Changes In Gross Primary Production Research Articles

Elevated atmospheric CO2 concentration and vegetation structural changes contributed to gross primary productivity increase more than climate and forest cover changes in subtropical forests of China

Abstract. The subtropical forests of China play a pivotal role in the global carbon cycle and in regulating the global climate. Quantifying the individual and combined effects of forest cover change (FCC), vegetation structural change (e.g. leaf area index (LAI)), CO2 fertilisation, and climate change (CC) on the annual gross primary productivity (GPP) dynamics of different subtropical forest types are essential for mitigating carbon emissions and predicting future climate changes, but these impacts remain unclear. In this study, we used a processed-based model to comprehensively investigate the impacts of these factors on GPP variations with a series of model experiments in China's subtropical forests from 2001 to 2018. Simulated GPP showed a significant increasing trend (20.67 gCm-2yr-1, p<0.001) under the interaction effects of FCC, LAI change, rising CO2, and CC. The CO2 fertilisation (6.84 gCm-2yr-1, p<0.001) and LAI change (3.79 gCm-2yr-1, p=0.004) were the two dominant drivers of total subtropical forest GPP increase, followed by the effects of FCC (0.52 gCm-2yr-1, p<0.001) and CC (0.92 gCm-2yr-1, p=0.080). We observed different responses to drivers depending on forest types. The evergreen broad-leaved forests showed the maximum carbon sequestration rate due to the positive effects of all drivers. Both the FCC (0.19 gCm-2yr-1, p<0.05) and CC (1.22 gCm-2yr-1, p<0.05) significantly decreased evergreen needle-leaved forest GPP, while their negative effects were almost offset by the positive impact of LAI changes. Our results indicated that LAI outweighed FCC in promoting GPP, which is an essential driver that needs to be accounted for in studies and ecological and management programmes. Overall, our study offers a novel perspective on different drivers of subtropical forest GPP changes and provides valuable information for policy makers to better manage subtropical forests to mitigate climate change risks.

Spatiotemporal variation and response of gross primary productivity to climate factors in forests in Qiannan state from 2000 to 2020

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in properly determining the maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in the LUE-estimated GPP. The recently developed P model, a new LUE model, captures the adaptability of vegetation to the environment and simplifies parameterization. Site-level studies have proven the superior performance of the P model over LUE models. As a representative karst region with significant changes in forest cover in Southwest China, Qiannan is useful for exploring the spatiotemporal variation in forest GPP and its response to climate change for formulating forest management policies to address climate changes, e.g., global warming. Based on remote sensing and meteorological data, this study estimated the forest ecosystem GPP in Qiannan from 2000–2020 via the P model. This study explored the spatiotemporal changes in GPP in the study region over the past 20 years, used the Hurst index to predict future development trends from a time series perspective, and used partial correlation analysis to analyse the spatiotemporal GPP changes over the past 20 years in response to three factors: temperature, precipitation, and vapor pressure deficit (VPD). Our results showed that (1) the total amount of GPP and average GPP in Qiannan over the past 21 years (2000–2020) were 1.9 × 104 ± 2.0 × 103 MgC ha−1 year−1 and 1238.9 ± 107.9 gC m−2 year−1, respectively. The forest GPP generally increased at a rate of 6.1 gC m−2 year−1 from 2000 to 2020 in Qiannan, and this increase mainly occurred in the nongrowing season. (2) From 2000 to 2020, the forest GPP in Qiannan was higher in the southeast and lower in the northwest, indicating significant spatial heterogeneity. In the future, more than 70% of regional forest GPP will experience a weak increase in nonsustainability. (3) In Qiannan, forest GPP was positively correlated with both temperature and precipitation, with partial correlation coefficients of 0.10 and 0.11, respectively. However, the positive response of GPP to precipitation was approximately 70.47%, while that to temperature was 64.05%. Precipitation had a stronger restrictive effect on GPP than did temperature in this region, and GPP exhibited a negative correlation with VPD. The results showed that an increase in VPD inhibits GPP to some extent. Under rapid global change, the P model GPP provides new GPP data for global ecology studies, and the comparison of various stress factors allows for improvement of the GPP model in the future. The results of this study will aid in understanding the dynamic processes of terrestrial carbon. These findings are helpful for estimating and predicting the carbon budget of forest ecosystems in karst regions, clarifying the regional carbon absorption capacity, clarifying the main factors limiting vegetation growth in these regions, promoting sustainable regional forestry development and serving the “dual carbon goal.” This work has important guiding significance for policy formulation to mitigate climate change.

