Datasets:

DamiOresotu
/

hydrocause-knowledge-corpus

license: cc-by-4.0
language:
  - en
tags:
  - hydrology
  - flood-forecasting
  - retrieval-augmented-generation
  - knowledge-base
  - causal-reasoning
  - northumbria
  - defra
  - environment-agency
pretty_name: HydroCause Knowledge Corpus
size_categories:
  - 1K<n<10K
task_categories:
  - text-generation
  - question-answering
  - feature-extraction
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*

HydroCause Knowledge Corpus

The domain knowledge base released alongside the HydroCause-RAG framework. It is the "raw knowledge source" used by every downstream component:

The retrieval layer (Part 2-3) grounds numerical claims against it.
The P7 audit gate (Part 3 §3.4, SGS Audit) scores semantic reasoning against it.
The teacher-LLM synthesis (Part 5) drafts Q&A pairs from its passages.
The domain-tuned model HydroCause-3B-DK is fine-tuned on those Q&A pairs.

Composition

Sub-corpus	Passages	Source
policy	632	Northumberland LFRMS, DEFRA FCERM, EA guidance, Northumbria RBD FRMP
hydrology	4,147	Open-access papers (HESS/ESSD, CC BY 4.0) + curated seed summaries
metrics	12	NSE/KGE/SMAPE/MAE/RMSE/MBE/POD/FAR/CSI/F1/CFS/SGS
registry	24	HydroCause-RAG numerical truth registry (six catchments) with TimeSHAP-derived tau
total	4,815	90/10 stratified train / validation split

Intended uses

This corpus supports four categories of downstream tasks:

1. Operational hydrological forecasting

Grounding retrieval-augmented systems in verifiable flood-warning doctrine (WMO-1072, WMO IFM Tool 19).
Answering operator questions about lead time, threshold selection, ensemble spread, false-alarm tradeoffs.
Providing decision-support context for issued warnings.

2. Comparison of historical events to predicted

Hindcasting methodology and extreme-event extrapolation (Frame et al. 2022).
Return-period statistics, non-stationarity considerations, DEFRA climate-change allowances.
Historic UK flood chronology and post-event attribution.

3. Informed decision-making

Cost-loss analysis and warning-threshold calibration for a defined user community.
Statistical hydrology for return-level estimation (Helsel 2020).
UK Flood Forecasting Centre and Environment Agency operational practice.

4. General hydrological education

Catchment processes, unit hydrograph theory, flood routing, recession analysis.
Streamflow measurement (Rantz WSP 2175 vol 1) and discharge computation (Rantz vol 2, USGS TM 3-A8).
Machine-learning-in-hydrology foundations (Kratzert 2018/2024, Gauch 2021, Lees 2021, Coxon 2020).
HydroLLM-Benchmark-aligned Q&A synthesis for domain-adapted language models.

Fields

Each row has:

passage_id (str) — SHA-1 fingerprint
source_label (str) — machine-readable source identifier
source_url (str) — public URL if scraped, else notebook_bundled / hydrocause_registry
chunk_index (int) — index within the source document
subcorpus (str) — policy / hydrology / metrics / registry
text (str) — the passage
n_words (int) — word count
split (str) — train or validation

Loading

from datasets import load_dataset
ds = load_dataset("DamiOresotu/hydrocause-knowledge-corpus")
print(ds)

Sources and licences

Policy sub-corpus (UK Open Government Licence v3.0)

Northumberland County Council: Local Flood Risk Management Strategy — Action Plan.
DEFRA: Flood and Coastal Erosion Risk Management Policy Statement (2020); National FCERM Strategy for England.
DEFRA / Environment Agency: River Basin Management Planning Ministerial Guidance.
Environment Agency: Northumbria River Basin District Flood Risk Management Plan 2021-2027.

