Collaborative Research: Reading lithology from topography: How rock properties influence landscape form and evolution in the Guadalupe Mountains, TX and NM

景观的形状部分取决于它们下面的基岩的物理性质(例如，不同地区不同类型的岩石有多强，岩石的裂隙有多大，以及特定岩石有多容易分解成土壤和沉积物，也称为风化)。从景观形态(地形)中“读出”岩石属性应该是可能的，但我们目前缺乏一种可实际使用的定量关系。例如，发生滑坡的可能性取决于岩石和土壤的性质。过去的构造活动和未来沿断层发生地震的可能性也可以从地形中潜在地推断出来，但构造的影响必须与岩石性质对景观形态的控制分开。预测地表属性也将有助于规划自然灾害后或人类冲突期间进入偏远地区的机会。研究人员将开发基于瓜达卢佩山脉的教学模块，用于通过德克萨斯州奥斯汀大学教授的双招生(即高中生获得大学学分)地球科学课程，该课程为德克萨斯州的高中生提供大学学分。

The shape of landscapes partly depends on the physical properties of the bedrock that underlies them (e.g., how strong different rock types are in different places, how fractured the rocks are, and how easily particular rocks break down to make soil and sediment, also known as weathering). It should be possible to "read" rock properties from landscape form (topography), but we currently lack a quantitative relationship that can be used practically. For example, the likelihood of landslides depends on rock and soil properties. Past tectonic activity and the potential for future earthquakes along faults can also potentially be inferred from topography, but the effects of tectonics must be isolated from rock property controls on landscape form. Predicting land surface properties would also be beneficial for planning access to remote areas following natural disasters or during human conflicts. The researchers will develop teaching modules based on the Guadalupe Mountains to use in a dual enrollment (i.e., college credit for high school students) geoscience course taught through UT Austin that provides college credit to high school students across Texas. High school geoscience and environmental science teachers will be trained through intensive and fun field workshops to be taught in the Guadalupe Mountains.This project uses the Guadalupe Mountains in Texas and New Mexico as a natural laboratory to understand how rock properties can be inferred from topography. In addition to (a) using geographic information system analysis to relate meter-scale topographic data and digital geology maps, the researchers will also (b) make extensive field measurements of rock properties across this landscape, (c) determine long-term bedrock erosion rates by measuring cosmogenic radionuclide concentrations from rock samples, and (d) evaluate open-source computer models that predict how landscapes will evolve through time, and improve these models to better incorporate rock properties into their equations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.