Assessing Dynamic Topography from Rivers Characteristics in Patagonia Forelands

Stage de recherche de Master 2

Master Sciences de la Terre

Physique et Chimie de la Terre et des Planètes

Author: Louise JEANDET

Assessing dynamic topography from rivers characteristics in Patagonia forelands

ABSTRACT

In Patagonia, south of 46◦S, the Miocene opening of a slab window related to the subduction of the Chile ridge have potentially induced important dynamic topography on the continent. However, the link between mantle flow underneath Patagonia and the post-Miocene relief evolution in the Andean chain and its forelands is not well constrained, and climatic and tectonic factors could also play a role in Patagonia Miocene uplift. To investigate the role of slab window in Patagonia topography, I used two morphometric methods based on the idea that dynamic topography can be eroded away and provide geomorphological record. Stream profile analysis and knickpoints mapping shows a zone between 46 and 49◦S where erosion is important, correlating with the present-day location of dynamic topography previously modelled. However, concavity and steepness data can not be exploited because of important errors on their calculation, probably coming from the quality of the data and the smoothing parameters used in this study. Calculation of Sr index, a new morphometric method based on the catchments geometry, shows a strong and recent uplift in this same zone. This result is likely to be explained by dynamic topography, because this uplifted zone is too far from the belt to be mainly influenced by glacial and tectonic factors. Further SIG analysis combined to fluvial terraces dating and numerical modelling are necessary to precise the delimitation of the zone mainly influenced by dynamic topography and quantify this perturbation.

Supervisors: Xavier ROBERT and Laurence AUDIN Institut des Sciences de la Terre (ISTerre), IRD, Grenoble

June 4, 2014 Remerciements

Je remercie toute l’équipe TRB d’ISTerre pour leur accueil chaleureux. Je remercie tout particulièrement Xavier Robert et Laurence Audin, pour leur disponibilité, et leurs conseils. Merci également à Laurent Husson pour ses idées intéressantes sur mon travail, ainsi qu’à Jean Braun. Merci à Karim, Mallory et Cyril, pour leur bonne humeur quotidienne et pour les gateaux du Jeudi. Cécile, merci pour tes précieux conseils, pour tes contacts, et pour l’expérience des tribulation savantes, qui était très enrichissante. Egalement merci à Laura, Etienne, Olivier, Jérémy, et Amandine, pour ces quelques mois passés ensemble à ISTerre. 1 Introduction

According to plate tectonics theory and isostasy principle, the present day topography of the Earth is the result of horizontal motion of the plates, their interactions leading to vertical variations of crustal and lithospheric thickness. How- ever, a growing amount of evidences suggests that the mantle flow creates an important proportion of Earth topography. This low amplitude (1 km) and long wavelength (hundreds to thousands kilometres) topography (Braun, 2010) is the surface expression of the mantle flow generated by density anomalies. This flow interacts with the base of the lithosphere, inducing viscous stresses balanced by the gravitational stresses generated by the deflection of the Earth surface (Hager et al., 1985, Braun, 2010), generating dynamic topography (fig.1). This boundary deformation takes place on a time scale set by asthenospheric viscosity, similar to the post-glacial rebound characteristic time (1-5 104 years). Thus, dynamic topography can be considered as instantaneous in relation to mantle flow, and moving with it (Hager et al., 1985).

In subduction zones, high topography is created by plate convergence. These relief results, at first order, from lithospheric thickening and isostasy. But we have to consider dynamic topography because in subduction zones, density anomalies are very important under the overriding plates. However, dynamic topography is difficult to constrain in these zones, because it is often ... hidden by high tectonic structures. Patagonian Andes is an interesting place to study the surface effects of mantle upwelling in a subduction. The Andean chain borders the western margin of the South American plate. It is a belt of thickened crust, resulting from the late Mezosoic and Cenosoic subduction of several oceanic plates. Figure 1: Sketch comparing how mantle flow and horizontal plates motion creates topography, from Braun(2010).

In Patagonia (part of the continent south of 41◦S), middle Miocene corresponds to an important geodynamical transition, from a widespread marine transgression to a generalized uplift (Lagabrielle et al., 2004). This also corresponds to the opening and northward enlargement of an asthenospheric window (ﬁg.2) through the subducting plates, inducing an upwelling of mantle material (Breitsprecher and Thorkelson, 2009). Thermochronological, numerical, and geomorphological evidences (Guillaume et al., 2009, 2013) suggest that the opening of this slab window induced a wave of dynamic topography in Patagonia, propagating northward since 18 Ma. However, the link between dynamic topography and the Miocene uplift is not well constrained, and glacial isostatic rebound (Lange et al., 2014) or tectonic mechanism (Lagabrielle et al., 2004) have been proposed to explain it. Thus, what is dynamic topography contribution to the Miocene uplift of Patagonia ?

Constraining present and past dynamic topography is rather difficult and has been the focus of many recent research (e.g. Braun, 2010, Barnett-Moore et al., 2014, Flament et al., 2014, Moucha et al., 2008). In the Patagonian Andes, it is hidden by tectonic structures, much higher in amplitude, pre-existing to the opening of the slab window. Moreover, quan- tifying current dynamic topography requires a good knowledge of crustal structures and lithospheric thickness, necessary to compute the proportion of isostatically compensated topography in the global Earth topography (Braun, 2010). The best way to infer dynamic topography in the Patagonian Andes is to look at its supposed past situation, looking for a sedimentary, thermochronological, or geomorphological record. This requires that this wave of dynamic topography have have been eroded efficiently. Because of its nature (long wavelength, small amplitude and slow changing), dynamic topography was thought unlikely to be eroded, fluvial erosion needing hight slopes and rapid uplift rate to be efficient. However, recent studies based on

1 numerical modelling shown that dynamic topography can be eroded away (Braun, 2010, Braun et al., 2013). In many cases, dynamic topography directly plays a role on surface processes. Braun et al.(2013) show, using surface processes models, that a passing wave of low amplitude, long wavelength topography can disturb enough the large scale geometry of drainage network to produce an amplification of erosion, the efficiency of fluvial erosion depending on drainage area and slope. Moreover, even if it is eroded away, dynamic topography should be sustained by mantle flow, because viscous forces induced on the lithosphere are balanced by gravitational forces arising from surface deflection (Hager et al., 1985, Braun et al., 2013). Thus, a dynamic topography with a one kilometre amplitude could lead to several kilometres of erosion (Braun, 2010). Geological evidences have also shown that an uplift caused by the passing of a wave of dynamic topography can disturb the drainage network efficiently to provide evidences in the sedimentary and geomorphological record: for example on the Colorado plateau (Moucha et al., 2008, Robert et al., 2011), or on the Brasilian margin (Barreto et al., 2002, Flament et al., 2014).

