Meteorological Drought Monitoring in Northeastern China ... - MDPI

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Meteorological Drought Monitoring in Northeastern China Using Multiple Indices Fengping Li 1,2, *, Hongyan Li 1 , Wenxi Lu 1 , Guangxin Zhang 3 and Joo-Cheol Kim 4 1

2 3 4

*

Key Laboratory of Groundwater Resources and Environment, Ministry of Education, College of New Energy and Environment, Jilin University, Changchun 130021, China; lihongyan@jlu.edu.cn (H.L.); luwx999@163.com (W.L.) State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; zhgx@iga.ac.cn International Water Resources Research Institute, Chungnam National University, Daejeon 34134, Korea; kjoocheol@hanmail.net Correspondence: fpli@jlu.edu

Received: 7 November 2018; Accepted: 26 December 2018; Published: 3 January 2019

Abstract: Drought monitoring is one of the significant issues of water resources assessment. Multiple drought indices (DIs), including Percent of Normal (PN), Standardized Precipitation Index (SPI), statistical Z-Score, and Effective Drought Index (EDI) at 18 different timesteps were employed to evaluate the drought condition in Wuyuer River Basin (WRB), Northeast China. Daily precipitation data of 50 years (1960–2010) from three meteorological stations were used in this study. We found DIs with intermediate time steps (7 to 18 months) to have the highest predictive values for identifying droughts. And DIs exhibited a better similarity in the 12-month timestep. Among all the DIs, EDI exhibited the best correlation with other DIs for various timesteps. When further comparing with historical droughts, Z-Score, SPI, and EDI were found more sensitive to multi-monthly cumulative precipitation changes (r2 > 0.55) with respect to monthly precipitation changes (r2 ≤ 0.10), while EDI was more preferable when only monthly precipitation data were available. These results indicated that various indices for different timesteps should be investigated in drought monitoring in WRB, especially the intermediate timesteps should be considered. Keywords: precipitation-based drought indices (DIs); Standardized Precipitation Index (SPI); Effective Drought Index (EDI); Z-Score; Wuyuer River Basin

1. Introduction Drought is considered as one of the most significant natural disasters typically resulted from a climatic-induced anomaly of significantly decreased water availability over a certain period of time [1]. Serious drought condition can exert severe and disastrous damage to socio-economic activities, ecosystems, and humanity [2,3]. In recent years, droughts are generally expected to increase in both severity and frequency [4] due to climate change. There is no universal definition of drought [5], but it is basically categorized into meteorological drought, hydrological drought, agricultural drought, and socio-economic drought [5,6]. All definitions are determined on the basis of the insufficient moisture condition caused by a deficit in precipitation over a certain period [7–10]. However, a meteorological drought may develop quickly and end abruptly, and is generally an indicator of other types [11]. Drought monitoring is essential to risk management and is normally monitored by employing drought indices, which provide a quantitative assessment to identify drought occurrence, severity, and magnitude over a region. A variety of drought indices [12–15] have been developed and applied over Water 2019, 11, 72; doi:10.3390/w11010072

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the past decades. These include the Palmer Drought Severity Index (PDSI) [16], the Percent of Normal (PN) [17], Z-Score [18], the Standardized Precipitation Index (SPI) [19], the Surface Water Supply Index (SWSI) [20], the Standardized Precipitation Evapotranspiration Index (SPEI) [13], the Reconnaissance Drought Index (RDI) [21], Effective Drought Index (EDI) [22], Standardized Moisture Anomaly Index (SMAI) [23], and some other remote sensing based indices [24–27]. More detailed information about these indices are listed in Table 1. Table 1. Descriptions of different drought indices [13,16–26]. Drought Index PDSI

Full Name

Considered Variables

References

Palmer Drought Severity Index

Precipitation, Temperature, Soil moisture

[16]

Percent of Normal

Precipitation

[17]

Z-Score

Precipitation

[18]

Standardized Precipitation Index

Precipitation

[19]

SWSI

Surface Water Supply Index

Rainfall, Runoff, and Snow water content, Storage Reservoir Volume

[20]

SPEI

Standardized Precipitation Evapotranspiration Index

Precipitation, Evapotranspiration

[13]

RDI

Reconnaissance Drought Index

Precipitation, Potential Evapotranspiration

[21]

EDI

Effective Drought Index

Precipitation

[22]

SMAI

Standardized Moisture Anomaly Index

Precipitation, Runoff, Soil moisture, Evapotranspiration

[23]

LSWI

Land Surface Water Index

Remote Sensing Based Vegetation Indices

[24]

NDWI

Normalized Difference Water Index

Water and Vegetation information from MODIS (Moderate Resolution Imaging Spectroradiometer) images

PN Z-Score SPI

[25,26]

