Ramirez et al. (2026) HydroBlox: AI-assisted visual programming framework for enhanced scientific reproducibility in hydrology

Identification

• Journal: Environmental Modelling & Software
• Year: 2026
• Date: 2026-07-01
• Authors: Carlos Erazo Ramirez, İbrahim Demir
• DOI: 10.1016/j.envsoft.2026.107098

Research Groups

River-Coastal Science and Engineering, Tulane University, United States
ByWater Institute, Tulane University, United States

Short Summary

This study introduces HydroBlox, a client-side browser-based framework that supports the creation, execution, and export of hydrological workflows using a visual programming interface. The platform enables reproducible, portable, and extensible hydrological analyses across various spatial and temporal scales.

Objective

• Investigate the development of a web-based framework for enhancing scientific reproducibility in hydrology through visual programming interfaces.

Study Configuration

• Spatial Scale: Various (e.g., Iowa River Basin)
• Temporal Scale: Short-term to long-term (e.g., precipitation-streamflow response analysis)

Methodology and Data

• Models used: None specified, but utilizes modular web libraries for data retrieval, statistical analysis, and visualization.
• Data sources: Not explicitly stated, but likely includes satellite, observation, and reanalysis data.

Main Results

• HydroBlox enables the creation, execution, and export of hydrological workflows using a visual programming interface.
• The platform facilitates reproducible, portable, and extensible hydrological analyses across various spatial and temporal scales.
• Two case studies demonstrate the system's capacity to facilitate collaborative research, education, and low-code scientific computing in hydrology.

Contributions

• Original value lies in providing a browser-native, modular tool for visual workflow development and execution within a decentralized, client-side context.
• Enhances scientific reproducibility in hydrology by reducing barriers to entry through user-friendly interfaces and open-source tooling.

Funding

• Not explicitly stated.

Citation

@article{Ramirez2026HydroBlox,
  author = {Ramirez, Carlos Erazo and Demir, İbrahim},
  title = {HydroBlox: AI-assisted visual programming framework for enhanced scientific reproducibility in hydrology},
  journal = {Environmental Modelling & Software},
  year = {2026},
  doi = {10.1016/j.envsoft.2026.107098},
  url = {https://doi.org/10.1016/j.envsoft.2026.107098}
}