EXPOSED
The modal hydrologic technician splits time between muddy field work — wading streams for discharge measurements, servicing stage gauges and data loggers, pulling water quality samples — and screen work: rating-curve updates, QA of telemetered records, spreadsheet processing and report tables. The field half is genuinely hard to automate; the office half is exactly what AI and expanded sensor telemetry are eating, and continuous remote monitoring reduces the number of site visits needed per station. No license shields the role and clients never ask for a specific technician by name, so the surviving work is the person who can reach a remote gauge in a flood and diagnose why the sensor is lying.
Most of this decline happened after 2021 — it is not the pandemic dip.
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing.
BLS projection, 2024–2034
-2.1% 3,100 → 3,000 on the projections basis
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects -2.1% by 2034. This is the case where the score and the forecast agree, and it is the one worth taking seriously.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~400 openings a year on average, including replacing people who leave.
HydrographerHydro OperatorHydrologic AidHydrologic TechnicianHydrography TechnicianField Technician (Field Tech)Groundwater Monitoring TechnicianGIS Technician (Geographic Information System Technician)
Holding it up: embodiment . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Wading a stream with a Price AA meter or ADCP at high flow, re-installing a scoured gauge orifice line, and hand-tracing a shifted rating after a channel avulsion are not going to be done by software, but the record-review, hysteresis coding, hydrograph shifting and monthly data-table assembly that fill the winter office months are already semi-automated in AQUARIUS/Aquarius-Time-Series workflows, which is why this sits at 11 rather than the 15+ of a purely field trade.
Hands-on in uncontrolled environments Discharge measurements happen where the water is — cableways over flooding rivers, ice-cover measurements cutting holes on frozen channels, hip-boot wading in unstable gravel beds, driving 4WD to gauge houses on washed-out access roads — and the environment is uncontrolled by definition, since the highest-value measurements are the ones taken during the storm, not after it; 16 rather than 20 because a real share of the year is spent at a desk.
No licence, no signature requirement There is no state license to measure streamflow; USGS or contractor field training and a Water Data Quality certification or OSHA/swiftwater card are internal qualifications, and the published record carries the agency's or the supervising hydrologist's name, not the technician's — a 3 reflects that the only formal gate is employer-side.
Meaningful discretion Deciding whether a measurement is good enough to publish, whether to shift the rating or flag the record as estimated, and whether that abrupt stage drop is a channel change or a plugged intake are calls you make alone at the site under a standard that is written down but not mechanical; it stops at 8 because USGS/Techniques and Methods protocols and a reviewing hydrologist bound the discretion on both ends.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (11/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (3/20) is whether the law requires a licensed human to sign. Trust premium (5/20) is whether buyers specifically pay for a person. Judgment and accountability (8/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 16 of this occupation's 43 points (37%).
Embodiment (16/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
No occupation passed every test: close enough to hydrologic technicians on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 55/100, still EXPOSED.
Genuine two-tier split: if telemetry QA and spreadsheet report production are automated, what remains is sensor-fault diagnosis, ice-affected and backwater record estimation, and shifting-control rating analysis — the judgment tier USGS surface-water specialists do by hand. Task-mix shift alone, no new rule needed
If USGS and state cooperator programs keep tightening the requirement that discharge measurements used to update rating curves be made in person under high-flow conditions (current USGS policy already requires direct current-meter/ADCP measurements across the flow range, and index-velocity and image-velocimetry sites still need periodic field calibration), the irreducible field share rises as remote QA absorbs the desk half
If state engineering boards or water-rights courts require that discharge records used in adjudication, TMDL compliance, or FERC minimum-flow compliance be certified by a licensed PE or a credentialed hydrographer (an ASCE/AIH-style certification given legal weight, as some Western states already do for water-master measurements), the signature requirement attaches to the record
If flood-warning and dam-safety programs formally assign the call on whether a telemetered stage reading is trustworthy — the go/no-go on issuing a NWS flood forecast from a suspect gauge — to the on-call hydrographer rather than an automated flagging system, the role owns a consequential ambiguous decision
The limit. Trust premium has no realistic route: buyers are agencies and consultants purchasing a certified number, not a named person, and no procurement practice rewards technician identity. Total plausible ceiling is roughly the mid-50s; the field half protects the headcount that remains but the occupation is small and cooperator funding, not automation, sets its size.
| Orlando-Kissimmee-Sanford, FL | 110 | $64,750 +0% |
| Houston-Pasadena-The Woodlands, TX | 100 | — |
| Sacramento-Roseville-Folsom, CA | 60 | $70,510 +9% |
| Baltimore-Columbia-Towson, MD | 40 | $62,450 -4% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 40 | $64,570 +0% |
| Portland-Vancouver-Hillsboro, OR-WA | 40 | $82,360 +27% |
| Raleigh-Cary, NC | 40 | $70,750 +9% |
| Tampa-St. Petersburg-Clearwater, FL | 40 | $60,700 -6% |
| Portland-Vancouver-Hillsboro, OR-WA | 40 | $82,360 +27% |
| Santa Rosa-Petaluma, CA | 30 | $79,860 +23% |
| Raleigh-Cary, NC | 40 | $70,750 +9% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 43. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.