Collaborative Research: P2C2--Inferring Spatio-Temporal Variations in the Risk of Extreme Precipitation in the Western United States from Tree Ring Chronologies

合作研究:P2C2——从树木年轮推断美国西部极端降水风险的时空变化

基本信息

  • 批准号:
    1702273
  • 负责人:
  • 金额:
    $ 32.05万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-09-01 至 2021-08-31
  • 项目状态:
    已结题

项目摘要

This project aims to understand extreme cold-season precipitation dynamics along the west coast of the United States through an analysis of the moisture anomalies recorded by tree-ring chronologies across the coast and interior of the western U.S. Winters with high total precipitation amounts in the coastal regions are marked by a small number of extreme storms that exhibit distinct spatial patterns of precipitation across the coast and further inland. Building from this observation, this research will seek to develop a novel application of dendroclimatic evidence to explore the following questions: a) how is extreme precipitation variability expressed in a network of tree-ring chronologies; b) can this information provide insight on the space-time variability of storm tracks that cause these extreme events; and c) how can the joint variability of extreme precipitation and storm tracks be modeled to develop consistent, multi-centennial reconstructions of both.The broader impacts include the potential to better understand extreme precipitation events which exert significant social costs through both loss of life and substantial damage to property. Society manages this burden using risk mitigation strategies (e.g., infrastructure, insurance) designed for historic extreme precipitation variability that is estimated from relatively short instrumental records and may be changing under anthropogenic climate change. This project will help determine how the frequency, persistence, recurrence and geographic pattern of these impacts have varied in the past under natural climate variability. This will provide important insights into the causes of these shifts and the potential for predictability in the future. This project also provides support for graduate and post-doctoral researchers.Outreach efforts will focus on high-level discussions with the L.A. County Flood Control District, U.S. Army Corps of Engineers, and related agencies that manage extreme event risk in the region. This direct engagement will enhance the application of the research products, as well as provide guidance for their design. Innovative visualizations of research output will be made available through the Columbia Water Center for public use.
本项目旨在通过分析美国西部沿海和内陆地区树木年轮年表记录的水分异常,了解美国西海岸极端冷季降水动态。沿海地区总降水量高的冬季,以少数极端风暴为标志,这些风暴在沿海和内陆地区表现出明显的降水空间格局。基于这一观察结果,本研究将寻求开发一种新的树木气候证据应用,以探索以下问题:a)极端降水变率如何在树木年轮年表网络中表达;B)这些信息能否提供关于导致这些极端事件的风暴路径的时空变化的见解;c)如何对极端降水和风暴路径的联合变率进行建模,以建立一致的、多世纪的两者重建。更广泛的影响包括有可能更好地了解极端降水事件,这些事件通过生命损失和重大财产损失造成重大社会代价。社会利用风险缓解战略(如基础设施、保险)来管理这一负担,这些战略是根据相对较短的仪器记录估计的历史极端降水变异性而设计的,并且可能在人为气候变化下发生变化。该项目将有助于确定这些影响的频率、持续性、复发性和地理格局在过去自然气候变率下是如何变化的。这将为这些变化的原因和未来的可预测性提供重要的见解。该项目还为研究生和博士后研究人员提供支持。外联工作的重点将是与洛杉矶县防汛区、美国陆军工程兵团以及管理该地区极端事件风险的相关机构进行高层讨论。这种直接参与将加强研究产品的应用,并为其设计提供指导。研究成果的创新可视化将通过哥伦比亚水中心提供给公众使用。

