The science of flooding
Source: environment.data.gov.uk (2018-06-19)

Flood Risk for Extreme Events (FREE): Radiosonde, Wind Profiles Data and Model Output from the Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting project. The Exploitation of new data sources, data assimilation and ensemble techniques for storm and flood forecasting Project is a NERC Flood Risk for Extreme Events (FREE) Research Programme project (Round 1 - NE/E002137/1 - Duration January 2007 - April 2010) led by Prof AJ Illingworth, University of Reading. This project investigates possible methods of producing ensemble weather forecasts at high-resolution. These ensembles will be used with raingauge and river flow to improve methods of flood forecasting. The dataset includes radiosonde and wind profiles in England and Wales derived using Doppler radar returns from insects. The radial velocity measurements from insects were converted into VAD profiles by fitting a sinusoid to radial velocities at constant range. All measured profiles have been interpolated to the instrument location. 
Model output files from experiments assimilating radial winds from insects are also available.

Floods in the UK are often caused by extreme rainfall events. At present, weather forecasts can give an indication of a threat of severe storms which might cause flash floods, but are unable to say precisely when and where the downpours will occur, due to the complex range of processes and space-time scales involved. The first stage is to predict the air motions leading to convergence and ascent at a certain location where the precipitation will be initiated, then the development of the precipitation needs to be forecast, and hydrological models used to produce accurate, quantitative, probabilistic flood predictions. Data assimilation is a sophisticated mathematical technique that combines observations with model predictions to give an analysis of the current state of the atmosphere. This analysis may be used to initialise a weather forecast. Although precipitation is well observed by weather radar, attempts to assimilate radar data have had little success; by the time the rain develops the forecast model state is too far from the truth and the air motions are inconsistent with the position of the first radar precipitation echo.

We propose to overcome this problem by assimilating new types of data from weather radars. These provide information on the evolving humidity fields and air motions in the lower atmosphere so that the model can accurately track the developing storm before precipitation appears. The model used will be a new Met Office model that can be run with a resolution (i.e., grid-spacing) of order 1-4km. This enables storm-cloud motions to be explicitly calculated, rather than treated as a sub-grid-scale effect. Furthermore, current operational forecast models are only updated with observations every few hours; in the new approach the model will be updated much more frequently. This should yield weather forecasts with improved locations (in space-time) for rainfall events.

Initialisation errors are not the only cause of inaccuracies in storm-scale weather forecasts. Models are often run only for a small region of the world, and the data on the boundaries of this area provided from a larger-scale model. These data are known as lateral boundary conditions. Errors in these lateral boundary conditions and modelling errors also contribute to the errors in the forecast. Even if these errors were reduced, the nonlinear nature of the storm dynamics ensures that there is a limit, beyond which the value of deterministic forecasts becomes questionable. After that point it becomes important to determine the uncertainties in the forecast precipitation, so an ensemble approach is required. (An ensemble is a collection of perturbed forecasts that may be considered as a statistical sample of the forecast probability distribution.)

The appropriate construction of a storm-scale ensemble is an open question. We propose a structured approach where perturbations will be designed on the basis of physical insight into convective forcing mechanisms. The resulting probabilistic rainfall forecasts can be interfaced to hydrological models used for flood forecasting. For the first time, this project will allow different scales of application of these methods to be supported: ranging from localised flash flooding of small catchments, through to indicative first-alert forecasting with UK-coverage and forecasting of river discharges to the sea. The project will also assess the impacts of improvements in numerical weather prediction on flood forecast performance.

In this project we anticipate fruitful interactions between the different disciplines of observations and measurement, meteorology and hydrology. Radar assimilation software development and ensemble forecasts will take place using Met Office models, so improvements can be implemented operationally very easily. The use of operational radars makes this project well placed to take advantage of data from any extreme events occurring during the period of the study.

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FLOOD RISK FROM EXTREME EVENTS – FREE 
 
The science of flooding 
 
SCIENCE PLAN 
 
1. Introduction 
 
There is increasing evidence that more frequent flooding resulting from increases in 
the frequency and intensity of storms are likely to be one consequence of 
anthropogenic climate change in many parts of the world including the United 
Kingdom. Flooding in the UK is currently a major and costly environmental hazard 
with annual damage from floods of around £1 billion at present levels of protection. 
In the autumn of 2000 up to 11,000 properties were flooded, and the financial cost 
was in excess of £1.3 billion. Recent figures from Defra show that the UK’s assets at 
risk from flooding by the sea are valued at £132.2 billion, from fluvial flooding 
valued at £81.7 billion and from coastal erosion at £7.8 billion. 
 
