How Do We Measure Future Snow Cover?

During winter in the Northern Hemisphere, about one-third of Earth’s land surface is covered by snow. However, as a response to the intense global warming, snow cover has declined significantly since the 1960s, as indicated by the longest satellite imagery (Dahlman, 2016). Snow cover is very important for the global climate and ecosystem, working as an energy bank, radiation shield, insulator, reservoir, and water transport medium through its special physical properties (Pomeroy and Brun, 2001).

Scientists have developed field monitoring, remote sensing, and hydrological modeling to measure the dynamics of snow cover. Each of the three methods has its advantages in fulfilling this mission. For example, in situ observations provide the most accurate and the longest measurement records of snow cover; remote sensing is powerful in providing large-scale snow observations; hydrological models is the only way to give future predictions of snow.

However, when these approaches are used alone, a number of limitations are apparent. For example, there are only sparse ground-based stations over the mountainous regions, where snow measurement is always most important; remotely-sensed data and hydrological models have great errors at many times, due to our currently insufficient knowledge in snow physics and the natural environment.

In a recent study, Dong (2018) proposed a synthetic framework for improving the snow cover measurement by jointly applying in situ observations, remote sensing, and hydrological models (see Figure 1). Based on successful research attempts (Dong and Menzel, 2016), Dong (2018) suggests using point-based field observations to correct the areal-scale remotely sensed snow data.

Figure 1: A synthetic framework incorporating in situ observations, remote sensing, and hydrological models for the future snow cover research. Advantages (+) and shortcomings (-) of each approach are indicated. (Dong, 2018; Redrawn by the author). Republished with permission from the Elsevier from: https://doi.org/10.1016/j.jhydrol.2018.04.027.

The fusion of ground and remote sensing information will balance the high accuracy of the former and the broad coverage of the latter, and thus provide the best available snow observations. Particularly, in the light of rapidly increasing Internet-based digital photos, geographers are able to extract snow information from the geo-tagged photos with machine learning techniques, treating them as Volunteered Geographic Information (VGI) (see Figure 2). For example, many people like uploading their cell phone pictures to GoogleMaps or Flickr.

Figure 2: Examples of sensors (a) and websites (b) that can provide digital photos as Volunteered Geographic Information (VGI), potentially useful for snow observations in the future (Dong, 2018). Republished with permission from the Elsevier from: https://doi.org/10.1016/j.jhydrol.2018.04.027.

Those geo-tagged outdoor pictures provide snow presence/absence information, and these cell phones can work as sensors of climate stations. The massive web-based digital photos are valuable for strengthening the sparse network of the expensive ground stations, and thus are potentially useful in improving remotely sensed snow data. If these improved snow observations are used as the data input of hydrological models, snow scientists will probably generate more accurate predictions and provide more reliable water resource assessments.

This research is described in the article entitled Remote sensing, hydrological modeling and in situ observations in snow cover research: A review, recently published in the Journal of Hydrology. The project was completed by Chunyu Dong from the Institute of the Environment and Sustainability, University of California, Los Angeles.

References:

1. Dong, C. (2018). Remote sensing, hydrological modeling and in situ observations in snow cover research: a review. Journal of Hydrology, 561, 573-583.
2. Dong, C., & Menzel, L. (2016). Producing cloud-free MODIS snow cover products with conditional probability interpolation and meteorological data. Remote Sensing of Environment, 186, 439-451.
3. Pomeroy, J.W., & Brun, E. (2001). Physical properties of snow. Jones, H.G., Pomeroy, J.W., Walker, D.A., Hoham, R.W. (Eds.), Snow Ecology: An Interdisciplinary Examination of Snow-covered Ecosystems, Cambridge University Press, Cambridge, UK, pp. 45-118