Prediction of variability of precipitation in the Yangtze River Basin

Stoch Environ Res Risk Assess (2012) 26:157–176 DOI 10.1007/s00477-011-0464-x

Prediction of variability of precipitation in the Yangtze River Basin under the climate change conditions based on automated statistical downscaling Jing Guo • Hua Chen • Chong-Yu Xu Shenglian Guo • Jiali Guo

•

Published online: 23 April 2011 Ó Springer-Verlag 2011

Abstract Many impact studies require climate change information at a finer resolution than that provided by general circulation models (GCMs). Therefore the outputs from GCMs have to be downscaled to obtain the finer resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based approach is proposed for predicting the daily precipitation of 138 main meteorological stations in the Yangtze River basin for 2010–2099 by statistical downscaling of the outputs of general circulation model (HadCM3) under A2 and B2 scenarios. After that, the spatial–temporal changes of the amount and the extremes of predicted precipitation in the Yangtze River basin are investigated by Mann– Kendall trend test and spatial interpolation. The results showed that: (1) the amount and the change pattern of precipitation could be reasonably simulated by ASD; (2) the predicted annual precipitation will decrease in all sub-

J. Guo H. Chen (&) S. Guo J. Guo State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, People’s Republic of China e-mail: [email protected] J. Guo e-mail: [email protected] C.-Y. Xu Department of Geosciences, University of Oslo, P. O. Box 1047, Blindern, 0316 Oslo, Norway J. Guo HydroChina Huadong Engineering Corporation, Hangzhou 310014, People’s Republic of China C.-Y. Xu Department of Earth Sciences, Uppsala University, Uppsala, Sweden

catchments during 2020s, while increase in all sub-catchments of the Yangtze River Basin during 2050s and during 2080s, respectively, under A2 scenario. However, they have mix-trend in each sub-catchment of Yangtze River basin during 2020s, but increase in all sub-catchments during 2050s and 2080s, except for Hanjiang River region during 2080s, as far as B2 scenario is concerned; and (3) the significant increasing trend of the precipitation intensity and maximum precipitation are mainly occurred in the northwest upper part and the middle part of the Yangtze River basin for the whole year and summer under both climate change scenarios and the middle of 2040–2060 can be regarded as the starting point for pattern change of precipitation maxima. Keywords Climate change Statistical downscaling Mann–Kendall trend Precipitation The Yangtze River basin

1 Introduction Global warming is an irrefutable fact and the global average surface temperature has increased by about 0.74 ± 0.18°C during the past 100 years and China is one of the countries experiencing the most significant effects of global warming (IPCC 2007; Bate et al. 2008). The Yangtze River is the longest river in China and the third longest river in the world and has an irreplaceable role in supporting sustainable development of the society and economy in China. The water resources and the total productive value of industry and agriculture in the Yangtze River basin are 40% of the totals of that of China. The Yangtze River basin has about 400 million inhabitants, counting for about 1/3 of the population in the country. However, the

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Yangtze River is also a storm-flood river with uneven distribution of precipitation and frequent occurrence of flood and drought. The influence of climate change on water allocation in the Yangtze River, especially on flood, has attracted increasing attention and concern. According to the Yangtze Conservation and Development Report 2007 (Yang et al. 2007), climate change is closely correlated with frequent drought and flood disasters especially extreme floods in the Yangtze basin. The frequency of floods hazard which has been fed mainly by precipitation in the Yangtze River is higher than elsewhere in China. Owing to its vast territory, complicated topography and typical monsoon climate, precipitation exhibits a big variability both spatially and temporally, presenting a decline tendency from southeast to northwest. Zhang et al. (2005) detected an upward trend in summer precipitation for the middle and lower reaches of the Yangtze River basin in the second half of the last century. Su et al. (2004) found significant upward trends in precipitation for the middle and lower Yangtze reaches in the 1990s. In the early years of twenty-first century, the fluctuations of precipitation became mild, but the time when extreme rainfall events occurred had shown a dispersed trend. Su et al. (2005) pointed that increasing precipitation extremes in June in the Yangtze River would increase the probability of flooding if the observed trends of the last 40 years continue into the future. Much attention has been paid to analyze the impact of climate change on the variability of mean precipitation and the precipitation extremes in the Yangtze River basin from different viewpoints (Su et al. 2009; Xu et al. 2009; Zhang et al. 2008; Huang et al. 2010). At present, general circulation models (GCMs) and large-scale circulation predictors are the most important and effective tools and indicators for studying the impact of climate change. For example, Xu et al. (2009) found that heavy precipitation events for single days and pentads would increase in their intensity over the Yangtze River basin by analyzing future projections of climate extremes directly derived from an ensemble of coupled general circulation models (CGCMs), under a range of emission scenarios in the Yangtze River basin. However, it is well-known that the spatial resolution of GCMs grids is too coarse to resolve many important sub-grid scale processes and GCM output is often unreliable at sub-grid scales (Wilby et al. 1999; Xu 1999; Maraun et al. 2010), they perform poorly at smaller spatial and temporal scales relevant to the regional impact analyses (Wilby et al. 2008). To bridge the gap of mismatch of scale between GCMs and the scale of interest for regional impacts study, dynamical downscaling and statistical downscaling methodologies have been developed by hydrometeorologists to convert GCMs outputs into local meteorological variables (Fowler et al. 2007). Statistical

