Prediction of variability of precipitation in the Yangtze RiverBasin under the climate change conditions based on automatedstatistical downscaling

Jing Guo • Hua Chen • Chong-Yu Xu •

Shenglian Guo • Jiali Guo

� 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-

catchments during 2020s, while increase in all sub-catch-

ments 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 Yan-

gtze River is the longest river in China and the third longest

river in the world and has an irreplaceable role in sup-

porting sustainable development of the society and econ-

omy 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

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: chua@whu.edu.cn

J. Guo

e-mail: guojingking@163.com

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

123

Stoch Environ Res Risk Assess

DOI 10.1007/s00477-011-0464-x

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 Develop-

ment 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 fre-

quency 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 topog-

raphy 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 pre-

cipitation 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 circu-

lation models (GCMs) and large-scale circulation predic-

tors 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 unreli-

able 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 anal-

yses (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 hy-

drometeorologists to convert GCMs outputs into local

meteorological variables (Fowler et al. 2007). Statistical

downscaling is aimed to derive empirical relationships that

can transform large-scale features of the GCMs (Predic-

tors) to regional-scale variables (Predictands) such as pre-

cipitation, 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 tech-

nique 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 down-

scaling 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 gen-

eration 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 Qinghai-

Tibet 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) selec-

tion 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) pre-

diction 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

Stoch Environ Res Risk Assess

123

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 con-

centrated 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

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 sce-

narios 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 grid-

boxes 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 vari-

ables superposed on the map of the Yangtze River basin.

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

section; 6: Hanjiang River; 7: Dongtinghu Lake; 8: Poyanghu Lake;

9: The middle mainstream section; 10: the lower mainstream section;

11: Taihu Lake

Stoch Environ Res Risk Assess

123

3 Methodology

3.1 Automated statistical downscaling

3.1.1 Regression methods

As same to SDSM, the ASD model process can be con-

ditional 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: pre-

cipitation occurrence and precipitation amounts, as

described by Hessami et al. (2008):

Oi ¼ a0 þXn

j¼1

ajpij; Ri ¼ b0 þXn

j¼1

bjpij þ ei ð1Þ

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:

ei ¼ffiffiffiffiffiffiffiffiVIF

12

rZiSe þ b ð2Þ

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:

b ¼ Mobs �Md ð3Þ

VIF ¼ 12 Vobs � Vdð ÞS2

e

ð4Þ

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 per-

iod, respectively. Regression-based downscaling methods

often use multiple linear regressions, however, the non-

orthogonality 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-orthogo-

nality of the predictor vectors.

3.1.2 Predictor selection methods

Two methods are implemented based on backward step-

wise 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;k ¼Rij � RikRjkffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

1� R2ik

� �1� R2

jk

� �r 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:

Table 1 The candidates of atmospheric variables for predictors

No. Variables Description No. Variables Description

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 p5_z 500 hPa vorticity 24 r850 Relative humidity at 850 hPa

12 p500 500 hPa geopotential height 25 shum Near surface specific humidity

13 p5th 500 hPa wind direction 26 temp Mean temperature

Stoch Environ Res Risk Assess

123

t ¼ Rffiffiffiffiffiffiffiffi1�R2

n�2

q ð6Þ

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 precipi-

tation, 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 pre-

dictors 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 pre-

dictor 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 sur-

rounding eight grid-boxes were taken as candidate pre-

dictors; 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 grid-

box were selected; thirdly, the most suitable four grid-

boxes 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 sta-

tistical 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 mete-

orological stations, it could be inferred that the precipita-

tion 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 calibra-

tion 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 (Per-

centile90), 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 ¼RMSE

xð7Þ

where x is the mean of the observed values.

