Search Results (1,888)

Contemporary approaches to urban water management increasingly rely on concepts such as Nature-Based Solutions (NBSs), Green Infrastructure, and Blue-Green Infrastructure. Although these frameworks have gained broad acceptance, their typological character provides limited guidance for project-oriented decision-making, as they primarily describe infrastructure types rather than their functions within integrated hydrological systems. To address this gap, this study proposes a complementary functional-operational framework for interpreting ecological infrastructures in urban flood mitigation. Employing a qualitative comparative case study methodology, we analysed four diverse international models—the Dutch Water Squares (Rotterdam), Tokyo’s underground flood control system, Copenhagen’s Cloudburst Management Plan, and Singapore’s ABC Waters Programme—to examine the systemic interaction between grey, green, and blue infrastructures at different watershed scales. The results indicate that flood mitigation effectiveness depends less on the predominance of a single infrastructure type and more on the functional coupling among them. Specifically, three primary functions were identified: rapid conveyance (grey infrastructure), infiltration and thermal regulation (green infrastructure), and dynamic storage and biodiversity support (blue infrastructure). Despite the contextual limitations and varying scales of the selected cases, blue infrastructure universally emerges as a systemic buffer that enhances urban resilience by regulating excess volumetric flows. Ultimately, the proposed framework introduces an actionable interpretative layer that complements existing typological classifications, providing planners and urban designers with a robust, scalable basis for implementing integrated ecological infrastructures. Full article

Against the backdrop of global climate change, drought–flood abrupt alternation (DFAA) has become a major compound climate extreme threatening ecosystem stability and sustainable watershed management. This study investigated the spatiotemporal characteristics and ecological responses of DFAA in the Jialing River Basin, China, using meteorological and hydrological observations from 1971 to 2020. DFAA events were identified using the Standardized Weighted Average Precipitation Index (SWAP) and run theory, and their spatiotemporal heterogeneity was characterized using spatial autocorrelation analysis. The Long-duration DFAA Index (LDFAI) was derived using the WEP-L distributed hydrological model. Ecological responses during 2000–2020 were evaluated by integrating the Remote Sensing Ecological Index (RSEI), grey relational analysis, and a Copula-based conditional probability model. The results showed that drought-to-flood events exhibited stronger spatial clustering than flood-to-drought events. Ecosystem responses showed significant lag effects, averaging 6.9 months for spring–summer events and 5 months for summer–autumn events, with greater sensitivity during the summer–autumn period. Under DTF events, the probability of maintaining relatively high ecological quality was significantly higher than under FTD events, whereas FTD events were associated with a higher probability of ecological degradation. Under compound scenarios, consecutive same-type events were more conducive to ecosystem stability, while alternating sequences of different event types significantly amplified negative ecological stress and represented high-risk scenarios for ecological degradation. These findings provide scientific support for adaptive watershed management, ecological restoration, and climate change adaptation in drought–flood-prone regions. Full article

Utah Lake and Great Salt Lake share a watershed yet face distinct pressures: eutrophication, harmful algal blooms, hydrologic decline, and exposed-playa dust hazards, but their combined literature has never been systematically characterized. I analyzed 1383 peer-reviewed records using bibliographic coupling, Louvain community detection, latent Dirichlet allocation, and large language model-assisted annotation. The coupling network (1012 records, 7536 edges) split into 379 communities ($Q=0.541$), of which 342 were singletons and only 14 reached 10 or more papers, together holding 59% of coupled records. Of 10 main-text clusters, 6 are anchored in a lake and 4 in a topic rather than a system; just 1 is Utah Lake dominant. Great Salt Lake research spans broader disciplinary communities (brine-shrimp ecology, mercury cycling, dust and paleoclimate); Utah Lake research is more applied (native-species management, eutrophication, harmful algal blooms). Recent Utah Lake studies report no lake-wide chlorophyll-a trend across 1068 Landsat scenes (1984–2021) and dissolved phosphorus near ∼0.02–0.04 mg L⁻¹ over ∼50 years despite ∼300% population growth, implying internal sediment cycling dominates; recent Great Salt Lake work centers on hydrologic decline and playa dust hazards. The synthesis identifies five cross-cutting gaps, including dust-emission-to-exposure assessment, Utah Lake nutrient modeling, and predictive water management, and yields a reproducible, transferable framework. Full article

