Simulated Net Primary Productivity Research Articles

Influence of Terrestrial Nitrogen Dynamics on Mesoscale Near‐Surface Meteorological Fields

AbstractThe influence of biogeochemical cycles, particularly the nitrogen cycle, on near‐surface meteorological fields is a critical yet understudied aspect of regional climate modeling. Neglecting such interactions may compromise the accurate representation of vegetation growth and hydrological processes in climate models, consequently affecting the simulated regional near‐surface climate conditions. In order to quantify such effects, we coupled the nitrogen‐augmented Noah‐MP land surface model with the Weather Research and Forecasting (WRF) model v4.1.2 (hereafter WRF‐CN) for regional climate modeling. Compared to the default WRF simulation without nitrogen dynamics, the WRF‐CN simulated net primary productivity, gross primary productivity (GPP), and leaf area index (LAI) were all higher in the study region. Because WRF underestimated the observed GPP and LAI due to the fixed nitrogen limitation of plant growth, these higher estimations improved WRF‐CN's performance in modeling GPP and LAI, which translated into improved simulations of near‐surface climate. Specifically, for the 2‐m air temperature, compared to WRF, WRF‐CN reduced the mean absolute error and root mean square error by 14.45% and 14.19%, respectively, while increased the Nash‐Sutcliffe efficiency coefficient by 7.23%, with the most pronounced improvements in the regions dominated by croplands. Our findings shed light on the crucial interactions between biogeochemical processes and near‐surface meteorological conditions, emphasizing the significance of incorporating terrestrial nitrogen dynamics in regional climate models. These insights contribute to advancing our understanding of climate system dynamics and improving the accuracy of climate predictions at the mesoscale.

黄河流域草地净初级生产力时空动态及其驱动机制

PDF HTML阅读 XML下载 导出引用 引用提醒 黄河流域草地净初级生产力时空动态及其驱动机制 DOI: 10.5846/stxb202108182295 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金课题（41701503，U21A2014）；中国博士后科学基金（2018M642736） Spatiotemporal dynamics of grassland net primary productivity and its driving mechanisms in the Yellow River Basin Author: Affiliation: Fund Project: The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan) 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:黄河流域是生态系统的敏感区，流域草地生态系统在气候变化和人类活动驱动下发生了显著变化。基于多源遥感影像和气象站点资料等，采用改进后的CASA模型估算2001-2018年黄河流域草地净初级生产力（NPP），评估定量反演结果精度，分析研究时段内流域草地净初级生产力时空动态，探讨气候要素和人类活动对流域草地净初级生产力的影响。结果表明：1）基于改进的CASA模型能够高精度模拟黄河流域草地NPP。2）2001-2018年黄河流域草地NPP呈增加趋势，且在2013年发生突变。2001-2013年气候和人类活动均促进流域草地NPP增加，而2013年后人类活动抑制流域草地NPP增加的作用明显增强，抑制区域面积较前一阶段增加34.89%。整体上，人类活动对草地NPP的影响强度低于气候要素影响强度。3）耕地和建设用地面积的变化，是驱动实际净初级生产力动态的重要人类活动要素。耕地和建设用地面积的增加与草地面积减少、实际净初级生产力增加速率的降低紧密相关。伴随经济发展，应坚持实施退耕还林还草政策，并加强对草地生态保护的监督。 Abstract:The Yellow River Basin (YRB) is a sensitive area for ecosystems. The grassland ecosystem in the YRB has changed significantly under climate change and human activities. Combing with multi-source remote sensing images and meteorological site data, we estimated the grassland net primary productivity (NPP) in the Yellow River Basin from 2001 to 2018 using the improved CASA model in this paper. After quantitatively evaluating the accuracy of simulated NPP data, we analyzed the spatiotemporal dynamics of grassland NPP and its responses to climate changes and human activities in the YRB. Results showed that 1) the improved CASA-based model could simulate the grassland NPP in the Yellow River Basin with high precision. 2) Grassland NPP in the YRB showed an increasing trend from 2001 to 2018, with a sudden change in 2013. Climate and human activities contributed to the increase of grassland NPP from 2001 to 2013. After 2013, the effect of human activities inhibiting the increase of grassland NPP increased significantly, and the area of the inhibited area increased by 34.89% over the previous period. Overall, the influence intensity of human activities on grassland NPP was lower than that of climate factors. 3) Change of cropland and built-up land area was the important human activity that drove the dynamics of actual NPP. The increase of cropland and built-up land closely related to the decrease of grassland area and the reduction of increase rate of actual NPP. With the development of the economy, the "Grain for Green" program should be persistently implemented, and the supervision of grassland ecological protection should be strengthened. 参考文献 相似文献 引证文献

