Single satellite synthetic aperture radar (SAR) data are now regularly used
to estimate hydraulic model parameters such as channel roughness, depth and
water slope. However, despite channel geometry being critical to the
application of hydraulic models and poorly known a priori, it is not
frequently the object of calibration. This paper presents a unique method to
simultaneously calibrate the bankfull channel depth and channel roughness
parameters within a 2-D LISFLOOD-FP hydraulic model using an archive of
moderate-resolution (150 m) ENVISAT satellite SAR-derived flood extent maps
and a binary performance measure for a 30
Flooding of over one-third of the world's land area affected more than 2 billion people – 38 % of the world's population – between 1985 and 2003 (Dilley et al., 2005). Climate change forecasts also indicate that in the future there may be an increase in the frequency and pattern of flooding (European Environment Agency, 2012; European Commission, 2014; IPCC, 2014). One response to this global hazard has been an increasing demand for better flood forecasts (Schumann et al., 2009a). Flood inundation models have an important role in flood forecasting and there has been scientific interest in combining direct observations of flooding from remote sources with these inundation models to improve predictions because of the persistent decline in the number of operational gauging stations (Biancamaria et al., 2011a), as well as the reality that many river basins are inaccessible for ground measurement. Synthetic aperture radar (SAR) satellites have particular importance in this respect as they can discriminate between land and smooth open water surfaces over large scales. These microwave (radar) frequency satellites are capable of all-weather day/night observations and this makes them a particularly attractive option for observing floods. Currently active SAR satellites include RADARSAT-2, ALSOS-2/PALSAR-2, TerraSAR-X, TanDEM-X, Sentinel 1a and 1b and the COSMO SkyMed constellation. Historic data are also available from SAR satellites now out of operation such as ENVISAT, ERS1 and 2 and RADARSAT-1.
By processing SAR data, it is possible to produce binary maps of flood extent that can then be used either on their own or intersected with a digital elevation model (DEM) to produce shoreline water levels for model calibration and validation. Integration of SAR data with models is an established technique for reducing uncertainty in model predictions, as it updates/calibrates the model states/parameters with observed data (e.g. Andreadis et al., 2007; Biancamaria et al., 2011b; Domeneghetti et al., 2014; Giustarini et al., 2011; Garcia-Pintado et al., 2013, 2015; Hostache et al., 2009; Matgen et al., 2010; Mason et al., 2009, 2012; Montanari et al., 2009; Tarpanelli et al., 2013; Yan et al., 2014), with the aim of improving flood forecasts. Naturally, calibration of these hydraulic models is essential for accurate results, and calibration studies to date have largely focused on roughness. Aronica et al. (2002), Tarpanelli et al. (2013), Hall et al. (2005), Schumann et al. (2007) and Di Baldassarre et al. (2009a, 2010, 2011) have used flood extent maps to successfully find best-fit roughness parameter values. Mason et al. (2003) point to roughness being a dominant factor for shallow reaches in particular and Di Baldassarre et al. (2009b) found that the optimal roughness parameters depend on the timing of the SAR image and the magnitude of the flood event. Given this prior research, historic observations of flooding should have a particular role in model calibration and sensitivity testing.
The provision of good bathymetric data is also critical to the application of hydraulic models (Trigg et al., 2009; Legleiter and Roberts, 2009; Yan et al., 2015). Yet generally there are few ways to obtain bathymetry information for hydraulic models where no ground data measurements exist. River depth may be estimated (e.g. Durand et al., 2010 employed an algorithm based on the Manning equation or Moramarco et al., 2013 who created an entropy depth distribution using surface flow velocity data) or measured with optical satellites using reflectance as done by Legleiter and Roberts (2009) (though the method is best suited for clear and shallow streams). Hostache et al. (2015) also proposed a drifting GPS buoy to assimilate water elevation and slope data into a hydraulic model to define riverbed bathymetry, but overall passive and remote mechanisms are scarce. Spatially distributed river depths are rarely available and there is a strong argument that where channel geometry is a priori unknown it should also be estimated through calibration.
