Assessing the sensitivity of modelled estimates of N2O emissions and yield to input uncertainty at a UK cropland experimental site using the DailyDayCent model

N. Fitton*, A. Datta, K. Smith, J. R. Williams, A. Hastings, M. Kuhnert, C. F. E. Topp, P. Smith

*Corresponding author for this work

Research output: Contribution to journalArticle

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6 Downloads (Pure)

Abstract

Biogeochemical models such as DailyDayCent (DDC) are increasingly used to help quantify the emissions of green-house gasses across different ecosystems and climates. For this use they require parameterisation to represent a heterogeneous region or are site specific and scaled upwards. This requires information on inputs such as climate, soil, land-use and land management. However, each input has an associated uncertainty, which propagates through the model to create an uncertainty in the modelled outputs. To have confidence in model projections, an assessment of how the uncertainty in inputs propagated through the model and its impact on modelled outputs is required. To achieve this, we used a pre-defined uncertainty range of key inputs; temperature, precipitation, clay content, bulk density and soil pH, and performed a sensitivity and uncertainty analysis, using Monte Carlo simulations. This allowed the effect of measurement uncertainty on the modelled annual N2O emissions and crop yields at the Grange field experimental site to be quantified. Overall the range of model estimates simulated was relatively high and while the model was sensitive to each input parameter, uncertainty was driven by the sensitivity to soil pH. This decreased as the N fertiliser application rate increased, as at lower N application rates the model becomes more sensitive to other drivers of N mineralisation such as soil and climate inputs. Therefore, while our results indicate that DDC can provide a good estimate of annual N2O emissions and crop yields under UK conditions, reducing the uncertainty in the input parameters will lead to more accurate simulations.

Original languageEnglish
Pages (from-to)119-133
Number of pages15
JournalNutrient Cycling in Agroecosystems
Volume99
Issue number1-3
DOIs
Publication statusPublished - Jul 2014

Keywords

  • DailyDayCent
  • N2O emissions
  • crop yields
  • sensitivity analysis
  • Monte Carlo simulations
  • nitrous-oxide emissions
  • soil
  • daycent
  • simulation
  • grassland
  • ecosystem
  • fluxes

Cite this

@article{4f04862792ed4ac8bab8b5187699ebe5,
title = "Assessing the sensitivity of modelled estimates of N2O emissions and yield to input uncertainty at a UK cropland experimental site using the DailyDayCent model",
abstract = "Biogeochemical models such as DailyDayCent (DDC) are increasingly used to help quantify the emissions of green-house gasses across different ecosystems and climates. For this use they require parameterisation to represent a heterogeneous region or are site specific and scaled upwards. This requires information on inputs such as climate, soil, land-use and land management. However, each input has an associated uncertainty, which propagates through the model to create an uncertainty in the modelled outputs. To have confidence in model projections, an assessment of how the uncertainty in inputs propagated through the model and its impact on modelled outputs is required. To achieve this, we used a pre-defined uncertainty range of key inputs; temperature, precipitation, clay content, bulk density and soil pH, and performed a sensitivity and uncertainty analysis, using Monte Carlo simulations. This allowed the effect of measurement uncertainty on the modelled annual N2O emissions and crop yields at the Grange field experimental site to be quantified. Overall the range of model estimates simulated was relatively high and while the model was sensitive to each input parameter, uncertainty was driven by the sensitivity to soil pH. This decreased as the N fertiliser application rate increased, as at lower N application rates the model becomes more sensitive to other drivers of N mineralisation such as soil and climate inputs. Therefore, while our results indicate that DDC can provide a good estimate of annual N2O emissions and crop yields under UK conditions, reducing the uncertainty in the input parameters will lead to more accurate simulations.",
keywords = "DailyDayCent, N2O emissions, crop yields, sensitivity analysis, Monte Carlo simulations, nitrous-oxide emissions, soil, daycent, simulation, grassland, ecosystem, fluxes",
author = "N. Fitton and A. Datta and K. Smith and Williams, {J. R.} and A. Hastings and M. Kuhnert and Topp, {C. F. E.} and P. Smith",
note = "Acknowledgments This work contributes to the Defra funded projects AC0116: InveN2Ory, and AC0114: the Greenhouse Gas Platform. PS is a Royal Society-Wolfson Research Merit Award holder.",
year = "2014",
month = "7",
doi = "10.1007/s10705-014-9622-0",
language = "English",
volume = "99",
pages = "119--133",
journal = "Nutrient Cycling in Agroecosystems",
issn = "1385-1314",
publisher = "Springer Netherlands",
number = "1-3",

}

TY - JOUR

T1 - Assessing the sensitivity of modelled estimates of N2O emissions and yield to input uncertainty at a UK cropland experimental site using the DailyDayCent model

AU - Fitton, N.

