Upper Colorado River and Great Basin streamflow and snowpack forecasting using Pacific oceanic-atmospheric variability
Upper Colorado River and Great Basin streamflow and snowpack forecasting using Pacific oceanic-atmospheric variability
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DOI：
10.1016/j.jhydrol.2011.09.030
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发表时间：
2011-11-22
影响因子：
6.4
通讯作者：
Lamb, Kenneth
中科院分区：
文献类型：
--
作者：
Oubeidillah, Abdoul A.;Tootle, Glenn A.;Lamb, Kenneth
美国西部的水资源管理者，包括犹他州等地区，面临着管理稀缺资源的挑战，因此，严重依赖预测来分配和满足各种水需求。对科罗拉多河上游和大盆地的流量和积雪预报模型的改进是至关重要的。在本研究中，研究了利用海洋和气候变量作为预测因子来改进长提前期（3至9个月）的流量和积雪预报。利用奇异值分解（SVD）分析，确定了与科罗拉多河上游和大盆地源头流（和积雪）远相关的太平洋海温区和500 mbar位势高度区（Z(500)）。得到的太平洋海温和Z（500）区域被用来创建指数，然后用作非参数预测模型的预测因子。大多数预报结果是正的统计技能，这表明预报比气候学预报或无技能预报有改善。结果表明，太平洋海温指数更适合于长预期（6 ~ 9个月）的流量（和积雪）预报，而Z（500）指数更适合于短预期（3个月）预报。总之，预报模式的结果表明，在预报模式中加入太平洋-大气气候变率可以提高对河流流量和积雪量的预报。(C) 2011 Elsevier B.V.版权所有
Water managers in western U.S., including areas such as the State of Utah, are challenged with managing scarce resources and thus, rely heavily on forecasts to allocate and meet various water demands. The need for improved streamflow and snowpack forecast models in the Upper Colorado River and Great Basin is of the utmost importance. In this research, the use of oceanic and climatic variables as predictors to improve the long lead-time (three to nine months) forecast of streamflow and snowpack was investigated. Singular Value Decomposition (SVD) analysis was used to identify a region of Pacific Ocean SSTs and a region of 500 mbar geopotential height (Z(500)) that were teleconnected with streamflow (and snowpack) in Upper Colorado River and Great Basin headwaters. The resulting Pacific Ocean SSTs and Z(500) regions were used to create indices that were then used as predictors in a non-parametric forecasting model. The majority of forecasts resulted in positive statistical skill, which indicated an improvement of the forecast over the climatology or no-skill forecast. The results indicated that derived indices from Pacific Ocean SSTs were better suited for long lead-time (six to nine month) forecasts of streamflow (and snowpack) while the derived indices from Z(500) improved short-lead time (3 month) forecasts. In all, the results of the forecast model indicated that incorporating Pacific oceanic-atmospheric climatic variability in forecast models can lead to improved forecasts for both streamflow and snowpack. (C) 2011 Elsevier B.V. All rights reserved.