To quantify uplift and topography history, thermochronology can lead to misinterpretation (Guillaume et al., 2013) because it records temperature history of rocks. The link with uplift history is not obvious, because other parameters affect temperature history (erosion, burial...). But we can expect (Braun et al., 2013) that this topography have been efficiently eroded to disturb the drainage network and provide geomorphological record. For more than one century, geomorphologists have tried to understand the response of fluvial drainage network to perturbations (uplift, or subsidence) (Penck, 1919, Ahnert, 1970, Molnar and England, 1990, Whipple and Tucker, 1999). Ultimately, modelling these processes would be useful to go back to the uplift history of a region from the landscape morphology. If many factors controlling the erosion and transport processes still poorly constrained, and vary from one location to another, efficient geomorphological methods have been developed in order to get, at least, regional uplift and subsidence stories. Thus, I use geomorphology to infer the northward propagation of dynamic topography from drainage network characteristics. I used Geographic Information System (SIG) analysis to extract rivers and catchments morphometry in tree different areas (fig.2), in order to:

− find if the drainage network is responding to a broad uplift of the continent by a wave of erosion; − find if, qualitatively, there are differences in the age of this perturbation, from south to north; − quantify this potential uplift and find an absolute age to the beginning of this perturbation in each zone.

2 Geodynamical context

2.1 A late Cenozoic exhumation

During the middle Miocene, the southern portion of the Chile ridge (separating Nazca plate from Antarctic plate, see ﬁg.2) started subducting (Breitsprecher and Thorkelson, 2009). The subduction of this spreading ridge led to the opening and progressive enlargement of an asthenospheric window (also referred as slab window) underneath Patagonia (Breitsprecher and Thorkelson, 2009). The Chile Triple Junction (CTJ) is the point where Nazca, Antarctic and South American plates meet (ﬁg.2). This junction has been migrating northward during the last 18 Ma, and it is currently located at 46◦ 12’S (Lagabrielle et al., 2004). Its migration has been discontinuous because of the episodic sinking of several portions of ridges parallel to the trench, these episodes leading to the northward asthenospheric window enlargement. The formation of this slab window has produced alkaline magmatism in the Eastern foreland and caused a hiatus in the calc-alkaline arc volcanism (Corgne et al., 2001, Kay et al., 1993). At the present-day latitude of the CTJ, a late Miocene, strong exhumation have been demonstrated by:

− the exhumation of granitic plutons near the latitude of the CTJ around 4-3 Ma (Lagabrielle et al., 2004); − the rejuvenation of thermochronological ages between 48◦S and 46◦S, showing an acceleration of erosion since 5 Ma.

But the origin of this strong exhumation and its link with Miocene uplift and dynamic topography is much discussed and have been the focus of many research for the last 10 years.

2 Structural and tectonic arguments (Lagabrielle et al., 2004) are in agreement with a change in the compressional regime (with a faster and more orthogonal convergence), resulting in uplift as the simple consequence of compression along N-S thrust. Moreover, a climatic argument is developed by Thomson et al.(2010), who invokes erosive glaciation between 46 and 48 ◦S at these latitudes to explain thermochronological ages rejuvenation. Lange et al.(2014) also predicates the role of glaciers, in term of isostatic response (uplift) to recent ice mass changes. However, these studies does not take in account that changes in erosion rate coincides with the present-day latitude of the CTJ. Guillaume et al.(2013) proposed a link between this exhumation and dynamic topography, showing with thermochronology the northward migration of a late Cenozoic wave of erosion between 46◦ and 48◦. But these data coming from the eastern slope of the chain, they are quite controversial because of the possible inﬂuence of the glaciers, or local tectonics (for example along the southern part of the Liquiñe-Ofqui fault).

Figure 2: Tectonic setting of the Western Patagonia, with the interacting plates and their velocity vectors (Guillaume et al., 2013). The altitudes are extracted from SRTM90 V4.1 data set. The subduction ages of the different trench-parallel segments of the Chile ridge are from Lagabrielle et al.(2004). The position of the Chile Triple Junction (CTJ) is indicated in red. The present-day extent of slab window is the grey area. Its edges corresponds to the present-day reconstructed Antarctic and Nazca plate edges (Breitsprecher and Thorkelson, 2009). The position of the Liquiñe-Ofqui fault zone (LOFZ) is indicated (Cembrano et al., 2002). The red patch corresponds to the difference in dynamic topography calculated between 8 Ma and present-day (Guillaume et al., 2013), from inverse modelling thermochronological data. The blue frames are the tree studied zones (from North to South, RC = the rio Chico catchment zone; DM = the Deseado massif zone, RCo = the rio Coyle catchment zone). The main rivers of the Atlantic drainage are represented in blue. The black triangles onland represents captures areas, there peak shows the new flow direction (captures indicated with a letter are detailled in fig.3). The tilted fluvial terraces (Guillaume et al., 2009) are represented in yellow (southward tilted) and orange (northward tilted).

3 2.2 A late Miocene perturbation of drainage network

In Patagonia eastern forelands, there are geomorphological evidences for a post Miocene, large scale reorganisation of the drainage network (Guillaume et al., 2009), possibly linked with the northward propagation of dynamic topography.

− a regional tilt of fluvial terraces since at least the Miocene. Fluvial terraces located on the forelands, at the latitude of the CTJ, show a post-Miocene inclination, that is too far from the belt to be influence by any glacial rebound. Guillaume et al.(2009) show a change from northward to southward tilt of the fluvial terraces located south of the present location of the CTJ and a northward tilt of the terraces located north of 46◦S (fig.2). − changes of flow direction of the main rivers. Several fluvial systems draining the eastern part of the Andean chain exhibit changes in there drainage since the Miocene (see fig.3). The Rio Senguerr (fig.3a) exhibits upper Miocene flow direction changes to the north, possibly linked to a regional northward tilt (Guillaume et al., 2009). Location of the terraces show that the paleo Rio Senguerr abandoned the Hermoso Valley, being caught northward by the Musters lake before flowing into the rio Chico. This capture occurred during or after the middle Pliocene. Some other captures, even if they are poorly documented can be seen in satellite imagery and DEM. Located south of the CTJ, they show a southward change in the flow direction and may be linked to a southwards regional tilt. For example the Rio Chico flows toward the South (fig.3b), abandoning the present location of the ruta 25. Also, we notice the capture of the Viedma lake by the Argentino lake through the Leona river (fig3c).

For geomorphological study, I choose areas (see fig.2, blue frames) located in the forelands of the belt, to minimize a possible glacial rebound or tectonic influence. Because of the DEM resolution and the codes we used for river profile extraction, the studied zones have to exhibit a significant slope, so we did not analyse drainage network too close to the eastern margin. Finally the different zones have to display a large range of latitudes, to evidence some North-to-South variations.

Figure 3: Shaded DEM of the tree zones of river capture localized in ﬁg.2. The main drainage network (blue lines) has been calculated from the DEM (see methods). The orange contour in a. shows the location of ﬂuvial terraces tilted toward the North (Guillaume et al., 2009). The blue and red arrows show the paleo and the present-day direction of the rivers, respectively. In b and c the dark line shows the present-day position of the divide.

The northernmost area is the rio Senguerr-rio Chico catchment, which exhibit some evidences of northward tilt (terraces on fig.2 and capture on fig.3). The slab window has not reached these latitudes yet. The second area is the Deseado Massif zone, located south of the CTJ, at the latitude of the dynamic topography maximum (Guillaume et al., 2013). This massif of late Miocene asthenospherically derived lavas (Breitsprecher and Thorkelson, 2009) is situated north of the ruta 25 southward capture (see fig.3b). The southernmost studied area is the rio Coyle catchment, located 400 km south of the present-day location of the CTJ. The slab window reached these latitudes aproximately 10 Ma ago (Breitsprecher and Thorkelson, 2009, Lagabrielle et al., 2004). I used two different morphometric methods to extract uplift information from fluvial landscape in these zones.

