Net Primary Productivity Predictions Research Articles

Plant size traits are key contributors in the spatial variation of net primary productivity across terrestrial biomes in China

Understanding the spatial variability of ecosystem functions is an important step forward in predicting changes in ecosystems under global transformations. Plant functional traits are important drivers of ecosystem functions such as net primary productivity (NPP). Although trait-based approaches have advanced rapidly, the extent to which specific plant functional traits are linked to the spatial diversity of NPP at a regional scale remains uncertain.Here, we used structural equation models (SEMs) to disentangle the relative effects of abiotic variables (i.e., climate, soil, nitrogen deposition, and human footprint) and biotic variables (i.e., plant functional traits and community structure) on the spatial variation of NPP across China and its eight biomes. Additionally, we investigated the indirect influence of climate and soil on the spatial variation of NPP by directly affecting plant functional traits.Abiotic and biotic variables collectively explained 62.6 % of the spatial differences of NPP within China, and 28.0 %–69.4 % across the eight distinct biomes. The most important abiotic factors, temperature and precipitation, had positive effects for NPP spatial variation. Interestingly, plant functional traits associated with the size of plant organs (i.e., plant height, leaf area, seed mass, and wood density) were the primary biotic drivers, and their positive effects were independent of biome type. Incorporating plant functional traits improved predictions of NPP by 6.7 %–50.2 %, except for the alpine tundra on the Qinghai-Tibet Plateau.Our study identifies the principal factors regulating NPP spatial variation and highlights the importance of plant size traits in predictions of NPP variation at a large scale. These results provide new insights for involving plant size traits in carbon process models.

Bioenergy potential from agricultural by-product in 2030: An AI-based spatial analysis and climate change scenarios in a Chinese region

Addressing the pressing need for sustainable bioenergy sources amid the growing climate crisis, this study investigates potential crop bioenergy output in the Jianghan Plain, Hubei, China. The research leverages Backpropagation Artificial Neural Network models to predict the accumulated net primary productivity (NPP) of various crops, i.e., single rice, double rice, maize, and wheat, in Jianghan Plain. The models highlight a strong predictive performance, with the SE model achieving an R2 of 0.83 and wheat model an R2 of 0.90, implying these models can be utilized reliably for future NPP forecasting. Our evaluation of past and future NPP values reveals a negative correlation between the increase in NPP values and observed temperature trends. This emphasizes the significant impact of temperature on crop bioenergy potential. For future NPP predictions, scenario (S3a) under Temperature Control strategies at medium-effort mitigation yields a favourable outcome for all crops, particularly SE. Additionally, the results suggest that aggressive decarbonization and global warming mitigation strategies are vital for enhancing NPP values. However, the study also highlights potential trade-offs and stresses the necessity for region-specific climate reconciliation strategies; for instance, midland counties benefit from temperature control, whereas the sideline favours CO2 mitigation. The study further investigates the spatial distribution of potential bioenergy, with the southwestern part of the plain holding more bioenergy potential, providing an essential reference for future energy planning. By 2030, in Jianghan Plain, when fully integrated with reasonable climate responses, there could be substantial bioenergy potential from crop residues reaching (1.2–1.9) × 109 MJ, which is 11% less than the current.

Net primary productivity exhibits a stronger climatic response in planted versus natural forests

Compared to planted forest ecosystems (PFE), natural forest ecosystems (NFE) generally have a longer history and theoretically have stronger environmental adaptability. Changes in net primary productivity (NPP) due to global change have been studied for many forest types, yet the underlying mechanism remain poorly understood. Using 926 forest plots (including both PFE and NFE) established between 2005 and 2020 in China, we found that NPP exhibited stronger climate and soil nutrient responses, with greater environmental plasticity, in PFE versus NFE. Several functional traits (e.g., leaf area, leaf nitrogen and phosphorus content) differed between NFE and PFE, but only weakly predicted spatial variation in NPP, as compared to the environmental factors. Overall, NPP in both NFE and PFE showed consistent responses to climate and soil nutrient changes. These environmental responses were weaker in coniferousforests than broadleaved forests. Direct effects of climatic factors on NPP were stronger than indirect effects. Overall, the effects of functional traits on NPP were mediated by climatic and edaphic factors. These findings provide guidance for more reliable predictions of NPP and succession in NFE and PFE in the context of global warming.

Spatiotemporal Variation of Net Primary Productivity and Its Response to Climate Change and Human Activities in the Yangtze River Delta, China

Exploring the temporal and spatial changes, as well as driving factors, of net primary productivity (NPP) of terrestrial ecosystems is essential for maintaining regional carbon balance. This work focuses on the spatiotemporal variation and future trends of NPP and the response mechanisms of NPP to various driving factors. The Theil–Sen estimator, as well as Mann–Kendall and Hurst exponent methods, were used to analyze the spatiotemporal dynamics and future trends of NPP, and geographical detectors and correlation analysis were used to reveal the response of NPP to various driver changes to environmental factors. The results showed that the NPP was generally on an increasing trend in the Yangtze River Delta region from 2000 to 2019, with the average NPP value of 550.17 g C m−2 a−1, of which 85.90% was the increasing regions and 14.10% was the decreasing regions, showing a significant spatiotemporal heterogeneity characteristic. The trend of future changes in NPP is dominated by an anti-persistence trend in the study area, i.e., the opposite of the past trend. Notably, annual precipitation is the most significant positive driver of NPP; while NPP was negatively correlated with population, meanwhile, different land use/land cover (LULC) also significantly affected the spatial distribution of NPP. Besides, there was a two-factor enhanced interaction between the various drivers on NPP, with the highest interaction occurring between temperature and elevation. Overall, this study provides data support for future regional NPP predictions and ecosystem evaluations.

Uncertainties in ocean biogeochemical simulations: Application of ensemble data assimilation to a one-dimensional model

Marine biogeochemical (BGC) models are highly uncertain in their parameterization. The value of the BGC parameters are poorly known and lead to large uncertainties in the model outputs. This study focuses on the uncertainty quantification of model fields and parameters within a one-dimensional (1-D) ocean BGC model applying ensemble data assimilation. We applied an ensemble Kalman filter provided by the Parallel Data Assimilation Framework (PDAF) into a 1-D vertical configuration of the BGC model Regulated Ecosystem Model 2 (REcoM2) at two BGC time-series stations: the Bermuda Atlantic Time-series Study (BATS) and the Dynamique des Flux Atmosphériques en Méditerranée (DYFAMED). We assimilated 5-day satellite chlorophyll-a (chl-a) concentration and monthly in situ net primary production (NPP) data for 3 years to jointly estimate 10 preselected key BGC parameters and the model state. The estimated set of parameters resulted in improvements in the model prediction up to 66% for the surface chl-a and 56% for NPP. Results show that assimilating satellite chl-a concentration data alone degraded the prediction of NPP. Simultaneous assimilation of the satellite chl-a data and in situ NPP data improved both surface chl-a and NPP simulations. We found that correlations between parameters preclude estimating parameters independently. Co-dependencies between parameters also indicate that there is not a unique set of optimal parameters. Incorporation of proper uncertainty estimation in BGC predictions, therefore, requires ensemble simulations with varying parameter values.

Operational framework to predict field level crop biomass using remote sensing and data driven models

