Construction and Analysis of Freezing Damage Indices for Winter Wheat During the Overwintering Period in Northern Xinjiang, China

doi:10.21203/rs.3.rs-3104192/v1

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Research Article

Construction and Analysis of Freezing Damage Indices for Winter Wheat During the Overwintering Period in Northern Xinjiang, China

https://doi.org/10.21203/rs.3.rs-3104192/v1

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published 08 Nov, 2023

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Freezing damage indices of winter wheat during snowfall in the overwintering period in northern Xinjiang, China, were constructed using six climate factors: the negative accumulated temperature, average temperature, maximum cooling range, average snow depth, extreme minimum temperature, and average wind speed. A snow depth data processing method was designed. The principal component method was used to construct the freezing damage indices. The freezing damage indices were constructed and analyzed separately using three different sources of data: freezing damage conditions recorded by 49 meteorological stations during 1960 − 2020; freezing damage conditions recorded by 11 wheat observation stations during 1960 − 2020; and freezing damage conditions recorded by 83 meteorological observation stations during 2016 − 2020. Before the analysis of disaster factors, Kaiser–Meyer–Olkin measure of sampling adequacy and Bartlett's test of sphericity were applied to test the suitability of factor selection. The results were analyzed to determine freezing damage according to a Mann–Kendall mutation test. Historical freezing damage data were used to test the freezing damage indices. The results showed that the indices did reflect the freezing damage in northern Xinjiang. In the last 60 years, the interannual variation of the freezing damage indices in northern Xinjiang displayed a significant downward trend.The freezing damage indices changed abruptly around 1985, and the frequency, extent, and scope of severe freezing damage in northern Xinjiang significantly decreased under climate warming. At the same time, instability was apparent due to climate change, with an increase in freezing damage from 1990 to 2009, a decrease from 2010 to 2020, and an increase in the area over which mild and moderate freezing damage occurred in the past 5 years. The analysis of wheat observation stations showed that the maximum cooling range and extreme minimum temperature during the overwintering period had the greatest influence on freezing damage of winter wheat. A large part of the winter wheat crop experienced frequent freezing damage, and it is therefore necessary to predict and prevent freezing damage in advance, and adjust the regional planting regime according to climate change.

Freezing damage index

Overwintering period

Snow

Winter wheat

Climate change

Northern Xinjiang, China is an important wheat production and consumption area in northwest China, and more than 70% of the total wheat area is winter wheat (Ma et al., 2020).The winter wheat in this region suffered the most serious potential meteorological disaster is freezing damage(Wang et al., 2011). From 1961 to 2018,the area of wheat affected by freezing damage during this period was over 66,700 hectares, with the three most severely affected regions in descending order being Changji, Tacheng, and Yili (Zhu et al., 2019).Since the 1980s, the climate in northern Xinjiang had become significantly warmer(You et al.,2022), while climate variation and climate instability has increased (Maaz et al., 2017;Lim et al.,2021), temperature has changed rapidly, and the frequency(Zhang et al.,2014) and range of the mild and moderate freezing injury has increased (Wheeler et al.,2013;Meng et al.,2019;Song et al.,2022;Miron et al., 2022). This has resulted in a significant increase in the frequency of wheat disasters and has directly affected the winter wheat yield ( Jia et al., 2019;Zhao et al., 2022; Zhang et al., 2022).Therefore, it is necessary to study freezing damage research in northern Xinjiang to explore the occurrence characteristics, temporal evolution trend, and spatial distribution characteristics of the freezing damage regions in northern Xinjiang under a warming climate, providing a theoretical basis for the effective utilization of climatic resources and ensuring the safety of wheat production.

Freezing damage is an agricultural meteorological disaster that can occur in winter and early spring, when overwintering crops are affected by temperatures below 0˚C, resulting in the dehydration and freezing of plant tissues, and thus loss of physiological activity. This can ultimately lead to plant damage or even death (Würschum et al.,2017;Jia et al., 2019; Fu et al., 2022). Numerous studies of freezing damage have been conducted (Cann et al., 2020;Dashtseren et al., 2021;Liao et al., 2021;Ma et al., 2022). Meng et al. (2019) established overwintering freezing damage indices for winter wheat in northern China by the principal component method. Mu et al. (2015) compared the freezing resistance of winter and spring wheat in the Huang-Huai-Hai region in mid-winter. (Zhang et al., 2019) constructed and improved a freezing damage index for Korla fragrant pear trees in winter. Mo et al. (2013) and Zhang et al. (2016) selected temperature as the disaster factor to establish an overwintering freezing damage index of winter wheat in Beijing and Henan Province, respectively. Freezing damage in the overwintering period has also caused disasters for animal husbandry, forest fruits, vegetables, and cash crops (Wang et al., 2017; Yang et al., 2020; Bai et al., 2021;Huang et al., 2021; Kong et al., 2021; Yuan et al., 2021;Abasi et al., 2022;Liu et al., 2022). However, the existing freezing damage indices did not involve snow depth data(Kersebaum,2022;Zhu et al.,2022), which is an essential disaster causing factor in northern Xinjiang(Zhao et al.,2021).No freezing damage indices of northern Xinjiang was found in the study, and only relevant reference every factor standard of freezing damage were found.

