Separating impacts of storm characteristics and mitigation activities on suspended sediment: Implications for sampling to evaluate mitigation success

Mitigation activities are often implemented to reduce anthropogenic sediment loads to sensitive ecosystems, including coral reefs, but effectiveness is rarely documented due to a lack of pre-mitigation data and to the difficulty of separating the impacts of mitigation from those of storm size and intra- and inter-storm variability. Here we present a method using stormwise metrics to quantify the effectiveness of a sediment mitigation project in American Samoa, where an aggregate quarry covering <1 % of a small watershed (drainage area 1.8 km(2)) generated approximately half of the annual suspended sediment load to the coast. Rainfall, runoff, turbidity and suspended sediment concentrations (SSC, mg L-1), yield (SSY, tons km(-2) per unit time) and loads (SSL, tons per unit time) were monitored periodically over a five-year period upstream and downstream of the quarry. Mitigation included revegetation and placement of gravel on roads and processing platforms followed by installation of retention ponds. Storms were identified using automated storm separation, and a simple regression model using maximum area-normalized storm discharge (Qmax) estimated SSC, SSY and SSL for each mitigation stage (post-gravel, post-ponds) for a reference storm sequence. Regression parameters, and SSL and SSC for the reference storm sequence were calculated for a range of storm separation parameters. Monte Carlo sampling of the regression errors was used to calculate a mean and standard deviation of SSL and its change following mitigation. SSC was nearly 100x higher during large storms than during small storms, demonstrating the need to account for storm size to detect impacts of mitigation. After mitigation, SSL downstream of the quarry decreased by 63-82 % for all storms and by 88-92 % for small storms, and SSY downstream of the quarry was similar to SSY from the forested watershed upstream. Mitigation of sediment loads can target a small fraction of a watershed area that accounts for the majority of the human impact. Combining stormwise metrics with simple regression modeling and uncertainty analysis is a simple and novel way to detect and quantify the effectiveness of mitigation activities on sediment loads.