern Oscillation Index (SOI) and the Oceanic Niño Index (ONI). This analysis covers both annual and seasonal scales and spans the period from 1971 to 2019. Statistical tests (i.e., Mann–Kendall (MK) and Innovative Trend Analysis (ITA)) were used to observe the variations in the EPIs. The results revealed that the number of Consecutive Dry Days (CDDs) is increasing more than Consecutive Wet Days (CWDs); overall, the EPIs exhibited increasing trends, except for the Rx1 (max. 1-day precipitation) and Rx5 (max. 5-day precipitation) indices. The ENSO indicator ONI is a temperature-related ENSO index. The results further showed that the CDD value has a significant positive correlation with the SOI for most of the UIB (Upper Indus Basin) region, whereas for the CWD value, high elevated stations gave a positive relationship. A significant negative relationship was observed for the lower portion of the UIB. The Rx1 and Rx5 indices were observed to have a negative relationship with the SOI, indicating that El Nino causes heavy rainfall. The R95p (very wet days) and R99p (extreme wet days) indices were observed to have significant negative trends in most of the UIB. In contrast, high elevated stations depicted a significant positive relationship that indicates they are affected by La Nina conditions. The PRCPTOT index exhibited a negative relationship with the SOI, revealing that the El Nino phase causes wet conditions in the UIB. The ONI gave a significant positive relationship for the UIB region, reinforcing the idea that both indices exhibit more precipitation during El Nino. The above observations imply that while policies are being developed to cope with climate change impacts, the effects of the ENSO should also be considered.

Groundwater is an important source of freshwater. At the same time, anthropogenic activities, in particular, industrialization, urbanization, population growth, and excessive application of fertilizers, are some of the major reasons for groundwater quality deterioration. Therefore, the present study is conducted to evaluate groundwater quality by using integrated water quality indices and a geospatial approach to identify the different water quality zones and propose management strategies for the improvement of groundwater quality. Groundwater quality was evaluated through the physicochemical parameters (pH, chloride (Cl−), fluoride(F−), iron (Fe−2), nitrate (NO3−1), nitrite (NO2), arsenic (As), total hardness, bicarbonate (HCO3−), calcium (Ca+2), magnesium (Mg+2), color, taste, turbidity, total dissolved solids (TDS)) and microbiological parameters including total coliforms, fecal coliforms, and Escherichia coli of samples collected from the water and sanitation agency (WASA) and urban units. Irrigation parameters crucial to the assessment, including (electrical conductivity (EC), residual sodium carbonates (RSC), and sodium adsorption ratio (SAR)), were also collected at more than 1100 sites within the study area of upper and central Punjab. After collecting the data of physicochemical parameters, the analysis of data was initiated to compute the water quality index for groundwater quality, a four-step protocol in which the Analytical Hierarchy Process (AHP) was used to determine the weights of selected parameters by generating a pairwise matrix, on the relative importance of parameters using the Satty scale. The index was then classified into five classes for quality assessment of drinking water (excellent, good, medium, bad, and very bad) and four classes for irrigation water quality assessment (excellent, good, permissible, and unsuitable). After computing the index values for drinking as well as irrigation purposes, the values were interpolated, and various maps were developed to identify the status of groundwater quality in different zones of the study area. Mitigation strategies for water pollution involve source control, such as monitoring industrial discharge points and managing waste properly. Additionally, treating wastewater through primary, secondary, or tertiary stages significantly improves water quality, reducing contaminants like heavy metals, microbiological agents, and chemical ions, safeguarding water resources. The findings highlight significant regional variations in water quality issues, with heavy metal concerns concentrated notably in Lahore and widespread emerging microbiological contamination across all studied divisions. This suggests a systemic problem linked to untreated industrial effluents and poorly managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions indicate water quality ranging from marginal to unfit, underscoring the urgency for remediation. Conversely, other divisions fall within a medium class, potentially suitable for drinking purposes. Notably, microbiological contamination at 27% poses a major challenge for water supply agencies, emphasizing the critical need for pre-disposal primary, secondary, and tertiary treatments. These treatments could potentially rehabilitate 9%, 35%, and 41% of the study area, respectively, pointing toward tangible, scalable solutions critical for safeguarding broader water resources and public health. With the current pace of water quality deterioration, access to drinking water is a major problem for the public. The government should prioritize implementing strict monitoring mechanisms for industrial effluent discharge, emphasizing proper waste management to curb groundwater contamination. Establishing comprehensive pre-disposal treatments, especially primary, secondary, and tertiary stages, is imperative to address the prevalent heavy metal and microbiological issues, potentially rehabilitating up to 41% of affected areas. Additionally, creating proactive policies and allocating resources for sustainable groundwater management are crucial steps for ensuring broader water resource security and public health in the face of deteriorating water quality. Therefore, urgent regional action is needed to address escalating anthropogenic threats to groundwater, emphasizing the crucial need for proactive measures to safeguard public health and ensure sustainable water resources.

