Net Primary Productivity Simulation Research Articles

Evaluation of soil carbon simulation in CMIP6 Earth system models

Abstract. The response of soil carbon represents one of the key uncertainties in future climate change. The ability of Earth system models (ESMs) to simulate present-day soil carbon is therefore vital for reliably estimating global carbon budgets required for Paris Agreement targets. In this study CMIP6 ESMs are evaluated against empirical datasets to assess the ability of each model to simulate soil carbon and related controls: net primary productivity (NPP) and soil carbon turnover time (τs). Comparing CMIP6 with the previous generation of models (CMIP5), a lack of consistency in modelled soil carbon remains, particularly the underestimation of northern high-latitude soil carbon stocks. There is a robust improvement in the simulation of NPP in CMIP6 compared with CMIP5; however, an unrealistically high correlation with soil carbon stocks remains, suggesting the potential for an overestimation of the long-term terrestrial carbon sink. Additionally, the same improvements are not seen in the simulation of τs. These results suggest that much of the uncertainty associated with modelled soil carbon stocks can be attributed to the simulation of below-ground processes, and greater emphasis is required on improving the representation of below-ground soil processes in future developments of models. These improvements would help to reduce the uncertainty in projected carbon release from global soils under climate change and to increase confidence in the carbon budgets associated with different levels of global warming.

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.

Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland

Abstract. The Carnegie–Ames–Stanford Approach (CASA) model is widely used to estimate vegetation net primary productivity (NPP) at regional scales. However, the CASA is still driven by multisource data, e.g. satellite remote sensing (RS) data, and ground observations that are time-consuming to obtain. RS data can conveniently provide real-time regional information and may replace ground observation data to drive the CASA model. We attempted to improve the CASA model in this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) RS products, the GlobeLand30 RS product, and the digital elevation model data derived from radar RS. We applied it to simulate the NPP of alpine grasslands in the Qinghai Lake basin, which is located in the northeastern Qinghai–Tibetan Plateau, China. The accuracy of the RS-data-driven CASA, with a mean absolute percent error (MAPE) of 22.14 % and root mean square error (RMSE) of 26.36 g C m−2 per month, was higher than that of the multisource-data-driven CASA, with a MAPE of 44.80 % and RMSE of 57.43 g C m−2 per month. The NPP simulated by the RS-data-driven CASA in July 2020 shows an average value of 108.01 ± 26.31 g C m−2 per month, which is similar to published results and comparable with the measured NPP. The results of this work indicate that simulating alpine grassland NPP with satellite RS data rather than ground observations is feasible. We may provide a workable reference for rapid simulation of grassland NPP to satisfy the requirements of accounting carbon stocks and other applications.

Simulation and Analysis of the Effects of Land Use and Climate Change on Carbon Dynamics in the Wuhan City Circle Area.

In a climate and land use change context, the sequestration of atmospheric carbon in urban agglomeration is key to achieving carbon emission and neutrality targets. It is thus critical to understand how various climate and land use changes impact overall carbon sequestration in large-scale city circle areas. As the largest urban agglomeration in central China, carbon dynamics in the Wuhan City Circle area have been deeply affected by rapid urbanization and climate change in the past two decades. Here, we applied monthly climate data, spatially explicit land use maps, NDVI (Normalized Difference Vegetation Index) images and the CASA (Carnegie–Ames–Stanford Approach) model to estimate the spatial and temporal changes of carbon dynamics in the Wuhan City Circle area from 2000 to 2015. We designed six different scenarios to analyze the effects of climate change and land use change on carbon dynamics. Our simulation of NPP (Net Primary Productivity) increased from 522.63 gC × m−2 to 615.82 gC × m−2 in the Wuhan City Circle area during 2000–2015. Climate change and land use change contributed to total carbon sequestration by −73.3 × 1010 gC and 480 × 1010 gC, respectively. Both precipitation and temperature had a negative effect on carbon sequestration, while radiation had a positive effect. In addition, the positive effect on carbon sequestration from afforestation was almost equal to the negative effect from urbanization between 2000 and 2015. Importantly, these findings highlight the possibility of carrying out both rapid urbanization and ecological restoration simultaneously.

