Search Results (198)

Flash floods represent an extreme hydro-geomorphological hazard in the arid and semi-arid regions in northwestern Saudi Arabia, presenting major risks to significantly expanding urban regions, infrastructure, and sustainable development projects of Saudi Vision 2030. The present study assesses the flood susceptibility of the Wadi Al-Disha catchment (4212.81 km²) using a high-resolution 12.5 m spatial resolution ALOS-PALSAR digital elevation model (DEM). A dual-model approach, combining the scale-dependent Morphometric Ranking Method and the scale-independent El-Shamy approach, was applied over district 18 sub-catchments to investigate, analyze, and calculate 15 hydro-morphometric effective parameters. The Ranking Method model assigned sub-catchment SC-18 to the high-flood susceptibility zone, SC-4 as a low-flood susceptibility unit, and the rest of the 15 sub-catchments as moderate risk, with SC-9, the largest sub-catchment, scoring highest among the moderate rank, just below the high-susceptibility rank threshold. On the other hand, the El-Shamy Approach model defined SC-3 and SC-8 as high-flood susceptibility classes, while the remaining 16 sub-catchments reflect moderate-flood susceptibility classes. This proposed assessment is a physically-based susceptibility evaluation extracted from terrain morphometry; it does not consider socio-economic exposure or hydrological (rainfall–runoff) information, which we identify explicitly as scope boundaries below. Comparative validation with a regional study states that a bifurcation ratio indicates that tectonic rifting impacts bifurcation parameter values ($Rb>4.0$), forcing the El-Shamy Approach model to underestimate susceptibility in structurally complex, high-relief landscapes. To underestimate the impact of these flood susceptibilities and secure sustainable infrastructure in the very close Prince Mohammed bin Salman Royal Reserve, the study suggests designing targeted dams in rapid-velocity headwaters and creating an early warning system on the plateaus upstream. Full article

Mountainous regions often experience a hydrological paradox where high monsoon rainfall coincides with severe dry-season water scarcity. The Chittagong Hill Tracts (CHT) of southeastern Bangladesh represent this challenge, as steep terrain, fractured geology, rapid runoff and limited monitoring constrain sustainable water-resource planning. This review critically evaluated water-resource characteristics, modeling approaches and decision-support strategies for the CHT. The literature shows that standalone models such as SWAT, MODFLOW and SWMM are useful for specific water-cycle components but do not adequately represent cross-domain processes including deep recharge, baseflow and stream–aquifer exchange in steep, complex terrain. International analogue studies indicate that coupled surface water–groundwater models can improve process representation when streamflow, groundwater-level, lithological and recharge data are available. However, their direct calibration and validation in the CHT remain limited by sparse groundwater monitoring and weak hydrogeological characterization. CHT-related and comparable Bangladesh studies indicate that machine-learning (ML) models can improve predictive suitability mapping when representative training data and independent validation are available; however, their relative advantage over the Analytic Hierarchy Process (AHP) is application-specific and depends on data quality, validation design, spatial autocorrelation, and model interpretability. The review identifies three key gaps: inadequate groundwater monitoring, absence of calibrated coupled modeling for fractured aquifers, and limited integration of socio-economic factors in rainwater-harvesting feasibility. Finally, a staged modeling pathway is proposed. Full article

This article presents a scoping review of the literature on digital twins (DTs) for sustainable groundwater resources management, which constitutes a very recent and rapidly expanding research field, with literature moving quickly from conceptual frameworks to application-oriented systems. The literature, selected through the PRISMA 2020 methodology, is organized into seven sub-topics: AI and ML foundations, digital twin architectures and frameworks, aquifer-scale DT applications, agricultural and water–energy–food (WEF) nexus DTs, basin and urban water DTs, sensing, monitoring and IoT infrastructures, and governance, resilience and socio-hydrology. This structure shows how the field is shifting from monitoring and prediction toward integrated decision support, where process-based models, machine learning surrogates, real-time sensing and optimization are combined to support drought mitigation, saltwater intrusion control, irrigation management, climate adaptation and basin-scale planning. Across the reviewed studies, the most recurrent contributions are the construction of hybrid model architectures, the use of DTs to close the loop between observation and control, and the growing recognition that groundwater management must incorporate governance, stakeholder decision-making and socio-hydrological feedbacks. At the same time, the literature still faces key limitations, especially uncertainty quantification, interoperability between models and data streams, transferability to data-scarce settings and limited validation under real operational conditions. Future research should therefore focus on physics-informed and explainable AI, federated and scalable DT architectures, stronger coupling with socio-hydrological and governance frameworks, and more field-tested implementations that can demonstrate robust performance across diverse hydrogeological and institutional contexts. Full article

