Hot weather event-based characteristics of double-early rice heat risk: A study of Jiangxi province, South China

Ecological Indicators 113 (2020) 106148

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Hot weather event-based characteristics of double-early rice heat risk: A study of Jiangxi province, South China

T

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Jianying Yanga, Zhiguo Huoa,b, , Xiangxiang Lic, Peijuan Wanga, Dingrong Wua a

State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing 100081, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China c Agro-meteorological Center of Jiangxi Province, Nanchang 330096, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Extreme weather process/event Rice heat Tmax Flowering

Frequent occurrences of extreme hot weather create severe rice heat disasters. Precisely assess rice heat risk based on the identification of the particular period severely hit by hot weather events is of great merit to improve public planning to minimize the deleterious impact of rice heat. In this study, maximum temperature, disaster and phenophase data on rice in Jiangxi province (typical planting area for double early rice in South China) were integrated to represent the historical heat of early double-cropping rice, facilitating the identification of particular period severely hit by historical rice heat and construction of hot weather event-based evaluation level of rice heat. Afterwards, a rice heat index (RHI) were constructed and calculated based on hot weather events and the exact rice growth stage (days before/after flowering, DF). The results showed that (1) Heat disasters occur approximately 15 d before flowering and the DF −5 to 0 was determined to have the highest possibility of rice heat, followed by the DF −10 to −5, with 29.41 and 22.06% of heat disasters starting in each period, respectively. (2) The probability of moderate and light heat damage was more than 80% when 3–5 d of hot weather occurred in the DF −5 to 5, while the probability of moderate and severe heat damage increased to 100% when > 5 d of hot weather occurred in this period. More than 80% of > 8 d rice heat started in DF −15 to 0, with severe rice heat accounting for approximately 90% in such a period. (3) Severe, moderate and light rice heat for 3–5 d was identified at DF −6 to 3, 4–5 and 6–9, respectively. Similarly, severe, moderate and light rice heat lasting for 6–8 d and > 8 d started at DF −6 to 1, 2–5, 6–18 and −7 to −5, −4 to 4, 5–14, respectively. (4) A high RHI was mainly found in the middle and northeastern part of the study area from 1981 to 2015, with the RHI in most stations being greater than 0.25. Increasing trends of a high RHI occurred in the same areas of the RHI belt. Most stations in such areas exhibited slopes > 0.15/10a. The results can provide technical and theoretical support for targeted rice heat assessment, and it can also universally applied in relative researches on rice heat.

1. Introduction Increases in temperatures have been witnessed under the background of climate change with extreme hot weather increases in frequency and intensity in the past few decades (Alexander et al., 2006). Crop growth is sensitive to heat, and extremely hot temperatures can cause physiological damage and crop yield loss irreversibly, particularly during sensitive phases of the crop such as the reproductive period (Hatfield et al., 2011; Luo, 2011), where it can cause grain loss and yield reduction substantially. Rice (Oryza sativa L.) is mainly planted in the tropics and subtropics regions and exposure to heat potentially (Seck et al., 2012). Extreme hot weather is recognized as an severely threaten to rice cultivated in

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East Asia (Tao and Zhang, 2013a; Tao and Zhang, 2013b), Southeast Asia (Jagadish et al., 2015; Wassmann et al., 2009) and South Asia, where hot weather events have been evident with increased frequency and severity. Yields of rice fall dramatically when exposed to temperatures over the threshold during the growing season, inevitably causes irreversible damage of rice (Schlenker and Roberts, 2009), which is generally defined as rice heat. Amount of studies has demonstrated the rice heat during the crucial processes in both controlled environments and field trials, as well as the impacts on final yield and its components (Wheeler et al., 2000). The negative effect of heat on rice mainly include changing flowering dynamics, reducing seed set and finally influencing grain yield formation (Matsui et al., 2000; Kobata and Uemuki, 2004; Jagadish et al., 2011; Julia and Dingkuhn, 2013).

Corresponding author at: No. 46, Zhongguancun South Street, Haidian District, Beijing 100081, China. E-mail address: [email protected] (Z. Huo).

https://doi.org/10.1016/j.ecolind.2020.106148 Received 25 March 2019; Received in revised form 8 January 2020; Accepted 27 January 2020 1470-160X/ © 2020 Elsevier Ltd. All rights reserved.

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a

b

c

d

e

Fig. 1. Maps of study area-Jiangxi province. Notes: a is the location of the Jiangxi province, meteorological stations and spatial distribution of rice planting area in 2014; b is spatial distribution of annual average temperature; c is tendency of average temperature in each meteorological station; d is spatial distribution of annual accumulated days at Tmax ≥ 35 °C; e is tendency of accumulated days at Tmax ≥ 35 °C in each meteorological station; Time series of b, c, d and e is from 1981 to 2015.

events were averaged over such sensitive period, making it hard to interpret the effects of unique hot weather processes/events on rice, which is with higher significant to the assessment of heat process-lead rice heat intensity. Rice heat risk often be exaggerated or underestimated because of the obscure of the vulnerable growth period hit by hot weather events. To cope with the rice heat damage effectively, it is necessary to actively carry out disaster risk assessment, integrating the identification of particular phenological period severely hit by rice heat. As the world’s largest rice producer, China has characterized by a significant climate warming in the past few decades (Lobell et al., 2011; Tao and Zhang, 2013b), and South China is supposed as heat disaster areas with increasing hot weather extremes (Zhang et al., 2014). The transplant stage of double-early rice in South China is mainly in April, and harvest in July. The flowering period which is considered the most sensitive to heat is in June, corresponding well with the temporal peak of the high temperatures. Agro-disaster representations can be explored, integrating disaster records, meteorological and crop phenological data (Glade et al., 2009; Yang et al., 2016), for exploring the agricultural effect of a disaster weather process/event and developing a process based indicator (Yang et al., 2016; Yang et al., 2017; Zhang et al., 2017). In this study, we performed a study based on the representation of historical rice heat disasters, with a special emphasis on (i) exploring the rice heat-lead characteristics of hot weather processes/events, highlighting the particular period severely hit by historical rice heat; (ii) establish evaluation level of rice heat related to disaster weather process/events; and (iii) estimation of rice heat risk based on the rice heat evaluation level,

