Academia.eduAcademia.edu

Outline

Title
Abstract
Figures
Introduction
Simulated vs. Real River Planform Data
River Scaling Statistics
Simulation Results
Comparison Between Simulation and Empirical Data
Conclusions
References
All Topics
Earth Sciences
Earth-Surface Processes

Planform Geometry and Dynamics of Meandering Rivers

Profile image of Hans Henrik StolumHans Henrik Stolum
https://doi.org/10.1130/0016-7606(1998)110<1485:PGADOM>2.3.CO;2
visibility

…

description

14 pages

link

1 file

Abstract

Three freely meandering rivers in the Amazon basin were analyzed for statistical scaling properties and oxbow lake size-frequency distributions. The rivers are the Purus (central Amazon, planform data), Juruá (central Amazon, planform and oxbow lake data), and Madre de Dios (Peruvian Amazon, oxbow lake data). Long reaches were found to be power- law scaling over more than two orders of mag- nitude. These river planforms are self-affine fractals. Oxbow lake data suggest that the lakes are sampled from a skewed hyperbolic (Pareto) size-frequency distribution. To examine the long-term behavior of freely meandering rivers, a deterministic continuum model of meandering rivers has been used for extensive simulations of free meandering mo- tion. The simulation outcomes are consistent with a dynamical state of self-organized criticality, which has the following characteristic behavior: (1) stationary mean sinuosity of the final state; (2) robustness, in the sense that the same final state is reached from any initial conditions; and (3) formation of a spatiotemporal fractal structure. Sensitivity tests showed that this behavior is not affected by valley confinement down to a valley width of 50 w (river width), and by chute cutoffs of mature meanders up to 3 w long, but the average sinuosity value reached in the final state is sensitive to valley width less than 100 w, and chutes longer than 1.5 w. Comparison with empirical data confirmed the validity of the simulations as models of river meandering. All tests found data and simulation results to be in close agreement.

