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

Abstract

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.

How you teach changes who you reach: understanding the effect of teaching modality on student engagement, content interest, and learning in undergraduate hydrology

“…Prior hydrologic education investigations studying the impact of shifting teaching modalities away from lecturing have focused heavily on aggregated student perceptions of learning (Gallagher et al, 2021;Knoben & Spieler, 2022; S. W. Lyon et al, 2013;Merck et al, 2021;Pérez-Sánchez et al, 2022). The result that we observed was similar to prior studies noting improved student perceptions of learning as well as foundational research on studentled education.…”

“…7). Evaluating this result in the context of prior research on hydrology education is challenging because few studies have employed multiple methods of quantifying student outcomes beyond questionnaires distributed to students (Gallagher et al, 2021;Knoben & Spieler, 2022;S. W. Lyon et al, 2013;Merck et al, 2021;Pérez-Sánchez et al, 2022).…”

“…Integration of computer simulation into teaching exercises has long been explored for opportunities to increase student engagement above that of the lecture-centric approach (Burt & Butcher, 1986). Several studies focused on student appraisals of in-class activities suggest that course modules focused on hydrological simulations resulted in student perceptions of enriched educational experiences (Gallagher et al, 2021;Knoben & Spieler, 2022;S. W. Lyon et al, 2013;Merck et al, 2021;Pérez-Sánchez et al, 2022).…”

“…Another challenge is that prior studies place strong emphasis on group mean outcomes of hydrology courses (i.e., average perceptions, average assessment scores), rather than the outcomes of individuals (Gallagher et al, 2021;Knoben & Spieler, 2022;S. W. Lyon et al, 2013;Merck et al, 2021;Pérez-Sánchez et al, 2022).…”

A comprehensive open-source course for teaching applied hydrological modelling in Central Asia

“…In recent years, numerical modelling exercises are increasingly shared in the teaching community (e.g. Knoben and Spieler, 2022;Seibert and Vis, 2012). However, the focus lies on general hydrology or US hydrology, and comprehensive material on Central Asian hydrology is not readily available.…”