Geomorphology 118 (2010) 349–358
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Geomorphology
j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / g e o m o r p h
Conceptual assessment of (dis)connectivity and its application to the Ganga River
dispersal system
Vikrant Jain, S.K. Tandon ⁎
Department of Geology, Centre for Advanced Studies, University of Delhi, Delhi 11007, India
a r t i c l e
i n f o
Article history:
Received 17 September 2009
Received in revised form 1 February 2010
Accepted 3 February 2010
Available online 16 February 2010
Keywords:
Types of connectivity
Connectivity index
Sediment water fluxes
River response
Ganga River, India
a b s t r a c t
Connectivity in river dispersal systems is an important evolving concept and helps in understanding their
dynamics. In this study, the types of connectivity have been defined on the basis of two components, namely
“physical contact” and “transfer of material.” Based on this, four types of connectivity are recognized. These
are (a) active connected system, (b) inactive connected system, (c) partially active connected system, and
(d) disconnected system. Connectivity indices have been proposed based on the process understanding of
different types of connectivity. This conceptual understanding of connectivity has been applied to the Ganga
dispersal system, a large river system. Results of the connectivity analysis on the Ganga dispersal
system reveal that different sets of data such as sediment budgets, geochemical isotopic data and radiogenic
(238U–234U–230Th) data are noncoherent. Therefore, developing rigorous and well constrained quantitative
multidisciplinary data sets is needed to have a better understanding of connectivity at multiple spatiotemporal scales in large river dispersal systems; this would ultimately help model the dynamics of such
systems.
© 2010 Elsevier B.V. All rights reserved.
1. Introduction
The nature and pattern of flux in a large river basin determine the
basic processes of dispersal of material (water, sediment, organic
matter, and nutrients) and are critically linked to catchment
processes. Geomorphic processes within a landform, and landform
interaction in a river basin is mostly governed by sediment flux; hence
sediment flux has become an important component in geomorphic
studies of a river basin. A river basin can be classified into landscape
compartments or geomorphic provinces through which sediment
movement takes place. The compartments of river basins may be
connected or disconnected (Church, 2002; Harvey, 2002a). Connectivity is defined as the way in which different landscape compartments fit together in a catchment (Hooke, 2003; Brierley et al., 2006;
Stanford, 2006).
Connectivity between different compartments is useful in determining the sensitivity of a river basin to external factors such as
climate change, tectonic effects, river engineering works, or land use
change (Brunsden and Thornes, 1979; May, 2006; Gregory et al.,
2008; Chiverrell et al., 2009). In addition, connectivity is also useful in
explaining variation in sediment yield with little or no change in
external forcing, as small-scale processes like bed armouring,
sediment storage, vegetation cover, and remobilization may significantly change sediment yield at basin outlets (DeVente et al., 2006;
⁎ Corresponding author. Tel.: + 91 11 27667899; fax: + 91 11 27667093.
E-mail address: [email protected] (S.K. Tandon).
0169-555X/$ – see front matter © 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.geomorph.2010.02.002
Coulthard and VanDeWiel, 2007; Lesschen et al., 2009; López-Tarazón
et al., 2009). Hence, connectivity assessment based on sediment flux is
a major concern in the analysis of river response to external forcing.
The understanding of connectivity in a large river system characterized by diversity and complexity within and between its
different compartments is a fundamental requirement both for
understanding them as well as for river management, repair, and
rehabilitation. This study applies the concepts associated with
connectivity and its types to the Ganga dispersal system, with a
view to appraising its significance in gaining insights into catchment
processes, material and energy fluxes in a large multi-scale dispersal
system.
2. Connectivity components and types of connectivity
Connectivity has been defined in different ways through its analysis
in various disciplines including geomorphology (Harvey, 2002a; Hooke,
2003; Brierley et al., 2006), hydrology (Pringle, 2003; Bracken and
Croke, 2007), ecology (Lindenmayer and Fischer, 2006) and graph
theory (Foulds, 1993). In a geomorphic system, the connectivity
between two compartments has been defined either through physical
contact or through transfer of material, or both these components. In
general, geomorphological studies use the concept of connectivity in
relation to transfer of material, i.e., sediment, water, and nutrients across
well-defined compartment boundaries, as material transfer governs the
connectivity between landforms or processes—specific domains. Most
studies assume that transfer occurs only between physically connected
compartments (see Table 1). Some case studies have considered
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Table 1
Types of connectivity in geomorphic studies.