The main meteorological reasons for freezing damage of winter wheat in overwintering period were cold wave and accumulated temperature deficiency caused by long time low temperature.The cold wave can be represented by maximum cooling and extreme minimum temperature.The influence of accumulated temperature can be reflected by overwinter negative accumulated temperature and average temperature.During the winter cold wind invasion, the ground temperature changes violently, easy to cause freezing seedlings.In the overwintering period, the temperature is continuously low, the snow is covered for a long time, the photosynthesis of wheat fields is weak, the cold resistance is decreased, and there are dead seedlings or freezing damage.

This study comprehensively considered the main meteorological reasons for freezing damage of winter wheat during the overwintering growth period in northern Xinjiang. The negative accumulated temperature, average temperature, maximum cooling range, average snow depth, extreme minimum temperature, and average wind speed were used as the disaster factors to construct the freezing damage indices.A snow depth data processing method was innovative designed. Multinomial calculation were used to determine whether the selected factors were suitable. If they were,the principal component method was used to construct freezing damage indices.In order to comprehensively analyze the change of winter wheat freezing damage in northern Xinjiang under climate change, only the historical stable wheat observation stations have too few data, and the representation of wheat growing regions was not comprehensive.So the freezing damage indices were constructed separately using three different sources of data: the freezing damage conditions for the whole region; the freezing damage conditions for only wheat observation stations; and the freezing damage distribution for more observation stations over the past 5 years. Historical freezing damage data were used to adaptable test the freezing damage indices.The better adaptability of freezing damage indices can provide reference for regionalization and planting layout of winter wheat in this area.

2.1 Overview of the Study Area

The study period was selected as the overwintering period from 1960 to 2020. The research area was northern Xinjiang, China, which covers the area of 42.30°−49.15°N and 79.90°−96.05°E (Fig. 1). There were 83 meteorological observation stations in the study area. The overwintering period refers to the period from the first day of the previous year to the last day of the next year when the sliding average temperature of the 5 days passes through 0˚C, which was generally from October of the previous year to March of the next year, and was recorded as the overwintering period of the previous year(Meng et al.,2019;).

To ensure the integrity and continuity of data, a total of 49 meteorological observation stations (YI region) were selected for analysis using data from 1960 to 2020, and 11 wheat observation stations (YII region) were analyzed separately. Additionally, because the number of observation stations in northern Xinjiang increased to 83 in 2016, data from the 83 meteorological observation stations (YIII region) from 2016 to 2020 were selected to separately analyze the average distribution of freezing damage in the last 5 years. This enabled a more accurate regional analysis to be conducted and strengthened the accuracy of freezing damage prevention.

2.2 Data Sources

Meteorological data was obtained from the China Meteorological Science Data Sharing Service Network. The surface meteorological observation data from 83 meteorological stations in northern Xinjiang during 1960 − 2020 (or since the establishment of the station) were selected (Fig. 1), including daily mean temperature, daily snow depth, and average wind speed. The freezing damage information was obtained from meteorological stations at all levels, local civil affairs departments, media and newspapers, the Meteorological Disaster Book, and the "Disaster Direct Reporting System" of the China Meteorological Administration.The produced dataset can be accessed

via http://weather.cma.cn/

2.3 Snow Depth Data Processing Method

By analyzing the snow depth data from weather stations in northern Xinjiang during the winter period from 1960 to 2020, it was found that some daily snow depth data were missing.Stations without snow depth records accounted for 7% of the total data. To ensure the integrity of the data for follow-up research, the adjacent value algorithm was applied to the data without snow depth. The algorithm procedure to obtain the values of no-snow depth data stations for specific dates was as follows. The whole algorithm cycled 10 times, adding 10 km each time, and only calculated data for dates without a snow depth. Initially, the average snow depth of all meteorological stations with a longitude and latitude less than 10 km from the weather station with no-snow depth data was calculated as the snow depth value of the station on the date in question. When the area around the station with no-snow depth data reached 100 km and a value was still not obtained, the snow depth value of the station on that date was not included in the calculation. The final data with no-snow depth accounted for only 2.6% of the total data, which did not affect the later calculation of the annual average snow depth during the wintering period of each station. This method comprehensively considered the different snow depth conditions, all of which were then used in the index study.

