Statistical downscaling of extreme daily …folk.uio.no/chongyux/papers_SCI/HYP_14.pdfStatistical downscaling of extreme daily precipitation, evaporation, and temperature and construction

HYDROLOGICAL PROCESSESHydrol. Process. 26, 3510–3523 (2012)Published online 24 January 2012 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.8427

Statistical downscaling of extreme daily precipitation,evaporation, and temperature and construction of

future scenarios

Tao Yang,1*,† Huihui Li,1 Weiguang Wang,1 Chong-Yu Xu2 and Zhongbo Yu31 State Key Laboratory of Hydrology- Water Resources and Hydraulics Engineering, Hohai University, Nanjing 210098, China

2 Department of Geosciences, University of Oslo, P.O. Box 1047, Blindern, 0316 Oslo, Norway3 Department of Geoscience, University of Nevada Las Vegas, Las Vegas, NV89154, USA

*CHyNaE-m†PrXinSci

Co

Abstract:

Generally, the statistical downscaling approaches work less perfectly in reproducing precipitation than temperatures, particularlyfor the extreme precipitation. This article aimed to testify the capability in downscaling the extreme temperature, evaporation,and precipitation in South China using the statistical downscaling method. Meanwhile, the linkages between the underlyingdriving forces and the incompetent skills in downscaling precipitation extremes over South China need to be extensivelyaddressed. Toward this end, a statistical downscaling model (SDSM) was built up to construct future scenarios of extreme dailytemperature, pan evaporation, and precipitation. The model was thereafter applied to project climate extremes in the DongjiangRiver basin in the 21st century from the HadCM3 (Hadley Centre Coupled Model version 3) model under A2 and B2 emissionscenarios. The results showed that: (1) The SDSM generally performed fairly well in reproducing the extreme temperature.For the extreme precipitation, the performance of the model was less satisfactory than temperature and evaporation. (2) Both A2and B2 scenarios projected increases in temperature extremes in all seasons; however, the projections of change in precipitationand evaporation extremes were not consistent with temperature extremes. (3) Skills of SDSM to reproduce the extremeprecipitation were very limited. This was partly due to the high randomicity and nonlinearity dominated in extreme precipitationprocess over the Dongjiang River basin. In pre-flood seasons (April to June), the mixing of the dry and cold air originatedfrom northern China and the moist warm air releases excessive rainstorms to this basin, while in post-flood seasons (July toOctober), the intensive rainstorms are triggered by the tropical system dominated in South China. These unique characteristicscollectively account for the incompetent skills of SDSM in reproducing precipitation extremes in South China. Copyright © 2011John Wiley & Sons, Ltd.

KEY WORDS climate extremes; statistical downscaling; climate change; projection; scenarios

Received 16 August 2011; Accepted 10 November 2011

INTRODUCTION

The frequent occurrence of extreme weather events suchas heat waves and intense and persistent precipitationassociated with subsequent flooding have raised concernsthat human activity might have caused an alteration of theclimate system (Yang et al., 2008), which is believed tobe the culprit behind the severity of such events. There isalso a widespread belief that the climate system willcontinue to change under the prevailing human activityand that humanity will be faced with more of theseextreme events (Hundecha and Bardossy, 2008; Yanget al., 2011). This leads to the growing concerns andstudies on changes in frequency, intensity, and/ormagnitude of such events in the past and for estimatingclimate that will occur in the future.

orrespondence to: Dr. Tao Yang, Professor, State Key Laboratory ofdrology-Water Resources and Hydraulics Engineering, Hohai University,njing 210098, The People’s Republic of China.ail: [email protected] address: State Key Laboratory of Desert and Oasis Ecology,jiang Institute of Ecology and Geography, Chinese Academy ofences, Urumqi, China.

pyright © 2011 John Wiley & Sons, Ltd.

General circulation models (GCMs) and large-scalecirculation predictors are the most important and effectivetools and indicators for the climate impact study. Thesenumerical coupled models represent various earth systemsincluding the atmosphere, oceans, land surface, and sea-ice and offer considerable potential for the study ofclimate change and variability. Over the past decade, thesophistication of such models has increased, and theirability to simulate present and past global and continentalscale climates has substantially improved. However, theresolution of GCMs remains relatively coarse and doesnot provide a direct estimation of hydrological responsesto climate change. For example, the Hadley Centre’sHadcm3 model is resolved at a spatial resolution of 2.5�

latitude by 3.75� longitude, whereas a spatial resolution of0.125� latitude and longitude is required by hydrologicsimulations of monthly flow in mountainous catchment(Wilby et al., 2004). In other words, GCMs provideoutput at nodes of grid-boxes, which are tens of thousandsof square kilometers in size, whereas the scale of interestto hydrologists is of the order of a few hundred squarekilometers. Bridging the gap between the resolution ofclimate models and regional- and local-scale processes