Figures (19)
Figure 1. Map of the southern central Amazon basin, Brazil, showing the five measure river reaches. 100 w, while they are highly sensitive to chute cutoffs longer than 1.5 w. This result was ob- tained by setting a uniform chute length thresh- old, which cut off all meanders at the critical length. The result is therefore not entirely repre- sentative of natural chute processes, which occa- Many meandering rivers are spatially free. But the second condition, near absence of temporal forcing by discharge fluctuations, is rarely real- ized in nature. Sensitivity tests (Fig. 6) demon- strate that simulated rivers are not affected by valley confinement down to a valley width of
Figure 1. Map of the southern central Amazon basin, Brazil, showing the five measure river reaches. 100 w, while they are highly sensitive to chute cutoffs longer than 1.5 w. This result was ob- tained by setting a uniform chute length thresh- old, which cut off all meanders at the critical length. The result is therefore not entirely repre- sentative of natural chute processes, which occa- Many meandering rivers are spatially free. But the second condition, near absence of temporal forcing by discharge fluctuations, is rarely real- ized in nature. Sensitivity tests (Fig. 6) demon- strate that simulated rivers are not affected by valley confinement down to a valley width of
Figure 2. Freely meandering reaches of Jurua and Purus Rivers redrawn from Landsat satel- lite images and synthetic aperture radar (SAR) images. (A) Jurua 1 reach (from Baker, 1978). (B) Jurua 2 reach. (C) Purus 2 reach. (D) Purus 1 reach.
Figure 2. Freely meandering reaches of Jurua and Purus Rivers redrawn from Landsat satel- lite images and synthetic aperture radar (SAR) images. (A) Jurua 1 reach (from Baker, 1978). (B) Jurua 2 reach. (C) Purus 2 reach. (D) Purus 1 reach.
TABLE 1. SUMMARY DATA OF RIVER REACHES ~W—Tiver wiatn. tAlaskan rivers. Data from Montgomery (1996).
TABLE 1. SUMMARY DATA OF RIVER REACHES ~W—Tiver wiatn. tAlaskan rivers. Data from Montgomery (1996).
Figure 3. Synthetic aperture radar (SAR) image of Parana Breu (to the left) and Jurua River. The full reach of Parana Breu was used in this study. The image was obtained in the dry season, when the Parana has very low discharge. The planform was therefore made more clearly visible by contrast en- hancement. The image is otherwise unal- tered. It covers an area of 31 X70 km. From the Global Rainforest Mapping Project (http://southport.jpl.nasa.gov/amazon).
Figure 3. Synthetic aperture radar (SAR) image of Parana Breu (to the left) and Jurua River. The full reach of Parana Breu was used in this study. The image was obtained in the dry season, when the Parana has very low discharge. The planform was therefore made more clearly visible by contrast en- hancement. The image is otherwise unal- tered. It covers an area of 31 X70 km. From the Global Rainforest Mapping Project (http://southport.jpl.nasa.gov/amazon).
Figure 4. Discharge (10° m*/s) measured at the mouth of (A) Jurua and (B) Purus rivers (Richey et al., 1991). Years indicated by ab- scissa scale are from 1 September through 31 August. that meandering river planforms are self-affine monofractals with a natural scaling range of 2.4 orders of magnitude. The generating mechanism is the meander cutoff process, with the actual me- anders being the generator shape. The structure may be more precisely described as a prefractal because the self-affine geometry prevents it from spanning more than about seven iterations of the generator (in the limit of infinite recursion the structure becomes an ideal mathematical fractal). However, since naturally occurring fractals are al- ways finite, such prefractal structures are usually referred to as fractal when they span at least two
Figure 4. Discharge (10° m*/s) measured at the mouth of (A) Jurua and (B) Purus rivers (Richey et al., 1991). Years indicated by ab- scissa scale are from 1 September through 31 August. that meandering river planforms are self-affine monofractals with a natural scaling range of 2.4 orders of magnitude. The generating mechanism is the meander cutoff process, with the actual me- anders being the generator shape. The structure may be more precisely described as a prefractal because the self-affine geometry prevents it from spanning more than about seven iterations of the generator (in the limit of infinite recursion the structure becomes an ideal mathematical fractal). However, since naturally occurring fractals are al- ways finite, such prefractal structures are usually referred to as fractal when they span at least two
Figure 5. Qualitative comparison of simulated and actual free meandering river planforms. (A) Reach of Purus River. Redrawn from Landsat satellite image. (B) Simulated river reach. Sinuosity = 2.83, length /= 570 w, iteration = 5200 starting from a nearly straight initial planform. w— river width. The following values of free parameters were used (Stglum, 1997): Froude number: Fr = 0.5; friction coefficient: C= 0.01; channel width/depth: w/h, = 20; sediment transport parameters: M =5, B = 1.5. The meander hierarchy of the Jurua 1 reach scales over two orders of magnitude when mea- The inverse renormalization procedure intro- duces a sampling stratification that brings this out clearly. When each level (stratum) is repre- sented by its mean, a power-law relationship be- tween / and m becomes apparent (Fig. 12). The number of measurements that form each mean varies from 175 at the smallest scale to 2 at the second largest. If the hierarchical set of undula- tions had been self-similar, the ratio should re- main nearly constant (with only some random variation superimposed). The significant devia- tion from a power-law function at the highest
Figure 5. Qualitative comparison of simulated and actual free meandering river planforms. (A) Reach of Purus River. Redrawn from Landsat satellite image. (B) Simulated river reach. Sinuosity = 2.83, length /= 570 w, iteration = 5200 starting from a nearly straight initial planform. w— river width. The following values of free parameters were used (Stglum, 1997): Froude number: Fr = 0.5; friction coefficient: C= 0.01; channel width/depth: w/h, = 20; sediment transport parameters: M =5, B = 1.5. The meander hierarchy of the Jurua 1 reach scales over two orders of magnitude when mea- The inverse renormalization procedure intro- duces a sampling stratification that brings this out clearly. When each level (stratum) is repre- sented by its mean, a power-law relationship be- tween / and m becomes apparent (Fig. 12). The number of measurements that form each mean varies from 175 at the smallest scale to 2 at the second largest. If the hierarchical set of undula- tions had been self-similar, the ratio should re- main nearly constant (with only some random variation superimposed). The significant devia- tion from a power-law function at the highest
Figure 6. Sensitivity tests of confining valley width (A) and chute cutoff length threshold (B). Each value is the mean sinuosity <S> of 10 simulations of 30 000 iterations each, all with length /= 500 w; w—river width.
Figure 6. Sensitivity tests of confining valley width (A) and chute cutoff length threshold (B). Each value is the mean sinuosity <S> of 10 simulations of 30 000 iterations each, all with length /= 500 w; w—river width.
Figure 7. Reach of Murrumbidgee River, Australia, observed through resolution windows, each one decade wide, in total spanning roughly three orders of magnitude. Structure (mean- ders) occur at all scales, arranged in a nested hierarchy starting from the 100 m resolution pro- file. Note change of meander shape with change of scale, indicative of self-affine scaling sym- metry. Redrawn from map.
Figure 7. Reach of Murrumbidgee River, Australia, observed through resolution windows, each one decade wide, in total spanning roughly three orders of magnitude. Structure (mean- ders) occur at all scales, arranged in a nested hierarchy starting from the 100 m resolution pro- file. Note change of meander shape with change of scale, indicative of self-affine scaling sym- metry. Redrawn from map.
Figure 8. Scaling of each river reach as a whole measured with the divider method. (A) Purus 1 and 2. (B) Jurua 1 and 2. (C) Parana Breu. (D) Simulated reach in Figure 5B.
Figure 8. Scaling of each river reach as a whole measured with the divider method. (A) Purus 1 and 2. (B) Jurua 1 and 2. (C) Parana Breu. (D) Simulated reach in Figure 5B.
Define the whole flow Reynolds number Re = pUL/ 1, which measures the ratio of inertial to vis- cous forces acting on a fluid element (Furbish, 1997). U, L are the characteristic (mean) velocity and length (channel depth) of the flow. The fluid density and viscosity are p and |. Confinement of turbulence to a thin boundary layer occurs if Re is near the critical threshold for the steady-turbulent flow phase transition. This threshold depends on Figure 9. Data from Table 1. (A) Scaling range vs. length of measured reach. The two shortest reaches are Nowitna and Tok Rivers, Alaska (from Montgomery, 1996). (B) Fractal dimension (D) vs. sinuosity. The values fall in a broad region, and the two lines indicate the ap- proximate limits of natural variation possible.
Define the whole flow Reynolds number Re = pUL/ 1, which measures the ratio of inertial to vis- cous forces acting on a fluid element (Furbish, 1997). U, L are the characteristic (mean) velocity and length (channel depth) of the flow. The fluid density and viscosity are p and |. Confinement of turbulence to a thin boundary layer occurs if Re is near the critical threshold for the steady-turbulent flow phase transition. This threshold depends on Figure 9. Data from Table 1. (A) Scaling range vs. length of measured reach. The two shortest reaches are Nowitna and Tok Rivers, Alaska (from Montgomery, 1996). (B) Fractal dimension (D) vs. sinuosity. The values fall in a broad region, and the two lines indicate the ap- proximate limits of natural variation possible.
Figure 11. Self-affine scaling of meander shape geometry over two orders of magnitude (Ju- rua 1). See inset in Figure 12 for explanation of shape parameters.
Figure 11. Self-affine scaling of meander shape geometry over two orders of magnitude (Ju- rua 1). See inset in Figure 12 for explanation of shape parameters.
Figure 12. Allometric relationship between the mean values of / and m at each level of the un- dulation hierarchy; /—river length; m—depth (amplitude). Comparison of the Jurua 1 data (Figs. 10 and 11) and data from the simulated reach in Figure 5B.
Figure 12. Allometric relationship between the mean values of / and m at each level of the un- dulation hierarchy; /—river length; m—depth (amplitude). Comparison of the Jurua 1 data (Figs. 10 and 11) and data from the simulated reach in Figure 5B.
Figure 13. Pooled oxbow lake size-frequency distribution from Jurua 1 reach and Madre de Dios River and two statistically significant hypotheses (sampling from linear and Pareto distribution).
Figure 13. Pooled oxbow lake size-frequency distribution from Jurua 1 reach and Madre de Dios River and two statistically significant hypotheses (sampling from linear and Pareto distribution).
Figure 15. Cumulative oxbow lake size-fre- quency distribution from a single run of a sim- ulated river and two statistically significant hypotheses (sampling from the exponential and Pareto distributions). Figure 14. The size distribution of oxbow lakes compared to three common distribu- tions in nature, the normal, log normal, and Poisson. The oxbow lake distribution is signif- icantly different from all three.
Figure 15. Cumulative oxbow lake size-fre- quency distribution from a single run of a sim- ulated river and two statistically significant hypotheses (sampling from the exponential and Pareto distributions). Figure 14. The size distribution of oxbow lakes compared to three common distribu- tions in nature, the normal, log normal, and Poisson. The oxbow lake distribution is signif- icantly different from all three.
Figure 16. Air photograph of free meander- ing river, Amazon basin. The elongate bend in the foreground is a characteristic feature of high-sinuous meandering both in natural and simulated rivers.
Figure 16. Air photograph of free meander- ing river, Amazon basin. The elongate bend in the foreground is a characteristic feature of high-sinuous meandering both in natural and simulated rivers.
Figure 17. Comparison of tails of large oxbow lakes from the size-frequency distributions of river data (Fig. 13) and simulation result (Fig. 5B).
Figure 17. Comparison of tails of large oxbow lakes from the size-frequency distributions of river data (Fig. 13) and simulation result (Fig. 5B).
Figure 18. Grain piles and meandering rivers: both systems are attracted by a critical state.
Figure 18. Grain piles and meandering rivers: both systems are attracted by a critical state.

