Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Impacts of Land Cover Change on the Near-Surface Temperature in the North China Plain

Impacts of Land Cover Change on the Near-Surface Temperature in the North China Plain Hindawi Publishing Corporation Advances in Meteorology Volume 2013, Article ID 409302, 12 pages http://dx.doi.org/10.1155/2013/409302 Research Article Impacts of Land Cover Change on the Near-Surface Temperature in the North China Plain 1,2 1 3 4 3 Ruijie Qu, Xiaolin Cui, Haiming Yan, Enjun Ma, and Jinyan Zhan College of Geomatics, Xi’an University of Science and Technology, No. 58 Yanta Road, Xi’an 710054, China Center for Chinese Agricultural Policy, Chinese Academy of Sciences, No. 11A Datun Road, Anwai, Beijing 100101, China State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China School of Mathematics and Physics, China University of Geosciences, No. 388 Lumo Road, Wuhan 430074, China Correspondence should be addressed to Jinyan Zhan; zhanjy@bnu.edu.cn Received 11 June 2013; Accepted 30 July 2013 Academic Editor: Xiangzheng Deng Copyright © 2013 Ruijie Qu 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. This study first tested and verified the ability of the Weather Research and Forecasting (WRF) model to simulate the near-surface temperature in the North China Plain. Then the static land cover data in the WRF were replaced, and thereaer ft the modified WRF model was used to explore the impacts of land cover change on the near-surface temperature in the North China Plain in year 1992 and year 2005. The results indicated that the land cover change in the North China Plain, which was characterized by the regional urbanization, had led to significant changes in the near-surface temperature, increasing the regional near-surface temperature by 0.03 C/year on average. eTh spatial pattern of the climate change basically corresponded to that of the land cover change; for example, the temperature increased most significantly in the regions mainly consisting of cities and built-up area. Besides, there were some variations in the degree and range of influence of the land cover change on the temperature among seasons. The result can provide important theoretical support for the adaptation to climate change, scientific land cover change management, and land use planning. 1. Introduction mainly influences the climate at the local, regional, and global scales by changing the land surface characteristics, More andmoreattention hasbeenpaidtothe inufl ence altering the exchange of energy, water, and other materials of humanactivitiesonthe climatesysteminrecentyears between the land surface and the atmosphere and influencing as great progress has been made in the researches on the other biogeochemical processes. er Th e is great variation in global climate system and environmental change. IPCC AR4 the biogeochemical mechanism through which the LUCC pointed out that the human activities may account for 90% influences the climate in different regions; the climate change of the reasons for the global warming. There is very complex caused by LUCC is mainly through the land-atmosphere influence of human activities on the climate, with the land interaction and is closely related to the regional climate and use/cover change (LUCC) being considered as the major environmental background, terrain, vegetation, and so forth. influencing factor in the climate system [ 1]. The LUCC eTh refore, it is of great importance to study the influence of influences the climate system at various temporal and spatial LUCC on the regional climate. scales, and the land use change has contributed to 1/3 of the The North China Plain has been selected as the study area increase in global CO emission since the 1750s. Besides, a in this study. First, it has long been one of the most densely lot of observations and simulation experiments also indicate populated regions in China; the current regional city density that the LUCC at various spatial scales has been one of is still very high and the industries and agriculture are well the most important approaches through which the human activities exert influence on the climate [ 2–4]. The LUCC developed. The rapid economic development and increasing 2 Advances in Meteorology WRF terrestrial data (land use, soil, etc.) WPS Metgrid data Gridded data Simulated data (temperature, REAL.EXE WRFV3 precipitation etc.) POST ARW POST (GrADS/Vis5D) POST module Figure 1: WRF flow chart. population have ledtoverydramaticlandcover change in result [5–9]. The WRF model is a next-generation mesoscale this region, and the human disturbance to the environment is numerical weather prediction system designed to serve both especially signica fi nt, which greatly inu fl ences the sustainable atmospheric research and operational forecasting needs. The development of the whole China. Second, the North China ARW-WRF (Edition 3.3) has been used in this study. Plain is a typical area of the monsoon climate as well ARW-WRF includes three parts: WRF Preprocessing as the transient region between the humid and subhumid System (WPS), WRFV3, and POST (Figure 1). The WPS region and the arid and semiarid region. On the one hand, program is used primarily for real-data simulations. Its the plain agriculture can be sustained for a long time due functions include (1) defining simulation domains, (2) inter- the local climatic conditions. On the other hand, the local polating terrestrial data (such as terrain, land use, and soil climatic conditions also lead to more frequent droughts, types) to the simulation domain, and (3) degribbing and make the agricultural production extremely unstable, and interpolating meteorological data from another model to this consequently mayleadtogreater economic loss andmore simulation domain. Its main features include (1) GRIB 1/2 extensive social influence. er Th efore, it is of great importance meteorological data from various centers around the world; to study the influence of LUCC on the climate in the North (2) USGS 24 category land datasets; (3) map projections China Plain. for polar stereographic, Lambert Conformal, Mercator, and This study rfi st tested the ability of the Weather Research latitudelongitude; (4) nesting; (5) user-interfaces to input and Forecasting (WRF) model to simulate the change of the other static data as well as met data. WRFV3 runs and exports near-surface temperature in the North China Plain, based simulated data, including temperature, precipitation, and so on which the static land cover data in the WRF were then forth. POST part converts the results and makes the outputs replaced. er Th eaeft r the modified WRF was used to study visualized. the influence of the land cover change on the near-surface ARW oeff rs multiple physical parameterization schemes temperatureinthe NorthChina Plaininyear1992and year that can be combined in any way. The options typically 2005. eTh result can contribute to better understanding of the range from simple and efficient to sophisticated and more influencing factors of the climate in the North China Plain computationally costly and from newly developed schemes so as to minimize the negative influence and maximize the to well-tried schemessuchasthose in currentoperational positive influence on the regional climate, which is helpful to models. Table 1 lists some schemes. the scientific regional land use planning and management in China in the future. 2.2. Experiment Design. The location and size of the simula- tion area have great inu fl ence on the simulation result [ 10, 11]. The center line of the simulation area was set to be 36 Nand 2. Model Introduction and Experiment Design 117 Einthisstudy.TheLambert projection wasused, with ∘ ∘ 2.1. Model Introduction. With the development of the atmo- the two standard parallels being 26 Nand 46 N, respectively. spheric models and land surface process models, the numer- eTh spatial resolution was set to be 20 km, and there were 112 ical simulation has become a widely used approach to study grid points in the east-west direction and 97 grid points in the the influence of climate on vegetation. eTh regional climate north-south direction in the whole simulation area. model used in this study, WRF model, has been widely The lateral boundary forcing data came from the National used in the global climate and achieved good simulation Centers for Environmental Prediction (NCEP)/FNL dataset Advances in Meteorology 3 Table 1: Physical parameterization schemes. Table 2: Schemes of the simulation test. Land cover data used in Physical parameters Schemes Test Test period the WRF model Kessler, Lin et al., WRF Microphysics Control test 2005.10–2007.12 Land cover data of 1992 Single-Moment 3-class Sensitivity test 2005.10–2007.12 Land cover data of 2005 Cumulus Kain-Fritsch, Grell-Devenyi ensemble parameterization scheme, Betts-Miller-Janjic Dudhia (MM5), CAM scheme, Shortwave radiation Goddard of the physical process are the same in the two tests. They were both implemented with the climate forcing data between Longwave radiation RRTM, CAM, GFDL October of 2005 and December of 2007. Planetary boundary layer MRF, MYJ, YSU 5-layer thermal diffusion, Noah Land Land surface Surface Model, RUC Land Surface 3. Data Processingb Model 3.1. Processing of Land Cover Data. It is necessary to reclassify the land cover data with the USGS land cover classicfi ation and were updated every 6 hours. This dataset has the spatial which includes 24 land cover and land use types and set the ∘ ∘ resolution of 1×1 and the vertical height of 27 layers, and it spatial resolution to be 20 km according to the requirement of has been established and updated since July of 1999 with the the WRF model. Therefore, the land cover data of the IGBP data assimilation of almost all kinds of observation data (e.g., land cover classification were first reclassified with the USGS remote sensing data and ground-based observation data). In land cover classification system, and then the spatial resolu- comparison to the datasets of NCEP I, NCEP II, and EAR40, tion of thedatawas convertedfrom1km to 20km.Thetwo the NCEP/FNL dataset has higher accuracy and spatial datasets both have the spatial resolution of 1 km and adopt resolution, and it includes more kinds of environmental the IGBP classica fi tion. On the basis of the data of the IGBP variables. land cover classicfi ation, we formulated the transformation In the parameterization scheme of physical processes in method from the IGBP land cover classification to the USGS the model, the cumulus parameterization scheme adopted land cover classification ( Table 3) and established the land use the Grell-Devenyi ensemble scheme, the boundary layer and land cover dataset of the USGS classicfi ation of the North processschemewasYSU,andtheshortwaveradiationscheme China Plain. was the CAM scheme, while the land surface process scheme The LUCC data were further upscaled on the basis of the was Noah Land Surface Model. The boundary bueff r was set data mentioned above so as to embed the high resolution to be 4layersofgridpoints, andthe boundary conditions underlying surface data into the large-scale climate model. In adopted the relaxation scheme. eTh time interval of the this study, the 1 km resolution land cover and land use data of model integration was set to be 5 minutes, and that of the USGS classification were upscaled into the 20 km resolution radiation process and cumulus convection was 30 minutes data with the resampling function of ArcGIS. Besides, the and 5 minutes, respectively. er Th e were 27 layers in the three kinds of data were integrated in a system of 7 land vertical direction and the atmospheric pressure at the top cover types, and their area consistency and spatial consistency layer was 50 hPa. were analyzed so as to check the change of the classification The test scheme in this study is designed as follows accuracy of the land cover and land use data before and after (Table 2). In order to analyze the impact of land cover thereclassicfi ation andupscaling.Thetotal area dieff rence change on climate and reduce data errors, the land cover between the land cover data that were reclassified and data used in this study were extracted from the Chinese upscaled and the initial data was presented in Table 4.It