项目成果

期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
A 500-Year Tree Ring-Based Reconstruction of Extreme Cold-Season Precipitation and Number of Atmospheric River Landfalls Across the Southwestern United States
基于 500 年树轮的美国西南部极端寒冷季节降水量和大气河流登陆数量的重建
  • DOI:
    10.1029/2018gl078089
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    5.2
  • 作者:
    Steinschneider, Scott;Ho, Michelle;Williams, A. Park;Cook, Edward R.;Lall, Upmanu
  • 通讯作者:
    Lall, Upmanu
A Weather‐Regime‐Based Stochastic Weather Generator for Climate Vulnerability Assessments of Water Systems in the Western United States
  • DOI:
    10.1029/2018wr024446
  • 发表时间:
    2019-08
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    S. Steinschneider;P. Ray;Saiful Haque Rahat;J. Kucharski
  • 通讯作者:
    S. Steinschneider;P. Ray;Saiful Haque Rahat;J. Kucharski
Identifying weather regimes for regional‐scale stochastic weather generators
  • DOI:
    10.1002/joc.6969
  • 发表时间:
    2020-12
  • 期刊:
  • 影响因子:
    0
  • 作者:
    N. Najibi;S. Mukhopadhyay;S. Steinschneider
  • 通讯作者:
    N. Najibi;S. Mukhopadhyay;S. Steinschneider
Reconstructing Extreme Precipitation in the Sacramento River Watershed Using Tree‐Ring Based Proxies of Cold‐Season Precipitation
使用基于树环的寒冷季节降水代理重建萨克拉门托河流域的极端降水
  • DOI:
    10.1029/2020wr028824
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    5.4
  • 作者:
    Borkotoky, Swatah Snigdha;Williams, A. Park;Cook, Edward R.;Steinschneider, Scott
  • 通讯作者:
    Steinschneider, Scott
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Scott Steinschneider其他文献

A hierarchical Bayesian model of storm surge and total water levels across the Great Lakes shoreline – Lake Ontario
  • DOI:
    10.1016/j.jglr.2021.03.007
  • 发表时间:
    2021-06-01
  • 期刊:
  • 影响因子:
  • 作者:
    Scott Steinschneider
  • 通讯作者:
    Scott Steinschneider
A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California – Part I: Model evaluation
基于天气状况的随机天气生成器,用于加利福尼亚州基于过程的自下而上的气候风险评估 - 第一部分:模型评估
  • DOI:
    10.1016/j.cliser.2024.100489
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    3.2
  • 作者:
    N. Najibi;Alejandro J. Perez;Wyatt Arnold;Andrew Schwarz;Romain Maendly;Scott Steinschneider
  • 通讯作者:
    Scott Steinschneider
A statewide, weather-regime based stochastic weather generator for process-based bottom-up climate risk assessments in California – Part II: Thermodynamic and dynamic climate change scenarios
基于天气状况的随机天气生成器,用于加利福尼亚州基于过程的自下而上的气候风险评估 - 第二部分:热力学和动态气候变化情景
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    3.2
  • 作者:
    N. Najibi;Alejandro J. Perez;Wyatt Arnold;Andrew Schwarz;Romain Maendly;Scott Steinschneider
  • 通讯作者:
    Scott Steinschneider
Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins
利用深度学习对局部气候和降水计数据进行集成,以改进对无资料和部分有资料流域的流量重建
  • DOI:
    10.1016/j.jhydrol.2025.133764
  • 发表时间:
    2025-11-01
  • 期刊:
  • 影响因子:
    6.300
  • 作者:
    Sungwook Wi;Rohini Gupta;Scott Steinschneider
  • 通讯作者:
    Scott Steinschneider

Scott Steinschneider的其他文献

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{{ truncateString('Scott Steinschneider', 18)}}的其他基金

CAREER: CAS- Climate: Climate Adaptation Pathways in Eco-Hydrologic Systems with Physics-Informed Machine Learning
职业:CAS-气候:基于物理的机器学习在生态水文系统中的气候适应途径
  • 批准号:
    2144332
  • 财政年份:
    2022
  • 资助金额:
    $ 32.05万
  • 项目类别:
    Continuing Grant
WRF: Collaborative Research: Extended-range forecasts of atmospheric rivers for adaptive management of flood risk, water supply, and environmental flows in California
WRF:合作研究:大气河流的长期预测,用于加利福尼亚州洪水风险、供水和环境流量的适应性管理
  • 批准号:
    1803563
  • 财政年份:
    2018
  • 资助金额:
    $ 32.05万
  • 项目类别:
    Standard Grant

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