Details of analysis
Type of (dis)connectivity
References
4 dimensions of connectivity in ecology
3D connectivity between hyporheic corridor and its implications
toward landscape scale processes
Effect of spatial scale on ecological connectivity (connectivity enhances
in small zones)
Spatiotemporal variation in connectivity
Flood: enhances connectivity
Dam/levee: reduces connectivity
Cellular automata model for drainage network evolution and sediment
transfer
Hillslope–channel, gully–stream connectivity and role on dynamics of
whole river system
Reduction in connectivity is defined by decrease in sediment transfer,
though physical connection remains same
Coupling between landscape compartments
Interactions (material transfer) between physically connected systems
Transfer of material (water, sediment, and nutrients) between
physically connected corridors
Transfer of material between physically connected systems
Ward (1989)
Stanford and Ward (1993)
Interactions (material transfer) between physically connected systems
Ward and Stanford (1995)
Sediment transfer between physically connected system
DeBoer (2001)
Sediment transfer between physically connected system
Harvey (2001)
Sediment transfer,
Physical connection not required
Physical connection may vary temporally
Spatial scale ∝ temporal scale
Sediment transfer
Physical connection at normal event is not required
Sediment transfer, physical connection not required
Harvey (2002a)
Direction of connectivity (u/s to d/s: fast rate; d/s to u/s: slow rate)
Mapping of (dis)connected pockets in floodplains
(Physical) linkages between coarse sediments in a channel
Ex.: bar to bar, bar to bed, bed to bar, or vice versa
Cellular automata modeling to study catchment response to climate:
governed by nature and magnitude of connectivity
Characterisation of flow path and connectivity among different
constituents
Hillslope–channel connectivity
Hillslope–channel connectivity
Hyporheic connectivity and network growth
Sediment transfer between physically connected system
Bencala (1993)
Poole et al. (2002)
Hooke (2003)
Coulthard et al. (2002,
2005)
Fisher et al. (2007)
Transfer of material (water, sediment, nutrients) between physically
connected linkages
Physical connectedness between saturated area and slope failure region; Reid et al. (2007)
Transfer of coarse sediments
Sediment transfer, physical contact between landforms is not required
Chiverrell et al. (2009)
Material (water) transfer; physically connected system
Abrams et al. (2009)
material transfer (connectivity) between compartments that are not
physically connected (Poole et al., 2002; Hooke, 2003). Hooke (2003)
has also provided a classification of connectivity for physically
disconnected bars in a channel environment on the basis of nature of
coarse sediment flux. However, types of connectivity have not been
assessed on the basis of both physical connectedness and sediment
transfer. Further, no discussion is available of systems in which compartments are physically in contact or connected but no transfer takes
place between the compartments. These different scenarios require that
the concept of connectivity and its measurement be explored further
with the inclusion of all possibilities of material transfer and physical
contact.
Four types of connectivity are proposed on the basis of its two
components: (i) physical contact between compartments (Pc) and (ii)
transfer of material (Tm) (Fig. 1). Physical contact (Pc) represents the
qualitative and quantitative contact relationships between two compart-
ments. Further, these components (i.e., physical contact and material
transfer) may vary from one-dimensional to three-dimensional, which
will define the variable dimensions of connectivity. The first two
connectivity types are suggested in physically connected (Pc) systems.
Type 1 is named as active connected systems, which are characterized by
physically connected compartments with material transfer between
them. The material transfer may be one way (upstream to downstream)
or two ways (channel–floodplain transfer). Material transfer in a
physically connected system may cause significant change in the
compartments. Active channel–floodplain connectivity lies in this region,
where floodplain aggradation is a common phenomenon because of
frequent overbank flooding (Fig. 2a). Type 2, inactive connected systems
characterize systems with physically connected compartments but with
no material transfer ordinarily between them in a given time frame.
In a physically disconnected system, sediment transfer may occur
through aeolian or fluvial geomorphic agents, and these systems are
Fig. 1. Types of connectivity on the basis of two components, i.e., physical contact (Pc) and transfer of material (Tm).
V. Jain, S.K. Tandon / Geomorphology 118 (2010) 349–358
351
occurrences of unconfined floodplains (Stanford and Ward, 1993),
river discharge, and channel shape (Boulton et al., 2004; Mika et al.,
2008), whereas the processes (mixing pattern of surface and
groundwater) in the hyporheic zone are governed by geomorphic
complexity in the channel. The hyporheic process increases with an
increase in streambed undulations or an increase in bed porosity
(gravel-bed rivers) (Packman and Bencala, 2000; Boulton et al., 2004;
Mika et al., 2008). On a horizontal plane, anthropogenic disturbance of
a river system may smooth the bed profile or change the nature of
sediment supply (governed by lateral and longitudinal connectivity),
which may change the mixing processes in the hyporheic zone
(vertical plane). The pattern of vertical connectivity also varies with
channel morphology as vertical connectivity decreases with the
decrease in grain size, a result of lateral and longitudinal connectivity
(Kasahara and Hill, 2008). Hence, variation in connectivity in the
horizontal plane may impact the connectivity in the vertical plane.
