Improving model prediction of soil N2O emissions through Bayesian calibration

V Myrgiotis, M Williams, CFE Topp, RM Rees

Research output: Contribution to journalArticle

3 Citations (Scopus)
9 Downloads (Pure)

Abstract

The biogeochemical processes that lead to the production of N2O in arable soils are controlled by temporally and spatially varying drivers. The need for prediction of soil N2O emissions across scales means that agroecosystem biogeochemistry models are widely used to simulate N2O emissions. Due to the parameter-dense nature of agroecosystem models their parameters have to be calibrated according to the soil and climatic conditions of the intended area of application. Bayesian calibration is considered one of the most advanced ways to complete this task. In this study, we calibrate nine parameters of the Landscape-DNDC process-based agroecosystem model, which are key to its N2O prediction. The Metropolis-Hastings algorithm is used at four separate implementations in order to estimate parameter posterior distributions at four arable sites in the UK. The results of this process are visualised, summarised and assessed against measured N2O data from ten independent arable sites. The study shows that, in many cases, soil N2O emission peaks that were not predicted with the default model parameters were predicted after calibration. Overall, the prediction of soil N2O fluxes across all the sites that were considered was improved by 33% when using the calibrated parameters.
Original languageEnglish
Pages (from-to)1467 - 1477
Number of pages11
JournalScience of the Total Environment
Volume624
Early online date27 Dec 2017
DOIs
Publication statusFirst published - 27 Dec 2017

Fingerprint

soil emission
calibration
agricultural ecosystem
prediction
soil
biogeochemistry
parameter

Bibliographical note

1023324
1024888
1030795

Keywords

  • Bayesian calibration
  • Landscape-DNDC
  • Metropolis-Hastings
  • Modelling
  • N2O
  • UK croplands

Cite this

@article{8ac9b6f8be804f63892c8557a27e1459,
title = "Improving model prediction of soil N2O emissions through Bayesian calibration",
abstract = "The biogeochemical processes that lead to the production of N2O in arable soils are controlled by temporally and spatially varying drivers. The need for prediction of soil N2O emissions across scales means that agroecosystem biogeochemistry models are widely used to simulate N2O emissions. Due to the parameter-dense nature of agroecosystem models their parameters have to be calibrated according to the soil and climatic conditions of the intended area of application. Bayesian calibration is considered one of the most advanced ways to complete this task. In this study, we calibrate nine parameters of the Landscape-DNDC process-based agroecosystem model, which are key to its N2O prediction. The Metropolis-Hastings algorithm is used at four separate implementations in order to estimate parameter posterior distributions at four arable sites in the UK. The results of this process are visualised, summarised and assessed against measured N2O data from ten independent arable sites. The study shows that, in many cases, soil N2O emission peaks that were not predicted with the default model parameters were predicted after calibration. Overall, the prediction of soil N2O fluxes across all the sites that were considered was improved by 33{\%} when using the calibrated parameters.",
keywords = "Bayesian calibration, Landscape-DNDC, Metropolis-Hastings, Modelling, N2O, UK croplands",
author = "V Myrgiotis and M Williams and CFE Topp and RM Rees",
note = "1023324 1024888 1030795",
year = "2017",
month = "12",
day = "27",
doi = "10.1016/j.scitotenv.2017.12.202",
language = "English",
volume = "624",
pages = "1467 -- 1477",
journal = "Science of the Total Environment",
issn = "0048-9697",
publisher = "Elsevier",

}

Improving model prediction of soil N2O emissions through Bayesian calibration. / Myrgiotis, V; Williams, M; Topp, CFE; Rees, RM.

In: Science of the Total Environment, Vol. 624, 27.12.2017, p. 1467 - 1477.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Improving model prediction of soil N2O emissions through Bayesian calibration

AU - Myrgiotis, V

AU - Williams, M

AU - Topp, CFE

AU - Rees, RM

N1 - 1023324 1024888 1030795

PY - 2017/12/27

Y1 - 2017/12/27

N2 - The biogeochemical processes that lead to the production of N2O in arable soils are controlled by temporally and spatially varying drivers. The need for prediction of soil N2O emissions across scales means that agroecosystem biogeochemistry models are widely used to simulate N2O emissions. Due to the parameter-dense nature of agroecosystem models their parameters have to be calibrated according to the soil and climatic conditions of the intended area of application. Bayesian calibration is considered one of the most advanced ways to complete this task. In this study, we calibrate nine parameters of the Landscape-DNDC process-based agroecosystem model, which are key to its N2O prediction. The Metropolis-Hastings algorithm is used at four separate implementations in order to estimate parameter posterior distributions at four arable sites in the UK. The results of this process are visualised, summarised and assessed against measured N2O data from ten independent arable sites. The study shows that, in many cases, soil N2O emission peaks that were not predicted with the default model parameters were predicted after calibration. Overall, the prediction of soil N2O fluxes across all the sites that were considered was improved by 33% when using the calibrated parameters.

AB - The biogeochemical processes that lead to the production of N2O in arable soils are controlled by temporally and spatially varying drivers. The need for prediction of soil N2O emissions across scales means that agroecosystem biogeochemistry models are widely used to simulate N2O emissions. Due to the parameter-dense nature of agroecosystem models their parameters have to be calibrated according to the soil and climatic conditions of the intended area of application. Bayesian calibration is considered one of the most advanced ways to complete this task. In this study, we calibrate nine parameters of the Landscape-DNDC process-based agroecosystem model, which are key to its N2O prediction. The Metropolis-Hastings algorithm is used at four separate implementations in order to estimate parameter posterior distributions at four arable sites in the UK. The results of this process are visualised, summarised and assessed against measured N2O data from ten independent arable sites. The study shows that, in many cases, soil N2O emission peaks that were not predicted with the default model parameters were predicted after calibration. Overall, the prediction of soil N2O fluxes across all the sites that were considered was improved by 33% when using the calibrated parameters.

KW - Bayesian calibration

KW - Landscape-DNDC

KW - Metropolis-Hastings

KW - Modelling

KW - N2O

KW - UK croplands

U2 - 10.1016/j.scitotenv.2017.12.202

DO - 10.1016/j.scitotenv.2017.12.202

M3 - Article

VL - 624

SP - 1467

EP - 1477

JO - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

ER -