Remote Sensing (RS) based monitoring provides opportunities to acquire timely and reliable information on crop growth at diverse scales. Crop yield forecasts can help decision makers to formulate policies on maintaining national food reserves, sustaining food supply chains and attaining national food security. Inter field crop yield heterogeneity arising from varied field management and agricultural practices necessitates this forecasting to be done at field-level. Such field level information can aid farmers to identify gaps in water and farm management practices and take corrective actions, if needed. So far, no scalable tools are available to predict crop yields at field level that leverage the availability of open-access high-resolution RS data.This research implements an operational framework to predict field level crop biomass by evaluating different regression algorithms to develop data driven models, leveraging historical and near real time open-access high resolution optical satellite data from Sentinel-2, radar data from Sentinel-1 and evapotranspiration (ETa) and Net Primary Production (NPP) data from Food and Agriculture Organization’s (FAO) Water Productivity through Open-access Remotely sensed data (WaPOR) platform. NPP is used as a proxy for biomass production/yield. Five of the most commonly used regression algorithms were tested to build a data-driven model for sugarcane NPP prediction in Wonji-Shoa sugarcane estate, located in the Awash Basin, Ethiopia. The models tested were the Multivariate Linear Regression (MLR), Stepwise Multivariate Linear Regression (SMLR), Boosted Regression Trees (BRT), Support Vector Regression (SVR), and Random Forest Regression (RFR).The results revealed that for seasonal sugarcane NPP predictions, the linear regression models (MLR and SMLR) yielded more accurate predictions than the non-linear machine learning models (BRT, SVR and RFR) tested. The highest accuracy was achieved for MLR models for which estimates with 89% accuracy could be made 4 months prior to the harvest and with accuracies of 79% up to 200 days (approx. 6.5 months) before the harvest. The non-linear machine learning models, however, could not provide reliable estimates of sugarcane NPP (accuracies < 61%). Cumulative vegetation indices (VIs) were found to have higher predictive power than standard VIs for predicting future sugarcane NPP. Cumulative Enhanced Vegetation Index (EVI) was found to be the variable with the highest predictive power, followed by VH polarized Sentinel-1 Synthetic Aperture Radar data and WaPOR ETa. The study shows the usefulness of high-resolution RS information to predict seasonal NPP at field level. The methods presented here can be translated into an automated framework towards an operational system.

Impacts of two types of errors on the predictability of terrestrial carbon cycle

AbstractInitial errors and model parameter errors are two of the main factors that produce uncertainties in numerical simulations and predictions. It is crucial to determine in advance which of these types of errors should be reduced to improve numerical simulations and increase their prediction skill. In this study, a fundamental issue related to studies of the predictability about terrestrial carbon cycle is discussed. The relative importance of initial errors and model parameter errors in causing the prediction uncertainty of net primary production (NPP), which is part of the terrestrial carbon cycle, is assessed. The errors in NPP predictions related to initial errors and parameter errors are evaluated within the Lund–Potsdam–Jena (LPJ) model using the conditional nonlinear optimal perturbation approach for these two types of errors (CNOP‐I and CNOP‐P, respectively). This method explores the upper bounds of the prediction errors caused by initial errors and model parameter errors at a particular time in the future. The contributions of initial errors and model parameter errors to NPP prediction uncertainty are found to depend on climate backgrounds and the prediction timescale. For the moist and semimoist regions of China, initial errors play a more important role than model parameter errors in the prediction uncertainty of NPP for short prediction times. As the prediction time scale increases, model parameter errors lead to large errors in NPP predictions. However, in the arid and semiarid regions of China, the uncertainty in NPP predictions caused by model parameter errors is always larger than that caused by initial errors. The above results can be explained by analyzing variations in photosynthesis and autotrophic respiration. The numerical results also suggest that the skill of predictions of the terrestrial carbon cycle may be improved by reducing the different types of errors for different climatic regions and prediction timescales.

Identification of Key Physical Processes and Improvements for Simulating and Predicting Net Primary Production Over the Tibetan Plateau

AbstractThere are still considerable uncertainties related to the numerical simulation and prediction of net primary production (NPP) as an important part of terrestrial carbon sources and sinks over the Tibetan Plateau (TP). To reduce the uncertainty of numerical simulations and improve the ability of predictions, the key physical processes related to the uncertainty of simulated NPP are identified at nine observational stations over the TP. A sensitivity analysis of parameter combinations based on the Conditional Nonlinear Optimal Perturbation related to Parameters (CNOP‐P) approach, which can be used to assess the sensitivity of a parameter subset, is conducted for 28 target physical parameters in the Lund‐Potsdam‐Jena (LPJ) Wetland Hydrology and Methane Dynamic Global Vegetation Model (LPJ‐WHyMe v1.3.1). Firstly, the numerical results show that the uncertainties of physical parameters do lead to a large error in the simulated NPP over the TP, and the range of error varies from 72.4 (MS 3478) to 150.5 g C m−2 year−1 (Ngari station). Secondly, in areas of moderate precipitation over the TP, the photosynthesis is the main factor leading to high uncertainty in NPP modeling. In areas of low and high precipitation over the TP, the combined influences of hydrological processes and photosynthesis play a key role. Finally, eliminating the errors associated with the most sensitive and important parameter combinations led to the maximum benefit in terms of reducing the uncertainty of simulated NPP, when compared to that obtained with the traditional method. This study suggests that we should prioritize reducing the uncertainty of relatively sensitive parameter combinations among all physical parameters to improve the prediction or simulation ability of NPP over the TP.