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downscaling is aimed to derive empirical relationships that can transform large-scale features of the GCMs (Predictors) to regional-scale variables (Predictands) such as precipitation, temperature and streamflow (Tripathi et al. 2006). Compared with dynamical downscaling, statistical downscaling has several advantages such as comparatively cheap and computationally efficient, capable of estimating local-scale climatic variables from GCMs outputs, and easily transferable to other regions, etc. (Xu 1999). Due to its low expenditure on usage and the equivalent power as its dynamic competitor, the statistical downscaling technique has been widely employed in climate change impact assessments (Wilby et al. 1999; Huth 2002; Wetterhall et al. 2005; Tripathi et al. 2006; Ghosh and Mujumdar 2008; Chen et al. 2010). Statistical downscaling methods are generally classified into three categories (Fowler et al. 2007): regression models, weather typing schemes and weather generators (WGs). Among these statistical downscaling methods, regression models, which are used to directly quantify relationships between the predictands and a set of predictor variables, are possibly the most popular ones. SDSM is a hybrid of the WG and regression-based downscaling model, which is developed by Wilby et al. (1999, 2002). The stochastic component enables the generation of multiple simulations with slightly different time series attributes, but the same overall statistical properties. Many studies (Harpham and Wilby 2005; Dibike and Coulibaly 2005; Khan et al. 2006, Chu et al. 2010) have shown that this model has superior capability to evaluate local scale climate change impact. However, the procedure of predictors’ selection methods in SDSM is partly based on user’s subjective judgment. Hessami et al. (2008) developed a new tool under the Matlab environment named ASD, which was inspired by SDSM and improved the procedure in the selection of predictors. The purposes of this study are (1) to evaluate the applicability of the ASD model in the large geographic region of the Yangtze River basin which includes QinghaiTibet Plateau, Sichuan Basin and East China Plain area, and (2) to analyze the long term trend of precipitation in Yangtze River basin including future trends (2010–2099) which are predicted by GCM outputs and downscaled by the ASD method. To achieve the ultimate objectives, the study will be performed in the following steps: (1) selection of appropriate atmospheric predictors in the wide tempo-spatial space for the statistical downscaling model (ASD); (2) evaluation of the performance of the ASD method in downscaling precipitation in the Yangtze River in terms of carefully selected statistical criteria; (3) prediction of the future change of precipitation in the Yangtze River basin by using ASD; and (4) investigation of the future spatial and temporal changes of mean precipitation


and the precipitation extremes over the Yangtze River basin under the climate change projections. The main implications of the study are twofold: First, the skills and problems of the ASD downscaling method in the large geographical region with vast territory, complicated topography and typical monsoon climate as exemplified by the Yangtze River basin will be of both scientifically and practically beneficial for other researchers in this field, and second, the results will provide a valuable scientific basis and background information for water resources planning and management, including flood and drought prediction in the Yangtze River basin.

2 Study area and data The Yangtze River passes through nine provinces of China and has a total drainage area of 1.8 9 106 km2 (Fig. 1). Except for some areas located on the Tibet Plateau, most parts of the basin have a sub-tropical monsoon climate, and the southern part of the basin is close to tropical climate and northern part is near to temperate climate. The mean annual precipitation in the basin varies from 300 to 500 mm in the western region to 1,600–1,900 mm in the southeastern region and the precipitation is mostly concentrated in the summer season (from June to August), accounting for nearly half of the annual totals. To have a brief idea on the climate of the study region, the Yangtze

Fig. 1 Location of the Yangtze River basin and its sub-basins, meteorological stations and the grid-boxes of HadCM3 outputs in the Yangtze River basin. 1: Jinshajiang River; 2: Mintuojiang River; 3: Jialingjiang River; 4: Wujiang River; 5: The upper mainstream

159

River basin is divided into three parts along the longitude from west to east, which correspond well with the decrease in altitude (Xu et al. 2006). The upper, middle and lower regions as shown in Fig. 1 have a mean altitude of 2,551, 627 and 113 m above sea level (m.a.s.l), respectively. Furthermore, the Yangtze River basin is divided into 11 sub-catchments along the longitude from west to east (Fig. 1). Daily precipitation data of 138 meteorological stations during 1961–2001 provided by the National Climatic Centre of China were used in the current study. There are 26 different large-scale atmospheric variables (Table 1), which were derived from the daily reanalysis dataset of NCEP/NCAR for 1961–2001, as well as outputs of scenarios A2 and B2 of HadCM3 (Hadley Centre Coupled Model, version 3) from 1961 to 2099, representing the current climate condition and the future climate scenarios, respectively. The NCEP/NCAR reanalysis daily data and HadCM3 daily data both at a scale of 3.75° (long.) 9 2.5° (lat.) were downloaded freely from the internet sites, which had been normalized with respect to their 1961–1990 means and standard deviations. The geographical domain, 86.125°E–125.625°E, 21.25°N–38.75°N with 70 gridboxes was chosen to include all areas with noticeable influence on the circulation patterns that govern weather in the Yangtze River basin. Figure 1 also showed grid-boxes (3.75° lat. 9 2.5° long.) of large-scale atmospheric variables superposed on the map of the Yangtze River basin.

section; 6: Hanjiang River; 7: Dongtinghu Lake; 8: Poyanghu Lake; 9: The middle mainstream section; 10: the lower mainstream section; 11: Taihu Lake

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160


Table 1 The candidates of atmospheric variables for predictors No.

Variables

Description

No.

1

mslp

Mean sea level pressure

14

p5zh

500 hPa divergence

2

p_f

Surface airflow strength

15

p8_f

850 hPa airflow strength

3

p_u

Surface zonal velocity

16

p8_u

850 hPa zonal velocity

4

p_v

Surface meridional velocity

17

p8_v

850 hPa meridional velocity

5

p_z

Surface vorticity

18

p8_z

850 hPa vorticity

6

p_th

Surface wind direction

19

p800

850 hPa geopotential height

7

p_zh

Surface divergence

20

p8th

850 hPa wind direction

8

p5_f

500 hPa airflow strength

21

p8zh

850 hPa divergence

9

p5_u

500 hPa zonal velocity

22

rhum

Near surface relative humidity

10

p5_v

500 hPa meridional velocity

23

r500

Relative humidity at 500 hPa

11 12

p5_z p500

500 hPa vorticity 500 hPa geopotential height

24 25

r850 shum

Relative humidity at 850 hPa Near surface specific humidity

13

p5th

500 hPa wind direction

26

temp

Mean temperature

3 Methodology 3.1 Automated statistical downscaling 3.1.1 Regression methods As same to SDSM, the ASD model process can be conditional on the occurrence of an event (e.g. precipitation) or unconditional (e.g. temperature). Hence, the modeling of daily precipitation involves the following two steps: precipitation occurrence and precipitation amounts, as described by Hessami et al. (2008): n n X X O i ¼ a0 þ aj pij ; Ri ¼ b0 þ bj pij þ ei ð1Þ j¼1

where Zi are normally distributed random numbers, Se is the standard error of estimate, b is the model bias and VIF is the variance inflation factor. The NCEP reanalysis data are used for calibrating the ASD model. When using NCEP data, VIF and b are, respectively, set to 12 and 0. When using GCM data for scenario generation, the VIF and the bias can be set automatically using the following equations:

