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.

Highlights

  • Rice cultivation is a major source of anthropogenic methane and a prime target of greenhouse gas mitigation efforts (Tian et al, 2016; Smith et al, 2008)

  • 3.1 Methane emissions and the uncertainties derived from field measurements

  • Among the 495 methane flux measurements, 184 (37 % of all cases) came from paddies that were continuously flooded during the entire rice growing period, 50 (10 % of all cases) came from paddies with single midseason drainage, and 261 (53 % of all cases) came from paddies under multi-drainage

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Summary

Introduction

Rice cultivation is a major source of anthropogenic methane and a prime target of greenhouse gas mitigation efforts (Tian et al, 2016; Smith et al, 2008). Methane fluxes in rice paddies varied extensively with environmental and agronomic factors Certain factors, such as rice biomass (Bachelet and Neue, 1993), organic matter input (Kern et al, 1995), water management (Khosa et al, 2011; Mishra et al, 1997), paddy soil properties (Yao et al, 1999; Gaunt et al, 1997), climate (Sass et al, 1991), and rice varieties (Su et al, 2015; Ding et al, 1999), have been recognized as having significant impacts on methane emissions. Because so many factors affect the production, oxidation, and emission of methane from rice cultivation, the observed methane fluxes varied extensively both spatially and temporally

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