3.2. Temporal dimension of connectivity
3.1. Spatial dimensions and interrelationships
The magnitude or type of spatial connectivity and their interactions may change with time, which provides it the fourth dimension.
Most of the connectivity studies are based on short time scale(s)
related to modern process studies (see references in Table 1). The
concept of connectivity at large spatial scale and at longer timescales
is highlighted by Harvey (2002a) in his detailed analysis of connectivity at different spatial scales. The temporal nature of variability may
be driven by tectonic, climate, or land use changes.
Tectonics in the hinterland may increase or decrease longitudinal
connectivity. Uplift in the hinterland area will enhance connectivity
through increase in channel gradient. However an uplift-induced
structural barrier transverse to river flow reduces connectivity between landforms upstream and downstream of the barrier (Fig. 3).
Extreme events may erode channel bars or significantly cause
erosion of the terrace or floodplains. In this case, the disconnected
landforms may be connected suddenly for a short time, and sediment
supply will increase manifold. Hence, climate change may increase
sediment yield at the basin outlet not only by increase in hinterland
erosion processes (Blum and Tornqvist, 2000) but also by enhancing
the intracompartment and intercompartment connectivity. This
increase in sediment yield may not necessarily be related directly
with the hinterland erosion, but may be the result of increase in
coupling at different scales. On the other hand, rainfall increase may
also cause large landslides from the increase in pore pressure on
hillslopes (Hoek and Bray, 1977; Schmidt and Montgomery, 1995),
which in turn, form barrier(s) across the channel and reduce sediment
transfer (Pratt et al., 2002). Hence, climate change could influence
Spatial connectivity is a three-dimensional property that requires
to be analysed in longitudinal, lateral and vertical directions (Ward,
1989; Stanford and Ward, 1993; Brierley et al., 2006). The interaction
between these different connectivity dimensions is responsible for
different response of a whole catchment. Interaction between
connectivity dimensions can be visualized in different planes.
Longitudinal and lateral connectivity interacts in a horizontal plane,
while vertical–longitudinal and vertical–lateral connectivity interacts
in vertical planes. This interaction provides the basis for 3-D understanding of processes.
The effect of lateral and longitudinal connectivity on vertical
connectivity can be analysed in a hyporheic zone. Hyporheic zone is
characterized by mixture of surface water and groundwater and is an
important aspect in geomorphological and ecological studies (Dahm
et al., 2006). The dynamics of the hyporheic zone is characterized by
spatial and temporal variability of vertical connectivity. However, the
distribution and boundaries of the hyporheic zone are governed by
features related to connectivity in the horizontal plane, which include
Fig. 3. Temporal variation in connectivity between hinterland (mountain) and
sediment deposition (plains) area due to tectonic processes. (a) The hinterland and
the sediment deposition zone are connected. (b) Uplift in the midstream reaches has
changed the connectivity. The smaller streams will be deflected in response to tectonic
processes, which will break the connectivity along these streams.
Fig. 2. Channel–floodplain connectivity: (a) channel–floodplain area connected and
forms the active connected system. (b) Partially active connected and disconnected
systems, in which “physically disconnected” channel and lower terrace (T1) will be
connected episodically during high (50-year) return period flood, while channel and
upper terrace (T2) surface are disconnected.
designated as partially active connected systems (Type 3, Fig. 1).
Material transfer between physically disconnected compartments
may be episodic (e.g. during rare events) or relatively more frequent
to continuous (aeolian transfer of sediment) in nature. Terrace T1 in
Fig. 2b is partially connected with the channel and provides an
example of episodic connection. Further, downstream transfer of
sediment may occur regularly, and connectivity will affect the
processes and morphology of the receptor landform, for example,
connectivity between channel bars in a river channel (Hooke, 2003).
In the absence of material transfer in a physically disconnected
system, the compartments are designated as disconnected systems
(Type 4, Fig. 1). Terrace T2 in Fig. 2b is disconnected with the channel.
3. Dimensions of connectivity
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sediment supply in either way through enhancing or reducing connectivity in the channel.
