Impact of changes in climate and CO₂ on the carbon-sequestration potential of vegetation under limited water availability using SEIB-DGVM version 3.02

Documenting year-to-year variations in carbon-sequestration potential in terrestrial ecosystems is crucial for the determination of carbon dioxide (CO2) emissions. However, the magnitude, pattern and inner biomass partitioning of carbon-sequestration potential, and the effect of the changes in climate and CO2 on inner carbon stocks, remain poorly quantified. Herein, we use a spatially explicit individual based-dynamic global vegetation model to investigate the influences of the changes in climate and CO2 on the enhanced carbon-sequestration potential of vegetation. The modelling included a series of factorial simulations using the CRU dataset from 1916 to 2015. The results show that CO2 predominantly leads to a persistent and widespread increase in above-ground vegetation biomass carbon-stocks (AVBC) and below-ground vegetation biomass carbon-stocks (BVBC). Climate change appears to play a secondary role in carbon-sequestration potential. Importantly, with the mitigation of water stress, the magnitude of the above- and below-ground responses in vegetation carbon-stocks gradually increases, and the ratio between AVBC and BVBC increases to capture CO2 and sunlight. Changes in the pattern of vegetation carbon storage was linked to regional limitations in water, which directly weakens and indirectly regulates the response of potential vegetation carbon-stocks to a changing environment. Our findings differ from previous modelling evaluations of vegetation that ignored inner carbon dynamics and demonstrates that the long-term trend in increased vegetation biomass carbon-stocks is driven by CO2 fertilization and temperature effects that are controlled by water limitations.

Ground Cover—Biomass Functions for Early-Seral Vegetation

Vegetation biomass is commonly measured through destructive sampling, but this method is time-consuming and is not applicable for certain studies. Therefore, it is necessary to find reliable methods to estimate vegetation biomass indirectly. Quantification of early-seral vegetation biomass in reforested stands in the United States Pacific Northwest (PNW) is important as competition between the vegetation community and planted conifer seedlings can have important consequences on seedling performance. The goal of this study was to develop models to indirectly estimate early-seral vegetation biomass using vegetation cover, height, or a combination of the two for different growth habits (ferns, forbs, graminoids, brambles, and shrubs) and environments (wet and dry) in reforested timber stands in Western Oregon, USA. Six different linear and non-linear regression models were tested using cover or the product of cover and height as the only predicting variable, and two additional models tested the use of cover and height as independent variables. The models were developed for six different growth habits and two different environments. Generalized models tested the combination of all growth habits (total) and sites (pooled data set). Power models were used to estimate early-seral vegetation biomass for most of the growth habits, at both sites, and for the pooled data set. Furthermore, when power models were preferred, most of the growth habits used vegetation cover and height separately as predicting variables. Selecting generalized models for predicting early-seral vegetation biomass across different growth habits and environments is a good option and does not involve an important trade-off by losing accuracy and/or precision. The presented models offer an efficient and non-destructive method for foresters and scientists to estimate vegetation biomass from simple field or aerial measurement of cover and height. Depending on the objectives and availability of input data, users may select which model to apply.

Enhanced summertime ozone and SOA from biogenic volatile organic compound (BVOC) emissions due to vegetation biomass variability during 1981–2018 in China

