Collaborative Research: Reducing Uncertainty of Climatic Trends in the Sierra Nevada: An Ensemble-Based Reanalysis via the Merger of Disparate Measurements

合作研究:减少内华达山脉气候趋势的不确定性:通过合并不同测量进行基于集合的再分析

基本信息

  • 批准号:
    0943551
  • 负责人:
  • 金额:
    $ 19.61万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2010
  • 资助国家:
    美国
  • 起止时间:
    2010-09-01 至 2013-08-31
  • 项目状态:
    已结题

项目摘要

AbstractThe Sierra Nevada system plays an integral role in the hydrologic cycle, energy cycle, ecological systems, and in water resources supply in Western North America. Trends toward earlier spring snowmelt runoff and decreasing springtime observations of snow water equivalent (SWE) from in situ networks have been observed in the past fifty years. These trends will result in both increased likelihood of winter floods and decreased water availability. It is therefore urgent to develop detailed, process-based understanding of the observed changes. Moreover, it is vital to develop a spatially-continuous characterization of these trends, spanning the physiographic gradients present in the Sierra Nevada. Not all available information has been used in the diagnosis of the trends, viz. spaceborne remote sensing measurements. This means that trends have only been evaluated where particular stations are located (not over the entire Sierra Nevada). Moreover, formal estimates of observation uncertainty have not been taken into account in the assessment of the trends. Detailed understanding of the physical processes and complex sensitivity to climatic change across physiographic gradients has not been achieved. We thus propose a reanalysis utilizing all available datasets, including both in situ and remote sensing measurements. Our motivation is the unique and complementary characteristics of each of four primary datastreams: in situ snow measurements, streamflow, snow-covered area (SCA) derived from visible and near-infrared data, and passive microwave measurements. We will use ensemble model simulations of snowpack physical processes to provide a priori estimates of snowpack variables. Measurement models will be used to relate snowpack states to all four primary datastreams. The data assimilation analysis step calculates an a posteriori estimate of snowpack variables that takes into account all four datastreams, as well as meteorological data and scientific knowledge of snow physics processes. Estimates of the spatiotemporal uncertainty in each snowpack variable can be calculated directly from the ensemble. Trends will be assessed using these posterior estimates of snowpack states.During our two-year project, we will perform the reanalysis described above for the King River and Kaweah River basins in the Southern Sierra Nevada. Each of these river basins overlaps spatially with study areas from the Southern Sierra Critical Zone Observatory (https://snri.ucmerced.edu/CZO). The Providence CZO in the King River basin and the Wolverton CZO in the Kaweah River basin represent locations where ongoing research is leading to a deeper process-level understanding of the snow cover across elevation gradients, which may provide insight into the long-term trends that have been observed. We will focus this two-year project on developing solid methodology that could be applied to the entire Sierra Nevada in follow-on work. We will focus on development of the statistical and physical models of spatial variability and uncertainty that form the core of the reanalysis assimilation scheme. In particular, we will focus on development of models for relating observing network point measurements of snow water equivalent to grid-based estimates of snow properties, and on parameterization of the uncertainty models for the hydrometeorological inputs to the snow physics models.We will integrate the models, methods, and results from the work into existing undergraduate and graduate courses at UCLA, and into ongoing outreach work of the Byrd Polar Research Center. Additionally, the high-resolution modeling framework developed here could provide a unique testbed for future climate change studies, where atmospheric model output from regional or global climate models could be used as forcing to the offline model to add to the existing literature on how the Sierra snowpack and spring streamflow is expected to change.
内华达州山脉系统在北美西部的水文循环、能量循环、生态系统和水资源供应中起着不可或缺的作用。在过去的50年里,人们观察到了春季融雪径流提前和春季雪水当量(SWE)减少的趋势。这些趋势将导致冬季洪水的可能性增加,供水量减少。因此,迫切需要对观察到的变化进行详细的、基于过程的了解。此外,它是至关重要的,以发展这些趋势的空间连续表征,跨越自然地理梯度目前在内华达州。 并非所有现有的信息都被用于趋势分析,即空间遥感测量。这意味着仅在特定台站所在的地方(而不是整个内华达州)对趋势进行了评估。此外,在评估趋势时没有考虑到对观测不确定性的正式估计。详细了解的物理过程和复杂的敏感性,气候变化跨越地文梯度尚未实现。因此,我们建议利用所有可用的数据集,包括在现场和遥感测量的再分析。我们的动机是四个主要数据流中的每一个的独特和互补的特征:原位雪测量,流量,积雪覆盖面积(SCA)来自可见光和近红外数据,以及被动微波测量。我们将使用集合模型模拟积雪的物理过程,提供积雪变量的先验估计。测量模型将用于将积雪状态与所有四个主要数据流联系起来。数据同化分析步骤计算积雪变量的后验估计,其中考虑到所有四个数据流,以及气象数据和雪物理过程的科学知识。每个积雪变量的时空不确定性的估计可以直接从集合计算。在我们的两年项目期间,我们将对内华达州南部山脉的国王河和卡威河流域进行上述再分析。这些河流流域中的每一个在空间上都与南部塞拉利昂临界区观测站(https://snri.ucmerced.edu/CZO)的研究区域重叠。国王河流域的普罗维登斯CZO和卡威河流域的沃尔弗顿CZO代表了正在进行的研究正在导致对海拔梯度上的积雪进行更深入的过程级了解的位置,这可能会提供对已观察到的长期趋势的深入了解。我们将把这个为期两年的项目的重点放在发展坚实的方法,可以应用到整个内华达州的后续工作。我们将重点发展的空间变异性和不确定性,形成再分析同化方案的核心的统计和物理模型。特别是,我们将专注于开发模型,用于将雪水的观测网络点测量与基于网格的雪属性估计相关联,以及将水文气象输入的不确定性模型参数化到雪物理模型中。我们将把模型,方法和工作结果整合到加州大学洛杉矶分校现有的本科和研究生课程中,和伯德极地研究中心正在进行的外展工作。此外,这里开发的高分辨率建模框架可以为未来的气候变化研究提供一个独特的测试平台,其中区域或全球气候模型的大气模型输出可以用作离线模型的强迫,以增加现有的关于塞拉利昂积雪和春季径流预计将如何变化的文献。