VIF ¼

12ðVobs Vd Þ S2e

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Description

where Vobs is the variance of observation during calibration period, Vd is the variance of deterministic part of model output during calibration period, Se is the standard error, Mobs and Md are the mean of observation and the mean of deterministic part of model output during calibration period, respectively. Regression-based downscaling methods often use multiple linear regressions, however, the nonorthogonality of the predictor vectors can make the least squares estimates of the regression coefficients unstable. In addition to multiple linear regressions, the present model gives the possibility to use the ridge regression (Hoerl and Kennard 1970) to alleviate the effect of the non-orthogonality of the predictor vectors.

j¼1

where Oi are the daily precipitation occurrences, Ri are daily precipitation amounts, pij are predictors, n is number of predictors, a and b are model parameters, ei is modeling error and it is modeled under the assumption that it follows a Gaussian distribution: rffiffiffiffiffiffiffiffi VIF ei ¼ Zi Se þ b ð2Þ 12

b ¼ Mobs Md

Variables

ð3Þ ð4Þ

3.1.2 Predictor selection methods Two methods are implemented based on backward stepwise regression (McCuen 2003) and partial correlation coefficients to select the predictors in ASD (Hessami et al. 2008). Partial correlation is the correlation between two variables after removing the linear effect of the third or more other variables. The partial correlation between variables i and j while controlling for third variable k is: Rij Rik Rjk Rij;k ¼ rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 R2ik 1 R2jk

ð5Þ

where Rij is the correlation coefficient between variables i and j. For partial correlation method, the p value is used for eliminating any one of the insignificant predictors. The p value is computed by transforming the correlation R to create a t-statistic having n - 2 degrees of freedom, where n is the number of observations:


R t ¼ qffiffiffiffiffiffiffiffi 1R2

161

ð6Þ

n2

The probability of the t-statistic indicates whether the observed correlation occurred by chance when the true correlation is zero. 3.2 Mann–Kendall test The Mann–Kendall method was used to analyze historical and future trends of climate variables (i.e. mean precipitation, total precipitation and the precipitation extremes) over the Yangtze River basin in this study. The Mann– Kendall test was originally devised by Mann (1945) and further derived by Kendall (1975) as a non-parametric test for detecting trends and distribution of the test statistic. The M–K method has been widely used for detecting a trend in hydro-climatic time series (Zetterqvist 1991; Burn and Elnur 2002; Yue et al. 2002; Arora et al. 2005; Aziz and Burn 2006; Zhang et al. 2006; Chen et al. 2007). The significance level of a = 5% is used in the study.

4 Downscaling precipitation 4.1 Selection of predictor variables and predictor domain The climatic system is influenced by the combined action of multiple atmospheric variables in the wide tempo-spatial space. Therefore, any single circulation predictor and/or small tempo-spatial space are unlikely to be sufficient, as they fail to capture key precipitation mechanisms based on thermodynamics and vapor content (Wilby 1998). Wilby and Wigley (2000) found that in many cases, maximum correlations between precipitation and the circulation predictors occurred away from the location of the grid-box of the downscaled station and suggested that selection of predictor domain was a critical factor affecting the realism and stability of downscaling models. The Yangtze River basin is strongly controlled by the East Asian monsoon and has different atmosphere circulation in different seasons. So, it is one of the most important steps in a downscaling exercise to select appropriate predictor variables and predictor domain from GCM in the wide tempo-spatial space. For each meteorological station, the procedures of ASD for selecting suitable predictor variables and domain were as following: Firstly, all of the 26 atmospheric variables in the grid-box, where the object station located in, and the surrounding eight grid-boxes were taken as candidate predictors; secondly, the partial correlation method with the significance level of 0.05 was employed in each grid-box of

nine, respectively, and the suitable predictors in each gridbox were selected; thirdly, the most suitable four gridboxes in the nine were chosen based on the model explained variance R2, which indicated the simulated capability of the selected predictors of each grid-box; and finally, the predictors in each grid-box of the selected 4 grid-boxes were further selected with partial correlation coefficients greater than 0.15 based on the previous step, and then the integrated predictors of the four grid-boxes for the downscaling model were chosen. The results of predictors’ selection by using ASD were summarized in Table 2. From Table 2, it could be seen that the final value of explained variance R2 after selecting predictors was greater than or equal to the maximum value of explained variance R2* of nine atmospheric grid-boxes in 90 stations (accounting for 65.22% of all stations of the Yangtze River Basin). So the predictors’ selection method of ASD can improve the simulated capability of the statistical downscaling. Furthermore, it was also found that most grid-boxes located by the stations were almost included in most suitable grid of four grid-boxes for each station, and the other three surrounding boxes respectively located in the right-hand, the bottom-right and the bottom of the station located grid-box. From the south-east of the quadrate region for selecting predictors to the most meteorological stations, it could be inferred that the precipitation of the Yangtze River basin is closely related to the Western Pacific subtropical high and southeast monsoon. 4.2 Calibration and validation of the downscaling model Before downscaling of the future precipitation with GCM predictors, the relationship between the selected predictors and precipitation in all stations needs to be calibrated and validated by using NCEP/NCAR predictors. The calibration period was from 1961 to 1990 and the validation period was from 1991 to 2001. To evaluate the capacity of ASD, the root mean square error (RMSE) between the observed and ASD simulated series of the five statistics indices and the coefficient of variation of the RMSE (CV(R)) were used. The five statistics indices included mean precipitation amount per days (Mean), standard deviation value (Std), the 90th percentile of rain day amount (Percentile90), percentage of wet days (Wet) and maximum number of consecutive dry days (Cod) (described in Table 3). The CV(R) is a normalized measure of variability between two sets of data and defined as: CVðRÞ ¼

RMSE x

ð7Þ

where x is the mean of the observed values.

