Spatial-temporal Characteristics of Rainstorm, Flood Disaster Losses

and Disaster-inducing Factors in Sichuan Province of China

Xiehui Li, Lei Wang

and Xiaoran Chen

Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences,

Chengdu University of Information Technology, Chengdu, China

Keywords: Rainstorm and flood disasters, disaster losses, percentile method, rainstorm intensity, Sichuan Province of

China

Abstract: Analysis of spatial-temporal distributions and variation trend of meteorological disasters and disaster-

inducing factors can shed new light on meteorological disaster prevention and control, disaster relief planning

and adaptation to climate change. Rainstorm and associated flood disasters are among the most frequent and

serious meteorological disasters in Sichuan Province of China which spans part of the Qinghai-Tibet Plateau.

In this study, daily precipitation data at 42 weather stations in Sichuan Province from 1973 to 2012 were

collected and utilized along with statistics on rainstorms, flood disasters and socioeconomic status of 21 cities

and prefectures from 1985 to 2012. Indicators characterizing the losses caused by rainstorm and flood

disasters and rainstorm features were chosen. Critical disaster-causing precipitations were determined using

the percentile method. Thus, a comprehensive analysis was conducted over the spatial-temporal distribution

of the losses caused by rainstorms, flood disasters and the disaster-inducing factors in Sichuan Province, with

a thorough characterization of precipitation during the rainstorms. The results showed from 1985 to 2012, the

loss of crop area caused by the rainstorm and flood disasters and the loss rate showed a large fluctuation. The

increasing trend of annual average rainstorm intensity was more obvious than that of the annual average

rainstorm frequency. In the context of global warming, although the overall precipitation of Sichuan decreases,

both the probability and intensity of extreme precipitation events increase. The annual average rainstorm

volume and annual average rainstorm frequency shared similar spatial distribution patterns in the past 40

years. The overall frequency and the frequency of rainstorm processes of five intensity grades decreased from

east to west. The frequencies were much higher in the basin of eastern Sichuan than in the mountainous south-

western region and in the north-western plateau. The smallest frequency was found in the plateau of north-

western Sichuan. Within the basin, the frequency of rainstorm intensity in the west was higher than that in the

east.

1 INTRODUCTION

An analysis of 1970-2009 EM-DAT (Emergency

Events Database) data reveals 7,870 hydro-

meteorological related global disasters, causing the

loss of 1.86 million lives and economic damages of

US$ 1.954 trillion (adjusted to 2011 US$ exchange

rate), among which storms and floods account for

79% of the total number of disasters and cause 56%

of life losses and 85% of the economic losses (World

Meteorological Organization, 2013). Recent years

have witnessed a growing number of extreme weather

and meteorological events along with accelerating

urbanization and industrialization in many countries

the general context of global warming. This directly

results in a surge of losses related to meteorological

disasters. Among various types of meteorological

disasters, rainstorm and flood disasters are more

frequent and cause greater losses than other disasters.

Rainstorms are the primary cause of flood disasters

and are also the related primary disaster-inducing

factor. Many studies have been conducted at home

and abroad concerning spatial-temporal distribution

of rainstorms and associated losses. On basis of

available rainfall data from 1891 to 2000, Kulkarni et

al (2010) analyzed severe rainstorm characteristics of

the Godavari Basin in Peninsular India. The results

showed that 22 severe rainstorms affected the

Godavari Basin in the past 110 years, mostly during

the monsoon months of July and August. Hitchens

Li, X., Wang, L. and Chen, X.

Spatial-temporal Characteristics of Rainstorm, Flood Disaster Losses and Disaster-inducing Factors in Sichuan Province of China.

In Proceedings of the 7th International Conference on Water Resource and Environment (WRE 2021), pages 43-57

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

and Brooks studied the spatial and temporal

distributions of heavy hourly rainfalls in the United

States by using two high-resolution precipitation

datasets (Hitchens & Brooks, 2013). Higgins and

Kousky examined the changes in observed daily

precipitation over the United States between 1950-

1979 and 1980-2009 by using several simple

measures including mean, frequency, intensity and

return period. The results showed that multi-day

heavy precipitation events are increasing in the more

recent period (Higgins & Kousky, 2013). Wu

analyzed the changing characteristics of precipitation

during 1951-2013 for the contiguous United States

(CONUS). Results showed a strong increase of heavy

precipitations with extreme events increasing for

most of the CONUS with the exception of the west

region (Wu, 2015). Dauji (2019) discussed the

monsoon rainstorm characteristics for varying inter-

event intervals at a site on the western coast of India.

Shakeel et al. applied a mix research approach to

analyze the 2010-flood generating factors and

damages in districts Muzaffar Garh. The analysis

indicated that the flood was generated by extreme

rainfall event in the last week of July 2010 in the

upper catchment areas of River Indus. And the

analysis showed that the inundation incurred total

estimated economic loss of about 9.85 million US$

(Shakeel et al., 2021).

China is one of the countries with the highest

frequency of rainstorm and flood disasters and it

suffers enormous economic losses (Yu et al., 2018).

According to statistics, the annual average area of

crops covered by rainstorm and flood disasters from

1951 to 2015 was 12.07 million ha; the annual

average economic loss caused by rainstorm and flood

disasters from 1984 to 2018 was 103.8 billion RMB.

Therefore, factors inducing the rainstorm and flood

disasters and assessment of related losses have been a

research hotspot (Huang et al., 2021; Zhao et al.,

2014; Wu et al., 2014; Lin & Yang, 2014; Wang et

al., 2014; Zou & Ren, 2015; Zhao et al., 2017; Jiang

& Gao, 2019; Luo et al., 2020). Rainstorms are the

primary source of floods in Sichuan of China and the

most serious meteorological disasters in Sichuan

Basin. A major trigger mechanism for rainstorms in

basin regions is the coupling of vertical vorticity in

the Qinghai-Tibet Plateau and Sichuan Basin.

Extreme rainstorms can easily lead to floods,

mountain torrents and debris flows, causing high

casualties and economic losses. For example, from

June 8

to 11

, 2013 western Sichuan Basin

underwent an episode of extreme rainstorm. During

this rainstorm, the precipitation at Dujiangyan Station

from 20:00 of 8

to 20:00 of 9

was 415.99mm, and

the maximum at a local station exceeded 700mm.

