GCRI · 气候与灾害
复合危害
Compound hazards are combinations of drivers or hazards that together cause more harm than each would alone: heat with drought, 风暴潮 叠加 河流洪水, 野火 followed by heavy rain on burned slopes, or an 地震 during a pandemic. They are often missed because data, models and agencies are organised by single hazard. This page sets out the types of compound event, how they are analysed, and what evidence serves those who must plan for them.
Compound hazards: how the risk develops.
Multiple and cascading events. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Concurrent drivers
Two hazards occur together because they share a cause or because chance aligns them, such as high river flow and 风暴潮 in the same coastal storm.
Preconditioned systems
An earlier condition makes a later hazard worse: wet soils before heavy rain, drought before heat, or a burned slope before a storm.
Spatially compounding events
The same hazard strikes several regions at once, such as simultaneous crop failures in major producers, overwhelming the capacity to substitute between them.
Temporally compounding events
A sequence of hazards arrives before recovery from the first, so each one meets a weaker system and the total loss exceeds the sum of separate events.
全球气候信号。
Flood water, daytime surface heat and rain across the world, from NASA GIBS. Each layer carries its own date.
交互式地图需要JavaScript；图层列表和来源见下文。
图层 3
本页的气候信号
关于本图层
- 证据
- 观测（云层造成缺口）
- 来源
- VIIRS 全球洪水产品（NASA LANCE）
- 提供方
- NASA LANCE
- 许可协议
- NASA 开放数据
- 年份版本
- 每日；卫星探测；云层造成缺口
- 构建时的最新内容
- 2026-10-14
- 处理结果
- 约 250 米网格
- 不确定性
- 卫星探测；云层造成缺口。卫星或模型栅格数据，按提供方发布的原样呈现。云层、条带缺口和反演误差会造成缺口和噪声；数值是指标，而非某地的测量值。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
关于本图层
- 证据
- 观测（持续云层仍会造成缺口）
- 来源
- VIIRS NOAA-20 Level 3，8 天（NASA LANCE）
- 提供方
- NASA LANCE
- 许可协议
- NASA 开放数据
- 年份版本
- 8 天；卫星 8 天合成；持续云层仍会造成缺口
- 构建时的最新内容
- 2026-09-14
- 处理结果
- 约 1 公里网格
- 不确定性
- 截至所示日期的 8 天内地表的卫星皮肤温度，是高温风险暴露指标；不是气温、热指数，也不是高温预警。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
关于本图层
- 证据
- 估计值
- 来源
- GPM IMERG（NASA GES DISC）
- 提供方
- NASA GES DISC
- 许可协议
- NASA 开放数据
- 年份版本
- 每日；近实时卫星估算
- 构建时的最新内容
- 2026-10-12
- 处理结果
- 约 2 公里网格
- 不确定性
- 近实时卫星估算。卫星或模型栅格数据，按提供方发布的原样呈现。云层、条带缺口和反演误差会造成缺口和噪声；数值是指标，而非某地的测量值。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
关于本图层
- 证据
- 估计值
- 来源
- 世界网格化人口 v4.11（NASA SEDAC（哥伦比亚大学 CIESIN））
- 提供方
- NASA SEDAC（哥伦比亚大学 CIESIN）
- 许可协议
- CC BY 4.0
- 年份版本
- null；基于人口普查的估算，静态（2020）
- 处理结果
- 约 1 公里网格
- 不确定性
- 基于人口普查的 2020 年每平方公里人口估算，作为风险暴露背景展示；不是受任何危害影响的人数。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
关于本图层
- 证据
- 观测
- 来源
- VIIRS NOAA-20 校正反射率（NASA LANCE）
- 提供方
- NASA LANCE
- 许可协议
- NASA 开放数据
- 年份版本
- 每日；卫星图像
- 构建时的最新内容
- 2026-10-14
- 处理结果
- 约 250 米网格
- 不确定性
- 卫星图像。卫星或模型栅格数据，按提供方发布的原样呈现。云层、条带缺口和反演误差会造成缺口和噪声；数值是指标，而非某地的测量值。
- 经由以下途径到达
- gibs.earthdata.nasa.gov，仅在您允许实时地图后访问
键盘操作，地图获得焦点时
按住 Ctrl 或 ⌘ 并滚动以缩放
最新可用
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap贡献者 (ODbL) · Natural Earth
正在加载最新的NASA影像。
拖动以移动。按住 Ctrl 或 ⌘ 并滚动、双指缩放或使用按钮缩放；悬停可查看指针下的数值；重置可返回该区域。
气候信号，以全球为取景。NASA 发布的卫星和模型产品，各带自己的日期。它们是指标，而非对任何地点的评估。
