Search Results (820)

Mesh resolution is a central source of numerical uncertainty in two-dimensional flood modelling because it controls terrain representation, wetting–drying transitions, computational cost, and the transferability of calibrated parameters. Yet controlled evidence remains limited on whether two widely used solvers exhibit the same resolution response patterns across flood magnitudes. This study compares calibrated model configurations developed in HEC-RAS 2D version 7.0 and SMS 13.0–TUFLOW Classic for a mountainous to low-gradient reach of the Little River, Tennessee, USA, using identical terrain, land cover roughness, and hydrological boundary data. The models were calibrated with the 6–11 April 2025 event and independently validated with the 11–16 March 2026 event. A factorial experiment comprising two models, five cell sizes (15–55 ft), and five flow conditions (the observed event and Q₅₀, Q₁₀₀, Q₂₀₀, and Q₅₀₀ design floods) resulted in 50 simulations. The study simultaneously evaluates resolution sensitivity within each calibrated model and inter-model convergence under a common scenario matrix. Inundation extent was most resolution-sensitive during the lower-magnitude observed event, whereas inter-model extent differences decreased as flood magnitude increased. Outlet hydrographs and peak discharges were comparatively stable, but local water-depth distributions became less consistent as the mesh was coarsened, particularly in SMS-TUFLOW. HEC-RAS retained greater depth consistency, plausibly because its sub-grid property tables preserve cell-scale volume and conveyance information. Overall, mesh adequacy was output- and solver-specific, indicating that grid selection should be based on the intended hydraulic output and supported by explicit spatial stability testing. Full article

We present a global metadata comprising results from studies investigating the effects of cover crops (CCs) on six key soil hydraulic properties, namely total porosity, infiltration rate, saturated hydraulic conductivity, water retention at field capacity and permanent wilting points, and available water holding capacity. This data repository is the result of a global meta-analysis entitled “Cover Crop Performance and Functional Groups Regulate Improvements in Soil Hydrology: A Global Meta-analysis”. Globally, numerous studies have investigated the role of CCs on soil hydraulic properties, but the results have varied across sites and years. Hence, the objective of the meta-analysis was to synthesize the existing knowledge base to assess the overall effects of CCs on these soil hydraulic properties and evaluate how environmental and management factors moderate these overall CC responses. We searched for peer-reviewed research articles published through 5 October 2024 in the ISI Web of Science database, with reference checking following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 146 relevant articles were identified from which data on CC responses were extracted. The metadata consists of 1007 pairwise observations comparing CC vs. no-CC controls across diverse geographic regions worldwide. Moreover, we collected associated metadata for each pairwise comparison that includes a broad set of bibliographic, geographic, soil, climate, and management variables. Categorical variables were grouped into pre-defined factor levels or classes. Missing soil and climate data were filled using publicly available data products. Our data repository can be a valuable resource for the field and modeling community to identify knowledge gaps and guide future research. Full article

Alternanthera philoxeroides (A. philoxeroides) is one of the worst invasive alien species in the world and poses serious threats to both ecological security and agricultural production in China. However, the effects of environmental factors on its growth across different habitat types remain insufficiently understood. To reveal the determining natural factors influencing the growth of A. philoxeroides, fieldwork, including 50 sample quadrats in six field sites, was conducted in the Yangtze River Basin of Hubei Province. In every quadrat, soil properties, soil moisture, pH, soil temperature, soil nutrients (N, P, and K), light intensity, plant cover, and aboveground plant fresh biomass were measured. Cluster analysis showed that A. philoxeroides habitats can be divided into five cluster groups: wetland, grassland, forest understory, farmland and aquatic communities. The results showed that both total community biomass and A. philoxeroides biomass were positively correlated with water content, whereas no significant relationships were found between biomass and soil nutrients, including N, P, and K. The biomass of A. philoxeroides was significantly higher in aquatic habitats, while no significant differences were observed among the other four terrestrial habitats. These findings suggest that hydrological management, together with early control in aquatic habitats, may be an effective strategy to limit the spread of A. philoxeroides. Full article

