Observed trends and Variability of heatwave across Vietnam

From the threshold for each day calculated earlier, a heatwave event is counted when at least 3 consecutive days have the value of maximum temperature exceeds that threshold. The total[r]

VIETNAM NATIONAL UNIVERSITY, HANOI

VIETNAM JAPAN UNIVERSITY

VU THUAN YEN

OBSERVED TRENDS AND

VARIABILITY OF HEATWAVE ACROSS

VIETNAM

VIETNAM NATIONAL UNIVERSITY, HANOI

VIETNAM JAPAN UNIVERSITY

VU THUAN YEN

OBSERVED TRENDS AND

VARIABILITY OF HEATWAVE ACROSS

VIETNAM

MAJOR: CLIMATE CHANGE AND DEVELOPMENT

CODE: 8900201.02QTD

RESEARCH SUPERVISOR:

Prof Dr Phan Van Tan

Prof Dr Kusaka Hiroyuki

PLEDGE

I assure that this thesis is the results of my own research and has not been

published The use of other research’s result and other documents must comply

with regulations The citations and references to documents, books, research

papers, and websites must be in the lists of references of the thesis

AUTHOR OF THE THESIS

TABLE OF CONTENT

PLEDGE i

TABLE OF CONTENT ii

LIST OF TABLES iii

LIST OF FIGURES iv

ACKNOWLEDGEMENT v

CHAPTER 1: INTRODUCTION

1.1 Literature review

1.1.1 Heatwave in global scales 1

1.1.2 Heatwave in Vietnam 6

1.1.3 Research Gap 8

1.2 Necessity of research

1.3 Research question 11

1.4 Research hypothesis 12

1.5 Research objective 12

1.6 Scope of research 12

1.7 Research structure 13

CHAPTER 2: DATA AND METHODOLOGY 14

2.1 Data 14

2.2 Method 16

CHAPTER 3: RESULTS AND DISCUSSION 26

3.1 Daily threshold for defining heatwave 26

3.2 Spatial and temporal variations of heatwave across Vietnam 27

3.3 Trend of changes of heatwave characteristics 40

3.4 Limitations of thesis and further research orientation 42

3.5 Recommendations for using the research results 42

CHAPTER 4: CONCLUSION 44

REFERENCES 46

LIST OF TABLES

LIST OF FIGURES

Figure 2.1 Climatic sub-regions of Vietnam 15 Figure 2.2 Heatwave characteristics demonstration 22 Figure 3.1 Heatwave threshold: 90th percentile daily maximum temperature ……27 Figure 3.2 Average value of heatwave characteristic of 109 stations 28 Figure 3.3 Heatwave characteristics with 90th percentile threshold and ONI-based ENSO phases in the same period 31 Figure 3.4 Heatwave characteristics with fixed threshold 35° C 33 Figure 3.5 Inter- annual variation of Number of Heatwave (spell) across Vietnam (1980 - 2018) 35 Figure 3.6 Inter- annual variation of Mean duration of Heatwave (days) across Vietnam (1980 - 2018) 37 Figure 3.7 Inter- annual variation of Number of Heatwave days (day) across Vietnam (1980 - 2018) 38 Figure 3.8 Inter- annual variation of Number of hot days (day) across Vietnam (1980 - 2018) 39 Figure 3.9 Trend of changes of heatwave characteristics ………41

ACKNOWLEDGEMENT

After months of work on my research, I would first like to give the sincerest thanks to my supervisors, Prof Phan Van Tan and Prof Kusaka Hiroyuki Their guidance and support have made this research possible Prof Phan Van Tan has supported me all the time while Prof Kusaka Hiroyuki has allowed me to learn about the heatwave at Tsukuba University, during the research internship in Japan

I would like to thank Regional Climate Modeling and Climate Change (REMOCLIC) advanced Research Group, Climate Change - Induced Water Disaster, and Participatory Information System for Vulnerability Reduction in North Central Vietnam (CPIS) for providing the database of Vietnam meteorological station data Besides that, the Vietnam General Statistic Office also is a help for me in collecting data

CHAPTER 1: INTRODUCTION

1.1 Literature review

Heatwave has occurred worldwide, remarkably are record in 2003 of England and Western Europe killed about 2193; 70000 individuals, respectively (Coumou and Rahmstorf, 2012) Moreover, the 2010 Russia heatwave which lasted for over a month is responsible for around 54000 dead people (McMichael and Lindgren, 2011) Due to the significant impact of heatwave, numerous studies of heatwave were conducted regarding characteristics of heatwave, its trend, and impact, as well as the correlation with other factors related to climate change Heatwave studies are notable from 2010 to present in both international scales and local scales

1.1.1 Heatwave in global scales

Regarding international works, the scope of studies spread across the globe, continents, and countries Heatwave was defined very differently depending on research objectives, scope, and resources, geographic and climatic features In general, heatwave is defined as a period of at least three consecutive days the selected temperature value exceeds the threshold This threshold could be an absolute threshold (Smith, Zaitchik et al., 2013) or relative threshold (based on percentile determination) After define and detect heatwave events, heatwave characteristics were determined for analyzing trends and correlation with other phenomena that the author concerned

concentrate on changes in warm nights which are kind to firmly amplify health effects by inhibiting the recovery from the daytime heat and through sleep deprivation Combined hot days and tropical nights (CHT), which is the consecutive occurrence of a hot day (Maximum temperature Tmax > 35°C) and tropical nights (minimum temperature Tmin > 20°C), have been detected to explain spatial and temporal variance in excess mortality The simulated CHT values (1961-1990) differ substantially between models CHT is particularly sensitive to biases in model climatology due to their relation to absolute temperature threshold despite the particular interest of the spatial pattern of CHT changes Because of focusing on health impact, this study embeds some heat index which is related to heat stress For example, AT105F (exceedance of apparent temperature threshold) indicates the average number of summer days with maximum humidity-corrected AT exceeding 40.6°C

