Abstract
One of the most conspicuous features of a mountain belt is the main drainage divide. Divide location is influenced by a number of parameters, including tectonic uplift and horizontal advection. Thus, the topography of mountain belts can be used as an archive to extract tectonic information. Here we combine numerical landscape evolution modelling and analytical solutions to demonstrate that mountain asymmetry, determined by the location of the main drainage divide, increases with increasing uplift gradient and advection velocity. Then, we provide a conceptual framework to constrain the present or previous tectonic uplift and advection of a mountain belt from the location and migration direction of its main drainage divide. Furthermore, we apply our model to Wula Shan horst, Northeastern Sicily, and Southern Taiwan.
Introduction
Mountain belts provide natural boundaries on the Earth’s surface. They influence atmospheric circulation^{1,2}, determining regional weather and the distribution of hydrological systems^{3,4}, and limit the migration of plants and animals, affecting biodiversity^{3,5}. Mountain building is driven by tectonic forces (vertical uplift and horizontal advection, Fig. 1) and erosion^{6,7,8,9,10,11,12}. Consequently, many geohazards such as earthquakes and landslides are concentrated in mountainous regions^{13,14}. Systematic constraints on the spatiotemporal patterns of tectonic deformation help to improve the understanding of the interactions among lithosphere, hydrosphere, biosphere, and atmosphere. Various methods have been used to constrain patterns of tectonic deformation. For example, over short timescales of years to decades, a combination of global positioning system (GPS)measured horizontal velocities and Interferometric Synthetic Aperture Radar (InSAR) measurements can provide highaccuracy regional uplift rates^{15,16,17,18}. The average vertical slip rate of a fault can be obtained by offset measurements combined with precise dating of displaced materials^{19,20}. Longerterm exhumation and uplift history can be estimated by lowtemperature thermochronometry^{21,22}, inversion of river long profiles using topographic data^{23,24}, radiogenic isotope dating of lava flows^{25}, and combination of seismic reflection and other geological data (e.g., drill core)^{26}.
Here, we present a new method to provide constraints on the tectonic pattern and history of a mountain belt solely based on the location and mobility of its main drainage divide (MDD). First, we quantify the relationship between mountain asymmetry, determined by MDD location, and both uplift gradient and advection velocity through theoretical reasoning and landscape evolution modelling. Then, we illustrate the interaction and competition between tectonic deformation and erosion in determining MDD mobility. Collectively, we build a model to provide constraints on the present or previous uplift and horizontal advection of a mountain belt based on the current location and mobility of its MDD, and apply this model to Wula Shan horst, Northeastern Sicily, and Southern Taiwan. Specifically, we demonstrate that: (1) the difference in uplift rate between the two edges of Wula Shan horst is 0.14 mm/year; (2) the tectonic activity of Northeastern Sicily remains constant or has slightly changed; (3) the tectonic activity of Southern Taiwan may have increased, and we expect that under the persistence of present conditions, the main drainage divide of Southern Taiwan will migrate southeastwards.
Results
Relationship between uplift gradient and divide location
For landscape evolution simulations and analytical solution (see “Methods”), we consider a onedimensional (1D) asymmetric mountain belt within an uplift field that linearly increases from one edge to the other, with an uplift gradient λ (mm/year per km). Normalised divide location (d) is defined as the ratio between the distance from the MDD to the lower uplifting edge (or the width of the positive side, Fig. 1) and the width of the mountain belt. From an initial random topography without MDD, a mountain belt evolves towards steady state (uplift rate = erosion rate) with a divide position dependent on uplift gradient (Fig. 2a–d, Movie 1). For a spatially uniform uplift rate of 0.5 mm/year, the MDD forms near the centre (d = 49.7 ± 3.5%) of the mountain range (Fig. 2a), consistent with previous works^{6,10,27}. With a linear asymmetric uplift of 0.5 mm/year at the bottom edge and values from 1.0 mm/year (λ = 0.01 mm/year per km) to 10 mm/year (λ = 0.19 mm/year per km) at the top edge, the MDD migrates closer to the top boundary (Fig. 2b–d). Consequently, the normalised divide location increases from d = 57.6 ± 4.1% to a maximum of d = 73.3 ± 2.7% (Fig. 2e). Based on the stream power model, we obtain an analytical solution of the divide location, which is independent of erodibility (see “Methods”). The analytical solution generally agrees with the numerical results, and falls within their 95% confidence range (Fig. 2e). However, for the same uplift gradient, the analytical solution predicts higher d values than the results of numerical models. This may be because the Hack’s law^{28} exponent b used for the analytical solution (b = 1.6, see “Methods”) does not fit exactly with the simulations^{27}. In numerical models, the b values vary with uplift gradient (Supplementary Figs. 1–4). The analytical solution demonstrates that d generally increases with increasing b (Supplementary Fig. 5).