DayCent Model Predictions of NPP and Grain Yields for Agricultural Lands in the Contiguous U.S.

AbstractAccurate estimation of crop net primary production (NPP) and yields is fundamental for regional analyses of agroecosystem dynamics using process‐based models. In this study, we simulated croplands in the contiguous U.S. using the DayCent ecosystem model with new production algorithms. Crops were divided into crop variety groups based on regional varieties of three major crops (corn, soybeans, and winter wheat) and generic parameter values that were generated for each group. These varieties have been developed through crop breeding programs and enhance production of major crop types in different temperature and precipitation regimes. NPP and yields for the three major crops were evaluated at the county level with reported yields from the National Agricultural Statistics Service (NASS). The predictions of the multiyear average yields in all counties were more accurate than most other published results using process‐based models. DayCent predictions of yields produced an overall R2 of 0.54, 0.54, and 0.38 for corn, soybean, and winter wheat, respectively, with predictions for most counties within ±20% of the NASS reported yields. Our estimations of the total annual NPP for the three crops in the contiguous U.S. are 0.24, 0.09, and 0.06 Pg C yr−1 for corn, soybean, and winter wheat, respectively. Together, they contribute 7.3% to 14.8% of the total NPP for all vegetation in the contiguous U.S. We conclude that crop variety groups capture heterogeneity in NPP for major crop types and can improve biogeochemical model predictions of NPP for croplands.

Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis

Abstract. A range of applications analysing the impact of environmental changes due to climate change, e.g. geographical spread of climate-sensitive infections (CSIs) and agriculture crop modelling, make use of land surface modelling (LSM) to predict future land surface conditions. There are multiple LSMs to choose from that account for land processes in different ways and this may introduce predictive uncertainty when LSM outputs are used as inputs to inform a given application. For useful predictions for a specific application, one must therefore understand the inherent uncertainties in the LSMs and the variations between them, as well as uncertainties arising from variation in the climate data driving the LSMs. This requires methods to analyse multivariate spatio-temporal variations and differences. A methodology is proposed based on multiway data analysis, which extends singular value decomposition (SVD) to multidimensional tables and provides spatio-temporal descriptions of agreements and disagreements between LSMs for both historical simulations and future predictions. The application underlying this paper is prediction of how climate change will affect the spread of CSIs in the Fennoscandian and north-west Russian regions, and the approach is explored by comparing net primary production (NPP) estimates over the period 1998–2013 from versions of leading LSMs (JULES, CLM5 and two versions of ORCHIDEE) that are adapted to high-latitude processes, as well as variations in JULES up to 2100 when driven by 34 global circulation models (GCMs). A single optimal spatio-temporal pattern, with slightly different weights for the four LSMs (up to 14 % maximum difference), provides a good approximation to all their estimates of NPP, capturing between 87 % and 93 % of the variability in the individual models, as well as around 90 % of the variability in the combined LSM dataset. The next best adjustment to this pattern, capturing an extra 4 % of the overall variability, is essentially a spatial correction applied to ORCHIDEE-HLveg that significantly improves the fit to this LSM, with only small improvements for the other LSMs. Subsequent correction terms gradually improve the overall and individual LSM fits but capture at most 1.7 % of the overall variability. Analysis of differences between LSMs provides information on the times and places where the LSMs differ and by how much, but in this case no single spatio-temporal pattern strongly dominates the variability. Hence interpretation of the analysis requires the summation of several such patterns. Nonetheless, the three best principal tensors capture around 76 % of the variability in the LSM differences and to a first approximation successively indicate the times and places where ORCHIDEE-HLveg, CLM5 and ORCHIDEE-MICT differ from the other LSMs. Differences between the climate forcing GCMs had a marginal effect up to 6 % on NPP predictions out to 2100 without specific spatio-temporal GCM interaction.

