Skip to main content

Advertisement

Log in

Rise and Fall of Periodic Patterns for a Generalized Klausmeier–Gray–Scott Model

  • Published:
Journal of Nonlinear Science Aims and scope Submit manuscript

Abstract

In this paper we introduce a conceptual model for vegetation patterns that generalizes the Klausmeier model for semi-arid ecosystems on a sloped terrain (Klausmeier in Science 284:1826–1828, 1999). Our model not only incorporates downhill flow, but also linear or nonlinear diffusion for the water component.

To relate the model to observations and simulations in ecology, we first consider the onset of pattern formation through a Turing or a Turing–Hopf bifurcation. We perform a Ginzburg–Landau analysis to study the weakly nonlinear evolution of small amplitude patterns and we show that the Turing/Turing–Hopf bifurcation is supercritical under realistic circumstances.

In the second part we numerically construct Busse balloons to further follow the family of stable spatially periodic (vegetation) patterns. We find that destabilization (and thus desertification) can be caused by three different mechanisms: fold, Hopf and sideband instability, and show that the Hopf instability can no longer occur when the gradient of the domain is above a certain threshold. We encounter a number of intriguing phenomena, such as a ‘Hopf dance’ and a fine structure of sideband instabilities. Finally, we conclude that there exists no decisive qualitative difference between the Busse balloons for the model with standard diffusion and the Busse balloons for the model with nonlinear diffusion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Notes

  1. Notice however the extra b 2 in the coefficient in front of the nonlinear term. In Morgan et al. (2000), b is scaled out of the matrix \(\mathcal{M}_{c}\) in formula (3.24). That is, the matrix \(b\mathcal{M}_{c}\) in Morgan et al. (2000) plays the role of our matrix \(\mathcal{M}_{\mathrm{i}\omega_{c}}(a_{c},0,\mathrm {i}k_{c})\). This is equivalent to scaling \(\mathcal{A}\to b\mathcal{A}\) in (2.47).

  2. Notice that this rescaling of the Klausmeier system can be acquired from Klausmeier’s original nondimensional system (see Klausmeier 1999),

    $$ \everymath{\displaystyle} \left\{ \begin{array}{@{}rcl} u_{T} & = & \nu u_X + a-u - uv^2; \\[1mm] v_{T} & = & \delta^{2\sigma}v_{XX} - mv + u v^2, \end{array} \right. $$
    (2.52)

    by rescaling with \(x=\frac{a^{2}}{\nu}X\), t=a 2 T, v=aV, u=aU, \(A=\frac{1}{a^{2}}\), \(B=\frac{m}{a^{2}}\) and by further introducing \(0<\delta^{\sigma}:=\frac{a}{\nu}\ll1\). (From the estimates for a and ν in Klausmeier (1999), it can be deduced that indeed \(0<\frac{a}{\nu}\ll1\).)

  3. Note that we silently switched back to the original parameters A, B, and C in (1.5). We will comment on the relation between A, B, and C on the one hand and a, b, and c on the other hand in Sect. 3.2.

  4. With slight abuse of terminology, in the context of perturbations of periodic patterns we abbreviate the Turing–Hopf instability to ‘Hopf instability’ in the rest of this paper.

References

  • Amann, H.: Dynamic theory of quasilinear parabolic equations II. Reaction–diffusion systems. Differ. Integral Equ. 3, 13–75 (1990)

    MathSciNet  MATH  Google Scholar 

  • Aranson, I., Kramer, L.: The world of the complex Ginzburg–Landau equation. Rev. Mod. Phys. 74, 187–214 (2002)

    Article  MathSciNet  Google Scholar 

  • Busse, F.H.: Nonlinear properties of thermal convection. Rep. Prog. Phys. 41, 1929–1967 (1978)

    Article  Google Scholar 

  • Chen, W., Ward, M.J.: Oscillatory instabilities and dynamics of multi-spike patterns for the one-dimensional Gray–Scott model. Eur. J. Appl. Math. 20(2), 187–214 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  • Chossat, P., Iooss, G.: The Couette–Taylor problem. In: Mathematical Sciences, vol. 102. Springer, Berlin (1994)

    Google Scholar 

  • Deblauwe, V., Barbier, N., Couteron, P., Lejeune, O., Bogaert, J.: The global biogeography of semi-arid periodic vegetation patterns. Glob. Ecol. Biogeogr. 17(6), 715–723 (2008)

    Article  Google Scholar 

  • Devaney, R.L.: Reversible diffeomorphisms and flows. Trans. Am. Math. Soc. 218, 89–113 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  • Doedel, E.J.: AUTO-07P: Continuation and bifurcation software for ordinary differential equations. http://cmvl.cs.concordia.ca/auto (2007)

  • Doelman, A., Kaper, T.J., Zegeling, P.: Pattern formation in the one-dimensional Gray–Scott model. Nonlinearity 10, 523–563 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  • Doelman, A., Rademacher, J.D.M., van der Stelt, S.: Hopf dances near the tips of Busse balloons. Discrete Contin. Dyn. Syst. 5(1), 61–92 (2012)

    MathSciNet  MATH  Google Scholar 

  • Eckhaus, W.: Asymptotic Analysis of Singular Perturbations. Studies in Mathematics and Its Applications, vol. 9. North-Holland, Amsterdam (1979)

    MATH  Google Scholar 

  • Eckhaus, W., Iooss, G.: Strong selection or rejection of spatially periodic patterns in degenerate bifurcations. Physica D 39, 124–146 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  • Elwell, H.A., Stocking, M.A.: Vegetal cover to estimate soil erosion hazard in Rhodesia. Geoderma 15, 61–70 (1976)

    Article  Google Scholar 

  • Fowler, A.C.: Mathematical Models in the Applied Sciences. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge (1997)

    Google Scholar 

  • Gardner, R.A.: On the structure of the spectra of periodic traveling waves. J. Math. Pures Appl. 72, 415–439 (1993)

    MathSciNet  MATH  Google Scholar 

  • Gardner, R.A.: Spectral analysis of long wavelength periodic waves and applications. J. Reine Angew. Math. 491, 149–181 (1997)

    MathSciNet  MATH  Google Scholar 

  • Gilad, E., von Hardenberg, J., Provenzale, A., Shachak, M., Meron, E.: Ecosystem engineers: from pattern formation to habitat creation. Phys. Rev. Lett. 93(9), 098105 (2004)

    Article  Google Scholar 

  • Henry, D.: Geometric Theory of Semilinear Parabolic Equations. Lecture Notes in Mathematics, vol. 840. Springer, Berlin (1981)

    MATH  Google Scholar 

  • HilleRisLambers, R., Rietkerk, M., van den Bosch, F., Prins, H.H.T., de Kroon, H.: Vegetation pattern formation in semi-arid grazing systems. Ecology 82(1), 50–61 (2001)

    Article  Google Scholar 

  • Kato, H.: Quasi-linear equations of evolution, with applications to partial differential equations. In: Lecture Notes in Mathematics, vol. 448, pp. 25–70. Springer, Berlin (1975)

    Google Scholar 

  • Kealy, B.J., Wollkind, D.J.: A nonlinear stability analysis of vegetative Turing pattern formation for an interaction–diffusion plant-surface water model system in an arid flat environment. Bull. Math. Biol. 74, 803–833 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  • Kelly, R.D., Walker, B.H.: The effects of different forms of land use on the ecology of a semi-arid region in southeastern Rhodesia. J. Ecol. 64, 553–576 (1976)

    Article  Google Scholar 

  • Klausmeier, C.A.: Regular and irregular patterns in semi-arid vegetation. Science 284, 1826–1828 (1999)

    Article  Google Scholar 

  • van de Koppel, J., Rietkerk, M., van Langevelde, F., Kumar, L., Klausmeier, C.A., Frysell, J.M., Hearne, J.W., van Andel, J., de Ridder, N., Skidmore, A., Stroosnijdr, L., Prins, H.H.T.: Spatial heterogeneity and irreversible vegetation change in semiarid grazing systems. Am. Nat. 159(2), 209–218 (2002)

    Article  Google Scholar 

  • Kéfi, S., Rietkerk, M., van Baalen, M., Loreau, M.: Local facilitation, bistability and transitions in arid ecosystems. Theor. Popul. Biol. 71, 367–379 (2007a)

    Article  MATH  Google Scholar 

  • Kéfi, S., Rietkerk, M., Alados, C.L., Pueyo, Y., Papanastasis, V.P., ElAich, A., de Ruiter, P.C.: Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449, 213–217 (2007b)

    Article  Google Scholar 

  • Levefer, R., Lejeune, O.: On the origin of tiger bush. Bull. Math. Biol. 59(2), 263–294 (1997)

    Article  Google Scholar 

  • Macfadyan, W.A.: Soil and vegetation in British Somaliland. Nature 165, 121 (1950a)

    Article  Google Scholar 

  • Macfadyan, W.A.: Vegetation patterns in the semi-desert planes of British Somaliland. Geogr. J. 116, 199–211 (1950b)

    Article  Google Scholar 

  • Matkowsky, B.J., Volpert, V.A.: Stability of plane wave solutions of complex Ginzburg–Landau equations. Q. Appl. Math. 51, 265–281 (1993)

    MathSciNet  MATH  Google Scholar 

  • Mielke, A.: The Ginzburg–Landau equation in its role as modulation equation. In: Fiedler, B. (ed.) Handbook of Dynamical Systems, vol. II, pp. 759–835. Elsevier, Amsterdam (2002)

    Google Scholar 

  • Morgan, D.S., Doelman, A., Kaper, T.J.: Stationary periodic patterns in the 1D Gray–Scott model. Methods Appl. Anal. 7(1), 105–150 (2000)

    MathSciNet  MATH  Google Scholar 

  • Ni, W.-M.: Diffusion, cross-diffusion, and their spike-layer steady states. Not. Am. Math. Soc. 45(1), 9–18 (1998)

    MATH  Google Scholar 

  • Rademacher, J.D.M., Sandstede, B., Scheel, A.: Computing absolute and essential spectra using continuation. Physica D 229(2), 166–183 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  • Rademacher, J.D.M., Scheel, A.: Instabilities of wave trains and Turing patterns in large domains. Int. J. Bifurc. Chaos Appl. Sci. Eng. 17(8), 2679–2691 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  • Rietkerk, M., Ketner, P., Burger, J., Hoorens, B., Olff, H.: Multiscale soil and vegetation patchiness along a gradient of herbivore impact in a semi-arid grazing system in West Africa. Plant Ecol. 148, 207–224 (2000)

    Article  Google Scholar 

  • Rietkerk, M., Boerlijst, M., van Langevelde, F., van de Koppel, H.H.T.J., Kumar, L., Prins, H.H.T., de Roos, A.M.: Self-organization of vegetation in arid ecosystems. Am. Nat. 160(4), 524–530 (2002)

    Article  Google Scholar 

  • Rietkerk, M., Dekker, S.C., Wassen, M.J., Verkroost, A.W.M., Bierkens, M.F.P.: A putative mechanism for bog patterning. Am. Nat. 163(5), 699–708 (2004)

    Article  Google Scholar 

  • Sandstede, B.: Stability of travelling waves. In: Fiedler, B. (ed.) Handbook of Dynamical Systems, vol. II, pp. 983–1055. Elsevier, Amsterdam (2002)

    Google Scholar 

  • Satnoianu, R.A., Menzinger, M.: Non-Turing stationary patterns in flow-distributed oscillators with general diffusion and flow rates. Phys. Rev. E 62(1), 113–119 (2000)

    Article  MathSciNet  Google Scholar 

  • Satnoianu, R.A., Maini, P.K., Menzinger, M.: Parameter space analysis, pattern sensitivity and model comparison for Turing and stationary flow-distributed waves (FDS). Physica D 160, 79–102 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  • Scheel, A.: Radially symmetric patterns of reaction–diffusion systems. Mem. Am. Math. Soc. 165 (2003)

  • Schneider, G.: Nonlinear diffusive stability of spatially periodic solutions—abstract theorem and higher space dimensions. Tohoku Math. Publ. 8, 159–168 (1998)

    Google Scholar 

  • Sherratt, J.A.: An analysis of vegetation stripe formation in semi-arid landscapes. J. Math. Biol. 51, 183–197 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  • Sherratt, J.A., Lord, G.J.: Nonlinear dynamics and pattern bifurcations in a model for vegetation stripes in semi-arid environments. Theor. Popul. Biol. 71, 1–11 (2007)

    Article  MATH  Google Scholar 

  • Sherratt, J.A.: Pattern solutions of the Klausmeier model for banded vegetation in semi-arid environments I. Nonlinearity 23, 2657–2675 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  • Siero, E., Rademacher, J.D.M.: Stability of wavetrains in quasilinear parabolic systems on the real line (2012). In preparation

  • Thiery, J.M., D’Herbes, J.M., Valentin, C.: A model simulating the genesis of banded vegetation patterns in Niger. J. Ecol. 83, 497–507 (1995)

    Article  Google Scholar 

  • White, P.L.: Brousse tigrée patterns in Southern Niger. J. Ecol. 58, 549–553 (1970)

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Max Rietkerk for sharing his insights and the stimulating discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sjors van der Stelt.