It has commonly been thought that channel geometry and roughness traded off against each other (e.g. as in the well-known Manning equation) and therefore that they could not be uniquely identified at the same time. However, Garcia-Pintado et al. (2015) estimated channel friction and spatially variable channel bathymetry together using water levels derived from a sequence of real SAR overpasses (3 m resolution data from the COSMO-SkyMed constellation of satellites) and the ensemble transform Kalman filter. Durand et al. (2008) demonstrated that estimates of depth and water (i.e. friction) slope could be derived simultaneously from synthetic observations of water surface elevation integrated with a hydraulic model, though this research related more specifically to depth of flow, rather than depth of channel. Yoon et al. (2012) were also able to derive bed elevations from similar synthetic data. Mersel et al. (2013) progressed this further by proposing a slope-break method to locate optimal locations to measure flow depth, through low to high flows over time, using synthetic data. Durand et al. (2008), Yoon et al. (2012) and Mersel et al. (2013) used synthetic altimetry data which were created within the context of the upcoming Surface Water and Ocean Topography (SWOT) mission that will be able to resolve rivers over 100 m wide only.
Research to date has therefore demonstrated the feasibility of calibrating hydraulic model parameters governing channel depth and channel roughness simultaneously. This has been achieved using the higher-spectrum-resolution (up to 50 m) SAR images of flood extent. But because pixel size is inversely proportional to orbit revisit time, high-resolution data are available only infrequently. There is thus some benefit to also exploring the use of existing moderate-resolution (50 to 300 m) SAR data (such as the archive of 150 m resolution ENVISAT wide swath mode) to understand more about how channel depth and friction can be identified concurrently using coarser-resolution SARs, and whether a single SAR flood map is sufficient to achieve this or if a sequence of flood maps is more beneficial.
Therefore, the aim of this paper is to draw on this prior research for simultaneous channel roughness and depth calibration and extend it to determine whether medium-resolution SAR data can be used to concurrently estimate channel friction and geometry parameters in a hydraulic model. If so, a secondary aim is to determine if a single SAR-derived flood map is sufficient to do this or if a sequence of flood maps is more useful. For this, the identifiability technique presented by Wagener et al. (2003), namely dynamic identifiability analysis (DYNIA) is utilised. The objective of this paper is therefore to test the utility of the DYNIA identifiability technique in this specific context to find the SAR images with high parameter information and locate the likely optimum parameter values. This methodology particularly uses flood extent with an accuracy-scoring method that disregards the correct detection of “no water” pixels.
In Sect. 2, we describe the methodology with information on the hydraulic model, the data needed to run it and the methods used to select the range of model parameters. There is also an introduction to the procedure used to process the satellite data and create flood extent maps. Section 3 describes the study area and data used, whilst Sect. 4 presents and discusses the results (including whether SAR observations at particular times during a flood or particular combinations of images are more successful). Conclusions are presented in Sect. 5.
We use the LISFLOOD-FP hydraulic model with the sub-grid formulation of Neal et al. (2012) to simulate flood flows. LISFLOOD-FP (Bates and De Roo, 2000) is a 2-D hydraulic model for subcritical flow that solves the local inertial form of the shallow water equations using a finite difference method on a staggered grid. As input, the model requires ground elevation data describing the floodplain topography, channel bathymetry information (river width, depth and shape), boundary condition data consisting of discharge time series at all inflow points to the domain, water surface elevation time series at all outflow points and friction parameters which typically distinguish different values for the channel and floodplain. Of these data, floodplain topography information is readily available from airborne and satellite digital elevation models, boundary condition data can be taken from ground gauges, hydrologic models or statistical distributions and friction parameters are typically estimated from lookup tables or calibrated. Channel bathymetry can be taken from ground-surveyed cross sections; however, for much of the planet no such measurements exist and are impossible to obtain remotely. In this situation, channel bathymetry is a priori unknown and it is therefore sensible to also treat it as a parameter that must be calibrated along with the friction.
In order to describe bathymetry as a calibrated variable in this experiment,
river channel depth was parameterised as a linear scaling of reach-average
width. In general, this linear approach will not be appropriate over an
entire river network where the reach-averaged width to depth relationship
would be expected to change with bankfull discharge. However, the width of
the river chosen as a test case for this paper is constant along the
simulated reach, while we assume the depth of tributaries has an
insignificant impact on the flooding on the main stem. In effect, the
optimisation problem therefore simplifies to estimating reach-averaged
bankfull depth and Manning's
General scheme of the three processing steps of the flood detection algorithm.
Contingency table (after Stephens et al., 2014 and Mason, 2003).
