Search Results (167)

The level of participation and performance of water user associations (WUAs) in drained and irrigated areas is influenced by many factors. This paper aims to identify the main challenges to the functioning and performance of these associations in Poland and Ukraine using the methodology of international comparative analysis. We examined legal, organizational, and financial framework of WUAs performance in Poland and Ukraine based on selected case study areas. The results of the study indicate that creation of WUAs in both countries can be assessed as beneficial for sustainable water development in general. However, it is found that the actions intended to bring benefits can actually exacerbate the problem of drought and water shortages. Research shows that the lack of complete documentation on the layout of the drainage networks plays a huge constraint factor that can lead to problems with controlling the reconstruction of drainage networks and significant deterioration of water relations. Another significant problem is the restriction of the scope of WUA activities in Poland to those types of actions subsidized by the state, while lacking financial resources for other necessary activities. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Floods are the biggest hydrometeorological disaster, affecting millions annually. Thus, flood hazard assessment is crucial and plays a pivotal role in rational water management. This study was undertaken to evaluate flood hazards through the application of MCDM methods and a bivariate statistical model integrated with GIS. The methodologies applied were AHP, fuzzy AHP, and the frequency ratio. Eight flood-related criteria were considered—elevation, flow accumulation, geology, slope, land use/land cover (LULC), distance from the drainage network, drainage density, and rainfall index—for the construction of a Flood Hazard Map for each methodology, with the aim to delineate the regions within the study area most prone to flooding. The results demonstrated that around 34% of the Chalkidiki regional unit presents a high and very high hazard to the occurrence of floods. The comparison of the maps generated using DSC demonstrated that all models are capable of delineating high and very high hazard areas with overlap values varying from 0.8 to 0.98. The validation results indicated that the models exhibit sufficient performance in flood hazard mapping with AUC-ROC scores of 66.6%, 65.7%, and 76.5% for the AHP, FAHP, and FR models, respectively. Full article

This study was conducted in the Harran Plain within the framework of the Southeastern Anatolia Project (GAP) in Türkiye to evaluate the vulnerability of groundwater to contamination, with a special emphasis on the high salinity conditions attributed to agricultural and rural practices. The region is notably challenged by salinization resulting from intensive irrigation and insufficient drainage systems. The DRASTIC framework was used to assess groundwater contamination vulnerability. The DRASTIC framework parameters were numerically integrated using both the original DRASTIC framework and its modified version, serving as the basis for subsequent predictive analytics and soft computing model development. The primary aim was to determine the most effective predictive model for groundwater contamination vulnerability in salinity-affected areas. In this context, various models were implemented and evaluated, including artificial neural networks (ANNs) with varied hidden layer configurations, four different regression-based methods (MARS, TreeNet, GPS, and CART), and three classical regression analysis approaches. The modeling process utilized 24 adjusted vulnerability indices (AVIs) as target variables, with the dataset partitioned into 58.34% for training, 20.83% for validating, and 20.83% for testing. Model performance was rigorously assessed using various statistical indicators such as mean absolute error, root mean square error, and the Nash–Sutcliffe efficiency coefficient, in addition to evaluating the predictive AVIs through spatial mapping. The findings revealed that the ANNs and TreeNet models offered superior performance in accurately predicting groundwater contamination vulnerability, particularly by delineating the spatial distribution of risk in areas experiencing intensive agricultural pressure. Full article

As climate change intensifies, cities are experiencing more severe rainfall and frequent waterlogging. When rainfall exceeds the carrying capacity of urban drainage networks, it poses a significant risk to urban facilities and public safety, seriously affecting sustainable urban development. Compared with general urban built-up areas, they demonstrate greater vulnerability to rainfall-induced waterlogging due to their obsolete infrastructure and high heritage value, making it imperative to comprehensively enhance their waterlogging resilience. In this study, Qingdao’s historic urban area is selected as a sample case to analyze the interaction between rainfall intensity, the built environment, and population and business characteristics and the mechanism of waterlogging disaster in the historic urban area by combining with the concept of resilience; then construct a resilience assessment system for waterlogging in the historic urban area in terms of dangerousness, vulnerability, and adaptability; and carry out a measurement study. Specifically, the CA model is used as the basic model for simulating the possibility of waterlogging, and the waterlogging resilience index is quantified by combining the traditional research data and the emerging open-source geographic data. Furthermore, the waterlogging resilience and obstacle factors of the 293 evaluation units were quantitatively evaluated by varying the rainfall characteristics. The study shows that the low flooding resilience in the historic city is found in the densely built-up areas within the historic districts, which are difficult to penetrate, because of the high vulnerability of the buildings themselves, their adaptive capacity to meet the high intensity of tourism and commercial activities, and the relatively weak resilience of the built environment to disasters. Based on the measurement results, targeted spatial optimization strategies and planning adjustments are proposed. Full article

