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.

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.

scholarly journals CARDAMOM-FluxVal Version 1.0: a FLUXNET-based Validation System for CARDAMOM Carbon and Water Flux Estimates

2021 ◽  
Author(s):  
Yan Yang ◽  
A. Anthony Bloom ◽  
Shuang Ma ◽  
Paul Levine ◽  
Alexander Norton ◽  
...  
Keyword(s):  
Data Fusion ◽  
Land Surface ◽  
Water Flux ◽  
Biogeochemical Model ◽  
Model Data ◽  
Formulation Development ◽  
Ecosystem Carbon ◽  
Biosphere Model ◽  
Model Structures ◽  
Benchmarking System

Abstract. Land-atmosphere carbon and water exchanges have large uncertainty in land surface and biosphere models. Using observations to reduce land biosphere model structural and parametric errors is a key priority for both understanding and accurately predicting carbon and water fluxes. Recent implementations of the Bayesian CARDAMOM model-data fusion framework have yielded key insights into ecosystem carbon and water cycling. CARDAMOM analyses—informed by co-located C and H2O flux observations—have exhibited considerable skill in both representing the variability of assimilated observations and predicting withheld observations. While CARDAMOM model configurations (namely CARDAMOM-compatible biogeochemical model structures) have been continuously developed to accommodate new scientific challenges and an expanding variety of observational constraints, there has so far been no concerted effort to globally and systematically validate CARDAMOM performance across individual model-data fusion configurations. Here we use the FLUXNET-2015 dataset—an ensemble of 200+ eddy covariance flux tower sites—to formulate a concerted benchmarking framework for CARDAMOM carbon (GPP, NEE) and water (ET) flux estimates (CARDAMOM-FLUXVal version 1.0). We present a concise set of skill metrics to evaluate CARDAMOM performance against both assimilated and withheld FLUXNET-2015 GPP, NEE and ET data. We further demonstrate the potential for tailored CARDAMOM evaluations by categorizing performance in terms of (i) individual land cover types, (ii) monthly, annual and mean fluxes, and (iii) length of assimilation data. The CARDAMOM benchmarking system—along with CARDAMOM driver files provided—can be readily repeated to support both the intercomparison between existing CARDAMOM model configurations and the formulation, development and testing of new CARDAMOM model structures.

scholarly journals Improving land surface models with FLUXNET data

2009 ◽  
Vol 6 (2) ◽  
pp. 2785-2835 ◽  
Author(s):  
M. Williams ◽  
A. D. Richardson ◽  
M. Reichstein ◽  
P. C. Stoy ◽  
P. Peylin ◽  
...  
Keyword(s):  
Data Fusion ◽  
Land Surface ◽  
Formal Model ◽  
Initial Conditions ◽  
Data Availability ◽  
Parameter Estimates ◽  
Model Errors ◽  
Land Surface Models ◽  
Model Data ◽  
Surface Models

Abstract. There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for fusing LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF). MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model process validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT) – we would expect similar parameter estimates among sites sharing a single PFT. We conclude by identifying five major model-data fusion challenges for the FLUXNET and LSM communities: 1) to determine appropriate use of current data and to explore the information gained in using longer time series; 2) to avoid confounding effects of missing process representation on parameter estimation; 3) to assimilate more data types, including those from earth observation; 4) to fully quantify uncertainties arising from data bias, model structure, and initial conditions problems; and 5) to carefully test current model concepts (e.g. PFTs) and guide development of new concepts.

scholarly journals Improving land surface models with FLUXNET data

2009 ◽  
Vol 6 (7) ◽  
pp. 1341-1359 ◽  
Author(s):  
M. Williams ◽  
A. D. Richardson ◽  
M. Reichstein ◽  
P. C. Stoy ◽  
P. Peylin ◽  
...  
Keyword(s):  
Data Fusion ◽  
Land Surface ◽  
Formal Model ◽  
Initial Conditions ◽  
Data Availability ◽  
Parameter Estimates ◽  
Model Errors ◽  
Land Surface Models ◽  
Model Data ◽  
Surface Models

Abstract. There is a growing consensus that land surface models (LSMs) that simulate terrestrial biosphere exchanges of matter and energy must be better constrained with data to quantify and address their uncertainties. FLUXNET, an international network of sites that measure the land surface exchanges of carbon, water and energy using the eddy covariance technique, is a prime source of data for model improvement. Here we outline a multi-stage process for "fusing" (i.e. linking) LSMs with FLUXNET data to generate better models with quantifiable uncertainty. First, we describe FLUXNET data availability, and its random and systematic biases. We then introduce methods for assessing LSM model runs against FLUXNET observations in temporal and spatial domains. These assessments are a prelude to more formal model-data fusion (MDF). MDF links model to data, based on error weightings. In theory, MDF produces optimal analyses of the modelled system, but there are practical problems. We first discuss how to set model errors and initial conditions. In both cases incorrect assumptions will affect the outcome of the MDF. We then review the problem of equifinality, whereby multiple combinations of parameters can produce similar model output. Fusing multiple independent and orthogonal data provides a means to limit equifinality. We then show how parameter probability density functions (PDFs) from MDF can be used to interpret model validity, and to propagate errors into model outputs. Posterior parameter distributions are a useful way to assess the success of MDF, combined with a determination of whether model residuals are Gaussian. If the MDF scheme provides evidence for temporal variation in parameters, then that is indicative of a critical missing dynamic process. A comparison of parameter PDFs generated with the same model from multiple FLUXNET sites can provide insights into the concept and validity of plant functional types (PFT) – we would expect similar parameter estimates among sites sharing a single PFT. We conclude by identifying five major model-data fusion challenges for the FLUXNET and LSM communities: (1) to determine appropriate use of current data and to explore the information gained in using longer time series; (2) to avoid confounding effects of missing process representation on parameter estimation; (3) to assimilate more data types, including those from earth observation; (4) to fully quantify uncertainties arising from data bias, model structure, and initial conditions problems; and (5) to carefully test current model concepts (e.g. PFTs) and guide development of new concepts.