Extreme precipitation triggered extreme natural

disasters, causing 59 deaths, 174 missing people and

direct economic losses of 20.3 billion RMB (Xiao et

al., 2017). In 2018, according to the statistics of

Sichuan Climate Center, there were 8 local rainstorms

within the province. The average precipitation was by

21% higher than in normal years. From July 8

20:00 of 12

, an extreme rainstorm was observed at

Guanghan Station of Deyang City. The maximum

daily precipitation was 321.9mm, and the event

rainfall was 488.8mm, both of which were the largest

throughout the province. In mid-August 2020, there

was continuous rainstorm and heavy rainstorm in the

west of Sichuan Basin, and severe rainstorm occurred

in some areas. Among them, the hourly rainfall

intensity generally reached 50-80mm, and some

places exceeded 100mm. Focusing on the losses

caused by rainstorm and flood disasters and on the

spatial-temporal distribution features, Deng studied

the features of flood disasters in Sichuan and disaster

countermeasures taken since the founding of New

China. It was concluded that the major reasons for

extreme rainstorms and flood disasters in Sichuan

from 1952 to 1998 were abnormal climate changes

(Deng, 2001). Zhou et al (2011) used the daily

precipitation data of 133 weather stations in Sichuan

Province from 1961 to 2008 and analyzed variation

and influence of atmospheric precipitation in Sichuan

in the past 50 years. It was found that from the west

to the east of Sichuan Province, the annual average

number of rainstorm days showed the overall trend of

increase, decrease, and again increase. Qing et al

(2013) implemented statistical methods and wavelet

analysis to disaster census database from 1985 to

2009 and discussed the spatial-temporal distribution

features of losses caused by rainstorm and flood

disasters in Sichuan Province. The results showed

that the basic change trend of losses caused by

rainstorm and flood disasters was increasing, and the

proportion of direct economic loss caused by

rainstorm and flood to GDP (Gross Domestic

Product) and the disaster area of crops had a change

cycle of about 8a and 13-14a, respectively. Li and Mu

(2014) used daily precipitation data of 102 weather

stations from 1961 to 2010 in south-western Sichuan.

They analyzed the spatial-temporal distribution

features of rainstorms in south-western Sichuan in a

period of 50 years by using trend analysis, Mann-

Kendall mutation test and wavelet analysis. Deng et

al (2017) investigated the rainstorm and flood

disaster-inducing features in the past 30 years using

rainstorm and flood disaster-related economic loss

questionnaire forms for Sichuan from 1984 to 2010,

WRE 2021 - The International Conference on Water Resource and Environment

daily precipitation data of Sichuan from June to

September in the period from 1981 to 2010 and NCEP

(National Centers for Environmental Prediction)

reanalysis data. Thus, they derived the circulation

background for disaster-inducing rainstorms in

Sichuan during major flood periods in flood years.

Based on the above studies, we selected Sichuan as

the study area. Sichuan, located in south-western

China, has diversified geomorphic types and

frequently occurring meteorological disasters.

(a) Geographical location of Sichuan Province (b) Digital Elevation Model and Weather Stations

Figure 1: Overview of study area and administrative division of Sichuan Province and geographic locations of weather

stations.

As reported in this paper, daily precipitation data

at 42 weather stations in Sichuan Province from 1973

to 2012 were collected and utilized along with

statistics on rainstorm and flood disasters and

socioeconomic status of 21 cities and prefectures

from 1985 to 2012. Indicators characterizing the

losses caused by rainstorm and flood disasters and

rainstorm features were used. The critical disaster-

causing precipitation was determined using the

percentile method. The purpose was to conduct a

comprehensive analysis over the losses caused by

rainstorm and flood disasters in Sichuan Province and

over the spatial-temporal distribution of disaster-

inducing factors, with a thorough characterization of

rainfall of rainstorm events. The research findings can

shed new light on rainstorm and flood disaster

prevention and control, disaster relief planning and

adaptation to climate change for Sichuan Province in

the context of global warming.

2 MATERIALS AND METHODS

2.1 Study Area

Sichuan Province is located in southwestern China

beside the upper reaches of the Yangtze River (97°21'

～108°33'E, 26°03'～34°19'N) (Figure 1a). Sichuan

is featured by complex terrain and large difference in

elevation between the west and east, the range being

178-7,143m (Figure 1b). There are a variety of

climate types with frequent occurrence of

meteorological disasters. The annual precipitation of

the province is smaller in the west and larger in the

east, smaller in the plateau than in the basin, and

smaller in the hilly area inside the basin than in the

mountainous regions around it (Zhou et al., 2011). By

topography and landform, Sichuan Province is

divided into basin in the east, plateau in the northwest

(mainly referring to Ganzi Prefecture and Aba

Prefecture), and mountainous regions in the

southwest (mainly referring to Liangshan Prefecture

and Panzhihua City). According to statistics

supported by the meteorological disaster database

from 1985 to 2009, among the areas affected by

meteorological disasters, the drought area is the

largest one. However, rainstorm and flood disasters

cause the greatest losses, because they not only affect

agricultural production, but also the transportation

and industry sectors (Qing et al., 2013).

2.2 Data Sources

The meteorological data used in this paper mainly

came from daily precipitation dataset available on the

website of China Meteorological Administration

(http://data.cma.cn/). A total of 42 representative

weather stations were selected throughout Sichuan

Province, and the time period was from 1973 to 2012.

Before formal data processing, daily precipitation

Spatial-temporal Characteristics of Rainstorm, Flood Disaster Losses and Disaster-inducing Factors in Sichuan Province of China

data within 40 years were first inspected.

Interpolation was performed for the missing values,

so as to ensure the continuity and accuracy of data.

