Articles | Volume 26, issue 14
https://doi.org/10.5194/hess-26-3805-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-3805-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2022
Research article |
| 19 Jul 2022
| 19 Jul 2022
On the similarity of hillslope hydrologic function: a clustering approach based on groundwater changes
Fadji Z. Maina
CORRESPONDING AUTHOR
Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M. S. 74R-316C, Berkeley, CA 94704, USA
now at: NASA Goddard Space Flight Center, Hydrological Sciences Laboratory, 8800 Greenbelt Rd, Greenbelt, 20771 MD, USA
Haruko M. Wainwright
Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M. S. 74R-316C, Berkeley, CA 94704, USA
Peter James Dennedy-Frank
Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M. S. 74R-316C, Berkeley, CA 94704, USA
Erica R. Siirila-Woodburn
Energy Geosciences Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, M. S. 74R-316C, Berkeley, CA 94704, USA
Related authors
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
Revised manuscript under review for HESS
Short summary
Short summary
To manage Earth's water resources effectively amid climate change, it's crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
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
Short summary
In this work, we assess the effects of end-of-century extreme dry and wet conditions on the hydrology of California. Our results, derived from cutting-edge and high-resolution climate and hydrologic models, highlight that (1) water storage will be larger and increase earlier in the year, yet the summer streamflow will decrease as a result of high evapotranspiration rates, and that (2) groundwater and lower-order streams are very sensitive to decreases in snowmelt and higher evapotranspiration.
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
Revised manuscript under review for HESS
Short summary
Short summary
To manage Earth's water resources effectively amid climate change, it's crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
Zexuan Xu, Erica R. Siirila-Woodburn, Alan M. Rhoades, and Daniel Feldman
Hydrol. Earth Syst. Sci., 27, 1771–1789, https://doi.org/10.5194/hess-27-1771-2023, https://doi.org/10.5194/hess-27-1771-2023, 2023
Short summary
Short summary
The goal of this study is to understand the uncertainties of different modeling configurations for simulating hydroclimate responses in the mountainous watershed. We run a group of climate models with various configurations and evaluate them against various reference datasets. This paper integrates a climate model and a hydrology model to have a full understanding of the atmospheric-through-bedrock hydrological processes.
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
Short summary
In this work, we assess the effects of end-of-century extreme dry and wet conditions on the hydrology of California. Our results, derived from cutting-edge and high-resolution climate and hydrologic models, highlight that (1) water storage will be larger and increase earlier in the year, yet the summer streamflow will decrease as a result of high evapotranspiration rates, and that (2) groundwater and lower-order streams are very sensitive to decreases in snowmelt and higher evapotranspiration.
Zexuan Xu, Rebecca Serata, Haruko Wainwright, Miles Denham, Sergi Molins, Hansell Gonzalez-Raymat, Konstantin Lipnikov, J. David Moulton, and Carol Eddy-Dilek
Hydrol. Earth Syst. Sci., 26, 755–773, https://doi.org/10.5194/hess-26-755-2022, https://doi.org/10.5194/hess-26-755-2022, 2022
Short summary
Short summary
Climate change could change the groundwater system and threaten water supply. To quantitatively evaluate its impact on water quality, numerical simulations with chemical and reaction processes are required. With the climate projection dataset, we used the newly developed hydrological and chemical model to investigate the movement of contaminants and assist the management of contamination sites.
Haruko M. Wainwright, Sebastian Uhlemann, Maya Franklin, Nicola Falco, Nicholas J. Bouskill, Michelle E. Newcomer, Baptiste Dafflon, Erica R. Siirila-Woodburn, Burke J. Minsley, Kenneth H. Williams, and Susan S. Hubbard
Hydrol. Earth Syst. Sci., 26, 429–444, https://doi.org/10.5194/hess-26-429-2022, https://doi.org/10.5194/hess-26-429-2022, 2022
Short summary
Short summary
This paper has developed a tractable approach for characterizing watershed heterogeneity and its relationship with key functions such as ecosystem sensitivity to droughts and nitrogen export. We have applied clustering methods to classify hillslopes into
watershed zonesthat have distinct distributions of bedrock-to-canopy properties as well as key functions. This is a powerful approach for guiding watershed experiments and sampling as well as informing hydrological and biogeochemical models.
