Articles | Volume 12, issue 6
https://doi.org/10.5194/esurf-12-1227-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-12-1227-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Short Communication: Numerically simulated time to steady state is not a reliable measure of landscape response time
Earth and Environmental Sciences Department, Tulane University, New Orleans, LA, USA
Adam M. Forte
Department of Geology & Geophysics, Louisiana State University, Baton Rouge, LA, USA
Katherine R. Barnhart
Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, CO, USA
Department of Geological Sciences, University of Colorado at Boulder, Boulder, CO, USA
now at: US Geological Survey, Geologic Hazards Science Center, Golden, CO, USA
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Debris flows are mixtures of mud and rocks that can travel at high speeds across steep landscapes. Here, we propose a new model to describe how landscapes are shaped by debris flow erosion over long timescales. Model results demonstrate that the shapes of channel profiles are sensitive to uplift rate, meaning that it may be possible to use topographic data from steep channel networks to infer how erosion rates vary across a landscape.
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Cited articles
Adams, B. A., Whipple, K. X., Forte, A. M., Heimsath, A. M., and Hodges, K. V.: Climate controls on erosion in tectonically active landscapes, Sci. Adv., 6, eaaz3166, https://doi.org/10.1126/sciadv.aaz3166, 2020. a
Allen, P. A. and Densmore, A.: Sediment flux from an uplifting fault block, Basin Res., 12, 367–380, 2000. a
Anders, A. M., Roe, G. H., Montgomery, D. R., and Hallet, B.: Influence of precipitation phase on the form of mountain ranges, Geology, 36, 479, https://doi.org/10.1130/G24821A.1, 2008. a, b
Armitage, J. J., Duller, R. A., Whittaker, A. C., and Allen, P. A.: Transformation of tectonic and climatic signals from source to sedimentary archive, Nat. Geosci., 4, 231–235, 2011. a
Armitage, J. J., Whittaker, A. C., Zakari, M., and Campforts, B.: Numerical modelling of landscape and sediment flux response to precipitation rate change, Earth Surf. Dynam., 6, 77–99, https://doi.org/10.5194/esurf-6-77-2018, 2018. a
Attal, M., Tucker, G., Whittaker, A. C., Cowie, P., and Roberts, G. P.: Modeling fluvial incision and transient landscape evolution: Influence of dynamic channel adjustment, J. Geophys. Res.-Earth, 113, F03013, https://doi.org/10.1029/2007JF000893, 2008. a, b
Attal, M., Cowie, P. A., Whittaker, A. C., Hobley, D., Tucker, G. E., and Roberts, G. P.: Testing fluvial erosion models using the transient response of bedrock rivers to tectonic forcing in the Apennines, Italy, J. Geophys. Res.-Earth, 116, F02005, https://doi.org/10.1029/2010JF001875, 2011. a
Barnhart, K. R., Hutton, E. W. H., Tucker, G. E., Gasparini, N. M., Istanbulluoglu, E., Hobley, D. E. J., Lyons, N. J., Mouchene, M., Nudurupati, S. S., Adams, J. M., and Bandaragoda, C.: Short communication: Landlab v2.0: a software package for Earth surface dynamics, Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, 2020a. a
Barnhart, K. R., Tucker, G. E., Doty, S. G., Shobe, C. M., Glade, R. C., Rossi, M. W., and Hill, M. C.: Inverting Topography for Landscape Evolution Model Process Representation: 1. Conceptualization and Sensitivity Analysis, J. Geophys. Res.-Earth, 125, e2018JF004961, https://doi.org/10.1029/2018JF004961, 2020b. a
