Flood Forecasting — A National Overview for Great Britain

Pilling, Charlie; Dodds, V.; Cranston, Michael; Price, David; Harrison, Trevor D.; How, Andrew

doi:10.1016/b978-0-12-801884-2.00009-8

Cited by 21 publications

(28 citation statements)

References 21 publications

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“…The discussion of the UK flood forecasting practice prior to the start of FFIR is because many aspects of the FFIR programme have become part of the current operational system, or aspects of FFIR were designed to run alongside with the proposed developments at the time. Recent updates to the operational flood forecasting practice for the FFC and SFFS can be found in [6,8], respectively.…”

Section: Flood Forecasting In the Uk Prior To Ffirmentioning

confidence: 99%

“…The FFIR proposed end-to-end forecasting system ( Figure 1b) is superficially similar to that of the current operational system (Figure 1a). This similarity arises in part due to the first stages of the forecasting chain being a tried and tested method [6] and thus a strong starting point for such a system. The additions, apart from the added capability of being able to produce warnings for flooding from intense rainfall, of the FFIR system to the operational system described (Section 3.1) are set out below…”

Section: Improvements From the Ffir Proposed Systemmentioning

confidence: 99%

“…End-to-end forecasting has been around for some time and many meteorological or hydrological centres have some form of operational end-to-end flood forecast (e.g., [5,6]). The more sophisticated end-to-end forecasts go further down the chain, i.e., they include hydraulic components either through a look-up library of static flood maps (e.g., [7,8]) or by running hydraulic models (e.g., [4,9]).…”

Section: Introductionmentioning

confidence: 99%

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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

Flack

Skinner

Hawkness-Smith

et al. 2019

Water

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Recent surface-water and flash floods have caused millions of pounds worth of damage in the UK. These events form rapidly and are difficult to predict due to their short-lived and localised nature. The interdisciplinary Flooding From Intense Rainfall (FFIR) programme investigated the feasibility of enhancing the integration of an end-to-end forecasting system for flash and surface-water floods to help increase the lead time for warnings for these events. Here we propose developments to the integration of an operational end-to-end forecasting system based on the findings of the FFIR programme. The suggested developments include methods to improve radar-derived rainfall rates and understanding of the uncertainty in the position of intense rainfall in weather forecasts; the addition of hydraulic modelling components; and novel education techniques to help lead to effective dissemination of flood warnings. We make recommendations for future advances such as research into the propagation of uncertainty throughout the forecast chain. We further propose the creation of closer bonds to the end users to allow for an improved, integrated, end-to-end forecasting system that is easily accessible for users and end users alike, and will ultimately help mitigate the impacts of flooding from intense rainfall by informed and timely action.

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Section: Flood Forecasting In the Uk Prior To Ffirmentioning

confidence: 99%

Section: Improvements From the Ffir Proposed Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Flack

Skinner

Hawkness-Smith

et al. 2019

Water

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“…It is useful to consider concurrent river and coastal flooding. As Pilling et al [17] describe, a typical scenario could be a protracted stormy period bringing successive intense low pressure systems across Britain. This could result in a storm surge coinciding with high tides and large waves being driven onshore concurrent with saturated river catchments and high flows and further heavy rainfall.…”

Section: Integrated Modelling and Forecastingmentioning

confidence: 99%

New developments at the Flood Forecasting Centre: operations and flood risk guidance

Pilling¹

2016

WIT Transactions on the Built Environment

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The Flood Forecasting Centre (FFC) is a partnership between the UK Met Office and the Environment Agency, established in 2009, to provide an overview of flood risk across England and Wales. Based at the Met Office in Exeter, the FFC provides flood guidance services covering England and Wales, primarily for the emergency response community. The FFC provides forecasts for all natural sources of flooding, these being fluvial, surface water, coastal and groundwater. This involves an assessment of possible hydrometeorological events in the next five days as well as their likely impacts. In addition, during times of heightened flood risk, close communication between the FFC and the Environment Agency allows mobilization and deployment of staff and flood defences at longer lead times. This mobilization is becoming increasingly important and high profile within Government. This paper considers new developments in forecasting fluvial, surface water and coastal flood risk. This includes: (1) implementing Met Office deterministic and ensemble high-resolution numerical weather prediction data feeds to drive the distributed hydrological model, Grid-to-Grid (G2G); (2) a prototype for surface water hazard impact model; and (3) improvements in coastal flood forecasting. However, challenges remain, for example, improving forecasting at short lead times through improved nowcasting, also longer lead time forecasting beyond day 5, and integrated system modelling to, for example, capture the complex interactions between fluvial and coastal flooding.

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“…It has often been employed for energy management, unit commitment and load dispatch [6][7][8][9][10][11][12][13][14]. The high accuracy of load forecasting guarantees the safe and stable operation of power systems [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

Dong

Wang

et al. 2017

Energies

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Abstract:The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.

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Flood Forecasting — A National Overview for Great Britain

Cited by 21 publications

References 21 publications

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

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

New developments at the Flood Forecasting Centre: operations and flood risk guidance

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

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