Protection is priced against a number you have to build first.
Most water-risk business cases die in committee because the exposure figure was never built properly. Here is the method, the line items, and the mistakes that make a real exposure look like background noise.
Treatable volume, depth, exchange with adjacent water, organic load and the length of your exposed window each move the cost of delivery by a large factor. So the useful work happens on your side first, and it happens before anybody quotes anything.
What follows is the exercise we ask operators to run before a site read, because it converts a conversation about technology into a conversation about numbers. It takes a competent analyst about a fortnight with access to maintenance records and revenue data. It does not require us.
Six steps
- Define the window, not the year Mark the weeks in which your exposure actually sits. For most coastal assets that is a run of weeks inside one season; for some it is a single fortnight. Annualising a seasonal risk is the single most common way to make a real exposure look like background noise.
- Count the revenue inside the window Not annual revenue divided by fifty-two. The revenue that occurs in those specific weeks, which for a resort, a marina or a fishery is usually a wildly disproportionate share of the year.
- List the failure modes with their own frequencies Throttled intake, mortality event, closed frontage, membrane replacement brought forward, notification to a regulator. Each gets a probability drawn from your own last ten years, not from a supplier's slide.
- Apply a multiplier to the direct loss, and justify it Direct losses undercount. Published event studies consistently show total regional income loss several times the direct figure. Pick your multiplier from a documented comparable, write down which one, and let the committee argue with the source rather than with you.
- Subtract what you already spend Additional dosing, extra cleaning cycles, contingency crew, monitoring you added after the last event, the insurance loading you are now carrying. This is money already leaving the business because of water, and it belongs on the same page.
- Compare against held capacity, not against a call-out The relevant comparison is the annualised cost of holding treatment capacity across your window against the expected cost of the window failing. Comparing against an emergency response quotation flatters the wrong option.
The lines, and where each number lives
Every figure below already exists somewhere in your business. The work is retrieval and honesty, not modelling.
| Line | Where the number lives | The mistake that weakens it |
|---|---|---|
| Lost production | Production against nameplate, hour by hour, for the affected weeks in each of the last ten years | Using average annual availability, which buries a two-week collapse inside a good year |
| Energy penalty | Specific energy consumption during affected periods against clean-water baseline | Treating it as a tariff issue because it appeared on an energy invoice |
| Consumables and asset life | Membrane, element and media replacement dates against design life; cleaning-in-place frequency | Booking early replacement to routine maintenance, which erases the cause permanently |
| Stock loss | Mortality records with the dissolved-oxygen and temperature trace alongside them | Attributing the whole event to disease when the oxygen trace shows what preceded it |
| Closure and cancellation | Occupancy, cancellations, discounting and forward bookings for every season after each event until they return to trend | Stopping at the closure week and ignoring the soft demand trailing behind it |
| Compliance and disclosure | Notifications made, consent variations sought, legal and consultancy hours, insurance loading at renewal | Excluding it because it is not operational, when it is the line that compounds fastest |
| Mitigation already running | Additional dosing, contingency crew, added monitoring, standby arrangements introduced after prior events | Leaving it out, which understates both the exposure and the value of removing it |
Build these from your own records. A published loss figure tests whether your number is plausible; it does not stand in for it.
Sanity checks
What the published record says about order of magnitude.
Analysis of the global Harmful Algae Event Database covering 2000 to 2020 attributes more than US$8 billion of economic loss to fish-killing blooms alone, with the largest documented impacts in China. That is one stressor, one loss type, across two decades.
At the single-asset end there is no published benchmark worth quoting. What circulates as a desalination downtime cost traces back to industry commentary rather than to an operator, a regulator or a reviewed study. Your own throughput, tariff and replacement-water cost will give you a better number in an afternoon than any borrowed figure.
The events on our stressor breakdown give the sector-specific comparables: aquaculture, shellfish, desalination and tourism each fail differently and at different scales.
Two patterns turn up repeatedly in the situation reports Alarivean receives, and neither has a published statistic behind it, so no figure for either is quoted here. Operators who stop annualising land on an exposed window measured in weeks rather than in years. And a guest-facing closure leaves forward bookings soft for seasons after the water itself has cleared. Both belong in your model. Neither belongs in anybody's marketing.
Two ways to buy
Per incident, or per season.
An incident-priced service earns more when your water behaves worse. That is what the contract structure rewards, and structures outlast the people who signed them.
Held capacity inverts it. Vessels are committed to a defined zone for a defined window, with gases, crew, instrumentation and reporting inside the subscription. Proactive work in adjacent water — intercepting a bloom or a deficit forming next door before it becomes yours — sits inside the same commitment rather than being billed as a separate emergency.
For a finance function the practical difference is that one is a variable cost correlated with your worst outcomes and the other is a fixed cost that reduces them. Those go in different places in a plan, and they are treated differently by an insurer.
The step that decides everything is the calibration phase: a bounded demonstration area on your own water, with viability parameters agreed before it starts, so that the result is a measurement rather than a negotiation.
The rules our own figures follow
A figure for your water arrives after the site read. Another operator's published loss is a sanity check on order of magnitude and never an input to your case. Pond-scale and ballast-tank-scale validations are cited as what they are — evidence about the approach, and no open-water result has been published for it by anyone. And any percentage improvement in dissolved oxygen, bloom biomass or availability waits for a calibration phase to produce it.
Where the exercise above produces a number too small to justify the intervention, that is the answer that comes back.
The two documents behind the figures above
- Reviews in Aquaculture — analysis of the Harmful Algae Event Database, 2000–2020, 2024.
- Alarivean, Inc. — resilience programmes, controls and data services.
Asked by finance, not by engineering
Four commercial questions
What decides the price, and when do we get one?
Treatable volume, depth, exchange with adjacent water, organic load, window length, and distance from existing regional capacity. Each of those moves the answer materially and none of them can be inferred from a web form.
The figure for your asset comes after the site read, in writing, with its assumptions printed underneath. Quoted before anyone has read that water, it would be a guess wearing a decimal point.
Our exposure is one week a year. Is this relevant to us?
Possibly not. A single short window with modest losses is usually better covered by contingency and redundancy than by held capacity.
The method on this page is as useful for reaching that conclusion as for reaching the other one, and reaching it early costs nothing.
Can we build the case on published loss figures from comparable operators?
Use them to test the order of magnitude of your own number. Do not use them as the number.
Published event losses are aggregated across regions, sectors and methodologies you cannot inspect. A case built on your own maintenance records and revenue data will survive scrutiny; one built on somebody else's headline will not.
What happens if calibration shows the treatment does not work here?
The agreed parameters say so and the programme stops. That is the purpose of a calibration phase, and it is far cheaper to find out inside a bounded demonstration area than inside a service contract.
Agreeing the exit before the entry is not pessimism. It is the only version of this that a procurement team should accept.
When the number is built
What comes back from a site read.
The asset, the failure mode, the exposed weeks and the unit cost of an hour down. Alarivean returns a technical read on treatable volume and realistic effect, including the cases where the arithmetic does not support doing anything.