MODIS True Color Terra image from from NASA WorldView (worldview.earthdata.nasa.gov)

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1 Estimating the snowpack with remote sensing MODIS True Color Terra image from from NASA WorldView (worldview.earthdata.nasa.gov) Edward (Ned) Bair, Ph.D. Earth Research Institute, University of California, Santa Barbara USA Technical training (30 min), Data and Modeling 9/19/16

2 Outline for this talk 1. Motivation: Why is snow important? 2. Comparison of four widely used techniques used to estimate basin-wide snow water equivalent (SWE) 3. Introduction to our downscaling and SWE reconstruction methods. 4. Discussion of current work with reconstruction, including real-time SWE estimates using machine learning 5. Conclusion and pitch for collaboration and funding 2

3 Why is snow and ice important? One billion people worldwide depend on water from snow and ice melt (Barnett et al., 2005) In Central Asia, for example, the Amu Darya Basin is heavily dependent on snow and glacial melt, and the full potential for reservoirs has already been exploited (Immerzeel and Bierkens, 2012), meaning that adaptation to changes in melt timing are limited. Estimating basin-wide SWE is the greatest unsolved problem in snow hydrology (Dozier et al. 2016). Runoff ratio (snowfall/streamflow), from Barnett et al. (2005) Barnett, T. P., J. C. Adam, and D. P. Lettenmaier (2005), Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438(7066), , doi: /nature Dozier, J., Bair, E.H., and Davis, R.E. (2016) Advanced review: Estimating the spatial distribution of snow water equivalent in the world's mountains. WIRES Water. doi: /wat Immerzeel, W. W., and M. F. P. Bierkens (2012), Asia's water balance, Nature Geosci, 5(12),

4 We can map snow cover well in cloudfree conditions, but we don t know how deep the snow is MODIS True Color Terra image from from NASA WorldView (worldview.earthdata.nasa.gov) 4

5 Common methods to estimate basin-wide snow water equivalent (SWE) a) Spatial interpolation from snow pillows (automated), snow courses (manual), and satellite-based measurements of fractional snowcovered area (fsca) b) SWE reconstruction building the snowpack in reverse from energy balance components and fsca c) Passive microwave emission from soil attenuated by snowpack d) Numerical weather models w/ data assimilation (e.g. SNODAS) Dozier et al.,

6 Fractional snow covered area We use a spectral unmixing technique to estimate fsca, currently the MODIS Snow Covered Area and Grain Size (MODSCAG, Painter et al. 2009) We are working on a new spectral unmixing technique that uses background (i.e. snow free) spectra, rather than pure endmembers. We then smooth and interpolate these fsca estimates across cloudy days, placing greater weight on days with scans closer to nadir (Dozier et al. 2008). From Painter et al From Dozier et al Dozier, J., T. H. Painter, K. Rittger, and J. E. Frew (2008), Time-space continuity of daily maps of fractional snow cover and albedo from MODIS, Advances in Water Resources, 31, , doi: /j.advwatres Painter, T. H., K. Rittger, C. McKenzie, P. Slaughter, R. E. Davis, and J. Dozier (2009), Retrieval of subpixel snowcovered area, grain size, and albedo from MODIS, Remote Sensing of Environment, 113, , doi: /j.rse

7 a) Snow pillow and course interpolation Simple: interpolate point measurements and spread snow using fsca Pros: Can be very accurate Cons: Relies on a dense network of point measurements Rittger, K., Bair, E.H., Kahl, A., and Dozier, J. (2016). Spatial estimates of snow water equivalent from reconstruction. Advances in Water Resources 94, doi: /j.advwatres

8 b) SWE Reconstruction We developed the Parallel Energy Balance Model (ParBal) We know when the snow disappears, as fsca 0. From that, build the snowpack in reverse from the energy balance. Use downscaled reanalysis data and modeled solar geometry. Pros: no ground measurements req d; accurate Cons: only available retrospectively; only models ablation season, not accumulation; doesn t work well in ephemeral snowpacks; doesn t work on glaciers Steps in implementation of SWE reconstruction. Top left: MODIS image (tile h23v05) on 01 April 2014 of Hindu Kush range in Afghanistan; Top right: Fractional snow-covered area. Total snow-covered area is 209,000 km 2. Bottom left: Energy inputs (W m 2 ) to snowmelt model at 11:00 on 01 April Bottom right: SWE (mm) on 01 April 2014, derived by hourly backward melt calculation from dates of snow disappearance. Total SWE volume is 61 km 3, an average of 290 mm. 8

