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DEM BASED GEOMORPHIC ANALYSIS ALONG KALABAGH FAULT AND POTWAR PLATEAU TO CONSTRAIN SURFACE DEFORMATION: INFERENCES FROM REMOTE SENSING AND GIS.

Byline: J. Qureshi, S. A. Mahmood, A. S. Almas, H. M. Rafique, S.R. Ahmad and A. Masood

ABSTRACT

The Potwar Plateau and Kalabagh fault zone is a lower part of North West Himalayan fold and thrust belt (NWHFTB) and is a direct result of India-Eurasia collision. The interplay between tectonics and erosional environment has created a very unique topography, folds and thrust geometries in this region. The purpose of this research is to constrain active tectonics and related surface deformations based on geomorphic indices from Digital Elevation Model (DEM). The Shuttle Radar Topography Mission (SRTM) DEM with a spatial resolution of 90 m is a good dataset to constrain active tectonics and topographic analysis. The geomorphic indices (stream-length gradient index, concavity, steepness, drainage density and lineament density) were extracted automatically using standard algorithms.

Comprehensive analyses based on stream profiles, lineament density, stream density, drainage patterns, topographic relief, Landsat imagery and published geological maps revealed a relationship between geomorphometric indices and tectonics. The geomorphic indices analysis also indicates active tectonics that shows control over the topography in the Potwar plateau and Kalabagh fault zone. The geomorphic features and steepness index shows that the northern Potwar deformed zone is more uplifted than the southern Potwar platform zone. The spatial distributions of variable uplift rates are indicative of unique surface deformation within the Potwar plateau and Kalabagh fault zone.

Key words: DEM, Geomorphic Indices, Stream Profile, Active Deformation, India-Eurasia, Potwar Plateau, Kalabagh

INTRODUCTION

The Potwar Plateau is located near the western flank of India Eurasia collision zone. The topography of Potwar Plateau is an outcome of complex interplay between tectonics and erosion. The mountainous region is the most prominent topographic feature on the planet earth. In the absence of erosion the surface geometry of a mountainous region may reflect domination of tectonic processes (Mahmood and Gloaguen, 2011). The surface processes are controlled by climate, tectonics and erosion that cause shaping up the regional topography during the mountain range formation. There is a pronounced interaction between surface processes, topography and tectonics. During the last couple of decades an extensive research work has been carried out to investigate the impact of surface processes on topography (Burbank et al, 1996; Wobus et.al., 2006; Kirby and Whipple, 2001; Mahmood and Gloaguen, 2011). The interactions between tectonic processes and landscape evolutions have also been explored using numerical methods.

Earlier, most of the work focused on larger mountain belts such as Himalayas, Andes, Hindukush and Alpes. Among these mountain ranges Himalayas are the most recent, youngest topographic feature on the earth. This ergogenic belt is a series of north dipping Cenozoic fault in southern Tibet (Yin, 2006; Chen and Khan, 2009). Crustal shortening due to northward subduction of Indian plate beneath Eurasia is continuously creating new tectonic features on the north western flanks of Indian plate (Jaswal et al, 1997). The resulting intense deformation is occurring in the thrust wedge. The Surghar Range, Salt Range and Trans Indus salt range with abrupt relief stand just above the Punjab foreland basin of Pakistan. The Main Frontal Thrust (MFT) lies along the southern piedmont of the deformed ranges, and is a boundary with the Punjab foreland basin.

The spatial distributions of the tectonics in Sub-Himalayan Thrust Belt (SHTB) have a great attraction for many scientists because of its significance to better understand the Himalayan evolution (Ahmad et al, 2005). However, a very little attention has been given to the SHTB, especially towards the relationship between tectonics, erosion and topographic processes. The objective of this research is to examine the relationship between tectonics and surface processes and resulting active deformations in the SHTB by analyzing DEM-based geomorphic indices and visually interpreted structures from Landsat data. Stream profile analysis is the main focus of this research due to its ability to cater vital information about the neotectonics deformation and erosional processes in the study area (Mahmood and Gloaguen, 2011).

