The Mountain Front Flexure marks a dominant topographic step in the frontal
part of the Zagros Fold–Thrust Belt. It is characterized by numerous active
anticlines atop of a basement fault. So far, little is known about the
relative activity of the anticlines, about their evolution, or about how
crustal deformation migrates over time. We assessed the relative landscape
maturity of three along-strike anticlines (from SE to NW: Harir, Perat, and
Akre) located on the hanging wall of the Mountain Front Flexure in the
Kurdistan Region of Iraq to identify the most active structures and to gain
insights into the evolution of the fold–thrust belt. Landscape maturity was
evaluated using geomorphic indices such as hypsometric curves, hypsometric
integral, surface roughness, and surface index. Subsequently, numerical
landscape evolution models were run to estimate the relative time difference
between the onset of growth of the anticlines, using the present-day
topography of the Harir Anticline as a base model. A stream power equation
was used to introduce fluvial erosion, and a hillslope diffusion equation was
applied to account for colluvial sediment transport. For different time steps
of model evolution, we calculated the geomorphic indices generated from the
base model. While Akre Anticline shows deeply incised valleys and advanced
erosion, Harir and Perat anticlines have relatively smoother surfaces and are
supposedly younger than the Akre Anticline. The landscape maturity level
decreases from NW to SE. A comparison of the geomorphic indices of the model
output to those of the present-day topography of Perat and Akre anticlines
revealed that it would take the Harir Anticline about 80–100 and
160–200 kyr to reach the maturity level of the Perat and Akre anticlines,
respectively, assuming erosion under constant conditions and constant rock
uplift rates along the three anticlines. Since the factors controlling
geomorphology (lithology, structural setting, and climate) are similar for
all three anticlines, and under the assumption of constant growth and erosion
conditions, we infer that uplift of the Akre Anticline started 160–200 kyr
before that of the Harir Anticline, with the Perat Anticline showing an
intermediate age. A NW-ward propagation of the Harir Anticline itself implies
that the uplift has been independent within different segments. Our method of
estimating the relative age difference can be applied to many other
anticlines in the Mountain Front Flexure region to construct a model of
temporal evolution of this belt.
Introduction
The Zagros Fold–Thrust Belt (hereafter referred to as Zagros) is an active orogen that resulted from the
collision between the Arabian and Eurasian plates and contains the deformed
portions of the NE part of the former Arabian passive margin (Fig. 1;
Berberian, 1995; Mouthereau et al., 2012). Many aspects of the structural
configuration and the evolution of the Zagros Fold–Thrust Belt are by now
satisfactorily constrained, but the detailed spatial and temporal
distribution of deformation across the belt is not yet well understood. This
concerns especially the NW part of the belt in the Kurdistan Region of Iraq
(KRI) due to a lack of comprehensive studies and for geopolitical reasons
that make access to the field challenging. The style, timing, and relative
activity of front thrusts, deformation propagation, and along-strike
variations have not been sufficiently studied. It is not well known which
structures are currently the most active ones either.
Tectonic subdivision of the NW segment of the Zagros Fold–Thrust
Belt (modified after Berberian, 1995; Emre et al., 2013; Koshnaw et al.,
2017; Zebari and Burberry, 2015). Names within the parentheses are known in
the Iranian part of Zagros.
One of the morphologically most conspicuous structural elements of the Zagros
Fold–Thrust Belt is the Mountain Front Flexure (MFF), which separates the
High Folded Zone and the Foothill Zone (known in Iran as the Zagros Simply
Folded Belt and Zagros Foredeep, respectively; Figs. 1 and 2; Berberian,
1995; Jassim and Goff, 2006; McQuarrie, 2004; Mouthereau et al., 2012;
Vergés et al., 2011). In most parts of the Zagros
Mountains, the MFF marks a pronounced topographic step, separating folds
with high amplitudes, narrow wavelengths, and higher topography in the High
Folded Zone from folds with relatively low amplitudes, long wavelengths, and
lower topography in the Foothill Zone (Fig. 2). The MFF is characterized by
numerous active anticlines atop of fault strands emerging from a basement
fault. It was suggested that the onset of the MFF activity in the NW Zagros was about 5±1 Ma based on low-temperature thermochronology
(Koshnaw et al., 2017). The timing of this activity is expected to differ
along-strike of the belt and, hence, the initiation of uplift of the
anticlines on the hanging wall of the MFF is the key to understand this
temporal and spatial evolution. In the neighboring Iranian part, the MFF was
a relatively long-lived structure active from 8.1 to 7.2 Ma to about the
Pliocene–Pleistocene boundary. After that, only the southwesternmost
anticline remained active in front of the MFF. This was inferred from
progressive unconformities and magnetostratigraphy (Hessami et al., 2001,
2006; Homke et al., 2004).
Geological map of the
Zagros Fold–Thrust
Belt in KRI showing the
location of the three anticlines Harir, Perat, and Akre with respect to the
MFF that separates the High Folded Zone from the Foothill Zone (modified
after Csontos et al., 2012; Sissakian, 1997; Zebari and Burberry, 2015).
In active orogens, the main factor that contributes to building up topography
is ongoing convergence (e.g., Bishop, 2007; Burbank and Anderson, 2012;
Whittaker, 2012). Recent advancements in the availability of high-resolution
digital elevation models (DEMs) and geographic information system (GIS) software allows us to quantitatively
analyze the landscape (Bishop, 2007; Tarolli, 2014). Tectonic geomorphology
approaches and landscape maturity studies have been used extensively and
proven to be efficient in studying the relative tectonic activity of
different areas in contractional settings (Cheng et al., 2012; Mahmood and
Gloaguen, 2012; Ramsey et al., 2008; Regard et al., 2009). Nevertheless, the
NW part of the Zagros lacks modern studies on tectonic geomorphology – with
few exceptions. Bretis et al. (2011) detected sets of wind gaps (i.e.,
segments of river valleys abandoned due to lateral and vertical fold growth)
in the High Folded Belt, NE of the MFF, suggesting that larger folds grew by
linkage of smaller, shorter folds. Zebari and Burberry (2015) performed
detailed analyses of various geomorphic indices for numerous anticlines in
the High Folded Zone, concluding that the combination of clearly asymmetric
drainage patterns and the mountain front sinuosity index (Bull, 2007; Keller
et al., 1999) is a valuable tool for identifying putatively active
fault-related folds. Obaid and Allen (2017) studied the landscape maturity of
various anticlines within the Zagros Foothill Zone and constrained the order
of deformation of these anticlines by proposing an out-of-sequence
propagation of underlying faults into the foreland. They proposed that the
Zagros Deformation Front was among the earliest faults that have been
reactivated within the Foothill Zone.
