Average Gross Primary Production Research Articles

Grassland productivity in arid Central Asia depends on the greening rate rather than the growing season length

Climate change has induced substantial impact on the gross primary productivity (GPP) of terrestrial ecosystems by affecting vegetation phenology. Nevertheless, it remains unclear which among the mean rates of grass greening (RG), yellowing (RY), and the length of growing season (LOS) exhibit stronger explanatory power for GPP variations, and how RG and RY affect GPP variations under warming scenarios. Here, we explored the relationship between RG, RY, LOS, and GPP in arid Central Asia (ACA) from 1982 to 2019, elucidating the response mechanisms of RG, RY, and GPP to the mean temperature (TMP), vapor pressure deficit (VPD), precipitation (PRE), and soil moisture (SM). The results showed that the multi-year average length of greening (LG) in ACA was 22.7 days shorter than that of yellowing (LY) and the multi-year average GPP during LG (GPPlg) was 38.28 g C m−2 d −1 more than that of during LY (GPPly). RG and RY were positively correlated with GPPlg and GPPly, although the degree of correlation between RG and GPPlg was higher than that between RY and GPPly. Increases in RG and RY contributed to an increase in GPPlg (55.44 % of annual GPP) and GPPly (35.44 % of annual GPP). The correlation between RG and GPPlg was the strongest (0.49), followed by RY and GPPly (0.33), and LOS and GPP was the weakest (0.21). TMP, VPD, PRE, and SM primarily affected GPP by influencing RG and RY, rather than direct effects. The positive effects of TMP during LG (TMPlg), PRE during LG (PRElg), and SM during LG (SMlg) facilitated increases in RG and GPPlg, and higher VPD during LY (VPDly) and lower PRE during LY (PREly) accelerated increases in RY. Our study elucidated the impact of vegetation growth rate on GPP, thus providing an alternate method of quantifying the relationship between vegetation phenology and GPP.

Exploring the Spatiotemporal Alterations in China’s GPP Based on the DTEC Model

Gross primary productivity (GPP) is a reliable measure of the carbon sink potential of terrestrial ecosystems and is an essential element of terrestrial carbon cycle research. This study employs the diffuse fraction-based two-leaf light-use efficiency (DTEC) model to imitate China’s monthly GPP from 2001 to 2020. We studied the trend of GPP, investigated its relationship with climatic factors, and separated the contributions of climate change and human activities. The findings showed that the DTEC model was widely applicable in China. During the study period, China’s average GPP increased significantly, by 9.77 g C m−2 yr−1 (p < 0.001). The detrimental effect of aerosol optical depth (AOD) on GPP was more widespread than that of total precipitation, temperature, and solar radiation. Areas that benefited from AOD, such as Northwest China, experienced significant increases in GPP. Climate change and human activities had a primary and positive influence on GPP during the study period, accounting for 28% and 72% of the increase, respectively. Human activities, particularly ecological restoration projects and the adoption of advanced agricultural technologies, played a significant role in China’s GPP growth. China’s afforestation plan was particularly notable, with the GPP increasing in afforestation areas at a rate greater than 10 g C m−2 yr−1. This research provides a theoretical foundation for the long-term management of China’s terrestrial ecosystems and helps develop adaptive ecological restoration tactics.

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.

A study on the simulation of carbon and water fluxes of Dangxiong alpine meadow and its response to climate change

The alpine meadow ecosystem accounts for 27 % of the total area of the Tibetan Plateau and is also one of the most important vegetation types. The Dangxiong alpine meadow ecosystem, located in the south-central part of the Tibetan Plateau, is a typical example. To understand the carbon and water fluxes, water use efficiency (WUE), and their responses to future climate change for the alpine meadow ecosystem in the Dangxiong area, two parameter estimation methods, the Model-independent Parameter Estimation (PEST) and the Dynamic Dimensions Search (DDS), were used to optimize the Biome-BGC model. Then, the gross primary productivity (GPP) and evapotranspiration (ET) were simulated. The results show that the DDS parameter calibration method has a better performance. The annual GPP and ET show an increasing trend, while the WUE shows a decreasing trend. Meanwhile, ET and GPP reach their peaks in July and August, respectively, and WUE shows a “dual-peak” pattern, reaching peaks in May and November. Furthermore, according to the simulation results for the next nearly 100 years, the ensemble average GPP and ET exhibit a significant increasing trend, and the growth rate under the SSP5–8.5 scenario is greater than that under the SSP2–4.5 scenario. WUE shows an increasing trend under the SSP2–4.5 scenario and a significant increasing trend under the SSP5–8.5 scenario. This study has important scientific significance for carbon and water cycle prediction and vegetation ecological protection on the Tibetan Plateau.

