2. Research

Recent Publications

Madoff, R.D., Putkonen, J. (manuscript in prep), Regolith transport and winnowing dictate the grainsize distribution on hillslope

Madoff, R.D., Putkonen, J. (in submission), Climate-dependent global erosion rates for the past 21 ka

Madoff, R.D., Putkonen, J., 2016, Climate and hillslope degradation vary in concert; 85 ka to present, eastern Sierra Nevada, CA, USA, Geomorphology, v. 266, p. 33-40 doi:10.1016/j.geomorph.2016.05.010

Mack, G.H., Madoff, R.D., 2005, A test of models of fluvial architecture and paleosol development: Camp Rice Formation (Upper Pliocene-Lower Pleistocene), southern Rio Grande rift, New Mexico, USA, Sedimentology, v. 52, no.1, p. 191-211 doi.org/10.1111/j.1365-3091.2004.00687.x

Interests

My areas of research interests lie in Geomorphology and Sedimentology, specifically hillslope processes, sediment transport, and the effects of past climate on erosion rates and surface processes. I am interested in the effects of endogenic (tectonic) and exogenic (climatic) controls in a region that affect erosion at different temporal and spatial scales and how quantitative models of erosion through time can be used to discern the drivers of surface change and landscape evolution. I use a wide variety of field techniques to quantify sediment transport and surface change – from the sediment trap and pebble markers, and traditional geomorphic field methods, to terrestrial LiDAR. I use numerical modeling to hypothesize and test how different landscapes may respond to environmental changes, which is relevant study for dating landform surfaces and interpreting paleoecology, as well as for landscape management and mitigation of natural hazards. With research background from a M.S. thesis, focused in fluvial sedimentology and stratigraphy, I am also interested in studying sediment transport processes and depositional environments in various climates, past and present, in order to understand how the temporal scale of the controls and drivers on erosion have shaped landscapes and can be used to predict environmental changes.