In an active orogen such as the Zagros, a better understanding of the
temporal and spatial distribution of deformation due to ongoing tectonics
can be achieved with landscape modeling. In the last 2 decades, numerical
models have been extensively used to study landscape evolution (Chen et al.,
2014; Tucker and Hancock, 2010) and several software packages were
specifically developed for this purpose (e.g., Hancock and Willgoose, 2002;
Hobley et al., 2017; Refice et al., 2012; Salles and Hardiman, 2016; Tucker
et al., 2001). Most of these models include algorithms for bedrock fluvial
incision and hillslope creep as input parameters. Several studies have
constrained the landscape evolution with the involvement of the
corresponding tectonics and structures elsewhere (Collignon et al., 2016;
Cowie et al., 2006; Miller et al., 2007; Refice et al., 2012).
In this study, we assessed variations in the landscape maturity of three
anticlines (from SE to NW, the Harir, Perat, and Akre anticlines) located on
the hanging wall of the MFF by quantitatively analyzing landscape indices
(hypsometric curve, hypsometric integral, surface roughness, and surface
index) in order to distinguish more mature segments from less mature ones,
and to reconstruct the relative variation in uplift time and/or rate along
these anticlines. We then computed the difference in the onset of uplift
between more mature anticlines and less mature ones using a landscape
evolution model. The present-day topography of the least mature anticline
served as an input model for computing the time that it takes this anticline
to reach the same state as the most mature one. Also, three structural
cross sections were constructed across the three anticlines to delineate
their structural style and to link it with their landscape maturity.
Geological setting
The Zagros Fold–Thrust Belt is the result of the collision between the
Arabian and Eurasian plates (Fig. 1; Berberian, 1995; Mouthereau et al.,
2012). Continental collision started in the Early Miocene following the
progressive subduction of Neo-Tethyan oceanic lithosphere underneath Eurasia
(Agard et al., 2011; Csontos et al., 2012; Koshnaw et al., 2017; Mouthereau
et al., 2012). The Zagros Fold–Thrust Belt extends for about 2000 km from
the Strait of Hormuz in southern Iran to the KRI and further into SE Turkey.
Since the onset of collision, the deformation front has propagated
250–350 km southwestward, involving the northeastern margin of the
Mesopotamian Foreland Basin and the Persian Gulf into a largely
NW–SE-trending foreland fold–thrust belt (Mouthereau, 2011; Mouthereau et
al., 2007). The shortening across different sectors of the Zagros
Fold–Thrust Belt is estimated to range between 10 % and 32 % (Blanc
et al., 2003; McQuarrie, 2004; Molinaro et al., 2005; Mouthereau et al.,
2007; Vergés et al., 2011). GPS-derived horizontal velocities between
Arabia and Eurasia show present-day convergence rates between 19 and
23 mm yr-1 (McClusky et al., 2003). It is suggested that deformation
partitioning occurs between the external and internal portions of the Iranian
part of the Zagros Fold–Thrust Belt. While the internal Zagros Fold–Thrust
Belt currently accommodates 3–4 mm yr-1 of right-lateral displacement
along the Main Recent Fault (Fig. 1; Reilinger et al., 2006; Vernant et al.,
2004), the external part accommodates 7–10 mm yr-1 of shortening by
thrusting and folding (Hessami et al., 2006; Vernant et al., 2004),
2–4 mm yr-1 of which is taken up by the MFF in the Fars Arc (Oveisi
et al., 2009). However, no such estimates are available for the Iraqi segment
of the Zagros. It is hence not known how much of the total
Arabia–Eurasia plate convergence is being accommodated across the Iraqi part
of the Zagros Fold–Thrust Belt.
The NW segment of the Zagros Fold–Thrust Belt in the KRI is subdivided into
several NW-trending morphotectonic
zones. These zones from NE to SW are (i) Zagros Suture Zone, (ii) Imbricated Zone, (iii) High Folded Zone, and
(iv) Foothill Zone (Figs. 1 and 2; Jassim and Goff, 2006). These zones are
bounded by major faults in the area. The faults include Main Zagros Fault
separating the Zagros Suture Zone from the Imbricated Zone, High Zagros Fault
that separates the Imbricated Zone from the High Folded Zone, and the
Mountain Front Flexure that separates the High Folded Zone from the Foothill
Zone (Figs. 1 and 2; Berberian, 1995; Jassim and Goff, 2006).
The deformed sedimentary succession is composed of 8–12 km thick Paleozoic
to Cenozoic strata that rest on the Precambrian crystalline basement (Aqrawi
et al., 2010; Jassim and Goff, 2006). The thick sedimentary cover consists of
various competent and incompetent rock successions separated by detachment
horizons. The infra-Cambrian Hormuz salt, which acts as a basal detachment in
much of the southern and central Zagros in Iran, pinches out towards the
northwest (Hinsch and Bretis, 2015; Kent, 2010). Other intermediate
detachment horizons influence the structural style of the central Zagros in
Iran (e.g., Sepehr et al., 2006; Sherkati et al., 2006), but their behavior
is uncertain in NW Zagros due to limitations in outcrops and insufficient
seismic profiles southwest of the Main Zagros Fault. Some proposed detachment
levels include Ordovician and Silurian shales (Aqrawi et al., 2010),
Triassic–Jurassic anhydrites (Aqrawi et al., 2010; Hinsch and Bretis, 2015;
Zebari, 2013; Zebari and Burberry, 2015), and Lower Miocene anhydrite (Aqrawi
et al., 2010; Csontos et al., 2012; Jassim and Goff, 2006; Kent, 2010; Zebari
and Burberry, 2015).
The exposed geological units within the High Folded Zone are limited to
ca. 5 km thick Upper Triassic–Quaternary stratigraphic succession (Fig. 2; Jassim and
Goff, 2006; Law et al., 2014). Most anticlines are made up of Cretaceous
carbonate rocks, while Upper Triassic–Lower Cretaceous strata are only
exposed in the core of some anticlines. The Tertiary clastic rocks are
preserved within the adjacent synclines. Within the studied structures, the
Upper Jurassic–Lower Cretaceous Chia Gara and Lower Cretaceous Sarmord
formations only crop out in Bekhme and Zinta gorges and consist of medium to
thick bedded marly limestone, dolomitic limestone, and shale (Figs. 2 and 3).