Relationship between advection velocity and divide location
Generally, there are multiple possible situations for mountain belt kinematics. Here, we consider two simple parameterisations of mountain belt kinematics with advection^{6,27}. First, advection velocity is spatially uniform, hence, the only variable is velocity. Second, advection velocity decreases linearly from the positive side to the negative side (Fig. 1), corresponding to a constant rate of shortening^{6}. In this case, two independent parameters determine the distribution of advection: the velocity gradient and the velocity at the edge of the negative side. We obtain the analytical solutions for both cases (Fig. 3a). When advection velocity is spatially uniform, d increases linearly with velocity. When it is zero (i.e., no advection), the MDD forms at the centre of the mountain belt. When velocity reaches about 5.1 mm/year, d is 100%, namely, there is no MDD within the topography. The analytical solution shows that the velocity at which d reaches 100% depends on erodibility, mountain width, and hillslope length (see “Methods”). In the case of a constant rate of shortening, d increases with both velocity gradient and the velocity at the edge of the negative side, in agreement with previous works^{6,8,27}.
There are two firstorder scenarios when a mountain belt experiences uplift gradient and advection simultaneously, which we explore via three other numerical simulations (Fig. 3b–e). First, the positive side has a lower uplift rate than the negative side. In this case, both uplift and advection push the MDD towards the negative side (as in the case of Southern Taiwan, see discussion below). Second, the positive side has a higher uplift rate than the negative side. In this case, uplift gradient and advection push the MDD towards different directions (as in Northeastern Sicily, see discussion below). When uplift gradient and advection push the MDD in the same direction, the mountain belt becomes more asymmetric (Fig. 3b–d). In contrast, when they push the divide in different directions, their effects partially cancel out. In this case, the steadystate divide location is dominated by the driver with a higher contribution to divide migration, and the mountain belt is less asymmetric (Fig. 3b, c, e). Generally, the time needed to reach steady state decreases with increasing uplift gradient (Supplementary Fig. 6).
Discussion
Deriving tectonic information from the MDD
The MDD tends to migrate towards the side with a higher uplift rate^{10,27,29,30}, lower precipitation and/or higher rock resistance^{10,27,29,31,32,33}, and in the direction of advection^{6,8,27,32,34,35}. Divide dynamics can be understood conceptually by focusing on three forces: advection, asymmetric uplift, and asymmetric erosion. Erosion is controlled by tectonic deformation, which can start or increase instantaneously, but erosion itself takes some time to adjust to those changes. This leads to some temporary decoupling between tectonic forcing and erosion. Hence, during transient periods, one of these factors may be the dominant force controlling divide mobility. Without advection, the steadystate MDD is located in a position determined by uplift gradient (Fig. 2). If uplift gradient is zero (i.e., uniform uplift), the steadystate MDD is at the centre of the mountain belt, and the erosion rates are equal on both sides. Any asymmetry in divide location generates an asymmetry in slope and thus in erosion rate, leading to divide migration towards the centre of the range. Conceptually, when asymmetric uplift outweighs erosional contrast in influencing divide mobility, the MDD moves to the side with higher uplift rate (Fig. 4a). This further steepens the catchments on the side with higher uplift rate, and consequently, increases the erosional contrast between the two sides. This leads to an enhanced influence of the erosional contrast across the MDD in controlling divide mobility until the contributions from asymmetric uplift and erosional contrast become equal at steady state. Alternatively, when erosional contrast outweighs asymmetric uplift in controlling divide mobility, the MDD moves towards the side with lower erosion rate (Fig. 4b). As a consequence, the erosional contrast decreases, diminishing its contribution to divide mobility until the contributions of asymmetric uplift and erosional contrast become equal at steady state. The interaction and competition between advection and erosion in controlling divide mobility are similar to the pattern of uplift gradient and erosion (Supplementary Fig. 7).