Anthropogenic and land use change may result in the decrease of
channel roughness (Brooks and Brierley, 1997), increase of sediment
supply (Langedal, 1997), construction of civil-engineering barriers
(Brandt, 2000), and increase/decrease of channel slope through
channelization work (Emerson, 1971; Brooks, 1987; Brooks and
Gregory, 1988; Surian and Rinaldi, 2003). All these activities change
connectivity in a fluvial system. This temporal variation in connectivity
results in different sets of processes and resultant morphology/
landforms. The nature of longitudinal connectivity in a channel may
increase because embankment construction restricts flow and causes
concentration of flow energy followed by erosion of depositional
features in the channel. Embankments also disconnect floodplains from
channels (lateral connectivity), resulting in significant effects on
floodplain accretion rates, river basin ecosystems and agricultural
productivity in the floodplain region. Lateral channel–floodplain
disconnection is short lived, and breaching of embankments and
channel avulsion results in sudden channel–floodplain connectivity
causing significant hazards on the plains. In an avulsive system, the
nature of connectivity between active and abandoned channels in a
landscape highlights the instability of avulsive system(s). In the case of
connected systems, the active channel may (re)occupy the abandoned
channel, and the probability of avulsion will always be higher (Jain and
Sinha, 2003, 2004). Connectivity in such an avulsive system may be
measured through a function including distance between channels, and
channel slope values and would be helpful to define an unstable avulsive
system. For example, the active channel and abandoned channel on the
Kosi megafan are connected, which causes channel reoccupation and,
hence, results in a highly unstable avulsive system (Sinha, 1998, 2009).
Therefore, an understanding of the temporal dimension of lateral
connectivity is an important aspect in fluvial hazard studies.
dams (longitudinal connectivity between channel reaches) and
embankments (lateral connectivity between floodplain and channel)
(Fryirs et al., 2007a).
3.3. Connectivity: time–space considerations
4. Measurement of connectivity
On the basis of time and spatial scales the nature of connectivity
may be divided into four classes: namely (A) small space–short
timescale, (B) large space–long timescale, (C) large space–short
timescale, and (D) small space–long time scale combinations (Fig. 4).
The closely spaced, small spatial units generally remain well
connected; and hence, short timescale–small space combinations
(class A) are a common occurrence, e.g., hillslope–channel connectivity, reach-scale longitudinal connectivity or floodplain–channel
connectivity. In a hierarchical scheme, involving progressive integration of successively higher spatial scales, class A may shift toward
either class B (large space–long timescale) through trajectory 1 or
toward Class C (large space–short time scale) through trajectory 2
(Fig. 4). The assignment to a particular trajectory will depend upon
sediment residence time. A low energy fluvial system with development of large floodplains will be characterized by high sediment
residence time; the river system will follow trajectory 1 and basin
scale river system will be classified under class B. A high energy fluvial
system with low floodplain sediment accumulation and distribution
will be characterized with less sediment residence time and the basin
will follow trajectory 2 and be classified under class C. Class B type
connectivity in a large river system will result in relatively less
influence of climate change or hinterland tectonics because of
buffering effects. Further, these types of fluvial systems are characterized by low recovery potential in river rehabilitation programs
(Fryirs and Brierley, 2001). Thus, characterization of connectivity
classes is an important aspect to analyse the future states of fluvial
systems.
Class D connectivity represents a greater time lag between small
spatial units. It may either occur in a lower energy environment
(topography) characterized by low relief and gentler slope or
between the closely spaced spatial units separated by barriers like
Connectivity estimation in the past has been made on the basis of
physical contact only. It was either based on physical contact of
subbasin area (Fryirs et al., 2007b) or saturated zones along hillslopes
(Reid et al., 2007). As noted earlier, connectivity is defined on the
basis of two aspects: namely physical contact and amount of sediment
transfer. The integration of these two aspects will better define
connectivity between two compartments. A conceptual representation of the connectivity index for physically connected compartments
(Cpc) (type 1 and type 2 connectivity of Fig. 1) may be written as
Fig. 4. Nature of connectivity in a time–space diagram and its interrelationship: class A:
short time–small space connectivity; class B: long time–large space connectivity; class
C: short time–large space connectivity; class D: long time–small space connectivity.
Progressive integration of successive classes is defined through different trajectories.
Cpc = A⁎im
ð1Þ
where A is area of physically connected dimensions, and im is the rate
of material transfer. In an inactive connected system (type 2
connectivity), the value of connectivity index will be null because im
will be zero; whereas in an active connected system, the value of
connectivity index between any two compartments having similar
connected area (A) will be different on the basis of variability in
sediment transfer rate (im). Further, rate of material transfer for a
given area will be higher in high energy compartments. Hence,
relatively high energy compartments will have strong connectivity for
the same amount of physical connectedness (A), which highlights the
role of geomorphic aspects on connectivity measurement.