Barnhart, K. R., Tucker, G. E., Doty, S. G., Shobe, C. M., Glade, R. C., Rossi, M. W., and Hill, M. C.: Inverting Topography for Landscape Evolution Model Process Representation: 2. Calibration and Validation, J. Geophys. Res.-Earth, 125, e2018JF004963, https://doi.org/10.1029/2018JF004963, 2020c. a
Beeson, H. W., McCoy, S. W., and Keen-Zebert, A.: Geometric disequilibrium of river basins produces long-lived transient landscapes, Earth Planet. Sc. Lett., 475, 34–43, 2017. a
Braun, J. and Deal, E.: Implicit algorithm for threshold Stream Power Incision Model, J. Geophys. Res.-Earth, 128, e2023JF007140, https://doi.org/10.1029/2023JF007140, 2023. a
Brocard, G. Y., Willenbring, J. K., Miller, T. E., and Scatena, F. N.: Relict landscape resistance to dissection by upstream migrating knickpoints, J. Geophys. Res.-Earth, 121, 1182–1203, 2016. a
Campforts, B. and Govers, G.: Keeping the edge: A numerical method that avoids knickpoing smearing when solving the stream power law, J. Geophys. Res.-Earth, 120, 1189–1205, https://doi.org/10.1002/2014JF003376, 2015. a
Campforts, B., Schwanghart, W., and Govers, G.: Accurate simulation of transient landscape evolution by eliminating numerical diffusion: the TTLEM 1.0 model, Earth Surf. Dynam., 5, 47–66, https://doi.org/10.5194/esurf-5-47-2017, 2017 (code available at: https://github.com/wschwanghart/topotoolbox, last access: 28 October 2024). a, b, c
Campforts, B., Shobe, C. M., Overeem, I., and Tucker, G. E.: The art of landslides: How stochastic mass wasting shapes topography and influences landscape dynamics, J. Geophys. Res.-Earth, 127, e2022JF006745, https://doi.org/10.1029/2022JF006745, 2022. a, b
Carretier, S., Martinod, P., Reich, M., and Godderis, Y.: Modelling sediment clasts transport during landscape evolution, Earth Surf. Dynam., 4, 237–251, https://doi.org/10.5194/esurf-4-237-2016, 2016. a
Castelltort, S. and Van Den Driessche, J.: How plausible are high-frequency sediment supply-driven cycles in the stratigraphic record?, Sediment. Geol., 157, 3–13, 2003. a
Croissant, T. and Braun, J.: Constraining the stream power law: a novel approach combining a landscape evolution model and an inversion method, Earth Surf. Dynam., 2, 155–166, https://doi.org/10.5194/esurf-2-155-2014, 2014. a
Davy, P. and Lague, D.: Fluvial erosion/transport equation of landscape evolution models revisited, J. Geophys. Res.-Earth, 114, F03007, https://doi.org/10.1029/2008JF001146, 2009. a
Densmore, A. L.: Footwall topographic development during continental extension, J. Geophys. Res., 109, F03001, https://doi.org/10.1029/2003JF000115, 2004. a
Densmore, A. L., Allen, P. A., and Simpson, G.: Development and response of a coupled catchment fan system under changing tectonic and climatic forcing, J. Geophys. Res.-Earth, 112, F01002, hhttps://doi.org/10.1029/2006JF000474, 2007. a
Fernandes, N. F. and Dietrich, W. E.: Hillslope evolution by diffusive processes: The timescale for equilibrium adjustments, Water Resour. Res., 33, 1307–1318, 1997. a
Ferrier, K. L., Huppert, K. L., and Perron, J. T.: Climatic control of bedrock river incision, Nature, 496, 206–209, 2013. a
Forte, A. M. and Whipple, K. X.: Criteria and tools for determining drainage divide stability, Earth Planet. Sc. Lett., 493, 102–117, https://doi.org/10.1016/j.epsl.2018.04.026, 2018. a
Forzoni, A., Storms, J. E., Whittaker, A. C., and de Jager, G.: Delayed delivery from the sediment factory: Modeling the impact of catchment response time to tectonics on sediment flux and fluvio-deltaic stratigraphy, Earth Surf. Proc. Land., 39, 689–704, 2014. a