Hydroclimatic evidence is often carried too directly from global-scale attribution studies into local water-resources decisions, despite important differences among variables, methods, and spatial scales. This critical narrative review examines how climate variability, statistical trends, detection, attribution, non-stationarity, and risk should be distinguished when interpreting changes in precipitation, drought, streamflow, floods, groundwater, and water availability. The review compares global assessments, Mediterranean studies, and selected local examples to clarify what each line of evidence can—and cannot—support in adaptation planning. Human influence on global warming is unequivocal, and increases in atmospheric evaporative demand are well supported across many regions; anthropogenic influence has also been detected in several large-scale water-cycle responses. Historical changes in precipitation, river flooding, groundwater, and local drought remain spatially heterogeneous because internal variability interacts with circulation, storage, landscape properties, abstraction, infrastructure, and demand. Statistically significant trends do not by themselves establish hydrological importance or causation, while non-significant local trends do not imply an absence of operational risk. On this basis, the review proposes a scale-aware way of matching hydroclimatic evidence with system vulnerability and the degree of commitment involved in adaptation. Low-regret and adjustable measures can address current vulnerabilities under uncertainty, whereas costly, long-lived, or difficult-to-reverse interventions require stronger local evidence and stress testing across plausible futures. 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

Sequential extreme hydrological events driven by climate change pose severe risks to both crop survival and groundwater quality in flood-prone agricultural regions. The current study investigated the long-term soil hydrodynamics and potential perfluoralkyl and polyfluoralkyl substance (PFAS) leaching in a mountain tea (Sideritis raeseri) plantation in Thessaly plain, Greece, before, during and after the consecutive extreme storms Daniel and Elias (in September 2023). An 8-year simulation was performed, covering the years 2018–2025, including an initial calibration/validation phase (2018) followed by the 2019–2025 simulation of hydrodynamics and potential short-chain PFAS leaching, under two hypothetical solute transport scenarios. Model HYDRUS-1D was calibrated and validated using field data (precipitation, irrigation, evaporation, transpiration and biomass) from the 2018 growing season under four irrigation treatments (100%, 75%, 50% and 0% of ET_c, where ET_c is crop evapotranspiration) to secure reliable soil hydraulic properties and was subsequently extended to simulate soil water dynamics until 2025, when crop failure was observed. To quantify environmental risks, two hypothetical solute transport scenarios with different initial depths of contamination (0–20cm and 0–100 cm) and an initial short-chain PFAS soil pore water concentration of 0.1 ppb (equal to upper EU limit) were executed exclusively for the 2019–2025 period to simulate the potential leaching of the contaminants through the soil. The simulation successfully captured the agronomic reality, showing that prolonged waterlogging conditions persisted, suggesting a severe risk of root anoxia, potentially leading to the subsequent crop failure in 2025. Furthermore, solute transport modeling captured the leaching process and predicted that under typical Mediterranean hydrological conditions, short-chain PFAS had already potentially leached beyond the root zone prior to the extreme events. However, the sequential floods acted as an acute hydraulic flush, evacuating the residual chemicals towards deeper soil layers. Moreover, the 0–100 cm contaminated profile represents the most critical hazard to the underlying aquifer, delivering a higher load and elevated risks for the groundwater table than the shallow (0–20 cm) contamination profile. Full article

Biochar is a porous, carbon-rich soil amendment that can enhance soil water retention capacity by modifying pore structure and physicochemical properties. Understanding the soil−water characteristic curve (SWCC) of biochar-amended soils is essential for evaluating their hydrological behavior and promoting the application of biochar in engineering practice. Given the demonstrated feasibility and accuracy of machine learning methods for predicting soil parameters, this study employed six machine learning models, namely, decision tree, random forest, XGBoost, LightGBM, CatBoost, and artificial neural network, to predict the SWCC of biochar-amended soils based on a constructed dataset. Feature importance analysis and partial dependence analysis were further conducted to reveal the influence patterns of key variables. The results indicate that all six models exhibit good predictive capability, with gradient boosting models (XGBoost, CatBoost, and LightGBM) performing best. Suction is the dominant factor controlling the volumetric water content variation, while soil particle-size distribution and dry density provide the physical basis for water retention. Biochar content, pyrolysis temperature, and feedstock type further modulate the water retention capacity of amended soils. Overall, the findings demonstrate that machine learning approaches can effectively predict the SWCC of biochar-amended soils and provide insights into the controlling mechanisms of soil water retention. Full article

