Search Results (77)

A systems-oriented review of machine learning (ML) over the entire flood management spectrum, encompassing fluvial flood control, pluvial flood management, and resiliency-risk characterization was undertaken. Deep learners like long short-term memory (LSTM) networks perform well in predicting reservoir inflows and outflows. Convolution neural networks (CNNs) and other object identification algorithms are being explored in assessing levee and flood wall failures. The use of ML methods in pump station operations is limited due to lack of public-domain datasets. Reinforcement learning (RL) has shown promise in controlling low-impact development (LID) systems for pluvial flood management. Resiliency is defined in terms of the vulnerability of a community to floods. Multi-criteria decision making (MCDM) and unsupervised ML methods are used to capture vulnerability. Supervised learning is used to model flooding hazards. Conventional approaches perform better than deep learners and ensemble methods for modeling flood hazards due to paucity of data and large inter-model predictive variability. Advances in satellite-based, drone-facilitated data collection and Internet of Things (IoT)-based low-cost sensors offer new research avenues to explore. Transfer learning at ungauged basins holds promise but is largely unexplored. Explainable artificial intelligence (XAI) is seeing increased use and helps the transition of ML models from black-box forecasters to knowledge-enhancing predictors. 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

This study presents a flood risk assessment of five rural bridges along the monsoon-prone Khar–Mohmand Gat corridor in Northwestern Pakistan using an integrated hydrologic and hydraulic modeling framework. Hydrologic simulations for 50- and 100-year design storms were performed using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS), with watershed delineation conducted via Geographic Information Systems (GIS). Calibration was based on regional rainfall data from the Peshawar station using a Soil Conservation Service Curve Number (SCS-CN) of 86 and time of concentration calculated using Kirpich’s method. The resulting hydrographs were used in two-dimensional hydraulic simulations using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) to evaluate water surface elevations, flow velocities, and Froude numbers at each bridge site. The findings reveal that all bridges can convey peak flows without overtopping under current climatic conditions. However, Bridges 3 to 5 experience near-critical to supercritical flow conditions, with velocities ranging from 3.43 to 4.75 m/s and Froude numbers between 0.92 and 1.04, indicating high vulnerability to local scour. Bridge 2 shows moderate risk, while Bridge 1 faces the least hydraulic stress. The applied modeling framework effectively identifies structures requiring priority intervention and demonstrates a practical methodology for assessing flood risk in ungauged, data-scarce, and semi-arid regions. 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

This research addresses the challenge of bias in Remotely Sensed Rainfall (RSR) datasets used for hydrological planning in Uganda’s data-scarce, ungauged catchments. Four bias correction methods, Quantile Mapping (QM), Linear Transformation (LT), Delta Multiplicative (DM), and Polynomial Regression (PR), were evaluated using daily rainfall data from four gauged stations (Gulu, Soroti, Jinja, Mbarara). QM consistently outperformed other methods based on statistical metrics, e.g., for National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA_CPC) RSR data at Gulu, Root-Mean-Square Error (RMSE) was reduced from 29.20 mm to 19.00 mm, Mean Absolute Error (MAE) reduced from 22.44 mm to 12.84 mm, and Percent Bias (PBIAS) reduced from −19.23% to 1.05%, and improved performance goodness-of-fit tests (KS = 0.03, p = 1.00), while PR, though statistically strong, failed due to overfitting. A bias correction framework was developed for ungauged catchments, using predetermined bias factors derived from observed station data. Validation at Arua (tropical savannah) and Fort Portal (tropical monsoon) demonstrated significant improvements in RSR data when the bias correction framework was applied. At Arua, bias correction of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data reduced RMSE from 49.14 mm to 21.41 mm, MAE from 45.74 mm to 17.38 mm, and PBIAS from −59.83% to −8.18%, while at Fort Portal, bias correction of the CHIRPS dataset reduced RMSE from 28.35 mm to 15.02 mm, MAE from 25.28 mm to 11.35 mm, and PBIAS from −46.2% to 4.74%. Our research concludes that QM is the most effective method, and that the framework is a tool for improving RSR data in ungauged catchments. Recommendations for future work includes machine learning integration and broader regional validation. Full article

Extreme high flows can have negative economic, social, and ecological effects and are expected to become more severe in many regions due to climate change. Knowledge of maximum flow regimes and estimation of extreme rainfall is important, especially in ungauged dry regions, for planning and infrastructure development. In this study, we propose a regional method for estimating extreme flow regimes and modeled extreme rainfall using the extreme value theory, with examples from the Gobi region of Mongolia. The first step is to apply the Generalized Extreme Value (GEV) theory for the maximum rainfall data using 44-year observational data covering the period 1978–2022. Then, estimated rainfall with a 100-year return period is used for the empirical equation of the maximum flood calculation. As a result, most stations’ maximum rainfall follows a Fréchet distribution and 100-year return period rainfall values that range between 27.8–130.6 mm. The local reference value in the 100-year return period rainfall is defined as 90 mm for the whole Gobi region. Our results show that extremely high rainfall in the Gobi region has changed from −7% to 16%, leading to higher flood events. These findings further provide evidence for the maximum rainfall for flood calculation, climate change impact assessment, water resource planning, and management studies. Full article

