flood maps
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2022 ◽  
Author(s):  
Qianqian Zhou ◽  
Shuai Teng ◽  
Xiaoting Liao ◽  
Zuxiang Situ ◽  
Junman Feng ◽  
...  

Abstract. An accurate and rapid urban flood prediction model is essential to support decision-making on flood management, especially under increasing extreme precipitation conditions driven by climate change and urbanization. This study developed a deep learning technique-based data-driven flood prediction model based on an integration of LSTM network and Bayesian optimization. A case study in north China was applied to test the model performance and the results clearly showed that the model can accurately predict flood maps for various hyetograph inputs, meanwhile with substantial improvements in computation time. The model predicted flood maps 19,585 times faster than the physical-based hydrodynamic model and achieved a mean relative error of 9.5 %. For retrieving the spatial patterns of water depths, the degree of similarity of the flood maps was very high. In a best case, the difference between the ground truth and model prediction was only 0.76 % and the spatial distributions of inundated paths and areas were almost identical. The proposed model showed a robust generalizability and high computational efficiency, and can potentially replace and/or complement the conventional hydrodynamic model for urban flood assessment and management, particularly in applications of real time control, optimization and emergency design and plan.


Eos ◽  
2021 ◽  
Vol 102 ◽  
Author(s):  
J. Besl

A new online program can quickly map the outlines of past floods, allowing data-scarce countries to prepare for future disasters.


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.


2021 ◽  
Vol 13 (14) ◽  
pp. 2786
Author(s):  
Roya Narimani ◽  
Changhyun Jun ◽  
Saqib Shahzad ◽  
Jeill Oh ◽  
Kyoohong Park

This paper proposes a novel hybrid method for flood susceptibility mapping using a geographic information system (ArcGIS) and satellite images based on the analytical hierarchy process (AHP). Here, the following nine multisource environmental controlling factors influencing flood susceptibility were considered for relative weight estimation in AHP: elevation, land use, slope, topographic wetness index, curvature, river distance, flow accumulation, drainage density, and rainfall. The weight for each factor was determined from AHP and analyzed to investigate critical regions that are more vulnerable to floods using the overlay weighted sum technique to integrate the nine layers. As a case study, the ArcGIS-based framework was applied in Seoul to obtain a flood susceptibility map, which was categorized into six regions (very high risk, high risk, medium risk, low risk, very low risk, and out of risk). Finally, the flood map was verified using real flood maps from the previous five years to test the model’s effectiveness. The flood map indicated that 40% of the area shows high flood risk and thus requires urgent attention, which was confirmed by the validation results. Planners and regulatory bodies can use flood maps to control and mitigate flood incidents along rivers. Even though the methodology used in this study is simple, it has a high level of accuracy and can be applied for flood mapping in most regions where the required datasets are available. This is the first study to apply high-resolution basic maps (12.5 m) to extract the nine controlling factors using only satellite images and ArcGIS to produce a suitable flood map in Seoul for better management in the near future.


2021 ◽  
Vol 25 (7) ◽  
pp. 4081-4097
Author(s):  
Concetta Di Mauro​​​​​​​ ◽  
Renaud Hostache ◽  
Patrick Matgen ◽  
Ramona Pelich ◽  
Marco Chini ◽  
...  

Abstract. Coupled hydrologic and hydraulic models represent powerful tools for simulating streamflow and water levels along the riverbed and in the floodplain. However, input data, model parameters, initial conditions, and model structure represent sources of uncertainty that affect the reliability and accuracy of flood forecasts. Assimilation of satellite-based synthetic aperture radar (SAR) observations into a flood forecasting model is generally used to reduce such uncertainties. In this context, we have evaluated how sequential assimilation of flood extent derived from SAR data can help improve flood forecasts. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. To this end, two assimilation methods are explored and compared: the sequential importance sampling method (standard method) and its enhanced method where a tempering coefficient is used to inflate the posterior probability (adapted method) and reduce degeneracy. The experimental results show that the assimilation of SAR probabilistic flood maps significantly improves the predictions of streamflow and water elevation, thereby confirming the effectiveness of the data assimilation framework. In addition, the assimilation method significantly reduces the spatially averaged root mean square error of water levels with respect to the case without assimilation. The critical success index of predicted flood extent maps is significantly increased by the assimilation. While the standard method proves to be more accurate in estimating the water levels and streamflow at the assimilation time step, the adapted method enables a more persistent improvement of the forecasts. However, although the use of a tempering coefficient reduces the degeneracy problem, the accuracy of model simulation is lower than that of the standard method at the assimilation time step.


