Hydrologic Engineering Center Research Articles

Integration of UAV Digital Surface Model and HEC-HMS Hydrological Model System in iRIC Hydrological Simulation—A Case Study of Wu River

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.

Response of Floods to the Underlying Surface Changes in the Taojiang River Basin Using the Hydrologic Engineering Center’s Hydrologic Modeling System

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.

Assessing potential barriers to migratory Walleye in the Sandusky River, Ohio

AbstractObjectiveThe Sandusky River, Ohio, is one of the major Lake Erie tributaries that supports migratory Walleye Sander vitreus reproduction. Following the construction of the Ballville Dam in 1913, Walleye spawning was limited to an area downstream near Fremont, Ohio. After the removal of the dam in 2018, several natural and human‐made features were identified that may impede Walleye passage by collecting woody debris and altering flow conditions. In this study, we use hydrologic modeling and migratory sampling to understand the spatial and temporal dynamic nature of the blockages caused by the remaining anthropogenic and natural barriers.MethodsWe created a fine‐scale Hydrologic Engineering Center's River Analysis System (HEC‐RAS) model to examine these potential natural and human‐made barriers to Walleye passage during spawning. Using our model, we compared swim speeds (10 min maintained and burst) of small, medium, and large Walleye under three flow scenarios with five levels of blockage to assess whether the area was passable. Further, we conducted electrofishing surveys during annual spawning runs from 2020 to 2021 to assess Walleye presence upstream and downstream after dam removal.ResultOur model predicted that Walleye were able to pass the barriers under most of the scenarios by maintaining burst swim speed. Additionally, we captured Walleye above the former dam site in 2021, suggesting that Walleye can access newly available spawning habitat during certain river conditions.ConclusionRemoving the Ballville Dam restored fish passage, increased access to spawning habitat, and could improve Walleye production in the Sandusky River.

Advanced floodplain mapping: HEC-RAS and ArcGIS pro application on Swat River

AbstractFloods stand out as one of the most devastating environmental threats worldwide resulting in the tragic loss of human lives and significant damage to our essential infrastructure. This study focuses on creating floodplain maps for the two important reaches of Swat River in the Khyber Pakhtunkhwa Province of Pakistan, namely, Kalam–Khwazakhela and Khwazakhela–Chakdara reaches. The Advanced Land Observing Satellite Phased Array Type L-band Synthetic Aperture Radar (ALOS PALSAR) 12.5 m Digital Elevation Model (DEM) data has been used for this purpose. Furthermore, the sensitivity of the flood model was assessed for the Flood 2022 event, considering simulated flood depth, extent, and velocity in relation to various terrains derived from the 12.5-m ALOS PALSAR data. To estimate extreme flows for different return periods (2, 5, 10, 25, 50, and 100 years), the Log Normal (LN), Log Pearson III (LP3), and Generalized Extreme Value (GEV) distributions were employed for the frequency analysis. The GEV distribution turned out to be the best fit for modeling the Swat River for both the Chakdara and Khwazakhela gauge stations. To predict flood levels for the peak floods identified through frequency analysis and for the specific return periods the Hydrologic Engineering Center's River Analysis System (HEC-RAS) 2D simulations were performed. Subsequently the outcomes of this model were used to create floodplain maps using Geographic Information System Professional (ArcGIS Pro) software customized for various return periods. The analysis revealed a linear increase in flood inundation area with longer return periods. The floodplain maps developed hold significant importance for the governing authorities of the Swat region. These maps serve as essential tools for implementing proactive measures against potential infrastructure damage, thereby protecting against economic losses and enhancing public safety. This research effectively combines hydrological modeling and geospatial technology offering practical solutions for managing flood risks. It serves as a valuable guide for making well-informed decisions and promoting sustainable development in flood-prone areas.

