Total Gross Primary Production Research Articles

Simulating Climatic Patterns and Their Impacts on the Food Security Stability System in Jammu, Kashmir and Adjoining Regions, India

This study investigated the historical climate data and future projections under the SSP5-8.5 scenario for Jammu, Kashmir (J&K), and its adjoining regions in India. Agriculture is a critical economic pillar of this region, making it highly vulnerable to climate change. This study focused on temperature and precipitation trends. Statistical analysis and modeling methods, including cloud computing, were employed to predict changes and assess their impact on agricultural productivity and water resources. The results indicated that by 2100, the mean maximum and minimum temperatures are projected to increase by approximately 2.90 °C and 2.86 °C, respectively. Precipitation variability is expected to rise, with a mean increase of 2.64 × 10−6 mm per day. These changes have significant consequences for crop yield, water stress, and ecosystem dynamics. An analysis of Gross Primary Productivity (GPP) as a proxy for agricultural productivity using linear regression revealed a concerning trend. Although the total GPP of the study area remained stable over time, it declined by −570 g yr−1 in 2010, coinciding with a 1 °C temperature rise. Projections based on the expected 3 °C temperature increase by 2100 suggest a total GPP loss of −2500 g yr−1. These findings highlight the urgent need for proactive adaptation measures, including sustainable agricultural practices, improved water management, and enhanced socioeconomic infrastructure, to mitigate the impact of climate change and ensure long-term resilience and food security in the region.

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.

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.

Comparative assessment of leaf photosynthetic capacity datasets for estimating terrestrial gross primary productivity

The maximum Rubisco carboxylation rate normalized to 25 °C (Vcmax25) is a key parameter in terrestrial biosphere models for simulating carbon cycling. Recently, global distributions of Vcmax25 have been derived through various methods and different data, including field measurements, ecological optimality theory (EOT), leaf chlorophyll content (LCC), and solar-induced chlorophyll fluorescence (SIF). However, direct validation poses challenges due to high uncertainty arising from limited ground-based observations. This study conducted an indirect evaluation of four Vcmax25 datasets by assessing the accuracy of gross primary productivity (GPP) simulated using the Biosphere-atmosphere Exchange Process Simulator (BEPS) at both site and global scales. Results indicate that, compared to utilizing Vcmax25 fixed by plant functional types (PFT) derived from field measurements, incorporating Vcmax25 derived from SIF and LCC (SIF + LCC), or solely LCC, into BEPS significantly reduces simulated errors in the annual total GPP, with a 23.2 %–25.1 % decrease in the average absolute bias across 196 FLUXNET2015 sites. Daily GPP for evergreen needleleaf forests, deciduous broadleaf forests, shrublands, grasslands, and croplands shows a 7.8 %–27.6 % decrease in absolute bias, primarily attributed to reduced simulation errors during off-peak seasons of vegetation growth. Conversely, the annual total GPP error simulated using EOT-derived Vcmax25 increases slightly (2.2 %) compared to that simulated using PFT-fixed Vcmax25. This is primarily due to a significant overestimation in evergreen broadleaf forests and underestimation in croplands, despite slight increased accuracy for other PFTs. The global annual GPP simulated using Vcmax25 with seasonal variations (i.e., LCC Vcmax25 and SIF + LCC Vcmax25) yields a 4.3 %–7.3 % decrease compared to that simulated using PFT-fixed Vcmax25. Compared to FLUXCOM and GOSIF GPP products, the GPP simulated based on SIF + LCC Vcmax25 and LCC Vcmax25 demonstrates better consistency (R2 = 0.91–0.93, RMSE = 314.2–376.6 g C m−2 yr−1). This study underscores the importance of accurately characterizing the spatiotemporal variations in Vcmax25 for the accurate simulation of global vegetation productivity.

