Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model

Migliavacca, Mirco; Meroni, Michele; Busetto, Lorenzo; Colombo, R.; Zenone, Terenzio; Matteucci, Giorgio; Manca, Giovanni; Seufert, G.

doi:10.3390/s90200922

Cited by 41 publications

(19 citation statements)

References 49 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Randerson et al, 2009;Levis and Bonan, 2004;White et al, 2000;Migliavacca et al, 2009). Furthermore, recent multi-model synthesis studies have shown that spring phenology is poorly simulated by many terrestrial biosphere models (Richardson et al, 2012;Keenan et al, 2012b).…”

Section: Days) Compared To the Period 1982-1999 (52 Days)mentioning

confidence: 99%

On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

et al. 2012

View full text Add to dashboard Cite

Abstract. Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate system through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere.Terrestrial biosphere models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we used the Harvard Forest phenology record to investigate and characterize sources of uncertainty in predicting phenology, and the subsequent impacts on model forecasts of carbon and water cycling. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species, with 12 leaf bud-burst models that varied in complexity.Akaike's Information Criterion indicated support for spring warming models with photoperiod limitations and, to a lesser extent, models that included chilling requirements.We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO 2 emissions vs. low CO 2 emissions scenario). Parameter uncertainty was the smallest (average 95 % Confidence Interval -CI: 2.4 days century −1 for scenario B1 and 4.5 days century −1 for A1fi), whereas driver uncertainty was the largest (up to 8.4 days century −1 in the simulated trends). The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied among models (±7.7 days century −1 for A1fi, ±3.6 days century −1 for B1). The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per degree of warming) varied between 2.2 days • C −1 and 5.2 days • C −1 depending on model structure.We quantified the impact of uncertainties in bud-burst forecasts on simulated photosynthetic CO 2 uptake and evapotranspiration (ET) using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of forest seasonality, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and ET of 9.6 % and 2.9 %, respectively. A sensitivity analysis shows that a variation of ±10 days in bud-burst dates led to a variation of ±5.0 % for annual GPP and about ±2.0 % for ET.For phenology models, differences among future climate scenarios (i.e. driver) represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of ecosystem processes and in particular the simulation of carbon uptake by forest ecosystems, with a larger impact of uncertainties related to phenology model structure, followed by uncertainties related to phenolo...

show abstract

Section: Days) Compared To the Period 1982-1999 (52 Days)mentioning

confidence: 99%

On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Agro-BGC is an extension of the Biome-BGC 4.2 model that mechanistically simulates ecosystem water, nitrogen, and carbon cycles (Running and Hunt, 1993;Thornton and Rosenbloom, 2005). White et al (2000) performed an extensive sensitivity analysis on Biome-BGC Net Primary Productivity (NPP) with respect to vegetation parameters, and the model has been applied successfully to a variety of forest types (Migliavacca et al, 2009;Thornton et al, 2002;Ueyama et al, 2009), urban landscapes (Trusilova and Churkina, 2008), turf grasses (Milesi et al, 2005), agricultural fields (Wang et al, 2005), and heterogeneous landscapes at the regional, continental, and global scales Running et al, 2004;Schimel et al, 1997;Turner et al, 2007). Agro-BGC adds agricultural practices, enzyme-driven C 4 photosynthesis, crop phenology, and standing dead matter to Biome-BGC in order to simulate C 4 perennial grasses, such as switchgrass, in managed and unmanaged contexts.…”