Related papers

Fractal relation of mainstream length to catchment area in river networks
Baldassare Bacchi

Water Resources Research, 1991

Hjelmfelt 1988) using eight rivers in Missouri. The fractal dimension of river length, d, is derived here from the Horton's laws of network composition. This results in a simple function of stream length and stream area ratios, that is, d --max (1, 2 log Roe/log RA). Three case studies are reported showing this estimate to be coherent with measurements of d obtained from map analysis. The scaling properties of the network as a whole are also investigated, showing the fractal dimension of fiver network, D, to depend upon bifurcation and stream area ratios according to D --min (2, 2 log R B/log R A). These results provide a linkage between quantitative analysis of drainage network composition and scaling properties of river networks.

View PDFchevron_right
River network fractal geometry and its computer simulation
V. Nikora

Water Resources Research, 1993

The hierarchical ordinal and statistical models of river networks are proposed. Their investigation has been carried out on the basis of river networks computer simulation as well as on empirical data analysis. The simulated river networks display self-similar behavior on small scales (the fractal dimension D-• 1.52 and Hurst's exponent H = 1.0) and self-affine behavior on large scales (the lacunary dimension D• • 1.71, H • 0.58). Similar behavior is also qualitatively characteristic for natural river networks (for catchment areas from 142 to 63,700 km 2 we obtained D • • 1.87 and H •-0.73). Thus in both cases one finds a region of scales with self-affine behavior (H < 1) and with Do < 2. Proceeding from fractal properties of the river networks, the theoretical basis of scaling relationships L-• A t• and 3?-A *, widely used in hydrology, are given (L, 37, and A denote the main river length, the total length of the river network, and catchment area, respectively); fi = 1/(1 + H) and, = Do/2. Nn as well as those of Panov [1948] and Schumm [1956]: An+l An = A iR,•-! R A =• (3) An and Zavoianu' s [ 1985] Pn+l Pn = PIR•-1 Rp = Pn (4) where L n, N n, A n, and P n denote the length, the number, the catchment area, and its perimeter for n-order streams, respectively. The following relationships for estimating the

View PDFchevron_right
Fractal geometry of individual river channels and its computer simulation
V. Nikora

Water Resources Research, 1993

A new method for analyzing the self-similarity and self-affinity of single-thread channels is proposed. It permits the determination of the fractal scaling exponents, of the characteristic scales, and the evaluation of the degree of anisotropy for self-similar fractal lines. Based upon the application of this method to the Dniester and Pruth rivers we established the self-similarity of the river pattern on small scales and the self-affinity on large scales. For these rivers we obtained the fractal scaling exponents, the characteristic scales, and the anisotropy parameters. A computer model has been developed which simulates river patterns Whose fractal properties are close to the properties of natural objects. A generalized model of fractaI behavior of natural rivers is proposed. On the basis of self-affinity of natural and simulated rivers on large scales, a hypothesis has been formulated which explains the violation of the dimension principle in the well-known relation between the river length and the catchment area. In R L d=2 (3) In RA Copyright 1993 by the American Geophysical Union. Paper number 93WR00978. 0043-1397/93/93 WR-00978505.00 where R r denotes the stream length ratio and RA is the ratio of the catchment areas for the rivers of adjacent orders. However, Nikora [1988, 1991] pointed only to the local self-similarity of individual fiver channels pattern on small scales and to its possible self-affinity on large scales, and established that for different rivers and for different sections of one fiver the d dimension is not constant (d = 1.0-1.33). Thus the above conditions and therefore the resulting relations (2) and (3) are doubtful. The following dependencies were proposed for describing the fractal properties of singlethread and multithread channels [Nikora, 1988, 1991]: Lr/L = (L2/L1) a-• (4) NiL1/N2L 2 = (L2/L1) d-1 (5) respectively, where L is the straight-line length between points that limit the river section (or between the fiver head and the fiver mouth); L l and L 2 are the internal and external scales of fractality, respectively; L1 •-B, L 2 ---B0;/3' is the fiver width; B0 is the width of the fiver valley; and N1 and N 2 are the number of islands within the investigated fiver section whose sizes correspond to L1 and L2. The local but not global fractality in the pattern of single-thread channels was also revealed by Snow [1989], who treated, according to Richardson's method, the data on !2 United States rivers and obtained d values varying from 1.04 to 1.38. In addition, in the works by Feder [1988], Robert and Roy [1990], Rosso et al. [1991], and Nikora [1991] the authors point to a possible violation of self-similarity of fiver catchment forms which may be the reason of the index deviation in (1) from the 0.5 value. Specifically, in Feder's [1988] work the following relation is proposed: In RL d = 2 (6) In RB which was obtained assuming that the widths of the ½atchments remain constant as their dimension increases and that 3561

View PDFchevron_right
The Most Probable Hydrologic Response of Fractal River Networks
P. Claps

Water Science and Technology Library, 1996

An expression of the hydrologic response of fractal networks is derived by means of the classical approach used in statistical-mechanics. Probabilities of the rate of arrivals at the outlet of mass particles instantaneously injected into the network are maximized accounting for some suitable constraints. The analysis of the structure of fractal networks allows us to meaningfully set the required constraints, that are of the same form of these used by . The resulting hydrologic response is a Weibull distribution whose parameters are the fractal dimension and the total length of the network. A comparison with the similar form of arrival time distribution obtained by Troutman and showed it to be a particular case of the more general expression relative to fractal networks.