Gasparini, N. M., Tucker, G. E., and Bras, R. L.: Network-scale dynamics of grain-size sorting: Implications for downstream fining, stream profile concavity, and drainage basin morphology, Earth Surf. Proc. Land., 29, 401–421, 2004. a
Gasparini, N. M., Whipple, K. X., and Bras, R. L.: Predictions of steady state and transient landscape morphology using sediment-flux-dependent river incision models, J. Geophys. Res.-Earth, 112, F03S09, https://doi.org/10.1029/2006JF000567, 2007. a, b
Gasparini, N., Forte, A., and Barnhart, K.: Input files and codes for Gasparini, Forte, and Barnhart, ESURF, 2024 [Data set], Zenodo [data set], https://doi.org/10.5281/zenodo.13984467, 2024. a
Godard, V., Tucker, G. E., Burch Fisher, G., Burbank, D. W., and Bookhagen, B.: Frequency-dependent landscape response to climatic forcing, Geophys. Res. Lett., 40, 859–863, 2013. a
Goren, L.: A theoretical model for fluvial channel response time during time‐dependent climatic and tectonic forcing and its inverse applications, Geophys. Res. Lett., 43, 10753–10763, https://doi.org/10.1002/2016GL070451, 2016. a
Goren, L., Willett, S. D., Herman, F., and Braun, J.: Coupled numerical–analytical approach to landscape evolution modeling, Earth Surf. Proc. Land., 39, 522–545, 2014. a
Hack, J. T.: Studies of longitudinal stream profiles in Virginia and Maryland, in: vol. 294, US Government Printing Office, https://books.google.com/books?hl=en&lr=&id=BMHMKaKYdl0C&oi=fnd&pg=PA45&dq=Studies+of+longitudinal+stream+profiles+in+Virginia+and+Maryland&ots=wbRlAYT9ho&sig=2E3Y8Jfr-UtrVtZpJ6EO3qkEjBo#v=onepage&q=Studies of longitudinal stream profiles in Virginia and Maryland&f=false (last access: 24 October 2024), 1957. a
Han, J., Gasparini, N. M., Johnson, J. P., and Murphy, B. P.: Modeling the influence of rainfall gradients on discharge, bedrock erodibility, and river profile evolution, with application to the Big Island, Hawai'i, J. Geophys. Res.-Earth, 119, 1418–1440, 2014. a
Hilley, G., Strecker, M. R., and Ramos, V.: Growth and erosion of fold-and-thrust belts with an application to the Aconcagua fold-and-thrust belt, Argentina, J. Geophys. Res.-Solid, 109, B01410, https://doi.org/10.1029/2002JB002282, 2004. a
Hobley, D. E. J., Adams, J. M., Nudurupati, S. S., Hutton, E. W. H., Gasparini, N. M., Istanbulluoglu, E., and Tucker, G. E.: Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, 2017. a
Howard, A. D.: A detachment-limited model of drainage basin evolution, Water Resour. Res., 30, 2261–2285, https://doi.org/10.1029/94WR00757, 1994. a, b
Hurst, M. D., Grieve, S. W., Clubb, F. J., and Mudd, S. M.: Detection of channel-hillslope coupling along a tectonic gradient, Earth Planet. Sc. Lett., 522, 30–39, https://doi.org/10.1016/j.epsl.2019.06.018, 2019. a
Hutton, E., Barnhart, K., Hobley, D., Tucker, G., Nudurupati, S., Adams, J., Gasparini, N., Shobe, C., Strauch, R., Knuth, J., Mouchene, M., Lyons, N., Litwin, D., Glade, R., Giuseppecipolla95, Manaster, A., Abby, L., Thyng, K., and Rengers, F.: landlab [Computer software], Zenodo [code], https://doi.org/10.5281/zenodo.595872, 2020. a
Istanbulluoglu, E. and Bras, R. L.: Vegetation-modulated landscape evolution: Effects of vegetation on landscape processes, drainage density, and topography, J. Geophys. Res.-Earth, 110, F02012, https://doi.org/10.1029/2004JF000249, 2005. a
