A framework for the broad dissemination of hydrological models for non-expert users. (June 2023)

• Record Type: Journal Article
• Authors: Schaffhauser, Timo; Garijo, Daniel; Osorio, Maximiliano; Bittner, Daniel; Pierce, Suzanne; Vargas, Hernán; Disse, Markus; Gil, Yolanda
• Is Part Of: Environmental modelling & software. Volume 164(2023)
• Publication Date: 2023-06
• Subjects: Software metadata -- Model metadata -- Model encapsulation -- Model catalogs -- MINT -- Hydrological models; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105695
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Hydrological models are essential in water resources management, but the expertise required to operate them often exceeds that of potential stakeholders. We present an approach that facilitates the dissemination of hydrological models, and its implementation in the Model INTegration (MINT) framework. Our approach follows principles from software engineering to create software components that reveal only selected functionality of models which is of interest to users while abstracting from implementation complexity, and to generate metadata for the model components. This methodology makes the models more findable, accessible, interoperable, and reusable in support of FAIR principles. We showcase our methodology and its implementation in MINT using two case studies. We illustrate how the models SWAT and MODFLOW are turned into software components by hydrology experts, and how users without hydrology expertise can find, adapt, and execute them. The two models differ in terms of represented processes and in model design and structure. Our approach also benefits expert modelers, by simplifying model sharing and the execution of model ensembles. MINT is a general modeling framework that uses artificial intelligence techniques to assist users, and is released as open-source software. Highlights: An approach to facilitate model dissemination from expert modelers to non-experts Methods are proposed to simplify model complexity with software components Non-experts can easily modify selected parameters and execute models from experts Our approach makes models more findable, accessible, interoperable, reusable (FAIR) Various applications benefited from this approach within the MINT framework