Spatiotemporal Variation of Drought Frequency of Winter Wheat in Hebei Province

Liu Xianfeng1,2, Zhu Xiufang1,2*, Pan Yaozhong1,2, Zhao Anzhou1,2, Li Muyi1,2, Li Lin2 (1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;

2. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China)

Abstract—Based on the daily meteorological data of 23 meteorological stations during 1960-2013 across the study area and its surroundings, the spatiotemporal variation of drought frequency of winter wheat is investigated by using the crop water deficit index (CWDI). The results show that during the entire growth period, the sequence of drought frequency is extreme drought > severe drought > moderate drought > light drought. The frequencies of moderate and severe droughts presented increasing trends after 1995, whereas light and extreme droughts showed decreasing trends during the same period. In addition, the drought frequency decreased from seeding to maturation, and the fluctuation of drought frequency tended to intense with an increase in the level of drought, especially during green-up to jointing. During that period, the frequency of extreme drought presented a decreasing trend after 1995, whereas an increasing trend was detected in severe drought conditions. Moreover, significant spatial differences in the trend of drought frequency were detected. For example, severe drought presented an increasing trend in the northwest parts of the study area and a decreasing trend in the east. Increasing and decreasing trends in extreme drought were detected in the northern and southern regions of the study area, respectively. Further, the drought frequency was characterized as a decreasing trend during seeding to overwinter. The frequencies of light, moderate, and severe droughts presented decreasing trends, whereas extreme drought showed an increasing trend during green-up to jointing. On the contrary, a reverse trend was detected during tasselling to maturation.

Keywords—Crop water deficit index (CWDI); drought frequency; winter wheat; spatiotemporal variation)

I. INTRODUCTION The global climate has been experiencing climate change

characterized as global warming during the past hundred years, which has induced an increasing trend in frequency insensitive of extreme events [1]. As the main meteorological disaster, drought is characterized by a high frequency of occurrence, wide influence, and long duration and imposes a huge and far-reaching impact on agricultural production, ecological environment, and socio–economic conditions [2]. The global annual economic losses caused by drought have been estimated at 6~8 billion, which is far more than that by other meteorological disasters [3]. Due to the widespread impact, drought shows the most obvious effects on agricultural production [4]. It has therefore become the most severe disaster and has attracted significant attention from the scientific community.

Currently, mostly research in the monitoring and predicting of drought have adopted drought indices such as the precipitation anomaly index [5], Palmer drought severity index (PDSI) [6], self-calibrating PDSI [7], standardized precipitation index (SPI) [8], and crop water deficit index (CWDI) [9]. Of these, the CWDI is a synthesis index that combines three factors of soil, vegetation, and climate. In addition, the CWDI can accurately reflect crop water deficit conditions and is widely used in evaluating agricultural drought [10]. For example, Sui Yue et al. [11] analyzed the spatiotemporal distribution characteristics of drought for spring and summer maize during their respective growth periods by using the CWDI. Huang Wanhua et al. [12] and Dong Qiuting et al. [13] evaluated the spatiotemporal characteristics of drought frequency of spring maize in Hunan Province and in the northeast region of China.

Hebei Province, located in the North China Plain, is one of the major wheat-producing areas in China. Drought is the most prominent agro–meteorological disaster and poses a severe serious threat to wheat production in this area [14]. Researchers have paid significant attention to the spatiotemporal pattern of drought in spring maize; however, few works have studied the drought frequency in the winter wheat overwinter crop. Winter wheat, as a main crop, plays a key role in agricultural sustainable development and has significant importance for reducing disaster losses and ensuring national food security. Therefore, the purpose of our study is to calculate the frequencies of different classes of drought in 10-day intervals of four growth periods and analyze their temporal and spatial distribution characteristics by using the CWDI.

II. DATA AND METHODS

A. Data source and preparations The meteorological datasets, with 23 meteorological

stations during 1960–2013, were collected from the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn). They consist of daily mean temperature, daily maximum temperature, daily minimum temperature, relative humidity, sunshine hours, wind speed, and precipitation (Fig. 1). All datasets were preprocessed through strict quality control criteria including extreme testing and time conformance testing. In addition, 10-day agricultural disaster datasets recorded during 1991–2013 were also derived from the China Meteorological Data Sharing Service System.

∗ Corresponding author: Zhu Xiufang E-mail: zhuxiufang@bnu.edu.cn

The boundary of winter wheat was extracted from the ZY-3 satellite image during 2013.

