HydroPulse

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How can hydroclimate anomalies be detected and classified by separating short-term weather variability from persistent climate shifts across hydrologic regimes?

HydroPulse focuses on detecting, characterizing, and contextualizing hydroclimate anomalies using multi-source observational data. The project aims to distinguish transient weather-driven deviations from longer-term climate signals by analyzing hydrologic variables across space and time. By constructing baselines tied to seasonal and regional regimes, HydroPulse evaluates departures in snowpack, precipitation, and related indicators to identify anomalous behavior. The emphasis is on reproducible anomaly definitions that remain interpretable across different hydroclimatic contexts.

Research at a Glance

Primary Methods

Time-series analysis; anomaly detection; regime classification; statistical baseline construction.

Data Sources

Hydrologic observations (e.g., snowpack, precipitation); gridded climate reanalysis products; station-based time series datasets.

Outputs

Anomaly detection metrics; regime-aware baselines; spatial and temporal anomaly maps; analytical notebooks and pipelines.

GitHub Repo

https://github.com/blueleaflabs/hydropulse

Publication URL

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