网格化径流测量对美国本土干旱变化的影响
The Impact of a Gridded Streamflow Measure on Drought Variation in the Conterminous United States
- Wake Forest University（维克森林大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究利用专为匹配气象数据时空支持的网格化径流数据，通过扩展统计方法控制自相关和方差变异性，发现径流测量是美国干旱变率最重要的解释变量。
AI中文摘要：
干旱模型依赖于广泛的气象和水文输入。利益相关者根据不同的目的和优先级对干旱进行分类，并相应地依赖不同的测量来解释和预测干旱的发生。虽然许多气象输入以网格化数据产品的形式提供，但水文径流测量通常仅以点参考的测站测量形式提供。这导致依赖面状气象数据和点参考水文数据的研究出现错位问题。此类测站数据还可能存在显著的空间和/或时间缺失。许多地区仍未设站，而在设有测站的地区，设备故障会导致时间缺口。在本研究中，我们记录了美国本土现有网格化径流测量的价值，该测量专门设计用于匹配公开可用的气象和有序干旱测量的时空支持。我们使用这个同质化数据库来评估该径流测量在解释美国干旱变率中的相对重要性。这一评估首先要求我们解决观测干旱方差中的自相关性和变异性，这是对现有统计方法的两个扩展。在适当控制这两者之后，我们的结果表明，在一组广泛的气象变量中，径流测量通常是最具统计重要性的解释变量。我们探讨了该结果的空间变化及其一些驱动因素。
英文摘要：
Models for droughts draw on a wide range of meteorological and hydrological inputs. Stakeholders classify droughts according to different purposes and priorities, and accordingly rely on different measures to explain and predict the onset of drought. While many meteorological inputs are available as gridded data products, hydrological streamflow measurements are often only available as point-referenced gauge measurements. This leads to an issue of misalignment for studies relying on both areal meteorological and point-referenced hydrological data. Such gauge data can also have notable spatial and/or temporal missingness. Many areas remain ungauged, and where gauges exist, equipment malfunctions cause temporal gaps. In this study, we document the value of an existing gridded streamflow measure for the conterminous United States, one which was specifically designed to match the spatio-temporal support of publicly available meteorological and ordinal drought measurements. We use this homogenized database to assess the relative importance of this streamflow measure in explaining US drought variability. This assessment first requires that we address autocorrelation and variability in the variance of observed droughts, two extensions to existing statistical methodology. After suitably controlling for both, our results show that the streamflow measure is often the most statistically important explanatory variable from among a wide set of meteorological variables. We explore the spatial variation and some drivers of this result.