The relationship between d and λ (Fig. 2), together with the conceptual framework (Fig. 4a, b), allow us to derive tectonic information from the location and mobility of the MDD. Both of these parameters can easily be obtained from present topography^{33}. If the controls of precipitation, lithology, and advection on divide location can be ruled out as dominant, we can distinguish five possible cases. For a symmetric mountain belt, the uplift is uniform if the MDD is stable (Fig. 4c). When the MDD is migrating towards the top side, the uplift is higher on the top side, and was uniform or higher on the bottom side (Fig. 4d). For an asymmetric mountain belt, we can apply the theoretical relationship (Fig. 2e) to acquire a reference uplift gradient λ′ from the current divide location. If the MDD is stable, the uplift is higher on the steeper side with a gradient of λ′ (Fig. 4e). When the MDD of an asymmetric mountain belt is unstable, λ′ can provide a lower bound on the present or previous gradient. If the MDD is migrating towards the steeper side, the uplift is higher on the steeper side with a gradient greater than λ′ (Fig. 4f). When the MDD is moving to the gentlersloping (bottom) side, the uplift was higher on the top side with a gradient greater than λ′ (Fig. 4g). Additionally, if the present uplift is known to be higher on the top side, the uplift gradient should have decreased.
In principle, the current divide location and migration pattern can also be used to constrain advection pattern (Supplementary Fig. 7). However, there is no known way of establishing divide stability in the case with advection. Thus, our model is unable to derive advection information quantitatively until a method that could establish divide mobility under advection conditions is developed. Nevertheless, if the present advection velocity is known, our model can provide information on previous tectonic deformation, depending on the specific uplift and advection patterns (see case studies below).
Application to natural landscapes
For the Wula Shan horst in northern China, which may have negligible advection, the uplift rate is higher at the southern edge and decreases northwards^{30}. The present MDD is stable, with a normalised divide location of 60.8% (Supplementary Fig. 8), placing it in the case described in Fig. 4e. Thus, its present uplift gradient is estimated to be 0.008 mm/year per km using Eq. (10). As the mountain width is 17.5 km, the difference in uplift rate between the southern and northern edges is estimated to be 0.14 mm/year. Thus, if precise constraints on the uplift rate related to the southern piedmont fault with pronounced fault scarps^{30} are available, the uplift rate at the northern edge could also be estimated. The Wula Shan horst is a simple example. In more complex cases, when the present uplift gradient and advection velocity are known, our model can provide constraints on previous tectonic deformation. We discuss two further examples: Northeastern Sicily and Southern Taiwan.
In Northeastern Sicily, uplift is mainly controlled by the tectonic activity of the MessinaTaormina Fault (MTF, a normal fault), being higher on the SE side (Fig. 5a). The estimated average uplift gradient of 0.0092 mm/year per km (see “Methods”) pushes the MDD towards the SE. In the direction perpendicular to the MDD, the average advection velocity is estimated to be 0.46 or 0.77 mm/year (Fig. 5a) (see “Methods”). The northwestward advection pushes the MDD towards the NW. Both the mean annual precipitation (711 and 705 mm/year for the NW and SE flanks, respectively) and lithology are nearly symmetrical with respect to the MDD (Fig. 5b, c), and thus should have little influence on divide location. We choose the ratio between the width of the NW side and the mountain width as the normalised divide location, d. Although the dλ data points generally follow the trend of the analytical solution for mountain belts with uplift gradient, most points fall below the analytical solution due to the existence of advection (Fig. 2e). We numerically solve the divide location under different settings including both uplift gradient and advection (Supplementary Fig. 9, see Code availability), where no analytical solution is possible. For an advection velocity of 0.46 mm/year, the MDD will form at d = 56.7% under the present settings, comparable with the current divide location of 58.7%, implying that the tectonic activity remains constant over the response timescale of the MDD. This value only slightly decreases to d = 56.4% if we exclude the precipitation difference between the two sides of the range. By contrast, if we exclude uplift gradient and advection, the MDD is predicted at d = 49.4% and 62.2%, respectively. Alternatively, for an advection velocity of 0.77 mm/year, the MDD should form at d = 52.8% under the present settings, lower than the current divide location. Based on our model, we attribute this discrepancy to the decreased uplift gradient and/or increased advection velocity. In summary, we speculate that the tectonic activity of Northeastern Sicily remains constant or has slightly changed.