Spatiotemporal patterns and driving factors of gross primary productivity over the Mongolian Plateau steppe in the past 20 years

The Eurasian steppe is the largest temperate grassland in the world. The grassland of the Mongolian Plateau (MP) represents an important part of the Eurasian steppe with high climatic sensitivity. Gross primary productivity (GPP) is a key indicator of the grassland's production, status and dynamic on the MP. In this study, we calibrated and evaluated the grassland-specific light use efficiency model (GRASS-LUE) against the observed GPP collected from nine eddy covariance flux sites on the MP, and compared the performance with other four GPP products (MOD17, VPM, GLASS and GOSIF). GRASS-LUE with higher R2 (0.91) and lower root mean square error (RMSE = 0.99 gC m−2 day−1) showed a better performance compared to the four GPP products in terms of model accuracy and dynamic consistency, especially in typical and desert steppe. The parameters of the GRASS-LUE are more suitable for water-limited grassland could be the reason for its outstanding performance in typical and desert steppe. Mean grassland GPP derived from GRASS-LUE was higher in the east and lower in the west of the MP. Grassland GPP was on average 205 gC m−2 over the MP between 2001 and 2020 with mean annual total GPP of 322 TgC yr−1. 30 % of the MP steppe showed a significant GPP increase. Growing season precipitation is the main factor affecting GPP of the MP steppe across regions. Anthropogenic factors (livestock density and population density) had greater effect on GPP than growing season temperature in pastoral counties in IM that take grazing as one of main industries. These findings can inform the status and trend of the productivity of MP steppe and help government and scientific research institutions to understand the drivers for spatial pattern of grassland GPP on the MP.

Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity

Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (φ0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for φ0 (Topt_phi0). The roles of modified APAR and φ0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and φ0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m−2d−1, -0.14 g C m−2d−1, 1.77 g C m−2d−1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr−1 during 1981—2021, in the upper range of existing GPP products.

A dynamic-leaf light use efficiency model for improving gross primary production estimation

Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for GPP estimation. However, the two-leaf (TL)-LUE model using a ‘potential’ sunlit leaf area index (LAIsu) can separate a portion of LAIsu even when the canopy does not receive any direct radiation, leading to the underestimation of GPP under cloudy and overcast days. Here, we developed a dynamic-leaf (DL) LUE model by introducing an ‘effective’ LAIsu to improve GPP estimation, which considers the comprehensive contribution of LAIsu when the canopy does and does not receive direct radiation. In particular, the new model decreases LAIsu to zero when direct radiation reaches zero. Our evaluation at eight ChinaFLUX sites showed that (1) the DL-LUE model outperformed the most well-known BL-LUE (namely, the MOD17 GPP algorithm) and TL-LUE models in reproducing the daily in situ GPP, especially at four forest sites [reducing the root mean square error (RMSE) from 1.74 g C m−2 d−1 and 1.53 g C m−2 d−1 to 1.36 g C m−2 d−1 and increasing the coefficient of determination (R 2) from 0.74 and 0.79–0.82, respectively]. Moreover, the improvements were particularly pronounced at longer temporal scales, as indicated by the RMSE decreasing from 29.32 g C m−2 month−1 and28.11 g C m−2 month−1 to 25.81 g C m−2 month−1 at a monthly scale and from 231.82 g C m−2 yr−1 and 221.60 g C m−2 yr−1–200.00 g C m−2 yr−1 at a yearly scale; (2) the DL-LUE model mitigated the systematic underestimation of the in situ GPP by both the TL-LUE and BL-LUE models when the clearness index (CI) was below 0.5, as indicated by the Bias reductions of 0.25 g C m−2 d−1 and 0.46 g C m−2 d−1, respectively; and (3) the contributions of the shaded GPP to the total GPP from the DL-LUE model were higher by 0.07–0.16 than those from the TL-LUE model across the eight ChinaFLUX sites. The proposed parsimonious and effective DL-LUE model not only has great potential for improving global GPP estimations but also provides a more mechanism-based approach for partitioning the total GPP into its shaded and sunlit components.

