Unravelling snowscapes complexity and its impacts on modelled snow cover evolution in northern regions

当前和未来的气候变化正在重塑北部地区的生态系统。高纬度地区的加速变暖正在推动积雪数量和范围的减少，这影响了季节性河流洪水、土壤温度和微生物分解、气候和植被。因此，了解积雪对气候变化响应的驱动因素和模式对于预测积雪条件变化对生态系统和人类活动的影响非常重要。然而，积雪在景观中表现出很大的异质性，以响应不同的地形和植被，以及它们与天气过程的相互作用。测量和理解这些复杂的“雪景”（积雪覆盖的景观）的异质性是困难的，在大尺度陆地表面模型中表示这种异质性更具挑战性。该项目将在加拿大东部沿北北极梯度的三个试验点研究积雪空间异质性，使用最先进的无人机和卫星遥感方法结合直接测量。对这些数据进行分析，揭示积雪变化的主要环境驱动因素和主导空间尺度。利用多分辨率模拟实验研究了小尺度积雪异质性对粗尺度模式预估的影响。为了增加当前和气候条件下积雪预估的稳健性，将开发和比较几种方法，以包括粗尺度模型中积雪异质性的影响。该项目将培训多达六名寒区环境科学方面的高素质人才，他们具备实地考察仪器和后勤、遥感和建模方面的技能，并有能力应对未来环境科学和管理方面的挑战。在这个项目中产生的知识将对寒冷地区的气候变化影响评估、积雪影响地区的洪水预报以及在当前和未来气候下加拿大水电站水库融水流入的预测有实际的好处。改善用于气候影响评估的陆地表面模式中的积雪表示也将有助于指导北方地区的气候变化缓解和适应战略。

Ongoing and future climate change is reshaping the ecosystems of northern regions. The accelerated warming at high latitudes is driving reductions in the amount and extent of snow cover, which impacts seasonal river flooding, soil temperature and microbial decomposition, climate, and vegetation. Understanding the drivers and patterns of snow cover response to climate change is thus important to project the impacts of changing snow conditions on ecosystems and human activities. However, the snow cover displays large heterogeneity in the landscape, in response to varying topography and vegetation, and their interactions with weather processes. Measuring and understanding the heterogeneity of these complex 'snowscapes' (landscapes covered by snow) is difficult and representing this heterogeneity within large scale land surface models is even more challenging. This program will study the snow cover spatial heterogeneity at three experimental sites along a boreal-Arctic gradient in Eastern Canada, using state-of-the-art drone and satellite remote-sensing methods combined with direct measurements. These data will be analyzed to reveal the key environmental drivers and dominant spatial scales of snow cover variability. The impact of small-scale snow cover heterogeneity on coarse scale model projections will be studied using multi-resolution modelling experiments. Several methods will be developed and compared to include the effect of snow cover heterogeneity within coarse scale models, in order to increase the robustness of snow cover projections under present and climate conditions. The program will allow training up to six highly qualified personals in cold region environmental science, with skills in fieldwork instrumentation and logistics, remote-sensing and modelling, and well equipped to address future challenges in environmental science and management. The knowledge generated in this program will have practical benefits for climate change impact assessment in cold regions, flood forecasting in snow-affected regions, and projections of meltwater inflow to hydropower reservoirs in Canada under present and future climates. The improvement of snow cover representations within land surface models used for climate impact assessment will also help to guide climate change mitigation and adaptation strategies in northern regions.