Additional information

Communicated by Alan R. Champneys.

Appendix: Derivation of the Ginzburg–Landau Equation

Appendix: Derivation of the Ginzburg–Landau Equation

In this appendix, we outline the derivation of the Ginzburg–Landau equation for the amplitude \(\mathcal{A}\) of the pattern that appears at the Turing–Hopf bifurcation. Each of the four different cases of Fig. 1 can be derived from the expressions given in this appendix by considering either γ=1 or c=0, or both.

In Appendix A.1 we derive the Ginzburg–Landau equation for the special case that γ=1 and \(c=\sqrt{\frac{2}{3}b}\) that was presented in Sect. 2.5.3.

The Ginzburg–Landau Ansatz for patterns that emerge at the Turing–Hopf instability can, for the rescaled GKGS-system (2.38), be written as

(A.1)

where \(\mathcal{A}\) and X ij are functions of ξ=εx and τ=ε 2(xc g t) and c g the group velocity defined by (2.26). Substituting this expansion in the GKGS-system (2.38) and collecting terms of equal powers of ε and the Fourier modes \(\mathrm{e}^{\mathrm{i}(k_{*} x + \omega_{*} t)}\), we derive expressions for X 02,12,22,13 and Y 02,12,22,13 subsequently. Notice that the scaling in (2.38) has the advantage that the terms of order ε 2 only play a role in the equations for X 13 and Y 13.

As mentioned in paragraph 2.4, the solvability condition can be applied to solve an equation of the form

$$ \mathcal{M}_{\mathrm{i}\omega_*}(a_*,k_*,c)x=y. $$
(A.2)

The equations for X 1j ,Y 1j , j=2,3 are of this form, with

(A.3)

We briefly point out the construction of the set of solutions for (A.2). The construction for c=0 differs from that for c≠0.

If c≠0, the matrix in (A.3) has two eigenvalues, λ +=0 and

$$ \lambda_- = -\varGamma k_*^2 - \frac{a_*^2}{b^2} + \mathrm{i}ck_* -k_*^2 + b -\mathrm{i}\omega_*. $$
(A.4)

If \(y\in\operatorname{Rg} \mathcal{M}_{\mathrm{i}\omega_{*}}(a_{*},k_{*},c)\) and \(c\not =0\), the set of solutions to (A.2) is given by

$$x = \frac{1}{\lambda_-}\,y + \ker\,\mathcal{M}_{\mathrm{i}\omega_*}(a_*,k_*,c). $$

On the other hand, if c=0, we know from Proposition 1 that

$$a_*^{\gamma+1} = \gamma gb^{2\gamma+1} \quad\mbox{and}\quad k_*^2 = \frac {1}{2}(1-g). $$

It is then straightforward to show that

(A.5)

and that λ =0 if γ=1. It is also straightforward to show that both columns of \(\mathcal {M}_{\mathrm{i}\omega _{*}}(a_{*},k_{*},0)\) span the range. We call the second column v 1. So if y from (A.2) lies in the range, there exists an α∈ℝ such that y=αv 1. Hence, if c=0, the set of solutions to (A.2) can be presented as

(A.6)

By plugging in the expansion (A.1) into (2.38) one obtains at order \(\mathcal{O}(\varepsilon)\) an equation for (X 02,Y 02)T,

$$ \left( \begin{array}{c} X_{02} \\ Y_{02} \end{array} \right) = \biggl[-2 \frac{b^4}{a_*^3}|\eta_{\gamma,c}|^2 - 8\frac {b^2}{a_*} \operatorname{Re}(\eta_{\gamma,c}) \biggr] \left( \begin{array}{c} 1 \\ 0 \end{array} \right)\bigl|\mathcal{A}^2\bigr|. $$
(A.7)

We use shorthands x 02,y 02 for \(X_{02}=x_{02}|\mathcal{A}|^{2}\), \(Y_{02}=y_{02}|\mathcal{A}|^{2}\). The values for x 02 and y 02 can be read from (A.7). At order \(\mathcal{O}(\varepsilon E)\), we find equations of the form

$$ \mathcal{M}_{\mathrm{i}\omega_*}(a_*,k_*,c) \left( \begin{array}{c} X_{12} \\ Y_{12} \end{array} \right) = \left( \begin{array}{c} x_{12} \\ y_{12} \end{array} \right) \mathcal{A}_{\xi} $$

which can be solved if \((x_{1j},y_{1j})^{\mathrm{T}}\in\operatorname{Rg} \mathcal {M}_{\mathrm{i}\omega _{*}}(a_{*},k_{*},c)\). We find

$$ \begin{array}{@{}rcl} x_{12} & = & [-4b\mathrm{i}\varGamma k_* - 2bc]/\lambda_-; \\[0.2cm] y_{12} & = & [-\eta_{\gamma,c} c_{\mathrm{g}} - 2\mathrm{i}k_*\eta_{\gamma ,c}]/\lambda_-. \end{array} $$
(A.8)

