Transformative insights into the high-elevation climatology and dynamics of Andean hydrology using a new snow reanalysis dataset

世界山区是地球系统的一个重要组成部分，因为世界人口的很大一部分从受季节性融雪影响的流域获得供水。对于山区附近的半干旱或干旱地区，季节性积雪是一个非常有用的水库，在冬季储存水，在干燥，炎热的夏季释放水，使其可用于农业，工业和其他饮用水用途。据估计，超过10亿人生活在这些地区的下游。然而，在地球仪的许多地区，这些地区的现场数据网络严重缺乏。这使得对这些系统以及它们如何演变的基本理解变得困难。该项目将开发和应用新的方法，用于描述地球仪数据稀少地区的降雪过程。热带外的安第斯山脉是一个重要的山地系统的明显例子。许多智利和阿根廷最大的人口中心（特别是在南纬20°和40°之间）直接依赖于冬季以雪的形式储存的水。此外，就海拔和范围而言，安第斯山脉是南半球最重要的山脉，它在许多尺度上影响大气环流，最终影响整个南美洲大陆的水文。尽管雪在该地区的重要性，有一个显着缺乏到位的监测，这限制了解决有关该地区的重要水文气候问题的能力。这项建议的目的是更好地了解积雪如何随着地形和其他自然地理特征的变化而分布，并评估年际天气条件如何影响积雪随海拔的分布，以及安第斯积雪如何对气候趋势作出反应。这些目标是通过一个新的雪再分析数据集。再分析框架将现有的遥感信息和现有的气象数据集集成到一个数据同化系统中，从而得出雪水当量（SWE）估计值。该框架考虑了来自所有输入的不确定性来源，包括遥感数据和现有的粗气象强迫数据，以产生空间和时间上的高分辨率SWE估计。总体目标是表征SWE，通过驱动物理过程的链接解释表征，并使用此见解来改善水文预测。该数据集将用于回答与积雪地理变化及其对当地尺度自然地理特征（例如，地形增强和地形再分布）和天气或大尺度环流现象（如大气河流和ENSO事件）。这项分析的结果将提供新的见解积雪和融化模式在一个监测不足的地区，并将允许更好地了解大规模的环流现象和山区降雪过程之间的相互作用。该项目的结果（包括数据和数字编码）将广泛提供给整个社区，并应证明对安第斯山脉的地方和区域应用以及通过应用于其他山区而对更广泛的科学界是有用的。

Mountainous regions of the world are an important component of the Earth system given the fact that a significant fraction of the world's population obtains their water supply from watersheds influenced by seasonal snowmelt. For semi-arid or arid regions near mountainous areas, the seasonal snowpack represents an extremely useful reservoir that stores water during the winter, releases it during the drier, hotter summer months, making it available for agricultural, industrial and other potable uses. It is estimated that over 1 billion people live downstream of such regions. Yet in many areas of the globe the in situ data network in such areas is severely lacking. This makes having a basic understanding of these systems and how they are evolving difficult. This project will develop and apply new methods for characterizing snow processes in data-sparse regions of the globe. The extratropical Andes is a clear example of an important montane system. Many of Chile's and Argentina's largest population centers (particularly between latitudes 20° and 40°S) depend directly on the water stored as snow during the winter. Further, the Andes is the most significant mountain range in the Southern Hemisphere in terms of elevation and extent, and it impacts the atmospheric circulation across many scales, ultimately impacting the hydrology of the entire South American continent. Despite the importance of snow in this region, there is a significant lack of in-place monitoring, which limits the ability to address important hydroclimatological questions about this region. The aim of this proposal is to better understand how snow is distributed as a function of topographic and other physiographic characteristics and evaluate how inter-annual synoptic conditions influence snowpack distribution with elevation and how the Andean snowpack responds to climatic trends. These aims are addressed by means of a novel snow reanalysis dataset. The reanalysis framework integrates available remotely sensed information and existing meteorological datasets into a data assimilation system that results in snow water equivalent (SWE) estimates. The framework accounts for uncertainty sources from all inputs, including both the remotely sensed data and existing coarse meteorological forcing data to yield high-resolution SWE estimates in space and time. The overarching goals are to characterize SWE, explain the characterization via links to driving physical processes, and use this insight to improve hydrologic predictions. This dataset will be used to answer questions related to snowpack geographical variability and its dependence on both local scale physiographic characteristics (e.g., orographic enhancement and topographic redistribution) and synoptic or large-scale circulation phenomena (e.g. Atmospheric Rivers and ENSO events). The results of this analysis will provide new insights into the snow accumulation and melt patterns over a poorly monitored region and will allow for improved understanding of the interactions between large-scale circulation phenomena and snow processes in mountain regions. The results of the project (including data and numerical codes) will be made available widely to the community at large and should prove useful to local and regional applications in the Andes as well as to the broader scientific community via application to other montane regions.