4 3 Morphometric method

3.1 Theoretical basis

3.1.1 Stream proﬁles analysis

Topographic data from most of the steady-state natural ﬂuvial channels can be modelled by the Hack law, a power-law relation between the local slope S (also called channel gradient), and the upstream drainage area A :

−θ S = ks.A (1)

where θ is the concavity and ks the steepness of the river (Hack, 1973). This relationship is accurate for all detachment and transport-limited processes, above a threshold drainage area, Acr, interpreted as the transition from debris-ﬂow to ﬂuvial processes (Montgomery and Foufoula-Georgiou, 1993). The logarithmic form of this equation is usually used to obtain concavity and steepness index of rivers from topographic data, by a linear regression on the log(S)-log(A) plot:

log(S) = log(ks) − θ.log(A) (2) Simple models of ﬂuvial incision predict that, assuming a spatially uniform rock uplift rate U, θ is independent of

U(Whipple and Tucker, 1999). These models also predict that ks is controlled by U, both being linked by a simple power-law relation (Howard and Kerby, 1983). However, obtaining informations about uplift rate directly from rivers ks is not possible, because in natural settings, rivers diﬀers from these simple models, which not incorporate many factors expected to inﬂuence θ, ks, or both.

First, the link between concavity and the uplift rate, U, is not well constrained, even if natural data suggest little change of θ as a function of U. Thus, regional calculation of ks requires the use of a reference value θref for all the processed rivers (equation 3). The θref value is based on the regional mean of all the concavities, if regional data are available. This allow regional comparison between the normalized steepness values, ksn.

−θref S = ksn.Acent (3)

As there is not previous θ data for the region studied here, we processed all the ksn calculation with θref = 0.45, which corresponds to a good estimate of θ in other tectonically active regions (Wobus et al., 2006, Whipple et al., 2007). Thus, 0.9 all the Ksn values will be expressed in m .

In the log(A)-log(S) plot, for a given θref , the intercept ksn is calculated for a slope given by θref , for an area Acent being the middle of the data segment to ﬁt (ﬁg.4).

If the difference between θ and θref is important ( ≥ 0.2), the ksn value is only significant for a small drainage area near Acent (Wobus et al., 2006). Otherwise, an overestimated θref gives an overestimated ksn, and conversely (see fig.4).

Figure 4: Sketch showing how the diﬀerence between θ and θref qualitatively change the Ksn value. The dark dots are the slope vs drainage area data. The green line is the ﬁt of the data with Ksn and θ determined as free parameters. The red and the blue straight line are the linear regression calculated with a θref too big and too small, respectively.

5 Moreover, in natural data, the link between ks and U is more complicated than a simple power law. Thus, we do not know how to extract uplift rates directly from ksn mapping. The link between U and ks is expected to vary between geological settings, because of many factors not incorporated in these models likely to influence ks, as substrate rock properties and climatic factors. However, at local scale, where other parameters as geology and climate are well constrained, ksn calculations can lead to qualitatively compare low and high uplift zones (Wobus et al., 2006). Finally, a lot of natural rivers can not be modelised by the equation (1) because they exhibit several portions, each of them modelled by a constant value of θ and ks. But these variations are actually helpful to extract uplift information (Whipple and Tucker, 1999, Wobus et al., 2006, Barnett-Moore et al., 2014). Topographic data shows, along the rivers profiles, that two portions of constant θ and ksn can be separated by a very hight steepness and concavity zone, called a knickpoint. Here, based on Wobus et al.(2006) we separate two basic cases (fig.5):

− The river is in a transient state, re-equilibrating after an uplift signal. On the long profile, the knickpoint separate the old and new equilibrated profile. The knickpoints from different rivers in this zone will follow the same line of constant elevation, showing a wave of regressive erosion; − The river is crossing the limit between two zones of different uplift regimes. On the long profile, two segments of different steepness values are separated by a zone of hight absolute θ value, convex "knickzone". In this case, the knickpoints of several rivers will follow the trend of the accommodating shear zone.

Figure 5: Schematic long profiles comparing transient and steady-state river profiles. The long profile (elevation versus distance to mouth) is represented in black. The red dots are slope vs drainage area measurements. The dark straight line is the fit of the data. A. Transient profile reequilibrating after a base-level fall. The knickpoint propagates upstream during the propagation of the regressive erosion wave. B. Steady-state river profile displaying a downstream increase in steepness. This can come from a differential uplift rate along the profile, the hight steepness zone coming from an upstream stronger uplift .

6 3.1.2 R/Sr analysis The previous method has a major limitation. The comparison of several stream proﬁles implies that the geology, sedimentary supply and incision mechanisms have to be the same between all the rivers, in order to extract only tectonic information. Moreover, assuming that these conditions are fulﬁlled, this provide only qualitative informations. An other approach is to consider the drainage area’s geometry as a whole.

R The geometry of a drainage basin is usually described by its hypsometric curve Hb and the associated integral (Hb), an index proposed by (Strahler, 1952). The hypsometric curve of a drainage basin is the cumulative distribution of the relative elevations above basin mouth, represented as a function of the drainage area. Using undimensionless units, it R allows comparison between basins of all sizes. The hypsometric curve (Harlin, 1978) as well as the hypsometric area (Hb) have been used to roughly distinguish between early geologic stages of development of drainage basin and old, equilibrium stages (Strahler, 1957). Thus, the potential of this index to describe temporal evolution of a catchment has been suspected since many years. Many studies e.g. Lifton and Chase(1992) tried to correlate diﬀerential uplift timing with the hypsometric integral. If R spatial variations of (Hb) have been used to assess the propagation of an uplift wave in a homogeneous geological setting R (Delcaillau et al., 1998), the time required to reach a stable (Hb) and the relative inﬂuence on this index of tectonics, R climate and rock type were remaining open questions. Finally, it has been shown that lithology strongly controls (Hb) (Cohen et al., 2008, Hurtrez and Lucazeau, 1999). Moreover, the hypsometric integral correlates with basin size in some cases (Chen et al., 2003). Thus, because of two many parameters noising the uplift signal, the hypsometric integral itself can not be useful to detect changes in the uplift age at a time scale lower than 106 years.

The morphometric analysis we use has been built by Demoulin(2011). The working hypothesis is that the inherent geometric properties of a catchment (i.e, hypsometric integrals of basin, trunk stream, and tributaries) have different responses rates to an uplift event. Looking at the relationship between these integrals can open a window toward dating the last uplift event affecting the region. Moreover, because the morphometric index considered here is a ratio between hypsometric integrals, it must be unaffected by any spatial changes in geology or sedimentary supply, as soon as these parameters influence in the same way the trunc stream and the tributaries. The transient response of a drainage area to a perturbation of the base level (uplift) is characterised by a wave of regressive erosion, propagating from the outlet of the catchment along the drainage network. Demoulin(2011) have identified tree morphometric variables (at the catchment scale) from which the derived index R is sensitive to the time elapsed since the last uplift event. These variables are (fig.6): R − the hypsometric integral (Hr) of the trunk stream (i.e. the area below the trunk stream long profile) R − the hypsometric integral (Hn) of the tributaries R − the hypsometric integral (Hb) of the basin These tree morphological levels indicate respectively the short, middle, and long-term response to an uplift characterised by the base-level fall. Thus, Demoulin(2011) defined the following index: R R Ir Hn − Hr dl R = = R R (4) Ib Hb − Hn dl

where l is the dimensionless length (for Hr and Hn) and the dimensionless area for Hb.

