Reach scale floodplain inundation dynamics observed using airborne synthetic aperture radar imagery:...

Journal of Hydrology (2006) 328, 306–318

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Reach scale floodplain inundation dynamicsobserved using airborne synthetic apertureradar imagery: Data analysis and modelling

Paul D. Bates a,*, Matthew D. Wilson b, Matthew S. Horritt c,David C. Mason d, Nick Holden e, Anthony Currie f

a School of Geographical Sciences, University of Bristol, University Road, Bristol BS8 1SS, UKb Department of Geography, University of Exeter, Tremough Campus, Trelever Road, Penryn, TR10 9EZ, UKc Department of Civil Engineering, University of Bristol, Queens Walk, Bristol BS8 1TR, UKd Environmental Systems Science Centre, University of Reading, Whiteknights, Reading RG6 6AL, UKe Environment Agency Science Group – Technology, Rivers House, Lower Bristol Road, Bath BA2 9ES, UKf QinetiQ Ltd., St. Andrews Road, Malvern, Worcestershire WR14 3PS, UK

Received 8 April 2005; received in revised form 8 December 2005; accepted 21 December 2005

Summary In this paper, we use an airborne synthetic aperture radar to map river flood inun-dation synoptically at fine spatial resolution (1.2 m) along a �16 km reach of the River Severn,west-central England. Images were obtained at four times through a large flood event between8th and 17th November 2000 and processed using a statistical active contour algorithm to yieldthe flood shoreline at each time. Intersection of these data with a high vertical accuracy surveyof floodplain topography obtained from airborne laser altimetry permitted the calculation ofdynamic changes in inundated area, total reach storage and rates of reach dewatering. In addi-tion, comparison of the data to gauged flow rates, the measured floodplain topography and mapdata giving the location of embankments and drainage channels on the floodplain yields newinsights into the factors controlling the development of inundation patterns at a variety ofscales. Finally, the data were used to assess the performance of a simple two-dimensional floodinundation model, LISFLOOD-FP, and allows us, for the first time, to validate the dynamic per-formance of the model. This process is shown to give new information into structural weak-nesses of the model and suggests possible future developments, including the incorporationof a better description of floodplain hydrological processes in the hydraulic model to representmore accurately the dewatering of the floodplain.ª 2006 Elsevier B.V. All rights reserved.

KEYWORDSFloodplain inundation;Synthetic aperture radar;Hydraulic modelling

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022-1694/$ - see front matter ª 2006 Elsevier B.V. All rights reserved.oi:10.1016/j.jhydrol.2005.12.028
* Corresponding author. Tel.: +44 117 928 9108; fax: +44 117 928 9108/7878.E-mail address: [email protected] (P.D. Bates).

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Reach scale floodplain inundation dynamics observed using airborne synthetic aperture radar imagery 307

Introduction

Whilst the last 40 years has seen significant advances in ourunderstanding of the fluid dynamics of flow in compoundchannels, largely as a result of small- and large-scale labora-tory experiments (see for example Sellin, 1964; Shiono andKnight, 1991), very few observations of such flows are avail-able at the field scale. This is a result of the large area thatneeds to be covered and the hazardous nature of field sam-pling during flood flows. This is particularly true for wholefloodplain reaches greater than 1 km in length as at suchscales detailed ground observations become a practicalimpossibility. However, floodplainmanagement is being seenincreasingly in a catchment wide context (van Roon andKnight, 2001) and there is thus a need to understand flood-plain inundation dynamics at the reach scale (10–50 km) inorder to make informed decisions on flood risk management.

To date, remote sensing of floodplain inundation from sa-tellite platforms has not offered a solution to this problem asthe repeat time of typical sensors (35 days for the ERS1 andERS2 satellites, 7–10 days for RADARSAT) means that theacquisition of multiple images through a flood is only possiblein the very largest, continental scale basins (e.g., Sippelet al., 1998). Here, topography may not be well constrainedand it is therefore impossible to investigate the forces drivingsuch floodplain flow with any certainty. Given the large datavolumes involved, high accuracy and fine spatial resolutiontopographic data are typically only available over riverreaches <100 km in length and consequently it is typicallyonly possible to acquire a single satellite SAR image per event(e.g., Aronica et al., 2002). Hence, these data cannot beused to investigate inundation dynamics. An obvious solutionto the lack of observations is to use a numerical hydraulicmodel for unsteady flow to simulate dynamic inundation pat-terns in conjunction with available gauging station data andan appropriate resolution digital elevation model (DEM) asboundary conditions (see for example Bates et al., 2003).However, without data capable of validating these predic-tions the comparative performance of competing models isessentially unknown. To overcome these problems, in thispaper we describe the use of a military specification airbornesynthetic aperture radar to map floodplain inundation synop-tically at fine spatial resolution (1.2 m) over a �16 km flood-plain reach at four time instances through a large floodevent. This allows us, for the first time, to make field obser-vations of the dynamics of reach scale floodplain inundation.In ‘‘Data acquisition and analysis’’, we describe the acquisi-tion and analysis of the airborne radar and ancillary data forthe test reach, and evaluate whether this confirms and pos-sibly enhances our current understanding of floodplain fluiddynamics. Then, in ‘‘Flow modelling’’, we use the acquireddata to validate a simple two-dimensional hydraulic modelof reach scale flood inundation to demonstrate how the en-hanced spatial and temporal resolution of the airborne radardata allows a more detailed evaluation of current modelcapabilities than has hitherto been possible.