The increasing frequency of extreme rainfall events poses significant challenges to sustainable water resource management, leading to severe natural disasters. To mitigate these challenges, understanding the hydrological characteristics of watersheds, especially baseflow, is critical for enhancing watershed resilience and supporting sustainable water quality and resource management. However, conventional watershed models often neglect the accurate simulation of baseflow recession. This study proposes a method for calculating and applying the alpha factor for each hydrologic response unit (HRU) in the Soil and Water Assessment Tool (SWAT), considering both temporal and spatial variability in baseflow. The study watershed has undergone significant development, increasing the need for effective water management strategies that promote long-term sustainability. The alpha factor was computed using BFlow2021, and its effectiveness was evaluated by comparing recession and baseflow estimates under different methods. The results indicate that incorporating monthly HRU-specific alpha factors significantly improves model predictions of recession characteristics, highlighting the need for a more spatially and temporally detailed approach in hydrological modeling. The proposed methodology can help clarify the connection between recession and baseflow and can be applied to ungauged stations, offering a valuable tool for sustainable watershed and water quality management. Full article

Satellite rainfall estimates are robust alternatives to gauge precipitation, especially in Africa, where several watersheds and regional water basins are poorly gauged or ungauged. In this study, six satellite precipitation products, the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS); Tropical Applications of Meteorology Using Satellite and Ground-based Observations (TAMSAT); TRMM Multi-satellite Precipitation Analysis (TMPA); and the National Aeronautics and Space Administration’s new Integrated Multi-SatellitE Retrievals for Global Precipitation Measurement (GPM) early run (IMERG-ER), late run (IMERG-LR), and final run (IMERG-FR), were used to force a gauge-calibrated Soil & Water Assessment Tool (SWAT) model for the Congo River Basin, Central Africa. In this study, the National Centers for Environmental Prediction’s Climate Forecast System Reanalysis (CFSR) calibrated version of the SWAT was used as the benchmark/reference, while scenario versions were created as configurations using each satellite product identified above. CFSR was used as an independent sample to prevent bias toward any of the satellite products. The calibrated CFSR model captured and reproduced the hydrology (timing, peak flow, and seasonality) of this basin using the average monthly discharge from January 1984–December 1991. Furthermore, the results show that TMPA, IMERG-FR, and CHIRPS captured the peak flows and correctly reproduced the seasonality and timing of the monthly discharges (January 2007–December 2010). In contrast, TAMSAT, IMERG-ER, and IMERG-LR overestimated the peak flows. These results show that some of these precipitation products must be bias-corrected before being used for practical applications. The results of this study will be significant in integrated water resource management in the Congo River Basin and other regional river basins in Africa. Most importantly, the results obtained from this study have been hosted in a repository for free access to all interested in hydrology and water resource management in Africa. Full article

This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson’s r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson’s r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins. 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

Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems. Full article

This study employs the Soil and Water Assessment Tool (SWAT) model to evaluate soil loss within the Shilabati and Dwarkeswar River Basin of West Bengal, serving as a pilot investigation into soil erosion levels at ungauged stations during the post-monsoon season. Detailed data for temperature, precipitation, wind speed, solar radiation, and relative humidity for 2000–2022 were collected. A land use map, soil map, and slope map were prepared to execute the model. The model categorizes the watershed region into 19 sub-basins and 227 Hydrological Response Units (HRUs). A detailed study with regard to soil loss was carried out. A detailed examination of soil erosion patterns over four distinct time periods (2003–2007, 2007–2012, 2013–2017, and 2018–2022) indicated variability in soil loss severity across sub-basins. The years 2008–2012, characterized by lower precipitation, witnessed reduced soil erosion. Sub-basins 6, 16, 17, and 19 consistently faced substantial soil loss, while minimal erosion was observed in sub-basins 14 and 18. The absence of a definitive soil loss pattern highlights the region’s susceptibility to climatic variables. Reduced soil erosion from 2018 to 2022 is attributed to diminished precipitation and subsequent lower discharge levels. The study emphasizes the intricate relationship between climatic factors and soil erosion dynamics. Full article

Depth–discharge rating is required at gauged and ungauged locations for hydrologic modeling of alluvial systems to evaluate streamflow and manage regional water resources. Spanning low to high-flow conditions, manual field measurements are used to develop discharge ratings at gauged locations, producing continuous flow data from automated water depth measurements. The discharge rating is dependent on channel geometry, stream slope, vegetation, roughness coefficient, sediment load, and bank stability. To construct discharge ratings for many locations within larger model domains (hundreds to thousands of km²), intensive GIS and manual (spreadsheet) data manipulation are often required. In this analysis, available USGS gauging stations and readily available GIS coverages were used to learn and implement a novel method to characterize the depth–discharge relationships for hydrologic modeling of larger or complex areas using commonly available data and normalization techniques. The improved procedure simply uses drainage area, channel slope, and channel width, readily derivable GIS data, to develop discharge ratings for gauged and ungauged sections. The discharge rating curves for 70 USGS streamflow gauges were reproduced using the procedure. Then, the produced and observed discharge rating curves were compared to evaluate the accuracy of the method. In the analysis of streamflow depth predictions, the average Root Mean Squared Error was recorded at approximately 0.38 m (≈1.24 ft), with an interquartile range between 0.21 m and 0.49 m. The Mean Error remained centered around 0 m, with interquartile values ranging from −0.24 m to 0.24 m. Full article

Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Most of the RFFA techniques are based on the annual maximum (AM) flood model; however, research has shown that the peaks-over-threshold (POT) model has greater flexibility than the AM model. There is a lack of studies on POT-based RFFA techniques. This paper presents the development of POT-based RFFA techniques, using regularised linear models (least absolute shrinkage and selection operator, ridge regression and elastic net regression). The results of these regularised linear models are compared with multiple linear regression. Data from 145 stream gauging stations of south-east Australia are used in this study. A leave-one-out cross-validation is adopted to compare these regression models. It has been found that the regularised linear models provide quite accurate flood quantile estimates, with a median relative error in the range of 37 to 47%, which outperform the AM-based RFFA techniques currently recommended in the Australian Rainfall and Runoff guideline. The developed RFFA technique can be used to estimate flood quantiles in ungauged catchments in the study region. Full article