The escalating impacts of climate change and anthropogenic pressures on vulnerable ecosystems demand digital tools that make advanced modeling more accessible to conservation practitioners. This study presents a TRL-4 prototype that integrates a configurable Digital Twin (DT) core with a generative AI conversational interface for conservation-oriented modeling in Doñana National Park, Spain, a UNESCO World Heritage site facing significant environmental challenges. The main contribution is not the training of specific ecological forecasting models, but the validation of an end-to-end workflow that allows users to configure, execute, inspect, and interpret a predictive system through natural language. The prototype supports the prediction of conservation-relevant ecological indicators, including Iberian lynx population dynamics and waterbird abundance, using heterogeneous environmental, climatic, hydrological, and socio-demographic datasets. The architecture connects a structured YAML configuration, heterogeneous environmental and biological datasets, automated machine learning training, database-backed traceability, dashboard visualization, and SHAP-based interpretability. Through representative executions, the prototype demonstrates that non-technical users can select target and explanatory variables, configure preprocessing options, launch model training, generate predictions, and review their outputs without directly editing configuration files or running code. Although the predictive metrics obtained in selected runs remain preliminary and should be interpreted as diagnostics rather than evidence of general forecasting skill, the results show that conversational DTs can substantially reduce technical barriers to ecological modeling. By combining generative AI, cloud infrastructure, reproducible machine learning workflows, and explainable AI, the proposed architecture provides a strong foundation for future conservation decision-support systems that augment expert judgment while preserving human oversight, transparency, and critical interpretation. Full article

This research develops a theoretical framework for understanding water-risk education as an essential civic competence in the context of climate change, hydrological extremes and socio-environmental vulnerability. Through a narrative conceptual synthesis of the recent literature on water governance, climate and disaster-risk education, sustainability competences and social representations of risk, the study examines how schools and universities can move from a general awareness of water problems to situated, anticipatory and action-oriented learning. The analysis identifies three mutually reinforcing dimensions of water-risk competence: cognitive understanding of water as a complex socio-environmental system; procedural capacity to analyze exposure, vulnerability, preparedness and adaptation; and attitudinal and ethical orientation toward responsibility, care, equity and collective decision-making. The research proposes a curricular model that connects hydrological-cycle education, flood and drought risk, territorial literacy, governance and the Sustainable Development Goals, especially SDGs 6, 11 and 13. The model is intended as a theoretical basis for future empirical studies, teacher-training designs and didactic proposals. It argues that water-risk education should not be restricted to emergency information or isolated scientific content, but should become a cross-curricular competence for forming citizens capable of interpreting uncertain climate scenarios and participating in resilient water governance. Full article

Flooding is one of the most damaging natural hazards worldwide, particularly in data-scarce watersheds where long-term hydrometeorological records are limited. This study focuses on the R’Dom watershed in the Meknes region, Morocco, and aims to improve flood susceptibility and relative flood risk assessment by integrating remote sensing, Geographic Information Systems (GIS), and multi-criteria decision-making (MCDM) approaches. The Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP/FAHP) were applied to evaluate flood hazard, vulnerability, and overall risk using seventeen conditioning factors, including topographic, hydrological, geological, land-cover, socio-economic, and infrastructure-related variables. The Flood Hazard Index (FHI), Flood Vulnerability Index (FVI), and Flood Risk Index (FRI) were calculated to produce flood susceptibility, vulnerability, and relative flood risk maps. Model validation was performed using a point-based flood inventory dataset composed of 900 locations, including 450 flood and 450 non-flood points, compiled from historical flood information, field observations, local information, official reports, and satellite-based interpretation. The dataset was divided into 70% for training and 30% for testing, and model performance was assessed using receiver operating characteristic–area under the curve (ROC-AUC) analysis. The AHP and Fuzzy AHP models showed good to excellent predictive performance, with testing AUC values ranging from 0.767 to 0.935. The AHP-based models achieved the highest testing performance, while Fuzzy AHP remained useful for representing uncertainty in expert judgment and gradual spatial transitions. The final flood risk map indicates that approximately 18.78% of the study area, corresponding to 240.62 km², is classified as having a high to very high flood risk, mainly around the Meknes conurbation and locally near the El Hajeb region. These results provide a practical decision-support tool for identifying priority areas for flood mitigation, land-use planning, and watershed management. However, the proposed GIS–MCDA approach produces relative flood susceptibility and risk classes and does not replace hydrological or hydraulic modeling for estimating flood depth, discharge, inundation extent, or return-period-based flood hazard. Full article

