Recommendations for Improving Integration in National End-to-End Flood Forecasting Systems: An Overview of the FFIR (Flooding From Intense Rainfall) Programme

Flack, David L. A.; Skinner, Chris; Hawkness-Smith, Lee; O’Donnell, Greg; Thompson, Robert J.; Waller, John C.; Chen, Albert S.; Moloney, Jessica; Largeron, Chloé; Xia, Xilin; Blenkinsop, Stephen; Champion, Adrian J.; Perks, Matthew; Quinn, Niall; Speight, Linda

doi:10.3390/w11040725

Cited by 31 publications

(44 citation statements)

References 59 publications

(67 reference statements)

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“…Before developing a surface water forecasting system, it is important to establish realistic aims for the system that balance the end‐user requirements and the scientific and practicality feasibility (Flack, Skinner, et al, 2019). There are two recent developments that have been fundamental to making these discussions possible.…”

Section: The Challenge Of Surface Water Flood Forecastingmentioning

confidence: 99%

“…Moore et al (2015) concluded that due to run time constraints, there were limited linked hydrological and hydrodynamic models that had the potential to run in real time and those that could potentially be used in this way had not been developed with continuous updating of the system states in mind (Moore, Bell, Cole, & Jones, 2007; Speight et al, 2018). As computational power has increased over the past few years and cloud computing and graphical processing units (GPUs) have developed rapidly, these new technologies have emerged as a realistic and affordable way to run computationally demanding surface water flooding models in less time (see Flack, Skinner, et al, 2019; Glenis, Kutija, & Kilsby, 2018; Xia, Liang, Ming, & Hou, 2017). The benefit of real‐time hydrodynamic simulations is the ability to directly model the forecast spatial variability of rainfall on inundation and impacts at a city scale.…”

Section: Approaches To Surface Water Flood Forecastingmentioning

confidence: 99%

“…Surface water flooding in this context is usually the result of convective weather systems that cause intense rainfall over short periods of time. In the United Kingdom, this type of rainfall typically occurs in the summer months but can occur at any time of the year (Blenkinsop, Lewis, Chan, & Fowler, 2016; Flack et al, 2019; Hand, Fox, & Collier, 2004). Flash floods resulting from rapidly developing convection systems or alignment of storm cells can be particularly dangerous (Pilling, Dodds, Cranston, Price, & How, 2016) and affect large numbers of people if they occur over urban areas.…”

Section: Introductionmentioning

confidence: 99%

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Operational and emerging capabilities for surface water flood forecasting

et al. 2021

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Surface water (or pluvial) flooding is caused by intense rainfall before it enters rivers or drainage systems. As the climate changes and urban populations grow, the number of people around the world at risk of surface water flooding is increasing. Although it may not be possible to prevent such flooding, reliable and timely flood forecasts can help improve preparedness and recovery. Unlike riverine and coastal flooding where forecasting methods are well established, surface water flood forecasting presents a unique challenge due to the high uncertainties around predicting the location, timing, and impact of what are typically localized events. Over the past 5 years, there has been rapid development of convection‐permitting numerical weather prediction models, ensemble forecasting, and computational ability. It is now theoretically feasible to develop operational surface water forecasting systems. This paper identifies three approaches to surface water forecasting utilizing state‐of‐the‐art meteorological forecasts: empirical‐based scenarios, hydrological forecasts linked to presimulated impact scenarios, and real‐time hydrodynamic simulation. Reviewing operational examples of each approach provides an opportunity to learn from international best practice to develop targeted, impact‐based, surface water forecasts to support informed decision‐making. Although the emergence of new meteorological and hydrological forecasting capabilities is promising, there remains a scientific limit to the predictability of convective rainfall. To overcome this challenge, we suggest that a rethink of the established role of flood forecasting is needed, alongside the development of interdisciplinary solutions for communicating uncertainty and making the best use of all available data to increase preparedness. This article is categorized under: Engineering Water > Engineering Water

