Correlating River Steepness with Erosion Rate along the Sri Lankan Escarpment. Matthew Potako

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2 1 Correlating River Steepness with Erosion Rate along the Sri Lankan Escarpment Matthew Potako ABSTRACT A recent body of work has utilized cosmogenic isotopes as a measure of erosion rate in efforts to deconvolve the roles of climate and tectonics in influencing denudation rates. Sri Lanka stands out from the global data set in that it has a tropical climate, steep channels, and low erosion rates. One can characterize the steepness of rivers with greatly varying drainage areas through a normalized steepness index (k sn ). Sri Lanka was compared to a set of tectonically active mountainous regions to see how climate and tectonic activity might influence erosion rates and channel steepness. Sri Lanka is unusually steep for its erosion rates. Although it has the most erosive climate, it has some of the lowest erosion rates, suggesting that tectonic activity is a very large influence on erosion rates in mountainous regions.

3 2 1. INTRODUCTION It has long been known that erosion rates in mountainous regions are strongly correlated to topographic relief (Ahernt, 1970). Recent efforts in the past decade to deconvolve the roles of climate, primarily temperature and precipitation, and tectonics in influencing denudation rates utilize cosmogenic isotopes as a measure of erosion rate in various settings (e.g., Riebe et al., 2004; Von Blanckenburg, 2005). Although these studies generally show that wetter, warmer, and higher relief landscapes are associated with higher erosion rates, a couple notable exceptions occur. One of the most perplexing of these is in the highlands of Sri Lanka, where erosion rates are quite low (<10 m/myr, Von Blanckenburg et al., 2004) despite high precipitation (1 4 m/yr mean annual rainfall) and warm climate (~25 C mean annual temperature) and relatively high relief (Von Blanckenburg, 2005). In recent years, a body of work has shown correlations between landscape erosion rate and channel steepness, a measure of the gradients of rivers (Wobus et al., 2006), in systems at a dynamic equilibrium (Kirby and Whipple, 2012). Regions of high relief and steep channels tend to be associated with higher erosion rates (e.g., Ouimet et al., 2009), although the scaling relationships between steep channels and erosion rate are dictated by variations in both climate state (Rossi et al., 2011) and rock type (Kirby and Whipple, 2012). The evolution of topography along the Sri Lankan passive margin appears to be dominated by retreat of a rift-flank escarpment (Vanacker, 2006) thought to have formed during Mesozoic breakup of the Gondawanan supercontinent. It is composed of three geomorphically distinct regions, a high plateau and coastal plains which erode at exceptionally low rates, and deeply incised escarpment zones which erode ten times faster ( vs m/myr, Vanacker, 2006). It serves as a end member to compare with the global data set compiled by

4 3 Kirby et al. 2012, in that it is tectonically inactive, has an erosive climate, low erosion rates, and steep channels. For the purpose of this study, the relationships between channel steepness and erosion rates on the escarpment are compared to this global data to help shed light on the interactions between weathering, climate and tectonics. The data proves that Sri Lanka is unusually steep for its low erosion rates. 2. BACKGROUND 2.1 Regional Setting The island of Sri Lanka formed as a result of continental break-up of Eastern Gondwanna, beginning around 180 Myr ago. Between 130 and 120 Myr ago, the land drifted away from its original location and no tectonic activity has been recorded in the geologic record since (Chand et al. 2001). Sri Lanka therefore provides the opportunity to focus exclusively on the effects of climate on weathering and erosion in comparison to a global data set covering different climatic and tectonic settings (Blankenburgh et al., 2004). The island has eroded and formed three morphologically distinct zones, the most prominent being the mountainous center known as the Sri Lankan Highlands. There, elevation ranges from 500m to Fig. 1. Relief map of Sri Lanka revealing the Central Highlands (grey), the escarpment zone (grey-brown) and the coastal plains (green). Black lines are rivers of heavy drainage.

