System-scale detection of erosion and deposition is crucial in order to assess the transferability of findings from scaled laboratory and small field studies to larger spatial scales. Increasingly, synoptic remote sensing has the potential to provide the necessary data. In this paper, we develop a methodology for channel change detection, coupled to the use of synoptic remote sensing, for erosion and deposition estimation, and apply it to a wide, braided, gravel-bed river. This is based upon construction of digital elevation models (DEMs) using digital photogrammetry, laser altimetry and image processing. DEMs of difference were constructed by subtracting DEM pairs, and a method for propagating error into the DEMs of difference was used under the assumption that each elevation in each surface contains error that is random, independent and Gaussian. Data were acquired for the braided Waimakariri River, South Island, New Zealand. The DEMs had a 1Ð0 m pixel resolution and covered an area of riverbed that is more than 1 km wide and 3Ð3 km long. Application of the method showed the need to use survey-specific estimates of point precision, as project design and manufacturer estimates of precision overestimate a priori point quality. This finding aside, the analysis showed that even after propagation of error it was possible to obtain high quality DEMs of difference for process estimation, over a spatial scale that has not previously been achieved. In particular, there was no difference in the ability to detect erosion and deposition. The estimates of volumes of change, despite being downgraded as compared with traditional cross-section survey in terms of point precision, produced more reliable erosion and deposition estimates as a result of the large improvement in spatial density that synoptic methods provide.

In this paper, we describe an experiment in which the position of scientists with respect to flood risk management is fundamentally changed. Building on a review of three very different approaches to engaging the public in science, we contrast the normal way in which science is used in flood risk management in England and Wales with an experiment in which knowledge regarding flooding was co-produced. This illustrates a way of working with experts, both certified (academic natural and social scientists) and noncertified (local people affected by flooding), for whom flooding is a matter of concern, and where the event, flooding, is given agency in the experiment. We reveal a deep and distributed understanding of flood hydrology across all experts, certified and uncertified, involved in the experiment. This did not map onto the conventional dichotomy between 'universal' scientific expertise and 'local' lay expertise. By working with the event we harnessed, produced and negotiated a new and collective sense of knowledge, sufficient in our experiment to make a public intervention in flood risk management in our case-study location. The manner in which the academic scientists involved in the practice of their science were repositioned was radical as compared with normal scientific method. It was also radical for a more fundamental reason: the purpose of our experiment became as much about creating a new public capable of making a political intervention in a situation of impasse, as it was about producing the solution itself. The practice of knowledge generation, the science undertaken, worked with the hybridisation of science and politics rather than trying to extract science from it. key words flood risk management flooding scientific method participation co-production hybridisation

Abstract:High-resolution data obtained from airborne remote sensing is increasing opportunities for representation of small-scale structural elements (e.g. walls, buildings) in complex floodplain systems using two-dimensional (2D) models of flood inundation. At the same time, 2D inundation models have been developed and shown to provide good predictions of flood inundation extent, with respect to both full solution of the depth-averaged Navier-Stokes equations and simplified diffusion-wave models. However, these models have yet to be applied extensively to urban areas. This paper applies a 2D raster-based diffusion-wave model to determine patterns of fluvial flood inundation in urban areas using highresolution topographic data and explores the effects of spatial resolution upon estimated inundation extent and flow routing process. Model response shows that even relatively small changes in model resolution have considerable effects on the predicted inundation extent and the timing of flood inundation. Timing sensitivity would be expected, given the relatively poor representation of inertial processes in a diffusion-wave model. Sensitivity to inundation extent is more surprising, but is associated with: (1) the smoothing effect of mesh coarsening upon input topographical data; (2) poorer representation of both cell blockage and surface routing processes as the mesh is coarsened, where the flow routing is especially complex; and (3) the effects of (1) and (2) upon water levels and velocities, which in turn determine which parts of the floodplain the flow can actually travel to. It is shown that the combined effects of wetting and roughness parameters can compensate in part for a coarser mesh resolution. However, the coarser the resolution, the poorer the ability to control the inundation process, as these parameters not only affect the speed, but also the direction of wetting. Thus, high-resolution data will need to be coupled to a more sophisticated representation of the inundation process in order to obtain effective predictions of flood inundation extent. This is explored in a companion paper.

