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 insight 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 teaching module that introduces students to the basics of model structure uncertainty through two ready-to-use exercises. The module is short and can easily be integrated into an existing hydrologic curriculum, limiting the time investment needed to teach this aspect of modelling uncertainty. A trial application at the Technische Universität Dresden (Germany) showed that the exercises can be completed in less than two afternoons and that the provided setup effectively transfers the intended insights about model structure uncertainty. The module requires either Matlab or Octave, and uses the open-source Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT) and the open-source Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset.

Highlights

  • 15 The ability to use computer models to provide hydrologic predictions is a critical skill for young hydrologists (Seibert et al., 2013; Wagener and McIntyre, 2007)

  • Understanding uncertainties in the modelling process is an important skill for graduates, and necessary to interpret the results 20 from any modelling exercise

  • An informal survey circulated amongst educators in the earth sciences suggests that model structure uncertainty is less often part of the curriculum than data and parameter uncertainty are

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Summary

Introduction

15 The ability to use computer models to provide hydrologic predictions is a critical skill for young hydrologists (Seibert et al., 2013; Wagener and McIntyre, 2007). Modelling uncertainties can be roughly classified as relating to the input and evaluation data, the estimation or calibration of model parameters, and the choice of equations that make up the model structure These concepts should be an integral part of the hydrologic curriculum 5 (Wagener et al, 2012; AghaKouchak et al, 2013; Thompson et al, 2012) in a teaching structure that includes student-driven, hands-on exercises that reinforce the taught concepts (Thompson et al, 2012). A survey among 101 teachers in the earth and environmental sciences (see Supplementary Materials) shows large differences in how much time is spent on teaching hydrologic modelling in general, whether model-related uncertainty is part of the course and, if so, which aspects of uncertainty are covered. Just 6% of respondents that did not cover uncertainty in their classes stated that the topic would be covered in another course

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