1. Simulating Areal Snowcover Depletion and Snowmelt Runoff in Alpine Terrain Chris DeBeer and John Pomeroy Centre for Hydrology, Department of Geography and Planning University of Saskatchewan

2. Background Snowmelt runoff from Rocky Mountains is an important water resource High uncertainty in the future hydrological response to climate and/or landcover change Important to be able to better understand and predict likely changes for future water management –Requires robust and physically based models for simulating snow processes

3. Variability of Alpine Snow Processes Complexities in terrain and vegetation affect snow accumulation, redistribution, and melt –High spatial variability in snow water equivalent (SWE) –Large variation in energy for snowmelt during the spring Leads to a patchy snowcover as the spring progresses Significantly affects timing, rate, and magnitude of meltwater generation

4. Areal Snowcover Depletion (SCD) Melt rate computations applied to a distribution of SWE yield snowcovered area (SCA) over time (SCD curve) M a 100 mm M a 100 mm M a 200 mm M a 350 mm M a 200 mm M a 350 mm SCA = 0.92 SCA = 0.45 SCA = 0.05 Frequency distribution of SWEDerived SCD curve

5. Problems with SCD Approach in Alpine Terrain The approach assumes uniform melt rate over the SWE distribution – Energy balance melt rate computations depend on snowpack state (e.g. depth, density, SWE, temperature, etc.) – Melt rates are not uniform in alpine terrain Further problems with new snowfall part way through melt

6. Study Objectives Develop new theoretical framework for areal snowcover depletion (SCD) and meltwater generation Test framework using observations in alpine basin Determine how variability of SWE and snowmelt energy affect areal SCD and meltwater generation Incorporate framework within hydrological model and examine influence of variability on hydrograph

7. Development of Theoretical Framework Framework for areal SCD based on lognormal distribution SCD curve from uniform 30 mm/day applied snowmelt

8. Development of Theoretical Framework Frequency factor, K SWE (mm) K min, i K min, ii 0 Initial distribution 50 150 200 100 Following melt Melt depth for 50 mm initial SWE Melt depth for 100 mm initial SWE SWE K min, 1 K min, 2 0 Snowmelt Subsequent accumulation Foot Line representing the distribution can be discretized New snow added uniformly over remaining distribution 132132 Framework handles other important aspects of spatial snowmelt and new snowfall during spring

9. Field Study Site Marmot Creek Research Basin, Kananaskis Country, Alberta

10. Field Study Site Focused data collection at Fisera Ridge and Upper Middle Creek Fisera Ridge Mt. Allan

15. Field Methods and Observations Data collection over three years (2007-09) involved: –Meteorological observation –Snow surveys –Daily terrestrial photos –Lidar snowcover mapping –Streamflow measurement Ridgetop Station North Facing Station Southeast Facing Station

16. Field Methods and Observations 100s of snow surveys over 3 years Setup and maintenance of many instruments and met stations Dozens of manual stream discharge measurements

17. Terrestrial Oblique Photo Correction 1) Viewshed mask created from camera perspective 2) DEM projection in camera coordinate system 3) Correspondence established between DEM cells and image pixels 4) Image reprojection in DEM coordinates 2 & 3 1 4

18. May 7, 2007May 10, 2007May 14, 2007May 17, 2007May 19, 2007May 22, 2007May 26, 2007May 29, 2007May 31, 2007June 2, 2007June 4, 2007June 7, 2007June 10, 2007June 13, 2007June 18, 2007June 21, 2007June 24, 2007June 27, 2007July 1, 2007July 4, 2007July 9, 2007July 13, 2007 Areal Snowcover Observations Time lapse digital photography used to monitor areal SCD

19. Snowmelt Modelling and Validation Snowpack evolution simulated using the Snobal energy balance model within Cold Regions Hydrological Model (CRHM) platform Active layer Lower layer L v E H K K L L P E Soil layer G R U U Shortwave and longwave radiation inputs corrected for slope, aspect, skyview fraction using algorithms in CRHM (Q m = L V E + H - K + K + L - L + G - dU/dt)

20. Snowmelt Modelling and Validation Model performs well for depth, SWE, internal energy 2008 2009

21. Effects of Snow Mass and Internal Energy Differences in initial state have large influence on computation of snowmelt timing and rate Cold Content: Energy required to bring snowpack to 0 °C and satisfy liquid water holding capacity

22. Spatial – Temporal Snowmelt Variability SE-facing slope N-facing slope Differences in melt energy and SWE lead to large differences in snowmelt that change over time

23. Landscape Disaggregation for SCD Simulation SWE values on different slopes fit theoretical lognormal distribution S-facing slope N-facing slope

24. Simulation of Areal SCD over Landscape Framework applied to predict areal SCD Results were improved by considering separate distributions and melt rates on each slope Overall basin North facing South facing East facing NS = 0.92 RMSE = 0.09

25. Influence of Inhomogeneous Snowmelt Earlier and more rapid melt of shallow snow on some slopes led to an initial acceleration of SCD

26. Variability in melt over landscape and SWE dists. affects location, extent, and timing of meltwater generating area 0 – 5 mm/day 5 – 10 mm/day 10 – 20 mm/day exposed ground forested areas cliffs April 26April 29 May 2 May 5 Spatial Variability of Meltwater Generation

27. Process-based and conceptual model with spatial structure based on topography, land cover, and SWE distributions Hydrological Model Development

28. Model is capable of producing reasonable hydrographs with correct volume of runoff Model Evaluation for Snowmelt Hydrograph Total DischargeComponent Hydrographs

29. Various simulation approaches were used to examine influence on the basin hydrograph Hydrograph Sensitivity Analysis NS = 0.62 RMSE = 0.03 NS = 0.53 RMSE = 0.03 NS = 0.39 RMSE = 0.04 NS = 0.23 RMSE = 0.04

30. Other approaches were used to examine effects of forest canopy and soil depth, and inhomogeneous melt Hydrograph Sensitivity Analysis NS = -0.28 RMSE = 0.06 NS = 0.47 RMSE = 0.04 NS = 0.62 RMSE = 0.03 HomogeneousInhomogeneous

31. Novel framework that allows for physical, yet spatially simple snowmelt and SCD simulation –Incorporation of sub-grid distributions of internal energy for melt computation Application of the framework, together with a hydrological model showed the influence of the spatial variability of both SWE and snowmelt energy on areal SCD and snowmelt runoff in an alpine environment Key Conclusions, Significance

32. Important to take inhomogeneous melt into account for areal SCD simulations –Implications for remote sensing, climate models and modelling applications using depletion curves Effects are not as important for snowmelt runoff and hydrograph simulation, as other processes tend to overwhelm the response –Still important to account for spatial variability of snowmelt energy on the slope, and land cover scale Key Conclusions, Significance

33. Thank You NSERC CFCAS Canada Research Chairs Programme University of Calgary Biogeosciences Institute Nakiska Ski Resort Applied Geomatics Research Group Students and Staff of Centre for Hydrology