Flood risk assessment of the Abu Simbel temple complex (Egypt) based on high-resolution spaceborne stereo imagery

2018 ◽  
Vol 20 ◽  
pp. 458-467 ◽  
Author(s):  
Raghda El-Behaedi ◽  
Eman Ghoneim
2021 ◽  
Author(s):  
Enes Yildirim ◽  
Ibrahim Demir

Flood risk assessment contributes to identifying at-risk communities and supports mitigation decisions to maximize benefits from the investments. Large-scale risk assessments generate invaluable inputs for prioritizing regions for the distribution of limited resources. High-resolution flood maps and accurate parcel information are critical for flood risk analysis to generate reliable outcomes for planning, preparedness, and decision-making applications. Large-scale damage assessment studies in the United States often utilize the National Structure Inventory (NSI) or HAZUS default dataset, which results in inaccurate risk estimates due to the low geospatial accuracy of these datasets. On the other hand, some studies utilize higher resolution datasets, however they are limited to focus on small scales, for example, a city or a Hydrological United Code (HUC)-12 watershed. In this study, we collected extensive detailed flood maps and parcel datasets for many communities in Iowa to carry out a large-scale flood risk assessment. High-resolution flood maps and the most recent parcel information are collected to ensure the accuracy of risk products. The results indicate that the Eastern Iowa communities are prone to a higher risk of direct flood losses. Our model estimates nearly $10 million in average annualized losses, particularly in large communities in the study region. The study highlights that existing risk products based on FEMA's flood risk output underestimate the flood loss, specifically in highly populated urban communities such as Bettendorf, Cedar Falls, Davenport, Dubuque, and Waterloo. Additionally, we propose a flood risk score methodology for two spatial scales (e.g., HUC-12 watershed, property) to prioritize regions and properties for mitigation purposes. Lastly, the watershed-scale study results are shared through a web-based platform to inform the decision-makers and the public.


2020 ◽  
Author(s):  
Michael Andrew Manalili ◽  
Guy Schumann ◽  
Lara Prades ◽  
Sophia Rosa ◽  
Domingos Reane ◽  
...  

<p>Floods and their impacts are highly local in nature and vulnerable population and exposed assets are most at risk in coastal monsoonal regions. This is aggravated if the region is also exposed to tropical cyclones, such as Mozambique and the Licungo basin along the eastern coastline of the country.</p><p>In order to be better prepared against future high-impact flood events, Mozambique’s National Institute for Disaster Management (INGC) has mapped the watershed of the country’s central Licungo River with drones to reduce flood risks and improve emergency response planning. The mapping is intended to “minimize risks” and promote timely preparation of actions when cyclones and floods are expected in the area.</p><p>In the proposed project, the acquired drone terrain model and collected field data (water levels) will be used to drive a bespoke localized 2-D flood model to accurately reproduce flood hazard and risk in the central Licungo basin for the 2013 and 2015 flood disasters. In addition, high-resolution population and exposure layers have been used to define bespoke local flood risk maps.</p><p>Accurate flood risk assessment of past events at the local scale can better inform decision support systems and facilitate the decision-making process and preparedness for future high-impact events. Knowing who is at risk where and when is vital information that is missing in many vulnerable regions and is most of the time not available at the required local level.</p><p>Moreover, global or large-scale flood prediction models do not contain the necessary detail to infer meaningful flood risk at the local level and such models are known to be inaccurate, albeit they represent best efforts at the scales they are simulating. However, to what degree these models are wrong at the local scale of impact and what is needed to improve them is not known, largely because local flood data and bespoke predictions of flood risk are missing at the local scale for many vulnerable regions. The collected high-resolution data and the local flood risk assessment this project proposes would allow the validation of large-scale modeling efforts thereby advancing our understanding of model limitations and would create opportunities to improve them at large scales.</p>


2021 ◽  
Author(s):  
Nivedita Sairam ◽  
Fabio Brill ◽  
Tobias Sieg ◽  
Patric Kellermann ◽  
Kai Schröter ◽  
...  

<p>Floods affect people worldwide and account for more than USD 100 billion losses on average every year. Hazard, Exposure and Vulnerability are the three components that influence flood risk. Flood Risk Management (FRM) decisions especially, with respect to new flood defense schemes and resilience initiatives are generally taken based on the assessment of impacts for hazard scenarios. Current large-scale studies are comprehensive in terms of sectors covered in impact assessment. However, these studies often deploy generalized data and methods on the model components resulting in coarse risk estimates with low spatial resolution.</p><p>In this study, we use process-based models with 100m resolution on the national scale within a systems approach to develop and simulate a 5000 year flood event catalogue for Germany. The events are then analyzed per economic sector, including residential, commercial and agriculture sectors. The risk chain includes continuous simulation of high-resolution hazard maps, obtained from coupled hydrology and hydraulic models; NUTS3-level exposure asset values further disaggregated to ATKIS land-use data and calibrated object-level vulnerability models that provide high-resolution quantification of economic damage. Spatial dependence of flood events is addressed by the continuous simulation approach. For each model component in the risk assessment (hazard, exposure and vulnerability), uncertainty in data and methods are integrated into the risk predictions. Based on these simulations, we present a sector-wise flood risk assessment for Germany along with the reliability of the risk estimates. This process-based, systemic flood risk assessment is valuable for policy making, adaptation planning and estimating insurance premiums.</p>


10.1596/28574 ◽  
2017 ◽  
Author(s):  
Satya Priya ◽  
William Young ◽  
Thomas Hopson ◽  
Ankit Avasthi

MethodsX ◽  
2021 ◽  
pp. 101463
Author(s):  
Maurizio Tiepolo ◽  
Elena Belcore ◽  
Sarah Braccio ◽  
Souradji Issa ◽  
Giovanni Massazza ◽  
...  

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 104 ◽  
Author(s):  
Qiang Liu ◽  
Hongmao Yang ◽  
Min Liu ◽  
Rui Sun ◽  
Junhai Zhang

Cities located in the transitional zone between Taihang Mountains and North China plain run high flood risk in recent years, especially urban waterlogging risk. In this paper, we take Shijiazhuang, which is located in this transitional zone, as the study area and proposed a new flood risk assessment model for this specific geographical environment. Flood risk assessment indicator factors are established by using the digital elevation model (DEM), along with land cover, economic, population, and precipitation data. A min-max normalization method is used to normalize the indices. An analytic hierarchy process (AHP) method is used to determine the weight of each normalized index and the geographic information system (GIS) spatial analysis tool is adopted for calculating the risk map of flood disaster in Shijiazhuang. This risk map is consistent with the reports released by Hebei Provincial Water Conservancy Bureau and can provide reference for flood risk management.


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