As a consequence of economic growth (with, for example, increased building on flood 
plains) and climate change (increased frequency and severity of severe storms and sea 
level rise), these costs are set to rise further. It follows that an improved ability to 
forecast, quantify and manage flood risks is essential to protecting the public, property 
and infrastructure, and to maintaining a sustainable economy. It also impacts on our 
ability to investigate the impacts of climate variability and change. This will be 
achieved only from increasing our scientific understanding of the frequency, intensity 
and structural behaviour of such extreme events across several aspects of the earth 
system sciences. 
 
 In what follows we describe a five year integrated plan of interdisciplinary scientific 
research funded by the Natural Environment Research Council at the level of £6 
million, including management costs, aimed at addressing these issues over the period 
2005-2010. FREE will bring together researchers in the meteorological, hydrological, 
terrestrial and coastal oceanographic communities. The programme will focus on 
challenges in and around the UK, but will be built on world leading science that has 
strong linkages with research being undertaken elsewhere. 
 
      2   Background 
 
The risk of floods can be expressed as the probability of such an occurrence within 
the intended design life of a mitigation scheme, or as the typical probability in any 
year. The reciprocal of this annual exceedance probability defines a return period in 
years of such an occurrence. Extreme floods, which might occur beyond the length of 
formal records, can be investigated using both statistical methodologies and 
modelling techniques. 
 
A key issue in searching for plausible expectations of future probabilities of extreme 
floods is the evolution of precipitation extremes (Christensen et al, 2005), catchment 
hydrological response, storm surges (Lowe et al, 2001) and sea level rise (Church et 
al, 2001). Even though many climate models predict a decrease in average 
precipitation in European summers, the predictions do not prevent extreme 

 
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precipitation values from increasing over most of Europe (Christensen and 
Christensen, 2003, 2004). 
 
Regional models such as the Met Office’s Hadley Centre regional model HaDRM2 
predict significant reductions in return periods by an order of magnitude for annual 
maximum rainfall under climate change at several flood-sensitive locations. A rise in 
mean sea-level and increases in storm-surges due to changes in winds and pressure 
also combine to reduce return periods of extreme high water levels at coastal locations 
around the UK (Senior et al, 2002). 
 
Predictions at shorter time scales begin with a characterisation of the current state of 
the system referred to as initialisation. The future states of a system are then predicted 
according to our current understanding of the dynamic behaviour of the system. Hunt 
and Burgers (2002) point out that atmospheric, land surface and ocean phenomena 
occur on all scales, but there are strong interactions between characteristic phenomena 
over differing scales. This affects how models are formulated, how they represent 
physical processes and how they are interpolated as the resolution of the computations 
improves. Consequently many elements of model structures offer varying degrees of 
predictability. Hence it is of great importance that the uncertainties and limits of 
prediction are established and communicated to users. 
 
As the resolution of atmospheric models improves explicit forecasts of convection are 
possible (Done et al, 2004). However, variabilities on ranges of scales that are 
impossible to resolve remain, and therefore important issues in the formulation of 
models remain especially in terms of sub-grid scale processes.  
 
Data assimilation is the application of a set of mathematical techniques that provide 
physically consistent estimates of spatially distributed environmental variables (see 
for example Andersson et al, 2005). The estimates are usually based upon merging 
scattered and / or indirect measurements of states and parameters with dynamic 
models that impose physical consistency constraints. However, both the background 
model predictions and the measurements are uncertain, and data assimilation 
procedures merge these estimates depending upon the degree of uncertainty. 
 
To achieve the best results from the merging of observations and models it is 
necessary to consider the discretisation and degree of spatial variability in hydrologic 
models. 
 
The parameters of hydrological models are frequently estimated by minimising some 
form of cost function that involves the error between the model-generated flow and 
the measured flow. This approach is similar to that employed in meteorological 
variational analysis schemes (for a review see Rihan et al, 2005). Young (2002) 
reviews the statistical framework for data assimilation with stochastic transfer 
function models based on the use of the Kalman filter. One limitation of this approach 
is its assumption that the stochastic processes are Gaussian. In fact this limitation may 
be removed by using Baysian numerical methods (see for example Krzysztofowicz, 
1999) or Monte Carlo Markov Chain algorithms (see for example Vrugt et al, 2003). 
 
