visualization approaches for communicating real-time flood forecasting level and inundation...

Visualizationapproaches for communicating real-time£oodforecasting level and inundation informationD. Leedal1, J. Neal2, K. Beven1,3, P. Young1,4 and P. Bates2

1 Lancaster Environment Centre, Lancaster University, Lancaster, UK

2 School of Geographical Sciences, University of Bristol, Bristol, UK

3 Geocentrum, Uppsala University, Uppsala, Sweden

4 Centre for Resource and Environmental Studies, Institute of Advanced Studies, Australian National University, Canberra, Australia

Correspondence:

David Leedal, Lancaster Environment Centre,

Lancaster University, Lancaster, LA1 4YQ, UK

Email: [email protected]

DOI:10.1111/j.1753-318X.2010.01063.x

Key words

Carlisle; data-based mechanistic; inundation;

LISFLOOD-FP; real-time flood forecasting;

uncertainty.

Abstract

The January 2005 flood event in the Eden catchment (UK) has focused consider-

able research effort towards strengthening and extending operational flood

forecasting in the region. The Eden catchment has become a key study site within

the remit of phase two of the Flood Risk Management Research Consortium. This

paper presents a synthesis of results incorporating model uncertainty analysis,

computationally efficient real-time data assimilation/forecasting algorithms, two-

dimensional (2D) inundation modelling, and data visualization for decision

support. The emphasis here is on methods of presenting information from a new

generation of probabilistic flood forecasting models. Using Environment Agency

rain and river-level gauge data, a data-based mechanistic model is identified and

incorporated into a modified Kalman Filter (KF) data assimilation algorithm

designed for real-time flood forecasting applications. The KF process generates

forecasts within a probabilistic framework. A simulation of the 6-h ahead forecast

for river levels at Sheepmount (Carlisle) covering the January 2005 flood event is

presented together with methods of visualizing the associated uncertainty. These

methods are then coupled to the 2D hydrodynamic LISFLOOD-FP model to

produce real-time flood inundation maps. The value of incorporating probabilistic

information is emphasized.

Introduction

This paper provides an overview of the approaches used to

generate two complementary forms of probabilistic informa-

tion relevant to flood risk management together with meth-

ods of communicating and visualizing this information. The

research activity is focused on the city of Carlisle within the

Eden catchment (UK) and the analysis time frame encom-

passes the serious flooding event of January 2005. Figure 1

shows the location of the study site with markers for the

Environment Agency gauge sites used later.

Operational flood forecasting is concerned with tracking

and forecasting the movement of a flood wave through a

catchment system with the aim of providing accurate and

useful information to actors involved in flood risk manage-

ment. An ideal forecast would provide information of flows,

levels, velocities, timings, and inundation with enough lead

time to allow for the deployment of flood defences and/or

the evacuation of at-risk areas. Unfortunately, experience

has shown that the science of flood forecasting is many years

from being able to provide all this information in a

consistently accurate fashion, and worse, for the case of long

lead times involving meteorological simulation, the sensi-

tivity of environmental systems to initial conditions suggests

that accurate deterministic forecasting will never be possi-

ble. To account for the limitations of modelling and

forecasting methods, the relatively new field of uncertainty

analysis has developed to provide environmental modellers

with the tools to allow (and encourage) model output to be

seen within a probabilistic context.

Model uncertainty does not provide too serious an

obstacle when models are used as a virtual laboratory for

scientific enquiry; for example, the Lorenz equations pro-

vide an invaluable insight into the properties of chaotic

systems, and similarly, the Lotka–Volterra equations are

invaluable for exploring predator–prey relationships.

Neither of these classic models calls for a consideration of

parameter uncertainty in order to be fit for their required

purpose. However, many modern environmental models

serve the purpose of providing forecast data to inform policy

J Flood Risk Management 3 (2010) 140–150c� 2010 The AuthorsJournal Compilation c� 2010 Blackwell Publishing Ltd

mailto:[email protected]

and support decision making. Within this context, it could

be argued that a model study is only fit for purpose when

not only the forecast but also the nature of the uncertainty

associated with the forecast are considered together. Beven

(2008) presents an in-depth reflection on this issue.

