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International Journal ofBiometeorology ISSN 0020-7128 Int J BiometeorolDOI 10.1007/s00484-013-0645-z

Impacts of land cover changes on climatetrends in Jiangxi province China

Qi Wang, Dirk Riemann, Steffen Vogt &Rüdiger Glaser

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ORIGINAL PAPER

Impacts of land cover changes on climate trends in Jiangxiprovince China

Qi Wang & Dirk Riemann & Steffen Vogt & Rüdiger Glaser

Received: 8 May 2012 /Revised: 31 January 2013 /Accepted: 31 January 2013# ISB 2013

Abstract Land-use/land-cover (LULC) change is an impor-tant climatic force, and is also affected by climate change. Inthe present study, we aimed to assess the regional scaleimpact of LULC on climate change using Jiangxi Province,China, as a case study. To obtain reliable climate trends, weapplied the standard normal homogeneity test (SNHT) tosurface air temperature and precipitation data for the period1951–1999. We also compared the temperature trends com-puted from Global Historical Climatology Network (GHCN)datasets and from our analysis. To examine the regional im-pacts of land surface types on surface air temperature andprecipitation change integrating regional topography, we usedthe observation minus reanalysis (OMR) method. Precipita-tion series were found to be homogeneous. Comparison ofGHCN and our analysis on adjusted temperatures indicatedthat the resulting climate trends varied slightly from dataset todataset. OMR trends associated with surface vegetation typesrevealed a strong surface warming response to land barrennessand weak warming response to land greenness. A total of81.1 % of the surface warming over vegetation index areas(0–0.2) was attributed to surface vegetation type change andregional topography. The contribution of surface vegetation

type change decreases as land cover greenness increases. TheOMR precipitation trend has a weak dependence on surfacevegetation type change. We suggest that LULC integratingregional topography should be considered as a force in re-gional climate modeling.

Keywords Land use/land cover . Climate trend .

Homogenization . Regional topography

Introduction

The most important anthropogenic impacts on climate are theemission of greenhouse gases and changes in land use (Kalnayand Cai 2003). Land-use/land-cover (LULC) change washighlighted as a major climate force (National Research Coun-cil 2005). Anthropogenic LULC change can affect emissionsof CO2, CH4, biomass burning aerosols and dust aerosols.Land cover change itself can also modify surface energy andmoisture budgets through changes in evaporation and fluxesof latent and sensible heat, directly affecting precipitation andtemperature (Forster et al. 2007). In some cases, LULCchange response to climate, in the form of urbanization,agricultural activity and deforestation, may even exceed thecontribution from greenhouse gases (Roger and Pielke 2005;Dirmeyer et al. 2010). Previous work has well documented theimpacts of different land type changes on climate, both glob-ally and regionally (Lim et al. 2008; Anantharaj et al. 2010;Fall et al. 2010a; Costa and Pires 2010; Kishtawal et al. 2010;Strengers et al. 2010; Xiao et al. 2010). It has been reportedthat land use changes due to agriculture lead to decreasedsurface air temperatures (Mahmood et al. 2006; Roy et al.2007; Lobell and Bonfils 2008). The estimated urbanizationimpact differs significantly, and depends strongly on thecriteria used in classifying urban and rural areas (Easterlinget al. 1996; Hansen et al. 2001).

Q. WangGuangdong Academy of Forestry, Guangzhou 510520,People’s Republic of China

Q. Wang (*) :D. Riemann : S. Vogt : R. GlaserInstitute of Physical Geography,Albert-Ludwigs-University Freiburg, Werthmannstrasse 4,79098 Freiburg (im Breisgau), Germanye-mail: jenniferqi520@googlemail.com

D. Riemanne-mail: dirk.riemann@geographie.uni-freiburg.de

S. Vogte-mail: steffen.vogt@geographie.uni-freiburg.de

R. Glasere-mail: ruediger.glaser@geographie.uni-freiburg.de

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Owing to the difficulty in separating the influenceresulting merely from land cover, the observation minusreanalysis (OMR) method developed by Kalnay and Cai(2003) has been used recently to assess the impact of landuse change by taking the difference between observationsby surface stations and reanalysis data. The OMR methodtakes advantage of the insensitivity of reanalysis to landsurface types, and removes natural variability due tochanges in circulation (since they are also included in thereanalysis), thus isolating surface effects from greenhousewarming by subtracting the reanalysis from the surfaceobservations (Kalnay and Cai 2008). The advantage of theOMR method is that it provides a substantial surface climatechange signal arising from different land cover typesthrough separating near-surface warming from globalwarming.

Several studies have used the OMR method to assess thesurface air temperature trend caused by land cover change.Kalnay and Cai (2003) initially assessed the decadal surfacewarming associated with local land uses over the easternregion of the United States by subtracting the reanalysisfrom the observations. Regional surface warming identifiedby OMR has good agreement with the trends obtained byHansen et al. (2001) using nightlights to classify urban andrural stations (Kalnay et al. 2006). Fall et al. (2010b) appliedthe OMR method to investigate the impacts of the sensitiv-ity of surface air temperature trends to land cover changeover the conterminous US. The impacts of different landcover types on temperature in China and Argentina, as wellas globally, and the consequences of temperature associatedwith land use in the Tibetan Plateau, have been assessed bythe OMR method (Zhou et al. 2004b; Frauenfeld et al. 2005;Yang et al. 2009; Hu et al. 2010; Lim et al. 2005; Nuñez etal. 2008). Results confirm that the OMR method is a robusttool with which to capture land cover change patterns on aregional scale. Fall et al. (2010c) applied the OMR methodin precipitation in India. The results revealed that there is nowell defined rainfall pattern as a function of OMR trends.Currently, no study has examined the influence of landcover on climate in greater detail, especially in relation toregional characteristics, which play an important role inlocal and regional climates.

