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• W2502549309 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." @default.
• W2502549309 created "2016-08-23" @default.
• W2502549309 creator A5016135692 @default.
• W2502549309 creator A5053603075 @default.
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• W2502549309 date "2017-01-12" @default.
• W2502549309 modified "2023-10-11" @default.
• W2502549309 title "Uncertainties in the national inventory of methane emissions from rice cultivation: field measurements and modeling approaches" @default.
• W2502549309 cites W1021635124 @default.
• W2502549309 cites W109570235 @default.
• W2502549309 cites W1491219816 @default.
• W2502549309 cites W1506620195 @default.
• W2502549309 cites W1611404481 @default.
• W2502549309 cites W1802358548 @default.
• W2502549309 cites W1905469101 @default.
• W2502549309 cites W1971908985 @default.
• W2502549309 cites W1973333099 @default.
• W2502549309 cites W1974363385 @default.
• W2502549309 cites W1977624941 @default.
• W2502549309 cites W1981888630 @default.
• W2502549309 cites W1982748610 @default.
• W2502549309 cites W1986642247 @default.
• W2502549309 cites W1986881255 @default.
• W2502549309 cites W1988021924 @default.
• W2502549309 cites W1998810380 @default.
• W2502549309 cites W2004289518 @default.
• W2502549309 cites W2012503797 @default.
• W2502549309 cites W2013147911 @default.
• W2502549309 cites W2022496410 @default.
• W2502549309 cites W2023202112 @default.
• W2502549309 cites W2023317855 @default.
• W2502549309 cites W2023345736 @default.
• W2502549309 cites W2024053089 @default.
• W2502549309 cites W2024302921 @default.
• W2502549309 cites W2024941744 @default.
• W2502549309 cites W2033481223 @default.
• W2502549309 cites W2037808212 @default.
• W2502549309 cites W2040978908 @default.
• W2502549309 cites W2049143490 @default.
• W2502549309 cites W2049509270 @default.
• W2502549309 cites W2052233367 @default.
• W2502549309 cites W2052598506 @default.
• W2502549309 cites W2070076768 @default.
• W2502549309 cites W2074212128 @default.
• W2502549309 cites W2082194284 @default.
• W2502549309 cites W2082348901 @default.
• W2502549309 cites W2082986818 @default.
• W2502549309 cites W2083635532 @default.
• W2502549309 cites W2083854278 @default.
• W2502549309 cites W2087837082 @default.
• W2502549309 cites W2089792340 @default.
• W2502549309 cites W2094321179 @default.
• W2502549309 cites W2100490017 @default.
• W2502549309 cites W2106812540 @default.
• W2502549309 cites W2110315967 @default.
• W2502549309 cites W2113028642 @default.
• W2502549309 cites W2113910010 @default.
• W2502549309 cites W2122313513 @default.
• W2502549309 cites W2126201200 @default.
• W2502549309 cites W212855041 @default.
• W2502549309 cites W2128727222 @default.
• W2502549309 cites W2129229929 @default.
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• W2502549309 cites W2133339723 @default.
• W2502549309 cites W2136596893 @default.
• W2502549309 cites W2136751976 @default.
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• W2502549309 cites W2147862212 @default.
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• W2502549309 cites W2293062392 @default.
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• W2502549309 doi "https://doi.org/10.5194/bg-14-163-2017" @default.
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