Seismic reflection results show that the top of this rhyolite/basalt sequence dips (~8-11°) southwest away from the Boise foothills at depths of 200 to 800 m. Seismic methods enabled interpretation of aquifer depths along the profiles and located fault zones where injected water may encounter fracture permeability and optimally benefit the existing producing system. The acquisition and processing techniques used to locate the Boise injection well may succeed for other hydrogeologic and environmental studies in urban settings.

]]>Through sampling experiments on high-resolution LiDAR snow depth observations at six separate 1.17-km^{2} sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross-validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (*RMSE*), and *R*^{2}. Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR-measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross-validation bias at the spatial scale of inference. We model at the 30-m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill.

Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (*R*^{2}) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although *RMSE* and *R*^{2} (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match *RMSE* and *R*^{2} scores obtained by LiDAR-measured predictors.

Under cross validation, the *RMSE* and *R*^{2} skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30-m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross-validation bias but also maximize true *R*^{2}. The bagging tree model is found to generally outperform the other regression models in the study on several criteria.

Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres.

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