Recent research in fluvial geomorphology has emphasized the spatially distributed feedbacks amongst river channel topography, flow hydraulics and sediment transport. Although understanding of the behaviour of dynamic river channels has been increased markedly through detailed within-channel process studies, less attention has been given to the accurate monitoring and terrain modelling of river channel form using three-dimensional measurements. However, such information is useful in two distinct senses. Firstly, it is one of the necessary boundary conditions for a physically based, deterministic modelling approach in which three-dimensional topography and river discharge drive within-channel flow hydraulics and ultimately spatial patterns of erosion and deposition and therefore channel change. Secondly, research has shown that an alternative means of estimating the medium-term bedload transport rate can be based upon monitoring spatial patterns of erosion and deposition within the river channel. This paper presents a detailed assessment of the distributed monitoring and terrain modelling of river bed topography using a technique that combines rigorous analytical photogrammetry with rapid ground survey. The availability of increasingly sophisticated terrain modelling packages developed for civil engineering application allows the representation of topographic information as a landform surface. Intercomparison of landform surfaces allows visualization and quantification of spatial patterns of erosion and deposition. A detailed assessment is undertaken of the quality of the morphological information acquired. This allow some general comments to be made concerning the use of more traditional methods to monitor and represent small-scale river channel morphology.

[1] This study develops and assesses two methods for estimating median surface grain sizes using digital image processing from centimeter-resolution airborne imagery. Digital images with ground resolutions of 3 cm and 10 cm were combined with field calibration measurements to establish predictive relationships for grain size as a function of both local image texture and local image semivariance. Independently acquired grain size data were then used to assess the algorithm performance. Results showed that for the 3 cm imagery both local image semivariance and texture are highly sensitive to median grain size, with semivariance being a better predictor than image texture. However, in the case of 10 cm imagery, sensitivity of image semivariance and texture to grain size was poor, and this scale of imagery was found to be unsuitable for grain size estimation. This study therefore demonstrates that local image properties in very high resolution digital imagery allow for automated grain size measurement using image processing and remote sensing methods.

[1] Most past studies of river dune dynamics have concentrated on two-dimensional (2-D) bed forms, with constant heights and straight crest lines transverse to the flow, and their associated turbulent flow structure. This morphological simplification imposes inherent limitations on the interpretation and understanding of dune form and flow dynamics in natural channels, where dune form is predominantly three-dimensional. For example, studies over 2-D forms neglect the significant influence that lateral flows and secondary circulation may have on the flow structure and thus dune morphology. This paper details a field study of a swath of 3-D dunes in the Rio Paraná, Argentina. A large (0.35 km wide, 1.2 km long) area of dunes was surveyed using a multibeam echo sounder (MBES) that provided high-resolution 3-D detail of the river bed. Simultaneous with the MBES survey, 3-D flow information was obtained with an acoustic Doppler current profiler (ADCP), revealing a complicated pattern of dune morphology and associated flow structure within the swath. Dune three-dimensionality appears intimately connected to the morphology of the upstream dune, with changes in crest line curvature and crest line bifurcations/junctions significantly influencing the downstream dune form. Dunes with lobe or saddle-shaped crest lines were found to have larger, more structured regions of vertical velocity with smaller separation zones than more 2-D straight-crested dunes. These results represent the first integrated study of 3-D dune form and mean flow structure from the field and show several similarities to recent laboratory models of flow over 3-D dunes.

A strong relationship between dissolved organic carbon (DOC) and sulphate (SO 4 2À ) dynamics under drought conditions has been revealed from analysis of a 10-year time series (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002). Soil solution from a blanket peat at 10 cm depth and stream water were collected at biweekly and weekly intervals, respectively, by the Environmental Change Network at Moor House-Upper Teesdale National Nature Reserve in the North Pennine uplands of Britain. DOC concentrations in soil solution and stream water were closely coupled, displaying a strong seasonal cycle with lowest concentrations in early spring and highest in late summer/early autumn. Soil solution DOC correlated strongly with seasonal variations in soil temperature at the same depth 4-weeks prior to sampling. Deviation from this relationship was seen, however, in years with significant water table drawdown (4À25 cm), such that DOC concentrations were up to 60% lower than expected. Periods of drought also resulted in the release of SO 4 2À , because of the oxidation of inorganic/organic sulphur stored in the peat, which was accompanied by a decrease in pH and increase in ionic strength. As both pH and ionic strength are known to control the solubility of DOC, inclusion of a function to account for DOC suppression because of drought-induced acidification accounted for more of the variability of DOC in soil solution (R 2 5 0.81) than temperature alone (R 2 5 0.58). This statistical model of peat soil solution DOC at 10 cm depth was extended to reproduce 74% of the variation in stream DOC over this period. Analysis of annual budgets showed that the soil was the main source of SO 4 2À during droughts, while atmospheric deposition was the main source in other years. Mass balance calculations also showed that most of the DOC originated from the peat. The DOC flux was also lower in the drought years of 1994 and 1995, reflecting low DOC concentrations in soil and stream water. The analysis presented in this paper suggests that lower concentrations of DOC in both soil and stream waters during drought years can be explained in terms of drought-induced acidification. As future climate change scenarios suggest an increase in the magnitude and frequency of drought events, these results imply potential for a related increase in DOC suppression by episodic acidification.

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