UCLA

Seasonal snowpack is a vital water resource that impacts downstream water availability. However, accurately estimating snow storage and characterizing its spatiotemporal distribution remain challenging, in particular for data-scarce regions such as High Mountain Asia (HMA). In this dissertation, a newly developed snow reanalysis method is used to estimate snow water equivalent (SWE) over the HMA region, assessing its spatiotemporal distribution and quantifying the regional snow storage. The method assimilates fractional snow-covered area (fSCA) from the Landsat and MODIS platforms, over the joint Landsat-MODIS record (Water Year (WY) 2000 – 2017). A fine resolution (16 arc-second, ~480 m) and daily High Mountain Asia snow reanalysis (HMASR) dataset is derived and analyzed in the dissertation.The key conclusions are summarized as follows: 1) Snowfall precipitation is found underestimated in most precipitation products with sizeable uncertainty when evaluated in sub-domains of HMA. The research shows the potential of using satellite snow observations as a constraint, to infer biases and uncertainties in snowfall precipitation in remote regions and complex terrain where in-situ stations are very scarce. 2) Through examining the HMASR dataset, the domain-wide peak seasonal snow storage is quantified as 163 km3 when aggregated across the full HMA domain and averaged across WYs 2000-2017, with notable interannual variations between 114 km3 and 227 km3. 3) Existing global snow products over HMA on average underestimate the peak snow storage by 33% � 52% over the entire HMA, and the uncertainty in peak snow storage estimates is primarily explained by accumulation season snowfall (88%) over HMA, partly due to a wide range (uncertainty) in precipitation (snowfall). Ultimately, the snow storage and its spatiotemporal variations characterized in this work can be used to understand the role of seasonal snowpack in the regional climate and water cycle over this region.