Margulis Research Group

Department of Civil and Environmental Engineering


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Spatial and temporal analysis of Sierra Nevada snowpack using a fractional snow covered area data assimilation approach

(Sponsor: NASA Earth System Science Fellowship; Student Fellow: Manuela Girotto)

The overall goal of this research is to improve our knowledge of spatial and temporal controls of the Sierra Nevada snowpack. The spatial heterogeneity of the snowpack and a continuously changing climate affects a variety of processes including surface water discharge. Much of California, like many semi-arid regions worldwide, relies on snowmelt as the primary source of fresh water. A continuous space-time characterization of snow distribution will improve our ability to make better prediction and monitor this vital resource. Toward this end, this research proposes to reconstruct spatial and temporal snow water equivalent interannual patterns over a testbed domain located in the Southern Sierra Nevada that includes a range of physiographic conditions. The data assimilation technique will systematically merge Landsat and MODIS derived Vis/NIR snow cover area into a land surface model that, coupled together with a snow depletion model, will predict seasonal, continuous (in space and time) snow water equivalent estimates. The use of multi-sensor and multi-resolution products is critical for developing future operational snow products aimed at characterizing mountainous hydrological processes over large scale regions. Tradeoffs and synergisms of the varying spatial/temporal resolution will be explored to identify optimal resolutions that allows for an accurate estimation of basin-scale snow processes. The assimilation will produce unprecedented spatial/temporal continuity conditioned on multiple data stream that can be exploited to investigate spatial accumulation and ablation patterns while identifying physiographic controls. Understanding the geophysical controls of the spatial patterns of snow processes is critical for understanding the effects of climate variability on the snowpack water storage. In particular the analysis of results obtained by assimilating the entire Landsat record will provide an unprecedented dataset that can be analyzed to improve our knowledge on how climate change has and will affect the temporal and spatial distribution of snow melt water.

Maps showing the preliminary small headwater basin study area: a) Tokopah basin location in Sierra Nevada; and distribution of b) elevation and c) aspect. The Tokopah basin is 19 square kilometers in size and essentially vegetation-free.


Schematic showing the basic components and methodology of the probabilistic SWE reconstruction (or SWE reanalysis) scheme.


Preliminary results:

Comparison of SWE estimates from intensive in situ sampling and regression tree modeling (top row) to those from the probabilistic (middle) and deterministic (bottom row) SWE reconstruction techniques.

Illustration of prior (red) and posterior (blue) FSCA estimates (top panel) and SWE (bottom panel) for 1997 reanalysis. Black lines represent the deterministic reconstruction. Green circles represent assimilated FSCA observations (top panel) and indepdent ground-truth SWE estimates (bottom panel).


Results of robustness test to the number of available observations during ablation season. The key point is that the probabilistic approach is much more robust to missing data than the deterministic approach.


Datasets: The results shown above are explained in more detail in Girotto et al. (2013) in Hydrological Processes. If interested, please contact the PI about data availability.