Effective watershed management requires spatial prioritization frameworks that simultaneously account for hydrological effectiveness, economic feasibility, and social readiness to support sustainable decision-making. However, most existing prioritization approaches rely primarily on biophysical indicators and rarely integrate socio-economic dimensions within a unified decision-making framework. This study proposes an integrated spatial prioritization framework for sustainable watershed management and applies it to the Taleghan watershed (341.4 km²) in the central Alborz Mountains, Iran. Hydrological responses were simulated using a calibrated HEC-HMS model, whereas soil erosion was estimated using a locally calibrated RUSLE model. Technical effectiveness was quantified based on reductions in peak discharge and soil erosion. At the same time, economic feasibility was evaluated through probabilistic cost–benefit analysis using Monte Carlo simulation to account for economic uncertainty. Social readiness was assessed using a validated Social Readiness Index (SRI) developed through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM). The Technical Effectiveness Index (TEI), Economic Feasibility Index (EFI), and Social Readiness Index (SRI) were integrated using a hybrid Fuzzy AHP–TOPSIS approach to derive a Composite Priority Index (CPI) for 29 Hydrological Response Units (HRUs). The calibrated HEC-HMS model demonstrated satisfactory performance (NSE = 0.79–0.91), while the calibrated RUSLE model accurately reproduced observed soil erosion (R² = 0.76; RMSE = 4.2 t ha⁻¹ yr⁻¹). TEI, EFI, and SRI values ranged from 0.09–0.94, 0.05–0.91, and 0.28–0.84, respectively, indicating substantial spatial heterogeneity across the watershed. The integrated prioritization classified 7.8% of the watershed as very high priority for intervention, whereas sensitivity analysis showed that approximately 65% of HRUs maintained stable priority rankings under ±20% variations in criterion weights. The findings demonstrate that integrating hydrological, economic, and social dimensions provides a more robust and implementation-oriented basis for watershed planning than conventional single-criterion approaches. Overall, the proposed framework provides a decision-support approach that may be adapted to other semi-arid mountainous watersheds following appropriate local calibration. Full article

Urban hillside areas concentrate hydrological and geotechnical risks intensified by accelerated urbanization and climate change. Although Nature-Based Solutions (NbS) are increasingly recognized as promising strategies for urban resilience, their application in hillside environments remains limited in scientific literature. This study integrates bibliometric analysis and a Systematic Literature Review (SLR) based on searches conducted across Scopus, ScienceDirect, and Web of Science from 2020 to 2025. Of the 4435 retrieved publications, only 92 addressed the association between NbS, hydrological modeling, and urban hillside environments. This reduction suggests that research integrating these themes remains limited within the adopted search criteria. The results demonstrate that hillside occupation in the Global South is conditioned by socio-spatial inequality, increasing exposure to landslides, erosion, and hydrological hazards. NbS were shown to reduce runoff peaks and contribute to slope stabilization when strategically positioned and adapted to slope gradient and hydrological connectivity; however, their effectiveness depends on continuous maintenance and monitoring. Comparative assessment indicates that most hydrological models are still applied in isolation, limiting the representation of coupled infiltration, soil saturation, and subsurface instability processes. The results indicate that effective NbS implementation in urban hillside areas requires integrated modeling approaches, interdisciplinary frameworks, and risk-oriented urban planning, particularly in socio-environmentally vulnerable contexts. Full article

This paper examines the traditional ponds of the Kathmandu Valley through the lens of Biophilic Urbanism, situating them within the broader framework of water management system. The study utilised a case study approach, drawing on field observations and secondary sources to analyse the spatial distribution, ecological roles, and socio-cultural significance of three pond typologies. Findings suggest that these ponds are not merely historical artefacts or utilitarian water bodies, but integral components of a sophisticated socio-ecological system that played a significant role in balancing hydrological functions with sensory experiences and cultural practices. The study highlights how these water bodies embody a holistic model of the integration of nature into the built environment, offering critical insights for urban resilience, climate adaptation, and sustainable urban development in contemporary cities. Full article