Under the dual pressures of global climate change and rapid urbanization, urban drainage systems (UDS) face severe challenges caused by extreme precipitation events and altered surface hydrological processes. The drainage paradigm is shifting toward resilient systems integrating grey and green infrastructure, necessitating a comprehensive review of the design and operation of grey infrastructure. This study systematically summarizes advances in urban stormwater process-wide regulation, focusing on drainage network design optimization, siting and control strategies for flow control devices (FCDs), and coordinated management of Quasi-Detention Basins (QDBs). Through graph theory-driven topological design, real-time control (RTC) technologies, and multi-objective optimization algorithms (e.g., genetic algorithms, particle swarm optimization), the research demonstrates that decentralized network layouts, dynamic gate regulation, and stormwater resource utilization significantly enhance system resilience and storage redundancy. Additionally, deep learning applications in flow prediction, flood assessment, and intelligent control exhibit potential to overcome limitations of traditional models. Future research should prioritize improving computational efficiency, optimizing hybrid infrastructure synergies, and integrating deep learning with RTC to establish more resilient and adaptive urban stormwater management frameworks. Full article

Mapping urban pluvial flooding (UPF) in data-scarce regions poses significant challenges, particularly when drainage systems are inadequate or outdated. These limitations hinder effective flood mitigation and risk assessment. This study proposes an innovative approach to address these challenges by integrating deep learning (DL) models with traditional methods. First, deep convolutional generative adversarial networks (DCGANs) were employed to enhance drainage network data generation. Second, deep recurrent neural networks (DRNNs) and multi-criteria decision analysis (MCDA) methods were implemented to assess UPF. The study compared the performance of these approaches, highlighting the potential of DL models in providing more accurate and robust flood mapping outcomes. The methodology was applied to Lahore, Pakistan—a rapidly urbanizing and data-scarce region frequently impacted by UPF during monsoons. High-resolution ALOS PALSAR DEM data were utilized to extract natural drainage networks, while synthetic datasets generated by GANs addressed the lack of historical flood data. Results demonstrated the superiority of DL-based approaches over traditional MCDA methods, showcasing their potential for broader applicability in similar regions worldwide. This research emphasizes the role of DL models in advancing urban flood mapping, providing valuable insights for urban planners and policymakers to mitigate flooding risks and improve resilience in vulnerable regions. Full article

Floods and landslides are two distinct natural phenomena influenced by different conditioning factors, though some environmental triggers may overlap. This study applied eXtreme Gradient Boosting (XGBoost) to develop susceptibility maps for both phenomena, using a unified approach based on the same geospatial predictors. The approach integrated topographical, geological, and remote sensing datasets. Flood event data were collected from institutional sources using multi-source and high-resolution remotely sensed data. The landslide inventory was compiled based on historical records and geomorphological analysis. Key conditioning factors such as elevation, slope, lithology, and land cover were analyzed to identify areas prone to floods and landslides. The methodology was applied to the Basento River basin in Southern Italy, a region frequently impacted by both hazards, to assess its vulnerability and inform risk management strategies. While flood susceptibility is primarily associated with low-lying areas near river networks, landslides are more influenced by steep slopes and geological instability. The XGBoost model achieved a classification accuracy close to 1 for flood-prone areas and 0.92 for landslide-prone areas. Results showed that flood susceptibility was primarily associated with low Elevation and Relative Elevation, and high Drainage Density, whereas landslide susceptibility was more influenced by a broader and balanced set of factors, including Elevation, Drainage Density, Relative Elevation, Distance and Lithology. The resulting susceptibility maps offered critical approaches for land use planning, emergency management, and risk mitigation. Overall, the results demonstrated the effectiveness of XGBoost in multi-hazard assessments, offering a scalable and transferable approach for similar at-risk regions worldwide. Full article