Section: Modelmentioning

confidence: 99%

Reducing the impact of model scale on simulated, gridded switchgrass yields

Vittorio

Miller

2014

Environmental Modelling & Software

View full text Add to dashboard Cite

a b s t r a c tResults of gridded ecosystem simulations of bioenergy crops are used for estimating economic viability, environmental impacts, and potential land use change. Gridded model uncertainty propagates through these uses, thus we propose a simple method for estimating regional, spatial model error from sparse field data. We apply this method to the Agricultural-BioGeochemical Cycles (Agro-BGC) model to examine and reduce the model uncertainty associated with grid scale for simulated switchgrass yields in a 6 latitude Â 5 longitude (w300,000 km 2 ) region covering Illinois, United States of America. Based on three evaluation sites, changes in yield with scale result from complex intra-model interactions driven by a combination of meteorological rather than soil or terrain variables. Spatial bias of the regional mean significantly increases with increasing cell size for 11 of 15 measurement dates. This bias is primarily due to grid scale, thus bias correction of output yield reduces the model uncertainty associated with grid scale. The corresponding Root Mean Squared Error and Bias-Corrected RMSE (RMSE BC ) have effectively negligible trends with inconsistent signs. The range of RMSE BC for 2-year Average Mature August Yield (AMAY) is 267e285 g C m À2 across 3-to 3600-arcsec resolution (w90 mew100 km) with biases from 9 to 61 g C m À2 . AMAY bias significantly increases with increasing cell size. Spatial bias of the regional mean is relatively consistent for resolutions 1200 arcsec (w33 km) (AMAY bias <3%), and larger AMAY biases (4e13%) at coarse resolutions indicate poorly characterized spatial heterogeneity. Including the 68% confidence interval around bias-corrected values, AMAY ranges from 0 to 1116 g C m À2 across a 150-arsec grid, which is similar to the range reported for 24 eastern United States field sites. Spatial bias of the regional mean yield can vary across grid resolution by as much as 31% of the observed regional mean and can dramatically affect calculations dependent on the resolution of the estimate. We conclude that grid scale profoundly affects model accuracy such that regional studies must match evaluation and simulation scales and should utilize multi-scale analyses to determine robustness of results.

show abstract

“…Mo et al (2008) used the EnKF sequential data assimilation method to optimize the key parameters of the Boreal Ecosystem Productivity Simulator (BEPS) model; in models of eddy covariance fluxes, data assimilation with an EnKF successfully retrieved the seasonal and inter-annual variations in parameters related to photosynthesis and respiration in a boreal ecosystem. Migliavacca et al (2009) built a ProSailH-BGC model of a forest and farmland ecological system and assimilated flux data and remotely sensed NDVI data; the data assimilation optimized the model parameters and decreased carbon flux simulation errors in the model. Rayner et al (2005) used a Bayesian approach to assimilate inverse FPAR values generated by remote sensing and FPAR predicted by the BETHY (Biosphere Energy Transfer Hydrology) model.…”

mentioning

confidence: 99%

Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites

Zhang

Sun

Peng

et al. 2015

Sci. China Earth Sci.

View full text Add to dashboard Cite

Model simulation and in situ observations are often used to research water and carbon cycles in terrestrial ecosystems, but each of these methods has its own advantages and limitations. Combining these two methods could improve the accuracy of quantifying the dynamics of the water and carbon fluxes of an ecosystem. Data assimilation is an effective means of integrating modeling with in situ observation. In this study, the ensemble Kalman filter (EnKF) and the unscented Kalman filter (UKF) algorithms were used to assimilate remotely sensed leaf area index (LAI) data with the Biome-BGC model to simulate water and carbon fluxes at the Harvard Forest Environmental Monitoring Site (EMS) and the Dinghushan site. After MODIS LAI data from 2000-2004 were assimilated into the improved Biome-BGC model using the EnKF algorithm at the Harvard Forest site, the R 2 between the simulated and observed results for NEE and evapotranspiration increased by 7.8% and 4.7%, respectively. In addition, the sum of the absolute error (SAE) and the root mean square error (RMSE) of NEE decreased by an average of 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration decreased by 24.5% and 25.5%, respectively. MODIS LAI data of 2003 were assimilated into the Biome-BGC model for the Dinghushan site, and the R 2 values between the simulated and observed results for NEE and evapotranspiration were increased by 6.7% and 17.3%, respectively. In addition, the SAE values of NEE and ET were decreased by 11.3% and 30.7%, respectively, and the RMSE values of NEE and ET decreased by 10.1% and 30.9%, respectively. These results demonstrate that the accuracy of carbon and water flux simulations can be effectively improved when remotely sensed LAI data are properly integrated with ecosystem models through a data assimilation approach.

show abstract

Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model

Cited by 41 publications

References 49 publications

On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

Reducing the impact of model scale on simulated, gridded switchgrass yields

Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites

Contact Info

Product

Resources

About