USGS ScienceSearch

Geology topics

S. Liu

Publications and source records attributed to S. Liu.

At least 19 recordsLinked to original sources

Carbon and energy fluxes in cropland ecosystems: a model-data comparison

Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO 2 seasonal uptake over agricultural regions.

Biogeochemistry

NCWin — A Component Object Model (COM) for processing and visualizing NetCDF data

NetCDF (Network Common Data Form) is a data sharing protocol and library that is commonly used in large-scale atmospheric and environmental data archiving and modeling. The NetCDF tool described here, named NCWin and coded with Borland C + + Builder, was built as a standard executable as well as a COM (component object model) for the Microsoft Windows environment. COM is a powerful technology that enhances the reuse of applications (as components). Environmental model developers from different modeling environments, such as Python, JAVA, VISUAL FORTRAN, VISUAL BASIC, VISUAL C + +, and DELPHI, can reuse NCWin in their models to read, write and visualize NetCDF data. Some Windows applications, such as ArcGIS and Microsoft PowerPoint, can also call NCWin within the application. NCWin has three major components: 1) The data conversion part is designed to convert binary raw data to and from NetCDF data. It can process six data types (unsigned char, signed char, short, int, float, double) and three spatial data formats (BIP, BIL, BSQ); 2) The visualization part is designed for displaying grid map series (playing forward or backward) with simple map legend, and displaying temporal trend curves for data on individual map pixels; and 3) The modeling interface is designed for environmental model development by which a set of integrated NetCDF functions is provided for processing NetCDF data. To demonstrate that the NCWin can easily extend the functions of some current GIS software and the Office applications, examples of calling NCWin within ArcGIS and MS PowerPoint for showing NetCDF map animations are given.

Cartographic Journal

Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km 2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

Biogeosciences

Impacts of biofuels production alternatives on water quantity and quality in the Iowa River Basin

Corn stover as well as perennial grasses like switchgrass (Panicum virgatum) and miscanthus are being considered as candidates for the second generation biofuel feedstocks. However, the challenges to biofuel development are its effects on the environment, especially water quality. This study evaluates the long-term impacts of biofuel production alternatives (e.g., elevated corn stover removal rates and the potential land cover change) on an ecosystem with a focus on biomass production, soil erosion, water quantity and quality, and soil nitrate nitrogen concentration at the watershed scale. The Soil and Water Assessment Tool (SWAT) was modified for setting land cover change scenarios and applied to the Iowa River Basin (a tributary of the Upper Mississippi River Basin). Results show that biomass production can be sustained with an increased stover removal rate as long as the crop demand for nutrients is met with appropriate fertilization. Although a drastic increase (4.7–70.6%) in sediment yield due to erosion and a slight decrease (1.2–3.2%) in water yield were estimated with the stover removal rate ranging between 40% and 100%, the nitrate nitrogen load declined about 6–10.1%. In comparison to growing corn, growing either switchgrass or miscanthus can reduce sediment erosion greatly. However, land cover changes from native grass to switchgrass or miscanthus would lead to a decrease in water yield and an increase in nitrate nitrogen load. In contrast to growing switchgrass, growing miscanthus is more productive in generating biomass, but its higher water demand may reduce water availability in the study area.

Iowa;Minnesota

Automating calibration, sensitivity and uncertainty analysis of complex models using the R package Flexible Modeling Environment (FME): SWAT as an example

Parameter optimization and uncertainty issues are a great challenge for the application of large environmental models like the Soil and Water Assessment Tool (SWAT), which is a physically-based hydrological model for simulating water and nutrient cycles at the watershed scale. In this study, we present a comprehensive modeling environment for SWAT, including automated calibration, and sensitivity and uncertainty analysis capabilities through integration with the R package Flexible Modeling Environment (FME). To address challenges (e.g., calling the model in R and transferring variables between Fortran and R) in developing such a two-language coupling framework, 1) we converted the Fortran-based SWAT model to an R function (R-SWAT) using the RFortran platform, and alternatively 2) we compiled SWAT as a Dynamic Link Library (DLL). We then wrapped SWAT (via R-SWAT) with FME to perform complex applications including parameter identifiability, inverse modeling, and sensitivity and uncertainty analysis in the R environment. The final R-SWAT-FME framework has the following key functionalities: automatic initialization of R, running Fortran-based SWAT and R commands in parallel, transferring parameters and model output between SWAT and R, and inverse modeling with visualization. To examine this framework and demonstrate how it works, a case study simulating streamflow in the Cedar River Basin in Iowa in the United Sates was used, and we compared it with the built-in auto-calibration tool of SWAT in parameter optimization. Results indicate that both methods performed well and similarly in searching a set of optimal parameters. Nonetheless, the R-SWAT-FME is more attractive due to its instant visualization, and potential to take advantage of other R packages (e.g., inverse modeling and statistical graphics). The methods presented in the paper are readily adaptable to other model applications that require capability for automated calibration, and sensitivity and uncertainty analysis.