5 4 2500m a.s.l., precipitation is high (up to 5m/yr), and temperatures are tropical (~25 C mean annual temperature) (Von Blanckenburg, 2005). The highlands are surrounded by flat coastal plains. The morphology of the island is characteristic of passive rift margins in which steep escarpment zones separates the low relief central plateau from the low laying coastal planes (Fig. 1). The escarpment is characterized by deep fluvially incised valleys with abundant waterfalls, prominently seen as knick points along the river relief profiles (Vanacker et el., 2007). The escarpment is thus an erosional feature that was not formed by block uplift or other tectonic activity (Verstappen, 1987). The rivers flowing through the escarpment zones have a general NE-SW oriented rectangular flow pattern which transition to radial flow patterns on the coastal plains. This is peculiar since the geology of the island is relatively uniform, 90% of the island consisting of crystalline basement rocks formed during granulite and amphibolite facies metamorphism (Blanckenburg, 2004). The change in drainage pattern does not correspond with change in lithology, but roughly follows the transition between the escarpment zones and the coastal plains (Vanacker et al., 2005). The crystalline basement rock strike NNE-SSW and is evidence that the rectangular flow pattern is structurally controlled (Verstappen, 1987). Based on previous studies by Bremer and Verstappen, Vanacker suggests that the rectangular flow pattern was inherited from an ancient drainage system. The radial flow pattern of the coastal plains has not yet integrated itself in the islands interior and supports the hypothesis that the escarpment zones are arch-type with the primary drainage divide being located inland of the escarpment zone (Vanacker et el. 2005).

6 5 2.2 Erosional Data This study uses broad-scale regional patterns of long-term denudation rates derived from fluvial sediment cosmogenic nuclide data (Blanckenburg, 2004; Vanacker 2006). Spatially averaged erosion rates can be estimated using these cosmogenic nuclide concentrations in fluvial sediment (Granger et al., 1996). Blanckenburg and Vanacker both used the same sampling and analytical techniques to determine the erosion rates. Blanckenburg s samples range from the southern escarpment though the highlands to the northern escarpment while Vanacker s data focuses on the southern escarpment, the zone where topography is most pronounced (Fig. 2). Seventeen of Blanckenburg s samples from the river channels of six tributaries and the main trunk at the upper region of the Mahaweli River were used for Fig. 2. Blanckenburg s erosion data are purple while Vanacker s data are green along the escarpment. comparison All of these rivers catchments are large, on the order of km 2 and are heavily used for agriculture. Therefore, the samples were taken from small catchments (max 1km 2 ) that have unmodified forest cover and are assumed to only erode by natural processes (Blanckenburg et al., 2004). Vanacker followed a similar approach to the southern escarpment, sampling small catchments of pristine environment to evade anthropogenic influence. Twelve samples were taken from small tributaries of the Belihul Oya and Kerihul Oya, and from the main trunk stream of the Belihul Oya above and in the escarpment zone (Vanacker et al., 2006).

7 6 For completeness, both data sets are presented in Table 1. A detailed regional description of the soils, geology, climate and tectonics of Sri Lanka has been presented by Hewawasam et al. (2003) and Blanckenburg et al. (2004). 3. Methods 3.1 Data Handling NASA s SRTM 90m Version 4 DEM topographic data was retrieved from the CGIAR server ( This study used srtm_52_11.zip and srtm_53_11.zip spanning the entire island of Sri Lanka (Lat: 5-10N, Long: 75-85E). The downloaded format was in a geographic coordinate system and was reformatted to UTM zone 44N with rectangular, equidimensional pixels using the ArcMap coordinate converter. This is necessary for further processing. A variety of methods can be used to extract hydrological data from a DEM. Any suite of software that can form a pixel-to-pixel flow array and extract elevation, distance from the stream head, and contributing drainage area is sufficient for extracting long river profile data (Wobus et al. 2006). Methods developed by Snyder et al. (2000) and Kirby el at. (2003) utilize a group of built-in functions in ARC/INFO to create a flow accumulation arrays and delineate drainage basins, a suite of MATLAB scripts to extract and analyze stream profile data from these basins, and an Arcview interface for projecting the data on DEMs in a Geographic Information System (GIS) (Wobus et al., 2006). Pits and holes need to be filled to create flow direction arrays, but the raw DEM should always be used to extract profile data to ensure no data is lost or created at the early stage of analysis.

8 7 It is well supported that slope-area data often exhibit a pronounced scaling break at A cr = 10 6 m 2, which in represents the transition from hill slope colluvial channel flow to stream dominated channel flow (eg., Montgomery and Foufoula-Georgiou, 1993). This transition is seen in Sri Lanka but varies between 10 5 m 2 and 10 7 m 2 of drainage (see appendix A). There, channels above the scaling break have irregular scatter with convex up relief, while channels below the scaling break exhibit linear regressions with concave up relief. Plots of slope area data below the break may reveal smooth, linear trends along the profile or may exhibit multiple linear sections with scaling breaks between. Low resolution DEM s (30m-90m) often show significant scatter and can be smoothed to aid in interpretation. Smoothing does not affect the location of scaling breaks but does predictably lower steepness indices and reduce spikes in the data. Generally, steepness and concavity values will vary less than ten percent between smoothed and unsmoothed profile (Wobus et al., 2006). 90m data was appropriate for this study since the stream profiles cover a vast distance and drainage area. The low-resolution data was regressed with a smoothing window of 2500m, ten times that of smaller watersheds, to limit scatter. This aided in fitting models to the general shape of the profile through the escarpment instead of fitting many smaller regressions through the escarpment. 3.2 Regression of channel gradient and contributing drainage area Once prepared, one can begin to fit models to the streams based on slope-area data. This case was particularly challenging due to the usual relief created along the escarpment. Graded rivers generally follow power-law relationship between local channel slope (S) and the contributing drainage area upstream (A).