数据：NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap 贡献者 · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERT · NASA AIRS来源与许可
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA 开放数据
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA 开放数据
- VIIRS 近实时产品（洪水、地表温度、积雪覆盖、校正反射率），NASA LANCE；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 NASA 开放数据
- GPM IMERG 降水，NASA 降水处理系统；由 NASA 戈达德地球科学数据和信息服务中心（GES DISC）分发；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 NASA 开放数据
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). 采用 CC BY 4.0 许可。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 doi:10.7927/H49C6VHW. CC BY 4.0
- GHRSST Level 4 MUR 分析，NASA 喷气推进实验室；由 NASA 物理海洋学 DAAC（PO.DAAC）分发；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 NASA 开放数据
- ASTER GDEM 是 METI 和 NASA 的产品（ASTER Global Digital Elevation Model V003，NASA LP DAAC）；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。引用格式：NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA 开放数据
- 使用 Natural Earth 制作。免费矢量和栅格地图数据，见 naturalearthdata.com。 公有领域
- © OpenStreetMap 贡献者（ODbL），经由 NASA GIBS（海岸线、边界、道路和地名）。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 ODbL
- Blue Marble，NASA Earth Observatory；影像来自 NASA GIBS。颜色依据已发布的色标重绘；显示处的数值已按六边形网格平均（经修改）。 NASA 开放数据
- 地球仪和平面地图上的背景信息：海岸线、边界、道路和地名来自 NASA GIBS 参考图层（OpenStreetMap 数据，ODbL，© OpenStreetMap contributors）；Blue Marble 影像可作为可选底图；3D 地形和高程读数来自 ASTER GDEM 高程色彩指数（5 m 至 200 m 分级，为便于辨识而作了夸张处理）。边界按已发布的样式绘制，不代表对任何领土的立场。
- 实时事件：NASA Earth Observatory Natural Event Tracker（EONET）；事件仅供参考，并保留其原始来源链接。
- 实时：NASA EONET。事件仅供参考，不是主管机构的预警。
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Observed (EO): NASA AIRS. We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- 空气质量预报，5 天，PM2.5（CAMS，经由 Open-Meteo）：包含经修改的 Copernicus 大气监测服务 2026 年信息。
- 过去一年的每日气温（ERA5，经由 Open-Meteo）：包含经修改的 Copernicus 气候变化服务 2026 年信息。
- 过去一年的每日气温和日照（NASA POWER）：这些数据来自 NASA 兰利研究中心（LaRC）POWER 项目，该项目由 NASA 地球科学/应用科学计划资助。
- 天气数据来自 Open-Meteo.com (https://open-meteo.com/), 依据 CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). 授权。
- Optional layers (22 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
图层与日期
- 洪水水体，3 日合成：VIIRS 全球洪水产品，NASA LANCE · 2026年10月14日
- 地表温度，日间，8 天合成：VIIRS NOAA-20 3 级产品，8 天，NASA LANCE · 2026年9月14日
- 降水率，日均值：GPM IMERG，NASA GES DISC · 2026年10月12日
- 人口密度，2020 年：人口：哥伦比亚大学 CIESIN，世界网格化人口 v4.11（2018），NASA SEDAC， CC BY 4.0 · 静态
- 真彩色：VIIRS NOAA-20 校正反射率，NASA LANCE · 2026年10月13日
- 海面温度距平：GHRSST MUR25，NASA JPL PO.DAAC · 2026年10月12日
Compound hazards are where domain couplings show most.
- 水耦合Flood after drought, or drought and heat together, break assumptions in sector plans.What to watchJoint occurrence of extremes
- 食品耦合Concurrent extremes across breadbaskets are the food system’s worst case.What to watchConcurrent extremes in producing regions
- 能源耦合Low wind with cold or heat stresses power systems with high renewable shares.What to watchProlonged low-generation periods
Compound hazard analyses are designed for the 情景与压力测试系列. The typology of Zscheischler and colleagues (2018) classifies preconditioned, multivariate, temporally and spatially compounding events.
