How We Build Our Data
GDD methodology, data sources, and the confidence tiers behind every threshold on this site.
What are Growing Degree Days?
Growing degree days (GDD) measure the accumulated heat that drives plant and insect development. Instead of calendar dates, which shift year to year with weather, GDD gives a reliable way to predict when a plant will bloom, when a pest will emerge, or when a disease risk window opens.
Each day, we calculate how much the average temperature exceeds a base threshold. Those daily values accumulate from January 1. When the total reaches a species-specific threshold, that phenological event is expected. A lilac that blooms at 450 GDD₃₂ will bloom at 450 GDD₃₂ whether that falls in early April (warm year) or late April (cool year).
Why base 32°F?
HortGuide uses GDD base 32°F (GDD₃₂) as the standard for all display. Base 32 captures the slow heat accumulation during Puget Sound's mild winter months, when many woody plants are already responding to temperature. The traditional base 50°F misses this early-season signal. For full details, see our GDD explainer guide.
How We Derive GDD Thresholds
Our thresholds come from three complementary data streams, each with different strengths:
Multi-year Kent calibration
For each species with a known bloom date in this region, we look up the cumulative GDD₃₂ on that date across six years of Kent, WA weather data (2020-2025). This gives a local median, range, and variance. It's our most directly relevant data, but depends on accurate bloom-date estimates for each species.
NPN citizen science observations
The USA National Phenology Network collects phenological observations from trained volunteers across the country. We filter for Washington and Oregon observations, match them to our weather station network, and compute station-local GDD₃₂ thresholds. NPN data provides ground-truth validation: real people recording real bloom dates at real locations.
Published research
For pests and diseases especially, we draw on published GDD models from university extension programs. Key sources include Herms 2004 (OSU, Secrest Arboretum, Ohio), the UMD IPMnet Pest Predictive Calendar (Gill & Klick, mid-Atlantic), and PNW Handbook disease models. These are expert-calibrated but often from different climates, so we convert them to GDD₃₂ and flag the regional gap.
When multiple sources exist for the same species, we cross-validate. If NPN observations for a species diverge significantly from the Kent calibration, we flag it, because that divergence usually means our bloom-date estimate needs adjustment rather than that the GDD model is wrong.
Confidence Tiers
Every GDD threshold on this site carries a confidence tier. This is our honest assessment of how much you should trust the number for timing decisions in the Puget Sound lowlands.
Multi-year Kent calibration validated by NPN observations (within 20% divergence). This threshold has been checked against real bloom observations in Western Washington. Timing should be reliable within the range shown.
Multi-year Kent calibration based on 6 years of weather data, but without independent NPN validation. The threshold is derived from regional bloom-date estimates and local weather, but hasn't been confirmed against ground-truth observations. Use with moderate confidence; the range may be wider than shown.
Kent calibration with significant divergence from NPN observations (over 50%), or threshold converted from out-of-region research without local validation. Treat as a rough guide. Actual timing in your area may differ substantially.
Currently 103 of 146 plants with GDD data have been assigned confidence tiers. Tiers are recalculated annually as new field observations and weather data accumulate.
The 7-Station Weather Network
Phenology varies across the Puget Sound lowlands. Seattle's urban heat island runs warmer than Kent's Green River valley. Bellingham is 2-3 weeks behind on GDD accumulation. Sequim, in the Olympic rain shadow, has an entirely different moisture regime.
To capture these differences, we track weather data from 7 stations spanning the region: Kent (primary reference), Seattle, Tacoma, Olympia, Bellingham, Sequim, and Issaquah. Each station has daily records from 2020 to the present, fetched from the Open-Meteo historical and forecast APIs. The comparison between stations IS the product: it's what lets you adjust timing recommendations to your specific location.
For plant species with NPN observations near multiple stations, we compute station-local GDD₃₂ thresholds by cross-referencing observation dates against each station's own weather history. This means the bloom threshold for red flowering currant might be different for Bellingham than for Kent, reflecting real microclimate differences rather than a one-size-fits-all number.
Per-station thresholds
We currently have per-station GDD₃₂ thresholds for 91 plant species across the network, computed from over 4,500 matched NPN observations. Of those, 28 species have enough coverage (observations at 3 or more stations) to support direct cross-station comparison on the weather dashboard.
The method: for each NPN phenological observation in Washington and Oregon, we identify the nearest weather station (within ~50 km), look up that station's cumulative GDD₃₂ on the observation date from its own weather CSV, and record it. After IQR filtering to remove outliers, we compute the median GDD₃₂ per species, per station, per phenophase. The result is a threshold tuned to each station's actual climate, not a Kent number applied everywhere.
Typical station offsets from Kent (as of March 2026): Seattle runs about +134 GDD₃₂ ahead (urban heat island), Issaquah +55 (warmer east-side foothills), Tacoma +80, and Olympia +76 (both south Puget Sound marine influence). Bellingham lags by about -380 GDD₃₂ (1.4 degrees latitude north), and Sequim by -508 (rain shadow with cold clear nights). These offsets shift your timing by days to weeks depending on the season.
How Field Observations Improve the Data
Every phenological observation recorded on this site feeds back into the data. When a first bloom is logged with a photo, the date and location are used to look up the exact GDD₃₂ at the nearest weather station. Over time, these observations build a ground-truth dataset that validates, corrects, or refines the thresholds derived from research literature.
A species might start as "Extrapolated" with a threshold borrowed from Ohio research. After a season of local observations, it could move to "Regional estimate." With multiple years of confirmed observations matching the threshold, it earns "Local estimate." The system is designed to get more accurate over time, not just more populated.
Known Limitations
GDD models are useful approximations, not perfect predictors. Several factors limit accuracy:
Microclimate variation means that even within a single zip code, south-facing slopes, urban heat islands, and cold air drainage pockets can shift timing by a week or more. Our station network captures regional differences but can't account for your specific site.
Photoperiod and chilling requirements interact with temperature in ways GDD alone doesn't capture. Some species need a minimum number of cold hours before responding to warmth. We note these cofactors where known, but many species haven't been studied in detail.
NPN citizen science data carries inherent observer variability. Observers may record events a few days after they actually occur, slightly inflating GDD values. We use IQR filtering to remove outliers, but some noise remains.
Data Sources & Attribution
Weather data
Open-Meteo historical and forecast APIs. Daily temperature, precipitation, soil temperature, and derived metrics for all 7 stations.
Phenological observations
Data were provided by the USA National Phenology Network and the many participants who contribute to its Nature's Notebook program. Licensed under CC BY 4.0.
Plant phenology research
Herms, D.A. 2004. "Using degree-days and plant phenology to predict pest activity." Ohio State University, Secrest Arboretum. IPM Tactics and Guidelines. Primary source for GDD₅₀ to phenological event mapping.
Pest GDD thresholds
Gill, S. & Klick, S. UMD IPMnet Pest Predictive Calendar. University of Maryland Extension. Base 50°F pest emergence thresholds, converted to GDD₃₂ via piecewise interpolation calibrated against Kent accumulation curves.
Regional extension data
WSU HortSense fact sheets, PNW Pest Management Handbooks, and OSU Landscape Plants database provide host-pest-disease relationships, cultivar susceptibility, and regional pest phenology.
Questions about our methodology? Get in touch. See current weather data or browse plant profiles to see confidence tiers in action.