9 8 (1) S = k s A θ k s is referred to the as the channel steepness index and θ as the concavity index (Flint, 1974). Above a threshold drainage area (A cr ) that defines the start of a channel, systematic scaling between slope and drainage area can be fit with a simple regression. In some channels subject to spatial or temporal changes in lithology, climate or tectonics, the profile is characterized by separate segments with different scaling. This analysis was limited to rivers that could be confidently fit with the following model. In order to characterize the steepness of the channels, individual sections of slope-area data were fit using a reference concavity, θ ref resulting in normalized steepness indices, k sn. (2) S = k sn A θ ref This method is founded on the expectation that reference concavities fall within a narrow range (0.4 θ 0.6) and that profile gradients depend on contributing drainage area (Wobus et al., 2006). It allows one to compare profiles of rivers with greatly varying drainage area by leaving A as a free parameter (Wobus et al., 2006). A reference theta of 0.45 was used for this analysis. The normalized steepness index (k sn ) is a value representative of the centroid of the data bounded by the regression. Therefore, the k sn value is highly dependent on one s choice of regression limits. Sri Lankan rivers generally follow the above scaling relationship but have many pronounced knick points and irregularities. The normalized steepness indices derived from the rivers were constrained to channels through the escarpment which exhibit a close fit to this scaling behavior.

10 9 Using drainage area as a proxy for fluvial discharge and assuming it is related to incision rate, the global datasets reveal a non-linear functional relationship between erosion rate and normalized channel steepness (Harkins et al., 2007; Ouimet et al., 2009; DiBiase et al., 2010; Cyr et al., 2010) (Fig. 5). (3) k sn E p The nature of this scaling (the exponent p) varies widely among field sites due to differences in dominant erosion processes which affect the efficiency of erosion (Lague et al., 2005). Individual sights generally show a linear scaling relationship between channel steepness and erosion rate. Here, Sri Lanka is presented within this global data set to further explore the range of scaling relations formed between channel steepness and erosion rates.

11 10 4. RESULTS k sn vs. Erosion Rate k sn Mean (erosion) Range Basin-averaged erosion rate (m/ma) Fig. 3. k sn vs. basin averaged erosion rate. Filled symbols represent the mean erosion rate for a given river s k sn. Open symbols represent multiple erosion data for a given river and demonstrates the range averaged for results. k sn vs. Concavity along the Escarpment k sn Concavity South East North Fig. 4. k sn vs. concavity along the Sri Lankan escarpment. Cardinal directions indicate which side of the escarpment the rivers were located.

12 11 Normalized steepness index (m^0.9) Normalized Steepness index vs. Basinaveraged Erosion Rate E. Tibet, China (Quimet et al., 2009) Andes, Bolivia (Safran et al., 2005) NE. Tibet, China (Harkins et al., 2007) San Gabriel Mtns., USA (DiBiase et al., 2009) Apennine Mts., Italy (Cry et al., 2010) Appalachian Mts., USA (Miller et al., 2011) Escarpment, Sri Lanka (Potako, Kirby, 2013) Basin-averaged erosion rate (m/ma) 1000 Normalized Steepness index vs. Basinaveraged Erosion Rate Log normalized steepness index (m^0.9) E. Tibet, China (Quimet et al., 2009) Andes, Bolivia (Safran et al., 2005) NE. Tibet, China (Harkins et al., 2007) Log basin-averaged erosion rate (m/ma) Fig. 5. Empirical scaling relationships between channel steepness (k sn ) and erosion rate. (Kirby and Whipple, 2012)

