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

揭示雪景的复杂性及其对北部地区积雪演化模型的影响

• 批准号: RGPIN-2022-04631
• 负责人: Kinnard, Christophe
• 金额: $ 1.82万
• 依托单位: Université du Québec à Trois-Rivières
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765344
• 关键词: Unravelling; snowscapes; complexity; its; impacts

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.

当前和未来的气候变化正在重塑北方地区的生态系统。高纬度地区的加速变暖正在推动积雪数量和范围的减少，这会影响季节性河流洪水，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的反应的驱动因素和模式对于预测不断变化的积雪状况对生态系统和人类活动的影响至关重要。然而，雪盖显示大的异质性景观，在不同的地形和植被，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”（被雪覆盖的景观）的异质性是困难的，在大尺度陆面模型中表示这种异质性更具挑战性。该方案将利用最先进的无人驾驶飞机和卫星遥感方法结合直接测量，研究加拿大东部沿沿着北极梯度的三个实验地点的积雪空间异质性。将对这些数据进行分析，以揭示积雪变化的主要环境驱动因素和主要空间尺度。将利用多分辨率模拟实验研究小尺度积雪不均匀性对粗尺度模型预测的影响。将开发和比较几种方法，包括积雪异质性的影响，在粗尺度模型，以增加目前的气候条件下的积雪预测的鲁棒性。该计划将允许在寒冷地区环境科学方面培训多达六名高素质的人员，他们具有实地测量仪器和物流，遥感和建模方面的技能，并有能力应对环境科学和管理方面的未来挑战。在这个程序中产生的知识将有实际的好处，在寒冷地区的气候变化影响评估，在受雪影响的地区洪水预报，并在加拿大目前和未来的气候下，融水流入水电水库的预测。改进用于气候影响评估的陆面模型中的积雪表示也将有助于指导北方地区的气候变化减缓和适应战略。