Coordinated control of fine particulate matter (PM2.5) and ozone (O3) has become a new and urgent issue for China’s air pollution control. Biogenic volatile organic compounds (BVOCs) are important precursors of O3 and secondary organic aerosol (SOA) formation. China experienced a rapid increase in BVOC emissions as a result of increased vegetation biomass. We applied WRF-Chem3.8 coupling with MEGAN2.1 to conduct long-term simulations for impacts of BVOC emissions on O3 and SOA during 1981–2018, using the emission factors extrapolated by localized emission rates and annual vegetation biomass. In summer of 2018, BVOC emissions are 9.91 Tg (in June), which lead to an average increase of 8.6 ppb (16.75 % of the total) in daily maximum 8-h (MDA8) O3 concentration and 0.84 μg m−3 (73.15 % of the total) in SOA over China. The highest contribution to O3 is concentrated in the Great Khingan Mountains, Qinling Mountains, and most southern regions, while southern areas for SOA. Isoprene has the greatest contribution to O3 while monoterpene has the largest SOA production. BVOC emissions have distinguished impacts in different regions. Chengdu-Chongqing (CC) region has the highest O3 and SOA generated by BVOCs while Beijing-Tianjin-Hebei (BTH) region has the lowest. From 1981 to 2018, the interannual variation of BVOC emissions caused by increasing leaf biomass results in O3 concentration increasing by 7.38 % at an average rate of 0.11 ppb yr−1, and SOA increasing by 39.30 % at an average rate of 0.008 μg m−3 yr−1. Due to the different changing trends of leaf biomass by regions and vegetation types, O3 and SOA show different interannual variations. Fenwei Plain (FWP), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions have the most rapid O3 increment while the increasing rate of SOA in CC is the highest. BTH has the smallest enhancement in O3 and SOA concentration. This study will help to recognize the impact of historical BVOC emissions on O3 and SOA, and further provide the reliable scientific basis for the precise prevention and control of air pollution in China.

Soil Nutrient and Vegetation Diversity Patterns of Alpine Wetlands on the Qinghai-Tibetan Plateau

To predict the consequences of environmental change on the biodiversity of alpine wetlands, it is necessary to understand the relationship between soil properties and vegetation biodiversity. In this study, we investigated spatial patterns of aboveground vegetation biomass, cover, species diversity, and their relationships with soil properties in the alpine wetlands of the Gannan Tibetan Autonomous Prefecture of on the Qinghai-Tibetan Plateau, China. Furthermore, the relative contribution of soil properties to vegetation biomass, cover, and species diversity were compared using principal component analysis and multiple regression analysis. Generally, the relationship between plant biomass, coverage, diversity, and soil nutrients was linear or unimodal. Soil pH, bulk density and organic carbon were also significantly correlated to plant diversity. The soil attributes differed in their relative contribution to changes in plant productivity and diversity. pH had the highest contribution to vegetation biomass and species richness, while total nitrogen was the highest contributor to vegetation cover and nitrogen–phosphorus ratio (N:P) was the highest contributor to diversity. Both vegetation productivity and diversity were closely related to soil properties, and soil pH and the N:P ratio play particularly important roles in wetland vegetation biomass, cover, and diversity.

Bistatic scattering simulations of circular and linear polarizations over land surface for signals of opportunity reflectometry

Signals of Opportunity Reflectometry (SoOp-R) employs the communication system, GNSS (Global Navigation Satellite System) constellation and other potential Signals of Opportunity (SoOp) as the transmitters. In recent years, it has gained increased interests. Several experiments have been carried out, however it is still in the initial development stage. Theoretical predictions of SoOp Reflectometry for land surface parameters detection, such as soil moisture and vegetation biomass, should be carried out simultaneously. Meanwhile, at present less works are paid attention to the polarization study of the polarizations. The first-order radiative transfer equation models are employed here and they are developed according to the wave synthesis technique to get the various polarization combinations. Using the two models as analysis tools, we simulate the bistatic scattering at all potential SoOp Reflectometry bands, i.e., P-, L-, C- and X-band for circular polarizations and linear polarizations. While the original commonly used microwave scattering models are linear polarizations, here we compare the difference. Although the models can simulate bistatic scattering at any incident angles and scattering angles. Four special observation geometry are taken into considerations during the analysis. Using the developed models as tools, the developed models establish the relationship between the land surface parameters (such as soil moisture, soil roughness and vegetation water content, diameters et al.) and bistatic radar cross section. The forward scattering models developed here enables the understanding of the effects of different geophysical parameters and transmitter–receiver observation scenarios on the bisatic scattering at any polarization combinations for any potential SoOP reflectometry bands. Robust retrieval methods for soil moisture and vegetation biomass can benefit from the forward scattering models.