AGU Journal Highlights -- Aug. 14, 2007

Flood inundation dynamics for large remote flood plains are critical for understanding hydrological and biogeochemical processes in these important ecosystems. In particular, the Amazon River watershed, whose discharge comprises about 20 percent of total continental runoff, experiences seasonal heavy floods, effecting processes such as plant productivity, heavy metal accumulation, nutrient dynamics, and the carbon cycle. However, the remoteness of Amazon flood plains and wetlands make studying these locations difficult. Wilson et al. use topographic data from NASA's Shuttle Radar Topography Mission to model floodplain inundation on a section of the central Amazon. They then compare model results with sparsely-available flood gauge data and satellite-derived estimations of actual inundation extents. The authors find that their model is fairly accurate at high water levels, but that accuracy drops at low water levels due to initial model parameters and errors in the topographic data used. Nonetheless, they note that, with their method, predictions of floodplain dynamics can be used as quantitative inputs into biochemical and geomorphic studies requiring detailed hydrodynamic information.

Title: Modeling large-scale inundation of Amazonian seasonally flooded wetlands

Authors: Matthew Wilson: Department of Geography, University of Exeter, Exeter, U.K.;

Paul Bates: School of Geographical Sciences, University of Bristol, Bristol, U.K.;

Doug Alsdorf: School of Earth Sciences, Ohio State University, Columbus, Ohio, U.S.A.;

Bruce Fosberg: Instituto Nacional de Pesquisas da Amazonas, Manaus, Brazil;

Matthew Horritt: Halcrow Group Ltd., Wiltshire, U.K.;

John Melack: Bren School of Environmental Science and Management, University of California, Santa Barbara, California, U.S.A.;

Frederic Frappart, James Famiglietti: Department of Earth System Science, University of California, Irvine, California, U.S.A.

Source: Geophysical Research Letters (GRL) paper 10.1029/2007GL030156, 2007

14. Improving global vegetation models with satellite data on leaf area

To help monitor the effects of increased greenhouse gas emissions, models have been developed to quantify the spatial and temporal variations in carbon dioxide exchanges between the land surface and the atmosphere. However, many models show large differences in primary productivity and net carbon exchange, hindering scientists' ability to understand the current carbon cycle. To help resolve this, Demarty et al. study how global leaf area measurements from satellite data affect France's Dynamic Global Vegetation Model. They find that the added leaf-area data advances the model’s onset and end of the growing season in the high northern latitudes by 20 days and 40 days, respectively. This results in lower estimates of primary productivity, with large variations from one vegetation biome to another. The authors also show how measurements of primary productivity from ground-based monitoring stations can help minimize model errors. They note that the further use of satellite products could be substantially enhanced if more ground-based data were used to independently check model results.

Title: Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model