Pith Number

pith:DN64N7HP

pith:2026:DN64N7HP7EMSXFRHIYV2AFGXWC

not attested not anchored not stored refs resolved

Probabilistic Seasonal Streamflow Forecasting Across California's Sierra Nevada Watersheds with Agentic AI

Ignacio Lopez-Gomez, Michael P. Brenner, Tapio Schneider

An agentic AI workflow produces seasonal runoff forecasts that reduce watershed-averaged quantile error by up to 29% versus California's operational predictions.

arxiv:2605.16178 v1 · 2026-05-15 · physics.ao-ph

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\pithnumber{DN64N7HP7EMSXFRHIYV2AFGXWC}

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4 Citations open

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Claims

C1strongest claim

the agent-evolved model achieves superior skill for early-season cumulative April-July runoff predictions, reducing watershed-averaged quantile forecast error by up to 29%

C2weakest assumption

The Monte Carlo Tree Search over code space combined with the LLM agent can explore model architectures and features in a way that produces forecasts that generalize to future years whose hydroclimatic statistics differ from the 2021-2025 evaluation window due to ongoing climate shifts.

C3one line summary

An agentic AI workflow evolves an adaptive XGBoost quantile regression ensemble that reduces watershed-averaged forecast error by up to 29% versus California's operational forecasts for April-July runoff at 1-6 month leads across 23 Sierra Nevada sites.

References

66 extracted · 66 resolved · 4 Pith anchors

[1] Pagano, T. C. and Hartmann, H. C. and Sorooshian, S. , title =. Climate Research , year =

[3] and Lettenmaier, Dennis P

[4] and Cayan, Daniel R

[5] 2026 , howpublished = 2026

[6] and Lehner, Flavio and Ikeda, Kyoko and Clark, Martyn P

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:01:56.361323Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1b7dc6fceff9192b9627462ba014d7b0a36f2612a99fbaa705fda0896443dc22

Aliases

arxiv: 2605.16178 · arxiv_version: 2605.16178v1 · doi: 10.48550/arxiv.2605.16178 · pith_short_12: DN64N7HP7EMS · pith_short_16: DN64N7HP7EMSXFRH · pith_short_8: DN64N7HP

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DN64N7HP7EMSXFRHIYV2AFGXWC \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1b7dc6fceff9192b9627462ba014d7b0a36f2612a99fbaa705fda0896443dc22

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "94f36c2cb2771993f43ee8ce2011eae69ba24aebffbabaf0b57297e2ba304491",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.ao-ph",
    "submitted_at": "2026-05-15T16:59:29Z",
    "title_canon_sha256": "115869889a0e46b7f5106056b0dbf9911a9eecb2fc3bad094f2536eef15e3eec"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16178",
    "kind": "arxiv",
    "version": 1
  }
}