Improving Snow Water Equivalent Maps With Machine Learning of Snow Survey and Lidar Measurements

In the semiarid interior western USA, where a majority of surface water supply comes from mountain forests, high-resolution aerial lidar-based surveys are commonly used to study snow. These surveys provide rich information about snow depth, but they are usually not accompanied with spatially explicit measurements of snow density, which leads to uncertainty in the estimation of snow water equivalent (SWE). In this study, we use a novel approach to distribute similar to 300 field measurements of snow density with artificial neural networks. We combine the resulting density maps with aerial lidar snow depth measurements, bias corrected with a very large and precisely geolocated array of field-measured snow depths (similar to 4,000 observations), to create and validate maps of snow depth, snow density, and SWE over two sites along Arizona's Mogollon Rim in February and March 2017. These maps show differences between midwinter and late-winter snow conditions. In particular, compared to that of snow depth, the spatial variability of snow density is smaller for the later snow survey than the earlier snow survey. These gridded data also show that the representativeness of Snow Telemetry and other point measurements is different for the midwinter and late-winter snow surveys. Overall, the lidar artificial neural network SWE estimates can be as much as 30% different than if Snow Telemetry density were used with lidar snow depths to estimate SWE. Plain Language Summary In the western USA, a majority of surface water originates from mountain snowmelt. Knowing the quantity of water in the snowpack, called snow water equivalent (SWE), is critical for water supply forecasts and management of rivers and streams for water delivery and hydropower. In this study, we develop a new method to estimate SWE by combining aerial remote sensing maps of snow depth with snow density maps generated through machine learning of hundreds of field measurements of snow density. This study finds that on a given date, snow density can vary widely, highlighting the importance of considering its spatial variability when estimating SWE. These gridded data show that the representativeness of Snow Telemetry and other point measurements is different for the midwinter versus late winter snow surveys. In addition, we show that using spatially variable maps of snow density can impact watershed-scale SWE estimates by up to 30% as compared to using snow density measurements from commonly used snow monitoring stations. The method described in this study will be useful for generating SWE estimates for water supply monitoring, evaluating snow models, and understanding how changing mountain forests might impact SWE.