Kirby, E. and Whipple, K. X.: Expression of active tectonics in erosional landscapes, J. Struct. Geol., 44, 54–75, 2012. a
Kwang, J. and Parker, G.: Extreme memory of initial conditions in numerical landscape evolution models, Geophys. Res. Lett., 46, 6563–6573, 2019. a
Kwang, J. S. and Parker, G.: Landscape evolution models using the stream power incision model show unrealistic behavior when equals 0.5, Earth Surf. Dynam., 5, 807–820, https://doi.org/10.5194/esurf-5-807-2017, 2017. a
Kwang, J. S., Langston, A. L., and Parker, G.: The role of lateral erosion in the evolution of nondendritic drainage networks to dendricity and the persistence of dynamic networks, P. Natl. Acad. Sci. USA, 118, e2015770118, https://doi.org/10.1073/pnas.2015770118, 2021. a
Lague, D.: The stream power river incision model: evidence, theory and beyond, Earth Surf. Proc. Land., 39, 38–61, https://doi.org/10.1002/esp.3462, 2014. a
Lyons, N. J., Val, P., Albert, J. S., Willenbring, J. K., and Gasparini, N. M.: Topographic controls on divide migration, stream capture, and diversification in riverine life, Earth Surf. Dynam. 8, 893–912, https://doi.org/10.5194/esurf-8-893-2020, 2020. a, b
Mackey, B. H., Scheingross, J. S., Lamb, M. P., and Farley, K. A.: Knickpoint formation, rapid propagation, and landscape response following coastal cliff retreat at the last interglacial sea-level highstand: Kaua'i, Hawai'i, Bulletin, 126, 925–942, 2014. a
O'Hara, D., Karlstrom, L., and Roering, J. J.: Distributed landscape response to localized uplift and the fragility of steady states, Earth Planet. Sc. Lett., 506, 243–254, 2019. a
Refice, A., Giachetta, E., and Capolongo, D.: SIGNUM: A Matlab, TIN-based landscape evolution model, Comput. Geosci., 45, 293–303, 2012. a
Roe, G. H., Whipple, K. X., and Fletcher, J. K.: Feedbacks among climate, erosion, and tectonics in a critical wedge orogen, Am. J. Sci., 308, 815–842, 2008. a
Roering, J. J.: How well can hillslope evolution models “explain” topography? Simulating soil transport and production with high-resolution topographic data, GSA Bull., 120, 1248–1262, https://doi.org/10.1130/B26283.1, 2008. a
Roering, J. J., Kirchner, J. W., and Dietrich, W. E.: Hillslope evolution by nonlinear, slope-dependent transport: Steady state morphology and equilibrium adjustment timescales, J. Geophys. Res.-Solid, 106, 16499–16513, 2001. a
Romans, B. W., Castelltort, S., Covault, J. A., Fildani, A., and Walsh, J.: Environmental signal propagation in sedimentary systems across timescales, Earth-Sci. Rev., 153, 7–29, 2016. a
Rosenbloom, N. and Anderson, R. S.: Hillslope and channel evolution in a marine terraced landscape, Santa Cruz, California, J. Geophys. Res., 99, 14013–14029, 1994. a
Salles, T.: eSCAPE: Regional to Global Scale Landscape Evolution Model v2.0, Geosci. Model Dev., 12, 4165–4184, https://doi.org/10.5194/gmd-12-4165-2019, 2019. a
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. a
Shelef, E. and Hilley, G. E.: A unified framework for modeling landscape evolution by discrete flows, J. Geophys. Res.-Earth, 121, 816–842, 2016. a
Shobe, C. M., Tucker, G. E., and Barnhart, K. R.: The SPACE 1.0 model: a Landlab component for 2-D calculation of sediment transport, bedrock erosion, and landscape evolution, Geosci. Model Dev., 10, 4577–4604, https://doi.org/10.5194/gmd-10-4577-2017, 2017. a