For the orogenic wedge of Southern Taiwan, the GPSderived uplift gradient of 0.14 mm/year per km pushes the MDD towards the SE (Fig. 6a). Owing to advection, rock moves from the NW to the SE through the mountain belt coming to the surface near the Longitudinal Valley (Fig. 6b)^{6}. Accordingly, we choose the Longitudinal Valley as the reference frame for advection. Other points within the mountain belt are moving towards the SE with respect to the valley. This advection, with a velocity gradient of 0.35 mm/year per km (see “Methods”), also pushes the MDD towards the SE^{6}. The average annual precipitations of 2528 and 2231 mm/year for the NW and SE flanks, respectively (Fig. 6b), create an erosion contrast, pushing the MDD towards the SE. Thus, uplift gradient, advection, and precipitation all push the MDD towards the SE. Although the dλ data points generally follow the trend of the model results and analytical solution, the d values are still lower than the model prediction including uplift gradient only (Fig. 2e). Under the present settings, at steady state, the divide location is estimated to be at d = 78.1% (Supplementary Fig. 10), which is much higher than the current value of 64.7%. If we exclude the precipitation difference, the divide location will change slightly to d = 76.6%. Under the present tectonic settings, the mountain belt becomes symmetric at d = 49.2% only after imposing a tremendous precipitation difference (2 m/year and 8 m/year on the NW and SE sides, respectively). Thus, tectonic deformation primarily determines the divide location of Southern Taiwan, while precipitation is a secondary control. A similar feature has been observed in Southern Alps of New Zealand, where the uplift gradient and advection have pushed the MDD close to the NW coastline, despite precipitation rates of ~12 m/year and 1 m/year on the NW and SE sides, respectively, which tend to push the MDD towards the SE^{27}. We attribute the discrepancy between the current divide location of d = 64.7% and the predicted value of d = 78.1% to two possible reasons. One is that the uplift rates and advection velocity estimated from GPS observations over several decades may have been overestimated due to the fact that the study area displayed a high level of seismicity^{36} and hence is dominated by large interseismic elastic accumulation. The other possible reason is a recent increase in tectonic activity, which is likely due to the southward propagation of the arccontinent collision in Taiwan^{34,37,38,39}. Such a condition implies that the mountain belt is in a transient state with respect to its divide position. We can expect that under the persistence of present conditions, the MDD of Southern Taiwan will migrate southeastwards, approaching a divide location at d = 78.1%.
In simulations and analytical considerations, we assume a linear uplift gradient, as this requires fewer constraints and yields results that are readily compared to the existing numerical studies^{10,29}. Assuming a nonlinear uplift field described by a power law with exponent α (α = 1 is a linear uplift gradient; α ≠ 1 is a nonlinear uplift gradient), mountain asymmetry increases with increasing α (Supplementary Fig. 11). As a result, for nonlinear uplift gradients, the conceptual relationships described in Fig. 4 can be used to constrain uplift patterns rather than provide a quantitative uplift gradient value.
Our calculations and concepts offer an opportunity to obtain firstorder constraints on the present tectonic uplift and advection of a mountain belt and its history via simple observations of freely available remotesensing data. As such, this approach does not require excessive investments of time and money, and can, in principle, be widely applied. Although the method does not yield complete information, it may be a useful supplement to existing, more labourintensive methods.
Methods
Landscape evolution model
We conduct 23 numerical simulations (Figs. 2, 3b–e) using the TopoToolbox Landscape Evolution Model (TTLEM^{40}), a MATLABbased landscape evolution model contained in TopoToolbox 2^{41}. All models consisted of a 50 × 100 km rectangular domain, with a spatial resolution of 75 m, in which four edges are fixed to a constant elevation of 0 m. Other simulation parameters are set as follows: erodibility (k_{1}) = 3 × 10^{−6}/year, exponents of the stream power model m = 0.5 for discharge and n = 1 for slope, hillslope diffusivity = 0.03 m^{2}/year, and drainage area threshold = 0.2 km^{2}. We run the models for 300 Myr, with time steps of 0.1 Myr. The steadystate divide location is analysed based on the topographies of the models at 300 Myr.