13 12 Fig. 6. Rivers used to compare to erosional data. Fig. 7. Additional rivers used to compare concavity indices The analysis proved that Sri Lanka is steep compared to the rest of the data set. No clear trend emerges from the k sn vs. steepness index chart. Sri lanka is the steep for its given erosion rates but it is not ever steep compared to some of the other study areas, some of which have a steepness index 3 times that of Sri Lanka. 5. DISCUSSION Channels draining the Sri Lanka highlands are relatively steep give the low erosion rates inferred from 10 Be inventories in stream sediment. As seen in Figure 4, Sri Lanka sits above the other data, following a steeper trend. It is the only tectonically inactive location in this dataset, and since tectonically active regions often have higher erosion rate, it is reasonable that the data would fall where it does. Based on the assumption that both high precipitation and temperature increases erosion rates, Sri Lanka has the most erosive climate out of the data, yet among the lowest erosion rates. Lithology is another important control on erosion. Most mountainous regions are composed of crystalline basement rock, as these rock types are the only ones resistant

14 13 enough to hold high relief. Although Sri Lanka is composed of resistant crystalline gneiss, its erosion rates are still impressively low. Since all of the data share similar lithology, the graphs suggest that tectonic activity has a greater influence erosion rates than climate conditions. Erosion rates aside, the escarpment is not particularly steep, falling within the same range as the San Gabriel Mountains and the Apennine Mountains and falling in the lower range of Eastern Tibet and the Bolivian Andes. The steepest regions, Eastern Tibet and the Bolivian Andes, both lay on convergence zones are very tectonically active suggesting this is a major control on channel steepness. Additional rivers along the escarpment were plotted in a k sn vs. concavity index graph. There is no strong relationship between the two but some trends emerge. k sn does appear to be independent of the concavity index and centered around 120. Channel steepenss (k sn ) ranges from m 0.9 while concavity ranges from 0.27 to 0.89, showing that k sn is somewhat consistent over a wide range of concavity indices. This supports the assertion that k sn is a good proxy in comparing river steepness. This study suggests that active tectonics is a greater control on channel adjustment and erosion rates than previously thought. More research should be done over a greater climatic range and lithological range of tectonically inactive regions to help constrain these variables influence on erosion rates and channel steepness. 6. CONCLUSION Sri Lanka is unusually steep for its erosion rates. Sri Lanka is characterized by extremes of the factors that may influence erosion tropical climate, steep channels and no tectonic activity. It therefore provides us the opportunity to analyze a tectonically inactive land adjusted

15 14 to climatically controlled erosion. Erosion rates were taken from Blanckenburg (2004) and Vanacker (2006). Rivers were characterized using methods developed by Snyder et al. (2000) and Kirby et al. (2003). The data suggest that active tectonics has a profound influence on erosion rate in mountainous regions.

16 15 ACKNOWLEDGEMENTS Thank you, Dr. Eric Kirby, for taking me under your wing and opening up to the world of geomorphology. Your scripts were invaluable in this analysis and I m sure many others have and will appreciate the ease of use. Thanks for the patience and time at this busy point of your life. Brian Clark, thank you for helping me prepare my DEM for analysis and for helping me set up an organized file structure. I really appreciate your willingness to help and your positive attitude. It was great getting to know you. Dr. Peter Heaney, thank you for teaching the writing class and helping us through this daunting exercise. You made it as fun as it could be. If it was not for you, I would probably be drafting my introduction at this point. Russ Rosenburg, thanks for the motivation at the beginning of the project. You re help the first few days put me on a good track. Congratulations on your Master s degree. Al Neely, thank you Al for sharing the imac with me and keeping me company in the wee hours of the night.

17 16 WORKS CITED Ahnert, F., Functional relationships between denudation, relief, and uplift in large mid-latitude drainage basins. American Journal of Science 268, 243e263. Chand, S., Radhakrishna, M., Subrahmanyam, C., India-East Antarctica conjugate margins: riftshear tectonic setting inferred from gravity and bathymetry data, Earth Planet. Sci. Lett. 185 (2001) Cyr, A.J., Granger, D.E., Olivetti, V., Molin, P., Quantifying rock uplift rates using channel steepness and cosmogenic nuclide-determined erosion rates: examples from northern and southern Italy. Lithosphere 2, 188e198. DiBiase, R.A., Whipple, K.X., Heimsath, A.M., Ouimet, W.B., Landscape form and millennial erosion rates in the San Gabriel Mountains, CA. Earth and Planetary Science Letters 289, 134e144. Flint, J.J., Stream gradient as a function of order, magnitude, and discharge. Water Resources Research 10, 969e973. Granger, D. E., J. W. Kirchner, and R. Finkel (1996), Spatially averaged long-term erosion rates measured from in situ produced cosmogenic nuclides in alluvial sediment, J. Geol., 104, Harkins, N., Kirby, E., Heimsath, A., Robinson, R., Reiser, U., Transient fluvial incision in the headwaters of the Yellow River, northeastern Tibet, China. Journal of Geophysical Research- Earth Surface 112, F03S04. Hewawasam, T., F. Von Blanckenburg, M. Schaller, and W. Kubik (2003), Increase of human over natural erosion rates in tropical highlands constrained by cosmogenic nuclides, Geology, 31, Kirby, E., Whipple, K., Expression of active tectonics in erosional landscapes, Journal of Structural Geology, 44 (2012) Kirby, E., Whipple, K., Tang, W., and Chen, Z., 2003, Distribution of active rock uplift along the eastern margin of the Tibetan Plateau: Inferences from bedrock channel longitudinal profiles: Journal of Geophysical Research, v Lague, D., Hovius, N., Davy, P., Discharge, discharge variability, and the bedrock channel profile. Journal of Geophysical Research /2004JF Montgomery, D.R., and Foufoula-Georgiou, E., 1993, Channel network rep- resentation using digital elevation models: Water Resources Research, v. 29, p , doi: /93WR02463.