Simulating the Impacts of Drought and Warming in Summer and Autumn on the Productivity of Subtropical Coniferous Forests

The impacts of drought and/or warming on forests have received great attention in recent decades. Although the extreme drought and/or warming events significantly changed the forest demography and regional carbon cycle, the seasonality quantifying the impacts of these climate extremes with different severities on the productivity of subtropical coniferous forests remains poorly understood. This study evaluated the effects of seasonal drought and/or warming on the net primary productivity (NPP) of subtropical coniferous forests (i.e., Cunninghamia lanceolata and Pinus massoniana forests) from Hengyang–Shaoyang Basin in southern China using the Ecosystem Demography model, Version 2.2 (ED-2.2) and based on the datasets from forest inventory, meteorological reanalysis, and remotely sensed products. The results showed that the goodness of fit of the DBH-height allometric equations was better than that of the default in ED-2.2 after model calibration; the ED-2.2 model qualitatively captured the seasonality of NPP in the subtropical coniferous forests; and the mismatch between simulated annual NPP and MODIS-NPP (MOD17A3HGF) became smaller over time. The effect of seasonal drought on NPP was greater than that of warming; the decline rate of NPP gradually increased and decreased with time (from July to October) under the seasonal drought and warming scenarios, respectively; NPP decreased more seriously under the combined drought-warming scenario in October, with an average decrease of 31.72%, than the drought-only and warming-only scenarios; seasonal drought had an obvious legacy impact on productivity recovery of subtropical coniferous forests, but it was not the case for warming. With the increase in drought severity, the average values of soil available water and NPP together showed a downward trend. With the increase in warming severity, the average values of canopy air space temperature increased, but NPP decreased. Seasonal drought and/or warming limit forest production through decreasing soil moisture and/or increasing canopy air space temperature, which impact on plant photosynthesis and productivity, respectively. Our results highlight the significance of taking into account the impacts of seasonal warming and drought when evaluating the productivity of subtropical coniferous forests, as well as the significance of enhancing the resistance and resilience of forests to future, more severe global climate change.

How global change impacted the rise and fall of the Guge Kingdom

Climate changes and its influences on human society are of increasing concern in science communities. Based on the reconstructed climate data and CENTURY model, we simulated net primary productivity of grassland and the grain yield of highland barley during the pre-industrial millennium in Ali Prefecture, Tibet Autonomous Region. It showed that the variation of precipitation and temperature together affected the fluctuation of land productivity. Wavelet analysis results showed that the land productivity in Ali fluctuated within main periodic bands of 180 yr. We found that the rise and falls of the Guge Kingdom in Ali was synchronic with the fluctuation of land productivity, and the collapse of the Guge Kingdom was obviously related to the sudden change of climate in the 17th century, which turned dry and cold. By combining with historical studies, this study further proposes the mechanism of land productivity fluctuation under climate change on Guge Kingdom. Our findings are helpful to understand the relationship between climate change and social vulnerability, especially providing a typical case study of ancient plateau countries.

Quantifying the effects of the ‘Internet plus Ecology’ framework on carbon sink in the digital age: a representative study of Ant Forest in China

Ecological afforestation is a positive measure to increase the absorption of carbon dioxide and curb global warming. Ant Forest, a successful example of an ‘Internet + voluntary tree planting’ gamified app with more than 500 million users, has contributed to substantial progress on ecological afforestation in China. It represents a new model of transforming the environmental awareness and low-carbon actions of users (e.g. walking, sharing bikes, and reducing plastics and papers) into actual environmental benefits via planting trees. The implications of Ant Forest can provide useful references for linking ecosystem restoration with the internet worldwide. However, the spatial distribution and quantitative effects on the carbon sink of Ant Forest on a finer scale are not fully understood. In this study, 588 Ant Forest blocks with a total area of 136 314 ha were identified based on area of interest data using the web crawler approach. The forest blocks involved 20 cities in 7 provinces and included 11 varieties of trees. More than 90% of these forest blocks were located in Inner Mongolia, Qinghai, and Gansu Provinces, and mostly shrubs were planted. Based on the Carnegie Ames Stanford approach model, the net primary productivity (NPP) of Ant Forest blocks was estimated. The simulated total annual NPP of all Ant Forest areas was 1.06 × 1011 gC, and an obvious increasing trend in NPP from 2016 to 2020 was observed, indicating effective carbon sequestration. We found that Hippophae rhamnoides and Caragana korshinskii had carbon sink advantages over other shrubs due to their higher NPP values per unit area. By strengthening individuals’ low-carbon awareness for reducing carbon emissions and increasing forest NPP to enhance the carbon sink, Ant Forest uses a representative and inspirational ‘Internet plus Ecology’ framework that has much significance for achieving carbon neutrality in China and tackling global climate change.