The Lower Cretaceous succession of Qamchuqa and Upper Cretaceous Bekhme and
Aqra formations consist of thick bedded and massive reef limestone, dolomitic
limestone, and dolomite. These units are generally rigid and resistant to
erosion. Thus, they build the raised cores of anticlines. The Upper
Cretaceous–Tertiary succession consists primarily of clastic rocks, which
are mostly denuded, and alternating upper Paleocene and upper
Eocene limestone of Khurmala and
Pila Spi formations, respectively. They form a ridge surrounding the
anticlines (Figs. 2 and 3). Unconsolidated Quaternary sediments in the study
area consist of slope deposits, residual soil, alluvial fan deposits, and
river terraces.
Stratigraphic column of the exposed rock units in the area.
Thicknesses are given as in well Bijeel-1 (Fig. 2), which is located 5 km to
the south of Perat Anticline (modified after Law et al.,
2014). The column is
scaled to the stratigraphic thicknesses.
There is no agreement concerning the overall structural style of the NW
Zagros in KRI. Several authors (Al-Qayim et al., 2012; Ameen, 1991;
Fouad, 2014; Jassim and Goff, 2006; Numan, 1997) suggested that the Iraqi
part of the Zagros Fold–Thrust Belt reveals a combination of both thin- and
thick-skinned deformation. Partly relying on reflection seismic data, it was
also suggested that contraction has been localized on inherited
passive-margin normal faults in the basement, which were inverted during the
late stage of deformation since ca. 5 Ma (Abdulnaby et al., 2014; Burberry,
2015; Koshnaw et al., 2017). The structural relief across the MFF (Fig. 2) is
likely linked to blind thrusts in the basement (Al-Qayim et al., 2012; Ameen,
1991, 1992; Fouad, 2014; Koshnaw et al., 2017; Numan, 1997). The same linkage
between structural relief and a regional basement blind thrust is also
documented in the Iranian Zagros (Blanc et al., 2003; Emami et al.,
2010; Leturmy et al., 2010; Sherkati et al., 2006). Alternatively, Hinsch and
Bretis (2015) argued that the structural relief in the hanging wall of the
MFF is related to an underlying duplex structure that is linked to a stepped
detachment horizon rooting in a lower Paleozoic detachment in the internal parts of the orogen. The
relief has been attributed to the accumulation of the Hormuz salt in the
Iranian Zagros (McQuarrie, 2004). Even though the MFF is believed
to be a major blind thrust in the basement (Berberian, 1995), it is usually
mapped along the southwestern limb of the last high anticline where the Pila
Spi limestones or the Bekhme and Aqra limestones crop out (Fouad, 2014;
Jassim and Goff, 2006; Numan, 1997). Given that landforms in the vicinity of
the MFF indicate ongoing tectonic deformation, we suspect that these blind
faults might be active at present. Unfortunately, however, instrumental
seismicity in the entire region is too diffusely distributed to be attributed
to any particular faults (Jassim and Goff, 2006).
Structurally, this segment of the Zagros Fold–Thrust Belt is dominated by
NW–SE trending fault-related folds, the trend of folds changes to nearly
E–W to the west of the Great Zab river (a.k.a. Greater Zab River; Fig. 2). The folds are
usually S-verging and the related faults emerge to the surface within both
the Imbricated Zone and High Folded Zone, while they remain blind within the
Foothill Zone (Fouad, 2014; Hinsch and Bretis, 2015).
Data and methods
We calculated and analyzed landscape indices from DEMs for the studied
anticlines and built a landscape evolution model that simulates progressive
uplift and erosion of the landscape. We also constructed structural cross sections across
these anticlines based on literature data and our own field observations.
Geomorphic indices
The present-day relief in the study area resulted from a competition between
rock uplift triggered by horizontal contraction and erosion destroying it.
Parameters controlling these competing processes are the rate of tectonic
accretion, rock erodibility, and climate (Bishop, 2007; Burbank and Anderson,
2012).
In order to quantitatively analyze the landscape for the Harir, Perat, and
Akre anticlines (Figs. 2 and 4), we calculated hypsometric curves and
determined three geomorphic indices: (i) hypsometric integral, (ii) surface
roughness, and (iii) surface index. These are considered proxies for the
relative maturity of a particular landscape. The hypsometric curve and
integral refer to the distribution of surface area of a landscape with
respect to the elevation (Strahler, 1952). The surface roughness value is
mainly sensitive to incision (Andreani et al., 2014; Andreani and Gloaguen,
2016; Pike and Wilson, 1971); the surface index is a measure for the amount
of erosion. When referring to the results obtained by using this set of
geomorphic indices, we colloquially refer to them as “landscape maturity”
parameters.
Hypsometric curve
The hypsometric curve for a basin is the frequency distribution of elevation
of the watershed area below a given height (Strahler, 1952). Convex-shaped
hypsometric curves represent relatively youthful stages of the basin,
S-shaped and concave curves refer to more mature and old stages (Strahler,
1952). Hypsometric curves are usually calculated for a specific drainage
basin. In this study, we calculated the weighted mean of the hypsometric
curves for basins with areas > 0.25 km2 within each
anticlinal ridge, weighted by the basin area within the anticline. We
restricted our analyses to those basins where Upper Cretaceous carbonates are
exposed (Fig. 4). This allowed us to make realistic comparisons between the
three anticlines, neglecting the differences in rock erodibility that arise
when varying lithologies are included. Wind gaps and water gaps as well as
the plunging crests of the anticlines were also excluded from the
calculation.
Topography and slope maps of the studied anticlines obtained from
30 m resolution SRTM1 DEM data showing the location of water and wind gaps
across these anticlines.
Hypsometric integral
The hypsometric integral (HI) is the ratio of area under the hypsometric
curve (Strahler, 1952). It is used to highlight the erosional stage of a
landscape with high values corresponding to less mature landscapes and low
values indicating advanced stages of erosion. The hypsometric integral is
computed for a certain area by the following equation (Pike and Wilson,
1971):
HI=hmean-hminhmax-hmin,
where hmean, hmin, and hmax are the mean,
minimum, and maximum elevations (m) of the examined area.
Surface roughness
The surface roughness (SR) measures how much an area deviates from being
totally flat. It differentiates flat planar surfaces with values close to 1
from irregular surfaces with higher values. It increases with the increase
in incision by streams. The surface roughness is calculated using the
following equation (Grohmann, 2004; Hobson, 1972):
SR=TSFS,
where TS and FS are the areas (m2) of the actual topographic surface
and the corresponding projection of that surface onto a planar surface,
respectively.
