Search Results (169)

Drought is a complex environmental hazard to ecosystems and society. Decision-making on drought management options requires evaluating and predicting the extremity of future drought events. In this regard, quantifiable indices such as the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the standardized streamflow index (SSI) have been commonly used to characterize meteorological and hydrological drought. In general, the estimation and prediction of the indices require an extensive range of precipitation (SPI and SPEI) and discharge (SSI) datasets in space and time domains. However, there is a challenge for long-term and spatially extensive data availability, leading to the insufficiency of data in estimating drought indices. In this regard, this study uses satellite precipitation data to estimate and predict the drought indices. SPI values were calculated from the precipitation data obtained from the Centre for Hydrometeorology and Remote Sensing (CHRS) data portal for a study water basin. This study employs a hydrological model for calculating discharge and drought in the overall basin. It uses random forest (RF) and support vector regression (SVR) as machine learning models for SSI prediction for time scales of 1- and 3-month periods, which are widely used for establishing interactions between predictors and predictands that are both linear and non-linear. This study aims to evaluate drought severity variation in the overall basin using the hydrological model and compare this result with the machine learning model’s results. The results from the prediction model, hydrological model, and the station data show better correlation. The coefficients of determination obtained for 1-month SSI are 0.842 and 0.696, and those for the 3-month SSI are 0.919 and 0.862 in the RF and SVR models, respectively. These results also revealed more precise predictions of machine learning models in the longer duration as compared to the shorter one, with the better prediction result being from the SVR model. The hydrological model-evaluated SSI has 0.885 and 0.826 coefficients of determination for the 1- and 3-month time durations, respectively. The results and discussion in this research will aid planners and decision-makers in managing hydrological droughts in basins. Full article

Land cover is a crucial factor in controlling rainfall–runoff processes in mountain basins. However, various anthropogenic activities, such as converting natural vegetation to agricultural or urban areas, can affect this cover, thereby increasing the risk of flooding in cities. This study evaluates the hydrological behavior of two mountain basins in Loja, Ecuador, under varying land use scenarios. El Carmen small basin (B1), located outside the urban perimeter, and Las Pavas small basin (B2), within the urban area, were modeled using HEC-HMS 4.3 software. The results highlight the significant influence of vegetation degradation and restoration on hydrological processes. In degraded vegetation scenarios, peak flows increase due to reduced soil infiltration capacity, while baseflows decrease. Conversely, the conserved and restored vegetation scenarios show lower peak flows and higher baseflows, which are attributed to enhanced evapotranspiration, interception, and soil water storage. The study underscores the importance of ecosystem management and restoration in mitigating extreme hydrological events and improving water resilience. These findings provide a foundation for decision-making in urban planning and basin management, emphasizing the need for comprehensive and multidisciplinary approaches to develop effective public policies. Full article

The Red Sea region is considered one of the regions that suffer most from water scarcity among the Egyptian areas. This situation reinforces the importance of maximizing the utilization of available water sources. Rainwater and flood harvesting may form a good water source if good harvesting practices are applied. Natural pastures, Bedouin communities, and wild plants may be affected by severe droughts expected due to climate change. Additional water resources are very important to enhance the resilience of the Bedouin communities to probable droughts. Five main hydrographic basins are issued from Gebel El Sibai (+1435 m), including Wadi Esel, Wadi Sharm El Bahari, Wadi Sharm El Qibli, Wadi Wizr, and Wadi Umm Gheig. Detailed investigation of morphometric parameters, runoff/rainfall relationship, and flood volume using GIS and HEC-HMS model of each basin were estimated as well as natural vegetation. This study reveals that rainfall ranges from 84 mm to 0 mm, and a storm of 84 mm (highest event) is expected to occur every 42 years with a probability of 2.4%. Quantitative morphometric analysis implies that the area has good potential for flooding, especially Wadi Sharm El Qibli and Wadi Umm Gheig, where Wadi Sharm El Bahri represents the lowest priority for flooding. The flood volume of Umm Gheig basin is the greatest: 12 million m³ at the basin outlet with a rainfall event of 15 mm. Wadi Esel is expected to collect 8.7 million m³ due to the ratio of the impervious soil and rainfall quantity, Wadi Sharm El Bahari 2.1 million m³, Wadi Sharm El Qibli 1.6 million m³, and Wadi Wizer 1.04 million m³. Seven storage dams (SD1-SD7) were proposed to enhance the utilization of the surface water potentialities of this study area. Full article