The study of Pezza and Van Rensch (2012) brought a new perspective on large scale dynamics of severe heatwave events generally affecting southern Australia The study explored the large-scale synoptic associations with heatwave A better understanding of this connection is crucial for the improvement of forecast Heatwave In the study, the authors used daily maximum (after AM) and minimum (before AM local time) temperature of stations including Melbourne Airport, Adelaide Airport, and Perth Airport for the period from January 1979 to March 2008 Heatwave was defined as the periods of three consecutive days in which a temperature threshold was more than or equal to the threshold The 90th percentile of monthly climatology was used to avoid a seasonal bias (Simmonds and Richter 2000) In the meantime, the maximum temperature was met or exceed 90th percentile of the maximum temperature for the month in which the heatwave begins for a minimum of consecutive days

conditions For more detail, CTX90pct is the threshold which is the calendar day 90th percentile of Tmax, based on a 15-day window Thus, there is a different percentile value for each day of the year CTN90pct is the calendar day 90th percentile of Tmin EHF is based on two excess heat indices (EHIs) The study applied specifically to the Australian continent The data used are daily gridded observations of Tmin and Tmax, along with vapor pressure and precipitation at 0.05 x 0.05-degree resolution Perkin (2012) followed the similar methodology of Pezza and Van Rensch (2012) for an event of at least three days’ length, rather than events of at least six conservative days by Fischer and Schär (2010) They emphasized on annual values of HWF, HWN, HWD, and HWA for 30-year time slices, while Perkin (2012) calculated average heat wave magnitude (HWM) All five characteristics are calculated annually These are: HWM is the average daily magnitude across all heatwave events within a year; HWA indicates the hottest day of the hottest yearly event; HWN is the number of heatwave per year; HWD is the length (in days) of the longest event in a year; HWF is the total of participating heatwave days per year that satisfy the definition criteria

day that Tmax is over 35°C (Tan, Zheng et al 2007) While (Robinson 2001) use Tmin = 26.7°C and Tmax= 40.6°C At least one of these thresholds must be met on at least two consecutive days to define as a heatwave day, etc

The remarkable study of Russo and Dosio (2014) introduced a new Heat Wave Magnitude Index (HWMI) allowing to compare temporal variation between locations HWMI takes into account both the magnitude and duration of the heatwave This index enabled us to compare the projected future with the present heatwave HWMI is the maximum magnitude of heatwave within a year, in which heatwave is defined as a period of more than or equal three days in a row with a maximum temperature higher than the daily threshold for the reference period (1981-2010) The threshold is determined as the 90th percentile of daily maxima, centered on a 31-day window The minimum number of consecutive hot days required to be considered as heatwave deferred across regions Choosing 3-day lengths according to Perkins, Alexander (2012), or 6-day lengths according to Fischer and Schär (2010) depend on the locations Due to focusing on global scales, Russo (2014) chose the definition of three days length for a heatwave HWMI computation, according to the authors, is a multiple-stage process Firstly, the daily threshold is calculated for the reference period (1981-2010) The second stage is heatwave selection, in which for each specific year, all the heat waves are selected Heatwaves composed at least three days in a row with daily maximum temperature above the daily threshold After that, each heatwave is decomposed into n subhead waves, where a subhead wave is a heatwave of three consecutive days Next, the magnitude of each heatwave is defined as the sum of the magnitudes of the n subhead waves Finally, the heatwave magnitude index for a given year is identified This HWMI computation, are further used in several types of research about the heatwave

the combination of intensity and duration of high-temperature periods From that heatwave definition, heatwave metrics are calculated from investigated 10-years observation and 30-year model simulations Two heatwave characteristics HWN and HWF are defined the same as the previous study Some other characteristics: HWMD, HWLD, HWI, HWA are newly defined in detail following: HWMD (heatwave mean duration) is the average duration of heat waves per studied period; HWLD (heatwave longest duration) is the average duration of the longest heatwaves per year (years without heatwaves are excluded from the analysis); HWI (heatwave intensity) is the average temperature during heat waves per studied time; HWA (heatwave amplitude) is the average temperature of the hottest heatwave day per year (years without heatwaves are excluded from this analysis)

Ncongwane (2016) pointed out some detailed heatwave definitions in his work “Heatwave events and increasing temperatures in South Africa.” From his point of view, a heatwave is a prolonged period (day-long or week-long) of extensive heat

It is a period of hot weather when ambient temperatures are high, occurred or more days that are above 90th percentile average temperature, the daily maximum temperature is met or exceeds 35°C for at least conservative days Some regions in Africa used absolute indices like 5-day temperature is more than 35°C or 3-day temperature is more than 40°C Heatwave event occurred when the temperature of the given regions is higher than 5°C or more above the average of the hottest month of that place carrying on for at least days It is an extended period of unusually high atmosphere-related heat stress caused by temporary modifications in the lifestyle of the affected population who had may suffer adverse health consequences

calculated for a 30-year-long reference period The threshold is defined as the 90th percentile of daily maxima temperature, centered on a 31-day window

The most recent study of Zschenderlein, Fink (2019) comprehensively analyzed the process of determining heatwaves across different European climates for the period 1979-2016 The definition of heatwaves used a percentile-based HWMId (Russo, Sillmann et al 2015) A heatwave is defined as a period of three consecutive days or more with a daily maximum temperature higher a threshold This threshold is 90th percentile of daily maximum at the point of time 00:00, 06:00, 12:00, and 18:00 Universal Time-temperature at 2-meter height within a centered 31-day window in the years 1979-2016