Qina Yan, Haruko Wainwright, Baptiste Dafflon, Sebastian Uhlemann, Carl I. Steefel, Nicola Falco, Jeffrey Kwang, and Susan S. Hubbard
Earth Surf. Dynam., 9, 1347–1361, https://doi.org/10.5194/esurf-9-1347-2021, https://doi.org/10.5194/esurf-9-1347-2021, 2021
Short summary
Short summary
We develop a hybrid model to estimate the spatial distribution of the thickness of the soil layer, which also provides estimations of soil transport and soil production rates. We apply this model to two examples of hillslopes in the East River watershed in Colorado and validate the model. The results show that the north-facing (NF) hillslope has a deeper soil layer than the south-facing (SF) hillslope and that the hybrid model provides better accuracy than a machine-learning model.
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.
Beven, K. J. and Kirkby, M. J.:
A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant, Hydrol. Sci. B., 24, 43–69, https://doi.org/10.1080/02626667909491834, 1979.
Bormann, H.:
Towards a hydrologically motivated soil texture classification, Geoderma, 157, 142–153, https://doi.org/10.1016/j.geoderma.2010.04.005, 2010.
Bosch, J. M. and Hewlett, J. D.:
A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration, J. Hydrol., 55, 3–23, https://doi.org/10.1016/0022-1694(82)90117-2, 1982.
Brown, A. E., Zhang, L., McMahon, T. A., Western, A. W., and Vertessy, R. A.:
A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation, J. Hydrol., 310, 28–61, https://doi.org/10.1016/j.jhydrol.2004.12.010, 2005.
Brunner, P. and Simmons, C. T.:
HydroGeoSphere: A Fully Integrated, Physically Based Hydrological Model, Groundwater, 50, 170–176, https://doi.org/10.1111/j.1745-6584.2011.00882.x, 2012.
Carrillo, G., Troch, P. A., Sivapalan, M., Wagener, T., Harman, C., and Sawicz, K.:
Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient, Hydrol. Earth Syst. Sci., 15, 3411–3430, https://doi.org/10.5194/hess-15-3411-2011, 2011.
Carroll, R. W. H., Bearup, L. A., Brown, W., Dong, W., Bill, M., and Willlams, K. H.:
Factors controlling seasonal groundwater and solute flux from snow-dominated basins, Hydrol. Process., 32, 2187–2202, https://doi.org/10.1002/hyp.13151, 2018.
CGIAR-CSI:
Global Aridity Index and Potential Evapotranspiration Climate Database v2, https://cgiarcsi.community/2019/01/24/global-aridity-index-and-potential-evapotranspiration-climate-database-v2/ (last access: 22 August 2020) 2019.
Chadwick, K. D., Brodrick, P. G., Grant, K., Goulden, T., Henderson, A., Falco, N., Wainwright, H., Williams, K. H., Bill, M., Breckheimer, I., Brodie, E. L., Steltzer, H., Williams, C. F. R., Blonder, B., Chen, J., Dafflon, B., Damerow, J., Hancher, M., Khurram, A., Lamb, J., Lawrence, C. R., McCormick, M., Musinsky, J., Pierce, S., Polussa, A., Hastings Porro, M., Scott, A., Singh, H. W., Sorensen, P. O., Varadharajan, C., Whitney, B., and Maher, K.: Integrating airborne remote sensing and field campaigns for ecology and earth system science, Methods Ecol. Evol., 11, 1492–1508, https://doi.org/10.1111/2041-210x.13463, 2020.
Chaney, N. W., Van Huijgevoort, M. H. J., Shevliakova, E., Malyshev, S., Milly, P. C. D., Gauthier, P. P. G., and Sulman, B. N.:
Harnessing big data to rethink land heterogeneity in Earth system models, Hydrol. Earth Syst. Sci., 22, 3311–3330, https://doi.org/10.5194/hess-22-3311-2018, 2018.
