Improving flood models using LiDAR: progress and problems

Airborne scanning laser altimetry (LiDAR) is an important new data source for river flood modelling. LiDAR can give dense and accurate DTMs of floodplains for use as model bathymetry. Spatial resolutions of 0.5m or less are possible, with a height accuracy of 0.15m. LiDAR gives a Digital Surface Model (DSM), so vegetation removal software (e.g. TERRASCAN) must be used to obtain a DTM. An example used to illustrate the current state of the art will be the LiDAR data provided by the EA, which has been processed by their in-house software to convert the raw data to a ground DTM and separate vegetation height map. Their method distinguishes trees from buildings on the basis of object size. EA data products include the DTM with or without buildings removed, a vegetation height map, a DTM with bridges removed, etc. Most vegetation removal software ignores short vegetation less than say 1m high. We have attempted to extend vegetation height measurement to short vegetation using local height texture. Typically most of a floodplain may be covered in such vegetation. The idea is to assign friction coefficients depending on local vegetation height, so that friction is spatially varying. This obviates the need to calibrate a global floodplain friction coefficient. It’s not clear at present if the method is useful, but it’s worth testing further. The LiDAR DTM is usually determined by looking for local minima in the raw data, then interpolating between these to form a space-filling height surface. This is a low pass filtering operation, in which objects of high spatial frequency such as buildings, river embankments and walls may be incorrectly classed as vegetation. The problem is particularly acute in urban areas. A solution may be to apply pattern recognition techniques to LiDAR height data fused with other data types such as LiDAR intensity or multispectral CASI data. We are attempting to use digital map data (Mastermap structured topography data) to help to distinguish buildings from trees, and roads from areas of short vegetation. The problems involved in doing this will be discussed. A related problem of how best to merge historic river cross-section data with a LiDAR DTM will also be considered. LiDAR data may also be used to help generate a finite element mesh. In rural area we have decomposed a floodplain mesh according to taller vegetation features such as hedges and trees, so that e.g. hedge elements can be assigned higher friction coefficients than those in adjacent fields. We are attempting to extend this approach to urban area, so that the mesh is decomposed in the vicinity of buildings, roads, etc as well as trees and hedges. A dominant points algorithm is used to identify points of high curvature on a building or road, which act as initial nodes in the meshing process. A difficulty is that the resulting mesh may contain a very large number of nodes. However, the mesh generated may be useful to allow a high resolution FE model to act as a benchmark for a more practical lower resolution model. A further problem discussed will be how best to exploit data redundancy due to the high resolution of the LiDAR compared to that of a typical flood model. Problems occur if features have dimensions smaller than the model cell size e.g. for a 5m-wide embankment within a raster grid model with 15m cell size, the maximum height of the embankment locally could be assigned to each cell covering the embankment. But how could a 5m-wide ditch be represented? Again, this redundancy has been exploited to improve wetting/drying algorithms using the sub-grid-scale LiDAR heights within finite elements at the waterline.

Uses of airborne scanning laser altimetry for river flood modelling and coastal geomorphology

Two ongoing projects at ESSC that involve the development of new techniques for extracting information from airborne LiDAR data and combining this information with environmental models will be discussed. The first project in conjunction with Bristol University is aiming to improve 2-D river flood flow models by using remote sensing to provide distributed data for model calibration and validation. Airborne LiDAR can provide such models with a dense and accurate floodplain topography together with vegetation heights for parameterisation of model friction. The vegetation height data can be used to specify a friction factor at each node of a model’s finite element mesh. A LiDAR range image segmenter has been developed which converts a LiDAR image into separate raster maps of surface topography and vegetation height for use in the model. Satellite and airborne SAR data have been used to measure flood extent remotely in order to validate the modelled flood extent. Methods have also been developed for improving the models by decomposing the model’s finite element mesh to reflect floodplain features such as hedges and trees having different frictional properties to their surroundings. Originally developed for rural floodplains, the segmenter is currently being extended to provide DEMs and friction parameter maps for urban floods, by fusing the LiDAR data with digital map data. The second project is concerned with the extraction of tidal channel networks from LiDAR. These networks are important features of the inter-tidal zone, and play a key role in tidal propagation and in the evolution of salt-marshes and tidal flats. The study of their morphology is currently an active area of research, and a number of theories related to networks have been developed which require validation using dense and extensive observations of network forms and cross-sections. The conventional method of measuring networks is cumbersome and subjective, involving manual digitisation of aerial photographs in conjunction with field measurement of channel depths and widths for selected parts of the network. A semi-automatic technique has been developed to extract networks from LiDAR data of the inter-tidal zone. A multi-level knowledge-based approach has been implemented, whereby low level algorithms first extract channel fragments based mainly on image properties then a high level processing stage improves the network using domain knowledge. The approach adopted at low level uses multi-scale edge detection to detect channel edges, then associates adjacent anti-parallel edges together to form channels. The higher level processing includes a channel repair mechanism.