Data acquisition and analysis

In November 2000, the UK experienced widespread and pro-longed flooding (Marsh and Dale, 2002) resulting from rain-

fall accumulations well above the seasonal average.Overbank flows from multiple flood peaks were recordedin many river basins and resulted in inundation of some10,000 properties and approximately £1Bn in damages(Fleming et al., 2001). During this period an airborne syn-thetic aperture radar (ASAR) was used, opportunistically,to acquire several images of the flooding along the rivers Se-vern, Thames and Ouse. Of these, most data were collectedfor a �16 km reach of the lower river Severn around Upton-on-Severn in west central England (see Fig. 1). The riverthrough this reach consists of a stable channel 50–70 mwide and �7–10 m deep which meanders through a welldeveloped floodplain up to 2 km in width. The upstreamcatchment area is 6850 km2 and bankfull discharge is�330 m3 s�1, with the Q95 and Q10 being 15 and221 m3 s�1, respectively. Floods occur in response to heavyrainfall in the upper catchment, which comprises the up-lands of central Wales, and not to local rainfall. These floodwaves take of the order 2–4 days to arrive at the Uptonreach. Along the reach, embankments up to �2 m high havebeen constructed on either side of the channel. Drainage offloodplain water through the embankments takes place via�15 culverts terminating on the main river side in heavysteel covers. These culverts are typically located to allowminor floodplain drainage channels to flow into the Severnmain stem. The gates are closed by pressure at high mainriver water levels to prevent backwater flooding, but allowdrainage of floodplain water into the river after the mainflood wave has passed and river levels have dropped belowthe level of the steel covers. The floodplain topography iscomplicated by other structures including embanked roadsand a large natural island on which the settlement of Up-ton-on-Severn is built. The floodplain land use is predomi-nately pasture with some arable crops (see Fig. 2).

The floodplain topography down to the low water riverlevel along this reach was mapped by the EnvironmentAgency of England and Wales in March 2002 using their Op-tech ALTM2033 airborne laser altimeter (or LiDAR). This is ascanning LiDAR pulsing at 33 kHz mounted onboard a lightaircraft, typically flying at an altitude of �800 m and veloc-ity of �65 m s�1. The instrument collects first and last re-turn information and scans up to 19� off-nadir. At thetypical flight altitude this results in a swath width of�600 m and a ground resolution of �1 point per m2. Theraw LiDAR data were collected in WGS84 co-ordinates in lastreturn mode and then converted to the local OSGB36 ellip-soid model from which co-ordinates in terms of the BritishNational Grid (BNG) could be found. Data quality waschecked by the Environment Agency by comparing the LiDARtopography to data acquired in a simultaneous ground sur-vey of a flat area of short vegetation in the centre of thetest reach using a differential GPS. The DGPS generatedground truth topographic measurements accurate to<0.01 m in the horizontal and vertical. For the test areathe vertical root mean square error of the LiDAR data wasestimated to be 0.079 m based on a sample of 181 points,with all height differences between LiDAR and the DGPSdata being less than 0.25 m. These errors will increase inareas of steep slope and dense vegetation; however, asthe majority of the floodplain is of low slope and coveredin short vegetation, bare earth height errors are likely tobe consistent with the above estimate. An additional DGPS

Figure 1 Location map of the study reach.

308 P.D. Bates et al.

survey was also conducted by the authors to confirm theseerror measurements and yielded similar results to the Envi-ronment Agency survey. The LiDAR data were then pro-cessed using the LiDAR segmentation algorithms developedby Cobby et al. (see Cobby et al., 2001; Mason et al.,2003). The original LiDAR height values were first aggre-gated into a 3 m raster grid by selecting the minimum valuefalling within each grid cell. DEM generation then involvedremoval of surface features such as vegetation from thedata set of aggregated heights. Below water channelbathymetry was characterised from a previous ground sur-vey consisting of �20 cross sections conducted by the Envi-ronment Agency and supplemented by a boat survey at asmall number of key locations conducted in autumn 2003.

ASAR images were acquired for this reach during flights ofopportunity on the 8th, 14th, 15th and 17th of November,2000 through a large double-peaked overbank flood eventand represent our most complete view to date of the dynam-ics of river floodplain inundation over whole river reaches.Hydrometric data for this event were available from the Di-glis gauging station located �10 km upstream of the headof Upton reach. This gauge is maintained by the UK Environ-ment Agency and records stage values every 15 min. Thesestage values are then converted to an estimate of flow viaa rating curve. A similar 15 min data set was also availablefrom the Saxon’s Lode gauge in the centre of the test reach.However, this is known to underestimate peak flows by up to�200 m3 s�1 as a result of bypass flow around the gauge atdischarges >400 m3 s�1. Below this value the recorded flowat Diglis at any given time differed from that at Saxon’s Lodeby <3%. This suggests that flow does not attenuate signifi-cantly between the two gauging stations and that floodwavesare simply translated. For our analysis we therefore used therecorded discharge at Diglis with timing adjusted based on

the recorded wave travel time to give the hydrograph atthe head of the Upton reach. We assume that between Diglisand the head of the Upton reach any contributions of waterto the floodplain from local runoff and direct precipitationor losses via floodplain infiltration and evapotranspirationare small compared to likely errors in the gauged flow. Aswith all flow data acquired via a rating curve, errors will in-crease at higher flows and in particular will be greater whenflow is out of bank. Both these conditions apply here and pre-vious studies of rating curve accuracy in such situations indi-cate that errors in measured flow of up to ±20% may occur(Ervine and Baird, 1982). This is likely to be much greaterthan the net sum of all other components of the hydrologicalmass balance along the reach.

