flood depth
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2022 ◽  
Author(s):  
Noah A. Paoa-Kannegiesser ◽  
Charles H. Fletcher ◽  
Tiffany R. Anderson ◽  
Makena Coffman

Abstract Projecting sea level rise (SLR) impacts requires defining ocean surface variability as a source of uncertainty. We analyze data from a Regional Ocean Modeling System (ROMS) reanalysis for the region surrounding the main Hawaiian Islands to incorporate the ocean surface uncertainty in mapping SLR flood probabilities. By analyzing the ocean surface height component of the ROMS reanalysis, we create an ocean surface reference (ORS) as a proxy for MHHW. We model the NOAA Intermediate, Intermediate-high and High regional SLR scenarios for the years 2050 and 2100 at three field sites around Oʻahu; Waikīkī, Hauʻula, Haleʻiwa. We calculate a probability density function (PDF) by convolving the PDF of water level derived from the ROMS reanalysis data with the PDF of error associated with a digital elevation model of the study sites. The resulting joint-PDF of flood depth allows us to create two types of probability-based flood projections: (1) Maps illustrating varying flood depths for a given probability threshold and, (2) maps illustrating varying probability for a specific flood depth. We compare 80% probability flood projections using our ORS approach to projections using the TCARI grid, the standard NOAA method. We highlight the importance of uncertainty and user-defined probability in identifying pixels that function as tipping points distinguishing flooding styles.


2021 ◽  
Vol 13 (24) ◽  
pp. 13953
Author(s):  
Muhammad Saeed ◽  
Huan Li ◽  
Sami Ullah ◽  
Atta-ur Rahman ◽  
Amjad Ali ◽  
...  

Floods are the most frequent and destructive natural disasters causing damages to human lives and their properties every year around the world. Pakistan in general and the Peshawar Vale, in particular, is vulnerable to recurrent floods due to its unique physiography. Peshawar Vale is drained by River Kabul and its major tributaries namely, River Swat, River Jindi, River Kalpani, River Budhni and River Bara. Kabul River has a length of approximately 700 km, out of which 560 km is in Afghanistan and the rest falls in Pakistan. Looking at the physiography and prevailing flood characteristics, the development of a flood hazard model is required to provide feedback to decision-makers for the sustainability of the livelihoods of the inhabitants. Peshawar Vale is a flood-prone area, where recurrent flood events have caused damages to standing crops, agricultural land, sources of livelihood earnings and infrastructure. The objective of this study was to determine the effectiveness of the ANN algorithm in the determination of flood inundated areas. The ANN algorithm was implemented in C# for the prediction of inundated areas using nine flood causative factors, that is, drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use. For the preparation of spatial geodatabases, thematic layers of the drainage network, river discharge, rainfall, slope, flow accumulation, soil, surface geology, flood depth and land use were generated in the GIS environment. A Neural Network of nine, six and one neurons for the first, second and output layers, respectively, were designed and subsequently developed. The output and the resultant product of the Neural Network approach include flood hazard mapping and zonation of the study area. Parallel to this, the performance of the model was evaluated using Root Mean Square Error (RMSE) and Correlation coefficient (R2). This study has further highlighted the applicability and capability of the ANN in flood hazard mapping and zonation. The analysis revealed that the proposed model is an effective and viable approach for flood hazard analysis and zonation.


2021 ◽  
Author(s):  
Rubayet Bin Mostafiz ◽  
Carol Friedland ◽  
Md Adilur Rahim ◽  
Robert Rohli ◽  
Nazla Bushra

2021 ◽  
Vol 884 (1) ◽  
pp. 012025
Author(s):  
Pattaramone Manawongcharoen ◽  
Thitirat Panbamrungkij

Abstract Flooding is one of the main disasters in Thailand and Mueang Sing Buri is among those areas hit. Located on the Chao Phraya River Basin, in the central region of Thailand, the area receives a large amount of runoff during monsoon seasons which causes frequent flood disasters. The aims of this research are to create a flood hazard map and to estimate the number of people that may need shelter after the occurrence of a flood, and to evaluate whether the shelter capacity is adequate in Mueang Sing Buri. To explore the potential locations of emergency shelters, the relevant information related to flooding was initially recorded, such as building detail, flood depth, elevation map, and flood risk map. The available space of each building varies by the characteristics of building types. The calculation of shelter capacity thus depends on characteristics of the buildings, accessibility, and percent of vacant area. The emergency shelter assessment benefits many sectors in the design of preparation plans for hazard management.


2021 ◽  
Vol 884 (1) ◽  
pp. 012028
Author(s):  
Norawit Suwannakarn ◽  
Chanita Duangyiwa ◽  
Ekkamol Vannametee

Abstract Due to a rapid increase in urban and built-up areas, the East Coast – Gulf basin of Thailand faces flood hazards more frequently than in the past. In this study, we aim to assess the effects of building construction on flooding, and any link between them. The FloodMap model is used to simulate flooding in the study area in September 2015. Two flooding scenarios were designed; one based only on land surface elevation and the other one with building construction included on the land surface. According to the result, we found that human construction increases flood hazard in the study areas, particularly flood depth. Flood areas are also found to increase if the human factor is added into the model, but in a lesser extent. With human construction, paved road is found to have the highest flood potential compared to other types of road. Built up areas are more flooded, while flooding extent is almost similar to results from the scenario of no human construction in forest and agricultural areas.


2021 ◽  
Vol 297 ◽  
pp. 113367
Author(s):  
Chen Hao ◽  
Ali P. Yunus ◽  
Srikrishnan Siva Subramanian ◽  
Ram Avtar

2021 ◽  
Vol 877 (1) ◽  
pp. 012025
Author(s):  
Tabarak W. Mahdi ◽  
Ali N. Hillo

Abstract Proper flood control plays an important part in designing hydraulic structures and environmental safety measures. However, in Iraq, a clear understanding either of the estimation or management of the magnitude of flooding is yet to reach a higher level. This has resulted in grave and frequent damage to much property and life in Maysan town. Therefore, this study was focused on the Flood Hazard contro; in the River Tigris at the downstream of Al-Kut Barrage to the Al-Musandaq Escape, by adopting the HEC RAS model. Here, information related to the hydrological and topographical Digital Elevation Model (DEM) data were used as the input data. The hydrological data enabled the estimation of the flood depth of the river, for April 2019. All the geometric data were prepared by the HEC-RAS model. The unsteady-state model simulation was performed employing the input data. In this study, the best method for flood control and management is to design a weir having an optimal level, the optimum level that does not permit the passage of a flow that exceeds 700 m3/s to the city of Maysan during the flood season and a flow that is not below 250 m3/s during the dry season, is 9.406 m.


2021 ◽  
Author(s):  
Seth Bryant ◽  
Heather McGrath ◽  
Mathieu Boudreault

Abstract. Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do not correct for errors, leading to poor estimates. To provide more accurate gridded depth estimates of historical flooding, a new tool is proposed that integrates Height Above Nearest Drainage and Cost Allocation algorithms. This tool is tested against two trusted, hydraulically derived, gridded depths of recent floods in Canada. This validation shows the proposed tool outperforms existing tools and can provide more accurate estimates from minimal data without the need for complex physics-based models or expert judgement. With improvements in remote-sensing data, the tool proposed here can provide flood researchers and emergency managers accurate depths in near-real time.


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