It can be checked that, indeed, \((x_{1j},y_{1j})^{\mathrm{T}}\in\operatorname{Rg} \mathcal{M}_{\mathrm{i}\omega_{*}}(a_{*},k_{*},c)\). At order \(\mathcal{O}(\varepsilon E^{2})\) we find

$$ \left( \begin{array}{c} X_{22} \\ Y_{22} \end{array} \right) = \left( \begin{array}{c} x_{22} \\ y_{22} \end{array} \right)\mathcal{A}^2, $$

with

$$ \everymath{\displaystyle} \begin{array}{@{}rcl} x_{22} & = & \biggl(\frac{b}{a_*} \biggr)^2 \bigl(4k_*^2+2\mathrm{i}\omega_* -b\bigr)y_{22} - \biggl( \frac{b}{a_*} \biggr)^2\biggl(\frac{b^2}{a_*} \eta_{\gamma,c}^2 + 4a_*\eta_{\gamma,c}\biggr) , \\[0.4cm] y_{22} & = & \frac{ \frac{b^2}{a_*}\eta_{\gamma,c}^2 + 4a_*\eta_{\gamma,c} + 8b^2k_*^2\gamma(\gamma-1) (\frac{b^2}{a_*} )^{\gamma-2}}{(\frac{b^2}{a_*} )^2 ( 4k_*^2 + 2\mathrm{i}\omega_* - b ) (-4\varGamma k_*^2 - (\frac{a_*}{b} )^2 + 2\mathrm{i}ck_* ) - 2b} \\[0.5cm] &&{} + \frac{ (\frac{b}{a_*} )^2 (\frac{b^2}{a_*}\eta_{\gamma ,c}^2 + 4a_*\eta_{\gamma,c} ) (-4\varGamma k_*^2 - (\frac{a_*}{b} )^2 + 2\mathrm{i}ck_* ) }{ (\frac{b^2}{a_*} )^2 ( 4k_*^2 + 2\mathrm{i}\omega_* - b ) (-4\varGamma k_*^2 - (\frac{a_*}{b} )^2 + 2\mathrm{i}ck_* ) - 2b}. \end{array} $$
(A.9)

At order ε 2 E, we obtain equations for X 13 and Y 13. These equations can be written as

$$ \mathcal{M}_{\omega_*}(a_*,k_*,c) \left( \begin{array}{c} X_{13} \\ Y_{13} \end{array} \right) = \left( \begin{array}{c} I_1 \\ I_2 \end{array} \right). $$
(A.10)

The right-hand sides I 1,I 2 are built up by several terms. The nonlinear terms from the reaction kinetics generate

We define L tot as the sum of these expressions:

$$ L_{\mathrm{tot}} := \biggl(2\bar{\eta}_{\gamma,c}\frac{b^2}{a_*} + 4a_* \biggr)y_{22} + 2\frac{a_*}{b}(\eta_{\gamma,c} x_{02} + \bar{\eta}_{\gamma,c} x_{22}) + 2b\bigl(2| \eta_{\gamma,c}|^2 + \eta_{\gamma,c}^2\bigr) . $$
(A.11)

The nonlinear terms that appear from working out the nonlinear diffusion terms generate

Based on this we define

We then obtain for the right-hand side of the system

$$ \everymath{\displaystyle} \begin{array}{@{}rcl} I_1 &=& \biggl(-4\frac{a_*}{b}-L_{\mathcal {A},\mathrm{NLD}} \biggr)\mathcal{A}+ (L_{\mathrm{tot}}-L_{\mathrm{NLD}})|\mathcal{A}|^2 \mathcal{A} \\[3mm] &&{}- (cx_{12}+2b\varGamma+2\mathrm{i}k_*\varGamma x_{12}) \mathcal{A}_{\xi\xi} , \\[1mm] I_2 &=& 4\frac{a_*}{b}\mathcal{A}- L_{\mathrm{tot}}|\mathcal {A}|^2\mathcal{A} - ( c_{\mathrm{g}}y_{12}+ \eta_{\gamma,c}+2\mathrm{i}k_* y_{12})\mathcal{A}_{\xi\xi} + \eta_{\gamma,c}\mathcal{A}_{\tau} . \end{array} $$
(A.12)

To derive the Ginzburg–Landau equation, we impose the solvability condition (2.42) to (A.10):

$$ 2bI_2 - \bigl(k_*^2+\mathrm{i}\omega_*-b\bigr)I_1 = 0, $$
(A.13)

and obtain

1.1 A.1 The Special Case that γ=1 and \(c=\sqrt{\frac {2}{3}b}\)

In this section we present the expressions for x ij ,y ij , ij=02,12,22 for the special case that γ=1 and \(c=\sqrt{\frac {2}{3}b}\). In Proposition 2 we computed that for γ=1 and \(c=\sqrt{\frac{2}{3}b}\) one has

$$k_*^2=\frac{1}{3}b, \qquad a_*^2= \frac{1}{3}b^3, \qquad\omega_*=-\frac{1}{3}b\sqrt{2} \quad\mbox{and} \quad c_{\mathrm{g}}=-\sqrt{\frac{2}{3}b}. $$

Also, one computes the second component of a basis vector of the kernel of \(\mathcal{M}_{\omega_{*}}(a_{*},k_{*},c)\) and the nonzero eigenvalue of \(\mathcal{M}_{\omega_{*}}(a_{*},k_{*},c)\) as

$$\eta_{1,\sqrt{\frac{2}{3}b}} = \frac{1}{3}b (-2+\mathrm{i}\sqrt{2} ) \quad \mbox{and} \quad\lambda_+=\frac{2}{3}b\mathrm{i}\sqrt{2}. $$

These values are used to derive

The nonlinear terms from the reaction kinetics are

The sum of these expressions is

$$L_{\mathrm{tot}} := \frac{4}{99}(7-5\mathrm{i}\sqrt{2}). $$

The nonlinear diffusion terms are, of course, zero, so \(L_{\mathrm {NLD}}=L_{\mathcal{A},\mathrm{NLD}}=0\). We get for the right hand components as in (A.10):

These give the Ginzburg–Landau equation in (2.51):

$$\mathcal{A}_{\tau} = \frac{1}{3}(8+\mathrm{i}\sqrt{2}) \mathcal{A}_{\xi\xi } + \frac{2}{9}\sqrt{\frac{3}{b}}(5+\mathrm{i} \sqrt{2})\mathcal{A} - \frac{2}{33}(5-2\mathrm{i}\sqrt{2})b^2 \mathcal{|A|}^2\mathcal{A}. $$

1.2 A.2 Derivation of the Ginzburg–Landau Equation for the GKGS-System with c=0

In this section we present the expressions for x ij ,y ij , ij=02,12,22 for the special case that c=0. In Proposition 1 we computed that for c=0 one has

$$k_*^2=\frac{1}{2}(1-g)b, \quad\mbox{and} \quad a_*^{\gamma+1}=g\gamma b^{2\gamma+1}. $$

