Effect of water stress on ecosystem photosynthesis and respiration of a Leymus chinensis steppe in Inner Mongolia

Temporal changes in net ecosystem CO2 exchange and influential factors in an apple orchard in Northeast China

The apple orchards in Liaoning, one of the four major apple-producing areas in Bohai Bay, Northeast China, play a crucial role in regulating the carbon sink effect. However, there is limited information on the variation in carbon flux and its influential factors in apple orchards in this region. To address this, CO2 flux data were monitored throughout the entire apple growth seasons from April to November in 2017 and 2018 in the apple (Malus pumila Mill. cv Hanfu) orchard in Shenyang, China. The energy closure of the apple orchard was calculated, and variations in net ecosystem exchange (NEE) at different time scales and its response to environmental factors were analyzed. Our results showed that the energy balance ratio of the apple was 0.74 in 2017 and 1.38 in 2018. NEE was generally positive in April and November and negative from May to October, indicating a strong carbon sink throughout the growth season. The daily average NEE ranged from - 0.103 to 0.094 mg m-2 s-1 in 2017 and from - 0.134 to 0.059 mg m-2 s-1 in 2018, with the lowest values observed in June and July. NEE was negatively correlated with net radiation, atmospheric temperature, saturated vapor pressure deficit, and soil temperature. These findings provide valuable insights for predicting carbon flux in orchard ecosystems within the context of global climate change.

Responses of biomass accumulation and nutrient utilization along a phosphorus supply gradient in Leymus chinensis

Phosphorus (P) deficiencies are widespread in calcareous soils. The poor availability of nitrogen (N) and P in soils often restricts crop growth. However, the effects of P addition on plant growth and plant nutrient transport changes during the establishment of Leymus chinensis fields in Xinjiang are not clear. We investigated the responses of Leymus chinensis biomass and nutrient absorption and utilization to changes in soil N and P by adding P (0, 15.3, 30.6, and 45.9 kg P ha^-1 year^-1) with basally applied N fertilizer (150 kg N ha^-1 year^-1). The results showed that (a) Principal component analysis (PCA) of biomass, nutrient accumulation, soil available P, and soil available N during the different periods of Leymus chinensis growth showed that their cumulative contributions during the jointing and harvest periods reached 95.4% and 88%, respectively. (b) Phosphorus use efficiency (PUE) increased with the increase of P fertilizer gradient and then decreased and the maximum PUE was 13.14% under moderate P addition. The accumulation of biomass and nutrients in Leymus chinensis can be effectively improved by the addition of P fertilizer at 30.6 kg ha^-1. Different P additions either moderately promoted or excessively inhibited Leymus chinensis growth and nutrient utilization.

Remote Sensing Estimation and Spatiotemporal Pattern Analysis of Terrestrial Net Ecosystem Productivity in China

Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (εmax), was optimized by using vegetation classification data. Then, the NEP was estimated by coupling the optimized CASA model, geostatistical model of soil respiration (GSMSR) and the soil respiration–soil heterotrophic respiration (Rs-Rh) relationship model. The ground observations from ChinaFLUX were used to verify the NEP estimation accuracy. The results showed that the R2 of the optimized CASA model increased from 0.411 to 0.774, and RMSE decreased from 21.425 gC·m−2·month−1 to 12.045 gC·m−2·month−1, indicating that optimizing CASA model by vegetation classification data was an effective method to improve the estimation accuracy of NEP. On this basis, the spatial and temporal distribution of NEP in China was analyzed. The research indicated that the monthly variation of NEP in China was a single peak curve with summer as the peak, which generally presented the pattern of southern region > northern region > Qinghai–Tibet region > northwest region. Furthermore, from 2001 to 2016, most regions of China showed a non-significant level upward trend, but main cropland (e.g., North China Plain and Northeast Plain) and some grassland (e.g., Ngari in Qinghai–Tibet Plateau and Xilin Gol League in Inner Mongolia) showed a non-significant-level downward trend. The study can deepen the understanding of the distribution of carbon sources/sinks in China, and provide a reference for regional carbon cycle research.