Surface index
The surface index (SI; Andreani et al., 2014) combines elevations,
hypsometric integral, and surface roughness to map simultaneously preserved
and eroded portions of an elevated landscape. It is calculated using Eq. (3)
(Andreani and Gloaguen, 2016):
SI=(NHI⋅Nh)-NSR,
where NHI, Nh, and NSR are the
normalized hypsometric integral, elevations, and surface roughness values,
respectively. Elevated and poorly incised landscapes with high hypsometric
integral and low surface roughness show positive surface index values. Highly
dissected landscapes with a high surface roughness yield negative surface
index values. This means that the surface index is also sensitive to
elevation.
Digital elevation models
The geomorphic indices for this study were calculated from the 12 m
resolution TanDEM-X DEM (Krieger et al., 2007) obtained from the German
Aerospace Center (DLR) and the 30 m resolution SRTM1 (Shuttle Radar Topography Mission Global 1 arc second Data) DEM (NASA
JPL, 2013); these two inputs were used since different DEM inputs give
slightly different geomorphic results (Andreani et al., 2014; Koukouvelas et
al., 2018; Obaid and Allen, 2017). Geomorphic indices were calculated using
both the TanDEM-X and the SRTM1 data. However, the TanDEM-X data revealed
numerous artifacts and voids, which made calculations unstable and results
unreliable (also see the comparison in the Supplement). All results of the
geomorphic indices and subsequent calculations presented in the following
sections were calculated from a 100×100 cell (3km×3 km) moving window on the 30 m resolution SRTM1 data. A larger moving
window makes the obtained measurements smoother and vice versa. The size of
the moving window must be chosen based on the scale of the target; here we
targeted anticlines with wavelengths varying from 5 to 8 km. A 3 km moving
window covered almost an entire limb of an anticline. We also tested the
method proposed by Pérez-Peña et al. (2009) in order to account for
the neighboring cells in the calculation of the geomorphic indices. Rather
than using a moving window, this approach uses a spatial autocorrelation of
neighboring cells and maps clusters of high and low values of indices using
Moran's I statistic (Moran, 1950) and Gi* statistics (Ord and Getis,
1995). We have tested the same method here by calculating the HI for a
500m×500 m grid of the SRTM data. We applied a hot spot
analysis using Gi* statistics with a distance of 1.5 km to define
neighbor cells. Then, we resampled the HI map calculated from a 100×100 cell (3km×3 km) moving window to 500m×500 m grid from SRTM data. The calculations were performed using the
focal and zonal tool sets in ESRI ArcGIS 10.4 software. In addition, the SRTM
DEMs with 30 m resolution were used to extract topographic profiles,
drainage networks, watersheds, stream slopes, and upstream drainage areas
wherever required.
Modeling landscape evolution
We built a landscape evolution model to quantify the time difference in
between the maturity level of the more mature and less mature anticlines by
comparing the geomorphic indices of the evolved landscape with those of both
anticlines based upon the open-source Landlab toolkit (Hobley et al., 2017;
http://landlab.github.io, last access: 13 March 2019).
We used two components in our model: one simulating erosion due to fluvial
action and another simulating sediment transport along slopes due to
hillslope diffusion processes. Chen et al. (2014) showed that consideration
of only these two components is sufficient for many landscapes but cannot
model fluvial sedimentation. However, from field observations and from
satellite imagery, we know that no significant fluvial sedimentation takes
place on the slopes of the analyzed anticlines. On slopes of anticline
flanks, the detachment-limited erosion due to the fluvial system tends to be
the dominant process (Howard, 1994). To detect changes in the landscape due
to fluvial erosion through time, we applied the commonly accepted idea that
the rate of stream incision is directly proportional to the hydraulic shear
stress of a stream (Braun and Willett, 2013). Consequently, we used the
stream power incision law (Sklar and Dietrich, 1998; Whipple and Tucker,
1999):
∂z∂t=KAmSn,
where ∂z/∂t is the erosion rate (m yr-1); K is an
erodibility coefficient (yr-1 m(1-2m)) that encompasses the
influence of climate, lithology, and sediment transport processes; A is the
upstream drainage area (m2) and is typically taken as a proxy for
discharge (Wobus et al., 2006); S=∂z/∂x is the local
channel slope (m m-1); z is the elevation (m); and m and n are
the area and slope exponents, respectively. The stream power incision law
(Eq. 4) is derived since the upstream drainage area A scales with channel
discharge and channel width. The magnitude of the sediment flux in the
channel is assumed to equal unity in the standard detachment-limited stream
power model (Perron, 2017; Whipple, 2002). In the model, an incision
threshold (C) was included, below which no incision occurs (Hobley et al.,
2017).
To account for the provision of sediment due to hillslope diffusion
processes from slopes outside the river system, we used the hillslope
diffusion equation (Culling, 1963; Tucker and Bras, 1998):
∂z∂t=Kd∇2z,
where Kd is the diffusivity coefficient (m2 yr-1), z
is the elevation (m), and ∇2 is the Laplace operator, i.e., the
divergence of the gradient.
Finally, the overall evolution of the landscape in different time steps was
calculated as the uplift rate subtracted by the changes due to both fluvial
erosion and the hillslope diffusion (Temme et al., 2017):
∂z∂t=U-KAmSn-Kd∇2z,
where U is the uplift rate (m yr-1).
A DEM raster grid of the present-day less mature anticline (Harir) and the
surrounding basins (Fig. 5a) served as model input. The advantage of using
Harir Anticline was that the evolved drainage network overprinted the
pre-existing one. The boundary conditions were set as closed on all sides
except in pre-existing outlets in the input grid. The basins surrounding
Harir Anticline were also included in the input grid to minimize the effect
of the boundary conditions on the Harir Anticline itself. In the input raster
grid, a flow route of each cell was connected with neighboring cells both
diagonally and orthogonally. This means that each cell had the possibility to
be linked with eight surrounding cells across its sides and corners (Hobley
et al., 2017; Tucker et al., 2016).
(a) DEM grid, drainage network, and basins for the
present-day Harir Anticline that is used as an input for the model. White
lines are basin divides; (b)m/n plotted against regression values
of elevation–X plot for streams in the Harir Anticline. The highest
regression is achieved for m/n=0.41.
Concerning the parameter used in the model, the value of m/n, n, and K
were found following the methodology described by Harel et al. (2016), Mudd
et al. (2014), and Perron and Royden (2013), and by comparison with data from
Harel et al. (2016). The value of m/n was found by plotting the elevation
against X (elevation–X plot) for streams in the input grid (Fig. 5a), where
X is found following the equation described by Perron and Royden (2013):
X=∫xbxA0Axm/ndx,
where A0 is the reference drainage area (m2) of 166 160 m2
and x is the horizontal upstream distance (m). In this approach, we
ascribed values for m/n range from zero to one, and X was calculated for
each time from Eq. (7). The value of m/n with maximum regression (R2)
value in the elevation–X plot was taken as the best-fitting value,
which was 0.41 in our case for the present-day Harir Anticline's drainages
(Fig. 5b). This value of m/n is located within the theoretically predicted
values of m/n, which ranges from 0.3 to 0.7, based on the stream power
incision model (Kwang and Parker, 2017; Temme et al., 2017; Whipple and
Tucker, 1999).
