Keywords

Uncertainty; Modelling Decisions; Subjectivity; Hydrological Modelling

Start Date

6-7-2022 2:20 PM

End Date

6-7-2022 2:40 PM

Abstract

Without taking subjective factors into account, hydrologic and hydraulic modelling is already a complex process with multiple sources of uncertainty. The usually-considered sources are data, model structure, and parameter uncertainty. However, modellers make multiple decisions during the modelling process, e.g. related to the calibration period, pre-processing methods of forcing data and temporal resolution. These decisions affect the model results, which is another type of uncertainty: methodological uncertainty. This methodological uncertainty does encompass some elements of the usually-considered uncertainties. The motivations behind each decision can potentially differ per modeller. Hence, the consequent modelling results are inherently subjective, which results in a bias in modelling. Fourteen semistructured interviews were conducted with modellers at Dutch water boards and consultancy companies. These are analysed to identify different motivations behind modelling decisions in a practical setting. This will be an inductive content analysis focusing on the social processes behind and values within the modelling process, for instance the expectations of who is responsible for what. The exploration of motivations in hydrological modelling will enhance our understanding of the modellers’ influence in modelling. This serves as an incentive to clarify and improve modelling procedures.

Stream and Session

false

COinS
 
Jul 6th, 2:20 PM Jul 6th, 2:40 PM

A Myriad of Motivations: how modelling decisions are made

Without taking subjective factors into account, hydrologic and hydraulic modelling is already a complex process with multiple sources of uncertainty. The usually-considered sources are data, model structure, and parameter uncertainty. However, modellers make multiple decisions during the modelling process, e.g. related to the calibration period, pre-processing methods of forcing data and temporal resolution. These decisions affect the model results, which is another type of uncertainty: methodological uncertainty. This methodological uncertainty does encompass some elements of the usually-considered uncertainties. The motivations behind each decision can potentially differ per modeller. Hence, the consequent modelling results are inherently subjective, which results in a bias in modelling. Fourteen semistructured interviews were conducted with modellers at Dutch water boards and consultancy companies. These are analysed to identify different motivations behind modelling decisions in a practical setting. This will be an inductive content analysis focusing on the social processes behind and values within the modelling process, for instance the expectations of who is responsible for what. The exploration of motivations in hydrological modelling will enhance our understanding of the modellers’ influence in modelling. This serves as an incentive to clarify and improve modelling procedures.