Mapping Surface Water Presence and Hyporheic Flow Properties of Headwater Stream Networks With Multispectral Satellite Imagery

Abstract

AbstractGrowth and contraction of headwater stream networks determine habitat extent, and open a window to the hyporheic zone. A fundamental challenge is observation of this process: wetted channel extent is dynamic in space and time, with wetted channel length varying by orders of magnitude over the course of a single storm event in headwater catchments. To date, observational data sets are produced from boots‐on‐the‐ground campaigns, drone imaging, or flow presence sensors, which are often laborious and limited in their spatial and temporal extents. Here, we evaluate satellite imagery as a means to detect wetted channel extent via machine learning methods trained on local surveys of wetted channel extent. Even where channel features are smaller than the imagery's spatial resolution, the presence of surface water may be imprinted upon the spectral signature of an individual pixel. For two catchments in northern California with minimal riparian canopy cover and highly dynamic wetted channel extent, we train a random forest model on RapidEye imagery captured contemporaneously with the existing surveys to predict wetted channel extent (accuracy >91%). The model is used to produce length‐discharge (L‐Q) relations and to calculate spatially distributed estimates of channel hyporheic flow capacity and exchange. A sharp break in hyporheic flow capacity occurs from main stem channels to lower order tributaries, resulting in a stepped L‐Q relationship that cannot be captured by traditionally used power law models. Remotely sensed imagery is a powerful tool for mapping wetted channels at high spatial resolution.