Drought Analysis and the Characteristics of Hydro-meteorological

Changes in the Jinsha River Basin

Xing Qu

1,2

, Deng Pan

1,2

Junjun Huo

1,2,*

, Zhe Yuan

1,2

, and Yuanzhi Tang

1,2

Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan 430010, China

Hubei Provincial Key Laboratory of Basin Water Resources and Ecological Environment, Wuhan 430010, China

Keywords: Climate change, Drought analysis, Mann-Kendall test, Jinsha River Basin

Abstract: The Jinsha River Basin (JRB) is the most important tributary of the Changjiang River and the most ecological

vulnerable region to climate change. Therefore, to better understand the hydro-meteorology characteristics of

JRB and enhance hydrological forecasts, a temporal-spatial analysis of historical hydro-meteorological

elements and drought characteristics is required. In this study, 45 meteorological stations and 5 hydrologic

stations in JRB for the last 60 years of historical data were utilized for analyze the temporal-spatial distribution

and trends of hydro-meteorological elements, while drought characteristics in this basin were assessed using

SPI values at six-month scales (SPI-6). The results show that: (1) the three daily temperature types (minimum,

mean and maximum) and precipitation of the JRB all show an increase from the upper to lower reaches. (2)

the Mann-Kendall test analysis of hydro-meteorological elements’ annual values reveals that the mean

temperature of 42 stations is rising, while precipitation is rising at 25 stations. The runoff at all five hydrologic

stations is increasing. The temperature and precipitation in the upper reaches are the areas with the greatest

increase in the JRB, while the precipitation in the lower reaches is the only one that is on a downward trend.

(3) In the drought analysis based on SPI-6, the downstream of JRB, which is located in Sichuan and Yunnan

provinces, is the region with the most severe drought. There is no visible trend in drought duration at most

stations, and the drought magnitude analysis is dominated by a decreasing trend. However, the drought

intensity analysis is dominated by an uptrend, especially in the mid- and lower streams.

1 INTRODUCTION

Drought, the most complex natural phenomena, is

characterized by strong progressivity, a wide range of

influence, and large losses, which have attracted

widespread attention (Keyantash & Dracup, 2020).

For example, some areas in southern China, including

Yunnan, Guangdong, Guangxi, and Shanxi,

experienced heavy drought conditions in the winter of

2020 and spring of 2021, and a total of 470,000 rural

residents experienced drinking water difficulties as a

result of the drought. Generally, droughts can be

classified into three major types based on its cause:

meteorological, agricultural and hydrological

droughts (Wang et al., 2016). Meteorological drought

is mainly caused by poor precipitation and

atmospheric circulation anomalies (van Loon et al.,

2015); agricultural drought, also known as soil

moisture drought, is defined by a lack of soil water

(Van Hateren et al., 2020); and hydrological drought

is associated with water shortage in rivers, lakes,

groundwater and other water bodies (van Loon,

2015). To assess the drought characteristics, several

drought indices are the most widely used (McKee et

al., 1993; Welford et al., 1993; Shukla et al., 2008),

including the standardized precipitation index (SPI)

(McKee et al., 1993), the soil moisture drought index

(SMDI) and the standardized runoff index (SRI). In

recent decades, numerous research based on various

drought indicators, including drought duration and

drought severity, have been carried out in some

watersheds of China. For example, Xu et al. (2015a)

based on the 3-month scale SPI constructed a

multidimensional clustering method to assess drought

risk of China during 1961-2012. The findings show

that two of the most extreme drought swept through

more than half of China's non-arid regions, occurring

from 1962 to 1963 and from 2010 to 2011. Zhai et al.

(2010) used monthly scale degree data to calculate

annual average SPI and PDSI values for

approximately five hundred meteorological stations

in China over the last 50 years. They discovered that

426

Qu, X., Pan, D., Huo, J., Yuan, Z. and Tang, Y.

Drought Analysis and the Characteristics of Hydro-meteorological Changes in the Jinsha River Basin.

In Proceedings of the 7th International Conference on Water Resource and Environment (WRE 2021), pages 426-434

ISBN: 978-989-758-560-9; ISSN: 1755-1315

the upper and lower Changjiang River or the

mountainous northwest region have significant

positive trends in these indices. Xiang et al. (2020)

combined a multidimensional Copula function

hydrological approach that can applied in

hydrological drought risk assessment, especial.

According to the findings, mild to moderate

hydrological droughts dominated the study area from

1961 to 2018.

