Search Results (302)

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

Rapid urbanization and increasingly frequent extreme rainfall events have intensified stormwater challenges, underscoring the need for watershed-scale strategies that integrate blue-green infrastructure (BGI). This study evaluates the stormwater control performance of combined initial reservoir storage level regulation, river water level adjustment, and green infrastructure (GI) implementation in the 42.4 km² Baihuayong watershed of Guangzhou, China. A coupled stormwater model (SWMM) was developed, calibrated, and coupled with TELEMAC-2D to simulate schemes varying initial reservoir storage levels (30.6 m to 27.6 m), river water levels (11 m to 8 m), and GI proportions (0–45%) under 2- to 100-year rainfall events. Results show that lowering initial reservoir storage levels from 30.6 m to 27.6 m enhanced runoff reduction by ~40% and reduced discharged water volume by ~30%, though overflow mitigation remained limited. Decreasing river water levels from 11 m to 8 m reduced flooded areas by up to 8.3%, with diminishing benefits below 9 m. Increasing GI coverage from 0% to 45% reduced overflow nodes from 236 to 192 and flood extent from 10.76 ha to 9.20 ha under moderate storms, but improvements were modest during extreme events. A synergistic configuration, combining a low initial reservoir storage level (27.6 m), low river water level (8 m), and a high GI proportion (35–45%), yielded the most comprehensive improvements. These findings demonstrate the strong potential of integrated BGI for watershed-scale flood resilience and provide quantitative guidance for sponge city planning. Full article

An integrated early warning system (EWS) for compound coastal and fluvial flooding is developed for Pyrgos, Western Greece, where low-lying geomorphology and past storm events highlight the need for rapid, impact-based forecasting. The methodology couples historical and climate-informed metocean and river discharge datasets within a numerical modeling framework consisting of a mild-slope wave model, the CSHORE coastal profile model, and HEC-RAS 2D inundation simulations. A weighted K-Means clustering approach is used to generate representative extreme scenarios, yielding more than 4000 coupled simulations that train and validate Artificial Neural Networks (ANNs). The optimal feed-forward ANN accurately predicts spatially distributed flood depths across the HEC-RAS grid using only offshore wave characteristics, water level, and river discharge as inputs, reducing computation time from hours to seconds. Blind tests demonstrate close agreement with full numerical simulations, with average differences typically below 5% and minor deviations confined to negligible water depths. These results confirm the ANN’s capability to emulate complex compound flooding dynamics with high computational efficiency. Deployed as a web application (EWS_CoCoFlood), the system provides actionable, near-real-time inundation forecasts to support local civil protection authorities. The framework is modular and scalable, enabling future integration of urban and rainfall-induced flooding processes and coastal morphological change. Full article

Rating curves are derived from the combined measurement of water levels and discharges in rivers. This curve is used to convert observed water levels into flow rates, thereby generating discharge time series. Traditionally, rating curves are computed using exponential regression, which often neglects the underlying hydraulic conditions. Consequently, such curves may provide reasonable estimates of average flow but become unreliable under extreme conditions (e.g., high water levels). This research proposes a strategy for estimating discharge at high water levels using hydraulic modelling to support designers and practitioners in interpreting the upper range of the stage–discharge relationship. The methodology was applied to assess the rating curve for high flows in the Magdalena River at the Magangué reach (Bolívar, Colombia). Daily discharge records from 1974 to 2023 were analysed. The maximum historical discharge recorded was 11,127 m³/s (in 2010), while the mean annual peak discharge was 7904 m³/s. The proposed methodology yielded Manning’s roughness coefficients ranging from 0.046 to 0.052 and achieved satisfactory performance, with a Nash–Sutcliffe Efficiency (NSE) of 0.99. Results demonstrated that the traditional regression-based method tends to underestimate maximum discharges relative to a properly calibrated upper section of the rating curve. The analysis revealed systematic underestimation by the conventional approach, with discrepancies of up to 4.2% in determining maximum discharges. These findings emphasise the importance of incorporating hydraulic modelling to refine rating curves for high-flow conditions, thereby improving the reliability of design discharges. Full article

