Slide Hazel FLOOD Frequently Asked Questions divining LAB

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Our founders draw on a combined forty years of domain expertise in climate adaptation and design of the built environment. We sell on-demand resilience analytics as a service to companies and communities seeking solutions to urban water threats. Our tools identify true physical vulnerability on all properties, enabling hi-precision understanding of risks—and a quicker path to solutions.

We don’t just estimate risk. Our passion is to accelerate solutions.

Use HazelFLOOD to stress-test buildings, neighborhoods, critical infrastructures, and whole cities. Gauge vulnerabilities and strategically prioritize the right interventions. Prevent losses, mitigate climate impacts, and build water security. Fast.

DiviningLAB is solution-driven.

How is DiviningLAB different?

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Most flood models on the market are probabilistic. Probabilistic tools are designed to answer the question: what’s the likelihood (probability) of a storm of intensity X flooding my site?

HazelFLOOD is deterministic. HazelFLOOD is designed to answer the question other models don't: what will happen on my site when storm of intensity X (or Y or Z) occurs?

Determinism delivers certainty.

How is HazelFLOOD different?

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HazelFLOOD is certain. Its resolution runs between 3m and 0.5m. It provides full 2D physics-based hydrodynamics at that resolution, describing rain and riverine flows’ actual interaction with the built environment.

Other models differ in computational techniques, which result in dramatic differences in precision and fidelity. Some leading probabilistic models are resolved to 30m, and then projected onto higher resolution terrain models (3m) to simulate high fidelity. Most are river-driven (1D), sometimes with rain added. This does not provide a true 2D (physical) description of water’s interaction with the built environment. It provides a graphic coding or characterization of an area.

The difference: HazelFLOOD’s full 2D hydrodynamic modeling is not a graphic shorthand of approximated risk. HazelFLOOD is actual. Certain.

HazelFLOOD is water-real.

Why is determinism important?

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Coupling the determinism of flood behavior with the probabilistic likelihood of a flood event is now a feature of HazelFLOOD.

We can plug in state-of-the-art probabilistic forecasting from the world’s leading research centers to provide a view of risk in the near future. The result: forecasts can be multi-month predictions of rainfall intensities, or near-casted hourly or weekly predictions of likely precipitation levels.

HazelFLOOD is interoperable with the world’s best probabilistic data.

Is deterministic modeling a limitation?

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Most existing flood models work within low to middle-of-the- road climate projections (RCP 4.5), and a minimum number of storm intensities: 5-, 20-, 100-, and 500-year storms.

HazelFLOOD is not locked into a low or middle-of-the-road climate projection. It offers users the ability to plug in an open-ended spectrum of possible storm intensities, from a summer shower to a 1,000-year flood event, of any duration, from minutes to days.

HazelFLOOD does not bank on moderate projections; it gives users dynamic, on-demand, scenario-based answers for any level of storm severity.

HazelFLOOD is climate-real.

Within what climate parameters does HazelFLOOD operate?

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There are four main flood types: fluvial (rivers topping their banks); coastal storm surge; sea level rise; and pluvial (rain). HazelFLOOD’s model is driven by one type in particular: rain. We build on our backgrounds in design of the built environment to model pluvial flooding and its behavior on urban surfaces better than it has ever been modeled.

HazelFLOOD is city-real.

What kind of floods does Hazel model?

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Two reasons:
(1) It is where the pain is greatest: In storms like Harvey, Irma, Maria, Sandy, Michael, the greatest losses are not from wind or storm surge; they are from rain. City rain.

(2) It is where the tools are weakest: rainfall in urban areas (city rain) is widely recognized as the least-well modelled of the flood perils. Pluvial, or rain-based, flooding—also sometimes, and confusingly, called flash flooding, urban flooding, inland flooding, or overland flows—is harder to model than fluvial (river-based) flooding. Pluvial (rain-based) flooding on urban surfaces requires solving more complex shallow water equations (swe).

Global leaders in water engineering reviewed early stage HazelFLOOD development and noted that Hazel’s lightning-fast computation of shallow water equations for overland urban flows (city rain) is potentially a game-changer:

HazelFLOOD “could revolutionize flood modelling.”

Why is modelling city rain so important?

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By getting rain right—super-fast shallow water equations— we get a bonus: we model and integrate the impacts of river-based flooding on cities with much higher precision.

HazelFLOOD integrates river-based flooding into rain-driven models.

Is the focus on city rain a limitation?
Aren’t some cities impacted not only by rain-based flooding but also rivers (for example, Charleston and Chicago)?

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HazelFLOOD is designed to be interoperable with coastal storm surge and sea level rise models.

What about the other two kinds of flooding?

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We are leveraging HazelFLOOD’s precision and speed to build out HazelALERT, providing near-term warnings for improved short-term disaster preparedness and real-time emergency response effectiveness.

Can HazelFLOOD provide flood alerts?

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Yes, unlike most models, HazelFLOOD accounts for existing infrastructures including storm drain inlets, catch basins, dykes, and channels.

HazelFLOOD is infrastructure-real.

Does HazelFLOOD take stormwater and flood control infrastructures into account?

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Wood from the hazel tree—preferably "a forked twig of one year's growth”—is a traditional material for divining rods, hand-held tools used to uncover hidden water sources and locate new infrastructures.

In Celtic folklore, the hazel tree is seen as a source of water, wisdom, and inspiration.

Why is your tool called Hazel?

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Hadley and Peter Arnold met while researching the architecture of ancient water systems in Rome. They have been married and working together for 25 years.

Why do the co-founders have the same last name?

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