Preprints
https://doi.org/10.5194/hess-2021-520
https://doi.org/10.5194/hess-2021-520
 
26 Oct 2021
26 Oct 2021
Status: a revised version of this preprint is currently under review for the journal HESS.

On the similarity of hillslope hydrologic function: a process-based approach

Fadji Zaouna Maina1,a, Haruko M. Wainwright1, Peter James Dennedy-Frank1, and Erica R. Siirila-Woodburn1 Fadji Zaouna Maina et al.
  • 1Energy Geosciences Division, Lawrence Berkeley National Laboratory 1 Cyclotron Road, M.S. 74R-316C, Berkeley, CA 94704, USA
  • anow at: NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, 8800 Greenbelt Rd, Greenbelt, 20771, MD, USA

Abstract. Hillslope similarity is an active topic in hydrology because of its importance to improve our understanding of hydrologic processes and enable comparisons and paired studies. In this study, we propose a holistic bottom-up hillslope similarity classification based on a region’s integrative hydrodynamic response quantified by the seasonal changes in groundwater levels. The main advantage of the proposed classification is its ability to describe recharge and discharge processes. We test the performance of the proposed classification by comparing it to seven other common hillslope similarity classifications. These include simple classifications based on the aridity index, topographic wetness index, elevation, land cover, and more sophisticated machine-learning classifications that jointly integrate all these data. We assess the ability of these classifications to identify and categorize hillslopes with similar static characteristics, hydroclimatic behaviors, land surface processes, and subsurface dynamics in a mountainous watershed, the East River, located in the headwaters of the Upper Colorado River Basin. The proposed classification is robust as it reasonably identifies and categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics (and hence hillslopes with similar hydrologic function). In general, the other approaches are good in identifying similarity in a single characteristic, which is usually close to the selected variable. We further demonstrate the robustness of the proposed classification by testing its ability to predict hillslope responses to wet and dry hydrologic conditions, of which it performs well when based on average conditions.

Fadji Zaouna Maina et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-520', A. D. Parsekian, 05 Feb 2022
    • AC1: 'Reply on RC1', Fadji Zaouna Maina, 09 Mar 2022
  • RC2: 'Comment on hess-2021-520', Anonymous Referee #2, 10 Feb 2022
    • AC2: 'Reply on RC2', Fadji Zaouna Maina, 09 Mar 2022

Fadji Zaouna Maina et al.

Fadji Zaouna Maina et al.

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