scholarly journals Agencies Collaborate to Better Monitor and Model the Environment

Eos ◽  
2018 ◽  
Vol 99 ◽  
Author(s):  
Brenda Rashleigh ◽  
Thomas Nicholson
Keyword(s):  
Data Fusion ◽  
Environmental Modeling ◽  
Model Data

Interagency Collaborative for Environmental Modeling and Monitoring: Monitoring and Model Data Fusion; Rockville, Maryland, 24–25 April 2018

scholarly journals Constraining a complex biogeochemical model for CO<sub>2</sub> and N<sub>2</sub>O emission simulations from various land uses by model–data fusion

2017 ◽  
Vol 14 (14) ◽  
pp. 3487-3508 ◽  
Author(s):  
Tobias Houska ◽  
David Kraus ◽  
Ralf Kiese ◽  
Lutz Breuer
Keyword(s):  
Data Fusion ◽  
Arable Land ◽  
Shallow Groundwater ◽  
Scale Model ◽  
Biogeochemical Model ◽  
N2o Emissions ◽  
Model Errors ◽  
Model Data ◽  
Validation Data ◽  
Fusion Concept

Abstract. This study presents the results of a combined measurement and modelling strategy to analyse N2O and CO2 emissions from adjacent arable land, forest and grassland sites in Hesse, Germany. The measured emissions reveal seasonal patterns and management effects, including fertilizer application, tillage, harvest and grazing. The measured annual N2O fluxes are 4.5, 0.4 and 0.1 kg N ha−1 a−1, and the CO2 fluxes are 20.0, 12.2 and 3.0 t C ha−1 a−1 for the arable land, grassland and forest sites, respectively. An innovative model–data fusion concept based on a multicriteria evaluation (soil moisture at different depths, yield, CO2 and N2O emissions) is used to rigorously test the LandscapeDNDC biogeochemical model. The model is run in a Latin-hypercube-based uncertainty analysis framework to constrain model parameter uncertainty and derive behavioural model runs. The results indicate that the model is generally capable of predicting trace gas emissions, as evaluated with RMSE as the objective function. The model shows a reasonable performance in simulating the ecosystem C and N balances. The model–data fusion concept helps to detect remaining model errors, such as missing (e.g. freeze–thaw cycling) or incomplete model processes (e.g. respiration rates after harvest). This concept further elucidates the identification of missing model input sources (e.g. the uptake of N through shallow groundwater on grassland during the vegetation period) and uncertainty in the measured validation data (e.g. forest N2O emissions in winter months). Guidance is provided to improve the model structure and field measurements to further advance landscape-scale model predictions.

Vadose Zone Model-Data Fusion: State of the Art and Future Challenges

2012 ◽  
Vol 11 (4) ◽  
pp. vzj2012.0140 ◽  
Author(s):  
Johan A. Huisman ◽  
Jasper A. Vrugt ◽  
Ty P.A. Ferre
Keyword(s):  
Data Fusion ◽  
Vadose Zone ◽  
State Of The Art ◽  
Zone Model ◽  
Model Data ◽  
Future Challenges

scholarly journals Description and validation of an intermediate complexity model for ecosystem photosynthesis and evapotranspiration: ACM-GPP-ETv1

2019 ◽  
Vol 12 (6) ◽  
pp. 2227-2253 ◽  
Author(s):  
Thomas Luke Smallman ◽  
Mathew Williams
Keyword(s):  
Stomatal Conductance ◽  
Data Fusion ◽  
Terrestrial Ecosystem ◽  
Ecosystem Model ◽  
Sensitivity Analyses ◽  
Model Data ◽  
Time Step ◽  
Terrestrial Ecosystem Model ◽  
Intermediate Complexity ◽  
Large Ensembles

