Abstract

  • LULCCs contribute one third to cumulative anthropogenic CO$_2$ emissions from 1850 to 2019
  • great important, high uncertainty
  • integrate new high-resolution LULCC dataset (HILDA+) into BLUE
  • lower $E_{LUC}$ compared to LUH2-based estimates
  • decreasing global $E_{LUC}$ trends instead of increasing
  • higher spatial resolution covers pristine-remaining areas better

1. Introduction

  • net CO2 flux from land-use and land-cover change ($E_{LUC}$) is key component of global C cycle
  • highly uncertain for many reasons
  • HILDA+: Historic Land Dynamics Assessment, 1/100 x 1/100 degrees resolution
  • allows BLUE to compare $E_{LUC}$ coming from different LULCC forcings, try to identify sources of uncertainty, candidates:
    • initialization time
    • spatial resolution to investigate role of successive transition
  • BLUE compuationally efficient, hence high resolution of HILDA+ can be used
  • 0.001 degrees would be even better for field-scale resolution of 1 ha
  • satellite data cannot be used directly because of mix of anthropogenic and environmental effects
  • goal: highlight spatial and temporal uncertainties in $E_{LUC}$ related to
    • LULCC reconstructions
    • resolution of LULCC forcing
    • initialization year

2. Methodology

  • includes C transfer to pools of different lifetimes
  • BLUE simulations with 3 different inputs (HILDA+ at 0.25 and 0.01 degrees, LUH2 at 0.25 degrees)
  • with HILDA+ 0.25 four different initialization years: 1900, 1920, 1940, 1960
  • more initialization dates with model HYDE3.2 based on LUH2
  • HILDA+ does not provide information and wood harvest and does not distinguish between primary and secondary land, hence preprocessing required

3. Land-use change emission based on HILDA+ and LUH2

  • divergent $E_{LUC}$ trends
  • gross fluxes smaller with HILDA+
  • lower resolution, higher component fluxes, lower resolution leads to much more in-cell transitions
  • inititialization year leads to small changes if initialized at least 60 yr earlier

4. Discussion

  • alignment of results depends on regions
  • disagreement of LULCC datasets since 2000 needs to be solved
  • implementation of shifting cultivation needs to be revised
  • spatial resolution has significant influence
  • effect of “successive transitions” probably also important for DGVMs and other BKMs
  • initializiation of 60 years prior to analyzed period seems sufficient

5. Conclusions

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