Environmental Modelling and Software

Quantifying the hydrological responses to climate change in an intact forested small watershed in southern China

Responses of hydrological processes to climate change are key components in the Intergovernmental Panel for Climate Change (IPCC) assessment. Understanding these responses is critical for developing appropriate mitigation and adaptation strategies for sustainable water resources management and protection of public safety. However, these responses are not well understood and little long-term evidence exists. Herein, we show how climate change, specifically increased air temperature and storm intensity, can affect soil moisture dynamics and hydrological variables based on both long-term observation and model simulations using the Soil and Water Assessment Tool (SWAT) in an intact forested watershed (the Dinghushan Biosphere Reserve) in Southern China. Our results show that, although total annual precipitation changed little from 1950 to 2009, soil moisture decreased significantly. A significant decline was also found in the monthly 7-day low flow from 2000 to 2009. However, the maximum daily streamflow in the wet season and unconfined groundwater tables have significantly increased during the same 10-year period. The significant decreasing trends on soil moisture and low flow variables suggest that the study watershed is moving towards drought-like condition. Our analysis indicates that the intensification of rainfall storms and the increasing number of annual no-rain days were responsible for the increasing chance of both droughts and floods. We conclude that climate change has indeed induced more extreme hydrological events (e.g. droughts and floods) in this watershed and perhaps other areas of Southern China. This study also demonstrated usefulness of our research methodology and its possible applications on quantifying the impacts of climate change on hydrology in any other watersheds where long-term data are available and human disturbance is negligible.

Guangdong Province

Hydrological effects of the increased CO 2 and climate change in the Upper Mississippi River Basin using a modified SWAT

Increased atmospheric CO 2 concentration and climate change may significantly impact the hydrological and meteorological processes of a watershed system. Quantifying and understanding hydrological responses to elevated ambient CO 2 and climate change is, therefore, critical for formulating adaptive strategies for an appropriate management of water resources. In this study, the Soil and Water Assessment Tool (SWAT) model was applied to assess the effects of increased CO 2 concentration and climate change in the Upper Mississippi River Basin (UMRB). The standard SWAT model was modified to represent more mechanistic vegetation type specific responses of stomatal conductance reduction and leaf area increase to elevated CO 2 based on physiological studies. For estimating the historical impacts of increased CO 2 in the recent past decades, the incremental (i.e., dynamic) rises of CO 2 concentration at a monthly time-scale were also introduced into the model. Our study results indicated that about 1–4% of the streamflow in the UMRB during 1986 through 2008 could be attributed to the elevated CO 2 concentration. In addition to evaluating a range of future climate sensitivity scenarios, the climate projections by four General Circulation Models (GCMs) under different greenhouse gas emission scenarios were used to predict the hydrological effects in the late twenty-first century (2071–2100). Our simulations demonstrated that the water yield would increase in spring and substantially decrease in summer, while soil moisture would rise in spring and decline in summer. Such an uneven distribution of water with higher variability compared to the baseline level (1961–1990) may cause an increased risk of both flooding and drought events in the basin.

Mississippi River Basin

Soil carbon sequestration potential for "grain for green" project in Loess Plateau, China

Conversion of cropland into perennial vegetation land can increase soil organic carbon (SOC) accumulation, which might be an important mitigation measure to sequester carbon dioxide from the atmosphere. The “Grain for Green” project, one of the most ambitious ecological programmes launched in modern China, aims at transforming the low-yield slope cropland into grassland and woodland. The Loess Plateau in China is the most important target of this project due to its serious soil erosion. The objectives of this study are to answer three questions: (1) what is the rate of the SOC accumulation for this “Grain for Green” project in Loess Plateau? (2) Is there a difference in SOC sequestration among different restoration types, including grassland, shrub and forest? (3) Is the effect of restoration types on SOC accumulation different among northern, middle and southern regions of the Loess Plateau? Based on analysis of the data collected from the literature conducted in the Loess Plateau, we found that SOC increased at a rate of 0.712 TgC/year in the top 20 cm soil layer for 60 years under this project across the entire Loess Plateau. This was a relatively reliable estimation based on current data, although there were some uncertainties. Compared to grassland, forest had a significantly greater effect on SOC accumulation in middle and southern Loess Plateau but had a weaker effect in the northern Loess Plateau. There were no differences found in SOC sequestration between shrub and grassland across the entire Loess Plateau. Grassland had a stronger effect on SOC sequestration in the northern Loess Plateau than in the middle and southern regions. In contrast, forest could increase more SOC in the middle and southern Loess Plateau than in the northern Loess Plateau, whereas shrub had a similar effect on SOC sequestration across the Loess Plateau. Our results suggest that the “Grain for Green” project can significantly increase the SOC storage in Loess Plateau, and it is recommended to expand grassland and shrub areas in the northern Loess Plateau and forest in the middle and southern Loess Plateau to enhance the SOC sequestration in this area.