Flood-susceptibility mapping (FSM) is crucial for effective flood prediction and disaster prevention. Traditional methods of modeling flood vulnerability, such as the Analytical Hierarchy Process (AHP), require weights defined by experts, while machine-learning and deep-learning approaches require extensive datasets. Remote sensing is also limited by the availability of images and weather conditions. We propose a new hybrid strategy integrating deep learning with the HEC–HMS and HEC–RAS physical models to overcome these challenges. In this study, we introduce a Weighted Residual U-Net (W-Res-U-Net) model based on the target of the HEC–HMS and RAS physical simulation without disregarding ground truth points by using two loss functions simultaneously. The W-Res-U-Net was trained on eight sub-basins and tested on five others, demonstrating superior performance with a sensitivity of 71.16%, specificity of 91.14%, and area under the curve (AUC) of 92.95% when validated against physical simulations, as well as a sensitivity of 88.89%, specificity of 93.07%, and AUC of 95.87% when validated against ground truth points. Incorporating a “Sigmoid Focal Loss” function and a dual-loss function improved the realism and performance of the model, achieving higher sensitivity, specificity, and AUC than HEC–RAS alone. This hybrid approach significantly enhances the FSM model, especially with limited real-world data. Full article

Hydrological processes can be highly influenced by changes in land use land cover (LULC), which can make hydrological modelling also very sensitive to land cover characterization. Therefore, obtaining up-to-date LULC data is a crucial process in hydrological modelling, and as such, different sources of LULC data raises questions on their quality and applicability. This is especially true with new data sources, such as citizen science-based land cover maps. Therefore, this research aims to explore the influence of LULC data sources on hydrological models via their parameterization and by performing sensitivity analyses. Kiffissos catchment, in Greece, a poorly gauged and highly urbanized basin including the city of Athens, is the case study area. In total, 12 continuous hydrological models were developed by mainly varying their structure and parametrization (lumped and gridded) and using three LULC datasets: coordination of information on the environment (CORINE), Urban Atlas and Scent (citizen-based). It was found that excess precipitation is negligibly contributed to by soil saturation and is dominated by the runoff over impervious areas. Therefore, imperviousness was the main parameter influencing both sensitivity to land cover and parameterization. Lastly, although the parametrization as lumped and gridded models affected the representation of hydrological processes in pervious areas, it was not relevant in terms of excess precipitation. Full article

This study aimed to enhance flood forecasting accuracy in the Liangfeng River basin, a small karst watershed in Southern China, by incorporating the Available Reservoir Capacity of Karst (ARCK) into the HEC-HMS model. This region is often threatened by floods during the rainy season, so an accurate flood forecast can help decision-makers better manage rivers. As a crucial influencing factor on karstic runoff, ARCK is often overlooked in hydrological models. The seasonal and volatile nature of ARCK makes the direct computation of its specific values challenging. In this study, a virtual reservoir for each sub-basin (total of 17) was introduced into the model to simulate the storage and release of ARCK-induced runoff phenomena. Simulations via the enhanced model for rainfall events with significant fluctuations in water levels during 2021–2022 revealed that the Nash–Sutcliffe efficiency coefficient (NSE) of the average simulation accuracy was improved by more than 34%. Normally, rainfalls (even heavy precipitations) during the dry season either do not generate runoff or cause negligible fluctuations in flow rates due to long intervals. Conversely, relatively frequent rainfall events (even light ones) during the wet season result in substantial runoff. Based on this observation, three distinct types of karstic reservoirs with different retaining/releasing capacities were defined, reflecting variations in both the frequency and volume of runoff during both seasons. As a real-time environmental variable, ARCK exhibits higher and lower values during the dry and rainy seasons, respectively, and we can better avoid the risk of flooding according to its special effects. Full article

Jordan experienced flash floods that resulted in numerous fatalities and injuries. This research focuses on identifying the Wadi Mujib’s flash flood potential zones and evaluating their potential magnitude. In this work, hydrological models were developed by integrating GIS settings with HEC-HMS software (V. 4.11). The hydrological model for Wadi Mujib is simulated in this research by means of the Soil Conservation Service (curve number method) while using rainfall data from 1970 to 2022. The results show that the optimum curve number values (CN) were 78.5 at normal antecedent moisture content. Additionally, in order to aid in the decision-making process for flash flood warnings, a flash flood potential index (FFPI) was also introduced based on four main physiographic parameters (slope, land use, plant cover, and soil texture) ranging from 1 to 10. The accumulative chart’s FFPI threshold, which indicates the areas with the highest potential for flash floods, was set at 95% or above. The FFPI threshold was chosen using the accumulative chart of FFPI, which shows that the FFPM threshold value is 7 and covers 13.39% of the study area. Full article