Forest Carbon Flux Simulation Using Multi-Source Data and Incorporation of Remotely Sensed Model with Process-Based Model

Forest carbon flux is critical to climate change, and the accurate modeling of forest carbon flux is an extremely challenging task. The remote sensing model (the MODIS MOD_17 gross primary productivity (GPP) model (MOD_17)) has strong practicability and is widely used around the world. The ecological process (the Biome-BioGeochemical Cycles Multilayer Soil Module model (Biome-BGCMuSo)) model can describe most of the vegetation’s environmental and physiological processes on fine time scales. Nevertheless, complex parameters and calibrations pose challenges to the application and development of models. In this study, we optimized all the input parameters of the MOD_17 model for the calibration of the Biome-BGCMuSo model to obtain GPP with continuous spatiality. To determine the contribution of input parameters to the GPP of different forest types, an Extended Fourier Amplitude Sensitivity Test (EFAST) was performed on the Biome-BGCMuSo model firstly. Then, we selected the sample points of each forest type and its different ecological gradients (30 for each type), using the GPP simulation value of the optimized MOD_17 model corresponding to the time and space scale to calibrate the Biome-BGCMuSo model, to drive the calibrated Biome-BGCMuSo, and we simulated the different forest types’ net primary productivity (NPP). According to dendrochronological measurements, the NPP simulation results were verified on the whole regional scale. The results showed that the GPP values of different forest types were highly sensitive to C:Nleaf (C:N of leaf), SLA1 (canopy average specific leaf area in phenological phase 1), and FLNR (fraction of leaf N in Rubisco). The coefficient of determination (R2) between the simulated forest NPP and the measured NPP was 0.64, and the root-mean-square (RMSE) was 26.55 g/C/m2/year. Our study aims to reduce uncertainty in forest carbon fluxes simulated by the Biome-BGCMuSo model, providing feedback for understanding forest ecosystem carbon cycling, vegetation productivity, and climate change.

The climate change mitigation potential of annual grasslands under future climates.

Composted manure and green waste amendments have been shown to increase net carbon (C) sequestration in rangeland soils and have been proposed as a means to help lower atmospheric CO2 concentrations. However, the effect of climate change on soil organic C (SOC) stocks and greenhouse gas emissions in rangelands is not well understood, and the viability of climate change mitigation strategies under future conditions is even less certain. We used a process-based biogeochemical model (DayCent) at a daily time step to explore the long-term effects of potential future climate changes on C and greenhouse gas dynamics in annual grassland ecosystems. We then used the model to explore how the same ecosystems might respond to climate change following compost amendments to soils and determined the long-term viability of net SOC sequestration under changing climates. We simulated net primary productivity (NPP), SOC, and greenhouse gas fluxes across seven California annual grasslands with and without compost amendments. We drove the DayCent simulations with field data and with site-specific daily climate data from two Earth system models (CanESM2 and HadGEM-ES) and two representative concentration pathways (RCP4.5 and RCP8.5) through 2100. NPP and SOC stocks in unamended and amended ecosystems were surprisingly insensitive to projected climate changes. A one-time amendment of compost to rangeland acted as a slow-release organic fertilizer and increased NPP by up to 390-814 kg C ha-1 year-1 across sites. The amendment effect on NPP was not sensitive to Earth system model or emissions scenario and endured through the end of the century. Net SOC sequestration amounted to 1.96 ± 0.02 Mg C ha-1 relative to unamended soils at the maximum amendment effect. Averaged across sites and scenarios, SOC sequestration peaked 22 ± 1 years after amendment and declined but remained positive throughout the century. Though compost stimulated nitrous oxide (N2 O) emissions, the cumulative net emissions (in CO2 equivalents) due to compost were far less than the amount of SOC sequestered. Compost amendments resulted in a net climate benefit of 69.6 ± 0.5 Tg CO2 e 20 ± 1 years after amendment if applied to similar ecosystems across the state, amounting to 39% of California's rangeland. These results suggest that the biogeochemical benefits of a single amendment of compost to rangelands in California are insensitive to climate change and could contribute to decadal-scale climate change mitigation goals alongside emissions reductions.