Rice heat risk, which is an important part of disaster management, is conducive to the prevention and mitigation of natural rice heat disasters. Analysis of rice heat risk can provide ex ante design of policies and measures to anticipate effects of heat disaster on agricultural production (Zhang et al., 2018). Many studies emphasize heat risk in rice reproductivity phase, i e. heading, flowering and early filling as a basal influence period in the identification of hazard prone regions (Sun and Huang, 2011; Zhang et al., 2018) using empirical statistical approaches or ensemble simulation models, and geographically mapped the rice heat risk based on the meteorological index integrated cropping calendar. For example, Teixeira et al. (2013) simulate a potential decrease due to heat stress for rice, maize, wheat and soybeans at a global scale used the Global Agro-Ecological Zones (GAEZ) model, and 35℃ was supposed as critical temperature threshold for rice during a 30-day period centred in the midpoint of productive phase. Gourdji et al. (2013) analysed the heat exposure of crops by calculating accumulated physiologically critical temperatures in the world 30 d before and after flowering. Yang and Chen (2007) analysed the occurrence rules of rice heat risk in south China based on the frequency of days with a Tmax ≥ 35 °C during the rice reproductive stage (from late July to early August). To assess the impact of rice heat, two basic aspects must be considered. First is the hot weather event, including the occurring time, duration, intensify, etc. Second is the crop resistance ability against heat, what is always varies with rice growth (Yang et al., 2016). Although researches based on the entire sensitive stages can better explain the yield variation corresponding to temperature rise or climate change, difference of resistance in rice growth stages was neglected and yield damage intensity factors or the effects of hot weather processes/ 2

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3

130 counties 1981–2006

Daily maximum temperature

Flowering date of double early rice

Rice heat records

Meteorology

Crop

Hazard

China Meteorological Disasters Book (Jiangxi Provinces) and the Yearbook of Meteorological Disasters in China (2001; 2002;2003; 2004; 2005; 2006)

130 counties 1981–2015

Data index Classification

Table 1 Details information of data used in this research.

Meteorological, crop and hazard data we used in this study (Table 1). Meteorological data from 85 weather stations were obtained from the National Meteorological Information Center, China Meteorological Administration (NMIC, CMA), including daily max temperature datasets from 1981 to 2015. Phenological data, i.e., flowering data, of double early rice for 1981 to 2015 from 130 counties is derived from the observation data of the Agro-meteorological Center of Jiangxi Province. These data mainly concern the beginning and ending of the rice flowering stage during 1981 to 2015. Early rice begins to flower from early June in southern Jiangxi and flowering is delayed until late June in the northern part of the province. Variations were detected in the analysis years (1981–2015). Average dates of rice flowering in 130 counties in each year were correlated with data from the nearest meteorological station to resolve the mismatch between the station sites and the counties. Rice heat disasters can be found in the China Meteorological Disasters Book (Jiangxi Provinces) (Chen and Wen, 2006) and the Yearbook of Meteorological Disasters in China (China Meteorological Administration, 2008–2015), including the time (occurrence data, duration days), location, and effects on rice growth or productivity, covering the period from 1981 to 2006. Datasets include meteorological, crop and hazard data from 1981 to 2006 were used to construct rice heat samples, facilitating the evaluation level construction of rice heat. Meteorological, crop date of 1981–2015 were used for the calculation of RHI.

Source

2.2. Meteorological, crop and hazard data

Agro-meteorological Center of Jiangxi Province

Period

Area scope

Utilization

Jiangxi Province is a major agricultural province located in southeast China between 24˚29′14″and 30˚4′41″N and 113˚34′36″and 118˚28′58″E with a total area of 166,900 km2 and a total population of approximately 44.6 million in 2010. Rice is the main crop in Jiangxi province. A map of rice planting area in each counties, generated by National Bureau of Statistics of China (NBSC) based on data from agricultural census, is shown in Fig. 1-a. The dominated rice variety is double cropped rice, and the planting area of double cropped rice stands first among provinces with ~1.39 million ha, accounting for approximately 25% of the total cultivated area of double-cropped rice in China (National Bureau of Statistics of China NBSC, 2014). This region has a subtropical, humid monsoon climate, with 1480–2080 annual average sunshine hours, an annual average temperature of 16.3–19.5 °C (Fig. 1-b), and an average annual rainfall of 1340–1940 mm. However, Jiangxi is a high-temperature prone region, with most part of the region more than 20 d at Tmax ≥ 35 °C (Fig. 1-d). Additionally, average temperature and accumulated days at Tmax ≥ 35 °C were detected increased in more than 95% stations (Fig. 1-c and Fig. 1-e). Eighty percent of the meteorological stations in Jiangxi had positive trends for the mean temperature of high-temperature days (MTHTD), the maximum value of the daily mean temperature (MVDMT), and the mean temperature of days during an extreme continuous high-temperature event (MTECHTE) over 50 recent years (Jin et al., 2016). The growth period of double early rice corresponds well with the temporal distribution of hot weather, and overwhelming majority of heat disasters for rice were witnessed in rice reproductive stage (Tian and Cui, 2015). Lack of supplemental irrigation is also an important factor clearly causing an extreme high-temperature event for rice plantings.

85 stations

2.1. Study area

1981–2015

2. Materials and methods

National Meteorological Information Center, China Meteorological Administration (NMIC, CMA)

to gain temporal and spatial characteristics of rice heat in the study area.

1) Construct of rice heat samples (1981–2006) 2) Calculation of rice heat index (1981–2015) 1) Construct of rice heat samples (1981–2006) 2) Calculation of rice heat index (1981–2015) Construct of rice heat samples (1981–2006)

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2.3.4. Classification of damage Historical disaster data for rice heat has been recorded in the China Meteorological Disasters Book and Yearbook of Meteorological Disasters in China. The records were originated by the China Meteorological Administration, and surveyed by skilled technical personnel in the meteorological department following standard field observation references. Heat-lead symptoms varies with rice growth phases. For example, heat stress at booting could cause spikelet sterility, while obvious yield decrease observed in rice grain filling via so-called “hightemperature forced mature”. So, key words such as affected, damaged and destroyed were used to characterize rice heat intensity in local disaster survey. Usually, “disaster-affected” refers to > 10% reduction of rice production as a result of disaster. “ Disaster-damaged” and “disaster-destroyed” mean 30% and 80% productivity decreases caused by disaster, respectively. Light, moderate, and severe rice heat were identified according to damage descriptions. At the light heat damage level, rice fields suffered from hot weather, and rice growth was affected. At the moderate level, high temperature significantly impaired the growth of rice, and rice was damaged with a productivity decrease. Severe heat damage caused rice to be seriously destroyed with little or no production.

2.3. Disaster sample construction Disaster information can be gained by documentary evidence of historical agro-disaster events (Yang et al., 2016; Kjeldsen et al., 2014; Ng et al., 2015). Damaging event and methodology data can be incorporated to identify the hot weather conditions that can trigger crop losses (Ana et al., 2015). Temperature data was firstly rechecked according to the historical disaster records, identifying the exactly starting time of hot weather events. Then, historical rice heats were represented by the construction of historical disaster samples, integrating occurring times, locations of hot weather, disaster records, and the growth stages of early rice. The historical disaster samples include high-temperature duration, heat damage level and starting time. 2.3.1. Conditions triggering rice heat Rice is sensitive to the meteorological environment conditions of increasing temperatures. Tao et al. (2008) evaluated the adaptability of rice under the condition of daytime temperatures (40 to 42 °C) during the flower period. Matsui et al. (2001) believed that daytime temperatures of more than 37.5 °C affects the pollen germination of rice, and the number of fertilization is significantly reduced when at higher temperatures (40 °C). Generally, Tmax ≥ 35 °C was the most popular used index in rice heat analysis (Satake and Yoshida, 1978; Shi et al., 2008; Tian et al., 2010; Teixeira et al., 2013; Tao and Zhang, 2013b; Zhang et al., 2014). The national standard of the People's Republic of China (GB/T 21985–2008, 2008) point out that pollen development and flowering pollination are affected when the maximum temperature of flowering is ≥ 35 °C for 3 consecutive days or the average temperature ≥ 30 °C, while yield and rice quality are affect when this hot process occur in fill-milking stage via high-temperature forced mature, constituting a rice heat disaster. Therefore, Tmax ≥ 35 °C for more than 3 d was identified as trigger for early rice heat in this study.

2.3.5. Data coupling Continuous days with Tmax ≥ 35 °C were first rechecked to identify the starting time and duration of the hot weather according to the historical early rice heat records. Historical rice heat samples were constructed by coupling historical temperature data with rice growth stages. Consider the rice heat disaster of late June 1991 in Guixi, Jiangxi Province, for instance. Records state that “hot weather occurred in Guixi in late June, premature rice appeared, and rice was damaged with yield decreased”. “Moderate” is was considered according to the damage classification principles. Temperature data were obtained from the Guixi Meteorological Station from the 23rd of June with 8 d Tmax ≥ 35 °C. According to the phenological record of early rice in 1991 in Guixi, the 23rd of June is approximately 3–5 d after early rice flowering. Thus, based on the hot weather duration, damage level and the starting time of the hot weather (demonstrated by DF), the rice heat disaster sample was constructed as follows: 8 d (duration) – moderate – 4 d (DF). Finally, a database including 205 rice heat disaster samples using the formation of hot weather duration (3–5 d, 6–8 d, and > 8 d), damage level (light, moderate, severe), and starting time demonstrated by DF were constructed based on historical data analysis and disaster records. Thirty-five samples were identified as light rice heat, while 78 samples were identified as moderate and 92 samples were identified as severe. Detailed quantity of rice heat samples in damage level and hot weather duration combinations is showed in Table. 2.

2.3.2. Target period of rice heat The relative susceptibility of growth stages to heat likely differs between regions, for the unique combinations of crop phenology and local climate. For example, cereal are largely perplexed by episodes of hot weather during grain filling in Mediterranean region (Maracchi et al., 2005; Olesen et al., 2011). Most heat stress threatened the postanthesis period of crop in Australia (Savin and Nicolas, 1996; Savin et al., 1997). Early rice in Jiangxi is heading in early June. Afterwards, it blossoms on the same day or following days to complete the fertilization process. High temperatures in Jiangxi Province generally begin in mid-May. Based on existing research combined with expert experience, the period from 20 d before flowering (−20 d) to mid-late filling is highlighted in this paper to demonstrate the regularity of rice heat. With the flowering date taken as the centre point, days before/after flowering (DF) is considered to be from −20 d to approximately 30 d (negative means the days before flowering, while positive means the days after flowering).

2.4. Analysis of historical rice heat samples Characteristics of historical rice heat samples were studied based on probability analysis. As a fuzzy mathematical set-value method for samples, information diffusion allows for optimizing the use of fuzzy sample information to offset the information deficiency (Huang 2001, 2002a, 2002b). The method can turn an observed sample into a fuzzy set, meaning that a single point sample is turned into a set-value sample, which is always used in probability analysis. In this study, the probability distribution of the starting/ending time for a heat disaster was first established in the rice reproductive stage based on the information diffusion method. Afterwards, the probability distribution of the heat intensity was analysed for 3–5 d, 5–8 d and > 8 d to obtain detailed characteristics of rice heat.

2.3.3. Heat duration Summer (June – August) in Jiangxi is often controlled by a subtropical anticyclone and is prone to continuous high-temperature weather. Gao and Wang (2009) assessed the heat stress of rice in South China based on 3 d, 5 d and ≥ 8 d as discriminated high-temperature heat disaster grades when Tmax ≥ 35 °C. Short-term hot weather (generally 3–5 d) gradually begins in late May in Jiangxi with a cumulative probability approximately 30% (Fig. 2-a). Jiangxi begins to enter a heat period with sustained high-temperature weather in late June. Continuous 8 d had the highest probability of occurrence with a probability of 7.17%, followed by 9 d hot weather process with a probability of 7.10% (Fig. 2-b). So, this article proposes that 3–5 d, 6–8 d and > 8 d of consecutive hot weather with a Tmax ≥ 35 °C causes effects of heat-intensity.

2.5. Evaluation level construction of rice heat Based on the designation of the best DF distribution fit in each 4

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a

b (%)

100 Probability density

Cumulative probability

(%)

80 60 40 20 0 3

5

7 9 11 Duration days

13

7.5 7 6.5 6 5.5 5 4.5 4

15

3

5

7 9 11 Duration days

13

15

Fig. 2. Cumulative probability and probability density of duration days of rice heat disaster. n

Table 2 Detailed quantity of data in rice heat level and hot weather duration combinations. Rice heat level

3–5 d

6–8 d

>8 d

Light Moderate Severe

21 29 7

6 23 12

8 26 73

RHI =

∑ Pi Qi i=1

here, n is the disaster intensities, i.e., the light, moderate, and severe rice heat, n = 3; Pi is the weight of intensity in level i. The weights of light, moderate, and severe rice heat were 1, 2 and 3, respectively, principally referring to the expert decision and related references (Yang et al., 2016; Yang et al., 2017); Qi is the possibility of rice heat in level i, which is calculated by information diffusion method (as described in 2.4). The information diffusion method in RHI quantified can compensate for insufficient information in time series from 1981 to 2015, making the evaluation results more accurate.

disaster sample being 3–5 d, 6–8 d and > 8 d of hot weather duration, the evaluation threshold of rice heat was calculated by the confidence interval estimation of DF in the datasets.