Shifting mammal communities and declining species richness along an elevational gradient on Mount Kenya.

Conservation areas encompassing elevation gradients are biodiversity hotspots because they contain a wide range of habitat types in a relatively small space. Studies of biodiversity patterns along elevation gradients, mostly on small mammal or bird species, have documented a peak in diversity at mid elevations. Here, we report on a field study of medium and large mammals to examine the impact of elevation, habitat type, and gross primary productivity on community structure. Species richness was observed using a camera trap transect with 219 sites situated across different habitat types from 2329 to 4657 m above the sea level on the western slope of Mt Kenya, the second highest mountain in Africa. We found that the lowest elevation natural habitats had the highest species richness and relative abundance and that both metrics decreased steadily as elevation increased, paralleling changes in gross primary productivity, and supporting the energy richness hypothesis. We found no evidence for the mid-domain effect on species diversity. The lowest elevation degraded Agro-Forestry lands adjacent to the National Park had high activity of domestic animals and reduced diversity and abundance of native species. The biggest difference in community structure was between protected and unprotected areas, followed by more subtle stepwise differences between habitats at different elevations. Large carnivore species remained relatively consistent but dominant herbivore species shifted along the elevation gradient. There was some habitat specialization and turnover in species, such that the elevation gradient predicts a high diversity of species, demonstrating the high conservation return for protecting mountain ecosystems for biodiversity conservation.

Optimal representation of spring phenology on photosynthetic productivity across the northern hemisphere forests

Accurate simulation of the onset of spring is crucial for predicting the photosynthetic productivity of forest ecosystems. Nonetheless, the potential of phenology simulations on predictions of forest gross primary productivity (GPP) remains poorly understood, with previous studies generally focused on predicting phenology dates themselves or on limited scales. Here, we constructed a framework through the critical process of leaf growth to couple a terrestrial biosphere model—FORCCHN2 with five widely used phenology models, including one-phase (involving heat forcing), two-phase (considering chilling demand), and multi-phase models (integrating photoperiod effects) through the critical process of leaf growth. We evaluated the GPP performance from the multiple forest biomes in 74 eddy covariance (EC) sites and compared the total trends to satellite observations across the northern hemisphere forests. The predictions of the five models could reproduce 62.41–67.24 % variations of the GPP observations in all EC sites. In the deciduous broadleaf forest sites, we found the two-phase and multi-phase coupled models performed better than the one-phase coupled model. Chilling may play an essential role in controlling photosynthetic activity in deciduous broadleaf forests. Moreover, the multi-phase coupled model integrated photoperiod effects performed best at capturing GPP changes of the whole northern hemisphere forests. The predictions also showed the GPP in the northern hemisphere ranged from 21.68 to 26.06 Pg C year−1, and the total GPP showed an increasing trend. This study provides local and regional available evidence for the impact of phenology on photosynthesis with climate warming. The results highlight the necessity for enhancing understanding of the phenology factors on GPP and integrating the appropriate phenology representation to improve predictions of photosynthetic productivity for forests, especially in prediction to large scales and long-term trends.

Comparison of carbon and water fluxes and the drivers of ecosystem water use efficiency in a temperate rainforest and a peatland in southern South America