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Section: The Challenge Of Surface Water Flood Forecastingmentioning

confidence: 99%

Section: Approaches To Surface Water Flood Forecastingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Operational and emerging capabilities for surface water flood forecasting

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“…The FFIR programme described itself as "A five year NERC funded programme aiming to reduce the risk of damage and loss of life caused by surface water and flash floods" (Flooding from Intense Rainfall, 2019). The programme, based in and focussed on the UK, brought to-gether experts from several universities, environmental consultancies, the Met Office, the Environment Agency, and the British Geological Survey to better understand the role intense and localised rainfall events had on both rural and urban flooding, with a strong focus on the end-to-end forecasting on events (Dance et al, 2019;Flack et al, 2019). Thunderstorms, driven by strong convection in summer months, form and dissipate rapidly and can be highly localised covering just a 1-3 km wide area.…”

Section: The Research Contextmentioning

confidence: 99%

Flash Flood!: a SeriousGeoGames activity combining science festivals, video games, and virtual reality with research data for communicating flood risk and geomorphology

Skinner

2020

Geosci. Commun.

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Abstract. The risk of flooding around the world is large and increasing, yet in many areas there is still a difficulty in engaging the public with their own flood risk. Geomorphology is a science that is linked to flooding and can exacerbate risks, but awareness of the science with the public is low and declining within academia. To increase awareness, it is important to engage the public directly with the science and those who are working to reduce flood risks – this starts by inspiring people to seek out further information through positive experiences of the science and researchers. Here, a new design model is presented to engage the public with specific research projects by using useful components offered by the popular mediums of games, virtual reality (VR), and science festivals to allow the public to get “hands on” with research data and models: SeriousGeoGames. A SeriousGeoGames activity, Flash Flood!, was developed around real geomorphology survey data to help engage the public with a flood-risk-related research project by placing them in a river valley as it undergoes a geomorphically active flooding from an intense rainfall event. Flash Flood! was exhibited at two science-focussed events, and formal evaluation was captured using a short questionnaire, finding that the majority of audience had a positive interaction (95.1 %) and wanted to know more about flooding (68.0 %) and geomorphology (60.1 %). It is hoped these interactions will increase the likelihood that future engagements with relevant agencies will be more fruitful, especially when it matters most.

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“…Therefore, there is an urge for measures to protect people and property from natural disasters [4][5][6]. Suppose the area and size of the damage can be predicted before the actual disaster then it can help to prevent and make adequate preparation for disaster management [7][8][9]. Therefore, studies were conducted to predict the damage.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Heavy Rain Damage Using Deep Learning

Lee

Choi

Shin

et al. 2020

Water

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Heavy rain damage prediction models were developed with a deep learning technique for predicting the damage to a region before heavy rain damage occurs. As a dependent variable, a damage scale comprising three categories (minor, significant, severe) was used, and meteorological data 7 days before the damage were used as independent variables. A deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN), which are representative deep learning techniques, were employed for the model development. Each model was trained and tested 30 times to evaluate the predictive performance. As a result of evaluating the predicted performance, the DNN-based model and the CNN-based model showed good performance, and the RNN-based model was analyzed to have relatively low performance. For the DNN-based model, the convergence epoch of the training showed a relatively wide distribution, which may lead to difficulties in selecting an epoch suitable for practical use. Therefore, the CNN-based model would be acceptable for the heavy rain damage prediction in terms of the accuracy and robustness. These results demonstrated the applicability of deep learning in the development of the damage prediction model. The proposed prediction model can be used for disaster management as the basic data for decision making.

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Recommendations for Improving Integration in National End-to-End Flood Forecasting Systems: An Overview of the FFIR (Flooding From Intense Rainfall) Programme

Cited by 31 publications

References 59 publications

Operational and emerging capabilities for surface water flood forecasting

Operational and emerging capabilities for surface water flood forecasting

Flash Flood!: a SeriousGeoGames activity combining science festivals, video games, and virtual reality with research data for communicating flood risk and geomorphology

Prediction of Heavy Rain Damage Using Deep Learning

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