Search Results (24)

Groundwater resources are critical in sustaining rapidly growing coastal urban regions like Dili City, Timor-Leste, where aquifers are prone to contamination. To inform groundwater pollution prevention and control in the Quaternary intergranular aquifer, a GIS-based groundwater vulnerability assessment was carried out using DRASTIC, modified DRASTIC, and modified DRASTIC–AHP methodologies. It confirmed that the central to northern urban area was the most vulnerable, while the southern part was the least vulnerable to contamination. Model performance was validated by correlating vulnerability indices with measured groundwater quality parameters, showing that the modified DRASTIC–AHP was the most accurate. The areas classified as having very low, low, moderate, high and very high vulnerability were 23.1%, 23.1%, 20.6%, 12.8%, and 19.2%, respectively, with high vulnerability along the northern coastline and Comoro River alluvial channel. Sensitivity analysis supports model robustness and identifies recharge, aquifer media, and hydraulic conductivity as the dominant controlling factors. The integrated modeling and sensitivity framework provides an efficient basis for prioritizing protection measures and infrastructure upgrades (e.g., sewerage) to reduce contamination risks. A key management implication is that centralized wastewater management is preferable to current practices for mitigating ongoing groundwater degradation. Full article

Protecting groundwater against pollution from agricultural sources is a key aspect of sustainable management of soil and water resources. Implementation of sustainable strategies for agricultural production can be supported by modeling tools, which allow us to quantify the effects of different agricultural practices in the context of groundwater vulnerability to contamination. In this study we present a method to assess groundwater vulnerability to nitrate pollution based on a combination of the SWAT agro-hydrological model and the DRASTIC index method. SWAT modeling was applied to assess different scenarios of agricultural practices and identify solutions for sustainable management of soil and groundwater and reduction of nitrate pollution. The developed method was implemented for groundwater resources in a study area (Puck Bay region, southern Baltic coast), which represented a complex multi-aquifer system formed in Quaternary fluvioglacial deposits (sand and gravel) separated by moraine tills. In order to investigate the effects of different agricultural practices, 12 scenarios have been defined, which were grouped into four classes: crop type, fertilizer management, tillage, and grazing. An overlay index structure was applied, and ratings and weights to several factors were assigned. All analyses were processed using GIS tools, and the results are presented in the form of maps, which categorize groundwater vulnerability to nitrate pollution into five classes, ranging from very low to very high. The results reveal significant variability in groundwater vulnerability to nitrate pollution in the study area. Agricultural practices have a very strong influence on groundwater vulnerability by controlling both recharge rates and nitrogen losses from the soil profile. The most pronounced increases in vulnerability were associated with scenarios involving excessive fertilization and intensive grazing. Among crop types, potato cultivation appears to pose the greatest risk to groundwater quality. Full article

The Huangshui River Basin is located in the transition zone between the Loess Plateau and the Qinghai–Tibet Plateau, characterized by a fragile hydrological and ecological environment. Groundwater serves as a vital water source for local economic development and human livelihood. With the acceleration of urbanisation and climate change, groundwater resources face challenges such as pollution and over-exploitation. This study employs an improved DRASTIC model, tailored to the characteristics of the groundwater system in the Huangshui River Valley of the upper Yellow River, to integrate groundwater resources, groundwater environment, and ecological environment systems. Improving the DRASTIC model for groundwater vulnerability assessment. A two-tiered evaluation system with nine indicator parameters was proposed, including six groundwater quality vulnerability indicators and five groundwater quantity vulnerability indicators. Fuzzy analytic hierarchy process and entropy weight method were used to determine the weights, and Geographic Information System (GIS) spatial analysis was employed to evaluate groundwater vulnerability in the Huangshui River basin in 2006 and 2021. The results indicate that the proportion of areas with high groundwater quality vulnerability increased from 10.7% in 2006 to 31.57% in 2021, while the proportion of areas with high groundwater quantity vulnerability decreased from 22.33% to 14.02%. Overall, groundwater quality vulnerability in the Huangshui River basin is increasing, while groundwater quantity vulnerability is decreasing. Based on the evaluation results of water quality and quantity vulnerability, protection zoning maps for water quality and quantity were compiled, and preventive measures and recommendations for water quality and quantity protection zones were proposed. Human activities have a significant impact on groundwater vulnerability, with land use types and groundwater extraction coefficients having the highest weights. This study provides a scientific basis for the protection and sustainable use of groundwater in the Huangshui River basin. Full article

