Search Results (185)

This study introduces a novel methodology for assessing ice-jam flood hazards along river channels. It employs empirical equations that relate non-dimensional ice-jam stage to discharge, enabling the generation of an ensemble of longitudinal profiles of ice-jam backwater levels through Monte-Carlo simulations. These simulations produce non-exceedance probability profiles, which indicate the likelihood of various flood levels occurring due to ice jams. The flood levels associated with specific return periods were validated using historical gauge records. The empirical equations require input parameters such as channel width, slope, and thalweg elevation, which were obtained from bathymetric surveys. This approach is applied to assess ice-jam flood hazards by extrapolating data from a gauged reach at Fort Simpson to an ungauged reach at Jean Marie River along the Mackenzie River in Canada’s Northwest Territories. The analysis further suggests that climate change is likely to increase the severity of ice-jam flood hazards in both reaches by the end of the century. This methodology is applicable to other cold-region rivers in Canada and northern Europe, provided similar fluvial geomorphological and hydro-meteorological data are available, making it a valuable tool for ice-jam flood risk assessment in other ungauged areas. Full article

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

Accurate estimation of long-term average (LTA) discharge is fundamental for water resource assessment, infrastructure planning, and hydrological modeling, yet it remains a significant challenge, particularly in data-scarce or ungauged basins. This study introduces an advanced machine learning framework to estimate long-term average discharge using globally available hydrological station metadata from the Global Runoff Data Centre (GRDC). The methodology involved comprehensive data preprocessing, extensive feature engineering, log-transformation of the target variable, and the development of multiple predictive models, including a custom deep neural network with specialized pathways and gradient boosting machines (XGBoost, LightGBM, CatBoost). Hyperparameters were optimized using Bayesian techniques, and a weighted Meta Ensemble model, which combines predictions from the best individual models, was implemented. Performance was rigorously evaluated using R², RMSE, and MAE on an independent test set. The Meta Ensemble model demonstrated superior performance, achieving a Coefficient of Determination (R²) of 0.954 on the test data, significantly surpassing baseline and individual advanced models. Model interpretability analysis using SHAP (Shapley Additive explanations) confirmed that catchment area and geographical attributes are the most dominant predictors. The resulting model provides a robust, accurate, and scalable data-driven solution for estimating long-term average discharge, enhancing water resource assessment capabilities and offering a powerful tool for large-scale hydrological analysis. Full article

Sea level rise due to climate change poses an increasing threat to coastal ecosystems, infrastructure, and human settlements. However, accurately estimating sea level changes in regions without tide gauge observations remains a major challenge. While satellite altimetry provides wide spatial coverage, its accuracy diminishes near coastlines. In contrast, tide gauges offer high precision but are spatially limited. This study aims to develop an artificial neural network-based model for estimating relative sea level changes in coastal regions where tide gauge data are unavailable. Unlike conventional forecasting approaches focused on future time series prediction, the proposed model is designed to learn spatial distribution patterns and temporal rates of sea level change from a fusion of satellite altimetry and tide gauge data. A normalization scheme is applied to reduce inconsistencies in reference levels, and Bayesian optimization is employed to fine-tune hyperparameters. A case analysis is conducted in two coastal regions in South Korea, Busan and Ansan, using data from 2018 to 2023. The model demonstrates strong agreement with observed tide gauge records, particularly in estimating temporal trends of sea level rise. This approach effectively compensates for the limitations of satellite altimetry in coastal regions and fills critical observational gaps in ungauged areas. The proposed method holds substantial promise for coastal hazard mitigation, infrastructure planning, and climate adaptation strategies. Full article

The Sylhet Division of Bangladesh, highly susceptible to monsoon flooding, requires effective flood risk management to reduce socio-economic losses. Flood frequency analysis is an essential aspect of flood risk management and plays a crucial role in designing hydraulic structures. This study applies regional flood frequency analysis (RFFA) using L-moments to identify homogeneous hydrological regions and estimate extreme flood quantiles. Records from 26 streamflow gauging stations were used, including streamflow data along with corresponding physiographic and climatic characteristic data, obtained from GIS analysis and ERA5 respectively. Most stations showed no significant monotonic trends, temporal correlations, or spatial dependence, supporting the assumptions of stationarity and independence necessary for reliable frequency analysis, which allowed the use of cluster analysis, discordancy measures, heterogeneity tests for regionalization, and goodness-of-fit tests to evaluate candidate distributions. The Generalized Logistic (GLO) distribution performed best, offering robust quantile estimates with narrow confidence intervals. Multiple Non-Linear Regression models, based on catchment area, elevation, and other parameters, reasonably predicted ungauged basin peak discharges (R² = 0.61–0.87; RMSE = 438–2726 m³/s; MAPE = 41–74%) at different return periods, although uncertainty was higher for extreme events. Four homogeneous regions were identified, showing significant differences in hydrological behavior, with two regions yielding stable estimates and two exhibiting greater extreme variability. Full article

