%0 Report
%D 2004
%T Suspended Sediment and Turbidity Patterns in the Middle Truckee River, California for the Period 2002 - 2003
%A Gayle L. Dana
%A Anna K. Panorska
%A Richard B. Susfalk
%A David McGraw
%A W. Alan McKay
%A Michael Dornoo
%C Reno
%I Desert Research Institute
%P 98
%U http://www.truckee.dri.edu/tmdl/SedimentPatternsMiddleTruckeeDRI2004.pdf
%X EXECUTIVE SUMMARY The principal goal of the Truckee River suspended sediment study was to estimate the sediment loads for the Truckee River in California, and to characterize the existing range of sediment loads and variability according to total amount, maximum, duration, timing and frequency of sediment transport events. Our primary objective in support of this goal was to develop a sediment surrogate that could be measured continuously in the Truckee River. This work supports development of numeric targets and load allocations for the Truckee River sediment Total Maximum Daily Load (TMDL) in California. The report is divided into three chapters: 1) Develop a model to predict suspended sediment concentrations from multiple variables, including turbidity; 2) Estimate suspended sediment loads; and 3) Assess spatial and temporal properties of turbidity. Transport changes in suspended sediment concentration (SSC) due to natural or human-induced causes are difficult to characterize because SSC varies rapidly and unpredictably during storm events. Capturing the extreme variation in SSC during storms requires sampling at high temporal frequency, which is usually impractical and expensive. As such, easier-to-measure surrogate variables are monitored continuously with in-situ instrumentation. Thus, a continuous turbidity record, supplemented with selected measurements of SSC to derive the turbidity-SSC relationship, can provide an efficient and cost effective method for estimating transported suspended sediment loads. Our project builds on and extends previous work by the U.S. Geological Survey (USGS) in the choice of the general class of statistical modeling techniques-regression methods. First, we use multivariate regression techniques, which allows for inclusion of multiple variables characterizing the river processes to the model. Second, since the Truckee River is expected to have high variability in sediment and turbidity, we explored parametric as well as nonparametric (robust) statistical techniques. Third, since we have evidence from previous work that the relationship between turbidity and sediment may be nonstationary, we used local regression models. These allow for different functional relations to be built for different parts of the data set that increases the overall fit of the model to the data. Additionally, since extreme sediment discharge events may be of major interest, we made every effort to capture them with our models, and not discard them (before modeling) as outliers. Fourth, we included error estimates (confidence intervals) for the predictions made using our models. The primary data sources for building the model and calculating sediment loads were flow, measured by the USGS at three sites, and turbidity measured by the California Department of Water Resources (CalDWR) at four sites. In addition, the turbidity instrumentation measured water temperature and specific conductivity. In order to develop a statistical relationship between suspended sediment concentrations and the explanatory variables (here discharge, turbidity, specific conductivity, and water temperature), SSC samples needed to be collected throughout the year as the explanatory variables varied. Therefore SSC was collected monthly at all four turbidity collection sites and weekly during snowmelt. Additionally, SSC was collected at Farad during thunderstorm events. Model Development to Predict Suspended Sediment Concentrations We developed a multiple linear regression model (MLR) for predicting SSC from various combinations of turbidity, water temperature, stream flow and specific conductivity: iv a) Full model: turbidity, flow, water temperature and specific conductivity; b) Turbidity, water temperature and specific conductivity; c) Flow only; and d) Water temperature only Models with fewer predictive variables were explored because it is often the case that only one or two (e.g., flow, temperature) of these variables are measured continuously in the field. A statistically “good” model with few predictive variables could be very useful for future studies. As part of the process of developing the predictive models, the statistical relationships between individual variables were analyzed. The “best” model developed was one in which SSC was predicted from all four explanatory variables (turbidity, flow, water temperature, and specific conductivity). The R2 was 0.73, that is, the explanatory variables explained about 73% of the variability of SSC. From a statistical standpoint, this was found to be a “good” model, in that the mathematical assumptions under which the model was constructed satisfied statistical diagnostic tests. In this model, site was not found to be significant, so one equation can be used for all four sites (rather than a separate equation having to be used for each site). The model developed to predict SSC from three explanatory variables (turbidity, water temperature and specific conductivity; flow removed from the model) explained about 58% of the variation in SSC. In this model, site was significant and it was necessary to develop a separate predictive equation for each site. The model with flow as the only explanatory variable resulted in a multiple R2 value of 0.39, but the statistical properties of this model were not good. There was no statistical relationship between SSC and water temperature, so a model could not be built with temperature as the only explanatory variable. Executive Summary continued on website or attachment below.
%8 03/2004
%> https://truckeeriverinfo.org/files/truckee/SedimentPatternsMiddleTruckeeDRI2004.pdf