In a physically disconnected system (partially active connected
system, type 3 connectivity), the connectivity (transfer) between two
compartments will depend on the energy condition and distance
between compartments. Hence, connectivity index between physically disconnected compartments (Cpd) can be conceptually represented as
Cpd = E = d
ð2Þ
V. Jain, S.K. Tandon / Geomorphology 118 (2010) 349–358
where E is energy in the system, which represent the driving force in
the compartment; and d is distance between the compartments.
5. Assessment of connectivity in a large river system: issues
and complexity
Large river systems have been defined on the basis of physical
parameters, namely channel length, basin area, or sediment load
(Potter, 1978; Tandon and Sinha, 2008). Generally, river systems having
more than an 800,000 km2 basin area and/or a 2500 km channel length
are classified as large river systems (Hovius, 1998; Tandon and Sinha,
2008). The study of large river systems includes the issues of multiple
scales, patterns, processes, hierarchy, diversity, multicausality and
variability in its different compartments. Multicausality and complexity
causes nonlinear response of a large river system to any disturbance,
hence, understanding of large river system(s) requires a detailed study
of (dis)connectivity between different compartments to understand the
complex nature of a large river system.
5.1. The Ganga River system
The concept of connectivity is applied to a large river system—the
Ganga River system. The Ganga River is one of the world's largest
dispersal systems with 2510 km channel length, 980,000 km2 basin area,
and a particularly very high suspended sediment load of 524 Mt/y. It is
characterized by a vast diversity of geomorphic forms and operating
processes that are related to the available energy in different compartments and energy transfer between compartments. The genetic
classification of the Ganga River system on the basis of energy distribution
353
(stream power) allows the recognition of five major compartments,
namely, (1) Himalayan hinterland extending from source to mountain
exit divisible into western (1A) and eastern (1B) parts; (2) cratonic
hinterland comprising the Aravalli, Bundelkhand, and Singhbhum belts;
(3) northern alluvial plains divisible into western (3A) and eastern (3B)
parts; (4) southern alluvial plains divisible into western (4A) and eastern
(4B) parts; and (5) Lower Ganga Plains and delta (Fig. 5) (Tandon et al.,
2008). Understanding of catchment-scale surface processes in the Ganga
dispersal system needs to be based on the analysis of connectivity
between these compartments (intercompartment connectivity) and of
connectivity between the landforms and processes within the individual
compartment (intracompartment connectivity).
5.1.1. Intercompartment connectivity in the Ganga dispersal system
The neighbouring compartments in the Ganga plains are actively
connected as both components, including physical connectedness and
sediment transfer, are important in this case and Eq. (1) can be used
for connectivity assessment between compartments; whereas, connectivity between distant compartments could be assessed through
Eq. (2). The nature of longitudinal connectivity between different
compartments is shown in Fig. 6a.
The hinterland area (source area) provides sediments to the Ganga
dispersal system; the Ganga plains act as temporary storage for
sediments as well as a zone of transportation. The Himalayan
hinterland is a younger Cenozoic orogenic belt that is characterized
by higher relief and steeper slopes in comparison to the cratonic
hinterland of Precambrian age. The high relief is also responsible for
orographic precipitation from the monsoon system and causes high
rainfall (1400–2000 mm) over the Himalaya, whereas the cratonic
Fig. 5. Map of the Ganga dispersal system showing different compartments based on energy distribution (stream power) and allogenic factors (tectonics and sea level change).
Numbers in the figure mark different compartments in the Ganga dispersal system and its details are provided in the text (after Tandon et al., 2008).
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Fig. 6. The compartments and connectivity in the Ganga dispersal system at different
scales: (a) intercompartment connectivity and (b) intracompartment connectivity.
hinterland is a drier region (600–1200 mm rainfall) (Singh, 1994).
Steeper slopes and higher rainfall are responsible for higher values of
the driving force (stream power = f {discharge (Q), channel slope (s)},
and hence the Himalayan hinterland is better suited to flush the
sediment to the Ganga plains in comparison to the cratonic hinterland
area. High rate of material transfer (im) is responsible for better
connectivity between the Ganga plains and the Himalayan hinterland
area relative to the cratonic hinterland (see Eq. (1)). Higher sediment
generation in the Himalaya and better connectivity with the Ganga
plains is responsible for dominance of the Himalayan-derived
sediments in the Ganga plains (Goodbred, 2003; Sinha et al., 2009).