A High-Resolution Simulation of Forest Ecosystem Carbon Budget Change with Complex Terrain

Forest ecosystems represent important carbon sinks within terrestrial ecosystems. Simulating carbon budgets in forest ecosystems located within complex terrains forms the foundation for understanding global carbon cycle dynamics. In this study, employing the FORCCHN model and utilizing mountain climate data as forcing parameters, the impact of the terrain factors of the Changbai Mountain Nature Reserve on carbon budget change in 1998–2017 was studied. The dynamics and distribution patterns of carbon fluxes at different slope aspects and elevations were discussed. Findings demonstrated that between 1998 and 2017, the per-unit area gross primary production (GPP), net primary production (NPP), and net ecosystem production (NEP) for the Changbai Mountain Reserve were 839.35, 462.03, and 192.72 gC m −2 a −1 , respectively. The carbon fluxes varied significantly with elevation: the GPP of the north, south, and west and the NPP of the east, south, and west decreased, and the NEP of the south and west increased. The total amount in GPP and NPP of the Changbai Mountain Nature Reserve fluctuated slightly year by year, but the carbon sequestration capacity decreased from year to year. The reserve had an average annual total GPP, NPP, and NEP of 1.62, 0.89, and 0.37 TgC a −1 , respectively. The north was the strongest carbon sink in the study area, contributing 35.98%, 35.88%, and 36.80% to the total amount of GPP, NPP, and NEP, respectively. This study emphasized the significance of precise mountain meteorological data to accurately quantify the carbon fluxes of mountain forest ecosystems.

A novel partitioning of gross primary production and water use efficiency for sustaining water and food security using Budyko hypothesis

This study coupled the green water and blue water accounting with the existing standard Budyko framework and Fu's 1-parameter Budyko framework to diagnose the basin hydrological behavior. Both Budyko frameworks were employed to determine green water consumption (ETGreen) and blue water consumption (ETBlue) which, in turn, were used to map the blue water index (BWI) hotspots and green water index (GWI) bright spots. The relative contributions of green water and blue water were quantified for sustaining water and food security. A new methodology is proposed using BWI and GWI for partitioning the Gross Primary Production (GPP) and Water Use Efficiency (WUE) into GPPBlue, GPPGreen and WUEBlue and WUEGreen. The methodology was applied to five sub-basins of the Central Godavari River Basin (CGRB): Purna, Dhalegaon, GR Bridge, Yeli and Delta. Results showed that all five basins exhibited larger deviations from the theoretical Budyko curve of Fu's 1-parameter Budyko framework than did the standard Budyko framework and the Dhalegaon basin showed the largest deviations. The partitioning of GPP and WUE by the proposed methodology showed that the proportion of GPPGreen to the total GPP was much higher than that of the GPPBlue. Similarly, the proportion of WUEGreen to WUE was more than that of WUEBlue. The mapping of GPPBlue and GPPGreen, and WUEBlue and WUEGreen showed that the Delta and Yeli basins had the highest values of both GPPGreen & GPPBlue and WUEBlue and WUEGreen (bright spot basins) and the Dhalegaon and parts of GR Bridge basin had the lowest values (hot spot basins). The proposed partitioning of GPP and WUE will help identify the relative contributions of green water and blue water (for managing agricultural waters) and formulate agronomical and engineering practices for stakeholders and policy makers for increasing the overall WUE and GPP to sustain water and food security.

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.

Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method

Maize is one of the main food crops and is widely planted in China; however, it is difficult to get timely and precise information on yields. Because of the benefits of remote sensing technology, satellite-based models (e.g., eddy covariance light use efficiency, EC-LUE) have a lot of potential for monitoring crop productivity. In this study, the gross primary productivity (GPP) of maize in the NCP was estimated using the EC-LUE model, and the GPP was subsequently transformed into yield using the harvest index. Specifically accounting for the spatiotemporal variation in the harvest index, the statistical yield and estimated GPP from the previous year were used to generate region-specific harvest indexes at the county scale. The model’s performance was assessed using statistical yield data. The results demonstrate that the increase in the total GPP in the summer maize-growing season in the NCP is directly related to the increase in the planting area, and the harvest index has significant heterogeneity in space, and the fluctuation in time is small, and the estimated yield can simulate 64% and 55%, respectively, of the variability in the yield at the county and city scales. The model also accurately captures the inter-annual changes in yield (the average absolute percentage errors are less than 20% for almost all years), but model performance varies by region. It performs better in continuous areas of maize-growing. The results from this study demonstrate that the EC-LUE model can be applied to estimate the yield from a variety of crops (other than winter wheat) and that it can be used in conjunction with a region-specific harvest index to track the production of large-scale crops.