In the model, n=1.7 and K=3.0×10-6 yr-1 m-0.4
were used; these values were estimated as mean of K and n in Harel et
al. (2016) for those areas that are comparable with our study area in aspect
of lithology, climate, and precipitation. The value of m was 0.7. We used
an incision threshold of C=1.0×10-5 m yr-1, which is
widely adopted for erosion of an upland landscape (Hobley et al., 2017). A
present-day annual mean precipitation of ca. 0.7 m yr-1 was used
through the time due to the lack of nearby paleoclimate data with good
quality. The average of the modeled precipitation anomaly data for Lake Van
in SE Turkey (200 km to the NNW of the studied anticlines) is close to zero
(Stockhecke et al., 2016; Supplement). The current elevation of the Bekhme
and Aqra formations in the crest of Harir Anticline is about 1500 m a.s.l.
Above that, 2070 m of Upper Cretaceous–Miocene units (Law et al., 2014) and
300 m of upper Miocene Lower Bakhtiari were
exhumed before exposure of the Bekhme and Aqra formations. If we consider
that the Lower Bakhtiari have been deposited close to sea level before onset
of the MFF ca. 5 Ma, there would be 3872 m of rock uplift at a rate of
∼0.0007 m yr-1, which was used in the model. This rate of
vertical uplift is reasonable for the area and it matches with the vertical
uplift in Kurdistan that has been presented by Tozer et al. (2019). Since
soil (regolith) is rare and very thin when present on the slopes, a low
diffusivity coefficient of Kd=0.001 m2 yr-1 was
used (Fernandes and Dietrich, 1997).
There are minor variations in lithology between the three anticlines (they
consist of a thick pile of dominantly Cretaceous carbonate) and no variation
in climate can be expected on such a relatively local scale. Therefore, no
significant variances are expected in the used parameters. Lastly, the
parameters were calibrated by comparing the nature of the evolved landscape
to other anticlines within the High Folded Zone that are cored by Cretaceous
carbonates and more mature than the Harir Anticline to evaluate how
realistic the evolved landscape is.
ResultsLandscape maturity
The three studied anticlines are composed of the raised Cretaceous
carbonates in their crests, whereas the Tertiary clastic rocks have been
denuded, but conserved in the adjacent synclines. The three anticlines are
dissected by rivers that form water gaps across them. Bekhme and Zinta
gorges cut the Perat and Akre anticlines, respectively. We also observed
wind gaps, such as those in the NW end of Harir Anticline (Zebari and
Burberry, 2015). Therefore, neither the location of these water and wind
gaps nor the plunging tips of anticlines have been considered in
interpreting the geomorphic indices as proxies for relative landscape
maturity.
The anticlines reach up to ca. 1500 m a.s.l. The minimum altitude is ca. 700
in the adjacent synclines and ca. 400 m in the Great Zab river (a.k.a. Greater Zab River) course.
The hypsometric curves for the three anticlines are presented in Fig. 6.
Harir Anticline's curve is more convex, and its shape is close to the
youthful stage of Strahler's diagram (Ohmori, 1993; Strahler, 1952) with
78 % of the area above the mean elevation, while Akre Anticline is less
convex and close to a mature stage with only 50 % of the area above the
mean elevation. Perat Anticline's values are located in between and closer to
the Harir Anticline curve with 64 % of its area above the mean elevation.
Present-day hypsometric curves of the studied anticlines. The curves
are calculated as a total weighted mean for drainage basins within each
anticline. We only use those parts where Upper Cretaceous carbonate rocks
crop out and we exclude wind gaps, water gaps, and the plunging tips of
anticlines. n is the number of basins used in the calculation of the
hypsometric curve for each anticline.
The three calculated geomorphic indices (HI, SR, and SI) seem to be
substantially influenced by the local structure, and wind and water gaps
(Fig. 7). Hypsometric integral values vary between 0.2 and 0.77, with lower
values in the adjacent synclines and higher values in the crest of the
anticlines. The HI values decrease toward the plunging ends of the anticlines
and at gorges, e.g., Perat Anticline's HI values are minimum at the Great
Zab river. In general, Harir Anticline shows higher values than the other two
anticlines. Harir Anticline has a broad crest and has been incised by narrow
valleys. This makes the mean elevation within the moving window in the
calculation close to the maximum elevation and, thus, causes higher values of
the hypsometric integral. Perat Anticline shows high values of HI on its
crest to the west of Bekhme Gorge. Among the three anticlines, Akre Anticline
shows the lowest values of HI in its central part, which is due to the presence
of more incised and wider valleys. Elevation drops rapidly from the hinge of
the anticline toward the limbs, which causes the mean elevation within the
window to fall.
Surface index maps for the three anticlines calculated from 100×100 cell (3km×3 km) and moving windows;
(a) hypsometric integral, (b) surface roughness, and
(c) surface index.
The surface roughness values range between 1 and 1.33 in the area. The lowest
values of the SR are also present in the adjacent synclines and in the
plunging tips of the anticlines. The highest values are associated with the
location of water gaps. These are areas where rivers are deeply incised at both
Bekhme and Zinta gorges. Harir Anticline has lowest surface roughness values.
Perat Anticline shows the highest value of SR especially in its northern
limb. Akre Anticline has moderate SR values in its central segment and
western side where a wind gap is present.
The results of the surface index range between -0.04 to 0.70 in the three
anticlines studied. Few locations show negative values. These are associated
partly with adjacent synclines and with Bekhme Gorge. Apart from these
locations, the area shows positive surface indices. Harir Anticline exhibits
higher values on its broad crest. Perat Anticline shows moderate values of SI
on its crest to the west and east of Bekhme Gorge. For Akre Anticline, SI
values are lower than in both Harir and Perat anticlines, with highest values
on its crest east of Zinta Gorge. These high values of SI highlight the flat
areas with high elevation and high hypsometric integral. The surface index
values also highlight the Pila Spi and Khurmala limestone ridges encircling
the anticlines with values close to zero.
