An improved Biome-BGC model for estimating net primary productivity of alpine meadow on the Qinghai-Tibet Plateau

Regulation of biophysical drivers on carbon and water fluxes over a warm-temperate plantation in northern China

Plantations have great potential for carbon sequestration and play a vital role in the water cycle. However, it is challenging to accurately estimate the carbon and water fluxes of plantations, and the impact of biophysical drivers on the coupling of carbon and water fluxes is not well understood. Thus, we modified the phenology module of the Biome-BGC model and optimized the parameters with the aim of simulating the gross primary productivity (GPP), evapotranspiration (ET) and water use efficiency (WUE) of a warm-temperate plantation in northern China from 2009 to 2020. Photosynthetically active radiation (PAR) showed significant positive correlations on GPP and WUE during the first stage of the growing season (S1: from early April to late July). Active accumulated temperature (Taa) mainly controlled the changes in GPP and ET during the second stage (S2: between the end of July and early November). Throughout the growing season, soil water content dominated daily GPP and WUE, whereas Taa regulated ET. The optimized Biome-BGC model performed better than the original model in simulating GPP and ET. Compared with the values simulated by the original model, root mean square error decreased by 7.89 % and 15.97 % for the simulated GPP and ET, respectively, while the determination coefficient increased from 0.77 to 0.81 for simulated GPP and from 0.51 to 0.62 for simulated ET. The results of this study demonstrated that the optimized model more accurately assessed carbon sequestration and water consumption in plantations.

Growing-season carbon budget of alpine meadow ecosystem in the Qinghai Lake Basin: a continued carbon sink through this century according to the Biome-BGC model

BACKGROUND

The alpine meadow is one of the most important ecosystems in the Qinghai-Tibet Plateau (QTP), and critically sensitive to climate change and human activities. Thus, it is crucial to precisely reveal the current state and predict future trends in the carbon budget of the alpine meadow ecosystem. The objective of this study was to explore the applicability of the Biome-BGC model (BBGC) in the Qinghai Lake Basin (QLB), identify the key parameters affecting the variation of net ecosystem exchange (NEE), and further predict the future trends in carbon budget in the QLB.

RESULTS

The alpine meadow mainly acted as carbon sink during the growing season. For the eco-physiological factors, the YEL (Yearday to end litterfall), YSNG (Yearday to start new growth), CLEC (Canopy light extinction coefficient), FRC:LC (New fine root C: new leaf C), SLA (Canopy average specific leaf area), C:Nleaf (C:N of leaves), and FLNR (Fraction of leaf N in Rubisco) were confirmed to be the top seven parameters affecting carbon budget of the alpine meadow. For the meteorological factors, the sensitivity of NEE to precipitation was greater than that to vapor pressure deficit (VPD), and it was greater to radiation than to air temperature. Moreover, the combined effect of two different meteorological factors on NEE was higher than the individual effect of each one. In the future, warming and wetting would enhance the carbon sink capacity of the alpine meadow during the growing season, but extreme warming (over 3.84 ℃) would reduce NEE (about 2.9%) in the SSP5-8.5 scenario.

CONCLUSION

Overall, the alpine meadow ecosystem in the QLB generally performs as a carbon sink at present and in the future. It is of great significance for the achievement of the goal of carbon neutrality and the management of alpine ecosystems.

A review of applied research on low-carbon urban design: based on scientific knowledge mapping

The construction of low-carbon cities is an essential component of sustainable urban development. However, there is a lack of a comprehensive low-carbon city design and evaluation system that incorporates "carbon sink accounting-remote sensing monitoring-numerical modelling-design and application" in an all-around linkage, multi-scale coupling, and localized effects. This paper utilizes the Citespace tool to evaluate low-carbon city design applications by analyzing literature in the Web of Science (WOS) core collection database. The results reveal that low-carbon cities undergo four stages: "measurement-implementation-regulation - management." The research themes are divided into three core clustering evolutionary pathways: "extension of carbon sink functions," "spatialisation of carbon sink systems," and "full-cycle, full-dimensional decarbonisation." Applications include "Utility studies of multi-scale carbon sink assessments," "Correlation analysis of carbon sink influencing factors," "Predictive characterisation of multiple planning scenarios," and "Spatial planning applications of urban sink enhancement." Future low-carbon city construction should incorporate intelligent algorithm technology in real-time to provide a strong design basis for multi-scale urban spatial design with the features of "high-precision accounting, full-cycle assessment and low-energy concept."

