Conceptual framework addressing timescale mismatch uncertainty: Nitrous-oxide (N2O) modeled and measured, Kansas, USA

Miguel A. Arango, Aavudai Anandhi and Charles W. Rice

Ecological Modelling, 2023, vol. 486, issue C

Abstract: 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.

Keywords: Known-unknowns framework; Theoretical framework to represent uncertainty; Model uncertainty; Three sensitivity analysis approaches; Uncertainty during model calibration and validation; Denitrification–Decomposition (DNDC) model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304380023002661
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:486:y:2023:i:c:s0304380023002661

DOI: 10.1016/j.ecolmodel.2023.110536

Access Statistics for this article

Ecological Modelling is currently edited by Brian D. Fath

More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().