Flooding in the Kathmandu Valley has intensified in recent years due to rapid urbanization, unregulated land-use change, and insufficient drainage infrastructure. Existing flood hazard assessments are often based on low-resolution datasets and lack proper field validation. This study presents an integrated flood modeling framework that combines Unmanned Aerial Vehicle (UAV)-derived Digital Elevation Models (DEMs), field-based flood measurements, and hydrological simulations to assess urban flood hazards in the Bagmati-Nakkhu confluence, Nepal. High-resolution UAV-derived DEM and field survey data, including flood marks and high-water levels, were used as the foundation for the analysis. Hydrological modeling was conducted using the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) to estimate the peak discharges of the Nakkhu River (2000–2024), which were then used to derive design flows for return periods of 5 to 150 years using the Gumbel distribution. These flows were used as boundary condition inputs for the Hydrologic Engineering Center—River Analysis System (HEC-RAS) to simulate flood depth and inundation extent under different scenarios. Flood extents for the 27 September 2024 event were derived from Sentinel-2 imagery and validated against surveyed flood marks. Additionally, land use/land cover (LULC) mapping based on UAV data was used to support flood impact analysis. The results show that flood depths ranged from approximately 0.5 m to 2.8 m, with inundation areas increasing by 35–50% under extreme rainfall. Model validation demonstrated strong agreement with simulated results, with deviations generally within ±0.3–0.5 m. Scenario analysis further indicates that urban expansion significantly increases runoff and flood extent, particularly in low-lying areas near the river confluence. Socio-economic exposure analysis for the 27 September 2024 event indicates that approximately 2569 residents (56.4% of the study zone population) and 4.011 km (77.42%) of the local road network were exposed to inundation. Overall, the results demonstrate that integrating high-resolution UAV data, field observations, and hydrological modeling greatly improves the accuracy and reliability of flood hazard assessments in data-scarce urban environments. Full article

Climate change is reshaping agricultural systems by altering temperature and rainfall regimes, increasing the frequency of extreme events, and intensifying risks to crop productivity, water use, and farm decision-making. As climate–agriculture research expands rapidly, it becomes increasingly difficult to identify consolidated knowledge domains, emerging priorities, and evidence gaps. This study maps the structure and evolution of this literature using 219,261 Scopus-indexed documents selected from 290,560 records published between 1990 and 2025. A text-mining workflow combined BERTopic-based semantic modeling with supervised thematic classification into 18 macro-themes, while annual shares, z-scores, and document-level primary–secondary co-framing were used to assess temporal salience and cross-theme coupling. The results show sustained growth in research output, with 53.67% of publications produced between 2016 and 2025, and strong geographical concentration in the United States and China, which together account for 41.98% of the corpus. Hydrology and water management, crop production, impact assessment, and atmospheric processes remain central pillars, while socio-economic vulnerability, food security, sustainability, biotechnology, and greenhouse gas mitigation have gained prominence. The resulting evidence map provides a reproducible overview of the climate–agriculture knowledge landscape and can support research prioritization and policy design for climate-resilient agrifood systems. Full article

The aim of this systematic literature review was to analyse how hydrological–hydraulic modelling, through the assessment of surface stormwater runoff behaviour, can support the participatory management of socio-environmental risks such as flooding, flash floods, and landslides. For this, 31 publications dating from 2015 to 2025 were selected from Scopus, ScienceDirect and Web of Science databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, to examine the importance of integration between modelling and community participation for risk management. The results indicate that, despite recent advances, most studies still prioritise either the technical application of modelling or community participation, without articulating the two approaches in risk analysis and management processes. There is a scarcity of methods that effectively combine local knowledge into the collaborative construction of scenarios and in the continued use of modelling as a tool for monitoring flood risks to disseminate community information. It was observed that studies carried out in developing countries use simpler methods, using community participation as an alternative to the absence of data. In developed countries, however, studies use more advanced methodologies through institutionalised processes. In contexts marked by high vulnerability, the integration of community participation and technical tools, such as hydrological–hydraulic modelling, represents a promising pathway toward more equitable and efficient risk management practices, aligning with sustainability agendas such as the Sustainable Development Goals (SDGs). Full article

