Simulating microbial denitrification with EPIC: Model description and evaluation

Izaurralde, R. C.; McGill, W. B.; Williams, J. R.; Jones, Curtis D.; Link, Robert; Manowitz, David H.; Schwab, Dagmar; Robertson, G. Philip; Millar, N.

doi:10.1016/j.ecolmodel.2017.06.007

Cited by 22 publications

(16 citation statements)

References 50 publications

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“…), DNDC (Li , ), and EPIC (Izaurralde et al. , ). DayCent is a daily time‐step modification to the CENTURY model, which functions on a monthly time‐step and was originally developed to simulate dynamics of soil organic matter, C, N, P, and S (Parton et al.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, EPIC was originally designed to simulate interactions between soil erosion and crop productivity and later modified to include an N 2 O submodel (Izaurralde et al. ). DNDC (i.e., denitrification decomposition) was developed specifically to simulate N 2 O emissions from agricultural soils, and has since been adapted to a variety of specific ecosystem and agroecosystem contexts (Giltrap et al.…”

Section: Introductionmentioning

confidence: 99%

“…For this work, we selected three models that have been developed, tested, and are currently used for the estimation of N 2 O emissions from agricultural fields: DayCent (Del Grosso et al 2000, Parton et al 2001, DNDC (Li 2000(Li , 2007, and EPIC (Izaurralde et al 2012(Izaurralde et al , 2017. DayCent is a daily time-step modification to the CEN-TURY model, which functions on a monthly time-step and was originally developed to simulate dynamics of soil organic matter, C, N, P, and S (Parton et al 1988).…”

Section: Introductionmentioning

confidence: 99%

“…Day-Cent was specifically developed to simulate the dynamics of N 2 O production, which are influenced by daily events such as tillage, fertilization, and precipitation (Del Grosso et al 2000. Similarly, EPIC was originally designed to simulate interactions between soil erosion and crop productivity and later modified to include an N 2 O submodel (Izaurralde et al 2017). DNDC (i.e., denitrification decomposition) was developed specifically to simulate N 2 O emissions from agricultural soils, and has since been adapted to a variety of specific ecosystem and agroecosystem contexts (Giltrap et al 2010).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

Gaillard

Jones

Ingraham

et al. 2018

Ecological Applications

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Process-based models are increasingly used to study agroecosystem interactions and N O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. This work provides an evaluation of simulated N O flux from three process-based models: DayCent, DNDC, and EPIC. The models were calibrated and validated using data collected from two research sites over five years that represent cropping systems and nitrogen fertilizer management strategies common to dairy cropping systems. We also evaluated the use of a multi-model ensemble strategy, which inconsistently outperformed individual model estimations. Regression analysis indicated a cross-model bias to underestimate high magnitude daily and cumulative N O flux. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and we found significant variation in model estimates of heterotrophic respiration, denitrification, soil NH , and soil NO , which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Our results suggest a bias in the model estimation of N O flux from agroecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

Gaillard

Jones

Ingraham

et al. 2018

Ecological Applications

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“…In addition to the enhanced interface and tools for SWAT integration (displayed by the red dash box in Figure ), the released system has many updates to FEST‐C EPIC, which was originally based on EPIC version 0509. Some parameterizations related to the carbon and N cycles are being further tested and updated following the advancement of the model (e.g., Izaurralde et al, ). The EPIC is also updated with an additional approach in computing percolation and lateral subsurface flow following a more recent version of the model (Doro et al, ).…”

Section: Fest‐cmentioning

confidence: 99%

An Integrated Agriculture, Atmosphere, and Hydrology Modeling System for Ecosystem Assessments