The hydrograph and timing of ASAR overflights are shownin Fig. 3. These data indicate that the first ASAR acquisitionoccurred just before the second flood peak at a discharge of657 m3 s�1. The second discharge peak of 694 m3 s�1 oc-curred at 21:30 h on the 8th November, and the remainingthree ASAR images were acquired on the falling limb ofthe hydrograph as the floodplain was dewatering at dis-charges of 312, 266 and 211 m3 s�1, respectively. Dischargewas approximately at bankfull discharge at the time of theASAR overflight on the 14th, and below bankfull dischargeon the 15th and 17th. No image data were acquired on thehydrograph rising limb: a result which is typical of many pre-vious studies of this type (see for example Bates and De Roo,2000; Horritt and Bates, 2001; Horritt and Bates, 2002) be-cause of the rapid rate of rise.

The imagery were acquired with an airborne syntheticaperture radar system developed for use by the UK militaryand operated by QinetiQ Ltd. (formerly part of the DefenceEvaluation Research Agency, DERA). This instrument,known as the enhanced surveillance radar (ESR), is a fully

Figure 2 LiDAR digital elevation model (DEM) of the lowerriver Severn reach at a spatial-resolution of 3 m, after theremoval of vegetation and buildings. Flow direction is fromnorth to south. No-data areas are shown in white. Within thesurveyed area, these are caused by reflectors such as bodies ofstanding water (including the river) which do not return a signalback to the sensor.


polarimetric X band radar (wavelengths of 2.4–3.75 cm)capable of operation between 9.45 and 9.95 GHz. The mainantenna is 360� scanning and space stabilised in azimuthand elevation. It is mounted underneath the fuselage of aBAC 1-11 aircraft. There is a trade-off between swathwidth, number of polarimetric modes collected and groundresolution, with the data shown in Fig. 5 being typical (i.e.,a 2 km wide swath at 1.2 m resolution). All data reported inthis paper were collected in VV polarisation mode at 1.2 mresolution.

For this event two satellite SAR scenes were also ac-quired on the 9th November: one from the RADARSAT instru-ment at 06:21 h and one from ERS-2 instrument at 11:06 h.Whilst both satellite instruments are X band radars, RADAR-SAT has previously been shown to be better configured tomap floodplain inundation (Horritt and Bates, 2002) andwas therefore compared to the ASAR image acquired onthe 8th November to determine the likely improvement ininundation mapping performance to be gained in movingto higher resolution imagery. This is shown in Fig. 4 and

demonstrates the large improvement in image quality thatresults from the move from a general satellite instrumentto a military specification airborne radar. In part, thisimprovement is due to the order of magnitude better reso-lution, but may also be due to instrument differences whichmean that the airborne radar is better configured for floodmapping. In particular, the airborne radar is able to acquirereturns over the urban area of Upton-on-Severn which arecoherent and allow the shoreline here to be delimited in away which is not possible from the satellite data.

The ASAR images were first geo-corrected and then pro-cessed to yield the shoreline vector using a statistical activecontour algorithm or Snake (see Horritt, 1999; Horritt et al.,2001). Processing of any synthetic aperture radar data is notstraightforward due the high degree of speckle in the imag-ery and low signal-to-noise ratio. However, Fig. 4 demon-strates that this is a significantly reduced problem in theASAR imagery compared to the information from satellitesensors used previously in studies of this type (e.g., Batesand De Roo, 2000; Horritt and Bates, 2002). The Snake algo-rithm has been shown to be capable of segmenting a radarimage into wet and dry zones to an accuracy of �1 pixel.The output from the Snake algorithm was therefore a shore-line vector for each image theoretically accurate to�1 pixel(or �1.2 m) and with no obvious misclassification errors.These vectors are shown in yellow in Fig. 5. Because the datawere acquired opportunistically, it was not possible to col-lect simultaneous ground data with which to validate thesederived shorelines. However, the shorelines are consistentwith each other, the local topography as determined fromthe LiDAR survey and the known local drainage network onthe floodplain (also shown on Fig. 5 in blue). By intersect-ing the ASAR data with the LiDAR topography we were alsoable to estimate the inundated area and the volume of wateron the floodplain at the time of each image. By calculatingthe difference in volume between each image we could alsoestimate rates of floodplain dewatering. To achieve this, thewater surface at the time of each image was approximatedas planar surface using stage measurements from the waterlevel recorders at Saxon’s Lode and Mythe Bridge (a stage-only gauging station at the southern edge of the reach) ascontrol points. Flood volume for each cell predicted asflooded in the ASAR images was then calculated as the cellarea multiplied by the difference in height between the pla-nar water surface and the LiDAR topography at that point.These values and associated error bars are given in Table 1and in Fig. 6. Based on previous studies (Ervine and Baird,1982) we assume the error in gauged discharge for overbankflow for this reach may be as much as ±20%. Assessing the er-ror in ASAR estimates of flooded area is more difficult due tolack of ground truth data, however for satellite SAR systemsthis has been found to be of the order of ±10% (Horritt et al.,2001). Fig. 4 implies that the ASAR data will be significantlymore accurate than this due to its higher resolution and con-figuration more suited to flood remote sensing. We thereforeestimate errors in the determination of inundation extent tobe at most ±5%. When combined with the errors and assump-tions made in the volume calculation (LiDAR height errors of�0.08 cm rmse, differences between the true water surfaceand the planar approximation) this leads to a conservativeestimate of error on calculated volume, and hence dewater-ing rates, of ±10%.