Modifying the maximal light-use efficiency for enhancing predictions of vegetation net primary productivity on the Mongolian Plateau

ABSTRACTMaximal light-use efficiency (LUE), ɛmax, is a measure of the conversion efficiency of the photosynthetically active radiation absorbed by plants to net primary productivity (NPP), based on the principle that an optimal plant productive capability exists when the LUE is at its maximum. ɛmax is an important parameter for modelling regional NPP and is conventionally applied at the biome level using a constant value. In this study, we estimated type-specific ɛmax values for three dominant land cover types on the Mongolian Plateau: 0.621 g C MJ–1 (MJ is the mega Joules and 1 MJ = 106 J) for meadow steppe, 0.534 g C MJ–1 for typical steppe, and 0.520 g C MJ–1 for desert steppe. With these steppe-specific modified ɛmax values, we were able to examine changes in NPP for 2001–2015 on the plateau, as well as their likely responses to regional climate change. The use of different ɛmax values for each steppe type improved the accuracy of the Carnegie Ames Stanford Approach (CASA) Biosphere model predictions of grassland NPP by 18.8% (R2 = 0.48 to R2 = 0.57; R2 is the coefficient of determination) over the observation period. Previous studies based on a constant ɛmax (0.541 g C MJ–1) appear to underestimate ɛmax and NPP in meadow steppe, highlighting the importance of setting type-specific ɛmax values for different land cover types in the remote-sensing modelling of grassland NPP. However, more detailed maps of biome sub-classes, with species composition, would be valuable for future attempts to determine appropriate ɛmax values. The growing season (April–October) NPP on the plateau increased significantly from 2001 to 2015, with an annual increment of 4.44 g C m–2 y–1. This trend was strongly governed by the change in summer NPP across the plateau. In comparison, NPP in the spring and autumn did not influence the change in total NPP, which was likely due to their relatively small values. Summer precipitation and the related drought stress were the chief factors responsible for plateau-scale NPP changes, due to the high proportion of summer precipitation and NPP in the annual totals. This may induce important environmental features, such as dzud (a Mongolian term for a severe winter) and desertification on the plateau.

Modeling terrestrial ecosystem productivity of an estuarine ecosystem in the Sundarban Biosphere Region, India using seven ecosystem models

The net primary production (NPP) is a key indicator for assessing the terrestrial carbon pools and fluxes from the atmosphere to biosphere in any given ecosystem. Adequate measurement of the sensitivity and uncertainty of regional and global carbon pools and fluxes in different climatic and anthropogenic regimes is needed to properly investigate the terrestrial carbon balance. Remote sensing light use efficiency (LUE) approaches can be used to quantify the terrestrial NPP accurately. Using LUE models, NPP was calculated for last two decades of an estuarine ecosystem, the Sundarban Biosphere region, India. Results from seven LUE models: Carnegie-Ames-Stanford Approach (CASA), Global Production Efficiency Model (GLO-PEM), Vegetation Photosynthesis Model (VPM), Eddy Covariance-Light Use Efficiency (EC-LUE), MODerate resolution Imaging Spectroradiometer (MOD17), Temperature and Greenness (TG), Greenness and Radiation (GR) models were compared to ascertain model consistency for NPP estimation during the study period 2000–2013. To optimize structural biases in the model accurate parameterization and systematic multi-method assessment were employed. The influence of the input drivers (biophysical, bioclimatic and environmental stress) on model performances was evaluated. Best model performances were observed over cropland, followed by mixed forest and mangrove ecoregion, respectively. Amongst all model, EC-LUE simulated higher NPP at mangrove ecoregion, while the MOD17 model simulated lower NPP in most of the evaluated biomes. In addition, TG and GR models exhibited larger unexplained variances and found statistically significant at mixed forest site. This error was attributed to the absence of environmental stress factors used to drive these model. GLO-PEM and VPM corroborate with NPP prediction among all LUE models. All seven LUE models predict the statistically significant NPP across the biomes and, the poor model performance is attributed to the different parameterization scheme executed for defining the biophysical and stress variables. Biophysical drivers mostly controlled the model performances, followed by environmental stress and bioclimatic drivers, respectively. This analysis is suggesting that the TG and GR model (driven only by the biophysical factors) could be useful in NPP predictions in the regions with no meteorological inputs and real-time eddy covariance flux tower measurement.