We estimate the time needed to reach steady state using the efolding time. The mean elevation of the models increases to a maximum and then decreases to be relatively stable after reaching steadystate (Supplementary Fig. 6). We use y = a + b × e^{−t/c} to fit the elevation data from the highest point to 300 Myr, where y is the mean elevation, t is time, a and b are constants, e is the Euler number, and c is the efolding time. The steadystate time is estimated as four times of the efolding time plus the time used to reach the highest elevation. Hack’s parameters (k_{2} and b) are calculated within the TopoToolbox 2^{41}.
Analytical solution for uplift gradient
At steady state, according to the detachmentlimited stream power model:^{42}
where S is the gradient of river channel, U is uplift rate, k_{1} is erodibility, A is the upstream drainage area, and m and n are positive constants. According to Hack’s law that describes the relationship between length of river channel and drainage area:^{28}
where x is the distance from the MDD, k_{2} and b are constants. We assume that the mountain width is M, and the uplift rate of the lower uplift edge is U_{1}, which linearly increases to the higher uplift edge with the uplift rate gradient λ. Thus, the uplift rate at the lower uplift side is
and the uplift rate at the higher uplift side is
where D is the distance between the MDD and the lower uplift edge. For the lower uplift side, the elevation at the MDD is
similarly, the elevation at the MDD for the higher uplift side is
Assuming m = 0.5 and n = 1, we then obtain the elevation at the lower uplift side as
and the elevation at the higher uplift side as
At the MDD, the elevations on both sides are the same, i.e., Eqs. (7) and (8) are equal. In our numerical models, assuming U_{1} = 0.5 mm/year and M = 50 km, we have
The average value of b for the 20 numerical models is 1.6 (Supplementary Fig. 1). There is no systematic correlation between uplift gradient and Hack’s parameters (Supplementary Figs. 1–4), which is supported by the observations from natural landscapes that exhibit selfsimilarity of drainage basins^{43}. Using b = 1.6 in Eq. (9), we have
We plot the relationship between λ and normalised divide location, d (d = D/M) (Fig. 2e) in MATLAB based on Eq. (10).
Analytical solution for advection
The advection theory is based on refs. ^{4,27}.
The base level of the positive side and the negative side are located at x = D and x = M – D, respectively. We use m = 0.5, n = 1, and b = 2, as there is no closeform solution for other values of b^{27}. The steadystate slope for the positive side can be expressed as
and for the negative side,
where S < 0, U and V are uplift rate and advection velocity, respectively.
We consider two cases for mountain belts with advection. One is the advection velocity linearly decreases from the edge of the positive side to the edge of the negative side. In the other case, advection velocity is spatially uniform. For the first case, at the edge of the positive side, the advection velocity is V_{p}, which linearly decreases to V_{r} at the boundary of the negative side, with an advection velocity gradient of γ = (V_{p} – V_{r})/M.
The velocity on the positive side is
and the velocity on the negative side is
Combining the above equations, we obtain the slope of the positive side as
and the slope of the negative side as
The boundary condition for the positive side is H(x = D) = 0, and for the negative side is H(x = M – D) = 0.
Integrating Eqs. (15) and (16) along the river profile, we can obtain the elevation of the positive side as
and the elevation for the negative side as
At the boundary between hillslope and channel (x = Xc), the two sides of the mountain belt have the same elevation, thus,
Therefore, we can calculate the divide location as
For a spatially uniform velocity (V), i.e., V_{p} = V_{r}, the divide location is
We plot the relationship between d and advection velocity (Fig. 3a) based on Eqs. (20) and (21) using M = 50 km, k_{1} = 3 × 10^{−6}/year, and Xc = 1.6 km.
Data collection and processing for natural landscapes
Northeastern Sicily, Italy
Northeastern Sicily is a ~50km long NESW trending peninsula (Fig. 5a). The SE flank (average slope = 18.3°) is steeper than the NW flank (average slope = 14.6°). Since the EarlyMiddle Pleistocene, Northeastern Sicily has been affected by strong uplift driven by extensional faults^{44}, especially the MTF (Fig. 5a), which actively deforms the Late Quaternary marine terraces and Holocene coastal notches^{45,46}.
