Controls on decadal erosion rates in Qilian Shan: Re-evaluation and new insights into landscape evolution in north-east Tibet

    Controls on decadal erosion rates in Qilian Shan: re-evaluation and new insights into landscape evolution in north-east Tibet Wang Yizhou, Zhang Huiping, Zheng Dewen, Zheng Wenjun, Zhang Zhuqi, Wang Weitao, Yu Jingxing PII: DOI: Reference:

S0169-555X(14)00347-X doi: 10.1016/j.geomorph.2014.07.002 GEOMOR 4834

To appear in:

Geomorphology

Received date: Revised date: Accepted date:

31 October 2013 1 July 2014 2 July 2014

Please cite this article as: Yizhou, Wang, Huiping, Zhang, Dewen, Zheng, Wenjun, Zheng, Zhuqi, Zhang, Weitao, Wang, Jingxing, Yu, Controls on decadal erosion rates in Qilian Shan: re-evaluation and new insights into landscape evolution in north-east Tibet, Geomorphology (2014), doi: 10.1016/j.geomorph.2014.07.002

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ACCEPTED MANUSCRIPT Controls on decadal erosion rates in Qilian Shan: re-evaluation and new insights into landscape evolution in north-east Tibet

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*Corresponding author. Tel: +86 18810404633; E-mail address:[email protected]

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Wang Yizhou*, Zhang Huiping, Zheng Dewen, Zheng Wenjun, Zhang Zhuqi, Wang Weitao, Yu Jingxing

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State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China

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Abstract: Available data from the Qilian Shan in north-east Tibet suggested that decadal-scale erosion rates were closely correlated with local topographic gradient, but not with climatic factors. However, a climatic change to more arid condition was proposed to explain the discrepancy between short-term and long-term erosion rates. In order to re-evaluate the topographic, tectonic and climatic influences on erosion, we adopted five parameters (slope, mean local relief, historical cumulative seismic moment, runoff coefficient of variation and fault density) to study 11 drainage basins in north-east Tibet. Our results showed that terrain gradient, rock fracture density and rainstorm intensity had strong influence on erosion rates while 60-year cumulative seismic moments of historical earthquakes showed weaker correlations. There was a spatial variation in the erosional mechanisms across the basin, with detachment-limited dominant around the ridges (slope >20º) and deposition dominant in the flat areas. The variation may lead to the discrepancy between short-term and long-term erosion rates. In general, our study support the ‘bath-tub’ model for low relief intermountain basins, hence providing new insights into the landscape evolution of the Qilian Shan in northeastern Tibetan Plateau. Key words: Decadal erosion process; Fault density; Terrain steepness; Storm; Qilian Shan; Northeastern Tibet 1. Introduction Landscape evolution of an active mountain can be described as a competition between tectonic processes that elevate topography and erosion that destroys it (Burbank et al., 1996; 2003; Reiners and Brandon, 2006; Molnar et al., 2006, 2007; Champagnac et al., 2012). In the dynamic process, tectonics, climate and erosion are often interlinked and play critical roles. For example, horizontal compression can cause crustal thickening which then raises earth’s surface via isostatic compensation. 1

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A rising landscape would increase orographic precipitation, leading to higher erosion rates; in turn, high and focused erosion rates may change the structural pattern (Dahlen and Suppe, 1988; Willett, 1999; Beaumont et al., 2001; Willett and Brandon, 2002; Reiners et al., 2003; Roe et al., 2008). Tectonics can also fracture rock, which helps to increase erosion rates. Furthermore, the focused erosion due to river or glacier carving will drive the isostatic uplift of rocks, and possibly surface uplift of peaks and ridges (Wager 1937; Holmes, 1944, 1965; Molnar and England, 1990). Erosional processes are controlled by multiple factors such as tectonics, lithology, climate, and topography. However, how these factors affect erosion remains a puzzle (Champagnac et al., 2012). Many suggest that the topographic variables, such as average slope (Aalto et al., 2006), local relief (Ruxton and McDougall, 1967; Montgomery and Brandon, 2002; Portenga and Bierman, 2011; DiBiase et al., 2010), local curvature (Roering et al., 1999), basin relief ratio (Summerfield and Hulton, 1994), and catchment size (Milliman and Meade, 1983; Milliman and Syvitski, 1992), play the most important role. Indeed, all these factors are associated with the steepness of terrain, and indicate that on a short time scale, steep slopes lead to higher erosion rates. However, in the long run, erosion may result in increased relief as localized along the river channel. Meanwhile, the background erosion rate is uniform across the landscape, hence not changing the relief (Champagnac et al., 2014). Therefore, it is difficult to conclude whether it is the terrain steepness that leads to higher erosion or the erosional processes that increase the relief. At the same time, climatic variables such as precipitation, discharge, and runoff can also play key roles in the short-term erosion process (Galy and France-Lanord 2001; Gabet et al., 2008). In addition, the degree of rock fracturing (Burbank et al., 1996; Molnar et al., 2007; Clarke and Burbank, 2010), the historical seismicity and storm-driven runoff variability (Dadson et al., 2003) also affect the decadal erosion rates. Among the active growing plateau margins, the northeastern Tibetan Plateau is considered to be one of the youngest fronts (Tapponnier et al., 2001). Learning the erosion distribution and rates is key to understanding how the topography, climate, tectonics, and lithology have interacted. Pan et al. (2010) presented decadal erosion rates of 11 drainage basins and suggested strong correlations with slope and mean local relief, but weak correlations with precipitation, temperature, discharge, and runoff. However, some other aspects still need to be explored, such as how to quantify the modern tectonic scenarios, lithology, storm and terrain steepness among the Qilian Shan, whether these variables contribute to short-term catchment erosion, and whether there is any spatial difference of erosion within the drainage basins. In order to answer these questions, we first quantify the potential variables related to erosion. We take historical cumulative seismic moments as a proxy for tectonic movement; the runoff coefficient of variation for climate; fault density for lithology; and mean local relief and slope for topography. Then we carry out 2

ACCEPTED MANUSCRIPT correlation and regression analyses for catchment-wide decadal erosion rates based on the work of Pan et al. (2010) with all variables to determine which potential variables might contribute more to erosion rates.