Against the backdrop of climate change and compounded by human activities, increasing water scarcity has triggered a series of drought disasters, which have already severely impacted both ecological environments and socioeconomic production. The SWAT model, recognized for its strong portability and superior spatial heterogeneity, has gained widespread acceptance in fields such as hydrology and environmental science, and is extensively applied in hydrological simulation studies across large-scale river basins. Hydrological models of the study area can be constructed in the SWAT model to simulate changes in hydrological variables by conducting spatial discretization, parameter specification, and boundary condition definition. Standardized drought index can effectively reflect the spatiotemporal variations in drought disasters, holding significant importance for clarifying and predicting drought characteristics. This study took the Luanhe River Basin as the research area, constructed a watershed hydrological model based on SWAT, and projected changes in the basin’s hydrological processes for the period 2030–2060. Based on the model’s projected data, we calculated drought indices and extracted drought events for the basin. The results indicate the following: (1) During the simulation period, only 30% of the years in the Luanhe River basin had annual runoff above the long-term average, with a range of 228.18 mm. The range of mean annual runoff across sub-basins was 173.32 mm. Drought and uneven water resource allocation over both spatial and temporal scales coexisted, and this issue is expected to intensify under future climate warming and drying. (2) The mid-reaches of the Luanhe River are more prone to drought compared to the upper reaches for its higher water demand. However, due to a stronger capacity for ecological restoration, droughts there are mostly of low intensity in the mid-reaches. In contrast, the upper reaches experience more periods classified as severe or extreme drought, and the drought events encountered are generally more intense than those in the mid-reaches. (3) The method proposed in this study can screen extreme drought events based on outliers in the characteristic values of drought events. Taking the simulation from this study as an illustration, anomalies in drought event characteristic values suggest a potential basin-scale, prolonged extreme drought event in the Luanhe River Basin from June 2038 to July 2042. Proactive drought prevention policies should be formulated for this period. The findings of this study provide guiding significance and practical value for drought assessment, risk management, and policy application in the Luanhe River Basin. This study methodologically combines hydrological model predictions with drought event responses, providing a novel method for predicting basin-scale drought conditions and issuing early warnings for extreme drought events. Full article

This study evaluates whether existing long-term monitoring data are sufficient to distinguish natural background concentrations from anthropogenic influences on chemical river water quality in Lake Sevan basin. A comprehensive dataset covering physicochemical parameters, nutrients, and trace metals was analyzed for nine major tributaries with different geological settings and land-use characteristics. Multivariate statistical analysis was applied to identify baseline conditions and deviations attributable to human activities. The results indicate that water chemistry is primarily controlled by lithology and hydrological regime, particularly in minimally impacted headwater regions. In contrast, elevated concentrations of nutrients (e.g., nitrate and phosphate) and selected trace elements were associated with agricultural runoff, urban discharge, and localized industrial inputs. Spatial patterns reveal clear gradients of increasing anthropogenic impact downstream and in densely populated sub-basins. The study also demonstrates that, while the current monitoring network is suitable for assessing the overall chemical status of rivers, it is less effective in defining natural background levels and quantifying individual pollution sources due to limited upstream reference conditions. Overall, this approach provides a scientific basis for improved water quality management and policy implementation in the Lake Sevan basin. The findings highlight the importance of integrating long-term monitoring data with statistical tools to support sustainable watershed management in vulnerable catchments. Full article

The Upper Yellow River (UYR) basin is the predominant runoff-yielding area of the entire watershed. Accurately simulating annual runoff is crucial for water resources management. While the Budyko framework effectively captures long-term hydro-thermal equilibrium, it struggles to represent nonlinear dynamics, flow channel routing, and spatial interconnections. Here, we propose a hybrid physics- and data-driven approach by coupling the Budyko framework (Fu’s equation) with an eXtreme Gradient Boosting (XGBoost) model, integrating upstream channel routing and antecedent storage-lag features using long-term hydrologic observations for nonlinear runoff simulation and driver attribution. To resolve the feature multicollinearity on machine learning attributions, input variables were consolidated into three groups: precipitation driven, evaporation limitation, and flow storage lag. The results demonstrate that the Budyko–XGBoost coupled model enhances annual runoff prediction accuracy compared to the standalone Fu equation and pure XGBoost, raising the coefficient of determination (R²) to 0.63–0.86 (mean R² = 0.75) and capturing both nonlinear dynamics and turning points, alongside reductions of 6.2% in the mean RMSE (18.77 mm) and 11.0% in the MAE (13.66 mm) compared to the pure XGBoost model (mean R² = 0.70, RMSE = 20.01 mm, and MAE = 15.35 mm). Group-level SHAP attributions reveal that flow storage-lag drivers (R_lag and $R_{\mathit{lag}}^{\prime }$) exert a primary control on runoff evolution across all the stations. Spatially, secondary drivers exhibit heterogeneity: in relatively humid, energy-limited regions (Maqu), high precipitation promotes positive runoff deviations, whereas in arid/semi-arid, water-limited reaches (e.g., Guide, Xunhua, Xiaochuan, and Lanzhou stations), high precipitation is absorbed by severe soil moisture deficits and reservoir interception, exerting a negative effect on runoff deviation. Full article

