Abstract

Drought indices that compute drought events by their statistical properties are essential stratagems for the estimation of the impact of drought events on a region. This research presents a quantitative investigation of drought events by analyzing drought characteristics, considering agro-meteorological aspects in the Heilongjiang Province of China during 1980 to 2015. To examine these aspects, the Standardized Soil Moisture Index (SSI), Standardized Precipitation Index (SPI), and Multivariate Standardized Drought Index (MSDI) were used to evaluate the drought characteristics. The results showed that almost half of the extreme and exceptional drought events occurred during 1990–92 and 2004–05. The spatiotemporal analysis of drought characteristics assisted in the estimation of the annual drought frequency (ADF, 1.20–2.70), long-term mean drought duration (MDD, 5–11 months), mean drought severity (MDS, −0.9 to −2.9), and mild conditions of mean drought intensity (MDI, −0.2 to −0.80) over the study area. The results obtained by MSDI reveal the drought onset and termination based on the combination of SPI and SSI, with onset being dominated by SPI and drought persistence being more similar to SSI behavior. The results of this study provide valuable information and can prove to be a reference framework to guide agricultural production in the region.

HIGHLIGHTS

  • This research is to examine the agricultural and meteorological drought variations by using Standardized Precipitation Index and Standardized Soil Moisture Index while composite drought anomalies (agricultural and meteorological) were analyzed by using Multivariate Standardized Drought Index in the study area.

  • This study also analyzes the behavior of Multivariate Standardized Drought Index relative to the Standardized Precipitation Index and Standardized Soil Moisture Index over the study area.

  • The spatiotemporal patterns of agro-meteorological drought were also examined.

  • Moreover drought characteristics (Frequency, Duration, Intensity and Severity) were also analyzed.

  • This study will also be helpful to choose one drought index for multi climatic parameters (precipitation, soil moisture) rather than a single factor base drought index.

INTRODUCTION

Drought is a main natural hazard that has a spatiotemporal impact on agriculture and water resources worldwide. It has a distinct nature and is very hard to detect because it occurs gradually and can affect a substantial area. Drought has been classified into four classes, namely, meteorological, hydrological, socioeconomic, and agricultural (Heim 2002). Meteorological drought occurs due to the inadequacy of precipitation relative to the normal precipitation in the area. Whereas, an agricultural drought sets in due to the deficiency of soil moisture, and socioeconomic drought mainly prevails because of the imbalance between supply and demand ratios (Mishra & Singh 2010). On the other hand, hydrological drought is described as a phenomenon where a lower amount of water is available (groundwater, river flow) than normal demand (Hill & Polsky 2007). Several types of drought indices have been formulated to give complete information on historical drought and to use the present situation in a historical perspective. The behavior of drought indices imitates different conditions related to the delayed agricultural impact (e.g., soil moisture deficit) and hydrological impacts (e.g., low stream flow). In recent times, a large number of drought indices have been recommended in the research literature and widely applied in many regions worldwide (Barua et al. 2010; Jiang et al. 2015). For example, Standardized Precipitation Index (SPI) has been used for the impact assessment of climate change (Wehner 2013). The approach of SPI can be used for various land surface or climatic variables such as Standardized Soil Moisture Index (SSI) (Hao & AghaKouchak 2014). SSI is used for the assessment of agricultural drought. SPI is an appropriate indicator for the assessment of drought onset while SSI describes the persistence of drought more consistently (Mo 2011).

However, dependency on only one climatic variable (e.g., precipitation), which does not give complete information on agriculture drought and absence of evapotranspiration, reduces its efficiency (Gocic & Trajkovic 2014). Therefore, drought index should be able to quantitatively characterize the severity of drought by assimilating the data of several factors, such as evapotranspiration and precipitation into a single numerical digit (Zargar et al. 2011). Many studies have indicated that a single drought index might not illustrate the true descriptions of drought anomalies and, therefore, a multi-index approach should be used for the comprehensive assessment of droughts (Keyantash & Dracup 2004; Moghimi et al. 2020). Therefore, Multivariate Standardized Drought Index (MSDI) was proposed to use the combined information of meteorological (precipitation) and ground conditions (soil moisture) to provide a single numerical value of drought.

Similarly, spatiotemporal trends of agro-meteorological droughts are also significant for drought risk management over northeast China. Many studies have indicated the drought variability over China. For example, Wang et al. (2015a) examined several extreme drought episodes from the temporal evaluation of the SPI values of 20 meteorological stations in the Heilongjiang Province of China. Qian & Zhu (2001) also examined the variations of drought from 1880 to 1998 over seven regions in China and observed that the drying trends are increasing over north China post-1970s. Similarly, droughts also occurred frequently over northeast China. Songnen Plain is the main grain production area in northeast China (Liang et al. 2011). Regional drought studies show that spring and autumn seasons were highly prone to drought, which is realistic for the Songhua River Basin. However, in August and September, 79 and 97% of the stations, respectively, indicated that drought became more severe in the Songhua River Basin (Song et al. 2015). The region has been adversely affected by droughts over the past 50 years which caused huge economic loss (Xie et al. 2003). Shen et al. (1980) pointed out that the occurrence of drought events during the spring season was related to the shortage of precipitation and soil water storage of the preceding autumn in northeast China. Therefore, MSDI was proposed for this study because it has the advantage of combing the information of drought events from soil moisture and precipitation by applying the joint distribution function of these variables.