1.1.2 Heatwave in Vietnam

Besides international researches, the studies of Vietnam related to heat extreme pay attention to hot days There has been no work related to the heatwave in Vietnam On the paper of “Magnitude and trends in extreme monthly temperature in Vietnam in the period of 1961-2007.” (Ha and Tan, 2009), authors analyses the magnitude and trends in extreme monthly minimum (Tm) and maximum temperatures (Tx) of climate sub-regions in Vietnam from 1961 to 2007 The analysis was based on daily minimum and maximum temperatures that were collected from 58 observation stations The findings indicated the rise by about 0.9°C per decade of the monthly minimum temperature of Vietnam which is much quicker compared with the rate of global average temperature increasing; while the monthly maximum temperature inconsiderably dropped by 0.1°C per decade The magnitude and trend change of Tm, Tx were not the same across Vietnam These values vary from region to sub-region The highest value is found over the Northwest part of Vietnam The change of extreme temperature, especially the rapid increase of monthly minimum temperature, is the cause of the reduction in the number of cold spells and the increase in the number of heatwaves and droughts in Vietnam

the model at each station is applied to calculate the number of hot days in the future Projection in the middle of the 21st century for Vietnam shows that under the medium scenario RCP4.5 the number of the hot day increases from 20-40 days compared to the baseline period (1986-2005) Under high scenarios RCP 8.5, the number of hot days increases about 30-60 days averaging over Vietnam It is about 30-40 for Northern Vietnam and 50-70 days for Southern Vietnam

According to Khiem (2017), the research results of the forecastability of REGCM4 model for the highest temperature and the number of hot days in May, June, and July for La Nina 1988-1989 and El Nino 1997-1998 at observation stations in the North Central region RegCM4 model consists of two nested regions with a resolution of 60 km and 20 km with input data from the CFS model of the American Environmental Forecasting Center It is calculated that except for some stations in the western mountains of Nghe An, Ha Tinh, Quang Binh provinces, the threshold of hot days in most stations is greater than 35°C Predictability the heat of the model in both La Nina and El Nino periods are better match for May and worse in June and July

to provide the scientific basis for policy-making and appropriate adaptation measures in each condition, contributing to risks reduction, ensuring sustainable development for the region

1.1.3 Research Gap

Heatwave-related studies in Vietnam have limitations for understanding deeply about heatwave due to the fixed threshold They limit heat extreme events to hot days and hot spells, while the understanding of characteristics of the heatwave is crucial for projection in the context of climate change Therefore, the study in this thesis would define heatwave and calculate heatwave characteristics based on references to the crucial studies in the world, together with available data of Vietnam This thesis would define heatwave according to the relative threshold (percentile calculated), simultaneously, the reference to the absolute threshold (35°C) given by the Vietnam Meteorological and Hydrological Administration (VNMHA) Along with that, some heatwave characteristics are based on Perkins and Alexander (2012) and some other indicators to calculate The detailed explanation would be covered in Chapter 02

1.2 Necessity of research

In the context of climate change, a heatwave is one of the most extreme event influents disastrously worldwide It affects negatively on natural, industrial and human systems (Perkins, 2015) Natural ecosystems are broken down due to extreme heat It is the fact that extreme heat contributed to 500 wildfires over Russia in 2010, as well as the worse Australian bushfires on record in 2009 resulting in 173 deaths and 3500 trees destroyed (Karoly, 2009) Those trees were homes of Australian flying foxes In the temperature up to 42°C, lactating mothers and their offspring fall out of the trees Over 12 years, more than 3000 flying foxes suffered from heat-related deaths (Welbergen, Klose et al ,2008)

a crop failure of 25% yearly, and a deficit of 15 USD billion (1% of gross domestic product) of total economic loss The record heatwave event of 2010 fainted Russian with loss harvests of 30% (Barriopedro, Fischer et al., 2011) Extreme temperatures impact adversely on other crops such as rice (Lanning, Siebenmorgen et al., 2011), as well as bovine livestock and their mild production (Dunn, Mead et al., 2014)

The deadly heat waves that have recently occurred worldwide have raised public awareness and concern about their destructive impacts on the mortality morbidity and (Im, Choi et al., 2017) Over the last decades, there has been a remarkable run of record-shattering heat waves damaging In 2003, an intense heatwave occurred over Western Europe (with temperatures the highest since 1950) was responsible for over 70 000 deaths (Coumou and Rahmstorf, 2012) In the August 2003 heatwave temperatures reached 38.5°C in England, and there were 2,193 heat-related deaths across the UK in just 10 days (Committee, 2018) In 2009, a heatwave over southeastern Australia killed 374 people (DHS, 2009) The 2010 Russian heatwave set forests ablaze to the historic heat wave in Texas in 2011 and the “Summer in March” in the U.S Midwest in 2012 (Trenberth, Meehl et al., 2012), which lasted for over a month, led to a death toll of around 54000 individuals (McMichael and Lindgren, 2011)

Extreme heat events lead to heat stress and can increase heat-related morbidity (García-Herrera, Díaz, et al., 2010) The human body cannot tolerate conditions exceeding 37°C At temperatures of 27°C and relative humidity of 40%, some healthy individuals may begin to experience heat stress with prolonged activity or exposure Heat stress causes fatigue, headache, and muscle cramps, while heat stroke can lead to death, even among healthy people Certain groups of people – those with chronic health conditions like diabetes or high blood pressure, and farmers, construction workers, and other outdoor laborers – are at greater risk of suffering heat stress and heat stroke during heat waves (Opitz-Stapleton, Sabbag et al., 2016)

above 40°C for long enough without proper cooling, it can lead to heatstroke, serious complications, and damage to the internal organs' body and nerve area, even death