2.4 Construction and Inspection of Freezing Damage Indices

The principal component analysis method was used to construct a comprehensive variable with clear physical significance that could effectively reflect freezing damage in northern Xinjiang. The comprehensive freezing damage indices were constructed using three different sources of data, which were also independent and comparable. The methods used to calculate specific disaster factors are shown in Table 1.

Table 1

Calculation of the six different disaster factors in the overwintering period
Factor	Calculation
Maximum cooling range	The maximum cooling range of daily average temperature within 72 h during the overwintering period
Extreme minimum temperature	The extreme minimum temperature during the overwintering period
Negative accumulated temperature	Accumulative value of daily average temperatures < 0˚C during the overwintering period
Average temperature	The mean daily average temperature during the overwintering period
Average snow depth	The mean daily average snow depth during the overwintering period
Average wind speed	The mean daily average wind speed during the overwintering period

Annual freezing damage index of each station in each region:

Y = a₁*X₁ + a₂*X₂ + a₃*X₃ + a₄*X₄ + a₅*X₅ + a₆*X₆ (1)

In Eq. (1), X₁, X₂, X₃, X₄, X₅, and X₆ represent the standardized values of negative accumulated temperature, average temperature, maximum cooling range, snow depth, extreme minimum temperature, and average wind speed, respectively, during the annual overwintering period of each station in the three regions (YI, YII, and YIII). a₁, a₂, a₃, a₄, a₅, and a₆ are the respective weight coefficients of the corresponding disaster factors.

The specific calculation processes were as follows. (1) The annual overwintering periods of each station from 1960 to 2020 were determined. (2) Considering the characteristics of northern Xinjiang, the disaster factors were determined. (3) Six disaster factors were calculated separately for each station during the annual overwintering period. (4) The disaster factors were calculated and standardized separately for all stations in each region (YI, YII, and YIII) in all years. (5) The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy, Bartlett's test of sphericity, and a common factor variance calculation of the disaster factors were conducted to determine whether the selected factors were suitable. If they were, the next step was carried out. (6) The first several principal components whose cumulative variance contribution rate was ≥85% were selected for analysis. The first three principal components were extracted from regions YI and YII, and the first four principal components were extracted from region YIII. (7) The eigenvalues and eigenvectors of the six disaster factors corresponding to each principal component were calculated separately. (8) The first several principal components were calculated through the variance contribution and eigenvectors of each principal component. (9) The expressions YI, YII, and YIII were obtained and used as the comprehensive indices of each region according to the calculation results.

Through the above calculations, the annual freezing damage indices YI, YII, and YIII of each station in northern Xinjiang during the 1960 − 2020 overwintering period were obtained. The adaptability of the freezing damage indices were tested using historical disaster data. After verification, temporal and spatial distribution maps of each freezing damage index Y were constructed for analysis.

2.1 Method of Inspection

Before the disaster factors were analyzed, the KMO test, Bartlett's test of sphericity, and common factor variance calculations were performed. The KMO test was used to check for any correlations and partial correlations between variables, with values ranging from 0 to 1. The closer the KMO value was to 1, the stronger the correlation and weaker the partial correlation between variables, and the better the effect of the factor analysis. A KMO statistic value above 0.7 was considered more suitable for analysis, while a KMO statistic value below 0.5 was unsuitable for analysis. When the significance (P value) of the Bartlett's test of sphericity was below 0.05, the data were in accordance with the standard and had a spherical distribution, and the variables were somewhat independent of each other. The common factor variance calculation determines the degree of common factor variance of each factor, and the cumulative contribution rate of several common factor variances. The higher the degree of common factor variance, the higher the representativeness or explanation rate of the extracted common factor for the original variable, and therefore the better the overall effect.

