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

世界上的山区是地球系统的一个重要组成部分，因为世界上很大一部分人口从受季节性融雪影响的流域获得供水。对于靠近山区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、工业和其他饮用用途。据估计，有超过10亿人生活在这些地区的下游。然而，在全球许多地区，严重缺乏这些地区的现场数据网络。这使得对这些系统及其演变的基本理解变得困难。这个项目将开发和应用新的方法来描述全球数据稀疏地区的雪过程。温带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（特别是在纬度20°和40°S之间）在冬季直接依赖以雪的形式储存的水。此外，安第斯山脉在海拔和范围上都是南半球最重要的山脉，它影响着多个尺度的大气环流，最终影响整个南美大陆的水文。尽管该地区的雪很重要，但严重缺乏现场监测，这限制了解决该地区重要水文气候学问题的能力。本建议的目的是更好地了解积雪如何作为地形和其他地理特征的函数分布，并评估年际天气条件如何影响积雪分布随海拔的变化以及安第斯积雪如何响应气候趋势。这些目标是通过一个新的雪再分析数据集来解决的。再分析框架将可用的遥感信息和现有的气象数据集整合到一个数据同化系统中，从而产生雪水当量（SWE）估算。该框架考虑了所有输入的不确定性来源，包括遥感数据和现有的粗糙气象强迫数据，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联系解释表征，并利用这种见解来改进水文预测。该数据集将用于回答与积雪地理变率及其对局地尺度地理特征（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）的依赖相关的问题。这一分析的结果将为监测不足地区的积雪和融化模式提供新的见解，并将使人们更好地了解山区大尺度环流现象与积雪过程之间的相互作用。该项目的成果（包括数据和数字代码）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有用。

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.