Yonghong Hu

Nested high-resolution modeling of the impact of urbanization on regional climate in three vast urban agglomerations in China

[1] In this paper, the Weather Research and Forecasting Model, coupled to the Urban Canopy Model, is employed to simulate the impact of urbanization on the regional climate over three vast city agglomerations in China. Based on high-resolution land use and land cover data, two scenarios are designed to represent the nonurban and current urban land use distributions. By comparing the results of two nested, high-resolution numerical experiments, the spatial and temporal changes on surface air temperature, heat stress index, surface energy budget, and precipitation due to urbanization are analyzed and quantified. Urban expansion increases the surface air temperature in urban areas by about 1C, and this climatic forcing of urbanization on temperature is more pronounced in summer and nighttime than other seasons and daytime. The heat stress intensity, which reflects the combined effects of temperature and humidity, is enhanced by about 0.5 units in urban areas. The regional incoming solar radiation increases after urban expansion, which may be caused by the reduction of cloud fraction. The increased temperature and roughness of the urban surface lead to enhanced convergence. Meanwhile, the planetary boundary layer is deepened, and water vapor is mixed more evenly in the lower atmosphere. The deficit of water vapor leads to less convective available potential energy and more convective inhibition energy. Finally, these combined effects may reduce the rainfall amount over urban areas, mainly in summer, and change the regional precipitation pattern to a certain extent.

Improved monitoring of urbanization processes in China for regional climate impact assessment

Regional climate is influenced by land surface processes through energy exchange between land and atmosphere at various scales. The performance of climate model simulation is largely influenced by land cover parameterization, especially over areas that experience rapid change of land surface characterization. Accurate land cover datasets suited for climate modeling are urgently needed to improve model parameterization for better simulation. In this study, fused urban land cover datasets have been developed by combining multi-source urban land cover products based on cross-comparison and detailed validation from medium resolution images during recent decades. Fractional cover values at different spatial scales were aggregated from integrated land cover datasets to derive improved estimates of the urban extent of big cities in China. Urbanization in China as captured by the new dataset can be divided into steadily and rapidly increasing periods with the urban area increasing by 2.5 and 6.7xa0% per year, respectively. Rapid urbanization mostly happens in the urban fringe, especially in eastern China, with mass conversion of cropland and woodland to urban land. An improvement in climate model simulation could be achieved using the fused data applied in climate modeling for examining the impact of rapid urbanization on the regional climate.

Multi-scale remote sensing estimates of urban fractions and road widths for regional models

Landuse in East Asia has changed substantially during the last three decades, featured with expansion of urban built-up at unprecedented scale and speed. The fast expansion of urban areas could contribute to local and even regional climate change. However, current spatial datasets of urban fractions do not well represent the extent and expansion of urban areas in the regions, and that best available satellite data and remote sensing techniques have not been well applied to serve regional modeling of urbanization impacts on near surface temperature and other climate variables. Better estimates of localized urban fractions are badly needed. Here we use high and mid resolution satellite data to estimate urban fractions and road width at local and regional scales. With our fractional cover, data fusion, and differentiated threshold approaches, more spatial details of urban cover are demonstrated than previously reported in many global datasets. Many city clusters were merging into each other, with gradual blurring of boundaries and disappearance of gaps among member cities. Cities and towns were more connected with roads and commercial corridors, while wildland and urban green areas have become more isolated as patches among built-up areas. Average road width in commercial areas was 37.2xa0m in Beijing (north, temperate) and 24.2xa0m in Guangzhou (south, tropical), which are greater than these listed in model default values. Those new estimates could effectively improve climate simulation at local and regional scales in East Asia.

