Geosciences and Geological and Petroleum Engineering, Missouri University of Science and Technology, Rolla, MO, 65409, USA.
Sci Rep. 2019 Sep 6;9(1):12828. doi: 10.1038/s41598-019-49278-8.
Groundwater depletion in many areas of the world has been broadly attributed to irrigation. However, more formal, data-driven, causal mechanisms of long-term groundwater patterns have not been assessed. Here, we conducted the first Granger causality analysis to identify the "causes" of groundwater patterns using the rice-producing parishes of Louisiana, USA, as an example. Trend analysis showed a decline of up to 6 m in groundwater level over 51 years. We found that no single cause explained groundwater patterns for all parishes. Causal linkages were noted between groundwater and area harvested, number of irrigation wells, summer precipitation totals, and drought. Bi-directional linkages were noted between groundwater and rice yield, suggesting feedback between both time series. Causal linkages were absent between groundwater and many drivers where significant correlations were noted, highlighting the importance of using robust causal relationships over illusive correlations to detect the cause. These results advance our understanding of groundwater dynamics and can reveal a key connection between food and groundwater.
世界上许多地区的地下水枯竭都被广泛归因于灌溉。然而，长期地下水模式的更正式、数据驱动的因果机制尚未得到评估。在这里，我们进行了第一次格兰杰因果关系分析，以使用美国路易斯安那州的水稻种植堂区为例，确定地下水模式的“原因”。趋势分析显示，在 51 年内地下水水位下降了多达 6 米。我们发现，没有一个单一的原因可以解释所有堂区的地下水模式。在地下水和收获面积、灌溉井数量、夏季降水总量和干旱之间发现了因果关系。地下水和水稻产量之间存在双向联系，表明这两个时间序列之间存在反馈。在地下水和许多驱动因素之间没有因果关系，尽管这些驱动因素存在显著相关性，这突出表明在检测原因时，使用稳健的因果关系而不是虚幻的相关性非常重要。这些结果提高了我们对地下水动态的理解，并可以揭示食物和地下水之间的关键联系。