Statistics on disasters and socioeconomic status were

collected from Encyclopedia of China

Meteorological Disasters (Sichuan Volume), Sichuan

Flood and Drought Disasters, Sichuan Disaster Relief

Yearbook, Sichuan Statistical Yearbook and related

literature (Deng et al., 2017). Since the disaster

statistics before 1984 were unavailable, the time span

of statistics on disasters and socioeconomic status

was from 1985 to 2012. Figure 1b shows the

administrative division of Sichuan Province and

geographic locations of 42 weather stations; Table 1

presents the administrative affiliation of these

weather stations. There are 18 prefecture-level cities

and 3 autonomous prefectures affiliated to Sichuan

Province. As to the distribution of weather stations,

there are 14 stations (No. 1-14) located in the plateau

of north-western Sichuan, 7 (No. 15-21) in the

mountainous region of south-western Sichuan and 21

(No. 22-42) in the basin of eastern Sichuan.

Table 1: Detailed information of weather stations in Sichuan Province used in the study.

Prefectures and

Cities

Numbers and names of weather stations

Ganzi Prefecture 1 Seda, 2 Litang, 3 Daocheng, 4 Kangding, 5 Jiulong, 6 Xinlong, 7 Daofu, 8 Shiqu, 9 Dege

Aba Prefecture 10 Ruoergao, 11 Maerkang, 12 Hongyuan, 13 Xiaojin, 14 Songpan

Liangshan

Prefecture

15 Muli, 16 Yuexi, 17 Zhaojue, 18 Leibo, 19 Yanyuan, 20 Xichang, 21 Huili

Ya'an City 22 Ya'an, 23 Hanyuan

Leshan City 24 Emeishan, 25 Leshan

Chengdu City 26 Wenjiang, 27 Dujiangyan

Mianyang City 28 Pingwu, 29 Mianyang

Suining City 30 Suining

Ziyang City 31 Ziyang

Yibin City 32 Yibin

Panzhihua City 33 Panzhihua

Bazhong City 34 Bazhong

Nanchong City 35 Langzhong, 36 Gaoping

Dazhou City 37 Wanyuan, 38 Daxian

Guangyuan City 39 Guangyuan

Neijiang City 40 Neijiang

Luzhou City 41 Naxi, 42 Xuyong

2.3 Main Research Methods

2.3.1 Measurement Indicators of Rainstorm

and Flood Disaster Losses and

Rainstorm Features

Frequency of rainstorm and flood disasters and

degree of damage caused are important factors for

characterizing the impact of disasters. To measure the

degree of damage caused by rainstorm and flood

disasters, we use the following indicators: area of

crops covered by natural disasters (the sown area of

crops reduced by more than 10% due to disasters),

area of crops affected by natural disasters (the sown

area of crops reduced by more than 30% due to

disasters), area of total crop failure (the sown area of

crops reduced by more than 80% due to disasters),

absolute and relative values of direct economic

losses. Relative indicators included loss rate (i.e.,

direct economic loss caused by rainstorm and flood

disasters of the year concerned/GDP of the year

concerned) and ratio of crop loss area to total

farmland area. Rainstorms are the primary reason for

flood disasters. The following measures are used to

characterize the rainstorms: annual average rainstorm

volume, annual average contribution rate of

WRE 2021 - The International Conference on Water Resource and Environment

rainstorms (ratio of annual average rainstorm volume

to annual average total precipitation), annual average

rainstorm frequency (ratio of cumulative number of

rainstorm days to actual length of time period

concerned) and annual average rainstorm intensity

(ratio of annual average rainstorm precipitation to

annual average frequency of rainstorms). The

regional rainstorm value is expressed as the integrated

arithmetic mean at all weather stations in this region.

2.3.2 Percentile Method

Percentile, as a position indicator, is used to describe

the distribution of a group of sample values. The

combined use of several percentiles can

comprehensively describe the distribution of samples.

Percentile can be calculated using the empirical

formula below (Wang et al., 2011):

  





jji

XXpQ



(1)



31int pnpj 





(2)



jpnp 



 31



(3)

where, Q

(p) is the i

percentile; X is the sample

sequence in an ascending order; p is the percentile

rank; n is the number of sequences; j is the j

sequence; int is the integer-valued function, the return

value is the closest integer by rounding downwards; γ

is the weight of (j+1)

sequence.

2.3.3 Precipitation during the Rainfall

Process and Critical Disaster-causing

Rainfall

The higher the precipitation intensity and the higher

the frequency of heavy precipitation, the more severe

losses are caused by rainstorm and flood disasters.

Hence, the more destructive the rainstorm and flood

disasters, the higher is the hazard degree of disaster-

inducing factors. The hazard degree of rainstorm and

flood disaster-inducing factors can be characterized

by rainstorm frequency and intensity, using the

following method: (1) A rainstorm process is taken

into consideration if it lasts several consecutive days

with rainfalls, and it is required that the precipitation

is larger than 50mm on at least one day; (2)

Precipitations for these processes spanning up to 10

days or more at each weather station over the years

are determined. Precipitations of each episode at all

weather stations constitute a sequence, and 10 such

sequences with varying time lengths are built. For

each sequence, the precipitations are ranked in an

ascending order and the precipitations in the 98

, 95

, 80

and 60

percentiles are calculated. These

values are taken as the critical disaster-causing

precipitations (rainstorm intensity); (3) Based on

these percentiles, rainstorm intensity is divided into 5

grades: precipitations in the 60

-80

percentile are of

grade 1 (flood-inducing); those in the 80

-90

percentile are of grade 2 (mild flood); those in the

-95

percentile are of grade 3 (moderate flood),

those in the 95

-98

percentile are of grade 4 (severe

flood) and those above 98

percentile are of grade 5

(extreme flood); (4) According to the index, the

frequencies of annual rainstorm and flood processes

with different intensity are determined at each station,

and the spatial distribution maps of rainstorm

frequency are plotted for each intensity grade (Li et

al., 2013).

3 RESULTS AND DISCUSSION

3.1 Spatial-temporal Distribution

Features of Losses Caused by

Rainstorm and Flood Disasters

3.1.1 Interannual Variation and Monthly

Distribution Features

Sichuan is located in southwestern China, where

agriculture accounts for a large proportion of the

national economy. Due to lower economic

development level, Sichuan is susceptible to natural

disasters. Every year, huge economic losses and

casualties are caused by rainstorms and floods, and

the losses involve mostly the agricultural production.

