Assessment of suspended sediment export and dynamics using in‐line turbidity sensors and time series statistical models

Abstract

AbstractThe Coln is an ecologically sensitive river in a limestone dominated catchment with no major tributaries. Three in‐line turbidity sensors were installed to monitor changes in the dynamics of suspended sediment transport from headwaters to the confluence. The aims were to (i) provide estimates of yield (t km−2 year−1) and likely drivers of suspended sediment over ~3 years and (ii) assess turbidity dynamics during storm events in different parts of the catchment. In addition, the sensor installation allowed a novel wavelet analysis based on identifying groups of turbidity peaks to estimate transport times of suspended sediment through the catchment. Yearly suspended sediment yields calculated for the upper catchment were typically less than 4 t ha−1 year−1 being similar to other UK limestone or chalk‐based rivers. Time series autoregressive integrated moving average models including explanatory variable regression modelling indicated that river discharge, groundwater level and water temperature were all significant predictors of turbidity levels throughout the year. However, high model residuals demonstrate that the models failed to capture random turbidity events. Five parts of the time series data were used to examine sediment dynamics. Plots of scaled discharge verses turbidity demonstrated that in the upper catchment, after initial suspended sediment generation, sediment quickly became limited. In the lower catchment, hysteresis analysis suggested that sediment dilution occurred, due to increasing base flow. The novel wavelet analysis demonstrated that during winter ‘sediment events’ identified as groups of turbidity peaks, took ~18 h to pass from the first sensor in the upper catchment to the second sensor (10.3 km downstream of sensor 1) and 24 h to the third sensor (23.3 km from sensor 1). The work demonstrates the potential for using multiple turbidity sensors and time series statistical techniques in developing greater understanding of suspended sediment dynamics and associated poor water quality in ecologically sensitive rivers.