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HydroPulse

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

HydroPulse detects, characterizes, and contextualizes hydroclimate anomalies using multi-source observational data.

Inputs: Hydrologic variables (snowpack, precipitation, and related indicators) drawn from multiple observational sources across space and time
Method: Constructs baselines tied to seasonal and regional regimes, then measures departures against them
Key distinction: Separates transient, weather-driven deviations from longer-term climate signals
Emphasis: Reproducible anomaly definitions that stay interpretable across different hydroclimatic contexts

Research at a glance

Code github.com/blueleaflabs/hydropulse

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