Advances in Flood Early Warning: Ensemble Forecast, Information Dissemination and Decision-Support Systems

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrological and Hydrodynamic Processes and Modelling".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 19678

Special Issue Editors

School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: digital watershed and hydroinformatics; extreme hydrological events (floods and droughts) under climate change; sustainable development of water resources
Special Issues, Collections and Topics in MDPI journals
School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Interests: agent-based models for water resources management; opinion dynamics and flood evacuation; machine learning in environmental data analysis
Center for Climate Physics, Institute for Basic Science, Busan, Republic of Korea
Interests: hydrological extremes (floods and droughts); groundwater-surface water interaction; hyporheic zone study; large-scale water resource system optimization; water resources planning and management; water resource economics and policy
Special Issues, Collections and Topics in MDPI journals
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong
Interests: artificial intelligence; hydrology; soft computing; water quality; meta-heuristic algorithm; hydrodynamic; rainfall; runoff
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Floods are highly destructive, usually causing enormous losses to life and property. It is, therefore, important and necessary to develop effective flood early warning systems (e.g., numerical weather prediction) and disseminate the information to the public through various information sources (e.g., social media), to prevent or at least mitigate damage to lives and property. For flood early warning, qualitative and quantitative methods can be developed by taking advantage of state-of-the-art techniques. For instance, different temporal and spatial scales can be considered, both ground data and remote sensing data can be used as the input, and a service-oriented architecture can be proposed for efficient flood forecasting. In addition, a general opinion dynamics model can be developed to simulate how individuals update their flood hazard awareness. Such developments can offer new insights into modeling flood disasters, including facilitating more accurate forecasts, more efficient communication, and more timely evacuation.

This Special Issue aims to collect the latest methodological developments and applications in the field of flood early warning. Potential topics include, but are not limited to, the following:

  • Flood dynamics, mechanisms and processes
  • Development of methods for flood early warning, especially in ungauged basins
  • Improvement of flood forecasting and information dissemination using various information sources
  • New methods/techniques for flood risk analysis, vulnerability analysis and evacuation behavior
  • Empirical analysis of flood warnings and flood-mitigation practices during real-world flood events

Dr. Haiyun Shi
Dr. Erhu Du
Dr. Suning Liu
Prof. Kwok-wing Chau
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Flood early warning
  • Ensemble flood forecast
  • Numerical weather prediction
  • Service-oriented architecture
  • Social media
  • Individual behavior
  • Evacuation decisions

Published Papers (4 papers)

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Editorial

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3 pages, 181 KiB  
Editorial
Advances in Flood Early Warning: Ensemble Forecast, Information Dissemination and Decision-Support Systems
by Haiyun Shi, Erhu Du, Suning Liu and Kwok-Wing Chau
Hydrology 2020, 7(3), 56; https://doi.org/10.3390/hydrology7030056 - 13 Aug 2020
Cited by 3 | Viewed by 2502
Abstract
Floods are usually highly destructive, which may cause enormous losses to lives and property. It is, therefore, important and necessary to develop effective flood early warning systems and disseminate the information to the public through various information sources, to prevent or at least [...] Read more.
Floods are usually highly destructive, which may cause enormous losses to lives and property. It is, therefore, important and necessary to develop effective flood early warning systems and disseminate the information to the public through various information sources, to prevent or at least mitigate the flood damages. For flood early warning, novel methods can be developed by taking advantage of the state-of-the-art techniques (e.g., ensemble forecast, numerical weather prediction, and service-oriented architecture) and data sources (e.g., social media), and such developments can offer new insights for modeling flood disasters, including facilitating more accurate forecasts, more efficient communication, and more timely evacuation. The present Special Issue aims to collect the latest methodological developments and applications in the field of flood early warning. More specifically, we collected a number of contributions dealing with: (1) an urban flash flood alert tool for megacities; (2) a copula-based bivariate flood risk assessment; and (3) an analytic hierarchy process approach to flash flood impact assessment. Full article