View PDFchevron_right
The Generation and Scaling of Longitudinal River Profiles
Gareth Roberts

Journal of Geophysical Research: Earth Surface, 2019

The apparent success of inverse modeling of continent-wide drainage inventories is perplexing. An ability to obtain reasonable fits between observed and calculated longitudinal river profiles implies that drainage networks behave simply and predictably at length scales of O(10 2-10 3) km and time scales of O(10 0-10 2) Ma. This behavior suggests that rivers respond in an organized way to large-scale tectonic forcing. On the other hand, stream power laws are empirical approximations since fluvial processes are complex, nonlinear, and probably susceptible to disparate temporal and spatial shocks. To bridge the gap between these different perceptions of landscape evolution, we present and interpret a suite of power spectra for African river profiles that traverse different climatic zones, lithologic boundaries, and biotic distributions. At wavelengths ≳10 2 km, power spectra have slopes of −2, consistent with red noise, demonstrating that profiles are self-similar at these length scales. At wavelengths ≲10 2 km, there is a crossover transition to slopes of −1, consistent with pink noise, for which power scales according to the inverse of wavenumber. Onset of this transition suggests that spatially correlated noise, perhaps generated by instabilities in water flow and by lithologic heterogeneities, becomes more prevalent at wavelengths shorter than ∼100 km. At longer wavelengths, this noise gradually diminishes and self-similar scaling emerges. Our analysis is consistent with the concept that complexities of river profile development can be characterized by an adaptation of the Langevin equation, by which simple advective models of erosion are driven by a combination of external forcing and noise.

View PDFchevron_right
Geometry of river networks. I. Scaling, fluctuations, and deviations
Peter Sheridan Dodds

Physical review. E, Statistical, nonlinear, and soft matter physics, 2001

This paper is the first in a series of three papers investigating the detailed geometry of river networks. Branching networks are a universal structure employed in the distribution and collection of material. Large-scale river networks mark an important class of two-dimensional branching networks, being not only of intrinsic interest but also a pervasive natural phenomenon. In the description of river network structure, scaling laws are uniformly observed. Reported values of scaling exponents vary, suggesting that no unique set of scaling exponents exists. To improve this current understanding of scaling in river networks and to provide a fuller description of branching network structure, here we report a theoretical and empirical study of fluctuations about and deviations from scaling. We examine data for continent-scale river networks such as the Mississippi and the Amazon and draw inspiration from a simple model of directed, random networks. We center our investigations on the sc...

View PDFchevron_right
Morphodynamic regime and long‐term evolution of meandering rivers
stefano lanzoni

Journal of Geophysical Research, 2009

1] In the present contribution we focus our attention on the long-term behavior of meandering rivers, a very common pattern in nature. This class of dynamical systems is driven by the coexistence of various intrinsically nonlinear mechanisms which determine the possible occurrence of two different morphodynamic regimes: the subresonant and the superresonant regimes. Investigating the full range of morphodynamic conditions, we objectively compare the morphologic characteristics exhibited by synthetically generated and observed planimetric patterns. The analysis is carried out examining, through principal component analysis, a suitable set of morphological variables. We show that even in the presence of the strong filtering action exerted by cutoff processes, a closer, although not yet complete, similarity with natural meandering planforms can be achieved only by adopting a flow field model which accounts for the full range of morphodynamic regimes. We also introduce a new morphodynamic length scale, L m , associated with spatially oscillating disturbances. Once normalized with this length scale, the relevant morphologic features of the simulated long-term patterns (e.g., the probability density function of local curvature and the geometric characteristics of oxbow lakes) tend to collapse on two distinct behaviors, depending on the dominant morphologic regime.

View PDFchevron_right
On the analysis of the medium term planform dynamics of meandering rivers
Ronald Gutierrez

Water Resources Research, 2014

The continuous wavelet transform is applied to the analysis of curvature signals from both synthetic meanders and 52 realizations from 16 natural meanders ranging from class B to class G (Brice classification), thus providing information on the spatial distribution of their arc-wavelength spectrum, and therefore, representing an objective characterization of meanders. Past research has studied the meander dynamics by using the centerline (short-term frame) and the valley centerline (long-term frame). The present study introduces a medium term frame, termed the mean center (MC), which is defined as the medium term coherent wave being present in the meander planimetry for a period that is strongly governed by the occurrence of cutoff events; although in the absence of them, it is present for $10 to $30 years. The MC is obtained by using a methodology that combines the capabilities of the principal component analysis and the discrete wavelet transforms. The application of wavelet cross correlation shows that peaks in the centerline curvature are strongly correlated with those of the MC suggesting that (1) a linear relationship between them may be associated to bank processes and, (2) in all other cases, a higher nonlinear relationship may be induced by autogenic hydrodynamic processes. In freely meandering rivers, compound bends, multiple loops, and cutoff events are associated to peaks in the MC local curvature. We define the planform amplitudes as the orthogonal distance of the centerline from mean center. Planform amplitudes (orthogonal distance of the centerline from mean center) are normally distributed and ranges from 2 to 20 river mean widths.

View PDFchevron_right
Constructal view of scaling laws of river basins
Antonio Heitor Reis

Geomorphology, 2006

River basins are examples of naturally organized flow architectures whose scaling properties have been noticed long ago. Based on data of geometric characteristics, Horton [Horton, R.E., 1932. Drainage basin characteristics. EOS Trans. AGU 13, 350–361.], Hack [Hack, J.T., 1957. Studies of longitudinal profiles in Virginia and Maryland. USGS Professional Papers 294-B, Washington DC, pp. 46–97.], and Melton [Melton, M.A, 1958. Correlation structure of morphometric properties of drainage systems and their controlling agents. J. of Geology 66, 35–56.] proposed scaling laws that are considered to describe rather accurately the actual river basins. What we show here is that these scaling laws can be anticipated based on Constructal Theory, which views the pathways by which drainage networks develop in a basin not as the result of chance but as flow architectures that originate naturally as the result of minimization of the overall resistance to flow (Constructal Law).