Similar approaches are employed in storm surge models. The initial state of the Dutch 
Continental Shelf Model (Gerritsen et al, 1995) is optimized by a Kalman filtering 

 
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module that assimilates water levels along the UK coast in combination with oil 
platform data. Modelling of wind-wave propagation in coastal areas requires 
improvement in air-sea interaction processes which need observations of wind, waves 
and bathymetry. Of particular importance are improvements to the long-term 
evolution of estuary hydrodynamics and morphology, and the interaction of the 
shoreline and offshore sediments. 
 
Due to strong nonlinearities in the behaviour of systems, the diverse spatial scales 
involved, new observations of the water cycle and a lack of knowledge of the 
uncertainties in models and observations, much research is needed in the field of data 
assimilation. 
 
The use of ensembles in both meteorological (see for example Du et al, 1997, Hamill 
and Colucci, 1997) and hydrologic (see for example De Roo et al, 2003) forecasting is 
one approach to dealing with uncertainty. This approach generally assumes that the 
uncertainty in flow predictions is primarily attributed to uncertainty in the input i.e. 
rainfall to the hydrologic model. However, uncertainty in model parameter estimates 
(calibration and spatial generalisation e.g. Lamb and Kay, 2004) and model structure 
also require attention. 
 
Areas at risk of inundation need to be modelled. This requires calibration and 
validation of model storage capacity and flow resistance due to various sub-grid 
natural and man-made elements in the terrain. In both fluvial and coastal areas the 
uncertainty involved in predicting inundation area involves flow routing models 
which require accurate topographical data, information on roads, levees and other 
infrastructure and, where necessary, reliable bathymetry which may indicate changes 
with time during storms. 
 
A US Workshop on Predictability and Limits-to-Prediction in Hydrologic Systems 
(National Research Council, 2003) stressed the conceptual framework for hydro-
meteorological predictability as that of a coupled atmosphere-land-ocean system. 
Regular variations of water through the system have superimposed upon them 
irregular fluctuations or changes caused by the chaotic dynamics of the atmosphere-
land-ocean system. 
 
To address the wide range of coupled models representing water related processes it is 
necessary to consider an appropriate framework providing a generic interface between 
models. Such a framework was, for example, developed as the basis of the River Flow 
Forecasting System (RFFS) (Moore et al, 1990).   More recently, for example, 
HarmonIT - an EU 5th Framework project, sought to provide a framework based upon 
an open modelling interface, the OpenMI (Moore and Tindall, 2004). Work is on-
going to ensure that this approach is developed into a self sustaining standard.  
 
 
3   Mission statement 
 
 
 
To reduce the flood risk in the UK from extreme events 
 
The overall aim of this research programme is to improve the prediction of river 
catchment and coastal floods occurring from extreme events, accompanied by 

 
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quantitative measures of uncertainty which take into account the non-stationarity of 
environmental conditions.   
 
 
4    Broad objectives 
 
In carrying out research within the Earth system of specific relevance to flooding it 
will be necessary to, 
• improve the estimation and prediction of flood risk from extreme 
events through considering the processes involved as an integrated 
system; 
• seek ways to reduce uncertainty and improve the quantification of 
flood risk; and 
• identify and articulate critical guidance on how flood risk is changing. 
Achieving this will involve the coupling of a range of models, operating over 
differing time and spatial scales, of the atmosphere generating rainfall forecasts, 
through hydrological (including ‘continuous simulation’), hydraulic and storm surge 
models to impact models and decision making tools. Socio-economic aspects, 
although important, will be regarded as on the fringe of this programme with work 
concentrating on the environmental science aspects of this directed topic.  
 
5. Scope of the programme 
 
In 2003/4 the Office of Science and Technology promoted the Foresight project on 
Future Flooding. The results from this project show that, under certain scenarios, 
flood risk (measured as flood probability times consequences) could rise twenty-fold 
over the next 100 years or so. Even with a well funded response to this risk it was felt 
that future flooding could still be approximately twice the severity that we now 
experience. In response to this conclusion Defra and the EA have reviewed their joint 
research programme in flood and coastal risk management (Defra / EA, 2005) within 
the context of the first government response to the autumn 2004 Making Space for 
Water consultation exercise (Defra, 2005). 
 