Tectonic setting of the study area: Fold and thrust belts are created as a result of collisional zones. The Himalayan Fold Thrust Belt (HFTB) is a result of subduction of Indian plate beneath the Eurasian plate. The Himalayan orogeny is comprised of Main Karakoram thrust (MKT), Shyok Suture zone (SSZ), Main Mantle Thrust (MMT). Main Central Thrust (MCT), Main Boundary Thrust (MBT) and Salt Range Thrust (SRT) as major tectonics boundaries (see Figure1). The North West Himalayan Fold and Thrust Belt (NWHFTB) is comprised of the region between MMT and SRT, whereas Hazara Kashmir Syntaxes (HKSN) and Nanga Parbat Haramosh Massif (NPHM) mark its eastern boundary. Towards west some thrust faults in eastern Afghanistan marks the western margin of NWHFTB (Monalisa et al, 2007). The NWHFTB consists of thin skinned tectonics of Eocene time. Potwar plateau (PP) extends down from MBT in the north to the salt range in the south is seismically less active part of NWHFTB and structurally complex.

Jehlum and Kalabagh faults mark its eastern and western boundaries respectively.

Mangla and Maira faults are present in the eastern section of PP and are approximately 10 km long. They are active features and are oriented with a dip slip movement that has been recorded along their traces (Nakata et.al, 1991). Another prominent active tectonic feature in the PP region is the Kheri Murat fault in the tectonic map of the PP with different local faults (Fault no. 57, see Figure 2).

In this fig, the GPS velocity vectors (Red) with respect to Eurasia fixed reference frame from the purple vector is transformed from velocities with respect to India fixed. Note the direction and decreasing GPS velocities towards north showing convergence and anticlockwise rotation of India. Abbreviations of fault names: AM, Alburz Marmul, CbF, Central Badakhshan Fault , HF, Herat Fault, CF, Chaman Fault; MoF, Mokar Fault, GzF, Gardez Fault, KoF, Konar Fault, MBT, Main Boundary Thrust; MFT, Main Frontal Thrust, MMT, Main Mantle Thrust, and MKT, Main Karakoram Thrust, Reshun Fault, SF, Sarobi Fault, ST, Spinghar Thrust. (Source: Mahmood and Gloaguen, 2011).

DATASETS AND METHODS

Datasets: Remote sensing data is now frequently and readily available in variety of formats and spatial resolutions, and influence the quality of extracted geomorphic features. Two data sets were employed to extract the geomorphic indices and structural features. These are SRTM DEM 90 m and Enhanced Thematic Mapper (ETM+) mosaic (see Figures 2,9,10,11 and 12) to describe the geomorphic features including hack index, concavity and steepness index, relative uplift rate and relief.

Data processing: To have a complete map view of the Potwar Plateau and the surroundings we made a mosaic of ETM+ data. The quality of Landsat mosaic was improved by fine adjustment, color balancing, brightness matching and georeferencing so that, structures can easily be identified. To better understand natural landscape physiography and spectral signature in the Potwar Plateau, different band combinations were prepared to have a good visual interpretation of structures. For this purpose, these ETM+ bands (7, 4, 2) as red, green and blue were cofused to have a good visual interpretation of both natural and geological features respectively (see Figure 2). This approach was quite handy for the visualization of target features (lineaments and drainage networks) from Landsat imagery for the PP (see Figure 2). The drainage network was extracted automatically using Matlab-based algorithm from the SRTM DEM 90 m.

The SRTM DEM is unable to collect data at some places with rugged relief, due to which pits/holes are accumulated as part of the dataset and can pose problems for the smooth extractions of stream network, which is used for the stream profile analysis. These holes are fixed using Inverse Distance Weighted (IDW) interpolation that produced location dependent values for such pits/ holes in DEM.

