scholarly journals A review of advances in China’s flash flood early-warning system

2018 ◽  
Vol 92 (2) ◽  
pp. 619-634 ◽  
Author(s):  
Changjun Liu ◽  
Liang Guo ◽  
Lei Ye ◽  
Shunfu Zhang ◽  
Yanzeng Zhao ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System

scholarly journals A Flash Flood Early Warning System for Rural Kenya: A Pilot Study

2018 ◽  
Vol 7 (4.38) ◽  
pp. 1310
Author(s):  
Prof. Dr. Ir Vinesh Thiruchelvam ◽  
Mbau Stella Nyambura
Keyword(s):  
Climate Change ◽  
Early Warning ◽  
Rural Areas ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System ◽  
Flash Floods ◽  
Short Term ◽  
Moderation Effect ◽  
Early Warnings

The cost of climate change has increased phenomenally in recent years. Therefore, understanding climate change and its impacts, that are likely to get worse and worse into the future, gives us the ability to predict scenarios and plan for them. Flash floods, which are a common result of climate change, follow increased precipitation which then increases risk and associated vulnerability due to the unpredictable rainfall patterns. Developing countries suffer grave consequences in the event that weather disasters strike because they have the least adaptive capacity. At the equator where the hot days are hotter and winds carrying rainfall move faster, Kenya’s Tana River County is noted for its vulnerability towards flash floods. Additionally, this county and others that are classified as rural areas in Kenya do not receive short term early warnings for floods. This county was therefore selected as the study area for its vulnerability. The aim of the study is therefore to propose a flash flood early warning system framework that delivers short term early warnings. Using questionnaires, information about the existing warning system will be collected and analyzed using SPSS. The results will be used to interpret the relationships between variables of the study, with a particular interest in the moderation effect in order to confirm that the existing system can be modified; that is, if the moderation effect is confirmed.       

scholarly journals Advanced Tools for Flood Management: An Early Warning System for Arid and Semiarid Regions

2021 ◽  
pp. 209-223
Author(s):  
Ekkehard Holzbecher ◽  
Ahmed Hadidi ◽  
Nicolette Volp ◽  
Jeroen de Koning ◽  
Humaid Al Badi ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System ◽  
Integrated Water Resources Management ◽  
Early Warning Systems ◽  
Flood Management ◽  
Special Focus ◽  
Semiarid Regions ◽  
Smart Technologies

AbstractTechnologies concerning integrated water resources management, in general, and flood management, in particular, have recently undergone rapid developments. New smart technologies have been implemented in every relevant sector and include hydrological sensors, remote sensing, sensor networks, data integration, hydrodynamic simulation and visualization, decision support and early warning systems as well as the dissemination of information to decision-makers and the public. After providing a rough review of current developments, we demonstrate the operation of an advanced system with a special focus on an early warning system. Two case studies are covered in this chapter: one specific urban case located in the city of Parrametta in Australia in an area that shows similar flood characteristics to those found in arid or semiarid regions and one case regarding the countrywide Flash Flood Guidance System in Oman (OmanFFGS).

scholarly journals A Flash Flood Early Warning System for Rural Kenya: A Pilot Study

2018 ◽  
Vol 7 (4.38) ◽  
pp. 810
Author(s):  
Prof. Dr. Ir Vinesh Thiruchelvam ◽  
Mbau Stella Nyambura
Keyword(s):  
Climate Change ◽  
Early Warning ◽  
Rural Areas ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System ◽  
Flash Floods ◽  
Short Term ◽  
Moderation Effect ◽  
Early Warnings

The cost of climate change has increased phenomenally in recent years. Therefore, understanding climate change and its impacts, that are likely to get worse and worse into the future, gives us the ability to predict scenarios and plan for them. Flash floods, which are a common result of climate change, follow increased precipitation which then increases risk and associated vulnerability due to the unpredictable rainfall patterns. Developing countries suffer grave consequences in the event that weather disasters strike because they have the least adaptive capacity. At the equator where the hot days are hotter and winds carrying rainfall move faster, Kenya’s Tana River County is noted for its vulnerability towards flash floods. Additionally, this county and others that are classified as rural areas in Kenya do not receive short term early warnings for floods. This county was therefore selected as the study area for its vulnerability. The aim of the study is therefore to propose a flash flood early warning system framework that delivers short term early warnings. Using questionnaires, information about the existing warning system will be collected and analyzed using SPSS. The results will be used to interpret the relationships between variables of the study, with a particular interest in the moderation effect in order to confirm that the existing system can be modified; that is, if the moderation effect is confirmed.   

scholarly journals iCRESTRIGRS: A coupled modeling system for cascading flood-landslide disaster forecasting

2016 ◽  
Author(s):  
Ke Zhang ◽  
Xianwu Xue ◽  
Yang Hong ◽  
Jonathan J. Gourley ◽  
Ning Lu ◽  
...  
Keyword(s):  
Slope Stability ◽  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Significant Risk ◽  
Warning System ◽  
Prototype System ◽  
Coupled Modeling ◽  
Hazard Management ◽  
Landslide Disaster