Condon, L. E., Maxwell, R. M., and Gangopadhyay, S.:
The impact of subsurface conceptualization on land energy fluxes, Adv. Water Resour., 60, 188–203, https://doi.org/10.1016/j.advwatres.2013.08.001, 2013.
Coon, E. T., David Moulton, J., and Painter, S. L.: Managing complexity in simulations of land surface and near-surface processes, Environ. Modell. Softw., 78, 134–149, https://doi.org/10.1016/j.envsoft.2015.12.017, 2016.
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Meng, J.: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res.-Atmos., 108, 8842, https://doi.org/10.1029/2002JD003118, 2003.
Dai, Y., Zeng, X., Dickinson, R. E., Baker, I., Bonan, G. B., Bosilovich, M. G., Denning, A. S., Dirmeyer, P. A., Houser, P. R., Niu, G., Oleson, K. W., Schlosser, C. A., and Yang, Z.-L.: The Common Land Model, B. Am. Meteorol. Soc., 84, 1013–1024, https://doi.org/10.1175/BAMS-84-8-1013, 2003.
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008.
Devadoss, J., Falco, N., Dafflon, B., Wu, Y., Franklin, M., Hermes, A., Hinckley, E.-L. S., and Wainwright, H.: Remote Sensing-Informed Zonation for Understanding Snow, Plant and Soil Moisture Dynamics within a Mountain Ecosystem, Remote Sens.-Basel, 12, 2733, https://doi.org/10.3390/rs12172733, 2020.
ESS-DIVE: About ESS-DIVE, ESS-DIVE, https://ess-dive.lbl.gov, last access: 5 July 2022.
Falco, N., Balde, A., Breckheimer, I., Brodie, E., Brodrick, P. G., Chadwick, K. D., Chen, J., Dafflon, B., Henderson, A., Lamb, J., Maher, K., Kueppers, L., Steltzer, H., Wainwright, H., Williams, K., and Hubbard, S. S.: Plant species distribution within the Upper Colorado River Basin estimated by using hyperspectral and lidar airborne data, Watershed Function SFA, ESS-DIVE repository [data set], https://doi.org/10.15485/1602034, 2020.
Fan, Y., Clark, M., Lawrence, D. M., Swenson, S., Band, L. E., Brantley, S. L., Brooks, P. D., Dietrich, W. E., Flores, A., Grant, G., Kirchner, J. W., Mackay, D. S., McDonnell, J. J., Milly, P. C. D., Sullivan, P. L., Tague, C., Ajami, H., Chaney, N., Hartmann, A., Hazenberg, P., McNamara, J., Pelletier, J., Perket, J., Rouholahnejad‐Freund, E., Wagener, T., Zeng, X., Beighley, E., Buzan, J., Huang, M., Livneh, B., Mohanty, B. P., Nijssen, B., Safeeq, M., Shen, C., Verseveld, W. van, Volk, J., and Yamazaki, D.: Hillslope Hydrology in Global Change Research and Earth System Modeling, Water Resour. Res., 55, 1737–1772, https://doi.org/10.1029/2018WR023903, 2019.
Ferguson, I. M. and Maxwell, R. M.: Role of groundwater in watershed response and land surface feedbacks under climate change, Water Resour. Res., 46, W00F02, https://doi.org/10.1029/2009WR008616, 2010.
Foster, L. M. and Maxwell, R. M.: Sensitivity analysis of hydraulic conductivity and Manning's n parameters lead to new method to scale effective hydraulic conductivity across model resolutions, Hydrol. Process., 33, 332–349, https://doi.org/10.1002/hyp.13327, 2019.
Goulden, T., Hass, B., Brodie, E., Chadwick, K. D., Falco, N., Maher, K., Wainwright, H., and Williams, K.: NEON AOP Survey of Upper East River CO Watersheds: LAZ Files, LiDAR Surface Elevation, Terrain Elevation, and Canopy Height Rasters, Watershed Function SFA, ESS-DIVE repository [data set], https://doi.org/10.15485/1617203, 2020.