3. Results

2.5.1. DFs distribution fitting test Modelling of climate data using various mathematical models has been performed to provide a better understanding of the data pattern and its characteristics, which involve studying a sequence of special climate processes. Distributions such as normal, exponential, Poisson, uniform, Rayleigh, and Weibull are usually used for discrete probability distributions of meteorological characteristics (Eagleson, 1978; Yang et al., 2016; Yang et al., 2017; Zhang et al., 2017). In this study, three common candidate distributions, normal, uniform and exponential, were chosen to describe the discrete probability distribution of DF in 9 datasets, briefly representing historical rice heat processes. As a widely applicable statistical method to determine the cumulative distribution function of a continuous random variable, e.g., tropical storms, precipitation level, wind force, extreme temperature, etc. (Vlček and Huth, 2009; Crutcher, 1975), the Kolmogorov–Smirnov (K–S) goodness-of-fit test was applied to test the distribution of the DF in 9 datasets.

3.1. Historical record-based characteristics of rice heat 3.1.1. Occurrence of rice heat The probability of the starting data of rice heat was calculated based on the disaster samples. The ending date was calculated from the given starting date and duration. As shown in Fig. 3, the heat disaster occurred approximately 15 d before flowering and lasted approximately 35 d after flowering. DF −5 to 0 was detected as having the highest possibility of rice heat, followed by DF from −10 to −5, with 29.41 and 22.06% of the heat disaster starting in each period, respectively. Hot weather started 0–10 d after flowering, which accounts for approximately 20% of the total rice heat, while hot weather beginning 10–20 d after flowering produced slight rice heat formation, with 6.37 and 7.35% of rice heat starting in DF 10–15 and 15–20, respectively. Table 3 provides insight into the proportions of the different durations (3–5 d, 6–8 d and > 8 d) of rice heat in different ranges of DF. Rice heat percentages of 86.96, 71.43 and 51.56 started with DF −15 to −10, −10 to −5, −5 to 0, respectively, induced by hot weather persisting for more than 8 d. Thirty-five percent and 39.29% rice heat

2.5.2. Threshold identification of each level of rice heat The confidence interval estimation is always chosen to present the discrete characteristics of extremes (Yang et al., 2016), and this method was used in this research to determine the DF threshold in the sample sets. Each threshold of DF in the rice heat disaster level (light, moderate, severe) for durations of 3–5 d, 6–8 d, and > 8 d was calculated refer to Friday and Monday (1974).

Ending Date

Starting Date

40%

Probability

30%

2.6. Development of rice heat index

20%

10%

The risk analysis of rice heat refers to the probability or recurrence of rice heat under a certain time within a given geographical area. Extreme frequency, extreme intensity, extreme percent and number of days of extremes are always used as evaluation indices for meteorological risk analysis (Moriondo et al., 2011; Wang et al., 2016; Zhang et al., 2018). In this study, frequency of rice heat in each level (light, moderate, and severe) from 1981 to 2015 were calculated based on the evaluation level constructed previously. Then, the rice heat index (RHI) was calculated as followings, to effectively demonstrate the risk for rice heat in the region.

0%

Days before/after Flowering (DF)

Fig. 3. Probability of starting and ending calendar of rice heat disaster. Notes: different rice reproductively period was showed as DF with a range of 5 day; full line demonstrate starting date and broken Line demonstrate ending date. 5

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a

Table 3 Proportions of different duration (3–5 d, 6–8 d and > 8 d) rice heat in different range of DF. Distance from flowering

Light

Duration

Severe

30%

3–5 d

6–8 d

>8 d

4.35 16.67 26.56 35.71 42.86 42.86 37.50

8.70 11.90 21.88 39.29 23.81 21.43 37.50

86.96 71.43 51.56 25.00 33.33 35.71 25.00

Probability

(−15 to −10] (−10 to −5] (−5 to 0] (0–5] (5–10] (10–15] (15–20]

Moderate

35%

25% 20% 15% 10% 5% 0%

Days before/after Flowering (DF)

Table 4 Starting calendar of rice heat at different intensities. Rice heat level

Minimum date

Maximum date

Mean date

Mean duration

3–5 d

Light Moderate Severe

−12 −9 −7

18 16 6

4.76 2.57 −1.57

3.86 4.00 3.43

6–8 d

Light Moderate Severe

6 −12 −11

18 16 6

13.00 1.61 −2.58

7.00 6.96 6.83

>8 d

Light Moderate Severe

6 −9 −13

18 12 6

13.38 1.35 −5.92

11.60 15.40 18.10

Light

Moderate

Severe

35% 30% Probability

Duration

b

25% 20% 15% 10% 5% 0%

Days before/after Flowering (DF)

was triggered by 3–5 and 6–8 d hot weather duration, respectively, when DF 0–5, while 3–5 d heat duration dominated at DF > 5.

c Light

Moderate

Severe

35%

3.1.2. Intensity of rice heat Detailed information about the hot weather starting time for 3–5 d, 6–8 d and > 8 d durations of light, moderate and severe rice heat is shown in Table. 4. For rice heat with a similar duration, the impact of rice heat intensity is closely related to the DF of hot weather. Hot weather beginning before flowering may trigger severe rice heat, with mean DFs for 3–5 d, 6–8 d and > 8 d rice heat in the severe category of −1.57, −2.58, and −5.92, respectively. There are obvious lags for DF under severe, moderate and light heat damage. For example, the mean start date of 6–8 d of severe rice heat was DF −2.58, while it was delayed to DF 1.61 and 13 in moderate and light rice heat damage events, respectively. To quantify the effects of high-temperature episodes on the rice reproductive stage and to compare the temporal dynamics of DF-based intensities for 3–5 d, 6–8 d and > 8 d rice heat, respectively, rice heatcausing probabilities for 3–5 d, 6–8 d and > 8 d of hot weather duration were described separately as shown in Fig. 4. The rice heat intensity appeared to increase sharply with a smaller DF for 3–5 d and 5–8 d durations of rice heat before or after flowering, while severe rice heat for a > 8 d duration was more likely before flowering, accounting for 18.87, 23.58 and 23.58% of the total > 8 d duration of rice heat in DF −15 to −10, −10 to −5 and −5 to −0, respectively. The probability of moderate and light heat damage was more than 80% when 3–5 d of hot weather occurred in the DF −5 to 5 (Fig. 4-a), while the probability of moderate and severe heat damage increased to 100% when > 5 d of hot weather occurred in this period (Fig. 4-b and Fig. 4-c). > 80% of rice heat with a > 8 d duration started in DF −15 to 0, with severe rice heat for > 8 d accounting for approximately 90% of the damage in such a period. Notably, a Tmax ≥ 35 °C for longer than 3 d may lead to moderate and severe rice heat when such a period starts before flowering, especially for a > 8 d duration of hot weather. Rice heat was mainly light or moderate when hot weather started 5 d after flowering.

