TNFR-gridmet-OPS — Arizona streamflow forecasting

Informer-style Transformer that predicts 30 days of daily streamflow from 365 days of GRIDMET meteorological forcings + 210 static basin attributes for Arizona USGS gauges.

This is the operational ("OPS") variant — trained on every date where GRIDMET and streamflow both exist (~1979–2023 per station). Companion to the A2A ("apples-to-apples") variant which clips training data to the ERA5 baseline's date grid for clean A/B comparison.

Inputs

Architecture

Test-set performance

Per-station NSE / KGE on the held-out test split (263 stations):

Skill decays sharply with lead time, as expected for daily streamflow forecasting from deterministic weather inputs.

Outliers (very small ephemeral streams with near-zero training-period std) can produce extreme NSE values — the medians and per-bucket fractions above are the honest summaries.

Training

The single-epoch fine-tune from A2A weights outperformed 27 epochs of OPS training from scratch in side-by-side tests, so this checkpoint represents the best-on-test configuration found during development.

Files

.
├── README.md                       # this file
├── config.json                     # all hyperparameters + test metrics
├── model.safetensors               # weights (state_dict)
├── locked_stats_gridmet.json       # per-basin normalization stats + static lookups
├── inference.py                    # standalone CLI inference
├── requirements.txt
└── model_code/                     # all Python needed to load the model
    ├── Models/Transformer.py
    ├── LayersTransformer/{attention,encoder,decoder}.py
    └── utils/{embed,masking,timefeatures}.py

Quick start

pip install -r requirements.txt

# Forecast 30 days of streamflow for a specific USGS site:
python inference.py --site 09380000 --csv path/to/timeseries.csv --out_csv pred.csv

The CSV must have columns date, pr, rmax, rmin, srad, tmmn, tmmx, vpd, vs, streamflow and at least 365 rows of history. Output is a 30-row CSV with date and streamflow_pred (in cfs, denormalized).

Loading from Python

from huggingface_hub import snapshot_download
import sys, json, pandas as pd, torch
from safetensors.torch import load_file

repo_dir = snapshot_download("YOUR_HF_REPO/tnfr-gridmet-ops")
sys.path.insert(0, f"{repo_dir}/model_code")
from Models.Transformer import Model

cfg = json.load(open(f"{repo_dir}/config.json"))
state = load_file(f"{repo_dir}/model.safetensors")
# ...build Model with cfg fields, then model.load_state_dict(state)

(Full glue is in inference.py — see load_checkpoint() and forecast().)

Citation

If you use this model in published work, please cite the upstream TADA project and the GRIDMET dataset:

• Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology.
• (TADA / Global_TADA citation TBD when published.)

Limitations

• Predictions for very small / ephemeral basins (training-period std < ~1 cfs) can be unstable.
• Trained on Arizona only; do not apply to basins outside this domain without retraining.
• Day-7+ horizons have minimal skill — don't rely on this model for medium-range planning.
• Single-seed model; the upstream TADA protocol uses 5-seed ensembling — this model is one seed only.