Abstract. The variability and drivers of carbon and water fluxes and their relationship to ecosystem water use efficiency (WUE) in natural ecosystems of southern South America are still poorly understood. For 8 years (2015–2022), we measured carbon dioxide net ecosystem exchange (NEE) and evapotranspiration (ET) using eddy covariance towers in a temperate rainforest and a peatland in southern Chile. NEE was partitioned into gross primary productivity (GPP) and ecosystem respiration (Reco), while ET was partitioned into evaporation (E) and transpiration (T) and used to estimate different expressions of ecosystem WUE. We then used the correlation between detrended time series and structural equation modelling to identify the main environmental drivers of WUE, GPP, ET, E and T. The results showed that the forest was a consistent carbon sink (−486 ± 23 g C m−2 yr−1), while the peatland was, on average, a small source (33 ± 21 g C m−2 yr−1). WUE is low in both ecosystems and likely explained by the high annual precipitation in this region (∼ 2100 mm). Only expressions of WUE that included atmospheric water demand showed seasonal variation. Variations in WUE were related more to changes in ET than to changes in GPP, while T remained relatively stable, accounting for around 47 % of ET for most of the study period. For both ecosystems, E increased with higher global radiation and higher surface conductance and when the water table was closer to the surface. Higher values for E were also found with increased wind speeds in the forest and higher air temperatures in the peatland. The absence of a close relationship between ET and GPP is likely related to the dominance of plant species that either do not have stomata (i.e. mosses in the peatland or epiphytes in the forest) or have poor stomatal control (i.e. anisohydric tree species in the forest). The observed increase in potential ET in the last 2 decades and the projected drought in this region suggests that WUE could increase in these ecosystems, particularly in the forest, where stomatal control may be more significant.

The impact of drought time scales and characteristics on gross primary productivity in China from 2001 to 2020

ABSTRACT Drought events have occurred frequently in the past and have had profound effects on Gross Primary Productivity (GPP). However, the role of different drought event characteristics in the response of GPP to drought is not well understood. To address this issue, we investigated the spatiotemporal response patterns of Chinese terrestrial ecosystem GPP to different time scales of meteorological droughts, and explored the difference of GPP response to drought events under various drought characteristics. Our research revealed that: Different time scales of meteorological droughts significantly impacted the GPP of 17.3%–25.6% area of China. Among them, the impact range of 1-month and 12-month meteorological drought was the smallest and largest respectively. GPP along the 400 mm isohyet line was more susceptible to drought. Second, grasslands and shrubs are more susceptible to drought than forested areas. Among the characteristics of drought events, drought severity had the greatest impact on GPP changes, while drought frequency had the least impact. An increase in drought frequency intensified the impact of 1-month meteorological drought on GPP. Increased drought duration and severity weakened the impact of 3-month meteorological drought on GPP and intensified the impact of 1-month and 12-month meteorological droughts on GPP. Our study reveals the impact of different time scales drought and drought characteristics on plant productivity, and provides more possibilities for understanding the effect of drought on plant productivity.

Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRV), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR2 = 0.85), followed by NIRV (meanR2 = 0.84), NDVI (meanR2 = 0.76), and kNDVI (meanR2 = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR2 = 0.85). In capture during drought, NIRV and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR2 = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIRV, and kNDVI will improve our understanding of GPP changes.

Using phenology to unravel differential soil water use and productivity in a semiarid savanna

AbstractSavannas are water‐limited ecosystems characterized by two dominant plant types: trees and an understory primarily made up grass. Different phenology and root structures of these plant types complicate how savanna primary productivity responds to changes in water availability. We tested the hypothesis that productivity in savannas is controlled by the temporal and vertical distribution of soil water content (SWC) and differences in growing season length of understory and tree plant functional types. To quantify the relationship between tree, understory, and savanna‐wide phenology and productivity, we used PhenoCam and satellite observations surrounding an eddy covariance tower at a semiarid savanna site in Arizona, USA. We distinguished between SWC across two different depth intervals (shallow, <0–30 cm and deep, >30–100 cm). We found that tree greenness increased with SWC at both depths, while understory greenness was only sensitive to the shallower SWC measurements. Onset of ecosystem dormancy, estimated from satellite observations close to the eddy covariance tower, explained more variability in annual gross primary productivity (GPP) than in other phenometrics. Higher SWC led to an extended growing season, caused by delayed dormancy in trees, but the understory showed no evidence of delayed dormancy in wetter periods. We infer that the timing of ecosystem scale dormancy, driven by trees, is important in understanding changes in a savanna's GPP. These findings highlight the important effects of rainfall during the winter. These findings suggest that savanna GPP is conditional on different responses to moisture availability in each of the dominant vegetation components.

