AGU Fall Meeting 2024 poster - Digital eaRth Twins Hydrology Systems

The "Digital Earth" (DE) metaphor is highly beneficial for both end users and hydrological modelers. In this contribution, we analyze different categories of models with the aim of incorporating them into Digital eARth Twin Hydrology systems (DARTHs). We emphasize that DARTHs are not merely models; they are a comprehensive infrastructure that hosts specific types of models and provides essential services for connecting to input data. We advocate for a modeling-by-component strategy to meet the requirements of the DE. We envision four technological steps to advance from the current state of modeling.

1. Decomposition of Models: Models are broken down into interacting modules, with agnostic parts handling inputs and outputs separated from model-specific parts containing algorithms.
2. Software Layers Addition: Appropriate software layers are added to enable transparent model execution in the cloud, independent of hardware and operating systems, without human intervention.
3. Cloud Execution:
4.  Interchangeability of Models: Models can be selected as interchangeable without providing deceptive answers. This includes the use of hypothesis testing, error estimation, literate programming, and guidelines for clean, informative code.

We argue for the urgency of making DARTHs open source in alignment with the open-science movement. DARTHs should promote a new participatory approach to hydrological science, allowing researchers to cooperatively contribute to characterizing and controlling model outcomes in various territories.
Finally, we discuss three enabling technologies within the context of DARTHs: Earth observations (EOs), high-performance computing (HPC), and machine learning (ML). We explore how these technologies can be integrated into the overall system to enhance scientific research and generate knowledge.

By clicking here please find the pdf of the poster.  Please find here the slides used in videos.