资源环境科技发展态势分析平台

Groundwater levels are typically measured at only a limited number of points in a catchment. Thus, upscaling these point measurements to the catchment scale is necessary to determine subsurface flow paths and runoff source areas. Here we present a data-driven approach composed of time series clustering and topography-based upscaling of shallow, perched groundwater dynamics using groundwater data from 51 monitoring sites in a 20-ha prealpine headwater catchment in Switzerland. The agreement between the upscaled (modeled) and measured groundwater dynamics was strong for most of the 19-month study period for the upslope and footslope locations but weaker at the beginning of events and for the midslope locations. However, these differences between measured and modeled groundwater levels did not significantly affect modeled groundwater activation, that is, the time when groundwater levels were within the more transmissive soil layers near the soil surface. The resulting groundwater activation maps represent the groundwater response across the catchment and highlight the dynamic expansion and contraction of the subsurface runoff source areas, particularly along the channel network. This is in agreement with the variable source area concept. However, there were also isolated active zones that did not get connected to the stream during rainfall events, highlighting the need to distinguish between variable active and variable stream-connected runoff source areas. Our data-driven approach to upscale point measurements of shallow groundwater levels appears useful for studying catchment-scale variations in groundwater storage and connectivity and thus may help to better understand runoff generation in mountain catchments.

Plain Language Summary For a better understanding of how runoff in streams is generated, we need to know how groundwater levels respond across a catchment. However, groundwater can usually only be measured at a few selected points, and interpolation between these points does often not result in realistic groundwater response patterns. Here we present a data-driven approach based on groundwater level data from 51 sites in a catchment in Switzerland for a 19-month study period. We grouped the monitoring sites into six clusters with similar groundwater level dynamics. We then determined the topographic characteristics of the sites in each cluster and assigned the average relative groundwater level for the monitoring sites in a cluster to all other sites in the catchment with similar topographic characteristics. By doing so, we created sequences of maps of groundwater levels across the entire study catchments. These maps show an expansion and contraction of the areas where the groundwater level is close to the surface and which of these areas are connected to the stream channels. These maps are useful to identify from which parts of the catchment streamwater may come during a rain event, which helps to improve our understanding of runoff generation processes.