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Table 2 Results of ASD model before and after predictor selection in each station of the Yangtze River basin Station

R2*

R2

Station

R2*

R2

Station

R2*

R2

Station

R2*

R2

Station

R2*

R2

Station

R2*

R2

52908

0.31

0.32*

56386

0.22

0.18

57178

0.28

0.25

57483

0.31

0.33*

57707

0.28

0.27

58236

0.32

0.30

56004

0.28

0.28*

56459

0.25

0.22

57206

0.28

0.28*

57494

0.34

0.36*

57713

0.27

0.28*

58238

0.32

0.28

56021

0.30

0.29

56462

0.28

0.28*

57232

0.35

0.37*

57504

0.25

0.27*

57722

0.28

0.24

58259

0.32

0.31

56029

0.27

0.28*

56475

0.23

0.24*

57237

0.33

0.36*

57516

0.27

0.29*

57731

0.25

0.22

58265

0.30

0.27

56034

0.32

0.31

56479

0.25

0.23

57245

0.33

0.39*

57537

0.26

0.32*

57741

0.26

0.25

58321

0.29

0.32*

56038

0.31

0.29

56485

0.29

0.25

57253

0.31

0.32*

57545

0.30

0.32*

57745

0.27

0.28*

58326

0.30

0.27

56096

0.21

0.21*

56492

0.22

0.22*

57259

0.30

0.30*

57554

0.30

0.32*

57766

0.29

0.27

58343

0.31

0.31*

56144

0.28

0.28*

56543

0.24

0.24*

57265

0.34

0.35*

57562

0.28

0.28*

57774

0.29

0.32*

58345

0.31

0.30

56146

0.23

0.24*

56565

0.25

0.22

57279

0.31

0.30

57574

0.29

0.33*

57776

0.28

0.33*

58358

0.28

0.34*

56152

0.31

0.31*

56571

0.20

0.20*

57306

0.27

0.28*

57583

0.32

0.33*

57793

0.31

0.29

58402

0.32

0.28

56167 56172

0.27 0.25

0.27* 0.21

56586 56651

0.25 0.24

0.25* 0.21

57313 57348

0.30 0.29

0.31* 0.31*

57584 57598

0.29 0.32

0.29* 0.29

57799 57803

0.29 0.29

0.29* 0.29*

58407 58424

0.34 0.31

0.27 0.29

56178

0.19

0.18

56671

0.28

0.30*

57355

0.30

0.31*

57602

0.21

0.23*

57816

0.28

0.31*

58436

0.35

0.35*

56182

0.25

0.22

56684

0.23

0.25*

57378

0.29

0.33*

57606

0.26

0.27*

57825

0.25

0.27*

58437

0.34

0.27

56188

0.24

0.25*

56691

0.30

0.31*

57385

0.29

0.30*

57608

0.22

0.24*

57832

0.29

0.32*

58464

0.28

0.29*

56193

0.25

0.26*

56768

0.29

0.31*

57399

0.32

0.26

57614

0.24

0.26*

57845

0.29

0.31*

58519

0.29

0.29*

56196

0.26

0.28*

56778

0.27

0.28*

57405

0.25

0.27*

57622

0.30

0.33*

57853

0.28

0.27

58527

0.34

0.34*

56247

0.23

0.20

57106

0.32

0.34*

57411

0.27

0.28*

57633

0.27

0.29*

57866

0.25

0.26*

58606

0.29

0.28

56251

0.27

0.26

57127

0.33

0.32

57426

0.31

0.32*

57649

0.29

0.30*

57872

0.28

0.28*

58608

0.31

0.28

56287

0.26

0.22

57134

0.32

0.33*

57447

0.34

0.37*

57655

0.27

0.28*

57896

0.27

0.28*

58626

0.31

0.31*

56294

0.24

0.26*

57143

0.31

0.34*

57458

0.29

0.27

57669

0.28

0.34*

57965

0.23

0.21

58634

0.33

0.32

56357

0.27

0.26

57144

0.30

0.34*

57461

0.28

0.30*

57671

0.27

0.25

57972

0.29

0.29*

58715

0.32

0.29

56385

0.30

0.29

57156

0.25

0.19

57476

0.28

0.32*

57682

0.31

0.33*

57993

0.26

0.25

58813

0.32

0.32*

2

2

Note: R * is the maximum value of explained variance of ASD model in nine atmospheric grid-boxes and R is final value of explained variance of ASD model after predictor selection for each station. Superscript ‘*’ indicates the R2 value equal or larger than R2* value

Table 3 Precipitation indices to evaluate the performance of statistical downscaling models Indices

Definition

Unit

Time scale

Mean

Mean precipitation amount

mm/day

Month

Std

Standard deviation value

mm/day

Month

Percentile90

90th percentile of rain day amount

mm

Month

Wet

Percentage of wet days (threshold C 0.1 mm)

%

Month

Cdd

Maximum number of consecutive dry days

day

Month

The calibration and validation results were shown in Table 4. It could be seen that the mean values of the estimated statistics indices in each sub-catchment of the Yangtze River basin in calibration and validation periods were close to those of observed series and the most relative biases between them were about 10% except for Cdd index. The RMSEs of most statistical indices were small in each sub-catchment in the calibration period except for Cdd index, and the CV(R) values of most statistical indices were below 0.1. However, in the validation period, the RMSE of most statistical indices were greater than the values in the

123

calibration period and the most CV(R) values were greater than 0.2. The comparison of RMSE of the statistics indices also showed that the Std and the Cdd were simulated poorer than the other three statistics indices, which suggested a relative weak capacity of ASD to capture the extreme events of precipitation process, as in most other statistical downscaling models (e.g. Srikanthan and McMahon 2001), and this defect of stochastic precipitation models will need to be remedied (Wilks 1989; Gregory et al. 1993). According to Wilby et al. (2004), this might attribute to the more stochastic nature of precipitation

2.03 0.39

Validation 3.54 0.62

3.04 0.15 2.97 0.58

Validation

4.50 0.92

Validation 3.46 0.69

Validation

3.43 0.87

3.13 0.69

Calibration 3.13

Validation

Average

3.15

3.01 0.16

Calibration 3.06 Validation 3.34

2.90 0.21 2.94 0.96

3.25 0.21

Validation

3.58

Calibration 3.42

3.52

3.40 0.20

Calibration 3.56

4.87

4.27 0.16

Calibration 4.39

Taihu Lake

The lower main stream section

The middle main stream section

Poyanghu Lake

3.72 0.78

3.63 0.21

Dongtinghu Lake Calibration 3.80

3.84

2.27 0.53

2.28

Validation

2.35 0.15

Calibration 2.45

2.92

Calibration 3.15

Validation

Hanjiang River

The upper main stream section

2.98 0.13 3.14 0.68

Calibration 3.09

Validation

Wujiang River

3.10

Calibration 2.63 Validation 2.37

2.50 0.18 2.44 0.57

2.87 0.15

Validation

2.81

Calibration 2.97

2.01

Jialingjiang River

Mintuojiang River

1.90 0.06

Calibration 1.91

Jinshangjiang River

0.22

0.05

0.07 0.28

0.24

0.06

0.20

0.06

0.19

0.04

0.22

0.05

0.22

0.06

0.20

0.05

0.22

0.04

0.07 0.24

0.21

0.05

0.20

0.03

8.68 2.45

8.77 1.05

6.87 1.95

6.53 1.47

6.89 1.54

7.12 0.94

6.73 1.96

6.49 0.75

6.15 1.15 6.14 2.09

5.71 1.50

5.67 1.16

3.87 0.77

3.78 0.46

7.81

7.71

8.38 8.83

9.53

9.12

9.55

9.52

7.47 2.12

7.28 1.03

7.82 1.24 7.87 2.46

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26.06 22.87 6.44

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25.34 23.38 5.00

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39.62 40.19 0.72

43.81 45.71 4.84

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44.10 45.20 4.32

47.66 47.95 0.63

30.40 33.54 4.21

34.13 35.09 1.12

43.64 46.75 4.80

47.20 47.51 0.58

49.51 53.30 5.37

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38.38 38.73 0.52 34.38 37.90 5.27