Figures 2 and 3 show the loss of crop area caused by

the rainstorm and flood disasters and the ratios of

such losses to local GDP (loss rate) in Sichuan from

1985 to 2012. The area of crops covered and affected

by the rainstorm and flood disasters all fluctuated

greatly in the past 28 years, and the changing trend

follows the polynomial distribution law of the fifth

power (Figure 2). Among the area of crops covered

by the rainstorm and flood disasters, the greatest

losses of occurred in 2010, the area of crops covered

by the disaster being 1.508 million ha; the second

largest loss occurred in 1998, the area of crops

covered by the rainstorm and flood disasters being

1.416 million ha; the loss was the smallest in 1994,

the area of crops covered by the rainstorm and flood

disasters being 0.192 million ha. Among the area of

crops affected by the rainstorm and flood disasters,

the greatest loss occurred in 1998, the crop area

affected was 0.819 million ha; the second largest loss

Spatial-temporal Characteristics of Rainstorm, Flood Disaster Losses and Disaster-inducing Factors in Sichuan Province of China

occurred in 2003, the crop area affected being 0.786

million ha; the loss was the smallest in 2008, the crop

area affected being only 16 thousand ha. Taken

together, 1998 was the year with the largest loss of

crop area affected by rainstorm and flood disasters,

and 2008 was the year least affected. In 1998

catastrophic floods occurred in the entire drainage

basin of the Yangtze River. Sichuan, located in the

upper reaches of the Yangtze River, suffered from 15

rainfall events as well as floods, landslides and debris

flows in different cities and prefectures from May to

September 1998. These natural disasters caused direct

economic loss of about 12.3 billion RMB, and 1998

was the year with most severe crop loss in Sichuan in

the 20

century (Feng & Luo, 1995).

From 1985 to 2012, the direct economic loss

caused by rainstorm and flood disasters fluctuated

within the range from 45.08 to 0.087 billion RMB;

the loss was the greatest in 2010 and the smallest in

1985. GDP increased continuously from 1985 to

2012, the variation range being from 42.115 to

2,382.78 billion RMB. The loss rate calculated on this

basis also showed a larger fluctuation, and the

changing trend follows the polynomial distribution

law of the third power (Figure 3). The interannual

variation was considerable, the loss rate was the

highest in 2010, the value being 26.23‰; the second

highest was found in 1989, the value being 18.73‰;

the smallest was found in 2006, the value being

1.44‰ and the average 8.97‰. Over the 28 years, the

values were above the averages in 9 years, and were

below the averages in 19 years; the values were all

below the averages in 11 consecutive years from 1999

to 2009, showing an apparent decreasing trend. Thus,

the loss caused by rainstorm and flood disasters was

not only closely related to disaster itself, but also to

the economic development level and the disaster

defense ability.

Figure 2: Loss of crop area due to rainstorm and flood

disasters from 1985 to 2012.

Figure 3: Ratios of loss caused by rainstorm and flood

disasters to GDP from 1985 to 2012 (loss rate).

Figure 4 shows the percentages of monthly

average loss caused by rainstorm and flood disasters

to annual average loss related to disasters from 1985

to 2012. Rainstorm and flood disasters mainly

occurred in the time span from March to November

in Sichuan, and most of them in the period from June

to September. The ratios of direct economic losses,

areas of crops covered and affected by the rainstorm

and flood disasters and areas of total crop failure in

these four months to the corresponding annual

averages were 95.8, 86.6, 85.4 and 90.6, respectively.

These values were especially higher in July,

accounting for 42.8%, 38%, 40.5% and 43.6% of the

annual total, respectively. The second highest

economic loss and crop loss area were found in

August and June, while those of March, April and

October accounted for the smallest percentages. This

was especially true in March, where the percentages

were all below 0.03%. Figure 5 shows the

percentages of monthly average crop loss area due to

rainstorm and flood disasters to total farmland area

from 1985 to 2012. Most of the crop loss occurred in

the time from June to August, especially in July. The

ratios of area of crops covered and affected by the

disasters to total farmland area were 5.87 and 2.16,

respectively; the highest percentages occurred in July,

the values being 2.19% and 0.88%, respectively.

Figure 4: Percentages of monthly average loss caused by

rainstorm and flood disasters to annual average loss related

to disasters from 1985 to 2012.

WRE 2021 - The International Conference on Water Resource and Environment

Figure 5: Percentages of monthly average crop loss area due

to rainstorm and flood disasters to total farmland area from

1985 to 2012.

3.1.2 Spatial Distribution Characteristics of

Losses in Cities and Prefectures

Sichuan is featured by high diversity of terrain and

climate types, and there are large variations in

socioeconomic development level across the cities

and prefectures. Rainstorm and flood disasters had

therefore a varying impact on different regions.

Figure 6 shows the spatial distributions of average

direct economic loss and loss rate due to rainstorm

and flood disasters in 21 cities and prefectures from

1985 to 2012. The average direct economic loss due

to rainstorm and flood disasters (Figure 6a), was

higher in Dazhou, Mianyang and Nanchong City,

which are located in the north-eastern Sichuan Basin.

The average direct economic losses were 0.483, 0.434

and 0.214 billion RMB. Regions with the lowest

average direct economic loss were Ya’an, Aba

Prefecture and Neijiang City, the values being 48, 34

and 14 million RMB, respectively. Due to difference

in economic aggregate in 21 cities and prefectures,

the absolute direct economic loss cannot fully reflect

the degree of loss caused by rainstorm and flood

disasters in different regions. Figure 6b shows the

spatial distribution of loss ratio. It can be seen that the

absolute average direct economic loss was smaller in

Ganzi Prefecture in the plateau of north-western

Sichuan, the value being 76 million RMB. Ganzi

Prefecture ranked 15

among 21 cities and

prefectures. However, while the annual average GDP

(6.007 billion RMB) of Ganzi Prefecture was the

smallest of the 21 cities and prefectures, its loss rate

was the highest (12.63‰). The loss rates of Dazhou,

Guangyuan and Mianyang City, located in the north-

eastern Sichuan Basin, were also relatively high, the

values being 12‰, 9.12‰ and 8.18‰, respectively;

by contrast, the loss rates of Ziyang, Chengdu and

Neijiang City in the middle and eastern Sichuan Basin

were smaller, the values being 1.73‰, 0.43‰ and

0.42‰, respectively.

Figure 6: Average direct economic loss (100 million RMB) (a) and loss rate (‰) (b) due to rainstorm and flood disasters in

21 cities and prefectures from 1985 to 2012.

Spatial-temporal Characteristics of Rainstorm, Flood Disaster Losses and Disaster-inducing Factors in Sichuan Province of China

Figure 7: Percentages of average area of crops covered (a) and affected (b) by rainstorm and flood disasters to total farmland

area in 21 cities and prefectures from 1985 to 2012 (%).

Figure 7 shows the ratios of average areas of crops

covered and affected by rainstorm and flood disasters

to total farmland area in each city and prefecture from

1985 to 2012. Dazhou, Bazhong and Meishan located

in the north-western or south-western Sichuan Basin

were the top three cities with the highest ratio of

average area of crops covered by the rainstorm and

flood disasters to total farmland area (Figure 7a). The

percentages were 13.01%, 12.62% and 9.86%,

respectively; the last three locations were Guangyuan,

Aba Prefecture and Ganzi Prefecture in northern

Sichuan Basin or plateau of north-western Sichuan.

The percentages were 2.37%, 1.78% and 1.58%,

respectively. Dazhou and Bazhong in north-eastern

Sichuan Basin and Ya’an in south-western Sichuan

Basin were the top three cities with the highest ratio

of average area of crops affected by rainstorm and

flood disasters to total farmland area (Figure 7b). The

percentages were 5.80%, 4.73% and 3.78%,

respectively; the last three locations were Guangyuan

and Luzhou in eastern Sichuan Basin and Aba

Prefecture in north-western Sichuan Basin. The

percentages were 0.97%, 0.48% and 0.42%,

respectively.

The agriculture sector accounts for a considerable

proportion of Sichuan provincial economy. Among

various losses caused by the rainstorm and flood

disasters and secondary disasters, the loss caused to

the agricultural production is most direct and

significant. Based on the crop loss area and ratio of

such loss to total farmland area, Dazhou and Bazhong

City in north-eastern Sichuan Basin and Meishan and

Ya’an City in south-western Sichuan suffered the

most from severe rainstorm and flood disasters; by

contrast, Ganzi Prefecture and Aba Prefecture in the

plateau of north-western Sichuan, Guangyuan City in

northern Sichuan Basin and Luzhou City in southern

Sichuan Basin suffered the least. The results observed

are basically in agreement with other research

previously carried out in the southwestern China

(Zhao et al., 2017; Li & Mu, 2014) and Sichuan

Province (Xiao et al., 2017; Deng, 2001; Qing et al.,

2013; Deng et al., 2017; Deng, 1999; Feng & Luo,

1995). However, the year with the least loss of crop

area affected by rainstorm and flood disasters, as well

as the loss rate due to rainstorm and flood disasters in

21 cities and prefectures are different. The

discrepancies may be caused by the data with

different time periods used in the different studies.

3.2 Spatial-temporal Distribution

Features of Factors Inducing

Rainstorm and Flood Disasters

From the viewpoint of their cause, flood disasters can

be divided into rainstorm, barrier and snowmelt types.

Rainstorm-induced flood is the most important type

of flood in Sichuan, and most of the flood disaster

events belong to this type which is prevalent all over

the province. Heavy rainstorms often cause flood

disasters, and intensive precipitation in a certain

region at a certain time is usually the result of one or

several rainstorms. Thus, rainstorms are considered

the most important flood-inducing factor.

WRE 2021 - The International Conference on Water Resource and Environment

3.2.1 Analysis of Interannual Variation

Features of Rainstorms

Rainstorm refers to high-intensity rainfalls or rainfalls

with high cumulative volume within a certain time

period. According to the standard for heavy

precipitation developed by China Meteorological

Administration, rainfalls with precipitation volume of

50mm or more within 24h are considered rainstorms.

Annual average rainstorm volume can

comprehensively reflect the general size of daily

rainfall and the amount of rainstorm in a certain

region. This study analyzed the variation trends of

annual average rainstorm volume, annual average

contribution rate of rainstorms, annual average

rainstorm frequency and annual average rainstorm

intensity over the years at 42 weather stations in

Sichuan from 1973 to 2012 (figure omitted).

The results showed that although the annual

average precipitation of time series in Sichuan

demonstrated a mild decreasing trend over the years,

the annual average rainstorm volume, annual average

contribution rate of rainstorms, annual average

rainstorm frequency and annual average rainstorm

intensity increased, but insignificantly. Within the 40

years, the annual average rainstorm volume was the

largest in 1981, the value being 200.4mm; the years

1983 and 1998 came next, with values of 196.2 and

191.3mm, respectively; the year 1976 had the

smallest annual average rainstorm volume, 81.3mm.

The annual average contribution rate of rainstorms

had the largest value (20.7%). The year 1976 was the

smallest (9.5%) and the difference between the two

was 11.2%. The annual average rainstorm frequency

was the highest in 1998 with 2.6 days, followed by

the years 1981 and 1983, with values of 2.5 days and

2.4 days, respectively; the lowest occurred in 1976,

with the value of 1.2 days. The annual average

rainstorm intensity had the highest value of 91.5mm/d

in 2010, followed by the years 1985 and 1989, with

83.7 and 83.1mm/d, respectively; the lowest occurred

in 1976, with 65.6mm/d. The increasing trend of

annual average rainstorm intensity was more obvious

than that of the annual average rainstorm frequency.

This means that the composition of rainstorm

intensity was more extreme. In other words, in the

context of global warming, although the overall

precipitation of Sichuan decreases, both the

probability and intensity of extreme precipitation

events increase. The temporal trends in our work are

similar to previous studies in reporting variation

trends of precipitation and rainstorm over the past few

decades in Sichuan Province (Xiao et al., 2017; Zhou

et al., 2011; Li & Mu, 2014; Zhang et al., 2019; Li et

al., 2019).