3511STATISTICAL DOWNSCALING OF HYDROMETEOROLOGICAL EXTREMES

represents a considerable problem for the climate changestudies including the application of climate changescenarios to hydrological models. Thus, considerableeffort in the climate community has focused on thedevelopment of techniques to bridge the gap, knownas ‘downscaling’.More recently, downscaling has found wide application

in hydroclimatology for scenario construction and simu-lation of (1) regional precipitation (Kim et al., 2004;Wang et al., 2011); (2) low-frequency rainfall events(Wilby, 1998) (3) mean, minimum, and maximum airtemperature (Kettle and Thompson, 2004); (4) soilmoisture (Jasper et al., 2004); (5) runoff (Arnell et al.,2003) and streamflows (Cannon and Whitfield, 2002);(6) ground water levels (Bouraoui et al., 1999); (7)transpiration (Misson et al., 2002), wind speed (Faucheret al., 1999), and potential evaporation rates (Weisse andOestreicher, 2001); (8) soil erosion and crop yield (Zhanget al., 2004); (9) landslide occurrence (Buma and Dehn,2000), and (10) water quality (Hassan et al., 1998).Downscaling methods could be broadly classified into

two categories (Xu, 1999): dynamic downscaling andstatistical downscaling. Both techniques have theirstrengths and weaknesses. In dynamic downscaling, theGCM outputs are used as boundary conditions to drive aRegional Climate Model (RCM) or Limited Area Modeland produce regional-scale information up to 5–50 km.This method has superior capability in complex terrain orwith changed land cover. However, this method entailshigher computation cost and relies strongly on theboundary conditions provided by GCMs with considerableuncertainties. In contrast, statistical downscaling gains localor station-scale meteorological time series (predictands) byappropriate statistical or empirical relationshipswith surfaceor troposphere atmospheric features. Generally, statisticaldownscaling methods can deliver ensembles of dailyclimate that evolve in line with the large-scale, transientchanges of the host GCM. Moreover, given the advantagesof being computationally inexpensive, statistical downscal-ing method can access finer scales than dynamical methodsand relatively easily applied to different GCMs, parametersand regions (Wilby et al., 2004). Therefore, it has beenwidely employed in climate impact studies. However,statistical downscaling approaches need much longerhistorical time series to build the appropriate statisticalrelationship. In addition, one of the assumptions ofstatistical downscaling is still valid in the future. Thisassumption cannot be testified at present. The conclusionfrom the most recent studies is achieved in the statisticaland regional dynamical downscaling of extremes project(STARDEX, http://www.cru.uea.ac.uk/projects/stardex)that both statistical and dynamical downscaling techniquesare comparable for simulating current climate (Haylocket al., 2006; Schmidli et al., 2006). The statisticaldownscaling has been widely employed in climate changeimpact assessments (Wilby et al., 1999; Huth, 2002;Tripathi et al., 2006; Ghosh and Mujumdar, 2008), due toits low expenditure on usage and the equivalent poweras dynamic downscaling.

Copyright © 2011 John Wiley & Sons, Ltd.

In Wilby and Wigley’s study (2000), statisticaldownscaling techniques are described as three categories,namely: regression methods (e.g. Kim et al., 1984;Wigley et al., 1990; Storch et al., 1993); weather pattern-based approaches (e.g. Lamb, 1972; Hay et al., 1991;Bardossy and Plate, 1992); and stochastic weathergenerators (Katz, 1996). No matter whether the methodis simple or complex, it is always based on some kind ofa regression relationship. The statistical downscalingmodel (SDSM) is best described as a hybrid of stochasticweather generator and regression-based methods (Wilbyet al., 2002). Many comparative studies (Wilby et al.,1998; Dibike and Coulibaly, 2005) have shown that ithas superior capability to capture local-scale climatevariability and is, therefore, widely applied (Wilby andHarris, 2006).General practices in downscaling of monthly outputs

from a full range of GCMs were presented as above inpast years. However, research in constructing reliablescenarios of future climate extremes is still a challengeand inadequate so far (e.g. Wilby and Harris, 2006).Moreover, SDSM normally works worse in subtropicaland tropical regions than in inland regions for thatprecipitation in subtropical and tropical regions alwayspresents more than one flood season due to the effectof tropical cyclones, which are difficult to capture.Therefore, the main objective of the present study is totestify the capability of SDSM in downscaling extremeevents in temperature, evaporation, and precipitation in thesubtropical region in southern China and, if it is successful,to project their future patterns for the study region. Thisstudy strives to downscale extremes of temperature,evaporation, and precipitation in the study region, moreimportantly to identify the possible links between theunderlying driving forces and skills in downscalingprecipitation extremes in subtropical regions. It willcontribute to promote current downscaling knowledge insimilar subtropical regions of the world.

STUDY AREA AND DATA

Study area

Dongjiang River is located between 114.0 ~ 116.5�Eand 22.5 ~ 25.5�N (Figure 1). It has a 562 km longmainstream to the Boluo station with a drainage area of25,555 km2. The Dongjiang River is important not onlyfor the local region but also for Hong Kong because about80% of Hong Kong’s water supply comes from DongjiangRiver through cross-basin water transfer. Three majorreservoirs (i.e. Xingfengjiang Reservoir since 1959,Fengshuba Reservoir since 1973, and Baipenzhu Reservoirsince 1984) were built in the basin.Annual average air temperature is about 20.4�C. The

precipitation of Dongjiang River demonstrates strongseasonality due to a subtropical monsoon climate. Owingto the influence of typhoons, precipitation exhibits strongvariability in both spatial and temporal perspective. Theannual precipitation varies between 1500mmand 2400mm.