The phenomenon of arable land non-agriculturalization has become increasingly severe, posing significant threats to the security of arable land resources and ecological sustainability. This study focuses on Dachang Hui Autonomous County in Langfang City, Hebei Province, a region located at the edge of the Beijing–Tianjin–Hebei metropolitan cluster. In recent years, the area has undergone accelerated urbanization and industrial transfer, resulting in drastic land use changes and a pronounced contradiction between arable land protection and the expansion of construction land. The study period is 2016–2023, which covers the key period of the Beijing–Tianjin–Hebei synergistic development strategy and the strengthening of the national arable land protection policy, and is able to comprehensively reflect the dynamic changes of arable land non-agriculturalization under the policy and urbanization process. Multi-temporal Sentinel-2 imagery was utilized to construct a multi-dimensional feature set, and machine learning classifiers were applied to identify arable land non-agriculturalization with optimized performance. GIS-based analysis and the geographic detector model were employed to reveal the spatio-temporal dynamics and driving mechanisms. The results demonstrate that the XGBoost model, optimized using Bayesian parameter tuning, achieved the highest classification accuracy (overall accuracy = 0.94) among the four classifiers, indicating its superior suitability for identifying arable land non-agriculturalization using multi-temporal remote sensing imagery. Spatio-temporal analysis revealed that non-agriculturalization expanded rapidly between 2016 and 2020, followed by a deceleration after 2020, exhibiting a pattern of “rapid growth–slowing down–partial regression”. Further analysis using the geographic detector revealed that socioeconomic factors are the primary drivers of arable land non-agriculturalization in Dachang Hui Autonomous County, while natural factors exerted relatively weaker effects. These findings provide technical support and scientific evidence for dynamic monitoring and policy formulation regarding arable land under urbanization, offering significant theoretical and practical implications. Full article

Morocco’s Témara Plain relies heavily on its aquifer system as a critical resource for drinking water, irrigation, and industrial activities. However, this essential groundwater reserve is increasingly threatened by over-extraction, seawater intrusion, and complex hydrogeochemical processes driven by the region’s geological characteristics and anthropogenic pressures. This study aims to assess groundwater quality and its vulnerability to pollution risks and map the spatial distribution of key hydrochemical processes through an integrated approach combining Geographic Information System (GIS) techniques and multivariate statistical analysis, as well as applying the DRASTIC model to evaluate water vulnerability. A total of fifty-eight groundwater samples were collected across the plain and analyzed for major ions to identify dominant hydrochemical facies. Spatial interpolation using Inverse Distance Weighting (IDW) within GIS revealed distinct patterns of sodium chloride (Na-Cl) facies near the coastal areas with chloride concentrations exceeding the World Health Organization (WHO) drinking water guideline of 250 mg/L—indicative of seawater intrusion. In addition to marine intrusion, agricultural pollution constitutes a major diffuse pressure across the aquifer. Shallow groundwater zones in agricultural areas show heightened vulnerability to salinization and nitrate contamination, with nitrate concentrations reaching up to 152.3 mg/L, far surpassing the WHO limit of 45 mg/L. Furthermore, other anthropogenic pollution sources—such as wastewater discharges from septic tanks in peri-urban zones lacking proper sanitation infrastructure and potential leachate infiltration from informal waste disposal sites—intensify stress on the aquifer. Principal Component Analysis (PCA) identified three key factors influencing groundwater quality: natural mineralization due to carbonate rock dissolution, agricultural inputs, and salinization driven by seawater intrusion. Additionally, The DRASTIC model was used within the GIS environment to create a vulnerability map based on seven key parameters. The map revealed that low-lying coastal areas are most vulnerable to contamination. Full article

