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.

我们将重点关注内华达州、加州的南部山脉，使用分布式降雪和径流测量（可追溯到20世纪20年代）来评估每个盆地在年度和逐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）最适合现有的流量和积雪测量。 这种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）模拟的集合进行比较。 这将使我们能够评估模型的不确定性来源的地形降水的年度模式。准确的降水输入，无论是总量和随海拔的变化，在山区径流模型是至关重要的。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，这些大气模式的发展和改进一直受到阻碍，缺乏直接的降水测量，造成水文学家的问题摆在首位，我们最可靠的测量，从这些高海拔地区（分布式径流和雪水当量）是有用的线索，估计历史的空间和时间分布的降水。 我们的研究方法整合了气象学和水文学，利用水文学作为工具，以更好地了解气象学，并提供了一个长期的基准，可用于改善我们的预测和推进科学在这两个领域。 这项工作将提高对单个风暴的山区降水以及这些风暴在整个季节中如何聚集的理解。 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。 努力将集中在南部塞拉内华达州，加州，那里的短期和长期预测历史上比其他地区的准确性低，但这些预测对圣华金河谷农业至关重要，价值超过300亿美元。 研究结果还将有助于大气模型的发展，使这些模型可以更成功地用于预测全球山区降水。