2017
DOI: 10.3390/w9100776
|View full text |Cite
|
Sign up to set email alerts
|

Forecasting and Providing Warnings of Flash Floods for Ungauged Mountainous Areas Based on a Distributed Hydrological Model

Abstract: Flash floods occur in mountainous catchments with short response times, which are among the most devastating natural hazards in China. This paper intends to forecast and provide warnings of flash floods timely and precisely using the flash flood warning system, which is established by a new distributed hydrological model (the China flash flood hydrological model, CNFF-HM). Two ungauged mountainous regions, Shunchang and Zherong, are chosen as the study areas. The CNFF-HM is calibrated in five well-monitored ca… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

0
14
0

Year Published

2017
2017
2023
2023

Publication Types

Select...
7
2

Relationship

0
9

Authors

Journals

citations
Cited by 24 publications
(14 citation statements)
references
References 34 publications
0
14
0
Order By: Relevance
“…Throughout the relevant theories, technologies and practices of mountain torrents prevention all over the world, the understanding of triggering factors and formation mechanism of mountain torrents is still at a relatively macro level and partial qualitative level [11][12][13]. In recent years, systematic research on the formation and disaster-causing dynamics of flash floods in small watersheds, and research on the dynamic characteristics of flash floods [14] and simulation methods and technologies [15] have received widespread attention. In the southeastern coastal watershed in particular, while retaining the natural characteristics of mountainous areas, the rapid economic development in the middle and lower reaches, the accumulation of wealth and the population has magnified the flood risk.…”
Section: Introductionmentioning
confidence: 99%
“…Throughout the relevant theories, technologies and practices of mountain torrents prevention all over the world, the understanding of triggering factors and formation mechanism of mountain torrents is still at a relatively macro level and partial qualitative level [11][12][13]. In recent years, systematic research on the formation and disaster-causing dynamics of flash floods in small watersheds, and research on the dynamic characteristics of flash floods [14] and simulation methods and technologies [15] have received widespread attention. In the southeastern coastal watershed in particular, while retaining the natural characteristics of mountainous areas, the rapid economic development in the middle and lower reaches, the accumulation of wealth and the population has magnified the flood risk.…”
Section: Introductionmentioning
confidence: 99%
“…The safety of lives and properties is always threatened by the severe floods. Meteo-hydrological predictions are important for providing early flood warnings and preventing or reducing flood damages [2,3]. The high resolution Numerical Weather Prediction (NWP) models provided notable improvement in Quantitative Precipitation Forecast (QPF).…”
Section: Introductionmentioning
confidence: 99%
“…Most of those catastrophes have occurred in ungauged mountainous areas, but methods for classifying the flash flood risk index (FFRI) in ungauged mountainous areas remain few. There is some previous literature forecasting and providing warnings of flash floods for ungauged mountainous areas based on a distributed hydrological model [2][3][4][5][6][7][8]. For example, Reed et al [2] studied flash flood forecasting at ungauged locations with a distributed hydrologic model and threshold frequency-based method.…”
Section: Introductionmentioning
confidence: 99%
“…For example, Reed et al [2] studied flash flood forecasting at ungauged locations with a distributed hydrologic model and threshold frequency-based method. Wang et al [3] established a flash flood warning system based on a distributed hydrological model applied to two ungauged mountainous regions.…”
Section: Introductionmentioning
confidence: 99%