Dynamical Flash Flood Risk Forecast

被引:1
作者
Rapant, Petr [1 ]
Kolejka, Jaromir [2 ]
机构
[1] VSB Tech Univ Ostrava, Inst Geoinformat, Fac Min & Geol, 17 Listopadu 15-2172, Ostrava 70833, Czech Republic
[2] Czech Acad Sci, Inst Geon, Drobneho 301-28, Brno 60200, Czech Republic
来源
DYNAMICS IN GISCIENCE | 2018年
关键词
Flash flood; Risk prediction; Weather radar;
D O I
10.1007/978-3-319-61297-3_27
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
Flash floods represent very dynamical natural phenomenon. Mostly, they are the result of torrential rains which can rise suddenly in any part of a country and are tough to predict. Of course, there are many weather forecasting systems, but their spatial and temporal resolution is usually insufficient for these purposes. There are also monitoring systems which can either register precipitation over the ground (a network of rain gauge stations) or runoff in riverbeds (a network of hydrometric stations). Again, spatial (and possibly temporal) resolution is not sufficient, and in the case of runoff monitoring, there is a substantial delay between actual rainfall and registration of runoff in riverbeds. And, of course, when the hydrometric station registers higher runoff than the flash floods is running or even over. From the point of early warning, all these systems reveal disadvantages. Aside from these systems, there is one which provides us with timely information about the spatial and temporal distribution of precipitation intensity over the ground. That is weather radar. We will demonstrate possible usage of these data for dynamic prediction of flash flood risk distribution in space and time over the monitored area. Proper processing of these data in combination with soil saturation indicator established using Flash flood guidance methodology developed by the US Hydrologic Research Center can generate timely information usable for early warning with a substantially reduced level of false warnings.
引用
收藏
页码:373 / 381
页数:9
相关论文
共 50 条
[41]   Flash flood risk management modeling in indian cities using IoT based reinforcement learning [J].
Goyal, Himanshu Rai ;
Ghanshala, Kamal Kumar ;
Sharma, Sachin .
MATERIALS TODAY-PROCEEDINGS, 2021, 46
[42]   Dynamic Pluvial Flash Flooding Hazard Forecast Using Weather Radar Data [J].
Rapant, Petr ;
Kolejka, Jaromir .
REMOTE SENSING, 2021, 13 (15)
[43]   Impact of urbanization on desert flash flood generation [J].
Duaa Almousawi ;
Jaber Almedeij ;
Abdullah A. Alsumaiei .
Arabian Journal of Geosciences, 2020, 13
[44]   A gradient sensing middleware to handle flash flood [J].
Ahmed, Nova ;
Ghosh, Shuvashis ;
Hassan, Rifat Ahmed ;
Galib, Sian Iftekher ;
Azad, A. K. ;
Syrus, Minhaz Ahmed .
COMPUTERS & ELECTRICAL ENGINEERING, 2017, 62 :44-52
[45]   Shonabondhu: A Sensing Middleware to Handle Flash Flood [J].
Ahmed, Nova ;
Khan, Mahmudur Rahman ;
Syrus, Minhaz Ahmed .
SENSYS'15: PROCEEDINGS OF THE 13TH ACM CONFERENCE ON EMBEDDED NETWORKED SENSOR SYSTEMS, 2015, :381-382
[46]   Analysis of a Flash Flood in a Small Basin in Crete [J].
Sarchani, Sofia ;
Tsanis, Ioannis .
WATER, 2019, 11 (11)
[47]   Impact of urbanization on desert flash flood generation [J].
Almousawi, Duaa ;
Almedeij, Jaber ;
Alsumaiei, Abdullah A. .
ARABIAN JOURNAL OF GEOSCIENCES, 2020, 13 (12)
[48]   Urban flash flood in Gdansk - 2001; solutions and measures for city flood management [J].
Majewski, Wojciech .
INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT, 2008, 6 (04) :357-367
[49]   Modelling and validation of flash flood inundation in drylands [J].
Dan Gao ;
Jie Yin ;
Dandan Wang ;
Yuhan Yang ;
Yi Lu ;
Ruishan Chen .
Journal of Geographical Sciences, 2024, 34 :185-200
[50]   Modeling and validation of flash flood inundation in drylands [J].
Gao D. ;
Yin J. ;
Wang D. ;
Yang Y. ;
Lu Y. ;
Chen R. .
Dili Xuebao/Acta Geographica Sinica, 2023, 78 (09) :2271-2283