Inundation Mapping Research Articles

Impact Analysis of Land Use Changes on Inundation Map In The Ciliwung River Watershed

Impact Analysis of Land Use Changes on Inundation Map In The Ciliwung River Watershed

Real-time flood inundation monitoring in Capital of India using Google Earth Engine and Sentinel database

This study focuses on researching flood inundation and vulnerable areas in Delhi NCT using Remote Sensing (RS) and GIS techniques during flood from July 8 to July 15, 2023, with the entire analysis conducted through satellite and cloud-based processing methods, specifically employing the Google Earth Engine (GEE). Leveraging high-temporal satellites that gather data enables the identification of flooded zones in real-time, during floods, and in the aftermath. Analyzing data collected at different stages of a flood provides valuable insights for pinpointing affected areas. Water naturally flows from high to low elevation areas, and based on elevation data, lowest elevation regions, particularly along the Yamuna riverbank under Delhi NCT, are highly susceptible to flooding, are considered flood-prone areas and flood water inundated. A thorough comprehension and flood-prone area mapping, along with a map illustrating highly inundated zones. After obtaining flood inundation maps and overlaying them with Land Use and Land Cover (LULC) classified maps, the study identified specific areas that experienced flood inundation. The analysis generated a flood zone map, indicating that the flooded area encompasses approximately 110 km², within a total study area of 1488.4 km². The affected areas have elevations ranging from 200 to 210 meters, whereas the maximum elevation in the study area is approximately 326 meters. The GEE platform is employed for processing, utilizing a Supervised classification algorithm for LULC mapping, and an Inverse Distance Weight method for mapping temperature and rainfall. This study utilized the GEE platform to create pre- and post-flood maps based on Sentinel 1 satellite datasets. Generated DEM, and employed it to create various surface estimation maps, including a stream order map. The GIS is employed to enhance the efficiency of monitoring and managing flood disasters, with the high temporal and spatial resolution data playing a pivotal role in flood monitoring.

Analysis of Gongseng Dam Break-Induced Flood in East Java, Indonesia Through 2D Iber Software

The dam is a crucial river-crossing structure that ensures a sustainable water supply and offers numerous benefits. However, the potential hazard of dam failure is an imminent threat that could materialize unexpectedly. To comprehend the potential impact of dam break flood and identify vulnerable areas, it is essential to conduct rigorous analysis and simulate various dam failure scenarios. This comprehensive assessment is invaluable for informed land use planning and the development of effective emergency response plans. Therefore, this study aimed to analyze flood inundation resulting from the hypothetical failure of Gongseng dam, using Iber model. The modeling approach relied on a two-dimensional finite volume shallow water model, guided by specialized software. The scenarios for Gongseng dam break showed inundation areas of 12.57 km² and 7.55 km² for overtopping and piping failure, respectively. Overtopping failure resulted in the highest discharge, with Von Thun method causing severe damage due to wide break dimensions, and eventually leading to catastrophic consequences. However, this study showed that Froehlich method provided the most rational prediction for break parameters. In contrast to the other methods focusing solely on water height behind dam, Froehlich equation considered both the volume and height at the time of failure. Implementing dam break analysis held the potential to benefit downstream communities by providing inundation maps, thereby aiding in the mitigation of flood risks. Particularly, in situations with limited data and resources, as shown in this study, the cost-effective modeling method proposed could be an attractive option for simulating extreme flood induced by dam break.

Selection of Elevation Models for Flood Inundation Map Generation in Small Urban Stream: Case Study of Anyang Stream

To reduce flood damages, the Ministry of Environment in Korea has provided a flood inundation map so that people can expediently identify flood-prone areas. However, the current flood inundation maps have been produced based on the DEM which makes it difficult to represent realistic situations due to the lack of reproduction of land surface conditions. This study aims to provide more accurate and detailed flood inundation maps for flooding events due to river overflow in small urban areas. In this study, flood inundation analysis is performed using the river analysis system, HEC-RAS 2D, with the DSM and the DEM of urban areas in the Anyang Stream Basin, Korea to examine the differences in terms of terrain data and flooded area. Finally, for urban areas with dense buildings and congested road networks, the flood inundation analysis based on DSM can represent a more realistic flood situation and create an appropriate flood inundation map.