The objective of this research is to examine the agricultural and meteorological drought variations by using SPI and SSI while composite drought anomalies (agricultural and meteorological) were analyzed by using MSDI over various sub-regions. Moreover, this study also analyzes the behavior of the MSDI relative to the Rainfall Anomaly Index (RAI), SPI, and SSI over the study area. The spatiotemporal patterns of agro-meteorological drought were also examined by statistically analyzing frequency, duration, severity, and intensity of identified drought events over 36 years of study.

RESEARCH AREA AND DATA SETS

This study was conducted at five meteorological stations which include Downtown, Bayan, Shangzhi, Wuchang, and Fangzheng in the Heilongjiang Province of China. The research area is situated between 125°42′–130°10′E and 44°04′–47°40′N. The climate of the study area lies in the temperate zone continental monsoon with long winters and short summers. There is a high variability of precipitation that exists due to non-uniformity of precipitation within the climatic stations of the study area. Downtown and Bayan stations experienced higher variations in precipitation as compared to Shangzhi and Wuchang stations (Khan et al. 2016a). The mean annual precipitation varied between 500 and 600 mm (Chen et al. 2011). The area is also under the influence of the East Asian Monsoon, experiencing strong north winds in winter and south winds in summer (Zhang & Zhou 2015). The study area is situated in one of the three black soil belts of the world. The elevation is high in the northwest and southeast side, and low in the northeast and southwest. There are many mountains in the region, and the area above 300 m elevation accounts for 35.8% of the land area of the region. Songnen Plain and Sanjiang Plain are the two major agricultural plains of Heilongjiang and account for 37.0% of the total area (Zhang et al. 2019). Sanjiang Plain and Songnen Plain are the main grain production areas in the whole of China. The grain output accounts for about one-tenth of the country's total grain production. Heilongjiang is largely an agricultural province in China with abundant forest resources, diverse climatic conditions, and landforms. Poor management of irrigation water has led to serious problems in local water resources (Liu et al. 2017). Droughts and floods are frequent because of precipitation variability (Gu et al. 2017). Generally, a meteorological drought occurs due to the shortage of precipitation and agricultural drought occurs due to the insufficient amount of soil moisture. Therefore, drought duration is a very important factor for the sustainable growth of agriculture due to the fact that extended drought duration causes great loss of agricultural grain production (Bai et al. 2004). The location of the respective meteorological stations is presented in Figure 1.

Figure 1

Geographical location of the study area.

Figure 1

Geographical location of the study area.

Daily observed data of precipitation of five meteorological stations during 1980–2015 (36 years) were used in this research. Most of the soil moisture observations were measured with the thermostat-weight technique to a depth of 1 meter. The data were obtained three times a month, on the 8th, 18th, and 28th days of the month. Afterwards, three observations of data of soil moisture of each month were averaged to get the monthly time series data at the different climatic stations to a depth of 1 meter. All the meteorological stations are either classified as cropland or grassland and their data observations represent the agricultural soil moisture conditions at different locations in the study area. All the climatic data were collected from the Meteorological Administration of Heilongjiang Province, China.

The MODIS data sets obtained by NASA's Earth Observing System for study duration were used in this study (http://e4ftl01.cr.usgs.gov). The spatial and temporal resolutions of this NDVI data set are 1 × 1 km2 and one month, respectively. This NDVI is used widely to study changes in regional vegetation coverage because of its moderate spatial resolution and high quality resulting from robust treatments of water, clouds, and heavy aerosols. First, the data format and projection of the raw MODIS-NDVI data were transformed using MODIS Reprojection Tools. Next, the growing season NDVI was defined as the average NDVI from April to October for each year. The landcover map is prepared using remote sensing product. NDVI is defined based on the different bands of remote sensing product.

METHODS

Drought identifications

Drought identification is a very complex phenomenon. Drought may not be truly identified by using a single meteorological variable (e.g., precipitation). Multiple meteorological variables (e.g., precipitation, temperature, soil moisture, etc.) are more effective for the identification of drought phenomena. Therefore, we used multiple drought indices for the assessment of drought anomalies in the study area. The indices involved: (i) SSI to assess the agriculture droughts by using the soil moisture time series data; (ii) SPI to calculate the meteorological drought by using the precipitation time series data; and (iii) MSDI to measure the combined effect of agricultural as well as meteorological droughts (using soil moisture as well as precipitation time series data).

SPI is a generally used drought index for the estimation of the intensity of drought irregularities. The application of the SPI indicator has increased worldwide due to its advantages of being simple in application and the requirement of a small amount of data for its implementation (Shamshirband et al. 1980). The drought index is mainly suitable for the comparison of drought conditions between various time scale data sets and regions with divergent weather conditions (Bonaccorso et al. 2003; Khan et al. 2017).