Also, one computes the second component of a basis vector of the kernel of \(\mathcal{M}_{\omega_{*}}(a_{c},k_{c},c)\) and the nonzero eigenvalue of \(\mathcal{M}_{\omega_{*}}(a_{c},k_{c},c)\) as

$$\eta_{\gamma,0} = \frac{1}{2}(g-7)\frac{a_*^2}{b^2} \quad\mbox{and}\quad \lambda_- = \frac{1}{2}(g-7)\frac{a_*^2}{b^2} + \frac{1}{2}(1+g)b. $$

These values are used to derive

The sum of the nonlinear terms from the kinetics is

$$L_{\mathrm{tot}} := \biggl[\frac{8}{9}(18-2g)\gamma+ 6(5-g) \biggr] \frac {a_*^4}{b^3}. $$

Also we have

This gives the Ginzburg–Landau equation in (2.49):

$$\mathcal{A}_{\tau} = 2\sqrt{2}\,\mathcal{A}_{\xi\xi} + b_1(\gamma)\mathcal{A}+ \mathcal{L}_1(\gamma)|\mathcal{A}|^2\mathcal {A} $$

with

1.3 A.3 Derivation of the Ginzburg–Landau Equation for the Case c≫1: The Klausmeier Model and the GKGS-Model for c≫1

This appendix to Sect. 2.6 deals with an elaborate account on the scalings introduced in Sect. 2.6 that were used to derive the Klausmeier system (2.53) from the GKGS-system. Also, we derive the GLE for the Klausmeier system (2.53).

Scaling Analysis for the Klausmeier System as a Limit Case of the GKGS System

We remark that the equilibria for both systems (1.5) and (2.53) are the same. Patterns close to the equilibrium (U +,V +) can be described as

$$ \everymath{\displaystyle} \begin{array}{@{}rcl} U & = & \delta^{2\beta-\alpha}\bigl(\hat{U}_+ + \varepsilon\hat{U}(x,t)\bigr); \\[2mm] V & = & \delta^{\alpha-\beta}\bigl(\hat{V}_+ + \varepsilon\hat{V}(x,t)\bigr). \end{array} $$
(A.14)

Substitution of these expansions in (1.5) gives the leading order formulation (2.38). We are interested in the behavior of the GKGS-model for \(0<1/\sqrt{c}\ll\varepsilon\ll 1\). Since we know from Proposition 2 that \(a_{c}=\mathcal {O}(\sqrt{c})\), we put \(a_{*} = \bar{a}_{*} \sqrt{c}\) and obtain for (2.38),

$$ \everymath{\displaystyle} \begin{array}{@{}rcl} \delta^{3\beta-2\alpha} U_t & = & c^{-\frac{1}{2}(\gamma -1)}\gamma\biggl(\frac{b^2}{\bar{a}_*} \biggr)^{\gamma-1} U_{xx} + c U_x - \biggl[ c \frac{\bar{a}_*^2}{b^2} U + 2b V\biggr] \\[5mm] & & {}+ \varepsilon\biggl[\gamma(\gamma-1) \biggl(\frac {b^2}{\bar{a}_*\sqrt{c}} \biggr)^{\gamma-2} \bigl[U_{xx}U+(U_x)^2\bigr] \\[5mm] &&{}- \frac{b^2}{\bar{a}_*\sqrt{c}} V^2 - 2\frac{\bar{a}_* }{b}\sqrt{c} UV \biggr] \\[5mm] & & {}+ \varepsilon^2 \biggl[\gamma(\gamma-1) (\gamma-2) \biggl(\frac{b^2}{\bar{a}_*\sqrt{c}} \biggr)^{\gamma-3}\biggl[ U(U_x)^2+ \frac {1}{2}U^2U_{xx}\biggr] \\[5mm] & & {} + \gamma(\gamma-1)\frac{1}{\bar{a}_*\sqrt{c}} \biggl(\frac {b^2}{\bar{a}_*\sqrt{c}} \biggr)^{\gamma-1}U_{xx} + 2r\frac{\bar{a}_*}{b^2}\sqrt{c}U -UV^2 \biggr] , \\[5mm] V_t & = & V_{xx} + \biggl[\frac{\bar{a}_*^2c}{b^2}U+bV \biggr] + \varepsilon\biggl[\frac{b^2}{\bar{a}_*\sqrt{c}}V^2 + 2\frac{\bar{a}_*}{b} \sqrt{c}UV \biggr] \\[5mm] &&{}- \varepsilon^2 \biggl[2r\frac{\bar{a}_*}{b^2}\sqrt {c}U - UV^2 \biggr]. \end{array} $$
(A.15)

In order to derive the Klausmeier model, we must scale the components U and V such that the diffusion coefficient in the first component of (2.38) is of higher order in \(1/\sqrt{c}\) than the other terms in the equations. The other terms must balance at the same, highest order. In order to obtain this, we scale U, V, and r such that

$$ U = \frac{\bar{U}}{\sqrt{c}},\qquad V=\sqrt{c}\bar{V} \quad\mbox{and} \quad r=\bar{r}\sqrt{c}. $$
(A.16)

With these scalings we obtain for (A.15), by neglecting higher orders of δ and \(1/\sqrt{c}\),

$$ \everymath{\displaystyle} \begin{array}{@{}rcl} 0 & = & \bar{U}_{\tilde{x}} - \biggl[ \frac{\bar{a}_*^2}{b^2}\bar{U} + 2b\bar{V} \biggr] - \varepsilon\biggl[ \frac{b^2}{\bar{a}_*}\bar{V}^2 + 2 \frac {\bar{a}_*}{b}\bar{U}\bar{V} \biggr] + \varepsilon^2 \biggl[ 2\bar{r} \frac{\bar{a}_*}{b^2}\bar{U} -\bar{U}\bar{V}^2 \biggr] , \\[4mm] \bar{V}_{\bar{t}} & = & \bar{V}_{\bar{x}\bar{x}} + \biggl[\frac{\bar{a}_*^2}{b^2}\bar{U} + b\bar{V}\biggr] + \varepsilon\biggl[ \frac{b^2}{\bar{a}_*}\bar{V}^2 + 2 \frac {\bar{a}_*}{b}\bar{U}\bar{V} \biggr] - \varepsilon^2 \biggl[ 2\bar{r} \frac{\bar{a}_*}{b^2}\bar{U} -\bar{U}\bar{V}^2 \biggr]. \end{array} $$
(A.17)

