Search Results (4,053)

Urban underpasses are critical flood-prone hotspots during extreme rainfall, posing significant threats to urban resilience and infrastructure safety. However, a scale gap persists between catchment-scale hydrological models, which often oversimplify local geometry, and high-fidelity hydrodynamic models, which typically lack realistic boundary conditions. To bridge this gap, this study develops a multi-scale framework that integrates the Storm Water Management Model (SWMM) with 3D Computational Fluid Dynamics (CFD). The framework employs a unidirectional integration (one-way forcing), utilizing SWMM-simulated runoff hydrographs as dynamic inlet boundaries for a detailed CFD model of a 2.8 km underpass in Shanghai. Simulations across six design rainfall events (2- to 50-year return periods) revealed two distinct flooding mechanisms: a systemic response at the hydraulic low point, governed by cumulative inflow; and a localized response at entrance concavities, where water depth is rapidly capped by micro-topography. Informed by these mechanisms, an intensity-graded drainage strategy was developed. Simulation results show significant differences between different drainage strategies. Through this framework and optimized drainage system design, significant water accumulation within the underpass can be prevented, enhancing its flood resistance and reducing the severity of disasters. This integrated framework provides a robust tool for enhancing the flood resilience of urban underpasses and offers a basis for the design of proactive disaster mitigation systems. Full article

Water from mountain regions is a crucial natural resource because of its major economic, social, and environmental significance. Wildfires may disrupt the normal functioning of the hydrological cycle, limiting water resources for nearby areas and degrading water quality in mountainous regions as contaminants enter water systems from the burning of vegetation and soil. In August 2022, the Serra da Estrela mountain, situated in the Mediterranean biogeographical region, was affected by a large wildfire that consumed 270 km² of the Serra da Estrela Natural Park, often resulting in severe vegetation burn, although the soil burn severity was low to moderate in most of the area. The research objective is to assess the impact of this wildfire on the hydrogeochemistry of groundwater and surface water in the Manteigas-Covão da Ametade sector of Serra da Estrela in the context of a wildfire with limited soil burn severity. Groundwater and surface water samples were collected from October 2022 to September 2023 and were analyzed for pH, Total Organic Carbon, electrical conductivity, major ions, potentially toxic elements, iron (Fe), and Polycyclic Aromatic Hydrocarbons. A stormy event in mid-September 2022, occurring before the first sampling campaign, removed most of the ash layer and likely caused transient hydrogeochemical changes in streams. However, the analytical results from the sampled waters revealed that the post-wildfire hydrogeochemical effects are not evident. In fact, the hydrogeochemical changes observed in groundwater and surface water appear to be primarily influenced by the regular hydrological behaviour of aquifers and streams. The low to moderate soil burn severity, the high soil hydrophobicity, and the temporal distribution of precipitation explain why the hydrogeochemistry was primarily influenced by groundwater flow paths, the types and weathering of local lithologies, soil types, dilution effects following wet periods, and seasonal changes in the tributaries feeding into streams, rather than by post-wildfire effects. These outcomes provide valuable insights for water resource management and for developing strategies to mitigate wildfire impacts in mountainous environments. Full article

The aim of this article is to present the social–ecological system (SES) as a unit of analysis for sustainable water management under conditions of anthropogenic pressure in Poland. In the face of accelerating climate change and growing human impacts, Polish water systems are exposed to increasing ecological stress and to material and immaterial losses affecting local communities. The SES approach provides an integrative analytical framework that links ecological and social components, enabling a holistic view of adaptive and governance processes at multiple spatial scales, from municipalities to areas that transcend administrative boundaries. Methodologically, this study triangulates three complementary approaches to strengthen explanatory inference. This conceptual SES review defines the analytical categories used in the paper, the bibliometric mapping (Scopus database with VOSviewer) identifies dominant research streams and underexplored themes, and the qualitative Polish case studies operationalize these categories to diagnose mechanisms, feedbacks, and governance vulnerabilities under anthropogenic pressure. The bibliometric analysis identifies the main research streams at the intersection of SES, water management and sustainable development, revealing thematic clusters related to climate change adaptation, environmental governance, ecosystem services and hydrological extremes. The case studies - the 2024 flood, the 2022 ecological disaster in the Odra River, and water deficits associated with lignite opencast mining in Eastern Wielkopolska - illustrate how anthropogenic pressure and climate-related hazards interact within local SES and expose governance gaps. Particular attention is paid to attitudes and social participation, understood as configurations of behaviors, knowledge and emotions that shape decision-making in local self-government, especially at the municipal level. This study argues that an SES-based perspective can contribute to building the resilience of water systems, improving the integration of ecological and social dimensions and supporting more sustainable water management in Poland. Full article