Beyond a fixed number: Investigating uncertainty in popular evaluation metrics of ensemble flood modeling using bootstrapping analysis

AbstractEvaluation of the performance of flood models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as uncertainty bounds, Nash Sutcliffe efficiency, Kling Gupta efficiency, and the coefficient of determination, which are widely used in the model evaluation, the inherent properties and sampling uncertainty in these metrics are demonstrated. A comprehensive evaluation is conducted using an ensemble of one‐dimensional Hydrologic Engineering Center's River Analysis System (HEC‐RAS) models, which account for the uncertainty associated with the channel roughness and upstream flow input, of six reaches located in Indiana and Texas of the United States. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high‐flow scenarios, and various types of measurement errors in observations on the evaluation metrics are investigated using bootstrapping. Results show that the model performances based on the uniform and normal priors are comparable. The statistical distributions of all the evaluation metrics in this study are significantly different under different high‐flow scenarios, thus suggesting that the metrics should be treated as “random” variables due to both aleatory and epistemic uncertainties and conditioned on the specific flow periods of interest. Additionally, the white‐noise error in observations has the least impact on the metrics.

Assessing the impact of climate change on surface runoff of the Ubolratana Reservoir, Thailand

Assessing surface runoff in river basins is paramount for effectively managing water resources. Climate change significantly impacts the availability of water within these basins. The Ubolratana reservoir is vital for sustainable water supply, aquaculture, agricultural practices, and domestic needs. This research aims to evaluate the runoff availability of the reservoir by using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) under distinct climate change scenarios. The HEC-HMS model was calibrated based on observed rainfall and runoff data. To project future runoff dynamics, calibrated model parameters were coupled with the bias-corrected rainfall data of Can-ESM5, CESM2, and GFDL-ESM4 models for two Shared Socioeconomic Pathways (SSPs) climate scenarios (SSP 245 and SSP 585). The results show upward trends in both projected rainfall and runoff within the drainage area of the Ubolratana reservoir between 2022 and 2061. In summary, the findings of this research may be useful for regional water resources management and strategic planning endeavors.

Evaluating the effects of climate change on river water temperature downstream of a hydropower reservoir in western Canada

This paper evaluates the impact of climate change on the water temperature of the Nechako River near the town of Vanderhoof (British Columbia, Canada). To do so, the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) hydraulic and water temperature model was used with data from 10 climate models representing two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5) over two future time periods (2041-2070 and 2071-2100). The results showed an upward trend in projected water temperatures for all tested discharge rates from the impounding reservoir during the warmest periods of the year. The study found that water temperatures are expected to increase by up to 2.57 °C for the near future (2041-2070) and up to 3.56 °C on average for all flow scenarios studied for a far future (2071-2100) when using SSP5-8.5. The timing of the peak water temperature during the summer is also expected to shift, with maximum water temperatures occurring up to 10 days later than in the reference period. In 10.3% of the far future SSP5-8.5 scenarios, at least one day per summer had a mean daily temperature of at least 24 °C, which exceeds limits of 20 °C for sockeye salmon and 21 °C for white sturgeon which are considered detrimental for the fish. It has been shown that over 50% of sockeye salmon will stop their sustained swimming at water temperatures of 24 °C due to cardiac limitations.

Hydrological Modelling with HEC-HMS in Krueng Peudada Sub-Watershed Bireuen Regency

The flood that occurred in Krueng Peudada was influenced by several factors, such as watershed conditions. Heavy rains caused Krueng Peudada to overflow so that a number of villages adjacent to the river were submerged. Based on these problems, to minimize the occurrence of flooding in Krueng Peudada building, namely by carrying out occurrence of flooding in Krueng Peudada to be used to obtain peak discharge as a reference in building planning in Krueng Peudada using HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modelling System). The benefit of the research is that the resulting model can be useful as a guide in calculating flood discharge considerations. The scope of this research includes the research location at Krueng Peudada using annual maximum daily rainfall data, calculating the return period of rainfall using of Gumbel probability distribution, Normal Log, Normal, and Pearson III Log as the basis for calculating the discharge and continuing the calculation of the hours with Manonobe and ABM. The results of calculations of the Nakayasu Synthetic Unit Hydrograph Method and running from HEC-HMS found that the flow that occurred in the Krueng Peudada two sub-watershed was 2903m3/second and 2723,0 m3/second, it can be concluded that in determining watershed management the flood discharge obtainded has error as much as 6,2%. The difference in the results obtained is due to the number of sub-watersheds in the watershed based on different flows and the value of the curve number for each area of the sub-watershed.