Demoulin(2011) have shown that R varies with time, following several erosion steps (ﬁg.6): − an uplift signal, characterised by a base-level fall, thus an increase in all tree integrals, will left R unchanged;

− an erosion wave propagates primarily along the trunk streams, which reequilibrates ﬁrst. Hr rapidly decreases, while

Hn and Hb remain almost constant. This leads to a rapid increase of R;

− the erosion wave propagates along an increasing number of tributaries. Hn decreases strongly while the decrease of

Hr slows down and Hb remain unchanged, leading to a slow decrease of R with time.

7 Figure 6: Sketch illustrating how the diﬀerent components of a catchment re-equilibrate after an uplift (or base-level fall) and the consequent temporal variations of R. The ages represent the time lapse after the uplift event.

As the hierarchical structure of the catchment controls the behaviour of R, through the size contrast between tributaries and trunk stream, a correction from basin shape have to be applied. We apply to R the correction factor proposed by De- 1 4A moulin(2011) √ , where E = 2 where A is drainage area and Lb is maximal basin elongation, measured from its outlet. E π∗Lb

However, the evolution of R (rate, and amplitude of the variations) does not depends only of time. Based on a topographic dataset, Demoulin(2011) have shown that for catchments ≥ 150 km2 and draining an en-bloc uplift zone, the drainage area, A, controls at ﬁrst order the metric R. But the slope of the relationship R = f(ln(A)), is mainly controlled by the time eﬀect and thus this index, called Sr, can provide robust informations about the age of the last uplift event. Based on the analysis of several region worldwide, where the last uplift event is dated, Demoulin(2012) proposed a quantitative relationship between t and the metric R, where t is the age of the uplift in million years:

−4 t = 0.009 ∗ Sr (5)

This relation is accurate 0.1 Ma after the last uplift event (ﬁg.6c). For each studied zone, we choose catchments representative of all the catchment sizes, following a few important conditions:

− size conditions: the relationship between ln(A) and R requires drainages areas larger than 150 km2; − geometric conditions (see Annexes). Moreover, the catchment elongation correction is true for a uniform repartition of the tributaries around the trunk stream.

3.2 Data processing

3.2.1 Elevation data preparation

For rivers and catchment analysis, we used elevation data derived from the USGS/NASA SRTM (Shuttle Radar Topogra- phy Mission) data, available as 3 arc seconds (90 m resolution) Digital Elevation Models (DEM). These DEM are available as 5◦x5◦ tiles. Each tile is a grid (raster) of 6000x6000 pixels coding an integer altitude value. They are in geographic World Geodetic System (1984) coordinates. A global assessment of SRTM data indicates an absolute height error of 7.5 m for South America continent (Rodriguez et al., 2006). We use version 4.1 of SRTM data, no-data holes (where water or shadows prevent the quantification of elevation) have been filled with interpolation techniques to provide seamless topography surface. In order to extract stream profiles, knickpoints, log(S)-log(A) plot, and hypsometric curves, the elevation data of the drainage network was extracted from the DEM. DEM preparation and rivers extraction was processed with ArcGIS 10.2. We mosaiced 4 Chile and Argentina tiles to cover the studied zone. The mosaiced raster was projected to UTM WGS 1984 coordinate system, (zone 19). The negative values were removed and transformed into "NoData" cells.

8 The drainage network have been extracted following several steps (ﬁg.7):

− the one-cell pits (cells lower than their eight surrounding pixels), considered noisy, were filled by increasing each pit’s elevation to the elevation of the lowest surrounding pixel. − the flow direction array was calculated. Each pixel of the flow direction raster has a value coding in which of the eight surrounding pixels water is likely to flow (the surrounding pixel with the steepest drop in z value). − the flow accumulation raster was calculated by accumulating the weight of all cells flowing into each downslope cell.

Figure 7: Example of the tree main rasters used in drainage network extraction. A. Digital Elevation Model. Each pixel codes an altitude. B. Flow direction raster. Each pixel codes the direction (i.e. one of the eight surrounding pixels) where water in this cell is likely to ﬂow (see the legend). C. Flow accumulation raster, superimposed to the DEM. Here, only cells with a value higher than 62 (ﬂow accumulation at the outlet of a 0.5 km2 catchment) have been displayed in dark, showing the drainage network.

3.2.2 Stream proﬁle analysis

The DEM and the flow accumulation raster are exported to .mat binary files in order to be processed by Matlab codes developed by Whipple et al.(2007). This also requires the Arcgis profiler toolbox (available on http:\www.geomorphtools. org/tools.htm) useful for the data transfer between Matlab files and ArcGIS. The rivers extraction is processed by a Matlab routine follonwing a path of pixel downstream. This requires the previous creation of a text file to define the coordinates of the channel heads to analyse. To circumvent a bad definition of the channel head, a "search distance" of 10 pixels is defined. This is the distance Matlab search downstream from the pixel defined as the channel head to be sure it is really in the channel. Then, the code goes back upstream until it reaches the divide, before going back again downstream. It starts data extraction (elevation, distance, drainage area) when it reaches the pixel defined by a minimum accumulation (= the number of contributing pixels). Once the long profile is extracted, there are several problems in using raw pixel-to-pixel elevation data in the stream profile. Because DEM is created by interpolating digitized topographic contour maps, the conversion of topographic map to raster format give rises to artificial stair-steps associated with map contour. Moreover, because of the integer format of the DEM, these steps produce artificial flat zones with a slope equal to zero, impossible to process in log(S)-log(A) plot. For these reasons, the extracted stream profiles are re-sampled using a vertical contour sampling interval corresponding to the contour interval of the original data source. Further elevation data smoothing is processed, prior to calculate channel slopes, using an horizontal moving average window.

Two methods have been used to calculate ksn and θ values by linear regression.

Ksn automatic calculation. The drainage network is extracted above a drainage area Acrit which defines the channel heads. The linear regression is processed along all stream profiles, using a vertical moving window of size h. That means that at a certain point a with an elevation z, the code search for the first downstream point b with an elevation a-h. As in very flat zones, or lakes, a and b can be very far from each other, the linear regression on the segment ab is not significative and gives very hight ksn value. This automatic calculation have been processed regionally on all the eastern Patagonia drainage network above 8 2 ◦ Acrit = 10 m between 44 and 52 S in order to get a first North-South mapping of the Ksn values (fig.8). The lakes

9 and ﬂat zones were not taken in account. Then this rivers ksn have been used to calculate a regional ksn map by using the Inverse Distance Weighted (IDW) interpolation in ArcGIS. For each cell, this method calculates the ksn value by averaging ksn values of each cell in the neighbourhood of the processed cell, using a weight that decreases as a function a distance to the processed cell. The map resulting of this interpolation have been used to calculate ksn swath proﬁles.