We used Latin hypercube sampling (LHS) to take 1000 samples of the two
uncertain LISFLOOD-FP parameters
Because SAR satellites are capable of all-weather day and night observations
and can distinguish the differences between land and open water signal
returns, they are particularly useful for observations of flooding. To derive
flood extent maps from the SAR images, we adopted the method proposed by
Matgen et al. (2011) and developed by Giustarini et al. (2013) and
Chini et al. (2016). This method has three
steps as illustrated in Fig. 1. Firstly, the probability density function
(pdf) of the open water backscatter values in the SAR data is estimated.
This requires identification of the bimodal aspect to a histogram of
backscatter values so that “open water” values can be recognised from other
backscatter values. A theoretical pdf of water backscatter is then fitted to
this histogram using nonlinear regression techniques. The backscatter
threshold value (Th
In the last step, a reference image is used to remove pixels from the flood map that do not change between the flood and non-flood images (Hostache et al., 2012) – i.e. pixels which have “water-surface-like” radar responses and could be either bodies of permanent water or smooth surfaces such as car parks or flat roofs. This third step creates the final binary map of flood extent. Errors inherent in the SAR processing are, for simplicity, not considered in this paper.
We compare these SAR-derived flood maps against the simulated flood maps generated from LISFLOOD-FP output at the equivalent time step by using a contingency matrix shown in Table 1. Flood maps are compared pixel to pixel to determine if there is agreement or disagreement between the two paired maps on whether there is surface water present or not.
From this, a binary pattern performance measure is used to give a
deterministic indication of how well each LISFLOOD-FP-simulated flood map
has represented the observed data (Mason, 2003; Stephens et al.,
2014). We chose to use the critical success index (CSI, Eq. 1) as this
measure does not consider “correct rejections” – (D) in Table 1 – in
the calculation (Bates and De Roo, 2000; Horritt et al., 2001;
Aronica et al., 2002) and it weights over- and under-prediction
equally – B and C – respectively. CSI scales between 1
(indicating perfect skill in the model) and 0 (indicating no skill in the model).
Before comparing SAR and LISFLOOD-FP model results, an independent remote dataset is used to illustrate the impact of observation errors and gaps inherent in the SAR data from processing. This validation step makes use of a very high-resolution (0.2 m) aerial photograph taken by the Environment Agency of England and Wales (EA) on 24 July 2007 from an aircraft passing over at 11:30 GMT (details within Giustarini et al., 2013). A flood map shapefile was created from this imagery by manual definition of the flood boundary. This was then converted and upscaled to a raster with the same spatial resolution (75 m) of the LISFLOOD-FP model results. Both the ENVISAT data and the LISFLOOD-FP results (the highest-scoring models) are compared with these aerial data. A figure showing these flood extents and the CSI results from this comparison are given in Sect. 4.1 below.
To determine most likely values for
The first stage in the DYNIA method is to rescale the “objective function” (i.e. CSI scores) so that they add up to 1, which is done by dividing each model result by the sum of all scores. Next, computing the cumulative distribution of the rescaled objective function transforms the objective function into a support measure which sums to unity – the “cumulative support” – so that each support measure may be comparable. To obtain the information content (IC), a confidence limit is applied to the rescaled objective functions to exclude outliers. The width of the confidence limit depends on how the best-performing parameters are spread within the parameter space: a wide confidence limit suggests that the parameters are distributed within the parameter space evenly and IC is low, whereas a narrow confidence limit suggests that the best-performing parameters are located within a smaller range and IC is higher. To normalise results for these data, a transformation measure was used (1 minus the width of the confidence limits over the parameter range, normalised to run from 0 to 1), so a value close to 1 is equivalent to a high IC. The IC can have any value between 0 (no information in that observation for parameter identification purposes) and 1 (observation is most informative for the parameter). The IC results are shown in Sect. 4.2 below.
The second stage in DYNIA is to find the identifiability by locating where in the parameter–time space most parameter information can be found. This is achieved by examining a plot of cumulative support against a parameter value. Any deviation from a straight line gradient of this cumulative support indicates whether the parameter is conditioned by the objective function or not. The stronger the deviation, the stronger the conditioning/identifiability of the parameter variable. This is done using the marginal parameter distributions – interactions are therefore only implicitly accounted for. The final stage is to organise the data into bins and calculate the gradient of the cumulative support between them. The results from this examination are shown in Sect. 4.3 below. These results are represented using plots of the gradient of the cumulative support value versus the parameter of interest to indicate the strength of the identifiability in each case. The IC and identifiability for all single SAR acquisitions are shown along with particular SAR combinations/groupings: by flood event and by position in the flood hydrograph as detailed in Sect. 3.2 and Table 3.