British Columbia, Canada, is recognized for its abundant natural resources, including agricultural and aquaculture products, sustained by its diverse climate and geography. Water resource allocation in BC is governed by the Water Sustainability Act, enacted on 29 February 2016, replacing the historic Water Act. However, limited gauging of streams across the province poses challenges for ensuring water allocation while meeting Environmental Flow Needs. Overallocated watersheds and data-scarce watersheds in need of licensing highlight the need for robust streamflow prediction methods. To address these challenges, we developed a methodology that integrates the Drainage Area Ratio and Nearest Neighbors techniques to predict streamflows efficiently, without incurring additional financial costs. We utilized Digital Elevation Models and flow data from provincially and municipally managed hydrometric stations, as well as from the Water Survey of Canada, to normalize streamflows based on area, slope, and elevation. This approach ensures hydrological predictions that account for variability in hydrological processes resulting from differences in lumped-scale watershed characteristics. The method was validated using streamflow data from hydrometric stations maintained by the aforementioned entities. For validation, each station was iteratively treated as ungauged by temporarily removing it from the dataset and then predicting its streamflow using the proposed methodologies. The results demonstrated that the amalgamated Drainage Area Ratio–Nearest Neighbors approach outperformed the traditional Drainage Area Ratio method, offering reliable predictions for diverse watersheds. This study provides an adaptable and cost-effective framework for enhancing water resource management across BC. Full article

Estimating surface runoff in ungauged basins is important for planning and managing water resources, as well as for developing civil and environmental projects. Within the estimation of surface runoff are the minimum flows, which are important for assessing water availability and the possibility of granting water resources. To estimate surface runoff in ungauged basins, regionalization is a technique that has been used and consists of transferring variables, functions and/or parameters from gauged basins to the ungauged basin. This study reviews the minimum flow regionalization methods used in studies published between 2015 and 2023 in the CAPES, Scielo, Scopus and Web of Science databases. The regionalization methods were grouped according to their approach, namely the regionalization of hydrological signatures and the regionalization of hydrological model parameters. Most studies focused on regionalizing hydrological signatures, particularly minimum flows and flow duration curves. For regionalizing hydrological model parameters, common approaches included spatial proximity, physical similarity and regression techniques. Some methods can estimate the flow time series at the location of interest, which can be an advantage for estimating different statistics from the data; other methods focus on estimating a specific flow statistic. Most methods require several gauged basins in their study area to obtain reliable estimates of minimum flows in ungauged basins. The study discusses the advantages, disadvantages and limitations of each method. Full article

The increasing frequency and severity of hydrological extremes due to climate change necessitate accurate baseflow estimation and effective watershed management for sustainable water resource use. The Soil and Water Assessment Tool (SWAT) is widely utilized for hydrological modeling but shows limitations in baseflow simulation due to its uniform application of the alpha factor across Hydrologic Response Units (HRUs), neglecting spatial and temporal variability. To address these challenges, this study integrated SWAT with the Tree-Based Pipeline Optimization Tool (TPOT), an automated machine learning (AutoML) framework, to predict HRU-specific alpha factors. Furthermore, a user-friendly web-based program was developed to improve the accessibility and practical application of these optimized alpha factors, supporting more accurate baseflow predictions, even in ungauged watersheds. The proposed HRU-specific alpha factor approach in the study area significantly enhanced the recession and baseflow predictions compared to the traditional uniform alpha factor method. This improvement was supported by key performance metrics, including the Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R²), the percent bias (PBIAS), and the mean absolute percentage error (MAPE). This integrated framework effectively improves the accuracy and practicality of hydrological modeling, offering scalable and innovative solutions for sustainable watershed management in the face of increasing water stress. Full article

Knowledge of river bathymetry is crucial for accurately simulating river flows and floodplain inundation. However, field data are scarce, and the depth and shape of the river channels cannot be systematically observed via remote sensing. Therefore, an efficient methodology is necessary to define effective river bathymetry. This research reconstructs the bathymetry from existing global digital elevation models (DEMs) and water surface elevation observations with minimum human intervention. The methodology can be considered a 1D geometric inverse problem, and it can potentially be used in gauged or ungauged basins worldwide. Nine global DEMs and two sources of water surface elevation (in situ and remotely sensed) were analyzed across two study areas. Results highlighted the importance of preprocessing cross-sections to align with water surface elevations, significantly improving discharge estimates. Among the techniques tested, one that combines the slope-break concept with the principles of mass conservation consistently provided robust discharge estimates for the different DEMs, achieving good performance in both study areas. Copernicus and FABDEM emerged as the most reliable DEMs for accurately representing river geometry. Overall, the proposed methodology offers a scalable and efficient solution for cross-section reconstruction, supporting global hydraulic modeling in data-scarce regions. Full article

