Your search found 4 records
1 Alaminie, A.; Amarnath, Giriraj; Padhee, Suman; Ghosh, Surajit; Tilahun, S.; Mekonnen, M.; Assefa, G.; Seid, Abdulkarim; Zimale, F.; Jury, M. 2023. Application of advanced Wflow_sbm Model with the CMIP6 climate projection for flood prediction in the data-scarce: Lake-Tana Basin, Ethiopia [Abstract only]. Paper presented at the European Geosciences Union (EGU) General Assembly 2023, Vienna, Austria and Online, 24-28 April 2023. 1p. [doi: https://doi.org/10.5194/egusphere-egu23-1113]
Flood forecasting ; Climate change ; Hydrological modelling ; Climate models / Ethiopia / Lake Tana Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051891)
https://meetingorganizer.copernicus.org/EGU23/EGU23-1113.html?pdf
https://vlibrary.iwmi.org/pdf/H051891.pdf
https://vlibrary.iwmi.org/pdf/H051891.pdf
(0.28 MB) (289 KB)
2 Pakhale, G.; Nale, J. 2023. Progression of flood risk assessment in India at a decadal scale: a critical review. Water Policy, 25(12):1175-1186. [doi: https://doi.org/10.2166/wp.2023.185]
Floodplains ; Flood forecasting ; Flood control ; Risk assessment ; Vulnerability ; Policies ; Rainfall ; Socioeconomic aspects ; Infrastructure ; Remote sensing ; Geographical information systems / India
(Location: IWMI HQ Call no: e-copy only Record No: H052445)
https://iwaponline.com/wp/article-pdf/25/12/1175/1344873/025121175.pdf
https://vlibrary.iwmi.org/pdf/H052445.pdf
https://vlibrary.iwmi.org/pdf/H052445.pdf
(0.49 MB) (504 KB)
Floods are a recurrent natural phenomenon in India, including perennial occurrences in some parts of the country. Progressively, floods are transformed into flood hazards because of the anthropogenic activities in the flood plains and adjoining catchments, causing injuries, loss of lives, and property damage. Flood hazards, when considered in relation to vulnerability and exposure limits, describe the associated flood risk. This article aims to discuss the progression in flood risk assessment through several government policies and actions in India at a decadal scale from 1951 to 2020. While doing this, some important extreme flood events witnessed in those decades that shaped the perspectives, measures, action plans, and policies in the subsequent years are discussed. The review confirms that with the changing patterns of floods, associated hazards, and risks over the years, improvements in risk assessment approaches are noticeable on dual fronts. Technical advancements in flood risk assessment have corroborated the policy reforms. Albeit these developments, the issues related to the scale of study, data sources and resolutions, climatic variability, urban development, complex population dynamics, and their interrelationships in the context of flood risk need to be resolved with serious efforts. Addressing these issues through multidimensional strategies is imperative to aver robust flood risk assessment.
3 Yin, Z.; Saadati, Y.; Hu, B.; Leon, A. S.; Amini, M. H.; McDaniel, D. 2024. Fast high-fidelity flood inundation map generation by super-resolution techniques. Journal of Hydroinformatics, 26(1):319-336. [doi: https://doi.org/10.2166/hydro.2024.228]
Flooding ; Flood forecasting ; Public health ; Health hazards ; Neural networks ; Models ; Machine learning / United States of America / Miami River / Florida
(Location: IWMI HQ Call no: e-copy only Record No: H052612)
https://iwaponline.com/jh/article-pdf/26/1/319/1360891/jh0260319.pdf
https://vlibrary.iwmi.org/pdf/H052612.pdf
https://vlibrary.iwmi.org/pdf/H052612.pdf
(1.32 MB) (1.32 MB)
Flooding is one of the most frequent natural hazards and causes more economic loss than all the other natural hazards. Fast and accurate flood prediction has significance in preserving lives, minimizing economic damage, and reducing public health risks. However, current methods cannot achieve speed and accuracy simultaneously. Numerical methods can provide high-fidelity results, but they are time-consuming, particularly when pursuing high accuracy. Conversely, neural networks can provide results in a matter of seconds, but they have shown low accuracy in flood map generation by all existing methods. This work combines the strengths of numerical methods and neural networks and builds a framework that can quickly and accurately model the high-fidelity flood inundation map with detailed water depth information. In this paper, we employ the U-Net and generative adversarial network (GAN) models to recover the lost physics and information from ultra-fast, low-resolution numerical simulations, ultimately presenting high-resolution, high-fidelity flood maps as the end results. In this study, both the U-Net and GAN models have proven their ability to reduce the computation time for generating high-fidelity results, reducing it from 7–8 h down to 1 min. Furthermore, the accuracy of both models is notably high.
4 Haque, A.; Shampa; Akter, M.; Hussain, Md. M.; Rahman, Md. R.; Salehin, M.; Rahman, M. 2024. An integrated risk-based early warning system to increase community resilience against disaster. Progress in Disaster Science, 21:100310. [doi: https://doi.org/10.1016/j.pdisas.2023.100310]
Disaster risk reduction ; Flood forecasting ; Communities ; Resilience ; Early warning systems ; Model ; Sustainable Development Goals ; Vulnerability ; Villages ; Indicators ; River water ; Water levels / Bangladesh / Kurigram
(Location: IWMI HQ Call no: e-copy only Record No: H052633)
https://www.sciencedirect.com/science/article/pii/S2590061723000376/pdfft?md5=40313c2dfaa230bcc2d53032aa35f8bf&pid=1-s2.0-S2590061723000376-main.pdf
https://vlibrary.iwmi.org/pdf/H052633.pdf
https://vlibrary.iwmi.org/pdf/H052633.pdf
(9.74 MB) (9.74 MB)
The need to integrate Early Warning System (EWS) with Disaster Risk Reduction (DRR) has long been recognized in several global forums. In the year 2006, the United Nations International Strategy for Disaster Reduction (UNISDR) proposed an Integrated Risk-based EWS (IR-EWS) by integrating four elements: (1) Monitoring and warning service; (2) Risk knowledge; (3) Dissemination and communication; and (4) Response capability. Nearly after two decades of the UNISDR proposal, our study finds that there are still gaps in making IR-EWS operational. Our study also finds that works on conceptualizing integration of resilience against disaster with EWS as part of DRR (in line with SDG-13) has not yet been started. Against this backdrop, in this study we developed an IR-EWS for flood termed as Dynamic Flood Risk Model (DFRM) which contains: (1) simple risk-based warning numbers which are easily understandable and communicable to the community, with risk considered as a proxy for resilience; and (2) capital-based action plans in relation to community capital to reduce disaster risk and increase community resilience against disaster. The DFRM is applied in two flood-prone districts in Bangladesh and found to be acceptable to the community with reasonable accuracy. The model is the customized version of flood for generic IR-EWS. This study can be considered as the first attempt of the next generation IR-EWS where risk is represented by simple warning numbers and where EWS (as part of DRR) can be applied to increase the resilience.
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