Figure 3 Hydrograph of the flood event on the Severn in November/December recorded at the Diglis gauging station with timingadjusted according to the observed wave travel time to reconstruct the flow record at the head of the test reach. The verticaldashed lines indicate the timing of ASAR imagery on 8 November 2000 at 12:18 h; 14 November 2000, 12:42; 15 November 2000,16:48; and 17 November 2000, 13:44. The horizontal grey line indicates bankfull discharge, with the grey horizontal shading denotingthe likely error of ±10% in this value.

Figure 4 Comparison of: (a) RADARSAT and (b) ASAR imagery of flooding at Upton on Severn on 8 and 9 November 2000. Whilst theimages were acquired �18 h apart the discharge in the reach at time of acquisition differed by only �2%. Note the ease with whichthe shoreline can be delimited in the ASAR imagery and the additional detail available around the urban area of Upton on Severn.


Fig. 6 shows that decreases in storage and inundatedarea both lag decreases in discharge, with inundated areadecreasing slowly until bankfull discharge is reached on

the 14th. As one might expect, the inundated area then be-gins to decrease more rapidly after this point. It should benoted that Table 1 and Fig. 6 only show net storage change

Figure 5 ASAR images of the reach at a spatial resolution of 1 m acquired on: (a) 8 November 2000, (b) 14 November 2000, (c) 15November 2000, (d) 17 November 2000. The flood extent delineated using the Snake algorithm is shown in white. The timing of theimages in relation to the flood hydrograph is shown in Fig. 3. The flow direction is North to South.

Table 1 Estimates of discharge, inundated area and water stored within the reach for each ASAR image

Date and time Discharge(m3 s�1)

Normaliseddischarge

Inundatedarea (km2)

Normalisedinundatedarea

Reach storage(m3 · 107)

Normalisedstorage

Rate of reachdewatering sincelast image in m3 s�1

8 November, 12:18 657 ± 131 1.00 7.91 ± 0.4 1.00 2.23 ± 0.122 1.00 –14 November, 12:42 312 ± 62 0.47 6.41 ± 0.32 0.81 1.35 ± 0.14 0.61 �16.3 ± 1.615 November, 16:48 266 ± 53 0.40 6.19 ± 0.31 0.78 1.09 ± 0.11 0.49 �25.7 ± 2.617 November, 13:44 211 ± 42 0.32 4.43 ± 0.22 0.56 0.63 ± 0.06 0.28 �28.5 ± 2.9

As the extent of each ASAR image is different, inundated area and storage are only calculated for pixels which appear in all four images.Normalised values of discharge, inundated area and storage are calculated by setting the value of each variable at the time of the firstimage to 1. A graph of the normalised values is shown in Fig. 6.


as we cannot discriminate different components, both posi-tive and negative, of the floodplain mass balance that con-tribute to this. These components are floodplain dewateringto the channel and subsequent rapid flow out of the reach,infiltration to the floodplain subsurface, evapotranspirationto the atmosphere, direct precipitation inputs and local run-

off contributions. Evapotranspiration losses are likely to below given maximum air temperatures during the event were<10 �C. Similarly precipitation falling directly on to thefloodplain during the event was also low and for this reasonlocal runoff is also unlikely to be a significant component ofthe net storage change. Hence, we hypothesise that the

Figure 6 Normalised change in flow, inundated area and volume of water stored within the reach for the period 8 to 17 November2000. Normalised values are obtained by setting the value of each variable at the second flood peak to 1.


dominant components of net storage change are likely to befloodplain dewatering and infiltration loss to the floodplainsubsurface.

The image data acquired on the 8th November 2000 atpeak flow (Fig. 5a) shows substantial floodplain inundation,and along much of the reach the water level is above that ofthe near-channel embankments such that the floodplainflow is contiguous with that in the main stem. The floodplainisland on which the small town of Upton-on-Severn is built isvisible clearly in the centre of the image. Upton experiencesregular flooding during which riverside properties areflooded and the roads to the settlement are impassable,although much of the town remains dry. It appears fromthe imagery that there is no direct linkage between themain floodplain flow to the north of the island and the inun-dated area along the island’s western flank. This connectionseems to be blocked along the line of road which is presum-ably embanked and this would imply that flooding to thewest of the island must occur as a result of backwater flowalong a minor tributary valley which enters the main stem tothe south of the island. However, this is misleading as inreality a culvert beneath the road to the north of the islanddoes allow some floodplain water to take this route. Even athigh flow a number of cross-floodplain embankments (prin-cipally roads and a disused railway line) remain predomi-nately dry but, again, culverts through these featuresmean that the floodplain water is not compartmentalisedat this very high stage. Rather, the inundation pattern is lar-gely controlled by the gross valley morphology and, in some(but not all) areas, the flood shoreline abuts steep valleysides which determine its location.

As the floodplain dewaters (Fig. 5b–d) and the water le-vel in the main stem drops, the floodplain inundation be-

comes increasingly compartmentalised. By the time of thesecond image on the 14th November 2000 the dischargehad fallen by 53% from its peak to 312 m3 s�1. The inundatedarea had reduced by 19% and the stored volume by 39%. Theembankments on either side of the channel are now dryalong the majority of the reach, indicating that the flood-plain flow is largely disconnected from flow in the channel.Connectivity from channel to floodplain still exists in severalplaces, but these become increasingly limited as the mainstem water level falls. Despite the discharge falling belowbankfull after the 14th, a substantial area of the floodplainremains inundated, indicating that flood water takes a con-siderable time to return to the channel and drain from thereach. The images acquired on the 15th and 17th Novembershow that the compartmentalisation of the floodplain waterbecomes more pronounced and increasingly fragmented.Hedges along field boundaries become emergent abovethe water level and are picked out by the Snake processingalgorithm as dry strips of land as a result of increased radarbackscatter from these features.

