| 摘 要： |
Landscape management and planning is an important way to mitigate climate change. Previous studies have proved landscape pattern (composition and configuration) was correlated to thermal environment based on a single image data in time, but the static results were inconclusive and contradictory. In this study, a framework to investigate the dynamic relevance between landscape pattern change and near surface air temperature (NST) difference was constructed through the Weather Research and Forecasting Model (WRF) and geospatial methods including landscape metrics. The landscape pattern change of Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2000 to 2020 were examined and their impacts on NST were simulated. Results showed it was dominant that cropland, water and grass land urbanized in the landscape evolution process, which led BTH average NST rise by 0.05 & DEG;C and 0.177 & DEG;C in January and July, respectively. The dynamic relevance (DR) between landscape composition and configuration and NST of all land classifications over the two decades were investigated quantitatively. Results indicated the DR between the change of landscape pattern and NST in different periods was different and it was seasonal various. DR of composition metric was more significantly correlated than non area related spatial configuration metrics. In this twenty years, the DR of urban land (UBL) was positive, and it was negative for cropland (CPL). The landscape pattern of water (WTR) was positively correlated with NST in winter and negatively in summer. The DR of green space was an opposite correlation with DR of WTR in winter and summer, due to the newly added green space was mainly transformed from WTR. The dynamic perspective of relevance based on WRF and landscape indicators can enhance the understanding the effects of landscape pattern on thermal environment and provide a methodological framework for evaluating the dynamic relationship of landscape indicators and thermal effects. |
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