1410 Influence of Urban Land Cover Data Uncertainties on the Numerical Simulations of Urbanization Effects in Eastern China

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Ning Zhang, Nanjing Univ., Nanjing, China

Updating the urban land cover information has been proved a necessary method for the numerical studies of urban climate and urban atmospheric environment in China, a fast urbanizing country. However, there are uncertainties in the urban land use/urban land cover (ULUC) information in different datasets due to the uncertainties in raw data sources and produce methods. In this study, the WRF model is used to simulate the summer climate over Yangtze River Delta in July and August 2013, when a heat-wave episode occurred. Five numerical experiments are designed; one is the baseline experiment (NOURB) in which all urban land cover is removed, and different urban land use/land cover (ULUC) experiments from four different global land cover datasets are used in other four experiments. The differences between the ULUC experiments and the NOURB experiment are used to observe the urbanization effects. The results show remarkable urbanization effect in all ULUC experiments and the urbanization influences are most different over the ‘union-urban’ area, which defined as the area marked as urban area by at least one land cover dataset, because the uncertainties in urban land cover information is greatest in this area. All ULUC experiments show the regional influence of urbanization as well, which means urbanization effects are not limited in cities. The CCI experiment using ESA-CCI 2013 dataset showed the greatest urbanization influence and the highest effect index in all analyzed variables, because of the largest urban land cover area in the analysis domain. The urban land cover area in the GLC experiment is the smallest among the ULUC experiments, but it distributes in a more concentrated way than in other experiments; it indicates that both city size and city shape affect the regional influence.
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