Fig. 1 Study area and distribution of weather stations

B. Methods 1) Calculation of the crop water deficit index The CWDI is defined as the ratio between crop water

requirement and the actual supply of water. The calculation formula is [9]

432

1

−−−

−

×+×+×+×+×=

iii

ii

CWDIeCWDIdCWDIcCWDIbCWDIaCWDI , (1)

where CWDI is the CWDI value calculated during a 10-day period; CWDIi, CWDIi-1, CWDIi-2, CWDIi-3, and CWDIi-4 denote the CWDI in ith, i-1st, i-2nd, i-3rd, i-4th, and i-5th 10-day period, respectively; and a, b, c, d, and e are the corresponding weighting factors, with values of 0.3, 0.25, 0.2, 0.15, and 0.1, respectively. CWDIi in (1) is calculated as

⎩⎨⎧ −

=0

/)( ciicii

ETPETCWDI

ici

ici

PETPET

<> , (2)

where CWDIi is the ith 10-day period of the CWDI, ETci is the crop water requirement in the ith 10-day period (mm), and Pi is precipitation in the ith 10-day period (mm). The crop water requirement is calculated by

0ETKET cc ×= , (3)

where ETc is the crop water requirement, ET0 is reference crop evapotranspiration, and Kc is the crop coefficient. ET0 was calculated by using the Penman–Monteith model, and Kc was acquired by using the Food and Agriculture Organization of the United Nations (FAO) [15] in addition to the results of China’s major crop water requirement isogram research [16]. The Penman–Monteith model is [17]

( ) ( ))34.01(

273900408.0

2

2n

0 U

eeUT

GRET

as

++Δ

−+

+−Δ=

γ

γ, (4)

where △ is the slope of the saturation vapor pressure curve (kPa/°C), Rn is the net radiation (MJ/(m2·d)), G is the soil heat flux (MJ/(m2·d)), γ is the psychometric constant (kPa/°C), T is the mean temperature (°C), U2 is the wind speed at a height of 2 m (m/s), es is the mean saturation vapor pressure (kPa), and ea is the actual vapor pressure (kPa).

2) Analysis Our study analyzed the spatiotemporal variation of drought

frequency based on the statistical results of the 10-day period. The trend of drought frequency was derived by using a linear regression model. According to phonological data of winter wheat in Hebei Province obtained from the farming database management division of the People’s Republic of China Ministry of Agriculture, we divided the growth period into four growth intervals: (i) seeding to overwintering (October 1 to February 28–29 in the following year), (ii) green-up to jointing (March 1 to April 20), (iii) jointing to tasselling (April 21 to May 20), and (iv) tasselling to maturation (May 21 to June 30). In addition, we modified the standard of drought grade by using the agricultural disaster datasets for the 10-day period. The results are presented in Table 1.

Table 1 Agricultural drought grade of CWDI

Grade Values of CWDI (%) No drought CWDI≤35

Light drought 35＜CWDI≤50 Moderate drought 50＜CWDI≤65

Severe drought 65＜CWDI≤80 Extreme drought ＞80

III. RESULTS

A. Temporal variation of drought frequency 1) Whole growth period The light drought frequency showed a weakly increasing

trend during 1961–2012 with a linear tendency of 0.038 /10a (Fig. 2a). The average frequency was 2.49 (1~6.09), and the frequency fluctuated extensively during the 1980s and 2000-2012. Moreover, the five-year running mean indicated an increasing trend in light drought frequency during the 1980s and a decreasing trend between 2000 and 2010. The average moderate drought frequency was 4.70 (2.22~8.04). An increasing trend was detected during the study period, especially after year of 1995, in which a continuous upward trend was found (Fig. 2b). The severe drought frequency showed an increasing trend of 0.12 /10a. The average frequency was 7.13 (4.52~11.30). During 1980~2012, a reverse trend was detected before and after 1995, and an increasing trend was found after 1995 (Fig. 2c). In addition, a decreasing trend was detected in extreme drought frequency, with a linear tendency of −0.13 /10a. Moreover, the extreme drought frequency was highest, with an average of 11.25 (3.70~17.35). For the decadal scale, the highest frequency of extreme drought was observed during 1990s, whereas the

lowest frequency occurred during 2000~2012 (Fig. 2d). In summary, the sequence of different levels of drought frequency was extreme drought > severe drought > moderate drought > light drought. The moderate and severe drought

frequencies agreed with an increasing trend after 1995, whereas the light and extreme drought frequencies showed decreasing trends during the same period.

Fig. 2 Interannual variations of drought frequency at different levels of drought: a. light drought; b. moderate drought; c. severe drought; d. extreme drought.

