Dynamics of storm-driven suspended sediments in a headwater catchment described by multivariable modeling

Abstract

Purpose Previous research has shown that the rate at which suspended sediment is transported in watercourses depends primarily on discharge (Q) as the first-order control, but additional factors are thought to affect suspended sediment concentrations (SSC) as well. Among these, antecedent hydrological and meteorological conditions (e.g., rainfall depth and intensity, discharge prior to a runoff event and the duration of runoff events) may represent significant transport controlling mechanisms. Univariate models using Q-SSC rating curves often produce large scatter and nonlinearity, because many of the hydrological and biotic processes affecting the dynamics of sediment are non-linear and exhibit threshold behavior. The simulation of such highly non-linear processes is therefore an elusive task requiring consideration of several interrelated controlling variables. The aim of this study was to identify the major hydrological and meteorological controls determining the dynamics of SSC during storm-runoff events and the magnitude of SSC in a headwater catchment in Luxembourg. Materials and methods A parsimonious data-driven model (M5′ modular trees) was used to simulate SSC in response to the identified controlling variables. Antecedent hydrometeorological variables (e.g., antecedent precipitation depths, antecedent precipitation indices, and a suit of hydrological data) were used as input variables. Results and discussion Twenty-four-hour antecedent runoff volumes were determined as the major control explaining sediment depletion effects during high-flow periods, and a gradual decline of SSC as a runoff event progresses. The modeling results obtained by M5′ trees were then compared to conventional power-law rating curves. The M5′ model outperformed the rating-curve by being successful in describing the shape and magnitude of the analyzed sedigraphs. Therefore, we propose that incorporating antecedent hydrometeorological data into SSC prediction models may strongly enhance the accuracy of export coefficients. Two splitting criteria identified by the M5′ model tree (Q and antecedent runoff volume) were found and are discussed as possible thresholds responsible for the greatest nonlinearity in the Q-SSC relationship. Conclusions Our study highlights the dominant antecedent hydro-meteorological conditions acting as the major controls on the magnitude of SSC during episodic events in the headwater Huewelerbach catchment in Luxembourg. For future application, it would be interesting to extend and test the data-mining approach presented in this paper to other catchments, where other controls on sediment transport may be identified.

Suspended sediment transport dynamics in rivers: Multi-scale drivers of temporal variation

“…The amount of SS transport by rivers depends on the interaction of multiple drivers acting on different spatial and temporal scales (Bracken et al, 2015;Fryirs, 2013;Onderka et al, 2012;Poulenard et al, 2012;Rickson, 2014;Sear et al, 2003). In this section, we provide a concise summary of the main processes of fine sediment generation and transport on land (Section 2.1), and the factors driving spatial (Section 2.2) and temporal (Section 2.3) variability in SS transport within rivers.…”

“…Therefore, different parameters such as total discharge, peak discharge, water yield, time of rise and fall of hydrograph, total duration of a precipitation event, maximum 30-minute rainfall, mean rainfall intensity and antecedent rainfall are commonly included in models to estimate sediment transfer from the catchment to the river (e.g. Dominic et al, 2015;Duvert et al, 2010;Onderka et al, 2012;Seeger et al, 2004;Tena et al, 2014). In addition, snowmelt has been shown to be a dominant driver for SS transport in many parts of the world (e.g.…”

“…Second, SS transport will vary as a result of changes in the dominant sediment source(s). Sediment source variations are often the result of interactions between catchment characteristics and hydro-meteorological processes, causing complex feedback mechanisms and threshold behavior (Onderka et al, 2012). Changes in vegetation cover (e.g.…”

“…SS transport in rivers is highly non-linear in time and space, and is often characterized by threshold behavior and feedback mechanisms (Bracken et al, 2015;Onderka et al, 2012). The non-linear nature of soil erosion and transport of sediment at the catchment-scale results in spatiotemporal variations in sediment generation, transfer to the channel and transport through the river network.…”

“…Over the last few decades, a wide range of empirical approaches have been developed and applied, from single sediment rating curves to complex multivariate analysis techniques (Asselman, 2000;Francke et al, 2014;Onderka et al, 2012;Poulenard et al, 2012).…”