- Documented compound events with dates and sources.
- Joint statistics of extremes for a region.
存量、流量、延迟和一个临界阈值。
两个存量，即压力和 适应能力，由一个增强的增长回路、一个延迟的平衡响应以及压力超过能力时产生的损害相连；越过阈值后，系统可能发生突变。这是使用明示示例数值的教学模型，不是预测。
控制措施
- 压力增长
- 每年 6.0%
- 能力在降低压力方面的效力
- 每年 4.0%
- 对感知压力的投资响应
- 20%
- 感知延迟
- 4.0 年
- 压力的临界阈值
- 60 项指数
- 越过阈值后的放大
- 2.0 倍
- 模拟年数
- 50 年
读数
- 峰值压力
- 113 指数
- 期末能力
- 186 指数
- 超过阈值的年数
- 15.8 年
数字、方法与来源
| 年份 | 压力 | 能力 | 感知 |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
方法
- 系统动力学 Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- 临界转变与临界点 Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- 因果回路图 Sterman (2000), Business Dynamics, McGraw-Hill
- 不确定性扇形图（参数蒙特卡罗） Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- 单因素敏感性（龙卷风图） Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
数据
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
贯穿六大支柱，由 Nexus Consortium 牵头
- 实验 实验室
- 构建于 Foundry 中的气候与灾害
- 检验地点 Universe 中的气候与灾害
- 编入 Standards 中的气候与地球系统
- 发表于 Reports 中的气候与灾害
- 授课于 Academy 中的地球与环境科学学院
衡量什么，以及衡量得如何。
每个指标都注明其单位、频率、背后的来源类型以及数值将附带的证据标签。此处不显示数值：数值由计划依据其自身权限提供。
| 指标 | 单位 | 节奏 | 来源类型 | 标签 |
|---|---|---|---|---|
| Concurrent hot and dry days | count per season | seasonal | Station and reanalysis data | 观测 |
| Joint 重现期 | years for the combined event | per analysis | Multivariate statistical models | 估算 |
| Dependence between drivers | correlation or tail dependence coefficient | per analysis | Statistical analysis of paired records | 估算 |
| Events with multiple hazard types | 计数 | 年度 | Disaster event databases | 已报告 |
| Simultaneous breadbasket shortfalls | count of regions below a yield threshold | 年度 | Crop monitoring | 估算 |
| Recovery interval between events | 月 | per event sequence | Event records | 观测 |
Model compound hazards as a system. Run the cascade.
Assembled from the Nexus Standards area profile 复合与级联危害标准: the hazards it names, the 系统 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.
灾害
- Natech event
- Chemical spill or leak （在下游引发）
- Emergency-response overload （在下游引发）
- Toxic gas release (set off downstream)
- Industrial fire (set off downstream)
系统与耦合
- 复合与级联危害标准
- 水文气象危害标准 Compounding
- 地球物理危害标准 Triggering
- 关键基础设施与关键实体 级联
- 全球与系统性风险 机制
控制措施
NS-0章程与原则NS-3风险记录 UNI（语法、旧编码对照、分配机构）NS-9情境 UNI（部署、实例、计划实例）NS-11验证与监测（证据、证明、零信任符合性观测）NRS-H61危害族：级联失效（NHS-1 sys.cascade）NRS-H62危害类别：复合与多危害事件（NHS-1 sys.compound）NRS-H60危害类别：供应链与流动中断（NHS-1 sys.supply-chain）NRS-H94危害族：系统性网络事件（NHS-1 cyber.systemic-cyber）NRS-H107危害族：数字基础设施集中与单一化（NHS-1 tech.digital-concentration）NRS-H7危害类别：大尺度环流异常（NHS-1 atmo.climate-mode）
观测
- Copernicus EMS rapid mapping activations (listed for reference)
- OpenStreetMap planet and replication (listed for reference)
- ERA5 再分析（Copernicus Climate Data Store）（仅列作参考）
- 灾害造成的直接经济损失与全球国内生产总值之比
- Number of disruptions to basic services attributed to disasters
示例地理范围
- 孟加拉国：134 个六边形单元
- 今夜夜间灯光与停电（VIIRS 昼夜波段），NASA GIBS
- Carbon monoxide, smoke transport (AIRS), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
证据来自何处。
具名公共数据集与机构记录，各自注明许可类别。依协议获得的来源仅按其条款使用，绝不再次发布。
| 来源 | 它提供什么 | 许可类别 |
|---|---|---|
| IPCC AR6 Working Group I, chapter on weather and climate extremes | Assessment of compound events and their changes | Public, IPCC terms of use |
| UNDRR-ISC 危害信息概况 | Hazard definitions with notes on linked and cascading hazards | Public, UNDRR and ISC terms of use |
| EM-DAT 国际灾害数据库（CRED，鲁汶大学） | Events recorded with associated hazards | Free for non-commercial use with registration |
| ERA5 reanalysis | Consistent multi-variable fields for joint analysis | Free and open with attribution, Copernicus licence (to check current terms) |
| GEOGLAM 作物监测 | Monthly crop conditions in major producing and at-risk countries | Public, GEOGLAM terms |
How compound hazards is modelled, measured and tested.
