Up until 2023, California had been experiencing a series of droughts in different parts of the state, with some described as record breaking and historical. The adverse affect of a prolonged drought has resulted in reduced agricultural yields, depleted groundwater reserves and reservoirs, and heightened risk of wildfires.
The drought conditions of the state have now been at least partially offset by an incredibly wet 2022-23 winter in California. Particularly in the early months of 2023, California has seen record snowfalls in large areas of the state, easing some of the water concerns the state has had.
Measuring snow and forecasting snowmelt effects on river rise is important for planning for California’s water needs and to mitigate flooding. New methods and technologies can help with assessing snowpack levels and predicting the timing and amount of water runoff from snowmelt.
Why is snow pack important for California?
Snow in the winter months is critical for California’s water resources as it determines how much water is likely to be available for consumer and farming needs without having to extract from major reservoirs.
Record snowpack levels for 2023 in California
The snow survey measures that record mountain snowpack in April 2023 along the Sierra Nevada showed snowpack at about 221% above normal levels, with 237% above statewide averages being recorded across other areas of the state. This has translated into snow depths of 126.5 inches at Phillips Station south of Lake Tahoe.
This is in stark contrast to the 2022 snow survey in the Sierra Nevada that showed about 2.5 inches at measuring stations.
Overall, since 1950, this year’s snow is likely to be the most or second most along much of the Sierra mountains. This bodes well for water resources in the state; officials have estimated that the 126.5 inches of snow at Phillips Station could translate to 54 inches of water.
Providing much of the important data to forecast how much snowpack California has and how much that might translate to water, Airborne Snow Observatories Inc. provides snowpack data to California’s Department of Water Resources.
Geospatial technologies for measuring snow levels and snowmelt
While weather station data continue to provide snowpack and snowfall data, key to measuring total water resources available for California based on recent snowfall comes from lasers and spectrometers attached to aircraft.
Since 2013, the collaboration, which started as a partnership between NASA’s Jet Propulsion Laboratory and California’s Department of Water Resources, not only measures snowfall but applies the data to watershed models that estimate water runoff from snowpack data. In 2019, the collaboration was spun off into a company, Airborne Snow Observatories.
The Snow Surveys and Water Supply Forecasting Unit of DWR employs Airborne Snow Observatory (ASO) surveys to gather data on California’s primary snow-generating watersheds. These surveys cover 12 major areas and provide information on snowpack characteristics such as density, depth, reflectivity, and other factors at a detailed 3-meter resolution.
The aircraft operated by Airborne Snow Observatories that scan the state’s watersheds has led to both very accurate snowpack measurements and about 98% accuracy or better in forecasting seasonal water runoff.
The data have proven invaluable to now forecasting how much seasonal water will be available for farmers, cities, and other major water users across the state of California. Effectively, the technology used to measure snowpack is increasingly in the form of Laser altimetry (lidar).
Currently, ICESat-2 fires 10,000 laser pulses a second and can measure elevation of sea ice, snowpacks, and ice sheets. Combining measurements from airborne instruments, such as the ones used by California authorities, and satellite data, which provides broader coverage, is also being applied to enable large area estimates.
Modeling runoff from snowmelt
Methods for modeling watersheds from snowpack data include using Structure-from-Motion (SfM) technique in capturing point cloud data that can be fixed to known ground control points. A digital surface model is then created.
Differences in elevation between the natural and snowpack terrain allow snow depth to be measured. Statistical models for calculating snow runoff vary, but common techniques include using spatial regression models that take output raster image cells and determine water equivalent from volume of snow. Weather and terrain conditions are also utilized as that can affect total runoff.
Forecasting snowmelt helps California manage limited water resources
From California’s experience, it is clear seasonal water variability has become extremely volatile, which has significant impact on water available for use by farmers, urban consumers, and others.
Forecasting water from snowpack is one of the most critical steps in estimating how much annual discharge could be available for states that depend on seasonal runoff.
Using lidar spectrometers, airborne but also spaceborne, will be critical for authorities in future years, particularly in drought-sensitive regions. We can now forecast runoff at over 98% accuracy in estimating water runoff.
This accuracy will be needed if we are to create precise water policies that can best estimate how much water we should use without depleting our long-term water reserves.
 For more on a recent story discussing California’s record snowfall and snowpack, see: https://edition.cnn.com/2023/04/03/us/california-april-snow-survey-water-climate/index.html.
 For more on ICESat-2 and GEDI, including for lidar measurements, see: Camile Sothe, Alemu Gonsamo, Ricardo B. Lourenço, Werner A. Kurz, and James Snider. 2022. Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel. Remote Sensing 14, 20 (October 2022), 5158. DOI:https://doi.org/10.3390/rs14205158.
 For more on runoff and snow water equivalent from snow estimates using captured elevantion raster data, see: Tomasz Niedzielski, Mariusz Szymanowski, Bartłomiej Miziński, Waldemar Spallek, Matylda Witek-Kasprzak, Jacek Ślopek, Marek Kasprzak, Marek Błaś, Mieczysław Sobik, Kacper Jancewicz, Dorota Borowicz, Joanna Remisz, Piotr Modzel, Katarzyna Męcina, and Lubomir Leszczyński. 2019. Estimating snow water equivalent using unmanned aerial vehicles for determining snow-melt runoff. Journal of Hydrology 578, (November 2019), 124046. DOI:https://doi.org/10.1016/j.jhydrol.2019.124046.