The Food and Agriculture Organization recommends that the Penman–Monteith Method contains Equation 56 (PMF) as a widely accepted standard for reference evapotranspiration (ETo) calculation. Despite this, the PMF cannot be employed when meteorological variables are constrained; therefore, alternative models for ETo estimation requiring fewer variables must be chosen, which means that they perform at least as well as, if not better than, the PMF in terms of accuracy and efficiency. This study evaluated five machine learning (ML) algorithms to estimate ETo and compared their results with the standardized PMF. For this purpose, ML models were trained using monthly time series climatic data. The created ML models underwent testing to determine ETo under varying meteorological input combinations. The results of ML models were compared to assess their accuracy and validate their performance using several statistical indicators, errors (root-mean-square (RMSE), mean absolute error (MAE)), model efficiency (NSE), and determination coefficient (R2). The process of evaluating ML models involved the utilization of radar charts, Smith graphs, heatmaps, and bullet charts. Based on our findings, satisfactory results have been obtained using RBFFNN based on M12 input combinations (mean temperature (Tmean), mean relative humidity (RHmean), sunshine hours (Sh)) for ETo estimation. The RBFFNN model exhibited the most precise estimation as RMSE obtained values of 0.30 and 0.22 during the training and testing phases, respectively. In addition, during training and testing, the MAE values for this model were recorded as 0.15 and 0.17, respectively. The highest R2 and NSE values were noted as 0.98 and 0.99 for the RBFNN during performance analysis, respectively. The scatter plots and spatial variations of the RBFNN and PMF in the studied region indicated that the RBFNN had the highest efficacy (R2, NSE) and lowest errors (RMSE, MAE) as compared with the other four ML models. Overall, our study highlights the potential of ML models for ETo estimation in the arid region (Jacobabad), providing vital insights for improving water resource management, helping climate change research, and optimizing irrigation scheduling for optimal agricultural water usage in the region.

The tropopause is described as the distinction between the troposphere and the stratosphere, and the tropopause height (TPH) is an indicator of climate change. GNSS Radio Occultation (RO) can monitor the atmosphere globally under all weather conditions with a high vertical resolution. In this study, four different techniques for identifying the TPH were investigated. The lapse rate tropopause (LRT) and cold point tropopause (CPT) methods are the traditional methods for determining the TPH based on temperature profiles according to the World Meteorological Organization (WMO) definition. Two advanced methods based on the covariance transform (CT) method are used to estimate the TPH from the refractivity (TPHN) and the TPH from the bending angle (TPHα). Data from the Sentinel-6 satellite were used to evaluate the different algorithms for the identification of the TPH. The analysis shows that the CPT height is greater than the LRT height and that the CPT is only valid in tropical regions. The CPT height, TPHN, and TPHα were compared with the LRT height. In general, the TPHα had the largest value, followed by the TPHN, and the LRT had the lowest value of TPH at and near the equator. In the equatorial region, the maximum TPH results from the TPHα (approximately 17.5 km), and at the poles, the minimum TPH results from the LRT (approximately 9 km). The results were also compared with the European Center for Medium-Range Weather Forecasts (ECMWF), and there was a strong correlation of 0.999 between the different approaches for identifying the TPH from RO and the ECMWF model. The identification of the TPH is critical for the transfer of mass, water, and trace gases between the troposphere and stratosphere.

Depth–discharge rating is required at gauged and ungauged locations for hydrologic modeling of alluvial systems to evaluate streamflow and manage regional water resources. Spanning low to high-flow conditions, manual field measurements are used to develop discharge ratings at gauged locations, producing continuous flow data from automated water depth measurements. The discharge rating is dependent on channel geometry, stream slope, vegetation, roughness coefficient, sediment load, and bank stability. To construct discharge ratings for many locations within larger model domains (hundreds to thousands of km2), intensive GIS and manual (spreadsheet) data manipulation are often required. In this analysis, available USGS gauging stations and readily available GIS coverages were used to learn and implement a novel method to characterize the depth–discharge relationships for hydrologic modeling of larger or complex areas using commonly available data and normalization techniques. The improved procedure simply uses drainage area, channel slope, and channel width, readily derivable GIS data, to develop discharge ratings for gauged and ungauged sections. The discharge rating curves for 70 USGS streamflow gauges were reproduced using the procedure. Then, the produced and observed discharge rating curves were compared to evaluate the accuracy of the method. In the analysis of streamflow depth predictions, the average Root Mean Squared Error was recorded at approximately 0.38 m (≈1.24 ft), with an interquartile range between 0.21 m and 0.49 m. The Mean Error remained centered around 0 m, with interquartile values ranging from −0.24 m to 0.24 m.

