Groundwater for Sustainable Development最新文献

Identifying sustainable artificial recharge zones in arid cratons is challenging due to complex geology and limited natural recharge conditions, making accurate site selection and management difficult. This study integrates Vertical Electrical Sounding (VES), the Analytic Hierarchy Process (AHP), and Boolean analysis to identify sustainable artificial recharge zones in the arid Bundelkhand craton of India. Aquifer thickness and fractures emerged as critical determinants of groundwater recharge conditions, revealing varying degrees of suitability for recharge across the study area. Approximately 2.31% (13.36 km²) of the area along streams exhibited "very high" suitability, while 8.09% (45.82 km²) had "high" suitability. “Moderate" suitability covered 17.86% (101.66 km²), "low" suitability accounted for 38.85% (218.39 km²), and "very low" suitability represented 17.35% (98.75 km²) of the area. Recharge potential was highest in the northeast and central parts, with the middle of the watershed exhibiting the lowest potential. The study demonstrated that this integrated approach significantly improved precision from 71.40% to 85.70% and enhanced the F1 score from 0.833 to 0.923, surpassing the performance of the AHP method alone. The findings highlighted the importance of strategic selection and targeting of specific locations for artificial recharge, as only ∼18% of the study area was suitable for such efforts, despite ∼43% showing potential for groundwater. AHP with VES proves more precise and reliable than Fuzzy-AHP with VES, with AHP's conservative approach classifying 55.70% of the area as very low to low suitability compared to Fuzzy-AHP's 41.92%, ensuring only the most suitable sites are selected. VES offers cost-effectiveness, noninvasiveness, and rapid generation of a 1D subsurface model, balancing its lower detail compared to Electrical Resistivity Tomography. When combined with the AHP, VES enhances adaptability to changing conditions, emphasizing ecological preservation and climate change resilience. This approach effectively addresses water challenges in arid regions, contributing to sustainable water resource management.

Monitoring and predicting groundwater quality is essential for managing water resources, protecting public health, and mitigating environmental impacts. This study presents a comprehensive hydrogeochemical investigation aimed at understanding the general hydrochemistry, identifying the extent of saltwater intrusion and prediction of groundwater quality in the semi-arid coastal aquifers of Tuticorin, Tamil Nadu, India. Groundwater samples were collected during both pre- and post-monsoon seasons to capture seasonal variations and groundwater quality was evaluated using the entropy weighted water quality index (EWQI) and predicted through the Random Forest (RF) machine learning technique. The findings revealed that total dissolved solids (TDS) exceeded WHO limits in 85% of samples during the pre-monsoon season and 61% during the post-monsoon season, indicating significant groundwater quality issues. Hydrogeochemical facies analysis identified Na-Cl as the dominant water type across all seasons, with a higher prevalence in coastal alluvium regions, suggesting a strong lithological influence and ongoing saline water intrusion. The EWQI coupled RF method provided high predictive accuracy, with R² values of 0.955 and 0.975 and RMSE values of 6.1 and 5.5 for the pre- and post-monsoon periods, respectively. In addition, results obtained from the RF-EWQI model indicated that ∼11.24% of the study area falls within the extremely poor water quality category. This zone is primarily associated with fluvial, fluvial-marine, and aeolian formations. In terms of spatial distribution, the RF-EWQI values for both seasons exhibit a parallel trend with the seawater mixing index (SMI), suggesting that the poor groundwater quality is primarily linked to the coastal alluvium aquifer. This underscores the significant impact of saline water intrusion on groundwater quality, particularly in the coastal alluvium aquifer. This integrated approach presented here offers valuable insights for improving groundwater quality assessment and management.

Aquifers in arid and semi-arid coastal regions, such as along the Mediterranean rim, are severely affected by droughts. The natural decrease in water levels is often exacerbated by excessive abstraction, resulting in both degradation of water quality and the risk of seawater intrusion. In these regions it is crucial to conduct thorough monitoring of wells, employing a wide range of indicators to forecast and mitigate the consequences of decreased precipitation and intensified pumping. This study proposes an analysis and monitoring methodology involving the calculation of performance indicators based on the Standardized Groundwater level Index (SGI), supplemented with information on the optimal accumulation time of the Standardized Precipitation Evapotranspiration Index (SPEI). Atmospheric reanalysis datasets and in-situ groundwater level observations are used together to derive the groundwater system memory and find consistent optimal SPEI accumulation times at each individual location. The inverse of memory derived from the autocorrelation of the SGI is used to estimate each well's ability to recover from drought conditions. This value provides the most reliable indication of resilience and sustainability. In the Algarve, the average regional variability of groundwater level is well captured by the SPEI-12 index. However, groundwater memories and optimal SPEI accumulation times are spatially very heterogeneous varying between SPEI-5 and SPEI-48. Wells with shorter memories (<6 months) demonstrate greater sustainability, whereas those with longer memories (>16 months), whether situated inland or along the coast, exhibit lower resilience and lower sustainability. Coastal wells with groundwater levels close to sea level, exhibiting minimal resilience, are of particular concern and require intensified monitoring efforts to adapt management strategies.

Groundwater resources are at great risk of contamination due to increased industrial and agricultural activities, population growth and urban expansion. This study investigated factors controlling spatio-temporal variability in groundwater quality and nitrate concentration at the southern coast of Caspian Sea, Iran to provide public health risk assessment. Na-Cl (44.8%) and Ca-HCO₃ (58.6%) types water were the dominant hydrogeochemical facies in dry and wet seasons, respectively. Most of the examined groundwater samples were found unfit for drinking but appropriate for agricultural irrigation. The chemistry of groundwater predominantly influenced by combination of local lithology and ion exchange in aquifer as well as seawater intrsuin. Nitrate concentration varied from 0.05 to 200 mg/L with a mean value of 33.1 mg/L in which 13.7% and 27.5% of samples showed concentration higher than WHO's recommended value in dry and wet seasons, respectively. The highest nitrate concentrations were observed at locations in proximity to human settlements including cities, villages as well as agricultural lands. The identified pollution hotspots confirm nitrate contributions from un-treated wastewater effluents and agricultural practices with minimum contribution from industrial activities. The result of Monte Carlo simulation revealed that children were at highest risk from drinking of groundwater containing nitrate. This study highlights the urgent need for action to address the growing threat to groundwater quality and public health posed by contamination from various sources in the southern coasts of Caspian Sea.