The field of model-based operational flood forecasting is a

clear example of the application of numerical forecasting as a

tool to support policy decisions. In the United Kingdom for

example, a broad range of models, from simple regression

based equations to complex hydrodynamic models, are a

common component of systems designed to guard against

human exposure to flood risk. For a review of the main

families of flood forecasting model used by the United King-

dom’s Environment Agency, see Moore and Bell (2001).

Historically, the main objective in the field of hydrological

modelling for flood forecasting has been to focus attention on

improving the model’s deterministic forecasting skill. Incre-

mental improvements in deterministic forecasting should be

possible through new modelling approaches and better model

calibration brought about by the assimilation of the ever-

growing body of hydrological data. It is trivially clear how

improvements in deterministic forecasting skill will provide

real benefits in reducing human exposure to flood risk (i.e.

consistently accurate flood warnings). However, at present, the

use of probabilistic information as a tool for decision support

in operational flood forecasting is very much the exception

rather than the rule. As a result, the value to the decision-

maker, of information displaying the degree of uncertainty in

a numerical model’s forecast, is as yet underresearched.

The authors believe that probabilistic information should

take on a central role in the presentation of model output to

decision makers. There are two main obstacles to achieving

this goal: (1) models with a high parametric dimensionality

require complex and computationally intensive methods for

uncertainty estimation that may not be appropriate for

applications such as real-time flood forecasting, where

calculation time and computational stability are key require-

ments and (2) probabilistic information needs to be ex-

pressed in a form that adds usefulness rather than confusion

to model output. This paper addresses the above issues by

demonstrating a real-time flood forecasting application for

Carlisle that tackles issue (1) by utilizing the parametrically

efficient data-based mechanistic modelling and data assim-

ilation approach to generate point estimates of river levels.

These levels are then passed to a simplified two-dimensional

(2D) hydrodynamic model of the key urban flood risk site.

While the 2D model is computationally expensive, offline

methods can be used to generate libraries of scenarios that

can be drawn on in real time, thus providing real-time 2D

inundation visualization capability. Issue (2) is addressed by

presenting a range of methods for conveying the complex

information content of probabilistic real-time flood fore-

casting output in an intuitive way that may provide new and

useful information for decision support personnel.

Methods

Data-based mechanistic rainfall to levelforecasting

The data-based mechanistic (DBM) modelling approach,

developed by Young and colleagues (see Young, 1998 and the

Figure 1 Location of the study site centred on Carlisle (UK) with the location of the Environment Agency’s four level gauge sites used in this study

(marked with circles). Reproduced from the OS MiniScale 1:1000000 map by the permission of Ordnance Survey on behalf of the Controller of Her

Majesty’s Stationery Office r Crown Copyright. Lancaster University, Bailrigg, Lancaster. O.S. Licence No. A281220.

J Flood Risk Management 3 (2010) 140–150 c� 2010 The AuthorsJournal Compilation c� 2010 Blackwell Publishing Ltd

141Visualization approaches for communicating real-time flood forecasting

references therein), was used to identify and estimate rainfall

to river-level relationships for the four locations of study in the

Carlisle region (Sheepmount, Linstock, Harraby Green, and

Denton Holme). Following the identification/estimation pro-

cess, the DBM transfer function model for each location was

recast in equivalent state space form and incorporated into a

modified Kalman Filter (KF) data assimilation algorithm (the

original KF is described in Kalman, 1960). This process has

been described in detail for different catchments in Romano-

wicz et al. (2006) and Young (2002); this paper will only

present pertinent details from the above where necessary.

Figure 2 provides a basic visual description of the opera-

tion of the KF data assimilation and forecasting process.