Innovative methods for rapid flood inundation mapping in Pul-e-Alam and Khoshi districts of Afghanistan using Landsat 9 images: spectral indices vs. machine learning models

Innovative methods for rapid flood inundation mapping in Pul-e-Alam and Khoshi districts of Afghanistan using Landsat 9 images: spectral indices vs. machine learning models

Flood Impact Assessment in Koton Karfe Using Sentinel-1 Synthetic Aperture Radar (SAR) Data

Flood has proven to be an incessant menace in Nigeria more threatening to riverine areas. The most recent flood ensued in 2022 as a result of heavy rainfall and the release of water from Lagdo Dam in Cameroon which became very devastating in many areas notably the Koton Karfe area in Kogi State, causing business shutdowns and the loss of lives and properties. In this work, Sentinel-1 Synthetic Aperture Radar (SAR) imagery was used for flood inundation mapping, and the accompanying damages were investigated using Landsat derived Land cover maps of Koton Karfe during the 2022 devastating flood. Overall, the results obtained in this study show that the regions that felt the impact of the flood the most were the southern and western areas, which must have experienced such an impact due to their proximity to the rivers Niger and Benue and also the water coming from the upper stream part of Cameroon. Further findings revealed that during the flood period on October 13, 2022, the total inundated area in Koton Karfe was estimated to be 198.255 sq. km. In terms of damage assessment, the urban areas had reduced from 220.902 sq. km in May 2022 to 87.473 sq. km in November 2022. This shows that over 133 sq. km of the urban settlement have been lost, indicating that lives must have been lost, properties too, and humans must have been displaced. This research will assist in the space of flood emergency response and disaster management.

Inundation Sewers and Mapping the Prevalence Leptospirosis Throughout Rats in Central Java Province

Leptospirosis is a public health threat in countries with tropical climates, like Indonesia. Incidents in Central Java Province are still found every year and still threaten the local community. The goal of this study is to find out what connection there is between sewer puddles and mice that have found leptospira bacteria that can cause leptospirosis in the Regencies of Klaten, Sukoharjo, Karanganyar, and Boyolali in Central Java Province. This research uses descriptive analysis, a cross-sectional design study, and the Geographic Information System (GIS) to carry out mapping. From the research results, it was found that the rat species that were positive for leptospira bacteria were Rattus norvegicus (31%), Rattus tanezumi (44.8%), and Rattus argentiventer (24.1%). Apart from that, only the sampling area and the presence of puddles in the gutter had significant results with the mouse PCR results with p<0.002 and p<0.03. Due to this, there are still many mice infected with leptospira bacteria in Central Java Province. For this reason, it is hoped that the public can increase awareness to always maintain a clean environment so that mice cannot breed freely. Apart from that, related agencies can carry out more monitoring of areas where leptospirosis is still endemic.

Hindcast Insights from Storm Surge Forecasting of Super Typhoon Saola (2309) in Hong Kong with the Sea, Lake and Overland Surges from Hurricanes Model