We can now scale out b by putting

$$ \begin{array}{@{}l} \bar{U} = \tilde{U} b^{3/4}; \qquad \bar{V} = \tilde{b}^{1/4}V; \qquad \bar{a}_c = \tilde{a}_c b^{5/4}; \\[2mm] x = b^{-1/2}\tilde{x}; \qquad t = b^{-1/4}\tilde{t}; \qquad \bar{r}=\tilde{r} b^{5/4}, \end{array} $$
(A.18)

and obtain, to leading order in ε and neglecting higher order terms of δ and \(\frac{1}{\sqrt{c}}\),

$$ \everymath{\displaystyle} \begin{array} {@{}rcl} 0 & = & \tilde{U}_{\tilde{x}} - \bigl[ \tilde{a}_*^2\tilde{U} + 2\tilde{V}\bigr] - \varepsilon\biggl[ \frac{1}{\tilde{a}_*}\tilde{V}^2 + 2\tilde{a}_*\tilde{U}\tilde{V} \biggr] + \varepsilon^2 \bigl[ 2\tilde{r} \tilde{a}_*\tilde{U} -\tilde{U}\tilde{V}^2 \bigr] , \\[4mm] \tilde{V}_{\tilde{t}} & = & \tilde{V}_{\tilde{x}\tilde{x}} + \bigl[\tilde{a}_*^2\tilde{U} + \tilde{V}\bigr] + \varepsilon\biggl[ \frac{1}{\tilde{a}_*} \tilde{V}^2 + 2\tilde{a}_*\tilde{U}\tilde{V} \biggr] - \varepsilon^2 \bigl[ 2\tilde{r}\tilde{a}_*\tilde{U} -\tilde{U}\tilde{V}^2 \bigr]. \end{array} $$
(A.19)

This system is the one presented in (2.55).

Derivation of the GLE for the Klausmeier System: The Regime \(0<1/\sqrt{c}\ll\varepsilon\ll1\)

From (A.19) one derives the dispersion relation associated to the linearization about the background state (U +,V +) in the Klausmeier model, neglecting higher orders of ε,

$$ \det\mathcal{M}_{\lambda}(\tilde{a}_*,\mathrm {i}\tilde{k}) : = \det\left( \begin{array}{c@{\quad}c} -\tilde{a}_*^2+\mathrm{i}\tilde{k} & -2 \\ \tilde{a}_*^2 & 1-\tilde{k}^2 -\tilde{\lambda}\end{array} \right)=0. $$
(A.20)

We apply conditions (2.7a), (2.7b) to derive critical parameters. Working out the dispersion relation (A.20) using condition (2.7a),

(A.21a)
(A.21b)

From these relations one derives

$$ \tilde{k}^2\bigl(\tilde{k}^2-1\bigr) + \tilde{a}_*^4\bigl(\tilde{k}^2+1\bigr) = 0, $$
(A.22)

and by solving equation (A.21a) for \(\tilde{\omega}\) we get

$$ \tilde{\omega}_* = \tilde{k}_*\bigl(\tilde{k}_*^2-1\bigr) \frac{1}{\tilde{a}_*^2} , $$
(A.23)

and thus

$$ \frac{\partial\tilde{\omega}}{\partial\tilde{k}} = \frac{1}{\tilde{a}_*^2}\bigl(3\tilde{k}^2-1\bigr). $$
(A.24)

Differentiating (A.24) with respect to \(\tilde{k}\) and substituting equation (A.21b) into the result, we get

$$ 2\tilde{k}^2 - 1 + \tilde{a}_*^4 = 0. $$
(A.25)

Solving (A.22) and (A.25) for \(\tilde{a}\) and \(\tilde{k}\) then gives

$$ \tilde{a}_*^2 = \sqrt{2}-1 \quad\mbox{and}\quad \tilde{k}_*^2 = \sqrt{2}-1, $$
(A.26)

which are the expressions for large c that we had derived in Proposition 2. From these expressions for \(\tilde{a}_{*}\) and \(\tilde{k}_{*}\) we further derive the critical frequency and the group speed

$$ \everymath{\displaystyle} \begin{array}{@{}rcl} \tilde{\omega}_* & = & -\sqrt{2}\sqrt{\sqrt{2}-1}; \\[2mm] \tilde{c}_\mathrm{g} & = & -\frac{\partial\tilde{\omega}}{\partial \tilde{k}}\bigg|_{k=k_*} = -2+\sqrt{2}. \end{array} $$
(A.27)

From (A.20) it follows that the kernel and range of the linearization about the equilibrium \((\tilde{U}_{+},\tilde{V}_{+})\) equal

$$ \mathrm{ker}\,\mathcal{M}_{\mathrm{i}\tilde{\omega}_*}(\tilde{a}_*, \tilde{k}_*) = \left( \begin{array}{c} 2 \\ -\tilde{a}_*^2 +\mathrm{i}k_* \end{array} \right) $$
(A.28)

and

$$ \operatorname{Rg} \mathcal{M}_{\mathrm{i}\tilde{\omega}_*}(\tilde{a}_*, \tilde{k}_*) = \left( \begin{array}{c} -2 \\ 1-\tilde{k}_*^2 -\mathrm{i}\tilde{\omega}_* \end{array} \right) . $$

From the expression for the range of \(\mathcal{M}_{\mathrm{i}\tilde{\omega}_{*}}(\tilde{a}_{*}, \tilde{k}_{*})\) we derive that the equations

$$\mathcal{M}_{\mathrm{i}\tilde{\omega}}(\tilde{a}_*, \tilde{k}_*) x = f $$

can be solved for x if and only if \(f\in\operatorname{Rg} \mathcal {M}_{\mathrm{i}\tilde{\omega}_{*}}(\tilde{a}_{*}, \tilde{k}_{*})\), that is, if f fulfills the solvability condition

$$ 2f_2 + \bigl[1-\tilde{k}_*^2 - \mathrm{i}\tilde{\omega}_*\bigr]f_1 = 0. $$
(A.29)