Hack gradient index: The streams length-gradient index (SL index) correlates to streams power (Hack, 1973). It is related to the ability of a particular reach of stream to erode its bed and transport sediment (Keller and Pinter, 1996). The SL index is calculated for a particular reach of interest and defined as:

SL = (DH/DL) L

Where DH/DL is the channel slope or gradient of the reach, DH is the change in elevation of the reach and DL is the length of the reach (see Figure 3), and L is the total channel length from the point on the channel (Keller and Pinter, 1996).

Stream profile analysis: The drainage network of PP is extracted from DEM by calculating using D8 algorithm. The direction of the drainage flow is dependent on upslope and catchment area. Stream delineation algorithm can affect stream parameters (e.g.. slope, contributing area, local elevation, downstream distance and Strahler order). River profiles are selected based on least cost path analysis that calculates the downstream flow path. The selected individual streams are formulated in ASCII format for further processing. The extracted and selected streams have always some errors therefore, some smoothening algorithms are applied that depend upon number of nodes (i.e. the smoothing factor). The River Profile Analysis (RPA) was applied on every individual stream to calculate vital information based on the bedrock incision model.

This model states that the detachment limited channels do not observe continuous coverage of sediments, even at low flow because of equal stream gradients for erosion and uplift (see Figure 2). The faults scarp or lithologic contrast helps the streams to reach in a new equilibrium. Mathematically we can write:

(equation)

Where U and E represent uplift and erosion rates respectively. Equation (1) can be re written as:

(equation)

Where K is erosion efficiency factor which is directly related to sediments and rock strength. A is the upstream drainage area and S is the slope of the channel. The m and n are constants which are dependent on basin hydrology, hydraulic geometry and erosion process (Snyder et al, 2000), dz/dt is the rate of change of elevation with respect to time and if the landscape is in equilibrium under steady state condition then dz/dt is equal to 0. Therefore equation (2) can be written as:

(equation)

Where the coefficient (U/K)1/n is steepness of the river profile while m/n is the concavity of the profile. The stream power law is represented as:

(equation)

Where equation and ksn are concavity and steepness indices respectively and can be calculated directly by regressional analysis on the area slope data shown in the eq.(4) (Wobus et al, 2006). By combining eq.(3) and (4) we get the following relation to calculate relative uplift rates.

(equation)

Where ksnn is the normalized steepness index. Eq.(5) gives the relative uplift rate for the study area under steady state conditions for landscape evolution by selecting suitable values for m,n and K. RPA is performed on the selected trends for each individual stream to calculate geomorphic indices (concavity and steepness indices) after logarithmic regression analysis on the area slope values for each selected trend.

The knick zone which is represented by knickpoints migrates upstream as the channel responds to the uplift-rate change. This migration is dependent upon the lithology of both units and/or fault activity. The inset in figure 4 shows the slope vs. drainage area data for the longitudinal profiles in which the channel concavity index is the same for both the initial and final profiles, while the steepness, ksn, is considerably higher for the final profile. (modified from Snyder et al, 2000)

The mean concavity index "(theta)" is calculated by using the "(theta)" values of upper segments of all individual streams. Consequently, normalized steepness index "ksnn" is calculated by using this mean "(theta)". In eq. (5) the relative uplift rate "U" is a function of "ksnn", constants n and K. We calculate "U" in the Potwar Plateau by assuming constant values of n and K as described in available studies (Anderson et al, 1994; Tucker and Slingerland, 1996; Wobus et al, 2006).The knickpoints are important as their upstream migration helps to understand the landscape response to a local based level fall and the corresponding sediments fluxes from rejuvenated catchments (Bishop et al, 2005). We identified many knick points on individual stream profiles and their spatial distributions in the map view of Potwar Plateau are important to see the tectonic/ lithologic contrast.