Grabs, T., Seibert, J., Bishop, K., and Laudon, H.:
Modeling spatial patterns of saturated areas: A comparison of the topographic wetness index and a dynamic distributed model, J. Hydrol., 373, 15–23, https://doi.org/10.1016/j.jhydrol.2009.03.031, 2009.
Harman, C. and Sivapalan, M.: A similarity framework to assess controls on shallow subsurface flow dynamics in hillslopes, Water Resour. Res., 45, W01417, https://doi.org/10.1029/2008WR007067, 2009.
Hjerdt, K. N., McDonnell, J. J., Seibert, J., and Rodhe, A.: A new topographic index to quantify downslope controls on local drainage, Water Resour. Res., 40, W05602, https://doi.org/10.1029/2004WR003130, 2004.
Hubbard, S. S., Williams, K. H., Agarwal, D., Banfield, J., Beller, H., Bouskill, N., Brodie, E., Carroll, R., Dafflon, B., Dwivedi, D., Falco, N., Faybishenko, B., Maxwell, R., Nico, P., Steefel, C., Steltzer, H., Tokunaga, T., Tran, P. A., Wainwright, H., and Varadharajan, C.: The East River, Colorado, Watershed: A Mountainous Community Testbed for Improving Predictive Understanding of Multiscale Hydrological–Biogeochemical Dynamics, Vadose Zone J., 17, 180061, https://doi.org/10.2136/vzj2018.03.0061, 2018.
IGBP:
Global plant database published – IGBP [text], http://www.igbp.net/news/news/news/globalplantdatabasepublished.5.1b8ae20512db692f2a6800014762.html, last access: 17 October 2018.
Jefferson, J. L., Gilbert, J. M., Constantine, P. G., and Maxwell, R. M.:
Active subspaces for sensitivity analysis and dimension reduction of an integrated hydrologic model, Comput. Geosci., 83, 127–138, https://doi.org/10.1016/j.cageo.2015.07.001, 2015.
Kassambara, A.:
Practical guide to cluster analysis in R: Unsupervised machine learning, in: Vol. 1, Sthda, ISBN 13 978-1542462709, 2017.
Loritz, R., Kleidon, A., Jackisch, C., Westhoff, M., Ehret, U., Gupta, H., and Zehe, E.: A topographic index explaining hydrological similarity by accounting for the joint controls of runoff formation, Hydrol. Earth Syst. Sci., 23, 3807–3821, https://doi.org/10.5194/hess-23-3807-2019, 2019.
Lyon, S. W. and Troch, P. A.: Hillslope subsurface flow similarity: Real-world tests of the hillslope Péclet number, Water Resour. Res., 43, W07450, https://doi.org/10.1029/2006WR005323, 2007.
Lyon, S. W. and Troch, P. A.: Development and application of a catchment similarity index for subsurface flow, Water Resour. Res., 46, W03511, https://doi.org/10.1029/2009WR008500, 2010.
Maina, F. Z. and Siirila-Woodburn, E. R.:
The Role of Subsurface Flow on Evapotranspiration: A Global Sensitivity Analysis, Water Resour. Res., 56, e2019WR026612, https://doi.org/10.1029/2019WR026612, 2020.
Maina, F. Z., Siirila-Woodburn, E. R., Newcomer, M., Xu, Z., and Steefel, C.: Determining the impact of a severe dry to wet transition on watershed hydrodynamics in California, USA with an integrated hydrologic model, J. Hydrol., 580, 124358, https://doi.org/10.1016/j.jhydrol.2019.124358, 2020.
Maina, F. Z., Siirila-Woodburn, E. R., and Dennedy-Frank, P. J.: Assessing the impacts of hydrodynamic parameter uncertainties on simulated evapotranspiration in a mountainous watershed, J. Hydrol., 608, 127620, https://doi.org/10.1016/j.jhydrol.2022.127620, 2022.
Maxwell, R. M.:
A terrain-following grid transform and preconditioner for parallel, large-scale, integrated hydrologic modeling, Adv. Water Resour., 53, 109–117, https://doi.org/10.1016/j.advwatres.2012.10.001, 2013.
Maxwell, R. M. and Condon, L. E.: Connections between groundwater flow and transpiration partitioning, Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891, 2016.
