Teaching hydrological modelling: Illustrating model structure uncertainty with a ready-to-use teaching module

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.

• Publication year

  2021

• Venue

  Hydrology and Earth System Sciences Discussions

• Publication date

  2021-01-21

• Fields of study

  Not labeled

• Identifiers
  DOI 10.5194/HESS-2021-30
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments

  2020cited by this paper
• CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil

  2020cited by this paper
• CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

  2020cited by this paper
• Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations

  2019influential reference
• Upper and lower benchmarks in hydrological modelling

  2018cited by this paper
• The CAMELS-CL dataset : catchment attributes and meteorology for large sample studies – Chile dataset

  2018cited by this paper
• The CAMELS data set: catchment attributes and meteorology for large-sample studies

  2017influential reference
• How do hydrologic modeling decisions affect the portrayal of climate change impacts?

  2016cited by this paper
• From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions

  2016cited by this paper
• Accelerating advances in continental domain hydrologic modeling

  2015cited by this paper
• Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance

  2014cited by this paper
• Preface "Hydrology education in a changing world"

  2013cited by this paper
• It takes a community to raise a hydrologist: the Modular Curriculum for Hydrologic Advancement (MOCHA)

  2012influential reference
• Teaching hydrological modeling with a user-friendly catchment-runoff-model software package

  2012influential reference
• Incorporating student-centered approaches into catchment hydrology teaching: A review and synthesis

  2012influential reference
• An educational model for ensemble streamflow simulation and uncertainty analysis

  2012cited by this paper
• Catchment scale hydrological modelling: a review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications

  2011cited by this paper
• On the colour and spin of epistemic error (and what we might do about it)

  2011cited by this paper
• Hydrological field data from a modeller's perspective: Part 2: process‐based evaluation of model hypotheses

  2011cited by this paper
• “ Topography driven conceptual modelling ( FLEX-Topo ) ”

  2010cited by this paper
• Climate change impacts—throwing the dice?

  2009cited by this paper
• Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling

  2009cited by this paper
• Reconciling theory with observations: elements of a diagnostic approach to model evaluation

  2008cited by this paper
• Do Nash values have value?

  2007cited by this paper
• TOOLS FOR TEACHING HYDROLOGICAL AND ENVIRONMENTAL MODELING

  2007cited by this paper
• Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology

  2006cited by this paper
• Process controls of water balance variability in a large semi-arid catchment: downward approach to hydrological model development

  2001cited by this paper
• On the need for benchmarks in hydrological modelling

  2001cited by this paper
• This is How the Discussion Started

  1981cited by this paper
• A criterion of efficiency for rainfall-runoff models

  1978cited by this paper
• On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters

  1972cited by this paper
• River flow forecasting through conceptual models part I — A discussion of principles☆

  1970cited by this paper

• No citing papers are available for this paper.