The Egyptian Red Sea coast is periodically exposed to flash floods that cause severe human and economic losses. That is due to its hydro-geomorphological characteristics. Therefore, identifying flash flood hazards in these areas is critically important. This research uses an integrated approach of remote sensing data and GIS techniques to assess flash flood hazards based on morphometric measurements. There are 12 drainage basins in the study area. These basins differ in their morphometric characteristics, and their main streams range between the 4th and 7th order. The morphometric parameter analysis indicates that three wadis are highly prone to flooding, five wadis are classified as moderate hazard, and four wadis are rated under low probability of flooding. The study area has a probability offlooding, which could cause serious environmental hazards. To protect the region from flash flood hazards and the great benefit of rainwater, the study recommended detention, crossing, diversion, and/or storage of the accumulated rainwater by building a number of dams or culverts along the main streams of wadis to minimize the flooding flow.

Mining activities often leave behind a legacy of environmental challenges, with aging tailings ponds representing a significant concern due to their potential for leachate formation and subsequent contaminant release. Thus, this study employs Electrical Resistivity Tomography (ERT) to investigate the intricate pathways of leachate within an aging mining tailings pond, addressing the pressing environmental and human health concerns associated with potential contaminant release. Ten 2D ERT profiles were acquired at the El Mochito mine waste site, covering an area of approximately half a square kilometer. These profiles, ranging in length from 104 to 363 m, provided insights into subsurface conditions down to a maximum depth of 60 m. The subsurface mapping of the ERT data showed three different geoelectric layers. The uppermost layer, with a thickness of approximately 2.5 m and resistivity values ranging from 60 to 100 Ohm.m, was identified as a dry tailing/soil zone. Beneath it, the second layer exhibited moderately resistive values (30–60 Ohm.m) with varying thicknesses of 10–20 m, signifying a percolation/leaching zone (semi-saturated zone). The third layer, characterized by substantially low resistivity (1–30 Ohm.m), indicated saturation and the presence of conductive materials, strongly suggesting active leaching. Based on these findings, this study recommends further investigation through geochemical analysis of subsurface samples and more advanced geophysical imaging techniques to validate the distribution of anomalous zones and delineate remediation pathways. This study lays the foundation for future comprehensive research that will integrate geophysical surveys with geochemical analysis and establish 4D modeling techniques to monitor pollutant penetration over time, with a particular focus on mine waste tailings mapping. Plus, this study contributes valuable insights into the characterization of leachate pathways within mining tailings ponds, offering a foundation for informed environmental management and remediation strategies.

Geographically isolated wetlands (GIWs) play a critical role in regional hydrology, streamflow, groundwater recharge, evapotranspiration, and water quality. Despite their importance, research on quantifying discharges from these wetlands remains scarce. This study focuses on the hydrological characterization of GIWs in W-C Florida, a region where they cover a significant proportion of the land surface. The paper introduces a new procedure for developing Stage–Storage–Discharge (rating) characteristics essential for deterministic hydrological modeling in larger geographic areas. The approach synthesizes and extends previous research methodologies and offers simplified key modeling coefficients (m and n), which act as intelligent calibration knobs. These coefficients, when coupled with easily derivable physical attributes such as areas, slopes, and elevations, allow for the accurate simulation of downstream discharge timings and magnitudes, including flood behavior. The proposed method was tested using observational data from well-calibrated models. The results indicated a relative error of −7.2% for stage–storage and 15% for stage–discharge. GIS-based techniques were evaluated against surveyed data, and the results showed an accuracy of 0.16 m (0.52 ft) in estimating both the invert elevation and the maximum depth of GIWs. This effort will ultimately contribute to a better understanding and management of these critical ecosystems.

The impact of climate extremes, such as heat waves and extreme rainfall, can cause harvest failures, flooding, and droughts that ultimately threaten global food security, harming the region’s economy. Fluctuations in streamflow indicate the sensitivity of streamflow responding to extreme precipitation events and other climatic variables (temperature extremes) that play a significant role in its generation. Pakistan is also considered one of the climate change hotspot regions in the world. The devastating impacts have often occurred in recent decades due to an excess or shortage of streamflow, majorly generated from the Upper Indus Basin (UIB). To better understand climate extremes’ impact on streamflow, this study examined climate extremes and streamflow (Q) changes for three decades: 1990–1999, 2000–2009, and 2010–2019. Observed streamflow and meteorological data from nine sub-catchments across all climatic zones of the UIB were analyzed using RGui (R language coding program) and partial least squares regression (PLSR). Climatic variables were estimated, including precipitation extremes, temperature extremes, and potential evapotranspiration. The Mann–Kendal test was applied to the climatic indices, revealing that precipitation increased during the last 30 years, while maximum and minimum temperatures during the summer months decreased in the Karakoram region from 1990 to 2019. The spatiotemporal trend of consecutive dry days (CDD) indicated a more increasing tendency from 1990 to 2019, compared to the consecutive wet days (CWD), which showed a decreasing trend. PLSR was applied to assess the relation between climatic variables (extreme P, T indices, and evapotranspiration). It was found that the dominant climatic variables controlling annual streamflow include the r95p (very wet days) and R25mm (heavy precipitation days), maximum precipitation event amount, CWD, PRCPTOT (annual total precipitation), and RX5 (maximum five-day precipitation). The TXn (Min Tmax) and Tmax mean (average maximum temperature) dominate streamflow variables. Moreover, the impact of evapotranspiration (ET) on variations in streamflow is more pronounced in arid catchments. Precipitation is the predominant factor influencing streamflow generation in the UIB, followed by temperature. From streamflow quantification, it was found that climate-driven annual streamflow decreased during 1999–2019 in comparison to 1990–1999, with an increase in a few catchments like Kalam, which increased by about 3.94% from 2000 to 2010 and 10.30% from 2010 to 2019, and Shigar, which increased by 0.48% from 2000 to 2009 and 37.37% from 2010 to 2019 concerning 1990–1999. These variations were due to changes in these climatic parameters. The PLSR approach enables the identification of linkages between climatic variables and streamflow variability and the prediction of climate-driven floods. This study contributes to an enhanced identification and hydroclimatological trends and projections.