Stoch Environ Res Risk Assess

123

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

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 sug-

gested 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

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 0.27 0.27* 56586 0.25 0.25* 57313 0.30 0.31* 57584 0.29 0.29* 57799 0.29 0.29* 58407 0.34 0.27

56172 0.25 0.21 56651 0.24 0.21 57348 0.29 0.31* 57598 0.32 0.29 57803 0.29 0.29* 58424 0.31 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*

Note: R2* is the maximum value of explained variance of ASD model in nine atmospheric grid-boxes and R2 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

Stoch Environ Res Risk Assess

123

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0.7

70

.20

10

.65

9.7

62

.15

0.1

93

7.8

94

0.6

85

.91

0.1

61

0.2

38

.24

2.6

20

.26

Min

tuo

jian

g

Riv

er

Cal

ibra

tio

n2

.97

2.8

70

.15

0.0

56

.19

5.6

71

.16

0.1

71

3.4

51

3.0

31

.20

0.0

84

9.4

44

9.5

10

.41

0.0

16

.81

5.8

11

.27

0.1

9

Val

idat

ion

2.8

13

.54

0.6

20

.21

6.0

55

.71

1.5

00

.23

13

.45

13

.01

2.5

20

.18

46

.45

47

.11

5.8

40

.13

7.1

89

.09

2.0

60

.29

Jial

ing

jian

g

Riv

er

Cal

ibra

tio

n2

.63

2.5

00

.18

0.0

76

.94

6.1

51

.15

0.1

61

6.3

51

5.4

61

.64

0.1

03

8.3

83

8.7

30

.52

0.0

18

.10

7.1

21

.26

0.1

5

Val

idat

ion

2.3

72

.44

0.5

70

.24

6.5

36

.14

2.0

90

.35

16

.22

15

.62

3.4

50

.21

34

.38

37

.90

5.2

70

.16

8.9

87

.15

2.3

40

.26

Wu

jian

gR

iver

Cal

ibra

tio

n3

.09

2.9

80

.13

0.0

46

.97

6.4

90

.75

0.1

11

5.8

51

5.1

31

.25

0.0

85

3.2

45

3.2

30

.35

0.0

15

.80

4.8

81

.05

0.1

8

Val

idat

ion

3.1

03

.14

0.6

80

.22

7.2

16

.73

1.9

60

.27

16

.54

15

.57

3.6

40

.22

49

.51

53

.30

5.3

70

.11

6.4

24

.88

1.8

00

.28

Th

eu

pp

erm

ain

stre

amse

ctio

n

Cal

ibra

tio

n3

.15

3.0

40

.15

0.0

57

.58

7.1

20

.94

0.1

21

8.0

21

7.2

31

.49

0.0

84

7.2

04

7.5

10

.58

0.0

16

.49

5.6

81

.00

0.1

5

Val

idat

ion

2.9

22

.97

0.5

80

.20

7.2

36

.89

1.5

40

.22

17

.85

16

.98

3.3

40

.18

43

.64

46

.75

4.8

00

.11

7.1

95

.83

1.7

60

.25

Han

jian

gR

iver

Cal

ibra

tio

n2

.45

2.3

50

.15

0.0

66

.82

6.5

31

.47

0.2

21

8.0

41

6.8

61

.75

0.1

03

4.1

33

5.0

91

.12

0.0

39

.44

7.9

71

.83

0.1

9

Val

idat

ion

2.2

82

.27

0.5

30

.22

6.5

86

.87

1.9

50

.28

18

.90

16

.87

4.7

20

.23

30

.40

33

.54

4.2

10

.14

10

.33

8.1

12

.76

0.2

7

Do

ng

tin

gh

uL

ake

Cal

ibra

tio

n3

.80

3.6

30

.21

0.0

59

.24

8.7

71

.05

0.1

12

2.3

12

0.9

71

.98

0.0

94

7.6

64

7.9

50

.63

0.0

17

.19

5.8

71

.51

0.2

1

Val

idat

ion

3.8

43

.72

0.7

80

.22

9.2

98

.68

2.4

50

.29

23

.06

20

.89

4.9

30

.23

44

.10

45

.20

4.3

20

.10

7.8

88

.08

2.0

10

.26

Po

yan