With reference to Figure 2, at time period k, an observation

of river level becomes available together with an estimate of

the uncertainty associated with the observation; this is

compared with the model forecast at the corresponding

time period, which also has a measure of uncertainty

(indicated in the diagram by normal curves). Equations (2)

and (3) in the text provide a recursive solution to estimating

the minimum variance blend of these two values. The new

value then provides the starting point for the n-step ahead

forecasts of level into the future; the propagation of the

forecast variance provides forecast uncertainty information

useful for decision support.

Importantly, for probabilistic forecasting, the KF data

assimilation algorithm inherently generates an estimate of

the error covariance matrix at each time step. Equation (1)

shows the state space form for each model. Data assimilation

was then performed using the recursive implementation of

Eqns (2) and (3).

xk ¼ Fxk�1 þ Guk�d þ zk

yk ¼ hTxk þ xk ð1Þ

Forecast:

xkjk�1 ¼ Fxk�1 þ Guk�d

Pkjk�1 ¼ FPk�1FT þ s2kQr

ykjk�1 ¼ hTxkjk�1

ð2Þ

Correction:

xk ¼ xkjk�1 þ Pkjk�1h½s2 þ hTPkjk�1��1fyk � ykjk�1gPk ¼ Pkjk�1 � Pkjk�1h½s2 þ hTPkjk�1��1hTPkjk�1

yk ¼ hTxk

ð3Þ

where the elements of F, G, and h are determined by the

associated transfer function model parameters, uk is a vector

of input terms (rainfall or upstream level) usually trans-

formed according to a power law function, d is the time

delay between input and output (this provides the forecast

lead time), zk is the process noise entering the system at time

period k, xk is the observation noise applied to the measure-

ment of level at time period k, s2k is the variance of the

observation noise at time period k, and Qr is a diagonal

matrix where the entries along the diagonal are model

hyperparameters defining the ratio of process to observation

noise. The values of Qr can either be optimized or tuned

using pragmatic heuristic rules until an appropriate level of

simulation performance is achieved.

Importantly, it is assumed that the observation noise

variance is heteroscedastic, effectively providing a means to

place less confidence in observations as the river level

increases. An estimate of the level-dependent observation

noise variance is generated using Eqn (4).

s2k ¼ l0 þ l1y2

k ð4Þwhere l0 and l1 are the parameters determining the rela-

tionship between the forecast level and the observation noise

Figure 2 Diagram showing the operation of the Kalman Filter data assimilation and forecasting process for real-time flood forecasting.


142 Leedal et al.

variance. Again, these model hyperparameters can either be

optimized or determined heuristically, where, in general

terms, l0 controls the baseline extent of forecast confidence

intervals at low flows and l1 defines the degree of inflation in

the confidence intervals as the forecasted level increases.

Each model can generate n-step ahead forecasts of level

where the maximum value for n for each model is equal to

the model’s associated value of d. For the results presented

here, the value of d is 6 h for Sheepmount and 5 h for

Linstock, Harraby Green, and Denton Holme. In operation,

a forecast and correction cycle is carried out at each sample

period as a new level and rainfall observation arrive (the

‘present’ sample period is labelled k). This is followed by drepetitions of the forecast step to produce level forecasts for

k11 to k1d periods into the future. Crucially, for operation

within a probabilistic framework, the error covariance

matrix P from Eqns (2) and (3), together with Eqn (4), are

used to estimate the variance for the k1n forecasts using

Eqn (5).

varðykþnjkÞ ¼ s2k þ hTPkþnjkh ð5Þ

An additional step is the application of an adaptive scalar

gain term. This mechanism applies a corrective offset to the

level forecasts that can be used to account for slow drifts in

the forecast accuracy that may result from long time scale

non-stationarity in the catchment process. This algorithm is

not included here; the interested reader is directed to the

references above for a complete description.

