Search Results (185)

The paper investigates the integration of permeable pavements into sustainable urban mobility planning in Bologna, Italy. The study evaluates stormwater management benefits under the current and future climate conditions through hydrological and hydraulic modeling with EPA SWMM. A residential catchment of about 50 hectares was analyzed, with potential permeable pavement implementation on pedestrian paths and parking areas. Results show a 40% reduction in stormwater volumes under the current climate and 38% under future scenarios, confirming the resilience and effectiveness of green infrastructure. Findings support Bologna’s Urban Plan for Sustainable Mobility, highlighting permeable pavements as a strategy to enhance resilience and sustainability. Full article

Flood dynamics can be represented using a growing diversity of remote-sensing sources, digital elevation models (DEMs), hydrological/hydraulic models, and statistical validation techniques. However, no unified evidence-based framework is currently available for systematically comparing the documentary-methodological support of these alternatives. This study proposes a hybrid multicriteria index for assessing the documentary-methodological robustness of flood-mapping methodologies integrating remote sensing, DEMs, hydrological/hydraulic information, and statistical validation methods. Here, documentary-methodological robustness refers to the recurrence, traceability, structural support, and reporting consistency of methodological alternatives within the reviewed corpus; it does not represent technical accuracy, predictive performance, local suitability, or universal methodological superiority. A global documentary review comprising 173 case-study records was organized into six thematic dimensions: optical imagery, SAR imagery, image fusion, DEM information, hydrological/hydraulic information, and statistical validation. Variables and categories were normalized on a 0–1 scale and integrated into thematic base indices using a hybrid weighting strategy that combines author-defined methodological relevance with entropy-derived objective weights. A documentary coverage adjustment was incorporated to account for unequal representation among thematic dimensions. The results showed stronger documentary-methodological support for optical imagery and hydrological/hydraulic information, particularly for Landsat, NDWI, change detection, discharge data, water levels, and hydrodynamic modelling. SAR, image fusion, DEM, and statistical validation dimensions showed comparatively lower but still relevant documentary support. Sensitivity analysis across five $\lambda$ scenarios showed that five of the six representative methodological combinations retained their robustness class, whereas only the IRIH combination shifted from high to very high robustness. The proposed framework transforms a descriptive review into a quantitative and replicable decision-support instrument for screening methodological alternatives according to their documentary-methodological support. Independent empirical validation remains necessary before selecting a methodology for operational application. Full article

Agrivoltaic (AV) systems represent a promising strategy for integrating renewable energy production and agricultural activity on the same land unit, while contributing to soil water conservation under increasingly frequent drought conditions. This preliminary, single-site case study investigates the effects of a horizontal biaxial tracking AV system on soil water dynamics in a durum wheat field in the Po Valley (Borgo Virgilio, Mantua, Italy) over a full monitoring period, covering the final crop growth stages and the post-harvest bare soil phase (May–December 2024). Monitoring of soil temperature, volumetric water content (VWC), and soil water potential (SWP) was conducted at four depths (15, 30, 45, and 60 cm) at one representative monitoring station per treatment, comparing soil under AV cover (AVC) and in unshaded conditions (UC), located 10 m apart. Paired VWC and SWP measurements were used to derive site-specific soil water characteristic curves (SWCCs) and to calibrate the agro-hydrological model CRITERIA-1D, which was used to estimate available water (AW) in the first 80 cm of depth for both treatments. Measured VWC values were higher in the AVC profile than in the UC profile at all monitored depths throughout the May–September period, with differences persisting, although at lower values through October–December. Estimated AW was consistently higher under AVC than in UC during both the dry and wet periods. Despite higher VWC, the AVC profile showed more negative average SWP values at all depths during summer. This pattern is consistent with the shape of the derived SWCCs and may point to differences in water-retaining capacity between the two profiles, possibly related to structural modifications induced by 13 years of AV system operation. These preliminary findings suggest that AV systems could potentially improve soil water availability in the root zone of rainfed cereal crops and propose the hypothesis that long-term AV cover may act as a driver of changes in soil hydraulic properties, with implications for the sustainability and climate resilience of dryland farming systems. However, given the design of this case study, with only one monitoring point per treatment, the observed differences reflect the specific monitored locations and cannot fully disentangle the AV treatment effect from pre-existing spatial heterogeneity in soil properties. The preliminary results obtained in this study should therefore not be generalised beyond the specific conditions of this case study, and the interpretations proposed here should be treated as unproven hypotheses rather than established conclusions. Further studies with spatial replication and multi-year monitoring are needed to confirm these patterns. 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

