EARTH SURFACE PROCESSES AND LANDFORMS Earth Surf. Process. Landforms 34, 2031–2046 (2009) Copyright © 2009 John Wiley & Sons, Ltd. Published online 13 November 2009 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/esp.1888 Quantifying the temporal dynamics of wood in large rivers: field trials of wood surveying, dating, tracking, and monitoring techniques B.J. MacVicar, 1 * H. Piégay, 2 A. Henderson, 3 F. Comiti, 4 C. Oberlin 5 and E. Pecorari 6 1 Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1 2 CNRS UMR 5600 – Environnement Ville et Société, University of Lyon, Site of École Normale Supérieure – LSH, 15 Parvis René Descartes, BP 7000, 69342, Lyon cedex 07, France 3 Division of Planning and Local Assistance, Northern District, California Department Of Water Resources, 2440 Main Street, Red Bluff, California, 96080-2356, USA 4 Faculty of Science and Technology, Free University of Bozen-Bolzano, via Leonardo da Vinci 7, 39100 Bozen-Bolzano, Italy 5 CNRS UMR 5138, Archéométrie et Archéologie, Université Claude Bernard Lyon 1, 40 boulevard Niels Bohr, 69622 Villeurbanne Cedex, France 6 Department of Environmental Science, Ca’ Foscary University, Santa Marta – Dorsoduro 2137 30121 Venezia-Venice, Italy Received 21 February 2009; Revised 26 June 2009; Accepted 6 July 2009 *Correspondence to: B.J. MacVicar, Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1.E-mail: bmacvicar@uwaterloo.ca ABSTRACT: Wood plays an important role in stream ecology and geomorphology. Previous studies of wood in rivers have quantified spatial distributions but temporal dynamics remain poorly documented. The lack of such data is related to limitations of existing methods, especially when applied to large rivers. Five techniques are field-tested to assess their utility for quantifying the temporal dynamics in rivers: repeated high-resolution aerial surveys, the measurement of wood physical characteristics as proxies for 14 C dating, passive and active radio frequency identification (RFID) tags, radio transmitters, and video. The spatial distribution of wood is surveyed using aerial imagery with a resolution finer than 0·10 m. The estimation of temporal trends by repeated aerial-based surveys needs to consider vegetation growth and hiding. Wood residence times can be calculated using 14 C analysis, but the assessment of wood physical characteristics including decay status and wood density offers a cheaper, if less accurate, alternative. Wood resistance to penetration is tested but results are not significant. Radio transmitters are reliable for multi-year (~5 year) surveys and can be detected at 800 m. Passive RFID tags are limited by a read range of 0·30 m but are reli- able for longer term (>5 year) studies. Active RFID tags combine a moderate read range (10–300 m) and low cost with in-flood detection but require more testing. Video monitoring of wood passing on the surface of a river is successfully implemented. For a single flood on the Ain River (France), wood transport rates are an order of magnitude higher on the rising limb of the hydro- graph than on the falling limb. Overall, the techniques improve the ability to gather the data needed to understand wood transfer processes and calibrate budgets of wood in rivers. Copyright © 2009 John Wiley & Sons, Ltd. KEYWORDS: rivers; wood transport; wood budget; wood residence time; aerial imagery; radio tracking; video monitoring Introduction Field surveys of the volume and spatial distribution of wood in rivers have documented significant variation according to differences in geology, climate, topography, stream size, and human activities within the watershed (see Gurnell et al., 2002 for review plus Baillie and Davies, 2002; Kraft and Warren, 2003; Van der Nat et al., 2003; Webb and Erskine, 2003; Comiti et al., 2006; 2008a; Lassettre et al., 2008; Mao et al., 2008a). Mass balances (Benda and Sias, 2003) and physical descriptions of wood mobility (Braudrick and Grant, 2000, 2001; Bocchiola et al., 2006a, b; Manners and Doyle, 2008) have been formulated to provide a quantitative framework to understand this variability. Despite the construction of models and descriptions of active processes, there is a lack of accurate field data with which to interpret wood volumes and spatial distributions, parameterize equations on wood transport, and test hypotheses (Benda and Sias, 2003; Hassan et al., 2005). For example, longitudinal distributions of wood volumes are known to vary according to flood sequences (Piégay, 2003; Moulin and Piégay, 2004), yet studies of temporal patterns and variability that underlie the ‘snapshot’ distributions of wood are rare. In this study, we consider the lack of data to be a technical problem related to limitations inherent in existing