Characterizing Biases in Mountain Snow Accumulation From Global Data Sets
Characterizing Biases in Mountain Snow Accumulation From Global Data Sets
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DOI：
10.1029/2019wr025350
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发表时间：
2019-11
影响因子：
5.4
通讯作者：
M. Wrzesien;T. Pavelsky;M. Durand;J. Dozier;J. Lundquist
中科院分区：
文献类型：
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作者：
M. Wrzesien;T. Pavelsky;M. Durand;J. Dozier;J. Lundquist
山雪通过其季节性的积累、储存和融化在区域水收支中起着重要作用。然而，由于有限的观测网络和现有卫星仪器无法遥感山区的雪深或水当量，描绘大面积积雪的特征仍然很困难。模式提供了从山脉到大陆尺度估算雪水储存量（SWS）的一些能力。在这里，我们比较了四种常用的全球数据集，以了解它们之间是否存在关于山地SWS估计的共识。欧洲中期天气预报再分析中心、全球陆地数据同化系统、现代研究与应用回顾性分析第2版和变入渗能力等数据集与全球SWS总量的四个数据集平均值相差在±36%以内。当使用新的季节性山雪分类数据集提取山区时，四种数据产品的一致性更高，均在山区季节性SWS的±21%以内。然而，当与来自天气研究与预报（WRF）区域模式的SWS高分辨率（9公里）模拟相比时，四种全球产品与WRF估计的北美山区积雪量相差40-66%，负偏差高达651 km3，与密西西比河的年流量相当。如果我们将北美SWS的偏差扩展到全球山脉，全球数据集可能会丢失多达1,500 km3的SWS，相当于世界上所有河流流量的4%。SWS的潜在差异表明，在积雪占主导地位的地区，特别是山区，必须做更多的工作来表征水资源。
Mountain snow has a fundamental role in regional water budgets through its seasonal accumulation, storage, and melt. However, characterizing snow accumulation over large regions remains difficult because of limited observational networks and the inability of available satellite instruments to remotely sense snow depth or water equivalent in mountains. Models offer some ability to estimate snow water storage (SWS) on mountain range to continental scales. Here we compare four commonly used global data sets to understand whether there is a consensus regarding mountain SWS estimates among them. The data sets—European Centre for Medium‐Range Weather Forecasts Reanalysis‐Interim, Global Land Data Assimilation System, Modern‐Era Retrospective Analysis for Research and Applications version 2, and Variable Infiltration Capacity—agree to within ±36% of the four–data set average for total global SWS. When mountain areas are extracted using a new seasonal mountain snow classification data set, the four data products have more agreement, where all are within ±21% of the seasonal SWS for mountain regions. However, when compared to high‐resolution (9 km) simulations of SWS from the Weather Research and Forecasting (WRF) regional model, the four global products differ from WRF‐estimated North American mountain snow accumulation by 40–66%, with a negative bias up to 651 km3, comparable to the annual streamflow of the Mississippi River. If we extend the North America SWS bias to global mountains, the global data sets may miss as much as 1,500 km3 of SWS, equivalent to 4% of the flow in all the world's rivers. The potential difference of SWS suggests more work must be done to characterize water resources in snow‐dominated regions, particularly in mountains.