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CDT water outlook
When water is likely to be there on the Continental Divide Trail, source by source and week by week.
Trail-wide: when is water there?
Each cell is a 50-mile stretch of trail (left = Mexico border, right = Canada) in one month of a typical year. Values come from a model that adjusts for which year each observation comes from, so a wet year at one end of the trail doesn't masquerade as a season. Blank = not enough data.
One water source, by time of year
Varies a lot year to year. Some years this source runs dry and others it flows well, so treat the typical-year curve as a rough guide. The shaded range is wider to show that spread.
Typical year (model)Likely range (80%)
Quantity
Chance of dry
Water quality (where noted)
Sentiment
Road-crossing cache. Caches are not part of the water model above: they are stocked by trail angels, so there is no chance-of-dry curve. The bars show the typical number of gallons hikers report on hand in each month, from recent seasons only. Treat it as a rough guide, because a cache can be empty the day you get there.
Gallons typically reported on hand, by month
5+ gal2 to 5 galunder 2 galno bar = not enough reports that month
Wet and dry years
Trail-wide, how each year compared with an average year after accounting for season and source. Lines show the 95% range from resampling sources.
Quantity (points above or below an average year)
Chance of dry (an average source in an average season = 11%)
Hollow dot = limited data, so treat with caution. Green is better for hikers, orange is worse.
These are model estimates, not current conditions. Seasonal curves come from a regression with year as a fixed effect; each source's curve is pulled toward its 50-mile stretch and then the trail-wide curve, so sparse sources don't overfit, and the shaded band is an approximate 80% range. Hover a chart to read the model at any date. Quantity: 0 dry, 1 stagnant/pools, 2 trickle, 3 flowing, 4 abundant. Never rely on this as your only water information.
Generative AI was used to help write most of the code developed to create this front end tool.
Data were assembled, cleaned, processed, and verified directly before incorporating them into this tool, and were not generated by an LLM. This tool works differently from LLM-generated text. Results and calculations displayed by this tool are directly calculated using hard-coded methods and a fixed, underlying data set. As such, these results are not subject to hallucination, and remain repeatable.
Accuracy of displayed results depends on the reliability and accuracy of the underlying data and methods that are used.