Net primary productivity simulation and environmental response analysis of the Jianghe River basin in western Hubei Province based on the BEPS-TerrainLabV2.0 model
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摘要: 区域植被净初级生产力(net primary productivity, NPP)的模拟估算及其时空变化规律对区域植被质量以及碳收支平衡分析具有重大意义。该文以鄂西犟河流域为研究区域,在对土地覆盖、地形、土壤、气象、植被指数等输入数据预处理基础上,基于BEPS-Terrainlab V2.0模型对流域1986—2017年的NPP进行模拟估算,评价了其模拟精度,并探讨了流域NPP时空变化规律及其环境变化响应。结果表明: 流域NPP在年内尺度上呈单峰状分布(夏季高、冬季低); 年际上呈波动上升趋势。流域NPP在空间上呈南低北高分布特征。不同土地覆盖类型的NPP值表现为阔叶>混交>针叶>农田>城市; NPP随高程的增加呈上升趋势,不同土壤质地的NPP值表现为砂土>砂质壤土>壤质砂土>粉砂壤土。在日尺度上对NPP影响最大的是辐射和温度,通过0.01显著性检验; 年尺度上LAI(leaf area index, LAI)对NPP影响最大,通过0.01显著性检验。
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关键词:
- BEPS-TerrainlabV2.0模型 /
- 净初级生产力 /
- 时空变化 /
- 环境影响
Abstract: The simulation-based estimation and spatio-temporal variations of the net primary productivity (NPP) of regional vegetation hold critical significance for analyzing regional vegetation quality and carbon balance. This study investigated the Jianghe River basin in western Hubei Province. First, it pre-processed the input data, including land cover, topography, soil, meteorology, and vegetation indices. Based on this, it estimated the NPP of vegetation in the Jianghe River basin from 1986 to 2017 using the BEPS-TerrainlabV2.0 model, with the model's simulation accuracy evaluated. Moreover, this study explored the spatio-temporal variations of the NPP and its response to environmental changes. The results are as follows: ① The NPP of vegetation in the Jianghe River basin exhibited a unimodal distribution, with higher values in summer and lower values in winter, on an intra-annual scale, and a fluctuating rising trend on an inter-annual scale; ② The spatial distribution of the NPP manifested higher values in the north and lower values in the south; ③ The NPP values of different land cover types followed the sequence below: broad-leaved forests > mixed forests > coniferous forests > farmland > urban areas. The NPP rose with an increase in elevation. The NPP values of different soil textures rank below: sandy soil > sandy loam > loamy sand > silty loam; ④ Radiation and temperature manifested the strongest impact on NPP on a daily basis, and the leaf area index (LAI) exhibited the most significant influence on NPP on an annual basis, both passing the 0.01 significance test. -
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