Elevational Gradient of Climate-Driving Effects on Cropland Ecosystem Net Primary Productivity in Alpine Region of the Southwest China

Investigating elevational gradient of climate driving effects on cropland ecosystem net primary productivity (NPP) plays an important role in food security in alpine region. We simulated cropland NPP by coupling a remote sensing model with an ecosystem process model and explored elevational gradient of climate driving effects on it in an alpine region of the southwest China during 1981–2014. The results showed that cropland NPP increased significantly with a rate of 3.85 gC m−2 year−1 year−1 under significant increasing solar radiation and climate warming and drying, among which the increasing solar radiation was the main driving factor of the increasing NPP. The driving effect of climate warming on cropland NPP shifted from negative at low elevations to positive at high elevations, which was caused by the fragile ecosystem characteristics and frequent drought at low elevations and a higher temperature sensitivity of cropland ecosystem at high elevations. Different effects of climate warming on NPP change at different elevations caused different results when we analyzed the climate-driving effects on cropland NPP at different spatial scales. These results reminded us that we should take the elevational gradient of climate driving effects into account when we manage food security in the alpine region.

Assessing vulnerability of agriculture system to climate change in the SAARC region

Agriculture production is primarily influenced by human-induced management interventions whereas climate plays an important role. Climate change with increasing global carbon dioxide concentration and changes in temperature, precipitation and other climatic parameters play a significant role in influencing agriculture productivity. The assessment of the vulnerability of agriculture production under the influence of climate change can be done using process-based vegetation models. We assessed the vulnerability of agriculture production for the South Asian Association for Regional Cooperation (SAARC) nations comprising of Afghanistan, Bangladesh, Bhutan, India, Nepal, Maldives, Pakistan and Sri Lanka using the Joint UK Land Environment Simulator (JULES) model. Net Primary Productivity (NPP) was simulated under the forcing of climatic variables of the climate change scenarios of representative concentration pathways (RCP2.6, 4.5 & 8.5) for the year 2050 using JULES. JULES is a process-based model that simulates fluxes of carbon, energy, and momentum between the atmosphere, land surface, and water to estimate the productivity of a system. The sensitivity was calculated as the inter-annual dispersion from the average NPP of assessment period 2016 to 2050, whereas adaptive capacity was calculated as the slope of productivity regressed over the assessment period. The vulnerability was calculated by subtracting the values of adaptability from sensitivity. The JULES simulated NPP was also compared with Moderate Resolution Imaging Spectrometer (MODIS 17A) derived NPP for the period 2000–2014. The simulated NPP had a decreasing trend under projected climate change scenarios of 2050 for all the SAARC nations compared to recent years average of 2000–2014. The analysis reveals that one-third of the SAARC region is highly sensitive under low to moderate emission scenarios, while it was observed that under high emission scenarios the sensitivity would reduce with increased vulnerability. We observed that the vulnerability of the system is not alone influenced by sensitivity and any change in adaptive capacity or resilience would also impact the overall vulnerability of the system.

Evaluation of NPP using three models compared with MODIS-NPP data over China.

Estimating net primary productivity (NPP) is significant in global climate change research and carbon cycle. However, there are many uncertainties in different NPP modeling results and the process of NPP is challenging to model on the absence of data. In this study, we used meteorological data as input to simulate vegetation NPP through climate-based model, synthetic model and CASA model. Then, the results from three models were compared with MODIS NPP and observed data over China from 2000 to 2015. The statistics evaluation metrics (Relative Bias (RB), Pearson linear Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE)) between simulated NPP and MODIS NPP were calculated. The results implied that the CASA-model performed better than the other two models in terms of RB, RMSE, NSE and CC whether on the national or the regional scale. It has a higher CC with 0.51 and a smaller RMSE with 111.96 g C·m-2·yr-1 in the whole country. The synthetic model and CASA-model has the same advantages at some regions, and there are lower RMSE in Southern China (86.35 g C·m-2·yr-1), Xinjiang (85.53 g C·m-2·yr-1) and Qinghai-Tibet Plateau (93.22 g C·m-2·yr-1). The climate-based model has widespread overestimation and large systematic errors, along with worse performances (NSEmax = 0.45) and other metric indexes unsatisfactory, especially Qinghai-Tibet Plateau with relatively lower accuracy because of the unavailable observation data. Overall, the CASA-model is much more ideal for estimating NPP all over China in the absence of data. This study provides a comprehensive intercomparison of different NPP-simulated models and can provide powerful help for researchers to select the appropriate NPP evaluation model.