View PDFchevron_right
Simple statistical models for river networks
Jens Feder, Paul Meakin, Torstein Jøssang

Physica A: Statistical Mechanics and its Applications, 1991

The geometric scaling properties associated with simple river network models have been studied using computer simulations. In these models the river networks arc comprised of random trajectories which start at randomly selected positions on a square lattice and continue until they either reach an edge of the lattice or join a previous trajectory to form a branched network. For those cases where the trajectories are either self-avoiding random walks (SAWS) or indefinitely growing self-avoiding walks (IGSAWs) the river basins are compact but have fractal basin boundaries with a dimensionality near to 514. For the IGSAW model the longest river is a self-similar fractal with a fractal dimensionality close to or equal to that of the IGSAW itself. If the trajectory is a directed walk. then the model is very similar to the Scheidegger river network model. In this case the basin boundary and the longest channei are self-affine Brownian processes. The resuhs ohtaincd from the IGSAW and directed walk models can be dcsxibed in terms of a simple scaling model. For the SAW models this scaling picture does not apply and it appears that practical simulations are far from the asymptotic (large system size) limit.

View PDFchevron_right

References (73)

  1. Bagnold, R. A., 1960, Some aspects of the shape of river mean- ders: U.S. Geological Survey Professional Paper 282-E, p. 135-144.
  2. Bak, P., 1996, How nature works: New York, Springer (Coper- nicus), 205.
  3. Bak, P., and Chen, K., 1991, Self-organized criticality: Scien- tific American, v. 264, p. 26-33.
  4. Bak, P., and Creutz, M., 1994, Fractals and self-organized crit- icality, in Bunde, A., and Havlin, S., eds., Fractals in sci- ence: Berlin, Springer-Verlag, p. 27-47.
  5. Bak, P., and Paczuski, M., 1993, Why nature is complex: Phys- ical World, part 12, v. 6, p. 39-43.
  6. Bak, P., Tang, C., and Wiesenfeld, K., 1987, Self-organized criticality: An explanation of 1/f noise: Physical Review Letters, v. 59, p. 381-384.
  7. Bak, P., Tang, C., and Wiesenfeld, K., 1988, Self-organized criticality: Physical Review A, v. 38, p. 364-374.
  8. Baker, V. R., 1978, Adjustment of fluvial systems to climate and source terrain in tropical and subtropical environments, in Miall, A. D., ed., Fluvial sedimentology: Canadian Soci- ety of Petroleum Geology Memoir 5, p. 211-230.
  9. Boettcher, S., and Paczuski, M., 1996, Exact results for spa- tiotemporal correlations in a self-organized critical model of punctuated equilibrium: Physical Review Letters, v. 76, p. 348-351.
  10. Braga, G., and Gervasoni, S., 1989, Evolution of the Po River: An example of the application of historic maps, in Petts, G. E., Möller, H., and Roux, A. L., eds., Historical change of large alluvial rivers: Western Europe: Chichester, United Kingdom, Wiley, p. 113-126.
  11. Bretz, M., Cunningham, J. B., Kurczynski, P. L., and Nori, F., 1992, Imaging of avalanches in granular materials: Phys- ical Review Letters, v. 69, p. 2431-2434.
  12. Brice, J., 1973, Meandering pattern of the White River in Indi- ana-An analysis, in Morisawa, M., ed., Fluvial geomor- phology: London, George Allen & Unwin, p.179-200.
  13. Brice, J., 1974, Evolution of meander loops: Geological Soci- ety of America Bulletin, v. 85, p. 581-586.
  14. Dadson, S. J., 1998, Self-organized criticality and river mean- dering [Bachelor's thesis]: Oxford, United Kingdom, University of Oxford, School of Geography, 65 p.
  15. Dean, W. R., 1924, The stream-line motion of fluid in a curved pipe: Philosophical Magazine [London], v. 7, p. 673-695.
  16. Falcon Ascanio, M. A., and Kennedy, J. F., 1983, Flow in allu- vial river curves: Journal of Fluid Mechanics, v. 133, p. 1-16.
  17. Feder, J., 1988, Fractals: New York, Plenum, 283 p. Frette, V., Christensen, K., Malthe-Sørensen, A., Feder, J., Jøssang, T., and Meakin, P., 1996, Avalanche dynamics in a pile of rice: Nature, v. 379, p. 49-52.
  18. Furbish, D. J., 1988, River bend curvature and migration: How are they related?: Geology, v. 16, p. 752-755.
  19. Furbish, D. J., 1991, Spatial autoregressive structure in mean- der evolution: Geological Society of America Bulletin, v. 103, p. 1576-1589.
  20. Furbish, D. J., 1997, Fluid physics in geology: New York, Ox- ford University Press, 476 p.
  21. Gagliano, S. M., and Howard, P. C., 1984, The neck cutoff oxbow lake cycle along the lower Mississippi river, in Elliott, C. M. ed., River meandering: New York, Ameri- can Society of Civil Engineers, p. 147-158.
  22. Gibbs, R. J., 1967, The geochemistry of the Amazon River sys- tem. Part 1. The factors that control the salinity and com- position and concentration of the suspended solids: Geo- logical Society of America Bulletin, v. 78, p. 1203-1232.
  23. Gust, G., 1976, Observations on turbulent-drag reduction in a dilute suspension of clay in sea-water: Journal of Fluid Mechanics, v. 75, p. 29-47.
  24. Hasegawa, K., 1989, Universal bank erosion coefficient for me- andering rivers: American Society of Civil Engineers Jour- nal of Hydraulic Engineering, v. 115.
  25. Hickin, E. J., 1974, The development of meanders in natural river-channels: American Journal of Science, v. 274, p. 414-442.
  26. Hickin, E. J., and Nanson, G. C., 1975, The character of chan- nel migration on the Beatton River, northeastern British Columbia, Canada: Geological Society of America Bul- letin, v. 86, p. 487-494.
  27. Hooke, J. M., 1995, River channel adjustment to meander cut- offs on the River Bollin and River Dane, northwest Eng- land: Geomorphology, v. 14, p. 235-253.
  28. Hooke, J. M., and Redmond, C. E. 1992, Causes and nature of river planform change, in Billi, P., Hey, R. D., Thorne, C. R., and Tacconi, P., eds., Dynamics of gravel bed rivers: Chichester, United Kingdom, Wiley, p. 549-563.
  29. Howard, A. D., 1984, Simulation model of meandering, in Elliott, C. M., ed., River meandering: New York, Ameri- can Society of Civil Engineers, p. 952-963.
  30. Howard, A. D., 1992, Modelling channel migration and floodplain sedimentation in meandering streams, in Carling, C. A., and Petts, G. E., eds., Lowland flood- plain rivers: New York, John Wiley, p. 1-41.
  31. Howard, A. D., and Hemberger, A. T., 1991, Multivariate characterization of meandering: Geomorphology, v. 4, p. 161-186.