Groundwater vulnerability has gained widespread attention, particularly in Chiang Mai Province, one of Thailand’s fastest-growing cities, which is experiencing rapid development in both economic and social sectors. The expansion of urban communities and the industrial, tourism, and agriculture sectors has resulted in the overutilization of available resources, notably water resources. This overuse, coupled with the adoption of modern technology to boost productivity and meet market demands, has led to an increased reliance on groundwater to supplement surface water sources, providing benefits across all sectors. However, the economic and social growth plays a pivotal role in shaping the diversity of land use, encompassing residential, commercial, industrial, and agricultural activities. These activities, in turn, directly contribute to environmental pollution, particularly in terms of the risk of groundwater contamination in Chiang Mai Province. This study aims to predict the future vulnerabilities of groundwater resources under an ensemble of climate change scenarios and changes in land-use patterns. Chiang Mai Province in northern Thailand is one of the fastest-growing cities and therefore is experiencing rapid urbanization, as well as land-use pattern changes, which was important for the case study. The new DRASTIC model, namely the DRASTIC-LP model, combined with GIS-based techniques and overlay techniques, was used to generate the map of groundwater resource vulnerabilities. A point pollution source (P)-related land-use pattern (L) that represents contamination impacts was considered an additional new DRASTIC parameter. The study’s findings reveal the high reliability and maximum effectiveness of the new DRASTIC-LP model in assessing groundwater vulnerability and contamination-risk areas under a climate change scenario (by MIROC-ESM-CHEM model under RCP.8.5 scenario) and land-use pattern changes (by CA_Markov Chian Model) for both the current year (2020) and the next 50-year period (2021–2070). Furthermore, the new DRASTIC-LP model is employed to trace the movement of pollutants from high- to very high-risk areas based on the groundwater vulnerability and contamination-risk maps. The results highlight that waste disposal dumping sites pose a more critical distribution and movement of pollutants when compared to industrial sites. Additionally, unconsolidated aquifers and cracked consolidated rock aquifers show a potentially higher occurrence of pollutant distribution and movement when compared to consolidated aquifers. Consequently, the study’s outcomes are applied to formulate guidelines for the management and control of groundwater resource contamination. These guidelines serve as valuable tools for decision makers, aiding in pollution prevention and the effective management of contamination risks in groundwater resources. Full article

The evaluation of vulnerability is a crucial aspect in the sustainable development, utilization, and preservation of groundwater resources. This study utilizes a comprehensive approach, integrating systematic analysis of hydrogeological conditions and the utilization of observed and collected data. The evaluation of groundwater vulnerability in the Yishu River Basin (YRB) was conducted by employing the DRASTIC model, along with the zone overlay function of GIS software. Seven evaluation indicators were considered in this assessment. The findings demonstrate that the groundwater vulnerability in the YRB can be categorized into five divisions: excellent, good, medium, poor, and very poor, accounting for 14.5%, 42.3%, 27.9%, 14.0%, and 1.3% respectively. The areas with low vulnerability are predominantly located in the eastern part of the study area, covering the largest proportion of the total area. Conversely, areas with high vulnerability are found alongside both banks of the Shu River, forming narrow strips. Although these areas have smaller overall coverage, they contain dispersed water sources that require careful attention. These research findings provide valuable scientific insights and serve as a reference for urban planning, land use management, and groundwater resource protection in the YRB. The formulation and adoption of targeted protection measures in accordance with different groundwater vulnerability zoning, the formulation of scientific groundwater resource development and utilization programs, and execution of land resource planning are of great significance from the perspective of groundwater resource protection. Full article

Groundwater vulnerability reflects the risk level of groundwater contamination and its self-repairing ability, as well as its sustainability for use. Therefore, it provides significant scientific support for implementing measures to prevent groundwater contamination, especially in coastal areas. In this study, considering the lithology of vadose in valley plains and the extent of karst subsidence areas, a GIS-based DRASTIC model was employed to assess groundwater vulnerability in Tangshan City, a coastal area in China. The assessment results were presented and mapped using GIS, based on a comprehensive evaluation of seven parameters, including “Depth of groundwater, Vertical net recharge, Aquifer thickness, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity”. The identified groundwater vulnerability zones included the highest, higher, moderate, low vulnerability those four zones, which accounted for 4%, 53%, 25%, and 18%, respectively. In addition, according to the results of field investigation, the karst subsidence area and the mined-out coastal area were directly classified as the highest vulnerable areas and covered 1.463 km²; more attention is required here in subsequent groundwater protection processes and strategies. Finally, the groundwater pollution index was used to validate the groundwater vulnerability distribution results, and these two were in high agreement, with an R² coefficient of 0.961. The study is crucial for the rational utilization and protection of water resources in Tangshan City. Full article