This study evaluated a cost-effective UAV-based multi-sensor approach for characterizing river corridor conditions during and after moderate floods in two mountain river corridors in Northern Utah, USA: the Logan River and Blacksmith Fork River. These study reaches included urban, rural, and agricultural areas, hydraulic structures and bridges, and fish passage structures. A DJI Matrice 300 UAV was used with two separate payloads: an AgEagle Altum-PT multispectral camera and an R3 Pro V2 two-return LiDAR system. The workflow included UAV flight planning and data collection, post-processing of the multi-spectral and LiDAR sensor data, spatial resolution and accuracy assessment, and interpretation of the resultant data. The multi-spectral post-processing produced pansharpened orthomosaics with a spatial resolution of 0.0432 m, while the UAV LiDAR produced DSM/DTM products at 0.05 m resolution. LiDAR accuracy assessment showed vertical RMSE values of approximately 0.0602 m for the Blacksmith Fork and 0.0782 m for the Logan River. The results showed that multispectral imagery and 2-band LiDAR provided a cost-effective means for detailed remote sensing with each sensor providing complementary information for flood and river corridor assessment. Multispectral imagery supported interpretation of flood extent, vegetation condition, relative turbidity, and thermal patterns, while LiDAR captured terrain and surface features such as banks, levees, floodplain surfaces, channel modifications, and structures. The integrated datasets supported maximum flood extent mapping and flood-level estimation. These datasets can support reach-scale hydraulic modeling, catchment hydrology, river corridor ecology, floodplain conditions, and real-time monitoring of floods, in addition to quantification of flood hazards or post-flood impacts for municipalities and insurers. Full article

Traditional urban drainage systems face increasing pressures due to growing urbanization and climate change, which amplify stormwater volumes beyond the design capacity of sewer networks. These conditions often result in combined sewer overflows (CSOs) and wastewater treatment plant (WWTP) bypasses, discharging untreated, nutrient-laden effluents into receiving water bodies. Elevated nitrogen (N) and phosphorus (P) loads from these events exacerbate eutrophication and harmful algal blooms in lakes. The ENVISION project (Lake Pollution: Integrating Nature-Based Solutions Into Environmental Urban Planning for Risk Mitigation) addresses the impact of urban discharges on lakes and evaluates the potential of Nature-Based Solutions (NBSs) to mitigate N and P releases from CSOs. A coupled hydrological–hydraulic and probabilistic modeling framework is applied to the Lake Maggiore (Northern Italy) catchment as a case study, to assess the potential of NBSs in reducing CSO events and, consequently, the N and P loads discharged into the lake. Preliminary results from the hydrological–hydraulic approach are presented. Full article

The Cau river basin in northern Vietnam is experiencing increasing pressures on water resources due to rapid urbanization, industrial development, agricultural expansion, and inadequate wastewater management. Understanding the interactions between surface water, groundwater, and water quality is essential for developing effective and sustainable water management strategies. This study developed and applied a coupled MIKE SHE–MIKE 11 framework to simulate surface–groundwater connectivity and its influence on water quality dynamics in the Cau river basin. Hydrometeorological and water quality datasets collected during 2023–2024 were used to calibrate and test the integrated model at key monitoring locations, including Cha, Phuc Loc Phuong, and Dap Cau stations. The hydrological component demonstrated satisfactory performance, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.55 to 0.79 for water level simulations, indicating a reliable representation of surface and subsurface flow processes. Simulated river–aquifer exchange fluxes revealed pronounced spatial variability across the basin. Upstream reaches predominantly functioned as groundwater recharge zones, whereas the middle and downstream sections exhibited dynamic bidirectional exchanges governed by river stage fluctuations, hydraulic gradients, and local hydrogeological conditions. Water quality simulations for BOD₅, COD, NH₄⁺, total nitrogen (TN), and total phosphorus (TP) showed good agreement with observations, with calibration and testing errors generally remaining below 25%. Incorporating surface–groundwater interactions improved the representation of pollutant transport, residence time, and nutrient accumulation processes compared with conventional river-only simulations. The results demonstrate that river–aquifer connectivity plays a critical role in regulating both hydrological processes and water quality conditions in the basin. The coupled modeling framework provides a robust scientific basis for identifying critical interaction zones, assessing pollution risks, optimizing monitoring programs, and supporting integrated water resource planning. By explicitly linking hydrological connectivity with water quality dynamics, the proposed framework serves as a practical decision-support tool for sustainable water resource management in the Cau river basin and other river–aquifer systems facing increasing environmental pressures and progressive water quality degradation. Full article