Coastal pond-to-mangrove restoration has become a prominent Nature-based Solution, yet its short-term ecological effects on waterbird communities remain unclear. We assessed taxonomic, functional, and compositional responses of waterbirds to large-scale restoration in Bamen Bay, Hainan Island, using BACI-style comparisons between restored and unrestored aquaculture ponds in 2021 and 2023. Restored areas exhibited higher taxonomic α diversity and functional richness (p < 0.001), coinciding with rapid habitat diversification following hydrological reconnection. Species richness (p < 0.001), Shannon diversity (p < 0.01), and functional richness (p < 0.01) were consistently higher in restored areas than in aquaculture ponds. In contrast, β diversity patterns diverged between habitats: restored areas remained relatively stable, whereas aquaculture ponds showed greater between-year compositional change (p < 0.05). Guild-specific responses revealed contrasting patterns: herons showed higher diversity in restored habitats (p < 0.05), whereas shorebirds exhibited no significant changes (p > 0.05), consistent with their dependence on open mudflats that were only partially retained. Although no significant declines were detected, functional richness tended to be lower in 2023 (p > 0.05), and ongoing mudflat loss suggests potential long-term risks for mudflat specialists, warranting extended monitoring. Taken together, our findings suggest that effective pond-to-mangrove restoration in Bamen Bay should balance mangrove expansion with the retention of tidal flats and managed shallow-water habitats to support diverse waterbird assemblages. Full article

This study examines the hydrological response of the Klambu Dam Catchment in Central Java, Indonesia, to climatic and land cover changes from 2000–2023, with simulations extending to 2040. Utilizing CHIRPS satellite data calibrated with six ground stations, monthly precipitation and temperature datasets were analyzed and projected via linear regression aligned with IPCC scenarios, revealing a marginal temperature decline of 0.21 °C (from 28.25 °C in 2005 to 28.04 °C in 2023) and a 17% increase in rainfall variability. Land cover assessments from Landsat imagery highlighted drastic changes: a 73.8% reduction in forest area and a 467.8% increase in mixed farming areas, alongside moderate fluctuations in paddy fields and settlements. The Thornthwaite-Mather water balance method simulated monthly discharge, validated against observed data with Pearson correlations ranging from 0.5729 (2020) to 0.9439 (2015). Future projections using Cellular Automata-Markov modeling indicated stable volumetric flow but a temporal shift, including a 28.1% decrease in April rainfall from 2000 to 2040, contracting the wet season and extending dry spells. These shifts pose significant threats to agricultural and aquaculture activities, potentially exacerbating water scarcity and economic losses. The findings emphasize integrating dynamic land cover data, climate projections, and empirical runoff corrections for climate-resilient watershed management. Full article