Thirty surface soil samples were collected from soils around Al-Janabeen Dam, southwest Saudi Arabia to assess the contamination and environmental risk of heavy metals (HMs) using various pollution measurements, sediment quality guidelines (SQGs), and statistical methods. The average concentrations of HMs (μg/g) were listed in the following decreasing order: Fe (5487.73) > Mn (323.54) > Cr (37.52) > Cu (30.25) > Zn (24.55) > Ni (17.48) > Co (9.51) > Pb (7.50) > Cd (0.81). The investigated soils were very severely enriched, moderate severe enriched with Cu and Pb, and moderate enriched with Co, Cr, Ni and Zn. The reported values of Cd, Zn, Pb, and Co were lower than the ISQG-Low values, while few samples reported levels of Ni, Cr, and Cu between the ISQG-Low and ISQG-High values, implying a low risk of exposure to these HMs. Statistical analysis indicated natural sources for Cr, Mn, Fe, Co, and Ni; and anthropogenic effects for Cu, Zn, Cd, and Pb.

Assessing the impacts of climate change and land use/land cover changes on water resources within a catchment is essential because it helps us understand how these dynamic factors affect the quantity, quality, and availability of freshwater. This knowledge is crucial for making informed decisions about water management, conservation, and adaptation strategies, especially in regions facing increasing environmental uncertainties and challenges to water resource sustainability. In Pakistan’s Kunhar River Basin (KRB), this investigation explores the potential effects of shifting land use/land cover (LULC), and climate on stream flows. The SWAT (Soil and Water Assessment Tool), a semi-distributed hydrological model, and the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) dataset from multiple global climate models (GCMs) were used to evaluate these effects. The temperature and precipitation data were downscaled using the CMhyd software; for both shared socioeconomic pathways (SSP2 and SSP5), the top-performing GCM out of four was required to produce downscaled precipitation and temperature predictions while taking future land use characteristics into account. The output from the chosen GCM indicated that by the conclusion of the 21st century, relative to the reference period (1985–2014), the study area’s average monthly precipitation, highest temperature, and lowest temperature will be increasing. Precipitation is anticipated to increase between 2015 and 2100 by 20.5% and 29.1% according to the SSP2 and SSP5 scenarios, respectively. This study’s findings, which emphasize the need for project planners and managers taking into account the effects of climate and land cover changes in their management techniques, show that climate change can have a significant impact on the changing seasons of flows in the Kunhar River basin.

The accurate characterization and mapping of low-grade ore deposits necessitate the utilization of a robust exploration technique. Induced polarization (IP) tomography is a powerful geophysical method for mineral exploration. An integrated survey using electrical resistivity tomography (ERT) and IP was employed in this study to characterize and map (Zn-Pb-Ag) ore deposits in NE New Brunswick, Canada. The survey encompassed twelve parallel lines across the study area. The 2D and 3D inversion of the results provided a detailed image of the resistivity and chargeability ranges of subsurface formations. The boundaries of sulfide mineralization were determined based on resistivity values of (700–2000 Ohm.m) and chargeability values of (3.5 mV/V) and were found to be located at an approximate depth of 80–150 m from the surface. The findings were validated through a comparison with data from borehole logs and mineralogy data analysis. The size and shape of sulfide deposits were successfully characterized and mapped in the study area using this cost-effective mapping approach.

