4.7 Article

Statistical Mapping of Freshwater Origin and Fate Signatures as Land/Ocean Regions of Influence in the Gulf of Mexico

期刊

JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS
卷 124, 期 7, 页码 4954-4973

出版社

AMER GEOPHYSICAL UNION
DOI: 10.1029/2018JC014784

关键词

land; sea exchanges; river plume; sea surface salinity; river; Gulf of Mexico

资金

  1. NASA Science Utilization of the Soil Moisture Active-Passive Mission program [NNH15ZDA001N-SUSMAP]

向作者/读者索取更多资源

Here we present an observation-based study of the coupled land-ocean regions of influence for the transformation of precipitation over land into coastal river plume structure in the Gulf of Mexico (GoM). First, we locate the regions on land for which precipitation and runoff generation have the strongest relationship with local river discharge. Then we map, on average, the apparent unique contribution of individual river discharge forcing to specific features of river plume structure across the GoM. To this end, we employ a spatial-temporal lagged correlation analysis that relates satellite-based precipitation, soil moisture, and sea surface salinity observations to in situ river discharge for the three primary freshwater input sources for the GoM. On land, we find a likely source region for the northeastern GoM in the southeastern Mississippi basin at 16-day lead time, a likely source region for the northeastern GoM in the Mobile Bay basin at 3-day lead time and a likely source region for the Central GoM from the Texas basin region at 4-day lead time. In the ocean, we find statistically significant regions of distinct contribution for each of the three sources of freshwater on plume structure at lag times from weeks to several months. Though a statistical approach is limited in its interpretability, this result advances progress toward a predictive framework for mapping of the impacts of hydrological flood events from land into the ocean using observations alone.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据