Manual Ksn calculation. The rivers segments to ﬁt are chosen on the long proﬁle and the log(A)-log(S) plot. A

ﬁrst ﬁt of the data is processed with θ and ks as free parameters, using equation (2). Then ksn calculation is made with

θref fixed. This method have been processed on the main rivers of the Atlantic drainage (fig.9). A more detailed stream profile analysis was made on the Deseado massif zone, where the global study of the Ksn and the Sr calculations revealed promising.

θref 0.45 smoothing window 250 m sampling interval 12.192 m

auto ksn window 0.5 km 8 2 auto ksn Acrit 10 m search distance 10 pixels minimum accumulation 10

Table 1: Parameters used in the stream proﬁle analysis. .

3.2.3 Sr analysis The stream, drainage network and catchment elevation data are extracted by a few operations on the DEM and the ﬂow accumulation raster. For each catchment, the main stream source and mouth pixels have been chosen. The extraction from the DEM of the main stream, the tributaries, and the whole watershed, are processed by ArcGIS tools and automatised by two Python codes. These elevation data are exported to .dbf tables and processed by another code in Python 2.7 to extract the hypsometric integrals and calculate the R index for each catchment. These three codes were developed by Xavier Robert (2006) and I adapted it to this work.

4 Results

4.1 Stream proﬁle analysis

4.1.1 Regional study

For the automatic ksn calculation, the regional concavity is not available because the fit is directly processed with θref without any preliminary estimation of the θ values. Thus, we can not interpret directly the Ksn values calculated. However, ◦ we can notice a zone between 46 and 49 S exhibiting Ksn values greater than 200 (fig.8), which are not due to lake or flat zones artefacts. In the manual fit of main rivers profiles (fig.9), three rivers display a significative θ value (rio Chico, rio Santa Cruz

North and rio Coyle) because |θ − θref | ≤ 2. The 2σ error on linear regression is also small (ﬁg.9B). At least, these tree rivers can be compared in term of Ksn. The rio St Cruz North have a Ksn value ' 2 times greater than the two others.

These two methods used to calculate Ksn does not give the same Ksn ranges of values. The automatic calculation gives much higher values, because it processes the linear regression on smaller rivers portions than the manual ﬁt. But ◦ quantitatively, both results show an increase of rivers Ksn between 46 and 49 S.

The Ksn North-South swath profile shows hight Ksn mean and maximum zone (fig.10C). This zone does not correspond to particular hight elevations, compared to the rest of the profile, were Ksn is lower. But it is surrounded by a deep northern ◦ and southern incision (fig.10B), showing that in this area between 46 and 49 S, rivers displays hight Ksn values more likely due to a large-scale (' 300 km) incision zone than to local sharp reliefs.

10 8 2 Figure 8: Ksn values for the main drainage network (Acrit > 10 m ) obtained with automatic calculation. The lakes and

ﬂat zones, where the ksn is artiﬁcially very hight, have been removed from this mapping.

11 Figure 9: A. Ksn values manually fitted for the main rivers of the Atlantic drainage of the Patagonia Andes; rio Chico (RC), rio Deseado (RD), rio Serpiento (RS), rio santa Cruz (RstC), rio santa Cruz-north (RstCn), and rio Coyle (RCo). The portions of the rivers that can not be fitted because of important data scattering are represented with dotted blue line. The green dot is a knickpoint. The red patch is the dynamic topography proposed by Guillaume et al.(2013). B. log-log plot of the slope as a function of drainage area for three of the main rivers. The dark blue and cyan colors show the regressed and reference concavities, respectively. The pink crosses are slope data calculated from smoothed elevation data (smoothing window = 250 m). The red squares are log-bin averages of raw slopes data. The error on θ is the 2σ error on the regression. Long profils are also displayed (elevation as a function of distance to mouth), the shaded portion corresponding to the fitted segment in the log(A)-log(S) plot.

12 Figure 10: A. Map of the automatically calculated Ksn values of the drainage network in southern Patagonia, after interpolation. The light grey ﬁlled zones are edge artefact. The dark frame is the zone used for swath proﬁles presented in B. B. Altitude and

Ksn swath profiles calculated with the DEM and the interpolated Ksn, respectively. The main rivers are indicated (see fig9 for legend). For each profile, the mean value is represented in black, swathed by maximum and minimum value. In A and B, the current dynamic topography proposed by Guillaume et al.(2013) is represented in degraded red.

4.1.2 Deseado massif region

There is not typical regional concavity index. The rivers display a wide range of θ values (table2), with θmean = 1.17 ±

3.13. Because of this dispersion the use of θref = 0.45 is thus not signiﬁcant for all the processed streams. Moreover, θ displays an important 2σ error on the linear regression (see table2) for most of the rivers. This huge error is probably due an important scattering of slope data in the log(A)-log(S) plot. This can come from the average moving window we choose for elevation data smoothing (250 m). With SRTM 90 m data, the choice of a bigger smoothing window could give less scattered slope data (Wobus et al., 2006).

Because of this error, the mapping of the θ values (fig.11) as a function of |θref - θ| in only significant for some northern rivers, where the error is small enough to let us know if θref have been under or over-estimated (fig4). Qualitative comparison of ksn based on |θref - θ| is thus only possible for these rivers. In the northern part of the massif, the eastern rivers have a concavity greater than 0.65. Thus, the ksn calculation with θref = 0.45 is not significant. However, qualitatively, the ksn calculated values are underestimated (see fig.4). As the ksn calculated for these rivers are greater than for the western rivers (fig.12), we can assume that, in the northern part of the Deseado massif, eastern rivers display a steepness index much greater than in the western part. The rivers profiles of the northern rivers (fig.13) shows these rivers profiles are likely due to the transition between two different uplift regimes, the upstream, western part of the profile located in the zone where the uplift is the greatest.

13 Figure 11: θ values calculated for the Deseado massif rivers. The rivers segments in green have a θ value close enough to θref to have a significant ksn value (|θref - θ| ≤ 0.2). The rivers segments in red and blue have an over and underestimated ksn value, respectively (see fig4). The circled zone shows the rivers where this classification is really accurate, taking in account the 2σ error on the regression.

The knickpoints are located at a mean altitude of 756 m ± 152 m (fig. 12C). Except in the Northern part, where they are quite aligned, there map pattern doesn’t suggest a tectonic control. If they would be following the trend of an accomodating shear zone (Wobus et al., 2006), they would be following this zone. There regional repartition seems more likely depending on the altitude (fig.12C) even is they display a quite large range of elevation (' 300m). This dispersion can come from the vertical error of the DEM, or the error coming from the choice of the knickpoint pixel, particularly in area with important slopes. It can also come from the distance from the main mouth; assuming that the knickpoints velocity propagation is the same for each river, correcting these altitudes by the distance from the mouth of the main river could be useful to identify if these knickpoints are due to a propagating wave of regressive erosion. The detailed mapping of knickpoints for the southern capture area shows that this zone is subject to an intensive wave of regressive erosion. For example, the major knickpoint of river 15 (fig.12A) is also located on each of the streams’s tributaries (fig.12B), showing a wave of erosion starting propagating through the second order rivers.