Macronutrient processing by temperate lakes: A dynamic model for long-term, large-scale application

We developed a model of the biogeochemical and sedimentation behaviour of carbon (C), nitrogen (N) and phosphorus (P) in lakes, designed to be used in long-term (decades to centuries) and large-scale (104–105km2) macronutrient modelling, with a focus on human-induced changes. The model represents settling of inflow suspended particulate matter, production and settling of phytoplankton, decomposition of organic matter in surface sediment, denitrification, and DOM flocculation and decomposition. The model uses 19 parameters, 13 of which are fixed a priori. The remaining 6 were obtained by fitting data from 109 temperate lakes, together with other information from the literature, which between them characterised the stoichiometric incorporation of N and P into phytoplankton via photosynthesis, whole-lake retention of N and P, N removal by denitrification, and the sediment burial of C, N and P. To run the model over the long periods of time necessary to simulate sediment accumulation and properties, simple assumptions were made about increases in inflow concentrations and loads of dissolved N and P and of catchment-derived particulate matter (CPM) during the 20th century. Agreement between observations and calculations is only approximate, but the model is able to capture wide trends in the lakewater and sediment variables, while also making reasonable predictions of net primary production. Modelled results suggest that allochthonous sources of carbon (CPM and dissolved organic matter) contribute more to sediment carbon than the production and settling of algal biomass, but the relative contribution due to algal biomass has increased over time. Simulations for 8 UK lakes with sediment records suggest that during the 20th century average carbon fixation increased 6-fold and carbon burial in sediments by 70%, while the delivery of suspended sediment from the catchments increased by 40% and sediment burial rates of N and P by 131% and 185% respectively.

Stagnating crop yields: An overlooked risk for the carbon balance of agricultural soils?

The carbon (C) balance of agricultural soils may be largely affected by climate change. Increasing temperatures are discussed to cause a loss of soil organic carbon (SOC) due to enhanced decomposition of soil organic matter, which has a high intrinsic temperature sensitivity. On the other hand, several modeling studies assumed that potential SOC losses would be compensated or even outperformed by an increased C input by crop residues into agricultural soils. This assumption was based on a predicted general increase of net primary productivity (NPP) as a result of the CO2 fertilization effect and prolonged growing seasons. However, it is questionable if the crop C input into agricultural soils can be derived from NPP predictions of vegetation models. The C input in European croplands is largely controlled by the agricultural management and was strongly related to the development of crop yields in the last decades. Thus, a glance at past yield development will probably be more instructive for future estimations of the C input than previous modeling approaches based on NPP predictions. An analysis of European yield statistics indicated that yields of wheat, barley and maize are stagnating in Central and Northern Europe since the 1990s. The stagnation of crop yields can probably be related to a fundamental change of the agricultural management and to climate change effects. It is assumed that the soil C input is concurrently stagnating which would necessarily lead to a decrease of agricultural SOC stocks in the long-term given a constant temperature increase. Remarkably, for almost all European countries that are faced with yield stagnation indications for agricultural SOC decreases were already found. Potentially adverse effects of yield stagnation on the C balance of croplands call for an interdisciplinary investigation of its causes and a comprehensive monitoring of SOC stocks in agricultural soils of Europe.

Comparing MODIS Net Primary Production Estimates with Terrestrial National Forest Inventory Data in Austria

The mission of this study is to compare Net Primary Productivity (NPP) estimates using (i) forest inventory data and (ii) spatio-temporally continuous MODIS (MODerate resolution Imaging Spectroradiometer) remote sensing data for Austria. While forest inventories assess the change in forest growth based on repeated individual tree measurements (DBH, height etc.), the MODIS NPP estimates are based on ecophysiological processes such as photosynthesis, respiration and carbon allocation. We obtained repeated national forest inventory data from Austria, calculated a “ground-based” NPP estimate and compared the results with “space-based” MODIS NPP estimates using different daily climate data. The MODIS NPP estimates using local Austrian climate data exhibited better compliance with the forest inventory driven NPP estimates than the MODIS NPP predictions using global climate data sets. Stand density plays a key role in addressing the differences between MODIS driven NPP estimates versus terrestrial driven inventory NPP estimates. After addressing stand density, both results are comparable across different scales. As forest management changes stand density, these findings suggest that management issues are important in understanding the observed discrepancies between MODIS and terrestrial NPP.