RESULTS AND DISCUSSION

The distribution of the stream channels is not uniform because of the different relief and progressive tectonic activities in the PP and KBFZ. The extracted drainage densities and patterns present the variations of channel space and configurations in the SHTB. The principle goal of tectonic geomorphology is to extract tectonic information from the longitudinal profiles. These profiles contain tectonic information in the form of knickpoints and their Strahler order as well. In any profile, the knickpoints migrate upstream or downstream as the channel responds to the tectonic changes or the change of the lithology along the path of the stream.

The stream profiles show difference between initial low uplift and final high uplift zone (see figure 4). In a stream profile the transition between two different steepness values is usually bridged by a zone of very low or high concavity. Though, the concavity index remains the same for both initial and final stream profiles (Snyder et al, 2000; Kirby and Whipple, 2003; Wobus et al, 2006). Generally low concavity index indicates change in rock strength or enhanced downstream incision. The sharpness of knick points means neotectonic events, river captures or lithologic contrasts. A very sharp knick point means it has developed more recently (Wobus et al, 2006). We have investigated the Soan River which bisects the Potwar Plateau into two regions i.e. Northern Potwar Plateau Zone (NPDZ) and Southern Potwar Platform Zone (SPPZ) by using stream power law (Figures 2,4,eq.1-eq.5). Soan River is a four segment channel (see Figures 5a and 5b).

The presence of neotectonic feature can be observed with three knick points. KP1 is due to the presence of MBT, KP2 is located at the end of Rawal Lake in the outskirt of Islamabad, KP3 marks the interaction of Soan River with fault# 22 (Figures 2, 5a and 5b) and KP4 is due to lithological contrast. Based on morphology of the Soan River profile, four trends can be observed i.e. upper segment, middle segment, lower middle segment and lower segment. The first segment travels over relatively relic landscape having generally low steepness index (ksn= 281.07) and concavity index ( =0.43). These values indicate that this part of the area has been less eroded during the recent times or erosional processes. Second segment have intermediate steepness 283.51 and concavity index is 0.79, which is a case of intermediate surface uplift or erosional process. The lower middle segment has low steepness 261.12 and concavity 0.42, which means enhanced incision downstream of the Soan River.

The lower segment shows highest steepness 571.23 and lowest concavity 0.38 which means higher uplift conditions and relatively low erosion.

In the Indus River profile KP1 stands for Khairabad Thrust (KBT). KP2 indicates Attock range near the junction of Kabul and Indus River and this point is rising local base level for the Indus River due to active Attock range. KP3 and KP4 represent the interaction of Indus River with Kala-Chitta fold and thrust belt. KP5 is due to Choriakka fault. KP6 represents the interaction of Indus with Uchhri fault and finally KP7 is because of active dextral Kalabagh fault (see Figures 6a and 6b).

The sudden changes in the geomorphic indices indicate changes due to neotectonic activity and gradual change in lithology. Higher values of steepness index are observed in eastern section of the plateau because the steepness index is directly proportional to the relative uplift as compared to central and western section of the plateau, which means more active deformation. Previous studies (Moghal et al, 2003) also suggest the similar scenario. We analyzed stream# 58 from SPPZ and stream# 7 from NPDZ (see Figures 7a, 7b, 8a and 8b) to understand the relative uplift behavior. The stream morphology for both the cases represents two segments with identified knick points. The first segment of stream# 58 is a result of its interaction with the fault# 22 and second with the fault# 20 and have steepness and concavity index value (ksn = 37.76, = 0.79 and ksn = 125.87, (theta) = 0.53) respectively.

From NPDZ side stream# 7 is more deformed as the upper segment has more steepness index value as compared to the steepness index value from stream# 58 of SPPZ i.e., (ksn = 76.4 and ksn = 121.14) respectively (see Figures 8a and 8b). As steepness values are directly related to relative uplift (Wobus et al, 2006; Shahzad et al, 2009), so this analysis shows that NPDZ is more uplifted as compared to SPPZ, which is relatively more stable.

The detailed statistics for steepness and concavity index are given in the table 1.