gh

uL

ake

Cal

ibra

tio

n4

.39

4.2

70

.16

0.0

41

0.3

01

0.1

70

.95

0.0

92

5.4

32

4.6

51

.70

0.0

74

5.6

84

5.8

40

.60

0.0

17

.89

6.1

92

.02

0.2

6

Val

idat

ion

4.8

74

.50

0.9

20

.19

11

.21

11

.32

3.7

00

.34

29

.18

25

.47

5.6

50

.19

43

.81

45

.71

4.8

40

.11

8.2

56

.14

2.6

30

.32

Th

em

idd

le

mai

nst

ream

sect

ion

Cal

ibra

tio

n3

.56

3.4

00

.20

0.0

69

.52

9.0

10

.98

0.1

02

4.4

32

2.9

02

.19

0.0

93

9.6

24

0.1

90

.72

0.0

28

.47

6.8

71

.85

0.2

2

Val

idat

ion

3.5

23

.46

0.6

90

.20

9.5

59

.30

2.8

70

.29

25

.34

23

.38

5.0

00

.20

36

.72

39

.33

4.8

00

.13

9.0

66

.97

2.5

30

.28

Th

elo

wer

mai

nst

ream

sect

ion

Cal

ibra

tio

n3

.42

3.2

50

.21

0.0

69

.12

8.6

21

.18

0.1

32

3.5

12

1.8

32

.38

0.1

03

8.5

43

9.1

60

.77

0.0

28

.73

6.9

81

.98

0.2

3

Val

idat

ion

3.5

83

.43

0.8

70

.24

9.5

38

.81

2.0

50

.22

26

.06

22

.87

6.4

40

.25

34

.48

38

.84

5.7

60

.17

9.6

86

.99

3.0

90

.32

Tai

hu

Lak

eC

alib

rati

on

3.0

62

.90

0.2

10

.07

8.3

87

.82

1.2

40

.15

21

.61

20

.17

2.1

20

.10

38

.52

39

.25

0.8

10

.02

8.4

07

.21

1.3

10

.16

Val

idat

ion

3.3

42

.94

0.9

60

.28

8.8

37

.87

2.4

60

.27

24

.52

20

.69

6.7

40

.27

35

.90

38

.89

4.8

90

.14

9.3

47

.21

2.6

00

.28

Av

erag

eC

alib

rati

on

3.1

33

.01

0.1

60

.05

7.7

17

.28

1.0

30

.14

18

.98

17

.97

1.6

80

.09

42

.82

43

.30

0.6

50

.02

7.9

66

.64

1.5

30

.19

Val

idat

ion

3.1

53

.13

0.6

90

.22

7.8

17

.47

2.1

20

.27

20

.16

18

.28

4.4

20

.21

39

.75

42

.48

5.0

90

.13

8.5

97

.15

2.3

80

.28

Stoch Environ Res Risk Assess

123

occurrence and magnitude, and the regression-based sta-

tistical 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 cap-

turing 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

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 devia-

tions of simulated annual precipitation in different periods

were calculated and listed in Table 5. Under the A2 sce-

nario, the predicted annual precipitation during 2020s

onitatS)gnijnaN(83285noitatS)gnahciY(16475noitatS)ehiuhsgniQ(43065

0

0.5

1

1.5

2

2.5

3

3.5

4

1 2 3 4 5 6 7 8 9 10 11 12

Month

Mea

n (m

m/d

ay)

OBS

SIM

0

1

2

3

4

5

6

7

8

9

1 2 3 4 5 6 7 8 9 10 11 12

Month

Mea

n (m

m/d

ay)

OBS

SIM

0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9 10 11 12

Month

Mea

n (m

m/d

ay)

OBS

SIM

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10 11 12

Month

STC

D (

mm

/day

)

OBS

SIM

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11 12

MonthS

TC

D (m

m/d

ay)

OBS

SIM

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6 7 8 9 10 11 12

Month

ST

CD

(mm

/day

)

OBS

SIM

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7 8 9 10 11 12

Month

PRE

C90

(m

m/d

ay)

