Predicting and interpolating Canada's future precipitation extremes across climate regions

预测和插值加拿大未来跨气候区域的极端降水

• 批准号: RGPIN-2017-03864
• 负责人: Soulis, Eric
• 金额: $ 0.59万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=665664
• 关键词: Predicting; interpolating; Canada; future; precipitation

As Canada's climate evolves, a better understanding of the patterns of precipitation extremes is critical. Engineers use charts that track the historic intensity, duration, and frequency of rainfall events to ensure their designs meet drainage standards. Frequently engineers use outdated weather station datasets that are geographically distant to their design projects. The Proponent created a tool that interpolates these charts, known as IDF curves, between weather stations for the Ontario Ministry of Transportation (MTO). The proposed research program expands and improves upon this work by confirming time trends in the historic data and extrapolating the results to build a climate change model for precipitation. The Proponent's research will enable Canadian engineers to design infrastructure projects based on geographically-corrected precipitation patterns that will stay relevant for the entire design life of their projects, and improve scientific understanding of Canada's regional climatological processes. ******IDF extrapolation creates a solid basis for forecasting. The Intergovernmental Panel on Climate Change Special Report on Extreme Events (IPCC SREX) states that projected rises in temperature are virtually certain and it is likely that the frequency of heavy precipitation will increase. This research quantifies uncertainty and allows regulators and designers to refine their confidence limits by factoring regional complexities into their projections.******The key variables in this problem are extreme precipitation, extreme temperature, and time. The Clausius-Clapeyron Rate (CCR) is a theoretical ratio linking extreme precipitation and extreme temperature. As temperatures rise, precipitation rises between 5.9% and 7.7% per degree Celsius. Temperature is an excellent covariate not only because of its relationship to precipitation, but also because of the reliability of temperature projections.******To expand the Proponent's research across Canada is complex. Geographic factors such as mountains introduce new problems. The Proponent will develop regional equations to weigh these factors differently. Then, factoring time and temperature, and using the CCR, the Proponent will project precipitation extremes across the country.******This research will provide objectivity and consistency for infrastructure planning. It will grant valuable insight for applying IDF interpolation and projection to new regions, not just in Canada, but globally. It will save engineers design time, reduce the risk of costly project over and under design, and save millions annually for Canadian infrastructure construction. The research will also aid future hydrological and atmospheric research, as it will improve the scientific understanding of regional relationships between temperature and precipitation over time.

随着加拿大气候的演变，更好地了解极端降水的模式至关重要。工程师使用图表跟踪降雨事件的历史强度，持续时间和频率，以确保他们的设计符合排水标准。工程师经常使用过时的气象站数据集，这些数据集在地理上与他们的设计项目相距甚远。提案人创建了一个工具，用于为安大略交通部（MTO）在气象站之间插入这些图表（称为IDF曲线）。拟议的研究计划扩展和改进了这项工作，确认历史数据的时间趋势，并推断结果，建立一个气候变化模型的降水。该项目的研究将使加拿大工程师能够根据经过地理校正的降水模式设计基础设施项目，这些降水模式将在项目的整个设计寿命期间保持相关性，并提高对加拿大区域气候过程的科学认识。*IDF外推法为预测提供了坚实的基础。政府间气候变化专门委员会关于极端事件的特别报告指出，预计气温上升几乎是肯定的，强降水的频率可能会增加。这项研究量化了不确定性，并允许监管机构和设计人员通过将区域复杂性纳入其预测来完善其置信限。这个问题的关键变量是极端降水、极端温度和时间。Clausius-Clapeyron比率（CCR）是极端降水和极端温度之间的理论比率。随着气温上升，降水量每摄氏度上升5.9%至7.7%。温度是一个很好的协变量，不仅因为它与降水的关系，而且因为温度预测的可靠性。要在加拿大各地扩大提案人的研究是复杂的。山脉等地理因素带来了新的问题。建议者将制定区域方程式，以不同方式衡量这些因素。然后，考虑到时间和温度，并使用CCR，提案人将预测全国各地的极端降水量。这项研究将为基础设施规划提供客观性和一致性。它将为将IDF插值和预测应用于新地区提供宝贵的见解，不仅在加拿大，而且在全球范围内。它将节省工程师的设计时间，降低成本高昂的项目超过和低于设计的风险，并为加拿大的基础设施建设每年节省数百万美元。这项研究还将有助于未来的水文和大气研究，因为它将提高对温度和降水随时间变化的区域关系的科学认识。