Groundwater vulnerability assessment is of pivotal importance for the sustainable management of groundwater resources, particularly in regions with intense agricultural activity. This research primarily aims to assess and delineate groundwater vulnerability zones using a comparative approach of three different GIS-based modified models, namely Pesticide DRASTIC-LU, Nitrate SINTACS-LU and Nitrate NV index. For this reason, eight hydrogeological parameters were employed to analyze the spatial distribution of groundwater vulnerability in the Nea Moudania aquifer, Chalkidiki, Greece. This multi-model methodology was implemented to ascertain the most reliable method for the study area. Results indicated that the southern and southwestern parts of the study area exhibited the highest vulnerability potential, whilst the northern part displayed the lowest. Moreover, single-parameter sensitivity analysis has revealed that land use and topography were the most critical parameters of the vulnerability indexes, whereas hydraulic conductivity was the least influential. Finally, the three vulnerability models were validated with nitrate concentrations of groundwater samples. Results revealed that the Nitrate NV index was the most accurate method, trailed by the Pesticide DRASTIC-LU and the Nitrate SINTACS-LU. Full article

Rapid urbanization has led to drastic land-use/cover changes (LUCCs) and urban heat islands (UHIs), negatively altering the urban climate and air quality. LUCC’s significant impacts on human health and energy consumption have inspired researchers to develop nature-based solutions to mitigate UHIs and improve air quality. However, integrating GIS-CFD modeling for urban heat mitigation towards climate change adaptation was largely neglected for eco-sustainable urban design in rapidly urbanizing areas. In this study, (1) long-term LUCC and meteorological analysis were conducted in the Wuhan metropolitan area from 1980 to 2016; (2) to mitigate the adverse effects of LUCC under a speedy development process, the role and relevance of optimizing building morphology and urban block configuration were discussed; (3) and particular design attention in strategy towards climate change adaptation for environmental performance improvement was paid in Wuhan’s fast-growing zones. The results show that UHII in 1980 was less severe than in 2016. Air temperature (Ta) increased by 0.4 °C on average per decade in developing areas. This increases the severity of UHII in urban fringes. It is found obligatory for a nature-based design to adopt urban morphology indicators (UMIs) such as average building height (μBH), sky view factors (ψ_SVF), and building density (BD/λ_p = % of built area) towards these changes. Further, on-site measurement revealed that λ_p is the most effective indicator for increasing urban heat around the buildings and boosting UHII. Using UMIs and a combined three-in-one regulation strategy based on μBH of common building types of high-rise (BH^A), mid-rise (BH^B), and low-rise (BH^C) buildings can effectively contribute to regulating Ta and air movement within block configuration. As a result of this study’s strategy, urban heat is mitigated via reinforcing wind in order to adapt to climate change, which impacts the quality of life directly in developing areas. Full article

Groundwater resources of the alluvial aquifer Ghiss Nekkor, which covers an area of 100 km², are the main source of domestic and agricultural freshwater supply in the region of Al Hoceima in Morocco. Due to human activities (overexploitation, increase in agricultural activity), this alluvial aquifer has become very sensitive to chemical pollution. The principal objective of this current study is to develop and implement a calibration method to assess, map, and estimate the vulnerability of the Ghiss Nekkor alluvial aquifer to pollution risk. In this work, the GIS-based DRASTIC model was used to estimate the inherent vulnerability to contamination of the Ghiss Nekkor alluvial aquifer with seven standard hydrogeological parameters. Nitrate (NO₃) and electrical conductivity (EC) data were used to validate the DRASTIC map. The results of the vulnerability map analysis show that the vulnerability to contaminants varies from non-existent in the southwestern part of the plain (7.3% of the total area), to very high (14.5%). The vulnerability is moderate in the central and northeastern areas (26.9%), while it is high in the other areas (17.5%). Furthermore, the most sensitive areas are mainly concentrated near the coastal strip and the central plain on both sides of the Nekkor River. In these areas, the NO₃ and EC values are above the maximum allowable limit of the World Health Organization. The results suggest that the DRASTIC model can be an effective tool for decision-makers concerned about managing groundwater sustainability. Full article