Assessing Hydrological Response in the Timah-Tasoh Reservoir Sub-Catchments: Calibration and Validation using the HEC-HMS Model

Hydrological modelling is a tool that is frequently used for assessing the hydrological response of a basin as a result of precipitation. It is also a vital component as water resources and environmental planning management. The study deals with calibrating and validating the hydrological response in the sub-catchments of the Timah-Tasoh reservoir using the hydrological model named Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS). This study uses the SCS Curve Number, the SCS Unit Hydrograph, the constant monthly baseflow, and lag routing for the model development. The model was simulated for ten (10) years for calibration and nine (9) years for validation. The model calibration and validation efficiency were assessed using the coefficient of correlation (R). The findings show that the HEC-HMS model performs satisfactorily in simulating the observed daily inflow series, with the R-value of 0.4902-0.5139 during calibration and 0.5047-0.5559 during validation process. Thus, the result obtained from this study can be used as a preliminary development of hydrological modelling of the catchment of the Timah-Tasoh reservoir and can be used for extend application such as water inflow forecasting, impact of land use to the reservoir and others.

“Flood inundation mapping of upstream region in the Adyar River basin: Integrating hydrologic engineering centre's river analysis system (HEC-RAS) approach with groundwater considerations”

Chennai, the capital of Tamil Nadu, has grappled with recurrent devastating floods, notably in 2015 and 2021, originating from the upstream of the Adyar River basin. These events have profoundly impacted the city's growth sectors. The regions both upstream and downstream of the Adyar basin, covering Chennai and Chengalpattu districts, face increased vulnerability to severe riverine flooding due to rapid urbanization. The interplay of extensive urban development and river overflow during monsoon seasons has created conditions conducive to substantial flooding, necessitating the development of an effective flood model. This study utilizes digital elevation data and unit hydrograph analysis to estimate flood return probability water levels for various recurrence intervals, including the 10-year and 100-year events. Moreover, it investigates flood inundation, specifically in the Somangalam area, providing insights into the extent of water spread. To comprehensively address the flood dynamics, the study incorporates considerations for groundwater interactions. The overarching objective remains to identify the most vulnerable zones within the Adyar River region prone to flooding. Accurate identification of these vulnerable areas is crucial for policymakers and stakeholders to devise proactive planning and early warning measures, enhancing disaster preparedness and resilience in the face of potential flood disasters. This approach aims to safeguard the well-being of Chennai's residents and promote sustainable development in the region.

The effect of landuse dynamics on water balance components in the Upper Oum Er Rabia Basin, Morocco

The Upper Oum Er-Rbia Basin (UOERB) has been facing significant land use/land cover change and urban sprawl dynamics in recent years, in addition to significant climatic variability leading to disruption of precipitation intensity and quantity. Our approach is to identify the morphometric characteristics of the study region and monitor changes in land use and land cover by analyzing Landsat images. These factors form the input parameters for the HEC-HMS (Hydrologic Engineering Center - Hydrologic Modeling System) hydrological model for simulating daily historical hydro-climatic events. The classification of Landsat images shows that the Upper Oum Er-Rbia Basin has experienced significant landuse dynamics over the past 30 years, resulting in disrupted rainfall and recurrent flooding events. Overall, the hydrological simulation shows good agreement between observed and simulated flow hydrographs, with Nash criteria between 0.6 and 0.8. This knowledge enables us to better estimate the return flows in the catchment area, and to take appropriate action in terms of hydraulic management and development in the Upper Oum Er-Rbia Basin.