Ran

Yuan

Cooter

et al. 2019

J Adv Model Earth Syst

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We present a regional-scale integrated modeling system (IMS) that includes Environmental Policy Integrated Climate (EPIC), Weather Research and Forecast (WRF), Community Multiscale Air Quality (CMAQ), and Soil and Water Assessment Tool (SWAT) models. The centerpiece of the IMS is the Fertilizer Emission Scenario Tool for CMAQ (FEST-C), which includes a Java-based interface and EPIC adapted to regional applications along with built-in database and tools. The SWAT integration capability is a key enhanced feature in the current release of FEST-C v1.4. For integrated modeling demonstration and evaluation, FEST-C EPIC is simulated over three individual years with WRF/CMAQ weather and N deposition. Simulated yearly changes in water and N budgets along with yields for two major crops (corn grain and soybean) match those inferred from intuitive physical reasoning and survey data given different-year weather conditions. Yearlong air quality simulations with an improved bidirectional ammonia flux modeling approach directly using EPIC-simulated soil properties including NH 3 content helps reduce biases of simulated gas-phase NH 3 and NH 4 + wet deposition over the growing season.Integrated hydrology and water quality simulations applied to the Mississippi River Basin show that estimated monthly streamflow and dissolved N near the outlet to the Gulf of Mexico display similar seasonal patterns as observed. Limitations and issues in different parts of the integrated multimedia simulations are identified and discussed to target areas for future improvements. Plain Language SummaryComputer modeling tools with land-water-air processes are important for understanding nutrient cycling and its negative impacts on air and water quality. We have developed an integrated modeling system that includes agriculture, atmosphere, and hydrology components. The centerpiece of the system is a computer system that includes an agricultural ecosystem model and tools used to connect different modeling components. The agricultural system can conduct simulations for 42 types of grassland and cropland with the influence of site, soil, and management information along with weather and nitrogen deposition from the atmosphere component. An air quality computer model then uses information from the agricultural model, such as how much ammonia is in the soil, to predict how much ammonia gets in the air. Then, the watershed hydrology and water quality model uses the information from the agricultural and atmospheric models to understand the influence of agriculture and atmosphere on water quality. The paper demonstrates and evaluates the integrated modeling system on issues mainly related to N cycling. The system performs reasonably well in comparison with survey and observation data given the configured modeling constraints. The paper also identifies and discusses the advantages and limitations in each part of the system for future applications and improvements. Key Points:• Modeling components representing agriculture, atmosphere, and hydrology are integrat...

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Testing the EPIC Richards submodel for simulating soil water dynamics under different bottom boundary conditions

Longo¹,

Jones

Izaurralde

et al. 2021

Vadose Zone Journal

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Most biogeochemical models simulate water dynamics using the tipping bucket approach, which has been often found to be too simplistic to represent vadose zone dynamics adequately under shallow groundwater conditions. Recently, a solution to the Richards equation using the Mualem-van Genuchten model (Rich-vGM) has been added into the EPIC (Environmental Policy Integrated Climate) model to address this shortfall. Its performance was tested using lysimeters operating under free drainage (FD) and at a shallow water table and 120-cm depth [WT120]). Model accuracy was also compared with the upgraded tipping bucketbased method implemented into EPIC (the variable saturation hydraulic conductivity method [VSHC]). Soil water content (SWC) data were split into calibration and validation subsets. Model evaluation also included annual evapotranspiration (ET), percolation (PRK), and upward water movements to assess underlying soil water balance factors. The submodels provided accurate and similar results upon comparison with SWC measures under FD (Nash-Sutcliffe coefficient [NSE] = 0.26 and 0.61 using VSHC and Rich-vGM, respectively). The Rich-vGM model accurately reproduced observed SWC and ET (e.g., NSE = 0.70 and percentage bias [PBIAS] = −3.7% for WT120, respectively) although it slightly overestimated PRK (PBIAS = 47.8%, on average). Instead, VSHC proved unable to correctly simulate shallow groundwater conditions (e.g., NSE = −1.85 for WT60 SWC). Under shallow groundwater conditions, the Rich-vGM method is recommended, despite the additional data required and the need to define the bottom boundary conditions according to water table fluctuations. In conclusion, the Richards solver introduced and tested in EPIC improved the model's ability to represent complex biophysical and biogeochemical processes in terrestrial ecosystems associated with the hydrological balance.

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Simulating microbial denitrification with EPIC: Model description and evaluation

Cited by 22 publications

References 50 publications

Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

An Integrated Agriculture, Atmosphere, and Hydrology Modeling System for Ecosystem Assessments

Testing the EPIC Richards submodel for simulating soil water dynamics under different bottom boundary conditions

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Simulating microbial denitrification with EPIC: Model description and evaluation

Cited by 22 publications

References 50 publications

Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC models

Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC models

An Integrated Agriculture, Atmosphere, and Hydrology Modeling System for Ecosystem Assessments

Testing the EPIC Richards submodel for simulating soil water dynamics under different bottom boundary conditions

Contact Info

Product

Resources

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Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models

Underestimation of N₂O emissions in a comparison of the DayCent, DNDC, and EPIC models