Abstract
AbstractInteractions between water flow and patchy vegetation are governing the functioning of many ecosystems. Yet, numerical models that simulate those interactions explicitly at the submeter patch scale to predict geomorphological and ecological consequences at the landscape scale (order of km2) are still very computationally demanding. Here, we present a novel and efficient convolution technique to incorporate biogeomorphic feedbacks in numerical models across multiple spatial scales (from less than 1 m2 to several km2). This new methodology allows for spatially refining coarse‐resolution hydrodynamic simulations of flow velocities (order of m) around fine‐resolution patchy vegetation patterns (order of 10 cm). Although flow perturbations around each vegetation grid cell are not simulated with the same level of accuracy as with more expensive finer‐resolution models, we show that our approach enables spatial refinement of coarse‐resolution hydrodynamic models by resolving efficiently subgrid‐scale flow velocity patterns within and around vegetation patches (mean error, spatial variability, and spatial correlation improved by, respectively, 13%, 66%, and 49% on average in our test cases). We also provide evidence that our approach can substantially improve the representation of important biogeomorphic processes, such as subgrid‐scale effects on net sedimentation rate and habitable surface area for vegetation (respectively 66% and 39% better on average). Finally, we estimate that replacing a fine‐resolution model by a coarser‐resolution model associated with the convolution method could reduce the computational time of real‐life fluctuating flow simulations by several orders of magnitude. This marks an important step forward toward more computationally efficient multiscale biogeomorphic modeling.
Highlights
Over the last two decades, it has become increasingly clear that two-way interactions between biological and physical processes, so-called biogeomorphic feedbacks, play a key role in the formation and evolution of many landscapes (Corenblit et al, 2015; Murray et al, 2008; Reinhardt et al, 2010)
Flow perturbations around each vegetation grid cell are not simulated with the same level of accuracy as with more expensive finer-resolution models, we show that our approach enables spatial refinement of coarse-resolution hydrodynamic models by resolving efficiently subgrid-scale flow velocity patterns within and around vegetation patches
We described a novel methodology to account for subgrid-scale interactions between water flow and patchy vegetation in a relatively coarse-scale hydrodynamic model
Summary
Over the last two decades, it has become increasingly clear that two-way interactions between biological and physical processes, so-called biogeomorphic feedbacks, play a key role in the formation and evolution of many landscapes (Corenblit et al, 2015; Murray et al, 2008; Reinhardt et al, 2010). Vegetation patterns resulting from the interplay between climate, soils, and topography in turn exert important controls on the hydrologic and geomorphic processes that contribute to the formation of landscape morphology over the long term (Istanbulluoglu & Bras, 2005; Nakayama, 2012; Saco et al, 2007) While theory on such biogeomorphic landscape formation is widely adopted, numerical process-based models that integrate patch-scale (order of m2) vegetation-flow interactions with their impact on landscape-scale (order of km2) biomorphodynamics are still computationally very demanding (Le Hir et al, 2007) and limited to small-scale, simple-geometry cases (e.g., Carr et al, 2016; Crosato & Saleh, 2011; de Lima et al, 2015; Larsen et al, 2017; Schwarz et al, 2014; Yamasaki et al, 2019, for the ecosystems mentioned above). Alternative options at the landscape scale include empirical cellular automata (e.g., Fonstad, 2006; Larsen & Harvey, 2011; Murray & Paola, 2003) and process-based models that consider vegetation as a large homogeneous, nonpatchy continuum (e.g., Belliard et al, 2015; D'Alpaos et al, 2007; Sandi et al, 2018, for tidal salt marshes) which fail at representing small-scale feedbacks critical for habitat structure
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