Abstract

Abstract. Remotely sensed data from fluvial systems are extensively used to document historical planform changes. However, geometric and delineation errors inherently associated with these data can result in poor or even misleading interpretation of measured changes, especially rates of channel lateral migration. It is thus imperative to take into account a spatially variable (SV) error affecting the remotely sensed data. In the wake of recent key studies using this SV error as a level of detection, we introduce a new framework to evaluate the significance of measured channel migration. Going beyond linear metrics (i.e. migration vectors between diachronic river centrelines), we assess significance through a channel polygon method yielding a surficial metric (i.e. quantification of eroded, deposited, or eroded-then-deposited surfaces). Our study area is a mid-sized active wandering river: the lower Bruche, a ∼20 m wide tributary of the Rhine in eastern France. Within our four test sub-reaches, the active channel is digitised using diachronic orthophotos (1950 and 1964), and the SV error affecting the data is interpolated with an inverse-distance weighting (IDW) technique. The novelty of our approach arises from then running Monte Carlo (MC) simulations to randomly translate active channels and propagate geometric and delineation errors according to the SV error. This eventually leads to the computation of percentage of uncertainties associated with each of the measured planform changes, which allows us to evaluate the significance of the planform changes. In the lower Bruche, the uncertainty associated with the documented changes ranges from 15.8 % to 52.9 %. Our results show that (i) orthophotos are affected by a significant SV error; (ii) the latter strongly affects the uncertainty of measured changes; and (iii) the significance of changes is dependent on both the magnitude and the shape of the surficial changes. Taking the SV error into account is strongly recommended even in orthorectified aerial photos, especially in the case of mid-sized rivers (<30 m width) and/or low-amplitude river planform changes (<1 m2m-1yr-1). In addition to allowing detection of low-magnitude planform changes, our approach is also transferable as we use well-established tools (IDW and MC): this opens new perspectives in the fluvial context (e.g. multi-thread river channels) for robustly assessing surficial channel changes.

Highlights

  • In a fluvial context, remotely sensed data provide spatial information on historical lateral dynamics of river channels (Bollati et al, 2014; Cadol et al, 2010; Comiti et al, 2011; Gurnell et al, 1994; Hajdukiewicz and Wyzga, 2019; Lauer et al, 2017)

  • Whereas the total spatially variable (SV) error was reduced by a factor of 2 between 1950 and 1964 in sub-reach 4, it remained fairly stable in the three other ones, ranging from 0.6 to 1.2 m

  • Monte Carlo (MC) simulations for deposited surfaces in sub-reach 1 include an outlier with a value (2.5 × 103 m2) corresponding to 38 % of the mean measured value (6.8 × 103 m2)

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Summary

Introduction

Remotely sensed data provide spatial information on historical lateral dynamics of river channels (Bollati et al, 2014; Cadol et al, 2010; Comiti et al, 2011; Gurnell et al, 1994; Hajdukiewicz and Wyzga, 2019; Lauer et al, 2017). This is of crucial importance for creating a scientific framework applicable to sustainable management of hydrosystems, including river restoration (Biron et al, 2014; Piégay et al, 2005; Surian et al, 2009). The thorough review of Donovan et al (2019) reached the same conclusion: they encouraged the generalisation of SV error assessment and noted the potential need for testing SV-LoD on new metrics of lateral migration, including areal metrics of surface change

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