One of the most critical stages for developing groundwater resources for drinking water use is assessing the water quality. The use of a Water Quality Index (WQI) is considered an effective method of evaluating water quality. The objective of this research was to evaluate the performance of six multiple artificial intelligence techniques, i.e., linear regression (stepwise), support vector regression SVM (linear and polynomial kernels), Gaussian process regression (GPR), Fit binary tree, and artificial neural network ANN (Bayesian) to predict the WQI in Jizan, Southern Saudi Arabia. A total of 145 groundwater samples were collected from shallow dug wells and boreholes tapping the phreatic aquifer. The WQI was calculated from 11 physicochemical parameters (pH, TDS, Ca2+, Mg2+, Na+, K+, Cl−, SO42−, HCO3−, NO3−, and TH). The spatial distribution results showed that higher values of Cl− and SO42− were recorded in the places close to the coastline, indicating the occurrence of seawater intrusion and salinisation. Seven wells had a WQI of greater than 300, indicating that the water was unfit for consumption. The results showed that the GPR, linear regression (stepwise), and ANN models performed best during the training and testing stages, with a high correlation of 1.00 and low errors. The stepwise fitting model indicated that pH, K+, and NO3− were the most significant variables, while HCO3− was a non-significant variable for the WQI. The GPR, stepwise regression, and ANN models performed best during the training and testing stages, with a high correlation and low errors. In contrast, the SVM and Fit binary tree models performed the worst in the training and testing phases. Based on subset regression analysis, the optimum input combination for WQI model prediction was determined as these eight input combinations with high R2 (0.975–1.00) and high Adj-R2 (0.974–1.00). The resultant WQI model significantly contributes to sustainable groundwater resource management in arid areas and generates improved prediction precision with fewer input parameters.

Detecting mineralogical alteration by hydrocarbon micro-seepage from trap to surface across the deformed structure, through Comparative analysis study of hyperspectral image (EO-1, Hyperion), and multispectral (landsat7 and Advanced land imager (Ali) to map soil alteration by petroleum seepage with applying remote sensing techniques as (band ratios, supervised, SAM classification, and hydrocarbon detection and index (HD and HI) calculation to detect the potentiality of hydrocarbon seepage in the area. The type of spectral resolution accuracy available for each space imaging platform can be used to choose which is best in determining the target and whether exploration yields oil. Hyperspectral remote sensing data are integrated with a GIS framework Weighted Sum (Spatial Analyst) classified hydrocarbon seepage prospects into five potential zones very good, good, intermediate, poor, and very poor probable potentiality. The spatial lithologic carbonaceous alteration appears closely coincident along reactivated leaking faults that cause hydrocarbon micro-seepage and altered surface lithology and mineralogical cement. The hyperspectral images have proven their worth in identifying and studying hydrocarbon leaks and the resulting environmental pollution and mineral alteration, as well as use micro-seeps as a pathfinder to locations of new oil explorations discoveries that can use as a pre-drill prediction of hydrocarbon occurrence and detected a prospect area for hydrocarbon drill.

Saudi Arabia is seeking fresh groundwater resources to face the increase in anthropogenic activities. The groundwater storage variations and occurrence were investigated and the surface and subsurface structures influencing the groundwater resources in the research area were defined using a combined study of Gravity Recovery and Climate Experiment, aeromagnetic data, and electrical resistivity data with other relevant datasets. Results are: The groundwater storage fluctuation is calculated at −0.34 ± 0.01 mm/yr during the period 04/2002-12/2021. The area is receiving an average annual rainfall rate of 117.6 mm during the period 2002 to 2019. Three structural trends, defined in the directions of NS, NNW, and NNE are cutting the sedimentary cover and the basement rocks. The sedimentary cover ranges from 0 to 1.2 km thick. Vertical electrical sounding results indicate three main geoelectric layers: the surface geoelectrical layer of higher resistivity values (428-9626 Ω. m) is made up of unconsolidated Quaternary sediments; the water-bearing layer of saturated sands with a resistivity range between 5.1 and 153 Ω. m and with depths vary from 1 to 94 m, and highly fractured basement rocks with resistivity values ranging from 813 to 6030 Ω. m. The integrated results are useful in providing a comprehensive image of the study area’s surface and subsurface structures, as well as groundwater potential in the southwestern part of Saudi Arabia. Our integrated approach provides a reproducible model for assessing groundwater potential in arid and semiarid areas.

Recently, machine learning (ML) and deep learning (DL) models based on artificial intelligence (AI) have emerged as fast and reliable tools for predicting water quality index (WQI) in various regions worldwide. In this study, we propose a novel stacking framework based on DL models for WQI prediction, employing a convolutional neural network (CNN) model. Additionally, we introduce explainable AI (XAI) through XGBoost-based SHAP (SHapley Additive exPlanations) values to gain valuable insights that can enhance decision-making strategies in water management. Our findings demonstrate that the stacking model achieves the highest accuracy in WQI prediction (R2: 0.99, MAPE: 15.99%), outperforming the CNN model (R2: 0.90, MAPE: 58.97%). Although the CNN model shows a relatively high R2 value, other statistical measures indicate that it is actually the worst-performing model among the five tested. This discrepancy may be attributed to the limited training data available for the CNN model. Furthermore, the application of explainable AI (XAI) techniques, specifically XGBoost-based SHAP values, allows us to gain deep insights into the models and extract valuable information for water management purposes. The SHAP values and interaction plot reveal that elevated levels of total dissolved solids (TDS), zinc, and electrical conductivity (EC) are the primary drivers of poor water quality. These parameters exhibit a nonlinear relationship with the water quality index, implying that even minor increases in their concentrations can significantly impact water quality. Overall, this study presents a comprehensive and integrated approach to water management, emphasizing the need for collaborative efforts among all stakeholders to mitigate pollution levels and uphold water quality. By leveraging AI and XAI, our proposed framework not only provides a powerful tool for accurate WQI prediction but also offers deep insights into the models, enabling informed decision-making in water management strategies.