Super Typhoon Saola (2309) skirted past south-southeast of Hong Kong within 40 km on the night of 1 September 2023, posing a significant storm surge threat to Hong Kong. Given the close proximity of Saola with a peak intensity of about 210 km/h within 300 km of Hong Kong, a close call of the “super typhoon direct-hit” scenario, this case provides valuable insights from a hindcast review of storm surge forecasts and warning operation using the Sea, Lake and Overland Surges from Hurricanes (SLOSH) model, which is the operational storm surge model adopted by the Hong Kong Observatory (HKO). The performance of the HKO’s PRobabilistic Inundation Map Evaluation System (PRIMES) using both statistical and model ensemble approaches was also reviewed in this paper. Saola was a challenging case for operational forecasting of a compact TC structure with changes in storm size and intensity when it came close to Hong Kong. With major observations of storm structure using weather radar and dense automatic weather station, tide gauge and water level gauge networks, the high sensitivity of storm surge forecasts to the storm size parameter and the distance of closest approach was clearly revealed in the case of Saola. Even with a circularly symmetric TC parametric model like SLOSH, the hindcast review results illustrated that the model outputs were reasonably accurate during the closest approach of Saola given an accurate storm size and distance of closest approach were input, and using a highly computationally efficient storm surge model made it possible for the nowcasting of storm surges to handle compact and intense TC direct-hit cases in operational TC forecasting. Taking a nowcasting approach not only helps provide more reliable storm tide forecasts, but also facilitates the formulation of a better warning strategy when making final-call decisions in emergency response actions, based on the more frequent real-time analysis of TC position, intensity and storm size and the more accurate prediction of these parameters. A nowcasting workflow for storm surge operation was proposed in this paper.

XFIMNet: an Explainable deep learning architecture for versatile flood inundation mapping with synthetic aperture radar and multi-spectral optical images

ABSTRACT Floods are among the most dangerous natural disasters, and their frequency and severity are rapidly increasing due to climate change. Flood inundation mapping is critical for better understanding which areas are prone to flooding and identifying emerging floods. Recently, deep learning algorithms have been developed to improve the accuracy and timeliness of the production of these maps. In this study a new architecture, named Explainable Flood Inundation Mapping Network (XFIMNet) is presented, which is an explainable deep learning segmentation model for producing flood inundation maps from both synthetic aperture radar (SAR) and multi-spectral optical images. Comparative analysis with state-of-the-art (SOTA) models is performed, where the versatility of the model is demonstrated, in that it achieves superior performance across both image types, while the SOTA models are less consistent. The models are further evaluated with only the red, green, and blue (RGB) bands of the Sentinel-2 images, demonstrating that the full 13-band image is the superior choice for training a deep learning model. XFIMNet achieves an Intersection Over Union (IOU) of 0.59 with SAR images, 0.7 with optical, and 0.47 with the RGB bands, outperforming each of the SOTA models, as well as providing the most detailed and accurate visual segmentation mask, with minimal increase in the computation time for training or inference. The findings of this study highlight that XFIMNet is not only able to provide the most accurate segmentation mask but also prioritizes the most appropriate features when making decisions across both image types.

Flood Hazard Mapping and Risk Assessment in Narayani River Basin, Nepal

Floods, earthquakes, forest fires, landslides, and other natural hazard are common in Nepal. Among them, the flood is one of the natural disasters and that occurred in the Narayani River basin. The Narayani River Basin's exposures and vulnerabilities are at danger from a flood catastrophe of this kind, hence this studies attempts to reduce and control the risk of flooding in order to better manage disasters. The factors of flood hazard, flood exposures, and flood vulnerability are investigated as part of the ward-level flood risk assessment, which aims to prevent and manage the flood disaster. The hydraulic model (HEC-RAS) for the 2018 flood event was used in this study to create the GIS- based modeling of the flood inundation maps. Additionally, it calculated the various return periods for floods in the river basin—5, 10, 25, 50, 75, and 100 years. Furthermore, the flood extent was confirmed using the flood map produced by the Google Earth Engine (GEE) using remotely sensed techniques. For Hydraulic modelling, ALOS/PALSAR Digital Elevation Model (12.5m.) spatial resolution was used. In addition, RAS-MAPPER generated the geometric data for the hydraulic modeling, which was then transferred into HEC-RAS. This data included the cross-section, flow route, streamline, and bank lines. On both sides of the river, the necessary Manning value "n" values were computed for every cross-section. The steady-flow models of the anticipated flood hydrographs were created using the hydraulic model. Google Earth Engine (GEE) flood maps generated from Sentinel-1C radar satellite data were used to validate the results for the 2018 flood events. When comparing the simulated result's flood inundation area with the remote sensing data's flood area, the overlap area for the 2018 flood event is 65%. Additionally, the flood area is verified. In addition, the hydraulic model generated flow conditions for 5, 10, 25, 50, 75, and 100 year return periods. The river basin's surface water level and flood extent are both progressively rising. In order to effectively manage and prepare for potential flood hazards in the study area, an analysis of the flood risk assessment was conducted by taking into consideration three primary factors: the flood hazard map, flood exposure, and flood vulnerability. The layers of the population, crops, schools, hospitals, and road network were all exposed to flooding, and the factors that determined flood vulnerabilities were literacy, urban area, and agecomposition (less than 14 and more than 65). The higher flood risk area was found in Ward number 1, 3, 4,16,18,26 of Bharatpur metropolitan, Gaindakot municipality ward number 1 and 12, 15 of Madhyabindu Municipality.

