4.5 Article

Lithological composition of the North American continent and implications of lithological map resolution for dissolved silica flux modeling

Journal

GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS
Volume 11, Issue -, Pages -

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1029/2010GC003259

Keywords

lithology; silica; North America; rock types; Earth system model

Funding

  1. DFG [EXC177, HA7742/6-1]
  2. Utrecht University

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Lithological maps are important tools for applications sensitive to physical or chemical attributes of rocks. They aggregate rock types into a limited set of classes. Most studies on river elementary fluxes derived from chemical weathering use local lithological maps, and some upscale their results to global scales, using global lithological maps. It remains unclear if those maps sufficiently represent the heterogeneous local lithological settings. To analyze this in more detail a new lithological map of North America containing 262,111 polygons is presented and compared to three global lithological maps. The impact of the differences is demonstrated using four models of dissolved silica mobilization from the terrestrial land system into river systems (DSi mobilization), calibrated using each map. According to the new map, North America is composed of 62.8% sediments, 14.8% plutonics, 13.3% metamorphics, and 7.2% volcanics. In all four maps, the total proportions of rock types are comparable for North America, but their spatial distribution differs largely. The calculated DSi mobilization differs weakly between the maps (-20% to +9%) compared to the results of the model calibrated on the new map for North America but strongly (-59% to +38%) for tributary regions of regional seas around North America. The map comparison suggests that lithological classes of different data sets may represent different attributes although they may share the same name. Applications (e.g., Earth system models) considering lateral weathering derived matter fluxes should use data derived from different lithological source data sets with care.

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