The drought index is based on shape and scale parameters α and β in a gamma distribution (Wu et al. 2005). The cumulative probability of precipitation is measured by using the following dynamics (Edwards 1997):
formula
(1)
where T denotes the gamma function. The probability q (zero precipitation) can be measured by dividing dry month over the total extent of monthly data time series.
formula
(2)

H(P) denotes the cumulative probability.

For the computation of the value SPI, an equal-probable transformation through H (P) is used:
formula
(3)
where Φ denotes the standard normal cumulative distribution function.
SSI is calculated by using the nonparametric approach (Hao et al. 2013). MSDI is an extension of SPI developed by McKee et al. (1993), further extending SPI to a bivariate model (soil moisture and precipitation). At specific time scales (one month or six months), the joint probability function of precipitation (X) and soil moisture (Y) can be obtained as:
formula
(4)
where P represents the joint probability of soil moisture and precipitation. Furthermore, MSDI can be derived by joint probability p (Hao & AghaKouchak 2013):
formula
(5)
where Φ denotes the standard normal distribution function.
The empirical joint probability of the bivariate model can be calculated by using the Gringorten plotting position formula as (Gringorten 1963; Benestad & Haugen 2007):
formula
(6)
where n shows the number of observations and denotes the number of occurrences (. The joint probability is derived from Equation (6) and then plugged in Equation (5) for the calculation of MSDI.
Drought has been categorized into five classes (D0, D1, D2, D3, and D4). Svoboda et al. (2002) used the D scale for the better representation of drought through a range of values each classifying the different severity level. The value of D0 ranges between −0.50 and −0.70, D1 between −0.80 and −1.20, D2 between −1.30 and −1.50, D4 between −1.60 and −1.90, and D4 greater or equal to −2.00, as shown in Table 1. RAI was also used to analyze the distribution of precipitation in the years (1980–2015) with the greatest anomalies. The index was first developed by Van Rooy (1965).
formula
(7)
formula
(8)
where N = current monthly/yearly rainfall (mm), = monthly/yearly average rainfall of the historical series (mm), = average of the ten highest monthly/yearly precipitations of the historical series (mm), = average of the ten lowest monthly/yearly precipitations of the historical series (mm).
Table 1

Basic drought classifications

No.Drought categoryCriterion
Mild −0.5 to −0.70 
Moderate −0.8 to −1.20 
Severe −1.30 to −1.50 
Extreme −1.60 to −1.90 
Exceptionally dry ≥ −2.0 
No.Drought categoryCriterion
Mild −0.5 to −0.70 
Moderate −0.8 to −1.20 
Severe −1.30 to −1.50 
Extreme −1.60 to −1.90 
Exceptionally dry ≥ −2.0 

The classification of RAI is given in Table 2.

Table 2

Classification of Rainfall Anomaly Index (RAI) intensity

No.ClassificationCriterion
Extremely humid Above 4 
Very humid 2 to 4 
Humid 0 to 2 
Dry −2.0 to 0 
Very dry −2.0 to −4.0 
Extremely dry Below −4.0 
No.ClassificationCriterion
Extremely humid Above 4 
Very humid 2 to 4 
Humid 0 to 2 
Dry −2.0 to 0 
Very dry −2.0 to −4.0 
Extremely dry Below −4.0 

Drought events statistics

Agriculture and meteorological droughts events have been identified and characterized by the following statistics (He et al. 2016):

  • (1)

    Starting month of drought (SM): the starting month of precipitation deficit or soil moisture deficit period is designated as the starting month of drought.

  • (2)

    Duration of drought (DD): the period between the starting date and the terminating date of drought events is called drought duration.

  • (3)

    Drought severity (DS): cumulative precipitation or soil moisture deficiency of drought below the given threshold value of drought is called drought severity.

  • (4)

    Drought intensity (DI): the ratio of drought severity to the drought duration.

Moreover, annual average drought frequency (ADF) was calculated by using the total number of drought events identified from 1980 to 2015 divided by the total duration of the study period (36 years). Similarly, mean drought duration, mean drought intensity, and mean drought severity were also calculated for the identified drought events during the study period. Seasonal spatial variation of drought events was calculated by using the ratio of drought duration in each season (winter, spring, summer, and autumn) to the total drought duration. The months of various seasonal drought events are:

  • (1)

    spring drought (March, April, May)

  • (2)

    summer drought (June, July, August)

  • (3)

    autumn drought (September, October, November)

  • (4)

    winter drought (December, January, February).

Moreover, the Mann–Kendall trend test was applied at 95% significance level to the drought time series data of five meteorological stations at five sub-regions for the assessment of drought trends in the study area. The test was originally developed by Mann (1945), whereas its statistical distribution was derived subsequently by Kendall (1975). The detailed statistics (s) are as:
formula
(9)
where,
  • xi & xj= values of i and j(j>i) in data series

  • sgn(xjxi) = sign function whereas n denotes the data points
    formula
    (10)

The +ve values showed a strong increasing trend while −ve values showed a strong decreasing trend.

If the size of the sample is greater than ten the variance is:
formula
(11)
where ti = number of ties of extent i, m= number of tied groups.
ZS is computed as:
formula
(12)

The null hypothesis is rejected if |ZS| > 1.96.