Environmental Management

Evaluating the effects of future climate change and elevated CO 2 on the water use efficiency in terrestrial ecosystems of China

Water use efficiency (WUE) is an important variable used in climate change and hydrological studies in relation to how it links ecosystem carbon cycles and hydrological cycles together. However, obtaining reliable WUE results based on site-level flux data remains a great challenge when scaling up to larger regional zones. Biophysical, process-based ecosystem models are powerful tools to study WUE at large spatial and temporal scales. The Integrated BIosphere Simulator (IBIS) was used to evaluate the effects of climate change and elevated CO 2 concentrations on ecosystem-level WUE (defined as the ratio of gross primary production (GPP) to evapotranspiration (ET)) in relation to terrestrial ecosystems in China for 2009–2099. Climate scenario data (IPCC SRES A2 and SRES B1) generated from the Third Generation Coupled Global Climate Model (CGCM3) was used in the simulations. Seven simulations were implemented according to the assemblage of different elevated CO 2 concentrations scenarios and different climate change scenarios. Analysis suggests that (1) further elevated CO 2 concentrations will significantly enhance the WUE over China by the end of the twenty-first century, especially in forest areas; (2) effects of climate change on WUE will vary for different geographical regions in China with negative effects occurring primarily in southern regions and positive effects occurring primarily in high latitude and altitude regions (Tibetan Plateau); (3) WUE will maintain the current levels for 2009–2099 under the constant climate scenario (i.e. using mean climate condition of 1951–2006 and CO 2 concentrations of the 2008 level); and (4) WUE will decrease with the increase of water resource restriction (expressed as evaporation ratio) among different ecosystems.

Ecological Modelling

Study on a pattern classification method of soil quality based on simplified learning sample dataset

Based on the massive soil information in current soil quality grade evaluation, this paper constructed an intelligent classification approach of soil quality grade depending on classical sampling techniques and disordered multiclassification Logistic regression model. As a case study to determine the learning sample capacity under certain confidence level and estimation accuracy, and use c-means algorithm to automatically extract the simplified learning sample dataset from the cultivated soil quality grade evaluation database for the study area, Long chuan county in Guangdong province, a disordered Logistic classifier model was then built and the calculation analysis steps of soil quality grade intelligent classification were given. The result indicated that the soil quality grade can be effectively learned and predicted by the extracted simplified dataset through this method, which changed the traditional method for soil quality grade evaluation. ?? 2011 IEEE.

Conference Paper

Impacts of precipitation seasonality and ecosystem types on evapotranspiration in the Yukon River Basin, Alaska

Evapotranspiration (ET) is the largest component of water loss from terrestrial ecosystems; however, large uncertainties exist when estimating the temporal and spatial variations of ET because of concurrent shifts in the magnitude and seasonal distribution of precipitation as well as differences in the response of ecosystem ET to environmental variabilities. In this study, we examined the impacts of precipitation seasonality and ecosystem types on ET quantified by eddy covariance towers from 2002 to 2004 in three ecosystems (grassland, deciduous broadleaf forest, and evergreen needleleaf forest) in the Yukon River Basin, Alaska. The annual precipitation changed greatly in both magnitude and seasonal distribution through the three investigated years. Observations and model results showed that ET was more sensitive to precipitation scarcity in the early growing season than in the late growing season, which was the direct result of different responses of ET components to precipitation in different seasons. The results demonstrated the importance of seasonal variations of precipitation in regulating annual ET and overshadowing the function of annual precipitation. Comparison of ET among ecosystems over the growing season indicated that ET was largest in deciduous broadleaf, intermediate in evergreen needleleaf, and lowest in the grassland ecosystem. These ecosystem differences in ET were related to differences in successional stages and physiological responses.