Nordic socio-recreational ecosystem services in a hydropeaked river

Fluctuating energy prices call for short-term river flow regulation at hydropower plants (HPPs), which can lead to hydropeaking – the pulsating water flow downstream from a HPP. Hydropeaking can affect land use areas of regulated rivers and subsequently their socio–recreational ecosystem services (SRESs). These areas often offer a range of services, such as swimming, boating, fishing, hiking, cycling, and berry picking. Such activities hold significant value in Nordic culture and for human wellbeing. We have examined how SRES land use areas are affected by hourly hydropeaking in a reach of the Kemijoki River in Finland. First, we determined the state of hydropeaking in the river by employing two indicators, normalized daily maximum flow difference and sub-daily flow ramping. Next, we looked at the spatiotemporal impacts of peaking hydrology using inundation maps derived from 2D-hydrodynamic modeling and a high-resolution land use map with clearly identified SRES areas. Finally, we examined the hazards to hydraulic safety in the river channel in the context of instream recreation. Our results show that hydropeaking levels in the study area remained consistently high throughout the entire study period, from 2010 to 2021. This was the case in all seasons except for the spring of 2013, 2016 and 2019. We determined that hydropeaking impacts on SRESs are mostly felt in the littoral zone (0.84 km2 i.e., 3.1 % of the study area) during the summer season as 25 % (0.21 km2) of this zone is influenced by hydropeaking. In addition, multiple recreational use areas in this zone, such as beaches, riparian forest, and summer cottages, were found to be affected by hydropeaking. The results show that most of the river channel becomes hydraulically unsafe during high ramping flows. The highest hazard to instream recreation opportunities is likely to occur during summer. Consequently, hydropeaking can threaten the social and recreational services of Nordic rivers.

A Feature-Informed Data-Driven Approach for Predicting Maximum Flood Inundation Extends

As climate change increases the occurrences of extreme weather events, like flooding threaten humans more often. Hydrodynamic models provide spatially distributed water depths as inundation maps, which are essential for flood protection. Such models are not computationally efficient enough to deliver results before or during an event. To ensure real-time prediction, we developed a feature-informed data-driven forecast system (FFS), which interpreted the forecasting process as an image-to-image translation, to predict the maximum water depth for a fluvial flood event. The FFS combines a convolutional neural network (CNN) and feature-informed dense layers to allow the integration of the distance to the river of each cell to be predicted into the FFS. The aim is to ensure training for the whole study area on a standard computer. A hybrid database with pre-simulated scenarios is used to train, validate, and test the FFS. The FFS delivers predictions within seconds making a real-time application possible. The quality of prediction compared with the results of the pre-simulated physically-based model shows an average root mean square error (RMSE) of 0.052 for thirty-five test events, and of 0.074 and 0.141 for two observed events. Thus, the FFS provides an efficient alternative to hydrodynamic models for flood forecasting.