RESULTS AND DISCUSSION

Drought variations over the study area

The drought variations of different meteorological stations of Heilongjiang Province of China based on MSDI are shown in Figure 2. MSDI is used to assess the changes in agro-meteorological droughts over each meteorological station. Moreover, RAI was also used to evaluate the significant changes in precipitation trends which induced drought events in the study area. The results showed that most of the study area remained under mild and moderate droughts during the study period from 1980 to 2015 (mild and moderate drought duration 1982–1992, 1999–2001, and 2010–2015). Extreme and exceptional droughts were also observed during 36 years of drought analysis. Almost a 40% area was under drought conditions during the years 1990–1992 and 2004–2005 at almost all the meteorological stations, as shown in Figure 2. It was also noted that RAI showed a downward trend (decreasing pattern) during the above-mentioned drought years. Our results are consistent with previous studies. Extreme and severe droughts were experienced during 1990 and 1991 in the Songhua River Basin of China (Khan et al. 2016b, 2018). Similarly, 40% of the study area was examined under moderate drought at almost all stations from 1999 to 2001. The drought actually occurred in China and other parts of the world during the same period. Wang et al. (2015b) reported a drought episode during 2001 in northeast China. The reported dry condition might be due to the asymmetrical surface temperature of the sea which is related to the El Niño–Southern Oscillation (ENSO) phenomenon which significantly changes the precipitation patterns over northeast China, and happens to be one of the major drivers of droughts. Temperature is one of the important climatic parameters and crop production of northeast China is also dependent on the summer temperature (Ding 1980). ENSO affects the atmospheric circulation and climate conditions over China through its influence on western North Pacific heating and South Asian heating (Wu & Wang 2002). Moreover, it was noticed that the relationship between northeast summer temperature and ENSO had changed since the 1970s, and temperature displayed a higher tendency than normal El Niño Southern Oscillation developing in the summer after the 1970s (Zou et al. 2007). Many studies have been conducted to assess the effects of irregularities in the surface temperature of the sea on droughts in Europe (Barlow et al. 2002). However, very limited research has been conducted to address this issue in northeast China. Similarly, the decreasing trend of precipitation is also the main cause of droughts at the regional level (Yan et al. 2018; Fu et al. 2019). Irregular atmospheric circulation is the direct cause of drought and abnormal precipitation (Wang et al. 2017). Dascălu et al. (2016) observed more events of summer drought due to the decreasing trend of precipitation in east Romania.

Figure 2

Yearly drought variations on each station under various drought severity levels based on RAI and MSDI.

Figure 2

Yearly drought variations on each station under various drought severity levels based on RAI and MSDI.

The drought duration and severity analysis based on indices, namely, SPI, SSI, and MSDI indicated that SPI experienced an average of five drought events lasting five months or more in the study area. The most severe (D2) and extreme (D3) droughts were observed during the year 2007. MSDI exhibited a higher number of regular drought events with the record drought duration starting from September 1991 to May 1993 and then again from April 2002 lasting until May 2004, as shown in Figure 3. The SSI showed 12 drought events with the most extreme drought starting from April 2005 until January 2006. It was noted that MSDI detected the highest number of dry months as compared to SPI and SSI during the drought analysis. MSDI may be a good drought indicator for the assessment of dry conditions in the study area due to its multi-scalar approach. Many researchers have used MSDI for drought assessment and results showed that MSDI describes the drought onset as early as the SPI, while MSDI shows drought persistence similar to that of SSI. Additionally, MSDI displays a more severe drought condition when both the precipitation and soil moisture show a deficit. It was also observed that MSDI, similar to univariate SPI and SSI, provides the probability of occurrence and therefore can be used for risk analysis as well (Hao & AghaKouchak 2013).

Figure 3

Yearly drought comparison basin-wide by comparing SPI, SSI, MSDI, and RAI.

Figure 3

Yearly drought comparison basin-wide by comparing SPI, SSI, MSDI, and RAI.

The comparison of RAI trends with drought indices (SPI/SSI/MSDI) is shown in Figure 3. It was noted that SPI dominated from 1982 to 1993 and 2008 to 2015, while SSI assessed the drought anomalies in a better way from 1995 to 2007. Moreover, RAI results showed consistent trends during the above-mentioned period. It is worth mentioning that soil moisture shows less variability as compared to precipitation; therefore, soil moisture drought index describes the persistence of drought (Changnon 1987).

Spatiotemporal variations of drought events

Spatiotemporal changes of droughts have been investigated by using the statistical analysis on various parameters, such as drought duration, frequency, intensity, and severity of all the identified drought events in the study area during the study period. Spatial and seasonal variations of drought events were examined in the study area of Heilongjiang Province of China following a procedure described below.

