Ensemble and parameter uncertainty
Models are run across parameter sets and forcing ensembles; the spread is reported as the range, not collapsed to a mean.
GCRI · Water Nexus
Uncertainty quantification is what makes the evidence honest: every modelled value on this platform carries a range, a label and the assumptions behind it, so a decision-maker can see how much weight it bears.
Uncertainty quantification and model validation characterise how uncertainty in inputs, parameters and model structure affects predictions. They establish how much confidence a model’s outputs deserve. In water, climate and hazards and 핵심 기반시설, uncertainty quantification shows where risk estimates are robust and where they are fragile. It guides where better data or models would change decisions. The canonical description is on STEM Commons.
Models are run across parameter sets and forcing ensembles; the spread is reported as the range, not collapsed to a mean.
Uncertainty in inflow is carried into storage, generation, treatment and health effects, so the final range reflects every step.
Each value is labelled observed, published statistics, modelled, estimated, forecast or unknown, and the label travels with the value into every figure and table.
The sectors uncertainty quantification for water models touches and how strongly they depend on each other: what a study has to observe and model together.
서명
물과 그 파트너
물은 식량에 관개(0.8)를, 에너지에 냉각수와 수력(0.6)을, 생물다양성에 환경 유량(0.6)을 줍니다.
W · E · I · F · H · B
Datasets and records used by uncertainty quantification and model validation on this platform, with the role each plays and the label its values carry. Sources are listed in 출처 등록부.
| 데이터 | 역할 | 라벨 |
|---|---|---|
| Model ensembles | Spread and sensitivity | 모형 산출 |
| Observation error estimates | Input uncertainty | 추정 |
| 전문가 의견 도출 | Where no data exist | 추정 |
모형, 그 가정, 데이터, 불확실성을 과학자나 엔지니어가 확인할 형태로 제시합니다. 구조는 발행된 방법을 따릅니다. 제시된 값은 예시이며 결과가 아닙니다.
How far the outlook for uncertainty quantification for water models could spread, shown as ranges rather than a single line.
Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.
| 점검 | 수행 방식 |
|---|---|
| 민감도 분석 | The inputs that drive most of the output range are identified and reported |
| Calibration of probabilities | Stated probabilities are checked against outcomes where a record exists |
| Label audit | A sample of published values is traced back to the evidence behind its label |
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
Process explorer · Process
10 steps·4단계
절차
In IPCC AR6 Working Group II, climate risk results from interactions between hazards and a system’s exposure and vulnerability, and responses can add risk.
공개 출처를 바탕으로 한 외부 표준이나 방법의 개요입니다. 프레임워크가 말하는 내용을 설명할 뿐, 어떤 실행의 기록도 아닙니다. Nexus나 파트너가 어떤 단계를 수행, 인증, 승인한다는 것을 보여 주지 않습니다. 게이트는 방법의 한 단계일 뿐, Nexus의 승인이 아닙니다.
시스템 과학과 모델링 stewards uncertainty quantification and model validation for the Consortium and reviews how it is applied across platforms.
보고서 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.
개인정보, 민감한 인프라 세부 정보, 공유할 권리가 없는 자료는 절대 보내지 마십시오. 기여물은 사용 전에 검토됩니다. 기여물을 보낸다고 해서 저작자 지위나 편집 권한이 부여되지 않으며, 후원으로 조사 결과를 얻을 수 없습니다.
정보와 문의는 오늘부터 열려 있습니다. 문의에는 참조 번호가 부여되고 다음 단계가 안내됩니다. 신청, 결제, 컴퓨팅 할당, 등록은 해당 팀, 약관, 심사 역량이 갖추어졌을 때만 열립니다.
이 사이트의 지도, 데이터와 그림은 맥락과 일반 정보로 제시됩니다. 조언, 예보 또는 경보가 아니며, 정보의 한계는 면책 고지와 데이터 고지에 나와 있습니다.
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