Maxwell, R. M. and Miller, N. L.:
Development of a Coupled Land Surface and Groundwater Model, J. Hydrometeorol., 6, 233–247, https://doi.org/10.1175/JHM422.1, 2005.
McDonnell, J. J. and Woods, R.:
On the need for catchment classification, J. Hydrol., 299, 2–3, https://doi.org/10.1016/j.jhydrol.2004.09.003, 2004.
Noël, P., Rousseau, A. N., Paniconi, C., and Nadeau, D. F.:
Algorithm for Delineating and Extracting Hillslopes and Hillslope Width Functions from Gridded Elevation Data, J. Hydrol. Eng., 19, 366–374, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000783, 2014.
Oudin, L., Kay, A., Andréassian, V., and Perrin, C.: Are seemingly physically similar catchments truly hydrologically similar?, Water Resour. Res., 46, W11558, https://doi.org/10.1029/2009WR008887, 2010.
ParFlow: ParFlow hydrologic model, ParFlow, https://parflow.org/#download, last access: 5 July 2022.
Pribulick, C. E., Foster, L. M., Bearup, L. A., Navarre-Sitchler, A. K., Williams, K. H., Carroll, R. W. H., and Maxwell, R. M.:
Contrasting the hydrologic response due to land cover and climate change in a mountain headwaters system, Ecohydrology, 9, 1431–1438, https://doi.org/10.1002/eco.1779, 2016.
Rahman, M., Sulis, M., and Kollet, S. J.:
Evaluating the dual-boundary forcing concept in subsurface–land surface interactions of the hydrological cycle, Hydrol. Process., 30, 1563–1573, https://doi.org/10.1002/hyp.10702, 2016.
Richards, L. A.:
Capillary conduction of liquids through porous medium, J. Appl. Phys., 1, 318–333, https://doi.org/10.1063/1.1745010, 1931.
Ryken, A., Bearup, L. A., Jefferson, J. L., Constantine, P., and Maxwell, R. M.:
Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction, Adv. Water Resour., 135, 103473, https://doi.org/10.1016/j.advwatres.2019.103473, 2020.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.:
Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Schwanghart, W. and Scherler, D.:
Short Communication: TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences, Earth Surf. Dynam., 2, 1–7, https://doi.org/10.5194/esurf-2-1-2014, 2014.
Sivapalan, M., Takeuchi, K., Franks, S. W., Gupta, V. K., Karambiri, H., Lakshmi, V., Liang, X., McDonnell, J. J., Mendiondo, E. M., O'Connell, P. E., Oki, T., Pomeroy, J. W., Schertzer, D., Uhlenbrook, S., and Zehe, E.: IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences, Hydrolog. Sci. J., 48, 857–880, https://doi.org/10.1623/hysj.48.6.857.51421, 2003.
van Genuchten, M. T.:
A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils1, Soil Sci. Soc. Am. J., 44, 892, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.
Wagener, T., Sivapalan, M., Troch, P., and Woods, R.:
CatchmentClassification and Hydrologic Similarity, Geography Compass, 1, 901–931, https://doi.org/10.1111/j.1749-8198.2007.00039.x, 2007.
Wainwright, H. M., Uhlemann, S., Franklin, M., Falco, N., Bouskill, N. J., Newcomer, M. E., Dafflon, B., Siirila-Woodburn, E. R., Minsley, B. J., Williams, K. H., and Hubbard, S. S.: Watershed zonation through hillslope clustering for tractably quantifying above- and below-ground watershed heterogeneity and functions, Hydrol. Earth Syst. Sci., 26, 429–444, https://doi.org/10.5194/hess-26-429-2022, 2022.
Winnick, M. J., Carroll, R. W. H., Williams, K. H., Maxwell, R. M., Dong, W., and Maher, K.: Snowmelt controls on concentration-discharge relationships and the balance of oxidative and acid-base weathering fluxes in an alpine catchment, East River, Colorado, Water Resour. Res., 53, 2507–2523, https://doi.org/10.1002/2016WR019724, 2017.
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.
We propose a hillslope clustering approach based on the seasonal changes in groundwater levels...