Water Resources Research

Federal land management, carbon sequestration, and climate change in the Southeastern U.S.: a case study with fort benning

Land use activities can have a major impact on the temporal trendsandspatialpatternsofregionalland-atmosphereexchange of carbon. Federal lands generally have substantially different land management strategies from surrounding areas, and the carbon consequences have rarely been quantified and assessed. Using the Fort Benning Installation as a case study, we used the General Ensemble biogeochemical Modeling System (GEMS) to simulate and compare ecosystem carbon sequestration between the U.S. Army's Fort Benning and surrounding areas from 1992 to 2050. Our results indicate that the military installation sequestered more carbon than surrounding areas from 1992 to 2007 (76.7 vs 18.5 g C m -2 yr -1 ), and is projected to continue sequestering more carbon from 2008 to 2050 (75.7 vs 25.6 g C m -2 yr -1 ), mostly because of the proactive management approaches adopted on military training lands. Our results suggest that federal lands might play a positive and important role in sequestering and conserving atmospheric carbon because some anthropogenic disturbances (e.g., urbanization, forest harvesting, and agriculture) can be minimized or prevented on federal lands

Georgia

Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively. Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000–2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed GPP at eddy flux towers showed significant underestimation of ET and GPP due to lower net radiation of MERRA dataset. Applying a procedure to correct the systematic errors of global meteorological data would improve global estimates of GPP and ET. The revised RS-PM and EC-LUE models will provide the alternative approaches making it possible to map ET and GPP over large areas because (1) the model parameters are invariant across various land cover types and (2) all driving forces of the models may be derived from remote sensing data or existing climate observation networks.

Remote Sensing of Environment

Ignoring detailed fast-changing dynamics of land use overestimates regional terrestrial carbon sequestration

Land use change is critical in determining the distribution, magnitude and mechanisms of terrestrial carbon budgets at the local to global scales. To date, almost all regional to global carbon cycle studies are driven by a static land use map or land use change statistics with decadal time intervals. The biases in quantifying carbon exchange between the terrestrial ecosystems and the atmosphere caused by using such land use change information have not been investigated. Here, we used the General Ensemble biogeochemical Modeling System (GEMS), along with consistent and spatially explicit land use change scenarios with different intervals (1 yr, 5 yrs, 10 yrs and static, respectively), to evaluate the impacts of land use change data frequency on estimating regional carbon sequestration in the southeastern United States. Our results indicate that ignoring the detailed fast-changing dynamics of land use can lead to a significant overestimation of carbon uptake by the terrestrial ecosystem. Regional carbon sequestration increased from 0.27 to 0.69, 0.80 and 0.97 Mg C ha −1 yr −1 when land use change data frequency shifting from 1 year to 5 years, 10 years interval and static land use information, respectively. Carbon removal by forest harvesting and prolonged cumulative impacts of historical land use change on carbon cycle accounted for the differences in carbon sequestration between static and dynamic land use change scenarios. The results suggest that it is critical to incorporate the detailed dynamics of land use change into local to global carbon cycle studies. Otherwise, it is impossible to accurately quantify the geographic distributions, magnitudes, and mechanisms of terrestrial carbon sequestration at the local to global scales.

Alabama, Georgia

Simulated dynamics of carbon stocks driven by changes in land use, management and climate in a tropical moist ecosystem of Ghana

Sub-Saharan Africa is large and diverse with regions of food insecurity and high vulnerability to climate change. This project quantifies carbon stocks and fluxes in the humid forest zone of Ghana, as a part of an assessment in West Africa. The General Ensemble biogeochemical Modeling System (GEMS) was used to simulate the responses of natural and managed systems to projected scenarios of changes in climate, land use and cover, and nitrogen fertilization in the Assin district of Ghana. Model inputs included historical land use and cover data, historical climate records and projected climate changes, and national management inventories. Our results show that deforestation for crop production led to a loss of soil organic carbon (SOC) by 33% from 1900 to 2000. The results also show that the trend of carbon emissions from cropland in the 20th century will continue through the 21st century and will be increased under the projected warming and drying scenarios. Nitrogen (N) fertilization in agricultural systems could offset SOC loss by 6% with 30 kg N ha −1 year −1 and by 11% with 60 kg N ha −1 year −1 . To increase N fertilizer input would be one of the vital adaptive measures to ensure food security and maintain agricultural sustainability through the 21st century.

