A model-data fusion approach to analyse carbon dynamics in managed grasslands

Myrgiotis, Vasileios; Blei, Emanuel; Clement, R.; Jones, Stephanie; Keane, Ben; Lee, Mark A.; Levy, Peter; Rees, Robert M.; Skiba, U.; Smallman, T. Luke; Toet, Sylvia; Williams, Mathew

doi:10.1016/j.agsy.2020.102907

Cited by 10 publications

(14 citation statements)

References 84 publications

(78 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By assimilating observational data (LAI, soil C stocks etc) the algorithm updates the distribution of model parameters following the rules of Bayesian inference. CARDAMOM has been used in studies of ecosystem C fluxes in the past, including two recent studies using DALEC-Grass (Myrgiotis et al, 2020(Myrgiotis et al, , 2021. A key aspect of CARDAMOM is the use of ecological and dynamic constraints (EDC), which are conditions applied to the parameter sampling process in order to ensure the mathematical, ecological and biogeochemical sensibility (or "common sense") of the simulated system.…”

Section: Carbon Data Model Frameworkmentioning

confidence: 99%

“…A uniform distribution was used for each of the 31 DALEC-Grass parameters with the range for each parameter presented in Table A1 (Appendix A). In Myrgiotis et al (2020) DALEC-Grass parameter priors were refined through implementing the model using known vegetation management (cutting dates, livestock density time-series) and by assimilating field-measured LAI and CO 2 flux data. In Myrgiotis et al (2021) these model priors have been further refined using EO-based vegetation anomaly time-series (for vegetation management inference) and by assimilating Sentinel-2 based LAI time-series.…”

Section: Carbon Data Model Frameworkmentioning

confidence: 99%

“…CAR-DAMOM generates field-specific calibrations of DALEC-Grass by assimilating EO-based LAI time series. The algorithm has been validated against an extensive dataset of field measured C flux and biomass data from two managed grasslands in Scotland and further tested using 4 years of EO-based LAI data in 3 variably-managed fields in South West England (Myrgiotis et al, 2020(Myrgiotis et al, , 2021. Here, the MDF algorithm is implemented for 2017-2018 on a large sample of 1855 managed grassland fields in GB (England, Wales and Scotland).…”

Section: Introductionmentioning

confidence: 99%

“…The model's 31 parameters are presented in Table A1 in Appendix A . For detailed information on the model's concept, its structure, its validation against measured CO 2 fluxes and its testing under variable grassland management using EO data we refer to (Myrgiotis et al, 2020(Myrgiotis et al, , 2021.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The carbon budget of the managed grasslands of Great Britain constrained by earth observations

Myrgiotis

Smallman

Williams

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Grasslands cover around two thirds of the land area of Great Britain (GB) and are important reservoirs of terrestrial biological carbon (C). Outside a few well-monitored sites the quantification of C dynamics in managed grasslands is made complex by the spatio-temporal variability of weather conditions combined with grazing and cutting patterns. Earth observation (EO) missions produce high-resolution frequently-retrieved proxy data on the state of grassland canopies but synergies between EO data and biogeochemical modelling to estimate grassland C dynamics are under-explored. Here, we show the potential of model-data fusion (MDF) to provide robust near-real time analyses of managed grasslands of GB (England, Wales andScotland). We combine EO data and process-based modelling to estimate grassland C balance and to examine the role of management. We implement a MDF algorithm to (1) infer grassland management from vegetation reduction data (Proba-V), (2) optimise model parameters by assimilating leaf area index (LAI) data (Sentinel-2) and (3) simulate livestock grazing, grass cutting, and C allocation and loss to the atmosphere. The MDF algorithm was applied for 2017 and 2018 at 1855 fields sampled from across GB. The algorithm was able to effectively assimilate the Sentinel-2 based LAI time series (overlap = 80 %, RMSE = 1 gCm−2, bias = 0.35 gCm−2) and predict livestock densities per area that correspond with independent census-based data (r = 0.68). The mean total removed biomass across all simulated fields was 6 (±1.8) tDM ha−1 y−1. The simulated grassland ecosystems were on average C sinks in 2017 and 2018; the GB-average net ecosystem exchange (NEE) and net biome exchange (NBE) for 2017 was −232 ± 94 and for 2018 was −120 ± 103 gCm−2 y−1. The 2018 summer drought reduced C sinks, with a 9-fold increase in the number fields that were C sources (NBE > 0) in 2018 compared to 2017. We conclude that management in the form of sward condition and the timing, intensity and type of defoliation are key determinants of the C balance of managed grasslands. Nevertheless, extreme weather, such as prolonged droughts, can convert grassland C sinks to sources.

