“Detecting people”

Wednesday, 16 December 2015: 12:05
2006 (Moscone West)
Almut Arneth1, Thomas Pugh1, Andreas Krause1, Anita Bayer1 and Mats Lindeskog2, (1)Karlsruhe Institute of Technology, Karlsruhe, Germany, (2)Lund university, Lund, Sweden
Land-use change (LUC) is known to significantly affect biogeochemical cycles as well as surface energy partitioning – with important implications, ranging from understanding present-day measurements, to simulations of climate change and impacts on ecosystems, to assessments of the mitigation potential of land-based mitigation policies on ecosystems. When connecting observations of surface-atmosphere interactions and modelling at different scales, two important issues in this context are: legacy effects (e.g., to what degree and for how long does past LUC at a given location affect vegetation structure, CO2 fluxes and carbon pools), and sub-grid variability of the land-use change per se (e.g., whether bi-directional information about changes are taken into consideration). Both are important when bridging between scales (in time and in space) to enhance long-term observation networks.

This contribution to the session will be very much from a process-based modelling perspective. Using a second generation dynamic global vegetation model we will show how different land-use histories impact vegetation and soil recovery (carbon pool-size, fluxes) differently, depending on the type of previous land-use, its length, and on the type of biome. We also study the difference between “gross” and “net” LUC accounting for simulated carbon cycling. Two important aspects, considering the session’s objectives, are: 1) When establishing and developing observation networks, land-use history is key information for the interpretation of measured fluxes and needs to be collected and made available;, 2) Observation networks that “operate” solely in the natural science domain need to increasingly seek cooperation with socio-economic observations (such as land-use change, land management) in order to gain better understanding of coupled socio-ecological systems.