Spatial variations of drought

Figures 4(a)–4(d) show the spatial drought events variations obtained by ADF (average annual drought frequency), MDS (mean drought severity), MDI (mean drought intensity), and MDD (mean drought duration) in the study area of Heilongjiang Province of China during 1980–2015. The results indicated that the spatial distribution of ADF was not equally distributed (not frequent) at five evident drought-prone stations. As a statistical indicator of drought, the ADF values range from 1.2 to 2.7 times annually. The pattern of ADF was increasing from the Downtown station towards Fangzheng station as shown in Figure 4(a). Wu et al. (2011) observed significant increasing trends of drought frequency in north China. The results indicated that the Downtown station exhibited the minimum value of ADF 1.2 times while Fangzheng station experienced a maximum value of 2.7 times annually. Similarly, Figure 4(b) displays the mean drought duration in the study area during the study period of 36 years. The results indicated that 5 to 11 months of mean drought duration (MDD) was observed over the study area during the study period. Zhang & Zhou (2015) studied the drought history and noted the longest drought duration over north China. A decreasing trend of drought duration was seen to be expanding from the Fangzheng station (11-month drought) toward the Wuchang station (5-month drought). Figures 4(c) and 4(d) show the statistics of mean drought severity and mean drought intensity at the Downtown, Bayan, Wuchang, Shangzhi, and Fangzheng stations. The drought characteristics showed that MDS ranged between −0.95 and −2.80 in the region. The MDS is increasing from Fangzheng towards Downtown station. Fangzheng exhibits the minimum drought severity (−0.95) while Downtown has a maximum drought index (−2.85) as shown in Figure 4(d). Figure 4(c) shows the decreasing pattern of MDI from Downtown stations towards Fangzheng station. It illustrates that Downtown exhibited the highest mean drought severity and intensity while Fangzheng experienced the maximum number of months under drought condition during 1980–2015, which indicated that high risk of drought prevailed at Fangzheng in terms of drought duration. Our results showed the spatial distribution of agro-meteorological drought trends at five sub-regions. Heilongjiang Province is the key region for grain production in northeast China, which might be suffering more economic loss due to the droughts as concluded by the different drought statistical analyses (MDD, MDI, ADF, and MDS) in the present study. Many researchers have investigated the high probability of drought frequency in northeast China. He et al. (2016) noticed the increasing trend of drought intensity in northeast China. Therefore, a higher tendency of drought has a significant impact on regional social and economic development.

Figure 4

Spatial characteristics of drought distribution in the study area: (a) annual average drought frequency, (b) mean drought duration, (c) mean drought intensity, (d) mean drought severity.

Figure 4

Spatial characteristics of drought distribution in the study area: (a) annual average drought frequency, (b) mean drought duration, (c) mean drought intensity, (d) mean drought severity.

Seasonal distribution trends of droughts

Figures 5(a)–5(d) demonstrate the seasonal drought characteristics (mean drought duration) of identified drought events from 1980 to 2015 over the study area of Heilongjiang Province of China. The results explained the apparent spatial difference of mean drought duration ratio among the various seasonal drought maps (spring, summer, autumn, and winter), which reflect the seasonal drought variations in the region. The drought indices (SPI and SSI) showed a drought event spanning over one month in a year during the spring season in the study area. Drought index (MSDI) exhibited a two-month drought duration during the spring season in the study area. Many researchers noticed the drought during the spring season in northeast China. For example, He et al. (2016) observed the spring drought in northeast China and stated that the specified region warms up quickly which leads to droughts during the spring season due to the strong winds and more evaporation. The probability of spring droughts accounts for 20 to 35%. Moreover, the temperature increases rapidly during the spring season in north China which leads to drought due to the reduction in precipitation. Spring droughts were observed at the global level in general and Asia in particular. For example, Waseem et al. (2016) observed the driest period during March, April, and May in South Korea. Homsi et al. (2020) also demonstrated a prolonged drought in Syria which may continue in the future due to the decreasing trend of precipitation. This condition will aggravate the water crisis and agriculture, which is an essential source of livelihood for most of the rural population of the country. The mean summer drought duration was experienced for 0.8–2.30 months while autumn drought duration ranged from 0.85 to 2.18 months). Many researchers noticed drought during the autumn season as well. Li et al. (2019) also experienced a drought episode during the autumn season in the Songhua River Basin. It was noticed that spring and summer droughts showed the lowest drought duration at Downtown station followed by Bayan and Wuchang, while Fangzheng station exhibited higher drought duration followed by Shangzhi station. Moreover, almost the same pattern of drought duration was noticed during the summer drought analysis. The mean drought duration ranged from 0.87 to 2.2 months during winter drought analysis. Many researchers examined drought during winter in China. For example, Liu et al. (2019) noticed a drought episode during winter in the Hanjiang River Basin.

Figure 5

Drought duration during different seasons: (a) spring, (b) summer, (c) autumn, (d) winter.

Figure 5

Drought duration during different seasons: (a) spring, (b) summer, (c) autumn, (d) winter.

Drought changes over sub-regions

Figure 6 shows the variations of drought time series of SSI, MSDI, and SPI for the five selected sub-regions in the Heilongjiang Province of China during the study period. A Mann–Kendall trend test was applied to the drought results of SSI and SPI at a 95% confidence level as shown in Table 3. Drought time series data were divided into three periods, namely, event 1 (1980 to 1991), event 2 (1992 to 2003), and event 3 (2004 to 2015) and drought characteristics of significant drought events are shown in Table 4. Moreover, statistical analysis of time series data of the three events is shown in Table 5. The events 1 and 2 did not indicate any significant trend while event 3 exhibited some significant trend during SPI drought analysis in the whole study region except Fangzheng station. This showed that there are no significant changes in the drying pattern at Fangzheng station during the study period.

