Conditional interval reduction method: A possible new direction for the optimization of process based models

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • R. Hollós - , University Eötvös Loránd, Centre for Agricultural Research (Autor:in)
  • N. Fodor - , Centre for Agricultural Research, Czech University of Life Sciences Prague (Autor:in)
  • K. Merganičová - , Czech University of Life Sciences Prague, Slovak Academy of Sciences (Autor:in)
  • D. Hidy - , Hungarian University of Agriculture and Life Sciences (Autor:in)
  • T. Árendás - , Centre for Agricultural Research (Autor:in)
  • T. Grünwald - , Professur für Meteorologie (Autor:in)
  • Z. Barcza - , University Eötvös Loránd, Czech University of Life Sciences Prague (Autor:in)

Abstract

Application of process-based models at different spatial scales requires their proper parameterization. This task is typically executed using trial-and-error parameter adjustment or a probabilistic method. Practical application of the probabilistic methods is hampered by methodological complexity and lack of interpretability. Here we present a novel approach for the parameterization of process-based models that we call as conditional interval refinement method (CIRM). The method can be best described as the combination of a probabilistic approach and the advantages of the expert-based parameter adjustment. CIRM was demonstrated by optimizing the Biome-BGCMuSo biogeochemical model using maize yield observations. The proposed approach uses the General Likelihood Uncertainty Estimation (GLUE) method with additional expert knowledge, supplemented by the construction and interpretation of decision trees. It was demonstrated that the iterative, fully automatic method successfully constrained the parameter intervals meanwhile our confidence on the parameters increased. The algorithm can easily be implemented with other process-based models.

Details

OriginalspracheEnglisch
Aufsatznummer105556
Seitenumfang17
FachzeitschriftEnvironmental Modelling and Software
Jahrgang158
Frühes Online-Datum13 Okt. 2022
PublikationsstatusVeröffentlicht - Dez. 2022
Peer-Review-StatusJa

Externe IDs

Scopus 85140315593
ORCID /0000-0003-2263-0073/work/163765968

Schlagworte

Schlagwörter

  • Bayesian calibration, Decision tree, Model optimization, Parameter constraints