Impact of climate warming on drought

characteristics of summer maize in North China

Plain for 1961-2010

Yanan HU Key Laboratory of Agri-informatics, Ministry of Agriculture / Institute of Agricultural Resources and Regional Planning,

Chinese Academy of Agricultural Sciences Beijing, China

huyanan@caas.cn Tel: +86-010-82109645 Fax: 010-82105635

Yingjie LIU* Public Meteorological Service Center, Chinese Meteorological Administration

Beijing, China liuyingjie@cma.gov.cn

Zhengguo LI Key Laboratory of Agri-informatics, Ministry of Agriculture / Institute of Agricultural Resources and Regional Planning,

Chinese Academy of Agricultural Sciences Beijing, China

lizhengguo@caas.cn

Abstract—North China Plain (NCP) is the major grain production region of the rain-fed summer maize and its natural climatic conditions play a decisive role in maize growth process. In this study, we focused on temporal-spatial characteristics of drought during maize growth period (June–September), and wanted to access the impact of climatic warming trend on summer maize drought during its growing season in NCP. To understand these questions, we used monthly mean air temperature and monthly precipitation data of 27 stations in NCP from 1961–2010. First, jumping points of mean temperature and precipitation in maize growth period were fixed by Mann-Kendall test method. Second, the self-calibrating Palmer Drought Severity Index (SC-PDSI) was used to calculate the drought level and the drought distribution characters were captured by empirical orthogonal function (EOF). Third, analyses of drought occurrence probability were conducted under two scenarios, i.e. one was actual observations, and another consisted of monthly actual precipitation for each year and past 50 years average of monthly mean temperature. Results showed that mean temperature and total precipitation in maize growth period were in a slightly decreasing trend before1996, which was the jumping time point of mean temperature. After 1996, mean temperature of maize growth period was higher than the 50 years average of 0.4℃–1.0℃, while the trend of total precipitation in maize growth

period was transferred from decreasing to increasing. The spatial character of drought occurrence in maize growth period showed consistency in NCP, and the most prone to drought and the hardest drought area was located in Henan Province. Drought occurrence probability increased by an average of 10.1% due to climate warming appeared since mid-1990s, and above severe drought increased in an average of 9% at the same time. These increase extent were higher than that of the decreased ones before the mid-1990s when temperature was lower than 50 years average. In addition, the proportion of above severe drought occurrence also rose with the climate warming. Therefore, the warming trend would have a significant effect on drought occurrence of summer maize in NCP.

Keywords—climate warming; drought; summer maize; North China Plain

I. INTRODUCTION Drought is currently one of the main constraints to crop

production in rainfed system throughout the world [1, 2]. It is projected to worsen with anticipated climate change, and drought-affected areas are projected to increase in extent, which could have adverse effects on agriculture [3, 4].

Increases in mean annual temperatures have been evident in most major agricultural regions in the past few decades [5]. Previous study have reported that crop heat stress is expected

Sponsor: China Postdoctoral Science Foundation (2014M550895)

to increase by the end of the 21st century[6] and drought sensitivity in maize also has increase in the last two decades[1]. But there has been little work done to document temperature influence on drought changes already occurring in cropping systems. Here we examine the effect of temperature by using self-calibrating Palmer Drought Severity Index (SC-PDSI). The SC-PDSI uses both precipitation and surface air temperature as input, in contrast to many other drought indices that are based on precipitation alone [7]. By this nature, application of SC-PDSI in this study is allowed to account for the effect of surface warming.

II. MATERIALS AND METHODS

A. Site characterization of the study area The study area falls in five provinces in the North China

Plain: Hebei, Shandong, Henan, Beijing and Tianjin. The general slight slop of the ground is from west to east, i.e. 100m above sea level for the most in the west to near sea level in the east.

Twenty-seven sites (Fig.1) scattered in important summer-maize growth region were selected for the study. Annual mean temperature ranges from 8 to 15°C. The annual rainfall over 50–70% falls in summer when maize grows vigorously. In this region, primary maize cultivation pattern is rainfed.

B. Data Monthly weather data, viz., precipitation and mean

temperature recorded in the meteorological observatories located in the region were collected from the China Meteorological Data Sharing Service System (http://cdc.cma. gov.cn), for the period 1961–2010.

The Available Water Holding Capacity (AWC) value for each site was derived from the Harmonized World Soil Database (HWSD). HWSD data is produced and maintained by the Food and Agriculture Organization of the United Nations (FAO), the International Institute for Applied Systems Analysis (IIASA), International Soil Reference and Information Centre (ISRIC), Institute of Soil Science – Chinese Academy of Sciences (ISSCAS) and Joint Research Centre of the European Commission (JRC) [8].