Agriculture, Ecosystems and Environment

Quantifying the spatial details of carbon sequestration potential and performance

Upscaling the spatial and temporal changes of carbon stocks and fluxes from sites to regions is challenging owing to the spatial and temporal variances and covariance of driving variables and the uncertainties in both the model and the input data. Although various modeling approaches have been developed to facilitate the upscaling process, few deal with error transfer from model input to output, and error propagation in time and space. The author has developed the General Ensemble Biogeochemical Modelling System (GEMS) for upscaling carbon stocks and fluxes from sites to regions with measures of uncertainty. This chapter describes the GEMS model, its application to regional- and larger-scale areas, and the new results that demonstrate the challenges of upscaling. GEMS relies on site-scale biogeochemical models to simulate carbon dynamics at the site scale. The spatial deployment of the site-scale model in GEMS is based on the spatial and temporal joint frequency distribution of major driving variables (e.g., land cover and land use change, climate, soils, disturbances, and management). At the site scale, GEMS uses stochastic ensemble simulations to incorporate input uncertainty to quantify uncertainty transfer from input to output, and to identify trends in both input data and simulation results. It permits one to simulate the range of possible permutations of input values and identify the trends and variance in both the input data and results. Using data assimilation techniques, GEMS simulations can be constrained by field and satellite observations, including estimates of net primary production (NPP) from the Moderate Resolution Imaging Spectroradiometer (MODIS), grain yield and cropping practices, and forest inventories. The modeling philosophy embedded in GEMS makes it ideal for assimilating information with various uncertainties to support estimating the spatial details of carbon sequestration potential as well as dynamic monitoring of the performance of carbon sequestration activities over large areas. As a case study, GEMS is applied to simulate the spatial and temporal details of carbon sources, sinks, and uncertainty in the Ridge and Valley ecoregion in the eastern United States

Book chapter

Factors influencing leaf litter decomposition: An intersite decomposition experiment across China

The Long-Term Intersite Decomposition Experiment in China (hereafter referred to as LTIDE-China) was established in 2002 to study how substrate quality and macroclimate factors affect leaf litter decomposition. The LTIDE-China includes a wide variety of natural and managed ecosystems, consisting of 12 forest types (eight regional broadleaf forests, three needle-leaf plantations and one broadleaf plantation) at eight locations across China. Samples of mixed leaf litter from the south subtropical evergreen broadleaf forest in Dinghushan (referred to as the DHS sample) were translocated to all 12 forest types. The leaf litter from each of other 11 forest types was placed in its original forest to enable comparison of decomposition rates of DHS and local litters. The experiment lasted for 30 months, involving collection of litterbags from each site every 3 months. Our results show that annual decomposition rate-constants, as represented by regression fitted k-values, ranged from 0.169 to 1.454/year. Climatic factors control the decomposition rate, in which mean annual temperature and annual actual evapotranspiration are dominant and mean annual precipitation is subordinate. Initial C/N and N/P ratios were demonstrated to be important factors of regulating litter decomposition rate. Decomposition process may apparently be divided into two phases controlled by different factors. In our study, 0.75 years is believed to be the dividing line of the two phases. The fact that decomposition rates of DHS litters were slower than those of local litters may have been resulted from the acclimation of local decomposer communities to extraneous substrate. ?? 2008 Springer Science+Business Media B.V.

Plant and Soil

Resolving model parameter values from carbon and nitrogen stock measurements in a wide range of tropical mature forests using nonlinear inversion and regression trees

Objectively assessing the performance of a model and deriving model parameter values from observations are critical and challenging in landscape to regional modeling. In this paper, we applied a nonlinear inversion technique to calibrate the ecosystem model CENTURY against carbon (C) and nitrogen (N) stock measurements collected from 39 mature tropical forest sites in seven life zones in Costa Rica. Net primary productivity from the Moderate-Resolution Imaging Spectroradiometer (MODIS), C and N stocks in aboveground live biomass, litter, coarse woody debris (CWD), and in soils were used to calibrate the model. To investigate the resolution of available observations on the number of adjustable parameters, inversion was performed using nine setups of adjustable parameters. Statistics including observation sensitivity, parameter correlation coefficient, parameter sensitivity, and parameter confidence limits were used to evaluate the information content of observations, resolution of model parameters, and overall model performance. Results indicated that soil organic carbon content, soil nitrogen content, and total aboveground biomass carbon had the highest information contents, while measurements of carbon in litter and nitrogen in CWD contributed little to the parameter estimation processes. The available information could resolve the values of 2-4 parameters. Adjusting just one parameter resulted in under-fitting and unacceptable model performance, while adjusting five parameters simultaneously led to over-fitting. Results further indicated that the MODIS NPP values were compressed as compared with the spatial variability of net primary production (NPP) values inferred from inverse modeling. Using inverse modeling to infer NPP and other sensitive model parameters from C and N stock observations provides an opportunity to utilize data collected by national to regional forest inventory systems to reduce the uncertainties in the carbon cycle and generate valuable databases to validate and improve MODIS NPP algorithms.

Ecological Modelling