Collaborative Research: Unraveling Orographic Precipitation Patterns by Combined Hydrologic and Atmospheric Analysis

合作研究：通过水文和大气综合分析揭示地形降水模式

• 批准号: 1344595
• 负责人: Jessica Lundquist
• 金额: $ 24.64万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1344595&HistoricalAwards=false
• 关键词: Collaborative; Research; Unraveling; Orographic; Precipitation

Focusing on the Southern Sierra Nevada, California, we will use distributed snow and streamflow measurements (which date back to the 1920s) to assess what precipitation must have been for each basin on annual and storm-by-storm time-scales. Using iterative hydrologic simulations across a range of model structures in a Bayesian framework, we will determine which precipitation amounts and gradients (and with what uncertainty) best fit available measurements of streamflow and snow accumulation. This methodology will provide a ground-based estimate of high-elevation precipitation for times and locations where no high-altitude rain gauges are available and will allow us to assess long-term change. We will then use these new hydrologic-based precipitation datasets as a benchmark for atmospheric model performance, and will compare these with an ensemble of 14-year regional atmospheric model (WRF) simulations generated using different boundary conditions and microphysics schemes. This will allow us to assess sources of model uncertainty in annual patterns of orographic precipitation. Accurate precipitation inputs, both total amounts and changes with elevation, are critical to model streamflow in mountainous regions. However, in general these regions are grossly under-sampled in terms of precipitation measurements, and those gauges that do exist are notoriously unreliable. Atmospheric models can predict precipitation rates and distributions. However, development and improvement of these atmospheric models has been hindered by the lack of direct precipitation measurements that caused hydrologists problems in the first place.Our most reliable measurements from these high-altitude areas (distributed streamflow and snow water equivalent) are useful clues for estimating historic spatial and temporal distributions of precipitation. Our research approach integrates both meteorology and hydrology, using hydrology as a tool to better understand meteorology, and providing a long-term benchmark that can be used to improve our forecasts and advance science in both fields. This work will improve understanding of mountain precipitation for both individual storms and how those storms aggregate over an entire season. This knowledge is crucial for forecasting short-term floods, seasonal water resources, and long-term climate sensitivity. Efforts will focus on the Southern Sierra Nevada, California, where short-term and long-term forecasts have historically had lower accuracy than other regions, but where those same forecasts are critical for the San Joaquin Valley agriculture industry, valued at over $30 billion. The research results will also contribute to atmospheric model development, such that these models can be used more successfully to predict mountain precipitation worldwide.

我们将重点关注加利福尼亚州内华达山脉南部，使用分布式降雪和水流测量（可以追溯到 20 年代）来评估每个流域在年度和逐场风暴时间尺度上的降水量。 在贝叶斯框架中使用一系列模型结构的迭代水文模拟，我们将确定哪些降水量和梯度（以及不确定性）最适合现有的水流和积雪测量。 该方法将为没有高海拔雨量计的时间和地点提供高海拔降水的地面估计，并使我们能够评估长期变化。 然后，我们将使用这些新的基于水文的降水数据集作为大气模型性能的基准，并将这些数据与使用不同边界条件和微物理方案生成的 14 年区域大气模型 (WRF) 模拟集合进行比较。 这将使我们能够评估地形降水年度模式中模型不确定性的来源。准确的降水输入（包括总量和随海拔的变化）对于山区径流建模至关重要。然而，总的来说，这些地区在降水测量方面的采样严重不足，而且那些确实存在的测量仪也是出了名的不可靠。 大气模型可以预测降水率和分布。 然而，这些大气模型的发展和改进受到了缺乏直接降水测量的阻碍，这首先给水文学家带来了问题。我们从这些高海拔地区进行的最可靠的测量（分布式水流和雪水当量）是有用的线索估计降水的历史空间和时间分布。 我们的研究方法整合了气象学和水文学，利用水文学作为更好地了解气象学的工具，并提供一个长期基准，可用于改进我们的预测并推进这两个领域的科学发展。 这项工作将增进对单个风暴的山区降水以及这些风暴在整个季节如何聚集的了解。 这些知识对于预测短期洪水、季节性水资源和长期气候敏感性至关重要。 工作重点将集中在加利福尼亚州内华达山脉南部，该地区的短期和长期预测历来比其他地区的准确度较低，但这些预测对于价值超过 300 亿美元的圣华金谷农业产业至关重要。 研究成果还将有助于大气模型的开发，使这些模型可以更成功地用于预测全球山区降水。