The purpose of this study was to examine the impact onsurface air temperature and precipitation of land coverchange and topography together in Jiangxi province inChina. By analyzing OMR trends and incorporating regionaltopography into regression assessments of surface air temper-ature and precipitation, we assess the relationship betweenOMR trends and land vegetation types [derived fromsatellite-normalized difference vegetation index (NDVI)].OMR trends and the sensitivity of surface air temperatureand precipitation to land cover types are presented. The resultsof our study reveal the significance of regional land cover

change for the climate. The teleconnection effect, in whichregions remote from the landscape conversion have alteredsurface meteorology (e.g., as discussed in Avissar and Werth2005 and Chase et al. 2000), is not the focus of this study.

Materials and methods

Study site

The location of our study site is shown in Fig. 1. Jiangxi issituated in the basin of Poyang Lake along the middle rangeof the Yangtze River and has a total area about 170,458 km2.Mountains and hills account for 78 % of Jiangxi (JiangxiMeteorological Bureau 2010). Jiangxi is dominated by hu-mid subtropical climate, Cfa (Trewartha 1968). The annualmean temperature is 16–19 °C and annual mean precipita-tion is 1,400–2,400 mm. Lake Poyang—the largest fresh-water lake in China—is in the north of Jiangxi. It has a totalarea of 162,200 km2 (Jiangxi Meteorological Bureau 2010).Jiangxi is a province with short arable land that has experi-enced significant land cover change during the past twodecades. As the population increases, a large area of forest,lake and pasture has become occupied for agricultural andurban use (Department of Land and Resources of JiangxiProvince 2011).

Materials

For the surface observations, monthly mean temperature andprecipitation totals were obtained from the Computationaland Information Systems Laboratory (CISL) at the NationalCenter for Atmospheric Research (NCAR) (http://dss.ucar.edu) for 36 available stations over Jiangxi and sixcontiguous provinces, 1951–1999. Of these, only six sta-tions are located in Jiangxi (shown in Table 1). The spatialdistribution of the stations is shown in Fig. 2. All stationshave continuous monthly data for the entire 49-year periodexcept for the missing data for November and December in2000. The homogenized monthly surface instrumental tem-perature and precipitation dataset was obtained using the

Fig. 1 Location of the study site

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Standard Normal Homogeneity Test (SNHT). The GlobalHistorical Climatology Network (GHCN) data was used forcomparative analysis, which is available online at (http://lwf.ncdc.noaa.gov/oa/climate/research/ghcn/ghcngrid.html)from the National Oceanic and Atmospheric Administra-tion, National Climatic Data Center (Asheville, NC). BothNCAR and GHCN datasets have continuous records forstations in Jiangxi and its six contiguous provinces for theperiod 1951–1990. The data from GHCN has been homog-enized based on pairwise comparison (Menne and Williams2009).

Monthly surface air temperature mean and monthly meanof precipitation rate time series from NCEP-NCAR reanalysis(NNR) were used, both covering the period 1951–1999. Ad-vanced very high resolution radiometer (AVHRR) NDVI, 15-day composite product dataset at 1° resolution for the period

1982–2000 was derived from Global Land Cover Facility,Global Inventory Modeling and Mapping Studies (GIMMS)(GIMMS; http://glcf.umiacs.umd.edu/data/gimms/). We usedNDVI to represent surface vegetation types. NDVI and itsseasonal change were related to the decadal OMR and obser-vation trends. This NDVI dataset has been corrected forcalibration, view geometry, volcanic aerosols, and other ef-fects not related to vegetation change. Seasons are defined asthe 3-month averages for surface air temperature and NDVI,3-month sum for precipitation: DJF, MAM, JJA, and SON.Shuttle radar topography mission (SRTM) 90 m digital eleva-tion data supplied by Consortium for Spatial Information(CGIAR-CSI) was used as elevation factor for regressionassessment and correction of spatialization.

Methods

Maximum value composite of NDVI

Atmospheric calibration of original 15-day compositeAVHRR-NDVI data was done to reduce cloud and othernoise. The two calibrated 15-day NDVI images for eachmonth were composited first by taking the maximum valuewithin two 15-day time periods for each pixel to eliminatepixels with cloud cover. Then we averaged the monthlyNDVI values for each pixel into annual and seasonal meanNDVI values. The result was an almost cloud free data set ofpeak NDVI value from 1982 to 2000 covering Jiangxi. Themaximum-value composite technique has the advantage ofminimizing cloud contamination and off-nadir viewing ef-fects since these factors tend to reduce the NDVI valuesover green surfaces (Holben 1986; Holben and Fraser 1984).Applying the maximum value compositing process, thehighest value record in the time range was retained for eachpixel location (Holben 1986). Due to the growing circle ofvegetation in Jiangxi, the NDVI mean of growing season(April–October) was used to reflect the general vegetationstate for the entire study period.

Land cover types were indicated by NDVI categories.Each pixel on an image was assigned to a NDVI category.The NDVI value is defined as the ratio of the difference tothe total reflectance: (near-infrared − red) / (near-infrared +red). Green leaves commonly have larger reflectance in the

Table 1 Stations in Jiangxi usedin the analysis of regional trends Station ID LAT LON Elevation (meters above sea level)

Ganzhou 57993 25°51’ N 114°57’ E 149

Guangchang 58813 26°51’ N 116°20’ E 808

Guixi 58626 28°18’ N 117°13’ E 56

Jian 57799 27°07’ N 114°58’ E 334

Jiujiang 58502 29°44’ N 116°00’ E 56

Nanchang 58606 28°36’ N 115°55’ E 41

Fig. 2 Map of Jiangxi, showing locations of climate stations used inthis study [overlaid on a 90 m digital elevation model (DEM)]

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near-infrared than in the visible range. Clouds, water, andsnow have larger reflectance in the visible than in the near-infrared, so that negative values of the vegetation index maycorrespond to snow or ice cover, whereas the NDVI value isalmost zero for bare soils such as deserts. As a result, NDVIvalues can range from −1.0 to 1.0, with higher valuesassociated with greater density and greenness of vegetationcovers (Lim et al. 2008). Based on Wang et al. (2006) andJiangxi’s vegetation distribution, NDVI images after maxi-mum value composite were assigned as six categories,NDVI value (1) −1.0 to 0; (2) 0–0.2; (3) 0.2–0.45; (4)0.45–0.6; (5) 0.6–0.75; (6) 0.75–1.0. Different land covertypes in response to climate changes were further analyzed.

OMR method

Surface climate change trends are analyzed using OMRmethod suggested by Kalnay and Cai (2003). No surfacedata were included in the data assimilation of NNR. Due tothe insensitivity of the reanalysis to surface processes, sur-face effects caused by regional vegetation type changescould be separated from greenhouse warming. In thereanalysis (a statistical combination of 6-h forecasts andobservation), surface observations of temperature, moistureand wind over land were not used (Kistler et al. 2000). Thereanalysis, combined with its model parameterizations ofsurface processes, creates its own estimate of surface fieldsfrom the upper air observations. The surface parameters in areanalysis have less dependence on local characteristics thanthe actual surface observations. As a result, the reanalysisexcludes surface effects even though it should show climatechange to the extent that they influence the observationsabove the surface (Fall et al. 2010b; Kistler et al. 2000).

As described by Kalnay and Cai (2003), the OMR de-cadal trend is obtained by taking the average of two decadalmean differences (the 1990s minus the 1980s and the 1970sminus the 1960s). The trends were computed as changes in

decadal averages in order to reduce random errors. Only twodecadal trends, the decade 1990–1999 minus 1980–1989and 1970–1979 minus 1960–1969 were used. The trendchanges between the decade 1960–1969 minus 1950–1959and 1980–1989 minus 1970–1979 were excluded due to thescheduling changes during 1958 and changes in the observ-ing systems starting in 1979. Observation times of thereanalysis for the first decade (1948–1957) were done 3 hearlier than the current synoptic times. This implies lessreliable information extraction during the pre-1958 era. Fur-thermore, introduction of the satellite observing systemstarting in 1979 resulted in a significant change in climatol-ogy data. Before 1979, climatology was dominated more bymodel climatology in data-sparse areas, leading to the gen-eration of spurious trends (Kistler et al. 2000). These twomajor changes affect trends in the NNR. The two decadalchanges of the 1990s minus 1980s (20 years with satellitedata) and the 1970s minus 1960s (20 years essentiallywithout satellite data) were used. Thus, we obtain decadaltrends from two independent and largely homogeneous 20-year periods.

Results

Comparison between the temperature trends from GHCNand from our analysis on adjusted data

Figure 3 compares the time trends in homogenized annualmean temperature at six stations over Jiangxi (1951–1990)obtained from GHCN and from our analysis on adjusteddata. Temperature from both analyses shows a similar in-creasing trend but a slight difference in amplitude. Both arenot statistically significant, P>0.05. The temperature fromGHCN (0.48 °C/100a) was 0.30 °C/100a warmer than thatfrom our analysis (0.18 °C/100a). The temperature fromGHCN analysis in 1951–1954, 1986 and 1989, was slightly

Fig. 3 Surface air temperatureanomaly relative to 1961–1990averaged over Jiangxi (1951–1990), annual mean for GlobalHistorical ClimatologyNetwork (GHCN) (triangle-dashed line), annual mean foradjusted data (dotted line),5-year running average forGHCN (crossed-solid line) and5-year running average foradjusted data (solid line).P>0.05

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cooler than that from our analysis. The differences can beexplained by different station use and different methods forhomogeneity test in both analyses. Nevertheless, the overalltemperature trend was almost identical from both datasetsbut there was a slight difference in the increasing amplitude.

Spatial pattern of surface air temperature changes basedon local linear trends (1951–1990) is shown in Fig. 4, fromthe top to the bottom: annual mean, winter, spring, summerand autumn mean temperature, respectively. Our analysis(Fig. 4a) indicates more heterogeneity warming than GHCN(Fig. 4b). No noticeable warming center was observed fromeither analysis due to the heterogeneity. Warming in the westand east for both analyses was in similar amplitude. GHCNreveals a different pattern of high warming in the south andlow in the north. Concerning seasonal pattern, both analysesshow a similar spatial distribution (winter warming vs sum-mer cooling; autumn warming vs spring cooling) but differ-ent amplitudes (strong spring cooling from GHCN). Forboth analyses, strong and heterogeneous winter warming ispresented and summer cooling concentrates on the northalong Poyang Lake watershed. Spring cooling is noticeablein the south, Wuyi Mountain. However, our analysis indi-cates strong warming autumn in the west and weak in theeast but inversely from GHCN. The temperature computedfrom different datasets over the same region can thus showdifferent trends.