Improved soil surface nitrogen balance method for assessing nutrient use efficiency and potential environmental impacts within an alpine meadow dominated region

The soil surface nitrogen balance (SSNB) method is commonly used to assess the nutrient use efficiency (NUE) of agricultural systems and any associated potential environmental impacts. However, the nitrogen flow of wide natural grasslands and other natural areas differ from that of artificial croplands and mown grasslands. In this study, we integrated root growth and the important nutrient resorption process into the SSNB model and used the improved model to clarify the nitrogen (N) flow and balance in the Three Rivers Headwater Region (TRHR)-an area dominated by alpine meadows-from 2012-2019. In the grassland system, the N surplus (ΔN) was 0.274 g m^-2 year^-1, and root return (BLD) dominated the N input, accounting for 67% of the total input (3.924 g m^-2 year^-1). N resorption was the main internal N flow in the grassland system (1.079 g m^-2 year^-1), and 30% of grassland uptake (N_UP-grass). The ΔN of the agricultural system was 1.097 g m^-2 year^-1, which was four times that of the grassland, and chemical fertilizer was the largest input, accounting for 84% of the total input. The NUE in grassland was 93%, which suggests a risk of soil mining and degradation, while that of cropland was 76% and within an ideal range. The ΔN provides a robust measure of river N export, the TRHR was divided into three catchments, and the export coefficient was 16.14%-55.68%. The results of this study show that the improved SSNB model can be applied to a wide range of natural grasslands that have high root biomass and resorption characteristics.

Sensitivity Analysis of Biome-BGC for Gross Primary Production of a Rubber Plantation Ecosystem: A Case Study of Hainan Island, China

Accurate monitoring of forest carbon flux and its long-term response to meteorological factors is important. To accomplish this task, the model parameters need to be optimized with respect to in situ observations. In the present study, the extended Fourier amplitude sensitivity test (eFAST) method was used to optimize the sensitive ecophysiological parameters of the Biome BioGeochemical Cycles model. The model simulation was integrated from 2010 to 2020. The results showed that using the eFAST method quantitatively improved the model output. For instance, the R² increased from 0.53 to 0.72. Moreover, the root-mean-square error was reduced from 1.62 to 1.14 gC·m^-2·d^-1. In addition, it was reported that the carbon flux outputs of the model were highly sensitive to various parameters, such as the canopy average specific leaf area and canopy light extinction coefficient. Moreover, long-term meteorological factor analysis showed that rainfall dominated the trend of gross primary production (GPP) of the study area, while extreme temperatures restricted the GPP. In conclusion, the eFAST method can be used in future studies. Furthermore, eFAST could be applied to other biomes in response to different climatic conditions.

Improved Estimation of Aboveground Biomass of Disturbed Grassland through Including Bare Ground and Grazing Intensity

Accurate approaches to aboveground biomass (AGB) estimation are required to support appraisal of the effectiveness of land use measures, which seek to protect grazing-adapted grasslands atop the Qinghai-Tibet Plateau (QTP). This methodological study assesses the effectiveness of one commonly used visible band vegetation index, Red Green Blue Vegetation Index (RGBVI), obtained from unmanned aerial vehicle (UAV), in estimating AGB timely and accurately at the local scale, seeking to improve the estimation accuracy by taking into account in situ collected information on disturbed grassland. Particular emphasis is placed upon the mapping and quantification of areas disturbed by grazing (simulated via mowing) and plateau pika (Ochotona curzoniae) that have led to the emergence of bare ground. The initial model involving only RGBVI performed poorly in AGB estimation by underestimating high AGB by around 10% and overestimating low AGB by about 10%. The estimation model was modified by the mowing intensity ratio and bare ground metrics. The former almost doubled the estimation accuracy from R2 = 0.44 to 0.81. However, this modification caused the bare ground AGB to be overestimated by about 38 and 19 g m−2 for 2018 and 2019, respectively. Although further modification of the model by bare ground metrics improved the accuracy slightly to 0.88, it markedly reduced the overestimation of low AGB values. It is recommended that grazing intensity be incorporated into the micro-scale estimation of AGB, together with the bare ground modification metrics, especially for severely disturbed meadows with a sizable portion of bare ground.