Increasing gross primary productivity under soil warming and wetting on the Tibetan Plateau

The soil freeze-thaw process has undergone significant changes on the Tibetan Plateau (TP) in the context of global change, resulting in the changes of soil physical and chemical properties, thereby affecting the vegetation phenology and photosynthesis through affecting the utilization capacity of CO2 and light by vegetation. However, little is known about how soil temperature (ST) and soil moisture (SM) affect the gross primary productivity (GPP) on the TP at different seasons and elevations. In this study, the spatiotemporal variation patterns of GPP, ST, and SM were analyzed based on the Community Land Model version 5.0 (CLM5.0) simulations in order to illustrate the impacts of ST and SM in surface (0–10 cm) and root zone soil (0–100 cm) on GPP between 1979 and 2020. The results showed that the CLM5.0-based GPP and ST were in good agreement with in situ observations. ST, SM and GPP increased at the rates of 0.04 °C a−1, 2.4 × 10−4mm3 mm−3 a−1, and 5.36 g C m−2 a−2, respectively. SM dominated the variations of GPP in winter (64.3%), while ST almost was the dominant factor in other periods, especially spring (99.9%) and autumn (94.7%). The explanatory power of ST and SM for GPP increased with elevation, especially for ST. The relative contributions of ST and SM to GPP at different time scales in root zone soil were similar to those in surface soil. This study provided a new understanding of how soil freeze-thaw affected GPP changes on the TP in the context of the intensification of warming and humidification.

Distinct Contributions of Climate Change and Anthropogenic Activities to Evapotranspiration and Gross Primary Production Variations over Mainland China

In recent decades, China has experienced substantial climate change and significant vegetation greenness due to the extensive implementation of artificial ecological restoration programs. However, the quantitative contributions of climatic and anthropogenic drivers to the national variations in associated evapotranspiration (ET) and gross primary productivity (GPP) over China at different climate zoning sub-regions remain unclear. Based on the analysis of climate factor and vegetation disturbance trends created by anthropogenic activities, this study constructed a remote sensing-based ecological model consisting of Penman–Monteith–Leuning (PML) and light use efficiency (LUE) components. The proposed model simulated the spatiotemporal changes in ET and GPP between 1999 and 2018 over China. The contributions of climatic factors and anthropogenic activities to ET and GPP variations were quantitatively calculated by ridge regression. The results show that (1) both interannual ET and GPP markedly increased, by 1.32 mm yr−1 and 8.01 g C m−2 yr−1, respectively; (2) vegetation changes due to anthropogenic disturbance made the dominant contribution to GPP variations over China, while the dominant factor influencing ET changes differed by sub-region due to the joint effects of vegetation and climate; (3) temperature and precipitation positively affected ET, while wind speed, humidity, and solar radiation negatively contributed to ET in most parts of Mainland China. These findings may provide a workable, scientific reference for further ecological restoration decision-making processes in China.

The coincidence of climate extremes with sensitive crop growth phases: Projected impact on sustainable crop water use and crop yield in the IGB river basins

Increased climate variability and extremes are unequivocal with unprecedented impacts on water resources and agriculture production systems. However, little is known about the impacts of climate extremes at the intra-seasonal level which remained largely unexplored. We investigated the coincidence of climate extremes with sensitive crop growth phases of wheat and rice in the Indus, Ganges and Brahmaputra (IGB) river basins of South Asia. We also quantified the related impacts on irrigation water demand (IWD), gross primary production (GPP) and crop yields (CY) simulated by a hydrological-vegetation model (LPJmL) during 1981–2100 using RCP4.5-SSP1 and RCP8.5-SSP3 framework. The climate extremes revealed a higher frequency and intensity during crop growth phases with significant increasing trends in future. Diverse changes in IWD, GPP and CY are projected in future under the influence of crop phase-specific extremes. The crop phase-specific changes in the IWD of wheat and rice will intensify in the future. More than 50 % of the change in future wheat irrigation is caused by warm and dry extremes during the ripening phase. Whereas, increase in IWD for rice is mainly associated with warm extremes only. The crop phase-specific GPP shows a decreasing trend in future for both wheat and rice in the Western part of IGB with the largest decrease during the reproductive phase of wheat (up to 36 %) and vegetative phase of rice (>20 %). This decrease is clearly reflected in seasonal yields i.e., both wheat (20 %) and rice (12 %) showed a decrease in future linked with warm and dry extremes. However, in the Eastern part of IGB, the GPP will mostly increase in future during the three crop phases of wheat and rice. These results can be used to help develop efficient adaptation strategies considering seasonal changes and sensitive crop phases for sustained food and water security in South Asia.