C. Methodology of data analysis • Mann-Kendall test

The Mann-Kendall (MK) test has been used for detecting trend and abrupt changes in time series [9, 10]. The major advantages of this method are that it is simple to calculate and give explicit abrupt time for mean value. The n time series values X ( 1x , 2x ….., nx ) are rebuild to another relative ranks S ( nSSS ,,, 21 ).

1

k

k ii

S r=

=∑ ( ,2=k n,3 ) (1)

where, 1=ir for ji xx > and 0=ir for ji xx < ( ,1=j i,2 ).

With the hypothesis 0H (i.e., the dataset is random and independent), the statistic kUF is normally standard distributed with:

( )[ ]( )k

kkk

SVarSESUF −= nk ,2,1= ; 01 =UF ) (2)

where E ( kS ) and Var ( kS ) are the mean value and variance of kS respectively.

( )kE s =( 1)

4n n +

(3)

( )kVar s = ( 1)(2 5)72

n n n− − (4)

The KUB values are derived by processing the reverse sequence dataset of X (i.e., nx , 1−nx , 1x ). First, using the same processes (formulas 1–4) shown above to get its

KUF values; and then transform them into KUB with:

KK UBUF −= ( ,nK = 1,1−n ; 01 =UB ) (5)

A positive value of kUF indicates that there is a increasing trend and vice versa. The critical test statistic value at significance 95% level is 1.96. If | kUF |>1.96, there is a significant trend in X dataset. And the cross point by kUF and

KUB lines in plot is an abrupt changes. The Mann-Kendall test was carried out for summer maize growth season (Jun. to Sep.) for mean temperature and precipitation for all the study sites.

• Empirical orthogonal function analysis

The main features of spatial variability can be outlined using empirical orthogonal function (EOF) analysis [11]. EOF analysis is a statistical technique that transforms an original set of variables to a substantially smaller set of uncorrelated variables that represent most of the information of the original set of variables. And it is a useful tool to separate the matrix consisted by an original data set into two parts of spacial and time functions [10, 12]. For example, nmX × is a m×n matrix, where m equals the number of sites, and n equals the number of time series. There are two orthogonal matrices, V andT , such that,

TVX ×= (6)

Fig. 1 Location map of selected study sites.

The calculation of convariance matrix S can be expressed by TXXS = , where TX is the transpose of matrix .X The covariance matrix can then be diagonalized, and its eigenvalues ( λ ) and corresponding eigenvectors (V ) calculated. These eigenvectors are the empirical orthogonal functions (EOFs). The matrix T can then be calculated by the simple formula .XVT T=

Each row of T serves as a series of time coefficients that describes the time evolution of a particular EOF. The map associated with an EOF represents a pattern that is statistically independent and spatially orthogonal to the others. The eigenvalue indicates the variance accounted for by the pattern. In EOF analysis, the largest eigenvalue is always the first, with the subsequent eigenvalues steadily decreasing in amount [12]. And the largest contribution to total variance of the system comes from the first eigenvector.

• Drought calculation (SC-PDSI)

Drought recognition was based on the SC-PDSI, which is derived from the original Palmer Drought Severity Index (PDSI) and improve the performance the original index by dynamically replacing the empirical constants with values based on the characteristics of the local climate [13]. It makes spatial comparison of PDSI values more meaningful. Wells et al. (2004) described in details of SC-PDSI’s procedure that requires precipitation, temperature and the AWC value of the soil at each study site. Monthly SC-PDSI values for the maize growth season from June to September are averaged to get a seasonal growth SC-PDSI value for each site [14].Table 1 shows the classification of drought based on the PDSI.

Table1. Classification of the PDSI values

PDSI Value PDSI Category

0.49 to -0.49 Normal

-0.50 to -0.99 Incipient drought

-1.00 to -1.99 Mild drought

-2.00 to -2.99 Moderate drought

-3.00 to -3.99 Severe drought

Below -4.00 Extreme drought

To isolate the effect of temperature two set calculations of SC-PDSI were performed. These two calculations both used the observed 50-year time series of precipitation. While the one (named as S1) was with the temperature variable held constant (using repeated series of 50-year average values from 1961), the other one (named as S0) was with the observed 50-year time series of temperature. The difference between two calculations depends only on the specific climatic factor, i.e., temperature.

III. RESULTS

A. Temporal trends of climate during maize growth season The mean air temperature for the entire sites of study

during maize growth season ranged from 23.4℃ to 25.6℃, with a slight increase among the period of 1961–2010 (Fig. 2). While the regional average total precipitation with a reverse trend ranged from 251mm to 662mm (Fig. 2). Although the

Mann-Kendall test shows both trends of them were not significant (Fig. 3), large annual variations were found during the past 50 years.