Seasonal and interannual variations in ecosystem respiration in relation to temperature, moisture, and productivity in a temperate semi-arid shrubland

Understanding the temporal dynamics and influencing factors of ecosystem respiration (Reco) in semi-arid shrublands is critical for predicting how their carbon balance may respond to climate change. Using the eddy-covariance technique, we quantified the net ecosystem CO₂ exchange (NEE) in a semi-arid shrubland of northern China from July 2011 to December 2016, and partitioned NEE into Reco and gross primary productivity (GPP). Annual Reco varied from 300 g C m^-2 yr^-1 in 2014 to 426 g C m^-2 yr^-1 in 2012, and GPP ranged from 277 g C m^-2 yr^-1 in 2014 to 503 g C m^-2 yr^-1 in 2012. The relationship between half-hourly nighttime Reco and air temperature (T_a) was well-described by the Lloyd & Taylor model. Indicators of the seasonal temperature sensitivity (E₀ and Q₁₀) of Reco increased with both the annual integral and seasonal amplitude of GPP. However, when averaged into 1 °C T_a bins, nighttime Reco increased with T_a up to an optimal temperature of ~20 °C, above which it decreased with increasing T_a. Periods of low soil moisture in spring and summer markedly depressed Reco, contributing to its seasonal and interannual variations. In addition, low soil moisture had little effect on nighttime Reco when T_a was below 15 °C, but substantially reduced nighttime Reco when T_a was above 15 °C. Ecosystem respiration increased linearly with GPP at both seasonal and interannual scales, with the slope being 0.50 and 0.55, respectively. Our results have important implications for predicting Reco under climate change, considering continuous warming and increases in the frequency and intensity of extreme events (e.g., heat waves, droughts). Moreover, our results suggest that process-based carbon models should adequately represent the effects of substrate supply (e.g., by GPP) on Reco and its temperature sensitivity.

The response of carbon stocks of drylands in Central Asia to changes of CO2 and climate during past 35 years

Drylands are terrestrial ecosystems sensitive to climate change. There are totally drylands of 5.17 × 10⁶ km² (above 80% of global total temperate desert area) in Central Asia (CAS), in which significant increases of temperature and changes of precipitation have been detected in recent decades. However, environment-induced changes in terrestrial carbon stocks of these dryland ecosystems have not been well investigated. With the Arid Ecosystem Model (AEM), this study was devoted to analyze spatiotemporal changes of carbon stocks in drylands over CAS during the past 35 years (1980-2014) and to quantify contributions to these changes of various factors, including temperature, precipitation, and atmospheric CO₂ concentration. Over the study period, total stocks of vegetation carbon (VEGC), soil organic carbon (SOC), and litter carbon (LTRC) averaged 2.8 ± 0.05 Pg C, 45.2 ± 0.01 Pg C, and 0.3 ± 0.004 Pg C(1Pg = 10¹⁵ g) in CAS, respectively. Meanwhile, total carbon (TOTC) declined by 0.7 Pg C. Climate change caused TOTC to decrease by 1.3 Pg C. In contrast, CO₂ enrichment effect caused TOTC to increase by 0.9 Pg C. The effects of different factors on TOTC changes varied spatially. Precipitation was the dominant factor regulating TOTC change in 40.9% of the study area, mainly in the desert sparse shrub region in northwest Kazakhstan and the dryland region of southern Xinjiang of China, in which vegetation growth was mainly limited by water resource. CO₂ dominated the change of TOTC in 38.3% of the study area, mainly in the lower altitude regions of Tianshan mountain, in which the hydrothermal condition was relatively suitable for vegetation growth. Ecosystems in southern Xinjiang of China and northwest Kazakhstan are fragile to climate change.

Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems

Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R2 ≥ 0.84), than in the temperate steppe (R2 ≤ 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts.

