Community Workflows to Advance Reproducibility in Hydrologic Modeling: Separating model-agnostic and model-specific configuration steps in applications of large-domain hydrologic models

Abstract

This a preprint and has not been peer reviewed. Data may be preliminary.

HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists

“…Recently, in fact, it has become clear that ML and deep learning (DL) techniques can be interpreted and explained (Gharari et al, 2021); thus, they can be used as a tool for understanding (the process) (Arrieta et al, 2020) or model parameter learning (Tsai et al, 2021), instead of primarily for predictive purposes. In any case, ML growth has been mostly driven by a large variety of problems, for instance, computer vision applications, and speech and natural language processing in a way that has to be harmonized with the practices of more traditional ways of conceiving hydrological models.…”

“…That is to say, promoting diversity should be accompanied by the sharing of standards for the parts. The way to do it has been traced, for instance, in Knoben et al (2021), who argued that the whole models informatics can be separated into model-agnostic parts, which potentially can be shared, and model-specific parts, which could be differentiated among the various developers or research groups.…”

“…At present, both in research and in applications, the action of matching data and models is done offline by the researchers, but the DARTH vision would require that these data be automatically ingested and processed. Therefore, DARTHs should take care of these aspects by design and be able to abstract data from the algorithms, as also explained in Knoben et al (2021).…”

“…Traditionally engineered models tend to be "applications", meaning that they bundle together all of the features that are required to have an all-round modeling experience, but these are exactly the opposite of what is needed by DARTHs, where everything must be provided as a service and loosely tied. Knoben et al (2021) give a further clear description of MaaA. Most current models fall into this category.…”