The direct and indirect effects of the environmental factors on global terrestrial gross primary productivity over the past four decades

Gross primary productivity (GPP) is jointly controlled by the structural and physiological properties of the vegetation canopy and the changing environment. Recent studies showed notable changes in global GPP during recent decades and attributed it to dramatic environmental changes. Environmental changes can affect GPP by altering not only the biogeochemical characteristics of the photosynthesis system (direct effects) but also the structure of the vegetation canopy (indirect effects). However, comprehensively quantifying the multi-pathway effects of environmental change on GPP is currently challenging. We proposed a framework to analyse the changes in global GPP by combining a nested machine-learning model and a theoretical photosynthesis model. We quantified the direct and indirect effects of changes in key environmental factors (atmospheric CO2 concentration, temperature, solar radiation, vapour pressure deficit (VPD), and soil moisture (SM)) on global GPP from 1982 to 2020. The results showed that direct and indirect absolute contributions of environmental changes on global GPP were 0.2819 Pg C yr−2 and 0.1078 Pg C yr−2. Direct and indirect effects for single environmental factors accounted for 1.36%–51.96% and 0.56%–18.37% of the total environmental effect. Among the direct effects, the positive contribution of elevated CO2 concentration on GPP was the highest; and warming-induced GPP increase counteracted the negative effects. There was also a notable indirect effect, mainly through the influence of the leaf area index. In particular, the rising VPD and declining SM negatively impacted GPP more through the indirect pathway rather than the direct pathway, but not sufficient to offset the boost of warming over the past four decades. We provide new insights for understanding the effects of environmental changes on vegetation photosynthesis, which could help modelling and projection of the global carbon cycle in the context of dramatic global environmental change.

Hydrothermal Conditions in Deep Soil Layer Regulate the Interannual Change in Gross Primary Productivity in the Qilian Mountains Area, China

The variability in soil hydrothermal conditions generally contributes to the diverse distribution of vegetation cover types and growth characteristics. Previous research primarily focused on soil moisture alone or the average values of soil hydrothermal conditions in the crop root zone (0–100 cm). However, it is still unclear whether changes in gross primary productivity (GPP) depend on the hydrothermal conditions at different depths of soil layers within the root zone. In this study, the soil hydrothermal conditions from three different layers, surface layer 0–7 cm (Level 1, L1), shallow layer 7–28 cm (Level 2, L2), and deep layer 28–100 cm (Level 3, L3) in the Qilian Mountains area, northwestern China, are obtained based on ERA5-Land reanalysis data. The Sen-MK trend test, Pearson correlation analysis, and machine learning algorithm were used to explore the influence of these three soil hydrothermal layers on GPP. The results show that soil moisture values increase with soil depth, while the soil temperature values do not exhibit a stratified pattern. Furthermore, the strong correlation between GPP and deep soil hydrothermal conditions was proved, particularly in terms of soil moisture. The Random Forest feature importance extraction revealed that deep soil moisture (SM-L3) and surface soil temperature (ST-L1) are the most influential variables. It suggests that regulations of soil hydrothermal conditions on GPP may involve both linear and nonlinear effects. This study can obtain the temporal and spatial dynamics of soil hydrothermal conditions across different soil layers and explore their regulations on GPP, providing a basis for clarifying the relationship between soil and vegetation in arid mountain systems.