Table 3

Mann–Kendall trend test statistics

StationLong.Lat.Event 1
Event 2
Event 3
SPISSISPISSISPISSI
Downtown 126.77 45.77 −0.16 0.21 0.48 2.4* 1.3 
Bayan 127.35 46.08 0.47 −0.31 −0.16 −0.16 2.13* 1.3 
Shangzhi 127.97 45.22 0.47 −0.31 1.02 −0.16 2.4* 1.3 
Wuchang 127.15 45.22 −0.31 −0.62 −0.16 2.54* 1.3 
Fangzheng 128.3 45.45 0.78 −0.31 −0.47 −0.16 0.48 1.3 
StationLong.Lat.Event 1
Event 2
Event 3
SPISSISPISSISPISSI
Downtown 126.77 45.77 −0.16 0.21 0.48 2.4* 1.3 
Bayan 127.35 46.08 0.47 −0.31 −0.16 −0.16 2.13* 1.3 
Shangzhi 127.97 45.22 0.47 −0.31 1.02 −0.16 2.4* 1.3 
Wuchang 127.15 45.22 −0.31 −0.62 −0.16 2.54* 1.3 
Fangzheng 128.3 45.45 0.78 −0.31 −0.47 −0.16 0.48 1.3 

*Shows significance level at 5%.

Table 4

Drought characteristics of significant drought events in the study area

StationIndex
Event 1
Event 2
Event 3
SMDDDSDISMDDDSDISMDDDSDI
 SPI Aug-84 10 −0.86 −0.17 Oct-95 10 −1.19 −0.24 Jan-06 −1.15 −0.23 
Downtown SSI Feb-83 15 −1.40  Sep-93 31 −1.29 −0.12 Jan-04 22 −1.74 −0.17 
MSDI Dec-82 34 −1.71 −0.34 Aug-93 50 −1.81 −0.30 Jan-04 47 −1.67 −0.28 
SPI Jan-89 −1.23 −0.25 Mar-01 10 −1.50 −0.31     
Bayan SSI Feb-83 15 −1.40 −0.14 Feb-95 32 −1.50 −0.17 May-04 21 −1.75 −0.17 
MSDI Mar-82 42 −1.63 −0.33 Jan-95 49 −1.59 −0.14 Jan-04 38 −1.86 −0.17 
SPI     Aug-97 −0.89 −0.18 Sep-07 −1.73 −0.29 
Shangzhi SSI Jan-91 09 −1.40 −0.14 Feb-94 27 −1.50 −0.17 May-04 21 −1.75 −0.17 
MSDI Dec-82 50 −1.23 −0.14 Jan-93 52 −1.56 −0.14 May-04 44 −1.71 −0.24 
SPI Feb-89 −1.01 −0.20 Oct-95 15 −1.25 −0.25 May-07 −1.28 −0.18 
Wuchang SSI Feb-83 15 −1.10 −0.14 Mar-94 29 −1.50 −0.17 May-04 21 −1.75 −0.17 
MSDI Feb-82 47 −1.18 −0.11 Aug-93 51 −1.47 −0.12 May-04 50 −2.04 −0.34 
Fangzheng SPI Mar-82 10 −1.25 −0.25 Oct-97 −0.92 −0.18 Jun-07 −1.51 −0.25 
SSI Feb-83 15 −1.40 −0.14 Mar-94 32 −1.50 −0.17 May-04 21 −1.75 −0.18 
MSDI Mar-82 57 −1.40 −0.28 Jun-93 54 −1.67 −0.15 May-04 52 −1.74 −0.19 
StationIndex
Event 1
Event 2
Event 3
SMDDDSDISMDDDSDISMDDDSDI
 SPI Aug-84 10 −0.86 −0.17 Oct-95 10 −1.19 −0.24 Jan-06 −1.15 −0.23 
Downtown SSI Feb-83 15 −1.40  Sep-93 31 −1.29 −0.12 Jan-04 22 −1.74 −0.17 
MSDI Dec-82 34 −1.71 −0.34 Aug-93 50 −1.81 −0.30 Jan-04 47 −1.67 −0.28 
SPI Jan-89 −1.23 −0.25 Mar-01 10 −1.50 −0.31     
Bayan SSI Feb-83 15 −1.40 −0.14 Feb-95 32 −1.50 −0.17 May-04 21 −1.75 −0.17 
MSDI Mar-82 42 −1.63 −0.33 Jan-95 49 −1.59 −0.14 Jan-04 38 −1.86 −0.17 
SPI     Aug-97 −0.89 −0.18 Sep-07 −1.73 −0.29 
Shangzhi SSI Jan-91 09 −1.40 −0.14 Feb-94 27 −1.50 −0.17 May-04 21 −1.75 −0.17 
MSDI Dec-82 50 −1.23 −0.14 Jan-93 52 −1.56 −0.14 May-04 44 −1.71 −0.24 
SPI Feb-89 −1.01 −0.20 Oct-95 15 −1.25 −0.25 May-07 −1.28 −0.18 
Wuchang SSI Feb-83 15 −1.10 −0.14 Mar-94 29 −1.50 −0.17 May-04 21 −1.75 −0.17 
MSDI Feb-82 47 −1.18 −0.11 Aug-93 51 −1.47 −0.12 May-04 50 −2.04 −0.34 
Fangzheng SPI Mar-82 10 −1.25 −0.25 Oct-97 −0.92 −0.18 Jun-07 −1.51 −0.25 
SSI Feb-83 15 −1.40 −0.14 Mar-94 32 −1.50 −0.17 May-04 21 −1.75 −0.18 
MSDI Mar-82 57 −1.40 −0.28 Jun-93 54 −1.67 −0.15 May-04 52 −1.74 −0.19 