Progressive climate change and rapid urbanization are placing increasing pressure on water resources, highlighting the need for local stormwater retention and reuse in residential areas. This study aimed to evaluate the influence of geological-engineering and geotechnical conditions on the design of stormwater management systems and to develop a conceptual solution for a residential property in Kraków, Poland. Geological, hydrogeological, hydrological, and topographic conditions were assessed using archival data verified through field investigations. Rainfall data from the Kraków-Balice meteorological station (2014–2023) were used to estimate rainwater harvesting potential and evaluate system performance. The proposed system consists of surface and subsurface drainage, two storage tanks, and an infiltration well. The first tank collects roof runoff for non-potable domestic use, whereas the second stores water from the drainage system and paved surfaces for irrigation. The annual rainwater harvesting potential was comparable to the non-potable water demand of a five-person household, while water collected from the drainage system and paved surfaces was sufficient for irrigation. Monthly precipitation analysis revealed pronounced seasonal variability, with winter shortages and summer surpluses. Integrating the storage tanks with the infiltration well enabled effective management of excess stormwater while supporting groundwater recharge. The results demonstrate that consideration of geological-engineering conditions is essential for the effective and sustainable design of residential stormwater management systems. Full article

Accurate source identification is essential for the prevention and control of heavy metal(loid)s (HMs) contamination in farmland soils. Conventional source-apportionment approaches often rely on limited indicators and expert judgment, which can increase uncertainty in source interpretation. In this study, 800 topsoil samples collected from farmland in Ningxia, together with 24 environmental and anthropogenic variables, were used to develop element-specific machine learning models for Cd, Cr, Hg, Pb, and As. Five algorithms, including random forest (RF), Extra Trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and least absolute shrinkage and selection operator (LASSO-stacking), were compared, and Shapley additive explanations (SHAP) were used to interpret the variables statistically associated with the spatial variability of each metal. Positive matrix factorization (PMF) was further applied to identify potential source categories. Model performance varied among metals, indicating element-specific differences in predictability and controlling factors. SHAP analysis showed that precipitation, temperature, spatial coordinates (longitude/latitude), cropping intensity, and industrial-source fine particulate matter emissions were among the most important associated factors, although their relative importance differed across elements. PMF results suggested that 73.8% of Cd was associated with agricultural inputs, 87.6% of Hg with industrial atmospheric deposition, and 68.4% of Cr and 46.7% of As with natural sources, Pb showed relatively weak predictability, suggesting that its spatial variability may be influenced by unmeasured local or legacy inputs, while model-derived associations indicated possible links with spatial gradients, terrain-hydrological conditions, wind-related variables, and soil carrier properties. Overall, this study presents an integrated framework that combines machine learning-based associated-factor analysis with receptor-model source apportionment, providing a more nuanced understanding of HMs contamination in farmland soils and supporting targeted soil pollution prevention and control. Full article

Flash floods are among the most destructive natural hazards, often causing substantial loss of life and severe damage to infrastructure and property. Predicting flood-prone areas remains challenging because flood generation is controlled by complex interactions among topographic, hydrological, climatic, and environmental factors. In this study, six machine learning algorithms—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree Classifier (DTC), AdaBoost, and Artificial Neural Network (ANN)—were developed to predict flash-flood inundation locations using satellite-derived flood inventories from two major rainfall events in Wadi El-Darb and Wadi El-Allaqi, Egypt. Model performance was evaluated using accuracy, precision, recall, and F1-score. During model development, Random Forest and Decision Tree Classifier achieved the highest prediction accuracy (94%), followed by AdaBoost and ANN (92%), while Logistic Regression (89%) and SVM (88%) also produced satisfactory results. To evaluate model generalization, the trained models were independently validated using a rainfall event in Wadi Hodein (Egypt) and a major flash-flood event that occurred in Oman during April 2024. The external validation showed that AdaBoost achieved the highest predictive performance in both validation basins, with accuracies of 87% for Wadi Hodein and 83% for Oman, providing encouraging initial evidence of applicability across hydrologically similar arid watersheds, While AdaBoost and Logistic Regression maintained satisfactory performance during external validation, other algorithms exhibited noticeable reductions in recall and F1-score, particularly in the Oman case study, indicating variability in model generalization across independent watersheds These findings suggest that the proposed framework may support flood susceptibility assessment in ungauged arid environments with comparable hydrological characteristics, although further validation across a wider range of climatic and geological settings is needed. Overall, the results highlight the value of integrating satellite remote sensing with machine learning to support flood hazard assessment, disaster preparedness, early warning systems, and flood risk management in data-scarce regions. Full article