For example, in the Yamuna River basin, even though the Himalayan
hinterland area is less than the cratonic hinterland area, the Himalayan-
derived sediments are far more than the craton-derived sediments
(Goodbred, 2003; Sinha et al., 2009). The role of cratonic hinterland
further decreases in downstream compartments i.e., WGP and LGP,
which are at a distance and weakly connected (Eq. (2)) with the craton
(Goodbred, 2003).
Further, tectonic-related connectivity along the Himalayan Mountain Front (MF) is characterized by temporal variability at the million
year timescale (Gupta, 1997; Singh and Tandon, 2008). Transverse
growth of fold structures along MF has deflected several river
channels forcing them to debouch onto the plains in different
locations. Hence, the structural evolution of landforms has changed
the nature of connectivity. This temporal variation in connectivity was
also suggested as a major cause for generating new landforms, such as
megafans (Gupta, 1997).
In the Ganga plains, the West Ganga plains (WGP) are longitudinally connected with the East Ganga Plains (EGP) through the trunk
Ganga River, which is further connected with the Lower Ganga Plains
(LGP). Connectivity between these compartments of the Ganga plains
will depend on its storage characteristics and sediment residence
time. These aspects are discussed in detail in a later section.
The LGP and the Bay of Bengal are a connected system. The LGP
provides sediment to the Bengal delta with rapid sedimentation over
the Holocene; sea level fluctuations on longer timescales affect
sedimentation processes in the LGP. In this case, longitudinal connectivity will also be defined by downstream to upstream connectivity, as
variations in sea level at different temporal scales will affect geomorphic
processes in the LGP through headward propagation of incision
following a base-level fall. The downstream to upstream connectivity
in the LGP will depend on sediment supply from upstream, channel
slope and sea floor slope, and rate of sea level fall (Schumm, 1993; Van
Heijst and Postma, 2001). The high sediment supply, high channel slope
in comparison to the sea floor slope, and the slow rate of sea level change
decrease the downstream–upstream connectivity (Schumm, 1993; Van
Heijst and Postma, 2001). Further, as the effect of sea level rise moves
upstream (against the energy gradient), the rate of downstream effect
will be very slow and its effect will be limited in the upstream reaches
(Schumm, 1993; Blum and Tornqvist, 2000; Harvey, 2002a; Blum,
2008). In this case, the upstream compartments (WGP and EGP) cannot
be considered to be directly connected with the Bay of Bengal.
5.1.1.1. Sediment residence time. Connectivity between different compartments is governed in part by sediment residence time in the
particular compartment, as it indicates relative material transfer in a
given time interval (see Eq. (1)). Sediment residence time is defined as
the average storage time in a given geomorphic unit. In a river system,
sediment residence time depends on different landforms that can be
defined as buffers, barriers, and blankets in the catchment (Fryirs et al.,
2007a). Residence time is linked to a time lag in river response to
external controls as eroded sediment (signature of tectonic, climate, or
land use change) will be delayed in reaching its downstream reaches
and will also increase reaction time of the whole basin in response to
Table 2
Time for sediment movement in the Ganga plains on the basis of different approaches.
No. Observation
Basis of analysis
1
No sediment variation in the delta
of major Asian rivers throughout
Quaternary
Doubling of sediment volume in
the Bengal delta from 7–11 ka
Sediment load and radioactive
measurement
Total sediment
Modeling of sediment load on the basis
of diffusion process; significant buffering
of signal from large floodplains
Sediment budgeting on the basis of
Total sediment
Quaternary stratigraphy
238
U–234U–230Th disequilibria analysis
Suspended and
dissolved load
Sediment in the plains area to
interpret sediment provenance
87
2
3
4.