In our calculation, the results of geomorphic indices change with changing
the size of the moving window and the resolution of the input data (see
the Supplement). This was also detected by Andreani et al. (2014) and Obaid and
Allen (2017). Andreani et al. (2014) found that the DEM resolution does not
affect the hypsometric integral but it affects the surface roughness, while
the size of the moving window affects both hypsometric integral and surface
roughness. The results become smoother with an increasing size of the moving
window. Here, we found that it is reasonable to use a 100×100 cell
(3km×3 km) moving window, which covers approximately one limb of the
anticline with 6–7 km width. It therefore highlights the desirable signal.
The cluster map for the HI Gi* statistics was calculated following the
approach by Pérez-Peña et al. (2009) for the 500m×500 m
grid. We obtained results similar to the HI map calculated from a 100×100 cell (3km×3 km) moving window and resampled to 500m×500 m grid in terms of highlighting the cluster of high and low HI values
(Fig. S19a and b in the Supplement). This comparison proves that our method
is equally applicable and valid. We therefore ran all analyses based on the
100×100 cell moving window as described above.
According to the hypsometric curves and the geomorphic indices, we found that
there is a measurable difference in landscape maturity between the three
anticlines. We classified our anticlines as relatively mature (Akre
Anticline), moderately mature (Perat Anticline), and less mature (Harir
Anticline). The difference in the maturity level must be due to a difference
in one or more of the factors tectonics, climate, or rock erodibility. No
variation in the climate is expected for the scale of the studied area,
therefore its impact on the landscape maturity can be neglected. The three
anticlines show essentially the same lithology (Figs. 2 and 3), i.e., similar
lateral rock type and erodibility. Thus, the only factors that may vary along
the anticlines are uplift rate or onset time of the uplift. This can be
interpreted with one of the following scenarios: either the anticlines
started to uplift in the order (1) Akre, (2) Perat, and (3) Harir from west to east or all of them started at the same time but with different rates. In
the latter case, the uplift rate would have been highest at Akre and lowest
at Harir, exposing Akre to erosion earlier than Harir.
Landscape evolution model
The aged landscape from the model run (Figs. 8 and 9) is the result of
fluvial erosion and hillslope diffusion on the one hand and uplift due to
folding on the other hand. In the landscape modeling, various simulations
with different parameters and time spans were performed (Figs. S24–S27).
Harir Anticline was used as an input model and the landscape evolution model
was run for a time span of 10 up to 100 kyr and then it was run for a time
span of 20 kyr. The evolving drainage system overprints the pre-existing one
in the input and gradually becomes more deeply incised from the anticline
flanks carving toward its core (Fig. 8). Harir Anticline is a box-shaped
anticline with a wide and flat crest area. With ongoing incision towards the
core of the anticline, this plain crest narrowed gradually and finally became
a sharp ridge that divided the drainage basins of the SW flank from those of
the NE.
The input landscape (a), which is the present-day Harir
topography, and the evolved landscape through time; (b) 80,
(c) 100, (d) 160, (e) 180, (f) 200,
(g) 220, and (h) 240 kyr.
Hypsometric curves of the studied anticlines and those of the
evolved Harir landscape from the model for different time spans.
(a) The curves were calculated using the total weighted mean for
drainage basin within each anticline, indicating that the evolved landscapes
after 100 and 160 kyr are the closest ones to the present-day Perat and Akre
anticlines, respectively. n is the number of basins used in the calculation
of the hypsometric curve for each time; (b) the curves were
calculated for the entire anticline, indicating that the evolved landscapes
after 80 and 200 kyr are the closest ones to the
present-day Perat and Akre anticlines, respectively.
We compared the hypsometric curves of the model outputs to the present-day
curves of the anticlines. The hypsometric curves were calculated first as
total weighted mean for basins within the anticline and later calculated for
the entire anticlinal ridges (Fig. 9). Statistically (with minimum RMS), the
hypsometric curve of Harir Anticline was closest to the present-day Perat
Anticline after 100 and 80 kyr of erosion when using total weighted mean and
entire anticlinal ridge, respectively, in the calculation of the hypsometric
curves. The output curve after 160 and 200 kyr matched best with present-day
Akre Anticline, when using total weighted mean and entire anticlinal ridge,
respectively, in the calculation of the hypsometric curves. We conclude that it
will take Harir Anticline roughly 80–100 kyr to reach the maturity
level of Perat Anticline and about 160–200 kyr to reach the level of Akre
Anticline based on our model and comparison of the hypsometric curves if the
uplift rates of the three anticlines were the same. The other possibility is
that the anticlines started to grow at the same time but with different
uplift rates. In this case, it is not possible to find the difference in
uplift rates via our landscape modeling. Since the factors that control
geomorphology (lithology, structural setting, and climate) were similar for
all three anticlines, and under the assumption of constant growth and
erosional conditions, we infer that uplift of Akre and Perat anticlines
started, respectively, 160–200 and 80–100 kyr before Harir started to grow
if their uplift rates were the same.
Structural cross sections
across the three studied anticlines; (a) Harir section (modified
after Syan, 2014), (b) Perat section constructed from field data and
thrusts inferred from a seismic line by Csontos et al. (2012), and
(c) Akre section constructed from field data (see Figs. 2 and 4 for
the locations). In Akre and Perat anticlines the data were collected along
gorges and the topographic profile across the anticline along an adjacent
transect to the gorges are delineated by a dotted line. The shortening
percentage since Late Miocene is shown on each cross section.
Geometry of the studied anticlines
The structural cross sections for the three anticlines (Fig. 10) constructed
from field data and literature (Syan, 2014) show that the anticlines are
box-shaped with broad crests. They are asymmetrical verging toward the SW
with a nearly vertical or overturned forelimb. The three anticlines are
thrust-related, in accordance with published studies of the area (Csontos et
al., 2012), and have a thrust in their forelimb. Perat Anticline additionally
exhibits a back thrust in its NE limb. The shortening across the three
anticlines was calculated using line-length balancing. We found 26 %,
28 %, and 29 % shortening for Harir, Perat, and Akre anticlines,
respectively, and conclude that there is no significant variation. A
difference in the fold amplitude between the three anticlines can be
discerned in the cross sections. The amplitude of Perat Anticline is higher
than that of both Harir and Akre anticlines. Another difference concerns the
thickness of the Upper Cretaceous–middle Eocene clastic succession, which
dwindles toward the Akre Anticline. The whole Miocene succession, however, is
the thickest in the Akre Anticline. Both the Upper Cretaceous–Middle
Eocene and Miocene successions consist of highly erodible clastic rocks, and
the thicker Miocene succession in the Akre Anticline counterbalances the
thinner Upper Cretaceous–middle Eocene succession. Therefore, it is not
expected that these variations in the structural geometry and stratigraphic
thickness have a great impact on the variation in the landscape maturity in
the three anticlines and the landscape model.