This study estimates the levels of chemical contamination and the responses of biochemical and cytogenetic biomarkers in Unio pictorum from the Nemunas River after a large-scale fire at a tire storage and processing warehouse (in October 2019), as well as after the subsequent discharge of partially cleaned water used for firefighting. The impact of firefighting water (FW) on the River Nemunas ecosystem was assessed. Elevated levels of trace metals (Pb, Cu, Co, Cr, Al, Zn) in U. pictorum mussels collected downstream from the wastewater treatment plant (WTP) discharger were measured in the first year after the accident. Genotoxic aberrations in gill cells were significantly more frequent in mussels collected downstream of the WTP discharger, along with higher frequencies of cytotoxic damage and changes in acetylcholinesterase activity. PAH metabolite concentrations, including naphthalene (Nap) and benzo(a)pyrene (B(α)P), were also elevated in haemolymph in U. pictorum gathered downstream from the discharger, but differences were not statistically significant. The total sum of 16 PAH concentrations in mussels collected in 2021 and 2022 was over 5 times higher than those in 2020, and the profile of accumulated metals shifted, with Ni, Cd, Cr, and Pb concentrations decreasing while Zn increased significantly. Mussel haemolymph in 2021 contained the highest levels of B(α)P-type PAH metabolites, indicating increased oxidative stress and neurotoxic impact. The results of chemical analysis and the values of genotoxic aberrations determined in gill cells of U. pictorum collected in 2021 and 2022 indicate an increase in PAH contamination and geno-cytotoxic impact compared to the results of 2020; these changes might be related to the gradual cancellation of COVID-19 restrictions and restoration of routine activities. The study provided an opportunity to demonstrate the unique response of a less anthropogenically stressed ecosystem to the extreme impact of contamination related to the fire on the tire recycling plant. Full article

A macrotidal estuary with mountain-stream inputs (MEMSs) is characterized by strong hydrodynamic forcing, high turbidity, and complex channel morphology. This study combines field measurements (2005–2020) with a 2D hydrodynamic–sediment model to examine estuarine turbidity maximum (ETM) dynamics, erosion–deposition patterns, and the effects of engineering interventions in the Jiaojiang Estuary (JJE). Results show that the coupled influence of upstream floods and downstream macrotides produces highly seasonal and spatially variable water–sediment processes: mountain-stream floods exhibit sharp hydrodynamic fluctuations, and the estuary displays pronounced tidal-wave deformation. Over the 15-year observation period, the riverbed experienced alternating erosion (up to −3.5 m) and deposition (up to +4.2 m), with net erosion of 0.5–1.2 m occurring in most Ling River sections during high-discharge years. The ETM migrated about 30 km during spring tides, with near-bed suspended sediment concentrations reaching 50–60 kg/m³. Human activities—particularly historical sand mining—modified channel geometry and sediment composition, intensifying the exchange between bed material and suspended sediment and facilitating the formation and migration of the ETM. Extreme events further enhanced geomorphic adjustment: the post-Lekima (2019) flood produced maximum scour of −5.8 m in the upper Ling River and deposition of +3.2 m in the Jiaojiang main channel within weeks. Channel curvature and junction morphology strongly controlled flood-level distribution. Model experiments indicate that lowering shoal elevations and widening the cross-section at key constrictions can effectively reduce flood levels. Collectively, these findings clarify the morphodynamic evolution mechanisms of a MEMS system and provide quantitative guidance for flood-mitigation and estuarine-management strategies. Full article

