scholarly journals A NOAA-USGS demonstration flash-flood and debris-flow early-warning system

Fact Sheet ◽  
2005 ◽  
Author(s):  
Keyword(s):  
Debris Flow ◽  
Early Warning ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System

scholarly journals Learning Case Study of a Shallow-Water Model to Assess an Early-Warning System for Fast Alpine Muddy-Debris-Flow

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 750
Author(s):  
Antonio Pasculli ◽  
Jacopo Cinosi ◽  
Laura Turconi ◽  
Nicola Sciarra
Keyword(s):  
Debris Flow ◽  
Shallow Water ◽  
Early Warning ◽  
Debris Flows ◽  
Early Warning System ◽  
Warning System ◽  
Extreme Weather Events ◽  
Water Model ◽  
Computational Time ◽  
Processing Unit

The current climate change could lead to an intensification of extreme weather events, such as sudden floods and fast flowing debris flows. Accordingly, the availability of an early-warning device system, based on hydrological data and on both accurate and very fast running mathematical-numerical models, would be not only desirable, but also necessary in areas of particular hazard. To this purpose, the 2D Riemann–Godunov shallow-water approach, solved in parallel on a Graphical-Processing-Unit (GPU) (able to drastically reduce calculation time) and implemented with the RiverFlow2D code (version 2017), was selected as a possible tool to be applied within the Alpine contexts. Moreover, it was also necessary to identify a prototype of an actual rainfall monitoring network and an actual debris-flow event, beside the acquisition of an accurate numerical description of the topography. The Marderello’s basin (Alps, Turin, Italy), described by a 5 × 5 m Digital Terrain Model (DTM), equipped with five rain-gauges and one hydrometer and the muddy debris flow event that was monitored on 22 July 2016, were identified as a typical test case, well representative of mountain contexts and the phenomena under study. Several parametric analyses, also including selected infiltration modelling, were carried out in order to individuate the best numerical values fitting the measured data. Different rheological options, such as Coulomb-Turbulent-Yield and others, were tested. Moreover, some useful general suggestions, regarding the improvement of the adopted mathematical modelling, were acquired. The rapidity of the computational time due to the application of the GPU and the comparison between experimental data and numerical results, regarding both the arrival time and the height of the debris wave, clearly show that the selected approaches and methodology can be considered suitable and accurate tools to be included in an early-warning system, based at least on simple acoustic and/or light alarms that can allow rapid evacuation, for fast flowing debris flows.

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 Application and analysis of debris-flow early warning system in Wenchuan earthquake-affected area

2016 ◽  
Vol 16 (2) ◽  
pp. 483-496 ◽  
Author(s):  
D. L. Liu ◽  
S. J. Zhang ◽  
H. J. Yang ◽  
L. Q. Zhao ◽  
Y. H. Jiang ◽  
...  
Keyword(s):  
Debris Flow ◽  
Wenchuan Earthquake ◽  
Early Warning ◽  
Early Warning System ◽  
Prediction Accuracy ◽  
Warning System ◽  
Sichuan Province ◽  
Affected Area ◽  
The Wenchuan Earthquake ◽  
New System

Abstract. The activities of debris flow (DF) in the Wenchuan earthquake-affected area significantly increased after the earthquake on 12 May 2008. The safety of the lives and property of local people is threatened by DFs. A physics-based early warning system (EWS) for DF forecasting was developed and applied in this earthquake area. This paper introduces an application of the system in the Wenchuan earthquake-affected area and analyzes the prediction results via a comparison to the DF events triggered by the strong rainfall events reported by the local government. The prediction accuracy and efficiency was first compared with a contribution-factor-based system currently used by the weather bureau of Sichuan province. The storm on 17 August 2012 was used as a case study for this comparison. The comparison shows that the false negative rate and false positive rate of the new system is, respectively, 19 and 21 % lower than the system based on the contribution factors. Consequently, the prediction accuracy is obviously higher than the system based on the contribution factors with a higher operational efficiency. On the invitation of the weather bureau of Sichuan province, the authors upgraded their prediction system of DF by using this new system before the monsoon of Wenchuan earthquake-affected area in 2013. Two prediction cases on 9 July 2013 and 10 July 2014 were chosen to further demonstrate that the new EWS has high stability, efficiency, and prediction accuracy.

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.

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