Quantitative Assessment of NPP Changes in the Yellow River Source Area from 2001 to 2017

It is of great significance to quantitatively evaluate the ecological degradation and restoration of the Yellow River source area from 2001 to 2017. Net primary productivity (NPP) is the core indicator used to indicate the health of terrestrial ecosystems. Based on the improved CASA (Carnegie-Ames-Stanford Approach) model and MOD17A3 NPP products, 174 measured sample sites were used to validate the NPP simulation results, select the model with higher accuracy as the actual NPP simulation, and using Theil-Sen median trend analysis and Mann-Kendall test to investigate the dynamics of NPP changes. The results are as follows: (1) the accuracy of the CASA model is higher than that of the MOD17A3 NPP product, and it is more suitable for the simulation of the actual NPP in the source area of the Yellow River; (2) the Yellow River Source region has shown a fluctuating upward trend in net primary productivity over the past 17 years, with 50% of the region showing a slight improvement, 36% showing a significant improvement, 13% showing a slight decline, and only 1% showing a significant decline, the average annual growth rate of NPP is 2.6 g C/yr-1. Compared with 2001, the net primary productivity in 2017 increased by 5.24×1012g C.

Assessing the spatiotemporal variation of NPP and its response to driving factors in Anhui province, China.

Net primary productivity (NPP) of terrestrial ecosystems is an important metric of ecosystem functioning; however, the understanding of response mechanism of NPP to influencing factors and driving mechanisms are still limited. In this study, taking Anhui province as an example, spatio-temporal changes of NPP and its response to influencing factors were investigated for evaluating the effects of climate change and land use and land cover change (LUCC) on regional NPP. The Carnegie-Ames-Stanford Approach (CASA) model was employed for NPP simulation by using the MODIS normalized difference vegetation index (NDVI) data and meteorological data over 2001-2016. Combined domestic LUCC, the spatiotemporal distribution pattern and dynamic change characteristics of NPP under a long time series and its response to climate factors and human activities were analyzed in the Anhui province. The results indicated that from 2001 to 2016, total NPP had a fluctuated and decreased trend with the variation range between 30.52 and 38.07TgC in Anhui province. The multi-year average of total NPP was about 34.62TgC. The highest value was in 2008 and the lowest value was in 2011. Among them, amount of forestland NPP was the most. The spatial distribution of NPP shows that the high value area was mainly distributed in southern Anhui mountain areas and western Anhui Dabie mountain areas; the lower value was distributed in the middle in the study area. The area of which the NPP showed a slight decrease and essentially unchanged accounted for 59.35% and 31.82%, respectively. In general, the correlation between vegetation NPP and temperature was greater than that between precipitation. The vegetations NPP of eight land use types were all positively correlated with temperature. However, the other seven types of land use were negatively correlated with precipitation except cultivated land. In the past 16years, the decrease of cultivated land areas and the increase of urban and construction land areas contributed a lot to the decrease of vegetation NPP in Anhui province. The NPP changes of different land use types were closely related to climatic factors, land cover area, and vegetation types.

Variation of Net Primary Production and Its Correlation with Climate Change and Anthropogenic Activities over the Tibetan Plateau

Grasslands in the Tibetan Plateau are claimed to be sensitive and vulnerable to climate change and anthropogenic activities. Quantifying the impacts of climate change and anthropogenic activities on grassland growth is an essential step for developing sustainable grassland ecosystem management strategies under the background of climate change and increasing anthropogenic activities occurring in the plateau. Net primary productivity (NPP) is one of the key components in the carbon cycle of terrestrial ecosystems, and can serve an important role in the assessment of vegetation growth. In this study, a modified Carnegie–Ames–Stanford Approach (CASA) model, which considers remote sensing information for the estimation of the water stress coefficient and time-lag effects of climatic factors on NPP simulation, was applied to simulate NPP in the Tibetan Plateau from 2001 to 2015. Then, the spatiotemporal variations of NPP and its correlation with climatic factors and anthropogenic activities were analyzed. The results showed that the mean values of NPP were 0.18 kg∙C∙m−2∙a−1 and 0.16 kg∙C∙m−2∙a−1 for the original CASA model and modified CASA model, respectively. The modified CASA model performed well in estimating NPP compared with field-observed data, with root mean square error (RMSE) and mean absolute error (MAE) of 0.13 kg∙C∙m−2∙a−1 and 0.10 kg∙C∙m−2∙a−1, respectively. Relative RMSE and MAE decreased by 45.8% and 44.4%, respectively, compared to the original CASA model. The variation of NPP showed gradients decreasing from southeast to northwest spatially, and displayed an overall decreasing trend for the study area temporally, with a mean value of −0.02 × 10−2 kg∙C∙m−2∙a−1 due to climate change and increasing anthropogenic activities (i.e., land use and land cover change). Generally, 54% and 89% of the total pixels displayed a negative relationship between NPP and mean annual temperature, as well as annual cumulative precipitation, respectively, with average values of –0.0003 (kg∙C∙m−2 a−1)/°C and −0.254 (g∙C∙m−2∙a−1)/mm for mean annual temperature and annual cumulative precipitation, respectively. Additionally, about 68% of the total pixels displayed a positive relationship between annual cumulative solar radiation and NPP, with a mean value of 0.038 (g∙C∙m−2·a−1)/(MJ m−2). Anthropogenic activities had a negative effect on NPP variation, and it was larger than that of climate change, implying that human intervention plays a critical role in mitigating the degenerating ecosystem. In terms of human intervention, ecological destruction has a significantly negative effect on the NPP trend, and the absolute value was larger than that of ecological restoration, which has a significantly positive effect on NPP the trend. Our results indicate that ecological destruction should be paid more attention, and ecological restoration should be conducted to mitigate the overall decreasing trend of NPP in the plateau.

Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating.

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m2) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m2) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.

Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.

Simulation of Net Primary Productivity in Mongolia Using CASA Model, During 2000-2004

Vegetation net primary productivity (NPP) is always used as an indicator of carbon cycling in terrestrial ecosystems at landscape and regional scales. Based on the CASA model, we analyzed the spatiotemporal pattern of growing season NPP from 2000 to 2004 using MODIS/NDVI and its relationship with precipitation. The result shows that the annual NPP in Mongolia has a tendency to slightly decrease from 61.13 in 2000 to 60 gC/m2/yr in 2004, with an annual mean decrement of -0.259 gC/m2/yr. However, annual and inter-annual NPP trends showed spatial and temporal heterogeneity. NPP in forest and grassland has decreased with an average annual decrement of -1.03 (r2=0.262) and -0.49 (r2=0.324), meanwhile NPP in desert steppe and desert has increased with the annual average increment of 0.4327 (r2=0.322) and 0.2401 (r2=0.283), respectively. The correlation coefficient showed that mean growing season NPP in grassland and desert steppe were closely correlated with precipitation than forest and desert.

Simulating alpine vegetation net primary productivity by remote sensing in Qinghai Province, China

Primary productivity of ecosystem is important indicator about ecological assessment. Remote sensing technology has been used to monitor net primary productivity (NPP) of ecological system for several years. In this paper; the remotely sensed NPP simulation model of alpine vegetation in Qinghai Province of Tibet Plateau was set up based on the theory of light use efficiency. Firstly a new approach based on mixed pixels and Support Vector Machine (SVM) algorithm were used to correct simulated NPP values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Finally; spatial distribution and monthly variation characteristics of NPP in Qinghai Province in 2006 were analyzed in detail. The result showed that NPP of vegetation in Qinghai Province in 2006 ranged from 0 to 422 gC/m2/a and the average NPP was 151 gC/m2/a. NPP gradually increased from northwest to southeast. NPP of different vegetation types were obviously different. The average NPP of broad-leaved forest was the largest (314 gC/m2/a); and sparse shrub was the smallest (101 gC/m2/a). NPP in Qinghai Province significantly changed with seasonal variation. The accumulation of NPP was primarily in the period (from April to September) with better moist and heat conditions. In July; the average NPP of vegetation reached the maximum value (43 gC/m2). In our model; the advantage of traditional LUE models was adopted; and our study fully considered typical characteristics of alpine vegetation light use efficiency and environmental factors in the study area. Alpine vegetation is the most important ecological resource of Tibet Plateau; exactly monitoring its NPP value by remote sensing is an effective protection measure.

