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 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 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 Using model-data fusion to interpret past trends, and quantify uncertainties in future projections, of terrestrial ecosystem carbon cycling

2012 ◽  
Vol 18 (8) ◽  
pp. 2555-2569 ◽  
Author(s):  
Trevor F. Keenan ◽  
Eric Davidson ◽  
Antje M. Moffat ◽  
William Munger ◽  
Andrew D. Richardson
Keyword(s):  
Data Fusion ◽  
Carbon Cycling ◽  
Terrestrial Ecosystem ◽  
Model Data ◽  
Future Projections ◽  
Ecosystem Carbon

scholarly journals Global ecosystem-scale plant hydraulic traits retrieved using model-data fusion

2020 ◽  
Author(s):  
Yanlan Liu ◽  
Nataniel M. Holtzman ◽  
Alexandra G. Konings
Keyword(s):  
Data Fusion ◽  
Land Surface ◽  
Large Scale ◽  
Microwave Remote Sensing ◽  
Drought Response ◽  
Model Data ◽  
Plant Hydraulics ◽  
Vegetation Water Content ◽  
Functional Types ◽  
Vegetation Water

Abstract. Droughts are expected to become more frequent and severe under climate change, increasing the need for accurate predictions of plant drought response. This response varies substantially depending on plant properties that regulate water transport and storage within plants, i.e., plant hydraulic traits. It is therefore crucial to map plant hydraulic traits at a large scale to better assess drought impacts. Improved understanding of global variations in plant hydraulic traits is also needed for paramaterizing the latest generation of land surface models, many of which explicitly simulate plant hydraulic processes for the first time. Here, we use a model-data fusion approach to evaluate the spatial pattern of plant hydraulic traits across the globe. This approach integrates a plant hydraulic model with datasets derived from microwave remote sensing that inform ecosystem-scale plant water regulation. In particular, we use both surface soil moisture and vegetation optical depth (VOD) derived from the X-band JAXA Advanced Microwave Scanning Radiometer for EOS (AMSR-E). VOD is proportional to vegetation water content and therefore closely related to leaf water potential. In addition, evapotranspiration (ET) from the Atmosphere Land-Exchange Inverse model (ALEXI) is also used as a constraint to derive plant hydraulic traits. The derived traits are compared to independent data sources based on ground measurements. Using the K-means clustering method, we build six hydraulic functional types (HFTs) with distinct trait combinations – mathematically tractable alternatives to the common approach of assigning plant hydraulic values based on plant functional types. Using traits averaged by HFTs rather than by PFTs improves VOD and ET estimation accuracies in the majority of areas across the globe. The use of HFTs and/or plant hydraulic traits derived from model-data fusion in this study will contribute to improved parameterization of plant hydraulics in large-scale models and the prediction of ecosystem drought response.

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 Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion

2021 ◽  
Vol 25 (5) ◽  
pp. 2399-2417
Author(s):  
Yanlan Liu ◽  
Nataniel M. Holtzman ◽  
Alexandra G. Konings
Keyword(s):  
Data Fusion ◽  
Land Surface ◽  
Large Scale ◽  
Microwave Remote Sensing ◽  
Plant Functional Types ◽  
Drought Response ◽  
Model Data ◽  
Plant Hydraulics ◽  
Functional Types ◽  
Vegetation Water

Abstract. Droughts are expected to become more frequent and severe under climate change, increasing the need for accurate predictions of plant drought response. This response varies substantially, depending on plant properties that regulate water transport and storage within plants, i.e., plant hydraulic traits. It is, therefore, crucial to map plant hydraulic traits at a large scale to better assess drought impacts. Improved understanding of global variations in plant hydraulic traits is also needed for parameterizing the latest generation of land surface models, many of which explicitly simulate plant hydraulic processes for the first time. Here, we use a model–data fusion approach to evaluate the spatial pattern of plant hydraulic traits across the globe. This approach integrates a plant hydraulic model with data sets derived from microwave remote sensing that inform ecosystem-scale plant water regulation. In particular, we use both surface soil moisture and vegetation optical depth (VOD) derived from the X-band Japan Aerospace Exploration Agency (JAXA) Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; collectively AMSR-E). VOD is proportional to vegetation water content and, therefore, closely related to leaf water potential. In addition, evapotranspiration (ET) from the Atmosphere–Land Exchange Inverse (ALEXI) model is also used as a constraint to derive plant hydraulic traits. The derived traits are compared to independent data sources based on ground measurements. Using the K-means clustering method, we build six hydraulic functional types (HFTs) with distinct trait combinations – mathematically tractable alternatives to the common approach of assigning plant hydraulic values based on plant functional types. Using traits averaged by HFTs rather than by plant functional types (PFTs) improves VOD and ET estimation accuracies in the majority of areas across the globe. The use of HFTs and/or plant hydraulic traits derived from model–data fusion in this study will contribute to improved parameterization of plant hydraulics in large-scale models and the prediction of ecosystem drought response.

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 Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological "common sense" in a model-data-fusion framework.

