International Snow Science Workshop Grenoble – Chamonix Mont-Blanc – 2013 Operational SWE forecasts using a hybrid approach Edward H. Bair 1,2* , Robert Davis 1 , Karl Rittger 3,2 , and Jeff Dozier 4 , 1 US Army Corps of Engineers Cold Regions Research and Engineering Laboratory, Hanover, NH, USA 2 Earth Research Institute, University of California, Santa Barbara, CA, USA 3 California Institute of Technology, Jet Propulsion Laboratory, Pasadena, CA, USA 4 Bren School of Environmental Science & Management, University of California, Santa Barbara, CA, USA ABSRACT: Accurate spatial measurement of SWE in mountain watersheds is perhaps the most significant problem in snow hydrology. Because of the spatial variability of snow in these areas, operational models can have large errors, particularly in remote regions. We describe how operational needs led us to merge different approaches. Recently, SWE reconstruction has been shown to match well the rank order of snowmelt runoff across the Sierra Nevada, CA USA, especially for very dry and wet years. Ending with a date for peak SWE and starting with the disappearance of snow in a satellite image, reconstruction retrospectively builds the snow cover by calculating the amount of snow melted at each time step in each pixel. Operational experience has demonstrated the need for subjective context and perspective. We place the current water year into a historical perspective graphically by displaying a family of accumulation-depletion curves. Because reconstruction cannot estimate SWE prior to peak, we use an ensemble of normalized products for accumulation. The products in the ensemble depend on whether the study area is sparsely or heavily gauged. For instance, in the Sierra Nevada, a heavily gauged area, we use snow pillow interpolation and the Snow Data Assimilation System (SNODAS) for SWE accumulation. In Afghanistan, a sparsely gauged area, we use satellite- derived SWE from passive microwave emission for accumulation. From the normalized accumulation curves, we obtain the predicted rank of a water year prior to peak SWE. From the predicted rank, we find similar water years from the Reconstruction ensemble. This hybrid method provides a user with a series of SWE and depletion possibilities. By providing improved SWE estimates over the current operational model, we hope to improve information available to water managers of snowmelt- dominated watersheds. KEYWORDS: SWE, microwave, reconstruction 1 INTRODUCTION Accurate estimates of snow water equivalent (SWE) in mountain watersheds are a longstanding and unsolved problem. Operational models have high uncertainty, and this uncertainty has high costs for water users. For instance, April to July runoff forecasts for the American River in California’s Sierra Nevada have an 18% error on average, and sometimes exceed 100% (Dozier, 2011). Uncertainty stems from the heterogeneous nature of mountain snow. Spectroscopic techniques using satellite-based imagery in the visible and nIR bands have been successful at mapping snow covered area (SCA) at sub-pixel resolution (e.g., Rosenthal and Dozier, 1996; Painter et al., 2009). Measurements of SCA are combined with a Reconstruction technique (Martinec and Rango, 1981), which has successfully modeled SWE in large basins in the Rocky Mountains (Molotch, 2009) and the Sierra Nevada (Rittger, 2012). The main advantage of Reconstruction is that it provides spatially resolved SWE estimates without the need for extensive ground based observations. The biggest disadvantage is that reconstruction can only be run retroactively after snow disappears, as we discuss in Section 2.1. We suggest a hybrid approach for large scale SWE estimates over the entire water year. Rather than focus on accurate SWE mapping, we focus on rank order statistics, which may be more useful to water managers who place a premium on information that helps them discern between normal (business as usual) and extreme (very wet/very dry) years. This hybrid approach ranks SWE estimates from several real-time products into a family of accumulation curves. From this family of accumulation curves, the user can select one or more corresponding depletion (i.e. reconstruction) curves, chosen based on rank. Given a sufficient historical catalog that captures the range of variability, we can estimate peak SWE from the corresponding Reconstruction depletion curve. This classification is based on nearest neighbors, *Corresponding author address: Edward H. Bair, Earth Research Institute, University of California, Santa Barbara CA 93106-5131, USA, email: nbair@eri.ucsb.edu