Thus, knickpoint mapping and long proﬁles shape shows a transition between two uplift rates marked by the northern knickpoints, the greatest uplift being south of this limit. This is consistent with knickpoints evidences of a intense wave of regressive erosion south of this limit, propagating toward the west.

However, because of an important error, ksn and θ values can not be useful to extract more information.

14 Figure 12: A. Ksn values (θref = 0.45) for the rivers of the Deseado massif and the capture area (surrounded by the black square).

Ksn and θ values are shown in table2. Green dots represent the knickpoints. The green line represents the possible transition btween two diﬀerent uplift regims marked by the northern knickpoints, the greatest uplift rate being south of this line. The main divides are displayed in light grey. B. Zoom on the capture area, where all the streams of the studied catchment have been processed. The red triangle shows the capture. See A. for ksn legend. C. Altitude of the knickpoints marked in A. The dark line represents the mean altitude. The green area shows the 2σ conﬁdence interval.

Figure 13: Long profiles of river 10 and 7, Deseado massif. The numbers indicate the ksn value founded by linear regression on log(S)-log(A) profile. The red (hight ksn)and blue (low ksn) arrows indicate the regression limits. The green circle and arrow represents the knickpoint and knickzone, respectively. The light grey numbers indicate the reference of the streams (see fig.12A.)

15 4.2 R/Sr analysis

The R indice does not correlate with basin area in every studied zone. In the rio Chico catchment (fig.14), the R metric is clearly not correlated with catchment size. The important size of the area could explain this result, because of regional variability of the mechanism controlling the erosion. Thus, the zone have been separated in three region: the massif, the north and the south of the rio Chico course. But even locally, the R metric does not show any tendance either with respect to catchment size. There is no relation either between rivers flowing directly to the see and flowing into the rio Chico, respectively. In the rio Coyle catchment zone (fig.15), in spite of the weak number of catchments that could be analysed for geometric reasons (Annexes), we can assume that the R indice do not correlate with basin size either.

Figure 14: A. Topography of the Rio Chico watershed region, and its drainage network. The contoured areas are the watershed used for the Sr calculation (see table4.2). Catchments covered by larger watershed are not displayed here. The colors correspond to the three regions separated in the plot. The two beige watershed are not included in these regions. B. Ratio R as a function of ln(A), where A is the area of the watershed in km2.

Figure 15: A. Rio Coyle catchment topography and its main rivers (ﬂow accumulation > 10 000). The watershed used in the R calculation are represented. B. R/Sr plot for the Rio Coyle area.

16 In the Deseado massif region, the R metric correlates with basin size, giving a slope SR equal to 1.7 (ﬁg.16). This slope

Sr is far biggest than the usually measured Sr index founded by (Demoulin, 2012, Demoulin et al., 2013). If we assume that the relation proposed by Demoulin(2012) works for all geodynamical settings, we can not date the last uplift event, but we can at least say that it is very recent (ﬁg.17). However, catchments used to calibrate this equation drain tectonic settings less active than Patagonia, were important horizontal and vertical lithospheric motions are expected.

Figure 16: A. Topography of the region of the Deseado massif, with the processed catchments. Morphometric indices are shown in table2. The capture area is shown by the dark frame. The main drainage network is represented in blue. B. Regional dependence of the R metric on catchment size A on the Deseado massif region.

Figure 17: Time versus Sr relationship proposed by Demoulin(2012) from 8 regional studies where the last uplift event timing is known (from the oldest to the more recent one: North California, Central massif, Southern and center Rhenish shield, Big river (California), Voronehz massif (SW Russia), northern Rhenish shield and Scotland. The red dot corresponds to the measurement of this study. The horizontal error bars correspond to the standard error on the regression coeﬃcients on the regional R=f(ln A) ﬁt.

The vertical dotted line corresponds to the equilibrium Sr index catchments are supposed to trend after an uplift signal.

17 2 catchment A(km ) ln(A) L(km) E Ib Ir Ir/Ib R Ksn θ Ksn av θ av 1 287 5,661 29,9 0,4092 0,077 0,087 1,136 1,7762 16,6 0,3 ± 0.28 24,0 0,81 ± 0.29 2 154 5,034 19,5 0,5148 0,101 0,094 0,926 1,2906 19,1 0,48 ± 0.13 / / 3 595 6,389 43,0 0,4098 0,038 0,071 1,887 2,9485 26,9 0,78 ± 0.31 / / 4 411 6,018 36,3 0,3976 0,071 0,083 1,169 1,8534 26,2 0,53 ± 0.2 35,2 0,24 ± 0.28 5 645 6,469 54,2 0,2794 0,057 0,093 1,634 3,0920 49,4 1 ± 0.15 / / 6 194 5,270 22,2 0,5023 0,055 0,044 0,793 1,1194 13,7 0,85 ± 0.16 12,2 1 ± 0.33 7 406 6,006 41,3 0,3029 0,080 0,104 1,313 2,3853 22,2 0,49 ± 0.13 72,6 2,20 ± 0.54 8 1093 6,996 50,5 0,5459 0,053 0,198 3,717 5,0312 53,9 1,6 ± 0.55 / / 9 724 6,584 46,6 0,4242 0,061 0,092 1,501 3,5195 35,2 0,74 ± 0.12 / / 10 796 6,680 48,2 0,4362 0,044 0,105 2,399 3,9103 15,8 0,53 ± 0.21 58,1 0,60 ± 0.29 11 291 5,675 25,5 0,5695 0,094 0,142 1,506 2,0585 24,4 0,34 ± 0.042 / / 12 232 5,445 29,2 0,3466 0,071 0,094 1,317 2,2377 24,9 0,59 ± 0.15 / / 13 / / / / / / / / 31,8 0,4 ± 0.16 / / 14 / / / / / / / / 8,78 0,42 ± 0.17 28,3 5,20 ± 13 15 409 6,015 32,3 0,4998 0,040 0,056 1,375 1,9461 53.9 - 2.2 ± 8.3 41,7 1.50 ± 0.56 16 423 6,049 28,8 0,6506 0,071 0,143 2,015 2,4988 52,9 0,19 ± 0.025 / / 17 727 6,590 62,2 0,2395 0,013 0,020 1,544 3,1555 33,2 0,77 ± 0.54 166 7 ± 1 18 597 6,392 45,9 0,3608 0,023 0,041 1,763 2,9364 17,9 0,41 ± 0.2 / /

Table 2: Morphometry of the rivers studied in the Deseado massif and the capture area. A = catchment area; L = watershed length; E = elongation factor; Ir and Ib = hypsometric integrals deﬁned in 3.2; R = morphometric indice deﬁned in 3.2; Ksn = normalised steepness (θref = 0.45); θ = concavity ± 2σ error on the regression; Ksn av and θ av = concavity and normalized steepness downstream of the knickpoint, if the river displays a change in Ksn. The rivers with no data for SR analysis could not be processed because of bad hypsometric integrals (Annexes).