Flood risk assessment in urban areas is becoming increasingly important due to climate change and rapid urbanization. This study presents an integrated flood risk modeling framework for the city of Volos, Thessaly, Greece, coupling the Soil and Water Assessment Tool (SWAT) with HEC-RAS 2D for comprehensive hydrological–hydraulic analysis. The study area is characterized by complex geomorphology, intense urban development and the presence of torrent streams (Xirias, Krausidonas and Anavros) with a total catchment area of 166.25 km². Geospatial and hydrological datasets, including land use, soil types and a Digital Elevation Model, were integrated within SWAT to generate 19-year daily discharge time series. These outputs were linked to HEC-RAS 2D boundary conditions to simulate flood extent, depth and velocity under two scenarios: Baseline (Manning n = 0.05) and Nature-Based Solutions (Manning n = 0.15). Results show that NBS interventions reduce flooded area by 25.3%, maximum depth by 20.4%, and affected buildings by 28.7%, with a Benefit–Cost Ratio of approximately 2.2. The methodology provides valuable input for flood risk management, spatial planning and civil protection strategies in Mediterranean urban environments. Full article

This study addresses the impact of the location of transverse barriers on debris flow in the Libertad sub-basin, in Chosica, Peru. Intense seasonal rainfall in this region causes destructive flows that threaten infrastructure and human lives. Using geographic information system tools, hydrological models and hydraulic simulations, scenarios with barriers installed at different distances from the debris source were evaluated. The results indicate that the barrier located closest to the source (0.3L) is the most effective, achieving a reduction in velocity of 12.9% at the most critical urban monitoring point, the greatest volume retention capacity (790.02 m³), and the greatest decrease in flow escaping from the study area (65.7%). In contrast, barriers at 0.5L, 0.7L, and 0.9L show progressively lower effectiveness. This finding highlights the importance of a strategic design that optimises the position of the barriers according to the geomorphological and hydrological characteristics of the area. It is concluded that an adequate distribution of barriers, complemented with integrated watershed management strategies, can considerably mitigate the risks associated with debris flows in vulnerable urban areas. Full article

Tropical mid-size cities on alluvial floodplains face compounded flood challenges combining pluvial accumulation from intense convective storms, regulated river overflow, and aging drainage networks. This study presents an integrated framework for Monteria, Colombia (~450,000 inhabitants; Sinu River, Caribbean lowlands), within Colombian Decree 1807/2014 and structured in four phases. (1) Hazard: A Rain-on-Grid 2D HEC-RAS 6.6 model covering 4090 ha, calibrated against four gauged events, identifies three dominant pluvial mechanisms (poor hydraulic connectivity, limited evacuation capacity, downstream channel overflow), plus 17 critical fluvial erosion points affecting ~289 properties at 100-year return period. (2) Vulnerability: Depth-damage functions from 1465 household surveys yield 36.36% of 3015 assets in high risk and 57.77% in medium risk. (3) Measures: Scenario M2 (channel widening plus dikes, land-raising, retention lagoons) removes 80 ha of flooding while displacing 28 ha at COP 845 million pre-design cost. Non-structural measures include a Sustainable Urban Drainage Master Plan, IoT-based Early Warning System, minimum construction-elevation map, and land-management instruments. A Monte Carlo residual-risk model reduces baseline risk to 19.9% under full implementation. (4) Emergency: A February 2026 cold-front event was addressed with a 4300 m perimeter dike and six pump stations deployed jointly by the Regional Environmental Authority (CVS) and Municipal Administration. Full article

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

Indian metropolitan cities such as Mumbai grapple with rapid urbanisation, extreme urban density, high built-up areas, loss of green cover, and shrinking open spaces, resulting in increased impermeable surfaces, urban heat island effects, and frequent flooding occurrences. Modern stormwater management has increasingly been characterised by integrated grey-green approaches; however, cities in the Global North benefit from established policies, technical expertise, and financial resources that enable the systematic and large-scale integration of Blue-Green Infrastructure (BGI) through district-wide geospatial assessment frameworks, unlike many cities in the Global South. Despite growing interest in nature-based stormwater solutions, there remains a dearth of geospatial empirical research from India examining the placement, distribution, performance, and functionality of BGI integrated with existing stormwater management systems in cities such as Mumbai. Furthermore, hydrological modelling using tools such as the Storm Water Management Model (SWMM) for the design, planning, and implementation of BGI in Indian cities remains largely unexplored. This study explores the role of BGI strategies in improving urban stormwater management within high-density Indian cities under a 25-year return period extreme rainfall scenario. Using an integrated approach that combines QGIS-based spatial analysis with EPA-SWMM hydrologic-hydraulic modelling, the research examines runoff behaviour, identifies flooding hotspots, and evaluates the effectiveness of Low Impact Development (LID)-based BGI measures such as permeable pavements, infiltration trenches, and green roofs applied at the ward level in Mumbai’s F/North and G/North Wards. Detailed land use classification, spatial mapping, and rainfall simulation corresponding specifically to a 25-year return period rainfall event was used to assess pre- and post-intervention conditions. The findings indicate that the applied BGI measures led to a 12.6% reduction in peak runoff (137.6 m³/s to 120.2 m³/s) and a 5.5% decrease in total runoff volume (783,510 m³ to 740,410 m³). More importantly, the peak flooding flow rate decreased by 45% (94.1 m³/s to 51.7 m³/s), demonstrating that BGI measures can efficiently reduce peak flooding flows by extending runoff hydrographs during extreme rainfall events. These findings are specifically applicable to the simulated 25-year return period extreme rainfall scenario and may vary under different rainfall intensities or return periods. Less extreme events could potentially experience even greater relative reductions or prevent flooding altogether, while also easing downstream hydraulic loads. Overall, strategically placed BGI interventions can significantly reduce surface runoff and peak flow, thereby enhancing stormwater resilience within spatially constrained urban environments. This study provides a replicable, data-driven framework for catchment-scale stormwater planning in dense Indian cities under extreme rainfall conditions, offering practical insights into methods, local contextual considerations, and spatial planning strategies for policymakers and urban planners seeking to retrofit and adapt existing infrastructure under increasing hydrologic stress and climate variability. 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

