Abstract:
For the past few decades, climate change has accelerated alteration in weather patterns at a global scale due to elevated greenhouse gas emissions. Factors such as increased atmospheric CO2 concentration and subsequent elevated temperatures and alterations in precipitation are believed to have affected vegetation function and carbon cycle on global scale. Currently, the most important terrestrial carbon sink is the vegetation. The Hindu Kush Himalayan (HKH) region has a vast territory with various natural ecosystems providing carbon sink capacity through forests, grasslands, farmlands and wetland ecosystems. Different Dynamic Global Vegetation Models (DGVMs) are used to simulate shifting of vegetation due to climate change. In this study, the impacts of climate change in terms of forest carbon fluxes and vegetation productivity changes are assessed for HKH using the DGVM Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS). LPJ-GUESS is, driven by the ensemble of three climate models participating in the CMIP5 database (IPSL-CM5A-MR, MPI-ESM-LR and CCSM4). The model was run from 1850-2100 for two climate change scenarios (RCP 2.6 & RCP 8.5). The first approach of this study was to evaluate the ability of the LPJ-GUESS model, as forced by climate from a selection of Global Climate Models, by comparing it with observation-based estimates of vegetation carbon and satellite-derived estimates of gross primary productivity (GPP) and net primary productivity (NPP). A moderate to weak agreement was found between the modelled and remote-based estimations. This could be attributed to the absence of missing processes such as nitrogen cycle. The second approach of this study was to assess the possible temporal and spatial trends of carbon flux and carbon pools across HKH. A reduction is found in modeled net biome productivity and vegetation carbon pool from 1951-2005 primarily due to land use change. The simulations showed that the HKH will switch from source to sink by the end of 21st century in RCP2.6 and RCP8.5 in all models. Improving representations of existing processes and incorporating missing processes are needed to refine DGVMs and better project future climate change. The overall temporal and spatial patterns of carbon pool and carbon flux derived in this study can be used with confidence as a basis for policy making.
Key Words: Climate change, Carbon sink, Dynamic global vegetation models, Terrestrial forest
