Robert B. Thomas

Peak flow responses to clear‐cutting and roads in small and large basins, Western Cascades, Oregon: A second opinion

In this paper, we conduct a reanalysis of methods and data used by Jones and Grant (1996). Data from three small watersheds (60 -101 ha) and three pairs of large basins (60 - 600 km 2 ) in Oregons western Cascades were used to evaluate effects of timber harvest and road construction on peak flows. We could not detect any effect of cutting on peak flows in one of the large basin pairs, and results were inconclusive in the other two large basin pairs. One small watershed was 100% clear-cut, a second was 31% patch-cut with 6% of the area affected by road construction, and a third was held as a long-term control. Peak flows were increased up to 90% for the smallest peak events on the clear-cut watershed and up to 40% for the smallest peak flows on the patch-cut and roaded watershed. Percentage treatment effects decreased as flow event size increased and were not detectable for flows with 2-year return intervals or greater on either treated watershed. Treatment effects decreased over time but were still found after 20 years on the clear-cut watershed but for only 10 years on the patch-cut and roaded watershed.

Monitoring baseline suspended sediment in forested basins: the effects of sampling on suspended sediment rating curves

Rating curves are widely used for directly assessing changes in the suspended sediment delivery process and indirectly for estimating total yields. Four sampling methods were simulated over a 31-day record of suspended sediment from the North Fork of the Mad River near Korbel, California. The position and size of the four groups of plotted slope/intercept pairs indicated differences in bias and variance among the methods. Estimates of total yield for the 31-day period and for storms of three sizes were also biased according to sampling method. A standard bias-correcting technique improved yield estimates, but did not remove sampling bias uniformly. Methods of data collection have a large and systematic effect on the estimation of rating-curve parameters and on estimates of suspended sediment yield. Differences attributed to land management may, in fact, result from changes in sampling methods.

An evaluation of flow-stratified sampling for estimating suspended sediment loads

Abstract Flow-stratified sampling is a new method for sampling water quality constituents such as suspended sediment to estimate loads. As with selection-at-list-time (SALT) and time-stratified sampling, flow-stratified sampling is a statistical method requiring random sampling, and yielding unbiased estimates of load and variance. It can be used to estimate event yields or to estimate mean concentrations in flow classes for detecting change over time or differences from water quality standards. Flow-stratified sampling is described and its variance compared with those of SALT and time-stratified sampling. Time-stratified sampling generally gives the smallest variance of the three methods for estimating storm yields. Flow-stratified sampling of individual storms may fail to produce estimates in some short-lived strata because they may have sample sizes of zero. SALT will tend to give small samples and relatively high variances for small stroms. For longer and more complex hydrographs, having numerous peaks, flow-stratified sampling gives the lowest variance, and the SALT variance is lower than that of time-stratified sampling unless the sample size is very large. A desirable feature of flow-stratified sampling is that the variance can be reduced after sampling by splitting strata, particularly high flow strata that have been visited just once, and recalculating the total and variance. SALT has the potential to produce the lowest variance, but cannot be expected to do so with an auxiliary variable based on stage.

A comparison of selection at list time and time‐stratified sampling for estimating suspended sediment loads

Time-stratified sampling of sediment for estimating suspended load is introduced and compared to selection at list time (SALT) sampling. Both methods provide unbiased estimates of load and variance. The magnitude of the variance of the two methods is compared using five storm populations of suspended sediment flux derived from turbidity data. Under like conditions, the SALT coefficient of variation was 1.4–7.7 times that of time-stratified sampling. Time-stratified sampling performs well if the range of sediment flux in each stratum is small. This requirement can be met by using small sample sizes in many short strata. Theoretically, SALT sampling has the potential for smaller sampling variance; however, it is difficult to select an auxiliary variable that predicts flux well under diverse flow conditions. An “optimum” auxiliary variable formed from the largest storm performed about as well as time-stratified sampling for the larger storms. Time-stratified sampling ensures that specimens are collected in all storms, facilitating load estimation for individual storms. In contrast, SALT can better allocate sampling resources over different size storms, enabling efficient estimation of the total load for longer periods. Because time-stratified sampling is less sensitive to the way measurements are allocated to different parts of the population, it is preferred for estimating storm loads of multiple constituents from the same sample.

Streamflow record extension using power transformations and application to sediment transport

To obtain a representative set of flow rates for a stream, it is often desirable to fill in missing data or extend measurements to a longer time period by correlation to a nearby gage with a longer record. Linear least squares regression of the logarithms of the flows is a traditional and still common technique. However, its purpose is to generate optimal estimates of each days discharge, rather than the population of discharges, for which it tends to underestimate variance. Maintenance-of-variance-extension (MOVE) equations [Hirsch, 1982] were developed to correct this bias. This study replaces the logarithmic transformation by the more general Box-Cox scaled power transformation, generating a more linear, constant-variance relationship for the MOVE extension. Combining the Box-Cox transformation with the MOVE extension is shown to improve accuracy in estimating order statistics of flow rate, particularly for the nonextreme discharges which generally govern cumulative transport over time. This advantage is illustrated by prediction of cumulative fractions of total bed load transport.

A new model for bed load sampler calibration to replace the probability‐matching method

In 1977 extensive data were collected to calibrate six Helley-Smith bed load samplers with four sediment particle sizes in a flume at the St. Anthony Falls Hydraulic Laboratory at the University of Minnesota. Because sampler data cannot be collected at the same time and place as “true” trap measurements, the “probability-matching method” was used to derive surrogate pairs for calibration analysis. The method is invalid since it implicitly assumes that sampler and trap data have no sampling or measurement errors, it gives biased and highly variable results, and it does not contain information enabling model specification. A new calibration model was developed that regresses transformed individual sampler measurements on daily means of transformed trap data and incorporates within-day variation in trap rates to explain part of the sampler variation. Three small-nozzle samplers performed more uniformly than three large-nozzle samplers did. There is evidence that samplers with higher nozzle ratios collect more bed load in most particle size classes tested. However, between the two small-nozzle samplers with ratios of 3.22 and 1.40, significant differences could be detected for only one particle size. The standard sampler with a 76 × 76 mm nozzle trapped sediment less efficiently than a similar sampler with a 152×152 mm nozzle in three of four particle sizes tested. Limitations in the data restricted more definitive statements about the samplers, but the results of this study can be used to design a more rigorous calibration experiment.