Methods for interpolating stream width, depth, and current velocity

Interpolation is a type of modeling that can be used to estimate habitat variables throughout a stream based on measurements distributed along the stream's length, but little guidance is available to select the best method of interpolation. Thus, we compared several methods to determine which produced the most accurate interpolation of width, depth, and current velocity, separately. We also determined whether interpolation should be performed using separate datasets for riffles, runs, and pools or unstratified datasets. We measured stream width, maximum depth, and mean current velocity in a northern Michigan watershed. We tested seven methods of interpolation including global average, linear regression, cubic spline, moving average, Lagrange polynomials, Kriging, and Loess smoother. Accuracy of different methods was determined by comparing interpolated habitat conditions to actual values measured at points along the river. This study produced two main recommendations. First, when performing interpolations, data should be stratified by meso-habitat type (riffles, runs, and pools) only when habitat variables are different for each meso-habitat type and stratification does not increase distance between points such that interpolation accuracy is reduced. If habitat variables are similar for all meso-habitat types, knowing the meso-habitat type within which a point falls does not add information that will increase interpolation accuracy. Second, the Loess smoother with a smoothing parameter from 0.2 to 0.4 generally produced the most accurate interpolated values and is the method we recommend for similar situations.