Understanding how urbanization modifies stream thermal regimes is essential for assessing freshwater ecosystem responses to climate variability and land-use and land-cover (LULC) change. This study investigated air temperature (AT)–water temperature (WT) relationships at annual, monthly, and diurnal timescales in two adjacent, relatively flat watersheds with contrasting urbanization levels in North Central Texas: the urbanized Doe Branch and less urbanized Little Elm Creek during 2012–2021. A single harmonic analysis was applied to characterize annual and diurnal thermal patterns, including mean temperature, amplitude, and phase, while statistical analyses were used to evaluate seasonal and daily thermal variability and peak timing. At the annual scale, AT and WT metrics were strongly correlated at both sites (r = 0.85–0.94, p < 0.01) indicating that atmospheric conditions were the dominant control of annual stream temperature variability. Annual mean WT increased with AT, suggesting strong air–water thermal coupling and the potential for warmer stream temperatures under future climate warming. However, Doe Branch exhibited higher annual mean WTs, delayed seasonal peak WTs, and reduced annual temperature ranges compared with Little Elm Creek, reflecting the influence of urban watershed characteristics on seasonal thermal responses. At the diurnal scale, daily mean WT remained strongly coupled with daily mean AT, whereas daily temperature range and peak timing showed weaker relationships with AT. The greater variability in peak WT timing at Doe Branch suggests that short-term stream thermal dynamics were influenced by additional watershed characteristics beyond atmospheric forcing alone. Full article

Land-use and land-cover (LULC) change is a major driver of ecosystem degradation and ecosystem service loss in tropical landscapes. This study assessed the impacts of LULC changes between 1985 and 2023 on the potential of different LULC classes to supply ecosystem services in the Upper Rio Pardo Basin (Rio Pardo and Rio São João do Paraíso watersheds), northern Minas Gerais, Brazil. LULC data from the MapBiomas Project were used to quantify transitions among LULC classes within the study area. The potential supply of ecosystem services was assessed using the Burkhard matrix, adapted to local environmental conditions and informed by expert knowledge, while the relative importance of ecosystem services was evaluated through a participatory assessment involving local communities. The expansion of pasturelands and commercial forest plantations was identified as the primary driver of ecosystem service loss in the study region. Native ecosystems showed the highest potential to provide regulating, supporting, provisioning, and cultural ecosystem services, particularly water regulation, soil protection, carbon sequestration, and biodiversity conservation. In contrast, anthropogenic land uses were primarily associated with provisioning services and showed limited capacity to sustain regulating and supporting services, indicating clear trade-offs between production and ecosystem functioning. Local communities identified water-related services as the highest priority, followed by climate regulation, soil fertility, and food provision. These findings demonstrate the value of integrating expert-based and participatory approaches to ecosystem service assessment and provide a basis for territorial planning strategies that prioritize the conservation and restoration of native vegetation, particularly in hydrologically sensitive areas. Full article

Existing hydrologic landscape classifications, such as Hydrologic Landscape Regions (HLRs) and Hydrologic Response Units (HRUs), group catchments by similarity of climate, geology, and topography, but do not directly link this classification to runoff-generation function, limiting their use for ranking hydrological activity in data-sparse mountain regions. The concept of a landscape–hydrological background, integrating climatic–hydrological, soil–hydrological, and topo–hydrological components, was used as the basis for a classification framework applied to 15 model catchments in the East Kazakhstan Region. The proposed approach provides a functional representation of landscape organization by linking environmental characteristics directly to runoff-generation processes and hydrological activity. The analysis revealed two distinct hydrological domains: a runoff-generating mountain domain (Bukhtarma, Uba, and Ulba), where highly active hydrological landscapes occupy 55.2–63.9% of the catchment area, and a transitional mountain-foothill domain (Narym, Kurchum, and Kalzhyr), where their share decreases to 18.0–25.4%. The highest landscape diversity, with Shannon indices of 1.31–1.53, occurs in the transitional catchments, reflecting the most balanced combination of functional groups. This study demonstrates that hydrological activity is controlled by the combined influences of catchment size, relief differentiation, and the spatial extent of high-mountain environments rather than by elevation alone. The proposed framework has practical applications for watershed management, water resources planning, environmental monitoring, and climate change adaptation, and is particularly valuable for data-sparse mountain regions. Full article