2 catchment A(km ) ln(A) L(km) E Ib Ir Ir/Ib R 1 515 6,244 41,4 0,3826 0,060 0,154 2,558 4,1355 2 154 5,037 24,2 0,3347 0,036 0,105 2,942 5,0845 3 785 6,666 60,1 0,2768 0,027 0,128 4,813 9,1491 4 830 6,722 65,1 0,2495 0,011 0,101 9,324 18,667 5 941 6,846 56,4 0,3765 0,064 0,064 0,995 1,621 6 2443 7,801 88,0 0,4014 0,038 0,137 3,626 5,7241 7 3999 8,294 131,6 0,2940 0,008 0,058 7,312 13,484 8 1412 7,253 73,0 0,3375 0,017 0,027 1,570 2,7030

Table 3: Morphometry of the rivers studied in the rio Coyle catchment. See table2 for legend.

18 2 catchment A(km ) ln(A) L(km) E Ib Ir Ir/Ib R location 1 584 6,370 48,0 0,3228 0,0755 0,1166 1,5431 2,7162 rio Chico Sud 2 159 5,067 26,0 0,2989 0,0819 0,0932 1,1386 2,0826 rio Chico Sud 3 471 6,156 47,9 0,2616 0,0546 0,1312 2,4015 4,6954 rio Chico Sud 4 586 6,374 72,5 0,1421 0,0545 0,0732 1,3435 3,5644 rio Chico Sud 5 662 6,495 72,4 0,1607 0,0352 0,1371 3,8958 9,7169 rio Chico Sud 6 218 5,382 38,9 0,1830 0,0899 0,1005 1,1187 2,6150 rio Chico Sud 7 150 5,009 29,3 0,2221 0,0880 0,1350 1,5349 3,2570 rio Chico Sud 8 180 5,193 40,9 0,1370 0,0596 0,0390 0,6533 1,7650 rio Chico Sud 9 233 5,451 17,4 0,9800 0,0882 0,1298 1,4722 1,4872 rio Chico Sud 10 357 5,879 27,2 0,6150 0,0618 0,0526 0,8510 1,0851 rio Chico Sud 11 152 5,026 31,0 0,2018 0,0735 0,0436 0,5940 1,3222 rio Chico Sud 12 228 5,428 26,5 0,4127 0,0908 0,0529 0,5821 0,9062 rio Chico Sud 13 258 5,554 30,1 0,3630 0,0817 0,1366 1,6721 2,7753 rio Chico Nord 14 225 5,418 24,4 0,4822 0,0567 0,1007 1,7769 2,5587 rio Chico Nord 15 175 5,163 27,2 0,3007 0,0611 0,1896 3,1048 5,6622 rio Chico Nord 16 386 5,955 47,9 0,2139 0,0331 0,1497 4,5158 9,7632 rio Chico Nord 17 169 5,130 34,3 0,1831 0,0338 0,0771 2,2792 5,3269 rio Chico Nord 18 1527 7,331 76,0 0,3366 0,0336 0,1393 4,1513 7,1557 rio Chico Nord 19 231 5,442 34,0 0,2544 0,0744 0,1758 2,3634 4,6857 rio Chico Nord 20 140 4,943 21,5 0,3860 0,1044 0,0688 0,6592 1,0611 rio Chico Nord 21 155 5,042 19,2 0,5344 0,0961 0,0927 0,9647 1,3197 san Bernardo fold belt 22 340 5,830 39,7 0,2750 0,0788 0,0958 1,2157 2,3181 san Bernardo fold belt 23 343 5,838 30,4 0,4737 0,0508 0,1630 3,2105 4,6646 san Bernardo fold belt 24 702 6,554 32,7 0,8362 0,0551 0,1791 3,2502 3,5543 san Bernardo fold belt 25 471 6,155 45,2 0,2936 0,0394 0,0499 1,2655 2,3357 san Bernardo fold belt 26 195 5,272 34,3 0,2108 0,0376 0,0818 2,1790 4,7453 san Bernardo fold belt 27 750 6,620 57,8 0,2858 0,0479 0,1160 2,4231 4,5328 san Bernardo fold belt 28 293 5,681 32,8 0,3471 0,0556 0,0740 1,3307 2,2588 san Bernardo fold belt 29 650 6,477 57,2 0,2530 0,0553 0,0852 1,5405 3,0624 san Bernardo fold belt 30 149 5,005 19,8 0,4846 0,0880 0,2213 2,5143 3,6116 / 31 225 5,415 19,5 0,7522 0,0633 0,2213 3,4955 4,0303 / 32 365 5.900 32.7 0.4353 0.0647 0.1541 2.3811 3.6090 san Bernardo fold belt

Table 4: Morphometry of the rivers studied in the rio Chico catchment. See table2 for legend. The location corresponds to the three areas delimited in ﬁg.14

For Sr analysis, errors on the integrals values, drainage area, and R have not been calculated, for two reasons:

− tests realized on catchments of diﬀerent size shows that a change of several pixels in the choice of mouth and source of the main river poorly changes the R result; − as we used the logarithm of the drainage area, the errors on this measurement would be negligible.

19 5 Discussion

An intensive regressive erosion between 46 an 49◦S

◦ The results of ksn regional mapping (ﬁg.8 and9) show a zone between 46 an 49 S where the ksn are greater than south and north of this zone. In the Deseado massif, located on this hight ksn area, stream proﬁle and knickpoints mapping are consistent with a intensive wave of erosion due to hight uplift rates. The northern limit of this hight uplift zone could be inferred by the location of knickpoints. This comforts thermochronological results showing a wave of erosion at these latitudes (Guillaume et al., 2013, Thomson et al., 2010).

Moreover, Sr analysis shows that in the Deseado massif area, catchment morphology is controled at first order by re- equilibration after an uplift signal (fig.16). South and North of this area, the hydrological network is probably controlled by local factors, not allowing to calculate the Sr index in these regions. In the Deseado massif region, uplift should be strong enough to control the drainage network morphology. Thus, in Patagonia, at the latitudes of the CTJ, rivers profiles analysis shows an intensive erosion as inferred by Guillaume et al.(2013) and Thomson et al.(2010). But catchment analysis as a whole combined to knickpoints mapping shows that an uplift is mainly responsible of this erosion at these latitudes and thus, glacial erosion proposed by Thomson et al.(2010) can not be considered as the main control of this important exhumation.

Origin of this uplift

This uplifted zone being located at the same latitudes of previously proposed dynamic topography (Guillaume et al., 2013), mantle upwelling produced by the opening of the slab window seems likely to control at least in part this uplift. However, in this region, stream profiles analysis do not allow concluding about a northward motion of a wave of positive topography because we do not have sufficient resolution in ksn and knickpoints to infer any changes as a function of the latitude. The main other argument advanced to explain the elevations and the important erosion of Patagonia is the role of the isostatic response of glaciers to recent climatic changes and mass loss (Lange et al., 2014). However, the proposed uplift response is located south of the Deseado massif region, at the rio Coyle latitudes. If this isostatic response is likely to influence at least part of the Patagonia surrection, it should influence mainly this zone. However, we do not observe any drainage response to an en-bloc uplift in this zone (fig.15). Thus, isostatic response must be localised on the belt and its influence does not seems to extend in the forelands.

SR analysis accuracy to constrain recent uplift This method has been useful to infer that a large-scale elevation of the topography exerts a ﬁrst order control on the drainage network in the Deseado massif zone.