China is suffering from severe water scarcity, with

per capita water resources that are only one-third of

the global average. Extreme weather is becoming

more common due to the climate change and the

intensification of human activities, which makes

extreme droughts the most serious disaster in terms of

impact (Schubert et al., 2016; Mishra et al., 2010), and

has resulted in significant economic losses in the

affected regions. According to statistics, during the

period of 1997-2009, the affected areas and economic

losses in the country were clearly above the average of

the last 30 years, with a mega-drought occurring

almost every two years (Leng et al., 2015). According

to the data from the Ministry of Water Resources'

Water and Drought Disaster Bulletin, the loss of cash

crops due to drought in 2003 amounted to RMB 53.8

billion, while 33 million people were suffering from

drinking water shortages. Other examples include, the

2006 mega-drought in provinces of Sichuan and

Chongqing (Yu et al., 2014), at the same time the most

severe meteorological drought event since

meteorological records were kept in the southwest

from 2009 to 2012, which had disastrous and far-

reaching consequences for agriculture, society and the

economy. Extreme weather has become more

common in recent years, with the intensification of

global warming and the impact of human activities,

and drought disasters will continue to pose a

significant threat to China's food security, human and

animal drinking water safety, and ecological

environment security. Therefore, relevant departments

and scientific research institutions need to pay more

attention to drought disasters and conduct more in-

depth research (Xu et al., 2015a; Xu et al., 2015b).

The Jinsha River Basin (JRB) is abundant in

hydroelectric resources and is China's largest planned

hydropower base. This basin has significant spatial

and temporal differences in climate and is susceptible

to climate change (Wang et al., 2013), as well as

drought disasters have been frequented in recent

years, causing significant losses to local economic

and social development. However, the awareness of

drought in this basin is still insufficient, and the

ability of drought management and forecasting

capabilities are still limited. Therefore, research on

the hydro-meteorological changes’ spatiotemporal

features and droughts in the JRB is essential for

strengthening drought control and prevention in this

basin. In this paper, 45 meteorological stations and 5

hydrologic stations of JRB for the last 60 years of

historical data were utilized to clarify the

spatiotemporal distribution and trends of hydro-

meteorological elements, while drought

characteristics, including meteorological and

hydrological drought, in the basin were assessed

using SPI values at six monthly scales.

2 STUDY AREA

The JRB is the section of the Yangtze River from its

headwaters to Yibin (Figure 1). It includes the

Tongtian River and Tuotuo River, is located on the

western edge of China's Qinghai-Tibet Plateau,

Yunnan-Guizhou Plateau, and the Sichuan Basin, and

spanning five provinces (regions): Qinghai, Tibet,

Sichuan, Yunnan, and Guizhou. The area of JRB is

about 500,000 km

, accounting for 27.8% of the total

area of the Yangtze River Basin; the length of the

river is approximately 3,500 km, accounting for

55.5% of the Yangtze River's total length; and the

drop is approximately 5,100 m, which is rich in

hydropower energy.

Figure 1: The map of Jinshajiang River Basin (JRB), where

blue dots are meteorological stations and red triangles are

hydrologic stations.

Influenced by the landform and terrain, the

climate characteristics of JRB has clear distinct

distribution. A plateau climate dominates the basin's

upper and middle regions, while the lower portion of

the main stream from Qiaojia to Pingshan is the warm

temperate climate. The temperature increases

Drought Analysis and the Characteristics of Hydro-meteorological Changes in the Jinsha River Basin

427

progressively from upstream to downstream, from

northwest to southeast. It is cold in the Qinghai-Tibet

Plateau, where the average temperature is below 0°C

for 7 months of the year. And for more than 65% of

the regions, the annual average temperature is below

0°C. The general precipitation distribution in JRB

also increases gradually from northwest to southeast.

From June to October is the flooding season of JRB

when 75%-85% precipitation happens.

3 DATA AND METHODS

3.1 Data

The meteorological dataset was obtained from the

China Meteorological Data Sharing Service System

(He et al., 2020). There are 45 meteorological stations

within and around the watershed, among which only

34 stations are located within the JRB. The data series

range from 1950 to 2014, with 27 stations having time

series spanning more than 50 years, and data gaps for

some stations being filled with interpolation. Figure 2

shows the data series statistics for 30 stations in the

watershed.

Figure 2: Period of recorded meteorological daily data for

each of the 30 stations located within the JRB.

The five hydrological stations are Zhimenda,

Pingshan, Shigu, Panzhihua, and Huatan. Except for

a few empty values at Huatan station, the time series

of the other four stations were complete. These vacant

values were also filled by interpolation.The copyright

form is located on the authors’ reserved area.

