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This study uses a hydrologic‐balance model to evaluate the range of precipitation and temperature (P‐T) conditions required to sustain Lake Bonneville at two lake levels during the late Pleistocene. Intersection with a second set of P‐T curves determined from glacial modelling in the nearby Wasatch Mountains places tighter climatic constraints that suggest gradually increasing wetness from ~21 to 15 ka. Specifically, during the latter part of the Last Glacial Maximum (LGM) (~21–20 ka), Lake Bonneville approached its highest level under conditions roughly 9.5°C colder but only 7% wetter than modern. As the lake reached its pre‐flood Bonneville level (~18.2–17.5 ka), climate conditions were ~16% wetter and ~9°C colder than modern. Byca. 15–14.8 ka, Lake Bonneville abandoned the overflowing Provo level under conditions that were ~21% wetter and ~7°C cooler. These results suggest that regional LGM highstands were not caused by large increases in precipitation, but rather by a climatic optimum in which moderate wetness combined with depressed temperatures to create a positive hydrologic budget. Later highstands during Heinrich I from 17 to 15 ka were likely achieved under gradual increases in precipitation, prior to a transition to drier conditions after 15 ka.

Soil erosion diminishes agricultural productivity by driving the loss of soil organic carbon (SOC). The ability to predict SOC redistribution is important for guiding sustainable agricultural practices and determining the influence of soil erosion on the carbon cycle. Here, we develop a landscape evolution model that couples soil mixing and transport to predict soil loss and SOC patterns within agricultural fields. Our reduced complexity numerical model requires the specification of only two physical parameters: a plow mixing depth,L_p, and a hillslope diffusion coefficient,D. Using topography as an input, the model predicts spatial patterns of surficial SOC concentrations and complex 3D SOC pedostratigraphy. We use soil cores from native prairies to determine initial SOC‐depth relations and the spatial pattern of remote sensing‐derived SOC in adjacent agricultural fields to evaluate the model predictions. The model reproduces spatial patterns of soil loss comparable to those observed in satellite images. Our results indicate that the distribution of soil erosion and SOC in agricultural fields can be predicted using a simple geomorphic model where hillslope diffusion plays a dominant role. Such predictions can aid estimates of carbon burial and evaluate the potential for future soil loss in agricultural landscapes.