Seasonal variation of net ecosystem carbon exchange and gross primary production over a Loess Plateau semi-arid grassland of northwest China

Grassland ecosystems store approximately one-third of the global terrestrial carbon stocks, which play a crucial role in regulating the carbon cycle on regional and global scales, but the current scientific understanding of the variation in net carbon dioxide exchange (NEE) on grassland ecosystems is still limited. Based on the eddy covariance technique, this study investigated the seasonal variation of ecosystem respiration (Reco) and gross primary production (GPP) from 2018 to 2020 in a semi-arid grassland on the Loess Plateau in northwest China. The results indicated that the annual cumulative average NEE value was − 0.778 kg C/m2, the growing season cumulative value accounted for approximately 83.81%, which suggested that the semiarid grassland showed a notable soil carbon sink. The correlation analysis revealed that soil temperature (Ts) (RReco = 0.71, RGPP = 0.61) and soil water content (SWC) (RReco = 0.47, RGPP = 0.44) were the two main driving factors in modulating the variation of daily average GPP and Reco (P < 0.01). Therefore, the monthly average of GPP and Reco increased with the increase in Ts (RGPP = 0.716, P < 0.01; RReco = 0.586, P < 0.05), resulting in an increase in the carbon sequestration capacity of the grass ecosystem. This study also showed that soil moisture has a promoting effect on the response of Reco and GPP to Ts, and the correlation among GPP, Reco, and Ts was much stronger under wet conditions. For instance, the coefficient of determination of Reco and GPP with Ts under wet conditions in 2018 increased by 0.248 and 0.286, respectively, compared to those under droughty conditions. Additionally, the temperature sensitivity of Reco (Q10) increased by 46.13% compared to dry conditions. In addition, carbon exchange models should consider the synergistic effect of Ts and SWC as one of the main driving factors for theoretical interpretation or modeling. Under the potential scenario of future global warming and the frequent extreme weather events, our findings have important implications for predicting future CO2 exchange and establishing an optimal ecological model of carbon flux exchange.

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.

Warm Early Summer Compensated Reduction in Photosynthesis Caused by 2022 Late Summer Extreme Drought Over the Tibetan Plateau

AbstractThe Tibetan Plateau (TP) is ecologically delicate, its vegetation highly sensitive to climate variations. In 2022, China endured an unprecedented compound drought‐heatwave, escalating TP's August temperature to a record high since 2000. This study examined this event's impact on TP's photosynthesis using multi‐source satellite‐based products. We demonstrated persistent high temperatures from April to August 2022, accompanied by shifting soil water (SW). Initially moist from April to June, the soil turned notably dry, hitting its lowest point in August. Consequently, early summer witnessed a substantial enhancement in gross primary productivity (GPP) and near‐infrared reflectance of vegetation (NIRv) predominantly due to high temperatures, except for reduction in the eastern and southeastern TP. June recorded the largest positive GPP anomaly since 2000. As summer progressed, severe drought, not high temperatures, dominated, causing obvious negative anomalies in GPP and NIRv, especially in August, concentrated in the central and southwestern TP. Separating the growing season into May–June–July (MJJ) and August–September (AS) revealed a clear contrast. Enhancement in photosynthesis during MJJ compensated the decline in AS, making average positive GPP and weak negative NIRv anomalies in the entire growing season. Specifically, MJJ saw GPP magnitudes of 5.724 TgC mo−1 in FluxSat, 9.491 TgC mo−1 in GPP v2.2 and OCO‐2‐based Solar‐induced chlorophyll fluorescence (GOSIF), and 0.004 in NIRv. Conversely, AS experienced a decline: −4.802 TgC mo−1 in FluxSat, −2.837 TgC mo−1 in GOSIF GPP, and −0.006 in NIRv. This delicate balance underscores the intricate interplay between early growth conditions, subsequent environmental stressors, and vegetation's adaptive responses.

