IAHS2022-441, updated on 12 Apr 2023
IAHS-AISH Scientific Assembly 2022
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

The Applicability of Optical Satellite Photogrammetry for Snow Depth Derivation and Streamflow Estimates

James McPhee1,6, Thomas Shaw2, Simon Gascoin3, César Deschamps-Berger3, Pablo Mendoza1, Álvaro Ayala4, and Francesca Pellicciotti2,5
James McPhee et al.
  • 1Universidad de Chile, Dept. of Civil Engineering, Santiago, Chile
  • 2Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
  • 3Centre d'Etudes Spatiales de la Biosphère, Toulouse, France
  • 4Centro de Estudios Avanzados de Zonas Aridas, La Serena, Chile
  • 5Department of Geography, Northumbria University, Newcastle, UK
  • 6Advanced Mining Technology Center, Universidad de Chile, Santiago, Chile

Information about end-of-winter spatial distribution of snow depth is important for seasonal forecasts of spring/summer streamflow in high-mountain regions. However, obtaining detailed information about high mountain snowpacks is often limited by insufficient ground-based observations and uncertainty in the (re)distribution of solid precipitation. We utilize high-resolution optical images from Pléiades satellites to generate a 4 m snow depth map for a high mountain catchment of central Chile and test the potential of this high-resolution information for improving the representation of snow depth initial conditions (SDICs) in a glacio-hydrological model (TOPKAPI-ETH). We calibrate model parameters controlling glacier mass balance and snow cover evolution using ground-based and satellite observations, and consider the relative importance of SDICs compared to model parameters and forcings. Satellite-based estimates are negatively biased (median difference of −0.22 m) when compared against subdomain observations from a terrestrial LiDAR scan, though they replicate general snow depth variability well. However, the Pléiades dataset is subject to data gaps (17% of total pixels), negative values for shallow snow (12%), and noise on slopes >40–50° (2%). Snow depths (with an estimated error of ~0.36 m) relate well to topographical parameters such as elevation and northness in a similar way to previous studies. However, estimations of snow depth based upon topographic characteristics, physically based modeling or model spin-up cannot resolve localized processes (i.e., avalanching or wind scouring) that are detected by Pléiades, even when forced with locally calibrated data. We find that Pléiades SDICs improve the simulation of snow-covered area, glacier mass balance, and monthly streamflow compared to alternative SDICs for our glacio-hydrological model. Model simulations are found to be sensitive to SDICs in the early spring (up to 48% variability in modeled streamflow compared to the best estimate model), and to temperature gradients in all months due to its control on albedo and melt rates over a large elevation range (>2,400 m). Therefore, optical stereo-photogrammetry offers an advantage for obtaining SDICs that aid both the timing and magnitude of streamflow simulations, process representation (e.g., snow cover evolution) and has the potential for upscaling to larger spatial domains.

How to cite: McPhee, J., Shaw, T., Gascoin, S., Deschamps-Berger, C., Mendoza, P., Ayala, Á., and Pellicciotti, F.: The Applicability of Optical Satellite Photogrammetry for Snow Depth Derivation and Streamflow Estimates, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-441, https://doi.org/10.5194/iahs2022-441, 2022.