Quantitative Modeling of Landscape Evolution

Temme, A.J.A.M.; Schoorl, J.M.; Claessens, Lieven; Veldkamp, A.

doi:10.1016/b978-0-12-818234-5.00140-1

Cited by 4 publications

(3 citation statements)

References 190 publications

(148 reference statements)

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“…Landscape evolution modeling (see, e.g., reviews by Bishop, ; Codilean et al, ; Coulthard, ; Martin & Church, ; Pazzaglia, ; Pelletier, ; Temme et al, ; Valters, ; Willgoose, ; Willgoose & Hancock, ) commonly couples together models for surface hydrology, hillslope sediment transport, and erosion by river channels. We call the specific rules used for each of these components process laws .…”

Section: Introductionmentioning

confidence: 99%

Inverting Topography for Landscape Evolution Model Process Representation: 2. Calibration and Validation

et al. 2020

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We present a multimodel analysis for mechanistic hypothesis testing in landscape evolution theory. The study site is a watershed with well-constrained initial and boundary conditions in which a river network locally incised 50 m over the last 13 ka. We calibrate and validate a set of 37 landscape evolution models designed to hierarchically test elements of complexity from four categories: hillslope processes, channel processes, surface hydrology, and representation of geologic materials. Comparison of each model to a base model, which uses stream power channel incision, uniform lithology, hillslope transport by linear diffusion, and surface water discharge proportional to drainage area, serves as a formal test of which elements of complexity improve model performance. Model fit is assessed using an objective function based on a direct difference between observed and simulated modern topography. A hybrid optimization scheme identifies optimal parameters and uncertainty. Multimodel analysis determines which elements of complexity improve simulation performance. Validation tests which model improvements persist when models are applied to an independent watershed. The three most important model elements are (1) spatial variation in lithology (differentiation between shale and glacial till), (2) a fluvial erosion threshold, and (3) a nonlinear relationship between slope and hillslope sediment flux. Due to nonlinear interactions between model elements, some process representations (e.g., nonlinear hillslopes) only become important when paired with the inclusion of other processes (e.g., erosion thresholds). This emphasizes the need for caution in identifying the minimally sufficient process set. Our approach provides a general framework for hypothesis testing in landscape evolution.

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Section: Introductionmentioning

confidence: 99%

Inverting Topography for Landscape Evolution Model Process Representation: 2. Calibration and Validation

et al. 2020

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“…Tucker and Hancock (2010), Temme et al. (2013) and Nones (2020) for example, describe that also for landscape evolution applications, quasi‐steady and diffusive wave models are often applied. The approach presented here with linear stability analyses and numerical modeling could indicate the applicability range of the simplified landscape evolution models.…”

Section: Discussionmentioning

confidence: 99%

Accuracy Assessment of Numerical Morphological Models Based on Reduced Saint‐Venant Equations

Barneveld,

Mosselman,

Chavarrías

et al. 2024

Water Resources Research

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Sustainable river management often requires long‐term morphological simulations. As the future is unknown, uncertainty needs to be accounted for, which may require probabilistic simulations covering a large parameter domain. Even for one‐dimensional models, simulation times can be long. One of the acceleration strategies is simplification of models by neglecting terms in the governing hydrodynamic equations. Examples are the quasi‐steady model and the diffusive wave model, both widely used by scientists and practitioners. Here, we establish under which conditions these simplified models are accurate. Based on results of linear stability analyses of the St. Venant‐Exner equations, we assess migration celerities and damping of infinitesimal, but long riverbed perturbations. We did this for the full dynamic model, that is, no terms neglected, as well as for the simplified models. The accuracy of the simplified models was obtained from comparison between the characteristics of the riverbed perturbations for simplified models and the full dynamic model. We executed a spatial‐mode and a temporal‐mode linear analysis and compared the results with numerical modeling results for the full dynamic and simplified models, for very small and large bed waves. The numerical results match best with the temporal‐mode linear analysis. We show that the quasi‐steady model is highly accurate for Froude numbers up to 0.7, probably even for long river reaches with large flood wave damping. Although the diffusive wave model accurately predicts flood wave migration and damping, key morphological metrics deviate more than 5% (10%) from the full dynamic model when Froude numbers exceed 0.2 (0.3).

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“…Each metric focuses on specific attributes of landscape evolution, from the quantification of sediment transport to the replication of hydrological responses under varying climatic conditions. Notably, topographic accuracy emerges as a fundamental criterion, as it encapsulates the geomorphological fidelity of model simulations in replicating real-world landscapesTemme and Schoorl (2009).…”

mentioning

confidence: 99%

Introducing Iterative Model Calibration (IMC) v1.0: A Generalizable Framework for Numerical Model Calibration with a CAESAR-Lisflood Case Study

Banerjee,

Nguyen,

Fookes

et al. 2024

Preprint

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Abstract. In geosciences, including hydrology and geomorphology, the reliance on numerical models necessitates the precise calibration of their parameters to effectively translate information from observed to unobserved settings. Traditional calibration techniques, however, are marked by poor generalizability, demanding significant manual labor for data preparation and the calibration process itself. Moreover, the utility of machine learning-based and data-driven approaches is curtailed by the requirement for the numerical model to be differentiable for optimization purposes, which challenges their generalizability across different models. Furthermore, the potential of freely available geomorphological data remains underexploited in existing methodologies. In response to these challenges, we introduce a generalizable framework for calibrating numerical models, with a particular focus on geomorphological models, named Iterative Model Calibration (IMC). This approach efficiently identifies the optimal set of parameters for a given numerical model through a strategy based on a Gaussian neighborhood algorithm. We demonstrate the efficacy of IMC by applying it to the calibration of the widely-used Landscape Evolution Model, CAESAR-Lisflood, achieving high precision. Once calibrated, this model is capable of generating geomorphic data for both retrospective and prospective analyses at various temporal resolutions, and retrospective and prospective analyses at various temporal resolutions, specifically tailored for scenarios such as gully catchment landscape evolution.

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Quantitative Modeling of Landscape Evolution

Cited by 4 publications

References 190 publications

Inverting Topography for Landscape Evolution Model Process Representation: 2. Calibration and Validation

Inverting Topography for Landscape Evolution Model Process Representation: 2. Calibration and Validation

Accuracy Assessment of Numerical Morphological Models Based on Reduced Saint‐Venant Equations

Introducing Iterative Model Calibration (IMC) v1.0: A Generalizable Framework for Numerical Model Calibration with a CAESAR-Lisflood Case Study

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