  32. Howard, A. D., and Knutson, T. R., 1984, Sufficient conditions for river meandering: A simulation approach: Water Re- sources Research, v. 20, p. 1656-1667.
  33. Ikeda, S., Parker, G., and Sawai, K., 1981, Bend theory of river meanders. Part 1. Linear development: Journal of Fluid Mechanics, v. 112, p. 363-377.
  34. Johannesen, H., and Parker, G., 1989, Linear theory of river meanders, in Ikeda, S., and Parker, G., eds., River mean- dering: American Geophysical Union Water Research Monograph 12, p. 379-415.
  35. Knighton, D., 1998, Fluvial forms and processes: A new per- spective: New York, Wiley, and London, Hodder (Arnold), 383 p.
  36. Korvin, G., 1992, Fractal models in the earth sciences: Amster- dam, Elsevier, 396 p.
  37. Leopold, L. B., Wolman, M. G., and Miller, J. P., 1964, Fluvial processes in geomorphology: San Francisco, California, Freeman, 511 p.
  38. Leopold, L. B., 1994, A view of the river: Cambridge, Massa- chusetts, Harvard University Press, 298 p.
  39. Lewis, G. W., and Lewin, J., 1983, Alluvial cutoffs in Wales and the Borderlands: International Association of Sedi- mentologists Special Publication 6, p. 145-154.
  40. Mandelbrot, B. B., 1967, How long is the coast of Britain? Sta- tistical self-similarity and fractional dimension: Science, v.155, p. 636-638.
  41. Mandelbrot, B. B., 1983, The fractal geometry of nature: New York, Freeman, 468 p.
  42. Matthes, G. H., 1947, Mississippi river cutoffs: American So- ciety of Civil Engineers Proceedings 73, part 1, Paper 2329.
  43. Montgomery, K., 1996, Sinuosity and fractal dimension of me- andering rivers: Area, v. 28, p. 491-500.
  44. Mullen, C. P., and Kirwan, A. D. Jr., 1994, Surface flow struc- ture of the Gulf Stream from composite imagery and satellite-tracked drifters: Nonlinear Processes in Geo- physics, v. 1, p. 64.
  45. Nelson, J. M., and Smith, J. D., 1989, Flow in meandering channels with natural topography, in Ikeda, S., and Parker, G., eds., River meandering: American Geophysi- cal Union Water Research Monograph 12, p. 321-377.
  46. Nikora, V., 1991, Fractal structure of river planforms: Water Resources Research, v. 27, p. 1327-1333.
  47. Odgaard, A. J., 1987, Streambank erosion along two rivers in Iowa: Water Resources Research, v. 23, p. 1225-1236.
  48. Paczuski, M., and Bak, P., 1993, Theory of the one-dimen- sional forest-fire model: Physical Review E, v. 48, p. R3214-R3216.
  49. Parker, G., 1976, On the cause and characteristic scales of me- andering and braiding in rivers: Journal of Fluid Mechan- ics, v. 76, p. 457-480.
  50. Parker, G., and Andrews, E. D., 1986, On the time development of meander bends: Journal of Fluid Mechanics, v. 162, p. 139-156.
  51. Parker, G., and Johannesen, H., 1989, Observations on several recent theories of resonance and overdeepening in mean- dering channels, in Ikeda, S., and Parker, G., eds., River meandering: American Geophysical Union Water Re- search Monograph 12, p. 379-415.
  52. Peitgen, H-O., Jürgens, H., and Saupe, D., 1992, Chaos and fractals: New York, Springer-Verlag, 984 p.
  53. Pizzuto, J. E., and Meckelnburg, T. S., 1989, Evaluation of a linear bank erosion equation: Water Resources Research, v. 25, p. 1005-1013.
  54. Power, W. L., and Tullis, T. E., 1991, Euclidean and fractal models for the description of rock surface roughness: Journal of Geophysical Research, v. 96, p. 415-424.
  55. Räsänen, M., Neller, R., Salo, J., and Jungner, H., 1992, Recent and ancient fluvial deposition systems in the Amazonian foreland basin, Peru: Geological Magazine, v. 129, p. 293-306.
  56. Richey, J. E., Victoria, R. L., Salati, E., and Forsberg, B. R., 1991, The biogeochemistry of a major river system: The Amazon case study, in Degens, E. T., Kempe, S., and Richey, J. E., eds., Biogeochemistry of major world rivers. SCOPE 42: Chichester, United Kingdom, Wiley, p. 57-74.
  57. Rodriguez-Iturbe, I., and Rinaldo, A., 1997, Fractal river basins: Cambridge, England, Cambridge University Press, 547 p.
  58. Rodriguez-Iturbe, I., Marani, M., Rigon, R., and Rinaldo, A., 1994, Self-organized river basin landscapes: Fractal and multifractal characteristics: Water Resources Research, v. 30, p. 3531-3539.
  59. Schroeder, M. R., 1991, Fractals, chaos, power-laws: New York, Freeman, 429 p.
  60. Schumm, S., and Khan, H., 1972, Experimental study of chan- nel patterns: Geological Society of America Bulletin, v. 83, p. 1755-1770.
  61. Schumm, S. A., Mosley, M. P., and Weaver, W. E., 1987, Ex- perimental fluvial geomorphology: New York, Wiley, 413 p.
  62. Snow, R. S., 1989, Fractal sinuosity of stream channels: Pure and Applied Geophysics, v. 131, p. 99-110.
  63. Stølum, H-H., 1996, River meandering as a self-organization process: Science, v. 271, p. 1371-1374.
  64. Stølum, H-H., 1997, Fluctuations at the self-organized critical state: Physical Review E, v. 56, p. 6710-6718.
  65. Stølum, H-H., and Friend, P. F., 1997, Percolation theory ap- plied to simulated meanderbelt sandbodies: Earth and Planetary Science Letters, v. 153, p. 265-277.
  66. Sun, T., Meakin, P., Jøssang, T., and Schwarz, K., 1996, A sim- ulation model for meandering rivers: Water Resources Research, v. 32, p. 2937-2954.
  67. Swanson, J. E., 1984, Tazlina river meander loop-A case his- tory, in Elliott, C. M., ed., River meandering: New York, American Society of Civil Engineers, p. 231-239.
  68. Taylor, G. I., 1929, The criterion for turbulence in curved pipes: Royal Society of London Proceedings, ser. A, v. 124, p. 243-249.
  69. Tricart, J., 1977, Types de lits fluviaux en Amazonie brésili- enne: Annales de Geographie, v. 86, p. 1-54.
  70. Walters, W. H., and Simons, D. B., 1984: Long-term changes of lower Mississippi river meander geometry, in Elliott, C. M., ed., River meandering: New York, American So- ciety of Civil Engineers, 318-329.
  71. Wang, Z. Y., and Larsen, P., 1994, Turbulent structure of water and clay suspensions with bed load: American Society of Civil Engineers Journal of Hydraulic Engineering, v. 120, p. 577-600.
  72. Wang, Z. Y., Larsen, P., and Xiang, X. K., 1994, Rheological properties of sediment suspensions and their implications: Journal of Hydraulic Research, v. 4, p. 495-516.
  73. Wang, Z. Y., Larsen, P., Nestmann, F., and Dittrich, A., 1998, Resistance and drag reduction of flows of clay suspen- sions: American Society of Civil Engineers Journal of Hydraulic Engineering, v. 124, p. 41-49.

