HydroBlox: AI-assisted visual programming framework for enhanced scientific reproducibility in hydrology

Scientific workflow reproducibility for hydrological and environmental analyses remains a challenge due to the heterogeneity of data sources, analysis protocols, and evolving visualization needs. 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 integrates modular web libraries to perform data retrieval, statistical analysis, and visualization directly in the browser. Two case studies are presented to demonstrate the platform: precipitation–streamflow response analysis in the Iowa River Basin and SPI-based drought characterization using a workflow that produces ensemble drought scenarios. Results demonstrate the system’s capacity to facilitate reproducible, portable, and extensible hydrological analyses across a range of spatial and temporal scales. The study discusses the architecture, implementation, and capabilities of the system and explores its implications for collaborative research, education, and low-code scientific computing in hydrology.