Since \(\mathrm{Det}\,\mathcal{M}_{\mathrm{i}\tilde{\omega}_{*}}(\tilde{a}_{*}, \tilde{k}_{*}) = 0\), it follows that the unique solution to the equation \(\mathcal{M}_{\mathrm{i}\tilde{\omega}_{*}}(\tilde{a}_{*}, \tilde{k}_{*}) x = f\) is

$$x = \frac{1}{\lambda_-}f $$

with λ the nonzero eigenvalue of \(\mathcal{M}_{\mathrm{i}\tilde{\omega}_{*}}(\tilde{a}_{*}, \tilde{k}_{*})\),

$$ \lambda_- := \operatorname{Tr} \mathcal{M}_{\mathrm {i}\tilde{\omega}}(\tilde{a}_*, \tilde{k}_*) = -\tilde{a}_*^2 +\mathrm{i}k_* + 1-\tilde{k}_*^2 -\mathrm{i}\tilde{\omega}_*. $$
(A.30)

By using (A.28), the expansion (U,V) that describes the pattern near its onset (i.e., for \(\tilde{a} = \tilde{a}_{*} - \tilde{r}\varepsilon^{2}\)) can be written out as

$$ \left( \begin{array}{c} U \\ V \end{array} \right) = \mathcal{A}(\xi,\tau) \left( \begin{array}{c} 2 \\ \eta \end{array} \right) \mathrm{e}^{\mathrm{i}(\tilde{k}_* x + \tilde{\omega}_* t)} + \mbox{c.c.} + \mbox{h.o.t.}, $$
(A.31)

with the shorthand

$$\eta:= -\tilde{a}_*^2 +\mathrm{i}\tilde{k}_*. $$

As pointed out in at the begin of the Appendix, in the Ginzburg–Landau formalism one subsequently derives equations of the form \(X_{02}=x_{02}\mathcal{A}^{2}, Y_{02}=y_{02}\mathcal{A}^{2}, X_{12}=x_{12}\mathcal{A}_{\xi}, Y_{12}=y_{12}\mathcal{A}_{\xi}, X_{22}=x_{22}|\mathcal{A} |^{2}\), and \(Y_{22}=y_{22}|\mathcal{A}|^{2}\). The formulas for x ij and y ij are derived by substituting the expansion (A.31) in the leading order system (A.19) and collecting terms of order ε j−1 E i (with shorthand \(E=\mathrm{e}^{\mathrm{i}(\tilde{k}_{c} x + \tilde{\omega}_{c} t)}\)) and solving them for X ij and Y ij :

$$ \everymath{\displaystyle} \begin{array} {@{}rcl} x_{02} & = & -2\frac{1}{\tilde{a}_*^3}|\eta|^2 - 4 \frac{1}{\tilde{a}_*}(\bar{\eta}+ \eta), \\[4mm] y_{02} & = & 0, \\[2mm] x_{12} & = & \frac{-2}{\lambda_-} , \\[4mm] y_{12} & = & \frac{1}{\lambda_-}(\eta c_{\mathrm{g}} - 2\eta \mathrm{i} \tilde{k}_c), \\[4mm] x_{22} & = & \frac{ 2\mathrm{i}\tilde{k}_* [\frac{1}{\tilde{a}_*}\eta^2 + 4\tilde{a}_* \eta] }{ [ -\tilde{a}_*^2 + 2\mathrm{i}\tilde{k}_*]\cdot[4\tilde{k}_*^2 + 2\mathrm{i}\tilde{\omega}_* -1] - 2\tilde{a}_*^2 } , \\[5mm] y_{22} & = & \frac{1}{\tilde{a}_*^2}\bigl[4\tilde{k}_*^2 + 2\mathrm{i}\tilde{\omega}-1\bigr]x_{22} - \frac{1}{\tilde{a}_*^2}\biggl[ \frac{1}{\tilde{a}_*}\eta^2 + 4\tilde{a}_*\eta\biggr]. \end{array} $$
(A.32)

The Ginzburg–Landau equation that describes the onset of patterns in the Klausmeier system (A.19) reads

(A.33)

with

$$L_\mathrm{tot} = \frac{2\bar{\eta}}{\tilde{a}_*}y_{22} + 4\tilde{a}_*^2y_{22} + 2 \tilde{a}_*\eta x_{02} + 2\tilde{a}_*\bar{\eta}x_{22} + 4|\eta|^2 + 2\eta^2. $$

If one works out the parameter values for \(\tilde{a}_{*}^{2} = \sqrt{2}-1\) and \(\tilde{k}_{*}^{2} = \sqrt{2}-1\), the leading order system (A.19) becomes

$$ \everymath{\displaystyle} \begin{array} {@{}rcl} 0 & = & \tilde{U}_{\tilde{x}} - \bigl[ (\sqrt{2}-1)\tilde{U} + 2\tilde{V}\bigr] \\ [0.2cm] & & {}- \varepsilon\biggl[ \frac{1}{\sqrt{\sqrt{2}-1}}\tilde{V}^2 + 2 \sqrt{\sqrt{2}-1}\tilde{U}\tilde{V} \biggr] + \varepsilon^2 \Bigl[ 2 \tilde{r}\sqrt{\sqrt{2}-1}\tilde{U} -\tilde{U}\tilde{V}^2 \Bigr] , \\ [0.4cm] \tilde{V}_{\tilde{t}} & = & \tilde{V}_{\tilde{x}\tilde{x}} + \bigl[(\sqrt {2}-1) \tilde{U} + \tilde{V}\bigr] \\ [0.2cm] & & {}+ \varepsilon\biggl[\frac{1}{\sqrt{\sqrt{2}-1}}\tilde{V}^2 + 2 \sqrt{\sqrt{2}-1}\tilde{U}\tilde{V} \biggr] - \varepsilon^2 \Bigl[ 2 \tilde{r}\sqrt{\sqrt{2}-1}\tilde{U} -\tilde{U}\tilde{V}^2 \Bigr]. \end{array} $$
(A.34)

The matrix that describes the linear leading order part of this system is then

$$ \mathcal{M}_{\lambda}(\tilde{a}_*,k) : = \left( \begin{array}{c@{\quad}c} -\sqrt{2}+1+\mathrm{i}\sqrt{\sqrt{2}-1} & -2 \\[1mm] \sqrt{2}-1 & 2-\sqrt{2} +\mathrm{i}\sqrt{2}\sqrt{\sqrt{2}-1} \end{array} \right). $$
(A.35)