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8.25 6.14 2.63

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8.10 7.12 1.26 8.98 7.15 2.34

7.18 9.09 2.06

6.81 5.81 1.27

10.23 8.24 2.62

10.22 8.45 1.78

0.28

0.19

0.16 0.28

0.32

0.23

0.28

0.22

0.32

0.26

0.26

0.21

0.27

0.19

0.25

0.15

0.28

0.18

0.15 0.26

0.29

0.19

0.26

0.18

SIM RMSE CV(R)

Cdd (day/month) RMSE CV(R) OBS

38.66 39.82 0.65

SIM

Wet (%)

RMSE CV(R) OBS

13.45 13.03 1.20

10.65

SIM

Percentile90 (mm/month) RMSE CV(R) OBS

10.30 10.17 0.95

9.29

9.24

6.58

6.82

7.23

7.58

7.21

6.97

6.94 6.53

6.05

6.19

3.93

3.74

SIM

Std (mm/day)

OBS SIM RMSE CV(R) OBS

Mean (mm/day)

Periods

Sub-catchments

Table 4 Comparison of the statistics indices between observed and simulated results for each sub-catchment in the Yangtze River basin during calibration (1961–1990) and validation (1991–2001) periods based on NCEP predictors

Stoch Environ Res Risk Assess (2012) 26:157–176 163

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Stoch Environ Res Risk Assess (2012) 26:157–176 56034 (Qingshuihe) Station

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Fig. 2 Validation results of ASD for precipitation in 56034 (Qingshuihe), 57461 (Yichang) and 58238 (Nanjing) stations, respectively

occurrence and magnitude, and the regression-based statistical downscaling models often cannot explain entire variance of the downscaled variable. In order to visually show the skills of ASD in downscaling the precipitation in the Yangtze River basin, the comparison of the simulated and observed mean monthly values of the 5 indices in the validation period is shown in Fig. 2 for three randomly selected stations from upper, middle and lower reaches of the river, respectively. It is seen that ASD could capture the monthly values of the statistical indices of precipitation reasonably well. Above all, although ASD has some limitations in capturing the extreme events of precipitation process, it can reasonably well reflect the occurrence and the total amount of precipitation and can be utilized for statistical

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downscaling precipitation of the Yangtze River basin for practical uses. 4.3 Downscaling precipitation under future emission scenarios The validated ASD was used to downscale the large-scale predictor variables derived from A2 and B2 scenarios of HadCM3 and daily precipitations were simulated for the following periods: the current (1961–2001), 2020s (2010–2039), 2050s (2040–2069) and 2080s (2070–2099). Compared to the simulated current values, the deviations of simulated annual precipitation in different periods were calculated and listed in Table 5. Under the A2 scenario, the predicted annual precipitation during 2020s

12.86 1546.57 8.85 1491.63 2.26 1401.39 1370.37 17.75 1644.70 8.96 1521.91 1391.79 1396.75 Average

-0.35

38.30 2296.80 19.01 1976.40 5.78 1756.80 1660.77 37.71 2350.80 24.22 2120.40 1764.00 1707.04 Taihu Lake

The lower mainstream section

1746.00

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10.09

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1753.99 9.95

12.12 2289.60

1994.40 4.79

7.01 2185.20

1900.80 The middle mainstream section

0.31 2048.40

1764.00

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1813.97

Poyanghu Lake

-2.75

-1.59 17.28 1306.80 1958.40 4.37 11.89 1386.00 1868.40 -7.01 7.36 1234.80 1792.80 1327.93 1669.85 4.15 26.93 1407.60 2178.00 4.42 12.66 1411.20 1933.20 1339.20 1728.00 1351.49 1715.97 Hanjiang River Dongtinghu Lake

The upper mainstream section

1368.00

-0.91 0.70

17.79

11.17 1526.40 5.67 1450.80 0.16 1375.20 1372.98 18.34 1663.20 6.30 1494.00

12.29

1405.42

-2.66

1501.20

1256.40 7.14

9.32 1393.20

1198.80 -1.22

5.65 1346.40

1105.20 1118.88

1274.45 25.02

15.51 1310.40

1620.00 12.52

7.26 1216.80

1458.00 -0.81

1130.40

1285.20

According to the hydrometeorological characteristics of the Yangtze River basin, extreme precipitation events are the main causes for the flood hazards in the basin (Zhang et al. 2005). In this section, the spatial and temporal patterns of trends of precipitation extremes and precipitation intensity over the Yangtze River basin for 2010–2099 based on downscaled daily precipitation by ASD would be explored using Mann–Kendall trend test. In this study two groups of statistics were used for exploring the

Wujiang River

1134.49

would decrease in most sub-catchments of the Yangtze River basin except for Dongtinghu Lake and Poyanghu Lake regions. However, during 2050s and 2080s the annual precipitation is predicted to increase in all sub-catchments of the Yangtze River basin by about 3.14–24.22% and 4.15–40.71%, respectively. Under B2 scenario, the predicted annual precipitation would have mix-trends in each sub-catchment of Yangtze River basin during 2020s, but increase in all sub-catchments by about 1.59–19.01% during 2050s and 5.59–38.30% during 2080s, respectively; with one exception for Hanjiang River region during 2080s where a slight decrease was simulated. When Yangtze River basin was considered as a whole, the predicted annual precipitation would decrease by 0.35% during 2020s, but increase 8.96% during 2050s and 17.75% during 2080s, respectively, under A2 scenario; while successively increase during the 3 future periods by about 2.26, 8.85 and 12.86%, respectively, as far as B2 scenario was concerned. Above all, the amount of precipitation would be upward in the future in the Yangtze River basin according to these results. Furthermore, the spatial distribution patterns of the relative changes of mean annual precipitation of the Yangtze River basin between current period and future periods for the both climate change scenarios, were interpolated by using the inverse distance weighting method (IDW), which was based on the assumption that the interpolating surface should be influenced mostly by nearby points and less by more distant points. The interpolation results are geographically displayed in Fig. 3. It can be seen that the predicted precipitation under A2 scenario would decrease in most regions of the basin during 2020s, while increase in most regions of the basin during 2050s and 2080s. Under B2 scenario, the predicted precipitation would increase in most regions of the basin during all three future periods. The biggest increasing trend under both emission scenarios in the future periods would mostly dominate in the eastern part of the upper region and the southern and central parts of the middle region.