Drainage of the floodplain seems to take place via thenetwork of minor drainage channels on the floodplain, withparticular ‘catchment areas’ being drained preferentially(e.g., the dry area to the east of the Upton island at co-ordi-nates x = 386,500, y = 239,500 which is clearly demarcatedby the drainage network). Such preferential drainage of re-gions must reflect compartmentalisation of the floodplainand the fact that different parts of the floodplain drainagenetwork show variable levels of connectivity to the mainchannel. As hypothesised above, infiltration may also playa role in the dewatering of the floodplain. A first order esti-mate of floodplain infiltration can be obtained because weknow the inundated area at the time of each image and


can estimate an average saturated hydraulic conductivity,Ksat [L T�1], for the floodplain sediments. A limited boreholesurvey using a hand auger showed that the floodplain isunderlain by near uniform clay-silt sediments to a consider-able depth (i.e., below the limit of hand augering). This uni-formity is likely to be due to active re-working andhomogenisation of floodplain sediments by channel migra-tion over the Holocene for this reach and is consistent withthe pattern found at other sites we have sampled within theSevern basin (Bates et al., 2000). The high clay content sug-gests measurements of Ksat for these sediments will yieldvalues from 1 · 10�6 to 1 · 10�7 m s�1 with considerablespatial variability within this range. Similar to other sites,the alluvial sediments are also likely to be underlain at somedepth by gravel layers resulting from channel meandering(e.g., Haycock and Burt, 1993). These sediments will likelyhave higher Ksat values in the range 1 · 10�4–1 · 10�6 m s�1, and may act as localised preferential flowpaths, but are likely to comprise only a fraction of the totalfloodplain sediments. Moreover, despite the likely spatialvariability and large range of individual Ksat measurements,the average effective value of Ksat for the whole floodplainis likely to be much more constrained. As the floodplain sed-iments are predominately comprised of a relatively homo-geneous alluvium we here assume an average effectivefloodplain Ksat value of 0.5 ± 0.3 · 10�6 m s�1. The productof this value and the average inundation extent for the per-iod between each image acquisition gives a probable rate offloodplain dewatering, and hence a first order estimate ofthe volume lost through infiltration from image to image.This can be compared to the observed decrease in reachstorage which suggests that the infiltration accounts for only�21 ± 13% of total volume lost between the 8th and 14thNovember, �12 ± 7% between the 14th and 15th and�9 ± 4% between the 15th and 17th. It is noticeable thatinfiltration losses reduce in relative importance throughthe falling limb of the hydrograph because of accelerationin the rate of dewatering of the reach (see Table 1). Itshould also be noted that these estimates of floodplaindewatering and infiltration rates are average values in spacefor the entire floodplain and in time between image acqui-sitions. They therefore conceal a great deal of the spatialheterogeneity in floodplain inundation dynamics that isapparent when the individual images are compared.

The above data demonstrate that the dynamics of riverfloodplain inundation at this site is broadly controlled bythe mass flux entering the reach and subsequently deter-mined by the interaction between friction and gravity, assuggested by Ip et al. (1998). Floodplain inundation isclearly at least a two-dimensional process, but little evi-dence emerges at this scale of flow features that are evi-dently related to three-dimensional effects (e.g., Sellin,1964; Shiono and Knight, 1991; Sellin and Willets, 1996).The data thus further confirms that shallow water theoryis an appropriate starting point for the development of floodinundation models. At smaller, sub-reach scales (10–1000 m) the data also indicate an important role for localfeatures such as embankments, natural floodplain compart-ments, small drainage channels (e.g., Nicholas and Mitchell,2003) and vegetation (e.g., Nepf, 1999) in creating small-scale inundation features. However, such features may of-ten be characterised poorly, and are noticeably lacking from

scale model investigations of floodplain flow (e.g., Shige-Eda and Akiyama, 2003). Whilst we lack ASAR scenes fromthe hydrograph rising limb, the data are a major improve-ment on the single low resolution images that can typicallybe acquired for particular flood events using satellite SAR.

Flow modelling

Flood extent data has proved to be a valuable tool for cal-ibrating and validating numerical hydraulic models (e.g.,Bates and De Roo, 2000) as, unlike bulk hydrometric dataobtained from gauging stations, it is two-dimensional inspace, often available over wide areas and very sensitiveto small changes in water surface elevation and topogra-phy. Thus, to simulate flood extent correctly a model mustpredict accurately the water surface elevation over wholeriver reaches and include an accurate parameterisation oftopography and friction at an appropriate scale. To date,extent data used in hydraulic model validation studies havebeen obtained from ground survey (e.g., Romanowicz andBeven, 2003; Nicholas and Mitchell, 2003) or from imagerslocated on satellite platforms (e.g., Bates and De Roo,2000; Horritt and Bates, 2001). However, all data used thusfar have been in some way limited. Ground survey of floodextent is only possible over limited spatial areas, particu-larly if one wishes to repeat the survey over the courseof a flood in order to characterise wave propagation overthe floodplain, and is difficult to standardise between oper-ators. Remote sensing can overcome both these con-straints, however, as noted in the Introduction, the longrepeat times of satellite sensors means that sequences ofimages of flood extent changing dynamically through anevent only exist for the very largest basins where topogra-phy may not be well constrained. For regions where finespatial resolution and high accuracy topographic data areavailable, only single images of dynamic flooding have beenacquired previously (e.g., Horritt and Bates, 2002; Aronicaet al., 2002). The data described in ‘‘Data acquisition andanalysis’’ provide a solution to this problem and representpossibly the best data currently available for the validationof dynamic inundation models.