Prediction of forest NPP in Italy by the combination of ground and remote sensing data

Our research group has recently proposed a strategy to simulate net forest carbon fluxes based on the coupling of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC. The outputs of the two models are combined through the use of a proxy of ecosystem distance from equilibrium condition which accounts for the occurred disturbances. This modeling strategy is currently applied to all Italian forest areas using an available set of NDVI images and ancillary data descriptive of an 8-year period (1999–2006). The obtained estimates of forest net primary production (NPP) are first analyzed in order to assess the importance of the main model drivers on relevant spatial variability. This analysis indicates that growing stock is the most influential model driver, followed by forest type and meteorological variables. In particular, the positive influence of growing stock on NPP can be constrained by thermal and water limitations, which are most evident in the upper mountain and most southern zones, respectively. Next, the NPP estimates, aggregated over seven main forest types and twenty administrative regions in Italy, are converted into current annual increment of standing volume (CAI) by specific coefficients. The accuracy of these CAI estimates is finally assessed by comparison with the ground data collected during a recent national forest inventory. The results obtained indicate that the modeling approach tends to overestimate the ground CAI for most forest types. In particular, the overestimation is notable for forest types which are mostly managed as coppice, while it is negligible for high forests. The possible origins of these phenomena are investigated by examining the main model drivers together with the results of previous studies and of older forest inventories. The implications of using different NPP estimation methods are finally discussed in view of assessing the forest carbon budget on a national basis.

Predicted NPP spatiotemporal variations in a semiarid steppe watershed for historical and trending climates

Accurate prediction of net primary production (NPP) is very important to understanding cycling of carbon, water, and nutrients as influenced by climate change. However, the existing NPP models do not consider site-specific intrinsic characteristics (e.g., soil properties and topography) and have great limitations in arid and semiarid environment, which is the case of the Balagaer River watershed located in northeastern Inner Mongolia Autonomous Region of China. In those models, the estimation of evapotranspiration (ET), a pivotal variable representing biosphere–climate interactive effects, is oversimplified and thus NPP magnitude and/or trend can hardly be predicted with a satisfactory accuracy. For example, NPP predicted by some of the previous studies were too high and exhibited a trend that is opposite to what was observed. The objectives of this study were to: 1) formulate a better NPP prediction model; 2) use the model to evaluate spatiotemporal variations of NPP and their controlling factors. The evaluation was conducted in the Balagaer River watershed. The model uses the Penman–Monteith formula and a theoretical solution to the Budyko's hypothesis to estimate potential and actual ET, respectively. Also, the model includes CLIGEN as a stand-alone module to generate hypothetical climates using “perturbed” values of monthly statistics that are computed from observed daily data. The application of the model in the study watershed indicated that it well predicted both magnitude and spatiotemporal variations of NPP. The results showed a weak declining trend of NPP over the 56 record years and a clear spatial pattern that matches the site-specific intrinsic characteristics. Further, the results showed that NPP in the study watershed will probably increase slightly or stay stable as a result of climate change.

