Matthew A. Wolinsky

Constraints on landscape evolution from slope histograms

We analyzed >10,000 topographic slope histograms subsampled from digital elevation models (DEMs) of 28 landscapes throughout the continental United States. These landscapes are made up of diverse lithologies and have different climatic and tectonic histories, but their slope distributions are consistently unimodal and collectively exhibit a systematic change from positively skewed to negatively skewed as mean slope increases. This change not only occurs between landscapes, but can also occur within them, and is recorded in DEMs of different spatial resolution. The transition from positive to negative skewness cannot be accounted for by scaling of relief, but requires a fundamental change in landscape morphology. Process-based landscape modeling reproduces the trend and suggests that a change in the dominant hillslope process—from creep and/or wash to slope failure—is responsible for the transition from positive to negative skewness with increasing mean slope. This process transition is governed by a dimensionless uplift number that reflects the balance between processes of relief generation and denudation. The uplift number provides a basis for understanding the morphologic similarity between landscapes in our analysis, illustrating how different combinations of climate, tectonics, and lithology can lead to equivalent morphologies.

Digitizing the Sedimentary Record: Efficient Data Structures for Dynamic Stratigraphy

As the surface of the Earth evolves over geologic time sediment is transported from high mountains to low lying sedimentary basins, accumulating stratigraphy which stores a sedimentary record of past environments. Understanding the structure of this stratigraphy is essential for exploiting sub-surface resources such as oil and water, and for deciphering the sedimentary record. Sophisticated computational models of Earth-surface dynamics can be used to predict formation of stratigraphy, but require stratigraphy in a discrete form. Here I present a discrete stratigraphic data structure which enables simulation of dynamic stratigraphic evolution. The data structure is robust and efficient, but can be easily coupled to existing Earth-surface dynamics models with minimal effort due to a simple interface. This represents a significant advance which will allow widespread use of dynamic models to predict and invert stratigraphy for application in a variety of fields.