Modelbased Tier 3 inventories

Model-based inventories are developed using empirical, process-based or other types of advanced models. It is good practice to have independent measurements to confirm that the model is capable of estimating emissions and removals in the source categories of interest (Prisley and Mortimer, 2004). In general, seven steps are used to implement a Tier 3 model-based inventory (Figure 2.7).

Step 1. Select/develop a model for calculating the stock changes and/or greenhouse gas emissions. A model should be selected or developed that more accurately represents stock changes or non-CO2 greenhouse gas emissions than is possible with Tiers 1 and 2 approaches. As part of this decision, it is good practice to consider the availability of input data (Steps 3) and the computing resources needed to implement the model (Step 5).

Figure 2.7 Steps to develop a Tier 3 model-based inventory estimation system

Figure 2.7 Steps to develop a Tier 3 model-based inventory estimation system

Policy Factors

Step 2. Evaluation with calibration data. This is a critical step for inventory development in which model results are compared directly with measurements that were used for model calibration/parameterization (e.g., Falloon and Smith, 2002). Comparisons can be made using statistical tests and/or graphically, with the goal of demonstrating that the model effectively simulates measured trends for a variety of conditions in the source category of interest. It is good practice to ensure that the model responds appropriately to variations in activity data and that the model is able to report results by land-use category as per the conventions laid out in Chapter 3. Re-calibration of the model or modifications to the structure (i.e., algorithms) may be necessary if the model does not capture general trends or there are large systematic biases. In some cases, a new model may be selected or developed based on this evaluation. Evaluation results are an important component of the reporting documentation, justifying the use of a particular model for quantifying emissions in a source category.

Step 3. Gather spatio-temporal data on activities and relevant environmental conditions that are needed as inputs to a model. Models, even those used in Tiers 1 and 2 approaches, require specific input information in order to estimate greenhouse gas emissions and removals associated with a source category. These inputs may range from weather and soils data to livestock number, forest types, natural disturbances or cropping management practices. It is good practice for the input data to be consistent with spatio-temporal scale of the model (i.e., algorithms). For example, if a model operates on a daily time step then the input data should provide information about daily variation in the environmental characteristic or activity data. In some cases, input data may be a limiting factor in model selection, requiring some models to be discarded as inappropriate given the available activity and/or environmental data.

Step 4. Quantify uncertainties. Uncertainties are due to imperfect knowledge about the activities or processes leading to greenhouse gas fluxes, and are typically manifested in the model structure and inputs. Consequently, uncertainty analyses are intended to provide a rigorous measure of the confidence attributed to a model estimate based on uncertainties in the model structure and inputs, generating a measure of variability in the carbon stock changes or non-CO2 greenhouse gas fluxes. Volume 1, Chapter 3 provides specific guidance on appropriate methods for conducting these analyses. Additional information may also be provided for specific source categories later in this volume.

Step 5. Implement the model. The major consideration for this step is that there are enough computing resources and personnel time to prepare the input data, conduct the model simulations, and analyze the results. This will depend on the efficiency of the programming script, complexity of the model, as well as the spatial and temporal extent and resolution of the simulations. In some cases, limitations in computing resources may constrain the complexity and range of spatial or temporal resolution that can be used in implementing at the national scale (i.e., simulating at finer spatial and temporal scales will require greater computing resources).

Step 6. Evaluation with independent data. It is important to realise the difference between Steps 2 and 6. Step 2 involves testing model output with field data that were used as a basis for calibration (i.e., parameterization). In contrast, evaluation with independent data is done with a completely independent set of data from model calibration, providing a more rigorous assessment of model components and results. Optimally, independent evaluation should be based on measurements from a monitoring network or from research sites that were not used to calibrate model parameters. The network would be similar in principle to a series of sites that are used for a measurement-based inventory. However, the sampling does not need to be as dense because the network is not forming the basis for estimating carbon stock changes or non-CO2 greenhouse gas fluxes, as in a purely measurement-based inventory, but is used to check model results.

In some cases, independent evaluation may demonstrate that the model-based estimation system is inappropriate due to large and unpredictable differences between model results and the measured trends from the monitoring network. Problems may stem from one of three possibilities: errors in the implementation step, poor input data, or an inappropriate model. Implementation problems typically arise from computer programming errors, while model inputs may generate erroneous results if these data are not representative of management activity or environmental conditions. In these two cases, it is good practice for the inventory developer to return to either Steps 3 or 6 depending on the issue. It seems less likely that the model would be inappropriate if Step 2 was deemed reasonable. However, if this is the case, it is good practice to return to the model selection/development phase (Step 1).

During Step 2 that follows the selection/development step, it is good practice to avoid using the independent evaluation data to re-calibrate or refine algorithms. If this occurs, these data would no longer be suitable for independent evaluation, and therefore not serve the purpose for Step 6 in this inventory approach.

Step 7. Reporting and Documentation. It is good practice to assemble inventory results in a systematic and transparent manner for reporting purposes. Documentation may include a description of the model, summary of model input data sources, model evaluation results including sources of experiments and/or measurements data from monitoring network, stock change and emissions estimates and the interpretation of emission trends (i.e., contributions of management activities). QA/QC should be completed and documented in the report. For details on QA/QC, reporting and documentation, see the section dealing with the specific source category later in this volume, as well as information provided in Volume 1, Chapter 6.

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