To detect groundwater-bearing potential zones in Wadi Fatimah, western Saudi Arabia, geophysical data from three profiles of two-dimensional electrical resistivity tomography (ERT) and remote sensing data were gathered, integrated, and evaluated. The DEM and slope maps indicate that Wadi Fatimah has a high potential to store great amounts of groundwater. The maximum elevations range from 0 to 933 m, with an average elevation of 466 m AMSL. The amount of surface water that infiltrates into the ground is affected by the slope. Rainwater can be collected in low-sloped areas and percolate into the subsurface, replenishing groundwater. In the study area, the slope ranged from 0° to 38°. The slopes of Wadi Fatimah ranged from 0° to 9.1°, with highlands having slopes ranging from 9.1° to 38°. Wadi Fatimah has a high stream density. Furthermore, because it is unconfined, the groundwater-bearing zone reaches the ground surface and recharges continuously during the rainy season. The drainage density is 0.433 km/km2, which is considered normal for coarse drainage. Lithology, infiltration capacity, and topographic relief all have an impact on drainage texture. Because of the basement rocks’ low slope, a coarse drainage texture of 0.059 was calculated, indicating additional groundwater recharge from precipitation. Moreover, based on the 2D inversion results of the ERT data, Wadi Fatimah’s unconfined aquifer has a high potential for groundwater. This aquifer is distinguished by a zone of low resistivity (less than 100 Ω.m) and a depth of up to 50 m below the ground surface. This aquifer is underlain by the weathered/fractured and/or fresh basement rocks. Wadi Fatimah basin is recharged by rainfall creating a promising or strategic area for groundwater supply for future planning and urbanization projects in surrounding areas.

Jazan province on Saudi Arabia’s southwesterly Red Sea coast is facing significant challenges in water management related to its arid climate, restricted water resources, and increasing population. A total of 180 groundwater samples were collected and tested for important hydro-chemical parameters used to determine its adaptability for irrigation. The principal components analysis (PCA) was applied to evaluate the consistency/cluster overlapping, agglomeration in the datasets, and to identify the sources of variation between the 11 major ion concentrations (pH, K+, Na+, Mg2+, Ca2+, SO42−, Cl−, HCO3−, NO3−, TDS, and TH). The EC values ranged from excellent to unsuitable, with 10% being excellent to good, 43% permissible, and 47% improper for irrigation. The SAR classification determined that 91.67% of groundwater samples were good to excellent for irrigation, indicating that they are suitable for irrigation with no sodium-related adverse effects. Magnesium hazard values showed that 1.67% of the samples are unsuitable for irrigation, while the remaining 98.33% are suitable. Chloro-alkaline indices signify that most groundwater samples show positive ratios indicating that ion exchange is dominant in the aquifer. The Gibb’s diagram reflects that evaporation, seawater interaction, and water–rock interaction are the foremost processes impacting groundwater quality, besides other regional environmental variables. A strong positive correlation was declared between TDS and Na+, Mg2+, Ca2+, Cl−, SO42− in addition to TH with Mg2+, Ca2+, Cl−, SO42−, TDS, and also Cl− with Na+, Ca2+, Mg2+ were major connections, with correlation coefficients over 0.8 and p < 0.0001. The extracted factor analysis observed that TH, Ca2+, TDS, Cl−, and Mg2+ have high positive factor loading in Factor 1, with around 52% of the total variance. This confirms the roles of evaporation and ion exchange as the major processes that mostly affect groundwater quality, along with very little human impact. The spatial distribution maps of the various water quality indices showed that the majority of unsuitable groundwater samples were falling along the coast where there is overcrowding and a variety of anthropogenic activities and the possible impact of seawater intrusion. The results of the hierarchical cluster analysis agreed with the correlations mentioned in the factor analysis and correlation matrix. As a result, incorporating physicochemical variables into the PCA to assess groundwater quality is a practical and adaptable approach with exceptional abilities and new perspectives. According to the study’s findings, incorporating different techniques to assess groundwater quality is beneficial in understanding the factors that control groundwater quality and can assist officials in effectively controlling groundwater quality and also enhancing the water resources in the study area.