The response of ecosystem carbon fluxes to LAI and environmental drivers in a maize crop grown in two contrasting seasons

The eddy correlation technique was used to investigate the influence of biophysical variables and crop phenological phases on the behaviour of ecosystem carbon fluxes of a maize crop, in two contrasting growing seasons. In 2009, the reduced water supply during the early growing stage limited leaf area expansion, thus negatively affecting canopy photosynthesis. The variability of gross primary production (GPP) and ecosystem respiration (R eco) was mainly explained by seasonal variation of leaf area index (LAI). The seasonal variation of R eco was positively influenced by soil temperatures (T soil) in 2008 but not in 2009. In 2008, a contribution of both autotrophic and heterotrophic components to total R eco could be hypothesized, while during 2009, autotrophic respiration is supposed to be the most important component. Crop phenological phases affected the response of ecosystem fluxes to biophysical drivers.

Impacts of diffuse radiation on light use efficiency across terrestrial ecosystems based on Eddy covariance observation in China

Ecosystem light use efficiency (LUE) is a key factor of production models for gross primary production (GPP) predictions. Previous studies revealed that ecosystem LUE could be significantly enhanced by an increase on diffuse radiation. Under large spatial heterogeneity and increasing annual diffuse radiation in China, eddy covariance flux data at 6 sites across different ecosystems from 2003 to 2007 were used to investigate the impacts of diffuse radiation indicated by the cloudiness index (CI) on ecosystem LUE in grassland and forest ecosystems. Our results showed that the ecosystem LUE at the six sites was significantly correlated with the cloudiness variation (0.24≤R²≤0.85), especially at the Changbaishan temperate forest ecosystem (R² = 0.85). Meanwhile, the CI values appeared more frequently between 0.8 and 1.0 in two subtropical forest ecosystems (Qianyanzhou and Dinghushan) and were much larger than those in temperate ecosystems. Besides, cloudiness thresholds which were favorable for enhancing ecosystem carbon sequestration existed at the three forest sites, respectively. Our research confirmed that the ecosystem LUE at the six sites in China was positively responsive to the diffuse radiation, and the cloudiness index could be used as an environmental regulator for LUE modeling in regional GPP prediction.

Spatial patterns and climate drivers of carbon fluxes in terrestrial ecosystems of China

Understanding the dynamics and underlying mechanism of carbon exchange between terrestrial ecosystems and the atmosphere is one of the key issues in global change research. In this study, we quantified the carbon fluxes in different terrestrial ecosystems in China, and analyzed their spatial variation and environmental drivers based on the long-term observation data of ChinaFLUX sites and the published data from other flux sites in China. The results indicate that gross ecosystem productivity (GEP), ecosystem respiration (ER), and net ecosystem productivity (NEP) of terrestrial ecosystems in China showed a significantly latitudinal pattern, declining linearly with the increase of latitude. However, GEP, ER, and NEP did not present a clear longitudinal pattern. The carbon sink functional areas of terrestrial ecosystems in China were mainly located in the subtropical and temperate forests, coastal wetlands in eastern China, the temperate meadow steppe in the northeast China, and the alpine meadow in eastern edge of Qinghai-Tibetan Plateau. The forest ecosystems had stronger carbon sink than grassland ecosystems. The spatial patterns of GEP and ER in China were mainly determined by mean annual precipitation (MAP) and mean annual temperature (MAT), whereas the spatial variation in NEP was largely explained by MAT. The combined effects of MAT and MAP explained 79%, 62%, and 66% of the spatial variations in GEP, ER, and NEP, respectively. The GEP, ER, and NEP in different ecosystems in China exhibited 'positive coupling correlation' in their spatial patterns. Both ER and NEP were significantly correlated with GEP, with 68% of the per-unit GEP contributed to ER and 29% to NEP. MAT and MAP affected the spatial patterns of ER and NEP mainly by their direct effects on the spatial pattern of GEP.