Elsevier

Journal of Hydrology

Volume 398, Issues 3–4, 24 February 2011, Pages 246-259
Journal of Hydrology

Global uncertainty analysis of suspended sediment monitoring using turbidimeter in a small mountainous river catchment

https://doi.org/10.1016/j.jhydrol.2010.12.025Get rights and content

Summary

A major challenge confronting the scientific community is to understand both patterns of and controls over spatial and temporal variability of suspended sediment dynamics in rivers, as these sediment govern nutriment export, river morphology, siltation of downstream reservoirs and degradation of water quality. High-frequency suspended sediment monitoring programs are required to meet this goal, particularly research in highly erodible mountainous catchments which supply the sediment load of the entire downstream fluvial network. However, in this context, analysis of the data and their interpretation are generally limited by many sources of uncertainty in river monitoring. This paper proposes to estimate the global uncertainty of suspended sediment monitoring using turbidimeter in a small mountainous river catchment (22 km2; Southern French Alps). We first conducted a detailed analysis of the main uncertainty components associated with the turbidity approach, i.e. a widely used method to continuously survey the suspended sediment concentration (SSC). These uncertainty components were then propagated with Monte Carlo simulations. For individual records, SSC uncertainties are found to be on average less than 10%, but they can reach 70%. At the flood scale, the mean and the maximum SSC uncertainties are on average 20% (range, 1–30%), whereas sediment yield uncertainty is a mean 30% (range, 20–50% depending on the flood considered; discharge error, 20%). Annual specific sediment yield (SSY*) was then 360 ± 100 t km−2 year−1. Uncertainty components associated with the automatic pumping procedure, discharge measurement and turbidity fluctuation at the short time scale were found to be the greatest uncertainties. SSC and SSY uncertainties were found highly site- and time-dependent as they vary significantly with the hydro-sedimentary conditions. This study demonstrates that global uncertainty accounts for only a small part of inter-flood SSC and SSY variability. It outlines the controlling factors of land use, relief, geology and rainfall regime on suspended sediment yields.

Research highlights

► Uncertainties of suspended sediment concentrations and yields are highly site- and time-dependent. ► They significantly vary with the hydro-sedimentary conditions. ► At the Galabre River station, the uncertainty of the mean suspended sediment concentration during a flood is on average 20%. ► Sediment yield uncertainty is on average 30%. ► The automatic pumping procedure, the discharge measurement and the turbidity fluctuations are the greatest uncertainty components.

Introduction

Improving knowledge on suspended sediment yields, dynamics and water quality is one of today’s major environmental challenges addressed to scientists and hydropower managers (Owens et al., 2005). These advances will continue in the future as the acquisition of reliable and long-term suspended sediment concentration (SCC) time series are generalised to many hydrometric stations. In mountainous catchments, major fractions of the annual suspended sediment yields (SSY) are transported over a very short time period generally corresponding to several floods (e.g. Meybeck et al., 2003, Mano et al., 2009). Therefore high-frequency SSC monitoring is required for reliable SSC and SSY estimates. Nevertheless, a reliable and easy method to obtain a direct, continuous SSC measurement is not currently available. Although great progress is expected with, for instance, the backscatter acoustic method (Wren et al., 2000, Gray and Gartner, 2009), their application is still limited to large rivers and canals. However, problems remain in mountainous catchments where river channels generally show significant and rapid erosion–transport–sedimentation processes, with very high specific sediment yields (suspended as well as bed-load transport). Moreover, measuring suspended sediment dynamics in this restrictive context remains of prime importance, all the more so because these river basins determine the fine sediment delivery to the whole downstream lowland river system (Milliman and Syvitski, 1992, Hicks et al., 2000, Lenzi et al., 2003, Mathys et al., 2003, Meybeck et al., 2003, Mano et al., 2009, Navratil et al., 2010) and they govern reservoir siltation dynamics, water resources and aquatic habitat qualities (Wood and Armitage, 1997, House and Warwick, 1999, Rees et al., 1999, Valero-Garcés et al., 1999, Packman and Mackay, 2003, Owens et al., 2005).