Intensified Drought Enhances Coupling Between Vegetation Growth and Pregrowing Season Precipitation in the Drylands of the Silk Road Economic Belt

AbstractDryland ecosystems cover approximately 40% of terrestrial land surfaces and form a major component of global biogeochemical cycles, which are sensitive and vulnerable to climate change. Rapid climate change observably threatens vegetation growth and profoundly affects the global carbon cycle. However, interannual variations in vegetation growth, and changes in coupling between vegetation growth and precipitation in response to drought are not fully understood in the dryland ecosystems of the Silk Road Economic Belt (SREB). By combining remote sensing normalized vegetation indexes from GIMMS (GIMMS NDVI), simulated net primary productivity levels from TRENDYv2 (TRENDY NPP), terrestrial water storage change data (△TWS) derived from GRACE, and climate grid datasets, we investigated climate change, vegetation growth and nonlinear changes in coupling between vegetation growth and precipitation in the pregrowing and current seasons during 1982–2015. The results show intensified drought and extensive vegetation browning in all seasons, especially the summer, across the drylands of the SREB from 1993 to 2015. Precipitation in the pregrowing season significantly contributed to vegetation growth in the subsequent spring and summer, showing that the effects of precipitation in the pregrowing season on subsequent vegetation growth lags by almost two seasons. We also found that intensified drought significantly strengthened the coupling between vegetation growth and precipitation in the pregrowing and current seasons. These results highlight the nonlinear response of vegetation growth to intensified drought, and can be referenced to improve the coupling of land‐atmospheric models for drylands.

Decadal change and inter-annual variability of net primary productivity on the Tibetan Plateau

Net primary productivity (NPP) is an important indicator of plant dynamics and the net carbon exchange between the terrestrial ecosystem and atmosphere. Both the long-term shifts in climate mean (climate change) and short-term variations around the climate mean (climate variability) have impacts on NPP but studies examining both aspects of climate variations are rare especially in the data-scarce regions such as the Tibetan Plateau (TP). Here, we used a dynamic vegetation model to investigate the impacts of the changes and variabilities in temperature, precipitation, cloud cover and CO2 on NPP on the TP. The simulated NPP was evaluated using field and Moderate-Resolution Imaging Spectroradiometer NPP and was found to be reasonable. At monthly time scale, NPP significantly correlated concurrently and at 1-month lag with temperature, precipitation and cloud cover (coefficient of determination, R2, in 0.52–0.77). Annual NPP variability was high (low) where mean annual NPP was low (high). The effects of annual precipitation, cloud cover and temperature variability on annual NPP variability were spatially heterogeneous, and temperature variability appeared to be the dominant factor (R2 of 0.74). Whereas, NPP changes were very similar to CO2 increases across the TP (spatial correlation of 0.60), indicating that long-term changes in NPP were dominated by CO2 increases. For both variability and long-term changes in NPP, temperature was the major factor of influence (highest spatial correlation of 0.67). These findings could assist in making informed mitigation policies on the impacts of climate change and variability on ecosystem and local nomadic communities.