Accurate rainfall input is critical for reliable flood simulation, particularly in semi-arid watersheds with pronounced spatiotemporal precipitation heterogeneity. This study developed a radar–gauge rainfall fusion framework to improve HEC-HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model performance in the Liulin experimental watershed, Xingtai City, Hebei Province. Radar quantitative precipitation estimation (QPE) was generated via a dynamically optimized Z-I relationship, then fused with gauge observations using three methods—Geographical Differential Analysis (GDA), Conditional Merging (CM), and Random Forest (RF). The fused products drove a calibrated HEC-HMS model, evaluated over five representative flood events. All three methods corrected radar QPE underestimation. Under independent cross-validation, GDA and CM achieved comparable point-scale accuracy (CC ≈ 0.81, RMSE ≈ 5.7 mm), while RF showed lower generalization (CC ≈ 0.48, RMSE ≈ 8.7 mm) due to overfitting. In flood simulations, GDA performed most robustly, followed by RF and CM, all surpassing single-source inputs. Notably, CM’s higher statistical accuracy did not translate into better flood performance, indicating that optimal statistical fidelity does not guarantee optimal hydrological results. Peak discharge deviations persisted for short-duration intense storms and long-duration uneven rainfall events. This study confirms that radar–gauge fusion enhances rainfall input quality and provides a reliable approach for improving flood forecasting. Full article

Accurate reservoir inflow forecasting is essential for sustainable watershed management and low-carbon hydropower operation. Traditional fixed-weight ensemble models lack adaptability under non-stationary hydrological conditions, limiting their reliability in reservoir operation. This study proposes a Context-Aware Localized Weighting Ensemble (CALWE) framework for reservoir inflow forecasting. The framework constructs a predictive response space from heterogeneous model outputs, enabling context identification based on similarities in model prediction behaviors. A localized weighting strategy is then employed to adaptively determine model contributions across contexts. The framework was evaluated using daily reservoir inflow data from Xiaowan Hydropower Station in the Lancang River Basin and monthly inflow data from Xiluodu Hydropower Station in the Jinsha River Basin. Results demonstrate that CALWE outperforms individual models and conventional ensemble approaches in both cases. Compared with the best-performing individual benchmark model for each basin (i.e., SVR for Xiaowan and XGBoost for Xiluodu), CALWE achieved a relative RMSE reduction of 4.93% and an absolute NSE improvement of 0.007 for daily inflow forecasting at Xiaowan, while achieving a relative RMSE reduction of 5.32% and an absolute NSE improvement of 0.027 for monthly inflow forecasting at Xiluodu. SHAP analysis revealed scale-dependent feature contributions, with daily forecasts dominated by antecedent inflow information and monthly forecasts influenced by meteorological, land surface, and hydrological factors. These findings demonstrate that CALWE captures context-dependent inflow responses while providing interpretable insights into model predictions, thereby supporting sustainable watershed management and reservoir operation. Full article

The Tenagi Plains Basin periodically suffers either severe flooding or drought conditions especially due to the lack of both sufficient and proper water management infrastructure works, whilst at the same time playing a vital role for the survival and development of agriculture within its incorporated areas in Paggaion Municipality, Kavala Prefecture, Greece. Moreover, due to its limited reservoir storing capacity within its upper catchment area, some dams of relatively small capacity are suggested to be taking advantage of the great water resources regime of the entire watershed area, especially during hydrological years of increased rainfall. The present study, using hydrological modeling (“HEC-HMS” and Geographical Information Systems), explores the total water quantities of the entire watershed that would have been effectively controlled during the hydrological year 2024–2025. Full article

