Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process‐based model

Abstract

Process-based model analyses are often used to estimate changes in soil organic carbon (SOC), particularly at regional to continental scales. However, uncertainties are rarely evaluated, and so it is difficult to determine how much confidence can be placed in the results. Our objective was to quantify uncertainties across multiple scales in a processbased model analysis, and provide 95% confidence intervals for the estimates. Specifically, we used the Century ecosystem model to estimate changes in SOC stocks for US croplands during the 1990s, addressing uncertainties in model inputs, structure and scaling of results from point locations to regions and the entire country. Overall, SOC stocks increased in US croplands by 14.6 Tg C yr À1 from 1990 to 1995 and 17.5 Tg C yr À1 during 1995 to 2000, and uncertainties were AE 22% and AE 16% for the two time periods, respectively. Uncertainties were inversely related to spatial scale, with median uncertainties at the regional scale estimated at AE 118% and AE 114% during the early and latter part of 1990s, and even higher at the site scale with estimates at AE 739% and AE 674% for the time periods, respectively. This relationship appeared to be driven by the amount of the SOC stock change; changes in stocks that exceeded 200 Gg C yr À1 represented a threshold where uncertainties were always lower than AE 100%. Consequently, the amount of uncertainty in estimates derived from process-based models will partly depend on the level of SOC accumulation or loss. In general, the majority of uncertainty was associated with model structure in this application, and so attaining higher levels of precision in the estimates will largely depend on improving the model algorithms and parameterization, as well as increasing the number of measurement sites used to evaluate the structural uncertainty.

Uncertainties in estimating regional methane emissions from rice paddies due to data scarcity in the modeling approach

“…When partitioning the large region under consideration into spatially adjacent divisions, the within-cell variation must be accounted for first (King et al, 1991;van Bodegom et al, 2000;Ogle et al, 2003Ogle et al, , 2010). The baseline model estimate is usually established by running the model once in a cell.…”

“…1 represent procedures for estimating national methane emissions, and the hollow arrows describe the uncertainty assessments accompanying the model upscaling. Although many studies have demonstrated how to upscale a model to make regional estimations from various baseline scenarios (Matthews et al, 2000;Li et al, 2004;Ogle et al, 2010), the primary focus of the present study is the aggregation of the uncertainties in model estimations due to data scarcity.…”

“…It is preferable to partition the region so that the model inputs in the cells are as statistically independent of each other as possible (King et al, 1991;Ogle et al, 2003Ogle et al, , 2010. When data are scarce, however, the criterion of intercell independence may result in the partition of large cells, leading to a reduced level of spatial details.…”

“…After the Monte Carlo simulation is performed for a within-cell uncertainty analysis in each division, we face the problem of uncertainty upscaling. In the case of "independent" partitioning of the entire subject region, an independent random variable is assigned to depict variations in the model estimate for each division (IPCC, 2000;Ogle et al, 2010), the uncertainty upscaling can be quite simple, as explained by the statistical "law of large numbers". As previously noted, however, a paucity of data for some of the model variables and a small cell size may result in data sharing among divisions, which is problematic for the model variables that lack sufficient data to support fine-resolution partitioning.…”

“…To analyze the uncertainty due to errors in model inputs in each cell, the Monte Carlo simulation has been recognized as an effective method (IPCC, 2000), and it has been applied in many studies (Ogle et al, 2003(Ogle et al, , 2010Yu et al, 2012). Based on the probability distribution functions (PDFs) derived from measurements and/or a priori knowledge of the model inputs, the Monte Carlo method involves randomly and repeatedly drawing values from the PDFs to drive the model and produce varying model estimates.…”