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Scenario Simulation of the Influence of Land Use Change on the Regional Temperature in a Rapidly Urbanizing Region: A Case Study in Southern-Jiangsu, China

Scenario Simulation of the Influence of Land Use Change on the Regional Temperature in a Rapidly... Hindawi Publishing Corporation Advances in Meteorology Volume 2014, Article ID 159724, 12 pages http://dx.doi.org/10.1155/2014/159724 Research Article Scenario Simulation of the Influence of Land Use Change on the Regional Temperature in a Rapidly Urbanizing Region: A Case Study in Southern-Jiangsu, China 1 2 3 Xinli Ke, Enjun Ma, and Yongwei Yuan College of Land Management, Huazhong Agricultural University, Wuhan 430079, China School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan 430074, China Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China Correspondence should be addressed to Xinli Ke; kexl@igsnrr.ac.cn Received 29 August 2013; Accepted 4 December 2013; Published 16 February 2014 Academic Editor: Burak Guneral ¨ p Copyright © 2014 Xinli Ke et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It has been shown that land use change in urbanized region, especially urban land expansion, will influence regional climate. However, there has been very little research on the climate effects of the future land use change in a rapidly urbanizing region. Taking the southern part of Jiangsu province in China as the study area and through a scenario analysis, the influence of land use change on the regional temperature was analyzed from the perspective of land surface radiation budget and energy balance. eTh results indicated that (1) the monthly average temperature is significantly higher under the Rapid Economic Growth (REG) scenario than under the Cooperate Environmental Sustainability (CES) scenario in 2050, especially in the hottest month (July). (2) The range of high-temperature regions is much wider under the REG scenario than it is under the CES scenario in 2050. (3) The land surface net radiation and latent heat ux fl are two key factors through which land use change influences the regional temperature in the study area, and the latent heat u fl x plays a dominant role. (4) Land use change mainly influences the land surface net radiation by altering the land surface albedo and emissivity. These results are helpful to mitigate regional climate change eeff cts caused by land use change. 1. Introduction Research on the impacts of urbanization on climate mainly focuses on urban heat island and urban rain island. Urbanization is one of the most dramatic changes in the Sincethe urbanheatislandwas proposed in 1833,ithas world. Urban land expansion is a prominent feature of been verified that urban heat island exists in many cities of urbanization. With the rapid development of urbanization, dieff rent typesand sizes[ 8, 9]. A large number of studies the land surface is replaced by the impermeable ground such suggest that the urban heat island intensity exhibits different as cement, asphalt, which has made urban environmental characteristics along with the location and the type of the city, problems become increasingly serious [1, 2]. Among them, the conditions of periurban area and so on [10, 11]. Global- the increasing urban heat island effect has become the scale studies suggest that urbanization plays an important typical problem of the urban environmental change and has role in raising global surface temperature in recent years endangered the daily life and safety of the urban residents [12, 13]. However, almost all the studies have conrfi med [3, 4]. Urbanization as a typical performance of human land that the urbanization, especially urban land expansion will use activities can affect local and regional climate change, lead to the rising surface temperature. Urbanization changes andevenlarge-scale atmosphericcirculation [5]. Underlying the underlying surface and makes the urban areas temper- surfacechangewould causethe redistribution of Earth ature rise. Precipitation is another important aspect of the surface’s heat and moisture in the process of urbanization, impact of urbanization on regional climate. In the process causing changes in local and even regional climate [6, 7]. of urbanization, natural vegetation is replaced by asphalt, 2 Advances in Meteorology buildings, and other artificial land surfaces, which results in reducing moisture together affected convective activity thus changes on the surface roughness, albedo and soil moisture, realizing regional climate change [34]. It can clearly be seen and other surface properties, and affects regional precipita- that numerical simulation method provides support for the tion through their impacts on the radiation balance, water in-depth understanding of the process: urbanization, land balance, and other processes. Early studies showed that in surface change, key biogeophysical parameters change, and rapid urban development period, there will be unusually climate change. high precipitation in urban areas [14], and the areas which This paper took Southern-Jiangsu in China as the case are close to the metropolitan areas are more prone to have study area. Firstly, DLS (Dynamics of Land System) model heavy rains [15–17]. With the growth of the urban population, is employed to simulate future land use change in study raining in the aer ft noon seems to increase in large cities area at various scenarios. en Th the simulation results of land [14]. Another study showed that the increasing intensity usechangewereprocessed so as to satisfythe requirements of showers in monsoon season in the urban areas may be of underlying surface data for WRF (Weather Research relevant to heat island [18]. Further studies showed that effects and Forecast) model. On this basis, the WRF model was of urbanization on precipitation intensity showed different used to simulate the impacts of future land use change on laws in different terrains and climatic backgrounds [ 19]. In regional climate change, to scientifically understand the key addition, the impact of urbanization on precipitation is not parameters and the key process of the impacts of region’s limited to urban areas but also to urban downwind areas future land usechangeonregionalclimate change,and to [20, 21]. provide scientific basis for the rational regional land use As to research methods, research on impacts of urbaniza- planning to mitigate climate change. tion on climate change is mainly based on studies of obser- vational data and numerical simulation. Early researches 2. Study Area about impacts of urbanization on climate change mainly rely on observation site data, combined with demographic data China is the largest developing country in the world, expe- or land use data, using empirical orthogonal function [22], riencing rapid urbanization. Yangtze River Delta is one of principal component analysis [23, 24], and other methods themostrapidly urbanizing regionsinChina,and land use to select reference stations and urban stations. Through change caused by urban expansion is very intense in this area. comparative analysis of observational data discrepancies Studies have shown that the urbanization of Yangtze River between reference stations and urban stations, the impacts of Delta region has obvious eeff cts on its climate. Southern- urbanization on climate change can be assessed. This method Jiangsu is an important part of the Yangtze River Delta, the is simple, intuitive, and persuasive, but it is aeff cted by the impact of regional urban land expansion on climate change site classification results and the number of samples. To solve has also been widely concerned [35]. However, current this problem, Kalnay and Cai (2003) [25], based on the researches mainly focus on land use change and climate characteristics of NCEP/NCAR reanalysis data which did not change, revealing the impacts of urbanization on regional use ground-based observations information in the assimila- climate change. Few studies focus on the impacts of future tion process, proposed an idea which could reflect impacts urbanlanduse change on regional climatechange. eTh of urbanization on the regional climate change according scientific understanding of the impacts of future land use to comparative observations and reanalysis differences [ 25]. change on the regional climate change is the basis of a Although this approach has achieved good results [26], its scientific and rational land use planning to mitigate regional reliability has been disputed [27, 28]. In recent years, with the climate change. eTh refore, there is an urgent need to reveal development of remote sensing technology, the research of the impacts of future land use changes of typical urbanized the impacts of urbanization on regional climate change has areas on regional climate change. made a great progress by using remote sensing technology to extract information on land use changes and land sur- Southern-Jiangsu is located in the Yangtze River Basin face air temperature and precipitation data [29–31]. These in China, with superior geographical environment, pleasant methods provided a good support to describe the impacts of climate and convenient irrigation, vast plains, and fertile soil. urbanization on climate change. However, these methods are There are ve fi cities in Southern-Jiangsu, including Nanjing, inadequate to analyze the impact mechanism of urbanization Zhenjiang, Changzhou, Wuxi, and Suzhou (Figure 1). In on climate. Therefore, it is necessary to introduce numerical history, Southern-Jiangsu was China’s most prosperous place simulation in researching the key processes that urbanization because agriculture there was well developed. Southern- affects regional climate change from the perspective of Jiangsu is closed to Shanghai and it is a large hinterland of the mechanism. Sensitivity experiments of two-dimensional Shanghai, so it has geographical advantages. Because of its scale model showed that sensible heat u fl xes caused by proximity to Shanghai, Southern-Jiangsu developed strong urban surface changes played a key role in climate change, economic ties with Shanghai. and roughness changes affected the spatial heterogeneity of Southern-Jiangsu is one of the most populous and urban- climatechange[32]. Sensitivity test of RAMS/TEM coupled ized and the fastest growing economic regions in China. model indicated that urban heat island plays a key role in With Shanghai’s role in driving the economic development, inducing downwind convective systems [33]. Sensitivity test Southern-Jiangsu grows rapidly in economic development of WRF/NOAH coupled model indicated that urbanization and urbanization. In 2008, the regional GDP of Southern- mainly through two aspects of increasing heat sense and Jiangsu was 1.85 trillion Yuan, accounting for 6.4% of the Advances in Meteorology 3 ∘ ∘ ∘ 119 E 120 E 121 E km 0 25 50 12.5 32 N 32 N 31 N 31 N ∘ ∘ ∘ 119 E 120 E 121 E Cultivated land Water area Forestry Built-up area Grassland Unused land Figure 1: Location of Southern-Jiangsu. whole country. Southern-Jiangsu is also relatively dense and the network of transport of the study area and to work out with cities and towns, and urbanization level is high. In the distance from each 100 m× 100 m grid to railways, roads, 2008, Southern-Jiangsu’s urbanization rate reached 67.7%, and rivers. and per capita GDP reached 61,823 Yuan reaching the level eTh social and economic statistical data included the of moderately developed countries. population of Southern-Jiangsu, per capita retail sales of social consumer goods, the total investment in fixed assets, per capita sfi cal revenue, the gross output of the second 3. Data and Methodology industry, and grain yield per unit area from 2000 to 2008. eTh above data come from Jiangsu Statistical Yearbook. 