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Drought Analysis with SSI

SynHydro provides the Standardized Streamflow Index (SSI) for characterizing drought from streamflow data. This tutorial covers the SSI calculation and drought event extraction workflow.

Calculate SSI on observed data

SSI transforms raw flows into standardized anomalies by fitting a probability distribution within a rolling window. Values below -1 indicate drought; below -2 is extreme.

import synhydro

Q_daily = synhydro.load_example_data()
Q_monthly = Q_daily.resample("MS").sum()
site = Q_monthly.columns[0]

ssi_calc = synhydro.SSI(dist="gamma", timescale=12, fit_freq="ME")
ssi_calc.fit(Q_monthly[site])
ssi = ssi_calc.get_training_ssi()

print(f"SSI mean: {ssi.mean():.3f}")   # ~0
print(f"SSI std:  {ssi.std():.3f}")     # ~1

Extract drought events

get_drought_metrics identifies contiguous periods where SSI stays below -1:

metrics = synhydro.get_drought_metrics(ssi)
print(metrics[["start", "end", "duration", "severity", "avg_severity"]].head())

Each row is a drought event with its duration, severity (minimum SSI), and magnitude (cumulative deficit).

Visualize ensemble SSI

plot_ssi_timeseries computes SSI for each ensemble realization and overlays the observed SSI, making it easy to compare synthetic and historical drought behavior. Pass the ensemble (not a training SSI series) along with the observed flows.

from synhydro.plotting import plot_ssi_timeseries

gen = synhydro.KirschGenerator()
gen.fit(Q_monthly)
ensemble = gen.generate(n_realizations=20, n_years=20, seed=42)

fig, ax = plot_ssi_timeseries(
    ensemble,
    observed=Q_monthly[site],
    site=site,
    window=12,
    title=f"SSI-12 -- {site}",
)

SSI-12 timeseries with shaded drought-severity bands and ensemble percentile envelopes

Shaded zones mark moderate (-1 to -1.5), severe (-1.5 to -2), and extreme (< -2) drought conditions.

Choosing a distribution

Use compare_distributions to rank candidate distributions by AIC and Kolmogorov-Smirnov test: 

results = synhydro.compare_distributions(Q_monthly[site].dropna().values)
print(results)

Next steps

Previous: Monthly-to-Daily Pipeline | Next: Ensemble Validation