Modeling net primary productivity of the terrestrial ecosystem in China from 1961 to 2005

Net primary productivity (NPP) is the most important index that represents the structure and function of the ecosystem. NPP can be simulated by dynamic global vegetation models (DGVM), which are designed to represent vegetation dynamics relative to environmental change. This study simulated the NPP of China’s ecosystems based on the DGVM Integrated Biosphere Simulator (IBIS) with data on climate, soil, and topography. The applicability of IBIS in the NPP simulation of China’s terrestrial ecosystems was verified first. Comparison with other relevant studies indicates that the range and mean value of simulations are generally within the limits of observations; the overall pattern and total annual NPP are close to the simulations conducted with other models. The simulations are also close to the NPP estimations based on remote sensing. Validation proved that IBIS can be utilized in the large-scale simulation of NPP in China’s natural ecosystem. We then simulated NPP with climate change data from 1961 to 2005, when warming was particularly striking. The following are the results of the simulation. (1) Total NPP varied from 3.61 GtC/yr to 4.24 GtC/yr in the past 45 years and exhibited minimal significant linear increase or decrease. (2) Regional differences in the increase or decrease in NPP were large but exhibited an insignificant overall linear trend. NPP declined in most parts of eastern and central China, especially in the Loess Plateau. (3) Similar to the fluctuation law of annual NPP, seasonal NPP also displayed an insignificant increase or decrease; the trend line was within the general level. (4) The regional differences in seasonal NPP changes were large. NPP declined in spring, summer, and autumn in the Loess Plateau but increased in most parts of the Tibetan Plateau.

Spatio-temporal dynamics on the distribution, extent, and net primary productivity of potential grassland in response to climate changes in China

Net primary productivity (NPP) of grassland is one of the key components in measuring the carrying capacity of livestock. Not only are grassland researchers concerned with the performance of NPP simulation models under current climate conditions, they also need to understand the behaviour of NPP–climate models under projected climatic changes. One of the goals of this study was to evaluate the three NPP–climate models: the Miami Model, the Schuur Model, and the Classification Indices-based Model. Results indicated that the Classification Indices-based Model was the most effective model at estimating large-scale grassland NPP. Both the Integrated Orderly Classification System of Grassland and the Classification Indices-based Model were then applied to analyse the succession of grassland biomes and to measure the change in total NPP (TNPP) of grassland biomes from the recent past (1950–2000) to a future scenario (2001–2050) in a geographic information system environment. Results of the simulations indicate that, under recent-past climatic conditions, the major biomes of China’s grassland are the tundra and alpine steppe, and steppe, and these would be converted into steppe and semi-desert grassland in the future scenario; the potential grassland TNPP in China was projected to be 0.72 PgC under recent-past climatic conditions, and would be 0.83 Pg C under the future climatic scenario. The ‘safe’ carrying capacity of livestock that best integrates a wide range of factors, such as grassland classes, climatic variability, and animal nutrition, is discussed as unresolved. Further research and development is needed to identify the regional trends for the ‘safe’ carrying capacity of livestock to maintain sustainable resource condition and reduce the risk of resource degradation. This important task remains a challenge for all grassland scientists and practitioners.

MERIS GPP/NPP product for Estonia: I. Algorithm and preliminary results of simulation / MERIS’e GPP/NPP tulem Eesti jaoks: I. Algoritm ja mudelarvutuste esialgsed tulemused

Abstract. A light use efficiency (LUE) type model named EST_PP to simulate the yearly gross primary production (GPP) and net primary production (NPP) of Estonian land on a 1 km2 grid is described. The model is based on MERIS (MEdium Resolution Imaging Spectrometer) satellite images to describe the fraction of photosynthetically active radiation (fAPAR) and leaf area index (LAI) as well as meteorological reanalysis datasets on 11 km2 grid produced by Estonian Meteorological Institute (EMHI) and Tartu University (TU) by means of the HIRLAM (High Resolution Limited Area Model) numerical weather prediction model. The land cover map of Estonia needed for the model was derived using DMCii (Disaster Monitoring Constellation International Imaging) SLIM-6-22 (Surrey Linear Imager - 6 channel - 22 m resolution) images and ancillary information. The EST_PP model was run for the period from years 2003 to 2011. The results of GPP and NPP simulation are compared with the available global MODIS (Moderate Resolution Imaging Spectroradiometer) GPP/NPP product and with the Estonian statistical data on yearly volume increment in forests and on yield of agricultural crops. The NPP simulation results on coniferous and deciduous forests are compared with the data from tree ring analyses from different counties. These comparisons show us that the simulated country average yearly NPP values for Estonian forests agree reasonably well with the indirect estimates from other sources, taking into account the rather high uncertainty of the model predictions, uncertainty of forest inventory-based estimates and limited representativity of existing tree ring data. However, problems arise with the ability of present versions of EST_PP and MODIS NPP models to adequately simulate the regional differences of productivity and of variability of productivity in different years. The model needs some modification and the basic LUE principles to be tested in Estonia. Nevertheless, the MODIS NPP and EST_PP models offer additional possibilities to map yearly productivity and carbon sequestration by Estonian vegetation. There is a perspective to add the model-simulated NPP values into the national inventory datasets.