3.2 Methods

3.2.1 Mann-Kendall Test

The Mann-Kendall (M-K) test (Kendall, 1975;

Bjerklie, 2007) is used to determine whether the

variable of interest has a monotonic upward or

downward trend over time. Compared to parametric

linear regression analysis, it is not necessary to

consider whether the sequence under consideration

follows a specific probability distribution. The M-K

test is best seen as an exploratory technique,

especially useful for evaluating hydro-meteorological

data (McLeod et al., 1990), and is most useful for

identifying stations that have obvious or significant

changes and quantifying these findings (Hirsch et al.,

1982).

According to the null hypothesis H

, these sample

sequences are all independent and therefore have:

𝐻



:𝑃𝑟𝑜𝑏𝑌



𝑌



=0.5,

𝑤ℎ𝑒𝑟𝑒 𝑇



 𝑇



)

𝐻



:𝑃𝑟𝑜𝑏𝑌



𝑌





0.5 (2 − 𝑠𝑖𝑑𝑒𝑑 𝑡𝑒𝑠𝑡)

)

The M-K test statistic S is formulated by equation

(3) (Yue et al., 2002):

𝑆=𝑠𝑔𝑛𝑥



−𝑥















)

𝑠𝑖𝑔𝑛

(

𝑥

)



+1 𝑖𝑓 𝑥0

0 𝑖𝑓 𝑥=0

−1 𝑖𝑓 𝑥<0

)

The M-K test statistic Z is calculated using the

equation(5):

𝑍=

⎩

⎪

⎨

⎪

⎧

𝑆−1

𝜎

𝑆0

0 𝑆=0

𝑆+1

𝜎

𝑆<0

)

where the variance σ



is defined as:

𝜎



𝑛

(

𝑛−1

)(

2𝑛+ 5

)

where, n represents the length of the time series x

, x

;

and x

are the values for the years i and k,

respectively.

A positive (negative) of Z value indicates that the

test time series has an upward (downward) monotone

trend. Z is a test statistic that is used to determine the

significance of a trend. The null hypothesis, H

, is

tested using this test statistic. If, the expression has a

significance level, then it indicates that the null

hypothesis is invalid, i.e., the trend is significant.

Where α represents the significance level. Other

WRE 2021 - The International Conference on Water Resource and Environment

428

significance levels (e.g., 0.01 or 0.05) can usually be

used, but most previous studies set the significance

level at 0.05 before collecting the data (Wang et al.,

2020; Ahmad et al., 2015). In the following analyses,

a significance level of 0.05 with a Z

0.025

=1.96 has

been fixed for the corresponding tests.

3.2.2 Drought Analysis

Mckee (McKee et al., 1993; Shukla et al., 2008)

developed the SPI in 1993 to quantify the precipitation

deficit over multiple time scales. On time scales less

than a year, precipitation does not follow a normal

distribution. As a result, the variable is changed to give

the SPI has a Gaussian distribution. The following

expressions are used to calculate this index.

𝑆𝑃𝐼

=+𝑡−

𝑐



+𝑐



∗𝑡+𝑐



∗𝑡



1+𝑑



∗𝑡+𝑑



∗𝑡



+𝑑



∗𝑡





(7)

𝑡=



𝑙𝑛

𝐻(𝑃)





(8)

𝑓

𝑜𝑟 0<𝐻

(

𝑃

)

<0.5

𝑆𝑃𝐼

=−𝑡−

𝑐



+𝑐



∗𝑡+𝑐



∗𝑡



1+𝑑



∗𝑡+𝑑



∗𝑡



+𝑑



∗𝑡





(9)

𝑡=𝑙𝑛

1 − 𝐻

(

𝑃

)







(10)

𝑓

𝑜𝑟 0.5<𝐻

(

𝑃

)

Where P denotes the total precipitation that

happend in the given time-scale, H(P) denotes the

cumulative probability of the observed precipitation

during this period, while c













are

mathematical constants.

The interpretation of the SPI's

possible values is depicted in Table 1.

Table 1: Period classification based on the values of the

standardized precipitation index (SPI).

SPI Description

≥ 2.0 Extremely wet

(1.5, 2.0] Very wet

(1.0, 1.5] Moderately wet

(−1.0, 1.0] Near normal

(−1.5, −1.0] Moderately dry

(-2.0, -1.5] Severely dry

≤ -2.0 Extremely dry

Figure 3: Diagram of the run theory, where Dd was defined

as the number of months in order with SRI values less than

the threshold x

, and Ds was the sum of the absolute values

of all SRIs during the drought. (

Guttman, 1999).

