Analysis of Extreme Temperature and Drought Information of

Kunming

Zhanpeng Zhu and Zhongmu Li

Institute of Astronomy, Dali University, Dali, China

Keywords: Extreme Temperature information, drought information, Kunming.

Abstract: As greenhouse gas concentrations rise, widespread climate change increases the frequency and occur range

of extreme climate events. In order to explore the change of extreme temperature and its impact on drought

in Kunming, we analysis the interannual change trend of extreme temperature index (ETIs) and standardized

precipitation evapotranspiration index (SPEI) based on the meteorological series data of Kunming station

from the years 1959 to 2019, and studied the correlation between them. The results show that the warm

index (SU25, TN90P, TX90P and WSDI) increased significantly, while the cold index (FD0, TN10P and

TX10P) decreased significantly in ETIs; the rate of warming at night is higher than that at daytime. From

the years 1959 to 2019, the SPEI showed a downward trend with a rate of -0.47/ (10a). This shows that

Kunming has gradually changed from humid to arid in recent decades. In the correlation analysis, the warm

index was negatively correlated with SPEI, while the cold index was positively correlated with SPEI.

TN10P and TN90P were correlated on monthly and seasonal time scales opposite to the annual scale. ETIs

and SPEI were both negatively correlated in winter in Kunming.

1 INTRODUCTION

Under the influence of global warming, the water

cycle is accelerating, leading to frequent extreme

climate events. The global land area and population

that are affected by extreme climate events have

doubled (Lange et al., 2020). Drought in extreme

events reduced crop yields, increased wildfires and

desertification. To better understand the reasons for

the increase in extreme climate events, extreme

temperatures and droughts have been studied at

different regional scales.

On the global scale, the number of cold night

days decreases and the number of warm night days

increases in more than 70% of the world's regions

(Alexander et al., 2006). On the regional scale,

extreme temperature variations in the Asia-Pacific

region, northern South America, and Canada (Choi

et al., 2009; Aguilar et al., 2005; Zhang et al., 2000)

are essentially consistent with global changes.

However, the trend of drought varies in different

regions (Danandeh et al., 2020; Gao et al., 2017).

Kunming, one of the rapidly developing

representative cities in China, has experienced

frequent droughts and floods in recent years, which

have caused great losses to agricultural production

and socio-economics (He et al., 2021). Current

research mainly focuses on the study of extreme

climate index and drought change (Wu et al., 2019;

Yang et al., 2016), but did not study the specific

response between extreme climate index and

drought. Therefore, the study of ETIs trends and

their impact on drought will help us understanding

how temperature changes affect drought, as well as

have importance for disaster prevention and

agricultural development.

2 DATA SOURCES AND

RESEARCH METHODS

Data on daily precipitation, maximum temperature,

minimum temperature, and the average temperature

in Kunming from the years 1959 to 2019 were

provided by the National Meteorological Science

Data Center.

2.1 Quality Control

We use RClimDex software to control the quality of

the data for detecting inaccurate meteorological data

such as precipitation less than 0, daily minimum

Zhu, Z. and Li, Z.

Analysis of Extreme Temperature and Drought Information of Kunming.

DOI: 10.5220/0011921700003612

In Proceedings of the 3rd International Symposium on Automation, Information and Computing (ISAIC 2022), pages 273-277

ISBN: 978-989-758-622-4; ISSN: 2975-9463

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

273

Table 1: Definition of extreme temperature indices.

temperature higher than daily maximum temperature,

and outliers more than 4 times the standard

deviation. We use daily meteorological data to

calculate monthly mean temperature and monthly

precipitation.

2.2 Research Methodology

In this paper, we selected the key seven extreme

temperature indices from the ETIs recommended by

the Expert on Climate Change and Indicators

(ETCCDI).

2.2.1 Extreme Temperature Index

We used RClimDex 1.0 software to calculate the

ETIs, and the definitions of each index are shown in

Table 1. We used the Mann-Kendall (M-K)

statistical test and Sen slope to assess the magnitude

and significance of changes in each ETI. Pearson

correlation analysis were used to measure the

correlation between ETIs and SPEI.

2.2.2 Standardised Precipitation

Evapotranspiration Index

The standardized precipitation index (SPI) and SPEI

used by researchers in regional drought studies have

become the most important drought index

(Needalcov et al., 2015). Compared with SPI, SPEI

takes into account the effect of potential

evapotranspiration on drought (Dukat et al., 2022).

Comparative studies show that SPEI is more

reasonable (Suroso et al., 2021). Therefore, in this

paper, we calculate SPEI values on an annual time

scale, based on monthly mean temperature and

monthly total precipitation. When the SPEI value is

larger, it means that the area is wetter, while the

SPEI value is smaller, the area is drier. We use the

linear trend method to analysis the trend of SPEI in

Kunming from the years 1959 to 2019 and compare

the research results with the actual situation.

3 RESULTS AND ANALYSIS

Figure 1 shows the temporal variation

characteristics of ETIs.

3.1 Characteristics of Interdecadal

Variation in the Extreme

Temperature Index

All seven ETIs passed the 0.01 significance test.