Regarding heatwave, the significant rise of heat extremes witnessed associated with a small shift in global average temperature is adhere to climate change (Perkins, 2015) Intergovernmental Panel on Climate Change (IPCC) has concluded that strong evidence exists indicating that hot days and heavy precipitation events have become more frequent since 1950 due to increase of global mean surface air temperature over the last century (Stocker, Qin et al., 2013) Since 1950, the number of a heatwave across the globe has risen and become longer along with the hotter and more frequent of hottest days and nights (Trenberth, Meehl et al., 2012) Global warming boosts the probability of extreme events (Stocker, Qin et al., 2013) Global temperatures are very likely to continue rising in the foreseeable future (Zhao, Ducharne et al., 2015) As a result of human-induced changes in climate, global mean surface air temperature shows a rising trend over the last 100 years This has led to a worldwide increase in frequency, intensity, and duration of extreme heat events or heatwaves (Perkins et al., 2012)

According to VHMA, summer 2019 is witnessed to be one of the harshest summers in history when the heatwave has swept many countries in the world and a series of temperature records were rushed In June and July 2019, consecutive heatwave occurred, the temperature increased abnormally and there was no downward trend In June 2019, the temperature in Paris, France reached the highest point in history at 46°C; and in July the temperature here still reached the average level of 40.6°C Meanwhile, the Czech Republic, Belgium, the Netherlands, Slovakia, Austria, Andorra, Luxembourg, Poland, and Germany also experienced the hottest June in history constantly at record levels the temperature of these countries for decades When temperatures soared, prolonged heat has caused forest fires, railroad explosions, and dry air and water shortages

Vietnam, not an exception to the global context, is also affected by the heatwave Vietnam has considered one of the countries is most vulnerable to climate change (IPCC, 2013) In recent decades, the heatwave has had a significant impact on human health, especially exposure to an epidemic In the increasing global warming, the temperature of Vietnam is also significantly changed, leading to more extreme temperatures and more complicated phenomena The report on the trend of average surface temperature change of IPCC indicates that the global average temperature increase is 0.74 ° C ± 0.18°C During 1906-2005, in Asia, the average temperature has increased by 0.3-0.8°C in the last 100 years (Trenberth, Meehl et al., 2012) while in Vietnam the average temperature has increased to around 0.5-0.7°C in the last 50 years (1958-2007) (Ncongwane 2016)

In Vietnam, according to statistics of the National Meteorological Data Center (NMDC) in 2019, there were 13 broad-area heatwaves, including hot temperatures with record temperatures and most of them occurred in provinces of the North, North, and South Central Coast, April 18 - 26, 2019, the Northern and Northern Central regions recorded record heat, the temperature of the stations measured was Muong La (Son La Province) 42.2°C, Tuong Duong (Nghe An Province) 42.4°C, Tay Hieu (Nghe An Province) 42.2°C, Con Cuong (Nghe An Province) 42.0°C, Huong Khe (Ha Tinh Province) 43.4°C, Tuyen Hoa (Quang Binh Province) 43, 0°C, almost all temperatures measured at stations have exceeded annual temperature records

It is a significant importance to study heatwave in Vietnam; while there is a lack of research that has been conducted across Vietnam Therefore, this master thesis will be filling that gap The thesis focus on analyzes the trend and changes in the heatwave and its characteristics The results may benefit from heat early warning systems to reduce risk from the heatwave The preparedness in advance is one of the helpful adaptations solutions in the context of climate change

1.3 Research question

duration and intensity of extreme heat events Thus, this master thesis aims to answer the question:

Question 1: How are spatial and temporal variations of heatwaves across Vietnam? Question 2: What are heatwave characteristics could be calculated?

Question 3: What are the trends of change of heatwave characteristics in Vietnam? 1.4 Research hypothesis

In order to answer research questions and identify research clearly, this thesis assumes that in the context of climate change, the average global mean temperature and temperature fluctuations both increase (IPCC, 2013) leading to the rise of the heatwave and negative impact on human health performing through heat stress Therefore, this master thesis is chosen with the tittle “Observed change and variability of heatwave across Vietnam.”

1.5 Research objective

This research aims to:

+ select the suitable heatwave index and identify heatwave conditions across Vietnam in recent decades based on heatwave characteristics

+ understand solidly the spatial and temporal variations and changes of heatwave characteristics over Vietnam under the context of global warming

1.6 Scope of research

1.7 Research structure

The thesis contains four main parts including (1) introduction; (2) data & methodology; (3) results and discussions; and (4) conclusion

Chapter 1: Introduction – presents the overview of the research containing basic and key ideas of the thesis such as the necessity of research, research question, research hypothesis, research objective, the scope of research, literature review concentrating on briefly showing the study-related academic works in the past, research gap and research structure

Chapter 2: Data and Methodology – is a chapter in which, the data is described and the methodology of the study is expected to be discussed Moreover, the rationale for choosing the definitions and database will be explained along with how each indicator or factor has been calculated

Chapter 3: Results and discussion – is the chapter includes results analysis of heatwave characteristics, their trends, and some comments about them in the context of climate change in Vietnam, as well as some limitations of the study, recommendations for using the research results, and further research orientation

CHAPTER 2: DATA AND METHODOLOGY

2.1 Data

The data used in this thesis includes daily maximum temperature data in the summertime from April to September 30 for the period 1980 - 2018 (39 years) from 109 meteorological stations in seven sub-regions They are R1- Northwest (10 stations), R2- Northeast (32 stations), R3- Red River Delta (16 stations), R4- North Central (27 stations), R5- South Central (10 stations), R6- Central Highland (14 stations), and R7- Mekong River Delta (7 stations) The stations’ names and their identification (ID) are listed in Table Data are provided by the REMOCLIC team and available in the High-Performance Computing System for directly calculating and processing

The daily maximum temperature is real-time data in a day If the series of data have missing data (due to technical problems), the data are displayed as “-99.00” and are not processed to calculate When processing, errors are detected and corrected based on actual measurement Suspected data can be rechecked with original data and be compared with nearby meteorological stations