The original method proposed by Wagener et al. (2003) recommends a pre-selection of models before stage 1 by using only the top 10 % performing models. We deviate from this original method by using the complete sample of 1000 sets of CSI scores since we found this gave a clearer overview picture of identifiability with our data.
The objective of this paper is to determine if a grouping of SAR data provides more information than single data. Here, the method of obtaining the CSI “group” score is also a small departure from the original DYNIA method. These group scores are determined by multiplying each single model/SAR flood map CSI result with the CSI score of the next SAR flood map until all members of the particular group have been added. The unique combinations which comprise these groups are described in Table 3. This combining of CSI scores is done for results from each of the 1000 models/parameter scenarios. The next step is the same as for single CSI scores as described above – i.e. to rescale the objective function and compute the cumulative support. So, although multiplying CSI values will reduce the grouped score, it has no bearing, as it is the changes to the gradient of the cumulative support value that indicates parameter identifiability, not the CSI scores themselves. The group IC and identifiability results shown in Sect. 4.2 and 4.3 result from SAR data that was grouped by this multiplication of CSI scores.
The area around Tewkesbury (UK), located at the confluence of the Rivers Severn and Avon is our test location. Figure 2 illustrates the 30.5 km by 52.4 km model domain, showing the two main rivers and their tributaries.
Extent of the River Severn model.
Two separate LISFLOOD-FP models were created to test the methodology. Both models are at 75 m spatial resolution and use the same background DEM. Additionally, both models use the same gauged inflows and have a rectangular-shaped channel. At the lower end of the model, a “free” downstream boundary condition was applied with a fixed energy slope of 0.00007, based on the average valley slope.
The differences between the two separate models are in how bankfull channel
depth and Manning's channel roughness values are obtained. First, an
“observed” model was created using surveyed cross sections of the main
rivers to determine channel width and depth with a fixed Manning's channel
roughness parameter of 0.038 (a value representing a main channel, which is
clear with some winding and presence of stones/vegetation, from
Chow, 1959). The cross-section survey data were provided by the EA. Second, a “test” model was created in
which the depth parameter
For both the test and observed models, the Manning floodplain roughness
value was set at a standard 0.06 for the entire domain. This is a reasonable
average for the floodplain which is mainly crop and grassland (0.03–0.04)
but with the presence of some trees (0.12) and brush (0.07). The Manning
values for the floodplain and the river channel (
The European Space Agency (ESA) sourced ENVISAT ASAR WSM acquisitions used with equivalent flow and return period data for rivers Avon and Severn; gauged data were obtained from the EA.
Description of SAR groupings.
The July 2007 flood extents as observed by aerial photography (on
24 July 2007 at 11:30 GMT,
Observed flows obtained from the EA were used as inflow to both models. Forcing flows come principally from the gauging station on the River Severn at Bewdley but with additional inputs from three tributaries of the River Severn: River Stour (at Kidderminster), River Salwarpe (at Harford Hill near Droitwich Spa) and River Teme (at Knightsford Bridge near Knightwick). For the River Avon, flows from the Evesham gauging station were used, with two additional flow contributions from the Avon tributaries Bow Brook (at Besford) and the River Isbourne (at Hinton). A smaller input from a wetland area west of Tewkesbury was also included, with flows scaled by area from the Salwarpe gauged flows.
The River Severn flood events of March 2007 (simulation period: 19 February–29 April 2007), July 2007 (simulation period: 5 June–12 August 2007), January 2008 (simulation period: 26 November 2007–25 February 2008) and January 2010 (simulation period: 4 January–18 February 2010) were modelled. The dates were chosen so the model would start at least 10 days before the start of the flood and end after flows had returned to within the banks.