Sr/86Sr and εNd values of sediments
Sediment
Time for downstream transfer of signal
(high sediment pulse)
References
Himalaya to Bengal delta—1 million
years (decoupled at submillion scale)
Metivier and Gaudemer
(1999)
Himalaya to Bengal delta—1–2 ka (tight
coupling between source and sink)
Ghaghra R (from MF to confluence with
Ganga R) ➔ 100 ka
Rapti R (from MF to confluence with
Ganga R) ➔ 160–250 ka
Floodplain sediment Tight coupling between Himalaya and
(suspended load)
Ganga plain
(Goodbred and Kuehl,
1999; Goodbred, 2003)
Chabaux et al. (2003,
2006)
Rahaman et al. (2009)
V. Jain, S.K. Tandon / Geomorphology 118 (2010) 349–358
any external change (Brunsden and Thornes, 1979). Estimates of
residence time using different methods have resulted in vast
differences in time lag computations for the Ganga plains, thus
highlighting the importance of studying and understanding connectivity and the interconnections between compartments. These different
sets of data for residence time are summarized in Table 2. On the basis
of a sediment diffusion model, a duration of 106 years is suggested for
sediment movement from the Himalayan hinterland to the Bay of
Bengal (Metivier and Gaudemer, 1999). The value of residence time in
the diffusion model was based on the elevation difference between
upstream and downstream ends of the plain, channel length, floodplain
355
width and sediment yield (Metivier and Gaudemer, 1999). Further, a
recent study based on 238U–234U–230Th disequilibria series computed
the sediment residence time in transverse drainage systems from the
mountain front to the confluence with the Ganga River as 150 ka for the
mountain-fed Ghaghra River and 250 ka for the plains-fed Rapti River
(Chabaux et al., 2006). This suggests that the average residence time in
the EGP is in the range of 150–250 ka, with the implication that the
Himalaya and the Bengal delta region cannot be considered as
connected parts of the dispersal system within the time frame of
150 ka, i.e. Bengal delta will not record signatures of Late Quaternary
climate change and Himalayan tectonic pulses.
Fig. 7. (a) Sketches showing the effect of geomorphological features on the intracompartment (dis)connectivity. (b) Field photograph showing disconnected hillslope–channel
system due to presence of a terrace surface, which acts as a barrier. (c) Field photograph of a connected hillslope–channel system where all eroded sediments from the hillslope will
contribute to the channel.
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V. Jain, S.K. Tandon / Geomorphology 118 (2010) 349–358
In a different set of sediment budgeting studies, a twofold increase in
sediment delivery to the Bengal delta at ∼10 ka was directly related
with erosional processes in the Himalaya (Goodbred and Kuehl, 1999,
2000). This study assumes insignificant residence time in the large-scale
dispersal of sediment caused by millennial-scale climate change(s).
Such divergent data (Table 2) highlight the contrasting picture and
raises the question of how strongly or weakly are the sediment data
from the Bengal delta or Ganga plains regions connected directly to
the Himalaya, as well as the timescale(s) of these connections? A
better appreciation of connectivity is required to have an in-depth
understanding of this cause–effect relationship.
Land use changes in the Himalaya have been implicated for sedimentation of downstream reaches and flooding from the alluvial
channels on the Ganga Plains (Blaikie and Muldavin, 2004; Wasson
et al., 2008). Also, tectonic processes and climate change have been
advanced as major reasons for sediment erosion. It is therefore
appropriate to question whether the increased sedimentation in the
plains is the result of (i) land use change in recent times or (ii) time
lag in response to monsoon intensification or tectonic pulses during
Holocene or earlier times? Such questions need more data sets and
better estimation of sediment residence time in these compartments
to assess connectivity between upstream–downstream processes.
5.2.1. Intracompartment connectivity in the Ganga dispersal system
Each compartment of a large river system is characterized by an
assemblage of various landforms. The landforms and landform-scale
processes may be connected or disconnected (Fig. 6b). Hillslope–
channel connectivity is an example of lateral connectivity in a
geomorphic compartment. The sediment produced from hillslope
erosion processes may be directly contributed to the channels or
may remain stored in geomorphic units like pediments, terraces, or
floodplain surfaces between hillslopes and channels (Fig. 7). The
mapping and distribution of such geomorphic units is helpful in
assessing the lateral connectivity. Disconnectivity causes storage of
available sediment, which increases sediment residence time. The
Himalayan–Ganga plain (near Mountain Front) connectivity will
depend on hillslope–channel lateral connectivity and longitudinal
connectivity between channel reaches. For example, during the
early Holocene monsoon strengthening, the enhanced hillslope
erosion in the Marsyandi catchment, Nepal Himalaya, filled the
bedrock valleys in the Himalaya (Pratt et al., 2002). At that stage,
hillslope–channel was laterally connected; however, all sediment
could not move downstream, hence longitudinal connectivity was
relatively weak. After slope stabilization from enhanced vegetation
on hillslopes around 7 ± 1 ka, the valley sediments were transported
downstream (Pratt et al., 2002). This temporal variation in
hillslope–mountain channel–plains channel connectivity has caused
a time lag of around 4–5 ka and was responsible for nonlinear
variation in the sediment supply in downstream reaches in response
to climate change.
