SnoDRI: A Machine Learning Based Index to Measure Snow Droughts

Abstract

Snow plays a significant role in the hydrology of numerous regions across the globe. A major portion of precipitation above 45O N latitude falls as snow. The accumulated snow melts slowly and contributes to infiltration and runoff processes. Therefore, studying the quantity and fate of water from snowmelt is essential. Reduced snow storage would lead to snow droughts, which can have an enormous impact on the water resources of snow-dominated catchments, such as those in the western United States. For that reason, it is essential to identify the time and severity of snow droughts efficiently. This study proposes SnoDRI, a new index that could identify and measure snow drought events. SnoDRI is a machine learning-based index estimated from several snow-related variables utilizing novel machine learning algorithms. The model uses a combination of mutual information and a self-supervised learning algorithm of an autoencoder. We use random forests for feature extraction for SnoDRI and to assess the importance of each variable. We use NLDAS-2 reanalysis data from 1981 to 2021 for the Western United States to study the efficacy of the new snow drought index. The results are validated by verifying the coincidence of actual snow drought events and the interpretation of our new index. We will discuss how well the new drought index performs and help in better identification of snow droughts.