Long-chain n−3 PUFA: plant v. marine sources

Increasing recognition of the importance of the long-chain n-3 PUFA, EPA and DHA, to cardiovascular health, and in the case of DHA to normal neurological development in the fetus and the newborn, has focused greater attention on the dietary supply of these fatty acids. The reason for low intakes of EPA and DHA in most developed countries (0 center dot 1-0 center dot 5hairspg/d) is the low consumption of oily fish, the richest dietary source of these fatty acids. An important question is whether dietary intake of the precursor n-3 fatty acid, alpha-linolenic acid (alpha LNA), can provide sufficient amounts of tissue EPA and DHA by conversion through the n-3 PUFA elongation-desaturation pathway. alpha LNA is present in marked amounts in plant sources, including green leafy vegetables and commonly-consumed oils such as rape-seed and soyabean oils, so that increased intake of this fatty acid would be easier to achieve than via increased fish consumption. However, alpha LNA-feeding studies and stable-isotope studies using alpha LNA, which have addressed the question of bioconversion of alpha LNA to EPA and DHA, have concluded that in adult men conversion to EPA is limited (approximately 8%) and conversion to DHA is extremely low (< 0 center dot 1%). In women fractional conversion to DHA appears to be greater (9%), which may partly be a result of a lower rate of utilisation of alpha LNA for beta-oxidation in women. However, up-regulation of the conversion of EPA to DHA has also been suggested, as a result of the actions of oestrogen on Delta 6-desaturase, and may be of particular importance in maintaining adequate provision of DHA in pregnancy. The effect of oestrogen on DHA concentration in pregnant and lactating women awaits confirmation.

HESS Opinions ``Ensembles, uncertainty and flood prediction''

Ensemble predictions are being used more frequently to model the propagation of uncertainty through complex, coupled meteorological, hydrological and coastal models, with the goal of better characterising flood risk. In this paper, we consider the issues that we judge to be important when designing and evaluating ensemble predictions, and make recommendations for the guidance of future research.

Calibrating flood inundation models: identifying and addressing uncertainty in satellite observed flood extents

Improvements in the resolution of satellite imagery have enabled extraction of water surface elevations at the margins of the flood. Comparison between modelled and observed water surface elevations provides a new means for calibrating and validating flood inundation models, however the uncertainty in this observed data has yet to be addressed. Here a flood inundation model is calibrated using a probabilistic treatment of the observed data. A LiDAR guided snake algorithm is used to determine an outline of a flood event in 2006 on the River Dee, North Wales, UK, using a 12.5m ERS-1 image. Points at approximately 100m intervals along this outline are selected, and the water surface elevation recorded as the LiDAR DEM elevation at each point. With a planar water surface from the gauged upstream to downstream water elevations as an approximation, the water surface elevations at points along this flooded extent are compared to their ‘expected’ value. The pattern of errors between the two show a roughly normal distribution, however when plotted against coordinates there is obvious spatial autocorrelation. The source of this spatial dependency is investigated by comparing errors to the slope gradient and aspect of the LiDAR DEM. A LISFLOOD-FP model of the flood event is set-up to investigate the effect of observed data uncertainty on the calibration of flood inundation models. Multiple simulations are run using different combinations of friction parameters, from which the optimum parameter set will be selected. For each simulation a T-test is used to quantify the fit between modelled and observed water surface elevations. The points chosen for use in this T-test are selected based on their error. The criteria for selection enables evaluation of the sensitivity of the choice of optimum parameter set to uncertainty in the observed data. This work explores the observed data in detail and highlights possible causes of error. The identification of significant error (RMSE = 0.8m) between approximate expected and actual observed elevations from the remotely sensed data emphasises the limitations of using this data in a deterministic manner within the calibration process. These limitations are addressed by developing a new probabilistic approach to using the observed data.

