The sensitivity of tropical forests to climate variability and change in Bolivia

This thesis studies the sensitivity of wet and dry tropical forests to climate variability and change in Bolivia. The complexity of the problem was approached by integrating information from climate observations, climate projections, biomass measurements, remote sensing data, and a dynamic vegetation model (LPJ-GUESS). Meteorological observations (1960 to 2009) revealed a warming trend (0.1°C per decade), as well as a drying trend since 1985 (-4% during the wet, and -10% during the dry season). Anomalies were significantly affected by the two climate modes Pacific Decadal Oscillation (PDO), and El Niño Southern Oscillation (ENSO). A multi-model ensemble of 35 general circulation models from the CMIP3 and CMIP5 archive projected significant increases in temperature (2.5 to 5.9°C), but conflicting changes of annual rainfall for the end of this century. Climate observations were then used to drive LPJ-GUESS in order to simulate Bolivia’s present-day potential vegetation as a baseline for assessing climate change impacts. Results were compared to biomass measurements from 819 plots, and to remote sensing data. Using regional parameter values for allometric relations, specific leaf area, wood density, and disturbance interval, a realistic spatial transition from the evergreen Amazon to the deciduous dry forest was simulated. The model reproduced reasonable values for seasonal leaf abscission, vegetation carbon (cv), and forest fires. Modeled Gross Primary Productivity (GPP) and remotely sensed Normalized Difference Vegetation Index (NDVI) showed that dry forests were more sensitive to rainfall anomalies than wet forests. Modeled GPP was positively correlated to ENSO in the Amazon, and negatively correlated to consecutive dry days. Decreasing rainfall trends were simulated to decrease GPP in the Amazon. Using the same model configuration, the impacts of climate change were then assessed for two contrasting projections (wet and dry), revealing a large range of uncertainty. The loss of cv simulated under a dry scenario was primarily driven by a reduction in GPP, and secondarily by enhanced emissions from fires. In the wet forest, less precipitation and higher temperatures equally reduced cv, while in the dry forest, the impact of precipitation was dominating. The temperature-related reduction of cv was mainly due to a decrease in photosynthesis, and only to lesser extent because of more autotrophic respiration and less stomatal conductance as a response to an increasing atmospheric demand. Tropical dry forests were simulated to virtually disappear, regardless of the potential fertilizing effect of rising concentrations of atmospheric carbon dioxide, suggesting a higher risk for forest loss along the drier southern fringe of the Amazon. The possibility of a forest dieback poses a serious threat to biodiversity, as well as rural livelihoods. Adaption measures for Bolivian forests should prioritize actions related to deforestation, fire management, grazing, and water resource management. Progress in climate change impact assessments for the Amazon will greatly depend on our ability to reduce uncertainties of future rainfall projections, possibly through physical reasoning rather than pure ensemble statistics. Otherwise, large uncertainties will remain for climate change impact assessments in the Amazon, despite extensive model improvements.