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SummaryUpland forests are traditionally thought to be net sinks for atmospheric methane (CH 4 ). In such forests, in situ CH 4 fluxes on tree trunks have been neglected relative to soil and canopy fluxes.We measured in situ CH 4 fluxes from the trunks of living trees and other surfaces, such as twigs and soils, using a static closed-chamber method, and estimated the CH 4 budget in a temperate upland forest in Beijing.We found that the trunks of Populus davidiana emitted large quantities of CH 4 during July 2014-July 2015, amounting to mean annual emissions of 85.3 and 103.1 lg m À2 h À1 on a trunk surface area basis on two replicate plots. The emission rates were similar in magnitude to those from tree trunks in wetland forests. The emitted CH 4 was derived from the heartwood of trunks. On a plot or ecosystem scale, trunk CH 4 emissions were equivalent to c. 30-90% of the amount of CH 4 consumed by soils throughout the year, with an annual average of 63%. Our findings suggest that wet heartwoods, regardless of rot or not, occur widely in living trees on various habitats, where CH 4 can be produced.

[1] While numerous algorithms exist for predicting incident atmospheric long-wave radiation under clear (L clr ) and cloudy skies, few comparisons have been published to assess the accuracy of the different algorithms. Virtually no comparisons have been made for both clear and cloudy skies across multiple sites. This study evaluates the accuracy of 13 algorithms for predicting incident long-wave radiation under clear skies, ten cloud correction algorithms, and four algorithms for all-sky conditions using data from 21 sites across North America and China. Data from five research sites were combined with publicly available data from nine sites in the AmeriFlux network for initial evaluation and optimization of cloud cover estimates; seven additional AmeriFlux sites were used as an independent test of the algorithms. Clear-sky algorithms that excelled in predicting L clr were the Dilley, Prata, and Å ngström algorithms. Root mean square deviation (RMSD) between predicted and measured 30-minute or hourly L clr averaged approximately 23 W m À2 for these three algorithms across all sites, while RMSD of daily estimates was as low as 14 W m À2 . Cloud-correction algorithms of Kimball, Unsworth, and Crawford described the data best when combined with the Dilley clear-sky algorithm. Average RMSD across all sites for these three cloud corrections was approximately 24 to 25 W m À2 for 30-minute or hourly estimates and approximately 15 to 16 W m À2 for daily estimates. The Kimball and Unsworth cloud corrections require an estimate of cloud cover, while the Crawford algorithm corrects for cloud cover directly from measured solar radiation. Optimum limits in the clearness index, defined as the ratio of observed solar radiation to theoretical terrestrial solar radiation, for complete cloud cover and clear skies were suggested for the Kimball and Unsworth algorithms. Application of the optimized algorithms to seven independent sites yielded similar results. On the basis of the results, the recommended algorithms can be applied with reasonable accuracy for a wide range of climates, elevations, and latitudes.

Abstract. The savanna ecosystem is one of the most dominant and complex terrestrial biomes, deriving from a distinct vegetative surface comprised of co-dominant tree and grass populations. While these two vegetation types co-exist functionally, demographically they are not static but are dynamically changing in response to environmental forces such as annual fire events and rainfall variability. Modelling savanna environments with the current generation of terrestrial biosphere models (TBMs) has presented many problems, particularly describing fire frequency and intensity, phenology, leaf biochemistry of C3 and C4 photosynthesis vegetation, and root-water uptake. In order to better understand why TBMs perform so poorly in savannas, we conducted a model inter-comparison of six TBMs and assessed their performance at simulating latent energy (LE) and gross primary productivity (GPP) for five savanna sites along a rainfall gradient in northern Australia. Performance in predicting LE and GPP was measured using an empirical benchmarking system, which ranks models by their ability to utilise meteorological driving information to predict the fluxes. On average, the TBMs performed as well as a multi-linear regression of the fluxes against solar radiation, temperature and vapour pressure deficit but were outperformed by a more complicated nonlinear response model that also included the leaf area index (LAI). This identified that the TBMs are not fully utilising their input information effectively in determining savanna LE and GPP and highlights that savanna dynamics cannot be calibrated into models and that there are problems in underlying model processes. We identified key weaknesses in a model's ability to simulate savanna fluxes and their seasonal variation, related to the representation of vegetation by the models and root-water uptake. We underline these weaknesses in terms of three critical areas for development. First, prescribed tree-rooting depths must be deep enough, enabling the extraction of deep soil-water stores to maintain photosynthesis and transpiration during the dry season. Second, models must treat grasses as a co-dominant interface for water and carbon exchange rather than a secondary one to trees. Third, models need a dynamic representation of LAI that encompasses the dynamic phenology of savanna vegetation and its response to rainfall interannual variability. We believe that this study is the first to assess how well TBMs simulate savanna ecosystems and that these results will be used to improve the representation of savannas ecosystems in future global climate model studies.

There is a growing concern about excessive nitrogen (N) and water use in agricultural systems in North China due to the reduced resource use efficiency and increased groundwater pollution. A two-year experiment with two soil moisture by four N treatments was conducted to investigate the effects of N application rates and soil moisture on soil N dynamics, crop yield, N uptake and use efficiency in an intensive wheat-maize double cropping system (wheat-maize rotation) in the North China Plain. Under the experimental conditions, crop yield of both wheat and maize did not increase significantly at N rates above 200 kg N ha maize crop has higher N use efficiency than wheat crop. Higher NO 3 -N leaching occurred in maize season than in wheat season due to more water leakage caused by the concentrated summer rainfall. The results of this study indicate that the optimum N rate may be much lower than that used in many areas in the North China Plain given the high level of N already in the soil, and there is great potential for reducing N inputs to increase N use efficiency and to mitigate N leaching into the groundwater. Avoiding excess water leakage through controlled irrigation and matching N application to crop N demand is the key to reduce NO 3 -N leaching and maintain crop yield. Such management requires knowledge of crop water and N demand and soil N dynamics as they change with variable climate temporally and spatially. Simulation modeling can capture those interactions and is considered as a powerful tool to assist in the future optimization of N and irrigation managements.

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