In this paper, we compare the output of a simple two-dimensional flood inundation model, LISFLOOD-FP (Batesand De Roo, 2000) to the airborne synthetic aperture radardata acquired for the Upton reach. LISFLOOD-FP is a raster-based inundation model specifically developed to takeadvantage of fine spatial resolution topographic data sets(Bates and De Roo, 2000). Channel flow is represented usingthe kinematic approximation to the full one-dimensional St.Venant equations solved using a fully implicit Newton–Raphson scheme. Sufficient boundary conditions for thechannel model are provided by an imposed flow at the up-stream end of the reach. The channel is discretised as a sin-gle vector along its centreline separate from the overlyingfloodplain raster grid. At each point along the vector the re-quired channel parameters are the width, Manning’s n valueand bed elevation. The latter data gives the bed slope andalso the bankfull depth when the channel vector is com-bined with the floodplain DEM. Each channel parametercan be specified at each point along the vector and the modelinterpolates linearly between these with the constraint that


for the kinematic channel approximation the gradient mustbe everywhere negative.

Floodplain flows are treated using a storage cell ap-proach first developed by Cunge et al. (1980) and imple-mented for a raster grid to allow an approximation to a2D diffusive wave. Here we solve a continuity equationrelating flow into a cell and its change in volume:

ohi;j

ot¼

Qi�1;jx � Qi;j

x þ Qi;j�1y � Qi;j

y

DxDyð1Þ

and a momentum equation for each direction where flowbetween cells is calculated according to Manning’s law (onlythe x direction is given here):

Qi;jx ¼

h5=3flow

n

hi�1;j � hi;j

Dx

!1=2

Dy ð2Þ

where hi,j is the water free surface height at the node (i, j),Dx and Dy are the cell dimensions, n is the Manning’s fric-tion coefficient, and Qx and Qy describe the volumetric flowrates between floodplain cells. Qy is defined analogously toEq. (2). The flow depth, hflow, represents the depth throughwhich water can flow between two cells, and is defined asthe difference between the highest water free surface inthe two cells and the highest bed elevation. These equa-tions are solved explicitly using a finite difference discreti-sation of the time derivative term:

tþDthi;j � thi;j

Dt¼

tQ i�1;jx � tQ i;j

x þ tQ i;j�1y � tQ i;j

y

DxDyð3Þ

where th and tQ represent depth and volumetric flow rate attime t, respectively, and Dt is the model time step. To pre-vent the build up of oscillations in areas of deep water onthe floodplain with low free surface gradient we imple-mented a flow limiter (Horritt and Bates, 2001) of the form:

Qi;jx ¼ min Qi;j

x ;DxDyðhi;j � hi�1;jÞ

4Dt

!ð4Þ

This value is determined by considering the change in depthof a cell, and ensuring it is not large enough to reverse theflow in or out of the cell at the next time step. For each cellthe flux is calculated using both the Manning equation andthe flow limiter, DxDy(hi,j � hi�1,j)/4Dt, with the minimumvalue being chosen. A complete description of the model isgiven in Bates and De Roo (2000) and Horritt and Bates(2001).

The above model was applied to the Upton site using agrid spatial resolution of 18 m. This represented a compro-mise between computational cost and a need to representaccurately the floodplain micro-topography present in theLiDAR data. The model grid was constructed by areal aver-aging of the 3 m resolution ‘bare earth’ DEM to this coarserscale, however this was found to ‘smear out’ key topo-graphic features such as embankments. UK Ordnance SurveyLandLine data (a vector data set of features at 1:2500 scalein rural areas) were, therefore, used to identify theseembankments and retain their height values at the modelgrid scale. The channel centerline was discretised with�200 points. However, for simplicity the width, slope andManning’s n value were assumed constant and parameter-ised from the channel bathymetry data described above.

This resulted in a model channel 55 m wide, with a slopeof 0.0003 m m�1. The hydrograph shown in Fig. 3 was usedto characterise the time-varying mass flux into the headof the reach. A sensitivity analysis showed that small errors(up to ±6 h) in the timing of the hydrograph that may haveresulted from the approximation used to derive the flowat this point made a negligible difference to the model re-sults given the long, broad peaked hydrograph and thelength (�10 days) of the flood event. LISFLOOD-FP simula-tions were run with a time step of 5 s for the full 23 day dy-namic event from 13:45 on 28th October 2000 to 01:45 on20th November 2000. This resulted in �400 k time stepsfor a grid of 333 · 683 cells (giving �230 k cells in total).Each simulation took approximately 12 h on a 2.7 GHz PC.