To elucidate the elevational differentiation patterns of soil physical properties and nutrient contents within the Yuanyang Hani Terrace system and to clarify their influence on the spatial distribution of vegetation root systems, we systematically collected soil and root samples across varying elevations, slope aspects, and soil depths. A combination of soil physicochemical analyses, root distribution quantification, correlation analysis, and principal component analysis (PCA) was employed. The results indicated that soil bulk density exhibited a unimodal trend along the elevation gradient, initially decreasing and then increasing, with the following order: high-elevation sunny slope > high-elevation shady slope > low-elevation sunny slope > low-elevation shady slope > mid-elevation sunny slope > mid-elevation shady slope. The lowest bulk density values (1.10–1.17 g cm⁻³) were observed at mid-elevations, where porosity and infiltration rates reached their maxima (>54% and 1.31–1.34 mm min⁻¹, respectively), whereas the poorest conditions were found at high elevations. Shady slopes consistently outperformed sunny slopes, with the greatest aspect-induced divergence occurring at mid-elevations (bulk density difference: 0.079 g cm⁻³). An anomalous combination of a sharp increase in bulk density (1.33 g cm⁻³), alongside simultaneous peak porosity (54.74%) and infiltration rate (1.32 mm min⁻¹), was detected in the 20–40 cm layer, which was attributed to compaction-induced alterations in pore configuration while preserving preferential flow pathways. Most nutrient variables exhibited optimal levels at mid-elevations; however, total potassium and available phosphorus displayed maximal values at low elevations, likely related to chemical weathering intensity and plant uptake competition. Root systems were predominantly concentrated in the surface layer (0–20 cm), accounting for 87.88% of total root abundance, and showed significant positive correlations with soil moisture and porosity (r ≥ 0.89) and significant negative correlations with bulk density (r ≤ −0.97). The first principal component of PCA (explaining 73.4% of total variance) was dominated by moisture, porosity, and infiltration rate, whereas nutrient loadings were relatively low, indicating that physical hydrological processes constituted the primary drivers of environmental differentiation. Collectively, these findings elucidate the coordinated regulation of soil multi-attribute variations by elevation, slope aspect, and soil depth, thereby providing a theoretical basis for the sustainable management of terrace agroecosystems. Full article

Climate change models point to a possible rise in air temperature, ranging from 2 °C to 4 °C. Global changes will therefore have a particularly pronounced effect on the functioning of shallow water bodies known as peat ponds, i.e., habitats formed after peat extraction in peatlands. However, the extent of this impact is still unknown. The main objective of the study was to determine the impact of water level and water physicochemical properties on the functioning of selected groups of aquatic organisms in peat ponds of different origins. The study was conducted in spring, summer and autumn of the years 2024 and 2025, in 10 peat ponds with different trophic status and typology, located in the Polesie National Park in eastern Poland. Considerable fluctuations in water levels, resulting from a scarcity of precipitation, accelerated the drying out and overgrowth of peat ponds, particularly the alkaline and acidic types. Within the zoocoenotic communities, a significant decline in the abundance of planktonic species and an increase in the abundance of littoral or periphytic species were recorded (F = 6.24, p < 0.015). Regarding trophic structure, an increase in the abundance of mixotrophic species and a decrease in the abundance of top-level predators were observed. Hydrological changes were reflected in an increase in species richness and abundance of communities of organisms with broad ecological tolerance (mainly ciliates), and a decrease in the number of species and abundance of planktonic crustaceans and macroinvertebrates. The RDA model explains over 84% of the total variability, indicating excellent model fit to the data and a strong correlation between environmental variables and species composition. Based on p values, water level, temperature, pH, and COD were selected as significant variables, with conductivity, O₂, and P_tot demonstrating less statistical significance. Peat ponds ecosystems can serve as an excellent model system for studying the impact of intensifying climate change on the functioning of shallow water bodies. Full article