A number of future directions for research were identified. Of particular relevance 
here, Research Councils were urged to investigate 
• the impact of science and technology on the future flood and coastal 
erosion risk management; 
• climate change in particular sea level, surges and waves; and 
• hydrology – updating methods in current water industry practice plus 
next-generation methods, flood assessment for small catchments and 
urban, rural and mixed land use sub-catchment runoff. 
The FREE programme will seek to address these research needs within the limitations 
imposed by the project budget and taking cognsance of the on-going work in the 
EPSRC-led Flood Risk Management Research Consortium (FRMRC). 
 
The programme will concentrate on the analysis and understanding of flood risk from 
extreme events, defined as occurring with a recurrence frequency of 1 in 50 years or 
rarer, thereby including floods which result in loss of life, significant economic 
impact and/or widespread impact on the public, property and the national 
infrastructure. Proposals for studies linked to extremely rare events are encouraged. 
Events resulting from a series of linked occurrences whose joint probabilities exceed 

 
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1 in 50 years also often result in serious damage and risk of loss of life. These are 
equally important and are also considered here. This programme will therefore relate 
to the Defra “Source-Pathway-Receptor” framework. 
 
It is recognised that there is not one specific approach to environmental flood risk 
modelling. However, the programme will aim to use and extend existing modelling 
frameworks within which different systems may communicate with each other. The 
emphasis is not on developing new models from scratch, but on developing novel 
functionality to deliver pertinent results. At the assessment stage projects that do not 
demonstrate this integrated approach are likely to be either rejected, put together, or 
considered in a complementary way to each other, to achieve an integrated “clouds-
to-catchment-to-coast” approach to flood forecasting. 
 
It is important that FREE recognises and complements existing research and 
development programmes such as the Flood Risk Management Research Consortium 
(FRMRC), the Lowland Catchment Research (LOCAR) programme and the 
Catchment Hydrology And Sustainable Management (CHASM) programme. The 
FRMRC is concerned with the development of new flood modelling tools, real-time 
updating including aspects of new observational technology and other more 
engineering aspects of flood defence and warning. The objectives of the FRMRC 
include undertaking research to enhance flood risk management practice worldwide, 
demonstration of proof-of-concept applications and the delivery of tools and 
techniques to users. 
 
On the other hand LOCAR has addressed issues concerning the understanding and 
modelling of permeable catchment systems and their associated aquatic habitats at 
different spatial and temporal scales and for different land uses. CHASM seeks to 
understand catchment scale issues and the anthropogenic and future climate change 
influences within catchments leading to better sustainable management. 
 
FREE differs from these programmes in seeking to bring all the communities 
involved in fluvial, estuarine and coastal flood forecasting and warning of extreme 
events together to improve the scientific linkages between disciplines. These linkages 
are necessary to enable progress towards achieving a fully integrated system. The 
programme will focus on the scientific basis of risk and uncertainty throughout the 
full range of geophysical processes involved in all types of flooding occurrence in 
catchments, rivers, urban drainage systems, estuaries and coastal environments. 
 
6 Specific objectives 
 
 Objective 1: To develop and extend the science underpinning integrated modelling 
frameworks enabling models to work sensibly and more effectively together 
 
Wise investment in fluvial and coastal defences and the property thereby protected 
depends upon accurate quantification of future changes in the frequency and intensity 
of storms, river catchment responses (groundwater and river flows), coastal storm 
surges, estuarine hydrodynamics and morphology and tidal extremes. Climate, 
regional and mesoscale numerical weather forecasting models contain large 
uncertainties in their predictions of precipitation, wind and waves. It is necessary to 
couple these models to catchment, estuarine and coastal surge models to achieve both 

 
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reliable engineering design criteria for protection works and reliable flood warning 
systems. Similarly simplified models of the evolution of the shape or morphology of a 
beach are necessary to understand how best to develop more complex operational 
models. Research will be supported enabling the scientific basis of models to be used 
in ways which will enable more effective linkages between different model types 
leading to improvements in the development of modelling frameworks. This will be a 
major part of the programme. 
 