subset of theGlobalLandCover Characteristicsdatabase indicated that there was no significant difference in the total which was developed based on the AVHRR data with the area of each land cover type before and aer ft reclassification support of IGBP-DIS in 1992 and the China subset of the and upscaling and the total area was consistent on the MODIS land cover data product in 2005. The two datasets whole, indicating that the reclassification and upscaling were both adopt the IGBP classification and the time span is reasonable. relatively large. eTh land cover dataset of 1992 is downloaded There was a high overall consistency between the 1 km from land cover products of China on the website of Cold resolution initial data of the IGBP classification and USGS and Arid Regions Science Data Center. And the dataset of classification in year 1992 and year 2005, except the slight 2005 is extracted from the Chinese subset of the MODIS difference in the total area of grassland, water bodies, and land cover data product in 2005 which is downloaded from unused land. Besides, the 20 km resolution data of USGS p ft ://e4ftl01.cr.usgs.gov/MOTA/ . er Th e were two sets of tests; classicfi ation dieff red from both of the other two kinds of land onewas thecontrol test andthe otherwas thesensitivity test, cover data. eTh result of the comparison between the initial thedieff rence betweenwhich wasrelated to thelandcover data and the upscaled USGS data of year 1992 indicated the types of the underlying surface. eTh land cover data of 1992 area of irrigated cropland and pasture, grassland, and water was used in the control test and that of 2005 was used in bodies decreased by 2.69%, 7.78%, and 41.42%, respectively, the sensitivity test. In addition to replacing land cover data, while the area of dryland cropland and pasture, deciduous the other input parameters and the parameterization scheme broadleaf forest, urban and built-up land, and unused land 4 Advances in Meteorology Table 3: Remapping tables of land-cover and land-use classification. USGS land cover classification Correspondence IGBP land cover classification 01 Urban and built-up land 14←01 01 Evergreen needle leaf 02 Dryland cropland and pasture 13←02 02 Evergreen broadleaf 03 Irrigated cropland and pasture 12←03 03 Deciduous needle leaf 04 Mixeddryland/irrigatedcroplandand pasture 11←04 04 Deciduous broadleaf 05 Cropland/grassland mosaic 15←05 05 Mixed forest 06 Cropland/woodland mosaic 08←06 06 Closed shrublands 07 Grassland 09←07 07 Open shrublands 08 Shrubland 08←08 08 Woody savannas 09 Mixed shrubland/grassland 10←09 09 Savannas 10 Savanna 07←10 10 Grasslands 11 Deciduous broadleaf forest 17←11 11 Permanent wetlands 12 Deciduous needle leaf forest 02←12 12 Croplands 13 Evergreen broadleaf forest 01←13 13 Urban and built-up 14 Evergreen needle leaf forest 05←14 14 Cropland mosaics 15 Mixed forest 24←15 15 Snow and ice 16 Water bodies 19←16 16 Bare soil and rocks 17 Herbaceous wetland 16←17 17 Water bodies 18 Wooded wetland 19 Barren or sparsely vegetated 20 Herbaceous tundra 21 Wooded tundra 22 Mixed tundra 23 Bare ground tundra 24 Snow or ice Table 4: Comparison table of area percentage (%) of each land-cover and land-use types among various kinds of classification systems. 1992 2005 a b c a b c IGBP USGS USGS IGBP USGS USGS Irrigated cropland and pasture 4.09 4.09 3.98 3.87 3.87 3.62 Dryland cropland and pasture 66.11 66.11 66.52 64.52 64.52 65.43 Deciduous broadleaf forest 6.48 6.48 6.51 6.55 6.55 6.06 Grassland 7.07 7.37 6.52 6.79 6.78 7.96 Water bodies 3.09 2.00 1.81 3.27 2.43 2.08 Urban and built-up land 12.34 12.34 12.57 14.46 14.46 13.76 Unused land 0.82 1.61 2.08 0.54 1.39 1.09 Total 100 100 100 100 100 100 a b c Note: represents the 1 km resolution data of IGBP classification, represents the 1 km resolution data of USGS classification, and represents the 20 km resolution data of USGS classification. increased by 0.62%, 0.46%, 1.68%, and 153.66%, respectively. the highest, reaching 99.25%, followed by that of dryland By contrast, that of year 2005 indicated the area of irrigated cropland and pasture, which was 98.76%. The overall con- cropland and pasture, deciduous broadleaf forest, water sistency reached 96.84% and the Kappa coefficient was 0.95, bodies, and urban and built-up land decreased by 6.46%, indicating the reclassification result had high classification 7.43%, 36.35%, and 4.87%, respectively, while the area of accuracy. dryland cropland and pasture, grassland, and unused land increased by 1.41%, 17.29%, and 101.11%, respectively. The error matrix was used to assess the spatial consistency 3.2. Processing of Meteorological Data. eTh observation data, between the initial data and the data aeft r reclassification in whichwereusedtomakeacomparison with thesimulated this study. The result indicated that the consistency of the temperature in this study, came from the meteorological land cover types except the unused land all exceeded 95% stations in the North China Plain. eTh meteorological data (Table 5). The consistency of the urban and built-up land was of the same period (January 2006–December 2007) of the Advances in Meteorology 5 Table 5: Error matrix of accuracy assessment for reclassifying land-cover and land-use types. Irrigated cropland Dryland cropland Deciduous Water Urban and b b Grassland Unused land b b b b b and pasture and pasture broadleaf forest bodies built-up land Irrigated cropland 97.32 0.32 2.04 0.02 0.15 2.88 0.03 and pasture Dryland cropland 0.23 98.74 3.43 2.02 0.05 3.2 0.2 and pasture Deciduous 0.54 0.04 96.45 4.43 0.23 0.3 0.22 broadleaf forest Grassland 0.34 0.26 5.23 93 0.4 0.54 0.17 Water bodies 0.37 0.24 0.01 0.05 97.78 0.27 0.23 Urban and built-up 000 0 0 99.25 0 land Unused land 0.02 0.1 0.07 0.12 0.14 0.69 90.86 a,b Note: represent land-cover and land-use types before and aer ft reclassification. Overall accuracy = 96.84%, Kappa coefficient = 0.9503. simulation were used in this study. eTh 20 km resolution urban and built-up land mainly was converted from the temperature data were obtained by interpolating the monthly dryland cropland, accounting for 60.55% of the conversion average temperature data from the 57 meteorological stations from the dryland cropland (Figure 3). in the North China Plain with the Kriging interpolation method. 4.2. Ability of the WRF Model to Simulate the Temperature Change in the North China Plain. The test result obtained with the standard WRF model was rfi st compared with the 4. Results ground-based observation data to assess the ability of the 4.1. Characteristics of Land Cover Changes in the North China WRF model to simulate the climate in the North China Plain during 1992–2005. Figure 2 shows the LUCC of the Plain. eTh daily average temperature was calculated as the North China Plain, which was obtained by reclassification average value of the temperature at 00:00, 06:00, 12:00, and and upscaling of high resolution data. eTh map shows that the 18:00soastokeepitconsistentwiththe ground-based plainregionwasdominatedbycropland,whichaccountedfor observation criteria. eTh result indicates that the WRF model 70% of the total area of the North China Plain. The irrigated can simulate the spatiotemporal change of temperature very cropland mainly concentrated in the northern part of Jiangsu well (Figure 4). According to the monthly change of the daily Province and the southwestern part of Shandong Province. average temperature in the whole study area, the highest eTh deciduous broadleaf forest and grassland were mainly temperatures in the observation data and simulation data distributed in the mountainous and hilly areas, sea beaches, both appear around July, and the lowest temperatures in the banks of lakes and rivers, and so forth. eTh deciduous observation data and simulation data both appear around broadleaf forest was mainly distributed along the southern January. The decreasing rate of the temperature during part of YanshanMountain, easternpiedmontofTaihang September and November is a bit higher than that during Mountain, northern piedmont of Tongbai Mountain, and March and May; that is, the temperature decreases a little DabieMountain. Whilethe grasslandmainlyconcentratedin more quickly in the autumn than it increases in the Spring. the hilly areas and coastal areas in Shandong Province. eTh Accordingtothe spatialpattern of thedaily average urban and built-up land was scattered in the whole North temperatureinFebruaryand August,the simulation data China Plain and accounted for about 14% of the total land and the ground-based observation data both indicate that the area. temperature is lower in the north part and higher in the south eTh LUCC data of the North China Plain in 1992 and 2005 part, it is colder in the mountainous area and warmer in the were overlaid to further analyze the conversion and inner plain area in the regions at the same latitude, and it is warmer change of each land cover type. The result indicated that the in the inland region than in the coastal region. For example, LUCC was mainly characterized by the increase in the urban the temperature difference between the Fuyang observation and built-up land and decrease in the dryland cropland, the station in the extreme north and the Zunhua observation changing rate of which reached 2.12% and 1.59%, respectively. station in the extreme north is as high as 5 CinFebruary By contrast, the changing rates of other land cover types and 2-3 C in August. While that between the Chengshantou were not more than 0.5%. The result indicated that the observation station in the extreme east and the Shijiazhuang newly increased urban and built-up land was mainly located observation station in the extreme west is 1–1.5 Cand 0.2– in the Beijing-Tianjin-Tangshan zone and around large and 0.3 C, respectively (Figure 5). medium-sized cities such as Shijiazhuang, Zhengzhou, Ji’nan, There is still some difference between the observed and Qingdao, and Lianyungang; besides, the newly increased simulated temperatures; that is, the simulated temperature is 6 Advances in Meteorology N N 1992 1992 2005 2005 (km) (km) 0 50 100 200 200 0 0 0 50 100 200 200 0 0 Province boundary Province boundary 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 Grassland Grassland Deciduous broadleaf forest Deciduous broadleaf forest Water bodies Water bodies Herbaceous wetland Herbaceous wetland Barren or sparsely vegetated Barren or sparsely vegetated (a) (b) Figure 2: The LUCC map aeft r upscaling in 1992 and 2005. (km) (km) 0 50 100 200 0 50 100 200 Province boundary Province boundary The newly increased urban and built-up land The decreased dryland cropland The unchanged increased urban and built-up land The unchanged dryland cropland (a) (b) Figure 3: Map of the unchanged and newly increased urban and built-up land (a) and map of the unchanged and decreased dryland cropland (b). Advances in Meteorology 7 30 5 the observed one on average. The observation stations with 4 thelower simulatedtemperature aremainlylocated in Henan 3 Province and Hebei Province, while the observation stations 2 with the higher simulated temperature mainly concentrate in 1 the hilly area in Shandong Province and the Beijing-Tianjin- Tangshan zone (Figure 6). In summary, the analysis mentioned above indicates that −5 −1 3456789 10 11 12 1 2 the WRF model can simulate the seasonal change and spatial pattern of temperature in the North China Plain very well. Observed value Although there is some difference between the observed and Simulated value simulated value, with the simulated temperature being lower Observed value and simulated value than the observed temperature on the whole, there is no Figure 4: Comparisons of simulated and observed values of the signicfi ant dieff renceinthe spatialpatternsofthe observed monthly average temperature at 2 meters above the ground. and simulated temperatures on the whole. er Th e is only some large difference in very few areas, indicating that the WRF modelhas agreat advantageinthe simulation of theclimate in the plain area. lowerthanthe observed temperatureonthe whole. eTh one reason is that there is systematic error in the WRF model. When simulating the surface temperature of East Asia with 4.3.AnalysisofTestResults. eTh LUCC in the North China the regional climate model, cold