AU - Datta, A.

AU - Smith, K.

AU - Williams, J. R.

AU - Hastings, A.

AU - Kuhnert, M.

AU - Topp, C. F. E.

AU - Smith, P.

N1 - Acknowledgments This work contributes to the Defra funded projects AC0116: InveN2Ory, and AC0114: the Greenhouse Gas Platform. PS is a Royal Society-Wolfson Research Merit Award holder.

PY - 2014/7

Y1 - 2014/7

N2 - Biogeochemical models such as DailyDayCent (DDC) are increasingly used to help quantify the emissions of green-house gasses across different ecosystems and climates. For this use they require parameterisation to represent a heterogeneous region or are site specific and scaled upwards. This requires information on inputs such as climate, soil, land-use and land management. However, each input has an associated uncertainty, which propagates through the model to create an uncertainty in the modelled outputs. To have confidence in model projections, an assessment of how the uncertainty in inputs propagated through the model and its impact on modelled outputs is required. To achieve this, we used a pre-defined uncertainty range of key inputs; temperature, precipitation, clay content, bulk density and soil pH, and performed a sensitivity and uncertainty analysis, using Monte Carlo simulations. This allowed the effect of measurement uncertainty on the modelled annual N2O emissions and crop yields at the Grange field experimental site to be quantified. Overall the range of model estimates simulated was relatively high and while the model was sensitive to each input parameter, uncertainty was driven by the sensitivity to soil pH. This decreased as the N fertiliser application rate increased, as at lower N application rates the model becomes more sensitive to other drivers of N mineralisation such as soil and climate inputs. Therefore, while our results indicate that DDC can provide a good estimate of annual N2O emissions and crop yields under UK conditions, reducing the uncertainty in the input parameters will lead to more accurate simulations.

AB - Biogeochemical models such as DailyDayCent (DDC) are increasingly used to help quantify the emissions of green-house gasses across different ecosystems and climates. For this use they require parameterisation to represent a heterogeneous region or are site specific and scaled upwards. This requires information on inputs such as climate, soil, land-use and land management. However, each input has an associated uncertainty, which propagates through the model to create an uncertainty in the modelled outputs. To have confidence in model projections, an assessment of how the uncertainty in inputs propagated through the model and its impact on modelled outputs is required. To achieve this, we used a pre-defined uncertainty range of key inputs; temperature, precipitation, clay content, bulk density and soil pH, and performed a sensitivity and uncertainty analysis, using Monte Carlo simulations. This allowed the effect of measurement uncertainty on the modelled annual N2O emissions and crop yields at the Grange field experimental site to be quantified. Overall the range of model estimates simulated was relatively high and while the model was sensitive to each input parameter, uncertainty was driven by the sensitivity to soil pH. This decreased as the N fertiliser application rate increased, as at lower N application rates the model becomes more sensitive to other drivers of N mineralisation such as soil and climate inputs. Therefore, while our results indicate that DDC can provide a good estimate of annual N2O emissions and crop yields under UK conditions, reducing the uncertainty in the input parameters will lead to more accurate simulations.

KW - DailyDayCent

KW - N2O emissions

KW - crop yields

KW - sensitivity analysis

KW - Monte Carlo simulations

KW - nitrous-oxide emissions

KW - soil

KW - daycent

KW - simulation

KW - grassland

KW - ecosystem

KW - fluxes

U2 - 10.1007/s10705-014-9622-0

DO - 10.1007/s10705-014-9622-0

M3 - Article

VL - 99

SP - 119

EP - 133

JO - Nutrient Cycling in Agroecosystems

JF - Nutrient Cycling in Agroecosystems

SN - 1385-1314

IS - 1-3

ER -