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Carbon accounting results for the same city can differ due to differences in protocols, methods, and data sources. A critical review of these differences and the connection among them can help to bridge our knowledge between university-based researchers and protocol practitioners in accounting and taking further mitigation actions. The purpose of this study is to provide a review of published research and protocols related to city carbon accounting, paying attention to both their science and practical actions. To begin with, the most cited articles in this field are identified and analyzed by employing a citation network analysis to illustrate the development of city-level carbon accounting from three perspectives. We also reveal the relationship between research methods and accounting protocols. Furthermore, a timeline of relevant organizations, protocols, and projects is provided to demonstrate the applications of city carbon accounting in practice. The citation networks indicate that the field is dominated by pure-geographic production-based and community infrastructure-based accounting; however, emerging models that combine economic system analysis from a consumption-based perspective are leading to new trends in the field. The emissions accounted for by various research methods consist essentially of the scope 1-3, as defined in accounting protocols. The latest accounting protocols include consumption-based accounting, but most cities still limit their accounting and reporting from pure-geographic production-based and community infrastructure-based perspectives. In conclusion, we argue that protocol practitioners require support in conducting carbon accounting, so as to explore the potential in mitigation and adaptation from a number of perspectives. This should also be a priority for future studies.

This study investigated the ability of a HYDRUS 1D model for predicting the vertical distribution of potassium iodine (200 ppm) in soil columns after amendment with five different common remediation materials (gypsum, lime, fly ash, charcoal, and sawdust) at a rate of 2.5% (w/w), relative to an unamended control soil. Results showed that relative to the unamended soil, iodine leaching was decreased by all amendments but that the magnitude of the decreases varied with the soil amendment applied. Iodine content was highest in the upper layer of the soil columns and decreased progressively with soil depth. The model was evaluated via comparison of the model simulated values with measured values from the soil column studies. The results showed that the HYDRUS 1D model efficiency was near to 1, indicating the stimulated results near to the measured values. Therefore, this study showed that iodine leaching through a soil could be ascertained well using a HYDRUS 1D model. The model over predicted iodine leaching, results in a weak correspondence between the simulated and the measured results for iodine leaching. This suggests that the HYDRUS-1D model does not explain accurately different organic and inorganic amended soil and the preferential flow that occurs in these columns. This may be due to the fact that Freundlich isotherm, which is part of the transport equations, does not sufficiently describe the mechanism of iodine adsorption onto the soil particles. This study would help to select an amendment for an effective management strategy to reduce exogenous iodine losses from agro-ecosystems. This would also improve scientific understanding of iodine transport in soil profile.

AbstractInland waters are important emitters of the greenhouse gasses (GHGs) carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) to the atmosphere. In the framework of the 2nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP‐2) initiative, we review the state of the art in estimating inland water GHG budgets at global scale, which has substantially advanced since the first phase of RECCAP nearly 10 years ago. The development of increasingly sophisticated upscaling techniques, including statistical prediction and process‐based models, allows for spatially explicit estimates that are needed for regionalized assessments of continental GHG budgets such as those established for RECCAP. A few recent estimates also resolve the seasonal and/or interannual variability in inland water GHG emissions. Nonetheless, the global‐scale assessment of inland water emissions remains challenging because of limited spatial and temporal coverage of observations and persisting uncertainties in the abundance and distribution of inland water surface areas. To decrease these uncertainties, more empirical work on the contributions of hot‐spots and hot‐moments to overall inland water GHG emissions is particularly needed.

Although widely recognized as the key to climate goals, coal "phase down" has long been argued for its side effects on energy security and social development. Retrofitting coal power units with biomass and coal co-firing with a carbon capture and storage approach provides an alternative way to avoid these side effects and make deep carbon dioxide emission cuts or even achieve negative emission. However, there is a lack of clear answers to how much the maximum emission reduction potential this approach can unlock, which is the key information to promote this technology on a large scale. Here, we focus on helping China's 4536 coal power units make differentiated retrofit choices based on unit-level heterogeneity information and resource spatial matching results. We found that China's coal power units have the potential to achieve 0.4 Gt of negative CO2 emission in 2025, and the cumulative negative CO2 emission would reach 10.32 Gt by 2060. To achieve negative CO2 emission, the biomass resource amount should be 1.65 times the existing agricultural and forestry residues, and the biomass and coal co-firing ratio should exceed 70%. Coal power units should grasp their time window; otherwise, the maximum negative potential would decrease at a rate of 0.35 Gt per year.

AbstractWhile climate warming reduces the occurrence of frost events, the warming-induced lengthening of the growing season of plants in the Northern Hemisphere may actually induce more frequent frost days during the growing season (GSFDs, days with minimum temperature < 0 °C). Direct evidence of this hypothesis, however, is limited. Here we investigate the change in the number of GSFDs at latitudes greater than 30° N using remotely-sensed and in situ phenological records and three minimum temperature (Tmin) data sets from 1982 to 2012. While decreased GSFDs are found in northern Siberia, the Tibetan Plateau, and northwestern North America (mainly in autumn), ~43% of the hemisphere, especially in Europe, experienced a significant increase in GSFDs between 1982 and 2012 (mainly during spring). Overall, regions with larger increases in growing season length exhibit larger increases in GSFDs. Climate warming thus reduces the total number of frost days per year, but GSFDs nonetheless increase in many areas.

AbstractDrought is a main driver of interannual variation in global terrestrial net primary production. However, how and to what extent drought impacts global NPP variability is unclear. Based on the multi-timescale drought index SPEI and a satellite-based annual global terrestrial NPP dataset, we observed a robust relationship between drought and NPP in both hemispheres. In the Northern Hemisphere, the annual NPP trend is driven by 19-month drought variation, whereas that in the Southern Hemisphere is driven by 16-month drought variation. Drought-dominated NPP, which mainly occurs in semi-arid ecosystems, explains 29% of the interannual variation in global NPP, despite its 16% contribution to total global NPP. More surprisingly, drought prone ecosystems in the Southern Hemisphere, which only account for 7% of the total global NPP, contribute to 33% of the interannual variation in global NPP. Our observations support the leading role of semi-arid ecosystems in interannual variability in global NPP and highlight the great impacts of long-term drought on the global carbon cycle.

article i nfo In this study we use the 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) product to develop multivariate linear regression models that estimate canopy heights over study sites at Howland Forest, Maine, Harvard Forest, Massachusetts and La Selva Forest, Costa Rica using (1) directional escape probabilities that are spectrally independent and (2) the directional spectral reflectances used to derive the directional escape probabilities. These measures of canopy architecture are compared with canopy height information retrieved from the airborne Laser Vegetation Imaging Sensor (LVIS). Both the escape probability and the directional reflectance approaches achieve good results, with correlation coefficients in the range 0.54-0.82, although escape probability results are usually slightly better. This suggests that MODIS 500 m BRDF data can be used to extrapolate canopy heights observed by widely-spaced satellite LIDAR swaths to larger areas, thus providing wide-area coverage of canopy height.