Droughts are complex because of their causation and impact, so no index is expected to be universally suitable and can sufficiently characterize the complicated drought conditions in a comprehensive manner. Previous studies have demonstrated the advantages in employing more than one index for drought evaluation [28,29]. China is one of the most vulnerable countries to frequent and severe droughts. According to statistical analyses by He et al. [30], 60% of China has experienced various types of meteorological hazards. With the impact of global warming, droughts are gaining increased attention [31,32]. The Wuyuer Basin is located in Northeastern China, with an area of 23,110 km2 . It is one of concentrative and extensive distribution area of inland wetlands in China, with a Ramsar wetland (i.e., Zhalong wetland) located at the downstream of the basin. Water supply is vital for agriculture and wetland ecosystems in this basin. Spatial-temporal changes of temperature and precipitation, which may result in drought and floods, has been reported in the basin [33]. A better understanding of droughts is essential to service for the water management and wetland protection in this area. A more detailed investigation to evaluate the performance and inherent properties of drought indices is imperative and has a special priority in this basin. Therefore, the objective of this study is to (1) identify correlations among DIs for various time scales, and (2) investigate their performances in drought monitoring. The results of this study can provide valuable information for drought monitoring and water resources management in the region. 2. Materials and Methods 2.1. Study Area The Wuyuer River originates from the west of the Lesser Khingan Mountains and travels a total length of 576 km with a drainage area of 15,084 km2 . It flows through six counties, i.e., Bei’an, Kedong, Keshan and Baiquan, Yi’an, and Fuyu, before draining to Zhalong Wetland. Zhalong wetland

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The Wuyuer River originates from the west of the Lesser Khingan Mountains and travels a total 3 of 17 length of 576 km with a drainage area of 15,084 km2. It flows through six counties, i.e., Bei’an, Kedong, Keshan and Baiquan, Yi’an, and Fuyu, before draining to Zhalong Wetland. Zhalong wetland is one of the of most important Ramsar international wetlands andand supports is one the most important Ramsar international wetlands supportsdiverse diverseecosystems ecosystems and and endangered endangeredwildlife wildlifespecies. species.Wuyuer WuyuerRiver RiverBasin Basin(WRB) (WRB)has hasaa typical typical inland inland semi-arid semi-aridclimate climateand and isisthe largest inland river in Heilongjiang province located in Northeastern China (Figure 1). It is dry the largest inland river in Heilongjiang province located in Northeastern China (Figure 1). It is and in winter, yet yet hot hot andand rainy in summer. The annual total precipitation dry cold and cold in winter, rainy in summer. The annual total precipitationisisabout about500 500mm, mm, mainly mainlyconcentrated concentratedduring duringthe theperiod periodof ofJune Juneto toSeptember Septemberand andititisisthe themain mainsource sourceof ofthe the water water for for the the WRB. WRB. Annual Annual evapotranspiration evapotranspiration in in the the WRB WRB isis 1110 1110 to to 1780 1780 mm, mm, much much higher higher than than the the precipitation. precipitation. WRB WRB has hasaapopulation populationof of1.7 1.7million millioninhabitants, inhabitants,1.24 1.24million millionof ofwhich whichmake maketheir theirliving living from agriculture [33]. [33].The Thewater water supplied by river the river is recognized as to crucial to agricultural from agriculture supplied by the is recognized as crucial agricultural irrigation irrigation andhealth. wetland health. With of theglobal effects of global climate extreme hydroand wetland With the effects climate change, morechange, extrememore hydro-meteorological meteorological events (especially droughts) are area occurring this area [34,35].threats These topose severe events (especially droughts) are occurring in this [34,35].inThese pose severe agriculture threats to agriculture production and wetland conservation. However, droughts in WRB have been production and wetland conservation. However, droughts in WRB have been under studied and no under studied and no comprehensive is available. this study we aim to provide a comprehensive assessment is available.assessment In this study we aim toInprovide a comprehensive drought comprehensive drought assessment four DIsvarious (drought indices) with various timesteps. assessment using four DIs (drought using indices) with timesteps. Water 2019, 11, 72

Figure 1. The Wuyuer River Basin in Northeast China and the meteorological (dots) stations used in Figure 1. The Wuyuer River Basin in Northeast China and the meteorological (dots) stations used in this study. this study.

2.2. Data Collection 2.2. Data Collection Daily precipitation data were obtained from three meteorological stations across the basin (Figure 1) precipitation from China data Meteorological Administration for a period of across January to Daily were obtained from three (CMA) meteorological stations the1960 basin December (Figure 1) 2010. from China Meteorological Administration (CMA) for a period of January 1960 to From 2010. the basic information of precipitation shown in Table 2, it can be found that the precipitation December decreases the mountain areas of in the upper basinshown to the plain in the2,lower basin. The mean From from the basic information precipitation in Table it can be found thatvalue the of annual precipitation during from mmbasin to 523tomm, with an average of precipitation decreases from the1960–2010 mountainvaried areas in the424 upper the plain in the lowervalue basin. 471 mm for the basin. The standard deviation of precipitation in each station was 95 mm to 125 mm, The mean value of annual precipitation during 1960–2010 varied from 424 mm to 523 mm, with an being about of the mean The annual precipitation. average value25% of 471 mmstations’ for the basin. standard deviation of precipitation in each station was 95 mm to 125 mm, being about 25% of the stations’ mean annual precipitation.