The scientific basis of modelling frameworks requires a detailed analysis of 
appropriate procedures for the transfer of information across space and time scales 
and the different component models. They will aim to describe extreme flooding 
events such as those associated with flash floods, floods arising from long duration 
rainfall and coastal surges and flooding in locations such as the Thames estuary. Also 
included in the frameworks will be the ability to investigate critical areas at risk of 
flooding under conditions of human-induced climate change. Integration between the 
models needs to be at the heart of this system and methods developed for coupling 
components will be explored for their utility. The expected prediction time-scale for 
such systems range from a few days to many tens of years and beyond. Thus real-time 
and medium term forecasting are included, as is the quantification of flood 
frequencies. 
 
Objective 2: To identify and spread scientific improvements in model initialisation, 
data assimilation and the processing of forecast ensemble outputs across modelling 
communities 
 
Precipitation arises from processes occurring in larger-scale weather systems as well 
as from localised convection in conditions of small-scale forcing. For the 
predominantly forced systems, improvements in forecast skill will arise from 
scientific advances connected with better model resolution of smaller-scale features, 
improved observations for initialising forecast models, better utilisation of those 
observations within data assimilation techniques and improved design of ensemble 
prediction systems.  
 
Extreme events in estuaries do not necessarily follow the largest or longest-lasting 
storms, but often comprise a combination of factors at vulnerable times and places 
due to inputs from, and interactions between, both river catchments and ocean. The 
dynamic components of estuarine flooding include waves, tides, surges and river 
flows, and require understanding of the long-term evolution of estuary hydrodynamics 
and morphology, and the interaction of the same between on-shore and off-shore 
environments. Operational forecasting is limited for waves and surges offshore by the 
accuracy and resolution of wind forecasts, and hence to time-scales of hours to a few 
days. It is further limited near-shore by lack of knowledge of evolving bathymetry. 
Forecasting of river and upper estuarine water levels is limited by model structure and 
input data particularly rainfall estimates. 
 
Research is required on data assimilation techniques that allow forecasts to be updated 
and made more accurate using the latest observations of the atmospheric state, 
particularly from remotely sensed data (radar, satellites), river flow, geomorphology 
of river channels and estuaries and wind and waves. The investigation of the more 
general deployment of techniques developed across disciplines that takes greater 

 
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account of the error characteristics of the data will be undertaken. Optimal methods 
for data assimilation from ground-based catchment and coastal observation networks 
(tide gauges, coastal radar etc.) should also be investigated. Work going on in the 
FRMRC will be noted to ensure that research undertaken in FREE complements that 
comprising this programme. 
 
Objective 3: To understand and quantify the propagation of uncertainty within a 
changing environment and within rapidly changing catchments 
 
The chain of modelling and data assimilation involved in forecast construction for a 
river network requires research on the estimation and propagation of uncertainty and 
the related task of decision-making to trigger a flood warning. Recent advances made 
in model calibration and performance assessment, need to be utilised and linked to 
forecast uncertainty estimation using ensemble techniques. Modelling methodologies 
for forecasting very extreme events cannot rely on data being available for model 
calibration and demand innovative approaches. 
 
Of particular interest will be new approaches to deal with rapid response catchments, 
downscaling issues, identifying areas of vulnerability and the investigation of the 
impact on flood forecasts of meteorological models having associated with them 
improved representations of the hydrological cycle, both surface and subsurface 
flows. Also of importance is the understanding of the dynamics of the evolution of the 
shoreline and its interaction with river flows and off-shore currents. 
 
At longer forecast lead-times, and in the context of long-term frequency estimation, it 
becomes increasingly important to consider how the ocean and atmosphere interact. A 
basic question to be addressed is the magnitude of the relative contributions to 
flooding from natural variability and human forcing of climate change. For estimating 
the chance, for example, of a wetter than normal winter, advances in seasonal 
forecasting are needed. For this it is essential to develop coupled ocean-atmosphere-
land surface models. 
 
Confidence in all such results is reduced by poor spatial resolution in global coupled 
models, by uncertainties in physical processes and by a lack of estimates of 
uncertainty in the models. Research is needed to tackle these scientific problems. An 
example is the uncertainty associated with parameterisations and the resolving of 
convection within models. An improved understanding of the factors that determine 
changes in the frequency and intensity of storms in a warmer world will be a major 
theme. This will require improvements in knowledge of storm tracks, weather regimes 
and storm dynamics and physics, undertaken by using regional climate models. 
Investigation is also needed into the role of persistent precipitation events and the 
impact on storm tracks and mesoscale development 
 
Meteorological and hydrological models are closely linked, and in operational or 
strategic planning environments this needs to be recognised and implemented. 
Through evaporation and transpiration the hydrology provide important feedbacks to 
meteorological systems while the atmosphere drives both these processes. The 
underlying surface influences the amount of rainfall meteorological systems generate. 
 