1295.74

-0.36

165

5 Predicted trends of precipitation extremes under future emission scenarios

Jialingjiang River

5.59

29.88 1490.40

759.60 1.59

18.59 1360.80

730.80 2.09

7.92 1238.40

734.40 719.35

1147.48 40.71

8.79 781.20

1627.20 20.17

4.27 748.80

1389.60 4.60

-0.74 712.80

1209.60

718.10

2080s (mm) Changes (%) 2050s (mm) Changes (%) 2020s (mm)

1156.41 Mintuojiang River

Jinshangjiang River

Current (mm) Current (mm)

Sub-catchment

Table 5 Comparison of the changes of mean annual precipitation under future emission scenarios

Changes (%)

B2 A2

2020s (mm)

Changes (%)

2050s (mm)

Changes (%)

2080s (mm)

Changes (%)


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Fig. 3 Spatial distribution of relative changes of mean annual precipitation between the current period (1961–2001) and future periods (2020s, 2050s, 2080s) in the Yangtze River basin

characteristics of precipitation extremes: Group 1 includes (1) maximum daily precipitation, (2) frequency of rainy days, (3) precipitation intensity, and (4) frequency of nonrainy days. And group 2 includes frequency of rainy days and precipitation intensity for daily precipitation exceeding 90th percentiles. The one-day maximum precipitation within a year and in summer denoted annual maximum precipitation and seasonal maximum precipitation respectively. 5.1 Spatial distribution of MK trends of the precipitation extremes The precipitation in the Yangtze River basin has significant regional differences due to its large area, various terrains and vegetations, fickle climatic system and inconstant urbanization condition. Figures 4, 5, 6 and 7 illustrate the

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spatial distribution of MK trends of precipitation extremes over the Yangtze River basin. 5.2 Precipitation extremes as measured by Group 1 statistical indices The spatial distribution of MK trends of annual precipitation extremes during the future period (2010–2099) under A2 and B2 emission scenarios in the Yangtze River basin was drawn in Fig. 4. Under A2 scenario, about 2/3 of stations have no significant trends, while the rest 1/3 of stations mainly lie in the middle region (Fig. 4 IA2) have significant increasing trends in annual maximum precipitation. As for the frequency of rainy days, the number of stations with significant increasing/decreasing trend was 36.23/15.22% respectively, with 48.55% of the stations have no significant changing trend (Fig. 4 JA2). For the


167

Fig. 4 MK trend of annual extreme precipitation evens in the Yangtze River basin (2010–2099) calculated by 138 stations under A2 (IA2, JA2, KA2, LA2) and B2 (IB2, JB2, KB2, LB2) scenarios

precipitation intensity, the number of stations with significant increasing trend was 54.35%, with 43.48% of the stations have no significant changing trend, and most of the stations with significant precipitation increases locate in the middle region (Fig. 4 KA2). Most stations showed opposite sign for the change of frequency of non-rainy days as compared with those of frequency of rainy days (Fig. 4

LA2). Under B2 scenario, most of stations show no significant trends of daily maximum precipitation and precipitation intensity, while only 27 stations in the middle and lower region show significant increasing trend in the annual maximum precipitation and one station presents significant decreasing trend in precipitation intensity, which has similar changing trend as those under A2

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168


Fig. 5 MK trend of summer extreme precipitation evens in the Yangtze River basin (2010–2099) calculated by 138 stations under A2 (IA2, JA2, KA2, LA2) and B2 (IB2, JB2, KB2, LB2) scenarios

scenario (Fig. 4 IB2, KB2). As for the frequency of rainy days, the number of stations with significant increasing/ decreasing trend was 13.04/15.94% respectively, with 71.01% of the stations have no significant changing trend, which mainly lie in the upper Jinshajiang, Mintuojiang, Jialingjiang, and Dongtinghu regions (Fig. 4 JB2). As for the frequency of non-rainy days, the stations showed same

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sign with no significant changing trend and showed opposite sign with significant increasing/decreasing trend as compared with those of frequency of rainy day (Fig. 4 LB2). The spatial distribution of MK trends of the four indices for summer precipitation extremes in the Yangtze River basin was plotted in Fig. 5 and similar patterns exist as


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compared with those of the annual extremes. Under A2 scenario, 65.94% of stations have no significant trend, while the 32.61% of stations, which mainly locate in the middle region have significant increasing trends in summer maximum precipitation (Fig. 5 IA2). As for the frequency of rainy days, the number of stations showing significant increasing/decreasing trend and no significant trend for about 20.29/23.91 and 55.80%, respectively (Fig. 5 JA2). As for the frequency of non-rainy days, the opposite changing patterns can be observed (Fig. 5 LA2). For the precipitation intensity, the number of stations with no significant trend was 51.44%, with 46.38% of the stations have significant increasing trend, which mainly locate in the upper Jinshajiang, lower Mintuojiang, Wujiang, Dongtinghu Lake and Taihu Lake region (Fig. 5 KA2). Under B2 scenario, more than 83.30% of the stations show no significant trend in summer maximum precipitation, and only 22 stations mainly locate in the middle region show significant increasing trend and only one station also in the middle region has a significant decreasing trend (Fig. 5 IB2). For the frequency of rainy days, the number of stations with significant increasing/decreasing trend was 7.25/ 25.36% respectively, and the stations with increasing trend mainly locate in Mintuojiang region, while 67.39% of the stations have no significant changing trend (Fig. 5 JB2). Most stations showed opposite change patterns for the change of frequency of non-rainy days as compared with those of frequency of rainy days (Fig. 5 LB2). For the precipitation intensity, 63.77% of the stations show no significant trend, and most of remaining stations, which also mainly locate in the upper Jinshajiang, lower Mintuojiang, Wujiang, Dongtinghu Lake and Taihu Lake region, have significant increasing trend (Fig. 5 KB2).