Rainstorm occurrences in Sichuan are mainly

influenced by circulation factors on three large scales.

The first are the southwest and southeast monsoons

from India and western Pacific, which mainly

influence rainstorm intensity and variation; the

second are the activities of subtropical high in the

western Pacific and Qinghai-Tibet Plateau, which

mainly control the seasonal variation of rainstorms;

the third are abnormal atmospheric circulations in the

northern hemisphere, especially in the middle and

high latitudes of East Asia. For example, the locations

of Ural high, Okhotsk Sea high and Balkashi Lake

low trough are key large-scale circulation

backgrounds controlling rainstorm occurrences (Zhan

& Wen, 2006). Many studies have shown that under

the circulation background conducive to rainstorm

occurrence, there are four major types of weather

systems that influence rainstorm occurrences in

Sichuan: southwest vortex, low trough and low vortex

and shear line above the Qinghai-Tibet Plateau that

work with cool surface air, southwest low-level jet,

and western Pacific subtropical high (Li et al., 2014).

From May to September 1981, 6 episodes of

catastrophic precipitations occurred in Sichuan. Due

to the joint influence from 500hPa low trough,

southwest vortex and southeast low-level jet that

work with cool surface air, the extreme rainstorm in

Sichuan Basin from 9

to 14

of July was the most

severe. It caused the heaviest losses of the 6 episodes.

The rainstorm affected 141 cities and counties of

Sichuan. During the 6 days precipitation process, the

regions with precipitation above 100mm spanned

over an area of 173.6 thousand km

. This rainstorm

episode was the largest rainstorm and flood disaster

in Sichuan Basin in the 20

century and also one of

the extreme floods since the founding of New China.

Therefore, the year 1981 presents the highest annual

average rainstorm volume and also the highest annual

average contribution rate of rainstorms within the

period from 1973 to 2012 (Sichuan Water

Conservancy and Electricity Department, 1996).

3.2.2 Spatial Distribution Features of

Rainstorms

Spatial kriging interpolation was performed for the

annual average rainstorm volume and annual average

rainstorm frequency registered at 42 weather stations

of Sichuan from 1973 to 2012. The spatial distribution

diagrams of annual average rainstorm volume and

annual average rainstorm frequency in Sichuan over

the years are shown in Figure 8.

Spatial-temporal Characteristics of Rainstorm, Flood Disaster Losses and Disaster-inducing Factors in Sichuan Province of China

Figure 8: Spatial distribution diagrams of annual average rainstorm volume (a) and annual average rainstorm frequency (b)

over the years from 1973 to 2012.

The annual average rainstorm volume and annual

average rainstorm frequency in Sichuan shared

similar spatial distribution patterns over the years.

Both were higher in the east region than in the west

region. The lowest was observed in the plateau of

north-western Sichuan, followed by the mountainous

region of south-western Sichuan; the highest was

found in Sichuan Basin in the east. The annual

average rainstorm volume was above 200mm in the

entire Sichuan Basin. The annual average rainstorm

frequency was over 2 days, and there were large

differences in rainstorm volume and rainstorm

frequency within the basin. Rainstorms were fewer in

Neijiang, Ziyang and Suining City in the middle of

the basin, where the annual average rainstorm volume

was below 250mm and the annual average rainstorm

frequency was below 3.5 days. Regions with high

rainstorm volume and frequency were mostly found

in western and north-eastern basin. Ya’an, Leshan

and Meishan City in the western part of the basin had

an annual average rainstorm volume above 500mm

and annual average rainstorm frequency above 5

days. These were obviously regions with frequent

rainstorms. Dazhou and Bazhong City in the north-

eastern basin, where the annual average rainstorm

volume was above 400mm and annual average

rainstorm frequency above 4 days, were also regions

with frequent rainstorms. In addition, southern

Liangshan Prefecture and Panzhihua City in the

mountainous regions of south-western Sichuan were

also afflicted by frequent rainstorms, with annual

average rainstorm volume of about 200mm and

annual average rainstorm frequency of 3 days.

Spatial kriging interpolation was performed for

the monthly average rainstorm volume and monthly

average rainstorm frequency based on data registered

at 42 weather stations of Sichuan from 1973 to 2012

(figure omitted). The results showed that the spatial

distribution of monthly average rainstorm volume

and monthly average rainstorm frequency was similar

in Sichuan over 40 years; the months with larger

rainstorm volume also had higher rainstorm

frequency. Rainstorm in Sichuan showed apparent

seasonal features, with no rainstorms occurring in

winter. Rainstorms were fewer from March to May

and from October to November, and were uniformly

distributed. The months from June to September were

the major season of rainstorms, and local rainstorms

were more frequent from July to August. The monthly

average rainstorm volumes of western and north-

eastern basin were all above 100mm/month from July

to August, and the monthly average rainstorm

frequency reached over 2d/month.

Comprehensively, the influence of terrain on

rainstorm occurrences in Sichuan was identified.

There was a clear-cut boundary between the high- and

low-value regions of annual average rainstorm

volume, annual average rainstorm frequency over the

years, as well as monthly average rainstorm volume

and monthly average rainstorm frequency from July

to August. This boundary coincides with the

boundary of eastern Qinghai-Tibet Plateau in

Sichuan. Two conditions have to be met for

rainstorms to occur: one is the presence of abundant

water vapor, and the other is the rising air current. The

plateau in north-western Sichuan has high altitude

and the air, thinner than in the plains, has lower water

vapor content. Under the joint effect of plateau terrain

and south Asia high, East Asian monsoon and India

monsoon can hardly transport water vapor to the

plateau of north-western Sichuan. As a result, both

the annual and monthly average rainstorm volume

WRE 2021 - The International Conference on Water Resource and Environment

and frequency are low in this region. The basin, by

contrast, has lower altitude and more abundant water

vapor in the air. Besides, the basin is surrounded by

mountains on the four sides, and the water vapor

produced by evaporation and transpiration within the

basin is not likely to mix with that outside the basin.

Thus, the basin has more abundant water vapor.

When a strong disturbance is given by the

convergence of external conditions, precipitation is

more likely to erupt in the form of rainstorm.