The calculated results were subjected to a Mann–Kendall mutation test to determine mutagenicity (Fu et al., 1992;Xue et al., 2002; ). Under the premise that the UF curve (i.e., a statistical series calculated in time series order) exceeded the confidence line of the critical value (P = 0.05), the UB curve (i.e., a statistical series calculated in the reverse order of the time series) had two obvious crossover points between the confidence line. This indicated that the crossover point was a mutation point and was statistically significant. If the intersection was outside the confidence line, it was uncertain whether it was a mutation point.

3.1 Determination and Verification of Freezing Damage Indices

The principal component analysis method was used to establish the coefficient of freezing damage index Y, and then obtain the Y expression in the three regions (I, II, and III).

YI = 0.33307*X₁ + 0.258827*X₂ + 0.3044*X₃ + 0.00058*X₄ + 0.3248*X₅ + 0.2301*X₆

YII = 0.2459*X₁ + 0.1703*X₂ + 0.35453*X₃ + 0.21615*X₄ + 0.3068*X₅ + 0.2639*X₆

YIII = 0.27802*X₁ + 0.2049*X₂ + 0.2747*X₃ + 0.3043*X₄ + 0.2491*X₅ + 0.0634*X₆ (2)

In Eq. (2), the Y expressions were positively correlated with the six disaster factors, which physically indicated that the greater the negative accumulated temperature, the average temperature, the maximum cooling, the snow depth, the extreme minimum temperature, and the average wind speed during the winter, the more severe the freezing damage. The higher the freezing damage indices, the more severe the degree of freezing damage. With a weight coefficient > 0.3, YI showed that snow depth had the least influence on freezing damage in the overwintering period, while negative accumulated temperature, maximum cooling, and extreme minimum temperature in the overwintering period had the largest influence. YII only analyzed the wheat observation stations and found that all six disaster factors had an impact on the freezing damage indices, among which the maximum cooling and extreme minimum temperature in the overwintering period had the greatest impact. YIII analyzed the freezing damage from 2016 to 2020 and found that wind speed had the least influence on the freezing damage index, while snow depth in the overwintering period had the largest influence. The results showed that the weight of the disaster factors affecting freezing damage in northern Xinjiang were different for the three different regional indices.

Combined with the observed disaster situation in northern Xinjiang, the YI value could reflect the occurrence of freezing damage in northern Xinjiang, with the results approximately in accordance with the recorded historical disaster situation. In the winter of 1966, 27.5% of the winter wheat in the 150th regiment of the Corps Eighth Agricultural Division died, and some winter wheat in Mosuowan and Xiayedi, Shihezi City, also died. More than 80,000 animals died of hunger and cold in Changji Autonomous Prefecture, with a mortality rate of 34%. Four field workers froze to death and several got frostbite in Tacheng City, Emin County, Tacheng. In the winter of 1968, severe cold and a thin snow layer in Wener County, Bortara Autonomous Prefecture, was responsible for a large area of winter wheat death. All the winter wheat in Mosuowan, Shihezi City and the 150th regiment of the Corps Eighth Agricultural Division was lost to freezing damage. In the winter of 1976, Balikun in Hami region had an early snowfall, and all the wheat at the northern foot of the Tianshan Mountains was frozen before harvest, resulting in a decrease of more than 5 million kg in the regions winter wheat harvest. All the winter cabbage in Hami County suffered freezing damage, leading to a loss of about 10 million kg. In Changji Autonomous Prefecture, heavy snow killed 14,000 livestock, and at the beginning of the following year, 10 thousand mu of winter wheat were frozen to death in Emin County.

In the winter of 1984, seven people froze to death in Altay, Yining City in Yili region due to sustained low temperatures (-30˚C) accompanied by strong wind (force 7), which was a rare event. Fruit trees suffered a wide range of freezing damage, power plants shutdown, and 121 vehicles of the transportation department broke down. From January to March of the next year, the Bortala Autonomous Prefecture suffered freezing conditions that killed 34,500 mu of wheat, accounting for 50.8% of the total crop. Winter wheat was frozen to death over an area of 7300 mu in Emin County, Tacheng. Seven cold air intrusions killed 180,000 livestock and affected 11,927 people in 1560 households in Buerjin County, Altay Prefecture. In the winter of 1987, more than 10,000 livestock died of starvation during freezing conditions in Altay. More than 31,300 animals died in the Tacheng area. In the winter of 2009, there was a snowstorm of a once-in-60-years magnitude in Xinjiang, which exceeded the historical maximum in the same period. This snowstorm caused severe freezing damage to winter wheat in northern Xinjiang. Since the beginning of winter in 2010, the Xinjiang Altay region has been hit by three severe cold events and snowstorms, and the snow cover on Mt. Altay had a depth of more than 180 cm, which greatly affected local agricultural and animal husbandry production, road transportation, and the lives of people of all ethnic groups.