show abstract

Section: Carbon Data Model Frameworkmentioning

confidence: 99%

Section: Carbon Data Model Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The carbon budget of the managed grasslands of Great Britain constrained by earth observations

Myrgiotis

Smallman

Williams

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For each iteration of the MDF, the EDCs check that the sampled parameter sets used to the generate model outputs are broadly within the expected ecophysiology based on our understanding of wheat growth dynamics. Past research has demonstrated the successful use of EDCs when calibrating terrestrial ecosystem models [43,45]. We applied four EDCs, which principally constrained estimates related to the C pools and fluxes (Table 2).…”

Section: Model-data Fusion Approachmentioning

confidence: 99%

Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield

et al. 2021

Self Cite

View full text Add to dashboard Cite

Climate, nitrogen (N) and leaf area index (LAI) are key determinants of crop yield. N additions can enhance yield but must be managed efficiently to reduce pollution. Complex process models estimate N status by simulating soil-crop N interactions, but such models require extensive inputs that are seldom available. Through model-data fusion (MDF), we combine climate and LAI time-series with an intermediate-complexity model to infer leaf N and yield. The DALEC-Crop model was calibrated for wheat leaf N and yields across field experiments covering N applications ranging from 0 to 200 kg N ha−1 in Scotland, UK. Requiring daily meteorological inputs, this model simulates crop C cycle responses to LAI, N and climate. The model, which includes a leaf N-dilution function, was calibrated across N treatments based on LAI observations, and tested at validation plots. We showed that a single parameterization varying only in leaf N could simulate LAI development and yield across all treatments—the mean normalized root-mean-square-error (NRMSE) for yield was 10%. Leaf N was accurately retrieved by the model (NRMSE = 6%). Yield could also be reasonably estimated (NRMSE = 14%) if LAI data are available for assimilation during periods of typical N application (April and May). Our MDF approach generated robust leaf N content estimates and timely yield predictions that could complement existing agricultural technologies. Moreover, EO-derived LAI products at high spatial and temporal resolutions provides a means to apply our approach regionally. Testing yield predictions from this approach over agricultural fields is a critical next step to determine broader utility.

show abstract

Challenges in scaling up greenhouse gas fluxes: experience from the UK Greenhouse Gas Emissions and Feedbacks Programme

Levy

Clement

Cowan

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The role of greenhouse gases (GHGs) in causing global climate change is now well recognized (IPCC, 2013). Emissions of GHGs from terrestrial ecosystems play an important part in this, and the potential for feedback within the climate system which amplify the emissions of GHGs from natural ecosystems is substantial. Accurate estimates are therefore needed of the GHG balance of the land surface at regional and national scales, and over long time periods, if we are to understand the key driver of global change. Because of the large scale involved, in relation to the scale at which we can make observations, this presents a major challenge that spans the domains of biogeochemistry, ecology, remote sensing, and atmospheric science.

show abstract

A model-data fusion approach to analyse carbon dynamics in managed grasslands

Cited by 10 publications

References 84 publications

The carbon budget of the managed grasslands of Great Britain constrained by earth observations

The carbon budget of the managed grasslands of Great Britain constrained by earth observations

Combining Process Modelling and LAI Observations to Diagnose Winter Wheat Nitrogen Status and Forecast Yield

Challenges in scaling up greenhouse gas fluxes: experience from the UK Greenhouse Gas Emissions and Feedbacks Programme

Contact Info

Product

Resources

About