Model predictions of inundation extent at the time ofeach SAR overpass were compared with the observed imagesof flood extent (Fig. 5) using the measure of fit:

F ¼ NumðSmod \ SobsÞNumðSmod [ SobsÞ

ð5Þ

where Smod and Sobs are the sets of domain sub-regions (pix-els, elements or cells) predicted as flooded by the modeland observed to be flooded in the satellite imagery, andNum(Æ) denotes the number of members of the set. F there-fore varies between 0 for a model with no overlap betweenpredicted and observed inundated areas and 1 for a modelwhere these coincide perfectly. As with the calculationsof the observed inundated area and storage, F was onlyevaluated for pixels which appear in all four ASAR images.Friction parameters in the model were spatially disaggre-gated into a single value for the channel (nch) and a singlevalue for the floodplain (nfp) specified in terms of the Man-ning coefficient, n. As these lumped energy loss parameterscan only be crudely estimated a priori the model was cali-brated. To achieve this, nch and nfp were allowed to varywithin a physically realistic range to maximise the measureof fit between observed and predicted inundation extent inthe first ASAR image acquired on 8th November. This cali-bration study resulted in values of nch of 0.018 and nfp of0.04, respectively, which were then held constant through-out the simulation. Sensitivity studies showed that thechoice of image used to calibrate the model made no signif-icant difference to the resulting coefficients, which sug-gests that the assumption of parameter stationarity was areasonable one in this case. This is physically reasonable,as friction in deep alluvial channels such as this is largelya function of the grain size and channel geometry, both ofwhich will not change significantly during a flood of thisscale. A comparison of model and observed results for eachimage is shown in Fig. 7, whilst values of the F coefficientare given in Table 2.

Fig. 7 and Table 2 show that at around peak flow on the8th November the model does an excellent job at simulatingthe observed inundation extent, with almost no under-pre-diction by the model and only a very small amount ofover-prediction. Moreover, pixels that are incorrectly pre-dicted as flooded on the 8th November are all within closeproximity (<50 m) of the observed shoreline. Given errorsin the observed topography data of �10 cm rmse this islikely to be as good as can realistically be achieved by anynumerical hydraulic model. As the falling limb continues

Figure 7 Comparison of inundation extent predicted using LISFLOOD-FP with that predicted using the ASAR imagery: (a) 8November 2000; (b) 14 November 2000; (c) 15 November 2000; (d) 17 November 2000. Light blue indicates areas predicted as floodedusing both LISFLOOD-FP and the ASAR; dark blue represents areas predicted as flooded by the model but lying outside the limit of theASAR swath; red indicates areas predicted as inundated in LISFLOOD-FP but not in the ASAR; yellow indicates areas predicted asinundated in the ASAR but not LISFLOOD-FP; the underlying grey scale represents the DEM used in the model, with the lighter bandshowing the extent and location of the ASAR swath for each overflight. The blue vectors show the drainage network.


the model performance remains good, but the overall skilllevel falls from F = 0.89 on the 8th to F = 0.73 on the 17th.In each comparison, the model tends to over-prediction,although the response is spatially variable and areas of un-der-prediction also occur. Even by the 17th November thepredicted inundation extent is still a coherent match to

the observed data and the model may still be regarded asrelatively skilful. Areas of over-prediction tend to relateto regions of the floodplain that have drained rapidly alongparticular drainage networks, such as the area to the east ofthe Upton island noted in ‘‘Data acquisition and analysis’’above. Over-prediction here is unsurprising as these

Table 2 Goodness of fit between inundation extentobserved using airborne synthetic aperture radar and thatpredicted by the LISFLOOD-FP hydraulic model

Date F, full image F, common areas

8 November 2000 0.8756 0.890914 November 2000 0.7793 0.792415 November 2000 0.7800 0.784717 November 2000 0.6777 0.7251


features are not represented at the model grid scale, andare only partially captured in the LiDAR topography becauseof their small width (<1 m) and tendency to be lined by rel-atively dense vegetation which increases height errors. Con-sequently, the model retains excess water in particulartopographic compartments and, as a result, does not simu-late the drying of embankments on either side of the chan-nel correctly at these locations (for example the floodcompartment on the right bank at the downstream end ofthe reach). This may also be a result of the simplified repre-sentation of shallow water flow used in the model, the fail-ure to include hydrological components of the floodplainmass balance (infiltration, direct precipitation, evapotrans-piration and local runoff) or the fact that at higher flowsinundation may simply be easier to predict as the shorelinelies on steeper gradient slopes at the back of the floodplainor is well constrained by the valley sides or embankments.In the latter case, inundation extent becomes a less goodsurrogate measure for water depth and models may predictwater surface elevation poorly but still get inundation ex-tent correct.

As noted in ‘‘Data acquisition and analysis’’, we hypoth-esise that of the hydrologic components only infiltration willbe a significant volume loss for this flood event and our pre-vious analysis has suggested that this process accounts from9 ± 5% to 21 ± 13% of the net storage change within thereach between ASAR images. We define significant in thiscontext as being a volume comparable to the likely errorin gauged flow (±20%).