Sediment flux in the channel is a function of the properties of
longitudinal connectivity. Structural evolution of these areas is
changing the longitudinal connectivity among landforms in the
debouchment zone of the Ganga dispersal system. Over relatively
long time scales, the Himalayan thrusts are propagating southward,
resulting in active anticlinal and fault related uplifts. The intermontane Dehradun valley provides one such example, where the uplift of
the Mohand ridge has affected the connectivity between the
Mussoorie Range and the Ganga plains (Fig. 8). Before uplift of the
Mohand ridge (≅ 500,000 ka extrapolated from the Pabbi hills (Keller
et al., 1977), the Mussoorie range was a source area for the Ganga
plain sediment, and it was directly connected with it. However, after
uplift of the Mohand ridge, the Ganga plains have been less directly
connected with the Mussoorie range. The sediments from the
Mussoorie range directly fill the intermontane valley north of the
Mohand ridge. Further, activation of faults (namely the Santaurgarh
thrust, and the Bhauwala thrust) (Raiverman et al., 1983; Singh et al.,
2001) in the dun area has caused channel incision, which converted
some of the depositional areas into hanging wall uplift-related
sediment source areas. The incision has resulted in the availability
of terrace sediments for transport to tributaries of the Yamuna and
Ganga River systems. Part of the sediment from the Mussoorie Range
is delivered to the axial river in the intermontane valley and is
reworked into the Ganga plains. Similar examples have earlier been
reported from elsewhere, for example Marchante Ridge, Spain
(Harvey, 2002b) and Wheeler Ridge, California (Keller et al., 1998).
It suggests that sediment supply from an upstream area in response to
major events will mostly be a nonlinear function over relatively
longer time scale of 104–105 years and will be governed by upstream–
downstream connectivity.
Fig. 8. The components of structural-driven connectivity in a part of the Ganga River basin. FCC image of the hinterland-proximal plains area is showing that uplift of the Mohand
ridge has disconnected Mussoorie range–Ganga plains system, which was connected before uplift of the Mohand ridge (cf. Fig. 4).
V. Jain, S.K. Tandon / Geomorphology 118 (2010) 349–358
5.3. Ganga plains connectivity: time–space considerations
The imprints of geological, climatic and anthropogenic controls are
located in the evolutionary trajectory of a landscape. The sensitivity of
different compartments to these external controls is mediated by
connectivity in different dimensions. The assessment of intercompartment connectivity should be based upon field based understanding of intracompartment connectivity. Time–space relationships
provide a way to analyse cross-scalar relations and the relative roles
of individual compartments in landscape evolution (Fig. 4).
The limited data from the Ganga plains have been analysed from
different compartments in time–space domain to obtain a preliminary
insight into the evolutionary nature of such a large river system. As
noted earlier, the data on sediment flux (Galy et al., 1999; Metivier
and Gaudemer, 1999; Goodbred, 2003; Chabaux et al., 2006) show
that these are not coherent. If higher sediment residence time of
250 ka to 2 million years is considered after Metivier and Gaudemer
(1999) and Chabaux et al. (2006), the B type connectivity class for the
Ganga River basin is indicated through trajectory 1. However, if the
millennial-scale transfer of signals of processes between the Himalaya
and delta sedimentation are considered as suggested by Galy et al.
(1999) and Goodbred (2003), class C connectivity through trajectory
2 would be favored.
In order to obtain a detailed understanding of landscape evolution,
and also to resolve the noncoherence in the existing data, more data
using quantitative approaches on sediment flux at different scales are
required. This will then allow a dependable assessment of connectivity at the scale of the entire Ganga River system.
6. Conclusions
The conceptual assessment of (dis)connectivity and its application to
a large river dispersal system has resulted in the following conclusions.
1. Four types of connectivity—active connected, inactive connected,
partially active connected and disconnected are suggested on the
basis of both physical connectedness/contact and material transfer.
2. Multi-scale large river systems involve both space and time
hierarchy, and connectivity is a tool that can help understand
landscape trajectories using time–space diagrams. Time–space
domains (past, present, and future projection) of connectivity
should be analysed for obtaining insights on the evolutionary
trajectories of river systems.
3. (Dis)connectivity between landscape compartments is an important
factor to analyse the catchment response to external controls, such as
tectonic, climate change, or anthropogenic activity. Most of the
studies are carried out through sediment load/sediment transport in
the downstream reaches, without obtaining any understanding of
connectivity between different landscape compartments in a river
basin. The qualitative and quantitative understanding of connectivity
becomes even more important in large river systems like the Ganga
because of complexity and diversity in processes and patterns in
compartments in a multi-scale context.
4. The source to sink approach of the Ganga dispersal system needs
connectivity understanding between all compartments. The
neighbouring compartments in the Ganga plains are connected.
However, the connectivity understanding between all compartments of the Ganga plains requires more data of residence time at
different scales, as limited available data show conflicting values of
residence time.
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