AI enhanced environmental modelling workflows: Towards Automated Scientific Exploration in Hydrology

AI enhanced environmental modelling workflows: Towards Automated Scientific Exploration in Hydrology

Authors: Darri Eythorsson, Kasra Keshavarz, Cyril Thébault, Mohamed Ismaiel Ahmed, Raymond Spiteri, Alain Pietroniro and Martyn Clark

Modern hydrological modeling has evolved into a complex scientific endeavour requiring sophisticated workflows that span multiple scales, processes, and computational paradigms. While existing workflow solutions address specific technical challenges, the field lacks comprehensive frameworks that can support end-to-end modeling while maintaining reproducibility and scalability. This works introduces two complementary frameworks that aim to address these fundamental challenges: CONFLUENCE (Community Optimization Nexus For Large-domain Understanding of Environmental Networks and Computational Exploration) and INDRA (the Intelligent Network for Dynamic River Analysis).

CONFLUENCE implements a modular architecture that enforces workflow reproducibility through a unified configuration system while maintaining the flexibility needed to support diverse modeling applications. The framework provides comprehensive solutions for four critical workflow components: (1) flexible geospatial domain definition and discretization, (2) model-agnostic data acquisition and preprocessing, (3) extensible model setup and parameterization capabilities, and (4) comprehensive evaluation and optimization tools. This systematic approach enables efficient, reproducible, and transparent hydrological modeling across scales.

INDRA augments this foundation by implementing a network of specialized AI expert agents that support various components of the hydrological modeling workflow. Through structured dialogue between domain experts (including AI specialists in hydrology, hydrogeology, meteorology, data science, and geospatial analysis), INDRA provides context-aware guidance while maintaining complete provenance of modeling decisions and their justification. This AI-assisted approach helps address three critical challenges: (1) the growing complexity of modelling decisions, (2) the need for reproducible workflows and detailed documentation, and (3) the technical barriers limiting broader adoption of advanced modeling practices.

The integration of these frameworks aims to explore how automation and AI assistance can enhance rather than disrupt traditional modeling practices. By maintaining clear documentation of decisions and their justifications, these systems help build trust in model results while creating opportunities for recursive learning from previous modeling experiments. Our case studies, spanning scales from individual catchments to continental domains, showcase the frameworks' capabilities while highlighting their potential to transform how researchers’ interface with complex environmental modeling workflows.

This work aims to advance both operational and research oriented hydrological modeling practices, offering a foundation for reproducible, scalable, and interoperable modeling while maintaining scientific rigor and flexibility. The framework’s open-source nature and modular design create opportunities for community-driven development and extension, potentially accelerating scientific discovery in hydrological sciences.