Extracting subpixel vegetation NDVI time series for evaluating the mixed pixel effect on GPP estimation in urban areas

ABSTRACT The normalized difference vegetation index (NDVI) is the most widely used vegetation index for monitoring vegetation vigor and cover. As NDVI time series are usually derived at coarse or medium spatial resolutions, pixel size often represents a mixture of vegetated and non-vegetated surfaces. In heterogeneous urban areas, mixed pixels impede the accurate estimation of gross primary productivity (GPP). To address the mixed pixel effect on NDVI time series and GPP estimation, we proposed a framework to extract subpixel vegetation NDVI (NDVIvege) from Landsat OLI images in urban areas, using endmember fractions, mixed NDVI (NDVImix), and NDVI of non-vegetation endmembers. Results demonstrated that the NDVIvege extracted by this framework agreed well with the true NDVIvege cross seasons and vegetation fractions, with R2 ranging from 0.74 to 0.82 and RMSE ranging from 0.03 to 0.04. The NDVIvege time series was applied to evaluate vegetation GPP in Wuhan, China. The total annual GPP estimated with NDVIvege was 28-35% higher than the total annual GPP estimated with NDVImix, implying uncertainty in the GPP estimations caused by mixed pixels. This study showed the potential of the proposed framework to resolve NDVIvege for characterizing vegetation dynamics in heterogeneous areas.

Evaluation of the impacts of ozone on the vegetation productivity of woodland and grassland ecosystems in China

In the context of global warming, with the increasing tropospheric ozone concentration and the obvious trend of aridity, terrestrial ecosystems are experiencing multiple pressures. However, there are still many uncertainties in modeling the effects of ozone on ecosystem productivity. In this study, an ozone uptake module was coupled to the BEPS (Boreal Ecosystem Productivity Simulator) model, and the new version, the BEPS-O3 model, was used to simulate the impacts of ozone on the gross primary productivity (GPP) of woodland and grassland ecosystems in China. Observational data from four sites were used to verify and improve the models, namely, DXG (grassland), HBG (shrub), DHS (evergreen broad-leaved) and CBS (coniferous forest). The regional results show that the total GPP of forest, grassland and shrub in China was 3.62 Pg C, 0.75 Pg C 0.33 Pg C in 2016, respectively. The GPP loss of forests, grasslands and shrubs caused by ozone was 2.8% and 1.1% and 1.5% of the GPP in woodland and grassland ecosystems, respectively (excluding farmland). Subtropical regions experienced the worst GPP losses, although ozone concentrations were not high. However, higher stomatal conductance leads to higher ozone absorption. By comparing the parameters of previous studies, we conclude that the ozone uptake function was better than the ozone exposure function in modeling. Furthermore, the selection of the ozone uptake function will have a significant impact on the final simulation results, especially for grassland.

The impacts of ambient ozone pollution on China's wheat yield and forest production from 2010 to 2021

Near-surface ozone causes damages on both crop and forest but their long-term spatiotemporal changes in China have been insufficiently explored, preventing comprehensive policy making with food security and climate targets. Moreover, limitation exists in the current metrics for long-term regional ozone risk assessment, AOT40 (the accumulated hourly ozone over a threshold of 40 ppbv) and PODY (phytotoxic ozone dose over a threshold of Y nmol ozone m−2 PLA s−1), with ignorance of meteorological influence for the former and complicated data collection and calculation procedures for the latter. Here, we developed a new metric for ozone-induced risk on winter wheat, O3MET, which can be easily derived based on ozone concentrations and meteorological variables, and is suitable for long-term assessment of ozone-induced wheat loss at the regional scale. Combining with existing metric for forest (O3RH), we comprehensively quantified the ozone damages on winter wheat yield and forest gross primary production (GPP) for mainland China during 2010–2021, the period with fast growth of ozone level across the country. The annual average losses of wheat yield and forest GPP were estimated at 26.5 Mt and 552.6 TgC, accounting for 17% and 4% of the total yield and GPP without ozone impact, respectively. Heavy dual ozone-induced damages on both wheat and forest were presented in East and South China. The ozone-induced wheat yield loss and forest GPP loss were estimated to increase at a rate of 1.8 Mt/yr and 13.9 TgC/yr for the entire country, respectively, driven mainly by the enhanced ambient ozone level within the research period. Besides ecological impact, the ozone pollution in the developed eastern China resulted in serious health burden as well, thus effective actions on ozone pollution alleviation in the region is crucial for reducing its ecological and health risks simultaneously.

