Advancing stochastic modeling and diagnostics of change for hydroclimatic processes and extremes

推进水文气候过程和极端变化的随机建模和诊断

• 批准号: RGPIN-2019-06894
• 负责人: Papalexiou, SimonMichael
• 金额: $ 1.89万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=715891
• 关键词: Advancing; stochastic; modeling; diagnostics; change

We live in an era of unprecedented global hydroclimatic and environmental change that requires reliable tools to model and improve predictions in hydroclimatic extremes, quantify their uncertainty, and assess environmental changes under natural variability and human forcings. The long-term goal of this research program is to advance stationary and non-stationary spatiotemporal stochastic modeling with extensions and applications into downscaling methods, infilling of missing values (MVs), and robust diagnostics of hydroclimatic change. The four short-term objectives of this research are to: (i) increase our understanding of the spatiotemporal structure of precipitation and temperature at multiple scales and provide novel stationary and non-stationary stochastic models; (ii) introduce novel infilling methods and advance current maxima extraction schemes from records with missing values through the use of stochastic approaches; (iii) develop novel spatiotemporal downscaling schemes for climate model projections and historical data; and, (iv) create accurate diagnostics to detect and assess the significance of observed changes and trends in hydroclimatic extremes. To fulfill the objectives, big-data analyses will be performed using thousands in situ stations, reanalysis grid products, and climate model outputs. The spatiotemporal structure of precipitation and temperature will be mathematically described by a set of parsimonious parametric spatial and temporal correlation structures. The properties of the marginal distribution at multiple scales will be explored using an extended set of entropy-derived distributions that are consistent with the nature of these processes. An advanced spatiotemporal stochastic modeling framework will be developed by extending the parent Gaussian scheme in order to include time varying marginals and incorporate important field features, including anisotropy and storm kinematics. These advances will be embodied in infilling methods for MVs, downscaling schemes, and diagnostics of hydroclimatic change by exploiting the precise representation and simulation of the spatiotemporal structure as well as the marginal distribution of precipitation and temperature. The developed stochastic modeling tools and compiled databases will be freely available and benefit the scientific community through: (i) generating scientific knowledge as the developed methods are applicable beyond the field of hydroclimatology; (ii) improving hydrological modeling through precise stochastic modeling of complex processes such as precipitation; (iii) supporting informed decision making with robust diagnostics of hydroclimatic change and ready-to-use databases of reliable precipitation and temperature forcing data at multiple scales; and, (iv) improving the probabilistic prediction of risk and severity of extreme weather events.

我们生活在一个前所未有的全球水文气候和环境变化的时代，需要可靠的工具来模拟和改善水文气候极端情况的预测，量化其不确定性，并评估自然变异和人为强迫下的环境变化。 该研究计划的长期目标是推进平稳和非平稳时空随机建模，扩展和应用到降尺度方法，填充缺失值（MV）和水文气候变化的鲁棒诊断。 本研究的四个短期目标是：（i）增加我们对多尺度降水和温度时空结构的理解，并提供新的平稳和非平稳随机模型;（ii）引入新的填充方法，并通过使用随机方法从缺失值记录中提取当前最大值;（三）为气候模型预测和历史数据制定新的时空降尺度方案;（四）建立准确的诊断方法，以检测和评估观测到的水文气候极端变化和趋势的重要性。为了实现这些目标，将使用数千个原位站、再分析网格产品和气候模型输出进行大数据分析。降水和温度的时空结构将数学描述的一组简约的参数空间和时间相关结构。在多个尺度的边际分布的属性将探讨使用一组扩展的熵衍生的分布，这些过程的性质是一致的。一个先进的时空随机建模框架将通过扩展父高斯计划，以包括随时间变化的边缘，并纳入重要的字段功能，包括各向异性和风暴运动学。这些进展将体现在填充方法MV，降尺度计划，水文气候变化的诊断，利用精确的表示和模拟的时空结构，以及降水和温度的边缘分布。 开发的随机建模工具和汇编的数据库将免费提供，并通过以下方式使科学界受益：㈠产生科学知识，因为开发的方法适用于水文气候学领域以外的领域; ㈡通过对降水等复杂过程进行精确的随机建模，改进水文建模; ㈢通过对水文气候变化的强有力的诊断和随时可用的多尺度可靠降水和温度强迫数据数据库，支持知情决策;以及（iv）改善对极端天气事件的风险和严重性的概率预测。