Early Warning Systems for Flash Floods and Debris Flows in Vietnam: A Review

Ngo, Thi Thanh Huong; Vu, Ba Thao; Nguyen, Trung‐Kien

doi:10.1007/978-981-15-2184-3_162

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…They used rainfall data to derive cumulative rainfall thresholds and Precipitation thresholds can be used to construct early warning systems for debris floods and flash floods (Bezak et al 2016). Flash flood predictions depend on various factors, e.g., precipitation, soil moisture, water level, time of occurrence, flood duration, and flood peak time (Ngo et al 2020). One of the simplest threshold-based approaches for predicting flash floods is to detect extreme weather conditions using weather parameter-based indicators, such as cumulative precipitation.…”

Section: Proposed Equation Of This Studymentioning

confidence: 99%

Determining the Precipitation Intensity Threshold of Debris Flood Occurrence

Banihabib

Tanhapour

2021

Natural Disaster Science and Mitigation Engineering: DPRI Reports

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In this chapter, the precipitation threshold at which debris floods occur was evaluated experimentally, and the factors that influence debris flood occurrence, including the bed slope, sediment layer thickness, sediment grain size, length of alluvial flow direction, precipitation intensity, and time of debris flood occurrence, were examined. The impacts of these factors on debris flood initiation were investigated through dimensional analysis. Then, a method was developed to estimate the precipitation intensity threshold based on a set of laboratory tests. Furthermore, different methods for determining the precipitation intensity threshold at which debris floods are initiated were assessed and discussed. The results of the experiments showed that the effect of the sediment layer thickness on debris flood occurrence can be ignored. Moreover, by independently evaluating the effect of each factor on debris flood occurrence, it was found that the sediment length and average diameter of sediments are influential to debris flood initiation. The results of this research provide a better understanding of debris flood mechanisms and occurrence thresholds of debris floods and can be employed to prepare a forecasting model.

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Section: Proposed Equation Of This Studymentioning

confidence: 99%

Determining the Precipitation Intensity Threshold of Debris Flood Occurrence

Banihabib

Tanhapour

2021

Natural Disaster Science and Mitigation Engineering: DPRI Reports

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“…Automatic monitoring solutions are at the base of developing smart Early Warning (EW) systems for flood hazards, which usually exploit, among relevant input data, the water level of rivers [6,7]. Nonetheless, the solutions proposed in the literature and focused on EW do not go into the details of how the water level is measured, apart from saying the type of instrumentation adopted [8,9]. Commonly adopted technologies for monitoring river flood are pressure transducers, rangefinders, ultrasonic, radar as well as optical sensors [10].…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision System for Staff Gauge in River Flood Monitoring

et al. 2021

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Rivers close to populated or strategically important areas can cause damages and safety risks to people in the event of a flood. Traditional river flood monitoring systems like radar and ultrasonic sensors may not be completely reliable and require frequent on-site human interventions for calibration. This time-consuming and resource-intensive activity has attracted the attention of many researchers looking for highly reliable camera-based solutions. In this article we propose an automatic Computer Vision solution for river’s water-level monitoring, based on the processing of staff gauge images acquired by a V-IoT device. The solution is based on two modules. The first is implemented on the edge in order to avoid power consumption due to the transmission of poor quality frames, and another is implemented on the Cloud server, where the frames acquired and sent by the V-IoT device are processed for water level extraction. The proposed system was tested on sample images relating to more than a year of acquisitions at a river site. The first module of the proposed solution achieved excellent performances in discerning bad quality frames from good quality ones. The second module achieved very good results too, especially for what it concerns night frames.

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“…Furthermore, developing accurate early warning systems should be placed as the highest priority to reduce human losses [1]. Although early warning systems for floods are now available in many locations across the country [65][66][67], the majority of them are only at the level of scientific experiment or heavily depend on the experience of operating staff, indicating a potential risk of subjectivity. To further enhance the existing systems, it is important to leverage advances in remote sensing and climate models, for which many data products are now freely available (e.g., weather forecasts obtained from the European Centre for Medium-Range Weather Forecasts or Global Forecast System), and hydrologic models to provide flood forecasts with several days' lead time.…”

mentioning

confidence: 99%

Future Changes in Hydro-Climatic Extremes across Vietnam: Evidence from a Semi-Distributed Hydrological Model Forced by Downscaled CMIP6 Climate Data

Do,

Le,

et al. 2024

Water

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Flood hazards have led to substantial fatalities and economic loss in the last five decades, making it essential to understand flood dynamics in a warming climate. This study reports the first comprehensive assessment of projected flood hazards across Vietnam. We used downscaled climate data from the CMIP6 initiative, involving a total of 20 climate models, and streamflow projection simulated using a semi-distributed hydrological model. The assessment covers seven near-natural catchments, each representing a climate zone of the country. To evaluate climate change impacts on floods, the study simultaneously analyzes changes in three indices: (i) the annual hottest day temperature, to represent temperature extremes; (ii) the maximum daily rainfall amount, to represent rainfall extremes; and (iii) the discharge value exceeding 5% in a year, to assess streamflow extremes. Changes in the selected indices (relative to the reference period from 1985 to 2014) are assessed under four emission scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) and two future time slices (2036–2065 and 2070–2099). Although the robustness (as indicated by multi-model agreement) and significance (identified through the statistical test) of the changes vary substantially, depending on the selected indices and assessed time slices, an overall increase is consistently identified across all of the assessed hydro-climatic extremes (up to 4.8 °C for temperature extremes, 43 mm for rainfall extremes, and 31% for streamflow extremes). The findings suggest a potential increase in flood risk across Vietnam in a warming climate, highlighting the urgent need for improved flood preparedness and investment to reduce economic loss and mortality in an uncertain future.

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Early Warning Systems for Flash Floods and Debris Flows in Vietnam: A Review

Cited by 5 publications

References 3 publications

Determining the Precipitation Intensity Threshold of Debris Flood Occurrence

Determining the Precipitation Intensity Threshold of Debris Flood Occurrence

A Computer Vision System for Staff Gauge in River Flood Monitoring

Future Changes in Hydro-Climatic Extremes across Vietnam: Evidence from a Semi-Distributed Hydrological Model Forced by Downscaled CMIP6 Climate Data

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