For the entire NCP, the detection of total precipitation (Fig. 3-a) shows its abruption during the period from 1965 to 1975, but the MK test wasn’t able to get a unique abrupt point. An abrupt point existed in air mean temperature series at 1996 (Fig. 3-b). The trend of mean air temperature after 1996 declined significantly that however its values were higher in most years than the long-term average of mean temperature for entire sites. For total precipitation, it shows reverse trend for

Fig. 3 Mann-Kendall test of mean air temperature and total precipitation for

entire sites of study in NCP

Fig. 2 Temporal trends of the mean air temperature (T) and total

precipitation (Pre) during the growth season. Straight line is the linear regression line against year. **Significant at P<0.01

during the same period, with the lowest precipitation of 50 years in 1997–2002.

B. Estimation of EOFs Using the SC-PDSI time series derived from 50 years

monthly data of mean temperature and precipitation, the EOFs were determined based on EOF analysis. Fig. 4 shows the amount of variance accounting for each EOF. The first four EOFs were found to be necessary to explain more than 70% of total variance of the SC-PDSI time series. It is considered that those EOFs whose accumulated variance is more than 70% of total variance have significance in explaining the original data. Fig. 5 shows the first EOF which accounts for approximately 41% while the second and third EOFs account for about 16.2% and 7.7%, respectively. A north-south pattern was for the first EOF, with a steeper changes in south of Henan Province and the drought sensitivity center was located in Henan Province.

The coefficient time series indicate the variance of each EOF. The first three coefficient time series were given in Fig. 6, and the first one shows more fluctuations than the others. Its negative values generally indicate severe drought events, which is also coincident with the observation in NCP, such as 1966, 1968, 1972 and 1999–2000[15].

C. Changes of drought occurrence probabilities and temperature impact on it The threshold of PDSI value = –1, which is the criterion for

mild drought condition (Table 1), was introduced to indicate drought events occurrence. The value less than –3 was deemed as severe drought events occurrence. Here, we took into account of these two thresholds in analysis.

Fig. 7 shows the occurrence frequency of drought events (S0) above mild level or above severe level and its difference between S0 and S1 during the study period. For the difference of the drought frequency between S0 and S1 (D-values), there were two opposite appearances in before and after the turning point. The turning point of D-values was around 1995 and 1999 for above mild drought and above severe drought, respectively. For both PDSI <–1 and PDSI <–3, there were a significant increase of drought occurrence frequency in NCP under actual scenario S0.

From fig. 7-a, we can see that, compared with the D-values absolute averaged 3.2%. It was bigger for absolute averaged10.1% after the turning point. This indicated that more

differences of drought occurrence frequency existed between the S0 and S1 scenarios after 1995. The effect of temperature fluctuation on drought events above mild level was larger since 1995, along with abrupt increase of temperature. And the increased temperature took on positive effect from the negative before 1995. Same changing pattern appeared in fig. 7-b for drought events above severe level before and after 1999. It is quite obvious that higher temperature urged drought frequency, especially for above severe drought whose drought occurrence probability increased in an average of 9% since mid-1990s, compared with 2.4% before. This represents more areas suffered severe drought or over than before due to the warming climate.

IV. DISCUSION AND CONCLUSION In this study, we used the SC-PDSI index to evaluate the

drought. EOF analysis method was selected to reflect spacial and temporal characteristic of drought. The results shows that the drought events derived by SC-PDSI index were consistent with the observations recorded by CMA[15], which indicates that the SC-PDSI was suitable to detect drought event in NCP. The most prone to drought and the hardest drought area was located in Henan Province.

Over the past five decades, the regional scale averaged surface air temperature during summer maize growth season in NCP, showed slightly rising trend within fluctuation, but the regional scale averaged total precipitation followed a reverse trend. However, it was noteworthy that the average temperature had obvious increase from the end of 1990s, which was higher than the 50 years average of 0.4°C –1.0°C.

Fig. 6 Coefficient time series of first, second, and third EOFs

Fig. 5 Shape of first EOF

Fig. 4 Proportion of variance accounted for each EOF

The climate warming led to an increasing trend of drought

frequency according with increased temperature since the temperature abrupt point 1995. In addition, the proportion of above severe drought also rose with the climate warming from 2.4% to 9% in average. This result is similar with the findings of [16, 17]. As temperature rising, there will appear more extreme high temperature events, which is a reason for drought. Dai et al [7] found that coinciding with warming climate from the middle 1980s, very dry area over global land increased with temperature as more important thereafter. Here, we only analyzed the effects of temperature increase on drought during maize growth season for the past 50 years. A subsequent study should be carried on to detect the influence of temperature about the future.

ACKNOWLEDGMENT We thank Jie PAN for help processing data, and Peng

YANG, Wenbin WU and Huajun TANG for helpful comments on the manuscript.

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Fig. 7 Drought frequencies of above mild drought (a) and above

severe drought (b) and their difference between S0 and S1 scenarios. Straight lines show the linear trends against year.

*Significant at P<0.05