Hydrol. Process. 26, 3510–3523 (2012)

http://www.cru.uea.ac.uk/projects/stardex

Figure 2. The study area divided by the method of Thiessen polygons

Qiuxiang R.

Dongjiang R

.Xinfeng R

Xizhi R.

Li River Xun

wu

Riv

er

Baipengzh

u

Reservi

or

XingfengjiangReservior

FengshubaReservior

Boluo

Heyuan

Streamflow gauges

Reservior

25°

N24

° N

23°

N

114° E 115° E 116° E

longtitude° (E)

latit

ude°

(N

)Shenzhen

Figure 1. Map of Dongjiang river basin

3512 T. YANG ET AL.

More than 80% of the total annual precipitation falls in theflood seasons from April to September.

Data

Observed data sets. Measured daily maximumtemperature, minimum temperature, pan evaporation, andprecipitation were provided by China MeteorologicalAdministration for 41-year period 1961–2001 atfiveweatherstations (Table I). The areal weights of five stations werecalculated using the Thiessen polygons method (Figure 2).

Reanalysis predictor sets used in calibration. Twenty-six different large-scale atmospheric variables derivedfrom the daily reanalysis dataset of NCEP/NCAR in theperiod of 1961–2001 were used to calibrate and validatethe SDSMmodel, which were downloaded freely from theinternet sites at a scale of 3.75� 2.5� (http://www.cics.uvic.ca/scenarios/sdsm/select.cgi). The geographicalextent (112.5–116.25�N, 22.5–25�E) was chosen to coverthe whole area with noticeable influence on the circulationpatterns that govern the weather pattern observed overthe Dongjiang River basin.

Table I. Basic information of the five meteorological stations inthe study region

No ID Station Latitude(N) Longitude(E) Areal weight

1 59096 Lianping 24�22’ 114�29’ 0.2152 59102 Xunwu 24�57’ 115�39’ 0.2013 59293 Heyuan 23�48’ 114�44’ 0.3324 59298 Huiyang 23�05’ 114�25’ 0.0475 59493 Shenzhen 22�32’ 114�00’ 0.205


GCM predictor sets used in hindcast and projection. Thevalidated SDSM was used to downscale the large-scalepredictor variables derived from A2 and B2 scenarios ofHadCM3 (Hadley Centre Coupled Model version 3) inthe period of 1961–2099. Both scenarios are characterizedby a continuously increasing global population with aconsequent increase in the emission of greenhouse gasand with a higher rate in A2 than in B2. Maximumtemperature, minimum temperature, pan evaporation,and precipitation were simulated during the followingperiods: the current (1961–2001), 2020s (2010–2039),2050s (2040–2069), and 2080s (2070–2099).

METHODOLOGY

Downscaling method

The SDSM, developed by Wilby et al. (2002), isemployed in this study to build statistical relationshipsbetween GCM predictors and local climate variables. Thesoftware tool for SDSM is available from the internetsite: http://www.cisc.uvic.ca/scenarios/index.cgi?More_Info-Downscaling-Tool. The regional climate variablesconditioned by the large-scale state may be written as:

R ¼ F Lð Þ (1)

in which R is the predictand (a local climate variable), L isthe predictor (a set of large-scale climate variables), and F adeterministic/stochastic function conditioned by L and hasto be estimated empirically from historical observations.Three implicit assumptions are made in order to use thiskind of downscaling methods for assessing regionalclimate change: (1) the predictors are variables of relevanceand are realistically simulated by the GCM; (2) thepredictors employed fully represent the climate changesignal; and (3) the relationship is valid also under alteredclimate condition.

Predictor selecting. The climate system is influenced bythe combined action of multiple atmospheric variables in thewide tempo-spatial space. Therefore, any single circulation

Hydrol. Process. 26, 3510–3523 (2012)

http://www.cics.uvic.ca/scenarios/sdsm/select.cgi

http://www.cics.uvic.ca/scenarios/sdsm/select.cgi

http://www.cisc.uvic.ca/scenarios/index.cgi?More_Info-Downscaling-Tool.

http://www.cisc.uvic.ca/scenarios/index.cgi?More_Info-Downscaling-Tool.

