A snow water equivalent reanalysis approach to explore spatial and temporal variability of the sierra nevada snowpack

The availability and variability of snowmelt has become a serious concern because of increased water demand, and because of the high degree of uncertainty related to climate variability posing a threat to the magnitude and timing of this precious resource. Understanding the geophysical controls and interannual variability of the spatial patterns of seasonal montane snowpacks are critical for understanding the effects of a warmer climate on the snowpack water storage. To explicitly resolve snow hydrological controls in complex montane environments, it is necessary to provide high resolution spatially and temporally distributed estimates of snow water equivalent, while also taking into consideration the uncertainties in the system. Toward this end, this dissertation developed a retrospective data assimilation technique (SWE reanalysis) that aimed to optimally merge VIS-NIR remote sensing data into a snow prediction model, and at the same time, account for the limitations of measurements, forcings, and model errors. The SWE reanalysis was: first developed and implemented over a small region, in order to investigate the performance of the methods under their nominal scenarios; second implemented for the full Landsat-5 record (27 year) over a regional scale domain in order to test accuracy and gain insight on the spatial and interannual controls on the SWE patterns; third extended to the entire Sierra Nevada in order to benchmark the reanalysis for its application to the full Sierra Nevada and to preliminarly understand what are the spatial controls on SWE patterns. The key findings of this dissertation can be summarized as follows: 1) The SWE reanalysis approach provided accurate spatially and continuous estimates of SWE and of its uncertainties due to measurement, forcings, and model errors. 2) The methods were found to be robust to input errors such as biases in solar radiation and precipitation, and robust to the number of available VIS-NIR observations. 3) The application of the methods over the Kern watershed for the full Landsat-5 record suggested that SWE accumulation patterns were in general not interannually consistent and that the interannual variability was dependent on whether a dry or wet year was analyzed. 4) The trend test analysis showed that peak-SWE and day-of-peak have not drastically changed over the analyzed 27 years for the Kern River watershed, but suggested that the lower elevations may be more susceptible to climate variability and change. 5) Elevation was found to be the primary control on spatial patterns of peak-SWE and day-of-peak for the entire Sierra Nevada range; however different patterns were found across the watersheds of the Sierra Nevada depending on their location. Ultimately, the methods can be applied to the full Sierra Nevada and other montane regions over the modern remote sensing record to generate a dataset that should be useful to scientists and practitioners not only in hydrology, but other fields where seasonal snow processes are a key driver such as biogeochemistry, mountain meteorology, and water resource management.