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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

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").mean()
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

Visualize

from synhydro.plotting import plot_ssi_timeseries

fig, ax = plot_ssi_timeseries(ssi, title=f"SSI-12 - {site}")

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

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).

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