Using only SSC samples with a predefined sampling frequency most often leads to large errors in suspended sediment flux (SSF) estimates (e.g., Thomas and Lewis, 1993, Thomas and Lewis, 1995, Lewis, 1996, Phillips et al., 1999, Coynel et al., 2004, Moatar et al., 2006). Therefore, given the problems of direct high-frequency SSC estimates, turbidity measurement is currently used as a surrogate measurement (Lenzi et al., 2003, Brasington and Richards, 2000, Mathys et al., 2003, Orwin and Smart, 2004, Stott and Mount, 2007, Mano et al., 2009, López-Tarazón et al., 2009). This method is based on the quasi-continuous measurement of turbidity, the sensor being calibrated with suspended sediment collected during many flood events. It remains the easiest and most widely used method for suspended sediment monitoring with high-frequency acquisition (Wren et al., 2000, Downing, 2006, Némery et al., 2010). However, this method shows many limitations and uncertainties within river applications and particularly in mountainous catchments exhibiting a very high SSC and complex spatio-temporal SSY variability.

The present paper aims (1) to analyse the major uncertainty components (referred to as UCs) associated with the turbidity approach for suspended sediment monitoring in a highly erodible mountainous catchment; (2) to assess the propagated uncertainty on SSC and SSY with Monte Carlo simulations; and (3) to classify the UCs according to their relative effect on the final results. This work will provide a practical methodological framework that can be applied to other hydrometric stations using similar monitoring techniques.

This study was based on a 2-year data set (October 2007–December 2009) collected in a small mountainous river basin, the Galabre River in Southern French Pre-Alps. Nine major uncertainty components (UC1–UC9) associated with turbidity measurement were identified and are summarised in Table 1: the choice of a turbidimeter (UC1) and its calibration (UC8); the temporal (UC2, UC3) and spatial (UC4) field sampling strategies; the technical field problems (UC5); the field (UC6) and laboratory (UC7) water sample sampling and procedures; and finally, the discharge estimation (UC9). UC2, UC4 and UC9 were considered in this study in comparison to the literature studies and on-site characteristics. Others (UC1, UC3, UC5–UC8) for which literature data were insufficient or not transposable to this context were quantified with specific analyses.

Uncertainty propagation applied to long-term time series is quite difficult to perform since the UCs may be correlated with each other; so analytical computations remain very complex. Therefore, Monte Carlo simulations were undertaken to evaluate the combined effect of UCs and to estimate the propagation of uncertainties (Fig. 1; Coleman and Steele, 1999, Joint Committee for Guides in Metrology, 2008a, Joint Committee for Guides in Metrology, 2008b, Lacour et al., 2009).

Section snippets

Study area

The Galabre River (lat.: 44°10′27″N, long.: 06°12′59″E) is a small tributary of the Bléone River in the Rhône River catchment, France (drainage area, 35 km2; Fig. 2). Its Mediterranean and mountainous climate (with frost in winter and high-intensity rainfall in summer) and its geology result in a badlands topography, gully development and substantial transfer of sediment. Highly erodible areas cover about 2% of the river basin. Mean annual temperature ranges between 12 and 13 °C at 400 m ASL

Choice of a suitable turbidimeter (UC1)

Sensor fouling is frequent in rivers, so the choice of the turbidimeter must take into account the auto-cleaning procedure adapted to each site’s characteristics (e.g. fouling by algae, calcification, macro-invertebrate, sediment accumulation) to avoid any lack of data or temporal trend (Wren et al., 2000, Lewis and Eads, 2008). Currently available turbidimeters are mainly limited by their range of measurement. Consequently, the choice of a suitable turbidimeter is mainly governed by the range

Choice of a suitable turbidimeter (UC1)

We first verified that our material was well suited to the study site. The turbidity uncertainty (provided by the manufacturer) was low given that it was less than 4% for the entire range of turbidity. The sensor had never been saturated during the 2007–2009 study period; the maximum turbidity observed was 58 g l−1 SiO2 and the maximum SSC was 130 g l−1 (Fig. 4a). No signal trend was observed. Finally, we found that 95% of the total SSY was transported with SSC > 1 g l−1 (Fig. 4b). As a result, the low

Conclusions

Suspended sediment monitoring cumulates many uncertainties from field monitoring to the SSC and SSY computation procedure. Assessing uncertainties is therefore required because it would help distinguish the spatio-temporal variability of SSY attributed to natural (e.g. rainfall, erodibility, relief) or anthropogenic factors (e.g. land use change) from the variability attributed to monitoring uncertainties. Furthermore, the quantification of these uncertainties is of prime importance to a

Acknowledgments

This study was funded by the French ANR-STREAMS project (ANR Blanc 06-1_139157). The authors are grateful to Fred Malinur, Lucas Muller, Vincent Thiabault, Jean-Marie Miscioscia, and Michel Ricard for the field campaigns and laboratory analysis and technical assistance, Linda Northrup (English Solution) for editing the manuscript, and two anonymous reviewers who helped improve the earlier versions of this manuscript.

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