geoskill-heatwave-impact-assessment

热浪影响评估：逐像元 P90 分位数阈值 + 连续≥3天连通域热浪检测，Stull/简化湿球温度健康风险分级，人口暴露与脆弱性制图。Heatwave impact assessment: per-pixel P90 threshold with connected-run detection, wet-bulb temperature health risk, population exposure and vulnerability mapping. 输出热浪日数/暴露人口/脆弱性/湿球风险 GeoTIFF + 事件清单 JSON。

热浪影响评估 | Heatwave Impact Assessment

Performs heatwave detection and impact assessment on daily-maximum (or daily-mean) temperature time-series rasters, for heat-hazard risk screening, public-health emergency response, and urban thermal environment research. Core algorithm:

• Heatwave detection: uses the 90th percentile (P90) of the time series per pixel as the threshold and labels connected components along the time axis (scipy.ndimage.label); runs of consecutive exceedance lasting ≥ min-duration (default 3) days are classified as heatwave events. Outputs heatwave days, longest duration, event count, peak temperature, and a heatwave mask.
• Wet-bulb temperature: estimates Tw from peak temperature and relative humidity. stull uses the empirical formula of Stull (2011) (J. Appl. Meteorol. Climatol.); simple uses a simplified dry–wet-bulb depression approximation. Tw ≥ 35°C approaches the physiological tolerance limit of the human body.
• Health risk classification: Tw is classified into five levels — low (<26), moderate (26-28), high (28-30), severe (30-32), and extreme (≥32).
• Population exposure: overlays the heatwave mask on the population raster to derive an exposed-population raster and the total/proportion of exposed population.
• Vulnerability: normalized heatwave intensity × normalized population density yields a composite vulnerability index in [0,1].

Supports --synthetic mode, which generates simulated temperature, population, and humidity fields containing injected heatwave events (a sustained +9°C over the eastern half), allowing the full workflow to be verified offline without network access or real data.

Dependencies / 依赖

bash

pip install 'numpy' 'rasterio' 'scipy'

Usage / 使用方法

Basic Usage (bbox Only, Automatic Synthesis)

bash

python geoskill-heatwave-impact-assessment.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./out

Example 1: Synthetic Data, Offline Assessment

bash

python geoskill-heatwave-impact-assessment.py --bbox 116.0 39.0 117.0 40.0 --synthetic --n-dates 40 --output-dir ./out

Example 2: Simplified Wet-Bulb Temperature Method

bash

python geoskill-heatwave-impact-assessment.py --bbox 121.0 31.0 122.0 32.0 --synthetic --method simple --output-dir ./sh

Example 3: Real Temperature Time-Series Raster

bash

python geoskill-heatwave-impact-assessment.py --input tmax_ts.tif --threshold-pct 90 --min-duration 3 --output-dir ./real

Example 4: Stricter Heatwave Definition (P95 + ≥5 Days)

bash

python geoskill-heatwave-impact-assessment.py --input tmax_ts.tif --threshold-pct 95 --min-duration 5 --output-dir ./strict

Example 5: Stull vs. Simplified Wet-Bulb Temperature Comparison

bash

python geoskill-heatwave-impact-assessment.py --bbox 116 39 117 40 --synthetic --method stull --output-dir ./cmp_stull --quiet
python geoskill-heatwave-impact-assessment.py --bbox 116 39 117 40 --synthetic --method simple --output-dir ./cmp_simple --quiet

Output / 输出

File	Format	Description
heatwave_days.tif	GeoTIFF (float32)	Per-pixel heatwave days, EPSG:4326
exposed_population.tif	GeoTIFF (float32)	Exposed population (heatwave mask × population)
vulnerability.tif	GeoTIFF (float32)	Normalized vulnerability index [0,1]
wetbulb_risk.tif	GeoTIFF (float32)	Wet-bulb temperature health risk level 0-4
heatwave_events.json	JSON	Regional event inventory + exposure/risk statistics
output-manifest.json	JSON	Run manifest (input/output/QA/software versions)

Data Source / 数据源 / Source

• Input: local multi-band daily-maximum temperature time-series GeoTIFF (one day per band); population and humidity use synthetic placeholder fields in real mode (this skill is not bound to a specific population data source)
• Synthetic mode: locally generates temperature/population/humidity fields containing injected heatwaves, with no external data source
• Wet-bulb formula: Stull, R. (2011), J. Appl. Meteorol. Climatol. 50(11)