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2. Regional setting The Qilian Shan extends from 95°E to 103°E, and 37°N to 41°N (Fig. 1), and bounds the northeastern Tibetan Plateau. It trends WNW–ESE, stretches over 1000 km long, and is about 300 km wide. Peaking at about 5500 m, the Qilian Shan has a mean elevation of 4000 m between its front and the northern margin of the Qaidam Basin (Fig. 1). To the south, the mean elevation of the Qaidam Basin is about 3000 m, with local topographic relief around 300 m. To the north, the Hexi Corridor basin overlies the stable Alashan block with a lower surface elevation of 1500–2000 m. The present climate environment here is arid to semiarid, and the temporal-spatial difference of precipitation is relatively large (Zhang et al., 2011; Niu et al., 2012). Many inland rivers, the Danghe, Shule, Heihe, and Shiyang Rivers, which originate from high altitude areas, incise the steep margins of the Qilian Shan, and flow into the Hexi Corridor and its flanking basins (Zhang et al., 2014). Cenozoic exhumation of the Qilian Shan has produced a kilometers-thick coarsening-upward succession of lacustrine-fluvial deposits in the Hexi Corridor basin to the north and the Qaidam basin to the south. The rivers are still active and transport a great amount of sediment to the flanking basins (Pan et al., 2010). All these observations indicate that erosion and deposition around the Qilian Shan have controlled the Late Cenozoic landscape evolution. Devonian to Cambrian metamorphic rocks form most of the bedrock of the Qilian Shan, and have deformed since early Paleozoic (Song et al., 2003). The Mesozoic was dominated by the subsequent extension, as evidenced by the widespread deposition of Jurassic and Cretaceous continental sediment in the region (Vincent and Allen, 1999). Since the early Cenozoic, the reactive Qilian Shan orogenic belt has been undergoing contractional deformation which continues to today (Institute of Geology, China Seismological Bureau and Lanzhou Seismological Institute, 1993; Meyer et al., 1998; Yin et al., 2008). The Cenozoic tectonics around the Qilian Shan is characterized by folding, thrust faulting, and strike-slip faulting that accommodates part of the India-Eurasia plate convergence (Tapponnier et al., 1990; Yuan et al., 2011, 2013; Zheng et al., 2013, 2014). The presence of Quaternary folds and thrust faults (Tapponnier et al., 1990; Meyer et al., 1998), the distribution of historical and instrumentally located earthquakes, and thrust fault-plane solutions (Molnar and Lyon-Caen, 1989) collectively indicate that the ranges of the Qilian Shan are still growing as a result of shortening of the crust. Geodetic shortening rates between the Qaidam and Alashan block are determined to be 5–7 mm a-1 by GPS (Zhang et al., 2004), and active shortening deformation is distributed throughout the 3

ACCEPTED MANUSCRIPT 270 km wide Qilian Shan plateau (Institute of Geology, China Seismological Bureau and Lanzhou Seismological Institute, 1993; Métivier et al., 1998; Hetzel et al., 2004; Champagnac et al., 2010; Yuan et al., 2011, 2013).

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3 Methods In order to understand what contributed to the erosional processes, we compared data on several parameters related to tectonics, climate, lithology, and topography, with decadal erosion rates from Pan et al. (2010) and ran a regression to find the most important factor.

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3.1. Drainage basins and decadal erosion rates The decadal erosion rates of the 11 subbasins of the Danghe, Shule, Heihe and Shiyang Rivers were estimated using the annual sediment load including suspended load, estimated bed load and dissolved load (Pan et al., 2010). All the data were documented at relevant hydrological stations from thev 1950s to 2000 (Pan et al., 2010). The erosion rates of each basin were listed in both Pan et al. (2010) and Table 1. Drainage basins, associated rivers, and their topographic characteristics were extracted from the SRTM digital elevation model (DEM), with a resolution of 90 m (Farr et al., 2007; Fig. 1), using the Hydrology toolbox in the ArcGIS 9.3 software.

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3.2. Potential erosion-related variables We used the historical cumulative seismic moment as a proxy of modern tectonic activity; the fault density for lithology, runoff coefficient of variation for climate (Dadson et al., 2003), and slope and mean local relief for topography. As the mean annual discharge, runoff, annual precipitation and mean temperature showed weaker correlations with erosion rates (Pan et al., 2010), these parameters were not presented in this paper. Other topographic variables such as basin area, average elevation, maximum elevation, and basin roughness, which were unable to describe the steepness of topography and contributed little to erosion (Pan et al., 2010), were not included in our study. 3.2.1. Historical cumulative seismic moment for tectonics Due to the insufficient geodetic and geological measurements on fault slip rates, we relied on historical cumulative seismic moment as a tectonic parameter. The historical cumulative seismic moment, which was the sum of seismic moment tensors, were often used to estimate the strain that accumulates as a result of an earthquake occurrence. Values of the scalar seismic moment (M0) were calculated using MS (surface wave magnitude) ≥ 4.0 earthquakes from 1950 to 2009 in the Qilian Shan region (http://www.ceic.ac.cn/AdvSearchHandler; Editorial Board of China 4

ACCEPTED MANUSCRIPT Earthquake Yearbook, 1990). To estimate scalar moments, we used Kanamori’s relationship (Kanamori, 1977, 1983, 1994; Purcaru and Berckhemer, 1978, 1982; Hanks and Kanamori, 1979): (1)

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log M 0  1.5M S  9.1

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To get a spatial distribution of seismic moment, we selected local polynomial interpolation in ArcGIS 9.3. As there was no empirical formula to describe the relation between epicentral distance and seismic moment, we assumed quadratic energy decay with distance. This was often used in the empirical formula for earthquake magnitude, epicentral distance and seismic intensity (Wang et al., 2000). Using a moving ellipse window with variable semi-major and semi-minor axes encompassing at least 30 earthquake records, we obtained a map of extrapolated historical cumulative seismic moments as shown in Fig. 2. As mentioned above, the geodetic GPS velocity and the active fault slip rates would provide effective constraints on the regional deformation. However, the GPS observations were sparse in the Qilian Shan region, and active fault studies were concentrated within the Hexi Corridor basin and hardly conducted for the interior mountain region. Therefore, it was still difficult to estimate strain rates across the Qilian Shan (Zheng et al., 2013). Although a 60-year-record of seismic moment data would not provide an adequate sample size, we preferred to have them as a tectonic parameter in our analysis.

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3.2.2. Fault density for lithology Weak rocks are more prone to erosion than strong rocks. Rock hardness is an important variable (cf. values on the Moh’s scale). Due to the large scale of the basins, the rocks here were subjected to different geological and geographical conditions. Even for the same rock type, disparate hardness was displayed under various metamorphic or weathering processes. Therefore, we used rock fractures as a proxy for rock erodiblity (Molnar et al., 2007) rather than rock hardness. We used fault density as a proxy of rock erodiblity in the absence of medium (101–103m in length) or local (10-1–101m in length) fracture density around the Qilian Shan. Fault density was calculated by dividing the total length of mapped faults by the basin area. The faults were derived from a digitized 1:500,000 geological map. As available studies revealed, the active faults were mainly distributed within the basin (Hetzel et al., 2004), and its density could represent the fracture degree only in the basins. Faults used here were mostly determined during previous mapping (Bureau of Geology and Mineral Resources of Gansu Province, 1989; Chen et al., 2003), but the characters and ages were not well constrained, as shown in Fig. 3. Therefore, we were unable to determine the relations between the mapped geological faults and the in-situ fracture degree, as well as the fault geometry that can influence the spatial pattern of 5

ACCEPTED MANUSCRIPT fractures and the locations of fault curvature quantitatively. However, the fault orientations and length distributions were displayed in Fig. 3 to provide some available information.

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3.2.3. Runoff coefficient of variation for climate As the mean annual discharge, runoff, annual precipitation and mean temperature had little contribution to the erosion across the Qilian Shan, we introduced a new parameter, runoff coefficient of variation (RCV). RCV quantifies the inter-annual variation of runoff, and is defined as the standard deviation of runoff divided by its mean runoff (Dadson et al., 2003). In arid area, high-magnitude low-frequency flood would play a more important role in erosion (Molnar, 2001). Although runoff depends on climate, topography, vegetation, and human influence, we associate the variation of runoff largely to summer storms via the method of elimination: 1) As the basins were sparsely inhabited (Pan et al., 2010) and covered by the same type of forest in the past century (Miao et al., 2013), we could infer that the effect of human activities and vegetation had been negligible, and climate change was the key factor that controlled the inter-annual variation of runoff. 2) Glacial melts only constituted a small percentage of the runoff (Shen et al., 2001). Also, the variation in size of the base flow, which was mainly derived from underground water, was much less than that of runoff (Zhang et al., 2011). Hence, we only focused on two factors, precipitation and temperature, for the climate factor. 3) Furthermore, as the research on the upstream of the Heihe River showed, the summer precipitation, which was dominated by storms, exerted the most control of surface runoff (Li, 2008; Zhang et al., 2011). Thus, we related the inter-annual variation of runoff to summer storms. 3.2.4. Topographic parameters We define mean slope, or the steepness of terrain, as a mean value of slope all over the grid in each basin (Pan et al., 2010). In addition to the slope, we also determined the mean local relief for these drainage basins as the mean value of maximum − minimum elevations over all circular cells of a certain radius in each basin (the radius is 5 km in Pan et al., 2010). In order to determine a reasonable radius to estimate the relief value, we chose a series of radii, 150, 300, 1000, 2000, 2500, 3000, 3500, 4000, and 5000 m to calculate mean local relief (Fig. 4). We did correlation analysis between relief with different radius and catchment-wide erosion rates. The mean local relief increased with the increasing radius, and Fig. 4 showed that relief in the Danshui Basin was consistently the largest. Nevertheless, the correlation coefficient of erosion rates and radii did not show a simple linear relationship. Fig. 5 showed that the correlation coefficients increased with the radius and reached the maximum at about 2.5 km radius, and then 6

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decreased with the radius. If the radius was large enough to cover the whole basin, the mean local relief or basin relief hardly showed a correlation with erosion rates (Pan et al., 2010); therefore we chose 2.5 km as the radius to calculate the mean local relief of the drainage basins.

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3.3. Correlation and regression analysis We conducted bi-variate linear regression correlation analysis among the aforementioned variables listed in Table 1 and Fig. 6. We followed Champagnac et al. (2012) and defined the quality of correlation as follows: “no correlation” for |R| < 0.3, “weak correlation” for 0.3 < |R| < 0.5, “fair correlation” for 0.5 < |R| < 0.7 and “strong correlation” for |R| > 0.7. It would be informative to adopt the classical analysis based on levels of significance (critical R coefficients for P <0.05, <0.01 and <0.001). However, we may not derive the significant P-value owing to the limited number of observation. Therefore, we calculated actual P values of their correlation coefficients. As the potential factors may have high interrelationship which would lower the stability of the regression equation, we did principle component regression instead of direct multi-variable regression. Here was the procedure of this method: (1) To get new variables, all the original ones were subtracted from their mean values and divided by the standard deviation to eliminate the effects of dimension. (2) A correlation coefficient matrix of new variables was calculated, as well as eigenvalues and eigenvectors. Then the aggregative variables could be obtained from values of the eigenvectors for the load of original variables, and the aggregative variables were fully independent. (3) The contribution ratio of each aggregative variable was calculated by dividing the related eigenvalue by the sum of all eigenvalues. Therefore the accumulative contribution ratio of aggregative variables could be acquired. (4) We chose those aggregative variables whose accumulative contribution ratio was larger than 85% as the principal components. Then we did multiple linear regressions of the dependent variable and the principal components which were independent of each other. The “princomp” function in R-software was used to search for the principal components. 4. Results Erosion rates, cumulative seismic moments, fault densities, runoff coefficients of variation, slope, and mean local relief for each catchment are listed in Table 1. The catchment-average decadal erosion rates range from 0.02 to 0.2 mm a-1 with a mean of ~0.08 mm a-1 (Pan et al., 2010). The catchment average cumulative seismic moment for each drainage ranges from 5.73 to 15.23×106 N·m with a mean of 11.19×106 N·m. The fault density ranges from 0.17 to 0.61 km-1 and the mean is 0.36 7

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km-1. The runoff coefficient of variation ranges from 0.14 to 0.28 and the mean is 0.20. The mean local relief for each catchment ranges from 471 to 1028 m with a mean of 736 m. To eliminate the dimension effect from ranges of each variable, the erosion rates and all the variables are normalized as Anorm = (A − m)/s

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(2)

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where A is the parameter, Anorm is the normalized parameter, m is the mean of the population and s is the standard deviation of this population. The average of Anorm is 0 with a standard deviation is 1. The normalized results are shown in Fig. 6. As both Table 1 and Fig. 6 show, a broadly positive correlation between decadal erosion rates and all the variables can be observed. Generally, erosion rates increase with the fault density, runoff coefficient of variation, mean slope, and mean local relief. The lowest decadal erosion rate occurs in the Danghe basin, which is also characterized by the lowest values of fault density, runoff coefficient of variation, slope, mean local relief, and cumulative seismic moment. The Danshui basin shows a contrasting characteristic, with the highest values of decadal erosion rate, slope and mean local relief. However, as Fig. 6 shows, a straightforward linear relationship between erosion rates and those variables cannot be inferred. For example, both in the Taolai and Danshui basins, the values of cumulative seismic moment are high, but the decadal erosion rate in the Taolai basin is low. Moreover, the Huangyang basin has the highest runoff coefficient of variation (0.28), and higher erosion rates, but a relatively small cumulative seismic moment value. In addition, all these variables are not only positively correlated with the decadal erosion rates, but also positively correlated with each other. 5. Discussion

5.1. Potential controls on decadal erosion rates Since a broadly straightforward linear relationship between erosion rates and those variables could not be inferred directly (Table 1 and Fig. 6), correlation analysis and regression analysis were performed to understand relationships among these factors. The correlation coefficients among erosion and potential variables are presented in Table 2. Both slope and mean local relief reflected the steepness of terrain, and they had a high correlation coefficient value of 0.95 (n = 11, P < 0.001). This indicates that there was little difference between the two topographic parameters in describing the steepness of terrain. In fact, the mean local relief and mean slope could scale with each other via a linear function, as long as they were calculated over analysis window 8

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of the same dimension (Montgomery and Brandon, 2002). However, as the slope was highly dependent on DEM resolution (Polidori et al., 1991; Zhang and Montgomery, 1994), we chose mean local relief as the measure for steepness of terrain. The correlation coefficients between decadal erosion rates and fault density, runoff coefficient of variation, slope, and mean local relief were 0.59 (n =11, P < 0.1), 0.56 (n =11, P < 0.1), 0.61 (n =11, P < 0.05) and 0.68 (n =11, P < 0.05) respectively. We did bi-variate linear regression (Fig. 7a,b) for those potential variables and erosion. From the linear regression, we could see how much those variables contribute to the erosion, i.e., the erosion rates may increase about 0.18 mm a-1 as the fault density increases by 1 km-1 (Fig. 7a), 0.53 mm a-1 as the runoff variability increases by 1 (Fig. 7b), 0.06 mm a-1 as the slope increases by 10° (Fig. 7c) and 0.02 mm a-1 as the local relief increases by 100 m (Fig. 7d). Therefore, the erosion rates were closely related to rock fracturing (fault density) and storminess (runoff coefficient of variation) besides the steepness of terrain (mean local relief and slope). This could be because rainstorms will accelerate water erosion such as sheet erosion, rill erosion and gully erosion on the hillslopes, and fractures on the surface would facilitate erosional processes (Molnar and England, 1990; Molnar et al., 2007). In general, the causal relationship between erosion and topography was complicated as steeper landscapes would speed up erosion while erosion would steepen topography. However, since we were only concerned about decadal erosional changes, and the erosion rates reported by Pan et al. (2010) only covered the last 60 years, we could assume negligible change in the land topography during this short time period. Therefore, we interpreted the observed decadal erosion rate of the Qilian Shan to be controlled mostly by topographic features, instead of a driver of landscape evolution. With the quantitative results of how much each variable contributed to the decadal erosion, we could then establish the qualitative analysis of the relative significance of these parameters to the rate of erosion. To eliminate the effect of dimension and inter-relationship of the variables, we used principle component regression before comparing with the regression coefficients of each variable. As Fig. 8 shows, the coefficients of slope and local relief are much higher than the others and error bar are smaller. With the order of regression and correlation coefficients, we can conclude that the terrain steepness contributes a lot to erosion, and the fractures are a bit more important to erosion than storm, while the seismic energy makes limited contribution to erosion. Our correlation analysis also showed that the terrain steepness, rainstorm frequency, and rock fracture density were related to each other. For example, the coefficients of mean local relief with fault density and runoff coefficient of variation were 0.60 (n =11, P < 0.1) and 0.50 (n =11, P < 0.2), respectively. The correlation 9

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between mean local relief and fault density could be explained by two reasons. First, as about one-sixth of the faults mapped in Fig. 3 were thrust faults, the outcrop of the bed rock might have been uplifted by the fault activity, after which the subsequent fault slip continued to elevate the relief; second, the pre-existing faults could also have created the fractures in the country rock, making the bedrock more erodible, hence causing higher erosion rates and steeper landscapes in the long run. The close relationship between terrain steepness and rainstorm frequency could be explained by orographic effects on precipitation. The correlation coefficient between fault density and runoff coefficient of variation is 0.68 (n =11, P < 0.05). However, as the faults here were created mainly by pre-Holocene tectonics, it would be unreasonable to relate ancient tectonic movements or faults to modern storminess. Then the high coefficient value between fault density and runoff coefficient of variation may be because both of them were correlated with terrain steepness. The correlation coefficient between erosion rate and accumulative seismic moment in the Qilian Shan is low (R = 0.33, n =11, insignificant P). In the tectonically more active Taiwan orogen, widely distributed landslides induced by earthquakes contributed significantly to the erosion process (Dadson et al., 2003). The difference between Taiwan and the Qilian Shan is probably because the climate in the latter is so arid that the landslide mass accumulated in the intermountain basin could not be transported out of the basin immediately. It could also be because no big earthquakes occurred during our sampled time period. The accumulative seismic moment showed a weaker correlation with mean local relief, indicating that the tectonic contribution to local relief of the drainage basins did not apply in Qilian as in Taiwan and other active orogens (Dadson et al., 2003; Champagnac et al., 2012). As decades of records were too short to be representative, further tectonic contributions should be explored. 5.2. Distribution of denuded zones Field observations suggest that the steepest depositional slopes are roughly 3–5° (e.g. arid alluvial fans of Death Valley, California). Slightly steeper slopes usually correspond to erosion surfaces like pediments where sediment is in transport stage. Then the transition from soil-mantled (transport limited) to rocky (detachment limited) landscapes commonly occurs at slopes above 30° (Binnie et al., 2007; Dibiase et al., 2012). Since the slope within basins differed largely, we investigated whether the buffer effect of those flat terrains might have reduced the catchment-wide erosion rates and contributed to the spatial differences of erosion. Besides, we also tried to explore if the erosion mainly proceeds in a transported-limited style, a detachment-limited style, or both. As a consequence, we partitioned the slope of the 11 drainage basins into a series of ranges as Fig.9a shows. We also calculated the area proportion for every drainage basins (Fig. 9b,c). Then we derived the correlation 10

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coefficient of each area proportion and basin-wide erosion rates (Fig. 10a). Fig. 10a showed a linearly increasing correlation pattern between erosion rates and area proportion of each slope bin until the slope reached 30º. The lowest range of slopes (0–5º) corresponded to a negative correlation coefficient (−0.55). We also determined the depositional areas (Fig. 9) from the 1:500,000 geological map and their spatial distribution corresponded with flat areas (slope < 5 o). Besides, the percent depositional area also had a negative correlation with erosion (Fig. 10b) as well as a highly positive correlation with percent area of 0–5º bin (Fig. 10c). Therefore we decided to regard the flat area as depositional zones. The correlation coefficient increased with slope, and the values remained negative until the slope was about 15º, suggesting that sediment was deposited in the areas where slopes were less than 15º. When the slope increased to 15–20º, the correlation coefficient would be equal to 0, again indicating that these areas were nearly in a balance between erosion and deposition. Although the slope of those areas were beyond the steepest deposition slope angle, the less significant coefficients suggested that the soil-mantled (transport-limited) landscaped contributed little to decadal erosion. We obtained positive and significant correlations until the slope was over 20º. The correlation coefficient increased continuously to 0.53 until the slope approached 25–30º. When the slope was steeper than 30º, a threshold angle from

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transport-limited angle to detachment-limited (Binnie et al., 2007; Dibiase et al., 2012), the correlation coefficient values remained constant at ~0.63. This close

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positive correlation between erosion and slope suggested that areas with slopes steeper than 20o contributed most of the erosion within the drainage basins. Indeed, erosion usually scales, either linearly or non-linearly, with average slope (Dietrich et al., 2003; Aalto et al., 2006; Portenga and Bierman, 2011; Carretier et al., 2013), local relief (Ruxton and McDougall, 1967), basin relief (Ahnert, 1970, 1984; Summerfield and Hulton, 1994; Vance et al., 2003), lithology and relief (Schaller et al., 2001), and rainfall and relief (Henck et al., 2011). Our present analysis indicates that the steeper areas (slope > 20o) could be considered as the main denuding areas. If we assume that all the sediments were from the steeper areas, the minimum decadal erosion rates of those areas could be further estimated by dividing the sediment load by the area of denuded zones. We used ‘minimum’ here because it was hard to estimate deposition in those intermountain basins. The minimum erosion rates of the 11 basins shown in Table 3 tended to be much larger than the average rate for the entire catchments. Due to a spatial difference of erosion within a drainage basin, the erosion rates would be underestimated if we just divided the sediment load by the whole basin area without considering the deposition effect. Indeed, the decadal erosion rates (0.02–0.20 mm a-1) for the whole basins were much less than both river incision rates (0.09–0.25 mm a-1) inferred from dated terraces (Pan et al., 2003, 2007) and exhumation rates (0.3–0.5 mm a-1) from fission track (Hetzel et al., 2002) or apatite Helium chronology 11

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(Zheng et al., 2010), all of which were not for the whole drainage basin. The discrepancy might have resulted from perturbations due to glacial–interglacial transitions (Pan et al., 2003). However, there was no direct evidence to support this view, and the spatial difference of erosion within the basins could not be neglected. We preferred that this discrepancy might have been solely due to averaging the whole catchment area without considering the deposition. Our re-estimated minimum erosion rates of 0.1–0.5 mm a-1 for the steeper regions were very comparable with river incision rates and longer-term exhumation rates. A direct comparison of erosion rates is possible in certain catchments. Although 10 the Be-based millennial timescale denudation rates (Palumbo et al., 2011) in the Liyuan Basin (0.20 mm a-1) and the Heihe Basin (0.33 mm a-1) were both higher than the catchment-wide decadal erosion rates (0.09 and 0.11 mm a-1), they were well consistent with our re-estimated results (0.24 and 0.30 mm a-1). Therefore, we suggest that, when comparing the different time-scale erosion rates, deposition within the intermountain basins should be considered; otherwise, inferences drawn from catchment-average erosion and site-specific exhumation would be misleading, as Pan et al. (2010) argued for the Qilian Shan.

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5.3. Potential controls on millennial erosion rates Since we had access to the millennial erosion rates of some drainage basins in the middle part of the Qilian Shan (Palumbo et al., 2011; Hetzel, 2013; Fig. 11a,b)ed , it was meaningful to perform correlation and regression analyses for the long-term erosion and the potential variables. As we did not have the runoff data of these basins, the runoff coefficient of variation, a climatic character, was not calculated. Besides, we chose those basins with area > 100 km2 to make sure that we could count the fault density and seismic moment. All the results were shown in Table 4, and the correlation coefficients for erosion rates and potential variables were listed in Table 5. The correlation analysis showed that both slope (R = 0.69, n = 10, P < 0.05) and mean local relief (R = 0.71, n = 10, P < 0.05) were highly related with the millennial erosion, which was similar to the decadal-scale erosion. The bi-variate linear regression (Fig. 12) showed that the millennial erosion rates increased by 0.29 mm a-1 as the mean slope increased by 10°, and by 0.11 mm a-1 as the local relief increased by 100 m. The principle component regression (Fig. 13) showed that the weight of slope and local relief to erosion were larger than that of the other variables. The seismic energy also had a weak correlation with erosion. However, the fault density had no relationship with long-term erosion, perhaps because those basins were small and the large scale faults had limited ability to represent the fractures within small basins. In summary, terrain steepness always played a much more important role in erosion process than the other factors, both in the decadal scale and in the long run.

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5.4. Role of erosion on building the Qilian Shan The landscape evolution of the Qilian Shan resulted from both the growth of the Tibetan Plateau and the erosion during Cenozoic. Some ranges were uplifted by thrusting, and these ranges separated the Qilian Shan region into a basin and range pattern (Meyer et al., 1998; Tapponnier et al., 2001). For the northwestern Qilian Shan, from south to north, ranges and basins are: 1) the Danghe Nan Shan and Danghe basin, 2) the Daxue Shan and Taolai basin, and 3) the northern Qilian Shan and Hexi Corridor basin. Ranges are mainly composed of Paleozoic metamorphic rocks, and the basins are mostly filled with Cenozoic continental sediments. The basin and range pattern represents the first order topographic characteristic in the Qilian Shan region. Meyer et al. (1998) proposed a “bath-tub” model to explain this topographic feature. A 250 km long, 20 km wide swath elevation profile from the Danghe Nan Shan to the northern Qilian Shan (Fig. 14) revealed that the highest elevations of range peaks were nearly constant. The elevation profile also indicated a growing local relief from the Danghe Nan Shan to the northern Qilian Shan despite the decreasing elevation. In addition, the rivers incised much deeper into the northern Qilian Shan region such as the Taolai and Hongshuiba Rivers, and these regions were of much rougher landscape (Fig. 14). Low-temperature thermochronology suggests that the onset of uplift of both the Danghe Nan Shan and northern Qilian Shan was about 8–10 Ma, and the surface uplift rates have been about 0.68 mm a-1 (Sam, 2009) and 0.3–0.5 mm a-1 (Sam, 2009; Zheng et al., 2010), respectively. Therefore, the elevation and the terrain relief would become lower from SW to NE under the assumption of uniform erosion for the whole Qilian Shan. However, the terrain relief increases from SW to NE as Fig. 11 shows. Hence, we infer that the second-order topographic features were affected by erosion and deposition although the first order topographic feature was controlled by the Cenozoic tectonic activity, i.e. the basin and range pattern resulted from the thrust and subsequently rock uplift. However, erosion in the Danghe catchment is slow and the most of the sediments were trapped and deposited in the Danghe basin. Erosion of the higher steeper range areas and deposition within the basin across the Danghe catchment further smoothed the relief. In contrast, erosion rates in the Taolai River catchment and the Hongshuiba River catchment are high and most of the sediments are transported out of the basins, resulting in the high relief topography. This process was similar to the “bath-tub” model of Meyer et al. (1998). 6. Conclusion Cumulative seismic movement, fault density, the runoff coefficient of variation, slope and mean local relief of the 11 drainage basins in the Qilian Shan were analyzed in relation to the modern tectonic energy, rock fracture, storminess and terrain steepness of the basins. Correlation analysis results suggest that the steepness of 13

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terrain (mean local relief and slope), rock fracture (fault density) and climate (runoff coefficient of variation) contributed more to the decadal erosion rates, while the tectonics showed limited contribution. The correlation analysis also showed that the steepness of terrain was much more correlated with the rock fracture and rainstorm occurrence than with modern tectonic movements. By dividing slopes of these basins into a series of sections and calculating the correlation coefficients between fractions of areas of different slopes with erosion rates, we found that steeper areas (slope > 20o) contributed most to the erosion across the Qilian Shan. Based on the difference of uplift rates from SW to NE as well as the terrain relief, we conclude that the range and basin pattern resulted from thrust faulting and subsequently rock uplift during the Cenozoic, while the terrain relief resulted from the spatial difference of erosion and deposition, in a similar way to the “bath-tub” model.

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Acknowledgement We are grateful for grants from the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB03020201, XDB03020203), and from National Science Foundation of China (41272215, 41030317, 41272196). We would like to thank Peter Molnar, Lease O Richard, Jean-Daniel Champagnac, Mike Fullen and Qi Ou for for providing valuable suggestions.

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Fig. 1. Shaded-relief map of the Qilian Shan. HF – Houtang Fault, DNF – Danghe Nanshan Fault, YBF – Yemahe North Margin Fault, DF – Daxueshan Fault, YF – Yin’aocao Fault, CEF – Changma-Ebo Fault, QBF – Qilian Shan North Margin Fault, YMF – Yumushan Fault, LF – Longshoushan Fault, WTF – Wuwei-Tianzhu Fault. YM – Yumen, JT – Jinta, JQ – Jiuquan, ZY – Zhangye, WW – Wuwei, DLH – Delingha, MY – Menyuan. Rivers are shown in blue lines with 11 hydrological stations whose names are listed in the legend. The outlines of drainage basins are shown in white lines. Fig. 2. Historical cumulative seismic moment of the Qilian Shan. For the distribution of seismic moment, we conducted local polynomial interpolation using ArcGIS 9.3 and a moving ellipse window with variable semi-major and semi-minor axes encompassing at least 30 earthquake records. Blue dots show epicenters. The Heihe 20

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Fig. 3. Distribution of the faults in the Qilian Shan. (a) 1:500,000 geological map of Qilian Shan. (b) Fault length distribution for each drainage basin. The Heihe Basin includes the Babao Basin and the upper Heihe Basin. The Shiyang Basin includes the Xiying, Zamu and Huangyang Basins.

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Fig. 4. Mean local relief of individual basin versus the radius of the circular moving window.

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Fig. 5. Correlation coefficient between decadal erosion rates and mean local relief for different radius values of the circular moving window.

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Fig.6. Decadal erosion rates and normalized values of variables.

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Fig. 7. Bi-variate linear regression for decadal erosion rates and the potential variables. (a) Fault density vs. erosion rates, showing the effect of fractures to decadal erosion. (b) Runoff coefficient of variation vs. erosion rates, showing the effect of storm to decadal erosion. (c) Slope vs. erosion rates, showing the effect of terrain steepness to decadal erosion. (d) Mean local relief vs.erosion rates, showing the effect of terrain steepness to decadal erosion. Fig. 8. Principal component regression for decadal erosion rates and all the potential variables.

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Fig. 9. Slope distribution of the 11 drainage basins in the Qilian Shan. (a) Distribution map of slope. The outlines of drainage basins are shown by blue lines. Steep slopes often occur in the vicinity of thrust faults. (b) Percent area of slope bins in the Danghe, Changma, Taolai, Danshui and Liyuan Basins.(c) Percent area of slope bins in the upper Heihe, Babao, Heihe, Xiying, Zamu and Huangyang Basins. Fig. 10. Correlation and bi-variate regression analysis for decadal erosion rates, percent area of slope bins and that of depositional area. (a) Correlation coefficients of basin-wide decadal rates with percent area of slope bins. For coefficient below −0.4, “negative relation” and “deposition”; for coefficient between 0.4 and −0.4, “weak relation”; and for coefficient above 0.4, “positive relation” and “erosion”. (b) Bi-variate linear regression analysis for percent depositional area and decadal erosion rates. (c) Bi-variate linear regression analysis for percent area of 0–5° bin and depositional areas.

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Fig. 12. Bi-variate linear regression for millennial erosion rates and the topographical variables. (a) Slope vs. erosion rates, showing the effect of terrain steepness to millennial erosion. (b) Mean local relief vs.erosion rates, showing the effect of terrain steepness to millennial erosion. Fig. 13. Principal component regression for millennial erosion rates and all the potential variables.

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Fig. 14. Elevation profile along the section A–B in Fig. 9a. MIN, MAX, MEAN and RELIEF are the minimum, maximum, mean elevation and relief of the section respectively.

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Table 1. Decadal erosion rates and potential variables of the 11 drainage basins. DER – decadal erosion rates, CSM – cumulative seismic moment, FD – fault density, RCV – runoff coefficient of variation, MLR – mean local relief. Table 2. Correlation coefficients of each variable and decadal erosion rates.

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Table 3 Minimum erosion rates (Min E) for denuded areas with average slope >30o. Table 4. Millennial erosion rates and potential variables of 10 drainage basins. MER – Millennial erosion rates, CSM – cumulative seismic moment, FD – fault density, RCV – runoff coefficient of variation, MLR – mean local relief. Table 5. Correlation coefficients of each variable and millennial erosion rates.

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Figure10

32

MA

NU

SC R

IP

T

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AC

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D

Figure11

33

SC R

IP

T

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AC

CE P

TE

D

MA

NU

Figure12

34

MA

NU

SC R

IP

T

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AC

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D

Figure13

35

MA

NU

SC R

IP

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ACCEPTED MANUSCRIPT

AC

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Figure14

36

ACCEPTED MANUSCRIPT Table 1 Basin name

CSM (106 N·m)

FD (km-1)

RCV

Slope (o)

MLR (m)

DER (mm yr-1)a

1

Danghe

8.59

0.17

0.14

9.29

471.95

0.02

2

Changma

10.07

0.25

0.22

14.21

675.44

0.12

3

TaoLai

13.32

0.32

0.15

16.49

769.38

0.05

4

DanShui

14.82

0.59

0.24

23.58

T

Number

0.20

5

LiYuan

11.10

0.50

0.24

17.01

731.49

0.09

6

Upper

15.23

0.28

0.16

12.53

577.40

0.12

IP

BaBao

10.45

0.41

0.20

15.13

649.14

0.09

8

HeiHe

13.30

0.33

0.15

16.73

736.47

0.11

9

XiYing

12.11

0.25

0.18

22.47

874.97

0.09

10

ZaMu

5.73

0.26

0.25

21.17

770.51

0.11

11

Huangyang

8.37

0.61

0.28

19.78

809.78

0.12

—

Mean

11.19

0.36

0.20

17.13

735.94

0.08

—

Standard

0.05

4.35

147.95

0.05

MA

NU

7

deviation

2.93

0.15

CE P

TE

D

After Pan et al. (2010).

AC

a

SC R

Hehei

1028.78

37

ACCEPTED MANUSCRIPT Table2 Runoff variability

Fracture

Slope

Relief

Decadal erosion

P-value

0.156 0.648

-0.438 0.178

0.024 0.943

SC R

Fracture

0.677 0.022

R P-value Runoff variability

P-value Slope

MA

R P-value Relief

D

R

TE

P-value

0.326 0.329

0.508 0.110

0.598 0.052

0.588 0.057

0.578 0.063

0.502 0.116

0.564 0.071

0.947 0.000

0.614 0.045

NU

R

0.241 0.475

IP

R

T

Seismic energy

0.681 0.021

AC

CE P

Number of observation (n) is 11. Bold italic values indicate strong correlations, |R| > 0.7; bold values indicate fair correlations, 0.5 < |R| <0.7; italic values indicate weak correlations, 0.3 < |R| <0.5; regular values indicate no correlation, |R| < 0.3.

38

ACCEPTED MANUSCRIPT Table3

Danshu i 4

Liyua n 5

Upper Heihe 6

0.14

0.38

0.13

0.32

0.24

0.51

Ba bao 7

H ei he 8

0.3 0

0. 29

NU MA D TE CE P 39

Xi yi ng 9

Za mu 10

Huan gyan g 11

0.2 1

0.24

T

Taola i 3

IP

Cha ngm a 2

SC R

Dan ghe 1

AC

Basi n nam e Basi n num ber Min E (mm a-1)

0. 15

ACCEPTED MANUSCRIPT

Basin name

CSM (106 N·m)

FD (km-1)

Slope (o)

MLR (m)

MER (mm yr−1)

Q1

5.54

0.33

14.9

371

0.077

Q2

6.51

0.32

17.4

455

0.039

Q3

8.52

0.28

18.3

473

0.100

Q6

12.24

0.32

26.3

714

T

Table4

Q8

8.37

0.40

26.5

715

Q12

12.27

0.32

22.9

609

Q13

13.31

0.21

16.1

JG34

10.62

0.07

20.4

JG42

10.37

0.35

22.5

JG49

12.46

0.07

7.6

Mean

10.02

0.27

19.29

2.68

0.11

5.74

IP

0.196

SC R

deviation

0.422

424

0.325

542

0.314

610

0.216

206

0.047

511.90

0.26

159.15

0.24

NU

Standard

0.833

AC

CE P

TE

D

MA

In basins with name “Q-” are from the work of Palumbo et al. (2011) and “JG-” are from Hetzel (2013).

40

ACCEPTED MANUSCRIPT Table5 Fracture

Slope

Relief

Millennial erosion

0.445 0.120

0.017 0.963

0.062 0.865

0.415 0.233

0.620 0.056

0.597 0.068

0.190 0.599

0.998 0.000

0.687 0.028

P-value

R P-value Slope R

NU

P-value

SC R

Fracture

Relief

MA

R P-value

IP

R

T

Seismic energy

0.708 0.022

AC

CE P

TE

D

Number of observation (n) is 10. Bold italic values indicate strong correlations, |R| > 0.7; bold values indicate fair correlations, 0.5 < |R| <0.7; italic values indicate weak correlations, 0.3 < |R| <0.5; and regular values indicate no correlation, |R| < 0.3.

41

ACCEPTED MANUSCRIPT Highlights: We provide new insights into the landscape evolution of the Qilian Shan.



Storm and lithology also contribute to decadal erosion.



>20º slope significantly controls erosion.



Deposition effect of drainage basins leads to temporal variation of erosion rates.

AC

CE P

TE

D

MA

NU

SC R

IP

T



42