Rising vegetation activity dominates growing water use efficiency in the Asian permafrost region from 1900 to 2100

Permafrost play an important role in regulating global climate system. We analyzed the gross primary productivity (GPP), net primary productivity (NPP), and evapotranspiration (ET) derived from MODIS and three earth system models participated in the Coupled Model Inter-comparison Project Phase 6 (CMIP6) in the Asian permafrost region. The water use efficiency (WUE) was further computed. The simulated GPP, NPP, and ET show slightly increasing trends during historical period (1900–2014) and strong increasing trends in projection period (2015–2100), and projected impacts of climate change on all variables are greater under high-emission scenarios than low-emission scenarios. Further analysis revealed higher increases in GPP and NPP than that of ET, indicating that vegetation carbon sequestration governs the growing WUE under historical and projected periods in this region. The GPP, NPP and ET showed higher changing rates in western, central and southeast areas of this region, and WUE (WUEGPP, and WUENPP) shows the similar spatial pattern. Compared to MODIS-derived GPP, NPP, and ET during 2000–2014, Earth system models yield the best estimates for NPP, while slight underestimations for GPP and ET, and thus slight overestimations for WUEGPP and WUENPP. This study highlights the predominant role of vegetation activity in regulating regional WUE in Asian permafrost region under future climate change. Vegetation domination of the growing water use efficiency implies that the permafrost region may continue acting efficiently in sequestrating atmospheric carbon in terms of water consumption throughout the 21st century.

Changes of productivity with stand development in broadleaf-Korean pine forest in Changbai Mountain, China

Stand age is a key factor affecting carbon stocks and fluxes of forest ecosystem. Quantification of the changes in forest productivity with stand development is critically important for optimizing forest age structure, facilitating maximum utilization of resources, and better realizing the role of forests in regulating the uptake, storage, and emission of CO2. In this study, using space for time substitution approach, we established 12 chronosequence plots in the broadleaf-Korean pine forests of Lushuihe. Using a locally parameterized Biome-BGC model, we simulated the dynamics of net primary productivity (NPP) with stand development and examined the changes with stand development in NPP of broadleaf-Korean pine forests under four developmental scenarios. Results showed that the biomass in broadleaf-Korean pine forests of different age-classes ranked in the order of young stand < mid-age stand < mature stand < over-mature stand, with the average value of (224.35±20.68), (237.23±39.96), (259.16±19.51), and (357.57±84.74) t·hm-2, respectively. Modelled NPP in broadleaf-Korean pine forests of different developmental stages varied in the range of 489.8-588 g C·m-2·a-1, which were consistent with the observed data of MODIS NPP, highlighting the adequacy and accuracy of Biome-BGC model in simulating the carbon flux of broadleaf-Korean pine forests. Simulated NPP displayed a pattern of initial increase and later decrease with stand development, reaching peak in the mid-age stand and being smallest in the over-mature stand. Simulations of NPP in broadleaf-Korean pine forest under four developmental scenarios showed that, for the two scenarios with planted Korean pine forests experiencing either natural development or controlled cutting, NPP was highest in the mature stage; whereas for the two scenarios with initial natural secondary birch forests experiencing either natural development or controlled cutting, NPP was highest in the young stage.

Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System (CCDAS)

The terrestrial carbon cycle is an important component of the global carbon budget due to its large gross exchange fluxes with the atmosphere and their sensitivity to climate change. Terrestrial biosphere models show large uncertainties in simulating carbon fluxes, which impact global carbon budget assessments. The land surface carbon cycle is tightly controlled by soil moisture through plant physiological processes. Accurate soil moisture observations thereby have the potential to improve the modeling of carbon fluxes in a model-data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate six years of surface soil moisture provided by the SMOS satellite in combination with global-scale observations of atmospheric CO2 concentrations. We find that assimilation of SMOS soil moisture exhibits better performance on soil hydrology modeling at both global and site-level than only assimilating atmospheric CO2 concentrations, and it improves the soil moisture simulation particularly in mid- to high-latitude regions where the plants suffer from water stress frequently. The optimized model also shows good agreements with inter-annual variability in simulated Net Primary Productivity (NEP) and Gross Primary Productivity (GPP) from an atmospheric inversion (Jena CarboScope) and the up-scaled eddy covariance flux product (FLUXNET-MTE), respectively. Correlation between SIF (Solar Induced Fluorescence) and optimized GPP also shows to be the highest when soil moisture and atmospheric CO2 are simultaneously assimilated. In general, CCDAS obtains smaller annual mean NEP values (1.8 PgC/yr) than the atmospheric inversion and an ensemble of Dynamic Global Vegetation Models (DGVMs), but larger GPP values (167.8 PgC/yr) than the up-scaled eddy covariance dataset (FLUXNET-MTE) and the MODIS based GPP product for the years 2010 to 2015. This study demonstrates the high potential of constraining simulations of the terrestrial biosphere carbon cycle on inter-annual time scales using long-term microwave observations of soil moisture.

Assessing the response of forest productivity to climate extremes in Switzerland using model-data fusion.

The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long‐term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3‐PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960–2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha−1 year−1 km−1 for P. abies and 0.93 ± 0.010 Mg C ha−1 year−1 km−1 for F. sylvatica). During warm–dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm‐dry extremes. Importantly, cold–dry extremes had negative impacts on regional forest NPP comparable to warm–dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.

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

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

Modeling net primary productivity of tropical deciduous forests in North India using bio-geochemical model

The present work investigates the applicability of a widespread bio-geochemical model (Biome BGC) to simulate monthly net primary productivity (NPP) and leaf area index (LAI) of Indian tropical deciduous forests. We simulated the monthly NPP and LAI of three plant functional types (PFTs) [dry mixed (DM), sal mixed (SM) and teak plantation (TP)] having distinct tree species compositions, canopy structure, different carbon assimilation rates and microclimate within a broad tropical deciduous forest during 2011–2012. The parameterization of 11 major eco-physiological parameters of Biome BGC was performed from in-situ physiological measurements gathered from 9 long-term ecological research plots in above three PFTs and PFT specific indices were developed. Bimodal trends, with highest peak in September during autumn and second peak in January during winter were observed for simulated monthly NPP in all three PFTs. Simulated NPP (gC/m2/year) values were 408.8 and 414.6; 376.8 and 392.9; and 327.5 and 338.2 during 2011 and 2012 in DM, SM and TP PFTs respectively. Observed NPP (gC/m2/year) values ranged between 463.4 and 493.1; 498.0 and 529.5; and 542.1 and 677.9 in 2012 in DM, SM and TP PFTs respectively. Biome BGC simulated NPP were in positive agreement with observed NPP in all PFTs (R2 = 0.92, 0.83 and 0.72 in DM, SM and TP respectively). In all PFTs Biome BGC led to an underestimation of LAI. The current investigation evaluated the operational application of Biome BGC in Indian tropical deciduous forest and opens scope for further improvement for LAI algorithms for better in-situ LAI simulation.

Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types

Abstract. Dynamic global vegetation models (DGVMs) are used for studying historical and future changes to vegetation and the terrestrial carbon cycle. JULES (the Joint UK Land Environment Simulator) represents the land surface in the Hadley Centre climate models and in the UK Earth System Model. Recently the number of plant functional types (PFTs) in JULES was expanded from five to nine to better represent functional diversity in global ecosystems. Here we introduce a more mechanistic representation of vegetation dynamics in TRIFFID, the dynamic vegetation component of JULES, which allows for any number of PFTs to compete based solely on their height; therefore, the previous hardwired dominance hierarchy is removed. With the new set of nine PFTs, JULES is able to more accurately reproduce global vegetation distribution compared to the former five PFT version. Improvements include the coverage of trees within tropical and boreal forests and a reduction in shrubs, the latter of which dominated at high latitudes. We show that JULES is able to realistically represent several aspects of the global carbon (C) cycle. The simulated gross primary productivity (GPP) is within the range of observations, but simulated net primary productivity (NPP) is slightly too high. GPP in JULES from 1982 to 2011 is 133 Pg C yr−1, compared to observation-based estimates (over the same time period) between 123 ± 8 and 150–175 Pg C yr−1. NPP from 2000 to 2013 is 72 Pg C yr−1, compared to satellite-derived NPP of 55 Pg C yr−1 over the same period and independent estimates of 56.2 ± 14.3 Pg C yr−1. The simulated carbon stored in vegetation is 542 Pg C, compared to an observation-based range of 400–600 Pg C. Soil carbon is much lower (1422 Pg C) than estimates from measurements (&gt; 2400 Pg C), with large underestimations of soil carbon in the tropical and boreal forests. We also examined some aspects of the historical terrestrial carbon sink as simulated by JULES. Between the 1900s and 2000s, increased atmospheric carbon dioxide levels enhanced vegetation productivity and litter inputs into the soils, while land use change removed vegetation and reduced soil carbon. The result is a simulated increase in soil carbon of 57 Pg C but a decrease in vegetation carbon of 98 Pg C. The total simulated loss of soil and vegetation carbon due to land use change is 138 Pg C from 1900 to 2009, compared to a recent observationally constrained estimate of 155 ± 50 Pg C from 1901 to 2012. The simulated land carbon sink is 2.0 ± 1.0 Pg C yr−1 from 2000 to 2009, in close agreement with estimates from the IPCC and Global Carbon Project.