This study evaluated the allocation of residential detention tanks in the Alto da Boa Vista Condominium, Federal District, Brazil, using hydrological and hydraulic modeling using the PCSWMM software (version 7.6.3610). The objective was to investigate the impact of urbanization on local hydrology, considering the occurrence of erosive processes in the area. Critical points in the infrastructure and regions susceptible to flooding were identified. The methodology involved implementing residential detention tanks in different allocation scenarios, including the use of isochrones. Isochrones, which represent lines of equal concentration time in the drainage network, were employed to segment the basin into three main regions: upstream (ISO 1+2), central (ISO 3+4), and downstream (ISO 5+6). The isochrone-based scenarios enabled the assessment of the impact of concentrating residential detention tanks in these specific zones. Additionally, two other scenarios were analyzed: one with the residential detention tanks uniformly distributed throughout the basin and another without the presence of these devices. Finally, a scenario with a random distribution of residential detention tanks was tested, encompassing a total of 54 distinct configurations, to investigate the influence of different spatial arrangements on the basin’s hydraulic performance. The results indicated that the number of residential detention tanks installed is the main determinant for peak flow attenuation at the basin’s outlet. It was observed that, regardless of the distribution of the devices, whether in concentrated scenarios (upstream, central, and downstream, as defined by the isochrones) or in randomly distributed configurations, the results were similar. In all cases, installing residential detention tanks in more than 30% of the basin area resulted in an approximately 5% reduction in peak flow at the outlet. It is concluded that implementing residential detention tanks is an effective and feasible solution for sustainable stormwater management, significantly contributing to surface runoff control and peak flow mitigation in urbanized areas. Full article

Engineered loess-filled gullies, which are widely distributed across China’s Loess Plateau, face significant stability challenges under extreme rainfall conditions. To elucidate the regulatory mechanisms of antecedent rainfall on the erosion and failure processes of such gullies, this study conducted large-scale flume experiments to reveal their phased erosion mechanisms and hydromechanical responses under different antecedent rainfall durations (10, 20, and 30 min). The results indicate that the erosion process features three prominent phases: initial splash erosion, structural reorganization during the intermission period, and runoff-induced gully erosion. Our critical advancement is the identification of antecedent rainfall duration as the primary “pre-regulation” factor: short-duration (10–20 min) rainfall predominantly induces surface crack networks during the intermission, whereas long-duration (30 min) rainfall directly triggers substantial holistic collapse. These differentiated structural weakening pathways are governed by the duration of antecedent rainfall and fundamentally control the initiation thresholds, progression rates, and channel morphology of subsequent runoff erosion. The long-duration group demonstrated accelerated erosion rates and greater erosion amounts. Concurrent monitoring demonstrated that transient pulse-like increases in pore-water pressure were strongly coupled with localized instability and gully wall failures, verifying the hydromechanical coupling mechanism during the failure process. These results quantitatively demonstrate the critical modulatory role of antecedent rainfall duration in determining erosion patterns in engineered disturbed loess, transcending the prior understanding that emphasized only the contributions of rainfall intensity or runoff. They offer a direct mechanistic basis for explaining the spatiotemporal heterogeneity of erosion and failure observed in field investigations of the engineered fills. The results directly contribute to risk assessments for land reclamation projects on the Loess Plateau, underscoring the importance of incorporating antecedent rainfall history into stability analyses and drainage designs. This study provides essential scientific evidence for advancing the precision of disaster prediction models and enhancing the efficacy of mitigation strategies. Full article

Extracting drainage networks from a digital elevation model (DEM) with massive cells is time-consuming due to depressions and flats, where flow paths to the outlet cannot be extracted using downslope gradients. Algorithms based on a priority queue are an efficient solution for this task. However, the existing algorithms depend on the insertion order in a priority queue to determine flow directions for cells with equal elevation. This dependency increases the sorting time in the priority queue. Our study developed an improved algorithm (referred to as DZFlood), adopting a dual priority queue. The queue considers elevation and flow distance to outlets. Cells sharing equal elevation and flow distance can be randomly arranged to reduce time costs. A secondary correction is applied to select a more tortuous yet shortest flow path for each flat cell. The visual assessment results show that the flow paths derived by DZFlood are more accurate than five existing algorithms and consistent with the real rivers and lakes. The computation efficiency of DZFlood is 19.2% faster, on average, than that of another priority-queue-based algorithm named LCP. The relative difference between their runtimes is greater when a larger DEM is used. Over a DEM with 3 × 10⁸ cells, DZFlood performs at least 28% faster than LCP. Full article

The exploration of gold and copper is essential for the sustainable development of mining worldwide and in Brazil, given the dependency on copper imports. This study aims to reassess and identify promising areas for sulfide prospecting in southern Brazil, with technologies, including radar images (ALOS PALSAR) and software (PCI Geomatics CATALYST Professional Complete, version 2023, QGIS 3.38.1 (Grenoble), Spring 5.5.6, and Orient 3.20.0), for the extraction and processing of tectonic lineaments. The comparative analysis between these linear structures and the drainage networks allows for the assessment of alluvial gold minerals and disseminated copper in andesites, as observed in the abandoned Seival mines. The methods employed include the extraction of tectonic lineaments and the evaluation of mineral occurrences in the Hilário (volcanogenic) and Arroio dos Nobres (sedimentary) formations of the Bom Jardim Group (Neoproterozoic) and their drainage networks. As a result, this article concludes that the main tectonic alignment directions for copper and gold occurrences disseminated in andesites are predominantly E–W, N–S, N 5° W, N 58° W, N 62° E, and N 23° E, and for alluvial gold N–S and N 45° W. These results are crucial for reassessing abandoned mining areas and identifying the primary mineral orientations in rocks and the predominant orientation of alluvial deposits, serving as structural controls for discovering new mineral occurrences. It is concluded that geotechnologies have expanded the possibilities for study, enabling a more detailed analysis of tectonic lineaments and drainage systems and providing a valuable prospective guide for gold and copper mineral exploration. Full article