Abstract. Severe storm-triggered floods and landslides are two major natural hazards in the U.S., causing property losses of $6 billion and approximately 110–160 fatalities per year nationwide. Moreover, floods and landslides often occur in a cascading manner, posing significant risk and leading to losses that are significantly greater than the sum of the losses from the individual hazards. It is pertinent to couple hydrological and geotechnical modelling processes toward an integrated flood-landslide cascading disaster early warning system for improved disaster preparedness and hazard management. In this study, we developed the iCRESTRIGRS model, a coupled flash flood and landslide disaster early warning system, by integrating the Coupled Routing and Excess STorage (CREST) model with the physically based Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) landslide model. The iCRESTRIGRS system is evaluated in four river basins in western North Carolina that experienced a large number of floods, landslides and debris flows, triggered by heavy rainfall from Hurricane Ivan during September 16–18, 2004. The modelled hourly hydrographs at four USGS gauge stations show generally good agreement with the observations during the entire storm period. In terms of landslide prediction in this case study, the coupled model has a global accuracy of 89.5 % and a true positive rate of 50.6 %. More importantly, it shows an improved predictive capability for landslides relative to the stand-alone TRIGRS model. This study highlights the important physical connection between rainfall, hydrological processes and slope stability, and provides a useful prototype system for operational forecasting of flood and landslide.

scholarly journals Flash Flood Early Warning System in Colima, Mexico

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5231
Author(s):  
José Ibarreche ◽  
Raúl Aquino ◽  
R. M. Edwards ◽  
Víctor Rangel ◽  
Ismael Pérez ◽  
...  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System ◽  
Social Consequences ◽  
Long Term Effects ◽  
Government Officials ◽  
Flood Prediction ◽  
Monitoring Stations

This paper presents a system of sensors used in flash flood prediction that offers critical real-time information used to provide early warnings that can provide the minutes needed for persons to evacuate before imminent events. Flooding is one of the most serious natural disasters humans confront in terms of loss of life and results in long-term effects, which often have severely adverse social consequences. However, flash floods are potentially more dangerous to life because there is often little or no forewarning of the impending disaster. The Emergency Water Information Network (EWIN) offers a solution that integrates an early warning system, notifications, and real-time monitoring of flash flood risks. The platform has been implemented in Colima, Mexico covering the Colima and Villa de Alvarez metropolitan area. This platform consists of eight fixed riverside hydrological monitoring stations, eight meteorological stations, nomadic mobile monitoring stations called “drifters” used in the flow, and a sniffer with data muling capability. The results show that this platform effectively compiles and forwards information to decision-makers, government officials, and the general public, potentially providing valuable minutes for people to evacuate dangerous areas.

scholarly journals A Simple Early Warning System for Flash Floods in an Ungauged Catchment and Application in the Loess Plateau, China

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 426 ◽  
Author(s):  
Zhehao Li ◽  
Hongbo Zhang ◽  
Vijay Singh ◽  
Ruihong Yu ◽  
Shuqi Zhang
Keyword(s):  
Loess Plateau ◽  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System ◽  
Flash Floods ◽  
The Loess Plateau ◽  
Design Flood ◽  
Ungauged Catchments ◽  
Water Stage

Under climate change, flash floods have become more frequent and severe, and are posing a danger to society, especially in the ungauged catchments. The objective of this paper, is to construct a simple and early warning system, serving for flash floods risk management in the ungauged catchments of the Loess Plateau in China, and offer a reference for flash flood warning in other areas in the world. Considering the absence of hydrological data in the ungauged catchments, the early warning system for flash floods is established by combining the regional or watershed isograms of hydrological parameters and local empirical formulas. Therein, rainfall and water stage/flow are used as warning indices for real-time risk estimation of flash flood. For early warning, the disaster water stage was first determined according to the protected objects (e.g., residents and buildings), namely the critical water stage. The critical flow (flow threshold), was calculated based on the water stage, and the established relationship between water stage and flow using the cross-sectional measured data. Then, according to the flow frequency curve of the design flood, the frequency of critical flow was ascertained. Assuming that the rainfall and the flood have the same frequency, the critical rainfall threshold was calculated through the design rainstorm with the same frequency of the design flood. Due to the critical rainfall threshold being sensitive with different soil conditions, the design flood and frequency curve of flood flow were calculated under different soil conditions, and thus the rainfall threshold was given under different soil condition for early warning of the flash flood disaster. Taking two sections in Zichang County (within the Loess Plateau) as an example, we set the rainfall and water stage/flow thresholds to trigger immediate or preparation signals for the migration of the population along the river. The application of this method to the 7.26 flood events in 2017 in China, shows that the early warning system is feasible. It is expected that this simple early warning system can provide early warnings of flash floods in ungauged catchments in the Loess Plateau and other similar areas.

scholarly journals Feasibility study on operational use of neural networks in a flash flood early warning system

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
Glauston Roberto Teixeira de Lima ◽  
Graziela Balda Scofield
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System ◽  
Neural Model ◽  
Time To Peak ◽  
Input Variables ◽  
Deep Learning Neural Network

ABSTRACT Issuing early and accurate warnings for flash floods is a challenge when the rains that deflagrate these natural hazards occur on very short space-time scales. This article reports a case study in which a neural network-based hydrological model is designed to forecast one hour in advance if the water level in a small mountain watershed with short time to peak, situated in the city of Campos do Jordão in Brazil, will exceed its attention quota. This model can be a powerful auxiliary tool in a flash flood early warning system, since with it decision-making becomes semi-automated, making it possible to improve the warnings advance-accuracy tradeoff. A deep-learning neural network using Exponential Linear Unit activation functions was designed based on 3-years rainfall and water level data from 11 hydrometeorological stations of the National Centre for Monitoring and Early Warning of Natural Disasters. In the training of the neural network, two combinations of input variables were tested. The tuples in the test set were classified through voting with 60 classifiers. The first results obtained in Matlab environment with high percentages of true positives indicate that it is feasible to use the neural model operationally.

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