Viewshed analysis is a terrain visibility computation method based on the digital elevation model (DEM). With the rapid growth of remote sensing and data collection technologies, the volume of large-scale raster DEM data has reached a great size (ZB). However, the data storage and GIS analysis based on such large-scale digital data volume become extra difficult. The usually distributed approaches based on Apache Hadoop and Spark can efficiently handle the viewshed analysis computation of large-scale DEM data, but there are still bottleneck and precision problems. In this article, we present a multi-level distributed XDraw (ML-XDraw) algorithm with Apache Spark to handle the viewshed analysis of large DEM data. The ML-XDraw algorithm mainly consists of 3 parts: (1) designing the XDraw algorithm into a multi-level distributed computing process, (2) introducing a multi-level data decomposition strategy to solve the calculating bottleneck problem of the cluster’s executor, and (3) proposing a boundary approximate calculation strategy to solve the precision loss problem in calculation near the boundary. Experiments show that the ML-XDraw algorithm adequately addresses the above problems and achieves better speed-up and accuracy as the volume of raster DEM data increases drastically. Full article

The groundwater resources in the Nile Delta region are an important resource for freshwater because of rising water demand due to anthropogenic activities. The goal of this study is to quantify groundwater sensitivity to pollution in the Nile Delta by a modified GIS-based DRASTIC-LU model. In this study, we utilized two types of modified DRASTIC-LU models, generic and pesticide, to determine the groundwater vulnerability rates to contamination. The results of the generic DRASTIC-LU model showed that the research region, except for the northwestern part with moderate vulnerability of 3.38%, is highly and very highly vulnerable to pollution with 42.69 and 53.91%, respectively. Results from the pesticide DRASTIC-LU model, on the other hand, also confirmed that, except for the northwestern and southern parts with a moderate vulnerability of 9.78%, most the Nile Delta is highly and very highly vulnerable with 50.68 and 39.53%, respectively. A validation of the model generated was conducted based on nitrate concentrations in the groundwater and a sensitivity analysis. Based on the nitrate analysis, the final output map showed a strong association with the pesticide vulnerability model. Examining the model sensitivity revealed that the influence of depth to water and net recharge were the most important factors to consider. Full article

In this study, a new approach integrating a groundwater vulnerability method and a numerical model for predicting groundwater resource sustainability under actual and future conditions of exploitation (2010–2030) is proposed in the semi-arid region of the Tebessa-Morsott alluvial aquifer (northeastern Algeria). The groundwater vulnerability method-based DRASTIC model was used to evaluate and delineate the vulnerable areas using a GIS technique. The MODFLOW code, on the other hand, was used to calculate the dynamics of groundwater level under actual and future conditions of exploitation considering two scenarios. The results of the application of the DRASTIC method to the reference year conditions (year 2010) showed that the high and average vulnerability classes covered a wide zone of the study area, about 97%. These results were validated based on the nitrate concentration values (R² = 0.955). However, the results for predicting future groundwater vulnerability showed that groundwater vulnerability variation over time (period 2010–2030) was closely related to groundwater depth variation caused by the pumping rate, since the decreases in the piezometric level produce a worsening of groundwater vulnerability. To achieve better groundwater management, an experimental site for artificial recharge supplemented by hydro-chemical monitoring of the groundwater could be an effective remediation strategy. Full article

Bark beetle outbreaks are responsible for the loss of large areas of forests and in recent years they appear to be increasing in frequency and magnitude as a result of climate change. The aim of this study is to develop a new standardized methodology for the automatic detection of the degree of damage on single fir trees caused by bark beetle attacks using a simple GIS-based model. The classification approach is based on the degree of tree canopy defoliation observed (white pixels) in the UAV-acquired very high resolution RGB orthophotos. We defined six degrees (categories) of damage (healthy, four infested levels and dead) based on the ratio of white pixel to the total number of pixels of a given tree canopy. Category 1: <2.5% (no defoliation); Category 2: 2.5–10% (very low defoliation); Category 3: 10–25% (low defoliation); Category 4: 25–50% (medium defoliation); Category 5: 50–75% (high defoliation), and finally Category 6: >75% (dead). The definition of “white pixel” is crucial, since light conditions during image acquisition drastically affect pixel values. Thus, whiteness was defined as the ratio of red pixel value to the blue pixel value of every single pixel in relation to the ratio of the mean red and mean blue value of the whole orthomosaic. The results show that in an area of 4 ha, out of the 1376 trees, 277 were healthy, 948 were infested (Cat 2, 628; Cat 3, 244; Cat 4, 64; Cat 5, 12), and 151 were dead (Cat 6). The validation led to an average precision of 62%, with Cat 1 and Cat 6 reaching a precision of 73% and 94%, respectively. Full article