Calibrating flood inundation models: methods for assessing model performance

Satellite observed data for flood events have been used to calibrate and validate flood inundation models, providing valuable information on the spatial extent of the flood. Improvements in the resolution of this satellite imagery have enabled indirect remote sensing of water levels by using an underlying LiDAR DEM to extract the water surface elevation at the flood margin. Further to comparison of the spatial extent, this now allows for direct comparison between modelled and observed water surface elevations. Using a 12.5m ERS-1 image of a flood event in 2006 on the River Dee, North Wales, UK, both of these data types are extracted and each assessed for their value in the calibration of flood inundation models. A LiDAR guided snake algorithm is used to extract an outline of the flood from the satellite image. From the extracted outline a binary grid of wet / dry cells is created at the same resolution as the model, using this the spatial extent of the modelled and observed flood can be compared using a measure of fit between the two binary patterns of flooding. Water heights are extracted using points at intervals of approximately 100m along the extracted outline, and the students T-test is used to compare modelled and observed water surface elevations. A LISFLOOD-FP model of the catchment is set up using LiDAR topographic data resampled to the 12.5m resolution of the satellite image, and calibration of the friction parameter in the model is undertaken using each of the two approaches. Comparison between the two approaches highlights the sensitivity of the spatial measure of fit to uncertainty in the observed data and the potential drawbacks of using the spatial extent when parts of the flood are contained by the topography.

Estimators in capture–recapture studies with two sources

This paper investigates the applications of capture–recapture methods to human populations. Capture–recapture methods are commonly used in estimating the size of wildlife populations but can also be used in epidemiology and social sciences, for estimating prevalence of a particular disease or the size of the homeless population in a certain area. Here we focus on estimating the prevalence of infectious diseases. Several estimators of population size are considered: the Lincoln–Petersen estimator and its modified version, the Chapman estimator, Chao’s lower bound estimator, the Zelterman’s estimator, McKendrick’s moment estimator and the maximum likelihood estimator. In order to evaluate these estimators, they are applied to real, three-source, capture-recapture data. By conditioning on each of the sources of three source data, we have been able to compare the estimators with the true value that they are estimating. The Chapman and Chao estimators were compared in terms of their relative bias. A variance formula derived through conditioning is suggested for Chao’s estimator, and normal 95% confidence intervals are calculated for this and the Chapman estimator. We then compare the coverage of the respective confidence intervals. Furthermore, a simulation study is included to compare Chao’s and Chapman’s estimator. Results indicate that Chao’s estimator is less biased than Chapman’s estimator unless both sources are independent. Chao’s estimator has also the smaller mean squared error. Finally, the implications and limitations of the above methods are discussed, with suggestions for further development.

An application of the DEA double bootstrap to examine sources of efficiency in Bangladesh rice farming

In this article we examine sources of technical efficiency for rice farming in Bangladesh. The motivation for the analysis is the need to close the rice yield gap to enable food security. We employ the DEA double bootstrap of Simar and Wilson (2007) to estimate and explain technical efficiency. This technique overcomes severe limitations inherent in using the two-stage DEA approach commonly employed in the efficiency literature. From a policy perspective our results show that potential efficiency gains to reduce the yield gap are greater than previously found. Statistically positive influences on technical efficiency are education, extension and credit, with age being a negative influence.