SM, drought initiation time; DD, drought duration in months; DI, drought intensity; DS, drought severity.

Table 5

Statistical analysis of time series data

StatisticsDowntownShangzhiBayanFangzhengWuchang
 Mean 47.2567 58.9733 51.8325 50.4933 52.1942 
Event 1 Median 47.1583 62.5917 53.4792 48.6208 53.3625 
Std deviation 9.2645 11.1746 12.3612 6.1807 11.8789 
Mean 43.9394 54.3992 47.8765 49.9432 45.7939 
Event 2 Median 40.1583 51.8917 44.8917 49.475 43.2 
Std deviation 10.5889 12.6671 12.4111 12.4746 9.5459 
Mean 46.9212 50.2791 50.6206 49.1215 50.6401 
Event 3 Median 44.1625 49.0102 48.8056 50.2 50.9042 
Std deviation 9.5708 10.4698 8.5131 9.8463 11.6991 
StatisticsDowntownShangzhiBayanFangzhengWuchang
 Mean 47.2567 58.9733 51.8325 50.4933 52.1942 
Event 1 Median 47.1583 62.5917 53.4792 48.6208 53.3625 
Std deviation 9.2645 11.1746 12.3612 6.1807 11.8789 
Mean 43.9394 54.3992 47.8765 49.9432 45.7939 
Event 2 Median 40.1583 51.8917 44.8917 49.475 43.2 
Std deviation 10.5889 12.6671 12.4111 12.4746 9.5459 
Mean 46.9212 50.2791 50.6206 49.1215 50.6401 
Event 3 Median 44.1625 49.0102 48.8056 50.2 50.9042 
Std deviation 9.5708 10.4698 8.5131 9.8463 11.6991 
Figure 6

Drought variations captured by SPI, SSI, and MSDI over (a) Downtown, (b) Bayan, (c) Fangzheng, (d) Shangzhi, and (e) Wuchang stations.

Figure 6

Drought variations captured by SPI, SSI, and MSDI over (a) Downtown, (b) Bayan, (c) Fangzheng, (d) Shangzhi, and (e) Wuchang stations.

In the Downtown area located in the northwest part of the study area (Figure 6(a)), the greatest number of drought events occurred during 1993, 1995, and 2004. Many researchers have explained the drought tendency in northeast China. Wang et al. (2011) reported on the soil moisture and claimed that a significant drought tendency is exhibited for northeast China. Table 4 presents the characteristics of drought events by using various drought indices in the study area (DD more than or equal to four months) during 1980 to 2015. Table 4 describes the start time of drought (SM), drought duration (DD), drought severity (DS), and drought intensity (DI) based on the SPI, SSI, and MSDI indices. It was observed that MSDI detected the drought events earlier as compared to SPI and SSI during events 1 and 2, which may indicate the better capability of MSDI for the identification of dry spells in the study area. It was also observed that there were a higher number of months under dry condition during the MSDI analysis as compared to SPI and SSI in all three events. SPI relies on precipitation and SSI considers soil moisture, while MSDI depends on both factors (precipitation and soil moisture). Therefore, MSDI behaves as a multi-scalar index which detects drought onset earlier as compared to other drought indices.

Figure 6 indicates the drought events having a duration of two months or more, while Table 4 displays the events of drought having a duration of four months or more. Figures 6(b) and 6(c) indicate the SPI, SSI, and MSDI drought anomalies at Bayan and Fangzheng stations. The results revealed that MSDI prevailed at both meteorological stations in terms of longer drought duration due to the composite drought information of SSI and SPI. The longest drought duration was observed during the second event (1992–2003) at Bayan and Fangzheng stations. The highest drought intensity was observed during May 2004 at Fangzheng station, as shown in Figure 6(c). Table 4 also presents the exceptional drought (D4) during 2004. Event 3 (2004–2015) at Fangzheng showed that drought indices, namely, SSI and MSDI assessed the drought during May. It means that SSI behaves the same as MSDI in terms of drought persistence. As SSI is based on the soil moisture, the results indicated that it is more persistent as compared to SPI. It means that a longer duration of drought occurred during the soil moisture deficit period while a short duration of drought occurred during the precipitation deficit period because a higher amount of precipitation deficit occurred in a very short time. The use of multivariate drought indices such as MSDI is certainly significant. The main reason is that the soil moisture levels may remain high even long after precipitation in humid and semi-humid regions. In such regions, typically, precipitation detects the drought events earlier and soil moisture better describes the persistence of drought. MSDI assessed the drought onset similarly to SPI but describes the drought persistence more similarly to SSI. However, in arid regions, the indices, namely, SPI, SSI, and MSDI were more consistent, and MSDI did not provide additional information about drought. The main reason is the fact that, in arid regions, after each rainfall event, soil moisture evaporates relatively quickly. In such climates/regions, the soil moisture level is typically very low and meteorological and agricultural droughts occur at about the same time (Golian et al. 2015).

