scholarly journals Flood Management, Flood Forecasting and Warning System

2017 ◽  
Vol 6 (2) ◽  
pp. 33-38
Author(s):  
Ehsan Hajibabaei ◽  
Alireza Ghasemi
Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1571 ◽  
Author(s):  
Song ◽  
Park ◽  
Lee ◽  
Park ◽  
Song

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.


Author(s):  
W. H. Azad ◽  
M. H. Hassan ◽  
N. H. M. Ghazali ◽  
A. Weisgerber ◽  
F. Ahmad

2020 ◽  
Author(s):  
Charlie Pilling

<p>Set up in 2009, the UK Flood Forecasting Centre (FFC), is a successful partnership between the Environment Agency and the Met Office to provide national, operational, flood risk guidance. At the same time, we have a development programme to continuously improve flood forecasting. Operational for over a decade, the FFC has a strong portfolio and reputation amongst its users and customers. For example, the 2019 Responder Survey reported that 94% of those who have had contact with the FFC within the last 12 months are satisfied with the services provided.  </p><p>High impact, low probability events have been a feature of the first 10 years of the Flood Forecasting Centre. Probabilistic forecasting and risk-based approaches provide approaches to identify, forecast and warn for such events. Indeed, whilst these are currently successfully employed by various National Meteorological Hydrological Centres, there is also recognition (for example, World Meteorological Organisation) that effective forecasting and warning systems should be:</p><ul><li>‘<strong>impact-based’</strong>;</li> <li>driven by ensembles or realistic scenarios through an <strong>‘end-to-end’</strong> system (rather than precipitation ranges being simplified);</li> <li>more <strong>objective</strong>, so using new tools such as ensemble ‘sub-setting’, pattern recognition and machine learning to extract most value.</li> </ul><p>The Environment Agency is implementing a new Delft-FEWS forecasting system this year, termed Incident Management Forecasting System (IMFS). This will introduce a step change in capability for probabilistic impact-based forecasting. Initially, rainfall and coastal scenarios (termed ‘best-estimate’ and ‘reasonable worst case’) will be used to drive end-to-end forecasting, which includes for example impact data bases for property, infrastructure and communities. This is very much a stepping stone in the technical (systems) and adaptive (people, culture) transformation to a <strong>fully probabilistic, end-to-end, impact-based, flood forecasting. </strong></p><p>I will share some of our recent approaches to:</p><ul><li>objective, ensemble based, forecasting, including the Natural Hazards Partnership surface water hazard impact model (driven by the Met Office MOGREPS precipitation ensembles) which goes live this year;</li> <li>scenario generation and ensemble sub-setting to provide input to end-to-end, impact-based forecasting (IMFS);</li> <li>next steps in moving to a fully probabilistic, end-to-end, impact-based, flood forecasting and warning system</li> </ul><p>I will also highlight some of our current challenges that we would love to work with others to solve.</p>


Author(s):  
Karma Tsering ◽  
Kiran Shakya ◽  
Mir A. Matin ◽  
Jim Nelson ◽  
Birendra Bajracharya

AbstractFlooding is a chronic natural hazard with disastrous impacts that have magnified over the last decade due to the rising trend in extreme weather events and growing societal vulnerability from global socioeconomic and environmental changes (WMO 2011 in Manual on flood forecasting and warning (WMO-No. 1072)).


2021 ◽  
Vol 22 (2) ◽  
pp. 264-275
Author(s):  
Mirza Sarač ◽  
Maja Koprivšek ◽  
Oliver Rajković ◽  
Azra Babić ◽  
Merima Trako ◽  
...  

2021 ◽  
Vol 21 (3) ◽  
pp. 193-201
Author(s):  
Jaewon Jung ◽  
Hyelim Mo ◽  
Junhyeong Lee ◽  
Younghoon Yoo ◽  
Hung Soo Kim

Instances of flood damage caused by extreme storm rainfall due to climate change and variability have been showing an increasing trend. Particularly, a flood forecasting and warning system has been recognized as an important nonstructural measure for flood damage reduction, including loss of life. Flood forecasting and warning have been performed by the forecasts of flood discharge and flood stage using the physically based rainfall-runoff models. However, recently, studies involving the application of a machine learning-based flood forecasting models, which addresses the limitations of extant physically based flood stage forecasting models, have been performed. We may require various case studies to determine more accurate methods. Therefore, this study performed the real-time forecasting of the river water level or stage at the Gurye station of the Sumjin river with lead times of 1, 3, and 6 h by applying a long short-term memory (LSTM)-based deep learning model. In addition, the applicability of the LSTM model was evaluated by comparing the results with those from widely used models based on support vector machine and multilayer perceptron. Consequently, we noted that the LSTM model exhibited a relatively better forecasting performance. Therefore, the applicability of the LSTM model should be extensively studied for flood forecasting applications.


2013 ◽  
Vol 13 (2) ◽  
pp. 311-317 ◽  
Author(s):  
Jae Beom Park ◽  
Dong Soo Shin ◽  
Moo Jong Park ◽  
Bong Gwon Kang ◽  
Hyun Suk Shin

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 971
Author(s):  
Jung Hwan Lee ◽  
Gi Moon Yuk ◽  
Hyeon Tae Moon ◽  
Young-Il Moon

The flood forecasting and warning system enable an advanced warning of flash floods and inundation depths for disseminating alarms in urban areas. Therefore, in this study, we developed an integrated flood forecasting and warning system combined inland-river that systematized technology to quantify flood risk and flood forecasting in urban areas. LSTM was used to predict the stream depth in the short-term inundation prediction. Moreover, rainfall prediction by radar data, a rainfall-runoff model combined inland-river by coupled SWMM and HEC-RAS, automatic simplification module of drainage networks, automatic calibration module of SWMM parameter by Dynamically Dimensioned Search (DDS) algorithm, and 2-dimension inundation database were used in very short-term inundation prediction to warn and convey the flood-related data and information to communities. The proposed system presented better forecasting results compared to the Seoul integrated disaster prevention system. It can provide an accurate water level for 30 min to 90 min lead times in the short-term inundation prediction module. And the very short-term inundation prediction module can provide water level across a stream for 10 min to 60 min lead times using forecasting rainfall by radar as well as inundation risk areas. In conclusion, the proposed modules were expected to be useful to support inundation forecasting and warning systems.


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