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

GSTDTAP

项目编号	1641960
	Transformative insights into the high-elevation climatology and dynamics of Andean hydrology using a new snow reanalysis dataset
	Steven Margulis
主持机构	University of California-Los Angeles
项目开始年	2017
	2017
项目结束日期	2018-12-31
资助机构	US-NSF
项目类别	Continuing grant
项目经费	151359(USD)
国家	美国
语种	英语
英文摘要	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.
来源学科分类	Geosciences - Earth Sciences
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/70662
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Steven Margulis.Transformative insights into the high-elevation climatology and dynamics of Andean hydrology using a new snow reanalysis dataset.2017.