Location: Watershed Management ResearchTitle: Quantifying spatial distribution of snow depth errors from LiDAR using Random Forests) Author
Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/30/2013
Publication Date: N/A
Citation: Interpretive Summary: Highly heterogeneous mountain snow distributions strongly affect soil moisture patterns, local ecology, and ultimately the timing, magnitude, and chemistry of stream runoff. These snow distributions are dynamically changing in response to climate change. Models based on physical laws, referred to as physically-based models, are required to accurately simulate these processes in a changing environment. Models based on historical relationships (e.g. statistics-based or calibrated models) struggle when “non-normal” conditions such as these are encountered. Models must also contain a spatial component to capture vital spatial variability. Capturing these fundamental heterogeneities in a physically-based distributed snow model therefore requires appropriately scaled model structures. Fine spatial resolutions come with high computational costs whereas coarse resolutions tend to reduce accuracy. This work looks at how model scale – particularly the resolutions at which the forcing processes are represented – affects simulated snow distributions and melt in a 6 km2 catchment in southwestern Idaho, USA. It was found that in order to accurately simulate snowmelt in this catchment, the snow cover needed to be resolved to 100m. Wind and wind-affected precipitation – the primary influence on snow distribution – required similar resolution. Thermal radiation scaled with the vegetation structure (~100m), while solar radiation was adequately modeled with 100-250m resolution. Spatio-temporal sensitivities to model scale were found that allowed for further reductions in computational costs through the winter months with limited losses in accuracy. It was also shown that these modeling-based scale assessments could be associated with physiographic and vegetation structures to aid a priori modeling decisions.
Technical Abstract: There is increasing need to characterize the distribution of snow in complex terrain using remote sensing approaches, especially in isolated mountainous regions that are often water-limited, the principal source of terrestrial freshwater, and sensitive to climatic shifts and variations. We apply intensive topographic surveys, multi-temporal LiDAR, and Random Forest modeling to quantify snow volume and characterize associated errors across seven land cover types in a semi-arid mountainous catchment at a 1 and 4 m spatial resolution. The LiDAR-based estimates of both snow-off surface topology and snow depths were validated against ground-based measurements across the catchment. LiDAR-derived snow depths estimates were most accurate in areas of low lying vegetation such as meadow and shrub vegetation (RMSE = 0.14 m) as compared to areas consisting of tree cover (RMSE = 0.20-0.35 m). The highest errors were found along the edge of conifer forests (RMSE = 0.35 m), however a second conifer transect outside the catchment had much lower errors (RMSE = 0.21 m). This difference is attributed to the wind exposure of the first site that led to highly variable snow depths at short spatial distances. The Random Forest modeled errors deviated from the field measured errors with a RMSE of 0.09-0.34 m across the different cover types. The modeling was used to calculate a theoretical lower and upper bound of catchment snow volume error of 21-30%. Results show that snow drifts, which are important for maintaining spring and summer stream flows and establishing and sustaining water-limited plant species, contained 30 ± 5-6% of the snow volume while only occupying 10% of the catchment area similar to findings by prior physically-based modeling approaches. This study demonstrates the potential utility of combining multi-temporal LiDAR with Random Forest modeling to quantify the distribution of snow depth with a reasonable degree of accuracy.