scholarly journals Assessment of a physical-biogeochemical coupled model system for operational service in the Baltic Sea

Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 683-701 ◽  
Author(s):  
Z. Wan ◽  
J. She ◽  
M. Maar ◽  
L. Jonasson ◽  
J. Baasch-Larsen
Keyword(s):  
Baltic Sea ◽  
Inorganic Nitrogen ◽  
Biogeochemical Model ◽  
Observation Data ◽  
Model Assessment ◽  
Model Errors ◽  
The Baltic Sea ◽  
Statistical Measures ◽  
The Baltic ◽  
Operational Service

Abstract. Thanks to the abundant observation data, we are able to deploy the traditional point-to-point comparison and statistical measures in combination with a comprehensive model validation scheme to assess the skills of the biogeochemical model ERGOM in providing an operational service for the Baltic Sea. The model assessment concludes that the operational products can resolve the main observed seasonal features for phytoplankton biomass, dissolved inorganic nitrogen, dissolved inorganic phosphorus and dissolved oxygen in euphotic layers as well as their vertical profiles. This assessment reflects that the model errors of the operational system at the current stage are mainly caused by insufficient light penetration, excessive organic particle export downward, insufficient regional adaptation and some from improper initialization. This study highlights the importance of applying multiple schemes in order to assess model skills rigidly and identify main causes for major model errors.

Perspectives on greenhouse gas emission estimates based on Australian wastewater treatment plant operating data

2013 ◽  
Vol 69 (3) ◽  
pp. 451-463 ◽  
Author(s):  
D. W. de Haas ◽  
C. Pepperell ◽  
J. Foley
Keyword(s):  
Wastewater Treatment ◽  
Steady State ◽  
Greenhouse Gas ◽  
Greenhouse Gas Emission ◽  
Electrical Power ◽  
Research Work ◽  
Treatment Plant ◽  
Emission Factors ◽  
N2o Emissions ◽  
Operating Data

Primary operating data were collected from forty-six wastewater treatment plants (WWTPs) located across three states within Australia. The size range of plants was indicatively from 500 to 900,000 person equivalents. Direct and indirect greenhouse gas emissions were calculated using a mass balance approach and default emission factors, based on Australia's National Greenhouse Energy Reporting (NGER) scheme and IPCC guidelines. A Monte Carlo-type combined uncertainty analysis was applied to the some of the key emission factors in order to study sensitivity. The results suggest that Scope 2 (indirect emissions due to electrical power purchased from the grid) dominate the emissions profile for most of the plants (indicatively half to three quarters of the average estimated total emissions). This is only offset for the relatively small number of plants (in this study) that have significant on-site power generation from biogas, or where the water utility purchases grid electricity generated from renewable sources. For plants with anaerobic digestion, inventory data issues around theoretical biogas generation, capture and measurement were sometimes encountered that can skew reportable emissions using the NGER methodology. Typically, nitrous oxide (N2O) emissions dominated the Scope 1 (direct) emissions. However, N2O still only accounted for approximately 10 to 37% of total emissions. This conservative estimate is based on the ‘default’ NGER steady-state emission factor, which amounts to 1% of nitrogen removed through biological nitrification-denitrification processing in the plant (or indicatively 0.7 to 0.8% of plant influent total nitrogen). Current research suggests that true N2O emissions may be much lower and certainly not steady-state. The results of this study help to place in context research work that is focused on direct emissions from WWTPs (including N2O, methane and carbon dioxide of non-biogenic origin). For example, whereas non-biogenic CO2 contributions are relatively minor, it appears that opportunities to reduce indirect emissions as a result of modest savings in power consumption are at least in the same order as those from reducing N2O emissions. To avoid potentially high reportable emissions under NGER guidelines, particularly for methane, the onus is placed on WWTP managers to ensure that accurate plant monitoring operating records are kept.

scholarly journals Using manure as fertilizer for maize could improve sustainability of milk production

