Example of unsupervised classification of microtopographic elevation for the Seney, MI WET site (top panel) using k-means clustering (middle panel).Black, grey, and white classifications correspond with high-, intermediate-, and low elevation classifications.Microtopography was classified using three clusters based on a post hoc analysis of elevation distributions by Gaussian mixture models.The lower panel shows the distribution of height in the high-(solid), intermediate-(dot-dashed), and low-(dashed) elevation classifications.We term these microtopographic classes as high hummock, low hummock, and hollow/lawn.
The hummock-hollow classification framework used to categorize peatland ecosystem microtopography is pervasive throughout peatland experimental designs and current peatland ecosystem modelling approaches. However, identifying what constitutes a representative hummock-hollow pair within a site and characterizing hummock-hollow variability within or between peatlands remains largely unassessed. Using structure-from-motion (SfM), high resolution digital elevation models (DEM) of hummock-hollow microtopography were used to: 1) examine how much area needs to be sampled to characterize site-level microtopographic variation; and 2) examine the potential role of microtopographic shape/structure on biogeochemical fluxes using data from 9 northern peatlands. This data set is comprised of plot DEMs, supporting data, and the script used to analyze data and produce figures presented in the manuscript submitted to Biogeosciences Discussion "ASSESSING THE PEATLAND HUMMOCK-HOLLOW CLASSIFICATION FRAMEWORK USING HIGH-RESOLUTION ELEVATION MODELS: IMPLICATIONS FOR APPROPRIATE COMPLEXITY ECOSYSTEM MODELLING".
This is the dataset, that can be used to reproduce the results from the following paper: "A classification scheme to determine wildfires from the satellite record in the cool grasslands of southern Canada: considerations for fire occurrence modelling and warning criteria". The Landsat Images are a series of pngs obtained from https://landbrowser.airc.aist.go.jp/hotarea/ representing agricultural fires.
This study applied the Canadian Model for Peatlands (CaMP) to 63.9 million hectares of peatlands within boreal and temperate ecozones of Canada to assess the trends in atmospheric carbon (C) emissions and removals and C sequestration over 30 years (1990–2019). The CaMP modelled net ecosystem productivity (NEP) for peatlands within the study area indicated a net C sink at an annual mean rate of 30.9 Mt C y−1 (48.4 g C m−2 y−1). Net Biome Productivity (NBP), which accounts for losses of carbon due to wildfire, reduced the C sink to 19.0 Mt C y−1 (29.8 g C m−2 y−1). On an area-weighted basis, the Hudson Plains and the Boreal Plains had the highest NBP (34.9 and 34.0 g C m−2 y−1, respectively) and the Atlantic Maritime and Boreal Shield West had the lowest (25.3 and 24.6 g C m−2 y−1 respectively), with the Boreal Shield East having intermediate NBP (27.5 g C m−2 y−1). NBP was highest in peatlands with forest cover, rising with increasing nutrient status (bog < poor fen < rich fen). These modelled values compare well with long-term carbon accumulation rates found in the literature for Canadian peatlands ranging from 6 to 70 g C m−2 y−1. While most years peatlands were a net sink of C, years with extensive fires resulted in peatlands being a small net source of C. The study highlighted that forested peatlands were important in driving the C sequestration sink but were also sensitive to climate warming due to high rates of soil CO2 emission and large wildfire C emissions. This highlights an important, yet vulnerable role these forested peatlands play in Canada's national greenhouse gas accounting. While this research is the first to produce estimates of C sequestration and greenhouse gas emission and removal rates across such a large area of Canada, further research is required across peatland types and ecozones to improve parameterization, validation, and process representations. Our results stress the importance of ecozone-specific analyses and accounting for infrequent large fire years and fire risk in land management policy and carbon accounting.
Abstract Northern and tropical peatlands represent a globally significant carbon reserve accumulated over thousands of years of waterlogged conditions. It is unclear whether moderate drying predicted for northern peatlands will stimulate burning and carbon losses as has occurred in their smaller tropical counterparts where the carbon legacy has been destabilized due to severe drainage and deep peat fires. Capitalizing on a unique long-term experiment, we quantify the post-wildfire recovery of a northern peatland subjected to decadal drainage. We show that the moderate drop in water table position predicted for most northern regions triggers a shift in vegetation composition previously observed within only severely disturbed tropical peatlands. The combined impact of moderate drainage followed by wildfire converted the low productivity, moss-dominated peatland to a non-carbon accumulating shrub-grass ecosystem. This new ecosystem is likely to experience a low intensity, high frequency wildfire regime, which will further deplete the legacy of stored peat carbon.
Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in real time for global air-quality forecasting. The model uses a bottom-up approach, based on remotely-sensed hotspot locations and global databases linking burned area per hotspot to ecosystem-type classification at a 1-km resolution. Unlike other global forest fire emissions models, GFFEPS provides dynamic estimates of fuel consumption and fire behaviour based on the Canadian Forest Fire Danger Rating System. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3-hour time steps (in contrast to non-dynamic models that use fixed consumption rates and require collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in near-real-time forecasting applications as well as historical simulations for which data are available. A study was conducted running GFFEPS through a six-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model distinguished grass-dominated regions from forested, while also showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including GFAS, GFED4.1s and FINN1.5/2.5. GFFEPS estimated values lower than GFAS/GFED (80 %/74 %), and estimated values similar to FINN1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. An effort is underway to validate the model, with further developments and improvements expected.