3.1. Data Sources. eTh data includes the land use data, socioe- conomic data, and data of natural environmental conditions of Southern-Jiangsu. 3.2. DLS Model. DLS is a land use dynamic simulation model Land use data is mainly used for scenario simulations of based land use change mechanism, which in accordance land use change. In this study, land use data of Southern- with the driving forces analysis of land use change, scenario Jiangsu in 2000 and 2008 are obtained through remote sens- forecasting, and supply industrial allocation of land area and ing images interpretation. These land use data are composed space allocation carrying out dynamic simulations of land of six land types, including farmland, forestry land, grassland, use change from region and grid (Figure 2). DLS model built-up land, water bodies, and unused land. Among them, consists of four modules, including scenario analysis module, land use data in 2000 came from the Land Use Database of spatial analysis module, the conversion rules module and Data Center Resources and Environment, Chinese Academy spatial analysis modules. Scenario analysis module is used of Science [36]. The database consists of Landsat TM/ETM+ to express the changed needs of a variety of land use types image interpretation with a spatial resolution of30×30 m. under different scenarios. Spatial analysis module is used to Land usedatain2008isinterpreted by LandsatETM+ calculate the probability values of various land use types in images. each grid unit through spatial regression analysis for driving factors. Transfer rules module is used to express possibility The natural environmental conditions data included and ease of a certain type of land transfer to another type of DEM data of the study area, the distance from the city at land on each grid cell. Space allocation module implements all levels, the distance from the railways, the distance from spatial distribution pattern of various land use types under theroads,and thedistancefromthe rivers.DEM data came different scenarios on the grid. from the data of Shuttle Radar Topography Mission (SRTM) of NASA. This paper hierarchically calculated the distance eTh re are mainly four steps to carry out dynamic sim- from the city at all levels to each 100× 100 m grid, using ulation of land use based on DLS. First, analyze statistical the Landsat TM/ETM+ geometric correction in 2000 that relationship between land use types distribution and driving covered Southern-Jiangsu to outline the major river systems factors from the two scales of region and grid, measure 4 Advances in Meteorology Analysis on driving mechanism of land use change Scenarios analysis of land use change Environmental Transform Characteristics Transform rules probability condition Driving of historic Driving mechanism Climate land use Prediction tendency Change of regional forces of for patterns Population change of land use change land use structure land use and process Economic change of land use Management Scenarios analysis of change Patterns of land use change regional land use change Policy Balance of land use demand between Balance of land use demand between various industrials various regions Patterns of land use change Spatial distribution of land use change Figure 2: Framework of DLS model. WRF External data preprocessing WRF ARW model Visualization source system Ideal 3D Ideal 2D supercell hill grav baroclinic waves squall line NCL WRF terrestrial ARW post ARW model data (GrADS/Vis5D) RIP4 Real data WPS initialization Gridded data: WPP NAM, GFS, Real data (GrADS/ initialization RUC, NNRP and GEMPAK) AGRMET (soil) Figure 3: Framework of WRF model. effects of the natural environment and socioeconomic factors worked out. Under the linear hypothesis, land use change on temporal patterns of regional land use, and extract the process can be presented as the following formula: key factors which aeff ct land use types distribution. en, Th 𝑡 𝑡−1 Δ𝑌 =𝑓(...,𝑥 ,...)−𝑓(...,𝑥 ,...)=𝑓(Δ𝑥 ), (1) basedonthe historyoflanduse characteristicsand the 𝑖 𝑖 𝑖 𝑖 status of regional land use changes, predict trends that the 𝑡 𝑡−1 whereΔ𝑌 is thechangeareaoflanduse𝑌 ,𝑥 and𝑥 are the key factors inu fl ence land use patterns, and then select a 𝑖 𝑖 𝑖 𝑖 value of independent variables in time𝑡 and𝑡−1 ,respectively, reasonable scenario. According to supply-demand situation andΔ𝑥 is the changed value of independent variables. of different industries on land under this scenario during the time cross-section of forecast period, allocate area demand of different land types to various industries. Finally, by balance 3.3. WRF Model. WRF model is a new generation mesoscale analysis of grid-scale land type area’s demand and supply, weather forecasting model and assimilation model which achieve spatial distribution of dieff rent kinds of land use was jointly initiated by research institutes and scientists types on the grid scale and generate spatial pattern of land of universities in the United States [37]. This model is use. highly modular and layer-designed, the main program adds According to the estimated result of the experiential a number of optimization options in compiler, and the input model, the contribution on land use change of various inde- andthe output data arereadinavarietyofstandardformats. pendent variables can be calculated. Based on this, prediction WRF model consists of three parts, including preprocess- of land use in 2010 and 2050 in Southern-Jiangsu can be ing module of mode (WPS), main module of model (ARW), Advances in Meteorology 5 ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 118 E 119 E 120 E 121 E Zhenjiang Zhenjiang Nanjing Nanjing Changzhou Changzhou Suzhou Suzhou Wuxi Wuxi ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 118 E 119 E 120 E 121 E (a) (b) ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 118 E 119 E 120 E 121 E Zhenjiang Zhenjiang Nanjing Nanjing Changzhou Changzhou Suzhou Suzhou Wuxi Wuxi ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 118 E 119 E 120 E 121 E Urban and built-up land Urban and built-up land Dryland cropland and pasture Dryland cropland and pasture Irrigated cropland and pasture Irrigated cropland and pasture Mixed dryland/irrigated cropland and pasture Mixed dryland/irrigated cropland and pasture Cropland/grassland mosaic Cropland/grassland mosaic Cropland/woodland mosaic Cropland/woodland mosaic Grassland Grassland Shrubland Shrubland Mixed shrubland/grassland Mixed shrubland/grassland Deciduous broadleaf forest Deciduous broadleaf forest Evergreen broadleaf forest Evergreen broadleaf forest Evergreen needleleaf forest Evergreen needleleaf forest Mixed forest Mixed forest Water bodies Water bodies (c) (d) Figure 4: Results of land use change simulation. (a) and (b) show the simulation results of land use in Southern-Jiangsu in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation results of land use in Southern-Jiangsu in year 2050 under REG scenario and CES scenario, respectively. and assimilation module of mode and postprocessing tools simulation. ARW is the core part of the model and mainly of mode data (WRF-VAR) (Figure 3). Preprocessing module makes initialization and integration for simulation. of the model mainly determines the analog areas, providing WRF model is mainly applied to the weather and climate initial and boundary conditions of simulation, providing research when horizontal resolution is 1–10 km. It can also be topography and soil types data and entrusting to the grid area applied to numerical simulation, physical parameterizations of simulation, reentrusting the meteorological data to grid of research, data assimilation, and numerical ideal test and ∘ ∘ ∘ ∘ 31 N 32 N 31 N 32 N ∘ ∘ 31 N 32 N 31 N 32 N ∘ ∘ ∘ ∘ 31 N 32 N 31 N 32 N ∘ ∘ ∘ ∘ 31 N 32 N 31 N 32 N 6 Advances in Meteorology provide meteorological eld fi for air quality model. This paper Temperature simulated the future land use change in Southern-Jiangsu using the DLS model and made dynamic simulations to regional climate change under different underlying scenarios basedonthe WRFmodel,justtorevealthe impactsof Southern-Jiangsu’s future land use change on regional climate change. 4. Results 4.1. Scenario Analysis of Future Land Use in Southern- 5 Jiangsu. Future land use conditions in Southern-Jiangsu are simulated under two kinds of scenarios in this paper. Aeft r 30 years’ development under the reform and opening up −5 policy in China, the Southern-Jiangsu has achieved rapid 0 2 4 6 8 10 12 Month socioeconomic progress. Meanwhile, the Southern-Jiangsu’s resources and environment are also under tremendous pres- Land 2010 in REG Land 2050 in REG sure. Particularly, farmland resources in this area are facing Land 2050 in CES Land 2010 in CES significant stress of reduction under the circumstance of rapid urban expansion. Southern-Jiangsu’s resources and environ- Figure 5: Simulated monthly average temperature in Southern- mental pressures have become increasingly prominent in Jiangsu under different scenarios (unit: C).Land2010inREG sce- nario and Land 2010 in CES scenario represent the monthly average the process of rapid economic development. er Th efore, the temperature simulated with the LUCC data in 2010 under the REG development of Southern-Jiangsu is facing new opportu- scenario and CES scenario as the underlying data, respectively. Land nities and challenges. Against this background, this paper 2050 in REG scenario and Land 2050 in CES scenario refer to that set Southern-Jiangsu’s future land use scenarios as REG in 2050. scenario and CES scenario. eTh core of REG scenario is that land usedemands have thepriorityinlanduse change. Southern-Jiangsu’s land use change has served the purpose of economic development in the past 30 years; therefore, it in the small-medium cities, which puts great pressure on the surrounding cultivated land and forests. Under the canbeconsidered that Southern-Jiangsu’s land usescenario was the REG scenario in the past 30 years. The core of CES scenarios, the speed of economic development will be CES scenario is to achieve coordination between economic restrained to some degree, and the land use intensity will be further improved and the consumption of land resources due development and environmental protection. Therefore, the purposeofthe land useinCES scenario is to realizethe to economic development will also be restrained. Although there will still be some expansion of built-up land around the transformation of economic development so as to protect main urban areas of Nanjing, Zhenjiang, Suzhou, Wuxi, and natural resources and environment by sacrificing the speed of economic development rationally. Changzhou, the expansion degree is much limited compared to that under the REG scenario. eTh area of cultivated land This study simulated the land use change in Southern- and forests will decrease slightly due to the built-up land Jiangsu during 2010 to 2050 under the REG scenario and expansion, but the decreased area has been under control CES scenario with the DLS model (Figure 4). The result compared to that under the REG scenarios, in particular, the under the REG scenario suggests that the built-up land shrinkage of forests is well controlled in these regions. By expansion in 2010 mainly concentrated on the main urban comparison, the built-up land in small-medium cities still areas of Nanjing, Zhenjiang, Suzhou, Wuxi, and Changzhou, expands dispersedly, but the expansion speed is obviously which is consistent with the trend of the current land use restrained. change in Southern-Jiangsu. While the simulation result under the CES scenario indicates that the built-up land will expanddispersedlyinthe wholestudy area,the built-up 4.2. Impacts of the Future Land Use Change on the Regional land expansion around the main urban areas of Nanjing, Temperature in Southern-Jiangsu. Based on the simulation Zhenjiang, Suzhou, Wuxi, and Changzhou will be restrained results of the future land use change in Southern-Jiangsu, the to some degree. WRF was used to simulate the impacts of the land use change The simulation result in year 2050 indicates that there on the regional climate change in the future under different is still a great demand of economic development for the scenarios. The underlying surface data were first generated land resource under the REG scenario since the REG will through up-scaling and reclassifying the simulation results keep a high speed in Southern-Jiangsu. eTh built-up land of land use change in Southern-Jiangsu according to the will expand most obviously around the main urban areas of requirement of the WRF model. en Th the static underlying Nanjing, Zhenjiang, Suzhou, Wuxi, and Changzhou, where surface data in the WRF model were replaced with the theareaofcultivatedlandand forestswillfurther decrease. dynamic ones in 2010 and 2050 under the REG scenario and eTh re will also be some expansion of the built-up land CESscenario; thereaeft rthe future regional climatechange Temperature Advances in Meteorology 7 32.2 32.2 32 32 18 31.8 31.8 17 31.6 31.6 16 31.4 31.4 31.2 31.2 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (a) (b) 32.2 19 32.2 32 32 18 31.8 17 31.8 31.6 16 31.6 31.4 31.4 31.2 31.2 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (c) (d) Figure 6: Spatial pattern of the monthly average temperature in Southern-Jiangsu under different scenarios (unit: C). (a) and (b) show the simulation result of average temperature of land use in Southern-Jiangsu in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation result of average temperature of land use in Southern-Jiangsu in year 2050 under REG scenario and CES scenario, respectively. was simulated and na fi lly the climate eeff cts of different monthly average temperature under the REG scenario is only underlying surfaces were analyzed (Figure 5). slightly higher than that under the REG scenario. However, The simulation results indicate that the changing trends of Figures 6(c) and 6(d) indicate that the regions with high the monthly average temperatures under different scenarios temperature in 2050 under the REG scenario are much wider are consistent on the whole. The highest monthly average than the high temperature regions under the CES scenario, temperatures all appear in June, July, and August, and the especially around the center of big cities such as Nanjing, lowest ones all appear in November, January, and February. Zhenjiang, Suzhou, and Wuxi. According to the comparison This result shows that the changes of underlying surface do of results between the monthly average temperature in 2050 not aeff ct the monthly temperature change trend; it only under the REG scenario and other scenarios, the range of the aeff cts the value of average temperature. There is signifi- high-temperature regions is much wider in 2050 under the cant difference on the monthly average temperatures during REG scenario than other scenarios. different periods under different scenarios. The simulation The biggest difference in the monthly average temper- results indicate that the regional monthly average tempera- ature under different scenarios is in July; therefore, the ture in 2010 under the CES scenario is the lowest on the whole, impacts of different underlying surfaces on the temperature while that in 2050 under the REG scenario is the highest. can be more clearly revealed through comparing the spatial Besides, the monthly average temperature in 2010 under the pattern of the monthly average temperature in July (Figure 7). REG scenario is slightly higher than in 2050 under the CES Figure 7 shows that the impacts of different underlying sur- scenario. In addition, there are also some differences in the faces on the spatial pattern of the temperature in Southern- monthly average temperatures between different underly- Jiangsu in July are consistent with its spatial pattern on ing surfaces during different periods. Overall, the greatest the monthly average temperature, but the scope of high difference in the monthly average temperatures appears in temperatureismoresignicfi antinJuly. Taking theresults summer, while there is no significant difference in winter, in 2050 under the REG scenario and CES scenario (Figures with that in January being the slightest. 7(c) and 7(d)) as examples, the high-temperature region in eTh simulation result indicates that there are signicfi ant Nanjing has expanded into a separate continuous region impacts of the underlying surface on the spatial pattern of the in 2050 under the CES scenario, while the scope of the monthly average temperature under different scenarios, espe- high-temperature region in Zhenjiang is still very limited. cially in 2050 under the REG scenario and in 2010 under the Besides, the high-temperature regions in Suzhou, Wuxi, and CES scenario (Figure 6). Figures 6(a) and 6(b) suggest that Changzhou have also expanded into a large continuous there is no significant difference between the spatial pattern of district,but itsscope andtemperature rangeare both smaller the monthly average temperature on the underlying surfaces than the scope and temperature range in 2050 under the REG under the REG scenario and the CES scenario in 2010, and the scenario. 8 Advances in Meteorology 33 33 32.2 32.2 32 32 32 31.8 31.8 31 31 31.6 31.6 30 30 31.4 31.4 29 29 31.2 31.2 28 28 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (a) (b) 33 33 32.2 32.2 32 32 32 32 31.8 31.8 31.6 31.6 30 30 31.4 31.4 29 29 31.2 31.2 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (c) (d) Figure 7: Spatial pattern of the monthly average temperature in Southern-Jiangsu in July under different scenarios (unit: C). (a) and (b) show the simulation result of monthly average temperature in Southern-Jiangsu in July of land use in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation result of monthly average temperature in Southern-Jiangsu in July of land use in year 2050 under REG scenario and CES scenario, respectively. 4.3. Key Impact Mechanisms of the Future Land Use Change on scenarios, under which it will show no significant difference. the Regional Temperature in Southern-Jiangsu. According to In addition, Figures 8(c) and 8(d) indicate that there will the surface energy budget equation, there is close relationship not be significant difference in the downward long-wave between the surface net radiation, land surface albedo, down- radiation and downward shortwave radiation under all the ward shortwave radiation, downward long-wave radiation, scenarios. In summary, under the condition that there is and land surface emissivity: no significant difference between the downward long-wave radiation and downward shortwave radiation, there will be 𝑆 𝑙 𝑠 𝑙 4 (2) 𝑅 =𝑅 +𝑅 =(1−𝛼 )𝐹 +𝜀𝐹 −𝜎𝜀𝑇 , lowerlandsurface albedo andemissivityin2050under 𝑛 𝑛 𝑛 𝑑 𝑑 the REG scenario, which consequently greatly increases the where𝑅 is the surface net radiation,𝑅 is the short wave 𝑛 land surface net radiation and thus lays foundation for the radiation,𝑅 is the long-wave radiation,𝛼 is the land surface warming eeff cts. albedo,𝐹 is the downward shortwave radiation,𝜀 is the land This study analyzed the impacts of the spatial heterogene- ity of the land surface emissivity on the spatial pattern of surface emissivity,𝐹 is the downward long-wave radiation, temperature in the hottest month (July) since the difference in and𝑇 is the land surface temperature. The land net radiation thelandsurfaceemissivityisthemainreasonforthewarming is theenergysourceofthe land surfacetemperature change, effects in 2050 under the REG scenario ( Figure 9). this studyhas focusedonhow theunderlyingsurface change Figure 9 shows that the regions with the lower land influences the land surface albedo, downward shortwave surface emissivity is more widespread in 2050 under the REG radiation, downward long-wave radiation, and land surface scenario than under other three scenarios. Under all the four emissivity in order to clarify the key influencing mechanism scenarios, there are always continuous districts with lower of thefuturelanduse change on theregionaltemperature in land surface emissivity in Nanjing, Zhenjiang, Suzhou, Wuxi, Southern-Jiangsu (Figure 8). and Changzhou, where the urban land is the main part of The land use change in Southern-Jiangsu mainly influ- the underlying surface. However, the result clearly shows ences the land net radiation through exerting impacts on that the land surface emissivity in these continuous districts thelandsurface albedo andemissivity, andthe land use is obviously lower in 2050 under the REG scenario than change influences the spatial heterogeneity of the land surface other scenarios, which may be mainly because the underlying emissivity most greatly under both the scenarios (Figure 8). surfacewillchangemoregreatly in 2050 under theREG Figure 8(a) suggests that the land surface albedo will be scenario. the lowest in 2050 under the REG scenario, while it will show no significant difference under other scenarios. Besides, What is more, the land surface energy budget equation suggests that under the condition of certain land surface Figure 8(b) suggests that the land surface emissivity will be obviously lower in 2050 under the REG scenario than other net radiation, the underlying surface mainly influences the Advances in Meteorology 9 0.28 0.98 0.975 0.27 0.97 0.26 0.965 0.25 0.96 0.955 0.24 0.95 0.23 0.945 0.94 0.22 0.935 0.21 0.93 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Month Month Land 2010 in REG Land 2050 in REG Land 2010 in REG Land 2050 in REG Land 2050 in CES Land 2050 in CES Land 2010 in CES Land 2010 in CES (a) (b) 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Month Month Land 2050 in REG Land 2050 in REG Land 2010 in REG Land 2010 in REG Land 2010 in CES Land 2050 in CES Land 2010 in CES Land 2050 in CES (c) (d) Figure 8: Influence of the future land use change on the key biogeophysical parameters in Southern-Jiangsu. (a) and (b) show the inu fl ence of the future land use change on albedo and surface emissivity; (c) and (d) show the influence of the future land use change on downward long-wave radiation and downward shortwave radiation (W/m ). temperature through influencing the sensible heat ux, fl latent Figure 10 shows that the latent heat u fl x is obviously lower heat u fl x, and soil heat ux: fl in 2050 under the REG scenario than under other scenarios, andtherefore thedecreaseofthe latent heatufl xcausedbythe 𝑅 =𝐻+ LE+𝐺, (3) underlying surface change due to land use change can be seen as one of the main causes of the temperature rise in Southern- where𝑅 is thelandsurface netradiation,𝐻 is the sensible 𝑛 Jiangsu. heat u fl x, LE is the latent heat u fl x, and 𝐺 is the soil heat ux. fl In order to further analyze the impacts of the difference in the latent heat ux fl on the spatial pattern of temperature, Since there is generally very limited heat u fl x into the soil layer, the land surface net radiation is mainly influenced the spatial pattern of the latent heat u fl x under different by thesensibleheatflux andlatentheatflux,while the scenarios were further analyzed (Figure 11). It can be seen that underlying surface can directly influence the latent heat u fl x there is no significant difference in the spatial pattern of the and consequently influence the near-surface temperature. latent heat u fl x in Southern-Jiangsu under different scenarios. glw Albedo swdown Emiss 10 Advances in Meteorology 0.97 32.2 32.2 32 32 0.96 0.96 31.8 31.8 0.95 0.95 31.6 31.6 0.94 0.94 31.4 31.4 0.93 0.93 31.2 31.2 0.92 0.92 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (a) (b) 0.97 32.2 32.2 0.97 0.96 32 32 0.96 0.95 31.8 31.8 0.95 0.94 31.6 31.6 0.94 0.93 31.4 31.4 0.93 0.92 31.2 31.2 0.92 0.91 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (c) (d) Figure 9: Spatial pattern of the land surface emissivity in Southern-Jiangsu under different scenarios. (a) and (b) show the simulation result of land surface emissivity of land use in Southern-Jiangsu in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation result of land surface emissivity of land use in Southern-Jiangsu in year 2050 under REG scenario and CES scenario, respectively. the climate change. Taking Southern-Jiangsu, the typical region of urbanization in China, as the study area, this study analyzed the inu fl ence of land use change on the temperature under the REG scenario and CES scenario during 2010–2050 on the basis of simulation with the DLS model and WRF model. This study then analyzed the impacts of land use change on the key biogeophysical parameters such as surface net radiation, land surface albedo, downward shortwave radiation, downward long-wave radiation, and land surface emissivity from the perspective of land surface radiation budget and energy balance. Furthermore, the key influencing mechanisms of the future land use change on the regional temperature was analyzed. eTh main conclusions are as follows. (1) The land use change in Southern-Jiangsu shows 0 2 4 6 8 10 12 Month different changing trends under different scenarios, but it is mainly characterized by the expansion of urban land and Land 2010 in REG Land 2050 in REG shrinkageofthe cultivated land andforests.Under theREG Land 2050 in CES Land 2010 in CES scenario, the urban land expansion in Southern-Jiangsu will keep at a fast rate; the urban land will mainly expand around Figure 10: Comparison of the latent heat flux in Southern-Jiangsu 2 the central cities, mainly occupying the cultivated land and under different scenarios (unit: W/m ). forests. By contrast, the built-up land will expand dispersedly in the whole study area under the CES scenario, and the built- up land expansion around the main cities will be restricted to However, thevariation rangeofthe latent heat ufl xin2050is some degree. smaller under the REG scenario than under other scenarios. (2) The monthly average temperature in Southern- Jiangsu shows a consistent changing trend under different scenarios, but the temperature range shows signicfi ant dieff r- 5. Discussion and Conclusion ence.Thehighest valueofthe monthlyaverage temperature The scientific understanding of the impacts of land use appears in July under all the scenarios, while the lowest one change on the regional climate change provides the founda- appears in January. Besides, the regional monthly average tion for reasonable land use management so as to mitigate temperature is the highest in 2050 under the REG scenario lh Advances in Meteorology 11 160 160 32.2 32.2 32 32 120 120 31.8 31.8 31.6 80 31.6 80 31.4 31.4 40 40 31.2 31.2 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (a) (b) 160 160 32.2 32.2 32 32 120 120 31.8 31.8 80 80 31.6 31.6 31.4 31.4 40 40 31.2 31.2 20 20 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (c) (d) Figure 11: Spatial pattern of the latent heat ux fl in Southern-Jiangsu under different scenarios (unit: mm). (a) and (b) show the simulation result of latent heat u fl x of land use in Southern-Jiangsu in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation result of latent heat ux fl of land use in Southern-Jiangsu in year 2050 under REG scenario and CES scenario, respectively. and the lowest in 2010 under the CES scenario. In addition, great significance to guide land management practices to the difference in the monthly average temperatures is the mitigate the regional climate change. eTh precipitation as greatest in the summer and the smallest in the winter. another important aspect of the regional climate change has (3) There is significant influence of the underlying surface not been considered since this study mainly focused on the on the spatial pattern of temperature. eTh spatial pattern influencing mechanism of the land use change on the regional differs most greatly in 2050 under the REG scenario and temperature in rapidly urbanizing regions. eTh refore, it is in 2010 under the conservation scenario, especially in July. still necessary to carry out more in-depth research on the The range of the high-temperature regions is much wider in inufl ence of thelanduse change on theregionalclimate 2050 under the REG scenario than it is in 2010 under the change. CES scenario. The high-temperature regions are much wider in 2050 under the REG scenario than it is under the CES Conflict of Interests scenario, especially in big cities such as Nanjing, Zhenjiang, Suzhou, and Wuxi. eTh authors declare that there is no conflict of interests (4) The land use change in Southern-Jiangsu mainly regarding to the publication of this paper. influences the regional temperature through altering the land surface net radiation and latent heat u fl x. The land surface net radiation, which depends on the land surface albedo and Acknowledgments emissivity, downward long-wave radiation, and downward This research was financially supported by the National Key shortwave radiation, plays a dominant role in inu fl encing Programme for Developing Basic Science of China (Grant no. the temperature. Meanwhile, there is no significant influence 2010CB950900), the National Natural Science Foundation of thelanduse change on thespatial patternofthe latent of China (Grant no. 41101098), the Ministry of Education heat u fl x. In addition, the land surface albedo and emissivity Research of Social Sciences Youth funded Projects of China play the most important roles in inu fl encing the land surface (Grant no. 10YJC790121), and the National Department net radiation, and there is no significant influence of the Public Benetfi Research Foundation of the Ministry of Land underlying surface on the downward long-wave radiation and and Resources of China (Grant no. 201311001-5). downward shortwave radiation. Thisstudy analyzed theinufl enceofthe future land use change (especially the urban land expansion) on the References regional temperature in Southern-Jiangsu; it further analyzed theimpacts of land usechangeonthe keybiogeophysical [1] Q. H. 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Scenario Simulation of the Influence of Land Use Change on the Regional Temperature in a Rapidly Urbanizing Region: A Case Study in Southern-Jiangsu, China

Advances in Meteorology , Volume 2014 – Feb 16, 2014

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Hindawi Publishing Corporation
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Copyright © 2014 Xinli Ke et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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10.1155/2014/159724
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Abstract

Hindawi Publishing Corporation Advances in Meteorology Volume 2014, Article ID 159724, 12 pages http://dx.doi.org/10.1155/2014/159724 Research Article Scenario Simulation of the Influence of Land Use Change on the Regional Temperature in a Rapidly Urbanizing Region: A Case Study in Southern-Jiangsu, China 1 2 3 Xinli Ke, Enjun Ma, and Yongwei Yuan College of Land Management, Huazhong Agricultural University, Wuhan 430079, China School of Mathematics and Physics, China University of Geosciences (Wuhan), Wuhan 430074, China Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China Correspondence should be addressed to Xinli Ke; kexl@igsnrr.ac.cn Received 29 August 2013; Accepted 4 December 2013; Published 16 February 2014 Academic Editor: Burak Guneral ¨ p Copyright © 2014 Xinli Ke et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. It has been shown that land use change in urbanized region, especially urban land expansion, will influence regional climate. However, there has been very little research on the climate effects of the future land use change in a rapidly urbanizing region. Taking the southern part of Jiangsu province in China as the study area and through a scenario analysis, the influence of land use change on the regional temperature was analyzed from the perspective of land surface radiation budget and energy balance. eTh results indicated that (1) the monthly average temperature is significantly higher under the Rapid Economic Growth (REG) scenario than under the Cooperate Environmental Sustainability (CES) scenario in 2050, especially in the hottest month (July). (2) The range of high-temperature regions is much wider under the REG scenario than it is under the CES scenario in 2050. (3) The land surface net radiation and latent heat ux fl are two key factors through which land use change influences the regional temperature in the study area, and the latent heat u fl x plays a dominant role. (4) Land use change mainly influences the land surface net radiation by altering the land surface albedo and emissivity. These results are helpful to mitigate regional climate change eeff cts caused by land use change. 1. Introduction Research on the impacts of urbanization on climate mainly focuses on urban heat island and urban rain island. Urbanization is one of the most dramatic changes in the Sincethe urbanheatislandwas proposed in 1833,ithas world. Urban land expansion is a prominent feature of been verified that urban heat island exists in many cities of urbanization. With the rapid development of urbanization, dieff rent typesand sizes[ 8, 9]. A large number of studies the land surface is replaced by the impermeable ground such suggest that the urban heat island intensity exhibits different as cement, asphalt, which has made urban environmental characteristics along with the location and the type of the city, problems become increasingly serious [1, 2]. Among them, the conditions of periurban area and so on [10, 11]. Global- the increasing urban heat island effect has become the scale studies suggest that urbanization plays an important typical problem of the urban environmental change and has role in raising global surface temperature in recent years endangered the daily life and safety of the urban residents [12, 13]. However, almost all the studies have conrfi med [3, 4]. Urbanization as a typical performance of human land that the urbanization, especially urban land expansion will use activities can affect local and regional climate change, lead to the rising surface temperature. Urbanization changes andevenlarge-scale atmosphericcirculation [5]. Underlying the underlying surface and makes the urban areas temper- surfacechangewould causethe redistribution of Earth ature rise. Precipitation is another important aspect of the surface’s heat and moisture in the process of urbanization, impact of urbanization on regional climate. In the process causing changes in local and even regional climate [6, 7]. of urbanization, natural vegetation is replaced by asphalt, 2 Advances in Meteorology buildings, and other artificial land surfaces, which results in reducing moisture together affected convective activity thus changes on the surface roughness, albedo and soil moisture, realizing regional climate change [34]. It can clearly be seen and other surface properties, and affects regional precipita- that numerical simulation method provides support for the tion through their impacts on the radiation balance, water in-depth understanding of the process: urbanization, land balance, and other processes. Early studies showed that in surface change, key biogeophysical parameters change, and rapid urban development period, there will be unusually climate change. high precipitation in urban areas [14], and the areas which This paper took Southern-Jiangsu in China as the case are close to the metropolitan areas are more prone to have study area. Firstly, DLS (Dynamics of Land System) model heavy rains [15–17]. With the growth of the urban population, is employed to simulate future land use change in study raining in the aer ft noon seems to increase in large cities area at various scenarios. en Th the simulation results of land [14]. Another study showed that the increasing intensity usechangewereprocessed so as to satisfythe requirements of showers in monsoon season in the urban areas may be of underlying surface data for WRF (Weather Research relevant to heat island [18]. Further studies showed that effects and Forecast) model. On this basis, the WRF model was of urbanization on precipitation intensity showed different used to simulate the impacts of future land use change on laws in different terrains and climatic backgrounds [ 19]. In regional climate change, to scientifically understand the key addition, the impact of urbanization on precipitation is not parameters and the key process of the impacts of region’s limited to urban areas but also to urban downwind areas future land usechangeonregionalclimate change,and to [20, 21]. provide scientific basis for the rational regional land use As to research methods, research on impacts of urbaniza- planning to mitigate climate change. tion on climate change is mainly based on studies of obser- vational data and numerical simulation. Early researches 2. Study Area about impacts of urbanization on climate change mainly rely on observation site data, combined with demographic data China is the largest developing country in the world, expe- or land use data, using empirical orthogonal function [22], riencing rapid urbanization. Yangtze River Delta is one of principal component analysis [23, 24], and other methods themostrapidly urbanizing regionsinChina,and land use to select reference stations and urban stations. Through change caused by urban expansion is very intense in this area. comparative analysis of observational data discrepancies Studies have shown that the urbanization of Yangtze River between reference stations and urban stations, the impacts of Delta region has obvious eeff cts on its climate. Southern- urbanization on climate change can be assessed. This method Jiangsu is an important part of the Yangtze River Delta, the is simple, intuitive, and persuasive, but it is aeff cted by the impact of regional urban land expansion on climate change site classification results and the number of samples. To solve has also been widely concerned [35]. However, current this problem, Kalnay and Cai (2003) [25], based on the researches mainly focus on land use change and climate characteristics of NCEP/NCAR reanalysis data which did not change, revealing the impacts of urbanization on regional use ground-based observations information in the assimila- climate change. Few studies focus on the impacts of future tion process, proposed an idea which could reflect impacts urbanlanduse change on regional climatechange. eTh of urbanization on the regional climate change according scientific understanding of the impacts of future land use to comparative observations and reanalysis differences [ 25]. change on the regional climate change is the basis of a Although this approach has achieved good results [26], its scientific and rational land use planning to mitigate regional reliability has been disputed [27, 28]. In recent years, with the climate change. eTh refore, there is an urgent need to reveal development of remote sensing technology, the research of the impacts of future land use changes of typical urbanized the impacts of urbanization on regional climate change has areas on regional climate change. made a great progress by using remote sensing technology to extract information on land use changes and land sur- Southern-Jiangsu is located in the Yangtze River Basin face air temperature and precipitation data [29–31]. These in China, with superior geographical environment, pleasant methods provided a good support to describe the impacts of climate and convenient irrigation, vast plains, and fertile soil. urbanization on climate change. However, these methods are There are ve fi cities in Southern-Jiangsu, including Nanjing, inadequate to analyze the impact mechanism of urbanization Zhenjiang, Changzhou, Wuxi, and Suzhou (Figure 1). In on climate. Therefore, it is necessary to introduce numerical history, Southern-Jiangsu was China’s most prosperous place simulation in researching the key processes that urbanization because agriculture there was well developed. Southern- affects regional climate change from the perspective of Jiangsu is closed to Shanghai and it is a large hinterland of the mechanism. Sensitivity experiments of two-dimensional Shanghai, so it has geographical advantages. Because of its scale model showed that sensible heat u fl xes caused by proximity to Shanghai, Southern-Jiangsu developed strong urban surface changes played a key role in climate change, economic ties with Shanghai. and roughness changes affected the spatial heterogeneity of Southern-Jiangsu is one of the most populous and urban- climatechange[32]. Sensitivity test of RAMS/TEM coupled ized and the fastest growing economic regions in China. model indicated that urban heat island plays a key role in With Shanghai’s role in driving the economic development, inducing downwind convective systems [33]. Sensitivity test Southern-Jiangsu grows rapidly in economic development of WRF/NOAH coupled model indicated that urbanization and urbanization. In 2008, the regional GDP of Southern- mainly through two aspects of increasing heat sense and Jiangsu was 1.85 trillion Yuan, accounting for 6.4% of the Advances in Meteorology 3 ∘ ∘ ∘ 119 E 120 E 121 E km 0 25 50 12.5 32 N 32 N 31 N 31 N ∘ ∘ ∘ 119 E 120 E 121 E Cultivated land Water area Forestry Built-up area Grassland Unused land Figure 1: Location of Southern-Jiangsu. whole country. Southern-Jiangsu is also relatively dense and the network of transport of the study area and to work out with cities and towns, and urbanization level is high. In the distance from each 100 m× 100 m grid to railways, roads, 2008, Southern-Jiangsu’s urbanization rate reached 67.7%, and rivers. and per capita GDP reached 61,823 Yuan reaching the level eTh social and economic statistical data included the of moderately developed countries. population of Southern-Jiangsu, per capita retail sales of social consumer goods, the total investment in fixed assets, per capita sfi cal revenue, the gross output of the second 3. Data and Methodology industry, and grain yield per unit area from 2000 to 2008. eTh above data come from Jiangsu Statistical Yearbook. 3.1. Data Sources. eTh data includes the land use data, socioe- conomic data, and data of natural environmental conditions of Southern-Jiangsu. 3.2. DLS Model. DLS is a land use dynamic simulation model Land use data is mainly used for scenario simulations of based land use change mechanism, which in accordance land use change. In this study, land use data of Southern- with the driving forces analysis of land use change, scenario Jiangsu in 2000 and 2008 are obtained through remote sens- forecasting, and supply industrial allocation of land area and ing images interpretation. These land use data are composed space allocation carrying out dynamic simulations of land of six land types, including farmland, forestry land, grassland, use change from region and grid (Figure 2). DLS model built-up land, water bodies, and unused land. Among them, consists of four modules, including scenario analysis module, land use data in 2000 came from the Land Use Database of spatial analysis module, the conversion rules module and Data Center Resources and Environment, Chinese Academy spatial analysis modules. Scenario analysis module is used of Science [36]. The database consists of Landsat TM/ETM+ to express the changed needs of a variety of land use types image interpretation with a spatial resolution of30×30 m. under different scenarios. Spatial analysis module is used to Land usedatain2008isinterpreted by LandsatETM+ calculate the probability values of various land use types in images. each grid unit through spatial regression analysis for driving factors. Transfer rules module is used to express possibility The natural environmental conditions data included and ease of a certain type of land transfer to another type of DEM data of the study area, the distance from the city at land on each grid cell. Space allocation module implements all levels, the distance from the railways, the distance from spatial distribution pattern of various land use types under theroads,and thedistancefromthe rivers.DEM data came different scenarios on the grid. from the data of Shuttle Radar Topography Mission (SRTM) of NASA. This paper hierarchically calculated the distance eTh re are mainly four steps to carry out dynamic sim- from the city at all levels to each 100× 100 m grid, using ulation of land use based on DLS. First, analyze statistical the Landsat TM/ETM+ geometric correction in 2000 that relationship between land use types distribution and driving covered Southern-Jiangsu to outline the major river systems factors from the two scales of region and grid, measure 4 Advances in Meteorology Analysis on driving mechanism of land use change Scenarios analysis of land use change Environmental Transform Characteristics Transform rules probability condition Driving of historic Driving mechanism Climate land use Prediction tendency Change of regional forces of for patterns Population change of land use change land use structure land use and process Economic change of land use Management Scenarios analysis of change Patterns of land use change regional land use change Policy Balance of land use demand between Balance of land use demand between various industrials various regions Patterns of land use change Spatial distribution of land use change Figure 2: Framework of DLS model. WRF External data preprocessing WRF ARW model Visualization source system Ideal 3D Ideal 2D supercell hill grav baroclinic waves squall line NCL WRF terrestrial ARW post ARW model data (GrADS/Vis5D) RIP4 Real data WPS initialization Gridded data: WPP NAM, GFS, Real data (GrADS/ initialization RUC, NNRP and GEMPAK) AGRMET (soil) Figure 3: Framework of WRF model. effects of the natural environment and socioeconomic factors worked out. Under the linear hypothesis, land use change on temporal patterns of regional land use, and extract the process can be presented as the following formula: key factors which aeff ct land use types distribution. en, Th 𝑡 𝑡−1 Δ𝑌 =𝑓(...,𝑥 ,...)−𝑓(...,𝑥 ,...)=𝑓(Δ𝑥 ), (1) basedonthe historyoflanduse characteristicsand the 𝑖 𝑖 𝑖 𝑖 status of regional land use changes, predict trends that the 𝑡 𝑡−1 whereΔ𝑌 is thechangeareaoflanduse𝑌 ,𝑥 and𝑥 are the key factors inu fl ence land use patterns, and then select a 𝑖 𝑖 𝑖 𝑖 value of independent variables in time𝑡 and𝑡−1 ,respectively, reasonable scenario. According to supply-demand situation andΔ𝑥 is the changed value of independent variables. of different industries on land under this scenario during the time cross-section of forecast period, allocate area demand of different land types to various industries. Finally, by balance 3.3. WRF Model. WRF model is a new generation mesoscale analysis of grid-scale land type area’s demand and supply, weather forecasting model and assimilation model which achieve spatial distribution of dieff rent kinds of land use was jointly initiated by research institutes and scientists types on the grid scale and generate spatial pattern of land of universities in the United States [37]. This model is use. highly modular and layer-designed, the main program adds According to the estimated result of the experiential a number of optimization options in compiler, and the input model, the contribution on land use change of various inde- andthe output data arereadinavarietyofstandardformats. pendent variables can be calculated. Based on this, prediction WRF model consists of three parts, including preprocess- of land use in 2010 and 2050 in Southern-Jiangsu can be ing module of mode (WPS), main module of model (ARW), Advances in Meteorology 5 ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 118 E 119 E 120 E 121 E Zhenjiang Zhenjiang Nanjing Nanjing Changzhou Changzhou Suzhou Suzhou Wuxi Wuxi ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 118 E 119 E 120 E 121 E (a) (b) ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 118 E 119 E 120 E 121 E Zhenjiang Zhenjiang Nanjing Nanjing Changzhou Changzhou Suzhou Suzhou Wuxi Wuxi ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ 118 E 119 E 120 E 121 E 118 E 119 E 120 E 121 E Urban and built-up land Urban and built-up land Dryland cropland and pasture Dryland cropland and pasture Irrigated cropland and pasture Irrigated cropland and pasture Mixed dryland/irrigated cropland and pasture Mixed dryland/irrigated cropland and pasture Cropland/grassland mosaic Cropland/grassland mosaic Cropland/woodland mosaic Cropland/woodland mosaic Grassland Grassland Shrubland Shrubland Mixed shrubland/grassland Mixed shrubland/grassland Deciduous broadleaf forest Deciduous broadleaf forest Evergreen broadleaf forest Evergreen broadleaf forest Evergreen needleleaf forest Evergreen needleleaf forest Mixed forest Mixed forest Water bodies Water bodies (c) (d) Figure 4: Results of land use change simulation. (a) and (b) show the simulation results of land use in Southern-Jiangsu in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation results of land use in Southern-Jiangsu in year 2050 under REG scenario and CES scenario, respectively. and assimilation module of mode and postprocessing tools simulation. ARW is the core part of the model and mainly of mode data (WRF-VAR) (Figure 3). Preprocessing module makes initialization and integration for simulation. of the model mainly determines the analog areas, providing WRF model is mainly applied to the weather and climate initial and boundary conditions of simulation, providing research when horizontal resolution is 1–10 km. It can also be topography and soil types data and entrusting to the grid area applied to numerical simulation, physical parameterizations of simulation, reentrusting the meteorological data to grid of research, data assimilation, and numerical ideal test and ∘ ∘ ∘ ∘ 31 N 32 N 31 N 32 N ∘ ∘ 31 N 32 N 31 N 32 N ∘ ∘ ∘ ∘ 31 N 32 N 31 N 32 N ∘ ∘ ∘ ∘ 31 N 32 N 31 N 32 N 6 Advances in Meteorology provide meteorological eld fi for air quality model. This paper Temperature simulated the future land use change in Southern-Jiangsu using the DLS model and made dynamic simulations to regional climate change under different underlying scenarios basedonthe WRFmodel,justtorevealthe impactsof Southern-Jiangsu’s future land use change on regional climate change. 