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Daniel Kramer


Ph.D. student

Department of Applied Geomatics, Université de Sherbrooke

2500, boul. de l'Université
Université de Sherbrooke
Quebec, Canada
J1K 2R1

819.821.8000 extension 62506




Projet de recherche

Temporal and spatial snow characterization using high-resolution modeling, microwave remote sensing and UAVs in the Canadian High Arctic

Due to the observed climate change (warming) in the Canadian Arctic, the frequency of Extreme Winter Events (EWE) such as Rain-On-Snow Events (ROS) is rising. These events have multiple effects on the hydrological state (snow water equivalent, runoff), ecological processes (grazing conditions, migration) and the economical (infrastructure) and social well-being of northern communities (traditional routes, re-supply). To help adjusting to the upcoming changes, an operational snow monitoring network for the entire Canadian Arctic is of great importance. To contribute to this goal, an improvement of the space borne data for monitoring of the snow cover is as much essential as exploring new technologies and platforms to validate modeling results.

In particular, detecting EWE and ROS is a high priority, as events like these have the strongest and most direct effect on the surrounding environment. The spatial evolution of snow is a key-factor that will determine how the Arctic will change during the next decades. As snow plays a vital role in the energy exchange between the soil and the atmosphere, a shorter snow season and a smaller snow extent will change the albedo greatly. This is a main contributor to feedback-effects like the snow-albedo-feedback and the increased thawing of the permafrost. On a global scale, the results will help to provide a better understanding of the cryospheric processes under the currently observed warming and the fate of the Arctic.

The objectives of the project are: (a) Coupling of active and passive microwave data; (b) Automated snow simulations for Greiner watershed, Nunavut; (c) Validation of snow simulations and (d) Evaluation of UAV-potential. For objectives (a), (b) and (c), a model-chain of 3 models (SNOWPACK, MEMLS, DRMT-ML) will be developed and then compared to different sources of passively and active remote sensed data (space borne and fieldwork based). In (a), re-analysis and fieldwork data will calibrate SNOWPACK. Space borne data will be used to describe the impact of EWE on snow properties (detailed stratigraphic measurements of snow density, temperature, grain size, wetness and thermal conductivity will be used) and in-situ data from microwave sensors will be compared to satellite data. For objective (b) the model-chain will be modified to the local conditions at the CHARS station, Cambridge Bay, and then validated in (c). Therefore, full snowpits will be dug and they will focus on repetition to capture snow metamorphism. The area of the field experiment will be centered around satellite pixels, but will also be in line with transects and UAV flights. For (d), UAVs will be flown and the gain of information will be assessed. The flights will focus on retrieving snow depth via image displacement procedures. Specifically, the “Structure from motion” approach will be the key method. The derived snow depth map will be used to force the snow model chain at several time intervals.

The final contribution of this project will be an Arctic-calibrated snow model. It will be driven by automatic weather stations (AWS) and reanalysis data for local applications. Further, an algorithm to provide regular maps of snow depth from UAV-flights is envisaged.

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