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How long should we measure? An exploration of factors controlling the inter-annual variation of catchment sediment yield



Although it is well-known that catchment suspended sediment yields (SY; tons per square kilometre per year) can vary significantly from year to year, little information exists on the magnitude and factors controlling this variability. This is crucial to assess the reliability of average SY values for a given measuring period (MP) and is of great geomorphic significance. This paper aims to bridge this research gap.

Materials and methods

A worldwide database was compiled with time series of measured SY values. Data from 726 rivers (mostly located in Europe, the Middle East and the USA) were collected, covering 15,025 annual SY observations. The MPs ranged between 7 and 58 years, while catchment areas (A) ranged between 0.07 and 1.84 × 106 km2. For 558 catchments, the annual runoff depths corresponding to the SY observations were also available. Based on this database, inter-annual variability was assessed for each catchment, and relationships with factors potentially explaining this variability were explored.

Results and discussion

Coefficients of variation of SY varied between 6% and 313% (median 75%). Annual SY data were generally not normally distributed but positively skewed. Inter-annual variability generally increased with increasing average SY. No significant relationship was found between the inter-annual variability of SY and A, while weak but significant relationships were noted with the variability in annual runoff and rainfall depths. Detailed analyses of a sub-dataset corresponding to 63 catchments in Romania revealed no clear relationships between inter-annual variability of SY and land-use or topographic characteristics. Nevertheless, indications were found that variability was larger for catchments with erosion-prone land-use conditions. Using a Monte Carlo simulation approach, the effect of inter-annual variability on the reliability of average SY data was assessed. Results indicate that uncertainties are very large when the MP is short, with median relative errors ranging between −60% and 83% after 5 years of monitoring. Furthermore, average SY values based on short MPs have a large probability to underestimate, rather than to overestimate, the long-term mean. For instance, the SY value of a median catchment after a 1-year MP has a 50% probability of underestimating the long-term mean by about 22%. Uncertainties quickly decrease after the first few years of measurement but can remain considerable, even after 50 years of monitoring.


It is important to consider uncertainties associated with average SY values due to inter-annual variability, for example when attempting to predict long-term average SY values using a steady-state model, as such uncertainties put fundamental limits to the predictive capabilities of such models.

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The research described in this paper was conducted within the framework of the EC-DG RTD—6th Framework Research Programme (sub-priority—Research on Desertification—project DESIRE (037046): Desertification Mitigation and Remediation of land—a global approach for local solutions. M. Vanmaercke received grant-aided support from the Research Foundation—Flanders (FWO), Belgium. This work is also financially supported by the Eskişehir Osmangazi University (ESOGÜ), Commission for the Scientific Research Projects (project no: 200415022). We thank the many colleagues for sharing sediment yield data and in particular Kirsti Granlund and Jose Bernal from the Finnish Environment Institute for kindly providing data for various Finnish catchments. Furthermore, this manuscript benefited from the constructive comments of two anonymous reviewers.

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Vanmaercke, M., Poesen, J., Radoane, M. et al. How long should we measure? An exploration of factors controlling the inter-annual variation of catchment sediment yield. J Soils Sediments 12, 603–619 (2012). https://doi.org/10.1007/s11368-012-0475-3

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  • Measuring period
  • Monte Carlo simulations
  • Runoff
  • Scale dependency
  • Temporal variability
  • Uncertainty