Long-term and large-scale ecological restoration projects shaped the spatial heterogeneity in new forests

The implementation of long-term ecological restoration projects and policies over the past two decades has played a crucial role in advancing the achievement of Sustainable Development Goal 15. However, the resulting dynamics of new forests remain unclear. In this study, we initially examined the distribution and sources of new forests and subsequently investigated the gross primary productivity (GPP) variations within these forested regions. We discovered that new forests are mainly converted from previously cultivated land, which is primarily due to the Grain for Green Project. The new forests are mainly distributed in mid-low altitude areas and gentle slope regions, indicating that accessible areas are prone to damage but also easy to recover. Over approximately two decades of ecological restoration projects, the GPP of new forests has significantly increased trends and the growth rates of new forests vary significantly in different climatic zones. Moreover, the GPP and growth rates of new forests displayed spatial heterogeneity. Specifically, the GPP in the new forests was highest in the eastern region, followed by the central south region, while the northwest region had the lowest GPP. Meanwhile, the southwest region shows the highest GPP growth rate among new forests, while the northwest region has the slowest growth rate. Furthermore, the average GPP of new forests is lower than stable forests, but new forests in China grow faster (3.735 vs. 2.587), highlighting their substantial carbon potential. These findings indicate that it's crucial to account for variations in stand and forest change rates when modeling forest carbon sequestration potential.

Coupled primary production and respiration in a large river contrasts with smaller rivers and streams

AbstractAlthough time series in ecosystem metabolism are well characterized in small and medium rivers, patterns in the world's largest rivers are almost unknown. Large rivers present technical difficulties, including depth measurements, gas exchange (, ) estimates, and the presence of large dams, which can supersaturate gases. We estimated reach‐scale metabolism for the Hanford Reach of the Columbia River (Washington state, USA), a free‐flowing stretch with an average discharge of 3173 . We calculated from semi‐empirical models and directly estimated it from tracer measurements. We fixed at the median value from these calculations (0.5 ), and used maximum likelihood to estimate reach‐scale, open‐channel metabolism. Both gross primary production (GPP) and ecosystem respiration (ER) were high (GPP range: 0.3–30.8 g , ER range: 0.8–30.6 g ), with peak GPP and ER occurring in the late summer or early fall. GPP increased exponentially with temperature, consistent with metabolic theory, while light was seasonally saturating. Annual average GPP, estimated at 1500 g carbon , was in the top 2% of estimates for other rivers. GPP and ER were tightly coupled and 90% of GPP was immediately respired, resulting in net ecosystem production near 0. Patterns in the Hanford Reach contrast with those in small‐medium rivers, suggesting that metabolism magnitudes and patterns in large rivers may not be simply scaled from knowledge of smaller rivers.

Improving the Gross Primary Production Estimate by Merging and Downscaling Based on Deep Learning

A reliable estimate of the gross primary productivity (GPP) is crucial for understanding the global carbon balance and accurately assessing the ability of terrestrial ecosystems to support the sustainable development of human society. However, there are inconsistencies in variations and trends in current GPP products. To improve the estimation accuracy of GPP, a deep learning method has been adopted to merge 23 CMIP6 data to generate a monthly GPP merged product with high precision and a spatial resolution of 0.25°, covering a time range of 1850–2100 under four climate scenarios. Multi-model ensemble mean and the merged GPP (CMIP6DL GPP) have been compared, taking GLASS GPP as the benchmark. Compared with the multi-model ensemble mean, the coefficient of determination between CMIP6DL GPP and GLASS GPP was increased from 0.66 to 0.86, with the RMSD being reduced from 1.77 gCm−2d−1 to 0.77 gCm−2d−1, which significantly reduced the random error. Merged GPP can better capture long-term trends, especially in regions with dense vegetation along the southeast coast. Under the climate change scenarios, the regional average annual GPP shows an upward trend over China, and the variation trend intensifies with the increase in radiation forcing levels. The results contribute to a scientific understanding of the potential impact of climate change on GPP in China.

Temporal and spatial variations and influencing factors of gross primary productivity in Changbai Mountain Nature Reserve, China.

