Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. 1The following paper is the final version prior to publication on 22 September 2015. are proposed, the way in which indicators could contribute to classification is discussed. All of the methods described in Table 1 consider a hierarchy of spatial units, but the degree to which they develop the other aspects of the conceptual approach proposed by Frissell et al.(1986) varies widely.2. Many of the frameworks focus entirely on hydromorphological processes and forms that are either directly measured or inferred. This is because interactions between processes and forms control the dynamic morphology or behaviour of rivers and their mosaics of habitats.Hydromorphological processes drive longitudinal and lateral connectivity within river networks and corridors, the assemblage and turnover of physical habitats, and the sedimentary and vegetation structures associated with those habitats.3. Some frameworks are conceptual, providing a way of thinking about or structuring analyses of river systems, and interpreting their processes, morphology and function (e.g. Frissell et al., 1986;Habersack, 2000;Fausch et al., 2002;Thorp et al., 2006;Beechie et al., 2010;McCluney et al., 2014). Some frameworks are more quantitative, generating one or more indices or classifications of spatial units that support assessment of river systems (e.g. Rosgen, 1994;González del Tánago and García de Jalón, 2004;Merovich et al., 2013;Rinaldi et al., 2013Rinaldi et al., , 2015a MacDonald, 2002;Brierley and Fryirs, 2005;Beechie et al., 2010; Rinaldi et al., 2013a Rinaldi et al., , 2015.In some cases, theoretical or historical analyses or consideration of specific future scenarios are used to develop space-time understanding that can support management decisionmaking (e.g. Buffington, 1997, 1998;Montgomery and MacDonald, 2002;Benda et al., 2004;Brierley and Fryirs, 2005;McCluney et al., 2014 , 1997, 1998Montgomery and MacDonald, 2002;Benda et al., 2004;Brierley and Fryirs, 2005;Merovich et al., 2013;Rinaldi et al., 2013Rinaldi et al., , 2015a. Furthermore, some of the frameworks include indicators of human pressures and their impacts (e.g. Merovich et al., 2013;McCluney et al., 2014;Rinaldi et al., 2013Rinaldi et al., , 2015a.6. Finally, although most frameworks could be described as incorporating processes to some degree, some methods are particularly process-based, even when processes are inferred from forms and associations rather than being quantified by direct measurements.Frameworks that consider temporal dynamics and trajectories of historical change (see point 4, above) are particularly effective in developing understanding of processes and the impacts of changed processes cascading through time and across spatial scales.Although the list of frameworks presented in Table 1 is far from comprehensive, ...

Flash-floods that occur in Mediterranean regions result in significant casualties and economic impacts. Remote imagebased techniques such as Large-Scale Particle Image Velocimetry (LSPIV) offer an opportunity to improve the accuracy of flow rate measurements during such events, by measuring the surface flow velocities. During recent floods of the Ardèche river, LSPIV performance tests were conducted at the Sauze-Saint-Martin gauging station without adding tracers. The rating curve is well documented, with gauged discharge ranging from 4.8 m 3 s −1 to 2700 m 3 s −1 , i.e., mean velocity from 0.02 m s −1 to 2.9 m s −1. Mobile LSPIV measurements were carried out using a telescopic mast with a remotely controlled platform equipped with a video camera. Also, LSPIV measurements were performed using the images recorded by a fixed camera. A specific attention was paid to the hydraulic assumptions made for computing the river discharge from the LSPIV surface velocity measurements. Simple solutions for interpolating and extrapolating missing or poor-quality velocity measurements, especially in the image far-field, were applied. Theoretical considerations on the depth-average velocity to surface velocity ratio (or velocity coefficient) variability supported the analysis of velocity profiles established from available gauging datasets, from which a velocity coefficient value of 0.90 (standard deviation 0.05) was derived. For a discharge of 300 m 3 s −1 , LSPIV velocities throughout the river crosssection were found to be in good agreement (±10%) with concurrent measurements by Doppler profiler (ADCP). For discharges ranging from 300 to 2500 m 3 s −1 , LSPIV discharges usually were in acceptable agreement (< 20%) with the rating curve. Detrimental image conditions or flow unsteadiness during the image sampling period led to larger deviations ranging 30-80%. The compared performances of the fixed and mobile LSPIV systems evidenced that for LSPIV stations, sampling images in isolated series (or bursts) is a better strategy than in pairs evenly distributed in time.

This paper investigates the potential of fast flood discharge measurements conducted with a mobile LSPIV device. LSPIV discharge measurements were performed during two hydrological events on the Arc River, a gravel-bed river in the French Alps: a flood greater than the 10-year return period flood in May, 2008, and a reservoir flushing release in June, 2009. The mobile LSPIV device consists of a telescopic mast with a remotely controlled platform equipped with a video camera. The digital video camera acquired sequences of images of the surface flow velocities. Ground Reference Points (GRPs) were positioned using a total station, for further geometrical correction of the images. During the flood peak, surface flow velocities up to 7 m/s and large floating objects prevented any kind of intrusive flow measurements. For the computation of discharge, the velocity coefficient was derived from available vertical velocity profiles measured by current meter. The obtained value range (0.72e0.79) is consistent with previous observations at this site and smaller than the usual default value (0.85) or values observed for deeper river sections (0.90 typically). Practical recommendations are drawn. Estimating stream discharge in high flow conditions from LSPIV measurements entails a complex measurement process since many parameters (water level, surface velocities, bathymetry, velocity coefficient, etc.) are affected by uncertainties and can change during the experiment. Sensitivity tests, comparisons and theoretical considerations are reported to assess the dominant sources of error in such measurements. The multiplicative error induced by the velocity coefficient was confirmed to be a major source of error compared with estimated errors due to water level uncertainty, free-surface deformations, number of image pairs, absence or presence of artificial tracers, and cross-section bathymetry profiles. All these errors are estimated to range from 1% to 5% whereas the velocity coefficient variability may be 10%e15% according to the site and the flow characteristics. The analysis of 36 LSPIV sequences during both events allowed the assessment of the flood discharges with an overall uncertainty less than 10%. A simple hydraulic law based on the geometry of the three sills of the Pontamafrey gauging station was proposed instead of the existing curve that is fitted on available gauging data. The high flow LSPIV discharge measurements indicated that this new curve is more accurate for high discharges since they are evenly distributed in a AE10% interval around it. These results demonstrate the interest of the remote stream gauging techniques together with hydraulic analysis for improving stageedischarge relationships and reducing uncertainties associated with fast flood discharges.

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