Table 1: River profile statistics for Potwar Plateau and Kalabagh fault zone

Total Streams Studied###199

Total Segment Studied###293

Maximum Segments in a Stream###4

Lowest Concavity in the Area###0.16

Highest Concavity in the Area###2.8695

Lowest Steepness in the Area###4.0963

Highest Steepness in the Area 1074.9946

Mean Concavity Value for Ksn###0.45

The hack map shows predominantly more gradient values in the NPDZas compared to SPPZ. This map shows hack indexes interpolated (from topo to raster) from values of each segment of the automated extracted drainage network (segment interval = 200m). Known major fault traces are indicated by thick black lines. The highest hack index values which underline actually anomalously steep slopes are mainly encountered along plateau margins and near the fault traces.

We applied stream profile analysis on all the 199 streams automatically extracted from SRTM DEM and calculated the concavity and steepness indices. We prepared the relative uplift map for the Potwar Plateau and outskirts for the first time using above mentioned geomorphic indices. We have tried to correlate the relative uplift map to the recent ongoing deformation process. The relative uplift map shows differential uplift conditions in different parts of the study area. In the North North East (NNE) of the study area, the uplift rate ranges from 0.61 to 1.07 mm/year. In the western section it ranges from 0.23 to 0.60 mm/year. In the southern section these rates ranges from 0.07 to 0.35 mm/year.

This clearly suggests that the NNE section is being uplifted more as compared to the rest of the Potwar Plateau. The results obtained also show that the spatial flow pattern of the streams and their orientations are also controlled by the local faults e.g.. in the study area most of the local faults in the Potwar Plateau are NNE-SSW oriented, East-West oriented and so is the drainage network. The localized lineaments are playing their crucial role in the development of the shape of the local spatial drainage patterns, which are indicative of neotectonic control over the drainage network flow (see Figures 2 and 12). Stream profile analysis used the assumption of dynamic equilibrium under the steady state condition. Inspite of this limitation it is still a very powerful qualitative tool, which gives vital indications to understand the ongoing neotectonic processes despite of the fact the fluvial incision processes are not fully understood.

This limitation holds true, if the slope area data are examined in view of other geological information at hand. The spatial distribution of relative uplift rate in Potwar Plateau and its vicinity are quite variable. This is a clear indication that the present existence of different sections of the Potwar Plateau with different relative uplift rates were developed at different stages.

Conclusions: The automated drainage network based on DEM is an important tool for computer based analy of stream profiles as it gives information about the landscape and surface deformation in the study area. The geomorphometric analysis of the Soan River and its tributaries are capable of studying the behavior of the Soan basin. The Soan River exhibits a change in the course of its behavior which is related to the neotectonics and underneath lithologies of the Potwar Plateau. As the tectonics of the region continues to develop, in the same way the drainage network continues to be influenced accordingly. The relative uplift rate map of the study area indicates that NPDZ is deformed tectonically more (higher uplift) as compared to the SPPZ. The stream length gradient map also shows higher gradients on the NNE side than on the SSW side. The spatial drainage network seems to be controlled by the local and regional faults.

This suggests that lineaments and streams have a local correlation which is clear from the orientation style of drainage network and local faults. The major source of this influence is due to the thin skinned tectonics of the Potwar Plateau. This study can be further improved by using high resolution DEM and GPS data along with other detailed geological information. The Geomorphometric features are important and effective indicators for neotectonic studies in a young tectonic regime with low elevation.

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Department of Space Science, University of the Punjab, Lahore 54590, Pakistan, NESCOM, Islamabad, Pakistan, School of Physical Sciences, Department of Physics, University of the Punjab, Lahore 54590, Pakistan, Institute of Geology, University of the Punjab, Lahore 54590, Pakistan, Corresponding author's E-mail: jahanz4hope@yahoo.com
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Publication:Pakistan Journal of Science
Article Type:Report
Geographic Code:9PAKI
Date:Jun 30, 2012
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