Historic ENVISAT wide swath mode (WSM, 150 m resolution) data are available from the European Space Agency's ENVISAT catalogue. These were resized to 75 m resolution data. Previous research at this site has largely focused on the July 2007 flood event observations (Mason et al., 2012, 2014; Durand et al., 2014; Garcia-Pintado et al., 2013; Schumann et al., 2011). The present work makes use of other historic flood observations in this area – namely the floods of March 2007, January 2008 and January 2010. Details of the satellite acquisition times are shown in Table 2, along with hydrologic information on the flood taken from the gauging station at Saxons Lode in the middle of the model domain. Time to peak describes the number of hours between the start of the event and the peak of the flood. Flooding from sequential events or with high contributions from other sources such as groundwater will therefore have a greater time to peak.
We separated these 11 SAR observations into different categories by particular flood event (Sect. 4.3.2) or where the acquisition occurs on the flood hydrograph (Sect. 4.3.3). Table 3 shows how this segmentation of the 11 acquisitions into categories was devised.
In this paper, we compare the results of hydraulic model-generated flood maps with the SAR observations of flood extent in order to determine if the satellite data have information in terms of calibrating the model. However, with inherent errors in the SAR data from processing, it is worthwhile first to compare the SAR data with those from other available remote data to illustrate the impact of observation errors. For validation, the CSI score is calculated between the ENVISAT data and an aerial photograph of the River Severn taken on 24 July 2007.
Figure 3 illustrates the derived flood extent from these aerial data (Fig. 3a) with the ENVISAT WSM SAR-derived flood map (Fig. 3b) from the previous day. Highest-scoring LISFLOOD-FP simulation flood maps from the observed model (Fig. 3c) and test model (Fig. 3d) at the same time step as the ENVISAT data are included for comparison. The CSI results from this SAR aerial and SAR-LISFLOOD-FP model comparison are shown in Table 4.
CSI scores for July 2007 flood extent maps comparing results obtained using ENVISAT WSM SAR- and aerial-derived flood extents with hydraulic-model-generated flood extent.
It is clear that the observed and test LISFLOOD-FP models produce lower CSI scores with the SAR data than with the aerial data. This is to be expected, and other studies which have used higher-resolution SAR imagery for validation (e.g. Bates et al., 2006; Di Baldassarre et al., 2009a, 2010) have observed the same result. The aerial photograph-derived flood map was delineated manually and therefore has improved representation of flooding because there are no detection gaps in the flood extent, whereas SAR-derived flood extents rely on the correct detection of areas of water using a procedure which is vulnerable to issues of detection and processing. So while we may conclude that aerial imagery has the best level of detail in flood extent available here, these data can also be limited by observation extent and processing (i.e. manual delineation of the flood edge is still interpretive) and, as a resource, aerial imagery is not as frequently available as SAR data for observing flood events. It is also worth pointing out that, for the ENVISAT SAR data, describing flood extent using the semi-automated algorithm can be a faster solution than manually delineating flood extent from new photographs.
The scores and flood extent for the observed model are not better than the test model results as might be expected. This may be explained by the fact that while the bathymetry of the observed model does come from survey data, the (domain-average) channel roughness value is not calibrated in either model. While the test model had 1000 parameter-varying depth and roughness values, the observed model had a best estimate of domain-average channel roughness parameter (of 0.038). While appropriate for the main rivers, it is evident that the channel roughness value is not suitable for the narrower tributaries.
It is also of interest that when the aerial data are compared with the ENVISAT WSM SAR-derived flood maps (row 1, last column), CSI scores are similar to those obtained from the best hydraulic model results. This indicates that the hydraulic models are representing the observed flood extent for this flood accurately, within the limits of the available data. While sections of the flood are missing in the SAR data (for example, upper River Avon and River Severn) bias can be introduced. Ideally, these non-informative areas of the SAR data would be masked out to limit the impact, but with series of data each differently capturing a flood event this requires a more comprehensive analysis than available here. It is currently an active area of research; for example, Giustarini et al. (2016) propose flood probability maps from sequences of SAR data. These maps could be used to mask out low probability of flooding areas. Also Schlaffer et al. (2015) makes use of harmonic analysis to refine flood extent mapping – a mask could be created to obscure pixels with low signal to noise ratios.
Single SAR acquisitions are compared with LISFLOOD-FP modelled
flood maps.
