Brown, Kevin S., authorFassnacht, Steven R., author2022-09-202022-09-202019-01https://hdl.handle.net/10217/235761January 2019.Seasonal snow is a crucial component of water supply in Colorado and the western United States. Measurement of snow accumulation through the winter and spring allows water managers to forecast water supply for the growing season and take actions to ease flooding and drought. The Natural Resources Conservation Service’s (NRCS) snow telemetry (SNOTEL) network provides real-time data at a high cost per station and at single points. An evaluation of existing field measurements of snow depth taken in 2009 and 2010 was undertaken to determine if fine resolution depth measurements are justified. Fassnacht et al. (in press) showed that the snow depth variability can be substantial even at fine resolution. However, these data required extensive labor to collect and only represented one measurement in time. A low-cost method to measure snow variability around these stations or in underrepresented areas could improve snow forecasts by quantifying the representativeness of data from the current network. To this end, we trialed a method combining time lapse photography and computer vision techniques to find snow depth at five sites in Colorado during water year 2018. Different site configurations were trialed, and a best operating procedure was determined. The data gathered were not more accurate than current ultrasonic or laser snow depth measurement technologies. However, the low cost and versatility of this method may make it more applicable in certain situations.born digitalreportsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.image recognitionsnow depthmeasurementphotogrammetryspatial variabilityText