The cumulative effects of urban expansion on land surface temperatures in metropolitan JingjinTang, China

Rapid urbanization has resulted in the permanent conversion of large areas of cropland and natural vegetation to impervious surfaces and therefore greatly modified land surface properties and land-atmosphere interactions. This study sought to examine the urbanization process using Landsat images from 2001 to 2010 in metropolitan JingjinTang (JJT), a rapidly expanding urban cluster in northern China. We aggregated the original results of land use data as fractional cover information in 1u2009km and 10u2009km grids. Annual and seasonal land surface temperatures (LSTs) were processed from Moderate Resolution Imaging Spectroradiometer products. We used moving window and gradient analysis methods to examine the differences in LST between urban and other land types, further identifying LST increases in gradients of urbanization levels. Urban extent increased by 1.6 times, and approximately 45% newly developed areas were converted from croplands during this process. Emerging urban land in JJT has caused approximately 0.85u2009±u20090.68°C warming in terms of annual mean LST, and the greatest warming occurred in the summer. An increase in urban land of 10% in a 1u2009km grid in JJT would cause approximately a 0.21°C increase in annual LST. Urbanization also led to increases in daytime LSTs and nighttime LSTs by approximately 1.03u2009±u20091.38°C and 0.78u2009±u20091.02°C, respectively. The warming trend induced by urbanization exhibits clear seasonal and diurnal differences, and this warming trend is most likely caused by the cumulative effects of changes in land properties, radiation storage, and anthropogenic heat release by urbanization.

Assessing surface albedo change and its induced radiation budget under rapid urbanization with Landsat and GLASS data

Radiative forcing (RF) induced by land use (mainly surface albedo) change is still not well understood in climate change science, especially the effects of changes in urban albedo due to rapid urbanization on the urban radiation budget. In this study, a modified RF derivation approach based on Landsat images was used to quantify changes in the solar radiation budget induced by variations in surface albedo in Beijing from 2001 to 2009. Field radiation records from a Beijing meteorological station were used to identify changes in RF at the local level. There has been rapid urban expansion over the last decade, with the urban land area increasing at about 3.3xa0% annually from 2001 to 2009. This has modified three-dimensional urban surface properties, resulting in lower albedo due to complex building configurations of urban centers and higher albedo on flat surfaces of suburban areas and cropland. There was greater solar radiation (6.93u2009×u2009108xa0W) in the urban center in 2009 than in 2001. However, large cropland and urban fringe areas caused less solar radiation absorption. RF increased with distance from the urban center (less than 14xa0km) and with greater urbanization, with the greatest value being 0.41xa0W/m2. The solar radiation budget in urban areas was believed to be mainly influenced by urban structural changes in the horizontal and vertical directions. Overall, the results presented herein indicate that cumulative urbanization impacts on the natural radiation budget could evolve into an important driver of local climate change.

Modifications in vegetation cover and surface albedo during rapid urbanization: a case study from South China

The green vegetation fraction (GVF) and surface albedo are important land surface parameters often used for validation of climate and land surface models that are influenced largely by environmental gradients and human activities. In this study, fine resolution GVF and albedo values derived from Landsat Thematic Mapper/Enhanced Thematic Mapper Plus images from 1990 to 2000 were used to examine the relationship of both GVF and albedo values to the spatial gradients of parameters related to dramatic urbanization in the Greater Guangzhou metropolitan area, Guangdong Province, in South China. Moderate resolution GVF and albedo datasets derived from the MODIS Collection 5 product were used to analyze the seasonal variation of GVF and albedo with rapid urban expansion from 2001 to 2007. The results show that the shortwave albedo had a clear declining trend from the urban center to natural land in 1990. However, no obvious trend in shortwave albedo change was observed along urban–rural gradients caused by the expansion of low-albedo urban buildings and more heterogeneous land cover patterns in 2000. A threshold of GVF (~0.21) was estimated for determining the change of albedo associated with vegetation fraction. Vegetation cover modified by urban expansion changed surface reflectance and influenced the surface energy balance. It is suggested that a large portion of energy absorbed in an urban area is likely to be converted to thermal energy that heating up is near the surface and emitted as longwave radiation.