Changbai Mountain Nature Reserve (CNR) is a typical temperate forest ecosystem, and gross primary production (GPP) of which is closely related to topography and climate change. Research on the spatio-temporal variations and influencing factors of GPP in the CNR is of great significance for assessing growth status of vegetation and the quality of ecological environment. We calculated GPP in CNR using the vegetation photosynthesis model (VPM), and analyzed the influences of slope, altitude, temperature, precipitation, and total radiation. The results showed that the range of annual average GPP in CNR was 63-1706 g C·m-2·a-1 from 2000 to 2020 and that GPP decreased with the increases of altitude. Temperature played the most important role in driving the spatial varia-tion of GPP, with a significant positive correlation with GPP. During the study period, the overall annual GPP showed a significant increase trend in CNR, with an average annual increase of 13 g C·m-2·a-1. The areas with increase of annual GPP accounted for 79.9% of the total area, and the area proportion of annual GPP increase differed in each plant functional type. Annual precipitation was significantly negatively correlated with GPP in 43.2% of CNR, while annual mean temperature and annual total radiation were significantly positively correlated with GPP in 47.2% and 82.4% of CNR. GPP would increase continuously in CNR under the scenario of future global warming.

Enhanced Ecosystem Water‐Use Efficiency Under the More Diffuse Radiation Conditions

AbstractDiffuse and direct radiation can affect ecosystem gross primary production (GPP) and evapotranspiration (ET) differently, leading to their confounding impacts on ecosystem water‐use efficiency (WUE). Currently, how these two types of radiation affect ecosystem WUE through their different impacts on GPP and ET are still not fully understood. Here, we explored how diffuse and direct radiation affects ecosystem WUE at 133 FLUXENT sites taking advantage of a newly derived diffuse radiation fraction (Kd) data set. We found that increases in Kd consistently improved WUE for all vegetation types, but the process varied with Kd levels: when Kd ≤ 0.45, the improved WUE attributed to the greater increase in GPP than ET; when Kd in 0.45–0.51, the increase in GPP and decrease in ET jointly improved WUE; when Kd ≥ 0.51, the deeper decline in ET than GPP led to a continuously increasing WUE. Furthermore, by dividing radiation during growing seasons into diffuse (Kd ≥ 0.75) and direct (Kd ≤ 0.25) dominated conditions, the average GPP, ET, and WUE under diffuse conditions were respectively 1.18 ± 0.16, 1.10 ± 0.19, and 1.10 ± 0.21 times higher (P &lt; 0.001) than those under direct conditions for 133 sites. By using a random forest method, we further found that diffuse radiation dominated the variations of GPP, while direct radiation was the most important factor for ET. We suggest that future research on the ecosystem carbon‐water relationship should distinguish the different roles of diffuse and direct radiation on carbon and water fluxes, especially under high scattering conditions with severe aerosol pollution and cloudy skies.

Spatial and Seasonal Variation of the Primary Productivity in the Major Nursery Grounds of Hilsa (Tenualosa ilisha)

The present study was conducted to estimate the primary productivity of major nursery grounds of hilsa both spatially and seasonally. Gross primary productivity (GPP), net primary productivity (NPP), and community respiration (CR) were estimated in summer, rainy, and winter, respectively. The maximum GPP (0.68 gCm-3h-1) and NPP (0.27 gCm-3h-1) were noted during summer season and minimum (0.31 gCm-3 h-1 and 0.19 gCm-3 h-1) in rainy season at all the six stations. The annual average GPP varied from 0.36±0.008 gCm-3h-1to 0.70±0.1 gCm-3h-1. The NPP value varied from 0.23±0.001 gCm-3h-1to 0.51±0.09 gCm-3h-1and exhibited an increasing trend from the month of March to June. CR ranged from 0.12±0.003 gCm-3h-1 to 0.20±0.05 gCm-3h-1 and exhibited an increasing trend from the month of January to May whereas the study found that the CR has also seasonal pattern with maximum and minimum value during summer and winter respectively at all the stations.The data of primary productivity at all stations showed that except S6, all stations has a good nutrient profile and suitable for hilsa production.