Abstract. Photosynthesis (gross primary production, GPP) and evapotranspiration (ET) are ecosystem processes with global significance for climate, the global carbon and hydrological cycles and a range of ecosystem services. The mechanisms governing these processes are complex but well understood. There is strong coupling between these processes, mediated directly by stomatal conductance and indirectly by root zone soil moisture content and its accessibility. This coupling must be effectively modelled for robust predictions of earth system responses to global change. Yet, it is highly demanding to model leaf and cellular processes, like stomatal conductance or electron transport, with response times of minutes, over decadal and global domains. Computational demand means models resolving this level of complexity cannot be easily evaluated for their parameter sensitivity nor calibrated using earth observation information through data assimilation approaches requiring large ensembles. To overcome these challenges, here we describe a coupled photosynthesis evapotranspiration model of intermediate complexity. The model reduces computational load and parameter numbers by operating at canopy scale and daily time step. Through the inclusion of simplified representation of key process interactions, it retains sensitivity to variation in climate, leaf traits, soil states and atmospheric CO2. The new model is calibrated to match the biophysical responses of a complex terrestrial ecosystem model (TEM) of GPP and ET through a Bayesian model–data fusion framework. The calibrated ACM-GPP-ET generates unbiased estimates of TEM GPP and ET and captures 80 %–95 % of the sensitivity of carbon and water fluxes by the complex TEM. The ACM-GPP-ET model operates 3 orders faster than the complex TEM. Independent evaluation of ACM-GPP-ET at FLUXNET sites, using a single global parameterisation, shows good agreement, with typical R2∼0.60 for both GPP and ET. This intermediate complexity modelling approach allows full Monte Carlo-based quantification of model parameter and structural uncertainties and global-scale sensitivity analyses for these processes and is fast enough for use within terrestrial ecosystem model–data fusion frameworks requiring large ensembles.

scholarly journals Process models and model-data fusion in dendroecology

2014 ◽  
Vol 2 ◽  
Author(s):  
Joël Guiot ◽  
Etienne Boucher ◽  
Guillermo Gea-Izquierdo
Keyword(s):  
Data Fusion ◽  
Process Models ◽  
Model Data

Modeling and uncertainty analysis of carbon and water fluxes in a broad-leaved Korean pine mixed forest based on model-data fusion

2018 ◽  
Vol 379 ◽  
pp. 39-53 ◽  
Author(s):  
Xiaoli Ren ◽  
Honglin He ◽  
Li Zhang ◽  
Fan Li ◽  
Min Liu ◽  
...  
Keyword(s):  
Data Fusion ◽  
Uncertainty Analysis ◽  
Mixed Forest ◽  
Korean Pine ◽  
Water Fluxes ◽  
Model Data

scholarly journals Constraining a complex biogeochemical model for multi-site greenhouse gas emission simulations by model-data fusion

2017 ◽  
Author(s):  
Tobias Houska ◽  
David Kraus ◽  
Ralf Kiese ◽  
Lutz Breuer
Keyword(s):  
Data Fusion ◽  
Greenhouse Gas Emission ◽  
Behavioral Model ◽  
Scale Model ◽  
Biogeochemical Model ◽  
N2o Emissions ◽  
Model Errors ◽  
Model Data ◽  
Validation Data ◽  
Fusion Concept

Abstract. This paper presents results of a combined measurement and modelling strategy to analyse N2O and CO2 emissions from adjacent arable, forest and grassland sites in Germany. Measured emissions reveal seasonal patterns and management effects like fertilizer application, tillage, harvest and grazing. Measured annual N2O fluxes are 4.5, 0.4 and 0.1 kg N ha−1 a−1, while CO2 fluxes are 20.0, 12.2 and 3.0 t C ha−1 a−1 for the arable, grassland and forest sites, respectively. An innovative model-data fusion concept based on multi-criteria evaluation (soil moisture in different depths, yield, CO2 and N2O emissions) is used to rigorously test the biogeochemical LandscapeDNDC model. The model is run in a Latin Hypercube based uncertainty analyses framework to constrain model parameter uncertainty and derive behavioral model runs. Results indicate that the model is in general capable to predict the trace gas emissions, evaluated by RMSE as an objective function. The model shows reasonable performance in simulating the ecosystems C and N balances. The model-data fusion concept helps to detect remaining model errors like missing (e.g. freeze-thaw cycling) or incomplete model processes (e.g. respiration amount after harvest). It further elucidates identifying missing model input sources (e.g. uptake of N through shallow groundwater on grassland during the vegetation period) and uncertainty in measured validation data (e.g. forest N2O emissions in winter months). Guidance is provided to improve model structure and field measurements to further advance landscape scale model predictions.

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