The warm index (SU25, TN90P, TX90P and WSDI)

increased at rates of 1.05 d/a, 0.71 d/a, 0.49 d/a, and

0.92 d/a, respectively (Fig.1a and c). The cold index

(FD0, TN10P and TX10P) decreased at rates of

-0.32 d/a, -0.22 d/a,and -0.08 d/a, respectively

(Fig.1b and d). This indicates that the temperature

in Kunming increased significantly from the years

1959 to 2019, which is consistent with previous

research results (Wu et al., 2019). TN90P increased

at a higher rate than TX90P, and TN10P decreased

at a higher rate than TX10P, indicating that the

rising rate of temperature at night was higher than

that at daytime.

3.2 Characteristics of Interannual

Variation in SPEI

The interannual variation trend of SPEI in the study

area is shown in Figure 2. The SPEI in Kunming

showed a significant downward trend from the years

Classification Index Name Definitions Indices Unit

Extreme Cold

Index

Frost days Annual count when daily minimum

temperature < 0℃

FD0 d

Cold nights

Cold daytimes

Count of days where TN < 10th percentile

Count of days where TX < 10th percentile

TN10P

TX10P

Extreme Warmth

Index

Duration of

warm periods

Annual count of days with at least six

consecutive days in which Tmax > 90

percentile

WSDI

Summer days Annual count when daily maximum

temperature > 25℃

SU25 d

Warm daytimes Count of days where TX > 90th percentile TX90P d

Warm nights Count of days where TN > 90th percentile TN90P d

ISAIC 2022 - International Symposium on Automation, Information and Computing

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Figure 1: Changes of ETIs in Kunming from the years 1959 to 2019.

1959 to 2019, and the SPEI gradually changed from

positive to negative values. It shows that Kunming

has gradually changed from humid to arid in recent

80 years. The highest value (1.72) and the lowest

value (-2.29) of SPEI in 1970 and 2009 respectively

represent the wettest and dry years in Kunming. Tian

and Wan (2016) studied the occurrence regularity of

drought and flood disasters in Kunming, and the

results showed that the disasters gradually changed

Figure 2: Trends in annual scale SPEI.

from flood to drought. In 2010, the worst drought in

a century occurred in Kunming. The results of the

study are consistent with previous studies and

historical data.

3.3 Effects of Extreme Temperature

Changes on Drought

In order to understand the impact of extreme

temperature changes on drought, this paper

analyzed their relationships at different time scales

(monthly, quarterly and annually). Since Rclimdex

only provides monthly scale data for TN10P,

TN90P, TX10P and TX90P, this paper analyzed the

correlation between these four ETIs and SPEI on

monthly and quarterly time scales (Table 2).

At the annual scale, the extreme warm index

was negatively correlated with SPEI, while the

extreme cold index was positively correlated with

SPEI. This indicates that Kunming is more arid

when the warming index rises. As the cold index

drops, it gets wetter. This may be because the

precipitation in Kunming shows a decreasing trend

and the precipitation tends to be more concentrated

Analysis of Extreme Temperature and Drought Information of Kunming

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Table 2: Correlation coefficients between ETIs and SPEI in Kunming on different time scales.

Indices (Month) TN10P TN90P TX10P TX90P

SPEI -0.697

0.577

0.651

-0.762

Indices

(Annual)

TN10P TN90P TX10P TX90P FD0 SU25 WSDI

SPEI 0.831

-0.904

0.676

-0.896

0.732

-0.881

-0.819

Season Indices TN10P TN90P TX10P TX90P

Spring SPEI -0.754

0.723

0.671

-0.856

Summer SPEI -0.776

0.610

0.669

-0.870

Autumn SPEI -0.872

0.745

0.669

-0.797

Winter SPEI -0.019 -0.033 -0.135 -0.069

Notes: ** stands for passing the significance test with p<0.01.

(Wu et al., 2020), making most of the precipitation

lost due to runoff, and the temperature rise increases

the surface evaporation, making the drought in

Kunming more serious. However, at the monthly

scale, TN10P and TN90P show the opposite

correlation to the annual scale. Rising nighttime

temperatures make the

Kunming area wetter; falling

nighttime temperatures make the Kunming area

drier. At the seasonal scale, ETIs and SPEI showed

significant correlations in all seasons except winter.

It can be found that in winter, extreme temperature

and SPEI are all negatively correlated. This

phenomenon indicates that drought is most likely to

occur in winter in Kunming area. The correlations

between ETIs and SPEI were consistent with the

monthly scale in all seasons except winter.

4 CONCLUSIONS

Among the seven ETLs, the warm index (SU25,

TN90P, TX90P and WSDI) increased significantly

and the cold index (FD0, TN10P and TX10P)

decreased significantly. ETLs all passed the 0.01

significant test. SPEI showed a downward trend, and

Kunming gradually changed from humid to arid

from the years 1959 to 2019. SPEI was negatively

correlated with warm index and positively correlated

with cold index. On monthly and seasonal time

scales (except for winter), TN10P and TN90P

showed correlations opposite to the annual scale. On

shorter time scales, higher nighttime temperatures

made the Kunming area wetter. In winter, both

extreme temperature changes made Kunming drier.

This paper briefly analysis the trends of ETIs and

SPEI in Kunming and the correlation between ETIs

and SPEI. However, the correlation between extreme

precipitation indices and SPEI, and the specific

physical factors behind the correlation between

ETIs and SPEI need to be further investigated.

ACKNOWLEDGEMENTS

This work is supported by Yunnan Academician

Workstation of Wang Jingxiu (202005AF150025),

the National Natural Science Foundation of China

(No.11863002), and Sino-German Cooperation

Project (No. GZ 1284).

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