 
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Understanding of the role of land use and surface heterogeneity (including urban, 
rural and mixed land use) in catchment hydrology under climate variability is likely to 
require continuous simulation techniques to represent the full range of hydrological 
processes which determine flood generation. For integrated catchment-scale decision 
support, models must be capable of application to data-rich and data-sparse situations, 
that is, the science of the ‘ungauged site’ is vital to consideration of full spatial risk. 
 
Research is also needed into advanced statistical techniques, which can take rare 
events (meteorological, hydrological and coastal) fully into consideration in 
computation at high return periods. Joint-probability analyses of rainfall, water level 
and surge and wave occurrence with other parameters also need to be fully 
investigated.  
 
Objective 4: To develop techniques for uncertainty reduction at the output stage 
enabling the communication of risk assessment to the user community and its use by 
that community. 
 
A better understanding of the sensitivity of fluvial, surge and tidal water levels to 
uncertainties in model inputs (e.g. from atmospheric models; boundaries between 
models; initial hydrological and geomorphologic data) is needed so that both 
atmospheric and hydrological ensemble techniques can be developed to quantify and 
understand how best to present, uncertainty in outputs, which can be used as inputs to 
estuary and coastal surge models. This includes consideration of exceptionally rare 
flood events of high magnitude. 
 
The estimation of true spatial risk, as opposed to the spatial mapping of arrays of 
point risks, will be explored (that is, covering the co-location characteristics of 
catchment flooding). Consideration is needed of methodologies for catchment areas 
which are data-poor as well as data-rich. This research will also be extended to the 
joint likelihood of concurrent coastal and catchment flooding, using both modelling 
and statistical methodologies 
 
An alternative approach considers the modification of meteorological indices within 
Global Climate Models (in particular the North Atlantic Oscillation), which are 
known to be closely correlated with the likelihood of surges. Studies of how these 
indices might change in the future, can be used to infer changes in surge climatology. 
These approaches need to be consolidated, and thoroughly tested, to develop an 
overall view of surge modification around the UK. 
 
Objective 5: To enable mitigation of, and adaptation to, floods by the provision of 
advice on flood risk management system development. 
 
Land use and land management have been identified as potentially important 
considerations in flood risk. Urban development is a dramatic example of 
environmental change, but agricultural land management practices and natural 
estuarine morphology may also alter flood risk at some scales and some recurrence 
intervals. The development of new decision support tools is not the aim of FREE, but 
scientific studies of the effects of change at various scales, together with inundation 
mapping and spatial analysis of risk assessment are appropriate. It is likely that such 

 
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output will help improve the development of appropriate flood risk management 
systems. 
 
It is important to assess the impact on sediment transport and coastal geomorphologic 
change caused by cumulative extreme events (the increased frequency of major 
storms may prevent the system “recovering” between events). Improvements in 
understanding of wave “set up” are an important aspect of these processes. Such 
changes could have a substantial impact on bathymetry, susceptibility to storm surge 
and management options available to society. It is important that the evolving 
dynamics of open coasts (particularly rates of erosion) associated with rising sea level 
are factored in with regard to these issues. 
 
This meteorological, hydrological, hydraulic and coastal research should lead to the 
availability of tools for decision-making which will impact sustainable flood risk 
management and hence policy development relating to the requirements of the EU 
Water Framework Directive. 
 
Objective 6: To seek the use, or re-analysis and use, of existing datasets whilst 
organising and creating a methodology to enable specific data to be collected 
following any extreme event that might occur during the period of FREE. 
 
Whilst it is not the intention within the FREE programme to collect new data sets, it is 
recognised that the development and modelling of new methodologies using existing 
data will lead to new science outcomes which may need to be archived. It is 
anticipated that such output will be archived within the Research Council Data 
Centres using the e-science Data Grid. 
 
