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杨道涵, 吴静, 李纯斌, 等. 2024. 青藏高原土壤热通量估算及时空分布特征[J]. 气候与环境研究, 29(2): 113−124. doi: 10.3878/j.issn.1006-9585.2023.23028
引用本文: 杨道涵, 吴静, 李纯斌, 等. 2024. 青藏高原土壤热通量估算及时空分布特征[J]. 气候与环境研究, 29(2): 113−124. doi: 10.3878/j.issn.1006-9585.2023.23028
YANG Daohan, WU Jing, LI Chunbin, et al. 2024. Estimation and Spatiotemporal Distribution of Soil Heat Flux over the Qinghai–Tibetan Plateau [J]. Climatic and Environmental Research (in Chinese), 29 (2): 113−124. doi: 10.3878/j.issn.1006-9585.2023.23028
Citation: YANG Daohan, WU Jing, LI Chunbin, et al. 2024. Estimation and Spatiotemporal Distribution of Soil Heat Flux over the Qinghai–Tibetan Plateau [J]. Climatic and Environmental Research (in Chinese), 29 (2): 113−124. doi: 10.3878/j.issn.1006-9585.2023.23028

青藏高原土壤热通量估算及时空分布特征

Estimation and Spatiotemporal Distribution of Soil Heat Flux over the Qinghai–Tibetan Plateau

  • 摘要: 土壤热通量(Soil heat flux, G)是影响青藏高原地表能量平衡的关键因素,对其进行估算以及时空分布特征分析,可为该区地表能量平衡研究提供参考依据。本文基于2003~2018年中分辨率成像光谱仪(Moderate-Resolution Imaging Spectroradiometer, MODIS)数据、中国区域地面气象要素驱动数据集以及中国西部1 km全天候地表温度数据集,利用土地表面能量平衡算法(Surface Energy Balance Algorithm for Land, SEBAL)模型结合青藏高原原位观测数据G0对模型的适用性和计算精度进行评估,发现该模型对青藏高原的土壤热通量G模拟精度较高。在此基础上利用遥感数据重构了该地区2003~2018年的土壤热通量数据,并分析了G值的时空分布特征。结果表明:(1)多年G均值整体呈波动下降趋势,最大谷值出现在2011年,最大峰值出现在2016年;各季节中,除冬季外,其余季节G均值呈波动下降趋势,且G均值值域高低依次呈现:夏季>春季>秋季>冬季,G均值波动变化大小顺序与之相一致。(2)G均值分布特征具有明显的空间异质性,总体呈现出北部柴达木盆地及其周边地区最高,西南阿里等地区较高,其余大部分地区普遍较低的空间分布特征;各季节G均值的空间分布特征基本与总体空间分布特征一致。(3)中部及东南地区G均值主要呈增加趋势,北部、西部和西南地区G均值主要呈减少趋势;各季节中,G均值有增加趋势的地区面积占比冬季最多,夏季最少,有减少趋势的地区面积占比夏季最多,冬季最少。本研究结果证明了SEBAL模型对反演青藏高原土壤热通量G的适用性,且丰富了青藏高原地表能量平衡的研究内容。

     

    Abstract: Soil heat flux (G) is one of the key factors affecting the surface energy balance over the Qinghai–Tibetan Plateau. Estimation and analysis of its spatiotemporal distribution can provide reference basis for research on surface energy balance in the Qinghai–Tibetan Plateau. In this paper, the applicability and accuracy of the Surface Energy Balance Algorithm for Land (SEBAL) model were evaluated by combining the model inversion data with observation data over the Qinghai–Tibetan Plateau from 2003 to 2018 based on Moderate-Resolution Imaging Spectroradiometer (MODIS), the China regional surface meteorological element driven dataset, and the 1-km all-weather surface temperature dataset from western China. We found that the SEBAL model had high accuracy for inverting G over the Qinghai–Tibetan Plateau. Accordingly, G values were inverted using remote-sensing data and the spatiotemporal distribution characteristics of G in the region were analyzed during 2003–2018. The results showed the following. (1) The mean value of G showed a fluctuating downward trend over a multiyear period, with the maximum valley value appearing in 2011 and the maximum peak value in 2016. The mean value of G demonstrated a fluctuating downward trend in every season except in winter. The mean value ranges of G in different seasons showed the following trend: Summer > spring > autumn > winter, the fluctuation in the mean value of G corresponded to this order in magnitude. (2) The spatial distribution characteristics of the mean value of G exhibited obvious spatial heterogeneity, with the overall trend being highest in the northern Qaidam Basin and its surrounding areas, relatively high in the southwestern Ngari region, and generally lower in most other areas. The spatial distribution characteristics of the mean value of G in each season generally aligned with the overall spatial distribution characteristics. (3) In the central and southeastern regions, the mean value of G mainly showed an increasing trend, while in the northern, western, and southwestern regions, the mean value of G mainly showed a decreasing trend. Among all seasons, the proportion of areas with an increasing trend in G was highest in winter and lowest in summer, while the proportion of areas with a decreasing trend was highest in summer and lowest in winter. The results of this study demonstrate the applicability of the SEBAL model for inverting G and enriching the studies on the surface energy balance over the Qinghai–Tibetan Plateau.

     

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