declined to 13.04% and the number of stations with no obvious trend increased to 72.46% as for frequency of rainy days (Fig. 6 IB2, JB2). The spatial distribution of MK trends of the precipitation maxima defined by 90th percentiles in summer was displayed in Fig. 7. It can be seen from Fig. 7 IA2 and IB2 that the stations showing significant decreasing trend of the frequency of rainy days can be found in every sub-catchments except Poyanghu Lake and Dongtinghu Lake, and the proportions of these stations with decreasing trends were 23.91 and 22.46%, respectively for A2 and B2 scenarios. The number of stations with increasing trend for the frequency of rain days was 17.39% under A2 scenario (mainly locate in the upper Jinshajiang, Mintuojiang, Jialingjiang, Wujiang and Dongtinghu region) (Fig. 7 IA2), while the number declined to 5.80% under B2 scenario (mainly locate in the upper Jinshajiang, Mintuojiang and Jialingjiang region) (Fig. 7 IB2). There were 37.7 and 24.6% of the total stations having significant upward trends for the intensity of precipitation under A2 and B2 scenarios, which mostly locate in the Dongtinghu Lake, Wujiang, Mintuojiang and the upper of Jinshajiang region (Fig. 7 JA2, JB2). It can also be seen that only one station locates in the upper mainstream section was dominated by the significant decreasing trend of precipitation intensity under both climatic scenarios (Fig. 7 JA2, JB2). As for the frequency of rainy days and the intensity of precipitation, Fig. 7 shows that the number of the stations with no obvious trend exceeded 50% under the both climatic scenarios.

5.2.1 Precipitation extremes as measured by Group 2 statistical indices

In order to study the temporal variability of precipitation extremes in future, the temporal changes of MK trends of the annual and summer precipitation maxima in the upper, middle and lower regions of Yangtze River basin were respectively plotted in Figs. 8, 9, 10 and 11 which are classified using the same statistical indices as before.

Figures 6 illustrate the changes of annul precipitation maxima defined by daily precipitation exceeding 90th percentiles. Under A2 scenario, it could be detected that the number of stations with increasing/decreasing trend and no obvious trend was 35.51/14.49, and 50.00%, respectively (Fig. 6 IA2), and the stations with increasing trend mainly locate in the upper Jinshajiang, Mintuojiang and Dongtinghu region. As for the precipitation intensity exceeding 90th percentile, the number of stations with significant increasing/decreasing trend and no significant trend was 47.83/1.45, and 50.72%, respectively (Fig. 6 JA2), and the stations with increasing trend also mainly lie in the upper Jinshajiang, Mintuojiang, Wujiang and Dongtinghu region. Under B2 scenario, the similar changing pattern was exhibited as compared with those under A2 scenario, except that the number of stations with increasing trend

5.3 Temporal changes of MK trends of the precipitation extremes

5.3.1 Precipitation extremes as measured by Group 1 statistical indices Figure 8 demonstrates temporal changes of MK trends of the annual maximum precipitation under A2 and B2 scenarios in the Yangtze River basin. Under A2 scenario, the annual maximum precipitation in the lower region is in decreasing trend during 2010–2058 and in increasing trend after 2058 but neither of them is significant at [95% confidence level, while the changing patterns of the upper and middle regions are in no obvious trend during the first two decades, in increasing trend during 2030–2065 and in

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Fig. 6 MK trend of annual precipitation extreme evens defined as daily precipitation exceeding 90th percentiles in the Yangtze River basin (2010–2099) calculated by 138 stations under A2 (IA2, JA2) and B2 (IB2, JB2) scenarios

Fig. 7 MK trend of summer precipitation extreme evens defined as daily precipitation exceeding 90th percentiles in the Yangtze River basin (2010–2099) calculated by 138 stations under A2 (IA2, JA2) and B2 (IB2, JB2) scenarios

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IA2, IB2: maximum annual precipitation

middle Yangtze River basin

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lower Yangtze River basin

KA2, KB2: precipitation intensity

95% confidence leveal

LA2, LB2: frequency of non-rain day

Fig. 8 Temporal changes of MK trend Z-value of areal-averaged annual extreme precipitations in the Yangtze River basin (2010–2099) under A2 (IA2, JA2, KA2, LA2) and B2 (IB2, JB2, KB2, LB2) scenarios

significant increasing trend at [95% confidence level after 2065 (Fig. 8 IA2). Under B2 scenario, it is indicated that the annual maximum precipitation has no obvious changing patterns during 2010–2045 in the whole Yangtze River basin, while the changing patterns of middle region are in

significant increasing trend at [95% confidence level after 2050, and the same is true for upper and lower regions after 2075 (Fig. 8 IB2). As for the frequency of rainy days, the upper region does not show any obvious trend during 2010–2050 and after 2050 is in increasing trend, while the

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IA2, IB2: maximum precipitation in summer JA2, JB2: frequency of rain day KA2, KB2: precipitation intensity LA2, LB2: frequency of non-rain day

Fig. 9 Temporal changes of MK trend Z-value of areal-averaged summer extreme precipitations in the Yangtze River basin (2010–2099) under A2 (IA2, JA2, KA2, LA2) and B2 (IB2, JB2, KB2, LB2) scenarios

middle and lower regions show increasing trend after middle 2030s under A2 scenario (Fig. 8 JA2), but none of them is significant; while under B2 scenario, the upper region is in increasing trend during 2010–2025 but in

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decreasing trend after 2025, and the middle and lower regions are in decreasing trend from late 2020s to 2050 (Fig. 8 JB2). As for the number of non-rainy days, the opposite changing patterns under A2 and B2 are found


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95% confidence leveal Fig. 10 Temporal changes of MK trend Z-value of areal-averaged annual precipitation extremes defined by 90th percentiles in the Yangtze River basin (2010–2099) under A2 (IA2, JA2) and B2 (IB2, JB2) scenarios