Land Surface Greening and CO2 Fertilization More than Offset the Gross Carbon Sequestration Decline Caused by Land Cover Change and the Enhanced Vapour Pressure Deficit in Europe

Satellite observations have revealed strong land surface “greening” (i.e., increases in vegetation greenness or leaf area index (LAI)) in the Northern Hemisphere over the past few decades. European terrestrial ecosystems are a greening hotspot, but how they respond to land surface greening, climate change, CO2 fertilization, land use and land cover change (LULCC) and other factors is unclear. Here, we assessed how these interacting factors might be combined to alter terrestrial gross primary production (GPP) throughout Europe during the period of 2001 to 2016 using a process-based Farquhar GPP model (i.e., FGM). We found a more productive European terrestrial ecosystem and most of the GPP enhancement in Europe was explained by increases in LAI (62%) and atmospheric CO2 concentration (29%). Spatially, the spatial signature of the LAI and GPP trends both suggested widespread (72–73% of the vegetated area) greening phenomena across Europe, among which 23.7% and 13.3% were statistically significant (p < 0.05). The interannual trend of GPP estimated by the FGM (0.55% yr−1) was reasonable compared with other GPP products (0.47% yr−1 to 0.92% yr−1) and the observed LAI increasing rate (0.62% yr−1). FGM factorial simulations suggested that land surface greening (+35.5 Pg C yr−2, p < 0.01), CO2 fertilization (+16.9 Pg C yr−2, p < 0.01), temperature warming (+3.7 Pg C yr−2, p < 0.05), and enhanced downwards solar radiation (+1.2 Pg C yr−2, p > 0.05) contributed to the GPP enhancement, while the enhanced vapour pressure deficit (−5.6 Tg C yr−2, p < 0.01) had significant negative impacts on GPP, especially in 2006 and 2012, when extreme droughts struck south-eastern Europe. Meanwhile, approximately 1.8% of the total area of Europe experienced LULCC from 2001 to 2016 and LULCC exerted a small but significant (−1.3 Tg C yr−2, p < 0.01) impact on GPP due to decreases in the total number of vegetated pixels (−159 pixels yr−1). Although the LULCC effect was negative, the largest increase occurred in forested land (+0.9% of total area). In addition, the increasing trends for the annual mean LAI (0.01 m2 m−2 yr−1, p < 0.001) and total GPP (22.2 Tg C yr−2, p < 0.001) of forests were more significant and higher than those of other vegetation types, suggesting that European forests may continue to play important roles in combating climate change in the future with long-lasting carbon storage potential. These results provide the first systematic quantitative analysis of the driving force of enhanced gross carbon assimilation by European ecosystems by considering variations in leaf physiological traits with environmental adaptations.