9 c) Passive microwave At 36 GHz, the brightness temperature (TB36) is sensitive to SWE. To account for subnivean emission, a lower frequency that is transparent to the snowpack (typically 19 GHz, i.e. TB19) is also used, the difference in brightness temperatures (TB19-TB36) is used to estimate SWE. Pros: only satellite-based direct measurement of SWE Cons: doesn t work in wet or deep snowpacks; poor spatial resolution (10-25 km); doesn t work well under canopies or in the mountains 9

10 d) Numerical weather models and data assimilation Pros: can be used in austere and heavily instrumented areas Cons: still reliant on ground based observations; updates not popular with water managers; at least with SNODAS, questionable accuracy 10

11 Sierra Nevada as a test site for the Hindu Kush Since most areas the Hindu Kush have little or no ground truth, we use the Sierra Nevada USA to test and validate our model The Hindu Kush is a particularly good analog and candidate for reconstruction because of it is not monsoon influenced (dry summer). Unknown riders in the Hindu Kush Mt Tom from the Owens Valley, CA USA 11

12 Our SWE reconstruction technique: The Parallel Energy Balance Model (ParBal) At time j, the energy to melt snow, in W m -2, is the product of the possible energy MM pp,jj and the fractional snow cover ff SSSSSS,jj : MM jj = ff SSSSSS,jj MM pp,jj To estimate hourly (the model time step) SWE in mm at time j, MM jj is summed during contiguous periods when ff SSSSSS,jj > 0. MM pp,jj = RR jj + HH jj + LL jj + GG jj where R is net radiation, H and LL are sensible and latent heat exchanges, and G is heat flow in/out of the snowpack, all at time step j. The model solves the energy balance at each time step for snow surface temperature TT ss using the Newton-Raphson method, RR TT ss + HH TT ss + LL TT ss = 0 and is solved iteratively for TT ss. Solutions with TT ss > K indicate MM pp > 0. For that case, TT ss is set to K and MM pp is computed; otherwise MM pp = 0. 12

13 Downscaling example: net radiation 13

14 Validation of ParBal with the Airborne Snow Observatory (ASO) 14

15 Results from the Upper Tuolumne, CA USA Bair, E. H., K. Rittger, J. Dozier, and R. E. Davis (in review), Validating Snow Water Equivalent Reconstruction in California s Sierra Nevada Using Measurements from the NASA Airborne Snow Observatory, Water Resources Research. 15

16 But what about Central Asia? Now that we are confident in the modeled SWE from ParBal, we have reconstructed SWE for all of Afghanistan, with plans to cover all of High Mountain Asia 16

17 What about the glaciers, e.g. Kunar Basin, Afghanistan, Apr ? SWE, mm Permanent snow/ice, excluded 4.9 km 3 SWE, how much water equivalent is in the glaciers? 17

18 We can t estimate total water stored in the glaciers with ParBal, but we can estimate how much they melt 18

19 Upper Indus Basin water partitioning Rittger, K., M. J. Brodzik, E. H. Bair, A. Racoviteanu, A. Barrett, S. J. Kalsa, B. Raup, B. Armstrong, and J. Dozier (in preparation), Distinguishing snow and glacier melt in High Asia using MODIS, Water Resources Research. Funded by the Contribution to High Asia Runoff from Ice & Snow (CHARIS) project

20 Another use of ParBal: real-time SWE prediction with machine learning using a 6 layer neural network Reconstructed SWE 1) Day of year 2) Longitude 3) Latitude 4) fsca 5) Std. dev fsca 6) AMSR-E SWE Predicted SWE 20

21 Machine Learning Variables 4/1/2014 Raw MODIS Timespace smoothed MODSCAG fsca AMSR2 SWE, mm Reconstructed SWE, mm 21

22 Machine learning results 22

23 Thanks to my collaborators Jeff Dozier, University of California, Santa Barbara, CA USA Karl Rittger, University of Colorado, Boulder, CO USA Bert Davis, US Army Corps of Engineers Cold Regions Research and Engineering Laboratory, Hanover, NH USA 23