Resolving soil and surface water flux as drivers of pattern formation in Turing models of dryland vegetation: a unified approach

DOI10.1016/J.PHYSD.2020.132695zbMath1481.35366OpenAlexW3080839651MaRDI QIDQ2116274

Eric Siero

Publication date: 16 March 2022

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physd.2020.132695

zbMATH Keywords

model reductionreaction-diffusionself-organizationmodel comparisonminimal modeldesertification

Mathematics Subject Classification ID

Reaction-diffusion equations (35K57) PDEs in connection with biology, chemistry and other natural sciences (35Q92) Ecology (92D40) Second-order parabolic systems (35K40) Mathematical modeling or simulation for problems pertaining to biology (92-10)

Related Items (3)

Continuation and bifurcation in nonlinear PDEs - algorithms, applications, and experiments ⋮ Spatiotemporal dynamic analysis of an extended water-plant model with power exponent plant growth and nonlocal plant loss ⋮ Special issue: Advances in pattern formation

Uses Software

• pde2path

Cites Work

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• Assessing the robustness of spatial pattern sequences in a dryland vegetation model
• Turing instabilities in general systems

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