2018 ◽  
Vol 16 (1) ◽  
pp. e0601 ◽  
Author(s):  
José D. Jiménez-Calderón ◽  
Adela Martínez-Fernández ◽  
Fernando Prospero-Bernal ◽  
José Velarde-Guillén ◽  
Carlos M. Arriaga-Jordán ◽  
...  
Keyword(s):  
Nutritive Value ◽  
Greenhouse Gas Emission ◽  
N2o Emissions ◽  
Dairy Production ◽  
Maize Silage ◽  
Organic Fertilization ◽  
Positive Balance ◽  
Chemical Fertilization ◽  
Cow Performance ◽  
Ch4 And N2o

This study evaluated the effect of organic or chemical fertilization of maize on cow performance, economic outcomes, and greenhouse gas emission. Each type of maize silage according its different fertilization was used in two rations offered to two different groups of nine Friesian-Holstein cows throughout 4 months. The production cost of the maize silage was 8.8% lower for organic than for chemical fertilization. Both silages had similar nutritive value, except a higher concentration of starch in maize with organic fertilization, which allowed a reduction in the proportion of concentrate in the ration, saving 25.3 eurocents per cow in the daily ration, generating a positive balance of 21.8 eurocents per cow and day. The milk yield and composition were unaffected depending on the type of fertilization, whereas the estimation of CH4 and N2O emissions with chemical fertilization was higher than emissions with organic fertilization. As a result, it is possible to increase the sustainability and profitability of dairy production with reuse and recycling of manure.

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

Skin Friction Resistance of Ships

1957 ◽  
Vol 1 (03) ◽  
pp. 3-12
Author(s):  
F. H. Todd
Keyword(s):  
Skin Friction ◽  
Present Status ◽  
Comparative Studies ◽  
Scale Model ◽  
Small Scale ◽  
Model Data ◽  
Towing Tank ◽  
Friction Resistance ◽  
Small Scale Model

The International Towing Tank Conference (ITTC) is to hold its 8th meeting in Madrid in September of this year. One of the subjects to be discussed will be the perennial one of how to estimate the resistance of a ship from that measured on a small-scale model in a towing tank. The Skin Friction Committee of the Conference was charged at the last meeting in Scandinavia in 1954, with reviewing the available data and making recommendations to the Conference in Madrid which will, it is hoped, be universally acceptable. Such a decision would remove one of the principal difficulties experienced in the use of model data in comparative studies. It is believed that a review of the present status of our knowledge in this field may be of interest to the members of the Society at this time.

scholarly journals Physical-biogeochemical regional ocean model uncertainties stemming from stochastic parameterizations and potential impact on data assimilation

2019 ◽  
Author(s):  
Vassilios D. Vervatis ◽  
Pierre De Mey-Frémaux ◽  
Nadia Ayoub ◽  
Sarantis Sofianos ◽  
Charles-Emmanuel Testut ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Spatial Scales ◽  
Ecosystem Model ◽  
Ocean Model ◽  
Open Ocean ◽  
Small Scale ◽  
Biogeochemical Model ◽  
Frequency Error ◽  
Model Errors ◽  
Potential Impact

Abstract. We generate ocean biogeochemical model ensembles including several kinds of stochastic parameterizations. The NEMO stochastic modules are complemented by integrating a subroutine to calculate variable anisotropic spatial scales, which are of particular importance in high-resolution coastal configurations. The domain covers the Bay of Biscay at 1/36° resolution, as a case study for open-ocean and coastal shelf dynamics. At first, we identify uncertainties from assumptions subject to erroneous atmospheric forcing, ocean model improper parameterizations and ecosystem state uncertainties. The error regimes are found to be mainly driven by the wind forcing, with the rest of the perturbed tendencies locally augmenting the ensemble spread. Biogeochemical uncertainties arise from inborn ecosystem model errors and from errors in the physical state. Model errors in physics are found to have larger impact on chlorophyll spread than those of the ecosystem. In a second step, the ensembles undergo verification with respect to observations, focusing on upper-ocean properties. We investigate the statistical consistency of prior model errors and observation estimates, in view of joint uncertainty vicinities, associated with both sources of information. OSTIA-SST L4 distribution appears to be compatible with ensembles perturbing physics, since vicinities overlap, enabling data assimilation. The most consistent configuration for SLA along-track L3 data is in the Abyssal plain, where the spread is increased due to mesoscale eddy decorrelation. The largest statistical SLA biases are observed in coastal regions, sometimes to the point that vicinities become disjoint. Missing error processes in relation to SLA hint at the presence of high-frequency error sources currently unaccounted for, potentially leading to ill-posed assimilation problems. Ecosystem model-data samples with respect to Ocean Colour L4 appear to be compatible with each other only at times, with data assimilation being marginally well-posed. In a third step, we illustrate the potential influence of those uncertainties on data assimilation impact exercise, by means of multivariate representers and EnKF-type incremental analysis for a few members. Corrections on physical properties are associated with large-scale biases between model and data, with diverse characteristics in the open-ocean and the shelves. The increments are often characteristic of the underlying mesoscale features, chlorophyll included due to the vertical velocity field. Small scale local corrections are visible over the shelves. Chlorophyll information seems to have a very measurable potential impact on physical variables.

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