Figure 2.1 Climatic sub-regions of Vietnam include R1 (Northwest), R2 (Northeast), R3 (Red River Delta), R4 (North Central), R5 (South Central), R6

Table 2.1 Names, ID, and Regions belonged to 109 meteorological Stations

ID R Station Name ID R Station Name ID R Station Name ID R Station Name

1 TUANGIAO 29 YENBAI 57 HOIXUAN 83 DANANG TAMDUONG 30 THAINGUYEN 58 YENDINH 84 TRAMY MUONGTE 31 DINHLAP 59 BAITHUONG 85 QUANGNGAI SINHO 32 MONGCAI 60 THANHHOA 86 BATO LAICHAU 33 HUULUNG 61 SAMSON 87 QUYNHON DIENBIEN 34 TAMDAO 62 NHUXUAN 88 TUYHOA SONLA 35 PHUHO 63 QUYCHAU 89 NHATRANG BACYEN 36 QUANGHA 64 TINHGIA 90 PHANRANG YENCHAU 37 LUCNGAN 65 QUYHOP 91 PHANTHIET 10 MOCCHAU 38 HIEPHOA 66 TAYHIEU 92 DAKTO 11 BAOLAC 39 SONDONG 67 TUONGDUONG 93 KONTUM 12 TRUNGKHANH 40 TIENYEN 68 QUYNHLUU 94 PLEIKU 13 HAGIANG 41 BAICHAY 69 CONCUONG 95 ANKHE 14 HOANGSUPHI 42 VINHYEN 70 DOLUONG 96 AYUNPA 15 BACME 43 VIETTRI 71 VINH 97 BUONHO 16 CAOBANG 44 BAVI 72 HUONGSON 98 MDRAK 17 NGUYENBINH 45 SONTAY 73 HATINH 99 BMTHUOT 18 BACQUANG 46 CHILINH 74 HUONGKHE 100 DAKNONG 19 CHORA 47 HANOI 75 KYANH 101 DALAT 20 NGANSON 48 HAIDUONG 76 TUYENHOA 102 PHUOCLONG 21 SAPA 49 HOABINH 77 BADON 103 LIENKHUONG 22 THATKHE 50 PHULIEN 78 DONGHOI 104 DONGPHU 23 LUCYEN 51 HUNGYEN 79 DONGHA 105 BAOLOC 24 HAMYEN 52 THAIBINH 80 HUE 106 VUNGTAU 25 THANUYEN 53 NAMDINH 81 ALUOI 107 CANTHO 26 MUCANGCHAI 54 NHOQUAN 82 NAMDONG 108 RACHGIA 27 LANGSON 55 NINHBINH 109 CAMAU 28 TUYENQUANG 56 VANLY

Northwest Red River Delta South Central Northeast North Central Central Highland

R1 R3 R5 Region

7 (R7)

Mekong River Delta

R2 R4 R6

2.2 Method

analyzing the variability of the heatwave, and (4) Analyze the trends of heatwave characteristics with non-parametric’s Mann-Kendall test and Sen’s Slope method 2.2.1 Step 1: Define heatwave

A heatwave is a weather event that cannot be directly observed; rather it can be detected by a certain index There is a wide range of heatwave definitions that vary from authors to authors, studies to studies Each one has advantages and disadvantages In this study, a heatwave is defined as in Perkin and Alexander (2013) That is heatwave is a period that at least consecutive days, have a daily maximum temperature (Tx) is greater than the threshold In which, a threshold is a local 90th percentile for the reference period 1981-2010, 183 calendar days of summertime (from April to September 30) centered 31-days window Heatwaves are defined differently due to the scope and purpose of research Table 2.2 summarizes some definitions used in past studies about the heatwave

In this study, a heatwave is defined mostly similarly to Zschenderlein (2019) where the daily temperature of three consecutive days above the threshold The threshold is 90th percentile of daily maximum temperature with a centered 31-days window

Table 2.2 Different definitions of Heatwave

No Year Author(s) Scope of study

Heatwave Definition

1 2010 Fischer and Schar

European (1961-1990)

HW is a spell of at least consecutive days with maximum temperatures exceeding the local 90th percentile of the control period (1961-1990)

2 2012 Perkin Australia

(1951-2008); (1971-2008)

HW is or more consecutive days above one of the following: 90th

percentile for maximum

No Year Author(s) Scope of study

Heatwave Definition

minimum temperature, and positive extreme heat factor (EHF) condition

3 2014 Simone Russo Southern Europe, America, Indonesia, Africa, (1981-2010)

HW is the period ≥ consecutive days with maximum temperature above the daily threshold for the reference period 1981-2010

4 2015 Stefan Zacharias

Germany (1971-2000)

HW is periods of at leave consecutive days with the daily mean air temperature above 97.5th percentile of the all-season temperature distribution

5 2016 Katlego Ncongwane

South Africa HW is a prolonged period (5 or more days) that are above 90th

No Year Author(s) Scope of study

Heatwave Definition

6 2017 Guido Ceccherini

Africa (1981-2015)

HW is computed using (1) maximum HWMIdtx and (2)

minimum HWMIdtn daily

temperature 2019 Philipp

Zschenderlein

European (1979-2016)

HW is a period of at least days in a row with a daily maximum temperature above a threshold (90th

percentile of the daily maximum of 0000, 0600, 1200, and 1800 UTC temperature at 2m height within a centered 31-days window

Determine the threshold of the heatwave

In order to identify the heatwave event, a threshold is needed to calculate From previous researches about heatwave, the threshold could be fixed (absolute) values or relative values This research uses a daily relative threshold which is the 90th percentile for each station, centered 31-window method The detail of this method would be explained in the next part After having the series of thresholds calculate base on 30- year baseline data series, the number of heatwave events are counted base on the chosen heatwave definition