基于Biome-BGC模型及树木年轮的太白红杉林生态系统对气候变化的响应研究

PDF HTML阅读 XML下载 导出引用 引用提醒 基于Biome-BGC模型及树木年轮的太白红杉林生态系统对气候变化的响应研究 DOI: 10.5846/stxb201710161851 作者: 作者单位: 1 西北大学城市与环境学院，2 西北大学陕西省地表系统与环境承载力重点研究室,1 西北大学城市与环境学院，2 西北大学陕西省地表系统与环境承载力重点研究室,1 西北大学城市与环境学院，2 西北大学陕西省地表系统与环境承载力重点研究室,1 西北大学城市与环境学院，2 西北大学陕西省地表系统与环境承载力重点研究室,1 西北大学城市与环境学院，2 西北大学陕西省地表系统与环境承载力重点研究室,1 西北大学城市与环境学院，2 西北大学陕西省地表系统与环境承载力重点研究室 作者简介: 通讯作者: 中图分类号: 基金项目: 国家林业公益性行业科研专项（201304309）；陕西省自然科学基金项目（2014JQ5172）；黄土与第四纪地质国家重点实验室开放基金项目（SKLLQG1611） Response of Larix chinensis forest ecosystem to climate change based on Biome-BGC model and tree rings Author: Affiliation: Northwest University,Northwest University,Northwest University,Northwest University,Northwest University,Northwest University Fund Project: Foundation:National forestry public welfare industry scientific research project(201304309),Shaanxi Natural Science Foundation(2014JQ5172),State Key Laboratory of Loess and Quaternary Geology(SKLLQG1611)] 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:利用Biome-BGC模型模拟了1960-2013年太白山太白红杉林生态系统的净初级生产力（NPP），对其与太白红杉的径向生长关系进行了探讨，并分析了NPP值对气候变化的响应关系。结果表明：1960-2013年太白山太白红杉林北坡NPP年均值为305.33g C m-2 a-1，南坡为320.71g C m-2 a-1，南北坡的NPP值均呈现出一定的上升趋势，北坡的上升速率（0.47g C m-2 a-1）要小于南坡（1.29g C m-2 a-1），但是北坡太白红杉分布下限区NPP值波动浮动较大。且北坡太白红杉NPP值随着海拔高度的上升而逐渐下降，低海拔的变化振幅要大于高海拔地区，南坡无明显变化。多数采样点的模拟NPP值与树轮宽度指数年际变化趋势趋于一致，相关关系呈显著相关。太白红杉标准年表、模型模拟NPP值与气象因子的相关分析均表明太白红杉的生长与生长季气温的相关性显著高于降水，即生长季的气温是太白红杉生长的限制因子。气候的变化作为制约太白红杉生境的重要因素，影响了太白红杉树木的生长，进而对NPP的变化产生了影响。树木年轮很好的检验了Biome-BGC模型模拟结果。 Abstract:In this paper, the Biome-BGC model was used to simulate net primary productivity (NPP) of the Larix chinensis forest ecosystem during 1960-2013. The relationship between the NPP value and climatic factors, as well as the response of Larix chinensis radial growth to climate change were analyzed. The results showed that the average NPP of Larix chinensis on the northern slope of Mt. Taibai was 305.33 g C m-2 a-1 and 320.71 g C m-2 a-1on the southern slope during 1960-2013 years. The NPP values of both the northern and southern slopes increased. The rate increase of the northern slope (0.47 g C m-2 a-1) was lower than that of the southern slope (1.29 g C m-2 a-1). However, the range of NPP fluctuation was larger at the lower limit of the distribution of Larix chinensis. The NPP value of Larix chinensis on the northern slope decreased gradually with the increase in altitude. On the northern slope, the NPP amplitude at low altitude was higher than that at high altitude, whereas the southern slope showed no obvious change. The simulated NPP values for most sampling points were consistent with the interannual variation trend in the tree-ring width index, and the correlation was significant. The correlation between the simulated NPP and the meteorological factors showed that the connection between growth and temperature during the growing season was significantly higher than that of precipitation, indicating that temperature in the growing season was the major limiting factor for growth of Larix chinensis. As a result, the tree-ring data could be used to validate simulation results based on the Biome-BGC model. 参考文献 相似文献 引证文献