Conceptual framework addressing timescale mismatch uncertainty: Nitrous-oxide (N2O) modeled and measured, Kansas, USA - Arango, Miguel A.; Anandhi, Aavudai; Rice, Charles W. (Ecological modelling, 2023)
•The uncertainty arises from the differences in time scales between modeled and measured variables are not explicitly addressed in literature.•A conceptual framework was developed to represent this known-unknown uncertainty for a combinations of integration methods, management practices, sensitivity analysis methods, calibration, and validation performance measures.•The framework is demonstrated by applying it to Denitrification–Decomposition model modeled/measured N2O when the timescales are not equal.•Although the model is used as an example, the techniques described can be applied to many modeling problems across locations at multiple time scales. The uncertainty that arises from the differences in time scales between modeled and measured variables during sensitivity analysis, calibration, and validation in process-based models are often not addressed in the literature. A conceptual framework was developed to represent the uncertainty arising due to this mismatch in timescales. Modeling N2O fluxes from agricultural lands in Manhattan, Kansas using Denitrification–Decomposition (DNDC) model, and with measurements available at biweekly time scale is chosen in the demonstration. A conceptual framework was developed to represent the known-unknown uncertainty using integration methods, management practices, sensitivity analysis methods, calibration and validation performance measures. The known-known and known-unknown uncertainty were represented for combinations of three integration methods (mean, median and cumulative sum), four management practice combinations (till-urea, no-till-urea, till-compost, no-till-compost), three sensitivity analysis methods (two graphical approaches and an index based method), and two calibration and validation performance measures (ME, R2). In the framework, the unknown uncertainty was represented but not quantified. The various assumptions and some of the implications were also discussed. The framework followed in this exercise and insights gained can be applicable to other process-based models.