Uncertainties in the national inventory of methane emissions from rice cultivation: field measurements and modeling approaches

Abstract

Abstract. Uncertainties in national inventories originate from a variety of sources, including methodological failures, errors, and insufficiency of supporting data. In this study, we analyzed these sources and their contribution to uncertainty in the national inventory of rice paddy methane emissions in China and compared the differences in the approaches used (e.g., direct measurements, simple regressions, and more complicated models). For the 495 field measurements we collected from the scientific literature, the area-weighted 95 % CI (confidence interval) ranged from 13.7 to 1115.4 kg CH4 ha−1, and the histogram distribution of the measurements agreed well with parameterized gamma distributions. For the models, we compared the performance of methods of different complexity (i.e., the CH4MOD model, representing a complicated method, and two less complex statistical regression models taken from literature) to evaluate the uncertainties associated with model performance as well as the quality and accessibility of the regional datasets. Comparisons revealed that the CH4MOD model may perform worse than the comparatively simple regression models when no sufficient input data for the model is available. As simulated by CH4MOD with data of irrigation, organic matter incorporation, and soil properties of rice paddies, the modeling methane fluxes varied from 17.2 to 708.3 kg CH4 ha−1, covering 63 % of the range of the field measurements. When applying the modeling approach to the 10 km × 10 km gridded dataset of the model input variables, the within-grid variations, made via the Monte Carlo method, were found to be 81.2–95.5 % of the grid means. Upscaling the grid estimates to the national inventory, the total methane emission from the rice paddies was 6.43 (3.79–9.77) Tg. The fallacy of CH4MOD contributed 56.6 % of the total uncertainty, with the remaining 43.4 % being attributed to errors and the scarcity of the spatial datasets of the model inputs. Our analysis reveals the dilemma between model performance and data availability when using a modeling approach: a model with better performance may help in reducing uncertainty caused by model fallacy but increases the uncertainty caused by data scarcity since greater levels of input are needed to improve performance. Reducing the total uncertainty in the national methane inventory depends on a better understanding of both the complexity of the mechanisms of methane emission and the spatial correlations of the factors that influence methane emissions from rice paddies.

A comparative study of anthropogenic CH<sub>4</sub> emissions over China based on the ensembles of bottom-up inventories

“…As the largest coal producer worldwide, China's coal mine CH 4 emissions are still poorly quantified, and estimates vary significantly, from 14 to 28 Tg CH 4 yr −1 (Sheng et al, 2019). In addition, emissions from waste treatment are mainly focused on the total emissions of city-level or provincial wastewater in China (Du et al, 2018;Zhao et al, 2019). Emissions from Chinese landfills are estimated by Cai et al (2018) and Du et al (2017), but there remain gaps in spatial or temporal coverage.…”

Estimation of methane emissions based on crop yield and remote sensing data in a paddy field

“…The empirical regression method is useful for quantifying cumulative CH 4 emissions (CCE) and has the advantage of requiring fewer variables while providing high explanatory power. 13 This method has been used in different ecosystem research based on field experiments, for example, wetlands, 14 croplands, 15 and forests. 16 Wang et al 17 established several regression models to predict paddy GHG emission processes, such as CH 4 emission, oxidation, and plant-mediated transport, achieving high accuracy.…”

“…Additionally, due to complicated physicochemical formulas, the mechanistic models can act as a “black box” for the user. The empirical regression method is useful for quantifying cumulative CH 4 emissions (CCE) and has the advantage of requiring fewer variables while providing high explanatory power . This method has been used in different ecosystem research based on field experiments, for example, wetlands, croplands, and forests .…”

Environmental Indicator Principium with Case References to Agricultural Soil, Water, and Air Quality and Model‐Derived Indicators

“…A wide spectrum of models is in use for estimating environmental indicators, especially process‐based ones. All model‐predicted environmental indicators have a certain level of uncertainty, arising from our insufficient understanding and imperfect representation of the underlying processes involved, intentional simplifications of processes for practical considerations, the limited data available to drive the models, and the inherent randomness of natural systems (Giupponi, 1995; Radcliffe et al, 2015; Zhang et al, 2017). This uncertainty is intimately linked to the validity of model assumptions, quality of data input and data used for model evaluation, and how well the model parameters are estimated.…”

“…The persistent challenge in model development and improvement is to balance the complexity of the model between maximization of accuracy and practical considerations (e.g., ease of use, short run time). A recent study on uncertainty of national CH 4 emissions using a modeling approach revealed the dilemma between model performance and data availability: a model with better performance reduces uncertainty, but data scarcity can increase uncertainty (Zhang et al, 2017). Accordingly, it is important to select models with the required predictive accuracy, input data availability, while considering the spatiotemporal scales of the simulation (Sharpley, 2006).…”