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

Modelling managed forest ecosystems in Sweden: An evaluation from the stand to the regional scale

Incorporation of a forest management module in the dynamic vegetation model LPJ-GUESS has allowed the study and predictions of management treatment effects on the carbon cycle and on forest ecosystem structure. In this study, LPJ-GUESS is evaluated at the regional scale against observational data from the Swedish National Forest Inventory. Simulated standing volume is compared against observations for the four most common forest types in the country. Furthermore, eddy-covariance flux measurements from the Integrated Carbon Observation System (ICOS) are used to evaluate model predictions of net ecosystem exchange (NEE), gross primary productivity (GPP) and ecosystem respiration (Reco) at the site scale. The model results suggest an adequate representation of standing volume in monocultures of Norway spruce and Scots pine for regional simulations in southern and central Sweden, after an updated parameterization of the species. For northern Sweden, the standing volume in Norway spruce monocultures was overestimated with the updated parameter values. At the stand scale, the model produced mixed results for carbon fluxes when evaluated against eddy-covariance data for two sites, one in central and one in southern Sweden. The interannual variation of GPP was well captured for the central Swedish site, but the modelled average GPP for the period 2015–2019 was overestimated by 9%. For the southern Swedish site, GPP was underestimated by 15% for the corresponding period and the simulated interannual variation was half of the observed. The seasonal estimates of modelled net ecosystem exchange (NEE) deviated from observations and the simulated standing volume was underestimated by 25% for both sites. The results highlight further potential to perform species-specific calibration to capture latitudinal gradients in key ecosystem properties, and to incorporate additional characteristics of site quality which could benefit model accuracy at the scale of individual forest stands, both regarding simulated carbon fluxes and forest stand variables.

Tracking the influence of drought events on winter wheat using long-term gross primary production and yield in the Wei River Basin, China

The Wei River Basin (WRB) is the main winter wheat-producing area in Northwest China and plays a pivotal role in national food security and economic development. Drought is the main agro-meteorological disaster affecting winter wheat yield in this region, and the accurate assessment of the drought impacts on crop growth and yield is an important part of maintaining food security. However, commonly used assessment methods, such as correlation analysis and crop model simulation, have some limitations. In this study, based on standardized precipitation evapotranspiration index (SPEI) at multiple time scales, gross primary production (GPP) and yield data from 1990 to 2018, sensitivity analysis, Pearson correlation, and superposed epoch analysis (SEA) were used to analyze the spatiotemporal characteristics of drought in the WRB and its influence on winter wheat growth and quantitatively assess the impact of drought at different levels on winter wheat yield. The results were as follows: 1) Overall, the WRB was in a dry state with a drying trend during 1990–2018, and the droughts in spring, summer, and winter were the main driving forces of annual drought that seriously threatened the growth and yield of winter wheat, especially in spring (March–May), which is the key growth period of winter wheat. 2) The average annual GPP of winter wheat in the WRB showed a significant upward trend of 0.465 · 10a−1, and the central part of the WRB was the region where winter wheat GPP had higher sensitivity to drought. In general, winter wheat GPP during the growing season was most sensitive to SPEI-3, indicating that seasonal water deficits had the greatest impact on winter wheat growth. 3) The yield reduction of winter wheat caused by drought presented a spatial distribution that is heavy in the north and light in the south, and as the drought grade increased, the winter wheat yield decreased more significantly, even in irrigated areas. Therefore, relevant government departments still need to strengthen the risk management of agricultural droughts in the WRB and formulate reasonable policies to maintain food security.

Spatio-temporal dynamics of gross primary productivity in China from 1982 to 2017 based on different datasets

Understanding the spatio-temporal variations of gross primary productivity (GPP) of terrestrial ecosystem and its relationship with climatic factors can provide important basis for vegetation restoration and protection. Based on meteorological data and three public GPP datasets (EC-LUE GPP, GLASS GPP, and NIRv GPP), we syste-matically analyzed the spatial-temporal variations of GPP and its response to climate change in China during 1982-2017. All the results based on the three GPP datasets showed that the annual and seasonal GPP in China increased annually from 1982 to 2017, with that in 1998 and 2002 significantly being higher than the average level during the study period, and that in 1989 and 1992 significantly being lower than the average annual GPP. From 1982 to 2017, GPP showed a significant upward trend in most regions of China, with the regions with significant increases accounting for 75.7%, 73.0%, and 69.6% of the whole study area, respectively. There was a significant positive correlation between annual GPP and precipitation and temperature, but spatial heterogeneity was strong. Among them, the regions with positive correlation between GPP and temperature were mainly distributed in Northwest and Central China, while the regions with positive correlation between GPP and precipitation were mainly distributed in North China. There was obvious spatial-temporal heterogeneity in regions that GPP being affected by temperature and precipitation in different seasons. Temperature was the limiting factor of GPP in spring, autumn and winter, while summer GPP was mainly affected by precipitation.