Accurate precipitation estimation is essential for hydrological modeling and water resource management in arid regions; however, complex terrain and sparse meteorological station networks introduce substantial uncertainties into gridded precipitation datasets. This study evaluates the performance of nine widely used precipitation products in the arid region of Northwest China (ARNC) at both the meteorological station scale and the sub-basin scale, and applies the Bayesian Model Averaging (BMA) approach to merge multi-source precipitation estimates. The results reveal pronounced spatial heterogeneity and significant differences in performance among datasets, with the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement mission performing best at the station scale and the Famine Early Warning Systems Network Land Data Assimilation System performing best at the sub-basin scale. Compared with individual products, the BMA-merged precipitation demonstrates substantial improvements at both scales, providing higher coefficients of determination and agreement indices, and lower relative mean absolute error and relative root mean square error, indicating enhanced accuracy and robustness. The BMA-merged precipitation product generally exhibits superior and more spatially consistent performance than the individual datasets across the ARNC, thereby providing a more reliable basis for regional hydrological and climate-related applications. The merged dataset shows that the mean annual precipitation in the ARNC during 2000–2024 is approximately 230.4 mm, exhibiting a statistically significant increasing trend of 1.4 mm per year, with the strongest increases occurring in the Tianshan and Qilian Mountains. This study provides a reliable foundation for hydrological modeling and climate-change assessments in data-limited arid environments. Full article

Reliable forecasting of karst spring discharge is critical for sustainable groundwater resource management under the dual pressures of climate change and intensified anthropogenic activities. This study proposes a Heterogeneous Spatiotemporal Graph Attention Network (H-STGAT) to predict spring discharge dynamics at Shentou Spring, Shanxi Province, China. Unlike conventional spatiotemporal networks that treat all relationships uniformly, our model derives its heterogeneity from a graph structure that explicitly categorizes spatial, temporal, and periodic dependencies as unique edge classes. Specifically, a dual-layer attention mechanism is designed to independently extract hydrological features within each relational channel while dynamically assigning importance weights to fuse these multi-source dependencies. This architecture enables the adaptive capture of spatial heterogeneity, temporal dependencies, and multi-year periodic patterns in karst hydrological processes. Results demonstrate that H-STGAT outperforms both traditional statistical and deep learning models in predictive accuracy, achieving an RMSE of 0.22 m³/s and an NSE of 0.77. The model reveals a long-distance recharge pattern dominated by high-altitude regions, a finding validated by independent isotopic evidence, and accurately identifies an approximately 4–6 month lag between precipitation and spring discharge, which is consistent with the characteristic hydrological lag identified through statistical cross-covariance analysis. This research enhances the understanding of complex mechanisms in karst hydrological systems and provides a robust predictive tool for sustainable groundwater management and ecological conservation, while offering a generalizable methodological framework for similar complex karst hydrological systems. Full article

Accurate delineation of inland waterbodies is critical for applications such as hydrological monitoring, disaster response preparedness and response, and environmental management. While optical satellite imagery is hindered by cloud cover or low-light conditions, Synthetic Aperture Radar (SAR) provides consistent surface observations regardless of weather or illumination. This study introduces a deep learning-based ensemble framework for precise inland waterbody detection using high-resolution X-band Capella SAR imagery. To improve the discrimination of water from spectrally similar non-water surfaces (e.g., roads and urban structures), an 8-channel input configuration was developed by incorporating auxiliary geospatial features such as height above nearest drainage (HAND), slope, and land cover classification. Four advanced deep learning segmentation models—Proportional–Integral–Derivative Network (PIDNet), Mask2Former, Swin Transformer, and Kernel Network (K-Net)—were systematically evaluated via cross-validation. Their outputs were combined using a weighted average ensemble strategy. The proposed ensemble model achieved an Intersection over Union (IoU) of 0.9422 and an F1-score of 0.9703 in blind testing, indicating high accuracy. While the ensemble gains over the best single model (IoU: 0.9371) were moderate, the enhanced operational reliability through balanced Precision–Recall performance provides significant practical value for flood and water resource monitoring with high-resolution SAR imagery, particularly under data-constrained commercial satellite platforms. Full article