Shobe, C. M., Tucker, G. E., and Rossi, M. W.: Variable-Threshold Behavior in Rivers Arising From Hillslope-Derived Blocks, J. Geophys. Res.-Earth, 123, 1931–1957, https://doi.org/10.1029/2017JF004575, 2018. a
Simpson, G. and Castelltort, S.: Model shows that rivers transmit high-frequency climate cycles to the sedimentary record, Geology, 40, 1131–1134, 2012. a
Snyder, N. P., Whipple, K. X., Tucker, G. E., and Merrits, D. J.: Landscape response to tectonic forcing: Digital elevation model analysis of stream profiles in the Mendocino triple junction region, northern California, Geol. Soc. Am. Bull., 112, 1250–1263, 2000. a
Stark, C. P. and Stark, G. J.: A channelization model of landscape evolution, Am. J. Sci., 301, 486–512, 2001. a
Steer, P.: Short communication: Analytical models for 2D landscape evolution, Earth Surf. Dynam., 9, 1239–1250, https://doi.org/10.5194/esurf-9-1239-2021, 2021. a
Stolar, D., Roe, G., and Willett, S.: Controls on the patterns of topography and erosion rate in a critical orogen, J. Geophys. Res.-Earth, 112, F04002, https://doi.org/10.1029/2006JF000713, 2007. a
Stolar, D. B., Willett, S. D., and Roe, G. H.: Climatic and tectonic forcing of a critical orogen, in: Tectonics, Climate, and Landscape Evolution, Geological Society of America, https://doi.org/10.1130/2006.2398(14), 2006. a
Straub, K. M., Duller, R. A., Foreman, B. Z., and Hajek, E. A.: Buffered, incomplete, and shredded: The challenges of reading an imperfect stratigraphic record, J. Geophys. Res.-Earth Surface, 125, e2019JF005079, https://doi.org/10.1029/2019JF005079, 2020. a
Tarboton, D. G.: A new method for the determination of flow directions and upslope areas in grid digital elevation models, Water Resour. Res., 33, 309–319, https://doi.org/10.1029/96wr03137, 1997. a
Theodoratos, N., Seybold, H., and Kirchner, J. W.: Scaling and similarity of a stream-power incision and linear diffusion landscape evolution model, Earth Surf. Dynam., 6, 779–808, https://doi.org/10.5194/esurf-6-779-2018, 2018. a
Tofelde, S., Bernhardt, A., Guerit, L., and Romans, B. W.: Times associated with source-to-sink propagation of environmental signals during landscape transience, Front. Earth Sci., 9, 628315, https://doi.org/10.3389/feart.2021.628315, 2021. a
Tucker, G. and Whipple, K.: Topographic outcomes predicted by stream erosion models: Sensitivity analysis and intermodel comparison, J. Geophys. Res.-Solid, 107, 2179, https://doi.org/10.1029/2001JB000162, 2002. a
Tucker, G. E. and Bras, R. L.: Hillslope processes, drainage density, and landscape morphology, Water Resour. Res., 34, 2751–2764, https://doi.org/10.1029/98WR01474, 1998. a, b
Tucker, G. E., Gasparini, N. M., Bras, R. L., and Lancaster, S. L.: A 3D Computer Simulation Model of Drainage Basin and Floodplain Evolution: Theory and Applications, Technical report prepared for U.S. Army Corps of Engineers Construction Engineering Research Laboratory, 1999. a
Tucker, G. E., Lancaster, S. T., Gasparini, N. M., and Bras, R. L.: The channel-hillslope intergrated landscape development model (CHILD), in: Landscape erosion and evolution modeling, edited by: Harmon, R. S. and Doe, W. W., Springer, New York, 349–388, https://doi.org/10.1007/978-1-4615-0575-4_12, 2001a (code available at: https://github.com/childmodel/child, last access: 28 October 2024). a, b
Tucker, G. E., Lancaster, S. T., Gasparini, N. M., Bras, R. L., and Rybarczyk, S. M.: An object-oriened framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks, Comput. Geosci., 27, 959–973, 2001b. a
