geoskill-frost-risk-mapping

霜冻风险制图 | Frost Risk Mapping

Performs terrain correction and frost risk analysis on daily minimum temperature time-series rasters, for agricultural frost disaster warning, cropping system zoning, and ecological cold damage assessment. Core algorithm:

• Terrain correction (apply_terrain_correction): corrects the minimum temperature from a reference surface to the actual per-pixel terrain — · Lapse rate (default 6.5 °C/km): T(z) = T_ref − Γ·(z − z_ref), higher elevation means colder; · Cold-air pooling: uses the Topographic Position Index TPI (pixel elevation − neighborhood mean) to identify depressions, where pooling of descending cold air causes additional cooling (capped at −6 °C), forming "frost hollows"; · Aspect effect: slope/aspect are derived from the DEM; in the northern hemisphere, south-facing slopes warm and north-facing slopes cool, with the effect strengthening as slope increases.
• Frost statistics: per-pixel statistics of frost frequency (fraction of days with Tmin ≤ threshold), number of frost days, first frost date, last frost date, and frost-free period (longest consecutive run of days with Tmin > threshold).
• Risk classification: frost frequency is classified into five levels — 0 none / 1 low / 2 moderate / 3 high / 4 severe.

--synthetic mode generates a DEM containing a ridge and a central depression (frost hollow), plus an elevation-influenced daily minimum temperature time series (flat reference surface + terrain correction), so the physical relationships — "temperature decreases with elevation", "high frost risk at high elevations / in depressions" — can be validated offline without network access or real data.

Dependencies / 依赖

pip install 'numpy' 'rasterio' 'scipy'

Usage / 使用方法

Basic usage (bbox only; auto-synthesized)

python geoskill-frost-risk-mapping.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./out

Example 1: offline evaluation with synthetic data

python geoskill-frost-risk-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --n-dates 40 --output-dir ./out

Example 2: custom frost threshold (e.g. −2 °C light frost)

python geoskill-frost-risk-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --threshold -2 --output-dir ./light_frost

Example 3: real minimum-temperature time series + DEM

python geoskill-frost-risk-mapping.py --input tmin_ts.tif --dem dem.tif --threshold 0 --output-dir ./real

Example 4: no terrain correction (use raw temperatures directly)

python geoskill-frost-risk-mapping.py --input tmin_ts.tif --correction none --output-dir ./raw

Example 5: a different area

python geoskill-frost-risk-mapping.py --bbox 121.0 31.0 122.0 32.0 --synthetic --output-dir ./shanghai --quiet

Output / 输出

Data Source / 数据源 / Source

• Input: local multi-band daily minimum temperature time-series GeoTIFF (one day per band) + optional DEM GeoTIFF (meters); when the DEM is omitted, synthetic terrain is used
• Synthetic mode: locally generated ridge+depression DEM and elevation-influenced minimum temperature field; no external data source
• Methods: lapse rate, TPI-based cold-air pooling, and aspect-driven radiation are all classic microclimatological methods

Privacy / 隐私声明 / Privacy

• Fully offline by default; no network requests are made
• --synthetic mode reads no external data
• All computation is performed locally; user data is never uploaded

License / License

MIT

---

---

name: geoskill-frost-risk-mapping description: '霜冻风险制图：由日最低温时序与 DEM 做高程递减率、冷空气湖（TPI 洼地）与坡向地形修正，逐像元统计霜冻频率/霜冻日数、初霜日、终霜日与无霜期，并分级霜冻风险。Frost risk mapping: terrain-corrected min-temperature (lapse rate, cold-air pooling via TPI, aspect) with frost frequency, first/last frost date, frost-free period and risk classification. 输出霜冻风险/无霜期/频率 GeoTIFF + 统计 JSON。'

霜冻风险制图 | Frost Risk Mapping

对日最低温时序栅格执行地形修正与霜冻风险分析，用于农业霜冻灾害预警、种植 制度区划与生态冷害评估。核心算法：

• 地形修正（apply_terrain_correction）：把参考面上的最低温修正到逐像元 实际地形—— · 高程递减率（lapse rate，默认 6.5°C/km）：T(z) = T_ref − Γ·(z − z_ref)， 高程越高越冷； · 冷空气湖（cold-air pooling）：用地形位置指数 TPI（像元高程 − 邻域均值） 识别洼地，洼地积聚下泄冷空气而额外降温（封顶 −6°C），形成"霜穴"； · 坡向效应（aspect）：由 DEM 解算坡度/坡向，北半球南坡增温、北坡降温， 随坡度增大而增强。
• 霜冻统计：逐像元统计霜冻频率（Tmin ≤ 阈值天数占比）、霜冻日数、初霜日、 终霜日与无霜期（最长连续 Tmin > 阈值天数）。
• 风险分级：由霜冻频率分为 0 无 / 1 低 / 2 中 / 3 高 / 4 严重 五级。

支持 --synthetic 模式生成含山脊与中央洼地（霜穴）的 DEM，以及受高程影响的 日最低温时序（平坦参考面 + 地形修正），无需网络和真实数据即可离线验证 "温度随高程递减""高海拔/洼地霜冻风险高"等物理关系。

依赖

pip install 'numpy' 'rasterio' 'scipy'

使用方法

基本用法（仅给 bbox，自动合成）

python geoskill-frost-risk-mapping.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./out

示例 1：合成数据离线评估

python geoskill-frost-risk-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --n-dates 40 --output-dir ./out

示例 2：自定义霜冻阈值（如 -2°C 轻霜）

python geoskill-frost-risk-mapping.py --bbox 116.0 39.0 117.0 40.0 --synthetic --threshold -2 --output-dir ./light_frost

示例 3：真实最低温时序 + DEM

python geoskill-frost-risk-mapping.py --input tmin_ts.tif --dem dem.tif --threshold 0 --output-dir ./real

示例 4：不做地形修正（直接用原始温度）

python geoskill-frost-risk-mapping.py --input tmin_ts.tif --correction none --output-dir ./raw

示例 5：不同区域

python geoskill-frost-risk-mapping.py --bbox 121.0 31.0 122.0 32.0 --synthetic --output-dir ./shanghai --quiet

输出

数据源 / Source

• 输入：本地多波段日最低温时序 GeoTIFF（每波段一日）+ 可选 DEM GeoTIFF（米）； 缺省 DEM 时用合成地形
• 合成模式：本地生成山脊+洼地 DEM 与受高程影响的最低温场，无外部数据源
• 方法：气温直减率、TPI 冷池与坡向辐射均为经典微气候学方法

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