Research

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15 pages, 1354 KiB  
Article
Flash Flood Impact Assessment in Jeddah City: An Analytic Hierarchy Process Approach
by Umar Lawal Dano
Hydrology 2020, 7(1), 10; https://doi.org/10.3390/hydrology7010010 - 06 Feb 2020
Cited by 33 | Viewed by 5531
Abstract
Floods are among the most destructive natural hazards that cost lives and disrupt the socioeconomic activities of residents, especially in the rapidly growing cities of developing countries. Jeddah, a coastal city situated in Saudi Arabia, has experienced severe flash flood events in recent [...] Read more.
Floods are among the most destructive natural hazards that cost lives and disrupt the socioeconomic activities of residents, especially in the rapidly growing cities of developing countries. Jeddah, a coastal city situated in Saudi Arabia, has experienced severe flash flood events in recent years. With intense rainfall, extensive coastal developments, and sensitive ecosystems, the city is susceptible to severe flash flood risks. The objective of this article is to apply an Analytic Hierarchy Process (AHP) model to explore the impacts of flash flood hazards and identify the most effective approaches to reducing the flash flood impacts in Jeddah using expert’s opinions. The study utilizes experts’ judgments and employs the AHP for data analyses and modeling. The results indicated that property loss has the highest probability of occurrence in the events of a flash flood with a priority level of 42%, followed by productivity loss (28%). Injuries and death were rated the least priority of 18% and 12%, respectively. Concerning flood impact reduction alternatives, river management (41%) and early warning system (38%) are the most favorable options. The findings could assist the government to design appropriate measures to safeguard the lives and properties of the residents. The study concludes by underscoring the significance of incorporating experts’ judgments in assessing flash flood impacts. Full article
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15 pages, 6119 KiB  
Article
Copula-Based Bivariate Flood Risk Assessment on Tarbela Dam, Pakistan
by Saba Naz, Muhammad Ahsanuddin, Syed Inayatullah, Tanveer Ahmed Siddiqi and Muhammad Imtiaz
Hydrology 2019, 6(3), 79; https://doi.org/10.3390/hydrology6030079 - 30 Aug 2019
Cited by 19 | Viewed by 6556
Abstract
Flooding from the Indus river and its tributaries has regularly influenced the region of Pakistan. Therefore, in order to limit the misfortune brought about by these inevitable happenings, it requires taking measures to estimate the occurrence and effects of these events. The current [...] Read more.
Flooding from the Indus river and its tributaries has regularly influenced the region of Pakistan. Therefore, in order to limit the misfortune brought about by these inevitable happenings, it requires taking measures to estimate the occurrence and effects of these events. The current study uses flood frequency analysis for the forecast of floods along the Indus river of Pakistan (Tarbela). The peak and volume are the characteristics of a flood that commonly depend on one another. For progressively proficient hazard investigation, a bivariate copula method is used to measure the peak and volume. A univariate analysis of flood data fails to capture the multivariate nature of these data. Copula is the most common technique used for a multivariate analysis of flood data. In this paper, four Archimedean copulas have been tried using the available information, and in light of graphical and measurable tests, the Gumbel Hougaard copula was found to be most appropriate for the data used in this paper. The primary (TAND, TOR), conditional and Kendall return periods have been also determined. The copula method was found to be a powerful method for the distribution of marginal variables. It also gives the Kendall return period for the multivariate analysis the consequences of flooding. Full article
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15 pages, 4628 KiB  
Article
An Urban Flash Flood Alert Tool for Megacities—Application for Manhattan, New York City, USA
by Rafea Al-Suhili, Cheila Cullen and Reza Khanbilvardi
Hydrology 2019, 6(2), 56; https://doi.org/10.3390/hydrology6020056 - 24 Jun 2019
Cited by 11 | Viewed by 4369
Abstract
Urban flooding is a frequent problem affecting cities all over the world. The problem is more significant now that the climate is changing and urbanization trends are increasing. Various, physical hydrological models such as the Environmental Protection Agency Storm Water Management Model (EPA [...] Read more.
Urban flooding is a frequent problem affecting cities all over the world. The problem is more significant now that the climate is changing and urbanization trends are increasing. Various, physical hydrological models such as the Environmental Protection Agency Storm Water Management Model (EPA SWMM), MIKE URBAN-II and others, have been developed to simulate flooding events in cities. However, they require high accuracy mapping and a simulation of the underground storm drainage system. Sadly, this capability is usually not available for older or larger so-called megacities. Other hydrological model types are classified in the semi-physical category, like Cellular Automata (CA), require the incorporation of very fine resolution data. These types of data, in turn, demand massive computer power and time for analysis. Furthermore, available forecasting systems provide a way to determine total rainfall during extreme events, but they do not tell us what areas will be flooded. This work introduces an urban flooding tool that couples a rainfall-runoff model with a flood map database to expedite the alert process and estimate flooded areas. A 0.30-m Lidar Digital Elevation Model (DEM) of the study area (in this case Manhattan, New York City) is divided into 140 sub-basins. Several flood maps for each sub-basin are generated and organized into a database. For any forecasted extreme rainfall event, the rainfall-runoff model predicts the expected runoff volume at different times during the storm interval. The system rapidly searches for the corresponding flood map that delineates the expected flood area. The sensitivity analysis of parameters in the model show that the effect of storm inlet flow head is approximately linear while the effects of the threshold infiltration rate, the number of storm inlets, and the storm inlet flow reduction factor are non-linear. The reduction factor variation is found to exhibit a high non-linearity variation, hence requiring further detailed investigation. Full article
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