Related papers

Influence of Valley Type on the Scaling Properties of River Planforms
Anicet Beauvais

Water Resources Research, 1996

Scaling properties of 44 individual river planforms from the Cascade and Olympic Mountains of Washington State were defined using the divider method. Analysis of the standardized residuals for least squares linear regression of Richardson plots reveals systematic deviations from simple self-similarity that correlate with the geomorphological context defined by valley type. A single fractal dimension describes rivers flowing through bedrock valleys. Those flowing in inherited glacial valleys exhibit two distinct fractal dimensions, with a larger fractal dimension at small scales. Rivers flowing in alluvial valleys are also described by two fractal dimensions, but with a larger dimension at large scales. We further find that the wavelength of the largest meander defines an upper limit to the scaling domain characterized by fractal geometry. These results relate scaling properties of river planforms to the geomorphological processes governing valley floor morphology. GoodchiM, 1980; Mark and Aronson, 1984; GoodchiM and Mark, 1987; Klinkenberg, 1992], few studies relate D to processes governing these forms [Woronow, 1981; Phillips, 1993]. Indeed, the relatively narrow range of fractal dimensions describing a variety of natural patterns suggests the futility of searching for ties with physical processes [Turcotte, 1992]. Some geomorphic features, however, exhibit scale-dependent variations in D, motivating examination of physical causes of such variations [Church and Mark, 1980; GoodchiM, 1980; Dutton, 1981; Lam and Quattrochi, 1992; Beauvais et al., 1994]. Efforts to evaluate potential connections between geomorphological process and scaling properties of river planforms are complicated by the many ways to calculate D. Some workers calculate the fractal dimension of river planforms from the relation between mainstream length and basin area [Hack, 1957]: L -= I3A • (1) where oe is the length of the river planform,/3 is a constant of proportionality, andA is the drainage area. Mandelbrot [1977], and later Church and Mark [1980] and Hjelmfelt [1988], interpreted the exponent a as being half the fractal dimension of the river planform (i.e., a -D/2). The relatively small range of a for most drainage basins (see data compiled by Montgomery and Dietrich [1992]) implies that D defined in this manner is equal to approximately 1.2 for rivers in general [Mandelbrot, 1983; Tarboron et al., 1988; Turcotte , 1992]. This implies that the D derived from length-area relations is not useful for examining differences among rivers. Furthermore, Robert and showed the unreliability of using a to infer the fractal dimension of rivers due to the effect of cartographic generalization.

View PDFchevron_right
Self-organized river basin landscapes: Fractal and multifractal characteristics
Riccardo Rigon

Water Resources Research, 1994

In recent years a new theory of the evolution of drainage networks and associated landscapes has emerged, mainly in connection with the development of fractal geometry and of self-organized criticality (SOC) concepts. This theory has much improved our understanding of the mechanisms which determine the structure of natural landscapes and their dynamics of evolution. In the first part of this paper the main ideas in the theory of landscape self-organization are outlined, and some remarkable features of the resulting structures are presented. In the second part we apply theoretical tools developed in the context of multifractal fields to the study of the scaling properties of the field of elevations of SOC landscapes. We observe that such landscapes appear to be more complex than simple self-affine fractals, although in some cases a simple fractal framework may be adequate for their description. We also show that multiple scaling may emerge as a result of heterogeneity in the field properties reflecting climate and geology.

View PDFchevron_right
Models of Fractal River Basins
Achille Giacometti

Journal of statistical …, 1998

Two distinct models for self-similar and self-affine river basins are numerically investigated. They yield fractal aggregation patterns following non-trivial power laws in experimentally relevant distributions. Previous numerical estimates on the critical exponents, when existing, are confirmed and superseded. A physical motivation for both models in the present framework is also discussed.

View PDFchevron_right
On the long-term behavior of meandering rivers
L. Ridolfi, C. Camporeale

Water Resources Research, 2005

1] In spite of notable advances in the description of river morphodynamics, the longterm dynamics of meandering rivers is still an open question, in particular, regarding the existence of a possible statistical steady state and its scaling properties induced by the competing action of cutoffs and reach elongation. By means of extensive numerical simulations, using three fluid dynamic models of different complexity and analysis of real data from the Amazon, North America, and Russia, we show that the reach cutoffs, besides providing stability and self-confinement to the meander belt, also act as a dynamical filter on several hydrodynamic mechanisms, selecting only those that really dominate the long-term dynamics. The results show that the long-term equilibrium conditions are essentially governed by only one spatial scale (proportional to the ratio of the river depth and the friction coefficient) and one temporal scale (proportional to the square of the spatial scale divided by the river width, the mean longitudinal velocity, and the erodibility coefficient) that contain the most important fluid dynamic quantities. The ensuing statistical long-term behavior of meandering rivers proves to be universal and largely unaffected by the details of the fluid dynamic processes that govern the short-term river behavior.

View PDFchevron_right
River Meandering as a Self-Organization Process
Hans Henrik Stolum

Simulations of freely meandering rivers and empirical data show that the meandering process self-organizes the river morphology, or planform, into a critical state characterized by fractal geometry.The meandering process oscillates in space and time between a state in which the river planform is ordered and one in which it is chaotic. Clusters of river cutoffs tend to cause a transition between these two states and to force the system into stationary fluctuations around the critical state.