Moreover, due to the terrain effect, rainy centers are

mostly distributed on the windward slopes of the

mountainous region. For this reason, Ya’an, Leshan

and Dujiangyan City in the western part of the basin

are regions which not only have the highest annual

average rainstorm volume and frequency, but also the

highest rainstorm volume and frequency from July to

August. Besides the terrain factor, the southwest

vortex weather process has also an impact on

precipitation and acts in the basin as an important and

special weather system inducing rainstorm.

3.2.3 Analysis of Rainstorm Precipitation

Process

Using the above statistical methods for rainstorm

precipitation process and critical disaster-causing

precipitation, we constructed 10 rainfall sequences of

rainstorm process at 42 weather stations in Sichuan

from 1973 to 2012. Then, precipitations in the 98

, 90

, 80

and 60

percentiles were calculated for

different sequences, and the critical disaster-causing

precipitation was determined. Finally, the rainstorm

intensity was divided into 5 grades based on

percentiles. Table 2 shows the scope and frequency of

each grade of rainstorm intensity for rainstorms

lasting for different days according to rainstorm

volume thresholds determined by percentiles.

Table 2: Scope and frequency of rainstorm processes with different intensity grades and lasting for different days.

Days

Grade 1

(0.1mm)

Grade 2

(0.1mm)

Grade 3

(0.1mm)

Grade 4

(0.1mm)

Grade 5

(0.1mm)

Frequency

(times)

Rainstorm

value

(0.1mm)

1 703≤R<860 860≤R<1035 1035≤R<1222 1222≤R<1438 R>1438 339 500

2 858≤R<1080 1080≤R<1305 1305≤R<1603 1603≤R<1933 R>1933 602 500

3 947≤R<1197 1197≤R<1491 1491≤R<1863 1896≤R<2360 R>2360 538 500

4 1054≤R<1383 1383≤R<1792 1792≤R<2020 2020≤R<2403 R>2403 429 500

5 1253≤R<1615 1615≤R<1969 1969≤R<2307 2307≤R<2709 R>2709 313 500

6 1402≤R<1898 1898≤R<2177 2177≤R<2600 2600≤R<3034 R>3034 184 500

7 1394≤R<1833 1833≤R<2254 2254≤R<2723 2723≤R<2960 R>2960 160 500

8 1634≤R<2087 2087≤R<2694 2694≤R<2965 2965≤R<3357 R>3357 132 500

9 1712≤R<2204 2204≤R<2507 2507≤R<2752 2752≤R<3437 R>3437 78 500

≥10 2376≤R<3162 3162≤R<3958 3952≤R<4569 4569≤R<4935 R>4935 149 500

Figure 9: Process frequency and maximum precipitation in

10 rainfall sequences.

Figure 10: Precipitation for different grades of rainstorm

intensity in 10 rainfall sequences.

Spatial-temporal Characteristics of Rainstorm, Flood Disaster Losses and Disaster-inducing Factors in Sichuan Province of China

(1) Frequency distribution and maximum

precipitation of rainstorm precipitation processes

During 40 years (1973-2012), a total of 2,924

rainstorm precipitation processes lasting for different

days occurred. The frequency and maximum

precipitation of the 10 rainfall sequences are shown

in Figure 9. Using the duration of 5 days as the

demarcation point, it was found that the rainstorm

frequency decreased significantly if the duration was

above this threshold. The first and last five sequences

were treated as two separate entities. Rainstorms

lasting for a few days accounted for a greater

proportion. There were 2,221 rainstorm processes

shorter than 5 days, accounting for 75.98% of the

total, while there were only 703 rainstorm processes

longer than 5 days, accounting for 24.02% of the

total. From 1973 to 2012, the number of rainstorm

processes lasting for 2 days was the greatest. The

frequency and therefore the probability of rainstorm

processes lasting longer than 2 days decreased.

Generally, rainstorms shorter than 2 days occurred

more frequently than those lasting longer.

Figure 11: Spatial frequency distribution of overall rainstorm intensity and each grade of rainstorm intensity (a) Grade 1 (b)

Grade 2 (c) Grade 3 (d) Grade 4 (e) Grade 5 (f) Overall.

WRE 2021 - The International Conference on Water Resource and Environment

Among rainstorm rainfall processes lasting for

several days, the maximum precipitation also varied

significantly in different sequences. The maximum

precipitations of rainstorm processes lasting for 1 to

10 days and above were as follows: 185.9mm (1 day),

328mm (2 days), 449.6mm (3 days), 398.8mm (4

days), 481.1mm (5 days), 365mm (6 days), 333.1mm

(7 days), 393.1mm (8 days), 430.7mm (9 days) and

657.4mm (≥10 days). We concluded that except for

large difference in maximum precipitation between

rainstorm processes lasting for 1 day and for more

than 10 days, the maximum precipitation of other

rainstorm processes was mostly about 400mm.

(2) Spatial frequency distribution of overall

rainstorm intensity for each grade of rainstorm

intensity

The maximum precipitation varied a little for

rainstorm processes in different sequences. In some

cases, the maximum precipitation of rainstorm

processes lasting for longer days was smaller than

that of shorter rainstorms. However, maximum

precipitations are only extreme cases which cannot

reflect the general rule. Figure 10 shows the

precipitations of 5 intensity grades of rainstorm

processes in 10 sequences. As the duration of

rainstorms increased, the precipitation of each

rainstorm intensity grade increased as well. For 42

weather stations in Sichuan, the frequency of

rainstorms of each intensity grade was calculated over

the years from 1973 to 2012 according to the critical

disaster-causing precipitation (Table 2). The average

total number of episodes of each intensity grade in

every 10 years span was calculated as the frequency

at that station. The overall frequency of rainstorm at

the station was the sum of frequencies of rainstorms

of different intensity grades. The spatial distributions

of frequencies of rainstorms for each intensity grade

and overall rainstorm intensity and frequency at each

station are shown in Figure 11.

The results showed that the overall frequency and

the frequency of rainstorm processes of different

intensity grades decreased from east to west of

Sichuan Province. The frequencies were much higher

in the basin of eastern Sichuan than in the

mountainous south-western region and in the north-

western plateau. The smallest frequency was found in

the plateau of north-western Sichuan. Within the

basin, the frequency of rainstorm intensity in the west

was higher than that in the east. The high-value

regions of overall rainstorm intensity frequency were

mainly found in the western and north-eastern basin.