In the winter of 2012, 1 million mu of winter wheat in Changji Autonomous Prefecture suffered from freezing damage. The severity of freezing damage was related to the lack of overwintering irrigation water and the weak frost resistance caused by the early sowing date and strong growth. In the winter of 2018, a cold air mass intruded into northern Xinjiang, causing a low temperature of -30˚C in many areas. The low temperature caused severe frostbite in the roots of winter wheat and grapes, which was very harmful to the crops. During the overwintering period of 2020, winter wheat on the Hongqi farm of the Sixth Division of Xinjiang Production and Construction Corps suffered from freezing damage and a lot of crop death occurred. In general, the freezing damage indices reflected the actual historic freezing damage situation well and had a strong adaptability. They can therefore be used to represent the overwintering freezing damage of winter wheat in northern Xinjiang.

Using the historical disaster data of winter wheat freezing damage in northern Xinjiang, literature data (Mo et al., 2013; Zhang et al., 2016), and the corresponding freezing damage index values calculated here, the overwinter freezing damage of winter wheat was divided into the four levels given in Table 2. The corresponding relationships between freezing damage indices and disaster levels were established.

Table 2

Levels of winter wheat freezing damage during the overwintering period
Freezing damage indices	Disaster level
Freezing damage indices	Freeze-free	Mild	Moderate	Severe
Y	Y < 0.6	0.6 ≤ Y < 0.7	0.7 ≤ Y < 0.8	Y ≥ 0.8
Mortality rate (%)	< 8	8 − 15	16 − 30	> 30

3.2 Time Series Analysis of Freezing Damage Indices

According to Fig. 2, in the YI and YII regions, the KMO values were 0.681 and 0.666 respectively, the significance (P < 0.01) of Bartlett's test of sphericity for both regions, and the degree of common factor variance of each factor was > 0.7. The cumulative variance contribution rates were > 85%. By analyzing the first three principal components, the cumulative variances were 86.960% and 86.688% respectively. The indices decreased significantly with increasing time. The index ranges of regions YI and YII were 0.36 − 0.72 and 0.44 − 0.93, respectively, with average values of 0.51 and 0.64, and their tendency rates were − 0.0021/a and − 0.0027/a, respectively. The years with YI > 0.7 in region YI included 1966, 1968, and 1976, and the years with YI < 0.4 included 2006, 2008, and 2019. In Region YII, the years with YII > 0.8 included 1960’s, 1970’s and 1980’s .The index regions YI and YII were divided into three periods: 1960 − 1989, 1990 − 2009, and 2010 − 2020, whose tendency rates were − 0.0023/a and − 0.0038/a, 0.0013/a and 0.0024/a, and − 0.0115/a and − 0.0182/a, respectively. According to the above analysis, in the past 60 years, the most serious periods of frost damage were concentrated in 1960’s, 1970’s,and 1980’s. The average annual freezing damage index values were higher at wheat observation stations than at the 49 meteorological stations in northern Xinjiang. Over the whole study period the index values in regions YI and YII significantly decreased, but there was an increasing trend from 1990 to 2009.

The Mann–Kendall mutation test was used to analyze the change trend of freezing damage indices (YI, YII) from 1960 to 2020 in northern Xinjiang. According to the intersection point of UF (statistical series calculated in time series order) and UB (statistical series calculated in reverse of the time series order) curves in each region, the decrease in index values was an abrupt change phenomenon. According to Fig. 3, in the YI region, the coincidence points of the freezing damage indices, as determined by the intersection of the two curves, were 1982, 1984, and 1985, and the mutation year in the YI region was 1985. The coincidence point in the YII region was 1981.

In the YI and YII regions, the average freezing damage index values before the mutation were 0.56 and 0.71, while after the mutation the index values were 0.48 and 0.60, respectively. Therefore, the degree of frost damage in northern Xinjiang decreased after the 1980s compared with the situation before the 1980s, displaying a significant downward trend, although an obvious upward trend was observed from 2008 to 2012.

