Sebastian Wrede

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

PurposePrevious 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 methodsA parsimonious data-driven model (M5′ modular trees) was used to simulate SSC in response to the identified controlling variables. Antecedent hydro-meteorological variables (e.g., antecedent precipitation depths, antecedent precipitation indices, and a suit of hydrological data) were used as input variables.Results and discussionTwenty-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 hydro-meteorological 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.ConclusionsOur 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.