However, dating this event reveals not possible with this method, accurate for Sr values between 0.2 and 1 (ﬁg.17). The expected starting of drainage area perturbation is between 6 and 3 Ma, when the slab window opening started reaching these latitudes (Lagabrielle et al., 2004, Breitsprecher and Thorkelson, 2009). But this would be available for an uplift signal punctual in time, then letting the drainage network re-equilibrate without any perturbation.

The very hight slope Sr found in this study can be explained by several hypothesis:

− the uplift signal is very recent and corresponds to a climatic signal coming from isostatic response to ice loss in the glaciers; however, this is quite unlikely because of the arguments developed above; − this signal is older but still disturbing the drainage network, so it can not trend toward its equilibrium hypsometric curves. In this case, the bigger catchments would be continually disturbed, thus drainage network would be "blocked" on stade b (ﬁg.6), R being always increasing. Less disturbed because they are more far from the main drainage

outlet, the smallest catchments would have more time to equilibrate and would be following a lower slope Sr. This is consistent with our data from the Deseado massif (ﬁg.18).

20 The uplift disturbing Patagonia drainage network is thus probably a present-day and continuous signal. A late Miocene uplift driven by Lagabrielle et al.(2004) the change in compressional regime, with more orthogonal convergence, can not explain the location of the disturbed zone and would be more likely located all along the trench, south of the CTJ. Thus, the present-day and continuous characteristic of our uplift signal and its location at the present-day latitude of the CTJ makes it likely to be a mantle-driven dynamic topography signal. However, this hypothesis should be comforted by several studies. First, dating the terraces located near the ruta 25 capture (ﬁg.3b) would be useful for a better constrain of the mechanism at the origin of this main drainage change.

Moreover, a better resolution in the knickpoints and Ksn mapping would be important to infer the Northern propagation of the uplift signal. In fact, we can expect that as the regressive erosion propagates by the main West-East drainage network, regressive erosion will be more advanced toward the West at higher latitudes (ﬁg.19, green arrows). Finally, numerical modelling of hypsometric curves response to the northward propagation of a continuous signal of dynamic topography would be interesting to help explaining topographic data in this region.

Figure 18: Regional dependence of R on basin area in the Deseado massif region, separately for small and huge catchments.

6 Conclusion

The use of SRTM90 data set for Patagonia let us have a global view of drainage network characteristics and the possible influences of dynamic topography. ◦ Stream profile analysis shows, qualitatively, an increase of ksn values between 46 and 49 S coupled with a propagating regressive erosion. Sr analysis shows a base-level fall control on the catchments morphology in this same zone. Thus, this study shows some evidences for a dynamic topography controlling Patagonia uplift at least near the Deseado massif. It shows that this zone have to be more studied, with data of higher quality as SRTM 30 meters resolution data set. Further numerical studies as knickpoint propagation modelling (Demoulin et al., 2013, Barnett-Moore et al., 2014) and southern fluvial terraces dating could open a window toward more quantification of the erosion process and its link with dynamic topography in Patagonia.

21 Figure 19: Possible geodynamical inﬂuence of the northward opening of the slab window in Patagonia forelands. The present day location of the CTJ and the Chile ridge are shown, as well as the subduction trench in light grey. The black triangles represent the main captures displayed in ﬁg.3. The blue frame represents the Deseado massif zone, where uplift and regressive erosion have been inferred in this study. The line of red dots represents the northern limit of hight uplift zone, inferred by the knickpoints position. The dotted dark line shows the limits of the dynamic topography proposed by Guillaume et al.(2013). The extension of this zone, inferred by the presented results, is displayed in red. The temporal migration of the CTJ and the consequent supposed migration of a wave of regressive erosion are represented by the green arrows.

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24 Annexes

Hypsometric curves

Inﬂuence of the catchment geometry on hypsometric curves quality. Catchment C drains a large area around the main stream, so tributaries and basin hyspometric curves always remain above the trunk stream (A). In catchment D is very narrow, so the normalised altitude of the tributaries and basin decrease downstream faster than the main river elevation. The black dots represent the mouth and source points of the trunk stream. Typically, hypsometric curves like B were not used for R calculation.

1 Hypsometric curve of a catchment out of equilibrium. The basin and tributaries hypsometric curves are convex. On the contrary, hypsometric curve A of the previous ﬁgure is typical of an equilibrated proﬁle, with concave curves.

2 Code written in Python 2.7 to automatize the creation of ﬁles necessary to process stream proﬁle analysis for one river

######!\usr\bin\env python # −∗− coding : ISO−8859−1 −∗−

# You need to run this python script in ArcGIS (python terminal). # You need to have enabled the extension "spatial analyst"

#import system modules: importarcpy #needsARCGISonwindows from arcpy import env from arcpy.sa import ∗ import os from os import path, access, R_OK, mkdir #W_OK for write permission import sys

############################################################################### ####### SET your ENVIRONNEMENT ################# # set the working environnment # Path the stream profile analysis files are stored: env.workspace = "C:\Users\jeandetl\Documents\Str_pro_analysis\Bassins_Demoulin\deseado_massif" # Name of the input raster: # The input raster is the projected, uncorrected raster used as input for Demoulin analysis files preparation input_raster = "des_mass_inp" # Name of the rasters used for the calculations # (raster_fnme+"_corr", raster_fnme+"_fdir", raster_fnme+"_facc") wname = "w10" ######## END o f m o d i f i c a t i o n s ###################

#creates stream profile analysis folders

input_raster = os.path.join(env.workspace ,input_raster)

#create the structure of the stream profile folders for the river mkdir(os.path. join(env.workspace ,wname)) watershed = (os.path. join(env.workspace ,wname))

mkdir(os.path. join(watershed ,"arcmap")) mkdir(os.path. join(watershed ,"matlab")) mkdir(os.path. join(watershed ,"rasters"))

#extraction of the watershed by mask print "extract by mask"

outExtractByMask=ExtractByMask(input_raster , os .path. join(env.workspace ," bassins",wname+"_bass.shp")) outExtractByMask.save(os.path. join(watershed ," rasters",wname+"_inp"))

3 raster_fnme = (os.path. join(watershed ,"rasters",wname)) matlab_path = os.path. join(watershed ,"matlab",wname) print "extract values > 0 m" # Con outcon = Con(raster_fnme+"_inp",raster_fnme+"_inp","","VALUE >= 0") outcon.save(raster_fnme) p r i n t (" ") print("Fill holes") # F i l l h o l e s outfill = Fill(raster_fnme) outfill .save(raster_fnme+"_corr") print("Flow direction") # Flow direction outflowdir = FlowDirection(raster_fnme+"_corr") outflowdir .save(raster_fnme+"_fdir") print("Flow accumulation") # Flow accumulation outflowacc = FlowAccumulation(raster_fnme+"_fdir","","INTEGER") outflowacc . save(raster_fnme+"_facc") print("Rasters to ASCII") # Conversion arcpy .RasterToASCII_conversion(raster_fnme+"_inp",os .path. join (matlab_path+"dem. txt "))

arcpy.RasterToASCII_conversion(raster_fnme+"_facc",os .path. join(matlab_path+"acc . txt"))

print(matlab_path) print("Script finished")

#END of Script