Table III. Selected predictor variables for Dongjiang river basindownscaling

Predictors

Predictands

Tmax Tmin Pcpn Eva

1. Mslp √ √ √ √2. p__u √ √ √3. p__v √ √4. p__z √ √5. p500 √ √ √ √6. p850 √ √7. temp √ √ √ √8. p5zh √9. p5th √10. rhum √ √11. shum √12. r500 √13. r850 √14. p5_v √15. p5_z √

Where:mslp =mean sea level pressure; p__u = zonal velocity component @surface; p__v=meridional velocity component @ surface; p__z = vorticity@ surfacep500= 500 hPa geopotential height p850 = 850 hPa geopotentialheight p5th = 500 hPa wind direction; rhum= surface relative humidity;shum= surface specific humidity; r500 = relative humidity at 500 hPa;r850 = relative humidity at 850 hPa; p5_v = 500 hPa zonal wind; p5_z =Vorticity at 500 hPa; Pcpn= daily precipitation; Eva = daily evaporation;Tmax-=dailymaximumoftemperature; Tmin-=dailyminimumof temperature.


predictor and/or small tempo-spatial space are unlikely to besufficient, as they fail to capture key precipitation mechan-isms based on thermodynamics and vapor content (Wilby,1998). Wilby and Wigley (2000) found that in many cases,maximum correlations between precipitation and thecirculation predictors occurred away from the location ofthe grid-box of the downscaled station and suggested thatselection of predictor domain was a critical factor affectingthe realisation and stability of downscaling model.The climate inmany zones of China is strongly controlled

by the East Asian monsoon, where the atmosphericcirculation feature is quite different between winter andsummer, and the scale of circulation pattern is large. Thus,it is a big challenge to choose predictors in the wide tempo-spatial space (Samel et al., 1999). The procedure adopted inthe study for selecting suitable predictors for each predictandis as follows: Table IIFirst, all of the 26 atmospheric variables in each one

of four grid-boxes (covering the whole study area andsurrounding) were taken as potential predictors. Second,these variables were then screened by SDSM to determinewhat amount explained variance is when the predictandand predictor(s) were statistically compared. The user wasrequired to select predictors that produce the highestexplained variance (E) and lowest standard error (SE).Finally, the predictors identified in this study weresummarized in Table III. It was shown that differentatmospheric predictors control different local variables: themaximum and minimum temperature are more sensitiveto mean temperature at 2m, and 850-hPa geopotentialheight, mean sea level pressure, and 500-hPa geopotentialheight are more sensitive predictors for the pan evaporation.For the daily precipitation, the relative humidity at 500 hPaand surface relative humidity are the most sensitive factors.

Calibration and validation of SDSM. Before downscal-ing of future climate with GCM predictors, the relation-ship between the selected predictors and precipitation in

Table II. Extreme indices for temperatu

Precipitation-related indices

PavPnl90Px1dPx5dPxcddPq90Temperature-related indicesTxxTxnTxq90TnxTnnTnq10Pan evaporation-related indicesEx1dEx3dEx5dEx7d


all stations need to be calibrated by using NCEP/NCARpredictors. From the 41years of data representing present-dayclimate (1961–2001), the first 30 years (1961–1990) are usedfor calibrating the regressionmodel, while the rest 11 years ofdata (1991–2001) are used to validate the model.

Measures of performance assessment

Four different measures were used to evaluate theperformance of the model: the coefficient of efficiency(Ens), coefficient of determination (R2), ratio of simulatedand observed standard deviation (RS), and model biases.

re, pan evaporation, and precipitation

Mean of daily precipitation on all days [mm/day]Number of events> long-term 90th percentileThe maximum of daily precipitation in given period [mm]Maximum total precipitation from any consecutive 5 days [mm]Maximum number of consecutive dry days [day]Empirical 90% quantile of precipitation [mm]

The maximum of daily maximum temperature [�C]The minimum of daily maximum temperature [�C]Empirical 90% quantile of the daily maximum temperature [�C]The maximum of daily minimum temperature [�C]The minimum of daily minimum temperature [�C]Empirical 10% quantile of the daily minimum temperature [�C]

The maximum of daily pan evaporation [mm]Maximum total evaporation from any consecutive 3 days [mm]Maximum total evaporation from any consecutive 5 days [mm]Maximum total evaporation from any consecutive 7 days [mm]

Hydrol. Process. 26, 3510–3523 (2012)

3514 T. YANG ET AL.

The coefficient of efficiency (Ens) describes how well thevolume and timing of the calibrated predictand compares tothe observed predictand and is defined by

Ens ¼ 1�Pn

i¼1 Oi � Sið Þ2Pn

i¼1 Oi � �Oð Þ2(2)

in which

�O ¼ 1n

Xn

i-1Oi (3)

Table IV. Performance assessment for predictands in calibrationand validation

Items Periods Ens R2 bias RS

Daily maximumtemperature

Calibration 0.90 0.90 0 0.93Validation 0.90 0.90 �1.1 0.93

Daily minimumtemperature

Calibration 0.94 0.98 0 0.97Validation 0.94 0.94 �0.12 0.98

Daily pan evaporation Calibration 0.65 0.65 0 0.77Validation 0.61 0.65 0.42 0.83

Daily precipitation Calibration 0.50 0.50 0.39 0.67Validation 0.48 0.48 0.30 0.66

0 2 4 6 8 10 1220

24

28

32

36

40

Txx

(C

)T

nx(

C)

month

obsncepA2B2

0 2 4 6 8 10 1210

15

20

25

30

month

obsncepA2B2

0 2 4 6 8 10 1220

25

30

35

40

month

obsncepA2B2

Txq

90(

C)

Figure 3. Comparison of the indices of extreme temperature from observescenarios in val


Where n is the number of time steps, Oi is the observedpredictand at time step i, and Si is the simulatedpredictand at time step i. Coefficient of determinationR2 measures the amount of variation of a dependentvariable that is explained by variation in the independent.The closer the values of Ens and R2 equal to 1, the moresuccessful the model calibration/validation is.The ratio of standard deviation of the modelled and

observed indices describes the degree of dispersion ofvariables (Hundecha and Bardossy, 2008):

RS ¼ SsimSobs

(4)

Where Ssim is the standard deviation of the modeledindices and Sobs is the standard deviation of the observedindices. Model bias describes the amount of systemdeviation, which is defined by

bias ¼ 1n

Xn

i¼1Si � Oið Þ (5)

Txn

(C

)T

nn(

C)

0 2 4 6 8 10 120

5

10

15

20

25

30

35

month

obsncepA2B2

0 2 4 6 8 10 12-5

0

5

10

15

20

25

month

obsncepA2B2

0 2 4 6 8 10 120

5

10

15

20

25

30

month

obsncepA2B2

Tnq

10(

C)

d data and simulated by SDSM driven by NCEP and H3A2 and H3B2idation period

Hydrol. Process. 26, 3510–3523 (2012)


Indices of extreme climate predictands

Changes in extremes of climate events have receivedincreased attention in the last years (IPCC, 2007). Sincethe early 1990s, it has been known that the largestchanges in the climate under enhanced greenhouseconditions were likely to be seen in changes of extremes(Gordon et al., 1992). Kunkel et al. (1999) reported thatpotential changes in extreme events can generate greaterimpact on human activities and natural environment thanmean climate changes.The select implementation of indices to describe extreme

climate events should have several characteristics: relevant,easy to interpret, understandable for policy makers, andcovering both frequency and intensity description ofextreme processes comprehensively. The core indices ofclimate extremes recommended by STARDEX Projectfunded by the European Commission under the FifthFramework Programme (FP5) (STARDEX, 2001) wereused in this study. These core indices were shown inTable II. It should be noted that index of mean precipitationis also included in the list. The indices were used to examinethe skills of the downscaling method in constructingscenarios for both climate extremes and means.

Table V. Comparison of the extreme indices between observedand simulated results during calibration (1961–1990) andvalidation (1991–2001) periods based on NCEP predictors

Indices Periods Ens R2 bias RS

1. Txx Calibration 0.81 0.93 �1.25 1.08Validation 0.82 0.93 �2.40 1.09

2. Txn Calibration 0.88 0.94 1.70 1.03Validation 0.92 0.95 1.17 1.01

3. txq90 Calibration 0.91 0.96 �0.98 1.04Validation 0.87 0.95 �2.12 1.08

4. Tnx Calibration 0.86 0.95 �1.03 1.11Validation 0.85 0.93 �1.04 1.08

5. Tnn Calibration 0.96 0.97 0.77 1.02Validation 0.97 0.98 0.45 1.07

6. tnq10 Calibration 0.97 0.98 0.64 0.99Validation 0.97 0.98 0.52 1.00

7. Ex1d Calibration 0.40 0.67 �0.97 0.98Validation 0.69 0.74 �0.23 1.00



10. Ex7d Calibration 0.73 0.83 �3.35 0.86Validation 0.81 0.81 0.24 0.92

11. Pav Calibration 0.82 0.83 0.39 0.98Validation 0.81 0.82 0.29 0.89

12. pnl90 Calibration 0.49 0.71 �0.03 1.32Validation 0.55 0.71 0.03 1.24

13. px1d Calibration 0.2 0.47 �13.45 0.61Validation 0.06 0.40 �14.87 0.56

14. px5d Calibration 0.62 0.67 �12.85 0.76Validation 0.57 0.69 �16.6 0.69

15. Pxcdd Calibration 0.35 0.73 �4.29 0.87Validation 0.12 0.61 �3.54 1.03

16. pq90 Calibration 0.62 0.67 �2.28 0.68Validation 0.67 0.75 �2.51 0.68

RESULTS

Model calibration and validation

The calibration (1961–1990) and validation results(1991–2001) were shown in Table IV. It could be seenthat both the simulated maximum and minimum tem-peratures were closely consistent with observations. R2,Ens, and RS between simulated and observed temperatureexceeded or equaled to 0.9 in calibration and validation.The simulation of daily pan evaporation was lesssatisfactory (Ens and R2 were between 0.61 and 0.65).As for daily precipitation, Ens and R2 values for thedownscaled precipitation were about 0.5, much lower thandaily temperature and pan evaporation. The biases for themaximum temperature, minimum temperature, pan evap-oration, and precipitation were�1.1 C,�0.12C, 0.42mm/day, and 0.39mm/day in validation. In summary, thosebiases were acceptable for practical uses. The statisticalmodel built using SDSM is capable of reproducing dailyclimate variables.

Inter-comparison of extreme indices of downscaling forthe calibration and validation period

Temperature. Generally, the performance of a down-scaling model in constructing temperature indices is betterthan the performance of precipitation indices. It wasshown (Figure 3) that the pattern of seasonal variations oftemperature was well downscaled with all three datasets(NCEP/NCAR, H3A2, H3B2). In simulating the max-imum of daily maximum temperature (Txx) and empirical90% quantile of the daily maximum temperature (Txq90),the results from NCEP/NCAR were systematicallylower than observations in all seasons, while the simu-


lations from the H3A2 and H3B2 were closer toobservations. For the other four indices (the minimum ofdaily maximum temperature, Txn; maximum of dailyminimum temperature, Tnx; minimum of daily minimumtemperature, Tnn; and empirical 10% quantile of the dailyminimum temperature, Tnq10, Table II), the results fromNCEP/NCAR were relatively satisfactory. Tnx was under-estimated in summer and winter; instead, the minimum ofdaily maximum temperature (Txn) from the H3A2 andH3B2 were 6 �C overestimated in summer. As for Tnq10,the results from all three datasets were consistent with theobservations, while H3B2 provided a worst performancefor Tnn. Table V summarized the coefficient of efficiency(Ens), coefficient of determination (R2), ratio of standarddeviation (RS), and biases between the 16 downscaledand observed indices.

Pan evaporation. The performance for pan evaporationdownscaling was less satisfactory than daily temperature.The results for daily pan evaporation are provided byFigure 4. It can be seen that in simulating these four indices(Ex1d, Ex3d, Ex5d and Ex7d, 1 Table II), all the simulatedresults were lower than observations in September. Ingeneral, the seasonal patterns were well simulated, whilethe simulated magnitude was less satisfactory.

Hydrol. Process. 26, 3510–3523 (2012)

0 2 4 6 8 10 124

6

8

10

12

Ex1

d(m

m)

month

obsncepA2B2

0 2 4 6 8 10 1220

25

30

35

40

45

50

Ex5

d(m

m)

month

obsncepA2B2

0 2 4 6 8 10 1210

15

20

25

30

35

Ex3

d(m

m)

month

obsncepA2B2

0 2 4 6 8 10 1220253035404550556065

Ex7

d(m

m)

month

obsncepA2B2

Figure 4. Comparison of the indices of extreme pan evaporation from observed data and simulated by SDSM driven by NCEP and H3A2 and H3B2scenarios in validation period

0 2 4 6 8 10 120

2

4

6

8

10

12

14

pav(

mm

/day

)

month

obsncepA2B2

0 2 4 6 8 10 120

20

40

60

80

100

120

pnl9

0(da

y)

month

month month

month month

obsncepA2B2

0 2 4 6 8 10 120

50

100

150

px1d

(mm

)

obsncepA2B2

0 2 4 6 8 10 120

50

100

150

200

250

px5d

(mm

)

obsncepA2B2

0 2 4 6 8 10 120

5

10

15

20

25

30

35

pxcd

d(da

y)

obsncepA2B2

0 2 4 6 8 10 120

5

10

15

20

25

30

p90(

mm

)

obsncepA2B2

Figure 5. Comparison of the indices of extreme precipitation from observed data and simulated by SDSM driven by NCEP and H3A2 and H3B2scenarios in validation period

3516 T. YANG ET AL.

Copyright © 2011 John Wiley & Sons, Ltd. Hydrol. Process. 26, 3510–3523 (2012)

A2 scenario B2 scenario

-2

-1

0

1

2

3

4

5

6

7

Winter Spring Summer Autumn

TX

x (°

C)

2020s 2050s 2080s

-2

-1

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7


2020s 2050s 2080s

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2020s 2050s 2080s

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TX

n (°

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2020s 2050s 2080s

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txq9

0 (°

C)

2020s 2050s 2080s

0

1

2

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5


2020s 2050s 2080s

TX

n (°

C)

TX

x (°

C)

txq9

0 (°

C)

Figure 6. Changes (%) in extreme temperature between the period (1961–1990) and the period (2011–2099) under the H3A2 and H3B2 scenarios


Precipitation. Among the six indices in simulatingprecipitation extremes, four of them are associated withextreme wet events: 90th percentile (pq90), maximum ofdaily precipitation (px1d),maximum5-day total (px5d), andnumber of heavy events (pnl90). The maximum number ofconsecutive dry days (pxcdd) describes very dry events, andmean of daily precipitation on all days (pav) describeschanges of mean daily precipitation. The threshold of 1mmwas used for a wet day (Hennessy et al., 1999). A dry daywas defined as having less than 1-mm precipitation.The calibration and validation results from NCEP/

