Dynamic analysis of landscape drivers in the thermal environment of Guanzhong plain urban agglomeration

• Published in: International journal of applied earth observation and geoinformation 2024-11, Vol.134, p.104239, Article 104239
• Main Authors: Chen, Long, Li, Heng, Zhang, Chunxiao, Chu, Wenhao, Corcoran, Jonathan, Wang, Tianbao
• Format: Article
• Language: English
• Subjects: Dynamic analysis; Guanzhong Plain Urban Agglomeration; Landscape pattern; Near-surface temperature; Weather Research and Forecasting Model (WRF)
• ISSN: 1569-8432
• DOI: 10.1016/j.jag.2024.104239

•Near surface air temperature change driven by landscape transformation was simulated.•The dynamic correlation between landscape pattern and near surface air temperature was analyzed.•Importance of landscape factors and their interactions from a dynamic perspective was quantified.•This study discussed the direction of landscape planning in mitigating climate change. Climate change caused by rapid urbanization in the Guanzhong region of China is becoming an increasingly significant problem. Previous empirical studies have confirmed that landscape patterns inextricably linked with the thermal environment, but static results based on a single temporal cross section of image data provide only a partial understanding. In this paper, we constructed a dynamic framework using Weather Research and Forecasting Model (WRF) for temperature simulation and Geodetector to study the landscape factors and their interactions that influence near-surface temperature (NST) changes in the Guanzhong Plain Urban Agglomeration (GPUA) between 2000 and 2020. Results showed that the GPUA average NST increased by 0.012 °C and 0.053 °C in January and July from 2000 to 2020, respectively. In terms of the dynamic correlation between landscape patterns and NST, cropland (CPL) was negative, urban land (UBL) was positive, and the remainder of the landscapes differed in winter and summer. Furthermore, results from the Geodetector showed that UBL embodied a stronger influence in summer than during winter months. This finding helps to explain why the average NST increase is higher in summer than during winter. The Dynamic Q values (DQ) of the area-based landscape metrics were generally larger than those of other spatial configuration metrics, and the interaction results showed that the landscape metrics of various land-cover classifications were enhanced, indicating that the superposition effect among landscape metrics needs to be taken into account in landscape planning in addition to area factors. The study of the relationship between landscape patterns and thermal environment considering dynamic perspective using WRF offers an important theoretical reference allied with practical guidance for understanding and adapting to forthcoming change in our climate through which we can help drive sustainable development decisions of the GPUA.