Probability

30% 25% 20% 15% 10% 5% 0%

Days before/after Flowering (DF) Fig. 4. Disaster-causing probability for 3–5 d, 6–8 d and > 8 d hot weather duration. Notes: a is disaster-causing probability for 3–5 d; b is disaster-causing probability for 6–8 d; c is disaster-causing probability for > 8 d;

3.2. Evaluation level of rice heat DF is an important factor in the evaluation of rice heat intensity according to the record based historical rice heat representation. DF in the 9 data sets in the given hot weather duration and rice heat intensity combinations was calibrated to constructed the evaluation level of rice heat. Table 5 presents the results of distributed significance tests of DF in the 9 rice heat sample sets. DFs in those 9 sample sets were subjected to the normal distribution and exponential distributions with significance > 0.05, while 8 sample sets reached the significance level for uniform distributions, with 1 dataset dropped (> 8 d-severe). The Sig value of the normal distribution is closer to 1 than the exponential distribution in 8 sample sets, except for the > 8 d-severe combination, demonstrating that the normal distribution is generally more suitable for fitting the DF datasets sets (Yang et al., 2016). Parameters of the normal distribution and 95% confidence interval for each DF set were listed in Table 6, with the probability distributions of DFs shown in 6

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Table 5 Distributed significance tests of DF in rice heat samples. Types

3–5 d

Normal Exponential Uniform

6–8 d

>8 d

Light

Moderate

Severe

Light

Moderate

Severe

Light

Moderate

Severe

0.657 0.224 0.370

0.910 0.372 0.592

0.950 0.969 0.666

0.900 0.385 0.518

0.982 0.745 0.760

0.987 0.445 0.990

0.869 0.238 0.336

0.660 0.325 0.855

0.127 0.575 0.000

showing a high positive RHI trend was also located in the middle and northeast part of the study region, same as the high RHI-dominated region. Most stations in such areas were found with slopes > 0.15/ 10a. Slopes of RHI at 11 stations, including Shangrao, Yingtan, Jian, Lean, Zhangshu, Xingan, Taihe, Geyang, Nanchang, Jishui and Lianhua, were detected more than 0.2/10a. Overall, the middle and northeast parts of the study region are considered regions with a high frequency of and high potential for rice heat. Notably, RHI is determined by a combination of weather condition (hot weather events) and crop phenological process (i.e. DF). Spatial distribution of RHI showed general agreement with the average annual days at Tmax ≥ 35 °C, except the middle part of study region, including Xingguo, Ganxian, Yudu and Ruijin. As mentioned previously, extreme temperatures during booting stage to flowering can be detrimental to RHI. Lower rice hear risk detected in the middle part of study region can be attributed to the relative advance of heat-sensitive growing stages, which has minimized the negative effects of hot event on rice. Temporal RHIs showed different characteristics with temperature, no matter the average temperature tendency or the accumulated hot days. Although temperature increased significantly in the south part of the study region including Quannan, Dingnan, Longnan and Xunma, there was no corresponding increase in RHI. Generally, RHI results can partially reflect the adaptation of the agricultural system in study region. More evidence and recommendations for disaster prevention and mitigation can be provided based on the risk assessment, and ex ante design of policies and measures can be made to face rice heat disaster. Advice from government authorities and tremendous effort from farmers, including shifting crops and modifying the planting season should be well implemented in rice heat-prone areas.

Table 6 Parameters of normal distribution and 95% confidence interval for 9 DF sets (d). Duration

Level

μ

σ

95% confidence interval

3–5 d

Light Moderate Severe

4.7619 2.5714 −1.5714

9.82296 6.55138 4.68534

−0.29 to 9.23 −0.03 to 5.11 −5.90 to 2.76

6–8 d

Light Moderate Severe

13.0000 1.6087 −2.5833

4.81664 7.62615 4.92597

7.94–18.05 −1.69 to 4.91 −5.71 to 0.54

>8 d

Light Moderate Severe

13.3750 1.3462 −5.9178

4.71888 6.38713 3.72586

5.33–14.00 −1.23 to 3.92 −6.79 to −5.05

Fig. 5. The starting threshold for the rice heat at each duration (3–5 d, 6–8 d and > 8 d) of hot weather was identified according to the mean 95% confidence interval of the 9 DF sets, based on normal distributions. We determined the light, moderate and severe damage levels for a 3–5 d heat duration as an example. The 95% confidence intervals of the DF for light, moderate and severe rice heat were −0.29 to 9.23, −0.03 to 5.11 and −5.90 to 2.76, respectively. Therefore, the mean starting date of light, moderate and severe rice heat disasters with 3–5 d duration had a 95% probability of being located at −0.29 to 9.23, −0.03 to 5.11 d and −5.90 to 2.76 DF. Priority was given to the more intense disaster level with a lower and upper 95% probability as the threshold. We can conclude that hot weather with a 3–5 d duration starts approximately −6 to 3 DF and may lead to severe rice heat disaster, while rice heat intensity appears to be moderate when it starts 4 to 5 d after flowering. Light rice heat will be induced when hot weather occurs at 6 to 9 (exactly 9.23) d after flowering. The level of rice heat based on the starting calendar was constructed after rounding, as shown in Table 7. Severe, moderate and light rice heat for 3–5 d duration can be identified at DF −6 to 3, 4–5 and 6–9. Similarly, severe, moderate and light rice heat for 6–8 d and > 8 d started at −6 to 1, 2–5, 6–18 and −7 to −5, −4 to 4, 5–14 DF, respectively.