NCAR were shown in Table V. It indicated that theindices were not equally well modeled. Pav has thehighest performance (Ens> 0.8), while px1d (Ens< 0.3)and pxcdd (Ens< 0.4) were the worst reproducedindices, implying that the model still cannot fully capturethe true persistence of the precipitation occurrence process.Monthly precipitation can be better downscaled by SDSMthan the extreme precipitation. In general, the model couldsimulate most indices well, but the capability in simulatingheavy rainfall under abnormal climate and the persistenceof the precipitation occurrence was still limited.The inter-comparison between the simulated and

observed six indices in the validation period was shownin Figure 5. As for p90, px5d, and px1d, the simulationswere generally underestimated, and the underestimationwas rather obvious in summer under H3A2 and H3B2


scenarios. Underestimation of extremes to some extentcan be attributed to the short validation period which isheavily influenced by some extreme events with veryhigh return period. For instance, the underestimation ofpx1d and px5d in April was because Huiyang, Heyuan,and Shenzhen stations had recorded rain as high as 146.7,133.6, and 344mm/day on 14 April 2000. The returnperiod of the rainfall total in April in Shenzhen wasestimated to be 100 years approximately. Since thevalidation period only had 10 years, the simulation couldnot accurately capture some abnormal and extremestorms. Although the pxcdd was underestimated usingthe NCEP/NCAR in most seasons, the trend andvariability were well simulated. It should be noted thatthe results from H3A2 and H3B2 were less satisfactorycompared with the NCEP/NCAR data especially for px1dand pxcdd. In summary, the simulation results fromNCEP/NCAR data were closer to the observations thanthe results from H3A2 and H3B2.

Projected changes for future climate scenarios

1. Temperature

Changes in extreme temperature between the baselineperiod (1961–1990) and the future period (2011–2099)were shown in Figure 6. Under the H3A2 scenario, all six

Hydrol. Process. 26, 3510–3523 (2012)

0

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Figure 6. (Continued )

3518 T. YANG ET AL.

temperature indices will increase in future 90 years. Txx(6.2 C) and Tnx (4.8 C) showed the highest increase insummer, while Txn (5.5 �C) and Tnn (4.9 �C) increasemost considerably in spring. Txq90 and tnq10 will increasewith similar magnitude during different seasons. UnderH3B2 scenario, the projected Txx (in 2020s and 2050s) andTxn (in 2050s) will decrease slightly in spring, while theother four indices (Txq90, Tnx, Tnn, and Tnq10) showedupward trends. Therefore, the extreme temperature eventswill be more frequent in the future.

2. Pan evaporation

Figure 7 showed that all the indices of pan evaporation inH3A2 andH3B2 scenariowould increase by 10% (in 2020s)and 40% (in 2080s) in summer. However, the change trendsof H3A2 and H3B2 projections are opposite in winter: theprojections from H3A2 scenario are decreasing while aslight increase was projected from H3B2 scenario. Ex3d,Ex5d, and Ex7d would decrease in spring during 2020s, butthey would increase during 2050s and 2080s under H3A2scenario. Under the H3B2 scenario, they will decrease by5% during 2020s and 2050s and increase by 2% to 12% in2080s.

3. Precipitation

The projected changes of precipitation extremes (Figure 8)were inconsistent with temperature extremes. It can be


seen that under H3A2 scenario, the pav and p90 woulddecrease in winter and spring and increase in summer andautumn, while in H3B2, they showed decreasing trendonly in winter. As for pnl90, the number of events higherthan long-term 90th percentile will decrease in winter andspring and increase in summer and autumn, and this ismore obvious under H3A2 scenario. Projection of pxcddunder H3A2 scenario showed considerable increases onlyin winter. Under H3B2 scenario, pxcdd showed increasesin all seasons. For the px1d and px5d, the results of H3A2had distinct change patterns in different seasons andperiods. In the future, the maximum daily precipitation(px1d) and the cumulative 5-day total precipitation (px5d)under H3B2 scenario will increase.

DISCUSSION

In this section, we attempt to identify the linkagesbetween the underlying driving forces and skill scores indownscaling precipitation extremes over the Dongjiangbasin. During the calibration and validation of SDSMwith the NCEP/NCAR reanalysis data, the temperatureindices were downscaled rather perfectly, but SDSM wasnot very effective in downscaling precipitation extremes.This can be attributed to the reasons below.Dongjiang River basin located in southern China

suffers frequent rainstorms, and the major drivingforces are more complicated than in other inland regions

Hydrol. Process. 26, 3510–3523 (2012)


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Figure 7. Changes (%) in extreme evaporation between the period (1961–1990) and the period (2011–2099) under the H3A2 and H3B2 scenarios


(See Fig. 9). Hereby, the flood season (April to October)was divided into pre-flood and post-flood seasons for sakeof discussion. The pre-flood season (April to June) inSouth China is composed of the frontal precipitationperiod and the summer monsoon precipitation period(Qiao et al., 2010). In pre-flood season, the mainatmospheric general circulation system dominated inmiddle high latitude of the Eurasia is two-trough andone-ridge, which help cold air move toward the SouthChina. The Western Pacific Subtropical High was stableat 18�N, which creates favorable conditions for theprevailing of the southerly airstream in South China andcoastal areas. Meanwhile, the active cold air in thesouthern Hemisphere and strengthening of the cross-equatorial flow contributed to form and intensify lowtropospheric jet in China and northern South China Sea.A large amount of moisture and unstable air-mass with


high humidity and temperature is transported to the upperlevel. In this favorable situation, along with the specialtopography and underlying surface, difference of sea landdistribution, non-uniform heating, thermodynamic anddynamical processes in atmosphere and the interaction indifferent scales would release heavy rain to the SouthChina. Besides, unbalance force of atmospheric motionand the coupling reaction among convective cloud clusterand moisture frontal zone and low level jet lead to thecontinuation of strong storm. In post-flood season (July toOctober), the rainstorms are triggered by tropical system,such as tropical cyclone, inter-tropical convergence zone,and easterly wave. The tropical cyclone would not onlybring tremendous moisture; they form big rainstormdirectly due to the strong convergence and updraft. Ifcombined with outside system (cold air and westerly beltsystem), it will bring more intense rainfall into the region.

