An Autoencoder-based Snow Drought Index
Sinan Rasiya Koya Kanak Kanti Kar Shivendra Srivastava Tsegaye Tadesse Mark Svoboda Tirthankar Roy
In several regions across the globe, snow has a significant impact on
hydrology. The amounts of water that infiltrate the ground and flow as runoff
are driven by the melting of snow. Therefore, it is crucial to study the
magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow
storage, can drastically impact the water supplies in basins where snow
predominates, such as in the western United States. Hence, it is important to
detect the time and severity of snow droughts efficiently. We propose Snow
Drought Response Index or SnoDRI, a novel indicator that could be used to
identify and quantify snow drought occurrences. Our index is calculated using
cutting-edge ML algorithms from various snow-related variables. The
self-supervised learning of an autoencoder is combined with mutual information
in the model. In this study, we use random forests for feature extraction for
SnoDRI and assess the importance of each variable. We use reanalysis data
(NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy
of the new snow drought index. We evaluate the index by confirming the
coincidence of its interpretation and the actual snow drought incidents.