DiscussionLandscape maturity and modeling
Any relative change in the base level induced by tectonics or climate leads
to a change of erosion rates. A relict landscape survives when its uplift
is not completely counterbalanced by erosion (Andreani and Gloaguen, 2016;
Burbank and Anderson, 2012; Pérez-Peña et al., 2015). The relative
relict landscape and its distribution on these three anticlines atop the MFF
reveal clues about underlying tectonics since there is no significant
variation in climate and lithology.
Within the three studied anticlines, the geomorphic indices effectively
highlighted their incision. The surface index, which combines both
hypsometric integral and surface roughness, sets apart relict landscapes of
positive and high values from transient landscapes of negative values that
are preferentially incised (Andreani et al., 2014). There is a notable
relative deviation in areas where anticlines are crossed by rivers, e.g.,
Bekhme and Zinta gorges which show high surface roughness. Also, variations in surface index are found in
comparable areas in the three anticlines. Focussing on the crest of the
anticlines, we observe that the Harir Anticline shows higher values than the
two others. The lowest values are found in Akre Anticline. Harir has low
incision at elevated surfaces while Akre has a more incised uneven landscape,
because erosion has worked deeper into the core of the anticline. This can
also be inferred from the valley shapes. We observe tight V-shape valleys in
the flanks of Harir and open V-shape valleys in Akre (Fig. 11a and b). We
relate this difference to the tectonic uplift and to the effects of longer
erosion acting on Akre. The observation can be interpreted with one of the
following two premises: either the anticlines started to uplift successively
(first Akre, then Perat, and finally Harir) or all of them started at the
same time but with different uplift and exhumation rates (Akre the fastest,
Harir the slowest). In other words, the Cretaceous carbonates in Harir
Anticline were exposed to the erosion later than in Akre Anticline and,
consequently, incised less.
Different shape of valleys in the Harir (a) and Akre
anticlines (b). See Figs. 2 and 4 for the locations.
The current landscape of these anticlines exposes Cretaceous carbonates of
the Qamchuqa, Bekhme, and Aqra formations, which became exposed to erosion
only after unroofing of the entire Palaeogene–Neogene succession. The
upper Miocene–Pliocene Bakhtiari Group, which is the youngest stratigraphic
unit in the area, is affected by folding, as observed from growth strata
(Csontos et al., 2012). This has also been observed in the upper Bakhtiari
(Pliocene–Pleistocene) close to the MFF (Koshnaw et al., 2017). In between
Bekhme and Aqra and the Upper Bakhtiari formations, 2.37 km of the Upper
Cretaceous–Miocene clastic rocks interbedded with thin units of limestone
(Law et al., 2014) have been exhumed due to successive rock uplift in the
crest of the studied anticlines, triggered by shortening and erosion. They
are only preserved in the adjacent synclines. The Cretaceous carbonates
themselves have been exposed in the crests of Akre, Perat, and Harir
anticlines for ca. 0.9 km above the level of the other exhumed units. Based
on the thickness, the amount of the exposed Cretaceous carbonate makes
ca. 28 % of the total exhumed and exposed thickness in the crest of the
anticlines. Therefore, with both scenarios (different uplift time or
different uplift rate) and with assumption of constant (linear) rock uplift
rate through time, the Cretaceous carbonate in Harir Anticline was exposed to
erosion later than in the Akre Anticline (Fig. 12a).
(a) Diagram showing the exposure time of the Upper
Cretaceous carbonates in Akre and Harir Anticlines. Two different scenarios
are plotted for Harir: having a slower uplift rate than Akre or onset of
uplift later than Akre. (b) Channel slope–drainage area plots for
streams in both Akre and Harir Anticlines.
The steeper valley flanks in Harir Anticline compared to those of Akre also
support higher uplift rates of the Harir Anticline. Furthermore, the
relationship between stream slope and upstream drainage area at any given
point of the streams in the Harir Anticline is positive (Fig. 12b). This means
that the streams have a convex shape and the streams' segments with steeper
slopes are still located in the flanks of the anticline. In the Akre
Anticline, this relationship is negative (Fig. 12b), which means that the
streams have a concave shape and the segments with steeper slopes have
migrated toward the core of the anticline. This implies that tectonic
activity in the Harir Anticline is younger than in the Akre Anticline; in
other words, the Harir Anticline was exposed to erosion later than the Akre
Anticline. Therefore, the premise of having Harir Anticline starting its
uplift later than Akre Anticline is most likely. This is our preferred
scenario in the model for the successive tectonic evolution of the study area
presented below.
The geomorphic indices have been widely used to assess the landscape
maturity and, subsequently, active tectonics (Andreani and Gloaguen, 2016;
Mahmood and Gloaguen, 2012; Pérez-Peña et al., 2009). The
challenging aspect of landscape maturity modeling is to obtain an absolute
quantification of tectonics and the relevant time spans. The same holds true
with using a landscape evolution model to estimate the relative time
difference between two landscapes, because it is difficult to compare two
landscapes in terms of maturity by absolute means. The results of the
landscape modeling approach yielded a numerically derived estimate on the
relative age difference between the studied anticlines but without absolute
growth ages.
In the model, various parameters and two well-known landscape evolution
equations for the fluvial erosion and hillslope diffusion were used, but in
general it is impossible to mimic nature perfectly. The relative time
difference of landscape evolution of these anticlines was estimated from the
model assuming that the climate has not changed much during the evolution of
the landscape since there was not much variation in the precipitation based
on the modeled data (Stockhecke et al., 2016) and for the sake of
simplicity, admitting that climatic change has a significant impact on the
landscape. In addition, neither rock fall nor karstification were included in
the model for simplicity. Field observations suggest that karstification does
not have a significant impact on the landscape. Overall, the evolved
landscape from the model seems to be plausible in comparison with the other
anticlines that surround Harir Anticline, and the landscape models are more
mature with respect to the developed topography and to the overall drainage
patterns.
Total weighted mean hypsometric curves for drainage basin within the
studied anticlines as compared to those of the Shakrok and Safin anticlines.
The Harir's curve is more convex than those of both Shakrok and Safin. n is
the number of basins used in the calculation of the hypsometric curve for each
anticline.
Simplified history of the formation of anticlines during the
propagation of the deformation front over time in the study area. The Harir
anticline is likely the latest to have formed within the High Folded Zone in
its SE end. It occupies the position of a relay structure during the linkage
of two adjacent, but overlapping, segments of the deformation front. The
anticlines were outlined based on the exposure of Cretaceous carbonates.