Land surface temperature shaped by urban fractions in megacity region

Large areas of cropland and natural vegetation have been replaced by impervious surfaces during the recent rapid urbanization in China, which has resulted in intensified urban heat island effects and modified local or regional warming trends. However, it is unclear how urban expansion contributes to local temperature change. In this study, we investigated the relationship between land surface temperature (LST) change and the increase of urban land signals. The megacity of Tianjin was chosen for the case study because it is representative of the urbanization process in northern China. A combined analysis of LST and urban land information was conducted based on an urban–rural transect derived from Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectrometer (MODIS), and QuickBird images. The results indicated that the density of urban land signals has intensified within a 1-km2 grid in the urban center with an impervious land fraction >60xa0%. However, the construction on urban land is quite different with low-/mid-rise buildings outnumbering high-rise buildings in the urban–rural transect. Based on a statistical moving window analysis, positive correlation (R2u2009>u20090.9) is found between LST and urban land signals. Surface temperature change (ΔLST) increases by 0.062xa0°C, which was probably caused by the 1xa0% increase of urbanized land (ΔIF) in this case region.

Comparison of three different methods to identify fractional urban signals for improving climate modelling

Urbanization has changed the properties of the Earth’s surface and resulted in modification of the biogeochemical cycle and possible climate feedback at global and regional scales. Such climate effects are especially evident locally over short periods in megacity areas. Climate model simulation and urbanization process analysis are often limited by poor accuracy of land-cover products that largely neglect mixed urban-surface information below certain thresholds. The present study compares three urban land identification methods (fractional cover, overlapping parabolic interpolation, and threshold) used in remote sensing and climate model parameterization with Landsat Thematic Mapper images and Moderate Resolution Imaging Spectroradiometer land-cover data sets in a systematic evaluation. We also analyse deviation induced by scaling effects and its influence on the urban radiation budget to better understand the implications of land-surface parameter deviation on regional climate analysis. A positive linear relationship is found between the spatial scale and urban-area deviation based on combined analysis of the three land identification methods, and deviation trends levelled off with an increase in the spatial scale. Coarse-resolution land-cover products could not capture well the urbanization process indicated by reference data from Beijing between 2000 and 2009, especially in urban fringe areas where major urban expansion was detected. Detailed sub-pixel information was possibly neglected by threshold methods, which resulted in strong deviation between land-cover products and actual conditions. The overlapping parabolic interpolation method used in climate models also produced deviation in surface parameter derivation during nested simulation work. This might further affect model performance at the regional scale and should be considered in climate model simulation.

Derivation of fractional urban signals in better capturing urbanization process

Rapid urbanization has changed land use and urban structure in China and therefore greatly modified land surface properties and land–atmosphere interactions, causing further local and regional climate change. Climate model simulation and urbanization process analysis are usually limited by poor accuracy of coarse-resolution land use/cover products employed in regional climate models. This study sought to identify better urban representation from Landsat images and monitor urban expansion by change detection of spatial patterns and urban fractions in southeastern coastal region of China. We used the improved normalized indices-based method to classify urban and built-up areas from Landsat images in Jiading District, Shanghai. Classification results were evaluated at both the pixel scale and the model grid scale, with overall accuracy of 88% and k coefficient of 0.76. Moreover, urbanization process over the Guangzhou–Foshan–Dongguan area was examined from 2000 to 2009. We aggregated the original results of urban classification data from Landsat images as fractional cover information in 1-km grids. The total fractional urban change in 2000–2005 (10.65%) was approximately three times greater than in 2005–2009 (3.38%). We also compared the fractional cover of urban expansion with the corresponding period of MODIS land cover products. It showed that existing land cover products in models had deviations and could not capture well the underlying conditions and urbanization process. Different fractional covers of urban scenarios were expected to provide better inputs for accurate modeling of critical environmental feedbacks over expanding urban clusters.