Matthew W. Rossi

On the geochemical heterogeneity of rivers draining into the straits and channels of the Canadian Arctic Archipelago

Ten rivers across northern Canada and the Canadian Arctic Archipelago (CAA) were sampled during spring 2014 and summer 2015 to investigate their geochemical heterogeneity for comparison against larger North American (i.e., Mackenzie and Yukon Rivers) and Siberian rivers. In general, rivers draining the western and/or northern regions of the study area have higher solute concentrations and lower 87Sr/86Sr ratios compared to rivers draining the eastern and/or southern regions. The inorganic geochemical signatures largely reflect the bedrock geology, which is predominately carbonate in the western and/or northern regions and silicate in the eastern and/or southern regions. Riverine δ18O values primarily correlate with latitude, with only a few exceptions. Measurements of total alkalinity (TA) were combined with a regional analysis of bedrock geology and extrapolated to produce a range for the mean characteristic TA of rivers draining into the straits and channels of the CAA (628–819 µeq kg−1). Combining this estimate with contributions from the Mackenzie River yields a revised North American river runoff TA of 935–1182 µeq kg−1, which is much lower than that of the Mackenzie River (1540 µeq kg−1). This lower concentration suggests that TA may not be used to distinguish between North American and Siberian river contributions in regions such as Davis Strait.

Fracture density and grain size controls on the relief structure of bedrock landscapes

Fracture density and grain size controls on the relief structure of bedrock landscapes Roman A. DiBiase1, Matthew W. Rossi2, Alexander B. Neely1 1Department of Geosciences, Pennsylvania State University, University Park, PA 16802, USA 2Earth Lab, University of Colorado, Boulder, CO 80309, USA *To whom correspondence may be addressed: Roman A. DiBiase, rad22@psu.edu

Understanding and predicting extreme wildfires in the contiguous United States

Abstract Wildfires are becoming more frequent in parts of the globe, but predicting where and when wildfires occur remains difficult. To predict wildfire extremes across the contiguous United States, we integrate a 30 year wildfire record with meteorological and housing data in spatiotemporal Bayesian statistical models with spatially varying nonlinear effects. We compared different distributions for the number and sizes of large fires to generate a posterior predictive distribution based on finite sample maxima for extreme events (the largest fires over bounded spatiotemporal domains). A zero-inflated negative binomial model for fire counts and a lognormal model for burned areas provided the best performance. This model attains 99% interval coverage for the number of fires and 93% coverage for fire sizes over a six year withheld data set. Dryness and air temperature strongly predict extreme wildfire probabilities. Housing density has a hump-shaped relationship with fire occurrence, with more fires occurring at intermediate housing densities. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes should not be surprising, and that the contiguous United States may be on the verge of even larger wildfire extremes.Wildfires are becoming more frequent in parts of the globe, but predicting where and when extreme events occur remains difficult. To explain and predict wildfire extremes across the contiguous United States, we integrate a 30 year wildfire occurrence record with meteorological and housing data in spatiotemporal Bayesian models with spatially varying nonlinear effects. We compared models with different distributions for the number and sizes of large fires. A zero-inflated negative binomial model for fire counts and a lognormal model for burn areas provided the best performance. This model attains 99% interval coverage for the number of fires and 92% coverage for fire sizes over a five-year withheld data set. Dryness and air temperature strongly regulate wildfire risk, with precipitation and housing density playing weaker roles. Statistically, these drivers affect the chance of an extreme wildfire in two ways: by altering fire size distributions, and by altering fire frequency, which influences sampling from the tails of fire size distributions. We conclude that recent extremes that have burned hundreds of thousands of acres should not be surprising, and that the contiguous United States may be on the verge of experiencing even larger (million acre) wildfire extremes.