Abstract. In this study, we develop a watershed zonation approach
for characterizing watershed organization and functions in a tractable
manner by integrating multiple spatial data layers. We hypothesize that (1) a hillslope is an appropriate unit for capturing the watershed-scale
heterogeneity of key bedrock-through-canopy properties and for quantifying the co-variability of these properties representing coupled ecohydrological
and biogeochemical interactions, (2) remote sensing data layers and clustering methods can be used to identify watershed hillslope zones having
the unique distributions of these properties relative to neighboring parcels, and (3) property suites associated with the identified zones can be
used to understand zone-based functions, such as response to early snowmelt
or drought and solute exports to the river. We demonstrate this concept using unsupervised clustering methods that synthesize airborne remote
sensing data (lidar, hyperspectral, and electromagnetic surveys) along with satellite and streamflow data collected in the East River Watershed, Crested
Butte, Colorado, USA. Results show that (1) we can define the scale of
hillslopes at which the hillslope-averaged metrics can capture the majority
of the overall variability in key properties (such as elevation, net
potential annual radiation, and peak snow-water equivalent – SWE), (2) elevation and aspect are independent controls on plant and snow signatures, (3) near-surface bedrock electrical resistivity (top 20 m) and geological structures are
significantly correlated with surface topography and plan species
distribution, and (4) K-means, hierarchical clustering, and Gaussian mixture
clustering methods generate similar zonation patterns across the watershed.
Using independently collected data, we show that the identified zones
provide information about zone-based watershed functions, including
foresummer drought sensitivity and river nitrogen exports. The approach is
expected to be applicable to other sites and generally useful for guiding
the selection of hillslope-experiment locations and informing model
parameterization.