Reduced Net CO2 Uptake During Dry Summers in a Boreal Shield Peatland

AbstractPeatlands are globally important long‐term sinks of atmospheric carbon dioxide (CO2). However, there is concern that climate change‐mediated drying will reduce gross primary productivity (GPP) and increase ecosystem respiration (ER) making peatlands vulnerable to a weaker carbon sink function and potential net carbon loss. While large and deep peatlands are usually resilient to moderate summer drying, CO2 exchange in shallow Boreal Shield peatlands is likely more sensitive to drying given the reduced groundwater connectivity and water storage potential. To better understand the carbon cycling responses of Boreal Shield peatlands to meteorological conditions, we examined ecohydrological controls on CO2 fluxes using the eddy covariance technique at a shallow peatland during the summer season for 5 years, from 2016–2020. We found lower GPP in dry summer years. Mean summer water table depth (WTD) was found to be significantly correlated with summer total net ecosystem CO2 exchange (R2 = 0.78; p value = 0.046) and GPP (R2 = 0.83; p value = 0.03), where wet summers with a WT close to the peat surface sequestered more than twice the amount of CO2 than dry summers. Our findings suggest that shallow Boreal Shield peatland GPP may be sensitive to climate‐mediated drying as they may switch to a net CO2 source in the summer season when WTDs exceed a critical ecohydrological threshold for a prolonged period of time.

石羊河流域水分利用效率及其对饱和水汽压差的响应

PDF HTML阅读 XML下载 导出引用 引用提醒 石羊河流域水分利用效率及其对饱和水汽压差的响应 DOI: 10.5846/stxb202111243322 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学青年基金项目(41705062,32071606); 甘肃省气象局研究型业务重点项目(ZD2021-02); 云南省教育厅项目"云南省高校山地实景点云数据处理及应用重点实验室" Spatial-temporal dynamics of water use efficiency and responding to vapor pressure deficit in Shiyang River Basin, northwestern China Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:水分利用效率(WUE)是叶片通过光合作用调节水分生理过程的指标,是联系生态系统碳循环与水循环关系的关键,反映了植被生态系统对立地环境快速调整和资源的变化适应策略。基于卫星遥感和地面观测数据,利用光能利用率模型和蒸散发经验估算模型,模拟石羊河流域2000-2019年植被总初级生产力(GPP)和蒸散发(ET)数据,估算2000-2019年不同植被类型的WUE空间分布特征,研究GPP/ET/WUE与饱和水汽压差(VPD)的相关性,探讨干旱区不同类型植被对水分利用及胁迫的适应策略。结果表明:(1) 2000-2019年石羊河流域植被WUE、GPP和ET的平均值分别为0.80 gC m-2 mm-1、256.52 gC/m2和302.52 mm,其三者的空间分布特征表现为"南高北低",即由流域源头至下游逐渐减少的空间分布。(2) 近20年内,流域内WUE、GPP和ET的变化率的平均值分别为0.017 gC m-2 mm-1 a-1,6.99 gC m-2 a-1和3.80 mm/a,流域总体呈现波动上升的趋势,但城区和民勤耕地-荒漠交接区呈不同程度的减少趋势。(3) 流域内不同类型植被的WUE平均值关系为:森林>农作物>草地>灌丛>荒漠植被>湿地植被,WUE增加速度的平均值关系为:农作物>森林>草地>灌丛>荒漠植被>湿地植被,与GPP基本一致,其中ET在灌丛、荒漠植被、湿地植被地区呈现降低趋势。(4) 研究区内GPP/ET/WUE与VPD相关性的空间分布特征与石羊河径流方向具有高度一致性,WUE与VPD相关性分布特征受土地利用以及径流方向影响显著。WUE与VPD呈正相关的区域主要分布在河西走廊绿洲东部和荒漠区东部,呈负相关区域主要分布在祁连山高海拔地区、河西走廊绿洲中部和荒漠区西部。因此,正确揭示内陆河植被GPP/ET/WUE的空间分异以及变化趋势及其对驱动因素VPD的响应,是了解流域尺度中干旱区植被对全球干旱加剧的适应性策略前提,进而为生态保护提供决策服务。 Abstract:Water use efficiency (WUE) is the regulation index of leaf photosynthesis on the water physiological process, as the key to link the relationship between the ecosystem carbon cycle and the water cycle, and directly reflects the rapid adjustment of the ecosystem to the site environment and the adaptation strategy of resource changes. The Gross primary production (GPP) temporal and spatial characteristics derived from the data of MODIS-NDVI by the Light Energy Utilization Model, and the Evapotranspiration (ET) temporal and spatial characteristics derived from the meteorological data by the Thornthwaite Evapotranspiration Model during 2000-2019 in Shiyang River Basin (SRB). In this research, we estimated the spatial pattern and temporal variations of the GPP, ET and WUE in different vegetation types from 2000 to 2019, investigated the co-relationship between GPP/ET/WUE and Vapor pressure deficit (VPD), and discussed the response of adaptation strategies for water utilization and stress in the arid area among the different vegetation. The results show that: (1) from 2000 to 2019, the average annual value of total WUE, GPP, and ET in SRB were 0.80 gC m-2 mm-1, 256.52 gC/m2, and 302.52 mm, respectively; The spatial patterns of average annual WUE, GPP and ET in the SRB's vegetated areas varied widely, and the values decreased from upstream on the south side of the Qilian Mountains and middle to downstream on the north side of the desert. (2) During the 20 years, the average slope of WUE, GPP, and ET showed an obvious upward trend in almost whole basin, with the rate of 0.017 gC m-2 mm-1 a-1, 6.99 gC m-2 a-1 and 3.80 mm/a. The change rates of WUE, GPP and ET within the basin exhibited an upward trend, except for Liangzhou and the areas surrounding Minqin cropland according to the urbanization and industrial adjustment. (3) The WUE, GPP and ET annual average values, and slope of trends among the different land use types also varied significantly. Among various vegetation types, the highest values of WUE, GPP and ET appeared in forests, and the lowest values presented in wetlands; the highest change rate of different vegetation was forest, and the lowest one was the wetland. (4) Correlation analysis and statistical results indicated that the correlation distribution characteristics of GPP/ET/WUE and VPD in the study area were highly consistent with the runoff direction of the SRB. As the WUE was determined by GPP and ET, the correlation distribution between WUE and VPD had obviously spatial heterogeneity with the land-use and SRB runoff direction. The positive correlation was distributed in the eastern part of the Hexi Corridor oasis and the eastern desert area, and the negative correlation was distributed in the high-altitude areas of the Qilian Mountains, the middle of the Hexi Corridor oasis and the western part of the desert area. Overall, correctly revealing the spatial differentiation and changing trend of GPP/ET/WUE of inland river vegetation and its response to the driving factor VPD was the premise of understanding the adaptive strategy of vegetation in arid areas at the watershed scale to the intensification of global drought, to provide decision-making services for ecological protection. 参考文献 相似文献 引证文献

