Ecosystem carbon balance in the Hawaiian Islands under different scenarios of future climate and land use change

The State of Hawai\u27i passed legislation to be carbon neutral by 2045, a goal that will partly depend on carbon sequestration by terrestrial ecosystems. However, there is considerable uncertainty surrounding the future direction and magnitude of the land carbon sink in the Hawaiian Islands. We used the Land Use and Carbon Scenario Simulator (LUCAS), a spatially explicit stochastic simulation model that integrates landscape change and carbon gain-loss, to assess how projected future changes in climate and land use will influence ecosystem carbon balance in the Hawaiian Islands under all combinations of two radiative forcing scenarios (RCPs 4.5 and 8.5) and two land use scenarios (low and high) over a 90 year timespan from 2010 to 2100. Collectively, terrestrial ecosystems of the Hawaiian Islands acted as a net carbon sink under low radiative forcing (RCP 4.5) for the entire 90 year simulation period, with low land use change further enhancing carbon sink strength. In contrast, Hawaiian terrestrial ecosystems transitioned from a net sink to a net source of CO2 to the atmosphere under high radiative forcing (RCP 8.5), with high land use accelerating this transition and exacerbating net carbon loss. A sensitivity test of the CO2 fertilization effect on plant productivity revealed it to be a major source of uncertainty in projections of ecosystem carbon balance, highlighting the need for greater mechanistic understanding of plant productivity responses to rising atmospheric CO2. Long-term model projections such as ours that incorporate the interactive effects of land use and climate change on regional ecosystem carbon balance will be critical to evaluating the potential of ecosystem-based climate mitigation strategies

Implications of Future Water Use Efficiency for Ecohydrological Responses to Climate Change and Spatial Heterogeneity of Atmospheric CO2 in China

As the atmospheric carbon dioxide (CO2) increases substantially, the spatial distribution of atmospheric CO2 should be considered when estimating the effects of CO2 on the carbon and water cycle coupling of terrestrial ecosystems. To evaluate this effect on future ecohydrological processes, the spatial-temporal patterns of CO2 were established over 1951 - 2099 according to the IPCC emission scenarios SRES A2 and SRES B1. Thereafter, water use efficiency (WUE) was used (i.e., Net Primary Production/Evaportranspiration) as an indicator to quantify the effects of climate change and uneven CO2 fertilization in China. We carried out several simulated experiments to estimate WUE under different future scenarios using a land process model (Integrated Biosphere Simulator, IBIS). Results indicated that the geographical distributions of averaged WUE have considerable differences under a heterogeneous atmospheric CO2 condition. Under the SRES A2 scenario, WUE decreased slightly with a 5% value in most areas of the southeastern and northwestern China during the 2050s, while decreasing by approximately 15% in southeastern China during the 2090s. During the period of the 2050s under SRES B1 scenario, the change rate of WUE was similar with that under SRES A2 scenario, but the WUE has a more moderate decreasing trend than that under the SRES A2 scenario. In all, the ecosystems in median and low latitude areas had a weakened effect on resisting extreme climate event such as drought. Conversely, the vegetation in a boreal forest had an enhanced buffering capability to tolerate drought events

The General Ensemble Biogeochemical Modeling System (GEMS) and its Applications to Agricultural Systems in the United States

The General Ensemble Biogeochemical Modeling System (GEMS) (Liu, 2009; Liu et al., 2004c) was developed to integrate well-established ecosystem biogeochemical models with various spatial databases for the simulations of biogeochemical cycles over large areas. Figure 18.1 shows the overall structure of the GEMS. Some of the key components are described below. General Ensemble Biogeochemical Modeling System (GEMS) 310 Multiple Underlying Biogeochemical Models 310 Monte Carlo Simulations 311 Model Inputs: Management Practices and Others 311 Model Outputs 311 Data Assimilation 311 Simulation of Agricultural Practices: EDCM as an Example 312 Net Primary Production (NPP) and Improvements in Crop Genetics and Agronomics 312 Soil Carbon Dynamics 312 Impacts of Soil Erosion and Deposition 313 CH4 and N2O Fluxes 313 Study Areas and Modeling Design 314 Study Areas 314 Nebraska Eddy Flux Tower Sites 314 Regional Applications: Mississippi Valley and Prairie Potholes 315 Modeling Design 315 Results 316 Impacts of Management Practices on SOC at Site Scale 316 Quantification of Regional Carbon Stocks and GHG Fluxes 317 Prairie Pothole Region 317 Mississippi Valley 319 Discussion 32

Effect of Oak Chip Aging on the Flavor of Persimmon Brandy

Mopan persimmon brandy with an alcohol content of 42% (V/V), prepared by fermentation and distillation, was aged after being added with 5–20 g/L of Chinese-made moderately roasted oak chips. The volatile and non-volatile components of persimmon brandy were analyzed by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS), the total phenol content and antioxidant activity were determined, and sensory evaluation was also performed. The results showed that a total of 33 volatile components were identified by GC-MS, among which the major components were ethyl acetate, ethyl decanoate, and ethyl laurate. The content of volatile components was the highest upon the addition of 10 g/L of oak chips. The results of LC-MS showed that the number of non-volatile substances increased by 183 after aging. The total phenol content and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging capacity increased with increasing addition of oak chips, but was basically stable after 90 days of aging. In the sensory evaluation, persimmon brandy with 15 g/L of oak chip scored the highest (72.5 points)