Ways of making data collection more efficient and effective are included e.g. 
retention of mesoscale model data sets, aircraft and satellite data (including land use, 
terrain modelling and actual flood extents) and data on coastline changes. Existing 
data offer new opportunities for analysis within innovative modelling frameworks. In 
addition it is hoped that procedures to collect specific data following any extreme 
events which might occur during FREE will be developed, and these data lodged at 
the Research Council Data Centres using the e-science Data Grid. 
 
Objective 7: To engage with the national and international community and stimulate 
knowledge transfer and user engagement 
 
FREE represents a major national initiative for research into the science underpinning 
flood forecasting and warning. The programme will benefit from engagement with the 
national and international community through participation of those working in the 
programme with groups overseas. This will be encouraged, and where possible 
supported. Of particular importance are the World Meteorological Organisation’s 
APFM, GEWEX and World Weather Research (WWRP) programmes, the EU COST-
713 Action, the upcoming UNESCO-WMO flood initiative, the upcoming European 
Union Framework 7 and the European Science Foundation programmes. Efforts will 
be made to participate in appropriate workshops and be involved in consortium / 
proposal writing for these programmes. 
 

 
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To achieve knowledge transfer from research outputs to users it will be necessary to 
involve users and industry closely in the implementation of the research, and in the 
training of scientists. With this in mind Industrial CASE studentships are to be 
encouraged. FREE will allocate money to a knowledge transfer fund to encourage 
engagements of ideas with users, and with small businesses. Links will be developed 
to other schemes such as Faraday Partnerships. 
 
It is also envisaged that during the course of this programme there will be 
considerable interaction of scientists with user groupings (such as the Environment 
Agency and the Met Office) through Knowledge Transfer initiatives which 
participants are encouraged to identify. Further details will be provided in the 
Implementation Plan of the programme. 
 
Objective 8: To provide training opportunities through research studentships 
including CASE awards, and to encourage the involvement of students funded via 
other means in FREE activities 
 
The inter-disciplinary, multi-institute programme with relevance to Defra, 
Environment Agency, Met Office and consultancy activities offers excellent 
opportunities for younger researchers, and it is anticipated that a significant number of 
research studentships (for PhD training) will be supported. Those involving 
participation with user groups and industry are particularly sought. The participation 
of students in national FREE and international activities will also be strongly 
encouraged.  
 
Summary: 
 
The programme will concentrate on the environmental scientific research and training 
activities needed to meet these objectives of understanding river catchment and 
coastal flood risk. The interest will focus upon ways of using models within an 
integrated system to improve understanding of physical and dynamical processes. The 
emphasis will not be on developing new models, but will seek the development of 
novel techniques of using and extending existing model structures and output on all 
time scales. For example, appropriate areas of study are the information on the water 
cycle contained within mesoscale atmospheric models within ungauged catchments, 
the use of data on severe convective storm development that might have been 
gathered during field campaigns such as Hyrex and the recent Convective Storm 
Initiation Project (CSIP), the identification of changes in morphology in estuarine 
areas through the use of existing wind and wave data, or the development of new 
flood frequency estimation techniques. 
 
The interaction between atmospheric, hydrological, river, estuarine and coastal 
forecasting and flood frequency estimation models and techniques will involve the 
influence of topographic, morphologic and other controls. Representations of these 
interactions require improved description of water movement in the air, land and 
ocean phases. This offers an important opportunity for crosscutting research of value 
to the flood risk assessment need. There are new applications to be addressed 
encompassing 
the 
basic 
science 
underpinning 
first 
alert 
and 
global/continental/regional/local flood risk assessment systems. Applying and 
utilising advances in direct- and remotely-sensed monitoring of hydro-meteorological 

 
11
variables, soil moisture, river level and the coastal ocean will provide opportunities to 
improve fluvial and estuarine flood forecasting and warning. 
 
7 Deliverables 
 
Objective 1 
 
• Joint research to be undertaken by an identifiable integration of science 
communities. 
• Language integration across discipline boundaries leading to the 
training of a new generation of scientists having an integrated way of 
approaching environmental scientific research. 
• Synergy between different models specifically demonstrated by a 
prototype complete integrated system or through its component parts 
 
Objective 2 
 
• Specifying techniques for data assimilation through coupled models 
from meteorological, hydrological, hydraulic to coastal oceanographic 
models. 
• Transfer of data handling, quality control and assimilation techniques 
between disciplines. 
• Demonstrated complementarity with, but not duplication of, the 
FRMRC programme. 
 