deviation is a widespread Plain, which was characterized by the regional urbanization, phenomenon. had led to some change of the near-surface temperature. The annual daily average temperature of the 57 obser- The annual average temperatures in the control test and ∘ ∘ ∘ vation stations is 14.19 C on average, while the simulated sensitivity test were 14.61 Cand 14.64 C, respectively. eTh value is 12.74 C. According to the monthly temperature LUCC in the North China Plain made the regional near- change, the simulated value is lower than the observed surface temperature increase by 0.03 C/year. All the months value in most months except November and December, and except January and June were characterized by a temperature the maximum difference between them reaches 3.34 Cin increase during 1992–2005. Besides, the LUCC in the North August. By contrast, the data of the seasonal temperature ChinaPlain also ledtoanincreaseinthe near-surface change suggests that the simulated temperature is lower than temperatureinall theseasons,among whichthe temperature the observed temperature in all seasons. eTh difference is increment was the highest in the summer and the lowest ∘ ∘ ∘ most significant in the summer, reaching 2.68 C, while it in the winter, reaching 0.05 Cand 0.02 C, respectively. eTh is relatively small in the spring, autumn, and winter, being monthly and seasonal temperature differences in the control ∘ ∘ ∘ 1.67 C, 0.99 C, and 0.44 C, respectively (Table 6). test and sensitivity test were as shown in Figure 7. There are also some differences in the spatial patterns eTh spatial patterns of the temperature increase are of the observed and simulated temperatures. In comparison consistent in the spring and autumn on the whole, both with the observed temperature, the simulated temperature indicating a significant temperature increase in the North is higher in the mountainous area and lower in the plain China Plain (Figure 8). The amplitude of the temperature area. For example, there is a large difference between the increase is relatively small in the spring (generally around ∘ ∘ observed and simulated daily average temperature in Febru- 0.03 C), while it is very large in the autumn (above 0.04 C ary and August. er Th e are 49 observation stations with on average). eTh temperature increases most greatly in the significant difference between the observed and simulated summer, increasing by 0.05 C/year on average, exceeding ∘ ∘ daily average temperatures (reaching the significance level of 0.1 C in the Circum-Bohai-Sea region, and reaching 0.2 Cin 95%) in February, of which 39 stations have the simulated the Beijing-Tianjin-Tangshan zone. Besides, there are much value 2.00 C lower than the observed value on average. wider regions with a significant temperature in the summer Whilethe simulatedtemperature of theother 10 observatio than in the other three seasons. Although the temperature while the observation stations n stations is 1.09 Chigher increases in the winter on the whole, it still decreases in most than the observed temperature on average. The observation regions, especially in the Yanshan Mountain, Circum-Bohai- stations with the lower simulated temperature are mainly Sea region, Shandong Peninsula, and so forth. locatedinthe middle part of HebeiProvinceand Shandong eTh spatial pattern of the seasonal temperature change Province, while that with the higher simulated temperature corresponded to that of the LUCC on the whole. eTh tem- mainly concentrates in the eastern piedmont of Taihang perature generally increased in the regions where the urban Mountain and inner part of Henan Province. By contrast, and built-up land increases. eTh temperature increment was the differences between the observed and simulated daily very high in these regions, and the degree and range of average temperatures in August in 42 observation stations influence of the temperature increase varied very significantly reachthe signicfi ancelevel.Thesimulated valueofthe 17 among seasons. Taking the Beijing-Tianjin-Tangshan zone as out of the 42 observation stations is 0.51 C lower than the an example, the temperature increment was very large and observed value on average, while the simulated temperature therange of theinufl enceofthe temperaturerisewas very of the other 25 observation stations is 0.98 Chigherthan wide in the summer in this region. The regional temperature Observed value and simulated value ( C) Observed value and simulated value ( C) 8 Advances in Meteorology ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 114 0 0 E 117 0 0 E 120 0 0 E 123 0 0 E 114 0 0 E 117 0 0 E 120 0 0 E 123 0 0 E ∘ 󳰀 ∘ 󳰀 39 0 0 N 39 0 0 N ∘ 󳰀 ∘ 󳰀 39 0 0 N 39 0 0 N ∘ 󳰀 ∘ 󳰀 36 0 0 N 36 0 0 N ∘ 󳰀 ∘ 󳰀 36 0 0 N 36 0 0 N ∘ 󳰀 ∘ 󳰀 33 0 0 N 33 0 0 N ∘ 󳰀 ∘ 󳰀 33 0 0 N 33 0 0 N ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 114 0 0 E 117 0 0 E 120 0 0 E 114 0 0 E 117 0 0 E 120 0 0 E Simulated in Aug. Simulated in Feb. −2–0 3-4 24-25 0-1 4-5 25-26 5-6 1-2 26-27 2-3 27-28 (a) (b) Figure 5: Simulated value of the daily average temperature in the North China Plain in February and August. Table 6: Simulated and observed values of the seasonal average temperature at 2 meters above the ground surface in the North China Plain (unit: C). Winter Spring Summer Autumn Simulated value 0.89 12.76 23.03 14.27 Observed value 1.33 14.43 25.72 15.27 Difference between simulated value and observed value 0.44 1.67 2.68 0.99 increased by 0.06–2.8 Cinthe summer,and thetemperature in the northern hemisphere was simulated with the “CRU- ∘ ∘ change due to expansion of the urban and built-up land NNR” model at the5 ×5 resolution (the OMR trend value influenced a wide area around the Beijing-Tianjin-Tangshan of the urban and built-up land, crop land, broadleaf forest, ∘ ∘ ∘ zone. eTh temperature increment was largely the same in and bare land was 0.034 C/year, 0.02 C/year, 0.002 C/year, ∘ ∘ the spring and autumn, reaching 0.03–3 C. However, the and 0.02 C/year, resp.). However, the simulated result is temperature rise mainly influenced Beijing and Tianjin in still somewhat higher, which may be because the ERA40 the spring and Beijing and some area in the north part of reanalysis indirectly included the ground-based observation Hebei Province in the autumn. eTh temperature rise was data and consequently made the OMR trend values smaller only obvious in the regions where the urban and built- than the results obtained with the numerical simulation. up land increased, while in other regions the temperature eTh vegetation plays an important role in influencing generally decreased by 0.01–0.06 C, which might be because the near-surface temperature. For example, one of the main the wind velocity was generally very high in the north China reasons for the near-surface temperature changes in different in the winter and consequently reduced the temperature rise land cover and land use types is the amount and density of resulting from the increase in urban and built-up land [12, 13]. the vegetation. On the whole, the better the vegetation cover The temperature changes most greatly in the urban and is,the less thetemperature riseis.Itmay be becausethere built-up land amongall thelandcover typesinthe North is very little evaporation in the barren land, and the land China Plain (0.1 C/year), followed by the irrigated cropland surface heat mainly gets into the atmosphere in the form and pasture (0.06 C/year), while the temperature increases of sensible heat. By contrast, there is higher soil humidity most slightly in the grassland, with an increment of only in the densely vegetated land, which makes the land surface 0.01 C/year (Figure 9). The result is largely consistent with the heat mainly get into the atmosphere in the form of latent result of the research of Lim et al. [14], in which the climate heat and consequently reduces part of the temperature rise 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 Advances in Meteorology 9 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 114 0 0 E 117 0 0 E 120 0 0 E 123 0 0 E 114 0 0 E 117 0 0 E 120 0 0 E 123 0 0 E ∘ 󳰀 ∘ 󳰀 39 0 0 N 39 0 0 N ∘ 󳰀 ∘ 󳰀 39 0 0 N 39 0 0 N ∘ 󳰀 ∘ 󳰀 36 0 0 N 36 0 0 N ∘ 󳰀 ∘ 󳰀 36 0 0 N 36 0 0 N ∘ 󳰀 ∘ 󳰀 33 0 0 N 33 0 0 N ∘ 󳰀 ∘ 󳰀 33 0 0 N 33 0 0 N ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 114 0 0 E 117 0 0 E 120 0 0 E 114 0 0 E 117 0 0 E 120 0 0 E −2-−1 −5–−1 1-2 0–0.5 −1–−0.5 −1–0 2-3 0.5–1 3-4 0-1 −0.5–0 1–1.5 (a) (b) Figure 6: Difference between the observed and simulated daily average temperature in the North China Plain in February and August. 0.1 The change of the average near-surface temperature corresponding to each kind of land cover change was 0.08 summarized in this study. The following gur fi e shows the 0.06 temperature change in the eight major types of land cover 0.04 change that involve a large area of land (Figure 10). The result showed that the conversion from dryland crop to forest and 0.02 built-up land made the near-surface temperature increase 0 ∘ by 0.13 C/year, while the conversion from dryland crop to −0.02 grassland made the near-surface temperature decrease by 3456789 10 11 12 12 ∘ 0.1 C/year. By contrast, the other conversion types only made Spring Summer Autumn Winter the near-surface temperature increase by 0.01–0.04 C/year. The conversion from croplands to built-up lands can Monthly temperature difference lead to the changes in the roughness and albedo of the Seasonal temperature difference land surface, furthercause thechangeinthe radiationflux Figure 7: Monthly and seasonal temperature differences in the of thelandsurface,and consequently make theregional control test and sensitivity test. near-surface temperature increase. Besides, changes of the underlying surface due to the urbanization can alter the physical processes such as the energy balance of the land surface, lead to the “vfi e island eeff cts” (i.e., dark islands, heat islands, dry islands, wet islands, and rain islands), decrease thewindvelocityand result in thevariablecityclimate,and of the land surface. In addition, the heat island effect in consequently influence the structure and development of the the urban region also leads to the rise of the near-surface boundary layer and change the climate in a large area. More- temperature. By contrast, the temperature increment is less over, the conversion from grasslands to dryland croplands in the water bodies, mainly because the specific heat capacity candecreasethe albedo of thelandsurface,increasethe of water bodies is very large, which makes the temperature net radiation of land surface, and consequently make the increase very slowly and consequently makes the near-surface sensible heat increase and lead to the increase of the daily temperature lower [15]. Moreover, there is great difference average temperature. eTh conversion from dryland cropland between the irrigation intensities of the dryland cropland and irrigated cropland, which leads to great difference in the to forest makes the near-surface temperature increase, mainly because agricultural irrigation can usually make the evapo- physical characteristics of them and consequently makes the temperature increments differ greatly. transpiration and air humidity increase, which leads to the Sensitivity test and control test ( C) 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 10 Advances in Meteorology ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 110 0 0 E 115 0 0 E 120 0 0 E 125 0 0 E 110 0 0 E 115 0 0 E 120 0 0 E 125 0 0 E N Summer N Spring ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 40 0 0 N 40 0 0 N 40 0 0 N 40 0 0 N ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 35 0 0 N 35 0 0 N 35 0 0 N 35 0 0 N The boundary of The boundary of study area study area Province boundary Province boundary <−0.04 <−0.04 −0.04–−0.02 −0.04–−0.02 −0.02–0 −0.02–0 0–0.02 0–0.02 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 0.02–0.04 0.02–0.04 ∘ 󳰀 30 0 0 N 30 0 0 N 30 0 0 N 30 0 0 N 0.04–0.06 0.04–0.06 0.06–0.08 0.06–0.08 >0.08 >0.08 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 110 0 0 E 115 0 0 E 120 0 0 E 110 0 0 E 115 0 0 E 120 0 0 E (a) (b) ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 110 0 0 E 115 0 0 E 120 0 0 E 125 0 0 E 110 0 0 E 115 0 0 E 120 0 0 E 125 0 0 E N N Autumn Winter ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 40 0 0 N 40 0 0 N 40 0 0 N 40 0 0 N ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 35 0 0 N 35 0 0 N 35 0 0 N 35 0 0 N The boundary of The boundary of study area study area Province boundary Province boundary <−0.04 <−0.04 −0.04–−0.02 −0.04–−0.02 −0.02–0 −0.02–0 0–0.02 0–0.02 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 0.02–0.04 ∘ 󳰀 0.02–0.04 30 0 0 N 30 0 0 N 30 0 0 N 30 0 0 N 0.04–0.06 0.04–0.06 0.06–0.08 0.06–0.08 >0.08 >0.08 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 0 0 E 115 0 0 E 120 0 0 E 0 0 E 115 0 0 E 120 0 0 E 110 110 (c) (d) Figure 8: Difference between the near-surface temperature in the four seasons in the sensitivity test and control test (Unit: C). 