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Data points intensively sampling 46 North American biomes were used to predict the geographic distribution of biomes from climate variables using the Random Forests classification tree. Techniques were incorporated to accommodate a large number of classes and to predict the future occurrence of climates beyond the contemporary climatic range of the biomes. Errors of prediction from the statistical model averaged 3.7%, but for individual biomes, ranged from 0% to 21.5%. In validating the ability of the model to identify climates without analogs, 78% of 1528 locations outside North America and 81% of land area of the Caribbean Islands were predicted to have no analogs among the 46 biomes. Biome climates were projected...
This project developed a soil vulnerability index and map indicating where forest cover will be most affected by climate change. Using this map, researchers developed a greater understanding of potential changes in soil moisture and temperature regimes under future climate conditions. They then evaluated how this information could be used to improve vegetation models across the landscape. They compared the results of different modeling approaches to the soil vulnerability map, synthesized the state of knowledge and uncertainty, and introduced management implications for action.
Categories: Data, Project; Types: Map Service, OGC WFS Layer, OGC WMS Layer, OGC WMS Service; Tags: 2011, AK, AK, AK, AK, All tags...
Climate change has significant effects on critical ecosystem functions such as carbon and water cycling. Vegetation and especially forest ecosystems play an important role in the carbon and hydrological cycles. Vegetation models that include detailed belowground processes require accurate soil data to decrease uncertainty and increase realism in their simulations. The MC2 DGVM uses three modules to simulate biogeography, biogeochemistry and fire effects, all three of which use soil data either directly or indirectly. This study includes a correlation analysis of the MC2 model to soil depth by comparing a subset of the model’s carbon and hydrological outputs using soil depth data of different scales and qualities....
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Example simulation of maximum aboveground live carbon for Wind Cave National Park area in year 2000. Simulated by the dynamic global vegetation model MC1.


    map background search result map search result map Pacific Northwest Forest Soils:  Creating a Soil Vulnerability Index to Identify Drought Sensitive Areas Simulated Aboveground Live Tree Carbon for Wind Cave National Park North American vegetation model data for land-use planning in a changing climate: Simulated Aboveground Live Tree Carbon for Wind Cave National Park Pacific Northwest Forest Soils:  Creating a Soil Vulnerability Index to Identify Drought Sensitive Areas North American vegetation model data for land-use planning in a changing climate: