A deterministic framework for selecting a flood forecasting and warning system at watershed scale

2014 ◽  
Vol 8 (4) ◽  
pp. 356-367 ◽  
Author(s):  
S. Golian ◽  
J. Yazdi ◽  
M.L.V. Martina ◽  
S. Sheshangosht
Keyword(s):  
Warning System ◽  
Flood Forecasting ◽  
Watershed Scale ◽  
Forecasting And Warning ◽  
Deterministic Framework

scholarly journals Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1571 ◽  
Author(s):  
Song ◽  
Park ◽  
Lee ◽  
Park ◽  
Song
Keyword(s):  
Decision Making ◽  
Water Level ◽  
Real Time ◽  
Lead Time ◽  
Flash Flood ◽  
Warning System ◽  
Flood Forecasting ◽  
Urban Stream ◽  
Forecasting And Warning ◽  
Flood Warnings

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.

National Flood Forecasting and Warning System of Malaysia: An Overview

2019 ◽  
pp. 264-273
Author(s):  
W. H. Azad ◽  
M. H. Hassan ◽  
N. H. M. Ghazali ◽  
A. Weisgerber ◽  
F. Ahmad
Keyword(s):  
Warning System ◽  
Flood Forecasting ◽  
Forecasting And Warning

Moving towards a fully probabilistic, end-to-end, impact-based, flood forecasting and warning system

2020 ◽  
Author(s):  
Charlie Pilling
Keyword(s):  
Warning System ◽  
Flood Forecasting ◽  
Incident Management ◽  
Probabilistic Forecasting ◽  
Worst Case ◽  
Forecasting System ◽  
End To End ◽  
Forecasting And Warning ◽  
Environment Agency ◽  
The Uk

<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>

scholarly journals Enhancing Flood Early Warning System in the HKH Region

2021 ◽  
pp. 169-200
Author(s):  
Karma Tsering ◽  
Kiran Shakya ◽  
Mir A. Matin ◽  
Jim Nelson ◽  
Birendra Bajracharya
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Natural Hazard ◽  
Environmental Changes ◽  
Warning System ◽  
Flood Forecasting ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Forecasting And Warning

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)).

scholarly journals Improvement of the operational HEC-HMS hydrological model embedded in the Flood Forecasting and Warning System of the Sava River Basin

2021 ◽  
Vol 22 (2) ◽  
pp. 264-275
Author(s):  
Mirza Sarač ◽  
Maja Koprivšek ◽  
Oliver Rajković ◽  
Azra Babić ◽  
Merima Trako ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Warning System ◽  
Flood Forecasting ◽  
Sava River ◽  
Forecasting And Warning ◽  
The Sava River

scholarly journals Flood Stage Forecasting at the Gurye-Gyo Station in Sumjin River Using LSTM-Based Deep Learning Models

2021 ◽  
Vol 21 (3) ◽  
pp. 193-201
Author(s):  
Jaewon Jung ◽  
Hyelim Mo ◽  
Junhyeong Lee ◽  
Younghoon Yoo ◽  
Hung Soo Kim
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Warning System ◽  
Flood Forecasting ◽  
Flood Damage ◽  
Support Vector ◽  
Forecasting Models ◽  
Physically Based ◽  
Forecasting And Warning ◽  
Storm Rainfall

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.

scholarly journals Rainfall Thresholds Estimation to Develop Flood Forecasting and Warning System for Nakdong Small River Basins

2013 ◽  
Vol 13 (2) ◽  
pp. 311-317 ◽  
Author(s):  
Jae Beom Park ◽  
Dong Soo Shin ◽  
Moo Jong Park ◽  
Bong Gwon Kang ◽  
Hyun Suk Shin
Keyword(s):  
Warning System ◽  
River Basins ◽  
Flood Forecasting ◽  
Small River ◽  
Rainfall Thresholds ◽  
Forecasting And Warning

scholarly journals Integrated Flood Forecasting and Warning System against Flash Rainfall in the Small-Scaled Urban Stream

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 971
Author(s):  
Jung Hwan Lee ◽  
Gi Moon Yuk ◽  
Hyeon Tae Moon ◽  
Young-Il Moon
Keyword(s):  
Water Level ◽  
Urban Areas ◽  
Warning System ◽  
Flood Forecasting ◽  
Urban Stream ◽  
Lead Times ◽  
Short Term ◽  
Inland River ◽  
Prediction Module ◽  
Forecasting And Warning

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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