As noted previously, the SPI is intended to assess

the precipitation deficit for multiple timescales,

which can effectively measure the regional drought

conditions. Meteorological and soil moisture

conditions (agriculture) respond to anomalies in

precipitation on relatively short time scales (1-6

months), while water bodies such as streams and

reservoirs respond to precipitation anomalies on

longer time scales.

SPI on 1-month scale (SPI-1): the SPI values at

the 1-month scale map can show the percentage of

normal precipitation over a 30-day period. It is

relatively short in duration, but can more clearly

reflect the subtle changes of drought than other time

scales.

SPI on 3-month scale (SPI-3): SPI-3 is calculated

by counting precipitation over a three-month period,

which primarily reflects short- and medium-term

moisture conditions and assesses precipitation's

seasonal status. At the same time ,it also is the most

commonly used of several time scales and can

accurately reflect seasonal variations in drought.

SPI on 6-month scale (SPI-6): SPI-6 represents

the precipitation trend over the medium term. It can

be very useful in displaying precipitation over time.

SPI-6 drought information can also be expressed on a

stream or reservoir level, depending on geological

conditions and the timing of unusual precipitation in

the area.

SPI on 12-month or 24-month scale (SPI-12 or

SPI-24): These long-term precipitation patterns are

reflected in these time-scale SPIs. These time-scale

SPIs are also linked to stream and reservoir levels, as

well as longer-term groundwater levels.

-3

-2

Drought events

SPI

Drought Analysis and the Characteristics of Hydro-meteorological Changes in the Jinsha River Basin

429

Figure 4: Spatial distribution of the average of (a) daily

minimum temperature, (b) mean temperature, (c) maximum

temperature and (d) precipitation in the JRB over the period

1951-2014.

In this paper, the SPI-6 values are used to define

drought occurrence. According to run theory (Figure

3) (Zelenhasić & Salvai, 1987), three drought

characteristic indicators exist: drought duration (Dd),

drought severity (Ds), and drought intensity (Di).

Previous studies used a threshold value of -0.5

(McKee et al., 1993; Wu et al., 2019) or -1.0 (Kwak

et al., 2016) to statistics drought events. The threshold

value in this paper was set at -1.0.

4 RESULTS AND DISCUSSION

4.1 Basic Statistical Analysis of

Hydro-meteorological Series

The statistical analysis of the daily minimum

temperature, mean temperature, maximum

temperature and precipitation time series has been

performed on the basis of the 34 meteorological

stations data within the JRB. As shown in Figure 4,

the three temperature types and daily precipitation of

the JRB all show a phenomenon of increasing from

the upper to lower reaches. The average daily

temperature in the Yangtze River source region is

below 0 °C all year, with a maximum temperature of

no more than 10 °C. The Yunnan administrative

region of JRB has the highest average daily

temperature, with temperatures reaching 15 °C or

higher. In Figure 5, the graphical relationship

between the elevation of each meteorological station

and the values of the daily minimum, mean and

maximum temperature series is presented, and a

linear regression has been established between those

variables. According to the high values of the

coefficient R

, which are all greater than 0.8, the fit of

the models can be considered suitable.

Figure 5: Relation between the daily minimum, mean and

maximum temperatures and elevation of the 34

meteorological station.

4.2 Trends Analysis of

Hydro-meteorological Series

The annual values of hydro-meteorological elements

were first extracted for each station, and then the M-

K test was performed (Figure 6). The results show

y = -0.0069x + 22.45

R² = 0.953

y = -0.0062x + 26.031

R² = 0.9115

y = -0.0054x + 31.26

R² = 0.8144

-15

-10

-5

100015002000250030003500400045005000

Expected temperature, T (°C)

Elevation, Z (m a.s.l.)

Minimum T Mean T Maximum T

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430

that the mean temperature of 42 out of 45 stations has

an increasing trend, with 37 stations exhibiting a

significant increase. Meanwhile, for precipitation, 25

stations, which are mainly found in the mid- and

upper regions, have an upward trend, while 20

downstream stations have a downward trend, where

only 8 of them are significant. For runoff, all five

hydrologic stations have an increasing trend, but only

Huatan station is significant.

