Gridded Snow Water Equivalent Reconstruction for Utah Using Forest Inventory and Analysis Tree-Ring Data

Gridded Snow Water Equivalent Reconstruction for Utah Using Forest Inventory and Analysis Tree-Ring Data
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使用森林清查和年轮数据分析重建犹他州网格化雪水当量

DOI:
10.3390/w9060403
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发表时间:
2017
期刊:
影响因子:
3.4
通讯作者:
R. DeRose
R. DeRose
中科院分区:
环境科学与生态学3区
文献类型:
--
作者:
D. Barandiaran;S. Wang;R. DeRose

文献摘要

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山间西部的积雪观测是稀疏和短暂的,使它们难以用于描绘过去的变化和极端。这项研究提出了重建4月1日雪水当量(SWE)的1850-1989年期间使用增量核心收集的美国林务局,内部西部森林清查和分析程序(FIA)。在犹他州,SWE重建的38个雪道位置使用标准化的树轮指数的组合来自两个FIA增量核心和公开可用的树轮年表。这些单独的重建,然后内插到一个4公里的网格使用客观分析与海拔校正,以创建一个SWE产品。结果表明,观察到的SWE以及良好的对应区域树木年轮为基础的干旱重建的显着相关性。诊断分析表明,全州范围内的气候变率在年际和年代际的时间尺度上是一致的,增加了地理细节,这是不可能使用courser前仪器代理数据集。这种SWE重建为水资源管理人员和预报员提供了更好的空间分辨率,以检查积雪的过去变化,这将是重要的,因为未来的水文气候变化被放大的气候变化。
Snowpack observations in the Intermountain West are sparse and short, making them difficult for use in depicting past variability and extremes. This study presents a reconstruction of April 1 snow water equivalent (SWE) for the period of 1850–1989 using increment cores collected by the U.S. Forest Service, Interior West Forest Inventory and Analysis program (FIA). In the state of Utah, SWE was reconstructed for 38 snow course locations using a combination of standardized tree-ring indices derived from both FIA increment cores and publicly available tree-ring chronologies. These individual reconstructions were then interpolated to a 4-km grid using an objective analysis with elevation correction to create an SWE product. The results showed a significant correlation with observed SWE as well as good correspondence to regional tree-ring-based drought reconstructions. Diagnostic analysis showed statewide coherent climate variability on inter-annual and inter-decadal time-scales, with added geographical details that would not be possible using courser pre-instrumental proxy datasets. This SWE reconstruction provides water resource managers and forecasters with better spatial resolution to examine past variability in snowpack, which will be important as future hydroclimatic variability is amplified by climate change.