The first step in the methodology is to examine the accuracy of the test
model with changing parameter value using CSI. The ENVISAT WSM SAR and
LISFLOOD-FP CSI results were plotted against the
The black areas in Fig. 4 show that a number of
Figure 4 also illustrates the covariance and a linear dependency between
the two parameters. This was observed in all the SAR data. Although the
choice of parameter range emphasises it, there is a slightly greater skill
score sensitivity to changes in
Previous SAR-based assimilation studies (Hostache et al., 2009;
Mason et al., 2009; Di Baldassarre et al., 2009a) show that
with a known and fixed channel bathymetry there is sufficient sensitivity in
the roughness parameter to enable calibration. The above findings indicate
that the sensitivity of
Consequently, an important result of this paper is that – in this particular
experimental set-up with channel roughness parameter
Table 5 presents IC results for depth parameter
Grouping SAR data boosts the IC scores considerably, as can be seen in the right-hand side columns of Table 5. Group IC scores are estimated after the SAR data have been grouped together and CSI scores combined as described in Sect. 2.4. Different SAR groupings were tested as illustrated in Table 3 including combinations according to flood event, position on the hydrograph as well as all SAR data.
For IC, the July 2007 flood now no longer outperforms the rest and instead
combinations of images, like the March 2007 flood event, have greater
information on
Information content for
Nevertheless, the number of SAR flood maps combined appears to be important
also since the all SAR and early falling limb (just over half of these
SAR images; Table 3) groupings emerge as providing the highest IC. The March 2007
flood grouping also contains twice as many members as the July 2007 or
January 2010 flood groupings and outperforms both. Clearly, incorporating
data from multiple observations improves IC since combining SAR images (and
CSI scores) improves the likelihood of extracting information on the unknown
parameters. However, it is not simply a question of numbers, otherwise
falling limb (combining 6 SAR flood maps for an IC score of 0.64) would
not be approaching the success of all SAR (combining 11 SAR flood maps for
an IC score of 0.68). Nor is greater information necessarily revealed by
removing poor scorers (the all SAR IC score reduces from 0.68 to 0.64 when the
four lowest-scoring flood maps are removed from this grouping). Instead, the
solution may lie in using SAR flood maps around the peak and falling limb of
the flood since combining falling limb and “rising limb” observations
together yields an IC score of 0.65 but combining falling limb and
peak observations together provides an IC score of 0.67. Further work and
data are necessary to draw any firm conclusions for the
The identifiability of
Identifiability against
From the CSI contour plots as illustrated in Fig. 4, we see that the best-performing model parameter combinations are distributed fairly evenly within the parameter space, so a 90 % confidence limit was also applied to the data prior to measuring the gradient of cumulative distribution of rescaled support values and creation of these following plots.
Figure 5 shows the identifiability plots for all single SAR data, numbered
as in Table 2. Because these plots do not generally have a strong peak,
identifiability is relatively weak for the individual SAR observations. The
strongest response here occurs for
Taken collectively, these data provide inconclusive results. This generally
weaker identifiability suggests that parameter
This section illustrates identifiability when data from individual SAR
images are combined into flood events as indicated in Table 3. An
important characteristic of the flood event identifiability plots is that
the SAR acquisitions are taken together in close sequence. Garcia-Pintado et
al. (2013) found that a tight sequence of images could improve
model predictions. Combining observations in this way appears to focus the
location of the
Identifiability against parameter
Figure 6 shows that the March 2007 and January 2008 events produce a
stronger identifiability between
Figure 7 looks at identifiability at three stages of a flood hydrograph for
the
Identifiability against
Previous studies have found that the scheduling of SAR images is important for calibration of models. Di Baldassarre et al. (2009b) found that identification of the optimal model parameters depended on the timing of the SAR image acquisition and the magnitude of the flood event. Garcia-Pintado et al.'s (2013) paper established that to improve forecasting of water levels in a model, regular observations during the rising limb and then less frequent observations during the falling limb gave most success. Additionally, Schumann et al. (2009b) cautioned that SAR images acquired during the wetting and drying phases of a flood could be showing floodplain connections and dewatering processes unconnected with the hydraulics represented by the model.