Temperature trends of observation and reanalysis

Monthly surface air temperature anomalies for five decades(1950s–1990s) from the observation and NNR reanalysis

relative to the normal period 1961–1990 averaged overJiangxi are shown in Fig. 5. The correlation between obser-vations and NNR is also presented. There was a goodagreement in the inter-annual variability and the long-termtrends between observations and NNR in the 1960s, 1970s,1980s and 1990s, with correlation coefficients r=+0.80, +0.78, +0.79 and +0.76, respectively. Relatively low correla-tion is indicated in 1950s (r=+0.53), because the observa-tion times of the reanalysis for the first decade wereconsiderably less reliable than in later decades (Kistler etal. 2000). The difference (TOMR) between the observationsand NNR is the largest, increasing to 0.05 °C in the 1970s(Fig. 5). As is revealed in many other studies (e.g., Kalnayand Cai 2003; Lim et al. 2008), the NNR has a strong abilityto capture surface air temperature variations caused by at-mospheric storms, advection of warm/cold air, and varia-tions in the frequency or track of major storms. In contrast tothe actual surface observations, no statistically significantdifference was found in the NNR estimation of stationtrends. The NNR trend is not affected by surface propertychanges. These arguments suggest that we could attributethe differences between monthly or annually averagedsurface-temperature trends derived from observations andfrom the NNR primarily to land use change. Decadal trendscan be dominated locally by inter-annual and decadal tem-perature variability due to anomalies in the circulation ratherthan to land cover change effects that are excluded by takingthe differences between observation and NNR temperatures.

Figure 6 shows the decadal temperature trends for theobservations, the NNR and the difference between these twotrends (OMR) averaged at six stations over Jiangxi. The

Fig. 4 Surface air temperaturechange based on local lineartrends (1951–1990) for a ouranalysis and b the globalhistorical climatology network(GHCN), from the top to thebottom: annual mean, winter,spring, summer and autumnmean temperature, respectively

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reanalyses exhibit a weaker warming than observations, asobtained by Kalnay and Cai (2003) and Lim et al. (2005).This feature was found across most of Jiangxi province.Thus the OMR pattern obtained by subtracting thereanalyses from observations shows a positive trend. Thisis at least partially attributable to land use change. Theaverage warming amplitude of the observations, NNR andOMR was +0.078 °C , +0.051 °C and +0.027 °C per decaderespectively (Fig. 6). Therefore, about 35 % of the observa-tion warming could be associated with land surface propertychanges. The observation, NNR and OMR trends show anoverall consistent warming effect. The strong warming con-centrated on Northern Jiangxi, both for OMR and

observations. In comparison with NNR, the OMR trendindicates a weaker and inverse warming effect. To as-sess the surface air temperature trend associated withland surface types, we may conclude that the OMR has theadvantage of indicating the effect resulting from land coverchange on climate.

Precipitation trends of observation and reanalysis

Daily mean precipitation anomalies for five decades(1950s–1990s) from observation and NNR relative to thenormal period 1961–1990 over Jiangxi are shown in Fig. 7.The correlation between observations and NNR is also

Fig. 5 Monthly mean surfaceair temperature anomalies[observation (dashed red)] andNCEP-NCAR reanalysis(NNR) (solid blue; in °C) overJiangxi relative to 1961–1990.Five decades (1950s–1990s) arecompared. TOMR is observationminus reanalysis temperature. rdenotes correlation coefficientbetween NNR and surfaceobservations

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presented. There was a good agreement in the inter-annualvariability and the long-term trends between observations andthe NNR in the 1970s, 1980s and 1990s, with correlationcoefficients of r=+0.66, +0.64 and +0.67, respectively. Poorcorrelation is indicated in the 1950s and 1960s (r=+0.49 andr=+0.20 respectively). The results may stem from the lessreliable observing system during the 1950s. As revealed inFig. 7, the NNR has good ability to capture precipitationvariations during the 1970s, 1980s and 1990s. No statisticallysignificant difference was found between the NNR andactual surface observations. The NNR trend is notinfluenced by the change of surface information. There-fore the differences between precipitation trends derivedfrom observations and from the NNR could be attrib-uted primarily to land use change.

The decadal precipitation trends for observations, theNNR and the OMR averaged at six stations over Jiangxiare shown in Fig. 8. The observations exhibit a stronger

wetting than the NNR. Thus, the OMR pattern obtained bysubtracting reanalyses from observations indicates an overallpositive trend. This can be attributable partially to land usechange. The wetting amplitude of the observations, NNR andOMR was 0.320 mm, 0.024 mm and 0.296 mm per decade,respectively. In comparison with the observation precipitation,the NNR exhibits an inverse pattern. This may be due to thehigh variability of topographical gradients. As reported byZhao et al. (2004), who compared the observation andreanalysis precipitation in China and found more observationsthan the reanalyses. The spatial pattern of OMR precipitationwas similar to that of observations, but with a weaker wettingeffect. The feature of more wetting in the north and lesswetting in the south for both OMR and observation precipita-tion was revealed. The decadal trend of observation precipi-tation is in good agreement with that of the observationtemperature. To assess the precipitation change in responseto land surface types, the OMR trend of precipitation has theability to represent the effect of land cover change.

NDVI trend

Figure 9 indicates the geographical variations of the annualand seasonal mean NDVI in Jiangxi (1982–2000). Surfacevegetation patterns with their greenness ranges are shown.Different NDVI greenness ranges are good representa-tives of different land cover types. The range is fromthe least sparse vegetation cover, 0 to the highest den-sity vegetation canopy, 1.0. Water bodies with negativeNDVI values had nearly no seasonal change. The verysparse vegetation areas with vegetation index (0∼0.2)show a slight seasonal change. The areas characterizedby vegetation index (0.2∼0.75) show a noticeable sea-sonal change (Fig. 9b–e). Evergreen tree cover areas[NDVI (0.75∼1.0)] exhibit a relatively weak seasonalchange. An overall noticeable seasonal NDVI change isshown. The findings are similar to those of Wang and Li(2008)

Surface air temperature and precipitation trends with respectto surface vegetation type changes

Relationship between surface air temperature and surfacevegetation types

To examine the surface air temperature with respect to surfacevegetation type change in Jiangxi, we related the temperaturechange estimated by observation, NNR and OMR to landvegetation types. Decadal observation, NNR and OMR trendsof temperature at each grid point plotted against the NDVImean of growing season are shown in Fig. 10. The resultsshow that the decadal observation, NNR and OMR trendswere all inversely proportional to the surface vegetation index.

Fig. 6a–c Decadal trends of monthly mean surface air temperature.The observation minus reanalysis (OMR) trend per decade (in °C) ateach grid point was obtained by the average of the ‘1990s minus1980s’ and ‘1980s minus 1970s’ temperatures. The mean value ofthe decadal trend is denoted in each panel on the left. a Observation;b NNR and c OMR

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The decadal trend in NNR shows no significant relationshipwith NDVI (r=−0.02). Poor correlations between NDVI anddecadal temperature trends of OMR and observations areindicated (r=−0.20 and r=−0.25, respectively). The surfacewarming response to the surface vegetation cover was poorlyrepresented by OMR. In contrast, Lim et al. (2005, 2008) andFall et al. (2010b) reported good representative of OMRtrends. However, the large positive correlations in thescatterplots were all in the areas with low NDVI values(0–0.4) for OMR and observations, whereas this was notthe case for NNR. Consistent results obtained by Kalnay andCai (2003) pointed out that the NNR should not be sensitive toland use effects, although it will show climate changes to the

extent that they affect observations above the surface. However,NNR from modeling experiments reveals that a strong surfacewarming correlated well with low vegetation index areas (Xueand Shukla 1993; Dai et al. 2004; Hales et al. 2004), whichcontradicts our findings.

Relationship between precipitation and surface vegetationtypes

Decadal observation, NNR and OMR trends of precipitationwere associated with mean NDVI of growing season toillustrate the relationship between precipitation and surfacevegetation types in Jiangxi. The decadal observation, NNR

Fig. 7 Daily meanprecipitation anomalies[observation (dashed red)] andNCEP-NCAR reanalysis(NNR) (solid blue; in mm) overJiangxi relative to 1961–1990.Five decades (1950s–1990s) arecompared. TOMR is observationminus reanalysis precipitation. rdenotes correlation coefficientbetween NNR and surfaceobservations

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and OMR trends at each grid point was scatter-plotted withNDVI and is shown in Fig. 11. The results show thatdecadal observation, NNR and OMR trends are not signif-icantly correlated with NDVI (r=0.02, r=−0.01 and r=0.03 ,respectively). The OMR trend has low correlationwith surface greenness. Thus, surface vegetation typesresponse to precipitation was poorly represented by OMRtrend. The findings are consistent with those of Fall et al.(2010c), who found no good correlation between OMR pre-cipitation trend and surface vegetation types in India.

Trends of temperature and precipitation associatedwith different surface vegetation types

We related observation and OMR trends of temperatureand precipitation to surface vegetation types overJiangxi. The correlation coefficients are presented inTable 2. Both observation and OMR trends of temper-ature showed a significant negative correlation withland type [NDVI (0–0.2)]. The correlation coefficients

were −0.61 and −0.62, respectively. For both tempera-tures, the correlation decreased with the increase in vegetationgreenness. No significant correlation between other land typesand corresponding observation and OMR trends of tempera-ture is indicated. The finding is consistent with that of Lim etal. (2008), who showed that the OMR trend decreased withsurface vegetation greenness.

No significant correlation was indicated between bothobservation and OMR trends of precipitation and differentsurface vegetation types over Jiangxi (Table 2). For bothtrends of precipitation, the correlation does not reveal regu-lar change with vegetation greenness.

Surface air temperature/precipitation change as a functionof different surface vegetation types incorporating regionaltopography

Using ordinary least squares, surface air temperature andprecipitation (both for OMR and observation) were regressedon each surface vegetation type and corresponding regionaltopography including elevation, latitude, longitude and slopeaccording to the form:

Fig. 8a–c Decadal trend of daily precipitation averaged at stationsover Jiangxi. The OMR trend per decade (in mm) at each grid pointwas obtained by the average of the ‘1990s minus 1980s’ and ‘1980sminus 1970s’. The mean value of the decadal trend is denoted in eachpanel on the left. a Observation, b NNR, c OMR

Fig. 9a–e Vegetation index map derived from NDVI. a Annual mean,b winter (DJF), c spring (MAM), d summer (JJA) and e autumn (SON)

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Fig. 10 Scatter plot between decadal NDVI and temperature trend over Jiangxi. a OMR, b Observation, c NNR. Abscissa NDVI, ordinatetemperature trend, r correlation coefficient of all the data points

Fig. 11 As Fig. 10 but for precipitation trends

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Surface air temperature or precipitationð Þ¼ a � surface vegetation typeð Þ þ b � elevationð Þ þ c

� latitudeð Þ þ d � longitudeð Þ þ e � slopeð Þ þ f

Where: a, b, c, d, e are corresponding regression coeffi-cients of surface vegetation type, elevation, latitude, longi-tude and slope, and f is constant.

The regression results are indicated in Table 3. They arestatistically significant at 99 % level. Both the observationand OMR surface air temperature trends show a dependenceon vegetation greenness. The dependence integrating re-gional topography on each surface vegetation typeperformed better than that removing regional topography.A promising finding was that 81.1 % of surface warming(OMR temperature: 0.108 °C/decade) over low vegetationcover areas [NDVI (0–0.2)] was explained by regionalsurface vegetation type change and elevation, latitude, lon-gitude and slope; 49.1 % of surface warming was attribut-able to regional land cover change over these areas. Over theareas with the highest vegetation index (0.75–1.0), landcover change can explain 19.6 % of the surface warmingintegrating regional elevation, latitude, longitude and slope.The explanation ability was even poorer when removingregional topography. As land cover greenness increased,the dependence of decadal OMR trends of both surface airtemperature and precipitation on vegetation cover greennessgot worse (Table 3). The response of land cover change tothe OMR temperature trend was consistent with Lim et al.(2008) and modeling analysis by Dai et al. (2004); Hales etal. (2004) and Xue and Shukla (1993).

An overall poor response of land cover change to OMRand observation precipitation, both integrating and remov-ing regional topography, is revealed (Table 3). The resultswere similar to those of Fall et al. (2010c), who alsoobtained a poor land cover functional response to OMRprecipitation in India. As indicated in Table 2, 38.8 % ofthe surface drying (OMR precipitation: −0.063 mm/decade)over the areas with vegetation index (0–0.2) was attribut-able to regional land cover change and topography; 28.2 %of the surface drying can be explained without the regional

topography into regression assessment. The explanationlevel of the dependence of OMR precipitation on othersurface vegetation type change was worse. The worst per-formance was the areas with vegetation index (0.75–1.0).

Table 4 shows the decadal trends of surface air tempera-ture and precipitation in response to surface vegetation typesand the number of 1°×1°grids calculated for each land type.Results show that the OMR temperature trend decreased andthe OMR precipitation trend increased with the increase ofsurface greenness. OMR warming over the areas with lowvegetation cover [NDVI (0–0.20)] was the strongest, andthe areas with NDVI (0.20–0.45) indicate a second strongestOMR warming. OMR cooling was found over the areaswith vegetation index (0.45–1.0). Very dense vegetationcover signified by evergreen tree cover with less anthropo-genic activity exhibited greater cooling than areas withintermediate vegetation cover [NDVI (0.45–0.60)]. Theassessment reveals that surface warming is larger for areaswith low vegetation cover [NDVI (0–0.20)], anthropogen-ically developed, but smaller for areas covered with verydensity vegetation [NDVI (0.75–1.0)] (Table 4). OMR dry-ing is shown over areas with low vegetation cover [NDVI(0–0.20)] and water bodies [NDVI (−1.0–0.2)]. Oher landtypes indicate OMR wetting, and areas with vegetationindex (0.60–0.75) show the largest wetting.

Areas with vegetation index (0.6–1) generally are com-posed of tree cover, which leads to the suppression of surfacewarming and the increase of surface moisture through thestrong transpiration wetting and evaporation cooling fromleaves with 0.009 °C/decade and 0.126 °C/decade surfacecooling and 0.145 mm/decade and 0.005 mm/decade surfacewetting, respectively. This is in agreement with Giambellucaet al. (1997), Lim et al. (2008), Shukla et al. (1990) and Xueand Shukla (1993). Areas with moderate vegetation index(0.45–0.6) characterized by herbaceous and shrub cover show0.008 °C/decade surface cooling and 0.114 mm/decade sur-face wetting (Table 4). The moderate contribution is becausethe cooling and wetting feedbacks from leaves are weakerthan those in trees. Owing to the small amount of evaporationnegative feedback in low vegetation cover areas [NDVI (0–0.2)], a strong surface warming and drying effect separatedfrom the observations could be explained by regional surface

Table 2 Correlation coefficients(r) between surface air tempera-ture/precipitation and corre-sponding surface vegetationtypes

aTemperature-observation minusreanalysis (OMR)bPrecipitation-observation minusreanalysis (OMR)

Land cover types Temperature-observation

Temperature − OMRa Precipitation-observation

Precipitation − OMRb

NDVI (−1.0∼0) −0.47 −0.29 0.41 0.37

NDVI (0∼0.2) −0.61 −0.62 0.34 0.32

NDVI (0.2∼0.45) 0.35 0.41 0.40 0.25

NDVI (0.45∼0.6) −0.34 −0.43 −0.39 −0.24

NDVI (0.6∼0.75) −0.36 0.38 0.32 0.21

NDVI (0.75∼1.0) −0.26 −0.31 −0.30 −0.23

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Tab

le3

Sum

maryforregression

mod

elsof

differentsurfacevegetatio

ntypes

Vegetationcovertypes

NDVI(1)

NDVI(2)

NDVI(3)

NDVI(4)

NDVI(5)

NDVI(6)

Mod

elAR2

Sig.

AR2

Sig.

AR2

Sig.

AR2

Sig.

AR2

Sig.

AR2

Sig.

T-OMR1a

0.83

20.00

00.811

0.00

00.29

20.00

00.31

00.00

00.27

30.00

00.19

60.00

0

T-OMR2b

0.34

40.00

00.49

10.00

00.27

40.00

00.29

10.00

00.24

70.00

00.13

50.00

0

T-OBR1c

0.46

90.00

00.66

60.00

00.43

50.00

00.47

40.00

00.43

40.00

00.33

30.00

0

T-OBR2d

0.17

10.00

00.48

40.00

00.30

60.00

00.32

10.00

00.29

20.00

00.20

70.00

0

P-O

MR1e

0.32

10.00

10.38

80.00

00.32

10.00

00.19

40.00

00.17

90.00

00.13

20.00

0

P-O

MR2f

0.13

50.00

00.28

20.00

00.29

30.00

00.16

40.00

00.14

90.00

00.12

20.00

0

P-O

BR1g

0.43

30.00

00.23

30.00

20.25

60.00

00.16

10.00

10.14

20.00

10.15

80.00

0

P-O

BR2h

0.15

60.00

90.20

40.00

20.116

0.00

00.111

0.00

70.09

20.00

10.05

80.00

0

aPredictorsof

T-OMR1:

(Con

stant),NDVI,elevation,

latitud

e,long

itude

andslop

e.Dependent

variable

ofT-OMR1:

Observatio

nMinus

Reanalysis(O

MR)of

temperature

bPredictorsof

T-OMR2:

(Con

stant),NDVI.Dependent

variable

ofT-OMR2:

Observatio

nMinus

Reanalysis(O

MR)of

temperature

cPredictorsof

T-OBR1:

(Con

stant),NDVI,elevation,

latitud

e,long

itude

andslop

e.Dependent

variable

ofT-OBR1:

observationtemperature

dPredictorsof

T-OBR2:

(Con

stant),NDVI.Dependent

variable

ofT-OBR2:

observationtemperature

ePredictorsof

P-O

MR1:

(Con

stant),NDVI,elevation,

latitud

e,long

itude

andslop

e.Dependent

variable

ofP-O

MR1:

Observatio

nMinus

Reanalysis(O

MR)of

precipitatio

nfPredictorsof

P-O

MR2:

(Con

stant),NDVI.Dependent

variable

ofP-O

MR2:

Observatio

nMinus

Reanalysis(O

MR)of

precipitatio

ngPredictorsof

P-O

BR1:

(Con

stant),NDVI,elevation,

latitud

e,long

itude

andslop

e.Dependent

variable

ofP-O

BR1:

observationprecipitatio

nhPredictorsof

P-O

BR2:

(Con

stant),NDVI.Dependent

variable

ofP-O

BR2:

observationprecipitatio

n

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vegetation type change anthropogenically driven under thesame amount of radiative forcing. Our findings are in agree-ment with those of Diffenbaugh (2005), Lim et al. (2005,2008) and Fall et al. (2010b).

Discussion

We aimed to investigate the impact of regional land coverchange on variations of surface air temperature and precip-itation. The OMR method (observation minus reanalysis)was used to estimate the impact of changes in land use bycomputing the difference between the trends of the adjustedsurface observations (which reflect all sources of climateforcing, including surface effects) and NCEP/NCAR reanalysis(which contains only the forcing influencing assimilatedatmospheric trends). We analyzed the observation andreanalysis trends of decadal temperature and precipita-tion over Jiangxi and examined the sensitivity of surfaceair temperature/precipitation to land cover by using OMRtrends as a function of surface vegetation types and regionaltopography. Key findings are presented as follows:

(1) The OMR approach is a robust tool with which toexamine the temperature and precipitation trends driv-en by the impact of regional land-cover types becauseland surface observations are not used in assimilationof the data into a physically consistent atmosphericmodel; reanalysis data is thus insensitive to local sur-face properties. This characteristic of the provides uswith the possibility of detecting surface climate changeassociated with regional land cover types by taking thedifference between observed and reanalysis climatetime series.

(2) Our results show a good temporal and spatial consistencybetween observed and reanalyzed temperature/precipitationtrends, and exhibit the ability of reanalysis to captureregional inter-annual variability. Similar findings wereobtained by Fall et al. (2010b), Frauenfeld et al. (2005),Kalnay et al. (2006), Lim et al. (2008) and Zhou et al.(2004a).

(3) OMR trend analyses associated with land cover typesusing AVHRR-NDVI dataset in Jiangxi show that landsurface type with vegetation index (0–0.2) exhibits thelargest surface warming and the vegetation index(0.75–1.0) area indicates the strongest cooling trend.We found different positive OMR trends (0.108 °C perdecade) in contrast to the findings of Fall et al. (2010b)(0.034 °C per decade) on a regional scale and Lim et al.(2005) (0.3 °C per decade) on a global scale for baresoils. We find a strong surface warming response toland barrenness and weak warming response to landgreenness. The results are consistent with those of Daiet al. (2004), Diffenbaugh (2005), Hales et al. (2004)and Lim et al. (2008).