2014 ◽  
Vol 11 (8) ◽  
pp. 12733-12772 ◽  
Author(s):  
A. A. Bloom ◽  
M. Williams
Keyword(s):  
Data Fusion ◽  
Initial Conditions ◽  
Added Value ◽  
Carbon Pools ◽  
Model Parameters ◽  
Model Data ◽  
Terrestrial Carbon ◽  
Area Index ◽  
Ecosystem Carbon ◽  
Fusion Framework

Abstract. Many of the key processes represented in global terrestrial carbon models remain largely unconstrained. For instance, plant allocation patterns and residence times of carbon pools are poorly known globally, except perhaps at a few intensively studied sites. As a consequence of data scarcity, carbon models tend to be underdetermined, and so can produce similar net fluxes with very different parameters and internal dynamics. To address these problems, we propose a series of ecological and dynamic constraints (EDCs) on model parameters and initial conditions, as a means to constrain ecosystem variable inter-dependencies in the absence of local data. The EDCs consist of a range of conditions on (a) carbon pool turnover and allocation ratios, (b) steady state proximity, and (c) growth and decay of model carbon pools. We use a simple ecosystem carbon model in a model-data fusion framework to determine the added value of these constraints in a data-poor context. Based only on leaf area index (LAI) time series and soil carbon data, we estimate net ecosystem exchange (NEE) for (a) 40 synthetic experiments and (b) three AMERIFLUX tower sites. For the synthetic experiments, we show that EDCs lead to an an overall 34% relative error reduction in model parameters, and a 65% reduction in the 3 yr NEE 90% confidence range. In the application at AMERIFLUX sites all NEE estimates were made independently of NEE measurements. Compared to these observations, EDCs resulted in a 69–93% reduction in 3 yr cumulative NEE median biases (−0.26 to +0.08 kg C m−2), in comparison to standard 3 yr median NEE biases (−1.17 to −0.84 kg C m−2). In light of these findings, we advocate the use of EDCs in future model-data fusion analyses of the terrestrial carbon cycle.

scholarly journals Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological "common sense" in a model–data fusion framework

2015 ◽  
Vol 12 (5) ◽  
pp. 1299-1315 ◽  
Author(s):  
A. A. Bloom ◽  
M. Williams
Keyword(s):  
Data Fusion ◽  
Initial Conditions ◽  
Added Value ◽  
Carbon Pools ◽  
Model Parameters ◽  
Model Data ◽  
Terrestrial Carbon ◽  
Area Index ◽  
Ecosystem Carbon ◽  
Fusion Framework

Abstract. Many of the key processes represented in global terrestrial carbon models remain largely unconstrained. For instance, plant allocation patterns and residence times of carbon pools are poorly known globally, except perhaps at a few intensively studied sites. As a consequence of data scarcity, carbon models tend to be underdetermined, and so can produce similar net fluxes with very different parameters and internal dynamics. To address these problems, we propose a series of ecological and dynamic constraints (EDCs) on model parameters and initial conditions, as a means to constrain ecosystem variable inter-dependencies in the absence of local data. The EDCs consist of a range of conditions on (a) carbon pool turnover and allocation ratios, (b) steady-state proximity, and (c) growth and decay of model carbon pools. We use a simple ecosystem carbon model in a model–data fusion framework to determine the added value of these constraints in a data-poor context. Based only on leaf area index (LAI) time series and soil carbon data, we estimate net ecosystem exchange (NEE) for (a) 40 synthetic experiments and (b) three AmeriFlux tower sites. For the synthetic experiments, we show that EDCs lead to an overall 34% relative error reduction in model parameters, and a 65% reduction in the 3 yr NEE 90% confidence range. In the application at AmeriFlux sites all NEE estimates were made independently of NEE measurements. Compared to these observations, EDCs resulted in a 69–93% reduction in 3 yr cumulative NEE median biases (–0.26 to +0.08 kg C m−2), in comparison to standard 3 yr median NEE biases (–1.17 to −0.84 kg C m−2). In light of these findings, we advocate the use of EDCs in future model–data fusion analyses of the terrestrial carbon cycle.

scholarly journals Multi-source hydrological soil moisture state estimation using data fusion optimisation

2017 ◽  
Vol 21 (7) ◽  
pp. 3267-3285 ◽  
Author(s):  
Lu Zhuo ◽  
Dawei Han
Keyword(s):  
Soil Moisture ◽  
Data Fusion ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Local Linear Regression ◽  
Data Sources ◽  
Superior Performance ◽  
Flood Prediction ◽  
Time To Build ◽  
Moisture State

Abstract. Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. Although there have been a number of soil moisture products, they cannot be directly used in hydrological modelling. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from SAC-SMA (soil moisture), MODIS (land surface temperature), and SMOS (multi-angle brightness temperatures in H–V polarisations). The simple yet effective local linear regression model is applied for the data fusion purpose in the Pontiac catchment. Four schemes according to temporal availabilities of the data sources are developed, which are pre-assessed and best selected by using the well-proven feature selection algorithm gamma test. The hydrological accuracy of the produced soil moisture data is evaluated against the Xinanjiang hydrological model's soil moisture deficit simulation. The result shows that a superior performance is obtained from the scheme with the data inputs from all sources (NSE = 0.912, r = 0.960, RMSE = 0.007 m). Additionally, the final daily-available hydrological soil moisture product significantly increases the Nash–Sutcliffe efficiency by almost 50 % in comparison with the two most popular soil moisture products. The proposed method could be easily applied to other catchments and fields with high confidence. The misconception between the hydrological soil moisture state variable and the real-world soil moisture content, and the potential to build a global routine hydrological soil moisture product are discussed.

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