GSA Annual Meeting in Denver, Colorado, USA - 2016

Paper No. 282-12
Presentation Time: 11:30 AM


SCOTT, Daniel N., Geosciences, Colorado State University, 1482 Campus Delivery, Fort Collins, CO 80521 and WOHL, Ellen E., Geosciences, Colorado State University, Fort Collins, CO 80523-1482,

There exist many basic questions regarding the evolution of Earth’s surface that are extremely difficult to answer. For example: Which segments of a large, steep, unwadeable river (e.g., the Colorado River in the Grand Canyon) dominantly erode bedrock, and which segments dominantly erode alluvium? Will a wood jam in a river move during a flood of a given magnitude? Currently, we can use conceptual models, previous experience, or even empirical equations to guide our answers to questions such as these. However, we have no clear way to combine those tools into a single, quantitative model that can both utilize all known data on a subject as well as evolve to incorporate new data. Basic statistical models attempt to do this by determining how processes relate to one another, allowing for the construction of conceptual models that are guided by experience and empirical data. We must focus on turning such conceptual models into better predictive models to improve our understanding of geomorphic processes. Using constantly evolving datasets and models, we can build models whose predictions can be used to bring the collective knowledge of our field to bear in understanding and managing the land.

Here, I use the aforementioned two examples as questions that can test the efficacy of this idea. By collecting data on a large number of potential predictor variables, I attempt to create a procedure that will accomplish two objectives: 1) develop a data collection scheme that is accessible to multiple investigators working independently and 2) use this data collection scheme to inform a constantly evolving, quantitative model that will answer the questions posed above. Preliminary results analyzing the stability of wood jams under floods of varying magnitudes suggest that this approach can be successful in terms of developing an improving model that can accommodate new data to inform future predictions. Finally, I discuss potential paths forward that will allow geoscientists to incorporate evolving models into collaborative science and management.

  • GSA 2016 Dan Scott.pdf (10.3 MB)