Dana Parr

Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven with RCP8.5 climate projections from 19 GCMs

Future changes of terrestrial ecosystems due to changes in atmospheric CO2 concentration and climate are subject to a large degree of uncertainty, especially for vegetation in the Tropics. Here, we evaluate the natural vegetation response to projected future changes using an improved version of a dynamic vegetation model (CLM-CN-DV) driven with climate change projections from 19 global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The simulated equilibrium vegetation distribution under historical climate (1981–2000) has been compared with that under the projected future climate (2081–2100) scenario for Representative Concentration Pathway 8.5 (RCP8.5) to qualitatively assess how natural potential vegetation might change in the future. With one outlier excluded, the ensemble average of vegetation changes corresponding to climates of 18 GCMs shows a poleward shift of forests in northern Eurasia and North America, which is consistent with findings from previous studies. It also shows a general “upgrade” of vegetation type in the Tropics and most of the temperate zones, in the form of deciduous trees and shrubs taking over C3 grass in Europe and broadleaf deciduous trees taking over C4 grasses in Central Africa and the Amazon. LAI and NPP are projected to increase in the high latitudes, southeastern Asia, southeastern North America, and Central Africa. This results from CO2 fertilization, enhanced water use efficiency, and in the extra-tropics warming. However, both LAI and NPP are projected to decrease in the Amazon due to drought. The competing impacts of climate change and CO2 fertilization lead to large uncertainties in the projection of future vegetation changes in the Tropics.

Integrating Remote Sensing Data on Evapotranspiration and Leaf Area Index with Hydrological Modeling: Impacts on Model Performance and Future Predictions

AbstractUsing the Connecticut River basin as an example, this study assesses the extent to which remote sensing data can help improve hydrological modeling and how it may influence projected future hydrological trends. The dynamic leaf area index (LAI) derived from satellite remote sensing was incorporated into the Variable Infiltration Capacity model (VIC) to enable an interannually varying seasonal cycle of vegetation (VICVEG); the evapotranspiration (ET) data based on remote sensing were combined with ET from a default VIC simulation to develop a simple bias-correction algorithm, and the simulation was then repeated with the bias-corrected ET replacing the simulated ET in the model (VICET). VICET performs significantly better in simulating the temporal variability of river discharge at daily, biweekly, monthly, and seasonal time scales, while VICVEG better captures the interannual variability of discharge, particularly in the winter and spring, and shows slight improvements to soil moisture estimates. ...