Benchmarking Climate-Carbon Model Simulations against Forest FACE Data

Terrestrial carbon fluxes are an important factor in regulating concentrations of atmospheric carbon dioxide (CO2). In this study, we use a coupled climate model with interactive biogeochemistry to benchmark the simulation of net primary productivity (NPP) and its response to elevated atmospheric CO2. Short-term field experiments such as Free-Air Carbon Dioxide Enrichment (FACE) studies have examined this phenomenon but it is difficult to infer trends from only a few years of field data. Here, we employ the University of Victoria's Earth System Climate Model (UVic ESCM) version 2.8 to compare simulated changes in NPP due to an elevated atmospheric CO2 concentration of 550 ppm to observed increases in NPP of 23% ±2% from four temperate forest FACE studies between 1997 and 2002. We further compare two scenarios: elevated CO2 with climate change, and elevated CO2 without climate change, the latter being consistent with FACE methodology. In the climate change scenario global terrestrial and forest-only NPP increased by 24.5% and 27.9%, respectively, while these increases were 21.0% and 17.2%, respectively, in the latitude band most representative of the location of the FACE studies. In the scenario without climate change, terrestrial and forest-only NPP increased instead by 28.3% and 30.6%, respectively, while these increases were 24.3% and 14.4%, respectively, in the FACE latitudes. This suggests that the model may underestimate temperate forest NPP increases when compared to results from temperate forest FACE studies and highlights the need for both increased experimental study of other forest biomes and further model development.

Testing for a CO2fertilization effect on growth of Canadian boreal forests

[1] The CO2 fertilization hypothesis stipulates that rising atmospheric CO2 has a direct positive effect on net primary productivity (NPP), with experimental evidence suggesting a 23% growth enhancement with a doubling of CO2. Here, we test this hypothesis by comparing a bioclimatic model simulation of NPP over the twentieth century against tree growth increment (TGI) data of 192 Pinus banksiana trees from the Duck Mountain Provincial Forest in Manitoba, Canada. We postulate that, if a CO2 fertilization effect has occurred, climatically driven simulations of NPP and TGI will diverge with increasing CO2. We use a two-level scaling approach to simulate NPP. A leaf-level model is first used to simulate high-frequency responses to climate variability. A canopy-level model of NPP is then adjusted to the aggregated leaf-level results and used to simulate yearly plot-level NPP. Neither model accounts for CO2 fertilization. The climatically driven simulations of NPP for 1912–2000 are effective for tracking the measured year-to-year variations in TGI, with 47.2% of the variance in TGI reproduced by the simulation. In addition, the simulation reproduces without divergence the positive linear trend detected in TGI over the same period. Our results therefore do not support the attribution of a portion of the historical linear trend in TGI to CO2 fertilization at the level suggested by current experimental evidence. A sensitivity analysis done by adding an expected CO2 fertilization effect to simulations suggests that the detection limit of the study is for a 14% growth increment with a doubling of atmospheric CO2 concentration.

Estado-da-arte da simulação da taxa de fixação de carbono de ecossistemas tropicais

Este trabalho avalia o estado-da-arte de modelos de simulação da taxa de fixação de carbono, ou produção primária liquida (NPP), de ecossistemas tropicais em duas configurações diagnósticas (simulação em sítios micrometeorológicos e simulação forçada por dados climáticos e por produtos de sensoriamento remoto) e uma prognóstica (simulação por um modelo acoplado clima-vegetação). Os resultados indicam que os modelos de estimativa de NPP atingiram capacidade de fazer estimativas não-viesadas de valores médios regionais de NPP, tanto no modo diagnóstico, quanto no modo prognóstico, com erros inferiores a 5%, enquanto o valor esperado do erro em cada sítio é inferior a 10% no modo diagnóstico e inferior a 20% no modo prognóstico. Os modelos estado-da-arte são capazes de monitorar a taxa de fixação de carbono por ecossistemas tropicais de maneira rotineira, sendo esperados baixos erros para essas estimativas.