Ecological porous concrete (EPC) offers both structural performance and ecosystem services, yet an inherent contradiction exists between the ecological benefits of high porosity and mechanical performance. Traditional design methods focusing solely on macro-scale porosity fail to achieve synergistic optimization. This review comprehensively synthesizes the intrinsic correlations between EPC’s multi-scale pore structures and key properties from micro-, meso-, and macro-scale perspectives, drawing upon representative studies across experimental, numerical, and theoretical approaches. The microscale reveals interfacial transition zone bonding, capillary pore effects, and alkalinity regulation for vegetation compatibility. The mesoscale clarifies the control of effective porosity, tortuosity, and pore throats on fluid transport and root penetration. The macro-scale analyzes skeletal pore support for plant growth, hydrology, and slope stability. A cross-scale collaborative design approach is proposed, featuring microscopic reinforcement, mesoscopic continuity, and macroscopic moderation. This paper provides theoretical support for EPC’s transition from empirical to precision design, promoting low-carbon and large-scale applications in revetments, Sponge Cities, and slope restoration. Full article

Per- and polyfluoroalkyl substances (PFAS) are ultra-persistent contaminants characterized by exceptional chemical stability, high mobility, and widespread environmental occurrence, posing significant challenges for remediation. Phytotechnology has emerged as a promising nature-based approach, yet its effectiveness is strongly governed by PFAS physicochemical properties and plant–soil interactions. This review provides a mechanistic synthesis linking PFAS environmental behavior with phytotechnology performance by examining PFAS sources, transport pathways, and structure-dependent properties that control persistence, partitioning, and mobility, with an emphasis on differences between short- and long-chain compounds. These characteristics determine bioavailability and influence treatment outcomes. Plant uptake mechanisms, including root absorption, xylem translocation, and tissue accumulation, are discussed alongside rhizosphere processes such as sorption, microbial interactions, and hydrological dynamics that regulate PFAS retention and redistribution. Current evidence indicates that phytotechnology functions primarily as a form of phytomanagement rather than a destructive solution, as mineralization is limited and field-scale treatment remains low. Instead, plant–soil–microbe systems reduce PFAS mobility and exposure through stabilization and sequestration. Future research should prioritize strategies for short-chain PFAS, integration with sorptive amendments, and data-driven approaches to optimize phytomanagement performance. Full article

Green roofs are used as nature-based solutions for urban stormwater management and for improving the thermal performance of buildings. Their hydrological performance depends on structural properties and rainfall characteristics, but the relative importance of these factors has not been fully quantified. Therefore, this study aimed to identify the key variables controlling the hydrological effectiveness of a green roof. A conceptual model of a flat roof representing a typical single-family building in south-eastern Poland was developed in the Storm Water Management Model (SWMM), with a modeled roof area of 232 m² and 100% of the roof surface covered by the green roof LID system. A total of 24,576 simulation cases were analyzed, considering different values of soil thickness, berm height, initial saturation, vegetation-related storage, rainfall duration, rainfall probability, and rainfall temporal distribution. The hydrological response was evaluated using peak runoff reduction and cumulative runoff volume ratio determined at selected times after rainfall. Predictive models based on the eXtreme Gradient Boosting (XGBoost) algorithm were developed, and their interpretation was performed using the SHapley Additive exPlanations (SHAP) method. The main novelty of the study is its application-oriented framework combining SWMM simulations, XGBoost modeling, and SHAP explainability to distinguish the factors controlling peak runoff reduction and delayed runoff release from a green roof. The results showed that peak runoff reduction ranged from 10.97% to 100.00%, with a median of 99.91%, indicating a generally high capacity of the analyzed system to attenuate peak flow. In contrast, the cumulative runoff volume ratio increased over time, with median values rising from 0.05% immediately after rainfall to 7.91% after 24 h, confirming the significant retention and detention potential of the green roof. SHAP analysis revealed that peak runoff reduction was governed primarily by berm height, whereas cumulative runoff volume was controlled mainly by initial substrate saturation. The results confirm that different mechanisms control short-term and long-term green roof performance. Full article