Narrow but robust advantages in two-big-leaf light use efficiency models over big-leaf light use efficiency models at ecosystem level

This study aims to (1) investigate whether two-big-leaf light use efficiency (LUE) models (TL) outperform big-leaf LUE models (BL) by incorporating different gross primary productivity (GPP) responses in sunlit and shaded leaves; (2) explore the robustness of using the leaf area index (LAI), clumping index (Ω) and spherical leaf angle distribution to partition canopies into sunlit and shaded leaves across canopy architectures; (3) identify optimal light response forms in LUE models. To exclude influences of drivers of GPP other than radiation, we collected various formulations of GPP response functions to temperature, vapor pressure deficit, CO2, soil water supply, light intensity and cloudiness index to construct 5600 BLs and 1120 TLs. These models were evaluated at 196 globally-distributed eddy covariance sites from the FLUXNET observational network using the Nash-Sutcliffe model efficiency (NSE), root mean squared error and Bayesian information criterion. Across all sites, the best big-leaf model (BL*; NSE=0.82) was statistically equal to the best TL (TL*; NSE=0.84). However, daily dynamics in GPP under hot and dry conditions were best described using TL* in 17% of sites, highlighting the local importance in separating sunlit and shaded leaves. Across approaches to represent effective LAI, the best approach relies on using normalized difference vegetation index with a spherical or flexible leaf angle distribution across sites rather than satellite LAI and Ω. We also observed similar performance between non-rectangular hyperbola and reciprocal light response functions across TLs. Models degrade when the maximum LUE is not differentiated between sunlit and shaded leaves, but not when light saturation levels are the same. Despite functional differences, the best five TLs agree in a larger contribution of shaded leaf area to total GPP, resulting from higher LAI and LUE. Overall, these results suggest marginal but robust selection of TL compared to BL.