This study explores the hydrogeochemical and isotopic characteristics of groundwater in the Irapuato Valley and Celaya Valley Aquifers in central Mexico, specifically focusing on the role of CO₂ in mineral alteration during water-rock interaction. The study is grounded in the principles of hydrogeochemistry and stable isotope geochemistry, analyzing the impact of CO₂ and H₂SO₄ on the weathering of carbonates and silicates. Hydrogeochemical analysis, including Piper diagrams, and isotopic measurements (δ¹³C, δ¹⁸O, δ²H), were conducted on water samples from wells in four municipalities (Irapuato, Salamanca, Villagrán, and Juventino Rosas). The data was statistically evaluated using Shapiro-Wilk tests to assess normality, skewness, and kurtosis, ensuring the reliability of the findings. The results indicate that HCO₃⁻ dominates the groundwater composition, with CO₂ and H₂SO₄ significantly influencing mineral alteration processes. The isotopic data suggest that CO₂ is primarily released from carbonate rock degassing, with slight isotopic enrichment in δ¹³C due to water-carbonate interaction. Hydrothermal fluids contribute to the geochemical evolution of the aquifer, leading to the formation of minerals such as tridymite, alunite, and kaolinite. Additionally, some groundwater samples exhibit evidence of thermalism and water-rock interactions, influencing their isotopic signatures and temperatures. These findings underscore the importance of CO₂ in groundwater chemistry and highlight the need for further studies to understand regional flow dynamics and the potential impact of geothermal systems on water quality.

The Hub River Basin (HRB), a critical transboundary water source for Sindh and Baluchistan provinces in Pakistan, may face worsening water scarcity due to climate change and population growth. This study aims to assess the current state of water scarcity in the HRB and assesses its vulnerability to these pressures in future. To evaluate the baseline water scarcity in the HRB, a calibrated and validated Soil and Water Assessment Tool (SWAT) was established. Five General Circulation Models (GCMs) were employed to project the future climate under Representative Concentration Pathways (RCP 4.5 and 8.5) for the HRB. Sector-specific indicators were also used to assess the temporal and altitudinal sensitivity of the basin to climate change. These climate projections were incorporated in the SWAT model to simulate flows for three different periods: Early Future (EF; 2010–2039), Mid Future (MF; 2040–2069), and Far Future (FF; 2070–2099). The SWAT model results indicate significant increase in mean flows simulated by SWAT, ranging from 15.27 to 52.78 m³/s under RCP 4.5 and RCP 8.5 compared to baseline flows at HRB. Additionally, the study examines the temporal variation in basin stress and scarcity levels using Falkenmark and Water scarcity indicators. The findings indicate a general decrease in the basin's stress and scarcity levels, potentially benefiting water users of the HRB, especially under RCP8.5. This study offers crucial insights for shaping policies and strategies to adapt to climate change and population growth, ultimately aiming to minimize their impacts on HRB's water resources. By informing water managers and promoting sustainable water management practices, this research can help prevent future conflicts over water allocation and infrastructure development linked with the HRB.

Water quality is critical to human health and the environment, especially in arid and semi-arid regions. Hence, the objectives of this study were to assess drinking water quality, identify critical parameters, investigate spatial patterns, and investigate accurate predictive models for the water quality index (WQI) in the Kazerun county in southwest Iran. To address this issue using deterministic and probabilistic WQI, correlation matrix, fuzzy C-Means (FCM) clustering, geostatistics, and adaptive network-based fuzzy inference system (ANFIS) with FIS generation by fuzzy C-Means (FCM-ANFIS) and sub-clustering (SC-ANFIS).Various software tools, including Excel, MATLAB, Python, and GIS were used to analyze groundwater data collected from 25 sampling sites. Water parameters, including pH, Cl⁻, SO₄⁻², EC, NO₃⁻, NO₂⁻, Ca²⁺, Mg²⁺, and F⁻, were examined. The results showed that F⁻ levels were within acceptable limits set by the US EPA, but about one-third of sites posed potential health risks based on WHO guidelines. In one-third of regions, the levels of Mg²⁺ exceeded the recommended guidelines. In deterministic and probabilistic approaches, water quality was excellent in 68% and 81.3% of sites, respectively. Sobol sensitivity analysis identified SO₄⁻²> Mg²⁺>Cl⁻ > EC > F⁻ > NO₃⁻ as significant WQI variables. Spearman correlation matrix shows substantial positive correlations between WQI and EC, F⁻, SO₄⁻², Mg²⁺, and Cl⁻ were shown by the Spearman correlation matrix. Based on the FCM results, the southeast and central sites (56% of sites) have similar water quality. In comparison, the northern and four central sites (28% of sites) have distinct regional features, and the southern sites (16% of sites) had unique water quality characteristics. Geostatistical analyses showed that pH had the most substantial local clustering, while SO₄⁻² had significant high-value clustering. Furthermore, hot spot research revealed specific sites with high pH, F⁻, NO₃⁻, and Cl⁻ levels. The FCM-ANFIS model outperformed the SC-ANFIS model, emphasizing FCM clustering's importance in water quality forecasting accuracy.