Ya'an, Leshan and Meishan City in the western part

of the basin were high-frequency regions, followed

by Bazhong and Dazhou City in the north-western

basin. The frequency of 5 rainstorm intensity grades

(Figure 11a-e), decreased from grade 1 to grade 3, the

maximum decreasing from 17 times/10a for grade 1

to 3.6 times/10a for grade 3. The rainstorm frequency

increased slightly for grade 4 intensity and decreased

for grade 5 intensity. The frequency for grade 5

intensity was the smallest of all grades, with a

maximum of 3 times/10a. The overall rainstorm

frequency (Figure 11f) varied within the range of 0-

30 times/10a. Within the years covered by statistical

data, the overall rainstorm intensity frequency was 0

in most part of the plateau of western Sichuan. Ya’an,

Leshan and Meishan City in the western part of the

basin were regions with high overall rainstorm

intensity frequency, the maximum being 30

times/10a. Figure 12 shows the distribution of

stations with overall rainstorm intensity frequency

above 10 times/10a. The high-value regions were

mainly found in Ya’an (Ya'an and Hanyuan Station),

Dazhou (Wanyuan and Daxian Station), Leshan

(Emeishan and Leshan Station), Chengdu (Wenjiang

and Dujiangyan Station), Bazhong (Bazhong Station)

and Guangyuan (Guangyuan Station) City in western

and north-eastern basin. In these regions, the overall

rainstorm intensity frequency was above 15

times/10a, which agreed with the spatial distribution

features of rainstorms discussed in the above section.

In terms of spatial distribution features of rainstorms

and rainstorm precipitation process, our results are

also consistent with the previous studies in Sichuan

Province or neighboring regions (Xiao et al., 2017; Li

& Mu, 2014; Zhang et al., 2019), but our analysis is

more comprehensive.

Figure 12: Radar map of stations with overall rainstorm

intensity frequency above 10 times/10a.

Spatial-temporal Characteristics of Rainstorm, Flood Disaster Losses and Disaster-inducing Factors in Sichuan Province of China

4 CONCLUSIONS

Global climate warming and urbanization have

conducted to intensity and frequency changes of

meteorological disaster-inducing factors. Therefore,

it emphasized the challenge in the management of

meteorological disasters. This challenge has become

the high-priority issue for different countries in

coping with climate change. Analysis of spatial-

temporal distributions and variation trend of

meteorological disasters and disaster-inducing factors

can shed new light on meteorological disaster

prevention and control, disaster relief planning and

adaptation to climate change. We chose Sichuan

Province which spans part of the Qinghai-Tibet

Plateau as the research area because the region is

affected by heavy rainfall and increased flood

frequency. The daily precipitation data of 42 weather

stations in Sichuan from 1973 to 2012 were used,

along with statistics on rainstorm and flood disasters

and socioeconomic status of 21 cities and prefectures

from 1985 to 2012. The following main conclusions

were deduced:

(1) From 1985 to 2012, the loss of crop area

caused by the rainstorm and flood disasters and the

loss rate in Sichuan showed a large fluctuation as a

whole. Rainstorm and flood disasters mainly occurred

in the period from June to September in Sichuan. In

the past 28 years, the ratios of direct economic losses,

areas of crops covered and affected by the rainstorm

and flood disasters and area of total crop failure in

these four months to the corresponding annual

averages were 95.8, 86.6, 85.4 and 90.6, respectively.

These values were especially higher in July,

accounting for 42.8%, 38%, 40.5% and 43.6% of the

annual total, respectively.

(2) The average direct economic loss due to

rainstorm and flood disasters in 21 cities and

prefectures from 1985 to 2012, it was higher in

Dazhou, Mianyang and Nanchong City, which are

located in the north-eastern Sichuan Basin. Regions

with the lowest average direct economic loss were

Ya’an, Aba Prefecture and Neijiang City.

(3) Though the annual average precipitation in

Sichuan showed a mild decreasing trend over the

years, the annual average rainstorm volume, annual

average contribution rate of rainstorms, annual

average rainstorm frequency and annual average

rainstorm intensity increased at 42 weather stations

from 1973 to 2012, but insignificantly. The annual

average rainstorm volume and annual average

rainstorm frequency in Sichuan shared similar spatial

distribution patterns in the past 40 years. Both were

higher in the east region than in the west region. The

lowest was observed in the plateau of north-western

Sichuan, followed by the mountainous region of

south-western Sichuan; the highest was found in

Sichuan Basin in the east. The months from June to

September were the major season of rainstorms, and

local rainstorms were more frequent from July to

August.

(4) Among rainstorm rainfall processes lasting for

different days, except for large difference in

maximum precipitation between rainstorm processes

lasting for 1 day and for more than 10 days, the

maximum precipitation of other rainstorm processes

was mostly about 400mm.The overall frequency and

the frequency of rainstorm processes of five intensity

grades decreased from east to west of Sichuan

Province. The frequencies were much higher in the

basin of eastern Sichuan than in the mountainous

south-western region and in the north-western

plateau. The smallest frequency was found in the

plateau of north-western Sichuan. Within the basin,

the frequency of rainstorm intensity in the west was

higher than that in the east.

However, due to the lack of data, the analysis of

rainstorm and flood disaster losses is only 28 years,

and the time scale is relatively short. There are many

causes in inducing rainstorm and flood disaster in

Sichuan, including natural and human factors.

Therefore, the analysis on spatial-temporal

distribution characteristics of rainstorm have to be

expand. In the future, the improvement of data will

strengthen the analysis of longer time scale to identify

more disaster causing factors.

ACKNOWLEDGMENT

This research was financially supported by the Key

Research and Development (R&D) Project in Yunnan

Province (202103AC100028) and the Second Tibetan

Plateau Scientific Expedition and Research

Program (2019QZKK0105).

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Spatial-temporal Characteristics of Rainstorm, Flood Disaster Losses and Disaster-inducing Factors in Sichuan Province of China