Short‐term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

Abstract

Headwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts and the flowing drainage density (DD) varies over time. Monitoring the DD dynamics is essential to understand the processes controlling it. However, our knowledge of the event‐scale DD dynamics is limited because high spatial and temporal resolution data on the DD remain sparse. Therefore, our team monitored the DD dynamics and hydrologic variables in two 5‐ha headwater catchments in the Swiss pre‐Alps in the summer of 2021, through mapping surveys of the flow state and a wireless streamwater level sensor network. We combined the two data sources to calculate the DD at the event‐time scale. Our so‐called CEASE method assumes that flow in a channel reach occurs above a set of water level thresholds, and it determined the DDs with accuracies >94%. DD responses to events differed for the two catchments, despite their proximity and similar size. DD ranged from 2.7 to 32.2 km km$^{−2}$ in the flatter catchment (average slope: 15°). For this catchment, the discharge‐DD relationship became steeper when DD exceeded 20 km km$^{−2}$ and DD increased substantially with relatively small increases in discharge. For rainfall events during dry conditions, the discharge‐DD relationship showed counterclockwise hysteresis, likely due to initially high groundwater discharge from the area near the catchment outlet; once rainfall stopped, DD remained high during the streamflow recession due to rising groundwater levels throughout the catchment. For events during wet conditions, the discharge and DD responded synchronously. In the steeper catchment (average slope: 24°), the DD varied only from 7.8 to 14.6 km km$^{−2}$ and there was no hysteresis or threshold behaviour in the discharge‐DD relationship, likely because multiple groundwater springs maintained streamflow throughout the network during the monitoring period. These results highlight the high variability in DD and its dynamics across small headwater catchments.