Optimal Fusion of Multispectral Optical and SAR Images for Flood Inundation Mapping through Explainable Deep Learning

In the face of increasing flood risks intensified by climate change, accurate flood inundation mapping is pivotal for effective disaster management. This study introduces a novel explainable deep learning architecture designed to generate precise flood inundation maps from diverse satellite data sources. A comprehensive evaluation of the proposed model is conducted, comparing it with state-of-the-art models across various fusion configurations of Multispectral Optical and Synthetic Aperture Radar (SAR) images. The proposed model consistently outperforms other models across both Sentinel-1 and Sentinel-2 images, achieving an Intersection Over Union (IOU) of 0.5862 and 0.7031, respectively. Furthermore, analysis of the different fusion combinations reveals that the use of Sentinel-1 in combination with RGB, NIR, and SWIR achieves the highest IOU of 0.7053 and that the inclusion of the SWIR band has the greatest positive impact on the results. Gradient-weighted class activation mapping is employed to provide insights into its decision-making processes, enhancing transparency and interpretability. This research contributes significantly to the field of flood inundation mapping, offering an efficient model suitable for diverse applications. This study not only advances flood inundation mapping but also provides a valuable tool for improved understanding of deep learning decision-making in this area, ultimately contributing to improved disaster management strategies.

INSTANCE SEGMENTATION OF LIDAR DATA WITH VISION TRANSFORMER MODEL IN SUPPORT INUNDATION MAPPING UNDER FOREST CANOPY ENVIRONMENT

Abstract. Inundation mapping in forest and dense vegetated areas requires the ability to generate well defined Digital Terrain Models (DTM) to derive floodwater extent, depth, and duration. Due to the occlusion caused by overlapping leaves and branch structures of forest canopies, the ability to extract elevation point clouds through UAV and airborne optical imagery and photogrammetry is challenging. LiDAR is an active sensor that acquires direct 3D measurements by transmitting hundreds of thousands of laser measurements per second producing incredibly detailed mapping layers of not only the terrain but also forest attributes such as crown diameter, tree density and height that can support inundation mapping as well as hydrological models and monitoring of floods.In this research, we propose a methodology to map the inundated areas under canopies by using photon base Geiger Mode LiDAR point cloud dataset and a deep learning model to conduct instance segmentation of tree canopy. The method is to segment the vegetation from water and determine the gap fraction between trees to quantify the penetration through canopy for the detection of water pixels in vegetated areas. To conduct the segmentation Masked-attention Mask Transformer (Mask2Former) for universal segmentation model was implemented and trained to automate the extraction of tree crown segments from the LiDAR data. Furthermore, a semi-automatic experimental approach using a Canopy Height Model and watershed segmentation was applied to develop a rapid tree crown annotation strategy.

Analysis of Flood Inundation Mapping Using the Weighting Method in Urban Floods

Floods are one of the most destructive natural disasters globally and are projected to increase in many regions of the world. One of the areas affected by the floods is Barito Kuala Regency in South Kalimantan. Flood hazard maps and flood inundation maps are one way to identify areas prone to flooding and to find out the area of inundated areas due to flooding. Methods of scoring and weighting the parameters that cause flood inundation to map an area that has the potential for flooding. Flood inundation maps can be generated using the Normalized Difference Water Index (NDWI) method. The purpose of these two analyzes is to determine the distribution of flood hazard levels in Barito Kuala District, to evaluate flood-prone areas with flood-prone maps, and to determine the effectiveness of the two methods in identifying flood-prone areas and to determine the accuracy of the resulting flood inundation maps. Flood-prone areas were identified using the scoring method and parameter weighting to determine the distribution of flood vulnerability levels in Barito Kuala District. Parameters of flood vulnerability level used include land cover, slope, rainfall, drainage density, and soil type. Flood vulnerability level parameters were obtained from NDWI analysis on ArcGIS and ENVI. Image data during the dry season and rainy season are thresholded at <0.3 to eliminate data that is considered non-water resulting in flood inundation. The results of processing the level of flood hazard in Barito Kuala Regency are divided into 4 classes, namely the safe class with 0.5%, the less prone class is 10.11%, the moderate class is 89.37 and the vulnerable class is 0.00% of the area of Barito Kuala Regency with the majority of the areas in the moderate class. Savings District. The results of the analysis of the flood inundation map and the flood hazard map show that the moderate class of flood inundation is 72.06% of the total area of flood inundation

