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Abstract The increasing atmospheric nitrous oxide (N 2 O) concentration stems from the development of agriculture. However, N 2 O emissions from global rice‐based ecosystems have not been explicitly and systematically quantified. Therefore, this study aims to estimate the spatiotemporal magnitudes of the N 2 O emissions from global rice‐based ecosystems and determine different contribution factors by improving a process‐based biogeochemical model, TRIPLEX‐GHG v2.0. Model validation suggested that the modeled N 2 O agreed well with field observations under varying management practices at daily, seasonal, and annual steps. Simulated N 2 O emissions from global rice‐based ecosystems exhibited significant increasing trends from 0.026 ± 0.0013 to 0.18 ± 0.003 TgN yr −1 from 1910 to 2020, with ∼69.5% emissions attributed to the rice‐growing seasons. Irrigated rice ecosystems accounted for a majority of global rice N 2 O emissions (∼76.9%) because of their higher N 2 O emission rates than rainfed systems. Regarding spatial analysis, Southern China, Northeast India, and Southeast Asia are hotspots for rice‐based N 2 O emissions. Experimental scenarios revealed that N fertilizer is the largest global rice‐N 2 O source, especially since the 1960s (0.047 ± 0.010 TgN yr −1 , 35.24%), while the impact of expanded irrigation plays a minor role. Overall, this study provides a better understanding of the rice‐based ecosystem in the global agricultural N 2 O budget; further, it quantitively demonstrated the central role of N fertilizer in rice‐based N 2 O emissions by including rice crop calendars, covering non‐rice growing seasons, and differentiating the effects of various water regimes and input N forms. Our findings emphasize the significance of co‐management of N fertilizer and water regimes in reducing the net climate impact of global rice cultivation. , Plain Language Summary Nitrous oxide (N 2 O) is a greenhouse gas with ∼300 times greater effect on climate warming than carbon dioxide. Global croplands represent the largest source of anthropogenic N 2 O emissions. However, the contribution of global rice‐based cropping ecosystems to the N 2 O budget remains largely uncertain because of inconsistent observed results. Inspired by the increasing availability of reliable global data sets, we improved and applied a process‐based biogeochemical model by describing the dynamics of various microbial activities to simulate N 2 O emissions from rice‐based ecosystems on a global scale. Model simulations showed that 0.18 million tons of N 2 O‐N were emitted from global rice‐based N 2 O emissions in the 2010s, which was five times larger than that in the 1910s. In the context of regional contribution, southern China, northern India, and Southeast Asia are responsible for more than 80% of the total emissions during 1910–2020. Results suggest that N fertilizer is the most important rice‐N 2 O source quantitively and that increasing irrigation exerts a buffering effect. This study confirmed the potential mitigating effect of co‐managing N fertilizer and irrigation on mitigating rice‐based N 2 O emissions globally. , Key Points N 2 O emissions from global rice‐based ecosystem increased from 0.026 to 0.18 TgN yr −1 between 1910 and 2020 Irrigated rice‐based ecosystems showed larger N 2 O fluxes than rainfed rice globally due to higher N fertilizer use and frequent aerations N fertilizer represents the largest N 2 O source, and co‐management of N fertilizer and flooding regimes is important for mitigation