Aquatic environments that support tourism, including coasts, lakes, reservoirs, and estuaries, are experiencing accelerating eutrophication worldwide. This trend increases the frequency and intensity of algal blooms. These blooms undermine ecosystem services and weaken the socio-economic performance of destination areas. Despite these challenges, existing research remains fragmented. Aquatic sciences mainly examine nutrient enrichment and bloom dynamics. In contrast, tourism studies often treat blooms as episodic disturbances and rarely integrate exposure pathways, risk communication, or feedback to destination governance. This review synthesizes evidence across freshwater and marine systems to develop a coupled tourism–water ecosystem perspective. We link eutrophication drivers and bloom typologies to three dimensions. These are the degradation of tourism-supporting ecosystem services, compound health stressors, and communication filters. The first includes losses of water clarity and aesthetic value. The second involves multi-route exposure through contact, inhalation, and seafood ingestion. The third shapes perceived safety, trust, and behavioral adaptation. We further connect perceived health risks to observable tourist behaviors, including cancellation, destination substitution, and activity avoidance. These micro-level responses can aggregate into market-level demand contractions and consumption reallocation. They can also trigger regional economic cascades, including public management costs, employment impacts, and long-term reputational damage. Crucially, tourism is not merely a victim of blooms. It can also act as a reinforcing anthropogenic driver through wastewater burdens, infrastructure expansion, and pulse pressures. These pressures lower ecological resilience, especially under warming and hydrological stabilization. Finally, we identify governance leverage points. These include early-warning systems, threshold-based graded interventions, transparent risk communication, and integrated social–ecological modeling. These strategies can reduce uncertainty-driven losses and support adaptive destination management. Overall, this review reframes algal blooms as systemic social–ecological risks. It provides a structured basis for future empirical attribution and policy design in tourism-dependent waters under climate stress. Full article

Rapid urbanization and climate change are exerting unprecedented pressure on regional water resources, particularly in emerging megacities. This study examines the Xiong’an New Area (XNA) in the water-stressed North China Plain, where high-intensity urbanization coincides with rigorous ecological restoration mandates. To overcome the limitations of single-model assessments, a coupled SD–PLUS–InVEST framework was developed, integrating System Dynamics for socio-economic and policy drivers, Patch-Generating Land-Use Simulation for fine-scale urban expansion, and InVEST for hydrological process assessment. Projecting spatiotemporal water dynamics to 2035 under three Shared Socio-Economic Pathways (SSPs), results reveal that urbanization-driven water demand growth consistently outpaces climate-induced supply gains. While precipitation increases are projected to raise water yield by 8.91–19.58% by 2035, demand surges by up to ~26% under the extensive expansion scenario (SSP5–8.5), driven predominantly by impervious surface proliferation. External water transfers are projected to sustain 40–45% of total supply by 2035, yet this dependency introduces systemic vulnerabilities. Quantitative assessment further indicates severe spatiotemporal mismatches, with Seasonal Water Shortage Rates of 26.1–27.3% and a Spatial Mismatch Index rising from 0.44 to 0.98. These findings indicate that climate-driven precipitation increments alone cannot offset water deficits induced by unregulated urban sprawl, and that integrating strategic land-use planning, resilient infrastructure, and adaptive governance is essential for water security in rapidly developing regions. Full article

Climate change remains among the most pressing environmental challenges confronting the world, exerting profound pressure on both ecological systems and socio-economic development. To advance understanding of the evolution patterns and driving mechanisms governing hydroclimatic systems in arid and semi-arid regions, this study employed an integrated framework encompassing trend testing, change-point detection, periodicity and persistence analysis, and machine learning-based attribution. Focusing on the Mu Us Sandy Land from 1982 to 2023, we systematically investigated the spatiotemporal evolution, periodic characteristics, and driving mechanisms of hydroclimatic factors. Furthermore, future climate risks were assessed using CMIP6 multi-model data. The results showed that: (1) All four variables exhibited positive slopes, but only soil moisture showed a statistically significant long-term wetting trend (β = 0.025 × 10⁻³, p = 0.0008) and a clear global abrupt change in 2011; the upward tendencies of precipitation (p = 0.3946), potential evapotranspiration (p = 0.4970), and surface runoff (p = 0.1097) did not reach the 0.05 significance level. (2) Meteorological elements showed weak periodicity and strong anti-persistence (mean Hurst index = 0.379 for precipitation and 0.222 for PET), whereas hydrological elements exhibited clear seasonal–interannual periods and more random future variability with greater spatial heterogeneity (mean Hurst index = 0.436 for runoff and 0.414 for soil moisture). (3) Monthly changes were mainly associated with local surface processes. Vegetation dynamics were key predictors of precipitation, runoff, and soil moisture, while potential evapotranspiration was dominated by atmospheric demand, with limited influence from large-scale climate indices. (4) Under high-emission scenarios, imbalanced water–heat increases may lead to a higher likelihood of drought conditions. Full article