To test the proposition that failure to account for infil-tration losses may explain the deterioration in model per-formance during the hydrograph falling limb we includedan infiltration loss from each flooded cell based on the esti-mated saturated hydraulic conductivity of the floodplainsediments. This is a clearly only a first order approximationto the true complexity of floodplain hydrologic processes(see Woessner, 2000), but is a useful initial indication ofwhether infiltration loss is likely to have a significant effecton inundation extent predictions for this reach. Simulationswere run for Ksat values of 0.5, 1.0 and 2.0 · 10�6 m s�1

assuming a uniform value for the entire floodplain. Theseresults showed that whilst the volume of water lost to flood-plain sediments was as anticipated by the calculations ofinfiltration losses reported in ‘‘Data acquisition and analy-sis’’, the impact on predicted inundation extent was, in thiscase, minor. Values of the measure of fit, F, were changedby a maximum of only ±1% and are therefore small com-pared to other uncertainties. This is likely to be due tothe topography of the floodplain and the fact that absolutewater surface elevation change due to infiltration betweenimage acquisitions is actually quite low given the low Ksat

value of floodplain alluvial sediments. Hence there arefew large areas of shallow ponded water where the cumula-tive infiltration loss between ASAR images is sufficient tochange the modelled floodplain state from wet to dry. Theglobal measure of fit used to evaluate the model may alsoobscure compensating differences. The impact of infiltra-tion on inundation predictions clearly merits further investi-gation, however our initial conclusion is that at this site thiseffect is small and that the deterioration in model perfor-mance during floodplain dewatering is more likely to bedue to a failure to characterise minor floodplain drainagechannels correctly within the model or inherent deficienciesin the model formulation.

Conclusions

This paper has described the most detailed data set yetavailable to characterise the dynamics of reach scale riverfloodplain inundation. This consisted of four fine spatial res-olution images of flood extent acquired using an airbornesynthetic aperture radar (ASAR) during a large flood eventon a �16 km reach of the River Severn, west central Englandwhich occurred during November 2000. These data weresupplemented by a high vertical accuracy topographic sur-vey using an airborne laser altimeter (LiDAR) acquired at aresolution commensurate with the ASAR data. In combina-tion, these data allowed an analysis of the fluid dynamicsof river floodplain inundation at this scale. This re-con-firmed shallow water theory as an appropriate vehicle forunderstanding and modelling river flood inundation at thereach scale. However, the ASAR data also highlighted theimportance of local drainage networks, topographicallydelimited floodplain compartments, embankments and cul-verts in controlling inundation patterns at smaller (10–1000 m) scales during the hydrograph falling limb. Such fea-tures may often be poorly characterised even in fine spatialresolution topographic surveys and may, therefore, be diffi-cult to incorporate in numerical models of flood inundation.The floodplain at this site seemed to drain predominatelyunder gravity along networks of drainage channels on thefloodplain, with infiltration to floodplain sediments being asmall, but perhaps significant, component of the total vol-ume loss. However, inclusion of this infiltration loss param-eter in a numerical hydraulic model of this site showed onlya minor impact on predicted inundation extent.

Hydrograph data in conjunction with the LiDAR topo-graphic survey allowed construction of a typical two-dimen-sional numerical hydraulic model for this reach. Usingspatially uniform and calibrated channel and floodplain fric-tion factors led to predictions of inundation extent thatshowed good correspondence to the observed data, partic-ularly at peak flow when inundation is more strongly con-trolled by the overall valley morphology, gravity andfriction as represented at the model grid scale. Here, modelperformance (F = 0.89) is close to the error limit of the ob-served data as the likely correspondence between the ASARimages of inundation and the true flood extent is likely to beno better (in terms of F) than �0.95 based on our experi-ence with satellite SAR systems (e.g., Horritt et al.,2001). During the falling limb model ability remained gooduntil the fourth image on the 17th November when the dif-


ferences between predicted and observed inundation be-come more marked and the value of the F performancemeasure decreased to 0.72. This is likely to be due to eitherthe increasing dependence of the inundation pattern onsmall-scale features not represented at the model gridscale, the simplified representation of shallow waterhydraulics included in the model or the fact that inundationextent at higher discharges may simply be easier to predict.However, for practical modelling studies correct simulationof peak inundation is the key requirement, with representa-tion of the fine detail of floodplain dewatering of secondaryimportance. Hence, this research confirms the conclusion ofprevious studies (Bates and De Roo, 2000; Horritt and Bates,2001) that storage cell models can, when calibrated, simu-late peak inundation extent adequately but may have prob-lems representing dynamic changes in inundation extentdue to their typical scale of application and simplifiedhydraulic formulation.

Of the data used in the paper it is clear that most uncer-tainty surrounds the gauged flow values, which may be in er-ror by as much as ±20% according to Ervine and Baird (1982).Prior to the availability of high accuracy, fine spatial resolu-tion remotely sensed data sets, topography and flood extentfor model validation were largely unknown and the domi-nant sources of uncertainty in flood inundation modellingstudies. With the resolution of these data constraints atten-tion now needs to turn to the measurement of dischargeduring flood flows as this may, in the future, prove to be amajor constraint on our ability to further analyse floodplainsystems and discriminate between different components ofthe floodplain hydrological mass balance.

Whilst the ASAR data described in this paper do not fun-damentally alter our view of the floodplain inundation pro-cesses, they do allow a more detailed understanding thanhas hitherto been possible and allow an ability to quantifybasic parameters (floodplain storage volumes, dewateringrates etc.) for the first time. The data also allow insight intothe relative importance of factors controlling floodplaininundation at a variety of scales as well as providing anexcellent benchmark test data set for numerical hydraulicmodels. Further development of dynamic models to predictinundation extent depends on access to high accuracy syn-optic images at multiple points through an event. This paperhas demonstrated the utility of ASAR data for this task and itis hoped this will lead to model improvements in the future.

Acknowledgements

The work reported in this paper was funded by the UKEngineering and Physical Sciences Research Council GrantNumbers GR/S17116 and GR/S76304/01. The authors are ex-tremely grateful to the Environment Agency of England andWales and QinetiQ Ltd. for allowing access to their LiDARand ASAR data.

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