High-resolution forest carbon modeling for climate mitigation planning
over the 11-state RGGI+ region, USA
Abstract
Climate mitigation planning requires accurate information on forest
carbon dynamics. Forest carbon monitoring and modeling systems need to
step beyond the traditional Monitoring, Reporting, and Verification
(MRV) framework of current forest cover and carbon stock. They should be
able to project potential future carbon stocks with high accuracy and
high spatial resolution over large policy-relevant spatial domains.
Previous efforts have demonstrated the possibility and value of
combining a process-based ecosystem model (Ecosystem Demography, ED),
high-resolution (1-meter) lidar and NAIP data, field inventory data, and
meteorology and soil properties in a prototype carbon monitoring and
modeling system developed for the state of Maryland. Here we present
recent work on expanding the Maryland prototype to a 10x larger domain,
namely the Regional Greenhouse Gas Initiative (RGGI+) domain consisting
of the states of Maryland, Delaware, Pennsylvania, New York, New Jersey,
Rhode Island, Connecticut, Massachusetts, Vermont, New Hampshire, and
Maine. The system expansion includes an updated version of the ED
ecosystem model, improved initialization strategy, and expanded Cal/val
approach. High-resolution wall-to-wall maps of current aboveground
carbon, carbon sequestration potential, carbon sequestration potential
gap, and time to reach sequestration potential are provided at 90m
resolution across the RGGI+ domain. Total forest aboveground carbon
sequestration potential gap is estimated to be over 2,300 Tg C for the
RGGI+ region, about 1.5 times of contemporary aboveground carbon stock.
States and counties exhibit variations in carbon sequestration potential
gap, implying different policy planning for future
afforestation/reforestation and forest conservation activities. Here we
present the details of this new carbon monitoring and modeling system as
well as regional results, including evaluations of our estimates against
USFS Forest Inventory and Analysis (FIA) data, multiple wall-to-wall AGB
maps, and state-wide and county-wide future carbon sequestration
potential over time.