Introduction
Location Map
Base Map
Database Schema
Conventions
GIS Analyses
Flowchart
GIS Concepts
Results
Conclusion
References

Conclusion

Conclusion

In general, the five ski resorts had greater reported snow depths than those that were predicted using the two different methods of interpolation. However, the difference between reported and predicted varied significantly for each month and resort. Elevation alone was not a strong predictor of snow depth, which makes sense as many other factors such as slope, aspect, and wind currents play important roles in snow accumulation.

During this particular period, Loveland Ski Resort’s reported snow depths agreed most often with the values predicted using both methods of interpolation. Conversely, Breckenridge’s reported snow depths were larger than predicted most often.

Additionally, between the two methods of interpolation used in ArcGIS, Inverse Distance Weighting created smoother surfaces compared with Kriging and usually had greater predicted snow depths. We believe that this smoothing method resulted in slightly better predicted snow depths than kriging, but we do not have enough data to show statistically significant results as to the relative strengths of each interpolation.

With additional snow stations and other study locations, this method could be refined to determine how accurately ski resorts report snow depth. Our analysis is meant to show the power of GIS in visualizing spatial differences in snowpack and making comparisons to ski resort reports. We do not have enough data points for the size of this area to make a truly effective interpolation of snow depth. Further analysis would require many more data points and could be carried out in R or other statistics software to generate more statistically significant results.