How much snow is in the mountains and what is its fate? by Manuela Girotto

Water resources such as snow or groundwater can be estimated using satellite remote sensing observations and numerical models. Both models and observations have inherent uncertainties and limitations related to observation errors, model parameterization, and input uncertainties. A promising method to alleviate shortcomings in models and observations is data assimilation because it combines existing and emerging observations with model estimates, thus bridging scale and limitation gaps between observations and models. 

Using these tools, we can address the following science questions: How much water is stored as seasonal snow? How much is in the groundwater aquifers? Can we quantify hydrological changes due to human induced processes (e.g., irrigation)? This presentation will focus on the estimation of snow seasonal amounts in mountainous regions, the water towers of the world. They supply a substantial part of both natural and anthropogenic water demands and they are also highly sensitive and prone to climate change. Slides of the talk can be found by clicking on the above figure. 

 

The presentation was followed by an interesting discussion that you can see here below:

Identifying Snowfall Elevation Patterns by Assimilating Satellite- Based Snow Depth Retrievals

Precipitation in mountain regions is highly variable and poorly measured, posing important challenges to water resource management. Traditional methods to estimate precipitation include in-situ gauges, doppler weather radars, satellite radars and radiometers, numerical modeling and reanalysis products. Each of these methods is unable to adequately capture complex orographic precipitation. Here, we propose a novel approach to characterize orographic snowfall over mountain regions. We use a particle batch smoother to leverage satellite information from Sentinel-1 derived snow depth retrievals and to correct various gridded precipitation products. This novel approach is tested using a simple snow model for an alpine basin located in Trentino Alto Adige, Italy. We quantify the precipitation biases across the basin and found that the assimilation method (i) corrects for snowfall biases and uncertainties, (ii) leads to cumulative snowfall elevation patterns that are consistent across precipitation products, and (iii) results in overall improved basin-wide snow variables (snow depth and snow cover area) and basin streamflow estimates.

The analysis of the snowfall elevation patterns' spatial characteristics indicates that the proposed assimilation scheme results in more accurate spatial patterns in the snowfall distribution across the entire basin. The derived snowfall orographic patterns contribute to a comprehensive improvement of mountain hydrologic variables such as snow depth, snow cover area, and streamflow. The most significant enhancements in streamflow are observed during the spring and summer months when peak flow observations align more accurately with the posterior cases than the prior ones. These results primarily stem from the fact that the assimilation of Sentinel-1 assigns less snowfall to the lower-elevation regions of the basin, while higher rates are assigned to the higher elevation. As summer approaches, water is released more slowly from the higher elevation via snow-melt than in the prior case, which aligns better with observations. The assimilation of Sentinel-1 effectively downscales coarser-resolution precipitation products. While the prior snowfall cumulative elevation pattern has a small gradient across elevation bands, these patterns are consistent across elevations and precipitation products after the assimilation of snow depth retrievals. In conclusion, this study provides a framework for correcting snowfall orographic patterns across other seasonally-snow dominated mountain areas of the world, especially where in-situ data are scarce. The full paper can be found by clicking on the Figure above.
Reference


Girotto, Manuela, Giuseppe Formetta, Shima Azimi, Claire Bachand, Marianne Cowherd, Gabrielle De Lannoy, Hans Lievens, et al. 2023. “Identifying Snowfall Elevation Patterns by Assimilating Satellite-Based Snow Depth Retrievals.” The Science of the Total Environment, September, 167312. https://doi.org/10.1016/j.scitotenv.2023.167312.