Currently, one of the major threats to cities is the escalating risk of flooding, which is attributed to the alteration of climate and hastened urbanization. The purpose of this study was to introduce the Strahler ordering method for simplifying drainage networks and to avoid randomness in developing flooding models. A coupled hydrodynamic model that combines SWMM and LISFLOOD-FP was developed to simulate urban inundation. Results showed that the coupled model had satisfactory applicability for waterlogging simulation. The Strahler ordering method could construct clear topological relations of the drainage network and showed good performance in drainage network simplification. Higher-density drainage networks could increase peak discharge and total volume of discharge, while decreasing the maximum water depth and the total inundation area. Taking “5.29” rainstorm events as an example, compared to Level 3, the relative rates of change in the total flow and peak flow of Level 2 and Level 1 networks are −33.18% and −23.29%. The total inundation area was decreased from 14.14 ha to 1.43 ha when the level of drainage network hierarchy was increased from Level 1 to Level 3. This study highlights the importance of re-assessment of current and future urban drainage networks for coping with the changes in urban floods induced by local and large-scale changes. Full article

Decision-making in how to manage environmental issues around mine sites is generally a complicated task. Furthermore, the large amount of data and information involved in the management process is cumbersome. However, Decision Support Tools (DSTs) based on Geographic Information Systems (GISs) are of great interest to environmental managers in order to help them to make well-informed and thoroughly documented decisions regarding reclamation plans, especially for abandoned mine sites. The current study highlights the implementation of a cost-effective and efficient GIS-based database as a DST that will be used to assess and manage environmental challenges, particularly those related to water resources, such as hydrographic network issues surrounding mine sites. Based on GISs, a prototype of a national geodatabase was designed and implemented for Moroccan mine sites. It consisted of a set of GIS layers that facilitated the dissemination of an extensive array of multidisciplinary environmental data concerning Moroccan mines to decisionmakers. By applying GIS tools, such as buffer zone analysis, to environmental and hydrological datasets, high-priority mines requiring urgent intervention were identified based on their proximity to water resources, their acid mine drainage (AMD) potential, and their environmental impact on ecosystems. The results highlight the effectiveness of GIS-based approaches in assessing environmental risks, particularly concerning water resources, while also contributing to sustainable mining management in Morocco. Finally, using the GIS-based database is expected to raise the awareness of decisionmakers in government agencies and mining companies for implementing a reclamation program for mine sites. Full article

This study presents a comprehensive flood-hazard assessment and mapping of the Messapios River catchment in Evia Island, Greece, utilizing a combination of Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GISs). Flood-prone zones were identified based on five critical factors, which were determined to be the most influential in the watercourse when excessive discharge overwhelms the drainage network’s capacity: slope, elevation, proximity to stream channels, geological formations, and land cover. The Analytic Hierarchy Process (AHP) was applied to assign weights to these factors, while the final flood-hazard map was generated using the Weighted Linear Combination (WLC) method. The analysis revealed that 17.8% of the catchment, approximately 39 km², falls within a very high flood-hazard zone, while 18.02% (38.91 km²) is classified as highly susceptible to flooding. The flood-prone areas are concentrated in the central, southern, and western parts of the study area, particularly at the lower reaches of the catchment, on both sides of the main streams’ channels, and within the gently sloping, low-lying fan delta of the river. The study area has high exposure to flood hazards due to the significant population of approximately 9000 residents living within the flood-prone zones, a fact that contributes to the area’s potential vulnerability. Additionally, critical infrastructure, including five industrial facilities, the Psachna General High School, the local Public Power Corporation substation, about 21 km of the road network, and 21 bridges are located within the zones classified as having high and very high flood-hazard levels. Furthermore, about 35 km² of economically vital agricultural areas (such as parts of the Psachna and Triada plains) are situated in highly and very highly prone to floods zones. MCDA proved to be an effective and reliable approach for assessing and mapping flood-hazard distribution in the Messapios River catchment. The results provide valuable insights to assist decision-makers in prioritizing intervention areas and efficiently allocate resources. Full article