Satellite Video Remote Sensing for Flood Model Validation

AbstractSatellite‐based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video‐derived flood extents and velocities. Hydraulic simulations of a flood event with a 5‐year return period (discharge of 722 m3 s−1) were conducted using Hydrologic Engineering Center—River Analysis System 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer‐encoder, convolutional neural network (CNN)‐decoder deep neural network. We evaluate the influence of test‐time augmentation (TTA)—the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non‐contact‐based river surface velocity estimation from sequential satellite video frames. When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% with an average relative improvement of 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network‐based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space‐based video sensors for validating flood models and studying flood dynamics.

An Evaluation of the Impact of Urban Growth on Runoff in Abeokuta, Southwestern Nigeria

Abeokuta is one of the urban areas in Nigeria with high cases of runoff fatalities in recent times, indicating the need for a proper understanding of prominent runoff-generating mechanisms, as well as the causative factors. Consequently, this paper, which is focused on flood-prone settlements, is aimed at providing information on the impact of change in urban land use on runoff in the study area. The specific objectives were to determine the change in average runoff in terms of the Soil Conservation Service Curve Number (SCN-CN), and to assess the impact of urban growth on runoff in the area. The SCN-CN was derived from the 30m spatial Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). At the same time, land cover change was estimated using Landsat TM+ from 2000 and Landsat 8 OLI from 2018. Data were analysed using the Hydrologic Engineering Centre’s Hydrologic Modelling System (HEC-HMS) and ArcGIS (version 10.1). The results showed a 14% increase (from 39% in 2000 to 53% in 2018) in urban areas of selected catchments; and a relative increase in average CN from 76.9 units in 2000 to 79.9 units in 2018, suggesting an increase in runoff potential relative to the increase in urban/impermeable space in the catchment. Annual discharge depth increased from 891.84mm to 956.9mm, while peak discharge increased from 161.9m3 /s to 196.2m3 /s. Runoff in the study area tends to exhibit spatial variability that is similar to the pattern exhibited by built-up areas across the study area, suggesting that the development of built-up areas can explain runoff exacerbation in part of the area. The use of SCN-CN and satellite images makes the approach reproducible, and the mixed methods of geographic information system and hydrological model revealed the spatial variability typically hidden in stand-alone hydrological models. The paper recommends further studies with the use of less coarse datasets, as well as the implementation of policies that focus on sustainable urban growth in the region, and other cities.

A Computational Tool to Track Sewage Flow Discharge into Rivers Based on Coupled HEC-RAS and DREAM

Worldwide abatement of untreated sewage discharge into surface water is a challenging task. Sewage discharging into surface waters has a detrimental impact on water quality. This paper presents a MATLAB (R2018b) framework designed to identify sewage flow discharges into rivers from an inverse problem-solving perspective. The computational tool integrates a hydrodynamic model using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 5.0.0) and an open-source toolbox for Differential Evolution Adaptive Metropolis (DREAM) as the inverse problem method. The proposed framework can effectively infer discharge sources in scenarios of highly transient flow based on hydraulic data at pre-set monitoring sites. To validate its capabilities, one hypothetical case and two real cases of sewage flow discharges entering a river were used to test the developed modeling framework. The results based on three performance metrics showed that this mathematical tool can be extended to simulate complex hydrodynamic flow patterns. This accomplishment underscores its potential as a valuable asset for environmental monitoring and water quality restoration efforts.

Ensemble rainfall–runoff modeling of physically based semi-distributed models using multi-source rainfall data fusion

Abstract This study was aimed at ensemble rainfall–runoff modeling by Soil and Water Analysis Tool (SWAT), the Hydrologic Engineering Center's Hydraulic Modeling System, and Hydrologiska Byråns Vattenbalansavdelning of Gilgel-Abay watershed, Blue Nile basin, Ethiopia. For modeling, daily rainfall datasets of five gauges and three satellites, streamflow, and spatial data were used. In the modeling stage, first, the runoff was simulated separately using the rainfall data of gauges, satellites, and their fusion. Second, ensemble rainfall–runoff simulation of the rainfall dataset fusion-based runoff result was conducted via the proposed weighted average, simple average, and neural network (NNE) ensemble techniques. The results exhibited that all models are good in capturing the rainfall–runoff relationship; however, SWAT showed slight superiority by Nash–Sutcliffe efficiency of 0.807 and 0.821 for gauge and fusion data, respectively. The rainfall fusion ensemble model revealed significant improvement over modeling by satellite rainfall owing to the bias-correcting gauge rainfall over satellite rainfall. The NNE technique enhanced the efficiency of the low-performing satellite-rainfall-based model by 17.5% and the rainfall fusion-based model by 13.3% at the validation stage. In general, the result of this study points out that the rainfall datasets’ fusion from multi-sources would be worthy for the rainfall–runoff simulation of ungauged basins.