Structural style and regional tectonics
An orogenic bend is depicted in the area where the trend of structures
changes across the Great Zab river from NW–SE at the eastern side of the
river to nearly E–W at its western side. The course of the Great Zab River
is suggested to overlie a NE-trending transversal basement fault with
right-lateral displacement (Ameen, 1992; Burberry, 2015; Jassim and Goff,
2006) evidenced by an offset of the High Folded Zone propagation
forelandward. At the eastern side of the river the deformation has propagated
for about 25 km further than on its western side (Figs. 1 and 2). The origin
of this fault reaches back to the late Proterozoic tectonic history of the
Arabian Plate. This fault has been reactivated later in subsequent tectonic
events (Ameen, 1992; Aqrawi et al., 2010; Burberry, 2015; Jassim and Goff,
2006). This can also be noticed in the thickness of the sedimentary cover,
which is thinner to the west of the Great Zab River (Ameen, 1992; Zebari and
Burberry, 2015). This change in thickness is attributed to a series of uplift
events and erosional or non-depositional gaps during the Mesozoic (Ameen,
1992; Aqrawi et al., 2010). Further propagation of the deformation (Mountain
Front) in the eastern side of the Great Zab River may be due to the existence
of a thicker sedimentary cover than on the western side, which in turn may
have influenced the foreland-ward propagation of deformation (Marshak and
Wilkerson, 1992). The deference in propagation of deformation may also be due
to the convergence being accommodated differently across the curved
fold–thrust belt (Csontos et al., 2012). In the eastern side of the Great
Zab River, the convergence is accommodated across NW–SE trending structures
through belt-normal slip and right-lateral strike–slip components, while in
the western side of the river the convergence is accommodated only by a
belt-normal slip across E–W trending structures (Csontos et al., 2012;
Reilinger et al., 2006).
Zebari and Burberry (2015) found that anticlines to the east of the Great
Zab River (Harir, Shakrok and Safin anticlines) demonstrate pronounced
NW-ward propagation based on their geomorphic criteria, and the start point
of the NW-ward propagation of the Harir Anticline is close to its SE end.
This implies that progressing uplift in the hanging wall of the MFF was not
gradually continuing from the Akre Anticline towards the Perat Anticline and
further SE-ward to the Harir Anticline. The uplift progress is probably
rather partitioned into segments along the belt. In addition, other
anticlines to the south (Safin Anticline) and to the southwest (Shakrok
Anticline) of Harir Anticline are more mature than Harir Anticline itself,
based on their hypsometric curves (Fig. 13) and geomorphic indices
(Figs. S1–S18), implying that the forelandward propagation of the
deformation was also out of sequence in this part of the High Folded Zone.
This has also been noted in the Foothill Zone based on thermochronological
dating (Koshnaw et al., 2017) and landscape maturity (Obaid and Allen, 2017).
Thus, the most plausible scenario is that deformation in the Harir segment
started sometime after that in Akre segment (160–200 kyr according to our
landscape evolution modeling). Harir Anticline uplift would also postdate
Perat Anticline uplift (80–100 kyr) to the west and the onset of Safin and
Shakrok anticlines to the south and southeast, which are not included in the
model (Fig. 14). As discussed by Csontos et al. (2012), the fold relay
corresponds to the change in strain partitioning and rotation of the
horizontal stress direction from NE–SW to N–S in Late Pliocene (Navabpour et al., 2008; Navabpour and Barrier, 2012). During the latest stage of the N–S convergence, a right-lateral shear and superposed folding along the NW–SE trending anticlines (Csontos et al., 2012) can be observed from the relay of
the Shakrok, Harir, and Perat anticlines (Figs. 2 and 14). Applying this
concept requires a comprehensive paleostress analysis investigation
especially within these studied anticlines, which is beyond the scope of this
paper.
Conclusions
The geomorphic indices used in this study allowed us to quantitatively
differentiate between variably degraded landforms in the frontal Zagros
Mountains of NE Iraq. This area is characterized by active folding due to
ongoing convergence between the Eurasian and Arabian plates. Three active
thrust-related anticlines that are aligned along-strike of the MFF were studied
in detail. While the Akre Anticline shows deeply incised valleys indicative
of advanced erosion, the Harir and Perat anticlines have relatively smooth
surfaces and show younger landscape than Akre. We related this difference to
the underlying tectonics. This can be interpreted with one of the following
concepts: either anticlinal growth started at different times or all of them
started to grow at the same time, but with different surface uplift and
exhumation rates.
A comparison of the geomorphic index values of the model output with those of
the present-day topography of Akre and Perat anticlines revealed that it will take Harir Anticline about 160–200 kyr to
reach the maturity level of today's Akre Anticline, and about 80–100 kyr to
reach the maturity level of the Perat Anticline, assuming constant uplift
rates along the three anticlines. Due to similarity in the lithology,
structural setting, and climate along the three anticlines, and by assuming
constant growth and erosion conditions, we infer that Akre Anticline started
to grow 160–200 kyr before Harir Anticline. The onset of growth of Perat
Anticline lies in between that of Harir and Akre anticlines. A NW-ward
propagation of Harir Anticline itself implies that the uplift has been
independent within different segments rather than having been continuous from
the NW to the SE. Our method of estimating relative age differences in
variously degraded anticlines can be applied to many other anticlines along
the MFF and could eventually develop into a model of the temporal evolution
of this fold and thrust belt.
Data availability
For this
study we used the freely available SRTM1 data. TanDEM-X digital elevation
models were obtained from the DLR.
The supplement related to this article is available online at: https://doi.org/10.5194/se-10-663-2019-supplement.
Author contributions
MZ and KU designed
the study; MZ did the field work, the geomorphological analyses, and the
landscape modeling; MZ, CG, PN, and KU interpreted the results and wrote the
paper.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The German Academic Exchange Service (DAAD) is acknowledged for providing a
scholarship (Research Grants – Doctoral Programmes in Germany, 2016/17;
57214224) to the first author to conduct his PhD research in Germany. The
authors express their gratitude to the German Research Foundation (DFG)
project no. 393274947 for providing financial research support. The German
Aerospace Agency (DLR) and Thomas Busche in particular are thanked for
providing TanDEM-X digital elevation models. We also thank Bernard Delcaillau
and two anonymous peers for their very thorough reviews that helped us to
further clarify and improve numerous aspects of our work.
Review statement
This paper was edited by Federico Rossetti and reviewed by
Bernard Delcaillau and two anonymous referees.
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