The key advantages of applying the algorithm described

above are: (1) the parsimonious model structure results in

computationally efficient data assimilation and forecasting

schemes and (2) the resulting n-step ahead forecasts are

defined in the form of symmetrical probability distributions

(mean and variance) that provide useful probabilistic in-

formation for flood risk management. This paper goes on to

consider two applications for these output: (1) the direct

communication of flood warning using level prediction and

warning thresholds and (2) the provision of n-step ahead

probabilistic boundary conditions to 2D hydrodynamic

models that can then provide probabilistic forecasts for

inundation extents within urban locations. The methods

for the latter are considered next.

Coupled 1--2D real-time probabilistic inundationforecasting

LISFLOOD-FP is a 2D storage cell hydrodynamic model

that can usefully be used to estimate flood inundation extent

for flood risk mapping or, using the special offline methods

outlined here, as a means of providing inundation informa-

tion in real time. The model is introduced in Hunter et al.

(2005). The model presented here for Carlisle is described in

detail in Neal et al. (2009). The model has a 25 m resolution

raster representation of floodplain topography sampled

from a 1 m resolution LiDAR-based digital terrain model

(DTM). Embankment tops in the DTM were retained in the

course resolution model grid, as these were assumed to have

a significant impact on flood inundation. The three rivers

shown in Figure 1 (River Eden, Caldew and Petteril) were

included in the 1D channel model. The 1D channel model

used a Manning’s roughness value of 0.055, while the 2D

floodplain model used 0.06. These values were obtained from

a calibration using observations of maximum water level on

the floodplain measured shortly after the January 2005 flood

event. A detailed description of this model and its accuracy

was presented by Neal et al. (2009) and will not be repeated

here. However, the method for estimating flood probabilities

is sufficiently generic that any model of the hydraulics that is

capable of incorporating the three inflows could be used,

with the proviso that the accuracy of the inundation model

will affect the accuracy of the probability estimates.

The principal behind the approach used here is that a

multidimensional array of simulations representing differ-

ent flow conditions on each river can be used as a rapidly

accessible library of inundation extents, given any flow

conditions within a pre-defined range. By generating an

offline library of simulation results, flood inundation prob-

abilities can be assessed rapidly, which would not be the case

if an ensemble of inundation models were run in real time

based on the equivalent forecast. Similar approaches have

been suggested by Aronica et al. (2002), Romanowicz and

Beven (2003), and Purvis et al. (2008). While the offline

library of simulations approach provides huge advantages in

terms of processing speed, the fact that the library of

simulations is based on steady state flows at the peak levels

has the drawback of not considering the effect of flood wave

dynamics on inundation extent that is, an assumption is

made that the dynamics of the flow on each tributary are

unimportant relative to the uncertainty in the peak flow

itself. Also, from an uncertainty point of view, the hydraulic

model and the rating at each gauge were assumed to be

perfect in the results presented later.

The inundation extent library was defined by populating

a 3D matrix of simulation results, where each simulation

had a different combination of inflows sampled from a pre-

defined range and a uniform distribution. The number of

simulations could have been reduced had a sufficient

correlation existed between the observed levels at two or

more gauges, e.g. by specifying a fixed relationship between

levels at different gauges. Unfortunately, differences in the

arrival time of peak flows and shape of hydrographs for each

river precluded this approach.

Peak forecast levels between September 2003 and March

2005 plus two standard errors were used to determine the

maximum levels that would be simulated by the hydraulic

model for the inundation extent library.



Although the maximum level simulated for Linstock was

reduced from 7 to 5.75 m, because levels above 5.75 m

generated physically unrealistic flows when the rating curve

was applied, lower level limits for the inundation extent

library were arbitrarily chosen at a level where inundation of

the floodplain did not occur. The range of levels simulated

in the inundation extent library is shown in Table 1.