The Hengduan Mountains region (HMR) is vulnerable to flash flood disasters, which account for the largest proportion of flood-related fatalities in China. Flash flood regionalization, which divides a region into homogeneous subdivisions based on flash flood-inducing factors, provides insights for the spatial distribution patterns of flash flood risk, especially in ungauged areas. However, existing methods for flash flood regionalization have not fully reflected the spatial topology structure of the inputted geographical data. To address this issue, this study proposed a novel framework combining a state-of-the-art unsupervised Graph Neural Network (GNN) method, Dink-Net, and Shapley Additive exPlanations (SHAP) for flash flood regionalization in the HMR. A comprehensive dataset of flash flood inducing factors was first established, covering geomorphology, climate, meteorology, hydrology, and surface conditions. The performances of two classic machine learning methods (K-means and Self-organizing feature map) and three GNN methods (Deep Graph Infomax (DGI), Deep Modularity Networks (DMoN), and Dilation shrink Network (Dink-Net)) were compared for flash-flood regionalization, and the Dink-Net model outperformed the others. The SHAP model was then applied to quantify the impact of all the inducing factors on the regionalization results by Dink-Net. The newly developed framework captured the spatial interactions of the inducing factors and characterized the spatial distribution patterns of the factors. The unsupervised Dink-Net model allowed the framework to be independent from historical flash flood data, which would facilitate its application in ungauged mountainous areas. The impact analysis highlights the significant positive influence of extreme rainfall on flash floods across the entire HMR. The pronounced positive impact of soil moisture and saturated hydraulic conductivity in the areas with a concentration of historical flash flood events, together with the positive impact of topography (elevation) in the transition zone from the Qinghai–Tibet Plateau to the Sichuan Basin, have also been revealed. The results of this study provide technical support and a scientific basis for flood control and disaster reduction measures in mountain areas according to local inducing conditions. Full article

Accurately predicting the impacts of climate change on hydrological fluxes in ungauged basins continues to be a complex task. In this study, we investigated the transferability of the model parameters SCS-SMA-LR, available in the ATHYS platform, to simulate hydrological behavior within catchments of a large South Mediterranean transboundary basin, i.e., the Medjerda bordering Tunisia and Algeria, characterized by contrasting climatic and physiographic conditions. A robustness analysis was set up for donor and receptor catchments situated in the Medjerda catchment in Tunisia. The model was initially calibrated for two donor catchments, for the 127 km² catchment of the Lakhmess watershed situated on the right bank and for the 362 km² catchment of the Raghay watershed situated on the left bank of the Medjerda basin in Tunisia, using input data from 1990 to 1994. The model performance was evaluated through multiple accuracy criteria based on the Best Linear Unbiased Estimator (BLUE) for the automatic calibration to quantify the model simulation, proving its good performance. The temporal transferability was assessed by evaluating model performance, transferring the calibrated parameters for the two catchments as validation on data for 3-year periods outside the calibration domain to test the robustness of the model through a diachronic analysis from different decades, i.e., for the periods 1994–1997, 2001–2004, and 2014–2017, respectively. The spatial transferability was assessed by transferring the parameters calibrated on the donor catchments to be applied to the receptor catchments based on similarity and data availability. The model was upgraded to a greater catchment for data from 1994 to 2016 for the right bank, the Siliana Upstream catchment, and to the nearest catchment with a similar area for the data from 2008 to 2017 for the left bank of the Medjerda basin, the Bouheurtma catchment. The capacity of the soil reservoir and the flow velocity parameters proved to have an important impact on the modeling implementations at, respectively, 123.03 mm and 1 m/s for Raghay, and 95.05 mm and 2.5 m/s for Lakhmes. The results show that the space–time transfer process of model parameters produces an acceptable simulation of flow volumes and timing. The proposed methodology proved to be a successful way to monitor ungauged catchments and strengthens the robustness of the SCS-SMA-LR model for hydrological modeling and impact studies in ungauged basins of the Southern Mediterranean region. Full article