OBS

SIM

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5 6 7 8 9 10 11 12

Month

PRE

C90

(mm

/day

) OBS

SIM

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12

Month

PRE

C90

(mm

/day

)

OBS

SIM

0

10

20

30

40

50

60

70

80

90

1 2 3 4 5 6 7 8 9 10 11 12

Month

Wet

(%

)

OBS

SIM

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12

Month

Wet

(%

)

OBS

SIM

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 5 6 7 8 9 10 11 12

MonthW

et (

%)

OBS

SIM

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10 11 12

Month

CD

D (

day)

OBS

SIM

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10 11 12

Month

CD

D (

day)

OBS

SIM

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10 11 12

Month

CD

D (

day)

OBS

SIM

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

Stoch Environ Res Risk Assess

123

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 pre-

dicted 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% dur-

ing 2080s, respectively, under A2 scenario; while succes-

sively 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 Yan-

gtze 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 geo-

graphically 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.

5 Predicted trends of precipitation extremes

under future emission scenarios

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 pat-

terns 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 theTa

ble

5C

om

par

iso

no

fth

ech

ang

eso

fm

ean

ann

ual

pre

cip

itat

ion

un

der

futu

reem

issi

on

scen

ario

s

Su

b-c

atch

men

tA

2B

2

Cu

rren

t2

02

0s

Ch

ang

es2

05

0s

Ch

ang

es2

08

0s

Ch

ang

esC

urr

ent

20

20

sC

han

ges

20

50

sC

han

ges

20

80

sC

han

ges

(mm

)(m

m)

(%)

(mm

)(%

)(m

m)

(%)

(mm

)(m

m)

(%)

(mm

)(%

)(m

m)

(%)