Tucker, G. E., Hutton, E. W. H., Piper, M. D., Campforts, B., Gan, T., Barnhart, K. R., Kettner, A. J., Overeem, I., Peckham, S. D., McCready, L., and Syvitski, J.: CSDMS: a community platform for numerical modeling of Earth surface processes, Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, 2022. a
Val, P., Lyons, N. J., Gasparini, N., Willenbring, J. K., and Albert, J. S.: Landscape evolution as a diversification driver in freshwater fishes, Front. Ecol. Evol., 9, 788328, https://doi.org/10.3389/fevo.2021.788328, 2022. a
Ward, D. J. and Galewsky, J.: Exploring landscape sensitivity to the Pacific Trade Wind Inversion on the subsiding island of Hawaii, J. Geophys. Res.-Earth, 119, 2048–2069, 2014. a
Whipple, K. X. and Meade, B.: Controls on the strength of coupling among climate, erosion, and deformation in two-sided, frictional orogenic wedges at steady state, J. Geophys. Res., 109, F01011, https://doi.org/10.1029/2003JF000019, 2004. a
Whipple, K. X. and Meade, B.: Orogen response to changes in climatic and tectonic forcing, Earth Planet. Sc. Lett., 243, 218–228, 2006. a
Whipple, K. X. and Tucker, G. E.: Dynamics of the stream-power river incision model: Implications for height limits of mountain ranges, landscape response timescales, and research needs, J. Geophys. Res.-Solid, 104, 17661–17674, https://doi.org/10.1029/1999JB900120, 1999. a, b, c, d
Whipple, K. X. and Tucker, G. E.: Implications of sediment-flux-dependent river incision models for landscape evolution, J. Geophys. Res.-Solid, 107, ETG 3-1–ETG 3-20, https://doi.org/10.1029/2000JB000044, 2002. a
Whipple, K. X., Forte, A. M., DiBiase, R. A., Gasparini, N. M., and Ouimet, W. B.: Timescales of landscape response to divide migration and drainage capture: Implications for the role of divide mobility in landscape evolution, J. Geophys. Res.-Earth, 122, 248–273, https://doi.org/10.1002/2016JF003973, 2017. a, b, c, d
Whittaker, A. C.: How do landscapes record tectonics and climate, Lithosphere, 4, 160–164, 2012. a
Whittaker, A. C. and Boulton, S. J.: Tectonic and climatic controls on knickpoint retreat rates and landscape response times, J. Geophys. Res.-Earth, 117, F02024, https://doi.org/10.1029/2011JF002157, 2012. a
Willett, S. D. and Brandon, M. T.: On steady states in mountain belts, Geology, 30, 175–178, 2002. a
Willett, S. D., McCoy, S. W., Perron, J. T., Goren, L., and Chen, C.-Y.: Dynamic reorganization of river basins, Science, 343, 1248765, https://doi.org/10.1126/science.1248765, 2014. a
Willgoose, G., Bras, R. L., and Rodriguez-Iturbe, I.: A coupled channel network growth and hillslope evolution model: 1. Theory, Water Resour. Res., 27, 1671–1684, https://doi.org/10.1029/91WR00935, 1991. a
Zhang, Y., Slingerland, R., and Duffy, C.: Fully-coupled hydrologic processes for modeling landscape evolution, Environ. Model. Softw., 82, 89–107, 2016. a
Short summary
The time it takes for a landscape to adjust to new environmental conditions is critical for understanding the impacts of past and future environmental changes. We used different computational models and methods and found that predicted times for a landscape to reach a stable condition vary greatly. Our results illustrate that reporting how timescales are measured is important. Modelers should ensure that the measurement technique addresses the question.
The time it takes for a landscape to adjust to new environmental conditions is critical for...