1.北京师范大学水科学研究院,北京 100875
2.瓦地海绵环境科技(北京)有限公司,北京 100102
3.内蒙古自治区包头市 水利事业发展中心,内蒙古 包头 014060
樊正龙(2000—),男,硕士研究生,主要从事水文及水资源学等工作。
彭定志(1977—),男,博士,教授,主要从事水文及水资源学等工作。
收稿:2025-12-22,
修回:2026-03-26,
录用:2026-03-27,
网络首发:2026-04-24,
纸质出版:2026-06-25
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樊正龙,彭定志,龚雨薇,等.拉萨河流域土壤湿度记忆性时空特征及冻结深度变化归因分析[J].人民珠江,2026,47(6):50-60.
Fan Zhenglong,Peng Dingzhi,Gong Yuwei,et al.Analysis of the Spatiotemporal Characteristics of Soil Moisture Memory and the Attribution of Changes in Freezing Depth in the Lhasa River Basin[J].PEARL RIVER,2026,47(06):50-60.
樊正龙,彭定志,龚雨薇,等.拉萨河流域土壤湿度记忆性时空特征及冻结深度变化归因分析[J].人民珠江,2026,47(6):50-60. DOI: 10.3969/j.issn.1001-9235.2026.06.005.
Fan Zhenglong,Peng Dingzhi,Gong Yuwei,et al.Analysis of the Spatiotemporal Characteristics of Soil Moisture Memory and the Attribution of Changes in Freezing Depth in the Lhasa River Basin[J].PEARL RIVER,2026,47(06):50-60. DOI: 10.3969/j.issn.1001-9235.2026.06.005.
土壤湿度是连接气候信号与冻结过程的关键因子,其动态变化深刻影响着高寒地区水循环与生态系统,然而其记忆性(SMM)的垂向变化规律及冻结深度的影响机制尚不明确。以拉萨河流域为研究区,基于GLDAS土壤湿度(0~10、10~40、40~100、100~200 cm)、MODIS地表温度、降水、NDVI和积雪深度等多源数据,综合运用Sen’s slope趋势分析、MK检验、Hurst指数、偏相关分析和随机森林等方法,系统分析了2000—2020年土壤湿度与冻结深度时空变化规律,量化了不同深度土壤湿度记忆性空间格局,辨识了驱动冻结深度变异的主导因子。结果表明:①拉萨河流域土壤湿度变化呈现显著垂向分异,从表层大面积减少(92.2%)逐步转为中层“东增西减”的格局,至深层增减趋势稳定;②土壤湿度记忆性在流域表层较短(1~2个月),随深度的增加SMM在流域大部逐渐增强,并在农田区形成稳定的记忆核心(SMM在6—7月);③NDVI对冻结深度空间变异的贡献最大(54%),其次是地表温度(贡献率为26%),积雪深度和降水的影响范围有限;在考虑温度的作用下,植被覆盖与冻结深度存在着以负偏相关为主、局部正偏相关的复杂空间格局。揭示了拉萨河流域土壤湿度记忆性时空分布特征以及相关因子对冻结深度影响机制,对理解高寒区陆气相互作用与水资源可持续性具有重要意义。
Soil moisture is a key factor connecting climate signals and freezing processes
and its dynamic changes profoundly affect the water cycle and ecosystems in alpine regions. However
the vertical variation patterns of its soil moisture memory (SMM) and the mechanisms influencing freezing depth are still unclear. Taking the Lhasa River Basin as the study area
based on multi-source data such as GLDAS soil moisture (0~10
10~40
40~100
100~200 cm)
MODIS land surface temperature
precipitation
NDVI
and snow depth
this study comprehensively utilized methods such as Sen's slope trend analysis
Mann-Kendall test
Hurst index
partial correlation analysis
and random forest to systematically analyze the spatiotemporal variation patterns of soil moisture and freezing depth from 2000 to 2020. It quantified the spatial patterns of soil moisture memory at different depths and identified the dominant factors driving the variation of freezing depth. The results showed that soil moisture changes in the Lhasa River Basin exhibited significant vertical differentiation
gradually shifting from a large-scale decrease in the surface layer (92.2%) to a pattern of "increasing in the east and decreasing in the west" in the middle layer
with stable increasing and decreasing trends in the deep layer. Soil moisture memory was relatively short in the surface layer of the basin (1-2 months)
gradually increasing with depth in most parts of the basin
and forming a stable memory core in the farmland area (SMM in June-July). NDVI contributed the most to the spatial variation of freezing depth (54%)
followed by land surface temperature (contribution rate of 26%)
while the impact of snow depth and precipitation was limited. Considering the role of temperature
there was a complex spatial pattern of negative partial correlation and local positive partial correlation between vegetation coverage and freezing depth. This study reveals the spatiotemporal distribution characteristics of soil moisture memory in the Lhasa River Basin and the mechanisms by which related factors affect freezing depth
which is of great significance for understanding land-atmosphere interactions and water resource sustainability in alpine regions.
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