The map of uplift rate integrated since the Late Pleistocene (Fig. 5a) is interpolated using seven uplift data points from ref. ^{47}. Uplift gradients vary along the MDD, thus, we divide the whole 50kmwide natural landscape into 30 subswaths that are perpendicular to the MDD. The uplift rate differences between the SE and NW edges for each subswath are divided by the length of the subswath to obtain λ. Then, the distance between the NW edge and the MDD is measured as D. The ratio between D and mountain width (M) is d. Values of λ and d for every three subswaths are averaged as the values of the 5kmwide main swaths. Therefore, we have ten main swaths with λ and d values (Fig. 2e). The MDDs of Wula Shan horst, Northeastern Sicily, and Southern Taiwan are extracted using Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) data with 30 m spatial resolution.
Given the proximity of the SE coast to the MTF that is nearly fixed with respect to the hanging wall (the Calabria, Fig. 5a), the advection velocity is close to the horizontal slip rate on the MTF. Thus, we choose Western Calabria as the reference frame for advection. We calculate the average GPS velocity from three GPS data points along the coast of Western Calabria, and the average GPS velocity from eighteen GPS data points in Northeastern Sicily. The results show that the average advection velocity in Northeastern Sicily is 0.99 mm/year, from SE to NW (Fig. 5a). In the direction perpendicular to the MDD, the advection velocity is 0.77 mm/year. Advection could also be defined as the rock velocity with respect to the erosional boundary. Therefore, we use the SE coastline of Northeastern Sicily as reference frame for advection. We calculate the average GPS velocity from six GPS data points (Fig. 5a, blue arrows) along the SE coastline, and the average GPS velocity from the rest twelve GPS data points in Northeastern Sicily. The results show that the average advection velocity in Northeastern Sicily is 0.48 mm/year (Fig. 5a). In the direction perpendicular to the MDD, the advection velocity is 0.46 mm/year. Collectively, we consider that the advection velocity in Northeastern Sicily is 0.46 or 0.77 mm/year (Fig. 5a).
For the NW side, the Hack’s parameters are 0.35 and 1.65 for k_{2} and b, respectively. For the SE side, the Hack’s parameters are 0.24 and 1.88 for k_{2} and b, respectively (Supplementary Fig. 12). The mountain width of Northeastern Sicily is 40 km. The hillslope length (value of Xc) is measured as the straightline distance between the MDD and the locations where channels start to form. We measure 34 sites within Google Earth and obtained an average Xc of 1756 ± 294 m. The average channel steepness (ks) values are 143 m and 179 m for the NW and SE sides, respectively (see “Data availability”), calculated within TopoToolbox 2^{41}. We estimate erodibility using the constraints imposed by ks in the following way. In the firstlevel iteration, picking a trial value for the divide location, we integrate the gradient function to generate river long profiles for both sides of the mountain belt. We then fit a power law with a fixed exponent of −0.5 to the long profile by using a nonlinear leastsquare fitting algorithm, to obtain a value for ks. Erodibility is then iteratively adjusted until the bestfit slopearea relationship matched the observed ks. In a secondlevel iteration, the trial value for the divide position is adjusted until it matched the position calculated using the erodibilities obtained in the firstlevel iteration. We obtain k_{1} values of 5.4 × 10^{−6}/year and 4.9 × 10^{−6}/year for the NW and SE sides, respectively.
We write a MATLAB script (divide_location.m, see Code availability) that numerically integrates the gradient function in the stream power model to estimate the steadystate location. Uplift gradient, advection velocity, Hack’s parameters, hillslope length, erodibility, precipitation, and mountain width are required by the script. Our analytical solutions, i.e., Eqs. (10), (20), and (21), are independent of Hack’s coefficient, k_{2}, while the script needs k_{2} as input. The results show that the analytical solutions and the script match best with a k_{2} value of 1 (Supplementary Fig. 13). Thus, we use a k_{2} value of 1 for both Northeastern Sicily and Southern Taiwan as the input of the script. Using the script and the above parameters, we estimate the divide location under different settings (Supplementary Fig. 9).
Southern Taiwan
As an active collision zone, the Taiwan Orogen formed from the convergence between the Philippine Sea Plate and the Eurasian Plate that started a few million years ago and has continued to the present^{10,37}. A 40kmwide zone is selected for this study, because the uplift rate in this zone linearly increases from the NW edge to the SE edge (Fig. 6a). The SE flank (average slope = 21.5°) is steeper than the NW flank (average slope = 15.0°).