This study evaluates the applicability of hourly Global Precipitation Measurement Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) data for event-based flood modeling in the Sunshui River Basin, southwestern China, using the hydrologic modeling system (HEC-HMS) model. The accuracies of IMERG V6, IMERG V7, and the corrected IMERG V7 satellite precipitation products (SPPs) were assessed against ground rainfall observations. The performance of flood modeling based on the original and the corrected SPPs was then evaluated and compared. In addition, the ability of different numbers (one–eight) of ground stations to correct IMERG V7 data for flood modeling was investigated. The results indicate that IMERG V6 data generally underestimate the actual rainfall of the study area, while IMERG V7 and the corrected IMERG V7 data using the geographical discrepancy analysis (GDA) method overestimate rainfall. The corrected IMERG V7 data performed best in capturing the actual rainfall events, followed by IMERG V7 and IMERG V6 data, respectively. The IMERG V7-generated flood hydrographs exhibited the same trend as those of the measured data, yet the former generally overestimated the flood peak due to its overestimation of rainfall. The corrected IMERG V7 data led to superior event-based flood modeling performance compared to the other datasets. Furthermore, when the number of ground stations used to correct the IMERG V7 data in the study area was greater than or equal to four, the flood modeling performance was satisfactory. The results confirm the applicability of IMERG V7 data for fine time scales in event-based flood modeling and reveal that using the GDA method to correct SPPs can greatly enhance the accuracy of flood modeling. This study can act as a basis for flood research in data-scarce areas. Full article

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m³/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: ‘low risk’, ‘moderate risk’, ‘high risk’, and ‘very high risk’. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m³/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness. Full article

Limited availability of hydrometeorological data and lack of data sharing practices have added to the challenge of hydrological modelling of large and transboundary catchments. This research evaluates the suitability of latest near real-time global precipitation measurement (GPM)-era satellite precipitation products (SPPs), IMERG-Early, IMERG-Late and GSMaP-NRT, for hydrological and hydrodynamic modelling of the Brahmaputra Basin. The HEC-HMS modelling system was used for the hydrological modelling of the Brahmaputra Basin, using IMERG-Early, IMERG-Late, and GSMaP-NRT. The findings showed good results using GPM SPPs for hydrological modelling of large basins like Brahmaputra, with Nash–Sutcliffe efficiency (NSE) and R² values in the range of 0.75–0.85, and root mean square error (RMSE) between 7000 and 9000 m³ s⁻¹, and the average discharge was 20611 m³ s⁻¹. Output of the GPM-based hydrological models was then used as input to a 1D hydrodynamic model to assess suitability for flood inundation mapping of the Brahmaputra River. Simulated flood extents were compared with Landsat satellite-captured images of flood extents. In critical areas along the river, the probability of detection (POD) and critical success index (CSI) values were above 0.70 with all the SPPs used in this study. The accuracy of the models was found to increase when simulated using SPPs corrected with ground-based precipitation datasets. It was also found that IMERG-Late performed better than the other two precipitation products as far as hydrological modelling was concerned. However, for flood inundation mapping, all of the three selected products showed equally good results. The conclusion is reached that for sparsely gauged large basins, particularly for trans-boundary ones, GPM-era SPPs can be used for discharge simulation and flood inundation mapping. Full article

The adverse impacts of climate change are becoming more frequent and severe worldwide, and Sri Lanka has been identified as one of the most severely affected countries. Hence, it is vital to understand the plausible climate-change-driven impacts on water resources to ensure water security and socio-economic well-being. This study presents novel assessments on low-flow dynamics along the lower Kalu River Basin, Sri Lanka, and water availability during the dry spells of the 2030–2060 period. Bias-corrected daily precipitation projections of a high resolution (25 km × 25 km) NCC-NORESM1-M regional climate model is used here to force a calibrated HEC-HMS hydrological model to project catchment discharge during the future period considered under the two end-member Representative Concentration Pathways (i.e., RCP 2.6 and RCP 8.5). Our results show that the study area (i.e., Kuda Ganga sub-basin) may become warmer (in non-monsoonal periods) and wetter (in monsoon season) under both scenarios during the near future (2030–2040) when compared to the baseline period (1976–2005) considered. Consequently, the streamflow may reduce, making it the decade with the largest water deficit within the time horizon. The subsequent deficit volume assessment for the 2031–2040 period shows a probable water shortage (~5 million m³) under the RCP 2.6 scenario, which may last for ~47 days with an average daily intensity of 105,000 m³. Our results highlight the need of incorporating climate-change-driven impacts in water resources management plans to ensure water security. Full article