0.12 0.1 0.08 0.06 0.04 0.02 Barren or Urban and Dryland Irrigated Deciduous sparsely built-up cropland cropland broadleaf Grassland Water bodies Wetland vegetated land and pasture and pasture forest land Temperature 0.1 0.020.060.010.030.030.050.03 change Figure 9: Temperature change in various land cover types. Temperature change ( C/year) 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 Advances in Meteorology 11 0.15 0.1 0.05 −0.05 −0.1 −0.15 Conversion Conversion Conversion Conversion Conversion Conversion Conversion Conversion from from from from from from from from dryland grassland to forest to dryland dryland dryland forest to grassland cropland to dryland dryland cropland to cropland to cropland to grassland to forest grassland cropland cropland forest built-up land water bodies Temperature 0.01 0.02 0.04 0.04 0.13 0.12 0.04 −0.1 change Figure 10: Change of the near-surface temperature corresponding to each type of land cover change. increasing of the dew point temperature (when the pressure the degree and range of the influence of the temperature is 1000 Pa, Dew point temperature rising of 1 Cisequivalent rise varied greatly among seasons. eTh temperature changed ∘ ∘ to temperature increasing of 2.5 C). most significantly in the urban and built-up land (0.1 C/year), followed by theirrigatedcroplandand pasture(0.06 C/year). The temperature generally changed by 0.02–0.05 C/year in the forest, water bodies , and dryland cropland and pasture, 5. Conclusion and Discussion and it changed most slightly in the grassland (0.01 C/year). Among all the types of land cover change that involved a eTh regional climate model, the WRF model, was used to large land area, the conversion from dry land into forest and study the impacts of land cover change on the near-surface built-up land led to the greatest near-surface temperature temperature in the North China Plain on the basis of the land increment, reaching 0.13 C/year. While the conversion from coverdataoftheNorthChinaPlaininyear1992andyear2005 dry land and pasture into grassland made the near-surface in this study. The land cover change in the North China Plain during 1992–2005 was mainly characterized by the increase temperature decrease by 0.1 C/year. By contrast, the other conversions only made the near-surface temperature change in urban and built-up lands and the decrease in dryland by 0.01–0.04 C/year. croplands. The urban and built-up lands increased by 2.12%, and the dryland cropland decreased by 1.59%, while other Different types of land cover differ greatly in the physical characteristics, chemical processes, and biological processes, land cover types changed by no more than 0.5%. Besides, the which lead to the significant differences in the energy budget newly increased urban and built-up land was mainly located and water budget of the land surface and consequently have in the Beijing-Tianjin-Tangshan zone and around large and different impacts on the regional climate change. In compar- medium-sized cities such as Shijiazhuang, Zhengzhou, Ji’nan, ison to the increase in the greenhouse gases that has a global Qingdao, and Lianyungang. In addition, the newly increased influence, the land cover changes exert more influence at urban and built-up land mainly was converted from the theregionalscale.Theland-atmosphere feedback canchange drylandcroplandandaccountedfor60.55%oftheconversion from the dryland cropland during this period. the albedo and soil moisture, alter the evaporation process, and consequently inu fl ence the response of the regional near- eTh WRF model can reflect the seasonal change and surface temperature to the increase in the greenhouse gases. spatialpattern of thenear-surfacetemperature in theNorth Generally, the better the vegetation cover is, the less the China Plain very well, although there is some difference temperature riseis. For example, the urban heat island eeff ct between the observed and simulated values, with the sim- in the urban regions where there is less vegetation will lead to ulated temperature being a little lower on the whole. er Th e greater increase in the near-surface temperature. Besides, the is no significant difference in the spatial patterns of the temperature increment in the water bodies is generally lower observed and simulated temperatures on the whole, except and consequently leads to a lower near-surface temperature some slightly large difference in few areas. eTh result indicates since the water has a large specific heat capacity and its that the WRF model has a substantial advantage in simulating temperature generally increases more slowly. In addition, theclimate in theplain area.Thelandcover change in the North China Plain, which was mainly characterized the significant difference between the irrigation intensities of the dryland cropland and that of the irrigated cropland by the regional urbanization, has caused significant change also leads to great difference in their physical characteristics of thenear-surfacetemperature.Itled to aregionalnear- and consequently makes their temperature increments differ surface temperature increment of 0.03 C/year. Besides, the greatly. Moreover, the change of the underlying surface due spatial pattern of the temperature change corresponded to to the urbanization can alter the physical processes such that of the land cover change on the whole; that is, the as the energy balance of the land surface and consequently temperature mainly increased significantly in the regions lead to the climate change in a large area; for example, the where the urban and built-up land expanded; In addition, Temperature change ( C/year) 12 Advances in Meteorology conversion from thecroplandtothe urbanand built-up land [7] A. D. Jazcilevich, A. R. Garc´ıa, and L. G. Ru´ız-Suar ´ ez, “A study of air flow patterns affecting pollutant concentrations in the canchangethe roughnessand albedo of thelandsurface and Central Region of Mexico,” Atmospheric Environment,vol.37, consequentlycausethechangeintheradiationufl xoftheland no. 2, pp. 183–193, 2003. surface and lead to significant increase in the regional near- [8] Q. P. Li and Y. H. Ding, “Research progress in the eeff ct surface temperature. of vegetation change on regional climate,” Journal of Nanjing eTh re are still some uncertainties in the research on Institute of Meteorology,vol.27, no.1,pp. 131–140, 2004. the climatic effects of the land cover change, so it is still [9] J.Chen, P. Zhao,H.Liu,and X. Guo, “Modelingimpacts necessary to carry out more in-depth researches on a series of vegetation in western China on the summer climate of of issues such as the improvement of the climate model and northwestern China,” Advances in Atmospheric Sciences,vol.26, the reclassica fi tion method of the land cover change data, no. 4, pp. 803–812, 2009. especially the land cover data. Since this study is preliminary, [10] A. Seth and F. Giorgi, “eTh eeff cts of domain choice on summer there are still some deficiencies as follows. First, there is still precipitation simulation and sensitivity in a regional climate some dieff rencebetween theupscaledlandcover data and model,” Journal of Climate, vol. 11, no. 17, pp. 2698–2712, 1998. the initial data, which leads to some uncertainties in the [11] S. Y. Liu, CWRF ApplicationinEastChina MonsoonArea, simulated climatic eeff cts of the land cover change. Second, Nanjing University of Information Science and Technology, the result may change if the calculus of the data of a long Nanjing, China, 2006. period is carried out since the study of the impacts of the land [12] A. J. Arnfield, “Two decades of urban climate research: a review cover change on the temperature in the North China Plain of turbulence, exchanges of energy and water, and the urban is basedonthe sensitivitytestofthe numericalintegration heat island,” International Journal of Climatology,vol.23, no.1, of the data of only two years in this <study. In addition, pp. 1–26, 2003. there are some uncertainties in the result since there are [13] G.T.Johnson,T.R.Oke,T.J.Lyons,D.G.Steyn,I.D.Watson, various feedbacks within the climate system. In view of these and J. A. Voogt, “Simulation of surface urban heat islands under deficiencies, on the one hand, it is necessary to try more ‘IDEAL’ conditions at night part 1: theory and tests against field methods to upscale the data so as to reduce the inu fl ence of data,” Boundary-Layer Meteorology,vol.56, no.3,pp. 275–294, the errors in the data conversion. On the other hand, it is necessary to extend the integration time so as to guarantee [14] Y. Lim, M. Cai, E. Kalnay, and L. Zhou, “Observational evidence the certainties of the simulation result. of sensitivity of surface climate changes to land types and urbanization,” Geophysical Research Letters, vol. 32, no. 22, ArticleIDL22712,4pages,2005. Acknowledgments [15] W.Z.Su, Y. B. Yang,and G. S. Yang,“Distributional character- istics of urban thermal space and relations with land use/cover This research was financially supported by the Ministry of of Nanjing,” Scientia Geographica Sinica,vol.25, no.6,pp. 697– Science and Technology of China (Grant no. 2010CB950904) 703, 2005. and the National Natural Science Foundation of China (Grant no. 41071343). References [1] National Research Council, “Radiative forcing of climate change: expanding the concept and addressing uncertainties,” Tech. Rep., National Research Council, Washington, DC, USA, [2] M. Cai and E. Kalnay, “Response to the comments by Vose et al. and Trenberth. Impact of land-use change on climate,” Nature, vol. 95, pp. 427–214, 2004. [3] J.J.Feddema,K.W.Oleson, G. B. Bonanetal.,“Atmospheric science: the importance of land-cover change in simulating future climates,” Science, vol. 310, no. 5754, pp. 1674–1678, 2005. [4] R.Mahmood,S.A.Foster, andD.Logan,“eTh GeoProfile metadata, exposure of instruments, and measurement bias in climatic record revisited,” International Journal of Climatology, vol. 26,no. 8, pp.1091–1124,2006. [5] A. Jazcilevich, V. Fuentes, E. Jauregui, and E. Luna, “Simulated urban climate response to historical land use modification in the Basin of Mexico,” Climatic Change,vol.44, no.4,pp. 515– 536, 2000. [6] A. D. Jazcilevich, A. R. Garc´ıa, and L. G. Ru´ız-Suar ´ ez, “A modeling study of air pollution modulation through land-use change in the Valley of Mexico,” Atmospheric Environment,vol. 36,no. 14,pp. 2297–2307, 2002. International Journal of Journal of Ecology Mining The Scientific Journal of Geochemistry Scientifica World Journal Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Journal of Earthquakes Paleontology Journal Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Journal of Petroleum Engineering Submit your manuscripts at http://www.hindawi.com International Journal of Geophysics Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Advances in Advances in Journal of Advances in International Journal of Meteorology ClimatologyGeology Oceanography Oceanography Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Applied & Journal of Environmental International Journal of Journal of International Journal of Computational Soil Science Mineralogy Geological Research Atmospheric Sciences Environmental Sciences Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Volume 2014 Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com http://www.hindawi.com Volume 2014 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Meteorology Hindawi Publishing Corporation

Impacts of Land Cover Change on the Near-Surface Temperature in the North China Plain

Loading next page...
 
/lp/hindawi-publishing-corporation/impacts-of-land-cover-change-on-the-near-surface-temperature-in-the-4ZIuNoKV6D
Publisher
Hindawi Publishing Corporation
Copyright
Copyright © 2013 Ruijie Qu 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.
ISSN
1687-9309
eISSN
1687-9317
DOI
10.1155/2013/409302
Publisher site
See Article on Publisher Site

Abstract

Hindawi Publishing Corporation Advances in Meteorology Volume 2013, Article ID 409302, 12 pages http://dx.doi.org/10.1155/2013/409302 Research Article Impacts of Land Cover Change on the Near-Surface Temperature in the North China Plain 1,2 1 3 4 3 Ruijie Qu, Xiaolin Cui, Haiming Yan, Enjun Ma, and Jinyan Zhan College of Geomatics, Xi’an University of Science and Technology, No. 58 Yanta Road, Xi’an 710054, China Center for Chinese Agricultural Policy, Chinese Academy of Sciences, No. 11A Datun Road, Anwai, Beijing 100101, China State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China School of Mathematics and Physics, China University of Geosciences, No. 388 Lumo Road, Wuhan 430074, China Correspondence should be addressed to Jinyan Zhan; zhanjy@bnu.edu.cn Received 11 June 2013; Accepted 30 July 2013 Academic Editor: Xiangzheng Deng Copyright © 2013 Ruijie Qu 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. This study first tested and verified the ability of the Weather Research and Forecasting (WRF) model to simulate the near-surface temperature in the North China Plain. Then the static land cover data in the WRF were replaced, and thereaer ft the modified WRF model was used to explore the impacts of land cover change on the near-surface temperature in the North China Plain in year 1992 and year 2005. The results indicated that the land cover change in the North China Plain, which was characterized by the regional urbanization, had led to significant changes in the near-surface temperature, increasing the regional near-surface temperature by 0.03 C/year on average. eTh spatial pattern of the climate change basically corresponded to that of the land cover change; for example, the temperature increased most significantly in the regions mainly consisting of cities and built-up area. Besides, there were some variations in the degree and range of influence of the land cover change on the temperature among seasons. The result can provide important theoretical support for the adaptation to climate change, scientific land cover change management, and land use planning. 1. Introduction mainly influences the climate at the local, regional, and global scales by changing the land surface characteristics, More andmoreattention hasbeenpaidtothe inufl ence altering the exchange of energy, water, and other materials of humanactivitiesonthe climatesysteminrecentyears between the land surface and the atmosphere and influencing as great progress has been made in the researches on the other biogeochemical processes. er Th e is great variation in global climate system and environmental change. IPCC AR4 the biogeochemical mechanism through which the LUCC pointed out that the human activities may account for 90% influences the climate in different regions; the climate change of the reasons for the global warming. There is very complex caused by LUCC is mainly through the land-atmosphere influence of human activities on the climate, with the land interaction and is closely related to the regional climate and use/cover change (LUCC) being considered as the major environmental background, terrain, vegetation, and so forth. influencing factor in the climate system [ 1]. The LUCC eTh refore, it is of great importance to study the influence of influences the climate system at various temporal and spatial LUCC on the regional climate. scales, and the land use change has contributed to 1/3 of the The North China Plain has been selected as the study area increase in global CO emission since the 1750s. Besides, a in this study. First, it has long been one of the most densely lot of observations and simulation experiments also indicate populated regions in China; the current regional city density that the LUCC at various spatial scales has been one of is still very high and the industries and agriculture are well the most important approaches through which the human activities exert influence on the climate [ 2–4]. The LUCC developed. The rapid economic development and increasing 2 Advances in Meteorology WRF terrestrial data (land use, soil, etc.) WPS Metgrid data Gridded data Simulated data (temperature, REAL.EXE WRFV3 precipitation etc.) POST ARW POST (GrADS/Vis5D) POST module Figure 1: WRF flow chart. population have ledtoverydramaticlandcover change in result [5–9]. The WRF model is a next-generation mesoscale this region, and the human disturbance to the environment is numerical weather prediction system designed to serve both especially signica fi nt, which greatly inu fl ences the sustainable atmospheric research and operational forecasting needs. The development of the whole China. Second, the North China ARW-WRF (Edition 3.3) has been used in this study. Plain is a typical area of the monsoon climate as well ARW-WRF includes three parts: WRF Preprocessing as the transient region between the humid and subhumid System (WPS), WRFV3, and POST (Figure 1). The WPS region and the arid and semiarid region. On the one hand, program is used primarily for real-data simulations. Its the plain agriculture can be sustained for a long time due functions include (1) defining simulation domains, (2) inter- the local climatic conditions. On the other hand, the local polating terrestrial data (such as terrain, land use, and soil climatic conditions also lead to more frequent droughts, types) to the simulation domain, and (3) degribbing and make the agricultural production extremely unstable, and interpolating meteorological data from another model to this consequently mayleadtogreater economic loss andmore simulation domain. Its main features include (1) GRIB 1/2 extensive social influence. er Th efore, it is of great importance meteorological data from various centers around the world; to study the influence of LUCC on the climate in the North (2) USGS 24 category land datasets; (3) map projections China Plain. for polar stereographic, Lambert Conformal, Mercator, and This study rfi st tested the ability of the Weather Research latitudelongitude; (4) nesting; (5) user-interfaces to input and Forecasting (WRF) model to simulate the change of the other static data as well as met data. WRFV3 runs and exports near-surface temperature in the North China Plain, based simulated data, including temperature, precipitation, and so on which the static land cover data in the WRF were then forth. POST part converts the results and makes the outputs replaced. er Th eaeft r the modified WRF was used to study visualized. the influence of the land cover change on the near-surface ARW oeff rs multiple physical parameterization schemes temperatureinthe NorthChina Plaininyear1992and year that can be combined in any way. The options typically 2005. eTh result can contribute to better understanding of the range from simple and efficient to sophisticated and more influencing factors of the climate in the North China Plain computationally costly and from newly developed schemes so as to minimize the negative influence and maximize the to well-tried schemessuchasthose in currentoperational positive influence on the regional climate, which is helpful to models. Table 1 lists some schemes. the scientific regional land use planning and management in China in the future. 2.2. Experiment Design. The location and size of the simula- tion area have great inu fl ence on the simulation result [ 10, 11]. The center line of the simulation area was set to be 36 Nand 2. Model Introduction and Experiment Design 117 Einthisstudy.TheLambert projection wasused, with ∘ ∘ 2.1. Model Introduction. With the development of the atmo- the two standard parallels being 26 Nand 46 N, respectively. spheric models and land surface process models, the numer- eTh spatial resolution was set to be 20 km, and there were 112 ical simulation has become a widely used approach to study grid points in the east-west direction and 97 grid points in the the influence of climate on vegetation. eTh regional climate north-south direction in the whole simulation area. model used in this study, WRF model, has been widely The lateral boundary forcing data came from the National used in the global climate and achieved good simulation Centers for Environmental Prediction (NCEP)/FNL dataset Advances in Meteorology 3 Table 1: Physical parameterization schemes. Table 2: Schemes of the simulation test. Land cover data used in Physical parameters Schemes Test Test period the WRF model Kessler, Lin et al., WRF Microphysics Control test 2005.10–2007.12 Land cover data of 1992 Single-Moment 3-class Sensitivity test 2005.10–2007.12 Land cover data of 2005 Cumulus Kain-Fritsch, Grell-Devenyi ensemble parameterization scheme, Betts-Miller-Janjic Dudhia (MM5), CAM scheme, Shortwave radiation Goddard of the physical process are the same in the two tests. They were both implemented with the climate forcing data between Longwave radiation RRTM, CAM, GFDL October of 2005 and December of 2007. Planetary boundary layer MRF, MYJ, YSU 5-layer thermal diffusion, Noah Land Land surface Surface Model, RUC Land Surface 3. Data Processingb Model 3.1. Processing of Land Cover Data. It is necessary to reclassify the land cover data with the USGS land cover classicfi ation and were updated every 6 hours. This dataset has the spatial which includes 24 land cover and land use types and set the ∘ ∘ resolution of 1×1 and the vertical height of 27 layers, and it spatial resolution to be 20 km according to the requirement of has been established and updated since July of 1999 with the the WRF model. Therefore, the land cover data of the IGBP data assimilation of almost all kinds of observation data (e.g., land cover classification were first reclassified with the USGS remote sensing data and ground-based observation data). In land cover classification system, and then the spatial resolu- comparison to the datasets of NCEP I, NCEP II, and EAR40, tion of thedatawas convertedfrom1km to 20km.Thetwo the NCEP/FNL dataset has higher accuracy and spatial datasets both have the spatial resolution of 1 km and adopt resolution, and it includes more kinds of environmental the IGBP classica fi tion. On the basis of the data of the IGBP variables. land cover classicfi ation, we formulated the transformation In the parameterization scheme of physical processes in method from the IGBP land cover classification to the USGS the model, the cumulus parameterization scheme adopted land cover classification ( Table 3) and established the land use the Grell-Devenyi ensemble scheme, the boundary layer and land cover dataset of the USGS classicfi ation of the North processschemewasYSU,andtheshortwaveradiationscheme China Plain. was the CAM scheme, while the land surface process scheme The LUCC data were further upscaled on the basis of the was Noah Land Surface Model. The boundary bueff r was set data mentioned above so as to embed the high resolution to be 4layersofgridpoints, andthe boundary conditions underlying surface data into the large-scale climate model. In adopted the relaxation scheme. eTh time interval of the this study, the 1 km resolution land cover and land use data of model integration was set to be 5 minutes, and that of the USGS classification were upscaled into the 20 km resolution radiation process and cumulus convection was 30 minutes data with the resampling function of ArcGIS. Besides, the and 5 minutes, respectively. er Th e were 27 layers in the three kinds of data were integrated in a system of 7 land vertical direction and the atmospheric pressure at the top cover types, and their area consistency and spatial consistency layer was 50 hPa. were analyzed so as to check the change of the classification The test scheme in this study is designed as follows accuracy of the land cover and land use data before and after (Table 2). In order to analyze the impact of land cover thereclassicfi ation andupscaling.Thetotal area dieff rence change on climate and reduce data errors, the land cover between the land cover data that were reclassified and data used in this study were extracted from the Chinese upscaled and the initial data was presented in Table 4.It subset of theGlobalLandCover Characteristicsdatabase indicated that there was no significant difference in the total which was developed based on the AVHRR data with the area of each land cover type before and aer ft reclassification support of IGBP-DIS in 1992 and the China subset of the and upscaling and the total area was consistent on the MODIS land cover data product in 2005. The two datasets whole, indicating that the reclassification and upscaling were both adopt the IGBP classification and the time span is reasonable. relatively large. eTh land cover dataset of 1992 is downloaded There was a high overall consistency between the 1 km from land cover products of China on the website of Cold resolution initial data of the IGBP classification and USGS and Arid Regions Science Data Center. And the dataset of classification in year 1992 and year 2005, except the slight 2005 is extracted from the Chinese subset of the MODIS difference in the total area of grassland, water bodies, and land cover data product in 2005 which is downloaded from unused land. Besides, the 20 km resolution data of USGS p ft ://e4ftl01.cr.usgs.gov/MOTA/ . er Th e were two sets of tests; classicfi ation dieff red from both of the other two kinds of land onewas thecontrol test andthe otherwas thesensitivity test, cover data. eTh result of the comparison between the initial thedieff rence betweenwhich wasrelated to thelandcover data and the upscaled USGS data of year 1992 indicated the types of the underlying surface. eTh land cover data of 1992 area of irrigated cropland and pasture, grassland, and water was used in the control test and that of 2005 was used in bodies decreased by 2.69%, 7.78%, and 41.42%, respectively, the sensitivity test. In addition to replacing land cover data, while the area of dryland cropland and pasture, deciduous the other input parameters and the parameterization scheme broadleaf forest, urban and built-up land, and unused land 4 Advances in Meteorology Table 3: Remapping tables of land-cover and land-use classification. USGS land cover classification Correspondence IGBP land cover classification 01 Urban and built-up land 14←01 01 Evergreen needle leaf 02 Dryland cropland and pasture 13←02 02 Evergreen broadleaf 03 Irrigated cropland and pasture 12←03 03 Deciduous needle leaf 04 Mixeddryland/irrigatedcroplandand pasture 11←04 04 Deciduous broadleaf 05 Cropland/grassland mosaic 15←05 05 Mixed forest 06 Cropland/woodland mosaic 08←06 06 Closed shrublands 07 Grassland 09←07 07 Open shrublands 08 Shrubland 08←08 08 Woody savannas 09 Mixed shrubland/grassland 10←09 09 Savannas 10 Savanna 07←10 10 Grasslands 11 Deciduous broadleaf forest 17←11 11 Permanent wetlands 12 Deciduous needle leaf forest 02←12 12 Croplands 13 Evergreen broadleaf forest 01←13 13 Urban and built-up 14 Evergreen needle leaf forest 05←14 14 Cropland mosaics 15 Mixed forest 24←15 15 Snow and ice 16 Water bodies 19←16 16 Bare soil and rocks 17 Herbaceous wetland 16←17 17 Water bodies 18 Wooded wetland 19 Barren or sparsely vegetated 20 Herbaceous tundra 21 Wooded tundra 22 Mixed tundra 23 Bare ground tundra 24 Snow or ice Table 4: Comparison table of area percentage (%) of each land-cover and land-use types among various kinds of classification systems. 1992 2005 a b c a b c IGBP USGS USGS IGBP USGS USGS Irrigated cropland and pasture 4.09 4.09 3.98 3.87 3.87 3.62 Dryland cropland and pasture 66.11 66.11 66.52 64.52 64.52 65.43 Deciduous broadleaf forest 6.48 6.48 6.51 6.55 6.55 6.06 Grassland 7.07 7.37 6.52 6.79 6.78 7.96 Water bodies 3.09 2.00 1.81 3.27 2.43 2.08 Urban and built-up land 12.34 12.34 12.57 14.46 14.46 13.76 Unused land 0.82 1.61 2.08 0.54 1.39 1.09 Total 100 100 100 100 100 100 a b c Note: represents the 1 km resolution data of IGBP classification, represents the 1 km resolution data of USGS classification, and represents the 20 km resolution data of USGS classification. increased by 0.62%, 0.46%, 1.68%, and 153.66%, respectively. the highest, reaching 99.25%, followed by that of dryland By contrast, that of year 2005 indicated the area of irrigated cropland and pasture, which was 98.76%. The overall con- cropland and pasture, deciduous broadleaf forest, water sistency reached 96.84% and the Kappa coefficient was 0.95, bodies, and urban and built-up land decreased by 6.46%, indicating the reclassification result had high classification 7.43%, 36.35%, and 4.87%, respectively, while the area of accuracy. dryland cropland and pasture, grassland, and unused land increased by 1.41%, 17.29%, and 101.11%, respectively. The error matrix was used to assess the spatial consistency 3.2. Processing of Meteorological Data. eTh observation data, between the initial data and the data aeft r reclassification in whichwereusedtomakeacomparison with thesimulated this study. The result indicated that the consistency of the temperature in this study, came from the meteorological land cover types except the unused land all exceeded 95% stations in the North China Plain. eTh meteorological data (Table 5). The consistency of the urban and built-up land was of the same period (January 2006–December 2007) of the Advances in Meteorology 5 Table 5: Error matrix of accuracy assessment for reclassifying land-cover and land-use types. Irrigated cropland Dryland cropland Deciduous Water Urban and b b Grassland Unused land b b b b b and pasture and pasture broadleaf forest bodies built-up land Irrigated cropland 97.32 0.32 2.04 0.02 0.15 2.88 0.03 and pasture Dryland cropland 0.23 98.74 3.43 2.02 0.05 3.2 0.2 and pasture Deciduous 0.54 0.04 96.45 4.43 0.23 0.3 0.22 broadleaf forest Grassland 0.34 0.26 5.23 93 0.4 0.54 0.17 Water bodies 0.37 0.24 0.01 0.05 97.78 0.27 0.23 Urban and built-up 000 0 0 99.25 0 land Unused land 0.02 0.1 0.07 0.12 0.14 0.69 90.86 a,b Note: represent land-cover and land-use types before and aer ft reclassification. Overall accuracy = 96.84%, Kappa coefficient = 0.9503. simulation were used in this study. eTh 20 km resolution urban and built-up land mainly was converted from the temperature data were obtained by interpolating the monthly dryland cropland, accounting for 60.55% of the conversion average temperature data from the 57 meteorological stations from the dryland cropland (Figure 3). in the North China Plain with the Kriging interpolation method. 4.2. Ability of the WRF Model to Simulate the Temperature Change in the North China Plain. The test result obtained with the standard WRF model was rfi st compared with the 4. Results ground-based observation data to assess the ability of the 4.1. Characteristics of Land Cover Changes in the North China WRF model to simulate the climate in the North China Plain during 1992–2005. Figure 2 shows the LUCC of the Plain. eTh daily average temperature was calculated as the North China Plain, which was obtained by reclassification average value of the temperature at 00:00, 06:00, 12:00, and and upscaling of high resolution data. eTh map shows that the 18:00soastokeepitconsistentwiththe ground-based plainregionwasdominatedbycropland,whichaccountedfor observation criteria. eTh result indicates that the WRF model 70% of the total area of the North China Plain. The irrigated can simulate the spatiotemporal change of temperature very cropland mainly concentrated in the northern part of Jiangsu well (Figure 4). According to the monthly change of the daily Province and the southwestern part of Shandong Province. average temperature in the whole study area, the highest eTh deciduous broadleaf forest and grassland were mainly temperatures in the observation data and simulation data distributed in the mountainous and hilly areas, sea beaches, both appear around July, and the lowest temperatures in the banks of lakes and rivers, and so forth. eTh deciduous observation data and simulation data both appear around broadleaf forest was mainly distributed along the southern January. The decreasing rate of the temperature during part of YanshanMountain, easternpiedmontofTaihang September and November is a bit higher than that during Mountain, northern piedmont of Tongbai Mountain, and March and May; that is, the temperature decreases a little DabieMountain. Whilethe grasslandmainlyconcentratedin more quickly in the autumn than it increases in the Spring. the hilly areas and coastal areas in Shandong Province. eTh Accordingtothe spatialpattern of thedaily average urban and built-up land was scattered in the whole North temperatureinFebruaryand August,the simulation data China Plain and accounted for about 14% of the total land and the ground-based observation data both indicate that the area. temperature is lower in the north part and higher in the south eTh LUCC data of the North China Plain in 1992 and 2005 part, it is colder in the mountainous area and warmer in the were overlaid to further analyze the conversion and inner plain area in the regions at the same latitude, and it is warmer change of each land cover type. The result indicated that the in the inland region than in the coastal region. For example, LUCC was mainly characterized by the increase in the urban the temperature difference between the Fuyang observation and built-up land and decrease in the dryland cropland, the station in the extreme north and the Zunhua observation changing rate of which reached 2.12% and 1.59%, respectively. station in the extreme north is as high as 5 CinFebruary By contrast, the changing rates of other land cover types and 2-3 C in August. While that between the Chengshantou were not more than 0.5%. The result indicated that the observation station in the extreme east and the Shijiazhuang newly increased urban and built-up land was mainly located observation station in the extreme west is 1–1.5 Cand 0.2– in the Beijing-Tianjin-Tangshan zone and around large and 0.3 C, respectively (Figure 5). medium-sized cities such as Shijiazhuang, Zhengzhou, Ji’nan, There is still some difference between the observed and Qingdao, and Lianyungang; besides, the newly increased simulated temperatures; that is, the simulated temperature is 6 Advances in Meteorology N N 1992 1992 2005 2005 (km) (km) 0 50 100 200 200 0 0 0 50 100 200 200 0 0 Province boundary Province boundary 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 Grassland Grassland Deciduous broadleaf forest Deciduous broadleaf forest Water bodies Water bodies Herbaceous wetland Herbaceous wetland Barren or sparsely vegetated Barren or sparsely vegetated (a) (b) Figure 2: The LUCC map aeft r upscaling in 1992 and 2005. (km) (km) 0 50 100 200 0 50 100 200 Province boundary Province boundary The newly increased urban and built-up land The decreased dryland cropland The unchanged increased urban and built-up land The unchanged dryland cropland (a) (b) Figure 3: Map of the unchanged and newly increased urban and built-up land (a) and map of the unchanged and decreased dryland cropland (b). Advances in Meteorology 7 30 5 the observed one on average. The observation stations with 4 thelower simulatedtemperature aremainlylocated in Henan 3 Province and Hebei Province, while the observation stations 2 with the higher simulated temperature mainly concentrate in 1 the hilly area in Shandong Province and the Beijing-Tianjin- Tangshan zone (Figure 6). In summary, the analysis mentioned above indicates that −5 −1 3456789 10 11 12 1 2 the WRF model can simulate the seasonal change and spatial pattern of temperature in the North China Plain very well. Observed value Although there is some difference between the observed and Simulated value simulated value, with the simulated temperature being lower Observed value and simulated value than the observed temperature on the whole, there is no Figure 4: Comparisons of simulated and observed values of the signicfi ant dieff renceinthe spatialpatternsofthe observed monthly average temperature at 2 meters above the ground. and simulated temperatures on the whole. er Th e is only some large difference in very few areas, indicating that the WRF modelhas agreat advantageinthe simulation of theclimate in the plain area. lowerthanthe observed temperatureonthe whole. eTh one reason is that there is systematic error in the WRF model. When simulating the surface temperature of East Asia with 4.3.AnalysisofTestResults. eTh LUCC in the North China the regional climate model, cold deviation is a widespread Plain, which was characterized by the regional urbanization, phenomenon. had led to some change of the near-surface temperature. The annual daily average temperature of the 57 obser- The annual average temperatures in the control test and ∘ ∘ ∘ vation stations is 14.19 C on average, while the simulated sensitivity test were 14.61 Cand 14.64 C, respectively. eTh value is 12.74 C. According to the monthly temperature LUCC in the North China Plain made the regional near- change, the simulated value is lower than the observed surface temperature increase by 0.03 C/year. All the months value in most months except November and December, and except January and June were characterized by a temperature the maximum difference between them reaches 3.34 Cin increase during 1992–2005. Besides, the LUCC in the North August. By contrast, the data of the seasonal temperature ChinaPlain also ledtoanincreaseinthe near-surface change suggests that the simulated temperature is lower than temperatureinall theseasons,among whichthe temperature the observed temperature in all seasons. eTh difference is increment was the highest in the summer and the lowest ∘ ∘ ∘ most significant in the summer, reaching 2.68 C, while it in the winter, reaching 0.05 Cand 0.02 C, respectively. eTh is relatively small in the spring, autumn, and winter, being monthly and seasonal temperature differences in the control ∘ ∘ ∘ 1.67 C, 0.99 C, and 0.44 C, respectively (Table 6). test and sensitivity test were as shown in Figure 7. There are also some differences in the spatial patterns eTh spatial patterns of the temperature increase are of the observed and simulated temperatures. In comparison consistent in the spring and autumn on the whole, both with the observed temperature, the simulated temperature indicating a significant temperature increase in the North is higher in the mountainous area and lower in the plain China Plain (Figure 8). The amplitude of the temperature area. For example, there is a large difference between the increase is relatively small in the spring (generally around ∘ ∘ observed and simulated daily average temperature in Febru- 0.03 C), while it is very large in the autumn (above 0.04 C ary and August. er Th e are 49 observation stations with on average). eTh temperature increases most greatly in the significant difference between the observed and simulated summer, increasing by 0.05 C/year on average, exceeding ∘ ∘ daily average temperatures (reaching the significance level of 0.1 C in the Circum-Bohai-Sea region, and reaching 0.2 Cin 95%) in February, of which 39 stations have the simulated the Beijing-Tianjin-Tangshan zone. Besides, there are much value 2.00 C lower than the observed value on average. wider regions with a significant temperature in the summer Whilethe simulatedtemperature of theother 10 observatio than in the other three seasons. Although the temperature while the observation stations n stations is 1.09 Chigher increases in the winter on the whole, it still decreases in most than the observed temperature on average. The observation regions, especially in the Yanshan Mountain, Circum-Bohai- stations with the lower simulated temperature are mainly Sea region, Shandong Peninsula, and so forth. locatedinthe middle part of HebeiProvinceand Shandong eTh spatial pattern of the seasonal temperature change Province, while that with the higher simulated temperature corresponded to that of the LUCC on the whole. eTh tem- mainly concentrates in the eastern piedmont of Taihang perature generally increased in the regions where the urban Mountain and inner part of Henan Province. By contrast, and built-up land increases. eTh temperature increment was the differences between the observed and simulated daily very high in these regions, and the degree and range of average temperatures in August in 42 observation stations influence of the temperature increase varied very significantly reachthe signicfi ancelevel.Thesimulated valueofthe 17 among seasons. Taking the Beijing-Tianjin-Tangshan zone as out of the 42 observation stations is 0.51 C lower than the an example, the temperature increment was very large and observed value on average, while the simulated temperature therange of theinufl enceofthe temperaturerisewas very of the other 25 observation stations is 0.98 Chigherthan wide in the summer in this region. The regional temperature Observed value and simulated value ( C) Observed value and simulated value ( C) 8 Advances in Meteorology ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 114 0 0 E 117 0 0 E 120 0 0 E 123 0 0 E 114 0 0 E 117 0 0 E 120 0 0 E 123 0 0 E ∘ 󳰀 ∘ 󳰀 39 0 0 N 39 0 0 N ∘ 󳰀 ∘ 󳰀 39 0 0 N 39 0 0 N ∘ 󳰀 ∘ 󳰀 36 0 0 N 36 0 0 N ∘ 󳰀 ∘ 󳰀 36 0 0 N 36 0 0 N ∘ 󳰀 ∘ 󳰀 33 0 0 N 33 0 0 N ∘ 󳰀 ∘ 󳰀 33 0 0 N 33 0 0 N ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 114 0 0 E 117 0 0 E 120 0 0 E 114 0 0 E 117 0 0 E 120 0 0 E Simulated in Aug. Simulated in Feb. −2–0 3-4 24-25 0-1 4-5 25-26 5-6 1-2 26-27 2-3 27-28 (a) (b) Figure 5: Simulated value of the daily average temperature in the North China Plain in February and August. Table 6: Simulated and observed values of the seasonal average temperature at 2 meters above the ground surface in the North China Plain (unit: C). Winter Spring Summer Autumn Simulated value 0.89 12.76 23.03 14.27 Observed value 1.33 14.43 25.72 15.27 Difference between simulated value and observed value 0.44 1.67 2.68 0.99 increased by 0.06–2.8 Cinthe summer,and thetemperature in the northern hemisphere was simulated with the “CRU- ∘ ∘ change due to expansion of the urban and built-up land NNR” model at the5 ×5 resolution (the OMR trend value influenced a wide area around the Beijing-Tianjin-Tangshan of the urban and built-up land, crop land, broadleaf forest, ∘ ∘ ∘ zone. eTh temperature increment was largely the same in and bare land was 0.034 C/year, 0.02 C/year, 0.002 C/year, ∘ ∘ the spring and autumn, reaching 0.03–3 C. However, the and 0.02 C/year, resp.). However, the simulated result is temperature rise mainly influenced Beijing and Tianjin in still somewhat higher, which may be because the ERA40 the spring and Beijing and some area in the north part of reanalysis indirectly included the ground-based observation Hebei Province in the autumn. eTh temperature rise was data and consequently made the OMR trend values smaller only obvious in the regions where the urban and built- than the results obtained with the numerical simulation. up land increased, while in other regions the temperature eTh vegetation plays an important role in influencing generally decreased by 0.01–0.06 C, which might be because the near-surface temperature. For example, one of the main the wind velocity was generally very high in the north China reasons for the near-surface temperature changes in different in the winter and consequently reduced the temperature rise land cover and land use types is the amount and density of resulting from the increase in urban and built-up land [12, 13]. the vegetation. On the whole, the better the vegetation cover The temperature changes most greatly in the urban and is,the less thetemperature riseis.Itmay be becausethere built-up land amongall thelandcover typesinthe North is very little evaporation in the barren land, and the land China Plain (0.1 C/year), followed by the irrigated cropland surface heat mainly gets into the atmosphere in the form and pasture (0.06 C/year), while the temperature increases of sensible heat. By contrast, there is higher soil humidity most slightly in the grassland, with an increment of only in the densely vegetated land, which makes the land surface 0.01 C/year (Figure 9). The result is largely consistent with the heat mainly get into the atmosphere in the form of latent result of the research of Lim et al. [14], in which the climate heat and consequently reduces part of the temperature rise 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 Advances in Meteorology 9 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 114 0 0 E 117 0 0 E 120 0 0 E 123 0 0 E 114 0 0 E 117 0 0 E 120 0 0 E 123 0 0 E ∘ 󳰀 ∘ 󳰀 39 0 0 N 39 0 0 N ∘ 󳰀 ∘ 󳰀 39 0 0 N 39 0 0 N ∘ 󳰀 ∘ 󳰀 36 0 0 N 36 0 0 N ∘ 󳰀 ∘ 󳰀 36 0 0 N 36 0 0 N ∘ 󳰀 ∘ 󳰀 33 0 0 N 33 0 0 N ∘ 󳰀 ∘ 󳰀 33 0 0 N 33 0 0 N ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 114 0 0 E 117 0 0 E 120 0 0 E 114 0 0 E 117 0 0 E 120 0 0 E −2-−1 −5–−1 1-2 0–0.5 −1–−0.5 −1–0 2-3 0.5–1 3-4 0-1 −0.5–0 1–1.5 (a) (b) Figure 6: Difference between the observed and simulated daily average temperature in the North China Plain in February and August. 0.1 The change of the average near-surface temperature corresponding to each kind of land cover change was 0.08 summarized in this study. The following gur fi e shows the 0.06 temperature change in the eight major types of land cover 0.04 change that involve a large area of land (Figure 10). The result showed that the conversion from dryland crop to forest and 0.02 built-up land made the near-surface temperature increase 0 ∘ by 0.13 C/year, while the conversion from dryland crop to −0.02 grassland made the near-surface temperature decrease by 3456789 10 11 12 12 ∘ 0.1 C/year. By contrast, the other conversion types only made Spring Summer Autumn Winter the near-surface temperature increase by 0.01–0.04 C/year. The conversion from croplands to built-up lands can Monthly temperature difference lead to the changes in the roughness and albedo of the Seasonal temperature difference land surface, furthercause thechangeinthe radiationflux Figure 7: Monthly and seasonal temperature differences in the of thelandsurface,and consequently make theregional control test and sensitivity test. near-surface temperature increase. Besides, changes of the underlying surface due to the urbanization can alter the physical processes such as the energy balance of the land surface, lead to the “vfi e island eeff cts” (i.e., dark islands, heat islands, dry islands, wet islands, and rain islands), decrease thewindvelocityand result in thevariablecityclimate,and of the land surface. In addition, the heat island effect in consequently influence the structure and development of the the urban region also leads to the rise of the near-surface boundary layer and change the climate in a large area. More- temperature. By contrast, the temperature increment is less over, the conversion from grasslands to dryland croplands in the water bodies, mainly because the specific heat capacity candecreasethe albedo of thelandsurface,increasethe of water bodies is very large, which makes the temperature net radiation of land surface, and consequently make the increase very slowly and consequently makes the near-surface sensible heat increase and lead to the increase of the daily temperature lower [15]. Moreover, there is great difference average temperature. eTh conversion from dryland cropland between the irrigation intensities of the dryland cropland and irrigated cropland, which leads to great difference in the to forest makes the near-surface temperature increase, mainly because agricultural irrigation can usually make the evapo- physical characteristics of them and consequently makes the temperature increments differ greatly. transpiration and air humidity increase, which leads to the Sensitivity test and control test ( C) 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 10 Advances in Meteorology ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 110 0 0 E 115 0 0 E 120 0 0 E 125 0 0 E 110 0 0 E 115 0 0 E 120 0 0 E 125 0 0 E N Summer N Spring ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 40 0 0 N 40 0 0 N 40 0 0 N 40 0 0 N ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 35 0 0 N 35 0 0 N 35 0 0 N 35 0 0 N The boundary of The boundary of study area study area Province boundary Province boundary <−0.04 <−0.04 −0.04–−0.02 −0.04–−0.02 −0.02–0 −0.02–0 0–0.02 0–0.02 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 0.02–0.04 0.02–0.04 ∘ 󳰀 30 0 0 N 30 0 0 N 30 0 0 N 30 0 0 N 0.04–0.06 0.04–0.06 0.06–0.08 0.06–0.08 >0.08 >0.08 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 110 0 0 E 115 0 0 E 120 0 0 E 110 0 0 E 115 0 0 E 120 0 0 E (a) (b) ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 110 0 0 E 115 0 0 E 120 0 0 E 125 0 0 E 110 0 0 E 115 0 0 E 120 0 0 E 125 0 0 E N N Autumn Winter ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 40 0 0 N 40 0 0 N 40 0 0 N 40 0 0 N ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 35 0 0 N 35 0 0 N 35 0 0 N 35 0 0 N The boundary of The boundary of study area study area Province boundary Province boundary <−0.04 <−0.04 −0.04–−0.02 −0.04–−0.02 −0.02–0 −0.02–0 0–0.02 0–0.02 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 0.02–0.04 ∘ 󳰀 0.02–0.04 30 0 0 N 30 0 0 N 30 0 0 N 30 0 0 N 0.04–0.06 0.04–0.06 0.06–0.08 0.06–0.08 >0.08 >0.08 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 ∘ 󳰀 0 0 E 115 0 0 E 120 0 0 E 0 0 E 115 0 0 E 120 0 0 E 110 110 (c) (d) Figure 8: Difference between the near-surface temperature in the four seasons in the sensitivity test and control test (Unit: C). 