Province) However, during the summertime of recent years, these stations are reported that there are days of abnormally high temperatures causing health problems for local people because it over their thermal threshold Therefore, instead of using the absolute threshold (35°C), this research calculates the heatwave threshold by determining the relative threshold which is the 90th percentile of the daily maximum temperature which is applied in some authors researching previously about the heatwave

Firstly, the threshold is determined as the 90th percentile of daily maximum temperature, centered on a 31-day window (Russo, Sillmann et al., 2015) This thesis determines the percentile of each calendar day for 183 days, from April to September 30 (the six hottest summer months of the year) in the continuous 30-year time series (1981-2010) of each station by centered day window method The 31-day window method for determining the 90th percentile maximum temperature is described as follows

In order to calculate the 90th percentile of the maximum temperature of one identified day in 183 days in the baseline 30 years (1980-2010), we have to take the maximum temperature data for the previous 15 days and 15 days thereafter to obtain one series of daily maximum temperature data and we take the 90th percentile temperature in that series to determine the temperature range of that day's heatwave For example, for calculating the 90th percentile of May 16, then the author takes the maximum temperature value of 15 days beforehand (May 1) and 15 days after (May 31) for 30 years (1981-2010) and total we will have a data series of 930 values of maximum temperature days From that series of data, the author determines the 90th percentile of the maximum temperature and uses that temperature to determine the threshold for the heatwave

From that temperature threshold, the author can identify the heatwave characteristics

2.2.2 Step 2: Calculate heatwave characteristics

In this step 2, nine heatwave characteristics (HWN, HWF, HWDmean, HWDmx, Hdays, HWMag, HWAmp, HWS, HWSx) are calculated for each station for a 39-years period Heatwave characteristics are used to understand the heatwave in terms of duration, magnitude, intensity, and severity Nine heatwave characteristics used in this thesis include the number of heatwave events (HWN), heatwave frequency (HWF), hot days numbers (Hdays), heatwave mean duration (HWDmean), heatwave maximum duration (HWDx), heatwave magnitude (HWMag), heatwave amplitude (HWAmp), heatwave mean severity (HWS), heatwave maximum severity (HWSx)

Figure 2.2 Heatwave characteristics demonstration

Definitions of heatwave characteristics using in this thesis describe as follow 1) Number of heatwave (HWN) (unit: spell)

From the threshold for each day calculated earlier, a heatwave event is counted when at least consecutive days have the value of maximum temperature exceeds that threshold The total number of heatwave events in a year for each station is called the number of heatwaves per year, which is denoted as HWN

2) Heatwave frequency (HWF) (unit: day)

Since the heatwave events are detected in each year at each station, the heatwave frequency is calculated by the total number of days of those heatwave events, which is denoted as HWF

3) Heatwave mean duration (HWDmean) (unit: day)

𝐻𝑊𝐷𝑚𝑒𝑎𝑛 = HWF HWN

In which: HWF is the total number of heatwave day annually HWN is the number of heatwave events annually 4) Heatwave maximum duration (HWDx) (unit: day)

Amongst heatwave events each year, there will be a longest consecutive period in which the daily maximum temperature above threshold q90 The number of days in that period is the maximum length of the heatwave, denoted as HWDmx

5) Hot days (Hdays) (unit: day)

In a year of a station, all the days with temperatures exceeding the heatwave temperature threshold (Q90) and ignore the condition of consecutive days is called the number of hot days per year, denoted as Hdays

6) Heatwave magnitude (HWMag) (unit: Celsius degree)

Heatwave magnitude is defined as the average temperature of all heatwave days of each station annually, denoted as HWMag

7) Heatwave amplitude (HWAmp) (unit: Celsius degree)

Heatwave day’s amplitude is defined as the average highest temperature of all heatwave events in a year of each station, denoted as HWAmp

8) Heatwave severity (HWS) (unit: Celsius degree)

Heatwave severity is the sum of the difference between the temperature of all heatwave events and temperature threshold q90, or known as the average excess temperature above q90 threshold, denoted as HWS

HWSi = ∑𝐻𝑊𝐷𝑖(Txi − qi)

𝑛=1

In which:

Txi: temperature of all heatwave events in a year qi: threshold of day i detecting heatwave events HWDi: heatwave mean duration in that year

9) Maximum heatwave severity (HWSx) (unit: Celsius degree)

Maximum heatwave severity is the sum of the difference between the temperature of a heatwave event having the highest temperature and temperature threshold q90, denoted as HWSx

HWSx = Max ( hwsi; i = 1, hwn) 2.2.3 Step 3: Analyze the variability of the heatwave

In this step, the spatial and temporal variation of heatwave characteristics is analyzed First of all, thresholds are analyze by regions and through time In this part, inter-annual variation is evaluated Then, the relationship between heatwave characteristics with ENSO could be detected In this step, the comparison between the variability of heatwave applying 90th-percentile-threshold and absolute threshold (35°C) is conducted This fixed value of the threshold is used by Vietnam Meteorological Department currently to categorize hot days

2.2.4 Step 4: Analyze the trends of heatwave characteristics with non-parametric’s Mann-Kendall test and Sen’s Slope method

and phenomena considered The larger the absolute value of the slope, the stronger the trend of increase (decrease) The significance and reliability of the slope are determined by the Man-Kendall test In this study, trend values are indicated with a 5% significance level, meaning the probability of a type error is 5%

CHAPTER 3: RESULTS AND DISCUSSION

3.1 The daily threshold for defining heatwave

From the results of calculating the temperature threshold for the occurrence of a heatwave, we have a color chart showing the 90th percentile (q90) temperature threshold of 183 days for each of the climatic regions The horizontal axis represents the time axis, 183 days, while the vertical axis represents 109 stations from the climatic region R1 to R7 The color chart shows the temperature (the greener tone, the lower the temperature, the higher the degree are red)