Objective 3 
 
• Quantification of forecast and flood frequency estimation uncertainty 
across disciplines and within data and models. 
• Specifying and managing the propagation of uncertainty across 
discipline and model boundaries. 
• Estimating and utilising uncertainty in the physical processes 
represented in models. 
• Analysis of factors impacting changes in frequency and intensity of 
storms and extreme catchment and coastal flood events. 
• Preparing a scientific approach to the development of risk assessment 
tools. 
• New approaches for forecasting extreme events in rapid response 
catchments. 
• New approaches for forecasting extreme pluvial events in urban and 
built up areas. 
• Understanding and quantification of land use effects on flood risk at 
specific scales and recurrence intervals. 
• Analysis of the relative contributions to flooding from natural 
variability and human forcing of climate change. 
 
 
 
 

 
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Objective 4 
 
• Achieve a better understanding of the sensitivity of fluvial, surge and 
total water levels to uncertainties in model inputs. 
• Techniques for the inclusion of exceptionally rare river and coastal 
flood magnitudes. 
• Development of procedures for constraining and presenting risk and 
uncertainty to end users, and the impact of this uncertainty on the 
environment. 
• Consider the relationship of meteorological indices in global models to 
the likelihood of catchment flooding and coastal surges. 
 
Objective 5 
 
• Identifying the implications of uncertainty across disciplines for policy 
development e.g. within for example the framework of the EU Water 
Framework Directive. 
• Identifying consequent changes in engineering design risk in both 
fluvial and coastal zones arising from the impact of sediment transport 
and geomorphologic change caused by extreme events. 
• Development of a national network of world class scientists and 
professionals in this multi-disciplinary field. 
 
Objective 6 
 
• To develop for this programme a methodology for gathering new data 
and collating existing data for extreme events shortly after they occur. 
• Archiving at the BADC data sets derived from research studies and 
extreme events occurring during FREE. 
 
Objective 7 
 
• Quarterly updates describing activity and new opportunities presented 
by research outputs. 
• Identification of specific routes to user involvement and early adoption 
of science by operational agencies. 
• Demonstrations of outputs at appropriate events or workplaces. 
• Transfer of research outcomes to policy makers. 
 
Objective 8 
 
• Research degrees awarded from FREE supported research. 
• Involvement of a mix of cross-disciplinary students in FREE and 
related international activities. 
 
 
 
 
 

 
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8 Output measures 
 
The intention is to benchmark the position at the start of FREE, possibly through 
review material, a Workshop, or in terms of the bids received following the Call for 
Proposals. The achievements of the programme will be assessed through: 
 
 
Outputs  
• Future science priority measured by comparison with and input to the 
NERC science programme. 
• number of bids which are inter-disciplinary 
• number of CASE awards 
• scientific papers in high (top twenty) impact journals 
• number of publications with authorship from more than one discipline 
group  
• demonstration activities with stakeholder groups 
• inter-disciplinary workshops 
• reports on progress and achievements; final report 
• number of research degrees based on FREE research 
 
Outcomes 
• success in training scientists to think in an inter-disciplinary way (in 
making future research bids) 
• scientists working across disciplinary boundaries 
• the emergence of new research groups developing in a multi-
disciplinary way e.g. Environmental (ESSC) and the National Centre 
for Oceanographic Forecasting (NCOF) 
• the adoption of research outputs in activities of operational agencies 
and advisory services that improve the civil contingency response to 
flooding from extreme events 
• an increased understanding amongst user communities of the 
capabilities and limitations of predicting and assessing flood risk from 
extreme events and vice versa with research 
• influence on future policy making in the management of, and 
adaptation to, flood risk 
 
9 Project management 
 
The FREE Steering Committee (Chair: Professor Paul Hardaker (Met Office)) 
provides science direction and advice. The general scientific management of FREE 
will be undertaken by the Science Co-ordinator Professor Chris Collier (University of 
Salford) who will report to the Steering Committee and the NERC Science and 
Innovation Manager. Individual Principal Investigators (PIs) will manage their 
respective programmes liaising with the Science Co-ordinator to ensure that the 
programme objectives are met. The NERC Programme Administrator is Dr Sally 
Palmer.    
 
 
 
 

 
14
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