Privacy / 隐私声明 / Privacy

• Runs fully offline by default and makes no network requests
• --synthetic mode reads no external data
• All computation is done locally; user data is never uploaded

License / License

MIT

---

---

name: geoskill-heatwave-impact-assessment description: '热浪影响评估：逐像元 P90 分位数阈值 + 连续≥3天连通域热浪检测，Stull/简化湿球温度健康风险分级，人口暴露与脆弱性制图。Heatwave impact assessment: per-pixel P90 threshold with connected-run detection, wet-bulb temperature health risk, population exposure and vulnerability mapping. 输出热浪日数/暴露人口/脆弱性/湿球风险 GeoTIFF + 事件清单 JSON。'

热浪影响评估 | Heatwave Impact Assessment

对日最高温（或日均温）时序栅格执行热浪检测与影响评估，用于高温灾害风险 筛查、公共卫生应急响应与城市热环境研究。核心算法：

• 热浪检测：逐像元以时序第 90 百分位（P90）为阈值，沿时间轴做连通域 标记（scipy.ndimage.label），连续超阈值 ≥ min-duration（默认 3）天的过程判 为热浪事件，输出热浪日数、最长持续、事件数、峰值温度与热浪掩膜。
• 湿球温度：由峰值温度与相对湿度估算 Tw。stull 采用 Stull (2011) 经验式 （J. Appl. Meteorol. Climatol.），simple 采用简化干湿差近似。Tw ≥ 35°C 接近 人体生理耐受极限。
• 健康风险分级：由 Tw 分为低(<26)/中(26-28)/高(28-30)/严重(30-32)/极端(≥32) 五级。
• 人口暴露：热浪掩膜叠加入口栅格，得暴露人口栅格与暴露总量/比例。
• 脆弱性：归一化热浪强度 × 归一化人口密度，得 [0,1] 综合脆弱性指数。

支持 --synthetic 模式生成含注入热浪事件（东半部持续 +9°C）的模拟温度场、 人口与湿度场，无需网络和真实数据即可离线验证全流程。

依赖

bash

pip install 'numpy' 'rasterio' 'scipy'

使用方法

基本用法（仅给 bbox，自动合成）

bash

python geoskill-heatwave-impact-assessment.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./out

示例 1：合成数据离线评估

bash

python geoskill-heatwave-impact-assessment.py --bbox 116.0 39.0 117.0 40.0 --synthetic --n-dates 40 --output-dir ./out

示例 2：简化湿球温度方法

bash

python geoskill-heatwave-impact-assessment.py --bbox 121.0 31.0 122.0 32.0 --synthetic --method simple --output-dir ./sh

示例 3：真实温度时序栅格

bash

python geoskill-heatwave-impact-assessment.py --input tmax_ts.tif --threshold-pct 90 --min-duration 3 --output-dir ./real

示例 4：更严格的热浪定义（P95 + ≥5 天）

bash

python geoskill-heatwave-impact-assessment.py --input tmax_ts.tif --threshold-pct 95 --min-duration 5 --output-dir ./strict

示例 5：Stull vs 简化湿球温度对比

bash

python geoskill-heatwave-impact-assessment.py --bbox 116 39 117 40 --synthetic --method stull --output-dir ./cmp_stull --quiet
python geoskill-heatwave-impact-assessment.py --bbox 116 39 117 40 --synthetic --method simple --output-dir ./cmp_simple --quiet

输出

文件	格式	说明
heatwave_days.tif	GeoTIFF (float32)	逐像元热浪日数，EPSG:4326
exposed_population.tif	GeoTIFF (float32)	暴露人口（热浪掩膜×人口）
vulnerability.tif	GeoTIFF (float32)	归一化脆弱性指数 [0,1]
wetbulb_risk.tif	GeoTIFF (float32)	湿球温度健康风险等级 0-4
heatwave_events.json	JSON	区域事件清单 + 暴露/风险统计
output-manifest.json	JSON	运行清单（输入/输出/QA/软件版本）

数据源 / Source

• 输入：本地多波段日最高温时序 GeoTIFF（每波段一日）；人口与湿度在真实 模式下用合成占位场（本 skill 不绑定特定人口数据源）
• 合成模式：本地生成含注入热浪的温度/人口/湿度场，无外部数据源
• 湿球公式：Stull, R. (2011), J. Appl. Meteorol. Climatol. 50(11)

隐私声明 / Privacy

• 默认完全离线运行，不发起任何网络请求
• --synthetic 模式不读取任何外部数据
• 所有计算在本地完成，不上传用户数据

License

MIT