The purpose of this study is to enhance the performance of the mesohabitat model MesoHABSIM by lowering the necessary hydraulic modelling effort. This proof-of-concept study tests an application of the MesoHydraulics model to simulate the hydraulic characteristics of hydromorphological units (HMUs) occurring in a regulated river at different low discharges. In this quantitative approach, hydraulic patterns are transferred from a source site, where depth and velocity distributions were derived from field measurements and a 2D hydrodynamic model, to a target site, where only a single field hydrometric survey was conducted. Instead of modelling changes in individual hydraulic measurement values to estimate hydraulic responses to discharge, the model relies on statistical distributions of these values within HMUs. We were testing whether changes in the distribution of HMU’s and their hydraulics can be transferred between morphologically comparable river sections to serve as a sufficient hydraulic input for mesoscale habitat modelling. The hydrodynamic component of the River2D software (V.0.95a), routinely used in MesoHABSIM, served as a baseline for testing the MesoHydraulic model’s performance and for producing source data for deriving distribution functions. The test was conducted using data from two one-kilometre sites on the upper Oder River (Poland). The model transfers the HMU area distributions, along with corresponding depth and velocity frequency distributions, for a number of flows from one site (the source) to another (the target). The hydraulics at both sites were surveyed under single-discharge conditions. For the source site, the hydrodynamic model was applied to classify the HMU mosaic at three additional discharge stages. At the target reach, the HMU mapping was conducted based on survey data, and statistical frequency functions were used to model distributions of hydraulic patterns at discharges modelled for the source. The hydraulic model’s performance was evaluated at the target reach by comparing simulated hydraulics and HMU patterns with those modelled using River2D. Finally, both models were used to calculate habitat availability for the fish communities, and dissimilarities were observed. The resulting hydraulic distributions were similar, with an average affinity index of 90%. Higher affinity indices were reached at flows close to the measured value, with increasing model disagreement toward flow extremes, most notably for Run and Backwater units. Regardless, habitat models for the fish community were also highly correlated with R² = 0.98 for amounts of suitable habitat and almost identical habitat distribution among the species. Yet, the MesoHydraulics-based model slightly, but consistently, overestimated habitat availability. While the model was tested in a large and regulated river system, its accuracy may vary depending on the natural river morphology. Further research should evaluate modelling uncertainties and their applicability in less-modified water bodies. Full article

Climate change is advancing, sea levels are rising, and peak river discharges are increasing. Accelerated sea level rise (SLR) may pose a significant threat to the long-term habitability of the Netherlands. In the short term, further reinforcement of flood defenses is required. However, the key long-term question is which adaptation strategy will most effectively manage flood risk in the Netherlands. As part of the SLR Knowledge Programme, research was conducted on various long-term strategies, focusing on the feasibility of three approaches: Protect, Advance, and Accommodate. The Protect and Advance strategies aim to reduce flood risk primarily through the prevention of flooding. The Accommodate strategy, particularly in its more extreme form, emphasizes Managed Retreat, following the precautionary principle, or seeks to mitigate flood consequences rather than invest in Prevention. This study examined the economic implications of two opposing cornerstone strategies, Protect and Managed Retreat, as well as hybrid strategies that integrate elements of both, across different sea level rise scenarios. Additionally, the study includes a forward-looking assessment of the potential impacts on the financial sector, with particular attention to catastrophe insurance and capital requirements aimed at mitigating default risk. The findings indicate that a Managed Retreat strategy represents a last-resort option and cannot be implemented effectively without concurrent protective measures. Furthermore, the annual flood risk is only marginally reduced under the Accommodate strategy, even when combined with protective interventions, while its associated costs significantly exceed those of the Protect strategy. A combined approach integrating protection with localized Accommodate measures that support multi-functional land use, such as nature-based solutions and water storage, appears to offer a more promising strategy, if these values cover the costs. The results can be used to evaluate the effectiveness of possible adaptation strategies to sea level rise. Full article

This study aims to forecast the combined impacts of drought and flood in the future using an integrated framework. This framework integrates U-Net++, quantile mapping (QM), Copula models, and ISIMIP3b gridded large-scale discharge data (1985–2014, 2021–2050, and 2071–2100). Copula models analyze compound effects in four dimensions to determine return periods for droughts and floods. The standalone U-Net++ and its integration with multiple linear regression, multiple nonlinear regression, M5 model tree, multivariate adaptive regression splines, and QM downscaled ISIMIP3b model river flows. U-Net++QM outperformed other models, with a 58% lower RRMSE. Ensemble GCMs showed less uncertainty than other models in river flow downscaling. For the Ensemble model, the highest drought severity was −300, the maximum duration was 300 months, flood peak flow reached 12,000 m³/s, and intervals lasted up to 22 months. Moreover, the return periods of compound events for this model ranged from 50 to 3000 years. Future river flow projections, using the Ensemble model and emission scenarios (SSP126, SSP370, and SSP585), showed increased vulnerability in 2071 and 2025 versus the observed period. Introducing an integrated framework serves as a management tool for addressing extreme combined phenomena under climate change. Full article

