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.

持续和未来的气候变化正在重塑北部地区的生态系统。高纬度地区变暖加速导致积雪数量和范围减少，从而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，由于不同的地形和植被及其与天气过程的相互作用，积雪在景观中表现出很大的异质性。测量和理解这些复杂的“雪景”（被雪覆盖的景观）的异质性很困难，并且在大规模地表模型中表示这种异质性更具挑战性。该计划将利用最先进的无人机和卫星遥感方法与直接测量相结合，研究加拿大东部北北极梯度沿线三个实验地点的积雪空间异质性。将分析这些数据，以揭示积雪变化的关键环境驱动因素和主要空间尺度。将使用多分辨率建模实验研究小尺度积雪异质性对粗尺度模型预测的影响。将开发并比较几种方法，以将积雪异质性的影响纳入粗尺度模型中，以提高当前和气候条件下积雪预测的稳健性。该计划将培训多达六名寒冷地区环境科学领域的高素质人才，他们具备现场工作仪器和物流、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。该计划产生的知识将为寒冷地区的气候变化影响评估、受雪影响地区的洪水预报以及当前和未来气候下加拿大水电站融水流入预测带来实际好处。用于气候影响评估的地表模型中积雪表征的改进也将有助于指导北部地区的气候变化减缓和适应战略。