In vitro evaluation of fibrolytic enzymes as additives for maize (Zea mays L.) silage - III. Comparison of enzymes derived from psychrophilic, mesophilic or thermophilic sources

A completely randomised study was completed to examine the influence of fibrolytic enzymes derived from psychrophilic, (F), mesophilic, (L) or thermophilic (Ta) sources, applied at ensiling, on the chemical characteristics and in vitro rumen fermentation of maize silage, assessed using the Reading Pressure Technique (RPT). Treatments, all in triplicate, consisted of untreated maize forage or treated with preparations F, L, Ta or a mixture (1: 1, v/v) of F and L (FL), at two levels each, and ensiled for 210 days in plastic mini-silos. Addition of enzymes L decreased (P < 0.05) silage pH relative to the control, whereas enzyme Ta tended (P < 0.10) to reduce it. Preparations F, L and Ta tended to reduce (P < 0.10) the fibre contents of the silages, with effects being attributable to a decrease in the cellulose fraction. Starch contents were reduced (P < 0.05) in the treatments including enzyme F. End-point (96 h) gas production (GP) values did not differ among treatments, suggesting that enzymes did not change the total amount of fermentable substrate. However, consistent with the decrease in starch contents, adding enzyme F reduced (P < 0.05) GP at most incubation times. Addition of enzymes increased (P < 0.05) the initial (6 h) organic matter degradation (OMD) levels in all but one treatment (F), with increases of 14, 19, and 26% for preparations L, Ta, and FL, respectively, averaged across levels. Furthermore, the addition of enzymes increased (P < 0.05) the soluble OM losses, however, these increases did not fully account for the initial increase in OMD. The latter suggests that enzymes increased solubility and also altered silage structure, making it more amenable to degradation by ruminal microorganisms. As a result of the increase in OMD, without a concomitant increase in GP, the fermentation efficiency was greatly increased (P < 0.05) in enzyme treatments. Addition of enzymes to maize at ensiling, particularly those from the mesophilic and thermophilic sources used here, have the potential to increase the initial rate of silage OMD. (C) 2003 Elsevier B.V. All rights reserved.

Recovery of nitrogen from different sources following applications to winter wheat at and after anthesis

The effects of applying nitrogen (30 or 40 kg N/ha) to wheat crops at and after anthesis, after 200 kg N/ha had already been applied to the soil during stem extension, were studied in field experiments comprising complete factorial combinations of different cultivars, fungicide applications and nitrogen treatments. Actual recoveries of late-season fertilizer nitrogen (LSFN), as indicated by N-15 studies, interacted with cultivar and fungicide treatment, and depended on nitrogen source (Urea applied as a solution to the foliage, or as ammonium nitrate applied to the soil) and year. These interactions, however, were not reflected in apparent fertilizer recoveries ((N in grain with LSFN - N in grain without LSFN)/N applied as LSFN), or in the crude protein concentration. Apparent fertilizer recovery was always lower than actual recoveries, and declined during grain filling. Fertilizer treatments with higher actual fertilizer recoveries were associated with lower net renlobilisation of non-LSFN (net remobilised N = N in above ground crop at anthesis - N in non-grain, above ground crop at harvest). LSFN also increased mineral nitrogen in the soil at harvest even when applied as a solution to the foliage. These effects are discussed in relation to potential grain N demand. (c) 2006 Elsevier B.V. All rights reserved.

Pea-barley intercrops use nitrogen sources 20-30% more efficiently than the sole crops

Field pea (Pisum sativum L.) and spring barley (Hordeum vulgare L) were intercropped and sole cropped to compare the effects of crop diversity on the use of nitrogen sources in European organic crop-ping systems. Across a wide range of growing condi-tions pea-barley intercropping showed that nitrogen sources were used from 17 to 31% more efficiently by the intercrop than by the sole crops. Intercropping technologies offers the opportunity for organic cropping systems to utilize N complementarity between component crops, without compromising total crop N yield levels