Identifying the dominant mechanisms of water and soil salinization in arid and semi-arid endorheic basins is fundamental for our understanding of basin-scale water–salt balance and supports water resources management. In many inland basins, mineral dissolution, evaporation, and transpiration govern salinization, but disentangling these processes remains difficult. Using the Barkol Basin in northwestern China as a representative endorheic system, we sampled waters and soils along a transect from the mountain front through alluvial fan springs and rivers to the terminal lake. We integrated δ¹⁸O–δ²H with hydrochemical analyses, employing deuterium excess (d-excess) to partition salinity sources and quantify contributions. The results showed that mineral dissolution predominated, contributing 65.8–81.8% of groundwater salinity in alluvial fan settings and ~99.7% in the terminal lake, whereas direct evapoconcentration was minor (springs and rivers ≤ 4%; lake ≤ 0.2%). Water chemistry types evolved from Ca-HCO₃ in mountainous runoff, to Ca·Na-HCO₃·SO₄ in groundwater and groundwater-fed rivers, and finally to Na-SO₄·Cl in the terminal lake. The soil profiles showed that groundwater flow and vadose-zone water–salt transport control spatial patterns: surface salinity rises from basin margins (<1 mg/g) to the lakeshore and is extremely high near the lake (23.85–244.77 mg/g). In spring discharge belts and downstream wetlands, the sustained evapotranspiration of groundwater-supported soil moisture drives surface salt accumulation, making lakeshores and wetlands into terminal sinks. The d-excess-based method can robustly separate the salinization processes despite its initial isotopic variability. Full article

Hydroclimatic and geomorphological prerequisites for mudflow hazard were studied using data on several of the largest flood events in the Khamar-Daban mountain area (Lake Baikal, East Siberia) for the period from 1966 to 2022. The data include flood-forming precipitation and atmospheric circulation patterns, the amount of related suspended sediment discharge in the years of high floods, as well as terrain features favorable for the formation of catastrophic floods and mudflows. Floods and mudflows in the area can arise under conditions of extremely high daily precipitation (up to 200 mm or more) after the territory becomes moistened by prolonged rainfall under meridional air transport. The maximum water discharge correlates with a multifold increase in the suspended sediment discharge and turbidity. The increase in sediment discharge associated with maximum water discharge (floods) of ≤10% probability is apparently due to 4–9 times higher flow rates. On the other hand, the formation of the solid runoff component in the area is controlled geomorphologically by slope processes depending on slope steepness, elevation contrasts, and the thickness of soft sediments subject to denudation and transport. The geomorphological conditions are most favorable for the development of mudflows and catastrophic floods in the catchments of the Bezymyannaya, Slyudyanka, Khara-Murin, and Utulik rivers. Floods and mudflows are especially hazardous on the southern shore of Lake Baikal, encircled by the Khamar-Daban Range, where active mudflow processes pose risks to the towns of Slyudyanka and Baikalsk, as well as to the sludge storage facilities of the abandoned Baikal Pulp and Paper Mill. Full article

The 2011 Great Flood in Thailand exposed critical deficiencies in water management across the Chao Phraya River Basin, particularly in controlling inflows and discharges from major reservoirs such as Sirikit and Bhumibol. Inadequate rainfall monitoring at the Nakhon Sawan station further intensified the disaster’s impact. As climate change continues to amplify extreme weather events, this study aims to improve flood forecasting accuracy and promote sustainable water resource management aligned with the Sustainable Development Goals (SDGs 6, 11, and 13). Advanced climate data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were spatially refined and integrated with hydrological models to enhance regional accuracy. The Discrete Wavelet Transform (DWT) was applied for feature extraction to capture hydrological variability, while the Nonlinear Autoregressive Model with Exogenous Factors (NARX) was employed to model complex temporal relationships. A multi-model ensemble framework was developed to merge climate forecasts with real-time hydrological data. Results demonstrate significant model performance improvements, with DWT-NARX achieving 55–98% lower prediction errors (RMSE) compared to baseline methods and correlation coefficients exceeding 0.91 across all forecasting scenarios. Marked seasonal variations emerge, with higher inflows during wet periods and reduced inflows during dry seasons. Under RCP8.5 climate scenarios, wet-season inflows are projected to increase by 15.8–17.4% by 2099, while dry-season flows may decline by up to 33.5%, potentially challenging future water availability and flood control operations. These findings highlight the need for adaptive and sustainable water management strategies to enhance climate resilience and advance SDG targets on water security, disaster risk reduction, and climate adaptation. Full article