Global prediction of gross primary productivity under future climate change

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid–high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

Asymmetric response of vegetation GPP to impervious surface expansion: Case studies in the Yellow and Yangtze River Basins

Terrestrial gross primary production (GPP) changes due to impervious surfaces significantly impact ecosystem services in watersheds. Understanding the asymmetric response of vegetation GPP to impervious surface expansion is essential for regional development planning and ecosystem management. However, the asymmetric response of vegetation GPP to the impacts of impervious surface expansion is unknown in different watersheds. This paper selected the Yellow River and Yangtze River basins as case studies. We characterized the overall change in GPP based on changes in impervious surface ratio (ISR), determined impervious surface expansion's direct and indirect impacts on GPP in the two watersheds, and further analyzed the asymmetric response of the compensatory effects of indirect influences on the impervious surface expansion in different watersheds. The results showed that: (1) The vegetation GPP decreased with increasing ISR in the Yangtze River Basin, while that in the Yellow River Basin first increased and then reduced. (2) The direct impacts of increased ISR reduced vegetation GPP, while the indirect impacts both had a growth-compensating effect. Growth compensation stabilized at approximately 0.40 and 0.30 in the Yellow and Yangtze River Basins. (3) When the ISR was 0.34–0.56, the growth compensation could offset the reduction of GPP due to direct impact and ensure that the background vegetation GPP was not damaged in the Yellow River Basin. In contrast, the background vegetation GPP was inevitably impaired with increased ISR in the Yangtze River Basin. Therefore, this study suggests that the ISR should be ensured to be between 0.34 and 0.56 to maximize the impervious surface of the Yellow River Basin without compromising the background vegetation GPP. While pursuing impervious surface expansion in the Yangtze River Basin, other programs should be sought to compensate for the loss to GPP.

Potential evapotranspiration determines changes in the carbon sequestration capacity of forest and grass ecosystems in Xinjiang, Northwest China

Accurately understanding carbon stock in forest and grass ecosystems, determining carbon sequestration capacity (CSC), and identifying influencing factors over time are crucial for regional carbon budget assessment. This paper studied changes in gross primary productivity (GPP), net primary productivity (NPP), and net ecosystem productivity (NEP) of forest and grass in Xinjiang, China from 1990 to 2020, showing the evolution of CSC and identifying key factors. The results showed that the photosynthetic carbon sequestration capacity of forest-grass ecosystems increased significantly over 30 years, with a growth rate of 2.57 g C m−2 a−1. Total carbon sequestration capacity also showed an increasing trend, with NPP increasing from 7.42 g C m−2 per decade in the 1990 s to 34.91 g C m−2 per decade in 2010–2020. Net carbon sequestration capacity was also effectively increased, with NEP increasing from − 45.98 g C m−2 to 30.20 g C m−2 from 1990 to 2020, a clear shift from a carbon source to a sink. CSC is higher in mountainous areas and areas around rivers, and stronger in the northern regions. Forests are the most important carbon reservoir, with an annual net carbon stock of 0.18 million tonnes, and grasslands have a high carbon sequestration potential, with an annual photosynthetic carbon stock of 154 million tonnes. The ecological water transfer projects implemented in the Tarim River Basin have helped to increase the CSC. Potential evapotranspiration (PET) greatly affects carbon use efficiency, explaining 59.05% of it. The interaction of any selected factors with PET enhances the explanatory power. The study results offer scientific and theoretical backing for attaining regional carbon neutrality.

Sensitivity of simulated soil water content, evapotranspiration, gross primary production and biomass to climate change factors in Euro-Mediterranean grasslands

