Chapter 1: We developed a non-linear regression model from first principals to predict the percent of precipitation interception from forest canopies using lidar as a measure of forest structure. To find the best parameters for the model, we measured thoroughfall of rain (n = 21), fresh snow (n = 21), and old snow (n = 26) on plots in the boreal forest of the upper Eklutna Valley, Alaska. We calculated a set of twelve lidar metrics for each plot, and found the combined metric of mean height * cover to be the lidar metric most highly correlated to ln (throughfall) for rain (r = -0.81), fresh snow (r = -0.79), and old snow (r = -0.73). Using mean height * cover in the interception model, we predicted mean interception for rainfall (20% [...]