The forecasting model provided a best estimate and

standard error at the three gauge locations close to the edge

of the model domain. For each forecast issued by the DBM

model, the probability of simulation a in the library of bsimulations was calculated using a multivariate normal

probability density function for that forecast. It was as-

sumed that the uncertainty in the forecast stage was

represented well by a normal distribution. The method can

be applied where forecast errors at each gauge are correlated,

although this was assumed not to be the case here as each

forecast was driven by independent gauge data.

The probability of inundation in cell i of the flood

inundation model was then estimated using a multivariate

extension of the method used by Purvis et al. (2008):

Pi ¼Pb

a ma;iPaPb

a Pað6Þ

where Pi is the probability of inundation in cell i, Pa is the

multivariate probability density of library simulation a, and

ma,i is the value of the binary flood inundation map (0 = dry,

1 = wet) for simulation a and location i.

Results

Presenting point-level estimates forSheepmount

In this first section of the results, we present probabilistic real-

time flood forecasting information generated by the DBM

modelling and KF algorithm described above. The forecast is

a point estimate of the level of the River Eden at the

Sheepmount gauge site used by the Environment Agency as

an indicator site for flood warning. While this paper is more

concerned with describing the types of information useful for

real-time flood forecasting, how to generate that information,

and a discussion of how best to communicate that informa-

tion, it is worth noting that the results presented here are

drawn from well-developed research investigating novel

methods for real-time forecasting and are tested using the

unprecedented January 2005 Carlisle flood.

The point-level information is presented in a form that

replicates the information content that would be available to

personnel in real time. The information available includes:

(1) present and all previous observations of river level; (2)

present and previous rainfall/upstream inputs; (3) all pre-

vious 1–6-h ahead forecasts at corresponding time values

together with variance estimates; (4) 1–6-h forecasts into the

future together with variance estimates; and (5) the value of

warning thresholds. Figure 3 shows the first in a series of

example output for point-level real-time forecasting in

Cartesian form.

Figure 3 shows the forecast level passing through the

lower three warning thresholds. The inclusion of the con-

fidence interval provides a highly useful indication of the

level of confidence in each forecast value. The inclusion of

the past 6-h forecasts and the associated confidence intervals

provides the user with information about the past perfor-

mance of the forecast and confidence intervals. It is im-

portant to note, however, that the provision of forecast

confidence intervals provides flood management profes-

sionals with additional decision-making complexity raising

unforeseen issues such as who takes ‘ownership’ of the

uncertainty? For a case study and analysis of the treatment

of uncertainty in real-time flood risk management, see

McCarthy et al. (2007). Figures 4–7 show the progression

of the 2005 flood wave.

The probabilistic information encoded in the forecasting

visualizations is of key importance for decision support.

During the majority of extreme flood events covered by

model forecasting, the models perform less well than hoped

(see Spencer et al. (2006) for a post event analysis of the

performance of the Environment Agency’s models applied

to the Carlisle 2005 event). The disappointing level of

performance is not surprising, considering each large flood

event is unique, and it is very difficult to calibrate a model

for these rare events. Inevitably, any model attempting to

forecast complex hydraulic processes will do so with some

degree of error. The confidence intervals in the result

reporting are an attempt to communicate this to flood risk

management personnel.

Given a probabilistic estimate of level, the flood risk

management team can then inform decisions based on the

degree of risk. For example demountable defences may be

deployed at sensitive locations at a lower probability of

inundation compared with less sensitive locations in order

to best administer resources.

Issuing warning as the river level crosses specific thresh-

olds is a common practice in flood risk management.

Figures 3–7 show how the grey areas intersect with thresh-

olds as levels rise. Figure 8 shows the post event analysis in

Table 1 Range of levels and sampling intervals used to generate the

simulation library

Minimum

level (m)

Maximum

level (m)

Sampling

interval (m)

Linstock 1 5.75 0.25

Harraby Green 1 2.5 0.5

Denton Holme 0.5 5.75 0.25


144 Leedal et al.

terms of the actual times when the observed level crossed the

six threshold values and the cumulative probability curves of

level crossing the threshold estimated by the 6-h ahead

forecast.