3.3 Spatial Distribution Characteristics of the Freezing Damage Indices

3.3.1 The average freezing damage index values in the YI region from decade to decade

As shown in Fig. 4, the analysis was conducted region by region in six time periods over the whole 1960 − 2020 study period (YI region). During 1960 − 1970, the average YI value was 0.57, with values > 0.7 mainly distributed in the Altay region, and values > 0.8 mainly distributed in Fuyun and Qinghe counties. Values of YI between 0.6 and 0.7 were mainly distributed in the middle and eastern sections of the Tianshan Mountains northward to Altay, including Balikun Kazakh Autonomous County, Qitai County, Jimsar County, Midong District, Shihezi City, Wusu City, Emin County, Wulhe District, and Fuhai County.

During 1971 − 1980, the average YI value was 0.56, and areas with YI values > 0.6 were distributed in the same areas as in the 1960s, with the addition of Hutubi County in the middle of the Tianshan Mountains. The observation station in Balikun County had an average YI value > 0.7.

During 1981 − 1990, the average YI value was 0.51, with a significant reduction in the area with values > 0.6, which were distributed in the Altay area, as well as Shihezi City, Midong District, Qitai and Balikun stations.

During 1991 − 2000 and 2001 − 2010, the average YI values were 0.48 and 0.49, respectively. Areas with YI > 0.6 were located in the Altay area as well as the stations at Qitai, Midong, and Shihezi. During 2011 − 2020, the average YI value was 0.46. Areas with YI > 0.6 were concentrated in Altay, with areas with YI > 0.7 distributed in Qinghe and Fuhai counties.

3.3.2 The average freezing damage index values in the YIII region

As shown in Fig. 5, the average freezing damage index values in the last 5 years at 83 meteorological observation stations (YIII region) during 2016 − 2020 were selected for a regional analysis. The KMO value was 0.587, the significance (P < 0.01) of Bartlett's test of sphericity, and the degree of common factor variance of each factor were > 0.7. The cumulative variance contribution rate was ≥ 85%. The first four principal components were extracted for analysis, and their cumulative variance was 92.9%. The average YIII value was 0.50, with YIII values > 0.7 mainly distributed in Fuyun and Qinghe counties in the Altay region, in which YIII values > 0.8 were distributed in northeast Fuyun County. An area of 0.6 < YIII ≤ 0.7 was concentrated in the north of Changji Prefecture and extended to Altay and part of Tori County.

3.3.3 Distribution of high freezing damage index values in the last 20 years

According to Fig. 2, the average freezing damage index values in the overwintering period in 2009 were the highest in the last 20 years, and over the last five years the highest values occurred in 2018. The distributions of freezing damage according to the indices in the overwintering periods of 2009 in the YI region and 2018 in the YIII region are shown in Fig. 6. According to Fig. 6, in the overwintering period of 2009, YI values ≥0.8 were distributed in the whole of the Altay region, and an area of 0.7 ≤ YI < 0.8 was distributed around Altay and the stations in Qitai County, Changji City, and Shihezi City. An area of 0.6 ≤ YI < 0.7 was distributed in the middle and eastern sections of the Tianshan Mountains and extended north to the vicinity of Altay, bit not Urumqi City.

In the overwintering period of 2018, the area with YIII values ≥0.8 was significantly reduced compared to the situation in 2009, and the freezing damage was concentrated in Fuyun and Qinghe counties. An area of 0.7 ≤ YIII < 0.8 distributed in other parts of Altay, northern Changji Prefecture, and part of Qitai County. The area of 0.6 ≤ YIII < 0.7 was significantly larger than in 2009, and was distributed in Emin County, Toli County in the Tacheng area, and the middle and eastern sections of the Tianshan Mountains, extending north to the Altay area, but not Urumqi City. In 2009 and 2018, there were major wheat planting areas with mild and moderate freezing damage.

3.4 Frequency and Number of Winter Wheat Freezing Damage Occurrences

When the indices were compared with the relevant disaster data, YII had the highest coincidence with the wheat freezing disasters in the historical reports. The frequency and number of freezing damage occurrences at wheat observation stations in each 20 year period within the full study period were analyzed in detail.

Table 3

Distribution of freezing damage occurrences at wheat observation stations in the YII region
Period	1960 − 1980				1981 − 2000				2001 − 2020
Disaster level	Mild	Moderate	Severe	Total	Mild	Moderate	Severe	Total	Mild	Moderate	Severe	Total
Altay City	1	3	17	21	4	4	10	18	4	3	12	19
Tacheng City	4	8	5	17	6	2	3	11	2	4	0	6
Bole	10	6	1	17	5	2	1	8	8	0	0	8
Wusu	7	6	2	15	4	1	1	8	7	0	0	7
Shihezi City	8	3	2	13	5	3	2	10	7	3	1	11
Changji City	9	4	7	20	3	1	1	5	4	2	3	9
Qitai County	1	6	14	21	3	7	9	19	4	6	7	17
Yining City	3	3	1	7	2	0	1	3	1	0	0	1
Xinyuan County	2	0	0	2	1	0	0	1	0	0	0	0
Zhaosu County	8	2	0	10	2	1	0	3	2	0	0	2
Balikun County	0	6	15	21	7	5	5	17	7	9	1	17
Total	53	47	64		42	26	33		46	27	24

