Special features of the dayCent modeling package and additional procedures for parameterization, calibration, validation, and applications

Authors: Del Grosso, Stephen - Steve; PARTON, WILLIAM - Colorado State University; KEOUGH, CYNTHIS - Colorado State University; Reyes-Fox, Melissa

Submitted to: Soil Science Society of America Special Publication Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 4/29/2011
Publication Date: 9/1/2011
Citation: Del Grosso, S.J., Parton, W.J., Keough, C., Reyes-Fox, M.A. 2011. Special features of the dayCent modeling package and additional procedures for parameterization, calibration, validation, and applications. Soil Science Society of America Special Publication Book Chapter. p. 155-176.

Interpretive Summary: There is no perfect method to quantify many agroecosystem variables of interest and all measurement methods and models have strengths and weaknesses. Ground based measurements are limited because snapshots of state variables at small spatial and temporal scales, (e.g., standing biomass, chamber trace gas concentration) are used to infer plant growth and gas flux rates at larger scales. Models are a simplification of reality but have the advantage of offering full spatial and temporal coverage. The accuracy and utility of estimates based on both models and measurements vary across spatial and temporal scales. For example, at smaller scales, process based models usually agree more close with N flux observations than simple empirical models, but as scale increases, estimates based on different modeling and measuring methodologies tend to converge. When evaluating models, the following must be kept in mind. There is uncertainty in inputs used to drive models and measurements used to test model outputs. The reliability of some measurements (e.g. grain yields) is greater than others (e.g. annual soil surface N2O flux). For these reasons, model results should be put in perspective by comparing outputs not only with measurements but with out puts from other models. When testing models, results are almost always mixed with different outputs comparing favorably with measurements under some conditions but not others. In addition to evaluating models based on comparisons with measurements and outputs from other models, how well faulty model behavior can be explained, in terms of, for example, reliability of input data, should also be considered.

Technical Abstract: DayCent (Daily Century) is a biogeochemical model of intermediate complexity used to simulate flows of carbon and nutrients for crop, grassland, forest, and savanna ecosystems. Required model inputs are: soil texture, current and historical land use, vegetation cover, and daily maximum/minimum temperature and precipitation. When calibrating the model, we recommend testing model performance in the following order: soil water content, crop yields/plant growth, changes in soil organic matter levels, and N loss vectors. Different statistics should be used when evaluating model performance including correlation coefficients, root mean square error, and mean error. For vectors that are highly variable in time (e.g., N2O emissions), the model can represent treatment impacts on seasonal emissions correctly but not necessarily the timing at the daily scale. In addition to comparing model outputs with field observations, comparisons with alternative models are advocated to more fully evaluate model performance. N2, NH3, NOx losses are some of the most uncertain model outputs because these vectors are rarely measured in field experiments. Web accessible databases that include comprehensive model driver and testing data are needed to facilitate model comparisons and evaluation. DayCent has been used to simulate the impacts of climate and land use change on various crop, grassland, and forest systems around the world, and is currently used to estimate soil N2O emissions from cropped and grazed lands for the annual US inventory of greenhouse gases compiled by the EPA.