The Sierra Nevada system plays an integral role in the hydrologic cycle, energy cycle, ecological systems, and in water resources supply in Western North America. Trends toward earlier spring snowmelt runoff and decreasing springtime observations of snow water equivalent (SWE) from in situ networks have been observed in the past fifty years. These trends will result in both increased likelihood of winter floods and decreased water availability. It is therefore urgent to develop detailed, process-based understanding of the observed changes. Moreover, it is vital to develop a spatially-continuous characterization of these trends, spanning the physiographic gradients present in the Sierra Nevada. Not all available information has been used in the diagnosis of the trends, viz. spaceborne remote sensing measurements. This means that trends have only been evaluated where particular stations are located (not over the entire Sierra Nevada). Moreover, formal estimates of observation uncertainty have not been taken into account in the assessment of the trends. Detailed understanding of the physical processes and complex sensitivity to climatic change across physiographic gradients has not been achieved. We thus propose a reanalysis utilizing all available datasets, including both in situ and remote sensing measurements. Our motivation is the unique and complementary characteristics of each of four primary datastreams: in situ snow measurements, streamflow, snow-covered area (SCA) derived from visible and near-infrared data, and passive microwave measurements. We will use ensemble model simulations of snowpack physical processes to provide a priori estimates of snowpack variables. Measurement models will be used to relate snowpack states to all four primary datastreams. The data assimilation analysis step calculates an a posteriori estimate of snowpack variables that takes into account all four datastreams, as well as meteorological data and scientific knowledge of snow physics processes. Estimates of the spatiotemporal uncertainty in each snowpack variable can be calculated directly from the ensemble. Trends will be assessed using these posterior estimates of snowpack states. During our two-year project, we will perform the reanalysis described above for the King River and Kaweah River basins in the Southern Sierra Nevada. Each of these river basins overlaps spatially with study areas from the Southern Sierra Critical Zone Observatory (https://snri.ucmerced.edu/CZO). The Providence CZO in the King River basin and the Wolverton CZO in the Kaweah River basin represent locations where ongoing research is leading to a deeper process-level understanding of the snow cover across elevation gradients, which may provide insight into the long-term trends that have been observed. We will focus this two-year project on developing solid methodology that could be applied to the entire Sierra Nevada in follow-on work. We will focus on development of the statistical and physical models of spatial variability and uncertainty that form the core of the reanalysis assimilation scheme. In particular, we will focus on development of models for relating observing network point measurements of snow water equivalent to grid-based estimates of snow properties, and on parameterization of the uncertainty models for the hydrometeorological inputs to the snow physics models. We will integrate the models, methods, and results from the work into existing undergraduate and graduate courses at UCLA, and into ongoing outreach work of the Byrd Polar Research Center. Additionally, the high-resolution modeling framework developed here could provide a unique testbed for future climate change studies, where atmospheric model output from regional or global climate models could be used as forcing to the offline model to add to the existing literature on how the Sierra snowpack and spring streamflow is expected to change.
The Sierra Nevada mountain range plays an integral role in the hydrologic cycle, energy cycle, ecological systems, and in water resources supply in the Western U.S. It is representative of other large-scale mountain ranges that provide a key source of water in semi-arid regions of the globe. Trends toward earlier spring snowmelt runoff and decreasing springtime observations of snow water equivalent (SWE) from in situ networks have been observed in the past fifty years. These trends will result in both increased likelihood of winter floods and decreased water availability. It is therefore urgent to develop detailed, process-based understanding of the observed changes. Previously derived trends have only been evaluated where particular stations are located (not over the entire Sierra Nevada). A wealth of satellite-based remote sensing over the last 30 years could be used to better characterize how snowpacks are evolving. This project centered on developing new frameworks for estimating maps of how snow has evolved over a test-basin (Kern River) in the Sierra Nevada over the satellite-based remote sensing record. Example results of the map of the climatology of peak snow water equivalent (SWE) over the last 27 years in the Kern River watershed and the trends in peak SWE are shown in the attached Figure 1. The intellectual merit of this project was primarily based on developing and validating robust new methods for deriving spatially-continuous estimates of temporal trends in snowpack characteristics that span a range of mountain conditions. These methods will be extended to cover the entire Sierra Nevada and other key mountain ranges across the globe. The broader impacts of this project derive from the importance of understanding these trends for future scientific, sociologic, and economic forecasting. For decision-makers, the results will not only provide trends that may be attributable to climate change, but will provide uncertainty estimates on those trends that provide essential information needed in operations management in the context of a changing climate.
