Cooperative Institute for Research in Environmental Sciences

Gregory Tucker

Research Interests

My interests lie in geomorphology and landscape evolution. My group seeks to understand the physics of diverse geomorphic processes, and how these processes interact to shape terrain and move sediment. Some of our efforts are geared toward understanding the evolution of the earth's surface over geologic time; others focus on contemporary issues such as gully development and erosional threats to toxic waste repositories. We also develop and share open-source software technology to support computational modeling of diverse earth-surface processes. Through teaching, I strive to equip students with the skills in writing and quantitative analysis that they need to understand, investigate, and make informed decisions about our fascinating and dynamic planet. 

Current Research

The sciences of the earth’s surface are evolving rapidly, and new data, discoveries, and ideas continue to fuel the need for new computational models. Numerical models are crucial to the earth-science enterprise because they enable us to explore and visualize quantitative hypotheses, and compare hypotheses with data. Yet the task of building, modifying, and maintaining the necessary software behind earth-surface dynamics models can be a daunting one. To sustain progress, it’s important that computational software be sufficiently flexible and adaptable that it promotes, rather than impedes, the discovery process. To help meet this need, Greg Tucker and a team of colleagues at CIRES, Tulane University, and the University of Washington have created a software library that help scientists rapidly create, explore, modify, and combine two-dimensional numerical models. The Landlab Toolkit is a support system for model development that (1) is written in a modern high-level language with a rich set of scientific computing libraries, (2) takes care of common but labor-intensive tasks, such as grid creation and input/output, with a convenient set of functions and data structures, (3) packages useful operations and calculations into reusable components, and (4) provides a simple mechanism for a scientific programmer to combine components. Landlab is written in Python, thereby taking advantage of the rapidly growing popularity of Python as an efficient, high-level programming language for scientific computing. Landlab provides a gridding module that allows modelers to create and configure a grid in just one or a few lines of code. Grids may be structured (e.g., raster or hexagonal) or unstructured (Delaunay/Voronoi). State variables and other distributed data can be attached to a grid, and staggered-grid numerical schemes are easy to implement. Landlab includes a set of process components written by the development team to model a wide variety of processes. These include, for example, incident solar radiation on terrain, evapotranspiration, overland flow, soil creep, stream network erosion, and flexure of the lithosphere. Landlab aims to foster progress in earth-surface dynamics by helping modelers focus on their science rather than the computer code behind it.


Output from a simple landform evolution model written in Landlab, showing the development of ridges, valleys, and drainage networks in a hypothetical landscape. Color represents terrain height; scale is in meters. Domain is 1 km x 1 km. Image courtesy: Harrison Gray (Gray et al., 2017 in review)

Output from a rainfall-runoff model written in Landlab, showing hydrograph produced by a heavy rainstorm.

Output from a rainfall-runoff model written in Landlab, showing hydrograph produced by a heavy rainstorm. Location is Spring Creek, a tributary of the South Platte River in the Colorado Rockies. (From Hobley et al., Earth Surface Dynamics, 2017). Image courtesy: Jordan Adams (Adams et al., 2017 Geoscientific Model Development)

View Publications

  • Martner, BE, PJ Neiman and AB White (2007), Collocated radar and radiosonde observations of a double-brightband melting layer in northern California. Mon. Weather Rev. Version: 1 135 (5) 2016-2024, issn: 0027-0644, ids: 166GP, doi: 10.1175/MWR3383.1
  • Matrosov, SY, R Cifelli, PC Kennedy, SW Nesbitt, SA Rutledge, VN Bringi and BE Martner (2006), A comparative study of rainfall retrievals based on specific differential phase shifts at X- and S-band radar frequencies. J. Atmos. Ocean. Technol. Version: 1 23 (7) 952-963, issn: 0739-0572, ids: 069MH, doi: 10.1175/JTECH1887.1
  • Neiman, PJ, GA Wick, FM Ralph, BE Martner, AB White and DE Kingsmill (2005), Wintertime nonbrightband rain in California and Oregon during CALJET and PACJET: Geographic, interannual, and synoptic variability. Mon. Weather Rev. Version: 1 133 (5) 1199-1223, issn: 0027-0644, ids: 930CH, doi: 10.1175/MWR2919.1
  • Matrosov, SY, DE Kingsmill, BE Martner and FM Ralph (2005), The utility of X-band polarimetric radar for quantitative estimates of rainfall parameters. J. Hydrometeorol. Version: 1 6 (3) 248-262, issn: 1525-755X, ids: 943ZB, doi: 10.1175/JHM424.1
  • BOE, BA, JL STITH, PL SMITH, JH HIRSCH, JH HELSDON, AG DETWILER, HD ORVILLE, BE MARTNER, RF REINKING, RJ MEITIN and RA BROWN (1992), THE NORTH-DAKOTA THUNDERSTORM PROJECT - A COOPERATIVE STUDY OF HIGH-PLAINS THUNDERSTORMS. Bull. Amer. Meteorol. Soc. Version: 1 73 (2) 145-160, issn: 0003-0007, ids: HE464, doi: 10.1175/1520-0477(1992)073<0145:TNDTPA>2.0.CO;2