Hydrology sub-corpus — peer-reviewed research (Copernicus, CC BY 4.0)

Kratzert, F., Klotz, D., Brenner, C., Schulz, K., Herrnegger, M. (2018). Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrology and Earth System Sciences 22:6005-6022. DOI: 10.5194/hess-22-6005-2018.
Kratzert, F., Gauch, M., Klotz, D., Nearing, G. (2024). HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin. HESS 28:4187. DOI: 10.5194/hess-28-4187-2024.
Gauch, M., Kratzert, F., Klotz, D., Nearing, G., Lin, J., Hochreiter, S. (2021). Rainfall-runoff prediction at multiple timescales with a single Long Short-Term Memory network. HESS 25:2045-2062. DOI: 10.5194/hess-25-2045-2021.
Coxon, G., Addor, N., Bloomfield, J.P., Freer, J., Fry, M., Hannaford, J., et al. (2020). CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain. Earth System Science Data 12:2459-2483. DOI: 10.5194/essd-12-2459-2020.
Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., Dadson, S.J. (2021). Benchmarking data-driven rainfall-runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models. HESS 25:5517-5534. DOI: 10.5194/hess-25-5517-2021.

Hydrology sub-corpus — educational (public domain / CC BY 4.0)

Kizilkaya, D., Sajja, R., Sermet, Y., Demir, I. (2025). Towards HydroLLM: A Benchmark Dataset for Hydrology-Specific Knowledge Assessment for Large Language Models. Environmental Data Science (Cambridge). DOI: 10.1017/eds.2025.10006. CC BY 4.0.
Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., Gilroy, E.J. (2020). Statistical Methods in Water Resources. U.S. Geological Survey Techniques and Methods 4-A3, 458 p. DOI: 10.3133/tm4a3. U.S. Government public domain (17 USC §105).
Turnipseed, D.P., Sauer, V.B. (2010). Discharge Measurements at Gaging Stations. USGS Techniques and Methods 3-A8, 87 p. Public domain.
Corson-Dosch, H.R. et al. (2023). The Water Cycle. USGS General Information Product 221. DOI: 10.3133/gip221. Public domain.
Rantz, S.E. et al. (1982). Measurement and Computation of Streamflow, Volume 1: Measurement of Stage and Discharge. USGS Water-Supply Paper 2175, 284 p. DOI: 10.3133/wsp2175. Public domain.
Rantz, S.E. et al. (1982). Measurement and Computation of Streamflow, Volume 2: Computation of Discharge. USGS Water-Supply Paper 2175, 347 p. DOI: 10.3133/wsp2175. Public domain.

Hydrology sub-corpus — operational forecasting doctrine (WMO)

World Meteorological Organization (2011). Manual on Flood Forecasting and Warning. WMO-No. 1072, 97 p. ISBN 978-92-63-11072-5.
WMO / Global Water Partnership Associated Programme on Flood Management (2013). Flood Forecasting and Early Warning. Integrated Flood Management Tools Series No. 19.

Hydrology sub-corpus — extreme-event research

Frame, J.M., Kratzert, F., Klotz, D., Gauch, M., Shalev, G., Gilon, O., Qualls, L.M., Gupta, H.V., Nearing, G.S. (2022). Deep learning rainfall-runoff predictions of extreme events. HESS 26:3377-3392. DOI: 10.5194/hess-26-3377-2022.

Metric definitions and seed summaries

Metric definitions (NSE, KGE, SMAPE, MAE, RMSE, MBE, POD, FAR, CSI, F1, CFS, SGS) and concept seed summaries are original text composed for this release.

Registry-anchored passages

Derived from the HydroCause-RAG numerical truth registry produced by Parts 1.7-1.9 of the source notebook. Six Northumberland catchments (Acomb Gardenhouse, Acomb Millersfield, Haltwhistle, Riding Mill, Stocksfield, Morpeth) with per-catchment CFS, forecast metrics, and TimeSHAP-derived tau attributions.

The corpus (this compilation) is released under CC BY 4.0. Original source documents retain their original licences.

Methodological note

The hydrology sub-corpus deliberately combines peer-reviewed research articles with educational textbook-style material and the HydroLLM-Benchmark reference paper. This mirrors the methodology of Kizilkaya et al. (2025), who built HydroLLM-Benchmark from research articles and hydrology textbooks, ensuring that downstream Q&A synthesis (Part 5) and retrieval (Parts 2-3) can be defended against reviewer challenges about educational versus research grounding.

Citation

@article{oresotu2026hydrocauserag,
  title  = {HydroCause-RAG: A Causally Audited Retrieval-Augmented
            Framework for Explainable Time-Series Forecasting with
            Foundation-Model Portability and Local-Model Distillation},
  author = {Oresotu, Olanrewaju and Li, Yulei and Woo, Wai Lok},
  journal= {Knowledge-Based Systems},
  year   = {2026}
}