Figures 6(d) and 6(e) show the drought hazards at Shangzhi and Wuchang areas. The results show that the period from 1990 to 2008 was more vulnerable to drought in terms of its duration and severity. The year 2004 indicated the highest drought intensity (−1.75) in both areas (Shangzhi and Wuchang). It is concluded that SSI prevailed over SPI in terms of drought persistence while MSDI assessed the drought hazards earlier as compared to the agricultural (SSI) and meteorological (SPI) drought indices. It was also noted that SPI and SSI assessed the drought aspect differently. Moreover, statistics (mean, median, and standard deviation) of identified drought events are shown in Table 5. Additionally, NDVI was used to assess the vegetation dynamics in the study area. Generally, average NDVI for China increased with a linear trend of 1.4%/10 years (p < 0.01). The mean NDVI increased significantly during 2001–2002 at a rate of 4.96%. The mean NDVI during 2002–2012 exhibited a significant increase with a linear trend of 0.7%/10 years (p < 0.01). Moreover, the mean NDVI ranged from 0.4029 to 0.4170, with a change rate of 2.94%. Figure 7 shows that Heilongjiang Province has increased vegetation dynamics 57 (14%) and decreased 42.91 (5.02%) as confirmed by Liu et al. (2015). The farmland area increased 58.03 (1990), 58.60, (2000) and 60.85% (2010). NDVI was significantly positively correlated with precipitation with a correlation coefficient of 0.93 which shows that precipitation is the dominant climatic factor which affects the vegetation changing aspects in Heilongjiang Province. Dong et al. (2014) stated that drought phenomenon has increased and precipitation decreased due to the changes in land use/land cover. The annual NDVI values increased in the north and west, while they decreased in the eastern and western part during the early period of 2000. NDVI depends on type of land cover, and land cover of the study region was compared during the analysis period.

Figure 7

Land cover variations over study area from 1992 to 2015.

Figure 7

Land cover variations over study area from 1992 to 2015.

CONCLUSIONS

This research provides an overview of drought characteristics that occurred during the last 36 years (1980–2015). For drought analysis, three drought indices were used (SPI, SSI, and MSDI). Additionally, RAI was used to compare the precipitation variations with drought results obtained from drought indices. The summary of findings is as follows.

It was observed that most of the research area was dominated by mild and moderate drought conditions from 1980 to 2015. The findings of this study also indicated that 50% of the area was under extreme and exceptionally dry conditions during 1990–1992 and 2004–2005. The drought characteristic results (i.e., drought duration, drought severity) based on various drought indices (SPI, SSI, and MSDI) showed that SPI demonstrated, on average, five drought events lasting five months or more in the study area. MSDI exhibited a greater number of continuous drought events with the record drought length which starts from the year 1991 to 1993 and again from 2002 to 2004. The SSI showed 12 drought events with the most extreme drought event starting from the year 2005 to 2006. Spatial drought analysis statistics indicated that ADF varied from 1.2 to 2.7, MDD 5 to 11, and MDI −0.90 to −2.9) over 36 years. It was also observed that Fangzheng station exhibited a higher number of months under the dry condition as compared to other meteorological stations. Meanwhile, it was also noted that RAI results are consistent with other drought indices’ results. Mann–Kendall trend test analysis also showed that no significant drought trends were observed at Fangzheng, which implies that there are no changes in drying trends and drought might continue with the same drying pattern in the future in this area. Similarly, one- and two-month drought durations were experienced during the spring and summer season, respectively. It was also observed that MSDI assessed the drought earlier as compared to SPI and SSI. The results also revealed that the MSDI behaves more similarly to SSI in terms of drought persistence.

Based on the drought analysis, it was found that this study will be helpful for the better management of water resources in the region. Climatic variables such as precipitation are normally accepted for the explanation of drought variability. This study used data of very limited parameters including precipitation and soil moisture for the drought assessment. Therefore, there is still a need for further studies to explore how other factors (surface temperature, solar radiations, wind speed, humidity) can potentially affect the drought phenomenon. Furthermore, there is also a need for comprehensive research to observe the atmospheric circulation effects on drought variations.

ACKNOWLEDGEMENTS

The authors are greatly indebted to the National Key Research and Development Program of China (No. 2016YFC0400202) and Key R&D Project of Jiangsu Province (Modern Agriculture) (No. BE2018313). The authors declare no conflict of interest.

DATA AVAILABILITY STATEMENT

All relevant data are included in the paper or its Supplementary Information.

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