On the Architecture of Integration in Hydrological Modelling — Orchestrating Reproducible, Scalable, and Transparent Workflows with SYMFLUENCE

Key challenges in hydrologic modelling include characterizing and propagating uncertainty, diagnosing model sensitivity, and conducting robust hypothesis testing. Traditionally, hydrologic model workflows rely on fragmented and bespoke scripts that obscure key sources of uncertainty, confound model performance evaluation, and impede reproducibility. We present SYMFLUENCE, an open-source framework that operationalizes end-to-end hydrological simulations, from conceptualisation, through model compilation and data processing, to calibration and visualisation, by integrating models, data, and computation into a coherent, reproducible system architecture. 

SYMFLUENCE provides a modular pipeline spanning the full hydrological modelling lifecycle: domain definition and watershed delineation, sub-basin and hydrological response unit (HRU) discretization, multi-source data acquisition and preprocessing, model input preparation, model instantiation, parameter estimation, multi-objective evaluation, and visualization. Each stage offers interchangeable components—users can select among delineation tools (TauDEM, pysheds) and existing geofabric producs such as, MERIT-Basins and TDX Hydro, forcing datasets (e.g., ERA5, CERRA, CARRA,, AORC, etc.), discretization schemes, model structures, calibration algorithms, evaluation metrics, etc. while the framework manages technical execution. This separation of concerns allows researchers to express scientific choices (e.g., spatial resolution, process representation, objective functions) independently from their computational implementation, reducing the cognitive burden of workflow orchestration and enabling systematic comparison of modelling decisions. 

At its core, SYMFLUENCE employs a declarative YAML specification to define entire modelling experiments—including parameter bounds, sampling strategies, objective functions, and multi-criteria evaluation metrics. This design directly addresses reproducibility challenges by ensuring that modelling decisions are consistent and transparent across computing environments. The framework supports model-agnostic ensemble generation for uncertainty quantification and model optimisation across diverse model structures and model ecosystems (SUMMA, FUSE, GR4J, HYPE, NextGen, and others), automated provenance capture, and efficient parallel execution for large-domain sensitivity experiments. 

We present applications spanning single-basin calibration to continental-scale ensemble analyses. These cases illustrate how SYMFLUENCE enables rigorous benchmarking of model performance by holding computational infrastructure constant, allowing structural uncertainty to be isolated from implementation artifacts. By bridging technical infrastructure and scientific inference, SYMFLUENCE enables systematic and transparent exploration of alternative modelling options.