Similar to the method used for Northeastern Sicily, the 40kmwide area is cut into 30 subswaths and 10 main swaths that are perpendicular to the MDD. The uplift rate differences between the southeasternmost and northwesternmost data points for each subswath are divided by their distance to calculate λ. Then, the distance between the NW edge and the MDD is measured as D. The ratio between D and mountain width (M) is d. Values of λ and d for every three subswaths are averaged as the value of the 4kmwide main swath. Thus, we have ten main swaths with λ and d values (Fig. 2e).
The advection velocity gradient is measured between the NW mountain front and the Longitudinal Valley (Fig. 6b), and the latter is used as the reference frame of advection. We calculate the average advection velocity from the data near the NW mountain front. The results show that the advection velocity in this area is 32 mm/year, and 30 mm/year in the direction perpendicular to the MDD (Fig. 6b). The average advection velocity gradient for Southern Taiwan (0.35 mm/year per km) is obtained as the ratio of advection velocity (30 mm/year) and the distance (86 km) between the measured NW edge and the Longitudinal Valley.
For the NW side, the Hack’s parameters are 0.48 and 1.66 for k_{2} and b, respectively. For the SE side, the Hack’s parameters are 0.38 and 1.77 for k_{2} and b, respectively (Supplementary Fig. 12). The mountain width of Southern Taiwan is 130 km. We measure 46 sites within Google Earth and obtained an average Xc of 1556 ± 240 m. The average channel steepness is 197 m and 237 m for the NW and SE sides, respectively, calculated within TopoToolbox 2^{41}. Similar to the method used for Northeastern Sicily, in Southern Taiwan, we obtain k_{1} values of 5.8 × 10^{−5}/year and 4.1 × 10^{−5}/year for the NW and SE sides, respectively. Similarly, with the script and the above parameters, we estimate the divide location under different settings (Supplementary Fig. 10).
Data availability
Topographies of all the numerical models at 300 Myr and Supplementary Movie 1 are deposited at https://doi.org/10.6084/m9.figshare.12318965.v10. Lithology and GPSderived uplift rates of Southern Taiwan are acquired from https://www.moeacgs.gov.tw. For Southern Taiwan, GPS horizontal velocities in a fixed Eurasia frame are obtained from https://www.moi.gov.tw. Average annual precipitation of Southern Taiwan is downloaded from https://www.cwb.gov.tw. The 30 m spatial resolution digital elevation model data are obtained from http://www.gscloud.cn. The boundary of Sicily is downloaded from https://map.igismap.com/gisdata, under open database license www.opendatacommons.org/licenses/odbl. Other relevant data supporting the findings of the study are available in the Supplementary Information, or from the corresponding author upon request. Source data are provided with this paper.
Code availability
A MATLAB script (divide_location.m) to generate a divide location under different settings is deposited at https://doi.org/10.6084/m9.figshare.12318965.v10.
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Acknowledgements
We express our gratitude to Niels Hovius, Brian J. Yanites, Wolfgang Schwanghart, Rong Yang, Liran Goren, and Mimmo Palano for fruitful discussions that greatly improved this work. Special thanks are also given to Taylor Schildgen, Joel Scheingross, Mitchall D’Arcy, and Stefano Luigi Gariano for advice on choosing natural landscapes as case studies. This work was supported by the Basic Science Centre Programme for Multiphase Media Evolution in Hypergravity of the National Natural Science Foundation of China (No. 51988101), the National Natural Science Foundation of China (Nos. 41502203 and 41941016), the Second Tibetan Plateau Scientific Expedition and Research (No. 2019QZKK0708), and the National Science and Technology Major Project (No. 2017ZX05008001).
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C.H., C.J.Y., and J.T. contributed equally to this work. C.H. and G.R. conceived the idea. G.R. and C.H. directed the project. C.J.Y. and C.H. constructed the numerical models. J.T., C.H., and X.Y. derived the analytical solutions. C.H., C.J.Y., D.R.B., and J.T. contributed to data collection and processing of the natural landscapes. All authors contributed to the interpretation of the data. C.H. and J.T. wrote the original manuscript, and all authors participated in paper revisions.
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He, C., Yang, CJ., Turowski, J.M. et al. Constraining tectonic uplift and advection from the main drainage divide of a mountain belt. Nat Commun 12, 544 (2021). https://doi.org/10.1038/s41467020207482
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DOI: https://doi.org/10.1038/s41467020207482
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