18 17 Ouimet, W.B., Whipple, K.X., Granger, D.E., Beyond threshold hillslopes: channel adjustment to base-level fall in tectonically active mountain ranges. Geology 37, 579e582. Riebe, C., Kirchner, J., Finkel, R. (2004). "Erosional and climatic effects on long-term chemical weathering rates in granitic landscapes spanning diverse climate regimes". Earth and planetary science letters( x), 224(3-4), p.547. Rossi, M.W., Whipple, K.X., DiBiase, R.A., Heimsath, A.M., Climatic Controls on Steady State Erosion using the Relationship between Channel Steepness and Cosmogenic 10Be-derived Catchment Averaged Erosion Rates, 2011 Fall Meeting, AGU. American Geophysical Union, San Francisco, CA. Safran, E.B., Bierman, P.R., Aalto, R., Dunne, T., Whipple, K., Caffee, M., Erosion rates driven by channel network incision in the Bolivian Andes. Earth Surface Processes and Landforms 30, 1007e1024. Snyder, N., Whipple, K., Tucker, G., and Merritts, D., 2000, Landscape response to tectonic forcing: DEM analysis of stream profiles in the Mendocino triple junction region, northern California: Geological Society of America Bulletin, v. 112, no. 8, p Vanacker, F. von Blanckenburg, T. Hewawasam, P.W. Kubik, Constraining landscape development of the Sri Lankan escarpment with cosmogenic nuclides in river sediment, Earth and Planetary Science Letters, 253 (2007), pp Verstappen, T., Geomorphologic studies on Sri Lanka with special emphasis on the northwest coast, Int. J. Appl. Earth Obs. Geoinf. 1 (1987) von Blanckenburg, F., The control mechanisms on erosion and weathering at basin scale from cosmogenic nuclides in river sediment, Earth Planet. Sci. Lett. 237 (2005) von Blanckenburg, F., Hewawasam, T., Kubik, P., Cosmogenic nuclide evidence for low weathering and denudation in the wet, tropical highlands of Sri Lanka, J. Geophys. Res. 109 (2004) F Wobus, C., Whipple, L., Kirby E., Snyder, N. Johnson, J., Spyropolou, K., Crosby, B., Sheehan, D., 2006, Tectonics from topography: Procedures, promise and pitfalls: Geological Society of America, Special Paper 398.

19 18 APPENDIX A Erosion and Channel Steepness Data Vanacker Erosion Data River ksn (m^0.9) ksn error (m^0.9) sample Erosion rate (m/ma) Erosion rate error (m/ma) BO 9 2 HP-2g BO-U HP-1g BO-U BO-U BO-U BO-U BO-F2F BO-F ESP ESP-3g KO KO-F KO-T KO-G KO-G KO-R1F ESP-5g Blankenburg Erosion Data AO 90 3 AO AO NO NO NO MO MO MO BON BO BO UO UO MO MO MO UO UO HUG HUG HUG Plateau 79 8 M-PER M-HAG Additional Rivers River ksn (m^0.9) ksn error (m^0.9) concavity Concavity error ESC-S ECS-S ESC-S ECS-S ESC-S ESC-E ECS-N ESC-N TABLE 1. River and k sn from Potako and Kirby (2013), Vanacker erosion data from Vanacker et al. 2006), Blanckenburg erosion data from Blackenburg et al. (2004).

20 APPENDIX B Stream Regressions 19

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