The observation station in Zhaosu County was 1,851 m above sea level (asl), and the observation station in Balikun County was 1,679 m asl, while the other observation stations were between 460 and 930 m asl. The higher the altitude within the same region, the more frequently freezing damage occurred and more years there were with severe freezing damage. With the abrupt climate change in the 1980s, the frequency of freezing damage decreased significantly from 1981 to 2020, and the number of years with severe freezing damage decreased gradually over the 20 year periods. The number of years with mild and moderate freezing damage from 2001 to 2020 increased slightly compared with the number from 1981 to 2000. Compared with the previous 20 years, the number of years with severe freezing damage in Aletai and Changji stations increased slightly from 2001 to 2020, while the number years with severe freezing damage at the remaining winter wheat observation stations decreased. According to the frequency and number of freezing damage occurrences at winter wheat observation stations in the last 20 years, Shihezi, Changji, and Qitai were the main planting areas of winter wheat, where freezing damage often occurred. The Yili area (Yining City, Xinyuan County, Zhaosu County) was the best area for wheat planting, due to its low freezing damage index values and low frequency of freezing damage.

According to the analysis described above, most of the areas that experienced mild and moderate freezing damage were the main planting areas of winter wheat. Due to the high frequency of mild freezing injury, it is necessary to reduce and eventually prevent the future occurrence of wheat freezing damage. There are five measures that could be adopted to meet this aim. (1) Select good varieties. This is the best way to protect wheat from freezing damage. Wheat varieties with frost resistance have little or no losses in years when frost damage occurs. (2) Plant wheat well. Sowing wheat at the appropriate time on carefully prepared land with sufficient soil moisture is an effective approach. It is important to ensure that agricultural machinery is in good condition during sowing to ensure the same depth of sowing, and complete it at the same time. (3) Manage the crop well. The pre-winter management of the crop after wheat emergence should be strengthened to obtain strong wheat seedlings during the overwintering period. Water and fertilizer management should be maintained at reasonable levels after greening, which would increase the size of ears and strengthen disease prevention and insect control in the later stages. (4) Watering the wheat well before the spring cold wave. Watering could increase the ground temperature, improve the farmland microclimate, and significantly reduce the wheat frost damage. Farmers or cooperatives could use sprinkler and/or drip irrigation where they are available. (5) Spray foliar fertilizers to increase the stress resistance of wheat plants and reduce the occurrence of freezing damage.

Freezing damage to winter wheat is the result of a complex process involving many factors, such as pre-wintering climate factors, crop variety types, pre-winter cold resistance exercise, the geographical environment, and planting management (Li et al., 2017; Zhang et al., 2019; Ma et al., 2021; Liu et al., 2022;Vaitkeviciut et al., 2022; ). The cold resistance mechanism of wheat is subject to genetic and environmental factors, as well as the comprehensive influence of the complexity of the wheat growth cycle and variability of environmental factors, and therefore it is possible that there were errors in the disaster analysis. In the years of freezing damage, the freezing damage index of wheat observation stations, especially Changji region, has reached moderate or severe, while no winter wheat damage has been recorded in books in some years.Additionally, due to the imperfect historical disaster data regarding wheat freezing damage, there may have been inconsistencies in the application of freezing damage indices, so they have been constantly improved and optimized in later business applications and field inspections.

In future studies it will be necessary to establish prediction models of winter wheat freezing damage in northern Xinjiang. If the freezing damage indices are estimated in the overwintering period of each station before October of the current year, it could provide an early warning of potential freezing damage in northern Xinjiang. It will also be useful to cooperate with insurance companies regarding the freezing damage indices to provide cover for winter wheat losses in northern Xinjiang, and the financial losses associated with a reduction in grain production due to freezing disasters could therefore be minimized.