Jin

shan

gji

ang

Riv

er7

18

.10

71

2.8

0-

0.7

47

48

.80

4.2

77

81

.20

8.7

97

19

.35

73

4.4

02

.09

73

0.8

01

.59

75

9.6

05

.59

Min

tuo

jian

gR

iver

11

56

.41

12

09

.60

4.6

01

38

9.6

02

0.1

71

62

7.2

04

0.7

11

14

7.4

81

23

8.4

07

.92

13

60

.80

18

.59

14

90

.40

29

.88

Jial

ing

jian

gR

iver

11

34

.49

11

30

.40

-0

.36

12

16

.80

7.2

61

31

0.4

01

5.5

11

11

8.8

81

10

5.2

0-

1.2

21

19

8.8

07

.14

12

56

.40

12

.29

Wu

jian

gR

iver

12

95

.74

12

85

.20

-0

.81

14

58

.00

12

.52

16

20

.00

25

.02

12

74

.45

13

46

.40

5.6

51

39

3.2

09

.32

15

01

.20

17

.79

Th

eu

pp

erm

ain

stre

amse

ctio

n1

40

5.4

21

36

8.0

0-

2.6

61

49

4.0

06

.30

16

63

.20

18

.34

13

72

.98

13

75

.20

0.1

61

45

0.8

05

.67

15

26

.40

11

.17

Han

jian

gR

iver

13

51

.49

13

39

.20

-0

.91

14

11

.20

4.4

21

40

7.6

04

.15

13

27

.93

12

34

.80

-7

.01

13

86

.00

4.3

71

30

6.8

0-

1.5

9

Do

ng

tin

gh

uL

ake

17

15

.97

17

28

.00

0.7

01

93

3.2

01

2.6

62

17

8.0

02

6.9

31

66

9.8

51

79

2.8

07

.36

18

68

.40

11

.89

19

58

.40

17

.28

Po

yan

gh

uL

ake

20

42

.01

20

48

.40

0.3

12

18

5.2

07

.01

22

89

.60

12

.12

20

11

.06

20

80

.80

3.4

72

19

2.4

09

.02

22

14

.00

10

.09

Th

em

idd

lem

ain

stre

amse

ctio

n1

81

3.9

71

76

4.0

0-

2.7

51

90

0.8

04

.79

19

94

.40

9.9

51

75

3.9

91

75

3.2

0-

0.0

51

89

0.0

07

.75

19

08

.00

8.7

8

Th

elo

wer

mai

nst

ream

sect

ion

18

18

.54

17

46

.00

-3

.99

18

75

.60

3.1

41

95

8.4

07

.69

17

72

.46

17

28

.00

-2

.51

19

04

.40

7.4

41

95

4.8

01

0.2

9

Tai

hu

Lak

e1

70

7.0

41

76

4.0

03

.34

21

20

.40

24

.22

23

50

.80

37

.71

16

60

.77

17

56

.80

5.7

81

97

6.4

01

9.0

12

29

6.8

03

8.3

0

Av

erag

e1

39

6.7

51

39

1.7

9-

0.3

51

52

1.9

18

.96

16

44

.70

17

.75

13

70

.37

14

01

.39

2.2

61

49

1.6

38

.85

15

46

.57

12

.86

Stoch Environ Res Risk Assess

123

characteristics of precipitation extremes: Group 1 includes

(1) maximum daily precipitation, (2) frequency of rainy

days, (3) precipitation intensity, and (4) frequency of non-

rainy 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 maxi-

mum 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

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 precipita-

tion 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 precipi-

tation. 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

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

Stoch Environ Res Risk Assess

123

precipitation intensity, the number of stations with signif-

icant 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 sig-

nificant trends of daily maximum precipitation and pre-

cipitation 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

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

Stoch Environ Res Risk Assess

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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

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

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

Stoch Environ Res Risk Assess

123

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, Don-

gtinghu 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 sta-

tions 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 Mint-

uojiang, Wujiang, Dongtinghu Lake and Taihu Lake

region, have significant increasing trend (Fig. 5 KB2).

5.2.1 Precipitation extremes as measured by Group 2

statistical indices

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 Don-

gtinghu 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

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 dis-

played 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-catch-

ments 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 sce-

narios. 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, Ji-

alingjiang, 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 scenar-

ios, 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 fre-

quency 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.

5.3 Temporal changes of MK trends

of the precipitation extremes

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.

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 sce-

narios 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

Stoch Environ Res Risk Assess

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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

Stoch Environ Res Risk Assess

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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 chang-

ing 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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2010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090

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2010 2020 2030 2040 2050 2060 2070 2080 2090 2010 2020 2030 2040 2050 2060 2070 2080 2090

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JA2, JB2: frequency of rain day

KA2, KB2: precipitation intensity

LA2, LB2: frequency of non-rain day

IA2

JA2

KA2

LA2

IB2

JB2

KB2

LB2

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

Stoch Environ Res Risk Assess

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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

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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lower Yangtze River basin

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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

IA2

JA2

KA2

LA2

IB2

JB2

KB2

LB2

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

Stoch Environ Res Risk Assess

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(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 pre-

cipitation 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 fre-

quency of non-rainy days are opposite to what are shown in

Fig. 9 JA2 and JB2 (Fig. 9 LA2, LB2). As for the pre-

cipitation 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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JB2

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

Stoch Environ Res Risk Assess

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(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 sig-

nificant. 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 pre-

cipitation intensity is concerned (Fig. 11 JB2).

6 Conclusion

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

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 pre-

cipitation in the Yangtze River Basin during 2010–2099

under A2 and B2 emission scenarios were investigated.

Some interesting conclusions can be described as follows:

(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 indi-

cated 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 sub-

catchment of the Yangtze River basin was sensitive

to wind direction, specific humidity and zonal

velocity.

(2) 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

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IA2, IB2: frequency

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IA2

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JB2

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

Stoch Environ Res Risk Assess

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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 rea-

sonably produced.

(3) 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 precipi-

tation 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, respec-

tively, except for the Hanjiang Basin during 2080s, as

far as B2 scenario is concerned.

(4) The spatial and temporal change trends of precipita-

tion 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 inten-

sity 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

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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