FLOOD HAZARD MODELLING USING HYDRAULIC SIMULATION MODEL AND SATELLITE IMAGES: A CASE STUDY OF CHAJ DOAB, PUNJAB, PAKISTAN

The main objective of the study is to determine the highly risked areas and to estimate flood extend through different return periods. HecGeo-RAS is used along with Hydrologic Engineering Center River Analysis System (HEC-RAS) to build a model which have analyzed the flow of water. The river and floodplain geometry were obtained by SRTM-DEM and flood mapping is finalized by the vector layer analysis in ARC GIS. Frequency analysis for the period 1986-2014 (29 years), instantaneous discharge data performed by Gumbel's, and LP3. Results depicts that District Mandibahudin always flooded on both sides by Jehlum and Chenab rivers. An interpretation of findings explicitly revealed that the 50-year return cycle flood at Trimmu (5299 m 3 s-1) would inundate 500 percent more area than the natural surge. Urban areas are also vulnerable to flooding such as Mandibahudin, which were also evident in the July / August 2014 flooding.

Hurricane risk assessment in a multi-hazard context for Dominica in the Caribbean

Hurricanes can trigger widespread landslides and flooding creating compound hazards and multiple risks for vulnerable populations. An example is the island of Dominica in the Caribbean, where the population lives predominantly along the coast close to sea level and is subject to storm surge, with steep topography rising behind, with a propensity for landslides and flash river flooding. The simultaneous occurrence of the multiple hazards amplifies their impacts and couples with physical and social vulnerabilities to threaten lives, livelihoods, and the environment. Neglecting compound hazards underestimates overall risk. Using a whole island macroscale, (level-I) analysis, susceptibility scenarios for hurricanes, triggered landslides, and floods were developed by incorporating physical process parameters. The susceptibilities were combined with vulnerability indicators to map spatial patterns of hurricane multi-risks in Dominica. The analysis adopted a coupled approach involving the frequency ratio (FR), analytic hierarchy process (AHP), and geographic information system (GIS). Detailed hazard modelling was done at selected sites (level-II), incorporating storm surge estimates, landslide runout simulations, and steady flow analysis for floods. High-resolution terrain data and simulation models, the Rapid Mass Movement Simulation (RAMMS) and the hydrologic engineering center’s river analysis system (HEC-RAS), were employed. Ground validation confirmed reasonable agreement between projected and observed scenarios across different spatial scales. Following the United Nations Office for disaster risk reduction (UNDRR) call for the inclusion of local, traditional, and indigenous knowledge, feedback, and expert opinion to improve understanding of disaster risk, 17 interviews with local experts and 4 participatory workshops with residents were conducted, and findings were incorporated into the analysis, so as to gain insights into risk perceptions. The study’s outcomes encompass projections and quantification of hurricane compound hazards, vulnerabilities, accumulated risks, and an understanding of local priorities. These findings will inform decision-making processes for risk mitigation choices and community actions by providing a new framework for multi-hazard risk assessment that is easy to implement in combining different data forms.

