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

Abstract

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.

Constraining N cycling in the ecosystem model LandscapeDNDC with the stable isotope model SIMONE

“…), but compares well with the range of r 2 (0.01–0.45) values obtained in a model comparison study (Gaillard et al. ). The low r 2 value in our study was mainly a result of the bias in the timing of N 2 O emission peaks, which is a general problem in many studies (Gaillard et al.…”

“…The quality of achieved temperature simulations compares well with other models ( r 2 of 0.85, 0.91, and 0.93 for EPIC, DayCent and DNDC, respectively; Gaillard et al. ).…”

“…The low r 2 value in our study was mainly a result of the bias in the timing of N 2 O emission peaks, which is a general problem in many studies (Gaillard et al. ).…”

“…A model intercomparison study with DNDC, DayCent, and EPIC models even showed smaller agreements between measured and simulated values ( r 2 range: 0.31–0.55; Gaillard et al. ). The average deviation of simulated WC values was 2.1% and within the range of reported literature values (0.1–2.8%; Wolf et al.…”

Whether the Rock Fragment Content Should Be Considered When Investigating Nitrogen Cycle in Stony Soils?

“…Ideally, before the model outputs corrected by RFCs, simulations of N 2 O emissions using DPFs should be the best and using DNDC defaults should underestimate in the N 2 O emissions. This was due to that water‐filled pore space at field capacity always associated with peak denitrification and N 2 O emission rates (Bollmann & Conrad, ; Castellano et al, ; Gaillard et al, ). On our study hillslope, if the soil water contents at field capacity predicted by the DNDC model defaults or ROSETTA were adopted (around 0.30 m 3 m −3 ; Table ), the soil water contents were generally lower than the field capacity (Figure ).…”