Figure 8 demonstrates that the expected value (the

0.5 probability level) often misses the observed crossing

time by several hours (the date labels are 6 h apart).

However, for the four highest thresholds, the observed

crossing time falls within an �80% confidence range.

It should not be understated that flood risk manage

ment professionals are familiar with working with uncer-

tainty via experience, communication with others, and

professional judgement. The added value of probabilistic

information in the form of confidence intervals is to provide

some formal and rapidly accessible means of quantifying

forecast uncertainty that may support the judgement pro-

cess determining whether, when, and where inundation will

occur.

1--2D LISFLOOD-FP probabilistic real-timeinundation maps

This section of the results considers the visualization of 2D

inundation maps generated using the methods described

above. Again, research into both the point estimate and 2D

modelling as well as approaches to couple these methods is

Figure 4 Six forecast beginning 8 p.m., 7 January 2005. See Figure 3 for legend.

Figure 3 Six-hour ahead real-time flood forecasting figure for Sheepmount at 2 p.m., 7 January 2005. Included in the figure are present and previous

observed levels (solid black line), previous 6-h forecasts plus 99% and 95% confidence intervals (black dots plus light and dark grey patch), the present

time period (vertical line), warning threshold levels (horizontal lines), 1–6-h ahead level forecasts plus 99% and 95% confidence intervals (black dots

with red outline plus light and dark reddish-grey patch).



Figure 5 Forecast beginning midnight, 8 January 2005. See Figure 3 for legend.

Figure 6 Forecast beginning 1 p.m., 8 January 2005. See Figure 3 for legend.

Figure 7 Forecast beginning 7 p.m., 8 January 2005. See Figure 3 for legend. The time axis has been extended for contrast with Figures 3–6.


146 Leedal et al.

ongoing, but the authors are using useful inundation fore-

cast visualizations of the January 2005 Carlisle flood event

for illustration. The propagation of level plus uncertainty

information from the 1D DBM methods described above to

the 2D hydrodynamic model allows probabilistic estimates

of inundation extent to be generated in real time. Informa-

tion is held in the form of probability distributions for the

wetness of each cell within the LISFLOOD grid. This

information can be communicated to flood risk manage-

ment personnel in a number of ways. Using the peak flows

for the January 2005 event at Carlisle, Figures 9 and 10 show

inundation estimation maps colour coded with probabilistic

information. Figure 9 uses discrete increments of probabil-

ity, while Figure 10 presents a graduation in shade between

green (low probability) and red (high probability). Each

inundation figure also shows the location of wrack marks

(debris left behind following flooding) collected and logged

by the Environment Agency. The line of wrack marks

provides a surrogate measure for the outline of inundation

that actually occurred during the January 2005 event. This

outline once again demonstrates the importance of mapping

within a probabilistic framework as forecasted and observed

inundation correspond well in some locations and less well

in others.

For the purpose of testing, Figure 10 is included below,

which shows the deterministic depth map resulting from a

LISFLOOD-FP simulation using best available observed

levels for the January 2005 event (again with wrack loca-

tions). The 5-h 1–2D coupled forecast inundation map

shows a relatively good correlation to Figure 11, which is

effectively the best flood inundation estimate using the

LISFLOOD-FP model that would be available after river-

level data had been observed.

The results presented above show a number of novel and

potentially useful features. These include real-time level

forecast uncertainty bounds generated automatically by the

KF algorithm, the coupling of 1–2D modelling to allow

rapid calculation of inundation extent, the use of offline

methods to allow rapid estimates of inundation uncertainty

associated with the 2D model results, and both Cartesian

and map overlay methods in an attempt to clearly commu-

nicate uncertainty estimates to flood-risk management

professionals.

