1. Estimating the Spatial Distribution of Snow Water Equivalent and Snowmelt in Mountainous Watersheds of Semi-arid Regions Noah Molotch Department of Hydrology and Water Resources University of Arizona, Tucson

3. Climate Change: Impact on the Snowpack Current Trends in the Pacific Northwest negative source: Knowles and Cayan, 2002 Projected Sierra Snowpack April 1 Snow Water Equivalent trend, 1950–2000. (Mote, 2003)

4. source: Peterson et al., 1997 Rely on point values and/or basin average values of SWE. Current Streamflow Forecasts snow-course SNOTEL

5. Objective Improve snowmelt modeling using remotely sensed albedo. Do remotely sensed snow albedo data improve estimates of the timing and magnitude of snowmelt? Improve the utility of SNOTEL data in spatial applications. What is the spatial representativeness of SNOTEL SWE values in the Rio Grande headwaters? What is the optimal location for future observations?

6. Study Regions Kaweah Tuolumne Salt-Verde Upper Colorado Upper Rio Grande elevation, m 200 1100 2200 4300 N

7. Methods: Snowpack Mass Balance initial SWEdaily snowmelt SWE n = SWE 0 - M j 0 1 2 3 km N

8. Remotely Sensed Snow Surface Albedo May 21May 05June 18, 1997 mean albedo = 0.664 snow age = 11 days frequency mean albedo = 0.693 snow age = 1 day mean albedo = 0.686 snow age = 4 days > 3 3 - 2 2 - 1 +- 1 -2 - -1 -3 - -2 std. dev. N 0 1 2 km

9. Model Evaluation: Timing of Snow Cover Depletion SWE n = SWE 0 - M j if, M j, ≥ SWE 0, then, SWE n = 0 and SCA n = 0 ObservedModeled 0 1 2km N

10. Model Evaluation: Timing of Snowmelt R-squared increased from 0.59 to 0.73 using AVIRIS albedo. Peak runoff occurred 18 days versus 2 days before the observed peak. AVIRIS peak USACE peakobserved peak

11. Model Evaluation: Magnitude of Snowmelt assumed w/ update assumed albedo AVIRIS SWE difference, cm Modeled – Observed SWE M = SWE 0 when SWE n = 0, SWE n = SWE 0 - M j USACE

12. Summary Timing improved: hydrograph R-squared increased from 0.59 to 0.73 Magnitude error decreased from 36% to 2%

13. Methods: Assessing Watershed Scale Variability SNOTEL stations replace snow-courses Criteria: accessibility, avoid public disturbance Mostly along highways, flat ground, small forest clearings Develop SCA persistence map Colorado Wyoming SNOTEL Utah AVHRR

14. Methods: Assessing grid-element scale variability binary regression tree models (Molotch et al., 2004) 0 1 2 3 km elevation solar radiation vegetation density slope wind exposure SNOTEL N

15. Snow Cover Persistence in the Rio Grande Headwaters:1995 - 2002 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 2,3,6 51,4 June 1 2 3 4 56 # years with snow cover 0 1 2 3 4 5 6 7 8 # years with snow cover June May SNOTEL stations 6 1,5 2,3,4

16. Grid-element Scale Variability SNOTEL overestimated grid- element SWE. Less than 2.4% of domain satisfied criteria for optimality. 0 1 6 9 12 15 18 21 24 27 absolute deviance from mean (cm) Slumgullion SNOTEL

17. Summary At the watershed scale, SNOTEL sites are located in areas with persistent snow cover. Valley floor and alpine areas and areas far from Western Barrier are unsampled. At the grid-element scale, SNOTEL sites are often in high accumulation areas. More representative measurements can be obtained using the approach defined.

18. How can scientific opportunities created by technological advances in field sensors, remote sensing and computational modeling be best realized? snowpack streamflow evapotranspiration Future Research Thrust 1: The processes controlling energy and water fluxes in mountainous regions are not well understood. airborne satellite: 2012 At what scales does spatial variability of snow characteristics control water, energy, and carbon, fluxes and can remote sensing resolve this variability at these scales? How representative are current operational hydrologic observations? How can we obtain representative measurements in future observation networks.

19. What are the affects of seasonal, annual, and inter- decadal snow cover variability on carbon exchange? - direct influence: primary production and respiration. - indirect influence: drought and forest fires. - processes effect water cycle Need to realize feedback systems between hydrological, biogeochemical and ecological processes. Future Research Thrust 2: What is the relationship between inter-annual and inter-seasonal snowpack mass balance and hydrochemical fluxes?

20. Acknowledgements NSF STC for the Sustainability of semi-Arid Hydrology and Riparian Areas (SAHRA), NASA EOS IDS, Climate Assessment for the Southwest (CLIMAS). R. Bales, R. Davis, P. Guertin, B. Nijssen, J. Shuttleworth, J. Dozier, T. Painter, R. Brice, S. Fassnacht, M. Colee, W. Rosenthal. Field data collection teams.