Landscape determinants of pelagic and benthic primary production in northern lakes.

Global change affects gross primary production (GPP) in benthic and pelagic habitats of northern lakes by influencing catchment characteristics and lake water biogeochemistry. However, how changes in key environmental drivers manifest and impact total (i.e., benthic + pelagic) GPP and the partitioning of total GPP between habitats represented by the benthic share (autotrophic structuring) is unclear. Using a dataset from 26 shallow lakes located across Arctic, subarctic, and boreal northern Sweden, we investigate how catchment properties (air temperature, land cover, hydrology) affect lake physico-chemistry and patterns of total GPP and autotrophic structuring. We find that total GPP was mostly light limited, due to high dissolved organic carbon (DOC) concentrations originating from catchment soils with coniferous vegetation and wetlands, which is further promoted by high catchment runoff. In contrast, autotrophic structuring related mostly to the relative size of the benthic habitat, and was potentially modified by CO2 fertilization in the subarctic, resulting in significantly higher total GPP relative to the other biomes. Across Arctic and subarctic sites, DIC and CO2 were unrelated to DOC, indicating that external inputs of inorganic carbon can influence lake productivity patterns independent of terrestrial DOC supply. By comparison, DOC and CO2 were correlated across boreal lakes, suggesting that DOC mineralization acts as an important CO2 source for these sites. Our results underline that GPP as a resource is regulated by landscape properties, and is sensitive to large-scale global changes (warming, hydrological intensification, recovery of acidification) that promote changes in catchment characteristics and aquatic physico-chemistry. Our findings aid in predicting global change impacts on autotrophic structuring, and thus community structure and resource use of aquatic consumers in general. Given the similarities of global changes across the Northern hemisphere, our findings are likely relevant for northern lakes globally.

The controlling factors of ecosystem water use efficiency in maize fields under drip and border irrigation systems in Northwest China

The ecosystem water use efficiency (WUEe) is the critical link between water and carbon cycling in the terrestrial ecosystem. The evaluations of the biophysical factors influencing WUEe could reveal mechanisms of controlling WUEe. In order to save water in agriculture, drip irrigation (DI) has been promoted to replace border irrigation (BI) in northwest China. Different irrigation methods will cause differences in evapotranspiration (ETa), gross primary productivity (GPP), and WUEe. Further research is needed to quantify the contribution of each factor to the differences in ETa, GPP, and WUEe. To separate the different factors controlling ETa, GPP, and WUEe variability between DI and border BI, the 4-year data under DI and BI measured by eddy covariance systems were collected and analyzed. To explore the differences in ETa, GPP, and WUEe under different irrigation methods, Penman-Monteith (P-M) model and stomatal conductance-photosynthetic transpiration coupling (SMPT-SB) model complemented by perturbation analysis were employed. The results showed that the 4-year mean evapotranspiration (ETa) decreased by 8% under DI compared with the value under BI, and the mean gross primary productivity (GPP) under DI was 5% higher than the BI. The WUEe under DI was 14% higher than that under BI. The P-M model and the SMPT-SB model agreed well with ETa and GPP measured on daily time scales, providing confidence in their ability to separate the biological and physical controls of ETa, GPP, and WUEe. Based on the two models, the first-order Taylor series expansions of the total ETa, GPP, and WUEe derivatives were applied to the P-M model and SMPT-SB model and compared with measured differences in ETa, GPP, and WUEe. More than 60% of the difference in ETa caused by the two irrigation methods was attributed to variations in the coupled surface resistance, and drip irrigation mainly achieves water saving by reducing soil evaporation. The difference in maize growth due to irrigation methods was the main reason for the differences in GPP. The differences in vapor pressure deficit and the coupled surface resistance caused by irrigation methods were the main factors causing the difference in WUEe. It could be surmised that drip irrigation increased WUEe in maize fields by changing surface resistance, which mainly reduced soil evaporation.