Comment

This paper has focused on the need for probabilistic

forecasting in real-time flood forecasting using the 2005

Carlisle flood event as a test bed. The objective has been to

identify which information would be useful in real-time

flood forecasting applications, how that information can be

generated, and finally how that information can be visua-

lized and communicated. The last of these objectives is

probably the furthest outside the area of expertise of the

authors. While the authors are familiar with ‘good’ knowl-

edge presentation philosophies as encouraged by Tufte

(1984, 1990), further research is required to address issues

of visualization style and layout when presenting complex

model output information to personnel in real-time flood

forecasting environments.

Experience from point-level estimate real-time forecast-

ing simulation demonstrates that while continuous ad-

vances in modelling skill should make deterministic

forecasting gradually more accurate, it would be unwise to

assume that uncertainty could ever be completely eradicated

from model results. Therefore, any environmental model

Figure 8 Cumulative distribution plots showing the probability that the 6-h ahead forecast level will be greater than each of the six warning threshold

levels. Vertical lines show the observed time of crossing for each threshold.



study, and particularly studies that inform policy or risk

management decisions, should include appropriate uncer-

tainty analysis methods and communicate these results to

decision makers. The DBM method uses relatively simple

assumptions about the structure of the forecast errors, i.e., a

symmetrical distribution with a level-dependent variance.

Figure 10 Same information as for Figure 9 but visualized using a continuous scale of inundation probability.

Figure 9 Forecast and probabilistic visualization of the maximum inundation extent for January 2005 floods at Carlisle based on boundary conditions

supplied by 5-h forecasts of level by DBM models to Linstock, Harraby Green, and Denton Holme. Also shown in the figure are the locations of flood

wrack (debris) identified after the January 2005 event.


148 Leedal et al.

Further research needs to be carried out to refine and extend

the identification and parameterization of the forecast error

term. The mean value of the DBM forecast rarely crosses

flood warning thresholds at exactly the same time as the

observed level. However, the observed threshold crossing

times generally lie within the identified forecast confidence

intervals. This information would be useful in real-time

forecasting scenarios.

Experience from coupled 1–2D LISFLOOD-FP modelling

demonstrates that useful probabilistic forecasted inundation

maps can be created using a combination of parametrically

efficient DBM models to provide boundary conditions,

which are then used to draw from offline libraries of

previously calculated inundation scenarios. A variety of

colour coding and overlay methods can be applied to aid

clear and rapid communication of probabilistic informa-

tion. Clearly, a 2D inundation georeferenced map overlay

would provide an invaluable resource for operational real-

time flood management. These maps could for example be

transmitted and updated easily in real time to emergency

personnel equipped with GPS/PDAs or other readily avail-

able visualization technologies.

A potential drawback of the provision of probabilistic

information is a capacity to introduce confusion by present-

ing information in an inconsistent fashion from one model

or modelling approach to another. To address this issue,

much future research effort is being planned to develop

codes of best practice in uncertainty estimation and com-

munication with the intention of providing guidelines that

will advocate consistency across the flood risk management

discipline. Faulkner et al. (2007) present a roadmap out-

lining the issues and challenges to this end.

Acknowledgements

This research was carried out as part of RPA9 and SWP1 of

the Flood Risk Management Research Consortium

(FRMRC) phases 1 and 2. The principal sponsors of FRMRC

are: the Engineering and Physical Sciences Research Council

(EPSRC) in collaboration with the Environment Agency

(EA), the Northern Ireland Rivers Agency (DARDNI), the

United Kingdom Water Industry Research (UKWIR) Orga-

nisation, the Scottish Government (via SNIFFER), the Welsh

Assembly Government (WAG) through the auspices of the

Defra/EA, and the Office of Public Works (OPW) in the

Republic of Ireland. For details of the FRMRC, see http://

www.floodrisk.org.uk.

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Figure 11 Deterministic depth map estimates for the January 2005 Carlisle flood event using observed peak flows. This figure can be compared with

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