Cumulative effects, defined by the Canadian Council of Ministers of the Environment as “a change in the environment caused by multiple interactions among human activities and natural processes that accumulate across space and time”[1], are of broad scientific interest [2, 3, 4, 5, 6, 7] and in Canada are at the forefront of scientific and technical investigations in the context of environmental impact assessments (EIAs) [8, 9], policy development [10, 11, 12] and monitoring approaches [13, 14]. These considerations apply at both federal and provincial levels in Canada [15]. As of 1995, cumulative effect assessments are required for federal impact assessments [16] and as of 1993 for EIAs in Alberta [17, 18]. These assessments, whether conducted at project or regional scales, require the identification of a suite of important issues for the assessment area and the associated valued ecosystem components (VECs) defined as “components of the environment (biophysical and human) that are identified as important ecologically, socially, or economically and are the focus of attention in environmental assessment” [15]. In the pilot study described here, the issue of interest is climate change and the associated VECs of interest are carbon (C) storage and greenhouse gas (GHG) emissions and removals from the upland forested land-base as affected by anthropogenic (e.g., oil and gas development, forestry) and natural (e.g., wildfire, insect outbreaks) disturbances over time (1984–2012).
In 2003, a Federal-Provincial-Territorial Committee on Climate Change and Environmental Assessment released a guidance document [19] to support the integration of climate change considerations into environmental assessments in Canada [20]. The guidance describes two approaches to incorporating climate change in an environmental assessment; effects of a project on climate change (mainly contributions to GHG emissions), and effects of climate change on the project. Greenhouse gas emissions should be included in both project level assessments [19] and regional strategic environmental assessments [15] that consider climate change but, because GHGs are transboundary and important to a global environmental issue (i.e., climate change), their importance is greatest for regional strategic assessments.
Under Canada’s Impact Assessment Act (IAA), the assessment of a designated project must consider any cumulative effects that are likely to result from the project, in combination with other physical activities that have been or will be carried out [21].The IAA also requires that impact assessments of designated projects take into account the extent to which the effects of the project hinder or contribute to the Government of Canada’s ability to meet its climate change commitments. The Government of Canada published a strategic assessment of climate change which outlines GHG and climate change information requirements for projects under the IAA [22].
Canada is considered a leader in recognizing climate change considerations in an EIA [23] and guidance documents state that practitioners should seek to describe the project's direct and indirect GHG emissions and related effects, including possible large-scale impacts on C 'sinks' (e.g., impact on forests) [19]. This is highly relevant in the oil sands region (OSR) because it is located in the heart of Canada’s large boreal forest that plays a significant role in the national and global GHG balance [24]. If boreal forest C dynamics are significantly altered by the cumulative effects of anthropogenic and natural disturbances, then these disturbances are a major contributor to the GHG emissions and C storage VECs and should be included in EIAs in the region. Typically, EIAs considering climate change impacts include industrial emissions (e.g., [25, 26]) because they are the largest source of GHG emissions associated with a project, but they typically do not include changes to the forest that also can affect the GHG balance and contribute to cumulative effects. In cases where contributions to GHGs from the forest land-base have been included in an assessment, the approach to estimation has been simplified, and has not included changes over time (years) or accounting for years in which the forest land remains un-forested, and therefore does not take up C from the atmosphere (e.g., [27]). Although tools have not been specifically developed for EIAs to include cumulative effects on forest C (and therefore GHG emissions and C stocks) here we demonstrate how the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) [28], an existing forest C modeling framework, can be used to quantify the cumulative effects of anthropogenic and natural disturbances on the forest land-base and their effects on GHG emissions, the net C balance, and C storage.
The CBM-CFS3 [28, 29] is a recommended resource for EIA practitioners [19] and is the core model used in Canada’s National Forest C Monitoring Accounting and Reporting System [30, 31]. The CBM-CFS3 is consistent with the Intergovernmental Panel on Climate Change (IPCC) guidelines for estimating GHG emissions and removals from land use, land-use change and forestry (see Kurz et al. [28]). It is an annual time-step, stand-level, upland forest C model that is driven by forest inventory and merchantable yield curves commonly available in the forest sector. Non-biomass pools (e.g., snags, downed wood, litter and soil) are modelled using turnover and decomposition functions, and emissions estimation is based on C stock changes. The CBM-CFS3 simulates natural (e.g., wildfire, insect damage) and anthropogenic (e.g., forest harvesting, land-use change) disturbance effects by using a disturbance matrix (DM) [32, 33] that defines the proportion of C in a pool that is transferred to a downstream pool(s), to the atmosphere, or to harvested wood products as a result of a disturbance in the year that the disturbance occurs. Disturbance matrices are well developed for stand replacing wildfires, insect disturbance events and harvesting, but are generalized for oil and gas activities [29, 30]. Therefore, DMs for the common oil and gas exploration and development disturbance types for upland forests were developed as part of this pilot study. These refined DMs (described fully in the Results section [Energy sector disturbances] and in Appendix I) more accurately describe the variation in the impacts of different activities on existing C stocks in biomass, dead organic matter and soil C pools.
The availability of large volumes of remotely-sensed data and advances in computer science have enabled the development of a spatially-explicit and scalable version of the CBM-CFS modelling framework. By facilitating the integration of data from multiple sources, a spatially-explicit approach increases transparency, accuracy, consistency, completeness and comparability of estimates. The new Generic Carbon Budget Model (GCBM) builds on the science of the CBM-CFS3 but uses a new computing approach the enables the simulation of C dynamics of landscapes comprising millions of pixels. Prior to the development of GCBM, the CBM-CFS3 used a spatially-referenced approach, where the forest inventory data were associated with polygons of varying size, representing timber and land management regions, with no specific knowledge of where the forest stands or disturbances were located within the region. The spatially-explicit framework of the GCBM allows for grid-based modelling at a scale determined by the user. A spatially-explicit modelling prototype leading to the development of the GCBM has been applied at the scale of photo plots to assess C dynamics on agricultural lands reverting to woody land in Ontario [34], the scale of a watershed to assess the effect of reservoir expansion in British Columbia [35] and the scale of a region to test the integration of spatially-explicit Landsat derived data layers into C accounting with the CBM-CFS3 [36]. The GCBM is suitable for modelling fine-scale oil and gas disturbances in conjunction with coarse-scale disturbance effects from forest harvesting and wildfire over a large landscape area.
The main objective of this pilot study is to demonstrate the value of using an existing tool (GCBM) in providing outputs useful for EIAs by (1) developing and implementing methods that integrate spatially-explicit data on natural and anthropogenic disturbances derived from remote sensing time series and other data sources within a spatially-explicit modelling framework (GCBM) and (2) estimating the cumulative effects of multiple types of oil and gas disturbances, as well as disturbances from harvesting, wildfire, and insect disturbances on the C balance, GHG emissions and removals, and C storage in the study area within the OSR.