View PDFchevron_right

Related topics

  • Applied Mathematics
  • Fluvial Processes
  • Fluvial Geomorphology
  • Fluvial Sedimentology

    • Cited by

    Aquatic Biodiversity in the Amazon: Habitat Specialization and Geographic Isolation Promote Species Richness
    Blanca Rengifo

    Animals, 2011

    Simple Summary: The immense rainforest ecosystems of tropical America represent some of the greatest concentrations of biodiversity on the planet. Prominent among these are evolutionary radiations of freshwater fishes, including electric eels, piranhas, stingrays, and a myriad of small-bodied and colorful tetras, cichlids, and armored catfishes. In all, the many thousands of these forms account for nearly 10% of all the vertebrate species on Earth. This article explores the complimentary roles that ecological and geographic filters play in limiting dispersal in aquatic species, and how these factors contribute to the accumulation of species richness over broad geographic and evolutionary time scales.

    View PDFchevron_right
    The potential for dams to impact lowland meandering river floodplain geomorphology
    James Webb

    TheScientificWorldJournal, 2014

    The majority of the world&#39;s floodplains are dammed. Although some implications of dams for riverine ecology and for river channel morphology are well understood, there is less research on the impacts of dams on floodplain geomorphology. We review studies from dammed and undammed rivers and include influences on vertical and lateral accretion, meander migration and cutoff formation, avulsion, and interactions with floodplain vegetation. The results are synthesized into a conceptual model of the effects of dams on the major geomorphic influences on floodplain development. This model is used to assess the likely consequences of eight dam and flow regulation scenarios for floodplain geomorphology. Sediment starvation downstream of dams has perhaps the greatest potential to impact on floodplain development. Such effects will persist further downstream where tributary sediment inputs are relatively low and there is minimal buffering by alluvial sediment stores. We can identify several...

    View PDFchevron_right
    Generic theory for channel sinuosity
    José Constantine

    Proceedings of the National Academy of Sciences, 2013

    Sinuous patterns traced by fluid flows are a ubiquitous feature of physical landscapes on Earth, Mars, the volcanic floodplains of the Moon and Venus, and other planetary bodies. Typically discussed as a consequence of migration processes in meandering rivers, sinuosity is also expressed in channel types that show little or no indication of meandering. Sinuosity is sometimes described as "inherited" from a preexisting morphology, which still does not explain where the inherited sinuosity came from. For a phenomenon so universal as sinuosity, existing models of channelized flows do not explain the occurrence of sinuosity in the full variety of settings in which it manifests, or how sinuosity may originate. Here we present a generic theory for sinuous flow patterns in landscapes. Using observations from nature and a numerical model of flow routing, we propose that flow resistance (representing landscape roughness attributable to topography or vegetation density) relative to surface slope exerts a fundamental control on channel sinuosity that is effectively independent of internal flow dynamics. Resistance-dominated surfaces produce channels with higher sinuosity than those of slope-dominated surfaces because increased resistance impedes downslope flow. Not limited to rivers, the hypothesis we explore pertains to sinuosity as a geomorphic pattern. The explanation we propose is inclusive enough to account for a wide variety of sinuous channel types in nature, and can serve as an analytical tool for determining the sinuosity a landscape might support. geopatterns | landscape controls | threadlike flows S inuous, threadlike flows are ubiquitous features of landscapes on Earth and other planetary bodies . Sinuosity is typically discussed as a consequence of channel migration processes in meandering rivers (1-9), where flow through a channel with modifiable boundaries and a curved planform sets up internal flow instabilities that drive spatial patterns of bank erosion and accretion, which change planform curvature. The physical mechanisms by which a nearly straight channel evolves into a freely meandering planform have been studied intensively and with great success. Strath terraces and meander bend cutoffs are evidence that even bedrock river channels can migrate, adjusting their sinuosity over time (10, 11). Despite their prominence, rivers with migrating meanders are a subset of the sinuous channel types that exist: lunar and Venusian rilles (12, 13) are sinuous, static patterns in lava channels; drainage channels in tidal mudflats that show little or no morphologic evidence of migration behavior can be characterized as quasi-static sinuous patterns (14-16). Sinuosity in some channels is described has having been "inherited" from a preexisting morphology (10, 14), but the nature of the antecedence tends to be unspecific or unexplained.

    View PDFchevron_right
    On the long-term behavior of meandering rivers
    L. Ridolfi, C. Camporeale

    Water Resources Research, 2005

    1] In spite of notable advances in the description of river morphodynamics, the longterm dynamics of meandering rivers is still an open question, in particular, regarding the existence of a possible statistical steady state and its scaling properties induced by the competing action of cutoffs and reach elongation. By means of extensive numerical simulations, using three fluid dynamic models of different complexity and analysis of real data from the Amazon, North America, and Russia, we show that the reach cutoffs, besides providing stability and self-confinement to the meander belt, also act as a dynamical filter on several hydrodynamic mechanisms, selecting only those that really dominate the long-term dynamics. The results show that the long-term equilibrium conditions are essentially governed by only one spatial scale (proportional to the ratio of the river depth and the friction coefficient) and one temporal scale (proportional to the square of the spatial scale divided by the river width, the mean longitudinal velocity, and the erodibility coefficient) that contain the most important fluid dynamic quantities. The ensuing statistical long-term behavior of meandering rivers proves to be universal and largely unaffected by the details of the fluid dynamic processes that govern the short-term river behavior.

    View PDFchevron_right
    Decadal length changes in the fluvial planform of the River Ganga: bringing a mega-river to life with Landsat archives
    Paul Carling

    Remote Sensing Letters, 2013

    The Landsat programme, which was started in 1972, initiated an era of space-based Earth observation relevant to the study of large river systems through the provision of spatially continuous, synoptic and temporally repetitive multispectral data. Free access to the Landsat archive via the Internet from mid-2008 has enabled the scientific community to reconstruct the Earth's changing surface and, in particular, to reconstruct the planform dynamics of the world's largest rivers. The present research reconstructed the planform changes occurring in the lower reaches of one of the Asian mega-rivers, the River Ganga (Ganges), from 1972 to 2010 using the Landsat archive. Sequential river planform maps generated from the time-series revealed the pattern of evolution of the river system over the study period. Specifically, within the observed sequence, the river started as a single-thread channel that then began meandering at four locations. The meander bends increased in sinuosity until chute cut-offs were triggered, returning the river to a state similar to that at the beginning of the sequence. This periodic pattern is constrained by several hard points in the geology, and by the Farakka Barrage, meaning that the observed cyclic pulsing is likely to continue into the future. This constrained pattern has significant implications for local planners who may currently fear that the river is migrating laterally.

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved