Data Repository Spatiotemporal trends in erosion rates across a pronounced rainfall gradient: examples from the south central Andes Bodo Bookhagen 1 and Manfred R. Strecker 2 1 Geography Department, Ellison Hall 1832, UC Santa Barbara, CA 93106 4060, USA bodo@eri.ucsb.edu, Phone: +1 (805) 893 3568, Fax: +1 (805) 893 2578 2 Institute of Earth and Environmental Science, Universität Potsdam, Germany 1. Introduction In the auxiliary materials we provide additional and background information and figures. 2. Geologic and Climatic Setting
3. Methods and Data Figure DR 1: Topography of NW Argentina with cosmogenic nuclide sample locations of 41 samples, associated river catchments, and labels. See Table 1 for more detailed information on topographic and cosmogenic nuclide parameters. 2
Figure DR 2: Comparison of mean annual discharges from high spatial resolution (5x5km 2 ) TRMM 2B31 data with low spatial (30x30km 2 ), but high temporal TRMM3B42 data. In general the data agree well and show that TRMM2B31 results in higher discharge than TRMM 3B42 data. This is likely related to the fact that TRMM3B42 is scaled with discharge measurement [Huffman et al., 2007]. The overall mismatch between gauged and remotelysensed discharge is routed in neglecting evapotranspiration and ground water loss. Mean annual discharges were derived from 1998 to 2010. 3
B. Bookhagen and M.R. Strecker: Spatiotemporal trends in erosion rates Figure DR 3: Spatial comparison of TRMM 2B31 (A) and TRMM 3B42 (B) data. The TRMM 2B31 (A) data have a spatial resolution of 5x5km2 and were calibrated according to procedure described in Bookhagen and Strecker [2008]. TRMM 3B42 (B) are in a gridded data format with a spatial resolution of 30x30km2 and a temporal resolution of 3h [Huffman et al., 2007]. Data were processed from 1998 to 2010 [Bookhagen and Strecker, 2010]. 4
Note that the overall rainfall pattern is similar; however, the steep rainfall gradient is more accurately captured by the TRMM 2B31 (A) data. 5
Figure DR 4: (A) shows longitudinal river profile (black), mean annual rainfall (blue) and 3 km radius relief (pink) along the main stem of the Santa María catchment (see Figure 1 for location). (B) shows mean annual discharges along the main stem derived from homogenously distributed rainfall across the catchment (red), flow routed (or weighted) flow accumulation with TRMM2B31 rainfall (blue), and the product of the flow accumulation (drainage area or homogenously distributed rainfall) and TRMM2B31 rainfall. Note that the homogenously distributed rainfall overpredicts discharge by a factor of two to three, whereas the product of flow accumulation and TRMM rainfall shows significant variability along river profile. In our calculations we rely on the flow routed TRMM discharge. 6
Figure DR 5: Same as Figure DR 4, but for the Humahuaca catchment. In (B), note the large discrepancy of a factor of 2 between homogenously distributed rainfall (red) and flow routed TRMM discharge. Also, note the streamgauge measurement in (B) that indicates that the flow routed TRMM discharge provides more reasonable discharge estimates than any of the other two methods (cf. Figure 2). 7
Figure DR 6: Comparison of 72 basins (this study and Safran et al. [2005]) of specific stream power vs. normalized steepness value. Overall, there exists a very good relation between the two datasets. Here, specific stream power and normalized steepness index were derived from TRMM weighted discharges. The results are similar to drainage area weighted discharges. 8
B. Bookhagen and M.R. Strecker: Spatiotemporal trends in erosion rates 4. Results Figure DR 7: Comparison between specific stream power (SSP) based on drainage area (A) and TRMM discharge (B). SSP based on drainage area predicts high amounts on the dry eastern rims of the Altiplano Puna Plateau. In contrast, TRMM based SSP has much lower values in the dry regions, but higher values in wet areas at orographic 9
rainfall barriers. We only have calculated SSP for the eastern Andean slopes and do not include river elevations below 0.8 km. Figure DR 8: Normalized steepness index (K sn ) for drainage areas above 1km 2. (A) depicts original calculations derived according to procedure described in [Wobus et al., 2006]. (B) shows the interpolated and smoothed K sn values to produce a continuous surface based on drainage areas. (C) is the same calculation, but with discharge taken from calibrated TRMM satellite data. Note the difference in the semi arid to arid environments bordering the Altiplano Puna Plateau. 10
B. Bookhagen and M.R. Strecker: Spatiotemporal trends in erosion rates 5. Discussion Figure DR 9: 2 to 20 km2 catchments (thin white polygons in A) draped over a topographic hillshade. Shown are all catchments 500 m mean elevation for the study area (n=24 197). (B) regional distribution of arid ( 0.25 m/yr mean annual rainfall), moderate (0.25<x<0.5 m/yr), and humid ( 0.5 m/yr) areas. Solid white line indicates internally drained area of the Altiplano Puna Plateau. 11
Figure DR 10: Relation between mean catchment normalized steepness index (k sn ) and CRN derived catchmentmean erosion rate. The TRMM weighted k sn produces a reasonable fit for the data and suggests a power law exponent of ~1.4. Only the filled symbols were used for the error weighted fits (n=27). 12
Figure DR 11: Drainage area weighted SSP color coded according to the catchment's rainfall amount. Red circles denote arid catchments (<0.25 m/yr annual rainfall), green circles show moderately humid catchments (0.25 0.75 m/yr), and blue circles show humid catchments (> 0.75 m/yr). Filled circles are data use for producing the fit. 13
Figure DR 12: Spatial variation of channel width differences based on drainage area and TRMM2B31 discharge. Because width in the calculation of specific stream power relies on discharge, our predicted channel width differs between the TRMM weighted and unweighted data. Unweighted (or drainage area based) channels in the semiarid regions are predicted to be ~1.3 to 2 times wider than TRMM weighted channels, while channels in the humid areas are ~0.7 times as wide. 14
6. References Bookhagen, B., and M. R. Strecker (2008), Orographic barriers, high resolution trmm rainfall, and relief variations along the eastern andes, Geophysical Research Letters, 35(6). Bookhagen, B., and M. R. Strecker (2010), Modern andean rainfall variation during enso cycles and its impact on the amazon basin, in Neogene history of western amazonia and its significance for modern diversity, edited by C. Hoorn, et al., Blackwell Publishing, Oxford, U.K. Huffman, G. J., et al. (2007), The TRMM multisatellite precipitation analysis (TMPA): Quasi global, multiyear, combined sensor precipitation estimates at fine scales, Journal of Hydrometeorology, 8(1), 38 55. Safran, E. B., et al. (2005), Erosion rates driven by channel network incision in the bolivian andes, Earth Surface Processes and Landforms, 30(8), 1007 1024. Wobus, C., et al. (2006), Tectonics from topography: Procedures, promise, and pitfalls, in Tectonics, climate, and landscape evolution, edited by S. D. Willett, et al., pp. 55 74, Geological Society of America Special Paper 398. 15