Assessing future flood inundation in Nandigama through land use, land cover, and rainfall analysis

Abstract Frequent occurrences of high-intensity rainfall have made urban flooding a significant problem. In the present study, flood inundation maps were prepared for the years 2030 and 2035 based on changes in future land use and land cover (LULC) and rainfall patterns in Nandigama, located in Andhra Pradesh, India. The Hydrologic Engineering Center's Hydrologic Modelling System Hydrologic and Hydrologic Engineering Center–River Analysis System hydraulic models were used to assess future runoff and flood inundation areas for the above years. Future LULC and hydro-meteorological data were analysed and incorporated into the models. Predicted LULC maps showed a high level of agreement with actual LULC maps, as indicated by a Kappa index of 88.34%. The average peak runoff flowing out of the basin increased by 1.82%–3.43% for years 2030–2035, respectively. When considering the average inundation percentage area in Nandigama, it was found that 12.73% and 13.83% of the area flooded in both years. The study recommendations give more attention to the Nandigama flood management authority.

Hydrological risk assessment for Mangla Dam: compound effects of instant flow and precipitation peaks under climate change, using HEC-RAS and HEC-GeoRAS

Dams play a pivotal role in water resource management by storing and supplying water for a multitude of purposes. However, the looming threat of dam breach floods necessitates meticulous research and the simulation of potential failure scenarios. These endeavors are essential not only for comprehending the gravity of dam break floods but also for identifying vulnerable regions and informing emergency response strategies and land-use planning initiatives. This study employs a two-dimensional hydraulic model within the HEC-RAS (Hydrologic Engineering Center and River Analysis System) software to conduct an extensive dam breach analysis specifically focusing on the Mangla Dam located in Azad Kashmir, Pakistan. The analysis encompasses the prediction of various breach parameters, including the hydrograph of the breach flood, peak flow rates, arrival times of the flood, and the creation of inundation maps. Of primary concern is the Probable Maximum Floo, which drives the dam collapse model under unsteady flow conditions, accounting for both piping and overtopping failure scenarios. This study discerns the breach outflow hydrograph through the utility of HEC-RAS tools and evaluates hydraulic conditions at critical downstream locations. To dynamically route flood waves, the breach outflow hydrographs are harnessed. Furthermore, the HEC-RAS model is executed with breach parameters derived from five distinct empirical approaches, with ensuing outcomes subjected to rigorous comparative analysis. A comprehensive sensitivity study pertaining to breach parameters is also carried out to ascertain the sensitivity of peak flow and maximum stage. The results reveal peak flow rates of 174,850 m3/s and 177,850 m3/s in the downstream vicinity adjacent to the dam, translating into corresponding flooded areas of 379 km2 and 394 km2 attributable to piping and overtopping failures, respectively. The analysis of Land Use Land Cover data demonstrates that in the event of piping failure, 217 km2 of agricultural land and 56 km2 of urban areas would be completely submerged. Conversely, overtopping failure would inundate 220 km2 of agricultural land and 59 km2 of urban areas. The utilization of advanced remote sensing data, combined with flood modeling insights, equips engineers and stakeholders with invaluable knowledge. This knowledge, in turn, underpins strategic planning and well-informed decision-making processes, essential for addressing the potential global repercussions of similar catastrophes.

Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach

The occurrence of flash floods in urban catchments within the Mediterranean climate zone has witnessed a substantial rise due to climate change, underscoring the urgent need for early-warning systems. This paper examines the implementation of an early flood monitoring and forecasting system (EMFS) to predict the critical overflow level of a small urban stream on Lesvos Island, Greece, which has a history of severe flash flood incidents requiring rapid response. The system is supported by a network of telemetric stations that measure meteorological and hydrometric parameters in real time, with a time step accuracy of 15 min. The collected data are fed into the physical Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS), which simulates the stream’s discharge. Considering the HEC-HMS’s estimated outflow and other hydro-meteorological parameters, the EMFS uses long short-term memory (LSTM) neural networks to enhance the accuracy of flood prediction. In particular, LSTMs are employed to analyze the real-time data from the telemetric stations and make multi-step predictions of the critical water level. Hydrological time series data are utilized to train and validate the LSTM models for short-term leading times of 15 min, 30 min, 45 min, and 1 h. By combining the predictions obtained by the HEC-HMS with those of the LSTMs, the EMFS can produce accurate flood forecasts. The results indicate that the proposed methodology yields trustworthy behavior in enhancing the overall resilience of the area against flash floods.