4. Discussion 4.1. Relationship between hot weather processes/events and rice heat As proposed in previous statements (Matsui et al., 2000; Kobata and Uemuki, 2004; Mohammed and Tarpley, 2009; Jagadish et al., 2011; Julia and Dingkuhn, 2013), temperatures exceed the appropriate range during rice growth period, particularly the vital productive phases, inevitably cause irretrievable damage to the crop development. In this work, we explored the relationship between hot weather processes/ events and rice heat based on historical disaster data processing and reanalysis, highlighting the particular period severely hit by historical rice heat under the former research scope, i.e., the reproductive period. Given the background of the study area, hot weather duration for 3–5, 6–8 d and > 8 d in the period from 20 d before flowering (−20 d) to mid-late filling is targeted to demonstrate the regularity and characteristics of rice heat. Tmax ≥ 35 °C, duration and DF were proposed as thresholds for rice heat. For rice heat with a similar duration, the impact of rice heat intensity is closely related to the starting time of hot weather. An earlier starting date of hot weather tends to lead to a more severe heat disaster for rice, which means periods before and during flowering are more sensitive to heat. Hot weather before and during flowering may affect the flowering dynamics via day heat stress, shortening of the flowering period and forwarding peak flowering generally (Tao et al., 2008) with a reduction in seed set and finally grain weight (Mohammed and Tarpley, 2009). The influence of hot

3.3. Risk analysis RHIs across the region were calculated from 1981 to 2015 using 85 meteorological stations, integrating the rice heat evaluation indicators and meteorological and flowering data. Temporal and spatial characteristics are described in Fig. 6 and Fig. 7. High RHIs were mainly found in the middle and northeast part of the region (as shown in Fig. 6a), with the RHI for most stations being greater than 0.25. The highest RHIs were detected at the Guangfeng, Huangfeng and Guixi stations, with RHI values greater than 0.5. The RHIs in the southern and northern parts were comparatively lower, with the observed RHI for most stations being less than 0.1. The temporal characteristics of RHIs from 1981 to 2015 are shown in Fig. 7. The RHIs from the 1980s, 1990s, 2000s and 2010s were 0.12, 0.09, 0.34 and 0.07, respectively. The regional RHI from 2003 had the highest value (0.75), followed by that from 1988 (0.74) and 1994 (0.58). Regional RHI showed a positive trend (slope was 0.02/10a) from 1981 to 2015. The RHI from 82 stations showed an increasing trend. However, spatial variability of the magnitude of the increase of the RHI was found (Fig. 6b). The region 7

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a

7%

b

4%

6%

4% 5%

3%

4% Probability

Probability

3% 2% 2%

1%

3% 2%

Normal distribution type: N 4.76 9.822

1%

0%

0% -15

-10

-5

0

5

10

15

-15

20

-10

-5

9%

7%

6%

6%

5%

5% Probability

Probability

8%

7%

4% 3% Normal distribution type: N 1.57 4.692

1% -5

2%

0% 0

5

10

15

20

-15

-10

-5

15

20

7% 6% 5% Probability

Probability

10

8%

3% 2%

4% 3% Normal distribution type: N 2.58 4.932

2%

Normal distribution type: N 1.61 7.622

1%

1%

0%

0% 0

5

10

15

20

-15

-10

-5

Days before/after Flowering (DF)

0

5

10

15

20

Days before/after Flowering (DF)

9%

g

5

9%

f

4%

-5

0

Days before/after Flowering (DF)

6%

-10

20

Normal distribution type: N 13.00 4.812

1%

5%

-15

15

3%

Days before/after Flowering (DF)

e

10

4%

0% -10

5

9%

d

8%

2%

-15

0

Days before/after Flowering (DF)

Days before/after Flowering (DF)

c

Normal distribution type: N 2.57 6.552

1%

7%

h

8%

6%

7% 5%

6%

4% Probability

Probability

5% 4%

3% 2%

3%

2%

Normal distribution type: N 13.38 4.722

1%

0%

0% -15

-10

-5

Normal distribution type: N 1.32 6.392

1%

0

5

10

15

-15

20

-10

-5

0

5

10

15

20

Days before/after Flowering (DF)

Days before/after Flowering (DF)

12%

i

10% 8% Probability

6% 4%

Normal distribution type: N -5.92 3.732

2% 0% -15

-10

-5

0

5

10

15

20

Days before/after Flowering (DF)

Fig. 5. Probability distributions of DF sets of 3–5 d, 6–8 d and > 8 d rice heat disasters in each level. Notes: a is light for 3–5 d; b is moderate for 3–5 d; c is sever for 3–5 d; d is light for 6–8 d; e is moderate for 6–8 d; f is sever for 6–8 d; g is light for > 8 d; h is moderate for 6–8 d; i is sever for > 8 d; Blue rhombuses are the DF of disaster samples.

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Table 7 The evaluation of rice heat based on starting calendar (demonstrated by DF).

0.7

Level

DF

0.6

3–5 d

Light Moderate Severe

6–9 4–5 −6 to 3

0.5

Light Moderate Severe

6–18 2–5 −6 to 1

Light Moderate Severe

5–14 −4 to 4 −7 to −5

6–8 d

>8 d

RHI

Duration

y = 0.0025x + 0.1231 R² = 0.0135

0.4

0.34

0.3

0.2

0.12

0.09

0.1

0.07

0 1981

1986

1991

1996

2001

2006

2011

Fig. 7. Temporal characteristics of regional rice heat index (RHI).

weather on rice filling is controversial. It was reported that high temperature during the grouting period is conducive to the growth of rice yield (Kim et al., 2011), while some researches believed it affects the photosynthesis and material migration of rice (Tashiro and Wardlaw,1989). In this study, the effects of high temperature on early rice gradually decreased as early rice entered the mature stage. Analysis of historical rice heat characteristics under different combinations of hot weather durations (3–5, 6–8 d and > 8 d) and DF in this work can help us better acquire the extreme impression of hot weather processes/ events on crop disasters in South China. Accordingly, a great deal of knowledge regarding rice heat in simulating heat-led disasters has been found, which can be considered a basic guide for assessing the impacts of extreme heat on rice production.

analysis of limited disaster samples. It is also acceptable to chose other existing approaches if sufficient date available, such as copula functions, bivariables and multivariate probabilistic estimates (Zhang et al., 2018), for longer data series would benefit distribution fitting of variables. The use of the rice heat evaluation indicator can be serves as an effective methodology to assist in the identification of rice heat risk. Currently, the simplest approach to evaluate the influence of rice heat on yield based on various heat index (such as the number of days plants are subjected to heat stress, the harmful accumulated temperature, temperature anomaly, the maximum day temperature) calculation during the sensitive growing period related to crop yield formation. Previous indictors of heat stress and the proportion of yield loss related to high temperatures could represent heat stress during the sensitive stage of rice or characteristics of heat stress under climate change, whereas the crop damage due to hot weather processes/events could indicate the unique effects of rice heat. The evaluation indicator proposed in this study can be a easily applied tools in the assessment of the heat effect of hot weather processes/events in combination with past and current temperatures and/or temperature forecasts and real/predicted rice phenological data. Some physiological changes and yield loss can be predicted underlining the combination of heat type, hot weather starting time and rice developing specific stages. For example, if a hot process begins at DF −6 to 1 and last for 6–8 days, rice spikelet number, seed-setting rate will be reduced seriously with final yield loss reach to 80% approximately.

4.2. Utilization of rice heat evaluation level Based on the historical disaster representation, the agricultural meteorological disaster indicators, such as agro floods (Yang et al., 2017), drought (Wu et al., 2018), chill (Wang et al., 2019), can be constructed by statistical analysis of historical disaster in the previous literates. Information diffusion, K-S tests, normal distribution and interval estimations were selected in this research to gain the intensity of rice heat over a hot weather process/event substantially influenced by the start time and duration of the hot weather. Information diffusion and K-S tests can better defined randomness and uncertainty in the rice heat samples. The interval estimation method based on distribution fitting theoretically reflects the variety regulation of discrete samples of DF in rice disasters, avoiding the effects of subjective factors (Jena et al., 2014; Yang et al., 2016). Such methods are more valid in the

Fig. 6. Spatial distribution of regional rice heat risk and its tendency. Notes: a is the Spatial distribution average rice heat index (RHI) from 1981 to 2015; b is the tendency of RHI in 85 stations from 1981 to 2015. 9

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evaluation index unrealistic. Generally, it is accepted that the occurrence of rice heat and its intensify related most strongly to maximum temperature, and it was relatively simple and appropriate to consider the Tmax ≥ 35 °C, high-temperature duration and DF used in this study. Rice heat intensity is the focus of our research, whereas the effects on final yield are out of our scope. For one reason, farmers and agronomists would have difficulty capturing the yield loss caused by hot weather events because extreme hot weather mainly accelerate or retard crop biophysical processes and damage in its appearances (e.g., leaf colour and shape) is out of distinguishable (Rezaei et al., 2015). Although 205 rice heat in history was recorded or surveyed by skilled technicians, the yield reduction in discrete points can not be gained accurately. Secondly, the difficulty in separating yield loss caused by rice heat make it another reason for the lack of historical yield loss datasets -caused by heat events in study areas, because crop yield always impacted by both climatic and anthropogenic factors (e.g., variety selection and field management). In recent years, great effort has been made to assess extremes induced yield loss by crop model, and amount of researches on the rice heat are performed (e.g. Oryza2000, CERESrice, Rice-Grow, SIMRIW, APSIM), with heat response functions and sensitivities be calibrated and quantified (Bouman, 2001; Nissanka et al., 2015; Zhang et al., 2016). Results in our study can better assistant the applicability of crop model as a fundamental information for model improvement.

4.3. Potential adaption strategies to mitigate rice heat Over past decades, rice yields in most of the study area have been reported to decrease because of extreme temperature, with extreme temperature-lead yield loss ranging from 0.01 to 50% (Wang et al., 2016). In particular, South China areas were considered to be the most vulnerable areas to extreme temperature, which is consistent with the findings of many previous studies (Tao and Zhang, 2013a,b; Teixeira et al., 2013; Zhang et al., 2018). In our present work, it was worth noting that rice heat was more serious in the middle and northeastern part of the study area. The growth period of double early rice corresponded well with the temporal distribution of hot weather, and rice heat damage in severe level was detected more in such regions. In the case of Guixi (located in northeast of Jiangxi Province), early rice usually blooms from late June to early July, severe heat rate was more than 45% because of the overwhelming hot weather processes occurred in local rice heat-sensitive phases, i.e., flowering. Although tremendous efforts such as shifting crops and modifying the planting season has been taken place in south regions of China, rice heat has been witnessed in recent years. Furthermore, rice heat tend to increase under climate change with the enhancement of the probability of temperature peaks overlapping with the reproductive period (Tao and Zhang, 2013b), although crucial phenology phase exposed to heat seem to be shortened (Zhang et al., 2018). In coping with growing heat environmental conditions, rice heat management is urgently needed to escape or avoidance of heat stress during a crucial phenological period. Appropriate advance of sowing date can potentially be proposed because rice is more sensitive to heat before and during flowering than it is in the filling phase (Moriondo et al., 2011). Additional, It is also an effective way to deal with rice heat to select the varieties with better heat resistance (Jagadish et al., 2010; Julia and Dingkuhn, 2012), e.g., introgression of the early morning flowering gene from Oryza officinalis into O. sative has recently been reported beneficial to reduce the spikelet fertility (Ishimaru et al., 2010).

5 Conclusions To explore the agricultural effect of a weather disaster process/ event on rice, the particular period severely affected by historical rice heat was determined, historical disasters were represented by coupling historical temperature data and rice growth stages (DF), based on the time and location of the historical early rice heat disaster records. Heat disasters occurred approximately 15 d before flowering, while DF −5 to 0 had the highest possibility of rice heat, followed by DF −10 to −5. Disaster intensity appeared to increase sharply close to (before or after) flowering. Hot weather may lead to moderate and severe rice heat when it occurs before flowering. Rice heat was mainly light or moderate when hot weather started 5 d after flowering. Severe, moderate and light rice heat for 3–5 d durations can be identified as starting at DF −6 to 3, 4–5 and 6–9, respectively. Similarly, severe, moderate and light rice heat for 6–8 d and > 8 d durations started at DF −6 to 1, 2–5, 6–18 and −7 to −5, −4 to 4, 5–14, respectively. It is worth noting that the middle and northeast part of the study region are certified as high frequency and high potential regions for rice heat risk based on the RHI from 1981 to 2015, which suggests that we should take actions rather than just wait. Our study explicitly represented the effects of extreme hot weather on rice and better represented the responses of disaster intensity with a time lapse. Different degrees of crop sensitivity to heat stress can provide substantial knowledge for rice heat related researches. In addition, the resulting probabilistic changes could provide more robust information for autonomous adaptation strategies, such as the shift in sowing dates, choice of varieties with longer/shorter growth cycles, etc. Irrigation, fertilization and soil conditions should be addressed to elaborate on and enhance the knowledge of rice heat. Comprehensive risk assessment considering agronomic, socioeconomic, field management and other endogenous or exogenous factors (such as technology, infrastructures, physic factors, etc.) is our following focus point.