(Fig. 8 LA2, LB2). As for the precipitation intensity, the middle and lower regions are in increasing trend during 2018–2050 and the upper region is in decreasing trend during 2010–2038 and in increasing trend during 2038–2050, and the increasing trend in the whole Yangtze River basin becomes significant at [95% confidence level after 2050 under A2 scenario (Fig. 8 KA2). Under B2 scenario, the middle region shows the same changing pattern as under A2 scenario, and the upper and lower regions do not show any trend in the first three decades and are in increasing trend during 2040–2070 and these increasing trends are significant at [95% confidence level after 2070 (Fig. 8 KB2). The results of similar study conducted for summer precipitation extremes are shown in Fig. 9, which reveals a similar changing pattern as the annual precipitation extremes in some cases. Under both climatic scenarios, the maximum precipitation in the lower region is in increasing trend during the future 90 years and that of the middle region after the middle of this century (Fig. 9 IA2, IB2). The year when the increasing trend of the maximum precipitation in the lower region becomes significant is about 2050 under A2 and 2070 under B2 (Fig. 9 IA2, IB2). As for the frequency of rain day, under A2 scenario, the upper

region is in decreasing trend during 2015–2040 and in increasing trend thereafter but no significant trend is detected in the middle and lower regions (Fig. 9 JA2); under B2 scenario, the whole Yangtze River basin is in increasing trend during 2010–2022 and in decreasing trend after 2022 (Fig. 9 JB2). Meanwhile, these trends of frequency of non-rainy days are opposite to what are shown in Fig. 9 JA2 and JB2 (Fig. 9 LA2, LB2). As for the precipitation intensity, the lower region is in increasing trend during the future 90 years under both climatic scenarios, the middle region is in significant increasing trends until the middle of this century under both climatic scenarios, the upper region is in significant increasing trend after 2050s under A2 scenario and after middle 2070s under B2 scenario. 5.3.2 Precipitation extremes as measured by Group 2 statistical indices Figures 10 and 11 show the frequency and intensity of precipitation that exceeding 90th percentile under botJh emission scenarios. For the annual events, the frequency of precipitation exceeding 90th percentile will increase after 2038 in the middle and lower regions under A2 scenario

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95% confidence leveal Fig. 11 Temporal changes of MK trend Z-value of areal-averaged summer precipitation extremes defined by 90th percentiles in the Yangtze River basin (2010–2099) under A2 (IA2, JA2) and B2 (IB2, JB2) scenarios

(Fig. 10 IA2), and decrease after 2026 in the upper region under B2 scenario (Fig. 10 IB2). For the summer extremes, the frequency will increase after 2040 in the upper region under A2 scenario (Fig. 11 IA2), and decrease after 2030 in the whole basin under B2 scenario (Fig. 11 IB2). However, all the change trends for the frequency of precipitation exceeding 90th percentile mentioned above, were not significant. Under A2 scenario, the precipitation intensity will increase significantly after 2050 in the upper and middle regions for the whole year and summer (Figs. 10 JA2, 11 JA2), respectively. Under B2 scenario, the significant increasing trends of the annual precipitation intensity will appear after 2048 in the middle region, and after 2070 in the upper and lower regions for the whole year (Fig. 10 JB2), whereas, significant increasing trends only appear in the middle region after 2044, as far as the summer precipitation intensity is concerned (Fig. 11 JB2).

atmospheric variables from two GCMs were downscaled to obtained daily precipitation for the basin. The spatial– temporal changes of the amount and the extremes of precipitation in the Yangtze River Basin during 2010–2099 under A2 and B2 emission scenarios were investigated. Some interesting conclusions can be described as follows:

6 Conclusion

(2)

In this paper, the applicability of the ASD statistical downscaling model in downscaling daily precipitation in the Yangtze River basin was evaluated, and the large scale

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(1)

For selecting the predictor domain by ASD, it was found that the most suitable four grid-boxes for each station were almost situated at the south-east of the quadrate region of predictor selection, which indicated that the precipitation of the Yangtze River basin was closely related to the Western Pacific subtropical high and Southeast monsoon. According to the summary of the selection of predictor variables, it was clearly seen that precipitation in each subcatchment of the Yangtze River basin was sensitive to wind direction, specific humidity and zonal velocity. It has been proven that ASD is a successful statistical downscaling method in the study region. It can be seen that the mean values of the estimated statistics indices in each sub-catchment of the Yangtze River for both calibration and validation periods are similar


(3)

(4)

to observed data and the relative bias between them is generally within 10% and the simulation skill of ASD for the daily precipitation is gradually increased from the upstream to the downstream. Above all, the variation characteristics of precipitation can be reasonably produced. The results for downscaling precipitation under scenario A2 showed that the predicted annual precipitation would decrease in all sub-catchments during 2020s, while increase in all sub-catchments of the Yangtze River Basin during 2050s and 2080s, respectively. However, the predicted annual precipitation would have a mix-trend in each sub-catchment of Yangtze River basin during 2020s, but increase in all sub-catchments during 2050s and 2080s, respectively, except for the Hanjiang Basin during 2080s, as far as B2 scenario is concerned. The spatial and temporal change trends of precipitation maxima and precipitation intensity were explored by using Mann–Kendall test over the Yangtze River basin for 2010–2099 based on downscaled daily precipitation by ASD. The results revealed that the significant increasing trend of the precipitation intensity and maximum precipitation would mainly occur in the northwest upper part and the middle part of the Yangtze River basin for the whole year and summer under both climate change scenarios. However, other statistical indices for precipitation extremes showed the inconsistent trends in all situations. Hence, the northwest upper and middle Yangtze River basin might encounter higher risk of flood hazards in future. The extreme precipitation will be enhanced in the Yangtze River Basin, generally after about 2050, which was illuminated by significant increasing maximum precipitation and precipitation intensity. The period of 2040–2060 can be regarded as the starting point for pattern change of precipitation maxima. Extreme precipitation as measured by intensity will be in significant increasing trend after about mid-2040s in the upper part of the Yangtze River basin and about 2060s in the middle and lower parts of the Yangtze River basin.

Due to the uncertainties of GCMs, climatic scenarios, downscaling methods, and also trend analysis methods, the prediction of variability of precipitation in the Yangtze River basin under the climate change conditions in this study only serves as a reference for decision making and for further studies. Acknowledgments The study is financially supported by the National Program on Key Basic Research Project (973 Program) (2010CB428405) and National Natural Science Fund of China (50809049). The authors are greatly appreciated the Canadian

175 Climate Change Scenarios Network (CCCSN) for providing the downscaling tool (ASD) and the reanalysis products of the NCEP and HadCM3 outputs for the downscaling tool. The authors also thank the National Climate Centre of China for supplying the daily precipitation data of meteorological stations in the Yangtze River Basin.

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