Reconciling satellite with ground data to estimate forest productivity at national scales

Large scale forest productivity estimates are of increasing interest as more demands are made on forest resources. In principle three different methods are currently available: (i) forest growth data from forest research plots and/or forest inventory sampling points based on repeated tree observations, (ii) flux tower observations which record the gas exchange of the plant atmosphere interactions for a given vegetation type, and (iii) remotely sensed data for a continuous cover of net primary productivity estimates. In this paper we focus on the conceptual challenge in comparing “space based” moderate resolution imaging spectroradiometer (MODIS) satellite driven net primary production (NPP) vs. terrestrial “ground based” productivity estimates using forest increment data from 151 long term research plots with a well documented management history. The Austrian biomass functions are applied to derive ground based NPP estimates based on repeated tree observations from the plots. In addition we use BIOME-BGC as a diagnostic tool for exploring conceptual constraints among the two methods. The results of the study can be summarized as follows: (i) MODIS satellite driven annual NPP estimates provide a continuous productivity estimate across Austria and no significant differences between different daily climate input data sets were evident. (ii) MODIS NPP predictions provide forest productivity estimates of fully stocked forests with a complete crown cover. This is confirmed by the results of spin-up runs of the BIOME-BGC model. (iii) Terrestrial driven NPP predictions using the Austrian biomass functions compared well with MODIS driven estimates after addressing stand density effects of the forest plot data. The influence of stand density were known to be an integral component in reconciling “space based” satellite vs. “ground based” derived forest productivity estimates. After addressing stand density, computed forest productivity estimates compared well with MODIS-based estimates. This suggests that combining both methods will enhance our ability to generate forest productivity assessments across large forest areas.

Monitoring of Net Primary Production in California Rangelands Using Landsat and MODIS Satellite Remote Sensing

In this study, we present results from the CASA (Carnegie-Ames-Stanford Approach) model to estimate net primary production (NPP) in grasslands under different management (ranching versus unmanaged) on the Central Coast of California. The latest model version called CASA Express has been designed to estimate monthly patterns in carbon fixation and plant biomass production using moderate spatial resolution (30 m to 250 m) satellite image data of surface vegetation characteristics. Landsat imagery with 30 m resolution was adjusted by contemporaneous Moderate Resolution Imaging Spectroradiometer (MODIS) data to calibrate the model based on previous CASA research. Results showed annual NPP predictions of between 300 - 450 grams C per square meter for coastal rangeland sites. Irrigation increased the predicted NPP carbon flux of grazed lands by 59 grams C per square meter annually compared to unmanaged grasslands. Low intensity grazing activity appeared to promote higher grass regrowth until June, compared to the ungrazed grassland sites. These modeling methods were shown to be successful in capturing the differing seasonal growing cycles of rangeland forage production across the area of individual ranch properties.

Terrestrial plant production and climate change

The likely future increase in atmospheric CO(2) and associated changes in climate will affect global patterns of plant production. Models integrate understanding of the influence of the environment on plant physiological processes and so enable estimates of future changes to be made. Moreover, they allow us to assess the consequences of different assumptions for predictions and so stimulate further research. This paper is a review of the sensitivities of one such model, Hybrid6.5, a detailed mechanistic model of terrestrial primary production. This model is typical of its type, and the sensitivities of the global distribution of predicted production to model assumptions and possible future CO(2) levels and climate are assessed. Sensitivity tests show that leaf phenology has large effects on mean C(3) crop and needleleaved cold deciduous tree production, reducing potential net primary production (NPP) from that obtained using constant maximum annual leaf area index by 32.9% and 41.6%, respectively. Generalized Plant Type (GPT) specific parameterizations, particularly photosynthetic capacity per unit leaf N, affect mean predicted NPP of higher C(3) plants by -22.3% to 27.9%, depending on the GPT, compared to NPP predictions obtained using mean parameter values. An increase in atmospheric CO(2) concentrations from current values to 720 ppm by the end of this century, with associated effects on climate from a typical climate model, is predicted to increase global NPP by 37.3%. Mean increases range from 43.9-52.9% across different C(3) GPTs, whereas the mean NPP of C(4) grass and crop increases by 5.9%. Significant uncertainties concern the extent to which acclimative processes may reduce any potential future increase in primary production and the degree to which any gains are transferred to durable, and especially edible, biomass. Experimentalists and modellers need to work closely together to reduce these uncertainties. A number of research priorities are suggested. 'The green leaf or, to be more precise, the microscopic green grain of chlorophyll, is the focus, the point in the world to which solar energy flows on one side while all the manifestations of life on earth take their source on the other side.' Kliment Arkadievich Timiryazev The conclusions of a century of plant physiology, speech at Moscow University, 12 January 1901.