0.12 0.1 0.08 0.06 0.04 0.02 Barren or Urban and Dryland Irrigated Deciduous sparsely built-up cropland cropland broadleaf Grassland Water bodies Wetland vegetated land and pasture and pasture forest land Temperature 0.1 0.020.060.010.030.030.050.03 change Figure 9: Temperature change in various land cover types. Temperature change ( C/year) 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 󳰀󳰀 Advances in Meteorology 11 0.15 0.1 0.05 −0.05 −0.1 −0.15 Conversion Conversion Conversion Conversion Conversion Conversion Conversion Conversion from from from from from from from from dryland grassland to forest to dryland dryland dryland forest to grassland cropland to dryland dryland cropland to cropland to cropland to grassland to forest grassland cropland cropland forest built-up land water bodies Temperature 0.01 0.02 0.04 0.04 0.13 0.12 0.04 −0.1 change Figure 10: Change of the near-surface temperature corresponding to each type of land cover change. increasing of the dew point temperature (when the pressure the degree and range of the influence of the temperature is 1000 Pa, Dew point temperature rising of 1 Cisequivalent rise varied greatly among seasons. eTh temperature changed ∘ ∘ to temperature increasing of 2.5 C). most significantly in the urban and built-up land (0.1 C/year), followed by theirrigatedcroplandand pasture(0.06 C/year). The temperature generally changed by 0.02–0.05 C/year in the forest, water bodies , and dryland cropland and pasture, 5. Conclusion and Discussion and it changed most slightly in the grassland (0.01 C/year). Among all the types of land cover change that involved a eTh regional climate model, the WRF model, was used to large land area, the conversion from dry land into forest and study the impacts of land cover change on the near-surface built-up land led to the greatest near-surface temperature temperature in the North China Plain on the basis of the land increment, reaching 0.13 C/year. While the conversion from coverdataoftheNorthChinaPlaininyear1992andyear2005 dry land and pasture into grassland made the near-surface in this study. The land cover change in the North China Plain during 1992–2005 was mainly characterized by the increase temperature decrease by 0.1 C/year. By contrast, the other conversions only made the near-surface temperature change in urban and built-up lands and the decrease in dryland by 0.01–0.04 C/year. croplands. The urban and built-up lands increased by 2.12%, and the dryland cropland decreased by 1.59%, while other Different types of land cover differ greatly in the physical characteristics, chemical processes, and biological processes, land cover types changed by no more than 0.5%. Besides, the which lead to the significant differences in the energy budget newly increased urban and built-up land was mainly located and water budget of the land surface and consequently have in the Beijing-Tianjin-Tangshan zone and around large and different impacts on the regional climate change. In compar- medium-sized cities such as Shijiazhuang, Zhengzhou, Ji’nan, ison to the increase in the greenhouse gases that has a global Qingdao, and Lianyungang. In addition, the newly increased influence, the land cover changes exert more influence at urban and built-up land mainly was converted from the theregionalscale.Theland-atmosphere feedback canchange drylandcroplandandaccountedfor60.55%oftheconversion from the dryland cropland during this period. the albedo and soil moisture, alter the evaporation process, and consequently inu fl ence the response of the regional near- eTh WRF model can reflect the seasonal change and surface temperature to the increase in the greenhouse gases. spatialpattern of thenear-surfacetemperature in theNorth Generally, the better the vegetation cover is, the less the China Plain very well, although there is some difference temperature riseis. For example, the urban heat island eeff ct between the observed and simulated values, with the sim- in the urban regions where there is less vegetation will lead to ulated temperature being a little lower on the whole. er Th e greater increase in the near-surface temperature. Besides, the is no significant difference in the spatial patterns of the temperature increment in the water bodies is generally lower observed and simulated temperatures on the whole, except and consequently leads to a lower near-surface temperature some slightly large difference in few areas. eTh result indicates since the water has a large specific heat capacity and its that the WRF model has a substantial advantage in simulating temperature generally increases more slowly. In addition, theclimate in theplain area.Thelandcover change in the North China Plain, which was mainly characterized the significant difference between the irrigation intensities of the dryland cropland and that of the irrigated cropland by the regional urbanization, has caused significant change also leads to great difference in their physical characteristics of thenear-surfacetemperature.Itled to aregionalnear- and consequently makes their temperature increments differ surface temperature increment of 0.03 C/year. Besides, the greatly. Moreover, the change of the underlying surface due spatial pattern of the temperature change corresponded to to the urbanization can alter the physical processes such that of the land cover change on the whole; that is, the as the energy balance of the land surface and consequently temperature mainly increased significantly in the regions lead to the climate change in a large area; for example, the where the urban and built-up land expanded; In addition, Temperature change ( C/year) 12 Advances in Meteorology conversion from thecroplandtothe urbanand built-up land [7] A. D. Jazcilevich, A. R. Garc´ıa, and L. G. Ru´ız-Suar ´ ez, “A study of air flow patterns affecting pollutant concentrations in the canchangethe roughnessand albedo of thelandsurface and Central Region of Mexico,” Atmospheric Environment,vol.37, consequentlycausethechangeintheradiationufl xoftheland no. 2, pp. 183–193, 2003. surface and lead to significant increase in the regional near- [8] Q. P. Li and Y. H. Ding, “Research progress in the eeff ct surface temperature. of vegetation change on regional climate,” Journal of Nanjing eTh re are still some uncertainties in the research on Institute of Meteorology,vol.27, no.1,pp. 131–140, 2004. the climatic effects of the land cover change, so it is still [9] J.Chen, P. Zhao,H.Liu,and X. Guo, “Modelingimpacts necessary to carry out more in-depth researches on a series of vegetation in western China on the summer climate of of issues such as the improvement of the climate model and northwestern China,” Advances in Atmospheric Sciences,vol.26, the reclassica fi tion method of the land cover change data, no. 4, pp. 803–812, 2009. especially the land cover data. Since this study is preliminary, [10] A. Seth and F. Giorgi, “eTh eeff cts of domain choice on summer there are still some deficiencies as follows. First, there is still precipitation simulation and sensitivity in a regional climate some dieff rencebetween theupscaledlandcover data and model,” Journal of Climate, vol. 11, no. 17, pp. 2698–2712, 1998. the initial data, which leads to some uncertainties in the [11] S. Y. Liu, CWRF ApplicationinEastChina MonsoonArea, simulated climatic eeff cts of the land cover change. Second, Nanjing University of Information Science and Technology, the result may change if the calculus of the data of a long Nanjing, China, 2006. period is carried out since the study of the impacts of the land [12] A. J. Arnfield, “Two decades of urban climate research: a review cover change on the temperature in the North China Plain of turbulence, exchanges of energy and water, and the urban is basedonthe sensitivitytestofthe numericalintegration heat island,” International Journal of Climatology,vol.23, no.1, of the data of only two years in this <study. In addition, pp. 1–26, 2003. there are some uncertainties in the result since there are [13] G.T.Johnson,T.R.Oke,T.J.Lyons,D.G.Steyn,I.D.Watson, various feedbacks within the climate system. In view of these and J. A. Voogt, “Simulation of surface urban heat islands under deficiencies, on the one hand, it is necessary to try more ‘IDEAL’ conditions at night part 1: theory and tests against field methods to upscale the data so as to reduce the inu fl ence of data,” Boundary-Layer Meteorology,vol.56, no.3,pp. 275–294, the errors in the data conversion. On the other hand, it is necessary to extend the integration time so as to guarantee [14] Y. Lim, M. Cai, E. Kalnay, and L. Zhou, “Observational evidence the certainties of the simulation result. of sensitivity of surface climate changes to land types and urbanization,” Geophysical Research Letters, vol. 32, no. 22, ArticleIDL22712,4pages,2005. Acknowledgments [15] W.Z.Su, Y. B. Yang,and G. S. Yang,“Distributional character- istics of urban thermal space and relations with land use/cover This research was financially supported by the Ministry of of Nanjing,” Scientia Geographica Sinica,vol.25, no.6,pp. 697– Science and Technology of China (Grant no. 2010CB950904) 703, 2005. and the National Natural Science Foundation of China (Grant no. 41071343). References [1] National Research Council, “Radiative forcing of climate change: expanding the concept and addressing uncertainties,” Tech. Rep., National Research Council, Washington, DC, USA, [2] M. Cai and E. Kalnay, “Response to the comments by Vose et al. and Trenberth. Impact of land-use change on climate,” Nature, vol. 95, pp. 427–214, 2004. [3] J.J.Feddema,K.W.Oleson, G. B. Bonanetal.,“Atmospheric science: the importance of land-cover change in simulating future climates,” Science, vol. 310, no. 5754, pp. 1674–1678, 2005. [4] R.Mahmood,S.A.Foster, andD.Logan,“eTh GeoProfile metadata, exposure of instruments, and measurement bias in climatic record revisited,” International Journal of Climatology, vol. 26,no. 8, pp.1091–1124,2006. [5] A. Jazcilevich, V. Fuentes, E. Jauregui, and E. Luna, “Simulated urban climate response to historical land use modification in the Basin of Mexico,” Climatic Change,vol.44, no.4,pp. 515– 536, 2000. [6] A. D. Jazcilevich, A. R. Garc´ıa, and L. G. Ru´ız-Suar ´ ez, “A modeling study of air pollution modulation through land-use change in the Valley of Mexico,” Atmospheric Environment,vol. 36,no. 14,pp. 2297–2307, 2002. International Journal of Journal of Ecology Mining The Scientific Journal of Geochemistry Scientifica World Journal Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Journal of Earthquakes Paleontology Journal Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Journal of Petroleum Engineering Submit your manuscripts at http://www.hindawi.com International Journal of Geophysics Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Advances in Advances in Journal of Advances in International Journal of Meteorology ClimatologyGeology Oceanography Oceanography Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Applied & Journal of Environmental International Journal of Journal of International Journal of Computational Soil Science Mineralogy Geological Research Atmospheric Sciences Environmental Sciences Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Volume 2014 Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com http://www.hindawi.com Volume 2014

Journal

Advances in MeteorologyHindawi Publishing Corporation

Published: Oct 8, 2013

References