Through the color chart, generally, the threshold level is mainly above 29°C The highest threshold can be reached to above 40°C All regions, in the middle of summertime (hottest months June-July), the maximum temperature reach up to 39°C The distribution of the high-temperature threshold above 37°C is concentrated mainly in the R4 (North Central) region from April to August, and only reaches this high level in the r3 region but only in the hottest month of summer (June) Some stations with low-temperature thresholds (indicated by green color) below 25°C is stations located in high coordinates, in highland areas (ID Sinho, ID 21 Sapa, ID 105 Da Lat) due to the topography these stations are located In this region, there is a humid subtropical climate, cool all year round In the northern mountainous region, Yen Chau station (Son La Province) has the highest series of heatwave thresholds compared to all R1 stations which peaked on April 25 with a temperature of 37.7 °C because this station is in the area with the low elevations

In R6 (Central Highland), R7 (Mekong River Delta), the highest threshold values (up to over 34 degrees) not occur in the middle of the summer period but the early summer months (April and May) This is related to the rainy season characteristics of these areas In early May, the rainy season appears in the Central Highlands and the Mekong River Delta April is the last month of the dry season, the rainy season if from May onwards, with a rainy day and night regime Therefore, the maximum temperature at R6 and R7 decreases in this period

Figure 3.1 Heatwave threshold: 90th percentile of daily maximum temperature (Tx) at stations of climatic sub-regions for summertime (q90, °C), the stations on the left-side vertical axis are listed according to their position on a North

to South axis (North at the top), the climatic sub-regions that stations belong to are on the right-sides vertical axis

3.2 Spatial and temporal variations of heatwave across Vietnam

(variability) that on variability often refers to general fluctuations on the level of each year Variation refers to annual volatility Variability observes whether fluctuations have periodic or not Then, their period should be calculated

Overall, the variability of heatwave characteristics varies for each region In Figure 3.2, it is clear that HWS and HWSx of R6 and R7 are lower significantly compare with other seasons This low level implies that the severity of heatwave in these two regions, not intense event the amount of HWN quite similar to other regions

R3 and R4 remain the same at a high level Amongst all regions, most of the values of heatwave characteristics of R7 reach the highest average value (HWN: 2.7 spells; HWF: 13.6 days, HWDmx: days, Hdays: 24.7 days), while the HWS is the lowest one

Over the periods of 39 years, with a similar average number of heatwave events, the maximum heatwave severity of R1 and R4 is highest (6.9°C), comparing with other regions

Table 3.1 Average values of Heatwave characteristics of each region (1980-2018)

Region HWN (spell) HWF (days) Dmean (days) Dmx (days) Hdays (days) HWMag (°C) HWAmp (°C) HWS (°C) HWSx (°C)

1 2.4 3.5 4.4 10.0 20.7 28.3 28.9 4.5 6.9

2 2.3 3.5 4.2 9.5 20.1 30.1 30.7 4.2 6.3

3 2.5 3.4 4.2 10.3 20.9 31.3 32.0 4.8 7.4

4 2.5 3.5 4.5 10.8 19.6 32.1 32.7 4.5 6.9

5 2.1 3.1 3.9 9.0 19.0 27.5 27.9 2.7 4.3

6 2.1 3.2 4.2 9.8 19.6 23.1 23.5 2.3 3.7

7 2.6 3.2 4.8 12.9 24.8 24.1 24.4 1.8 3.3

Figure 3.3 shows the big picture of heatwave characteristics over 39 years (1980-2018) The HW characteristics include the number of heatwave event (HWN), mean heatwave duration (HWDmean), maximum heatwave duration (HWDmx), number of heatwave days (HWdays), number of hot days (Hdays), heatwave magnitude (HWMag), heatwave amplitude(HWAmp), heatwave severity (HWS), maximum heatwave severity (HWSx) over climatic sub-regions (1980-2018);

From the figure, it can be seen the relationship between the characteristics of heatwaves and ENSO phases The ENSO phase is one of three phases: El Nino, La Nina, and Neutral The characteristics of heatwaves are considered as the average values to calculate climate change trends

a heatwave that can be spread from one place to another While El-Nino is a seasonal phenomenon, it means that it can last several months and is quite persistent Therefore, heatwave and El-Niño may or may not be related But in general, when looking at the correlation chart, the El-Niño years often have favorable conditions for heatwaves to take place

Regarding HWN, HWN has a relatively little variation between regions but not much difference The maximum number of heatwaves a year on average can be up to or 10 times But normally the average fluctuates around times/year In the El Nino years, the number of heatwaves occurred more often and peaked at (8.35 times/year) But in the Neutral year, the number of heatwaves does not exceed times per year Regarding Hdays, in 2015, (the year of El Nino) was the year with the highest number of days in all the years (54.79 days) Neutral years, for many years, there are days when the temperature is above the heatwave threshold, but it does not exceed 30 days Regarding HWF, 2015 is the year with the largest number of days in all of the largest polar average in all years with 39.94 days of heatwave occurring For neutral years like 1983 with the largest HWF value (23.1 days) Regarding HWMag, most of the years in ENSO phases had HWMag values not significantly different from the lowest year (2002 -El Nino year) and 1996 (Neutral year) being the highest year with the temperature of 37.3°C Regarding HWS, the total average excess temperature that exceeds the threshold of the 1998 (La Nina Year) heatwave is 9.53°C / year; following with 1987 (El Nino Year) is the year with the second-highest average temperature (9.25°C / year)

In the year of El Nino, the average of HWN (3.2 spells/year), HWF (13.26 days/year), Hdays (23.16 days/year) are the highest values; while in La Nina year, the average HWM (35.6°C), HWS (5.29°C) is much higher than the rest year

