Project Info

Earth, fire and water – can topographic roughness help predict erosion and landslides after wildfires?

This MURF project will contribute to part of a new, larger project being undertaken by a graduate student beginning in Summer, 2019. The goal of the larger project is to determine whether topographic roughness can be used to identify and predict the effects of vegetation, land use history and lithology on hillslope erosion and catastrophic failure following wildfire. The effects of topographic roughness on hillslope erosion and failure processes are important but poorly understood. Vegetative roughness elements like trees and shrubs may control particle movement on steep hillslopes both by sapping kinetic energy from mobile sediment, and by capturing and storing sediment upslope. When vegetation is removed by wildfire, large volumes of sediment can be released to travel far downslope to the valley floor, seeding debris flows when the region next experiences flooding. In addition to vegetation, many characteristics that influence topographic roughness, such as lithology and land use history, can also affect post-wildfire erosion and landsliding through a range of parameters (e.g., soil infiltration and cohesion) that are challenging to constrain at small scales. To the extent that vegetation, lithology and land use history can be uniquely associated with metrics for topographic roughness, we hypothesize that roughness may then be usable as a convenient predictive proxy for these post-fire erosional effects. The MURF project will focus on building and mapping a collection of overlapping datasets characterizing topography, precipitation, vegetation, land use history, lithology, landslides, and other parameters before and after wildfire. Topographic data will be collected from both existing databases and terrestrial lidar scanning or drone photogrammetry field campaigns over the summer of 2019. The techniques and skills used in this project are somewhat flexible and will depend on student interests and goals, but could involve remote sensing, digital topographic data collection and mapping, data mining, machine learning, statistics, field work, and simple computer programming.

More Information

https://geology.mines.edu/project/danica-roth/

Grand Engineering Challenge: Engineer the tools of scientific discovery

Student Preparation

Qualifications

Required: basic prior experience with computer programming, ArcGIS or other GIS software, using online databases and data (especially topographic, precipitation, vegetation, and historical), field work.

Preference will be given to students who have previous familiarity with OR strong interest in developing skills in any of the following: terrestrial lidar/photogrammetry/point cloud collection and analysis, data mining, data science, data management, and machine learning techniques.

Time Commitment

15-20 hours/month

Skills/Techniques Gained

The techniques and skills used in this project are somewhat flexible and will depend on student interests and goals, but could involve remote sensing, digital topographic data collection and mapping, data mining, data management, database management, machine learning, statistics, field work, and simple computer programming.

Learning Outcomes:
– student will know how to find, navigate, work with and manage large spatial databases and datasets
– student will be able to apply basic principles and techniques to co-register and compare spatial datasets in GIS
– student will develop a basic conceptual framework for understanding and critically assessing connections between landscape parameters (climate, vegetation, lithology, land use and wildfire history, soil characteristics, etc.) and processes (erosion, debris flows, landslides, etc.)

Depending on student interests, goals and performance, this project may also lead to student co-authorship on scientific publications and presentation at scientific conferences.

Mentoring Plan

Student will have weekly individual meetings to discuss progress, challenges and questions, as well as email and drop-in availability. The student will also be included in lab group meetings and reading groups, which will provide exposure to a collegial research environment and provide networking and professional development opportunities. Additional supervision, collaboration and mentorship will be provided by the graduate student working on the larger project to which this work contributes. Depending on student interests, goals and performance, this project may also lead to student co-authorship on scientific publications and presentation at scientific conferences, and additional employment as a grader for geomorphology classes.