Articles | Volume 26, issue 14
https://doi.org/10.5194/hess-26-3805-2022
https://doi.org/10.5194/hess-26-3805-2022
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

Related authors

Coupling the ParFlow Integrated Hydrology Model within the NASA Land Information System: A case study over the Upper Colorado River Basin
Peyman Abbaszadeh, Fadji Zaouna Maina, Chen Yang, Dan Rosen, Sujay Kumar, Matthew Rodell, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-280,https://doi.org/10.5194/hess-2024-280, 2024
Preprint under review for HESS
Short summary
Projecting end-of-century climate extremes and their impacts on the hydrology of a representative California watershed
Fadji Z. Maina, Alan Rhoades, Erica R. Siirila-Woodburn, and Peter-James Dennedy-Frank
Hydrol. Earth Syst. Sci., 26, 3589–3609, https://doi.org/10.5194/hess-26-3589-2022,https://doi.org/10.5194/hess-26-3589-2022, 2022
Short summary
Sensitivity of meteorological-forcing resolution on hydrologic variables
Fadji Z. Maina, Erica R. Siirila-Woodburn, and Pouya Vahmani
Hydrol. Earth Syst. Sci., 24, 3451–3474, https://doi.org/10.5194/hess-24-3451-2020,https://doi.org/10.5194/hess-24-3451-2020, 2020
Short summary

Related subject area

Subject: Hillslope hydrology | Techniques and Approaches: Modelling approaches
Technical note: Monitoring discharge of mountain streams by retrieving image features with deep learning
Chenqi Fang, Genyu Yuan, Ziying Zheng, Qirui Zhong, and Kai Duan
Hydrol. Earth Syst. Sci., 28, 4085–4098, https://doi.org/10.5194/hess-28-4085-2024,https://doi.org/10.5194/hess-28-4085-2024, 2024
Short summary
Investigation of the functional relationship between antecedent rainfall and the probability of debris flow occurrence in Jiangjia Gully, China
Shaojie Zhang, Xiaohu Lei, Hongjuan Yang, Kaiheng Hu, Juan Ma, Dunlong Liu, and Fanqiang Wei
Hydrol. Earth Syst. Sci., 28, 2343–2355, https://doi.org/10.5194/hess-28-2343-2024,https://doi.org/10.5194/hess-28-2343-2024, 2024
Short summary
Rapid spatio-temporal flood modelling via hydraulics-based graph neural networks
Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci., 27, 4227–4246, https://doi.org/10.5194/hess-27-4227-2023,https://doi.org/10.5194/hess-27-4227-2023, 2023
Short summary
Understanding hydrologic controls of sloping soil response to precipitation through machine learning analysis applied to synthetic data
Daniel Camilo Roman Quintero, Pasquale Marino, Giovanni Francesco Santonastaso, and Roberto Greco
Hydrol. Earth Syst. Sci., 27, 4151–4172, https://doi.org/10.5194/hess-27-4151-2023,https://doi.org/10.5194/hess-27-4151-2023, 2023
Short summary
Elucidating the role of soil hydraulic properties on aspect-dependent landslide initiation
Yanglin Guo and Chao Ma
Hydrol. Earth Syst. Sci., 27, 1667–1682, https://doi.org/10.5194/hess-27-1667-2023,https://doi.org/10.5194/hess-27-1667-2023, 2023
Short summary

Cited articles

Andréassian, V., Lerat, J., Le Moine, N., and Perrin, C.: Neighbors: Nature's own hydrological models, J. Hydrol., 414–415, 49–58, https://doi.org/10.1016/j.jhydrol.2011.10.007, 2012. 
Aryal, S. K., O'Loughlin, E. M., and Mein, R. G.: A similarity approach to predict landscape saturation in catchments, Water Resour. Res., 38, 26-1-26–16, https://doi.org/10.1029/2001WR000864, 2002. 
Berghuijs, W. R., Sivapalan, M., Woods, R. A., and Savenije, H. H. G.: Patterns of similarity of seasonal water balances: A window into streamflow variability over a range of time scales, Water Resour. Res., 50, 5638–5661, https://doi.org/10.1002/2014WR015692, 2014. 
Berne, A., Uijlenhoet, R., and Troch, P. A.: Similarity analysis of subsurface flow response of hillslopes with complex geometry, Water Resour. Res., 41, W09410, https://doi.org/10.1029/2004WR003629, 2005. 
Beven, K. J.: Uniqueness of place and process representations in hydrological modelling, Hydrol. Earth Syst. Sci., 4, 203–213, https://doi.org/10.5194/hess-4-203-2000, 2000. 
Download
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.