Figure 6: Trend results in daily (a) mean temperature, (b)

precipitation and (c) runoff from 1951 to 2014 using the

Mann-Kendall test and with a significance level of 5%, the

size of the triangles is proportional to the slope of the

detected trend.

The increasing(decreasing) trend of temperature

or precipitation detected in each part of the basin

(upper, middle and lower) over the 64 years of the

analysis period are also represented in Figure 7 (only

significant trends have been taken into account). The

average volume of annual precipitation is presented

as well. The temperature and precipitation in the

upper reaches are the areas with the greatest increase

in the JRB, while the precipitation in the lower

reaches is the only one that is on a downward trend.

4.3 Drought Analysis

The SPI-6 is calculated for 45 stations within the JRB,

and then according to these values to assess the

drought event and the drought characteristics. The

spatial distribution of drought characteristics based

on SPI-6 values in December 2011 is illustrated in

Figure 8, which the Figure 8b is obtained by spatial

interpolation on the basis of Figure 8a. From this, it is

clear that the downstream of JRB, where is located in

the provinces of Sichuan and Yunnan, is the region

with the most severe drought in December 2011.

Figure 7: The increasing (or decreasing) trend of the

temperature and precipitation over the entire period (1951-

2014) in the upper (green), middle (yellow) and lower

(pink) parts of JRB.

Several studies (Xu et al., 2015

; Zhang et al.,

2012; Li et al., 2011) have concluded that the

southwestern region of China, as well as the mid- and

upper streams of the Changjiang River (including the

lower Jinsha River), are the most frequent drought

hazard regions in China, causing enormous losses to

the ecology and agricultural economy each year. For

example, Yunnan was hit by a once-in-a-century

drought in 2010, and western Guizhou and

northwestern Guangxi have reached mega-drought

status, with over 200,000 rural residents facing water

shortages (Zhang et al., 2012; Li et al., 2011).

Drought Analysis and the Characteristics of Hydro-meteorological Changes in the Jinsha River Basin

431

Figure 8: The spatial distribution of drought characteristics

of the JRB based on SPI-6 values in December 2011.

Figure 9: Trends in the drought duration based on SPI-6

using Mann-Kendall test with a 5% significance level.

Figures 9 to 11 show the trends of the drought

characteristics (drought duration, drought magnitude

and drought intensity) at 45 meteorological stations

within and near the JRB based SPI-6 values using M-

K test. There is no visible trend in drought duration at

38 of 45 stations, 4 with a decreasing trend and 1 with

an increasing trend. The drought magnitude analysis

is dominated by a decreasing trend, with 27 of the 45

stations showing a decreasing trend, but only 2 are

significant. The drought intensity analysis, on the

other hand, is dominated by an increasing trend, with

26 out of 45 stations in a increasing trend, while the

middle and lower regions of the JRB is more visible.

Figure 10: Trends in the drought magnitude based on SPI-

6 using Mann-Kendall test with a 5% significance level.

Figure 11. Trends in the drought intensities based on SPI-6

using Mann-Kendall test with a significance level of 5%.

5 CONCLUSIONS

Based on the multi-year data series of meteorological

and hydrologic stations within and around the basin,

the temporal-spatial variations of hydro-

meteorological elements in the JRB were analyzed,

and the basin drought characteristics were also

assessed using SPI-6 values. The following

conclusions are drawn:

The three temperature types (minimum, mean and

maximum) and daily precipitation of the JRB all

show a increasing from the upper to lower reaches.

The relationship between temperature and station

elevation is significantly negative linear, and the

coefficient of determination R

is greater more than

0.8.

The M-K test of annual values of mean

temperature, precipitation, and runoff reveals that the

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432

mean temperature of 42 of 45 stations is rising, while

precipitation is rising at 25 of them.The runoff at all

five hydrologic stations is increasing. The

temperature and precipitation in the upper reaches are

the areas with the greatest increase in the JRB, while

the precipitation in the lower reaches is the only one

that is on a downward trend.

In the drought analysis based on SPI-6, the

downstream of JRB, where is sited in the provinces

of Sichuan and Yunnan, is the region with the most

severe drought in December 2011. There is no

significant trend in drought duration at 38 of 45

stations, and the drought magnitude analysis is

dominated by a decreasing trend. There is, however,

a statistically significant increase trend of drought

intensity the middle and lower reaches.

ACKNOWLEDGEMENTS

This study was supported by [National Natural

Science Foundation of China] under the grant number

[41890824] and [National Public Research Institutes

for Basic R&D Operating Expenses Special Project]

under the grant number [CKSF2019433/SZ].

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