4. Results 4.1. Scenario Analysis of Future Land Use in Southern- 5 Jiangsu. Future land use conditions in Southern-Jiangsu are simulated under two kinds of scenarios in this paper. Aeft r 30 years’ development under the reform and opening up −5 policy in China, the Southern-Jiangsu has achieved rapid 0 2 4 6 8 10 12 Month socioeconomic progress. Meanwhile, the Southern-Jiangsu’s resources and environment are also under tremendous pres- Land 2010 in REG Land 2050 in REG sure. Particularly, farmland resources in this area are facing Land 2050 in CES Land 2010 in CES significant stress of reduction under the circumstance of rapid urban expansion. Southern-Jiangsu’s resources and environ- Figure 5: Simulated monthly average temperature in Southern- mental pressures have become increasingly prominent in Jiangsu under different scenarios (unit: C).Land2010inREG sce- nario and Land 2010 in CES scenario represent the monthly average the process of rapid economic development. er Th efore, the temperature simulated with the LUCC data in 2010 under the REG development of Southern-Jiangsu is facing new opportu- scenario and CES scenario as the underlying data, respectively. Land nities and challenges. Against this background, this paper 2050 in REG scenario and Land 2050 in CES scenario refer to that set Southern-Jiangsu’s future land use scenarios as REG in 2050. scenario and CES scenario. eTh core of REG scenario is that land usedemands have thepriorityinlanduse change. Southern-Jiangsu’s land use change has served the purpose of economic development in the past 30 years; therefore, it in the small-medium cities, which puts great pressure on the surrounding cultivated land and forests. Under the canbeconsidered that Southern-Jiangsu’s land usescenario was the REG scenario in the past 30 years. The core of CES scenarios, the speed of economic development will be CES scenario is to achieve coordination between economic restrained to some degree, and the land use intensity will be further improved and the consumption of land resources due development and environmental protection. Therefore, the purposeofthe land useinCES scenario is to realizethe to economic development will also be restrained. Although there will still be some expansion of built-up land around the transformation of economic development so as to protect main urban areas of Nanjing, Zhenjiang, Suzhou, Wuxi, and natural resources and environment by sacrificing the speed of economic development rationally. Changzhou, the expansion degree is much limited compared to that under the REG scenario. eTh area of cultivated land This study simulated the land use change in Southern- and forests will decrease slightly due to the built-up land Jiangsu during 2010 to 2050 under the REG scenario and expansion, but the decreased area has been under control CES scenario with the DLS model (Figure 4). The result compared to that under the REG scenarios, in particular, the under the REG scenario suggests that the built-up land shrinkage of forests is well controlled in these regions. By expansion in 2010 mainly concentrated on the main urban comparison, the built-up land in small-medium cities still areas of Nanjing, Zhenjiang, Suzhou, Wuxi, and Changzhou, expands dispersedly, but the expansion speed is obviously which is consistent with the trend of the current land use restrained. change in Southern-Jiangsu. While the simulation result under the CES scenario indicates that the built-up land will expanddispersedlyinthe wholestudy area,the built-up 4.2. Impacts of the Future Land Use Change on the Regional land expansion around the main urban areas of Nanjing, Temperature in Southern-Jiangsu. Based on the simulation Zhenjiang, Suzhou, Wuxi, and Changzhou will be restrained results of the future land use change in Southern-Jiangsu, the to some degree. WRF was used to simulate the impacts of the land use change The simulation result in year 2050 indicates that there on the regional climate change in the future under different is still a great demand of economic development for the scenarios. The underlying surface data were first generated land resource under the REG scenario since the REG will through up-scaling and reclassifying the simulation results keep a high speed in Southern-Jiangsu. eTh built-up land of land use change in Southern-Jiangsu according to the will expand most obviously around the main urban areas of requirement of the WRF model. en Th the static underlying Nanjing, Zhenjiang, Suzhou, Wuxi, and Changzhou, where surface data in the WRF model were replaced with the theareaofcultivatedlandand forestswillfurther decrease. dynamic ones in 2010 and 2050 under the REG scenario and eTh re will also be some expansion of the built-up land CESscenario; thereaeft rthe future regional climatechange Temperature Advances in Meteorology 7 32.2 32.2 32 32 18 31.8 31.8 17 31.6 31.6 16 31.4 31.4 31.2 31.2 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (a) (b) 32.2 19 32.2 32 32 18 31.8 17 31.8 31.6 16 31.6 31.4 31.4 31.2 31.2 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (c) (d) Figure 6: Spatial pattern of the monthly average temperature in Southern-Jiangsu under different scenarios (unit: C). (a) and (b) show the simulation result of average temperature of land use in Southern-Jiangsu in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation result of average temperature of land use in Southern-Jiangsu in year 2050 under REG scenario and CES scenario, respectively. was simulated and na fi lly the climate eeff cts of different monthly average temperature under the REG scenario is only underlying surfaces were analyzed (Figure 5). slightly higher than that under the REG scenario. However, The simulation results indicate that the changing trends of Figures 6(c) and 6(d) indicate that the regions with high the monthly average temperatures under different scenarios temperature in 2050 under the REG scenario are much wider are consistent on the whole. The highest monthly average than the high temperature regions under the CES scenario, temperatures all appear in June, July, and August, and the especially around the center of big cities such as Nanjing, lowest ones all appear in November, January, and February. Zhenjiang, Suzhou, and Wuxi. According to the comparison This result shows that the changes of underlying surface do of results between the monthly average temperature in 2050 not aeff ct the monthly temperature change trend; it only under the REG scenario and other scenarios, the range of the aeff cts the value of average temperature. There is signifi- high-temperature regions is much wider in 2050 under the cant difference on the monthly average temperatures during REG scenario than other scenarios. different periods under different scenarios. The simulation The biggest difference in the monthly average temper- results indicate that the regional monthly average tempera- ature under different scenarios is in July; therefore, the ture in 2010 under the CES scenario is the lowest on the whole, impacts of different underlying surfaces on the temperature while that in 2050 under the REG scenario is the highest. can be more clearly revealed through comparing the spatial Besides, the monthly average temperature in 2010 under the pattern of the monthly average temperature in July (Figure 7). REG scenario is slightly higher than in 2050 under the CES Figure 7 shows that the impacts of different underlying sur- scenario. In addition, there are also some differences in the faces on the spatial pattern of the temperature in Southern- monthly average temperatures between different underly- Jiangsu in July are consistent with its spatial pattern on ing surfaces during different periods. Overall, the greatest the monthly average temperature, but the scope of high difference in the monthly average temperatures appears in temperatureismoresignicfi antinJuly. Taking theresults summer, while there is no significant difference in winter, in 2050 under the REG scenario and CES scenario (Figures with that in January being the slightest. 7(c) and 7(d)) as examples, the high-temperature region in eTh simulation result indicates that there are signicfi ant Nanjing has expanded into a separate continuous region impacts of the underlying surface on the spatial pattern of the in 2050 under the CES scenario, while the scope of the monthly average temperature under different scenarios, espe- high-temperature region in Zhenjiang is still very limited. cially in 2050 under the REG scenario and in 2010 under the Besides, the high-temperature regions in Suzhou, Wuxi, and CES scenario (Figure 6). Figures 6(a) and 6(b) suggest that Changzhou have also expanded into a large continuous there is no significant difference between the spatial pattern of district,but itsscope andtemperature rangeare both smaller the monthly average temperature on the underlying surfaces than the scope and temperature range in 2050 under the REG under the REG scenario and the CES scenario in 2010, and the scenario. 8 Advances in Meteorology 33 33 32.2 32.2 32 32 32 31.8 31.8 31 31 31.6 31.6 30 30 31.4 31.4 29 29 31.2 31.2 28 28 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (a) (b) 33 33 32.2 32.2 32 32 32 32 31.8 31.8 31.6 31.6 30 30 31.4 31.4 29 29 31.2 31.2 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (c) (d) Figure 7: Spatial pattern of the monthly average temperature in Southern-Jiangsu in July under different scenarios (unit: C). (a) and (b) show the simulation result of monthly average temperature in Southern-Jiangsu in July of land use in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation result of monthly average temperature in Southern-Jiangsu in July of land use in year 2050 under REG scenario and CES scenario, respectively. 