基于随机森林模型的青藏高原冰川预测及分析

doi:10.12118/j.issn.1000-6060.2024.528

摘要/Abstract

摘要：

冰川是青藏高原自然资源调查监测的重点对象之一，冰川的调查监测与研究对青藏高原气候变化具有重要意义。以青藏高原冰川为研究对象，融合多源数据构建了一个随机森林模型（决定系数=0.72），获得了2000—2020年青藏高原逐年1 km尺度冰川预测数据集，分析了2000—2020年青藏高原冰川的空间分布特征和时空变化特征。研究表明：（1）青藏高原冰川空间分布特征为：主要分布于0°~40°坡度范围内，占比达97.92%；主要分布于4000~7000 m海拔范围内，占比达99.38%；总体呈现北坡多于南坡、西坡多于东坡的特点。（2）青藏高原冰川时空变化特征为：时间上，2000—2020年青藏高原冰川总体呈现显著退缩趋势。空间上，青藏高原边缘地区冰川存在显著变化趋势，从边缘向腹地显著变化趋势减弱，腹地以轻微变化趋势为主。（3）喜马拉雅山和念青唐古拉山冰川主要呈显著退缩趋势，喀喇昆仑山冰川主要呈轻微退缩趋势，昆仑山冰川呈轻微前进趋势与轻微退缩趋势并存的特点。

关键词: 冰川, 预测及分析, 随机森林模型, 空间分布, 时空变化, 青藏高原

Abstract:

Glaciers on the Qinghai-Xizang Plateau, China serve as critical indicators for natural resource monitoring and regional climate change analysis. This study investigates glacier dynamics across the plateau by integrating multi-source datasets and developing a robust random forest model (R²=0.72) to generate a 1 km-resolution annual glacier prediction dataset spanning from 2000 to 2020. Key findings include as follows: (1) Spatial distribution patterns: 97.92% of glaciers are located on slopes of 0°-40°, and 99.38% are distributed at elevations of 4000-7000 m. Glacier density is higher on northern slopes than on southern slopes, and western slopes exhibit greater coverage than eastern slopes. (2) Spatiotemporal changes: From 2000 to 2020, glaciers exhibited a clear retreat trend. Spatially, stronger variation signals were observed along the plateau margins, while interior regions showed relatively minor changes. (3) Regional trends: Glaciers in the Himalaya Mountains and Nyainqentanglha Mountains showed significant retreat, while those in the Karakoram Mountains experienced only slight retreat. The Kunlun Mountains exhibited a mixed pattern of slight advancement and retreat.

Key words: glaciers, predict and analysis, random forest model, distribution patterns, spatiotemporal changes, Qinghai-Xizang Plateau

张毅明, 汤宇磊, 冯俊波. 基于随机森林模型的青藏高原冰川预测及分析[J]. 干旱区地理, 2025, 48(8): 1342-1352.

ZHANG Yiming, TANG Yulei, FENG Junbo. Predicting and analysing glaciers in the Qinghai-Xizang Plateau: A random forest model[J]. Arid Land Geography, 2025, 48(8): 1342-1352.

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数据	来源	分辨率
冰川分布数据	国家青藏高原科学数据中心	1 km
NDVI数据	MODIS传感器	1 km
SRTM高程数据	美国太空总署、美国国防部国家测绘局	30 m
产水模数数据	中国科学院资源环境科学数据中心	1 km
气温格点数据	国家气象信息中心	0.5°
降水格点数据	国家气象信息中心	0.5°
土地利用数据	国家基础地理信息中心	30 m
土地利用数据	欧洲航天局	300 m
GDP数据	中国科学院地理科学与资源研究所	1 km
人口密度数据	社会经济数据和应用中心	1 km

坡度/(°)	占比/%		坡向/(°)	占比/%		海拔/m	占比/%
坡度/(°)	2017年	2020年	坡向/(°)	2017年	2020年	海拔/m	2017年	2020年
0~10	23.68	23.68	0.0~22.5	8.90	8.90	0~1000	0.00	0.00
10~20	36.46	36.46	22.5~67.5	15.28	15.27	1000~2000	0.00	0.00
20~30	27.61	27.61	67.5~112.5	13.38	13.38	2000~3000	0.11	0.01
30~40	10.17	10.17	112.5~157.5	12.79	12.82	3000~4000	3.33	0.56
40~50	1.92	1.92	157.5~202.5	13.29	13.29	4000~5000	26.39	18.29
50~60	0.16	0.16	202.5~247.5	9.84	9.85	5000~6000	49.79	69.99
60~70	0.00	0.00	247.5~292.5	8.70	8.66	6000~7000	19.79	11.10
70~80	0.00	0.00	292.5~337.5	10.78	10.73	7000~8000	0.56	0.05
80~90	0.00	0.00	337.5~360.0	7.04	7.10	8000~9000	0.03	0.00

M-K检验数据	Z值	P值
青藏高原全区冰川数据	-3.3519	<0.01
喜马拉雅山冰川数据	-2.3252	<0.05
喀喇昆仑山冰川数据	-1.1777	>0.05
昆仑山冰川数据	-0.0906	>0.05
念青唐古拉山冰川数据	-2.9291	<0.01