Under the combined influences of climate change and anthropogenic activities, the variability of basin streamflow has intensified, posing substantial challenges for accurate prediction. Although Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models characterize volatility in time series, many previous studies have neglected changes in series structure, leading to inaccurate identification of the form of volatility. Building on tests for structural breaks (SBs) in time series, this study first removes the series mean using an Autoregressive Integrated Moving Average (ARIMA) model and then incorporates Markov-switching (MS) to develop a multi-state MS-GARCH model. An asymmetric MS-GARCH (MS-gjrGARCH) variant is also incorporated to describe the volatility of streamflow series with SBs. Daily streamflow data from five hydrological stations in the middle reaches of the Yellow River are used to compare the predictive performance of SB-ARIMA-MS-GARCH, SB-ARIMA-MS-gjrGARCH, ARIMA-GARCH, and ARIMA-gjrGARCH models. The results show that daily streamflow exhibits SBs, with the number and timing of breakpoints varying among stations. Standard GARCH and gjrGARCH models have limited ability to capture runoff volatility clustering, whereas MS-GARCH and MS-gjrGARCH effectively characterize volatility features within individual states. The multi-state switching structure substantially improves daily streamflow prediction accuracy compared with single-state volatility models, increasing R² by approximately 5.8% and NSE by approximately 36.3%.The proposed modeling framework offers a robust new tool for streamflow prediction in such changing environments, providing more reliable evidence for water resource management and flood risk mitigation in the Yellow River basin. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

Landslide dams, typically composed of newly deposited, loose, and heterogeneous materials, are highly susceptible to failure induced by overtopping and seepage, particularly under extreme hydrological conditions. Accurate prediction of such breaching processes is essential for flood risk management and emergency response, yet existing models generally consider only a single failure mechanism. This study develops a mathematical model to simulate landslide dam breaching under the coupled action of overtopping and seepage erosion. The model integrates surface erosion and internal erosion processes within a unified framework and employs a stable time-stepping numerical scheme. Application to three real-world landslide dam cases demonstrates that the model successfully reproduces key breaching characteristics across overtopping-only, seepage-only, and coupled erosion scenarios. The simulated breach hydrographs, reservoir water levels, and breach geometries show good agreement with field observations, with peak outflow and breach timing predicted with errors generally within approximately 5%. Sensitivity analysis further indicates that the model is robust to geometric uncertainties, as variations in breach outcomes remain smaller than the imposed parameter perturbations. These results confirm that explicitly accounting for the coupled interaction between overtopping and seepage significantly improves the representation of complex breaching processes. The proposed model therefore provides a reliable computational tool for analyzing landslide dam failures and supports more accurate hazard assessment under multi-mechanism erosion conditions. Full article

Sustainable Urban Drainage Systems (SUDSs) are essential for stormwater management in urban areas, with varying hydrological, social, ecological, and economic benefits. Nevertheless, choosing the SUDS most appropriate for public spaces poses a challenge when balancing details/specifications against community decisions, primarily social implications and perceptions. Building on the SUDS design pillar of the amenity, this study outlines a three-phase methodological framework for selecting SUDS based on social facilitation. The first phase introduces the application of the Partial Least Squares Structural Equation Modeling (PLS-SEM) and Classificatory Expectation–Maximization (CEM) techniques by modeling complex social interdependencies to find critical components related to urban planning. A Likert scale survey was also conducted with 440 urban dwellers in Tunja (Colombia), which identified three dimensions: Residential Satisfaction (RS), Resilience and Adaptation to Climate Change (RACC), and Community Participation (CP). In the second phase, the factors identified above were transformed into eight operational criteria, which were weighted using the Analytic Hierarchy Process (AHP) with the collaboration of 35 international experts in SUDS planning and implementation. In the third phase, these weighted criteria were used to evaluate and classify 13 types of SUDSs based on the experts’ assessments of their sub-criteria. The results deliver a clear message: cities must concentrate on solutions that will guarantee that water is managed to the best of their ability, not just safely, and that also enhance climate resilience, energy efficiency, and the ways in which public space is used. Among those options considered, infiltration ponds, green roofs, rain gardens, wetlands, and the like were the best-performing options, providing real and concrete uses in promoting a more resilient and sustainable urban water system. The methodology was also used in a real case in Tunja, Colombia. In its results, this approach proved not only pragmatic but also useful for all concerned, showing that the socio-cultural dimensions can be truly integrated into planning SUDSs and ensuring success. Full article