Introduction: The upper Arlanza River (Duero Basin, Burgos, Spain) hosts a genetically distinct local lineage of brown trout (Salmo trutta fario), the “Arlanza strain”, largely free from hatchery-derived introgression, alongside other native cyprinids of conservation concern, including the Iberian chub (Achondrostoma arcasii, Vulnerable—IUCN). The river also supports the Iberian desman (Galemys pyrenaicus, Endangered—IUCN) and Eurasian otter (Lutra lutra). Despite these values, the study reach presents multiple transverse obstacles limiting longitudinal connectivity and degraded riparian cover in critical sections due to livestock erosion, compromising habitat quality for all species. Objective: This study aimed to design engineering interventions to improve fluvial and riparian habitat in a 4 km reach of the upper Arlanza River, restoring longitudinal connectivity and thermal refuge availability while strictly preserving the genetic integrity of the native Arlanza trout strain. Methodology: The reach was characterised through electrofishing surveys, riparian quality assessment (modified RQI index), hydraulic refuge evaluation (IR index), and hydrological analysis based on a 30-year flow record. Brown trout population dynamics were modelled using dimP 1.0 software, with a comparative analysis between upstream (Quintanar de la Sierra village) and downstream (Vilviestre del Pinar village) sampling points to identify connectivity bottlenecks. Engineering works were scheduled to avoid reproductive periods of all target species. Results: The upstream population showed a rejuvenated age structure (density: ~1.40 ind/m; mean length: 12.0 cm), consistent with good spawning conditions but limited growth capacity due to cold temperatures and low summer flows. The downstream point exhibited a severely reduced population (~0.10 ind/m), indicating marked loss of connectivity and habitat degradation. Priority intervention zones were identified in the Camping and lower Prado Mayor sub-reaches. Proposed measures included weir notching to restore fish passage, livestock watering points to reduce bank erosion, and riparian restoration by planting native species (Populus tremula, Betula alba, Salix spp.) protected with fences. Conclusions: Restoring longitudinal connectivity and riparian cover in the upper Arlanza River are essential to protect the genetically valuable Arlanza trout strain, the endangered G. pyrenaicus, and other native fish species, providing a transferable framework for headwater fluvial restoration that jointly addresses biodiversity conservation and genetic resource protection. Full article

This study adapted and applied a spatially distributed analytical model to estimate the annual representative leached fraction and the annual potential leached mass of atrazine in the Cauquenes catchment in Chile under contrasting Mediterranean hydroclimatic conditions. The model was based on van der Zee and Boesten and Rakonjac et al. and was modified to account for the strong seasonality of precipitation and evapotranspiration by using representative daily hydrological conditions derived from monthly averages. Spatially distributed soil, climate, land-cover, and atrazine application data were integrated at the pixel scale, including locally corrected soil organic carbon, hydraulic properties, precipitation, evapotranspiration, leaf area index, and annual atrazine dose. The model was applied to two contrasting years, 2018 and 2023, and outputs were aggregated at the pixel, land-cover, hotspot, and catchment scales. The results showed a marked hydroclimatic control on potential atrazine leaching. In the drier year, 2018, both the annual representative leached fraction and the annual potential leached mass were generally very low across the catchment, whereas in the wetter year, 2023, moderate-to-high leaching values became much more spatially extensive, and hotspot areas expanded substantially. At the catchment scale, potential leached mass increased from 0.088 kg in 2018 to 179.784 kg in 2023, while the percentage of applied mass potentially leached increased from 5.50 × 10⁻⁵% to 0.112%. Land-cover classes influenced the results both through the spatial allocation of atrazine application and through LAI-dependent partitioning of evapotranspiration. Global sensitivity analysis using the Morris method identified K_OC and DT50 as the dominant controls on annual potential leached mass, and spatial uncertainty propagation was performed. Overall, the proposed framework provides a potential annual screening estimate and may serve as a preliminary screening tool to prioritize areas for targeted monitoring and future model benchmarking in Chile. Full article