Return level estimation
Annual maxima or peaks over a threshold are fitted with generalised extreme value or generalised Pareto distributions, and return levels are reported with confidence intervals and record length.
GCRI · Avhewa û metirsî
Extreme value statistics estimate how large and how frequent rare events can be from limited records. In this field they give return levels for rainfall, floods, heat, wind and surge, and increasingly allow those levels to change with a warming climate.
Extreme value theory and xetera dûvê study the behaviour of rare, high-impact events. They provide methods to estimate the magnitude and frequency of extremes beyond the range of observed data. In climate and hazards and disaster and crisis, extreme value methods set design levels and capital buffers for rare events. Tail dependence analysis reveals when extremes in different pergalan tend to occur together. The canonical description is on STEM Commons.
Annual maxima or peaks over a threshold are fitted with generalised extreme value or generalised Pareto distributions, and return levels are reported with confidence intervals and record length.
Distribution parameters are allowed to vary with a covariate such as global mean temperature, so that return levels can be expressed for present and future climates.
Records from similar sites are pooled to estimate extremes where single records are short, with homogeneity of the region tested.
Daytime surface heat, rain and flood water across the world, from NASA GIBS. Each layer carries its own date.
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NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
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Qat û dîrok
Datasets and records used by extreme value theory and xetera dûvê on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Dane | Rol | Nîşan |
|---|---|---|
| Station and gauge annual maxima | Fitting inputs | ÇAVDÊRÎKIRÎ |
| Reanalysis extremes | Spatial coverage | MODELKIRÎ |
| Model large ensembles | Rare-event sampling | MODELKIRÎ |
| Historical and palaeo flood records | Extending records | TEXMÎNKIRÎ |
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Climate and hazards · Extreme value statistics · Run R-4223
generalised Pareto fit
| Shape ξ | 0.18 | −0.07 | 0.59 | heavy tail if above zero |
| Scale σ | 2.6 | 0.9 | 6.1 | units of the variable |
| Exceedances | 144 | declustered |
Fits are checked with diagnostic plots, goodness-of-fit tests and out-of-sample comparison. Sensitivity to threshold and record length is reported.
| Kontrol bikin | How it is done |
|---|---|
| Goodness of fit in the tail | Fits are checked with quantile plots focused on the largest values |
| Confidence intervals | Intervals are reported with every return level, using profile likelihood or bootstrap |
| Stationarity tests | Trends are tested before assuming a stationary distribution |
Extrapolation far beyond the data is highly uncertain. Changing climates and markets violate stationarity assumptions.
Assembled from the Nexus Standards area profile Standardên xetera avhewa: the hazards it names, the pergalan and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Metirsî
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Kontrol
NS-0 Destûr û prensîbNS-1 Çavdêrxaneya UNI û kataloga berheman (rêzimana v0.2: beşên qad, sektor, herêm û jîngehê)NS-3 UNIyên tomara xetereyê (rêzimanî, nexşeya veguhastina ji pergala kevin, desthilata veqetandinê)NS-3b Profîla metadata ya daneset û berheman (STAC, DCAT, PROV-O)NS-9 UNI yên çarçoveyî (belavkirin, mînak, mînakên bernameyê)NS-11 Piştrastkirin û çavdêrî (delîl, erêkirin, çavdêriya lihevhatina zero-trust)NS-14 Lihevhatin û nexşeya li standardên derveyîNRS-H65 Malbata metirsiyê: Binpêkirinên sînorên gerstêrkê (NHS-1 planet.boundary)NRS-H62 Malbata metirsiyê: Bûyerên tevhev û pir-metirsî (NHS-1 sys.compound)NRS-H71 Malbata metirsiyê: Felaketên kozmîk û yên dema kûr (NHS-1 planet.deep-time)NRS-H79 Malbata metirsiyê: Krîzên tenduristiya derûnî (NHS-1 health.mental)NRS-H75 Malbata metirsiyê: Nexweşiyên hesas li hember avhewayê (NHS-1 health.climate-sensitive)NRS-H7 Malbata metirsiyê: Anomaliyên gera mezin (NHS-1 atmo.climate-mode)Çavdêrî
Erdnîgariya mînakê
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How far the outlook for extreme value statistics for hazards could spread, shown as ranges rather than a single line.
Sîgortaya duyemîn û katastrof stewards extreme value theory and xetera dûvê for the Consortium and reviews how it is applied across platforms.
Process explorer · Process
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Pêvajo
Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, windahiya salane ya navînî, exceedance curves, PML and TVaR.
Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.
rêzeya Pergalên Stratejîk is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
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