To address the challenge of optimizing hydrological parameters for nitrogen pollution control in paddy polders, this study coupled the Stella eco-dynamics model with an external optimization algorithm and developed a nonlinear programming framework using the water surface ratio and inflow rate as decision variables and the maximum nitrogen removal rate as the objective function. The simulation and optimization conducted for the Hongze Lake polder area indicated that the model exhibited strong robustness, as verified through Monte Carlo uncertainty analysis, with coefficients of variation (CV) of nitrogen outlet concentrations all below 3%. Under the optimal regulation scheme, the maximum nitrogen removal rates (${\eta }_1$, ${\eta }_2$, and ${\eta }_4$) during the soaking, tillering, and grain-filling periods reached 98.86%, 98.74%, and 96.26%, respectively. The corresponding optimal inflow rates ($Q^{*}$) were aligned with the lower threshold limits of each growth period (1.20, 0.80, and 0.50 m³/s). The optimal channel water surface ratios ($A_1^{*}$) were 3.81%, 3.51%, and 3.34%, respectively, while the optimal pond water surface ratios ($A_2^{*}$) were 19.94%, 16.30%, and 17.54%, respectively. Owing to the agronomic conflict between “water retention without drainage” and concentrated fertilization during the heading period, the maximum nitrogen removal rate (${\eta }_3$) during this stage was only 37.34%. The optimal channel water surface ratio ($A_1^{*}$) was 2.37%, the pond water surface ratio ($A_2^{*}$) was 19.04%, and the outlet total nitrogen load increased to 8.39 mg/L. Morphological analysis demonstrated that nitrate nitrogen and organic nitrogen dominated the outlet water body. The “simulation–optimization” coupled framework established in this study can provides quantifiable decision-making tools and methodological support for the precise control and sustainable management of agricultural non-point source pollution in the floodplain area. Full article

This analysis examines long-term changes in water levels of the Mead and Glen Canyon reservoirs on the Colorado River. Both reservoirs display clear declining trends in water levels, particularly after 2003. The causes include a combination of climate change, megadrought, increased water consumption, and alterations in the hydrological regime. Lake Mead exhibits a stronger and more concerning decline than Lake Powell, including extreme drought conditions over the past three years. The Rescaled Adjusted Partial Sums (RAPS) analysis identifies three statistically distinct subperiods, with an unambiguous decline in the most recent period. The day-to-day (DTD) method indicates reduced day-to-day water level variability in Lake Mead following the commissioning of the Powell reservoir, confirming its regulating influence. The Standardized Hydrological Index (SHI) indicates an accelerating intensification of drought conditions over the past 20 years. Regression analysis confirms a strong relationship between the water levels of the two reservoirs, along with significantly increased water losses in the more recent period. The literature suggests that climate projections are highly unfavorable, with further reductions in Colorado River discharge expected. The study underscores the urgent need to adapt water-management policies and align consumption with the new hydrological realities. Full article

This study examines the hydroclimatic controls on reservoir storage dynamics in the Sinni River basin (southern Italy), with a specific focus on the Monte Cotugno dam—the largest earth-fill reservoir in Europe. Using monthly precipitation data (2000–2024) from eight gauges and standardized indicators (SPI at multiple timescales and SRI for storage), we apply robust trend, correlation, autocorrelation, and causality analyses, supported by advanced preprocessing (TFPW), to disentangle climatic influences from anthropogenic pressures. Results show a statistically significant and persistent decline in the SRI series, indicating progressive storage depletion, despite stationary or slightly positive trends in precipitation at annual and hydrologically relevant timescales. These findings highlight the dominant role of cumulative operational losses and systemic inefficiencies—rather than sustained climatic drying—as primary drivers of reservoir decline. Granger causality and lagged-correlation analyses reveal that multi-month to annual precipitation anomalies (SPI-3, SPI-6, SPI-12) exert the strongest influence on storage variations, yet the basin’s ability to convert rainfall into effective reservoir supply is severely constrained by infrastructural and management limitations. The study underscores the urgent need to integrate climate-based monitoring with infrastructural modernization and governance reforms to address the combined climatic and anthropogenic pressures increasingly affecting Mediterranean water systems. Full article