Grassland models often yield more uncertain outputs than arable crop models due to more complex interactions and the largely undocumented sensitivity of grassland models to environmental factors. The aim of the present study was to assess the impact of single-factor changes in temperature, precipitation, and atmospheric [CO2] on simulated soil water content (SWC), actual evapotranspiration (ET), gross primary production (GPP) and yield biomass, and also to link the sensitivity analysis with experimental results. We employed an unprecedented multi-model framework consisting of seven grassland models at nine sites with different environmental characteristics in Europe and Israel, with two management options at three sites. For warming/cooling and wetting/drying, models showed general consistency in the direction of SWC and ET changes, but less agreement regarding GPP and biomass changes. The simulated responses consistently revealed an overall positive effect of CO2 enrichment on GPP and biomass, while the direction of change differed for SWC and ET. Comparing with single-factor experimental manipulations, SWC simulations slightly underestimated the observed effect of warming, while the overall mean model sensitivity for biomass (+7.5%) closely matched the mean response observed with 1–2 °C warming (+6.6%). The models exhibited lower sensitivity of SWC to wetting or drying compared to the experiments. The overall mean sensitivity of biomass to drying was -4.3%, contrasting with the mean experimental effect size of -9.6%, which proved to be more realistic than the mean wetting effect (+3.2%, against +38.9% in the field trials). The simulated sensitivity of SWC to CO2 enrichment was markedly underestimated, while the biomass response (+12.0%) closely matched the observations (+17.5%). Although the multi-model averaging did not manifestly improve the realism of the simulations, it ensured a realistic response in the direction of change to varying conditions. The results suggest a paradigm shift in grassland modelling meaning that the usual practice of model optimisation/validation needs to be complemented by a sensitivity analysis following the approach presented. The results also highlight the importance of model improvements, especially in terms of soil hydrology representation, a key environmental driver of grassland functioning.

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.

Prominent Impact of Diurnal Rainfall Variations on Evapotranspiration and Gross Primary Productivity in Forests over Low Latitudes

Rainfall amount and its variability on various time scales can significantly influence ecosystem evapotranspiration (ET) and gross primary productivity (GPP). However, little attention has been paid to how ET and GPP respond to diurnal rainfall variations. Here we investigate the effect of diurnal rainfall variations on ET and GPP, based on data at 14 forest sites between 35°S-35°N from FLUXNET2015. Diurnal rainfall variations are represented by four indices. The timing of precipitation within one day is represented by the fraction of daytime precipitation (Frc) and the rainfall peaking time hrpeak. The frequency of precipitation (Freq) is expressed in hours with rainfall out of 24 hours on the day. An Unranked Gini index (UGi) is newly introduced to represent the unevenness of diurnal rainfall distribution in both timing and frequency. Our results illustrate prominent influences of diurnal rainfall variations on ET and GPP at the studied forest sites in lower latitudes. Given an amount of daily total precipitation, more daytime rainfall either in intensity or frequency (larger Frc or Freq), especially occurring around noon (shorter period from hrpeak), could lead to less daily total ET and GPP. More unevenly distributed rainfall being more away from noon (larger UGi) could result in more daily total ET and GPP. These influences become stronger with more daily total rainfall except for Freq. The regression coefficients of ET and GPP to UGi could reach 0.6, which is twice of Frc and hrpeak. During a rainfall, solar radiation, air temperature and vapor pressure deficit would decrease, leading to decreases in ET and GPP. The concurrent diurnal variations in rainfall and other meteorological variables drive changes in ET and GPP. Considering diurnal variations of rainfall in observations and models would be helpful to improve our understanding of atmosphere-biosphere interactions in the future.

Historical Attributions and Future Projections of Gross Primary Productivity in the Yangtze River Basin under Climate Change Based on a Novel Coupled LUE-RE Model

Attributions and predictions of gross primary productivity (GPP) under climate change is of great significance for facilitating a deeper understanding of the global and regional terrestrial carbon cycle and assessing ecosystem health. In this study, we have designed a novel approach to simulate GPP based on the satellite and meteorological data compiling the advantages of the light use efficiency model with regression methods (LUE-RE model), which overcomes the limitation of the satellite-based method in GPP simulation and projection in the future time without satellite data. Based on the proposed method, results show that GPP in the Yangtze River Basin shows a significant increase trend in the historical period. Elevated CO2 dominates the changes of GPP in the Yangtze River Basin. In the future, with the increase in elevated CO2 and climate change, the trend of GPP growth is more obvious. The growth slopes under different scenarios are 2.65 gCm−2year−1a−1, 12.34 gCm−2year−1a−1, 24.91 gCm−2year−1a−1, and 39.62 gCm−2year−1a−1. There are obvious seasonal differences in the future changes of GPP in the Yangtze River Basin, of which the GPP changes mostly in spring. The spatial patterns show that higher GPP is concentrated in the upper stream, while the low values are mainly concentrated in the middle reaches. This study contributes a new method to project GPP and highlights that stakeholders should pay more attention to the significant GPP increases in spring in the future.