Improved Characterization of Hydroclimatic Extremes through the Development of a Comprehensive Nonstationary Modelling Framework

通过开发综合非平稳建模框架改进极端水文气候特征

• 批准号: RGPIN-2017-05558
• 负责人: Najafi, MohammadReza
• 金额: $ 1.6万
• 依托单位: University of Western Ontario
• 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=749091
• 关键词: Improved; Characterization; Hydroclimatic; Extremes; through

Analysis, design, and management of civil infrastructure require accurate estimation of hydroclimatic extremes including floods and droughts. It is now widely recognized that the planet's climate, including its hydroclimate, is no longer stationary, in great measure due to land-use change and human influence on Earth's climate. Unprecedented changes in flood and drought events may threaten existing infrastructure and challenge future designs. This research program will develop innovative frameworks for accurate estimation of hydroclimatic extremes at regional and global scales, leading to more sustainable design and management of civil infrastructure. It comprises three short-term objectives, which constitute a cohesive and comprehensive nonstationary modelling framework, to characterize historical causes of change, develop predictive nonstationary models and predict future changes in extremes. 1- Systematic diagnosis of the historical drivers of change Understanding the causes of change in extreme events is required by engineers and decision makers for informed design and planning. A systematic framework will be developed to distinguish between the effects of land-use change (through urbanization, deforestation, among others) and climate change on hydroclimatic extremes. 2- Development of nonstationary physically-based and statistical models to predict hydroclimatic extremes Accurate and precise estimation of the magnitude and frequency of extremes is required for the reliable design and planning of civil infrastructure. New probabilistic frameworks based on Bayesian statistics will be developed to explicitly characterize non-stationarity in statistical regional frequency analysis (RFA) approaches. Also, the important role of nonstationary physically-based distributed hydrologic models for the accurate prediction of hydrologic extremes will be examined. 3- Future impacts of climate and land-use change on hydroclimatic extremesInnovative models will be developed to assess projected impacts of climate and land-use change on hydroclimatic extremes, and investigate their implications for infrastructure design and planning. Different sources of uncertainties, including internal climate variability and modelling errors, will be characterized. The role of nonstationary physically-based and statistical models to estimate projected changes in extremes will be investigated. This research will address an open question in the hydroclimate analysis that is how to estimate the nonstationary hydroclimatic extremes. It will change the way scientists and engineers estimate extreme events for future design and planning. The program will provide training for six graduate students in state-of-the-art physically-based and statistical models to solve critical water resources problems.

民用基础设施的分析、设计和管理需要对包括洪水和干旱在内的极端水文气候进行准确的估计。现在人们普遍认识到，地球的气候，包括水文气候，不再是静止的，这在很大程度上是由于土地使用的变化和人类对地球气候的影响。洪水和干旱事件的前所未有的变化可能威胁到现有的基础设施，并对未来的设计提出挑战。该研究计划将开发创新框架，以准确估计区域和全球范围内的水文气候极端情况，从而实现更可持续的民用基础设施设计和管理。它包括三个短期目标，构成一个连贯和全面的非平稳建模框架，以描述变化的历史原因，开发预测性非平稳模型，并预测未来极端情况的变化。1-工程师和决策者需要系统地诊断变化的历史驱动因素了解极端事件变化的原因，以便进行明智的设计和规划。将制定一个系统框架，以区分土地利用变化（通过城市化、森林砍伐等）和气候变化对水文气候极端情况的影响。2-开发非平稳的基于物理和统计的模型来预测水文气候极端事件准确和精确地估计极端事件的幅度和频率是民用基础设施的可靠设计和规划所必需的。将开发基于贝叶斯统计的新概率框架，以明确描述统计区域频率分析（RFA）方法中的非平稳性。此外，非平稳的物理为基础的分布式水文模型的重要作用，准确预测水文极端将被检查。3-气候和土地利用变化对水文气候极端情况的未来影响将开发创新模型，以评估气候和土地利用变化对水文气候极端情况的预计影响，并调查其对基础设施设计和规划的影响。将说明不确定性的不同来源，包括内部气候变化和模型误差。将研究非平稳的基于物理的和统计模型在估计极端情况下的预测变化方面的作用。这项研究将解决一个悬而未决的问题，在水文气候分析，即如何估计非平稳水文气候极端。它将改变科学家和工程师为未来设计和规划评估极端事件的方式。该计划将为六名研究生提供最先进的基于物理和统计模型的培训，以解决关键的水资源问题。