Flash Drought Mechanism and Characteristics in South Korea

A flash drought is a drought that occurs rapidly within a short period of time, owing to changes in weather and environmental factors that occur rapidly due to extreme weather caused by climate change. Drought damage occurs within a relatively short period of time, and extreme vegetation stress can damage crops due to reduced water supply. Thus, different monitoring methods are required for different drought types. In this study, ESI (Evaporative Stress Index) and EDDI (Evaporative Demand Drought Index) were calculated to evaluate the short-term drought and determine the mechanism that causes flash droughts. The ESI and EDDI both indicates faster responses to the 2017 spring drought event than the SPI (Standardized Precipitation Index), confirming their sensitive drought assessment abilities. A flash drought detection standard was established based on the drought category used in USDM (United States Drought Monitor), and seven flash drought events were detected in South Korea over 5 years (2014-2018). A correlation analysis indicates that, among the hydrometeorological factors, the correlation coefficients between soil moisture/maximum temperature and flash drought occurrence exhibited strong positive (negative) correlations (0.9 and -0.9, respectively). The carbon change pattern obtained using GPP (Gross Primary Productivity) differed, exhibiting a -60% average GPP reduction rate compared to three other regions (JB, CN, and CB) where flash droughts occurred in 2018. We focus our discussion on opportunities to extend flash drought detection and mechanism determination using two drought indices that must be addressed to improve flash drought monitoring in South Korea.

Intercomparison of global terrestrial carbon fluxes estimated by MODIS and Earth system models

Earth system models (ESMs) have been widely used to simulate global terrestrial carbon fluxes, including gross primary production (GPP) and net primary production (NPP). Assessment of such GPP and NPP products can be valuable for understanding the efficacy of certain ESMs in simulating the global carbon cycle and future climate impacts. In this work, we studied the model performance of 22 ESMs participating in the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) by comparing historical GPP and NPP simulations with satellite data from MODIS and further evaluating potential model improvement from CMIP5 to CMIP6. In CMIP6, the average global total GPP and NPP estimated by the 22 ESMs are 16% and 13% higher than MODIS data, respectively. The multi-model ensembles (MME) of the 22 ESMs can fairly reproduce the spatial distribution, zonal distribution and seasonal variations of both GPP and NPP from MODIS. They perform much better in simulating GPP and NPP for grasslands, wetlands, croplands and other biomes than forests. However, there are noticeable differences among individual ESM simulations in terms of overall fluxes, temporal and spatial flux distributions, and fluxes by biome and region. The MME consistently outperforms all individual models in nearly every respect. Even though several ESMs have been improved in CMIP6 relative to CMIP5, there is still much work to be done to improve individual ESM and overall CMIP performance. Future work needs to focus on more comprehensive model mechanisms and parametrizations, higher resolution and more reasonable coupling of land surface schemes and atmospheric/oceanic schemes.

Would the obtainable gross primary productivity (GPP) products stand up? A critical assessment of 45 global GPP products

Gross primary productivity (GPP) is a vital variable of the global carbon cycle, but the quantification of global GPP is subject to significant uncertainty due to the lack of direct observations at a global scale. Here, we evaluated and compared 45 GPP products in terms of their applicability to different vegetation types at various spatiotemporal scales. The results show that 44 GPP products and obsGPP (Model Tree Ensemble GPP derived from observations and named obsGPP) have similar global patterns with correlation coefficients greater than 0.8 except for NGT, where GOSIF, RS, and BESS are prominent. GPP products have the greatest variation in Suriname, with a mean 75th and 25th percentile difference value of 0.4748 (normalized), and we recommend RS, SDGVM and LPJ-wsl as they provide GPP estimates close to the average GPP. In terms of seasonal estimations, considerable disagreement occurs among the GPP products in winter, with a range from 118.76 to 314.95 gC/m2/season, among which JULES has the closest GPP value to the average GPP estimation. For studies concerning vegetation types preference is given to the LUE average GPP. The 45 GPP products are more consistent on grasslands but, have obvious differences for savannas. All GPP products have their own specific spatiotemporal scales, such as global or national scales or different seasons and different vegetation types (forest, grasslands, etc.). This study provides guidelines for selecting GPP products.