Cluster of excellence in water resources and climate modeling in alpine and polar environments

高山和极地环境水资源和气候建模卓越集群

• 批准号: RGPIN-2019-04858
• 负责人: Radic, Valentina
• 金额: $ 2.62万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737908
• 关键词: Cluster; excellence; water; resources; climate

The retreat of mountain glaciers worldwide is a key indicator of modern climate change, with mass loss from glaciers contributing to sea level rise and to river flows during dry and warm parts of the year. In western Canada, maintenance of low flows by glacier runoff is essential for hydro-power generation, agricultural irrigation and water supply for major cities and small towns. The coming decades will see rapid and unprecedented changes to glaciers worldwide. However, current projections are unable to resolve with certainty how individual river basins will be impacted by the loss of glacier ice in their headwaters.  The long-term objective of my research program is to use state-of-the-art field, modelling and data analysis methods to narrow the uncertainties in projections of glacial contributions to streamflow and sea level rise. My short-term objectives, each envisioned as a PhD project, will address the following key uncertainties: A) Uncertainty in future climate scenarios, given by Global Climate Models (GCMs), which drive the glacier mass changes. We will evaluate GCMs with historical climate observations and use the best performing GCMs for the projections.  B) Uncertainty in simple downscaling methods used to convert coarsely-resolved climate fields from GCMs to finely-resolved ones that drive melt processes at the scale of individual glaciers. We will develop advanced downscaling methods that incorporate regional climate modelling and machine learning methods.  C) Uncertainty in empirical melt models which cannot be adequately calibrated in the absence of observations. We will develop a physics-based model that calculates melt from the difference between energy input into the glacier (e.g. solar radiation) and energy output (e.g. thermal radiation). We will evaluate model performance against novel field observations of energy and mass fluxes at glaciers in western Canada.  D) Uncertainty in the treatment of glacier dynamics. We will incorporate a high-complexity model of glacier geometry evolution in our model of glacier mass balance.  E) Uncertainty in glacier mass loss on regional scale which cannot be adequately quantified in the absence of a historical record of mass changes at this scale. We will develop novel data analysis methods to use available long-term streamflow measurements as a proxy of glacier mass changes.  By resolving these uncertainties, we will provide projections of glacier contribution to streamflow at the level of detail necessary to inform local policy makers and user communities. The outcomes will define how climate change will tangibly express itself for Canadians and most of the world. Funded personnel will contribute to the cutting-edge research on risk assessment of freshwater resources under climate change, and will receive high-level training in geosciences, computing and data analysis preparing them for a broad range of opportunities in academia, government or private industry.

全球山区冰川的退缩是现代气候变化的一个关键指标，冰川的质量损失导致海平面上升，并在一年中干燥和温暖的部分导致河流流量。在加拿大西部，冰川径流维持低流量对水力发电、农业灌溉和大城市和小城镇供水至关重要。未来几十年，全球冰川将发生前所未有的快速变化。然而，目前的预测是无法确定地解决如何个别流域将受到影响的冰川冰在其headwaters.The我的研究计划的长期目标是使用国家的最先进的领域，建模和数据分析方法，以缩小预测的不确定性冰川的贡献流量和海平面上升。我的短期目标，每一个设想作为一个博士项目，将解决以下关键的不确定性：A）未来气候情景的不确定性，由全球气候模型（GCM），驱动冰川质量变化。我们将评估GCM与历史气候观测，并使用表现最好的GCM的预测。B）简单的降尺度方法的不确定性，用于转换粗分辨率的气候场从GCM到精细分辨率的，驱动融化过程中的单个冰川的规模。我们将开发先进的降尺度方法，结合区域气候建模和机器学习方法。C）经验融化模型的不确定性，在没有观测的情况下无法进行充分校准。我们将开发一个基于物理学的模型，该模型从冰川的能量输入（例如太阳辐射）和能量输出（例如热辐射）之间的差异计算融化。我们将评估模型的性能对新的实地观测的能量和质量通量在加拿大西部的冰川。D）在处理冰川动力学的不确定性。我们将把一个高度复杂的冰川几何形状演变模型纳入我们的冰川质量平衡模型中。E）在区域尺度上冰川质量损失的不确定性，在缺乏这种尺度上质量变化的历史记录的情况下，无法充分量化。我们将开发新的数据分析方法，利用现有的长期径流测量作为冰川质量变化的代理。通过解决这些不确定性，我们将提供必要的细节水平，告知当地政策制定者和用户社区冰川对径流的贡献预测。结果将决定气候变化将如何切实表达自己对加拿大人和世界上大多数人。获得资助的人员将为关于气候变化下淡水资源风险评估的前沿研究作出贡献，并将接受地球科学、计算和数据分析方面的高级培训，使他们为在学术界、政府或私营企业获得广泛机会做好准备。