Groundwater discharge is critical for maintaining river flow during dry seasons, especially in lowland areas. Despite its significance, groundwater resources have often been overlooked highlighting the need for comprehensive studies amidst growing pressure to develop new water resources. This study focuses on the Soyang River Basin, South Korea, including its ungauged northern regions, the nearby DMZ (Demilitarized Zone), using the physically based Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model. A three-year simulation was conducted to examine variable aquifer depth distribution patterns by assuming an inverse relationship between surface elevation and aquifer bottom depth. Three case studies (i.e., equal distribution, linear regression, and logarithmic regression) were evaluated and compared. The method to identity optimal aquifer depth distributions to enhance groundwater simulation accuracy in regions with significant topographical variation was incorporated. Groundwater levels at six monitoring sites showed that altitude-based variable aquifer depths outperformed the equal distribution case. The results showed strong agreement between simulated and observed values, particularly in the linear regression case with an R-squared statistic of 0.858 and Nash–Sutcliffe Efficiency index of 0.789, indicating that linear regression-based aquifer depth estimation can significantly improves long-term runoff modeling and groundwater simulation accuracy. The logarithmic regression case had the lowest relative peak error in peak flow. These findings highlight the importance of adjusting aquifer depth distributions in physically based hydrologic models to better reflect real-world conditions. Overall, this study contributes to advance groundwater modeling by integrating variable aquifer depth distributions into a physically based hydrologic model for large scale watersheds. Full article

Small- and medium-sized reservoirs significantly alter natural flood processes, making it essential to understand their impact on runoff for effective water resource management. However, the lack of measured data for most small reservoirs poses challenges for accurately simulating their behavior. This study proposes a novel method that utilizes readily available satellite observation data, integrating hydraulic, hydrological, and mathematical formulas to derive outflow coefficients. Based on the Grid-XinAnJiang (GXAJ) model, the enhanced GXAJ-R model accounts for the storage and release effects of ungauged reservoirs and is applied to the Tunxi watershed. Results show that the original GXAJ model achieved a stable performance with an average NSE of 0.88 during calibration, while the NSE values of the GXAJ and GXAJ-R models during validation ranged from 0.78 to 0.97 and 0.85 to 0.99, respectively, with an average improvement of 0.03 in the GXAJ-R model. This enhanced model significantly improves peak flow simulation accuracy, reduces relative flood peak error by approximately 10%, and replicates the flood flow process with higher fidelity. Additionally, the area–volume model derived from classified small-scale data demonstrates high accuracy and reliability, with correlation coefficients above 0.8, making it applicable to other ungauged reservoirs. The OTSU-NDWI method, which improves the NDWI, effectively enhances the accuracy of water body extraction from remote sensing, achieving overall accuracy and kappa coefficient values exceeding 0.8 and 0.6, respectively. This study highlights the potential of integrating satellite data with hydrological models to enhance the understanding of reservoir behavior in data-scarce regions. It also suggests the possibility of broader applications in similarly ungauged basins, providing valuable tools for flood management and risk assessment. Full article

Flash floods are catastrophic phenomena that pose a serious risk to coastal infrastructures, towns, villages, and cities. This study assesses the risk of flash floods in the ungauged Mekkah province region based on specific and effective morphometric and topographic features characterizing the study region. Shuttle Radar Topography Mission (SRTM) data were employed to construct a digital elevation model (DEM) for a detailed analysis, and the geographical information systems software 10.4 (GIS) was utilized to assess the linear, area, and relief aspects of the morphometric parameters. The ArcHydro tool was used to prepare the primary parameters, including the watershed border, flow accumulation, flow direction, flow length, and stream ordering. The study region’s flash flood hazard degrees were assessed using several morphometric characteristics that were measured, computed, and connected. Two different and effective methods were used to independently develop two models of flood vulnerability behaviors. The integrated method analysis revealed that most of the eastern and western parts of the studied province provide high levels of flood vulnerability. Due to it being one of the most helpful topographic indices, the integrated flood vulnerability final map was overlayed with the topographic position index (TPI). The integrated results aided in understanding the link between the general basins’ morphometric characteristics and their topographical features for mapping the different flood susceptibility locations over the entire studied province. Thus, this can be applied to investigate a surface-specific reduction plan against the impacts of flood hazards in the studied landscape. Full article

Flood frequency analysis at large scales, essential for the development of flood risk maps, is hindered by the scarcity of gauge flow data. Suitable methods are thus required to predict flooding in ungauged basins, a notoriously complex problem in hydrology. We develop a Bayesian hierarchical model (BHM) based on the generalized extreme value (GEV) and the generalized Pareto distribution for regional flood frequency analysis at high resolution across a large part of North America. Our model leverages annual maximum flow data from ≈20,000 gauged stations and a dataset of 130 static catchment-specific covariates to predict extreme flows at all catchments over the continent as well as their associated statistical uncertainty. Additionally, a modification is made to the data layer of the BHM to include peaks over threshold flow data when available, which improves the precision of the discharge level estimates. We validated the model using a hold-out approach and found that its predictive power is very good for the GEV distribution location and scale parameters and improvable for the shape parameter, which is notoriously hard to estimate. The resulting discharge return levels yield a satisfying agreement when compared with the available design peak discharge from various government sources. The assessment of the covariates’ contributions to the model is also informative with regard to the most relevant underlying factors influencing flood-inducing peak flows. According to the developed aggregate importance score, the key covariates in our model are temperature-related bioindicators, the catchment drainage area and the geographical location. Full article