The freezing damage indices established in this study can reflect the actual freezing damage to winter wheat in northern Xinjiang and can therefore be used to characterize the degree of winter wheat freezing damage during the overwintering period in this area. The weights of the disaster factors were different for the freezing damage indices in northern Xinjiang that used different sources of data. The negative accumulated temperature, maximum cooling temperature, and extreme minimum temperature in the overwintering period were the factors with the largest influence in the freezing damage index using data from 49 stable stations in northern Xinjiang during 1960 − 2020(YI region). The maximum cooling range and extreme minimum temperature had the greatest influence on the freezing damage of winter wheat when considering wheat observation stations only(YII region). Snow depth had the greatest influence in the freezing damage index using data from 83 stations in the last 5 years(YIII region).YII indicated that the cold wave weather during winter was the most likely to cause freezing damage to winter wheat. It further indicated that extreme weather had the greatest impact on winter wheat.Compared YI region and YIII region, it was found that snow depth in YI had the least influence on freezing damage and the wind speed weight was higher, while the snow depth weight in YIII was the largest and the wind speed weight was the least.With the change of climate, the snowfall in the overwintering period in this region increased significantly. As the snowfall increased, the wind could not directly affect the surface, and the influence of wind on freezing damage decreased significantly.

In the last 60 years, the scope and degree of severe and moderate freezing damage in the overwintering period of all stations in northern Xinjiang displayed a very significant decreasing trend. After the abrupt climate warming that occurred around 1985, the frequency, degree, and scope of severe freezing damage in the winter wheat area in northern Xinjiang significantly decreased. Based on a regional analysis, the Altay area experienced the largest area of serious freezing damage. According to the frequency and number of freezing damage occurrences at winter wheat observation stations in the last 20 years, in the middle and eastern sections of the Tianshan Mountains, the main planting areas of winter wheat, mild and moderate freezing damage often occurred. In recent years, although severe freezing damage has decreased, the area of mild and moderate freezing damage had increased, mainly in Tacheng area, which was also a winter wheat growing area. It was apparent that freezing damage has become a factor restricting the winter wheat yield in northern Xinjiang, and it is therefore necessary to take measures to prevent freezing damage to winter wheat. There is a need to consider the regionalization of winter wheat frost damage, adjust the planting structure in vulnerable areas, and reduce the loss caused by natural disasters.

Taking the northern Xinjiang region of China as an example, the construction and detailed analysis of freezing damage indices in the overwintering period were carried out, but there was no limit on the implementation area. The innovative construction and analysis methods used to develop the freezing damage indices can be used to analyze the freezing damage situation in other areas dominated by snowfall during the winter wheat overwintering period.

Acknowledgements This study was achieved within the National Key Research and Development Program of China (2018YFC1505602) project, together with China Xinjiang Meteorological Bureau Project (YD202212).

Author contribution Conceptualization:Zulian Zhang,Weiyi Mao; methodology and software:Zulian Zhang; Writing-Original Draft:Zulian Zhang,Mingquan Wang;Investigation:Zulian Zhang,Xunguo Huo,Mushajing•Aidaituli;Formal Analysis:Zulian Zhang;Data Curation:Zulian Zhang;Visualization:Zulian Zhang;Formal Analysis:Zulian Zhang;Data Curation:Mingquan Wang;Visualization:Xunguo Huo;Funding Acquisition:Weiyi Mao;Resources:Weiyi Mao;Writing-Review & Editing:Weiyi Mao;Supervision:Weiyi Mao,Yawen Gu;Validation:Yawen Gu,Guanping Cao;

Data availability Supplementary data to this article can be found online at http://data.cma.cn/.

Ethical approval Not applicable.

Consent to participate Not applicable.

Consent to publish Not applicable.

Competing interests The authors declare no competing interests.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

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Construction and Analysis of Freezing Damage Indices for Winter Wheat During the Overwintering Period in Northern Xinjiang, China

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Abstract

Figures

1 Introduction

2 Study Area and Methods

2.1 Overview of the Study Area

2.2 Data Sources

2.3 Snow Depth Data Processing Method

2.4 Construction and Inspection of Freezing Damage Indices

2.1 Method of Inspection

3 Results and Analysis

3.1 Determination and Verification of Freezing Damage Indices

3.2 Time Series Analysis of Freezing Damage Indices

3.3 Spatial Distribution Characteristics of the Freezing Damage Indices

3.3.1 The average freezing damage index values in the YI region from decade to decade

3.3.2 The average freezing damage index values in the YIII region

3.3.3 Distribution of high freezing damage index values in the last 20 years

3.4 Frequency and Number of Winter Wheat Freezing Damage Occurrences

4 Discussion

5 Conclusions

Declarations

References

Additional Declarations

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