This research investigates flood susceptibility in the mid- and downstream areas of Taiwan’s Wu River, historically prone to flooding in central Taiwan. The study integrates the Hydrologic Engineering Center—Hydrologic Modeling System (HEC-HMS) for flow simulations with unmanned aerial vehicle (UAV)-derived digital surface models (DSMs) at varying resolutions. Flood simulations, executed through the International River Interface Cooperative (iRIC), assess flood depths using diverse DSM resolutions. Notably, HEC-HMS simulations exhibit commendable Nash–Sutcliffe efficiency (NSE) exceeding 0.88 and a peak flow percentage error (PEPF) below 5%, indicating excellent suitability. In iRIC flood simulations, optimal results emerge with a 2 m resolution UAV-DSM. Furthermore, the study incorporates rainfall data at different recurrence intervals in iRIC flood simulations, presenting an alternative flood modeling approach. This research underscores the efficacy of integrating UAV-DSM into iRIC flood simulations, enabling precise flood depth assessment and risk analysis for flood control management. Full article

Due to underlying surface changes (USCs), the changes in the Taojiang River Basin’s flood generation conditions could impact the flooding process in the basin. However, most studies have typically focused on either land-use changes (LUCs) or soil and water conservation measures (SWCMs) to assess the impact of the USCs on floods, which may not provide a more comprehensive understanding of the response of floods to the USCs. To investigate how the USCs have altered the floods in the Taojiang River Basin, located upstream of Poyang Lake, China, the HEC-HMS model, which incorporates the influence of the USCs into the parameter calibration, is established in this study to investigate the flood processes on an hourly scale. The flood peak and the maximum 72 h flood volume are selected as two indexes and are applied to analyze the changes in floods caused by the USCs. The 1981–2020 period is divided into three sub-periods (i.e., 1981–1992, 1993–2007, and 2008–2020) based on the conditions of the USCs. It is found that the two indexes have exhibited decreasing trends, mainly due to the USCs during 1981–2020. Benchmarked against the baseline period of 1981–1992, the two indexes decreased by 3.06% (the flood peak) and 4.00% (the maximum 72 h flood volume) during 1993–2007 and by 5.92% and 7.58% during 2008–2020. Moreover, the impacts of the LUCs and SWCMs are separated through parameter adjustments in the model, revealing that the SWCMs played a dominant role in the USCs in the Taojiang River Basin. The quantification and assessment of the impact of the USCs on floods of different magnitudes revealed that the influence decreases with increasing flood magnitude. The results of this study improve our understanding of how USCs affect the flooding process and therefore provide support for flood control management under changing environments. Full article

This study focuses on the Sirba River Basin (SRB), a transboundary West African catchment of 38,950 km² shared by Burkina Faso and Niger, which contributes to flooding downstream in Niamey (Niger). The study uses the HEC-HMS hydrological model to explore the dynamics of the daily rainfall–runoff relationship over the period 2006–2020. The model is calibrated using observed rainfall at 13 meteorological stations within the river basin and observed discharges at the Garbey Kourou hydrometric station outlet. Two types of simulation are compared: (i) a continuous simulation (CS) over the period 2006–2020 and (ii) an event-based simulation (ES) using selected major flood events in 2010, 2012, 2013, 2015 and 2020. The results showed satisfactory model performance under both modeling schemes (R² = 0.84–0.87 for CS and R² = 0.94–0.98 for ES), with a superior performance of ES over CS. Also, significant differences in the distribution of calibrated model parameters for the percent impervious and the attenuation flood wave factor were observed. A sensitivity analysis revealed that the curve number, initial abstraction, lag time and routing time factors were influential on the model outputs. The study therefore underscores the model’s robustness and contributes crucial insights for flood control management and infrastructure planning in the SRB. Full article

Floods are among the most devastating disasters in terms of socio-economics and casualties. However, these natural disasters can be managed and their effects can be minimized by flood modeling performed before the occurrence of a flood. In this study, flood modeling was developed for the Göksu River Basin, Mersin, Türkiye. Flood hazard and risk maps were prepared by using GIS, HEC-RAS, and HEC-HMS. In hydraulic modeling, Manning’s n values were obtained from 2018 CORINE data, return period flow rates (Q25, Q50, Q100, Q500) were obtained from HEC-HMS, and the application was carried out on a 5 m resolution digital surface model. In the study area, the water depths could reach up to 10 m, and water speeds were approximately 0.7 m/s. Considering these values and the fact that the study area is an urban area, hazard maps were obtained according to the UK Department for Environment, Food and Rural Affairs (DEFRA) method. The results indicated that possible flood flow rates from Q25 to Q500, from 1191.7 m³/s to 1888.3 m³/s, were detected in the study area with HEC-HMS. Flooding also occurred under conditions of the Q25 flow rate (from 4288 km² to 5767 km²), and the impacted areas were classified as extremely risky by the DEFRA method. Full article