Climate change and irrigation expansion are expected to substantially alter hydrological processes and agricultural productivity in the semi-arid watersheds of northern Ethiopia; however, their combined impacts remain insufficiently quantified. This study evaluated the effects of future climate change and irrigation management on watershed hydrology and crop yield in the Geba watershed using the Soil and Water Assessment Tool Plus (SWAT+). The model was calibrated and validated using observed daily streamflow data for the 2006–2020 period and driven by an ensemble of five bias-corrected CORDEX Africa regional climate models (RCMs) under the RCP 4.5 and RCP 8.5 scenarios for the mid-century (2046–2060) and late-century (2086–2100) periods. Two agricultural management systems, namely rainfed and irrigation-rainfed integrated management, were evaluated. Model performance was satisfactory for streamflow simulation, with NSE values of 0.56 and 0.50 and KGE values of 0.64 and 0.54 during calibration and validation, respectively. The results indicate a progressive shift toward an evapotranspiration-dominated hydrological regime under future climate conditions. Under rainfed management, surface runoff and evapotranspiration increased by up to 60% and 30%, respectively, whereas groundwater recharge and lateral flow declined substantially. Irrigation scenarios intensified hydrological stress by reducing percolation, lateral flow, and water yield by up to 80%, 60%, and 55%, respectively. Statistical analyses revealed that climate forcing, management type, and their interactions significantly affected hydrological responses (p < 0.001), with emission pathways representing the dominant driver of variability. Crop responses varied considerably among management systems and crop types. Rainfed maize and wheat exhibited moderate yield increases under mid-century conditions, whereas teff consistently showed negative responses under most climate scenarios, indicating high vulnerability to warming and moisture stress. Under irrigation management, most crops experienced substantial yield reductions during late-century periods, although tomatoes showed localized gains under high-emission scenarios. Overall, the findings demonstrate that irrigation expansion alone may not provide sustainable adaptation under increasing climate stress because it intensifies evapotranspiration and reduces groundwater recharge. Integrated watershed management, climate-resilient crop selection, efficient irrigation practices, and soil-moisture conservation strategies are therefore essential for sustaining agricultural productivity and water availability in semi-arid Ethiopian watersheds. Full article

Understanding how land change affects water yield is essential for sustainable land management and water security. However, previous studies often rely on a single model or spatial scale, limiting their ability to capture fine-scale land-surface heterogeneity and basin-integrated hydrological processes; consequently, cross-scale consistency in water-yield patterns and drivers remains unresolved. This study developed a dual-spatial-scale framework for the Upper Yangtze River Basin. The Patch-generating Land-Use Simulation (PLUS) model projected land patterns for 2030, the Integrated Valuation of Ecosystem Services and Tradeoffs Annual Water Yield (InVEST-AWY) model mapped grid-scale water yield, the Soil and Water Assessment Tool (SWAT) model simulated sub-basin hydrological responses, and Geodetector identified dominant drivers. Their integration links local land transitions with basin-scale hydrological consequences under consistent scenarios, thereby overcoming the limitations of single-model assessments. Results showed that high water-yield areas were concentrated in the eastern and central basin, while the two scales exhibited similar spatial patterns but locally different magnitudes and trends. Built-up land under urban development was 7.96% greater than under ecological protection. Climatic variability increased runoff by 2115.33 m³/s under ecological protection, while precipitation dominated water-yield variation, with explanatory powers of 0.81 and 0.67 at the grid and sub-basin scales, respectively. These findings advance understanding of scale-dependent responses and support land optimization and watershed planning. Full article

The escalating frequency of extreme hydrological events under environmental uncertainty poses a severe socio-economic threat to floodplains such as the Brazilian Pantanal, the world’s largest tropical wetland. Mitigating dynamic flooding and drying cycles is highly challenging due to a critical scarcity of in situ monitoring, leaving flood risks poorly understood. To address these data gaps, this study presents an advanced deep learning forecasting framework that integrates multisource environmental data, fusing satellite-derived precipitation (CHIRPS) and global land data assimilation evapotranspiration (GLDAS) data with historical river gauge telemetry. Multi-layered neural network architectures were optimized and combined with progressive moving average filters (10− and 15−day windows) to capture the complex hydrometeorological patterns of the data-scarce Miranda River Watershed. The optimal deep learning configuration, utilizing a robust two-hidden-layer topology (15 and 60 neurons), consistently outperformed standard baselines. Although purely exogenous data blocks successfully minimized satellite noise and captured seasonal trends (NSE ≥ 0.92), structural underestimation of peak flows was observed. When incorporating the previous day’s streamflow (lag t−1) as a physical anchor, this limitation was noticeably alleviated, increasing both the Nash–Sutcliffe Efficiency (NSE) and Coefficient of Determination (R²) values above 0.99. While this performance surge is driven by the strong temporal persistence inherent to the autoregressive lag, it introduces an operational trade-off by restricting the forecast to a reactive 24 h window. In this regard, an evaluation of the operational forecast horizons revealed that the exogenous deep learning blocks maximize warning lead times, providing a vital tool for proactive civil defense and disaster risk reduction. Ultimately, this multisource framework establishes a methodological foundation for automated decision support systems, providing the high-accuracy streamflow forecasting capability required to support future flood mitigation frameworks. Full article