Hydrol. Process. 26, 3510–3523 (2012)


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(%

)2020s 2050s 2080s

Figure 8. Changes in extreme precipitation between the period (1961–1990) and the period (2011–2099) under the H3A2 and H3B2 scenarios

3520 T. YANG ET AL.

For example, under the influence of the hitting of typhoonand the cold air traveling from the north and northwestChina, a heavy rainstorm occurred in southernGuangdong providence on 24 September 1979. Thehighest rainfall of Huiyang exceeded 400mm. Themonsoon trough is another important driving forcecompared with tropical cyclone. It brings persistentrainfall to South China. As for the precipitation in winterand spring, the anomalous vapor transport of the westernPacific and the low level in the South China Sea were themain impact factors, which was caused by the ENSOteleconnection. The El Niño made the low-level anticyc-lone of the Philippine Sea abnormal, which offeredfavorable water vapor condition for the rainstorm. Inaddition, prevailing south wind contributed to thecontinuous water vapor convergence in south China.While in case of the La Niña, the opposite phenomenonoccurs. Therefore, the complex precipitation processes inDongjiang River basin increase the difficulty in precipi-tation simulation. This explains why the indices thatdescribed very wet events (maximum of daily precipita-tion, maximum 5-day total, number of heavy events) werenot simulated well.In addition, SDSM is not sufficiently powerful to capture

the features of extreme precipitation events similar with otherSDSMs (e.g. Srikanthan and McMahon, 2001). The defectof stochastic precipitation models need to be improved(Gregory et al., 1993). According toWilby et al. (2004), this


might attribute to the more stochastic nature of precipitationoccurrence and magnitude, and the regression-basedSDSMs often cannot explain entire variance of the down-scaled variable. Additionally, while there is a strongseasonal consistency between stations for a number ofpredictors (e.g. geopotential heights and humidity), theseasonal specific predictor also play an important role (e.g.surface divergence during the summer months, Fealy andSweeney, 2007). Hence, it is recommended the selectedpredictors at seasonal scale (or month scale) improve thedownscaling performance to a certain degree.

CONCLUDING REMARKS

In this study, the large-scale atmospheric variables fromGCMs output were downscaled to the regional scale inorder to investigate the spatial-temporal changes inextreme precipitation, temperature, and pan evaporationover the Dongjiang River basin during 2010–2099 underH3A2 and H3B2 emission scenarios. It will improvecurrent understanding on hydrological impacts underfuture climate change in the subtropical regions. Theresults for downscaling temperature under scenariosH3A2 and H3B2 showed that the temperature extremeevents would be more significant in the rest 21st century(2010–2099). Despite the similar changes supplied byboth scenarios, the magnitudes of the changes projected

Hydrol. Process. 26, 3510–3523 (2012)

Westward extension and northward of

Western Pacific subtropical high

Southward warm moist air

Two-trough and one-ridge circulation system

in middle high latitude of the Eurasia

Southward cold dry air

Forming cold and stationary front

Meso-and small-scale system convergence,

shear, convective activity

Rainstorm

insouth

China

The special topography

and underlying surface

Non-uniform heating,

difference of sea land

Tropical system ENSO cycle

Figure 9. Conceptual diagram explained the heavy rain processes in South China

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Figure 8. (Continued)


by the two scenarios are generally different. As to the panevaporation, the predicted value from H3A2 indicatedthat the maximum 1, 3, 5, and 7 days evaporation willdecrease in winter while increase in other three seasons in2010–2099. For H3B2, a general upward trend wasidentified in future. However, the projected changes forprecipitation-related indices are uncertain.


Although some preliminary results of changes indownscaled extreme indices are obtained in the presentwork, a number of uncertainties still exist in assessing thechanges of regional-scale extreme indices. More researchwork in the future, particularly the ensemble projectionsby higher resolution GCMs or especially RCMs, as wellas analyzing the uncertainties related to the model spread,

Hydrol. Process. 26, 3510–3523 (2012)

3522 T. YANG ET AL.

are needed for a more profound understanding of thefutures changes in climate extremes.

ACKNOWLEDGEMENTS

The work was jointly supported by grants from theNational Natural Science Foundation of China(40901016, 40830639, 40830640), a grant from the StateKey Laboratory of Hydrology-Water Resources andHydraulic Engineering (2009586612, 2009585512), andthe Fundamental Research Funds for the CentralUniversities (2010B00714), the Australian EndeavourFellowship Program, and CSIRO Computational andSimulation Sciences Transformational Capability Plat-form. Finally, cordial thanks are also extended to the editor,Professor Malcolm G. Anderson and two anonymousreferees for their valuable comments which greatlyimproved the quality of this paper.

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