项目成果

期刊论文数量(0)
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Michael Durand其他文献

Effects of volcanic air pollution on health
火山空气污染对健康的影响
  • DOI:
    10.1016/s0140-6736(00)03586-8
  • 发表时间:
    2001
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Michael Durand;J. Grattan
  • 通讯作者:
    J. Grattan
Geothermal ground gas emissions and indoor air pollution in Rotorua, New Zealand
  • DOI:
    10.1016/j.scitotenv.2004.10.023
  • 发表时间:
    2005-06-01
  • 期刊:
  • 影响因子:
  • 作者:
    Michael Durand;Bradley J. Scott
  • 通讯作者:
    Bradley J. Scott
Indoor air pollution caused by geothermal gases
  • DOI:
    10.1016/j.buildenv.2005.06.001
  • 发表时间:
    2006-11
  • 期刊:
  • 影响因子:
    7.4
  • 作者:
    Michael Durand
  • 通讯作者:
    Michael Durand
12. Management of Colorectal Liver Metastases (CRLM) – A Ten Year Single Institutional Experience
  • DOI:
    10.1016/j.ejso.2015.08.043
  • 发表时间:
    2015-11-01
  • 期刊:
  • 影响因子:
  • 作者:
    Michael Durand;Fiona Hand;Justin Geoghegan;Donal Maguire;Emir Hoti
  • 通讯作者:
    Emir Hoti
Using river hypsometry to improve remote sensing of river discharge
  • DOI:
    10.1016/j.rse.2024.114455
  • 发表时间:
    2024-12-15
  • 期刊:
  • 影响因子:
  • 作者:
    Michael Durand;Chunli Dai;Joachim Moortgat;Bidhyananda Yadav;Renato Prata de Moraes Frasson;Ziwei Li;Kylie Wadkwoski;Ian Howat;Tamlin M. Pavelsky
  • 通讯作者:
    Tamlin M. Pavelsky

Michael Durand的其他文献

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{{ truncateString('Michael Durand', 18)}}的其他基金

From Classrooms to Geosciences Careers: Developing, Testing and Disseminating a High School Module on Modeling Water in Urban Environments
从课堂到地球科学职业:开发、测试和传播城市环境中水建模的高中模块
  • 批准号:
    1203035
  • 财政年份:
    2012
  • 资助金额:
    $ 19.61万
  • 项目类别:
    Standard Grant

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Cell Research (细胞研究)
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    2008
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    24.0 万元
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    专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
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    10774081
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    2007
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    45.0 万元
  • 项目类别:
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