模型、其假设、数据和不确定性，以科学家或工程师会核查的形式呈现。结构遵循已发表的方法。所取数值仅为示意，不是研究发现。
不确定性区间
How far the outlook for compound hazards could spread, shown as ranges rather than a single line.
- 中心估计值
- 集合的中位数
- 范围
- IPCC 校准用语中的“可能”和“很可能”
- 集合
- 明示成员、情景和初始条件
- 配合阅读
- 结果对每项输入的敏感性
由谁决定，以及证据服务于什么。
Climate and Hazards prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| 谁来决定 | 问题 | 本页面服务的证据 |
|---|---|---|
| Civil protection and emergency agencies | Which combinations of hazards to plan and exercise for | Compound event typologies and storylines |
| Coastal and flood defence agencies | How joint surge and river flow change design standards | Joint return periods with dependence stated |
| Food security and trade ministries | How to prepare for simultaneous shortfalls among suppliers | Spatially compounding event analyses |
该主题产生的产出
- Typologies of compound events relevant to a country or sector
- Joint probability analyses with dependence stated
- Storylines that trace a compound event through its impacts
- Event sequence records showing recovery intervals
专家提出的问题。
The core question is what happens when hazards line up.
- Which combinations of hazards drive the largest impacts in a given system?
- Are the hazards statistically dependent, for example because they share a weather pattern?
- Can one event precondition another, such as drought before heavy rain?
- Could several regions that supply the same market be hit at once?
- Are emergency resources and insurance capacity sized for simultaneous events?
机制与驱动因素。
Many hazards share drivers. Large-scale atmospheric patterns can bring heat and drought together, or set up storms that deliver both surge and 极端降雨. Modes of 气候变率 can make simultaneous droughts or floods in distant regions more likely in the same year. Ignoring that dependence and treating hazards as independent tends to underestimate the chance of extreme combined outcomes.
Preconditioning and triggering create sequences. 野火 leaves slopes prone to debris flows; earthquakes loosen slopes before monsoon rains; a first storm can leave defences damaged before a second arrives. Impacts compound further when 系统 are already stressed, for example when a flood strikes during an outbreak.
如何研究，以及方法止于何处。
| 方法或数据 | 显示内容 | 局限 |
|---|---|---|
| Multivariate statistics and copulas | Dependence between hazard variables | Need long records; tail dependence is hard to estimate |
| Large ensembles of climate model runs | Rare combinations not seen in observations | Model biases can distort joint behaviour |
| Event-set and storyline approaches | Plausible, physically coherent combined events | Storylines are not probabilities |
| Impact-based and 系统 个模型 | How combined hazards affect infrastructure and services | Depend on uncertain vulnerability and dependency data |
如何研究，以及方法无法说明的内容。
Where this page’s references lead.
阅读本页时所对照的机构和文件。状态和版本会变化；每个链接都指向发布机构。
问题与解答。
What is a compound event?
Why are compound events underestimated?
What is a climate storyline?
What is a storyline approach?
Is the Universe mission theme on compound hazards active?
从本页贡献证据。
报告 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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