Abstract Process‐based land surface models are important tools for estimating global wetland methane (CH 4 ) emissions and projecting their behavior across space and time. So far there are no performance assessments of model responses to drivers at multiple time scales. In this study, we apply wavelet analysis to identify the dominant time scales contributing to model uncertainty in the frequency domain. We evaluate seven wetland models at 23 eddy covariance tower sites. Our study first characterizes site‐level patterns of freshwater wetland CH 4 fluxes (FCH 4 ) at different time scales. A Monte Carlo approach was developed to incorporate flux observation error to avoid misidentification of the time scales that dominate model error. Our results suggest that (a) significant model‐observation disagreements are mainly at multi‐day time scales (<15 days); (b) most of the models can capture the CH 4 variability at monthly and seasonal time scales (>32 days) for the boreal and Arctic tundra wetland sites but have significant bias in variability at seasonal time scales for temperate and tropical/subtropical sites; (c) model errors exhibit increasing power spectrum as time scale increases, indicating that biases at time scales <5 days could contribute to persistent systematic biases on longer time scales; and (d) differences in error pattern are related to model structure (e.g., proxy of CH 4 production). Our evaluation suggests the need to accurately replicate FCH 4 variability, especially at short time scales, in future wetland CH 4 model developments. , Plain Language Summary Land surface models are useful tools to estimate and predict wetland methane (CH 4 ) flux but there is no evaluation of modeled CH 4 flux error at different time scales. Here we use a statistical approach and observations from eddy covariance sites to evaluate the performance of seven wetland models for different wetland types. The results suggest models have captured CH 4 flux variability at monthly or seasonal time scales for boreal and Arctic tundra wetlands but failed to capture the observed seasonal variability for temperate and tropical/subtropical wetlands. The analysis suggests that improving modeled flux at short time scale is important for future model development. , Key Points Significant model‐observation disagreements were found at multi‐day and weekly time scales (<15 days) Models captured variability at monthly and seasonal time (42–142 days) scales for boreal and Arctic tundra sites but not for temperate and tropical sites The model errors show that biases at multi‐day time scales may contribute to persistent systematic biases on longer time scales

Abstract In forest ecosystems, the majority of methane (CH4) research focuses on soils, whereas tree stem CH4 flux and driving factors remain poorly understood. We measured the in situ stem CH4 flux using the static chamber–gas chromatography method at different heights in two poplar (Populus spp.) forests with separate soil textures. We evaluated the relationship between stem CH4 fluxes and environmental factors with linear mixed models and estimated the tree CH4 emission rate at the stand level. Our results showed that poplar stems were a net source of atmospheric CH4. The mean stem CH4 emission rates were 97.51 ± 6.21 μg·m−2·h−1 in Sihong and 67.04 ± 5.64 μg·m−2·h−1 in Dongtai. The stem CH4 emission rate in Sihong with clay loam soils was significantly higher (P &lt; 0.001) than that in Dongtai with sandy loam soils. The stem CH4 emission rate also showed a seasonal variation, minimum in winter and maximum in summer. The stem CH4 emission rate generally decreased with increasing sampling height. Although the differences in CH4 emission rates between stem heights were significant in the annual averages, these differences were driven by differences observed in the summer. Stem CH4 emission rates were significantly and positively correlated with air temperature (P &lt; 0.001), relative humidity (P &lt; 0.001), soil water content (P &lt; 0.001) and soil CH4 flux (P &lt; 0.001). At these sites, the soil emitted CH4 to the atmosphere in summer (mainly from June to September) but absorbed CH4 from the atmosphere during the other season. At the stand level, tree CH4 emissions accounted for 2–35.4% of soil CH4 uptake. Overall, tree stem CH4 efflux could be an important component of the forest CH4 budget. Therefore, it is necessary to conduct more in situ monitoring of stem CH4 flux to accurately estimate the CH4 budget in the future.

Atmospheric deposition of nitrogen (N) and phosphorus (P) far exceeding the pre-industrial levels have the potential to change carbon (C) dynamics in northern peatlands. However, the responses of soil C concentration and organo-chemical composition to different rates and durations of nutrient enrichment are still unclear. Here, we compared the short- (3 years) and long-term (10 years) effects of N and P fertilizations on the physicochemical properties of peat and porewater in a bog-fen complex in northern China. Our results showed that the short-term fertilization increased Sphagnum moss cover, while the expansion of vascular plants was observed owing to the long-term fertilization. The preserved soil C did not vary considerably after the short- and long-term fertilizations. The harsh soil conditions may impede the decomposition of organic matters by soil microorganisms during the short-term fertilization. For the long-term fertilization, the input of high-phenolic litters owing to vascular plant expansion likely exerted an important control on soil C dynamics. These processes constrained the variation in soil C concentrations when the addition rate and cumulative amount of external N and P increased, which will advance our understanding and prediction of the resilience of soil C storage to imbalanced nutrient enrichment of N and P in northern peatlands.