Observation and simulation of net primary productivity in Qilian Mountain, western China

We modeled net primary productivity (NPP) at high spatial resolution using an advanced spaceborne thermal emission and reflection radiometer (ASTER) image of a Qilian Mountain study area using the boreal ecosystem productivity simulator (BEPS). Two key driving variables of the model, leaf area index (LAI) and land cover type, were derived from ASTER and moderate resolution imaging spectroradiometer (MODIS) data. Other spatially explicit inputs included daily meteorological data (radiation, precipitation, temperature, humidity), available soil water holding capacity (AWC), and forest biomass. NPP was estimated for coniferous forests and other land cover types in the study area. The result showed that NPP of coniferous forests in the study area was about 4.4 tC ha −1 y −1. The correlation coefficient between the modeled NPP and ground measurements was 0.84, with a mean relative error of about 13.9%.

Application of a geographically‐weighted regression analysis to estimate net primary production of Chinese forest ecosystems

ABSTRACTAim The objective of this paper is to obtain a net primary production (NPP) regression model based on the geographically weighted regression (GWR) method, which includes spatial non‐stationarity in the parameters estimated for forest ecosystems in China.Location We used data across China.Methods We examine the relationships between NPP of Chinese forest ecosystems and environmental variables, specifically altitude, temperature, precipitation and time‐integrated normalized difference vegetation index (TINDVI) based on the ordinary least squares (OLS) regression, the spatial lag model and GWR methods.Results The GWR method made significantly better predictions of NPP in simulations than did OLS, as indicated both by corrected Akaike Information Criterion (AICc) and R2. GWR provided a value of 4891 for AICc and 0.66 for R2, compared with 5036 and 0.58, respectively, by OLS. GWR has the potential to reveal local patterns in the spatial distribution of a parameter, which would be ignored by the OLS approach. Furthermore, OLS may provide a false general relationship between spatially non‐stationary variables. Spatial autocorrelation violates a basic assumption of the OLS method. The spatial lag model with the consideration of spatial autocorrelation had improved performance in the NPP simulation as compared with OLS (5001 for AICc and 0.60 for R2), but it was still not as good as that via the GWR method. Moreover, statistically significant positive spatial autocorrelation remained in the NPP residuals with the spatial lag model at small spatial scales, while no positive spatial autocorrelation across spatial scales can be found in the GWR residuals.Conclusions We conclude that the regression analysis for Chinese forest NPP with respect to environmental factors and based alternatively on OLS, the spatial lag model, and GWR methods indicated that there was a significant improvement in model performance of GWR over OLS and the spatial lag model.

Continental Scale Variability in Ecosystem Processes: Models, Data, and the Role of Disturbance

Management of ecosystems at large regional or continental scales and determination of the vulnerability of ecosystems to large-scale changes in climate or atmospheric chemistry require understanding how ecosystem processes are governed at large spatial scales. A collaborative project, the Vegetation and Ecosystem Modeling and Analysis Project (VEMAP), addressed modeling of multiple resource limitation at the scale of the conterminous United States, and the responses of ecosystems to environmental change. In this paper, we evaluate the model-generated patterns of spatial variability within and between ecosystems using Century, TEM, and Biome-BGC, and the relationships between modeled water balance, nutrients, and carbon dynamics. We present evaluations of models against mapped and site-specific data. In this analysis, we compare model-generated patterns of variability in net primary productivity (NPP) and soil organic carbon (SOC) to, respectively, a satellite proxy and mapped SOC from the VEMAP soils database (derived from USDA-NRCS [Natural Resources Conservation Service] information) and also compare modeled results to site-specific data from forests and grasslands. The VEMAP models simulated spatial variability in ecosystem processes in substantially different ways, reflecting the models' differing implementations of multiple resource limitation of NPP. The models had substantially higher correlations across vegetation types compared to within vegetation types. All three models showed correlation among water use, nitrogen availability, and primary production, indicating that water and nutrient limitations of NPP were equilibrated with each other at steady state. This model result may explain a number of seemingly contradictory observations and provides a series of testable predictions. The VEMAP ecosystem models were implicitly or explicitly sensitive to disturbance in their simulation of NPP and carbon storage. Knowledge of the effects of disturbance (human and natural) and spatial data describing disturbance regimes are needed for spatial modeling of ecosystems. Improved consideration of disturbance is a key “next step” for spatial ecosystem models.