(4) OMR precipitation trend is also assessed as a function ofsurface vegetation types and regional topography. Theresults show that OMR trend is insensitive to differentsurface greenness. This may be due to the length of thedry season and the seasonality of precipitation. Fall et al.(2010c) revealed similar findings of precipitation trendsas a poor function of land cover change over India.However, NDVI has been shown to be a sensitive indi-cator of the inter-annual variability of precipitation(Prasad et al. 2005). A positive correlation between theannual mean NDVI and precipitation was indicated inarid regions of Central Asia (Ichii et al. 2002). Moreover,the sensitivity of NDVI to precipitation variability indrier regions was found to be higher than in other wetforest types (Wang et al. 2003). In agreement with ourresults, NDVI has a relatively better response to thevariation of precipitation over barer areas than areas ofdense vegetation. Prasad et al. (2005), Schultz andHalpert (1993) and Gómez-Mendoza et al. (2008) point-ed to the significance of lag in the correlation of precip-itation and vegetation types. The lag response of landcover change to precipitation requires to be considered.Despite the limited relationship indicated, our resultsprovide guidance for attribution of regional precipitation.

Our innovative findings indicate that 81.1 % of the sur-face warming over the areas with vegetation index (0–0.2)

Table 4 Decadal trends of temperature (°C/decade) and precipitation (mm/decade) response to surface vegetation types and the number of 1°×1°grids for the calculation of correlation between vegetation types and temperature/precipitation trend

Vegetation cover types Numberof grids

Temperature-observation (°C)

Temperature-OMR (°C)

Precipitation-observation (mm)

Precipitation-OMR (mm)

NDVI (−1.0∼0) 56 0.855 0.153 0.544 −0.241

NDVI (0∼0.20) 295 0.818 0.108 0.670 −0.063

NDVI (0.20∼0.45) 440 0.725 0.029 0.715 0.096

NDVI (0.45∼0.60) 762 0.708 −0.008 0.706 0.114

NDVI (0.60∼0.75) 942 0.708 −0.009 0.716 0.145

NDVI (0.75∼1.0) 395 0.595 −0.126 0.673 0.005

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was attributed to regional land cover change and topographytogether, whereas an explanation level of 49.1 % is obtainedwithout regional topography into regression assessment.The contribution capability of land cover change decreasesas land cover greenness increases. Regional climate changeis a complex process involving the interaction and feedbackwith LULC change. It is well known that regional climatechange is influenced heavily by regional topography. It isworth mentioning that the integral of the entire topographyhas a negligible effect on LULC change, and thus affectsregional climate only indirectly. The topography of hillylandscapes modifies land environment by changing thefluxes of water and energy, increasing the vulnerability ofland systems, which could become more accentuated underclimate change (drought, increased variability of precipita-tion). Model simulations show that cropland change is sig-nificantly related to a slope × elevation index in the southernMediterranean climate even under the high emission scenar-io (Ferrara et al. 2010). Daly et al. (2009) found that acomplex temperature landscape composed of steep gradi-ents in temporal variation is controlled largely by the gradi-ents in elevation and topographic position. The introductionof regional topography in the regression estimation allows amore accurate determination of which components formingprocesses have adapted to ongoing climate change, and towhat extent.

It should be noted that the method used to derive NDVIneeds further refinement and calibration. On the other hand,the estimate of land-cover influence on temperature andprecipitation is limited by station density (six stations overJiangxi are available). Kalnay and Cai (2003) have foundthat the correlation between the observation and reanalysistemperature is lower where the density of the meteorologicalstations is low, thus decreasing the ability of OMR to rep-resent land cover change. Notwithstanding this limitation,the results of this study suggest that land cover change andregional topography together contribute to regional surfaceair temperature change. To what extent this effect is stronglyland-type dependent remains to be determined. Moreover,we further confirm the ability of the OMR method to give aquantitative estimation of surface climate variability withrespect to land cover change on a regional scale.

This study provides important considerations in under-standing the impacts of land cover change on climatechange on a regional scale, especially in a hilly region.Our results also have important implications in the monitor-ing and modeling processes of regional climate. LULCchange should be considered along with greenhouse gas asa forcing factor in regional climate modeling. Furthermore,the application of the OMR method on a regional scaleneeds to be considered in greater detail. Also the combina-tion and correlation of different sensors and satellites willprovide better tools to understand the relationship between

LULC change and climate change. People and ecosystemsexperience the effects of environmental change regionallyand not as globally averaged. Besides greenhouse gases andaerosol-driven radiative forcings, the impact of local andregional LULC on climate merits more attention in futureregional-scale studies of global change.

Conclusions

OMR trends associated with different land surface typesshow a strong surface warming response to land barrennessand weak warming response to land greenness; 81.1 % ofthe surface warming over vegetation index areas (0–0.2) isattributed to regional land cover change and topography.The contribution capability of land cover change decreasesas land cover greenness increases. OMR precipitation trendhas a weak dependence on regional land surface types andtopography.

Acknowledgments The authors thank the German Academic Ex-change Service (DAAD) for supporting this work as part of the PhDwork of Q.W. We also thank the reviewers for their constructivecomments.

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