Abstract

Sedimentation is a problem for all reservoirs in the Black Hills of South Dakota. Before working on sediment removal, a survey on the extent and distribution of the sediment is needed. Two sample lakes were used to determine which of three interpolation methods gave the most accurate volume results. A secondary goal was to see if fewer samples could be taken while still providing similar results. The smaller samples would mean less field time and thus lower costs. Subsamples of 50%, 33% and 25% were taken from the total samples and evaluated for the lowest Root Mean Squared Error values. Throughout the trials, the larger sample sizes generally showed better accuracy than smaller samples. Graphing the sediment volume estimates of the full sample, 50%, 33% and 25% showed little improvement after a sample of approximately 40%–50% when comparing the asymptote of the separate samples. When we used smaller subsamples the predicted sediment volumes were normally greater than the full sample volumes. It is suggested that when planning future sediment surveys, workers plan on gathering data at approximately every 5.21 meters. These sample sizes can be cut in half and still retain relative accuracy if time savings are needed. Volume estimates may slightly suffer with these reduced samples sizes, but the field work savings can be of benefit. Results from these surveys are used in prioritization of available funds for reclamation efforts.

Highlights

  • Interpolation of data between sampled points is both powerful and timesaving

  • The three tested interpolation methods were similar in regards to their overall means and confidence intervals (One-way ANOVA (F(2234) = 0.13, p = 0.878)

  • The first part of this project was to determine the accuracy of three interpolation methods based on how well they predicted the values at the known points

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Summary

Introduction

Interpolation of data between sampled points is both powerful and timesaving. It allows for the estimation of values between the known values, yet provides a cost savings as workers do not need to collect excess data from many additional sites or repeated field excursions; accuracy questions regarding the interpolated values do arise after the algorithm is used [1,2,3]. Using sediment data is valuable as it allows for an initial investigation of sediment deposition, how it exists spatially within a reservoir and it makes available the background for repeat measures when investigating temporal changes [7]. Mounting evidence for appropriate watershed planning can assist or prolong the life expectancy of any given reservoir and its tendency to fill in with sediment. Deposition of sediment within a reservoir has been predicted through modeling processes but remains stochastic in nature in regards to natural fluctuations of inflow, water level, sediment concentrations and climatic conditions [8,9,10]

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