This study aimed to identify groundwater resources at Harrat Khaybar to meet the increasing demand for groundwater for drinking and other purposes, including agricultural and industrial activities. Aquifers were assigned based on integrated electrical resistivity tomography (ERT) and hydrogeological data. Twenty-three ERT profiles ranging 590–890 m long distributed across Harrat Khaybar were acquired. The ERT data were processed to obtain 2D subsurface geoelectrical sections that indicated a variable depth for groundwater as 6–280 m. To investigate water quality, 69 groundwater samples were collected from intensively pumped wells distributed throughout the Harrat. The depth to groundwater measured in these wells ranged 1.5–80 m. It was concluded that Harrat Khaybar is composed of basaltic lava flows underlain by alluvial deposits, which represent two significant water-bearing formations: sub-basaltic alluvial deposits and basalt flows. The depth to the groundwater abruptly deepened from 1.5 to 80 m in the northeast and southwest directions, which could be due to groundwater-bearing formations dipping in these directions or northwest-orientated tension faults. The groundwater quality was assessed in terms of total dissolved solids (TDS), which ranged from 225 to 8340 mg/l, and a TDS-drilling zone encountered in the western part of the Harrat, the following similar most likely resulted from groundwater mineralization. The concentration of TDS generally decreased toward the east in the groundwater flow direction. The pH (6.5–8.1) of the samples indicated that the groundwater pH meets quality standards for domestic and irrigation purposes. It is concluded that, the application of both methods will be of great benefit to identify the groundwater sources and its quality as well.

Coastal environments need continuous environmental risk assessment, especially with increasing coastal development and human activities. The present work evaluates the distribution, contamination, and environmental risk of potentially toxic elements (PTEs) in coastal sediments between Al-Jubail and Al-Khafji cities along the Arabian Gulf, Saudi Arabia, and documents the influence of background references applied in pollution indices. Thirty-two sediment samples were collected for analysis of Ni, Cu, Cr, As, Zn, Pb and Hg using ICP-AES. The ranges of PTEs (mg/kg) were in the following order: Cr (3.00–20.0), Ni (2.00–32.0), Zn (2.00–14.0), As (2.00–4.00), Pb (1.50–5.00), Cu (1.00–5.00), and Hg (0.50–1.00). The coastal sediments show severe enrichment with As and Hg, and no to minor enrichment and a low contamination with Cr, Cu, Cr, Zn, and Pb. Based on sediment quality guidelines, concentrations of Cu, Pb, Zn, As, and Cr do not represent a concern for benthic communities, while Ni and Hg show a risk for benthic communities in four and 17 sampled areas, respectively. Multivariate analysis indicated a geogenic source for Zn, Cr, Cu, Ni, and Pb, mixed natural and anthropogenic sources for As, and an anthropogenic source for Hg, mostly from oil pollution, sewage, and industrial effluents spreading near Al-Jubail industrial city.

Loveringite, a rare member of the crichtonite group with nominal formula (Ca,Ce)(Ti,Fe,Cr,Mg)21O38, was found in the Khamal layered mafic intrusion, the first known locality for this mineral in the Arabian Shield. The Khamal intrusion, a large post-collisional mafic complex, is lithologically zoned, bottom to top, from olivine gabbro through gabbronorite, hornblende gabbro, anorthosite, and diorite to quartz diorite. Loveringite is found near the base of the complex, as an intercumulus phase in olivine gabbro. Most loveringite grains are homogeneous, although a few grains are zoned from cores rich in TiO2, Al2O3, Cr2O3, and CaO towards rims rich in FeO*, ZrO2, V2O3, Y2O3, and rare earth elements (REE). Petrographic relations indicate that loveringite formed after crystallization of cumulus olivine, pyroxenes, and plagioclase. Anhedral and corroded crystals of loveringite are surrounded by reaction rims of Mn-bearing ilmenite and baddeleyite, suggesting that the residual liquid evolved into and subsequently out of the stability field of loveringite. The budget of incompatible elements (Zr, Hf, REE, U, and Th) hosted in loveringite is anomalous for a primitive mafic liquid. Saturation in loveringite is likely the result of early contamination of the primary melt by anatexis of country rock, followed by isolation of evolving liquid in intercumulus space that restricted communication with the overlying magma chamber. The zoned crystals likely reflect diffusive equilibration between residual loveringite grains and their reaction rims of ilmenite.