4.3. Key Impact Mechanisms of the Future Land Use Change on scenarios, under which it will show no significant difference. the Regional Temperature in Southern-Jiangsu. According to In addition, Figures 8(c) and 8(d) indicate that there will the surface energy budget equation, there is close relationship not be significant difference in the downward long-wave between the surface net radiation, land surface albedo, down- radiation and downward shortwave radiation under all the ward shortwave radiation, downward long-wave radiation, scenarios. In summary, under the condition that there is and land surface emissivity: no significant difference between the downward long-wave radiation and downward shortwave radiation, there will be 𝑆 𝑙 𝑠 𝑙 4 (2) 𝑅 =𝑅 +𝑅 =(1−𝛼 )𝐹 +𝜀𝐹 −𝜎𝜀𝑇 , lowerlandsurface albedo andemissivityin2050under 𝑛 𝑛 𝑛 𝑑 𝑑 the REG scenario, which consequently greatly increases the where𝑅 is the surface net radiation,𝑅 is the short wave 𝑛 land surface net radiation and thus lays foundation for the radiation,𝑅 is the long-wave radiation,𝛼 is the land surface warming eeff cts. albedo,𝐹 is the downward shortwave radiation,𝜀 is the land This study analyzed the impacts of the spatial heterogene- ity of the land surface emissivity on the spatial pattern of surface emissivity,𝐹 is the downward long-wave radiation, temperature in the hottest month (July) since the difference in and𝑇 is the land surface temperature. The land net radiation thelandsurfaceemissivityisthemainreasonforthewarming is theenergysourceofthe land surfacetemperature change, effects in 2050 under the REG scenario ( Figure 9). this studyhas focusedonhow theunderlyingsurface change Figure 9 shows that the regions with the lower land influences the land surface albedo, downward shortwave surface emissivity is more widespread in 2050 under the REG radiation, downward long-wave radiation, and land surface scenario than under other three scenarios. Under all the four emissivity in order to clarify the key influencing mechanism scenarios, there are always continuous districts with lower of thefuturelanduse change on theregionaltemperature in land surface emissivity in Nanjing, Zhenjiang, Suzhou, Wuxi, Southern-Jiangsu (Figure 8). and Changzhou, where the urban land is the main part of The land use change in Southern-Jiangsu mainly influ- the underlying surface. However, the result clearly shows ences the land net radiation through exerting impacts on that the land surface emissivity in these continuous districts thelandsurface albedo andemissivity, andthe land use is obviously lower in 2050 under the REG scenario than change influences the spatial heterogeneity of the land surface other scenarios, which may be mainly because the underlying emissivity most greatly under both the scenarios (Figure 8). surfacewillchangemoregreatly in 2050 under theREG Figure 8(a) suggests that the land surface albedo will be scenario. the lowest in 2050 under the REG scenario, while it will show no significant difference under other scenarios. Besides, What is more, the land surface energy budget equation suggests that under the condition of certain land surface Figure 8(b) suggests that the land surface emissivity will be obviously lower in 2050 under the REG scenario than other net radiation, the underlying surface mainly influences the Advances in Meteorology 9 0.28 0.98 0.975 0.27 0.97 0.26 0.965 0.25 0.96 0.955 0.24 0.95 0.23 0.945 0.94 0.22 0.935 0.21 0.93 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Month Month Land 2010 in REG Land 2050 in REG Land 2010 in REG Land 2050 in REG Land 2050 in CES Land 2050 in CES Land 2010 in CES Land 2010 in CES (a) (b) 0 2 4 6 8 10 12 0 2 4 6 8 10 12 Month Month Land 2050 in REG Land 2050 in REG Land 2010 in REG Land 2010 in REG Land 2010 in CES Land 2050 in CES Land 2010 in CES Land 2050 in CES (c) (d) Figure 8: Influence of the future land use change on the key biogeophysical parameters in Southern-Jiangsu. (a) and (b) show the inu fl ence of the future land use change on albedo and surface emissivity; (c) and (d) show the influence of the future land use change on downward long-wave radiation and downward shortwave radiation (W/m ). temperature through influencing the sensible heat ux, fl latent Figure 10 shows that the latent heat u fl x is obviously lower heat u fl x, and soil heat ux: fl in 2050 under the REG scenario than under other scenarios, andtherefore thedecreaseofthe latent heatufl xcausedbythe 𝑅 =𝐻+ LE+𝐺, (3) underlying surface change due to land use change can be seen as one of the main causes of the temperature rise in Southern- where𝑅 is thelandsurface netradiation,𝐻 is the sensible 𝑛 Jiangsu. heat u fl x, LE is the latent heat u fl x, and 𝐺 is the soil heat ux. fl In order to further analyze the impacts of the difference in the latent heat ux fl on the spatial pattern of temperature, Since there is generally very limited heat u fl x into the soil layer, the land surface net radiation is mainly influenced the spatial pattern of the latent heat u fl x under different by thesensibleheatflux andlatentheatflux,while the scenarios were further analyzed (Figure 11). It can be seen that underlying surface can directly influence the latent heat u fl x there is no significant difference in the spatial pattern of the and consequently influence the near-surface temperature. latent heat u fl x in Southern-Jiangsu under different scenarios. glw Albedo swdown Emiss 10 Advances in Meteorology 0.97 32.2 32.2 32 32 0.96 0.96 31.8 31.8 0.95 0.95 31.6 31.6 0.94 0.94 31.4 31.4 0.93 0.93 31.2 31.2 0.92 0.92 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (a) (b) 0.97 32.2 32.2 0.97 0.96 32 32 0.96 0.95 31.8 31.8 0.95 0.94 31.6 31.6 0.94 0.93 31.4 31.4 0.93 0.92 31.2 31.2 0.92 0.91 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (c) (d) Figure 9: Spatial pattern of the land surface emissivity in Southern-Jiangsu under different scenarios. (a) and (b) show the simulation result of land surface emissivity of land use in Southern-Jiangsu in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation result of land surface emissivity of land use in Southern-Jiangsu in year 2050 under REG scenario and CES scenario, respectively. the climate change. Taking Southern-Jiangsu, the typical region of urbanization in China, as the study area, this study analyzed the inu fl ence of land use change on the temperature under the REG scenario and CES scenario during 2010–2050 on the basis of simulation with the DLS model and WRF model. This study then analyzed the impacts of land use change on the key biogeophysical parameters such as surface net radiation, land surface albedo, downward shortwave radiation, downward long-wave radiation, and land surface emissivity from the perspective of land surface radiation budget and energy balance. Furthermore, the key influencing mechanisms of the future land use change on the regional temperature was analyzed. eTh main conclusions are as follows. (1) The land use change in Southern-Jiangsu shows 0 2 4 6 8 10 12 Month different changing trends under different scenarios, but it is mainly characterized by the expansion of urban land and Land 2010 in REG Land 2050 in REG shrinkageofthe cultivated land andforests.Under theREG Land 2050 in CES Land 2010 in CES scenario, the urban land expansion in Southern-Jiangsu will keep at a fast rate; the urban land will mainly expand around Figure 10: Comparison of the latent heat flux in Southern-Jiangsu 2 the central cities, mainly occupying the cultivated land and under different scenarios (unit: W/m ). forests. By contrast, the built-up land will expand dispersedly in the whole study area under the CES scenario, and the built- up land expansion around the main cities will be restricted to However, thevariation rangeofthe latent heat ufl xin2050is some degree. smaller under the REG scenario than under other scenarios. (2) The monthly average temperature in Southern- Jiangsu shows a consistent changing trend under different scenarios, but the temperature range shows signicfi ant dieff r- 5. Discussion and Conclusion ence.Thehighest valueofthe monthlyaverage temperature The scientific understanding of the impacts of land use appears in July under all the scenarios, while the lowest one change on the regional climate change provides the founda- appears in January. Besides, the regional monthly average tion for reasonable land use management so as to mitigate temperature is the highest in 2050 under the REG scenario lh Advances in Meteorology 11 160 160 32.2 32.2 32 32 120 120 31.8 31.8 31.6 80 31.6 80 31.4 31.4 40 40 31.2 31.2 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (a) (b) 160 160 32.2 32.2 32 32 120 120 31.8 31.8 80 80 31.6 31.6 31.4 31.4 40 40 31.2 31.2 20 20 31 31 118. 5 119 119. 5 120 120.5 121 118. 5 119 119. 5 120 120.5 121 (c) (d) Figure 11: Spatial pattern of the latent heat ux fl in Southern-Jiangsu under different scenarios (unit: mm). (a) and (b) show the simulation result of latent heat u fl x of land use in Southern-Jiangsu in year 2010 under REG scenario and CES scenario, respectively; (c) and (d) show the simulation result of latent heat ux fl of land use in Southern-Jiangsu in year 2050 under REG scenario and CES scenario, respectively. and the lowest in 2010 under the CES scenario. In addition, great significance to guide land management practices to the difference in the monthly average temperatures is the mitigate the regional climate change. eTh precipitation as greatest in the summer and the smallest in the winter. another important aspect of the regional climate change has (3) There is significant influence of the underlying surface not been considered since this study mainly focused on the on the spatial pattern of temperature. eTh spatial pattern influencing mechanism of the land use change on the regional differs most greatly in 2050 under the REG scenario and temperature in rapidly urbanizing regions. eTh refore, it is in 2010 under the conservation scenario, especially in July. still necessary to carry out more in-depth research on the The range of the high-temperature regions is much wider in inufl ence of thelanduse change on theregionalclimate 2050 under the REG scenario than it is in 2010 under the change. CES scenario. The high-temperature regions are much wider in 2050 under the REG scenario than it is under the CES Conflict of Interests scenario, especially in big cities such as Nanjing, Zhenjiang, Suzhou, and Wuxi. eTh authors declare that there is no conflict of interests (4) The land use change in Southern-Jiangsu mainly regarding to the publication of this paper. influences the regional temperature through altering the land surface net radiation and latent heat u fl x. The land surface net radiation, which depends on the land surface albedo and Acknowledgments emissivity, downward long-wave radiation, and downward This research was financially supported by the National Key shortwave radiation, plays a dominant role in inu fl encing Programme for Developing Basic Science of China (Grant no. the temperature. Meanwhile, there is no significant influence 2010CB950900), the National Natural Science Foundation of thelanduse change on thespatial patternofthe latent of China (Grant no. 41101098), the Ministry of Education heat u fl x. In addition, the land surface albedo and emissivity Research of Social Sciences Youth funded Projects of China play the most important roles in inu fl encing the land surface (Grant no. 10YJC790121), and the National Department net radiation, and there is no significant influence of the Public Benetfi Research Foundation of the Ministry of Land underlying surface on the downward long-wave radiation and and Resources of China (Grant no. 201311001-5). downward shortwave radiation. Thisstudy analyzed theinufl enceofthe future land use change (especially the urban land expansion) on the References regional temperature in Southern-Jiangsu; it further analyzed theimpacts of land usechangeonthe keybiogeophysical [1] Q. H. 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