Author(s):  
H. Liu ◽  
P. Van Oosterom ◽  
B. Mao ◽  
M. Meijers ◽  
R. Thompson

Abstract. Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical flood inundation maps and tables for loss analysis. Original modelling results recording critical flood evolution processes are overlooked due to cumbersome management and analysis. This certainly has drawbacks: the ‘static’ maps impart few details about the flood; also, the data fails to address new requirements. This significantly confines the use of flood maps. Recent development of point cloud databases provides an opportunity to manage the whole set of modelling results. The databases can efficiently support all kinds of flood risk queries at finer scales. Using a case study from China, this paper demonstrates how a novel nD-PointCloud structure, HistSFC, improves flood risk querying. The result indicates that compared with conventional database solutions, HistSFC holds superior performance and better scalability. Besides, the specific optimizations made on HistSFC can facilitate the process further. All these indicate a promising solution for the next generation of flood maps.


2021 ◽  
Vol 13 (13) ◽  
pp. 2430
Author(s):  
Afolarin Lawal ◽  
Hannah Kerner ◽  
Inbal Becker-Reshef ◽  
Seth Meyer

The inability of a farmer to plant an insured crop by the policy’s final planting date can pose financial challenges for the grower and cause reduced production for a widely impacted region. Prevented planting is primarily caused by excess moisture or rainfall such as the catastrophic flooding and widespread conditions that prevented active field work in the midwestern region of United States in 2019. While the Farm Service Agency reports the number of such “prevent plant” acres each year at the county scale, field-scale maps of prevent plant fields—which would enable analyses related to assessing and mitigating the impact of climate on agriculture—are not currently available. The aim of this study is to demonstrate a method for mapping likely prevent plant fields based on flood mapping and historical cropland maps. We focused on a study region in eastern South Dakota and created flood maps using Landsat 8 and Sentinel 1 images from 2018 and 2019. We used automatic threshold-based change detection using NDVI and NDWI to accentuate changes likely caused by flooding. The NDVI change detection map showed vegetation loss in the eastern parts of the study area while NDWI values showed increased water content, both indicating possible flooding events. The VH polarization of Sentinel 1 was also particularly useful in identifying potential flooded areas as the VH values for 2019 were substantially lower than those of 2018, especially in the northern part of the study area, likely indicating standing water or reduced biomass. We combined the flood maps from Landsat 8 and Sentinel 1 to form a complete flood likelihood map over the entire study area. We intersected this flood map with a map of fallow pixels extracted from the Cropland Data Layer to produce a map of predicted prevent plant acres across several counties in South Dakota. The predicted figures were within 10% error of Farm Service Agency reports, with low errors in the most affected counties in the state such as Beadle, Hanson, and Hand.


2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Binata Roy ◽  
Md. Sabbir Mostafa Khan ◽  
A. K. M. Saiful Islam ◽  
Khaled Mohammed ◽  
Md. Jamal Uddin Khan

AbstractBangladesh is one of the largest flood-prone deltas of the GBM (Ganges–Brahmaputra–Meghna) basins, and recently, it is categorized as the 7th worst climate-affected country in the world. Future climate change along with economic development, urbanization, and increase in population may worsen this situation manifolds. To cope with future flood situations and lessen probable flood losses, it is essential to develop flood maps of the major flood-prone rivers of Bangladesh considering climate change scenarios. In this study, the flood inundation of the Arial Khan River and its floodplain has been assessed for the predicted climate change scenario of RCP 8.5 (Representative Concentration Pathway 8.5) using open-source mathematical models. A calibrated and validated hydrologic model of GBM basins in SWAT (Soil and Water Assessment Tool) model has been used to estimate the future flow magnitudes at Bahadurabad Transit (Brahmaputra River) and Hardinge Bridge (Ganges River) using extreme emission scenario RCP 8.5. Using the flow magnitude of these two stations as the upstream boundaries, an HEC-RAS 1D model has been set up for the Brahmaputra, Ganges, and Padma rivers for generating future flow magnitude at the offtake of the Arial Khan River. Later, an HEC-RAS 1D-2D coupled model is set up for the Arial Khan River floodplain and flood maps are prepared considering flood depth, duration, and inundation extent. The flood assessment for different projections of RCP 8.5 shows that there is an increasing trend of flood in terms of depth, duration, and inundation from the 2020s to the 2080s. Hence, the floodplain becomes more hazardous by the end of this century. The climate change impact on the projected population for the RCP 8.5 scenario is assessed under SSP5 (Shared Socioeconomic Pathways 5) which indicates that the total flood-affected population will be nearly twice in the 2080s compared to the 2020s. So, future climate change is going to have a dreadful effect on the flood situation of the Arial Khan River floodplain.


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