Human activity has led to a rise in the demand for water, prompting Saudi Arabia to search for alternative groundwater supplies. Wadi As-Sirhan is one area that has experienced extensive agricultural growth and the severe over-exploitation of its groundwater resources. The groundwater drawn from the wadi should be continuously monitored to determine the best management options for groundwater resources and economic growth. The most recent Gravity Recovery and Climate Experiment (GRACE) mission and outputs of land surface models were combined to estimate the depletion rate of the groundwater of the Wadi As-Sirhan drainage basin in the northern region of Saudi Arabia throughout the period of April 2002–December 2021. The findings are: (1) the average GRACE-derived terrestrial water storage variation (ΔTWS) was calculated at −13.82 ± 0.24 mm/yr; (2) the soil moisture storage variation was averaged at +0.008 ± 0.004 mm/yr; (3) the GRACE-derived groundwater depletion rate was estimated at −13.81 ± 0.24 mm/yr; (4) the annual precipitation data over the Wadi As-Sirhan was averaged at 60 mm/yr; (5) The wadi has a minimal recharge rate of +2.31 ± 0.24 mm/yr, which may partially compensate for a portion of the groundwater withdrawal; (6) the sediment thickness shows an increase from 0 m at the southern igneous and volcanic rocks to more than 3000 m close to the Saudi–Jordanian border; (7) The wadi’s eastern, southern, and western portions are the sources of its tributaries, which ultimately drain into its northwestern portion; (8) change detection from the Landsat photos reveals considerable agricultural expansions over recent decades. The integrated method is useful for analyzing changes to groundwater resources in large groundwater reservoirs and developing environmentally appropriate management programs for these resources.

Eight time-domain electromagnetic profiles were carried out in Harrat Ithnayn, northwestern Saudi Arabia, to detect potential groundwater-bearing aquifers in the area. The collected data were processed and analyzed, with the results inferred or extrapolated between one-dimensional vertical soundings to obtain two-dimensional resistivity sections of the subsurface geological setting. We found that the resistivity values change laterally and vertically throughout the area. High resistivity values (greater than 100 Ohm-m) occupy most of the sections down to depths of approximately 100–150 m, whereas the low-resistivity regions of the section appear to be shallower than 100 m toward the west in the northern part of Harrat Ithnayn. The high-resistivity regions correspond to the Harrat basalts and are underlain by low resistivity (down to a few Ohm-m) in the saturated zones. There are also localized regions of low resistivity at depths of approximately 20–30 m, indicating shallow local groundwater aquifers. Aquifer thickness varies from approximately 50 to more than 100 m. In the southern part, the resistivity sections indicate that the main aquifer is at a depth of 150–200 m (possibly near 170 m) below the basalt. A saturated zone or shallow aquifer approximately 20–30 m thick also exists at a depth of approximately 15 m. In both the shallow and deep aquifers, the resistivity values indicate that there is good quality fresh water.

The present study was conducted in the Wadi Fatima catchment in western Saudi Arabia with the primary objective of finding the major hydrochemical differences among geothermal and non-geothermal groundwater samples. Based on the dissolved-silica content of 124 groundwater samples, the chalcedony geothermometer with no steam loss was used to classify them as geothermal or non-geothermal. The mean total dissolved solids (TDS) of the non-geothermal and geothermal samples were 1254 mg/L and 4389 mg/L, respectively. The high TDS of the geothermal samples can be attributed to the higher temperature of circulation leading to a greater degree of mineral dissolution, as well as the mixing of fresh, shallow groundwater with deeper saline water, as can be seen on Piper and Durov plots. Silicate weathering is the primary hydrochemical process active in the area. Based on the saturation indices of commonly dissolved mineral phases, the geothermal water samples showed higher saturation. Using chalcedony geothermometer can be an effective tool for classifying groundwater from low-temperature reservoirs as geothermal and non-geothermal. Further groundwater facies analysis, ionic relationships, and saturation indices can be employed to determine hydrochemical characteristics of these two different types of waters.

Changes in the terrestrial total water storage (TWS) have been estimated at both global and river basin scales from the Gravity Recovery and Climate Experiment (GRACE) mission and are still being detected from its GRACE Follow-On (GRACE-FO) mission. In this contribution, the sixth release of GRACE-FO (RL06) level-2 products applying DDK5 (decorrelation filter) were used to detect water mass variations for the Nile River Basin (NRB) in Africa and the Mega Aquifer System (MAS) in Asia. The following approach was implemented to detect the mass variation over the NRB and MAS: (1) TWS mass (June 2018–June 2021) was estimated by converting the spherical harmonic coefficients from the decorrelation filter DDK 5 of the GRACE-FO Level-2 RL06 products into equivalent water heights, where the TWS had been re-produced after removing the mean temporal signal (2) Precipitation data from Global Precipitation Climatology Project was used to investigate the pattern of change over the study area. Our findings include: (1) during the GRACE-FO period, the mass variations extracted from the RL06-DDK5 solutions from the three official centers—CSR, JPL, and GFZ—were found to be consistent with each other, (2) The NRB showed substantial temporal TWS variations, given a basin average of about 6 cm in 2019 and about 12 cm in 2020 between September and November and a lower basin average of about −9 cm in 2019 and −6 cm in 2020 in the wet seasons between March and May, while mass variations for the MAS had a relatively weaker temporal TWS magnitude, (3) the observed seasonal signal over the NRB was attributed to the high intensity of the precipitation events over the NRB (AAP: 1000–1800 mm yr−1), whereas the lack of the seasonal TWS signal over the MAS was due to the low intensity of the precipitation events over the MAS (AAP:180–500 mm yr−1).