Articles | Volume 26, issue 14
Research article
19 Jul 2022
Research article |  | 19 Jul 2022

On the similarity of hillslope hydrologic function: a clustering approach based on groundwater changes

Fadji Z. Maina, Haruko M. Wainwright, Peter James Dennedy-Frank, and Erica R. Siirila-Woodburn

Data sets

Plant species distribution within the Upper Colorado River Basin estimated by using hyperspectral and lidar airborne data N. Falco, A. Balde, I. Breckheimer, E. Brodie, P. G. Brodrick, K. D. Chadwick, J. Chen, B. Dafflon, A. Henderson, J. Lamb, K. Maher, L. Kueppers, H. Steltzer, H. Wainwright, K. Williams, and S. S. Hubbard

NEON AOP Survey of Upper East River CO Watersheds: LAZ Files, LiDAR Surface Elevation, Terrain Elevation, and Canopy Height Rasters T. Goulden, B. Hass, E. Brodie, K. D. Chadwick, N. Falco, K. Maher, H. Wainwright, and K. Williams

Model code and software

ParFlow hydrologic model ParFlow

Short summary
We propose a hillslope clustering approach based on the seasonal changes in groundwater levels and test its performance by comparing it to several common clustering approaches (aridity index, topographic wetness index, elevation, land cover, and machine-learning clustering). The proposed approach is robust as it reasonably categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics, hence a similar hydrologic function.