4.4. Uncertainties and limitations Thirty-five degrees Celsius is considered an extreme-temperature threshold for different stages of rice development (Sun and Huang, 2011; Wang et al., 2014; Zhang et al., 2014). However, some of heat tolerant varieties can avoid damage of organism in spite of experiencing hot weather completely (Prasad et al., 2006; Shi et al., 2013). For example, Jagadish et al. (2007) found that crop yield increased with day temperatures in an indica variety but decreased in a japonica variety. Otherwise, increasing night temperatures is confirmed a negative correlation with rice yield (Shi et al., 2013), with a 72% decrease founded in panicle fertility when night temperature increased from 27 °C to 32 °C (Peng et al., 2004; Mohammed and Tarpley, 2009). Although temperatures ≥ 35 °C might not play the best application in some specific regions, this threshold could be of practicality in regions with the same latitude and with similar rice varieties. In this study, use of this threshold could be a better way to analyse the historical disaster characteristics of the available dataset. It would be ideal to compare and validate the environment thresholds when more temperature datasets, such as 24-h temperatures, are available. Additionally, several other geological and topographical factors, as well as soil conditions and field management factors, can influence local canopy temperature. For example, wet land rice may be more robust than crops in dry environments. Wet land rice, with a large evaporation pressure deficit and most times with an supplementary supply of water through precipitation or irrigation, maintains canopy temperature in a low condition because of leaf transpirational cooling, which reduces the possibility of rice heat risk. However, other factors, for example, CO2, sunshine hours, plant diseases and insect pests that can aggravate or alleviate rice heat cannot be accurately simulated or are not available, making the construction of a comprehensive

CRediT authorship contribution statement Jianying Yang: Writing - review & editing, Methodology. Zhiguo Huo: Supervision. Xiangxiang Li: Investigation, Data curation, Resources. Peijuan Wang: Conceptualization. Dingrong Wu: Software. 10

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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This research was supported by the National Science Foundation of China, China (31701312), the Basic Research Funds-regular at the Chinese Academy of Meteorological Sciences, China (2017Z004), and the Science and Technology Development Fund at the Chinese Academy of Meteorological Sciences, China (2018KJ012). We gratefully acknowledge the anonymous reviewers for their valuable comments on the manuscript. References Alexander, L.V., Zhang, X., Peterson, T.C., Caesar, J., Gleason, B., Tank, A.M.G.K., Haylock, M., Collins, D., Trewin, B., Rahimzadeh, F., 2006. Global observed changes in daily climate extremes of temperature and precipitation. J. Geo. Res. Atmos. 111, 1042–1063. Ana, M., Petrović, Slavoljub S., Dragićević, Boris P., Radić, Ana Z., Pešić, Milanović, 2015. Historical torrential flood events in the kolubara river basin. Nat. Hazards 79 (1), 1–11. Bouman, B., 2001. ORYZA2000: Modeling Lowland Rice. International Rice Research Institute/Wageningen University and Research Centre, Los Banos, Philippines/ Wageningen, Netherlands. Chen, S.X., Wen, K.G., 2006. China Meteorological Disasters Book (Jiangxi Provinces). Meteorogogical Press, Beijing. China Meteorological Administration, 2008-2015. China Meteorological Disaster Yearbook. Meteorological Press, Beijing (In Chinese). Crutcher, H.L., 1975. A note on the possible misuse of the Kolmogorov-Smirnov test. J. Appl. Meteorol. 14, 1600–1603. Eagleson, P.S., 1978. Climate, soil, and vegetation: 2. The distribution of annual precipitation derived from observed storm sequences. Water Resour. Res. 14 (5), 713–721. Friday, F., Monday, M., 1974. Theory of probability and mathematical statistics. Am. Math. Society. 2, 223–224. Gao, S.H., Wang, P.J., 2009. Effects of heat on rice in the middle and lower reaches of the Yangtze river. China Meteorological Press, Beijing (In Chinese). Glade, T., Albini, P., Frances, F., 2009. The use of historical data in natural hazard assessments. Springer, Netherlands, pp. 131–140. Gourdji, S.M., Sibley, A.M., Lobell, D.B., 2013. Global crop exposure to critical high temperatures in the reproductive period: historical trends and future projections. Environ. Res. Lett. 8, 024041. Hatfield, J.L., Boote, K.J., Kimball, B.A., Ziska, L.H., Izaurralde, R.C., Ort, D., Thomson, A.M., Wolfe, D., 2011. Climate impacts on agriculture: implications for crop production. Agron. J. 103, 351–370. Huang, C.F., 2001. Information matrix and application. Int. J. Gen Syst 30 (6), 603–622. Huang, C.F., 2002a. An application of calculated fuzzy risk. Inf. Sci. 142 (1), 37–56. Huang, C.F., 2002b. Information diffusion techniques and small sample problem. Int. J. Inf. Technol. Decis. Mak. 1 (2), 229–249. Ishimaru, T., Hirabayashi, H., Ida, M., Takai, T., Sanoh, Y.A., Yoshinaga, S., Ando, I., Ogawa, T., Kondo, M., 2010. A genetic resource for early-morning flowering trait of wild rice oryza officinalis to mitigate high temperature-induced spikelet sterility at anthesis. Ann. Bot. 106, 515–520. Jagadish, S., Craufurd, P., Wheeler, T., 2007. High temperature stress and spikelet fertility in rice (Oryza sativa L.). J. Exp. Bot. 58, 1627–1635. Jagadish, S.V.K., Murty, M.V.R., Quick, W.P., 2015. Rice responses to rising temperatures – Challenges, perspectives and future directions. Plant 38 (9), 1686. Jagadish, S.V.K., Muthurajan, R., Oane, R., Wheeler, T.R., Heuer, S., Bennett, J., Craufurd, P.Q., 2010. Physiological and proteomic approaches to address reproductive stage heat tolerance in rice (Oryza sativa L.). J. Exp. Bot. 61, 143–156. Jagadish, S.V.K., Muthurajan, R., Rang, Z.W., Malo, R., Heuer, S., Bennett, J., Craufurd, P.Q., 2011. Spikelet proteomic response to combined water deficit and heat stress in rice (Oryza sativa cv. N22). Rice 4, 1–11. Jena, P.P., Chatterjee, C., Pradhan, G., Mishra, A., 2014. Are recent frequent high floods in Mahanadi basin in eastern India due to increase in extreme rainfalls? J. Hydrol. 517 (1), 847–862. Jin, H., Zhang, F., Yan, X., Jie, L., 2016. Recent changes of rice heat stress in jiangxi province, southeast china. Int. J. Biometeorol. 61 (4), 1–11. Julia, C., Dingkuhn, M., 2012. Variation in time of day of anthesis in rice in different climatic environments. Eur. J. Agron. 43, 166–174. Julia, C., Dingkuhn, M., 2013. Predicting temperature induced sterility of rice spikelets requires simulation of crop-generated microclimate. Eur. J. Agron. 49, 50–60. Kim, J., Shon, J., Lee, C.K., Yang, W., Yoon, Y., Yang, W.H., Kim, Y.G., Lee, B.W., 2011. Relationship between grain filling duration and leaf senescence of temperate rice under high temperature. Field Crops Res. 122, 207–213. Kjeldsen, T.R., Macdonald, N., Lang, M., Mediero, L., 2014. Documentary evidence of past

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