Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

Abstract

Estimating the impact of different sources of uncertainty along the modelling chain is an important skill graduates are expected to have. Broadly speaking, educators can cover uncertainty in hydrological modelling by differentiating uncertainty in data, model parameters and model structure. This provides students with insights on the impact of uncertainties on modelling results and thus on the usability of the acquired model simulations for decision making. A survey among teachers in the Earth and environmental sciences showed that model structural uncertainty is the least represented uncertainty group in teaching. This paper introduces a computational exercise that introduces students to the basics of model structure uncertainty through two ready-to-use modelling experiments. These experiments require either Matlab or Octave, and use the open-source Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) and the open-source Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) data set. The exercise is short and can easily be integrated into an existing hydrological curriculum, with only a limited time investment needed to introduce the topic of model structure uncertainty and run the exercise. Two trial applications at the Technische Universität Dresden (Germany) showed that the exercise can be completed in two afternoons or four 90 min sessions and that the provided setup effectively transfers the intended insights about model structure uncertainty.

Details

Original languageEnglish
Pages (from-to)3299-3314
Number of pages16
JournalHydrology and earth system sciences
Volume26
Issue number12
Publication statusPublished - 29 Jun 2022
Peer-reviewedYes

External IDs

Scopus 85133546452
Mendeley 795bbbad-eccd-34b2-9f2f-8504ac7fce77
WOS 000818956900001
unpaywall 10.5194/hess-26-3299-2022

Keywords