Working out the levels for the different expressions (A.32), we get

$$ \everymath{\displaystyle} \begin{array} {@{}rcl} x_{02} & = & (4-2\sqrt{2})\sqrt{\sqrt{2}-1,} \\ [2mm] y_{02} & = & 0, \\ [2mm] x_{12} & = & -\frac{1}{41} \Bigl[ 10-3\sqrt{2} -\mathrm {i}(40+29\sqrt{2})\sqrt{\sqrt{2}-1} \Bigr] , \\ [4mm] y_{12} & = & \frac{1}{82} \Bigl[78\sqrt{2}-96+\mathrm{i}(16 \sqrt {2}-108)\sqrt{\sqrt{2}-1} \Bigr], \\ [4mm] x_{22} & = & \frac{1}{69} \Bigl[(61\sqrt{2}+40)\sqrt{\sqrt {2}-1}+2\mathrm{i}(67\sqrt{2}-13) \Bigr], \\ [4mm] y_{22} & = & -\frac{2}{69} \Bigl[(10\sqrt{2}+42)\sqrt{\sqrt {2}-1}+\mathrm{i}(5\sqrt{2}-2) \Bigr], \end{array} $$
(A.36)

and

$$L_\mathrm{tot} = -44 + 32\sqrt{2} + \mathrm{i}[-20+18\sqrt{2}]\sqrt { \sqrt{2}-1}. $$

The Ginzburg–Landau equation then becomes

or, equivalently,

which is the equation presented in (2.56).

The GLE for the GKGS Model for c≫1: The Regime \(0<\varepsilon \ll1/\sqrt{c}\ll1\)

As in the previous section, we scale the leading order system of the GKGS model according to (2.54). We then obtain the leading order system (2.57). To first order in ε, the leading order system (2.57) reads

$$ \mathcal{M}^c_{\lambda}(\tilde{a}_*, \mathrm{i}\tilde{k}) : = \left( \begin{array}{c@{\quad}c} -\gamma a_*^{1-\gamma}c^{-\frac{1}{2}\gamma}b^{\frac {3}{4}\gamma-\frac {1}{4}}\tilde{k}^2+c^{\frac{1}{2}}[\mathrm{i}\tilde{k}-\tilde{a}_*^2] & -2c^{\frac{1}{2}} \\[2mm] \tilde{a}_*^2 & 1-\tilde{k}^2 -\tilde{\lambda}\end{array} \right). $$
(A.37)

First, we remark that for γ≥1 the linear part of the nonlinear diffusion in the GKGS-model is in leading order \({\leq}\mathcal{O}(c^{-1/2})\). Secondly, we remark that to leading order in c,

$$\det\mathcal{M}^c_{\lambda}(\tilde{a}_*,\mathrm{i}\tilde{k}) = c^{\frac{1}{2}} \mathcal{M}_{\lambda}(\tilde{a}_*,\mathrm{i}\tilde{k}) + \mathcal{O}\bigl(c^{\frac{1}{2}(1-\gamma)}\bigr) $$

with \(\mathcal{M}_{\lambda}(\tilde{a}_{*},\mathrm{i}\tilde{k})\) as defined in (A.20). Therefore, the critical \(\tilde{k}_{*}\), \(\tilde{a}_{*}\), \(\tilde{\omega}_{*}\), and \(\tilde{c}_{g}\) are to leading order in c as in (A.26) and (A.27).

The solvability condition is as in (A.29). Using the leading order system (2.57), we compute

$$ \everymath{\displaystyle} \begin{array} {@{}rcl} x_{02} & = & -2\frac{1}{\tilde{a}_*^3}| \eta|^2 - 4\frac{1}{\tilde{a}_*}(\bar{\eta}+ \eta), \\[4mm] y_{02} & = & 0, \\[2mm] x_{12} & = & \frac{l_0\cdot c^{-\frac{1}{2}\gamma}-2}{\lambda_-} , \\[4mm] y_{12} & = & \frac{1}{\lambda_-}(\eta c_{\mathrm{g}} - 2\eta \mathrm{i}\tilde{k}_c) , \\[4mm] x_{22} & = & \frac{1}{\tilde{a}_*^2} \bigl[4\tilde{k}_*^2 + 2\mathrm{i}\tilde{\omega}-1 \bigr]y_{22} - \frac{1}{\tilde{a}_*^2} \biggl[ \frac{1}{\tilde{a}_*} \eta^2 + 4\tilde{a}_*\eta \biggr] , \\[5mm] y_{22} & = & \frac{ -l_1c^{-\theta_1}-l_2c^{-\theta_2} \frac {1}{\tilde{a}_*}\eta^2 + 4\tilde{a}_* \eta+\frac {1}{a^2}[-l_3c^{-\theta_2} -a^2 +2\mathrm{i}\tilde{k}][\frac {1}{a}\eta^2 + 4a\eta] }{ [l_4 c^{-\theta_4} -\tilde{a}_*^2 + 2\mathrm{i}\tilde{k}_*]\cdot[4\tilde{k}_*^2 + 2\mathrm{i}\tilde{\omega}_* -1] - 2\tilde{a}_*^2 }.\!\!\!\!\!\!\!\!\!\!\!\!\!\! \end{array} $$
(A.38)

In (A.38) it is understood that all l i , i=0,…,4 do not depend on c and that θ i ≥0 for i=0,…,4. We have not computed the l i and θ i explicitly. This gives for the GLE of the GKGS-model in general form

(A.39)

with

We have not bothered about calculating the constant denoted by “const”, since for asymptotically large c the associated expressions only play a role at higher order. That is, for asymptotically large c≫1 it is immediate that (A.39) reduces to (A.33). Using the expressions (A.38) and inserting \(\tilde{k}\), \(\tilde{a}\), \(\tilde{\omega}\), and c g one obtains the GLE for the Klausmeier system (2.56).

Rights and permissions

Reprints and permissions

About this article

Cite this article

van der Stelt, S., Doelman, A., Hek, G. et al. Rise and Fall of Periodic Patterns for a Generalized Klausmeier–Gray–Scott Model. J Nonlinear Sci 23, 39–95 (2013). https://doi.org/10.1007/s00332-012-9139-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00332-012-9139-0

Keywords

Mathematics Subject Classification

Navigation