Upper Rio Grande studies around 6 snow telemetry (SNOTEL) sites Gin Flat pilot study During the winter of 2003/04, 10 ultrasonic depth sensors were installed (Gin Flat) in the upper Merced River basin. The sensors were installed in a spatial array to continuously measure the spatial & temporal distribution of snow. The study site is near the existing snowcourse (1930-present) & SNOTEL (1980-present) sites. The pilot study examined the physiographic variables within a 4-km2 open forest from the accumulation through ablation periods. Results The observed snow depth at the SNOTEL sites relative to the statistical distribution of interpolated & gridded snow depth based on the spatial snow survey was fairly consistent in 2001 & 2002; with significant positive snow depth bias at the Slumgullion site. Slum USJ WC Lilly 2002 2001 Optimal observation areas (shaded in black) around 4 SNOTEL sites (white rings), are areas with the lowest cumulative absolute deviance from the mean modeled snow depth. Areas enclosed by the dotted line at Upper San Juan had an April deviance of 2 cm. Only the Wolf Creek SNOTEL site was within the optimal area. Slumgullion Upper San Juan Wolf Creek Summit Lily Pond Representative measurement points Introduction The spatial representativeness of six SNOTEL stations in the Rio Grande headwaters was assessed using detailed observations of snow depth & density, remotely sensed data & binary regression tree models. The approach presented here is intended to improve the ability to upscale SNOTEL data for evaluating & calibrating remote sensing algorithms & initializing, evaluating, & updating modeling efforts at the regional scale. Ultrasonic sensors were generally within 3% of depth measured by probing. Probing around the SNOTEL pillow showed an error of 28%. Bias of ultrasonic sensors Vertical rectangles are + one std. deviation of values over 4-km2 area. Vertical lines are the range. Horizontal lines are mean snow depths. Accumulation & ablation rates over a 4-km2 study area at Gin Flat showed differences of as much as 50%. Intra-seasonal variability Gin Flat N elevation, m 200 Dependent variable Snow depth & density data collected around the SNOTEL sites on 22–27 April 2001 & 3–12 April 2002 are used to illustrate the findings. 1100 Upper Rio Grande sites 2200 4300 Range of SWE estimates from choosing 1-8 points with identical physiographic features (flat, open) for 3 different years, as % on mean SWE: historical peak (1983), low (1988), & average (1982). Although there are 10 points in the snow course, 2 are close to trees. Snow course variability Slum. URG MC USJ WC Lilly Independent variables Vertical rectangles are + one std dev. of values found over 16 km2 area. Vertical lines are the range of values. Horizontal lines are SNOTEL site values. Relative to the larger watershed in which they are located, the Upper Rio Grande SNOTEL sites are not representative of the physiographic variables known to control snow distribution. Rather, they are in an area of high snowcover persistence. Some sites also had a negative slope bias and hence positive solar radiation bias relative to the surrounding 1-16 km2 area. The 10 snow course measurement points, which are mostly in an open area, consistently had greater mean SWE than did the continuous measurement points at the 4-km2 open forest study area. Sensors vs. snow course Conclusions Although Rio Grande SNOTEL sites are not in representative locations, it is possible to identify points that are more representative of 1-16 km2 areas based on physiographic variables. Results such as these can be used to improve selection of representative SWE measurement points & guide use of point measurements to estimate SWE over broader areas. Intensive point measurements can be used to establish how physiographic variables interact to control SWE distribution, & representative areas defined based on those important variables. Gin Flat results show that a single point measurement is a poor estimator of SWE even in a physiographically homogeneous area. Using 4 or more points will reduce the uncertainty by 50% as compared to a single measurement. Acknowledgements The Upper Rio Grande work was supported through the an NSF Science & Technology Center grant to the U. Arizona (SAHRA). A number of colleagues from UA & DRI contributed to the field work. The Gin Flat study was supported by UC Merced, with the cooperation of Yosemite National Park w/ AVIRIS albedo w / constant albedo