Grassland Productivity Response to Climate Change in the Hulunbuir Steppes of China

As global climate change deeply affects terrestrial ecosystem carbon cycle, it is necessary to understand how grasslands respond to climate change. In this study, we examined the role of climate change on net primary productivity (NPP) from 1961 to 2010 in the Hulunbuir grasslands of China, using a calibrated process-based biogeochemistry model. The results indicated that: Temperature experienced a rise trend from 1961; summer and autumn precipitation showed a rise trend before the 1990s and decline trend after the 1990s. Winter and spring precipitation showed an ascending trend. Simulated NPP had a high inter-annual variability during the study period, ranging from 139 g Cm−2 to 348 g Cm−2. The annual mean NPP was significant and positive in correlation with the annual variation of precipitation, and the trend was first raised then fell with the turn point at the 1990s. Temperature had a 20–30 d lag in summer, but none in spring and autumn; precipitation had a 10–20 d lag in summer. The climate lag effect analysis confirmed that temperature had a positive effect on NPP in spring and a negative effect in summer.

Improved Modeling of Gross Primary Productivity of Alpine Grasslands on the Tibetan Plateau Using the Biome-BGC Model

The ability of process-based biogeochemical models in estimating the gross primary productivity (GPP) of alpine vegetation is largely hampered by the poor representation of phenology and insufficient calibration of model parameters. The development of remote sensing technology and the eddy covariance (EC) technique has made it possible to overcome this dilemma. In this study, we have incorporated remotely sensed phenology into the Biome-BGC model and calibrated its parameters to improve the modeling of GPP of alpine grasslands on the Tibetan Plateau (TP). Specifically, we first used the remotely sensed phenology to modify the original meteorological-based phenology module in the Biome-BGC to better prescribe the phenological states within the model. Then, based on the GPP derived from EC measurements, we combined the global sensitivity analysis method and the simulated annealing optimization algorithm to effectively calibrate the ecophysiological parameters of the Biome-BGC model. Finally, we simulated the GPP of alpine grasslands on the TP from 1982 to 2015 based on the Biome-BGC model after a phenology module modification and parameter calibration. The results indicate that the improved Biome-BGC model effectively overcomes the limitations of the original Biome-BGC model and is able to reproduce the seasonal dynamics and magnitude of GPP in alpine grasslands. Meanwhile, the simulated results also reveal that the GPP of alpine grasslands on the TP has increased significantly from 1982 to 2015 and shows a large spatial heterogeneity, with a mean of 289.8 gC/m2/yr or 305.8 TgC/yr. Our study demonstrates that the incorporation of remotely sensed phenology into the Biome-BGC model and the use of EC measurements to calibrate model parameters can effectively overcome the limitations of its application in alpine grassland ecosystems, which is important for detecting trends in vegetation productivity. This approach could also be upscaled to regional and global scales.

Response of Carbon Dynamics to Climate Change Varied among Different Vegetation Types in Central Asia

The effect of climate change on the spatio-temporal patterns of the terrestrial carbon dynamics in Central Asia have not been adequately quantified despite its potential importance to the global carbon cycle. Therefore, the modified BioGeochemical Cycles (Biome-BGC) model was applied in this study to evaluate the impacts of climatic change on net primary productivity (NPP) and net ecosystem productivity. Four vegetation types were studied during the period 1979 to 2011: cropland, grassland, forest, and shrubland. The results indicated that: (1) The climate data showed that Central Asia experienced a rise in annual mean temperature and a decline in precipitation from 1979 to 2011; (2) the mean NPP for Central Asia in 1979–2011 was 281.79 gC m−2 yr−1, and the cropland had the highest NPP compared with the other vegetation types, with a value of 646.25 gC m−2 yr−1; (3) grassland presented as a carbon source (−0.21 gC m−2 yr−1), whereas the other three types were carbon sinks; (4) the four vegetation types showed similar responses to climate variation during the past 30 years, and grassland is the most sensitive ecosystem in Central Asia. This study explored the possible implications for climate adaptation and mitigation.