No abstract

Understanding the rates and pattern of erosion is a key aspect of deciphering the impacts of climate and tectonics on landscape evolution. Denudation rates derived from terrestrial cosmogenic nuclides (TCNs) are commonly used to quantify erosion and bridge tectonic (Myr) and climatic (up to several kiloyears) time scales. However, how the processes of erosion in active orogens are ultimately reflected in Be concentration in the main stem Arun suggests that upstream sediment grains are fining to the point that they are operationally excluded from the processed sample. This results in 10 Be concentrations and denudation rates that do not uniformly represent the upstream catchment area. We observe strong impacts on 10 Be concentrations from local, nonfluvial geomorphic processes, such as glaciation and landsliding coinciding with areas of peak rainfall rates, pointing toward climatic modulation of predominantly tectonically driven denudation rates.

[1] Recent studies suggest that orogens can achieve a topographic steady state whereby equilibrium is reached between tectonics and erosion. However, steady state topography may not be the norm in many orogens experiencing large changes in climate or tectonics, which can produce topographic transients. The quantification of transient topography over geologic timescales requires reconstructing paleotopography, but this has proven difficult in many cases. This study investigates the utility of bedrock thermochronometer data to reconstruct orogen paleotopography over million year timescales. Apatite (U-Th)/He and fission track ages are integrated with a thermokinematic model for a single-parameter inversion of paleotopography. An iterative scheme is used that minimizes the misfit between predicted and observed cooling ages to identify the range of paleotopographies that could produce observed ages within sample uncertainty. Two approaches are considered. First, synthetic 2-D topographies are used to test the robustness of the approach. The following topographic evolution scenarios are considered: (1) lateral ridge migration, (2) topographic relief change, and (3) valley widening and deepening from glaciation. Second, the method is applied in three dimensions to existing data from the Coast Mountains of British Columbia, Canada. Results from both applications of the model suggest that (1) paleotopographic reconstruction will typically underpredict the magnitude of topographic change, especially relief change; (2) paleotopography is most successfully reconstructed after lateral ridge migration in long-wavelength topographies; and (3) reconstructed paleotopography from the Coast Mountains, British Columbia, suggests that glacial erosion may have the potential to remove drainage divides and laterally shift topographic ridges and peaks.Citation: Olen, S. M., T. A. Ehlers, and M. S. Densmore (2012), Limits to reconstructing paleotopography from thermochronometer data,

The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6-12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth's surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.