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0):Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).</p

Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales

While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi-site synthesis of how predictors of CH4 fluxes (FCH4) in freshwater wetlands vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, and random forests) in a wavelet-based multi-resolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by similar to 17 +/- 11 days, and lagged air and soil temperature by median values of 8 +/- 16 and 5 +/- 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat-dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1- to 4-h lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.Peer reviewe

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%-31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency similar to 0.52-0.63 and 0.53). UpCH(4) estimated annual global wetland CH4 emissions of 146 +/- 43 TgCH4 y(-1) for 2001-2018 which agrees closely with current bottom-up land surface models (102-181 TgCH4 y(-1)) and overlaps with top-down atmospheric inversion models (155-200 TgCH4 y -1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25 degrees from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ ORNLDAAC/2253).Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (similar to 30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001-2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state-of-the-art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground-based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties

Theoretical study of crystal phase effect in heterogeneous catalysis

\u3cp\u3eDensity functional theory (DFT) is a powerful tool to study heterogeneous catalysis nowadays. In past decades, numerous DFT calculations have been conducted to investigate the mechanism of catalytic reaction from which the rationale of catalyst design can be revealed. Because the catalyst electronic and geometric structures determine the intrinsic activity, corresponding composition, size, and morphology have been explored extensively to tune the structure–activity relationship for higher activity and selectivity. In this review, we focus on the recent theoretical progress of the crystal phase effect on catalysis. Catalysts with different crystal phases have different symmetries, and could expose very different facets with distinct electronic and geometrical properties, which would have significant influential on the activity and selectivity of the active sites as well as the site density. Exploration of the dependence of catalysis on the crystal phases provides a new rationale of catalysts design toward a high-specific activity. WIREs Comput Mol Sci 2016, 6:571–583. doi: 10.1002/wcms.1267. For further resources related to this article, please visit the WIREs website.\u3c/p\u3

Optimum Particle Size for Gold-Catalyzed CO Oxidation

The structure sensitivity of gold-catalyzed CO oxidation is presented by analyzing in detail the dependence of CO oxidation rate on particle size. Clusters with less than 14 gold atoms adopt a planar structure, whereas larger ones adopt a three-dimensional structure. The CO and O2 adsorption properties depend strongly on particle structure and size. All of the reaction barriers relevant to CO oxidation display linear scaling relationships with CO and O2 binding strengths as main reactivity descriptors. Planar and three-dimensional gold clusters exhibit different linear scaling relationship due to different surface topologies and different coordination numbers of the surface atoms. On the basis of these linear scaling relationships, first-principles microkinetics simulations were conducted to determine CO oxidation rates and possible rate-determining step of Au particles. Planar Au9 and three-dimensional Au79 clusters present the highest CO oxidation rates for planar and three-dimensional clusters, respectively. The planar Au9 cluster is much more active than the optimum Au79 cluster. A common feature of optimum CO oxidation performance is the intermediate binding strengths of CO and O2, resulting in intermediate coverages of CO, O2, and O. Both these optimum particles present lower performance than maximum Sabatier performance, indicating that there is sufficient room for improvement of gold catalysts for CO oxidation

# The British Cartographic Society 2005 REFEREED PAPER NCWin — A Component Object Model (COM) for Processing and Visualizing NetCDF Data

NetCDF (Network Common Data Form) is a data sharing protocol and library that is commonly used in large-scale atmospheric and environmental data archiving and modeling. The NetCDF tool described here, named NCWin and coded with Borland Czz Builder, was built as a standard executable as well as a COM (component object model) for the Microsoft Windows environment. COM is a powerful technology that enhances the reuse of applications (as components). Environmental model developers from different modeling environments, such as Python, JAVA, VISUAL FORTRAN, VISUAL BASIC, VISUAL Czz, and DELPHI, can reuse NCWin in their models to read, write and visualize NetCDF data. Some Windows applications, such as ArcGIS and Microsoft PowerPoint, can also call NCWin within the application. NCWin has three major components: 1) The data conversion part is designed to convert binary raw data to and from NetCDF data. It can process six data types (unsigned char, signed char, short, int, float, double) and three spatial data formats (BIP, BIL, BSQ); 2) The visualization part is designed for displaying grid map series (playing forward or backward) with simple map legend, and displaying temporal trend curves for data on individual map pixels; and 3) The modeling interface is designed for environmental model development by which a set of integrated NetCDF functions is provided for processing NetCDF data. To demonstrate that the NCWin can easily extend the functions of some current GIS software and the Office applications, examples of calling NCWin within ArcGIS and MS PowerPoin