Accurate river runoff prediction plays a vital role in water resource management, agricultural scheduling, disaster prevention, and climate adaptation. To address three long-standing challenges in multi-basin hydrological modeling—the insufficient nonlinear expressiveness of recurrent structures, underestimation of extreme high-flow events caused by sample imbalance, and weak cross-basin generalization—this study proposes a hybrid forecasting framework, KAN–WLSTM, that integrates physical priors with deep learning. Specifically, (i) the KAN replaces linear layers to achieve nonlinear mapping consistent with hydrological mechanisms; (ii) a WMSE loss is adopted to emphasize high-flow samples; (iii) Granger causality analysis is applied for causality-driven input selection; and (iv) Optuna is used to perform Bayesian-based adaptive hyperparameter optimization. Multi-scale experiments based on the CAMELS-GB dataset show that a 14-day lag window yields the best performance, with an average MSE = 1.77 ${({\mathrm{m}}^3/\mathrm{s})}^2$ and NSE of 0.81 across nine representative catchments. Comparative results indicate that the proposed model achieves the best or near-best scores in most metrics, outperforming traditional LSTM by 6.8% in MSE and 2.7% in NSE, while reducing peak discharge errors by up to 18%. In large-sample evaluations across 161 catchments, the KAN–WLSTM model attains an average and median NSE of 0.770 and 0.827, respectively, with the smallest variance and ranked first among all models, demonstrating outstanding robustness and generalization under diverse hydro-climatic conditions. Full article

Understanding sediment transport under varying flow regimes and its associated scouring–silting responses is fundamental for analyzing the coupled dynamics of hydrology and river morphology. However, in large alluvial rivers strongly modified by human interventions such as dam operations, identifying critical thresholds of scouring–silting transitions remains a major challenge. This study examines sediment transport dynamics and flow frequency patterns through statistical analysis and the coefficient of determination method, using daily discharge and suspended sediment concentration records from eight hydrological stations along the middle and lower Yangtze River, China, covering 1990–2023. The results demonstrate that the impoundment of the Three Gorges Reservoir substantially altered downstream hydrological and sediment regimes, leading to a more uniform flow frequency distribution, suppression of extreme flows, and increased prevalence of moderate discharges. These adjustments stabilized river flows and improved sediment transport efficiency. Distinct spatial variations were identified across seven river sections: upstream reaches shifted from bimodal scouring–silting patterns to scouring-dominated regimes, whereas downstream reaches exhibited weakened sediment deposition. Moreover, critical thresholds of both flow and sediment coefficients displayed systematic longitudinal shifts. Collectively, these findings provide new insights into water–sediment interactions under large-scale regulation and offer practical implications for sediment management in highly engineered river systems. Full article

In dam operations, sudden discharges during extreme rainfall events can pose severe flood risks to downstream communities. This study developed a dam discharge-based river water level forecasting model using a data-driven deep learning approach, long short-term memory (LSTM). To enhance predictive performance, physics-based HEC-RAS simulation outputs, including extreme events, were incorporated as additional inputs. The Seomjin River Basin in South Korea, which recently experienced severe flooding, was selected as the study area. Hydrological data from 2010 to 2023 were utilized, with 2023 reserved for model testing. Forecasts were generated for four lead times (3, 6, 12, and 24 h), consistent with the operational flood forecasting system of the Ministry of Environment, South Korea. Using only observed data, the model achieved high accuracy at upstream sites, such as Imsil-gun (Iljung-ri, R² = 0.92, RMSE = 0.27 m) and Gokseong (Geumgok Bridge, R² = 0.91, RMSE = 0.35 m), for a 6-h lead time. However, performance was lower at Gurye-gun (Songjeong-ri, R² = 0.72, RMSE = 1.48 m) due to the complex influence of two dams. Incorporating enhanced inputs significantly improved predictions at Gurye-gun (R² = 0.91, RMSE = 1.17 m at 3 h). Overall, models using only observed data performed better at upstream sites, while enhanced inputs were more effective in downstream or multi-dam regions. The 6-h lead time yielded the highest overall accuracy, highlighting the potential of this approach to improve real-time dam operations and flood risk management. Full article