Abstract Identifying key driving forces for streamflow variation is essential for improving sustainable water resource management in terms of understanding how changes in the watershed translate to changes in streamflow. In this study, the relationships between trends in total annual streamflow and trends in watershed characteristics across the contiguous U.S. during 1981-2016 are investigated with data from 2,621 USGS gages. The regions of homogeneous hydrologic change, i.e. watersheds that are undergoing similar statistically significant streamflow trends, are delineated and frequent pattern mining (i.e. Apriori algorithm) and variable importance (i.e. Random Forest) are used to derive the key driving forces for these regions. As expected, the trends in streamflow are highly associated with the trends of precipitation. In contrast, the influences of anthropogenic factors vary substantially across regions. Particularly, the influence of water use change tends to be significant in the regions dominated by agricultural land, e.g. Dakotas. The importance of land use change is highlighted in the regions with relatively large forest coverage, e.g. Northeast. However, these important identified water use changes are not frequently associated with the increasing streamflow in sub-regions, e.g. Great Lakes, and thus the significance of the water use impacts are site-specific. Therefore, the changes in climate and land use are frequently and importantly identified together in the sub-regions with increasing streamflow, which can be collectively used to discover the major causes of the streamflow trends in those regions. Although the impacts of changing water use are highlighted in the Southwest, climate trends are primarily responsible for the decreasing streamflow.

中文翻译：

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流量趋势归因频繁模式和变量重要性的数据驱动分析

摘要 在理解流域变化如何转化为水流变化方面，确定水流变化的关键驱动力对于改善可持续水资源管理至关重要。在这项研究中，使用来自 2,621 个 USGS 仪器的数据调查了 1981-2016 年美国本土年总流量趋势与流域特征趋势之间的关系。划定了均质水文变化的区域，即经历类似统计显着流量趋势的流域，并使用频繁模式挖掘（即 Apriori 算法）和变量重要性（即随机森林）来推导出这些区域的关键驱动力。正如预期的那样，水流趋势与降水趋势高度相关。相比之下，人为因素的影响因区域而异。特别是在以农业用地为主的地区，如达科他州，用水变化的影响往往是显着的。在森林覆盖率相对较大的地区，例如东北部，土地利用变化的重要性凸显。然而，这些重要的用水变化并不经常与子区域（例如五大湖）的流量增加相关，因此用水影响的重要性因地点而异。因此，气候和土地利用的变化在流量增加的子区域中频繁且重要地被共同识别，可以共同发现这些区域的流量趋势的主要原因。