2) Different growth period In order to compare the temporal variation of drought

frequency in different growth stages and to acquire a comprehensive understanding of drought in the winter wheat crops, we analyzed the characteristics of drought frequency changes through the four growth stages (Fig. 3). Except for the fourth stage, the overall drought frequency was extreme drought > severe drought > moderate drought. In addition, the frequency of extreme drought decreased from stage one to stage four at 5.93, 2.76, 1.49, and 1.06, respectively. For interannual variation, the annual fluctuation tended to be extensive from light drought to extreme drought, especially in the stage of green-up to jointing. During that period, the extreme drought frequency obviously decreased after 1995, whereas the frequency of severe drought presented an increasing trend during the same period (Fig. 3b). From the analysis above, we can conclude that the occurrence of extreme drought was more unstable and unpredictable.

B. Spatial variation of drought frequency 1) Whole growth period Fig. 4 exhibits the spatial trends of different levels of

drought. The tendencies in most areas were −0.1~0.1 /10a in light drought. The areas with decreasing trends of more than −0.1/10a were distributed only in Xingtai, while the increasing region was distributed mainly in Cangzhou, and Tangshan

(Fig. 4a). On the moderate level of drought, the drought frequency showed an increasing trend in the middle of Hebei in regions of Baoding, Langfang, and Cangzhou (Fig. 4b). In the middle of most areas, the trend of severe drought frequency was −0.1~0.1/10a. Baoding and Shijiazhuang presented increasing trends, and the decreasing areas were mainly distributed in the eastern part of the study area (Fig. 4c). For the extreme level of drought, obvious spatial differences in trends of drought frequency were detected, with decreasing and increasing trends appearing in northern and southern of Hebei, respectively. Changes in the middle areas of Hebei were negligible (Fig. 4d).

2) Different growth period We further calculated the trend of drought frequency in

different drought level from stage (i) to stage (iv) (Fig. 5). Overall, the trends of drought frequency in the first two stages were larger than those in the following two stages. The specific characteristics are as follows: (1) The drought frequencies of different drought levels showed mainly decreasing trends (Fig. 5a–d). (2) During stage (ii), the trends of drought frequency in light drought, moderate drought, and severe were all decreasing, whereas the extreme drought presented an increasing trend and was mainly distributed in the middle and southern regions of the study area (Fig. 5e–h). (3) On the contrary, the trends of drought frequency for moderate and severe droughts were increasing during stage

(iii), whereas the extreme drought showed a decreasing trend (Fig. 5i–l). (4) Similar to that in stage (iii), increasing trends were detected in the light drought, moderate drought, and severe drought, whereas extreme drought showed a decreasing trend during stage (iv) (Fig. 5m–p).

IV. SUMMARY AND DISCUSSION

A. Summary The spatiotemporal variation of drought frequency for the

winter wheat-growing areas in Hebei was investigated by using the CWDI. We determined that the sequence of drought frequency during the entire growth period was extreme drought > severe drought > moderate drought > light drought. The drought frequency decreased from seeding to maturation, and the fluctuation of drought frequency tended to intense with an increase in the level of drought.

This was especially apparent in the stages of green-up to jointing, in which the frequency of extreme drought presented a decreasing trend after 1995, whereas an increasing trend was detected in severe drought. Significant spatial differences in the trend of drought frequency were detected in our study. Severe drought presented an increasing trend in the northwestern part of the winter wheat-growing region, and a decreasing trend was shown in the east. For extreme drought, increasing and decreasing trends were detected in the northern and southern regions of the study area, respectively, whereas little variation occurred in the middle region. Furthermore, the drought frequency was characterized as a decreasing trend during the stage of seeding to overwinter. The frequencies of light, moderate, and severe droughts presented decreasing trends, whereas extreme drought showed an increasing trend at the stage of green-up to jointing. On the contrary, the reverse was detected during tasselling to maturation.

Fig. 3 Interannual variations of drought frequency at different growth stages: a. stage one; b. stage two; c. stage three; d. stage four. The green line denotes light

drought; blue, moderate drought; yellow, severe drought; and red, extreme drought.

Fig. 4 Trends of drought frequency at different level of drought: a. light drought; b. moderate drought; c. severe drought; d. extreme drought.

Fig. 5 Trends of drought frequency and classification of drought for different growth stages of winter wheat in Hebei Province.

B. Discussion Although some characteristics of drought frequency in

winter wheat in Hebei Province were detected, the conclusion was based on a simple drought index; significant uncertainty remains. Therefore, a synthesized drought index that couples multi-source data is urgently needed. For example, the vegetation drought response index (VegDRI) proposed by the National Drought Mitigation Center in Nebraska, United States, is an effective method. In addition, we did not consider the influence of irrigation and other human activity in our study, which caused uncertainty in our results. Moreover, the standard of drought grade is the key factor that results in different levels of drought among different drought indices. More accurate results require more systemic analysis and more complete data.

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