Open Research Data "HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows"
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HydroAgent — Open Research Data Package Companion data, intermediate results, figures, and reproduction scripts (Python) for the manuscript: HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows Submitted to AGU Advances, 2026.05. Distributed under FAIR principles: Findable (Zenodo DOI), Accessible (open formats), Interoperable (published schemas), Reusable (CC-BY-4.0 / MIT). What is in this package This package contains research artefacts only — everything needed to inspect, audit, and reproduce the figures and tables in the paper. The HydroAgent codebase itself (skills, contracts, adapters, calibration engine) is not included in this data package. hydro_agent_data_open/ ├── 00_schemas/ Pydantic-exported JSON Schemas for every artefact ├── 01_input_data/ Basin profile · 113 historical cases (1995–2019) · 14 validation events (2020–2024) ├── 02_step0_scheme/ XAJ calibrated parameter sets (5-type and global) + calibration metrics ├── 03_benchmark_results/ 5-LLM × 5-fold CV · stability (n=10) · Step1→Step2 chain · workflow case · cost table · canonical 2024 hydrographs ├── 04_figures/ Paper figures (Fig 1–5) and SI figures (S1–S4) as PDFs ├── 05_scripts/ Visualization (Python, with pinned dependencies) ├── 06_tables/ Supplementary tables as analyzable CSVs ├── MANIFEST.md File-level inventory with provenance pointers ├── CITATION.cff Machine-readable citation ├── metadata.yml Zenodo deposition metadata ├── LICENSE-DATA.txt Creative Commons Attribution 4.0 (CC-BY-4.0) └── LICENSE-CODE.txt MIT License See [MANIFEST.md](MANIFEST.md) for the full file-level inventory. Reproducing the paper figures # 1. Set up environment # Run these commands from the hydro_agent_data_open folder. pwd cd /path/to/hydro_agent_data_open #conda create -n hydro_agent_data_open python=3.10.11 #conda activate hydro_agent_data_open pip install -r 05_scripts/requirements.txt # 2. Reproduce each figure (output → 04_figures/) python 05_scripts/visualization/fig_step1_combined.py # Fig 3 python 05_scripts/visualization/EventsHydrograph/plot_selected_event_hydrographs.py # Fig 4 python 05_scripts/visualization/Canonical2024Hydrographs/plot_canonical_2024_hydrographs.py # Fig S1 python 05_scripts/visualization/LLMs_kfold_performance_costs.py # Fig 5 python 05_scripts/visualization/Step0Comparison/plot_paired_dumbbell.py # Fig S2 python 05_scripts/visualization/fig_step1_stability.py # Fig S3 python 05_scripts/visualization/fig_step1_part_2.py --llms deepseek chatgpt-5.4 gemini qwen-3.6-plus claude-4.6 # Fig S4 Each script reads from 03_benchmark_results/ and writes the corresponding PDF. The published versions of these PDFs are in 04_figures/ for byte-level comparison. Limits of reproducibility This package supports inspection of the released data, benchmark artefacts, tables, and figure-generation scripts. It does not include the HydroAgent codebase or live LLM execution environment, so it cannot fully regenerate LLM calls or benchmark production runs from scratch. LLM outputs are non-deterministic, and closed-source model versions may change over time. The References03_benchmark_results/step1_stability_gpt_n10/ directory is included to quantify this variability for the cited GPT-5.4 Step 1 runs. Data provenance Source This package Upstream Hourly hydrometeorological forcing (1995–2024) 01_input_data/basin_14194150/ CAMELSH dataset — Tran et al. (2025), Zenodo 16763144 Streamflow observations embedded in event JSONs USGS gauge 14194150, South Yamhill River at McMinnville, OR (waterdata.usgs.gov) 113 historical flood events + 14 validation events 01_input_data/historical_cases_113/, 01_input_data/validation_events_14/ Derived in this study (peak-over-threshold extraction) XAJ calibrated parameter sets 02_step0_scheme/ Calibrated in this study using DDS (Tolson & Shoemaker, 2007) K-fold CV, stability, Step1→Step2 benchmark results, and canonical 2024 hydrograph inputs 03_benchmark_results/ Generated in this study Citation If you use these data or scripts, please cite: This paper (preferred, supersedes the dataset DOI): HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows. AGU Advances, submitted in 2026. This dataset: Zenodo DOI 10.5281/zenodo.20304955 (assigned on upload — see CITATION.cff). Upstream CAMELSH dataset: Tran et al. (2025), Zenodo 10.5281/zenodo.16763144. Licenses Data (everything under 01_input_data/, 02_step0_scheme/, 03_benchmark_results/, 06_tables/): Creative Commons Attribution 4.0 International (CC-BY-4.0). Scripts (everything under 05_scripts/ and the helper scripts at this level): MIT License (MIT). Derived figures in 04_figures/ inherit the data license (CC-BY-4.0). Contact Corresponding author: Baoying Shan, baoying.shan@polimi.it Issues, errata, and reuse questions: contact the corresponding author. Version See [CHANGELOG.md](CHANGELOG.md). This README documents the initial release (v1.0) prepared alongside the AGU Advances submission.
