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

全球山地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增加。在加拿大西部，冰川径流维持低流量对水力发电、农业灌溉和主要城市和小城镇的供水至关重要。未来几十年，全世界的冰川将发生迅速而前所未有的变化。然而，目前的预测无法确定单个河流流域将如何受到其源头冰川冰损失的影响。我的研究计划的长期目标是使用最先进的实地，建模和数据分析方法来缩小冰川对水流和海平面上升的贡献预测的不确定性。我的短期目标，每一个都被设想为博士项目，将解决以下关键的不确定性：a)全球气候模型（GCMs）给出的未来气候情景的不确定性，这推动了冰川质量的变化。我们将利用历史气候观测来评估gcm，并使用表现最好的gcm进行预估。B)用于将粗略分解的气候场从gcm转换为精细分解的气候场的简单降尺度方法的不确定性，这些方法在单个冰川的尺度上驱动融化过程。我们将开发先进的缩小方法，结合区域气候建模和机器学习方法。C)在没有观测的情况下不能充分校准的经验熔体模型的不确定性。我们将开发一个基于物理的模型，根据冰川的能量输入（如太阳辐射）和能量输出（如热辐射）之间的差异来计算融化。我们将根据加拿大西部冰川的能量和质量通量的新实地观测来评估模型的性能。D)冰川动力学处理的不确定性。我们将在我们的冰川质量平衡模型中加入一个高度复杂的冰川几何演化模型。E)区域尺度上冰川质量损失的不确定性，由于缺乏该尺度上质量变化的历史记录，这种不确定性无法充分量化。我们将开发新的数据分析方法，利用现有的长期流量测量作为冰川质量变化的代理。通过解决这些不确定性，我们将在必要的细节水平上提供冰川对水流贡献的预测，以告知当地决策者和用户社区。选举结果将决定气候变化将如何对加拿大人和世界大多数国家产生切实的影响。资助人员将为气候变化下淡水资源风险评估的前沿研究做出贡献，并将接受地球科学、计算和数据分析方面的高水平培训，为他们在学术界、政府或私营企业的广泛机会做好准备。

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.