While here the number of SAR data within each category is limited, Fig. 7
shows there is still a difference in identifiability for these separate
phases. The strongest
The weakest identifiability for the
Identifiability against
Alternatively this divergence of findings for the optimum image time could be explained by the different experimental set-up and goals. Garcia-Pintado et al. (2013) made use of distributed and derived water levels to correct model inflow errors and improve model predictions with assimilation, whereas identifiability here makes use of SAR-derived flood extent to calibrate reach-averaged bathymetry and roughness parameters for the entire river network. Information obtained during the rising limb was the most useful time to correct inflows because the water level and channel volumes are most changing during this time, whereas this experiment, in locating the optimum bathymetry and roughness parameters, relies on mapping of flood extent (i.e. at bankfull and overbank). This is seen most usually in the peak and falling limb images where there is indeed flood extent but also where flows (at some locations within the model domain) are transitioning between channel and floodplain.
Figure 8 shows the identifiability result for all 11 SAR flood maps combined
and compares it with all the previous group results so far. As for the IC
results, this all SAR arrangement produces an observable improvement in
identifiability compared with the single SAR or flood event plots.
Although Sect. 4.3.1 shows that a single image does provide the
information needed to locate parameter
These results suggest that greatest information for parameter
The results above show that calibration is possible for the more dominant
depth parameter but that roughness is less easily located in this
simultaneous calibration methodology. So far, it is assumed that no ground
data are available to give prior information on either parameter and so the
ranges are deliberately broad. However, one or both parameters could be
constrained further with some knowledge of the catchment and standard look-up
tables (e.g. Phillips and Tadayon, 2006; Chow, 1959). Given
that even a cursory examination of Google Earth imagery shows regions of
meander and channel alteration, obstructions and changing vegetation along
the River Severn reach, the Manning channel roughness values are more
likely to lie between 0.035 and 0.055. This section shows that if we
constrain the
Figure 9 compares the identifiability for all SAR data for the full range
of models (roughness is not constrained; solid line) and for 236 models
which satisfy the constraint of having
The model responds to changes in channel friction by altering the speed of the flood wave and flow velocities. These results highlight the important reasons for calibrating this second parameter concurrently. If channel roughness were set too high, the flood wave would be delayed. If it were set too low, the flood wave would be too advanced.
Identifiability for 23 July 2007 at 10:27 GMT showing all data (solid
line) and with
This paper presents a methodology for dual calibration of bankfull depth and
channel roughness parameters of the LISFLOOD-FP sub-grid hydraulic model
using SAR data and a binary pattern classification measure based on flood
extent. Multiple models performed well initially, but by employing an
identifiability methodology we located the area of the parameter space with
highest information for the depth parameter
The methodology provides some information on which single and combinations of SAR flood maps would be most useful for calibration purposes. Single SAR flood maps would be sufficient to calibrate the depth parameter but the identifiability is much improved when multiple maps are combined. Combinations aligned according to particular flood events/magnitudes are not conclusively different, but using many or all available SAR images does offer a real improvement in identifiability. There are indications that combining maps with similar flood duration or stage of flood (i.e. SAR images acquired close to peak or just after) would be beneficial for calibrating the reach-average depth parameter, but further work is needed with more targeted observations than the 11 used here. For robustness, a good range of flood magnitudes should be used for calibration.
The channel roughness parameter
A benefit of this methodology is that, although we used gauged inflows within the model, in theory, the calibration methodology should work also with no recourse to ground data if good inflows can be simulated and a good DEM is available. The method also does not require a step to obtain water levels from the flood data. It does, however, make some simplifications and assumptions. First, the method assumes that as there are no errors in the return signals or processing of the ENVISAT WSM images and the derived flood maps therefore represent the true and full flood extent. In reality, there will be a chain of small errors in the processing of the data that would have an impact on the derived flood extent, and therefore also on the identifiability and IC results. This is particularly true for single SAR data which are compared against each other but perhaps less easily isolated in grouped SAR data as the combining of data smooths out errors and, by accumulation, compensates for perceived detection errors in the remotely sensed data. Understanding the impact of these individual errors on the final result would be an interesting follow-on experiment. The importance of the SAR resolution has not been tested here.
There is also error likely in the assumptions behind the model set-up. For
example, we assume that the channel depth can be approximated with a
parameter
The authors declare that they have no conflict of interest.
We thank the Environment Agency of England and Wales for providing the river cross-section data, DEM and gauging station data. The authors would also like to thank the editor and two reviewers for their valuable comments which helped to improve the manuscript.
Melissa Wood's contribution was supported by the National Research Fund of Luxembourg through the PAPARAZZI project (CORE C11/SR/1277979). Edited by: W. Wagner Reviewed by: C. M. Massari and G. J.-P. Schumann