USING REMOTELY SENSED DATA TO SUPPORT ...

SHSG 2005 Catchment Modelling - Pender et al

USING REMOTELY SENSED DATA TO SUPPORT FLOOD MODELLING ON THE RIVERS OUSE AND SEVERN S.P.Néelz1, G. Pender2, I. Villanueva3 and N.G.Wright4 Heriot Watt University and University of Nottingham 1. INTRODUCTION The Easter floods of 1998 and the autumn floods of 2000 resulted in large areas of the UK being inundated by floodwater, sometimes on several occasions. In the autumn of 2000 ten thousand properties were flooded and eleven thousand people forced into temporary accommodation, businesses were closed and roads and railways brought to a standstill. The total cost of this damage and disruption has been estimated at £1B. All of the reviews1, 2 following these events have called for improvements in the management of flood risk in the UK. Computer models of flood inundation are essential tools to inform management decisions regarding flood risk. Their strength lies in their flexibility and the fact that they allow not only the testing of flood scenarios based on current catchment conditions, but also a dynamic perspective through various flood defence, climate change and development assumptions. In recent years, the construction of computer models for flood predictions has been influenced by the increasing availability of low cost high resolution remotely sensed data. Remotely sensed data sets that are becoming increasingly available include: • LiDAR (Light Detection And Ranging). An airborne mapping technique which uses a laser to measure the distance between the aircraft and the ground. This technique results in the production of cost-effective terrain and vegetation height maps with 1 point per 4m2 density or greater and vertical rms errors of ~15 cm. Vegetation height can also be related to frictional resistance and hence LiDAR data may be able to assist in the process of model calibration. Such data are now routinely collected by the Environment Agency for use in flood risk assessment. • SAR (Synthetic Aperture Radar) This is a class of active radar system used in this context for flood extent mapping. SAR systems are mounted on both satellite and airborne platforms, with the latter having significant advantages in terms of resolution, responsiveness and accuracy. Satellite SAR systems such as the ERS sensors and Radarsat have repeat times of the order of 10-35 days and a resolution of 12.5 m. Airborne SAR systems, such as those flown by DERA for the Environment Agency during the autumn 2000 floods, are capable of responsive mode operation and much higher resolution (0.51

Research Associate, School of the Built Environment, Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS 2 Professor of Environmental Engineering, School of the Built Environment, Heriot-Watt University, Riccarton, Edinburgh, EH14 4AS 3 Research Associate, School of Civil Engineering, The University of Nottingham, University Park, Nottingham, NG7 2RD 4 Reader, School of Civil Engineering, The University of Nottingham, University Park, Nottingham, NG7 2RD

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1m pixel size). SAR is also now being routinely used to provide another source of topographic data to complement LiDAR. Such systems are capable of providing data with horizontal resolution of ~5m and vertical accuracy in the range 0.5-1m at sampling rates of up to ~500km2 per hour. CASI (Compact Airborne Spectrographic Imager) is a hyperspectral optical system capable of measuring light intensity in ~270 narrow (3-4 nm) bands of the optical spectrum. The Environment Agency fly such a system simultaneously with their LiDAR instrument and the output is used to obtain high resolution (~4m) classifications of land use based on differential absorption and reflection of incoming radiation by different surfaces. In the flood modelling context CASI data can be used to identify features on floodplains and provide additional data on vegetation properties that may be of use in the parameterization of boundary friction in flood models.

In combination, LiDAR, SAR and CASI data are capable of providing high resolution data sets to assist in parameterizing and validating flood prediction models. This paper evaluates the benefits to be gained from the use of remotely sensed data sets to support flood modelling using two existing computer models of the River Severn in the location of Upton-upon-Severn and the River Ouse between Skelton and Naburn (York area). The data available consisted of LiDAR derived Digital Terrain Models (DTM) and SAR estimates of inundation extent.

2. THEORETICAL OVERVIEW 2.1 1D modelling of channel flow Most 1D models for open-channel flow are based on the one-dimensional St-Venant, or shallow-water, equations of volume conservation: ∂Q ∂A + =q (1) ∂x ∂t and momentum conservation: AQ Q ∂Q ∂  Q 2  ∂H +   + gA −g =0 (2) ∂x ∂t ∂x  A  K2 where Q is the flow (m3/s), A is the cross section area (m2), q is any lateral inflow (m3/s/m), x is the longitudinal channel distance (m), t is time (s), H is the water surface elevation above datum (m), g is the gravitational acceleration (m/s2), K is the channel conveyance, where K2= A2R4/3/n2, n is Manning’s roughness coefficient, R is the hydraulic radius, defined by R=A/P, where P is the wetted perimeter. These equations model unsteady non-uniform open channel flow whilst taking into account the longitudinal hydrostatic gradient and the frictional resistance of the bed. Details on open channel hydraulics are available in many references3,4. Resistance to flow causing a loss of

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momentum is aggregated into a single conveyance term K, which is normally computed versus water level at each cross-section prior to simulating flood propagation. To solve these equations the river is divided into a series of reaches between surveyed crosssections5. Water levels and velocities are then computed at these discrete locations and at discrete times using numerical methods including Finite Elements, Finite Differences (FD), methods of characteristics, and Riemann solvers. The most widely used methods are implicit FD schemes, that include the Preissmann 4-point scheme6 (used in ISIS and InfoWorks-RS) and the Abbott-Ionescu 6-point scheme6 (used in MIKE 11).

3. DATA AVAILABLE TO THE PROJECT 3.1 River Severn During the 2000 floods four airborne SAR (ASAR) scenes were collected for a ~16km reach of the lower river Severn around Upton-upon-Severn in west central England. The floodplain topography is complicated by structures including drainage ditches, embanked roads and a large area of relatively high ground on which most of the settlement of Upton-upon-Severn is built. The floodplain land use is predominately pasture with a lesser amount of arable crops. The floodplain topography down to the low water river level along this reach was mapped by the Environment Agency in March 2002 using their Optech ALTM2033 airborne laser altimeter (or LiDAR). This is a scanning LiDAR pulsing at 33 KHz mounted onboard a light aircraft typically flying at an altitude of ~800m and velocity of ~65ms-1. The instrument collects first and last return information and scans up to 19° off-nadir. At the typical flight altitude this results in a swath width of ~600m and a ground resolution of ~1 point per m2. The raw LiDAR data were collected in WGS84 co-ordinates and then converted to the local OSGB36 ellipsoid model from which they can be converted by post-processing to co-ordinates in terms of the British National Grid (BNG). Data quality was checked by the UK Environment Agency by comparing the LiDAR topography to data acquired in a simultaneous ground survey of a flat area of short vegetation in the centre of the test reach using differential GPS. The differential GPS generated topography data accurate to