Permanent & Non-Permanent Water Identification Based on Image Fusion of Sentinel 1 & 2 Data (Case Study: Lamongan Regency)

The Earth’s surface consists of land and water. Surface water is separated into two groups: permanent water and temporary water. Flooding is temporary water that is often experienced in many places in Indonesia, including Lamongan Regency. In this area, at the beginning of 2022, seasonal floods caused many residents’ houses, agricultural land, and access roads to be submerged in water. This is due to its topography, which is characterized by lowlands and water. Land cover monitoring in large areas can be identified and analyzed using remote sensing. In this case, remote sensing is used to obtain information about surface water for separating permanent water and temporary water for comprehensive treatment in the long term. This study uses two primary sources of data: Sentinel-1 and Sentinel-2 satellite images from five years of observation from January 2018 to December 2022. Sentinel-1 is used to exploit its Synthetic Aperture Radar (SAR) sensor, which is sensitive to dielectric properties; thus, the water objects can be identified easily. Meanwhile, the Sentinel-2 satellite provides up to 10 meters of spatial-resolution satellite data. Processing was carried out using Image Fusion with Fuzzy Logic Min to merge the advantages of different data sources. The result of processing obtained that the probability of permanent water distribution in Lamongan Regency from 2018 to 2022 has differences in the total area every year. The total area of permanent water from Image Fusion is 12.769 km2. Meanwhile, the total area for flood inundation is 57.023 km2. A comparison of the processing results with comparative data is carried out for the calculation of the accuracy test. Overall accuracy for permanent water is 91%, with a kappa coefficient of 0.82. Then an overall accuracy for flood inundation obtained 90% with a kappa coefficient is 0.79. The result of this study is a permanent water map and flood inundation map of the Lamongan Regency, which is expected to be a reference in flood disaster mitigation activities.

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.

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.

Assessment of the structural uncertainty of hydrological models and its impact on flood inundation mapping

ABSTRACTFloods are frequent natural disasters, and the management of floods involves predicting flood hydrographs and inundation maps. However, the uncertainty associated with these predictions is a significant challenge in flood management. This study focuses on the uncertainty associated with the selection of mathematical models to estimate flood hydrographs and their impact on inundation map predictions. The study involved setting up 27 hydrological models using different mathematical models for the loss, transform, and routing methods within the Hydrologic Engineering center – Hydrologic Modelling system (HEC-HMS) model. The results showed that the peak flow estimation depends on the models adopted for predicting the flood hydrograph. This uncertainty was translated to the flood inundation maps, where the inundated area varied across different flood events. The study also found that the hydrological conditions prior to the start of the event impact the hydrological model predictive uncertainty, and ensemble predictions by Bayesian model averaging can reduce the uncertainty.

Dam Breach Modeling and Downstream Flood Inundation Mapping Using HEC-RAS Model on the Proposed Gumara Dam, Ethiopia

This study was, therefore, taken up to dam breach modeling and downstream flood inundation mapping. To achieve this objective, the software ArcGIS Version 10.4 extension program of HEC-GeoRAS Version 10 tool was used for HEC-RAS model development, to generate modeling reach and floodplain cross-sectional geometric data and for downstream inundation mapping and HEC-RAS Version 6.3.1 tool was used for subject dam break simulation and unsteady flood routing at downstream regions. Von Thun and Gillette regression equation was selected to estimate the breach parameter and the result shows that breach bottom width is 113 m, side slope 0.5H : 1V and breach development time is 0.85 hr for overtopping and bottom width is 111 m, and side slope 0.5H : 1V and breach development time is 0.83 hr for pipping. During analysis of flood routing the peak discharge at the dam site is 19,753.68 m3/s occurred at 4 hr for overtopping and 25,128.1 m3/s at time to peak 4 hr for pipping.