A Widespread Grain Size Bias in Detrital Cosmogenic Nuclide Studies: Implications for Sampling in Steep Terrain

Abstract:

Cosmogenic nuclides in stream sediment have been used to measure catchment-averaged erosion rates for nearly two decades. This has revolutionized understanding of mountain landscape evolution. To accurately reflect average erosion, sampled sediment must be representative of the entire landscape. However, mounting evidence suggests that the different grain sizes common in mountain streams often harbor different cosmogenic nuclide concentrations. This raises a fundamental challenge: what should we sample? A common practice is to sample sand, but this can lead to bias in inferred erosion rates. Overcoming the challenge of representative sampling requires understanding the source of the grain size bias. We demonstrate that variations in sediment sizes across slopes can produce large differences in nuclide concentrations in stream sediment. This emerges from both empirical observations from the Sierra Nevada and from a forward model of landscape erosion. We used the model to explore the kinds of catchments in which bias is likely to arise if we measured nuclides in sand alone. We found that in small or low-relief catchments, modeled nuclides in sand accurately reflect imposed erosion rates. Higher relief may produce a larger bias. For example, by sampling only sand in a catchment where both erosion rates and median grain size increase over 3 km of relief, we could underestimate average erosion rates by a factor of 4. Quantifying a signal of grain size bias in the real world is likely complicated by non-steady erosion processes (e.g. landslides) and other factors. Nevertheless, spatial variations in sediment size across slopes may partly explain observed differences in nuclide concentrations in streams. Using sediment tracing tools in conjunction with cosmogenic nuclides may improve sampling methods in landscapes susceptible to grain size bias. Our approach provides a way to quantify how grain size varies across landscapes and thus improve understanding of weathering and erosion in mountain settings.