In order to see the clear differences between the selection 90th-percentile threshold and absolute threshold, this study will use an additional threshold of 35°C to compare The fixed threshold 35°C is used according to the hot-spell standard of Vietnam (together with the concept of the number of hot spells hot spell is the threshold fixed 35 degrees Figure 3.5 shows the heatwave characteristics are calculated by using a threshold of 35°C

With 35 degrees-threshold, the picture is completely different At this time, the different regions change significantly magnitude temperature hovers mostly around 36°C While according to the 90th percentile, there are stations in the threshold years that are only 32°C- 33°C Therefore, this study chose the threshold q90 as more reasonable

Figure 3.4 Heatwave characteristics over climatic sub-regions with a fixed threshold of 35° C

Inter-annual variation HWN in R3, R4 shows the meaning results that verify the results of previous studies

Figure Inter- annual variation of Mean duration of Heatwave (days) across Vietnam (1980 - 2018)

Figure 3.7 and Figure 3.8, the picture is quite similar in value That means, the heat event lasts for a long time year by year from (1980-2018)

3.3 Trend of changes in heatwave characteristics

The trend changes in calculated on the Sen’s slope coefficient, which is determined by the median of the sequence elements If the slope coefficients are positive, the increasing trend is presented, while negative-slope coefficients represent a decreasing trend of the factors and phenomena considered The trend of change of HWN for every decade showing in Figure 3.9 is always positive, meaning the increasing trend

For characteristics including HWD, Hdays, HWF, HWS, and HWSx, the high positive slope occurred mostly to stations of R3 and the North of R4 The larger the absolute value of the slope, the stronger the trend of increase (decrease) In this study, absolute values are denoted by the size of the circle Hdays, HWF, HWS are concentrated in R3 (RRD), R4 is generally large scale This is the time when the subtropical high pressure encroaches on the west and there is a combination of moving to the east or expanding to the east of South Asia's low pressure That is when the two systems that combine and create the heat This is the extreme heat of the region, and the North Central Coast Therefore, only this area in the whole territory of Vietnam appears in many extreme temperatures This extreme hot event is related to the synoptic mechanism

Figure 3.9 The trend of changes of heatwave characteristics; in which a- HWN (number of heatwaves); b- HWDx (heatwave maximum duration); c- HWDmean (heatwave mean duration); d-Hday (number of hot days); e- HWF (number of heatwave days); f- HWMag (heatwave magnitude); g- HWAmp (heatwave amplitude); h- HWS (mean heatwave severity); i- HWSx (maximum

3.4 Limitations of thesis and further research orientation

One limitation of the method of calculation is El Niño in the year 1982-1983; 1987-1988 does not seem to be demonstrated through the characteristics An extremely strong 1997-1998 El Nino show in the chart is not clear This may mean that the method of calculating HWMag and HWAmp has not worked

Another limitation of the thesis is the test of the 31-day-window-method for a narrow period Since this method applied for mid-latitude regions in which the climate quite stable, does not vary like the tropical climate in Vietnam The long-days-window would result in moderating the values of the threshold during the transition of the season in which temperature changes in the wide range

In the context of climate change, the increase in heatwave has many negative consequences In order to study these effects in more detail, it is necessary to use indicators that affect health (heat stress) A prolonged heatwave can greatly affect human health, even affecting plants or animals Those are supposed to be suffered from "uncomfortable thermal condition" Based on the results of this study, further study of heat stress and its impact should be considered

3.5 Recommendations for using the research results

The impact of the heatwave is heavy on nature and social systems Through this study, the increased trend changes in the heatwave in Vietnam are clearly seen In the context of climate change, the trend is expected to rise even more Therefore, in order to protect our life, minimize damage in community health, agricultural production; it is necessary to have appropriate measures to cope with the above phenomenon

CHAPTER 4: CONCLUSION

In general, based on the series of heatwave characteristics’ analysis, this thesis concludes the overall increase tendency in number, frequency, and severity of heatwave occurred across Vietnam territory over decades (1980-2018)

In terms of heatwave thresholds, the level of the threshold of stations located in high elevator land is mostly lower than the average level amongst the climatic sub-regions containing these stations due to the humid subtropical climate of the area that these upland stations established In stations with low elevator, especially center is like big cities, the common threshold is above approximately 35°C since the Urban Heat Island (UHI) effect and Foehn wind effect

In terms of heatwave characteristics, El Nino years have the number of heatwaves (HWN), the total number of heatwave days of events (HWF), number of hot days (Hdays) have the highest average value The reason is that the rise of sea temperature of El-Nino years leading to the higher received temperature Otherwise, two characteristics include heatwave magnitude (HWMag) and mean heatwave severity (HWS) of La-Nina years is higher than both El Nino and Neutral years

In terms of trend, firstly, most characteristics of stations have increased over time, especially in the climatic sub-region R3 (Red River Delta) One possible reason for this increasing trend is rapid urbanization The other reason is related to the synoptic mechanism In R3, subtropical high pressure much encroaches and there is a combination of shifting to the east or expanding to the east of South Asia's low pressure that is when the two systems combine and create the heat Secondly, HWN, HWDx, Hdays, HWF, HWSx characteristics of the whole region tend to increase enormously in terms of the number of days, events, and Celsius degree due to global warming Otherwise, HWDmean, HWAmp, HWMag, HWS characteristics increase insignificantly and unclearly

the year); then gradually decreases to the R3, R5, R6, R1, R2, R7 at the beginning of April and the end of September This result is consistent with the general trend of extreme weather in global studies

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APPENDIX

Annex 1.a The trend of changes in the Number of heatwaves (events /decade)

Annex Heatwave threshold q90(°C): 90th percentile of daily maximum temperature (Tx) at stations of climatic sub-regions for