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Table 2. Statistical characteristics of precipitation in the Wuyuer River Basin. Elevation (m)

Coordinates

Statistics of Annual Precipitation Series (1960–2010)

Stations

Lat.

Lon.

Mean (mm)

Max (mm)

Min (mm)

Median (mm)

St.D

Beian Keshan Fuyu

270 235 163

48.28 48.05 47.80

126.52 125.88 124.48

523 497 438

785 901 748

312 252 285

499 492 418

123 110 125

2.3. Drought Indices Calculation Four precipitation-based DIs, Percent of Normal by Mean (PN), Z-Score, Standardized Precipitation Index (SPI), and Effective Drought Index (EDI), were selected in this study. Observed daily precipitation records were averaged by month and then were used to calculate DIs and multi-monthly precipitations. 2.3.1. Percent of Normal by Mean (PN) The PN is a drought index that is well known for its simple calculation and effectiveness in describing droughts. It can be computed based on the following equation [17]: PN (%) =

xi x

(1)

where xi is the observed precipitation value and x is the average value of a certain period. 2.3.2. Z-Score In brief, Z-Score is calculated as follows: Z-Score =

xi − x σ

(2)

where xi is the observed precipitation value and x is the average value of a certain period, and σ is the standard deviation of the records. 2.3.3. Standardized Precipitation Index (SPI) The SPI was developed to determine the precipitation deficit and can be calculated with multiple timesteps [19]. When calculating the SPI, the long-term precipitation data was first fitted to a probability distribution. As proposed in previous studies [36,37], this study used the gamma distribution to fit the long-term precipitation data. Then the fitted distribution was transformed to the standard normal distribution so that the SPI mean and variance are 0 and 1, respectively. A drought event occurs when the value of SPI is continuously negative and ends when the SPI becomes positive. More details about the calculation of SPI are available from some previous studies [36]. 2.3.4. Effective Drought Index (EDI) The EDI was proposed to provide an exact detection of occurrence and duration of droughts [21]. In contrast to other indices, EDI is a function of ‘precipitation needed to return to normal’ (PRN) and is calculated with a daily time step [38]. Specifically, EDI is calculated as follows: EDI = PRN/σ

(3)

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where σ is the standard deviation of PRN values for a relevant period, and PRN can be obtained as: N

1 i i =1

PRN = (EP− EP)/ ∑

(4)

where EP represents effective precipitation. Effective precipitation (EP) refers to the summation of all daily precipitation with a time reduction function. The EP for any day is a function of precipitation of the current day, as well as of previous days but with lower weights. It is calculated with the following equation: N

EP =

∑

"

m =1

m

∑ Pi

!

# /m

(5)

i =1

N is the duration of summation and Pi is the precipitation of i–1 days ago. In this study, a value of 365 days is chosen as the duration [26]. In this study, only EDI was calculated for one timestep because it is determined as the mean condition of precipitation over a year. For all the other DIs, the monitoring period can be either a month, 3 months, a season or a year. Therefore, to make a further understanding of timesteps effects on drought monitoring, they were computed for 18 different timesteps, i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, 24, 30, 36, and 48 months, respectively. 3. Results and Discussion 3.1. Correlations of Drought Indices for Different Timesteps The DI values were first standardized with integers based on the drought classifications (Table 3) for PN, Z-Score and SPI for 18 timesteps and EDI. Then Pearson correlation analysis was employed to detect the relationship among the calculated indices. Table 3. Classifications of droughts for different DIs.

Classifications PN (%) Z-Score SPI EDI

Extreme Drought

Severe Drought

Moderate Drought

Mild Drought

Normal-Wet Condition

4

3

2

1

0

≤40 ≤−2.00 ≤−2.00 ≤−2.00

40~55 −1.90~−1.50 −1.90~−1.50 −1.90~−1.50

55~80 −1.49~−1.00 −1.49~−1.00 −1.49~−1.00

80~100 −0.99~0.99 −0.99~0.99 −0.99~0.99

≥100 ≥1 ≥1 ≥1

The average correlation coefficients among the DIs in different timesteps were calculated for the basin (Figure 2). EDI was timestep independent, so the correlation coefficients between EDI and other DIs in different timesteps were listed in Table 4. Generally, DIs calculated using intermediate timesteps varied from 7 to 13 months correlated better (>0.5) with others. While, the correlations of both shorter (1 to 6 months) and longer (18–24 months) timesteps, especially one-month, two-month, and 48-month, with others being relatively low. Additionally, DIs in intermediate timesteps showed better correlation with both shorter and longer timesteps, while DIs in shorter or longer timesteps only showed better correlation with nearby timesteps. This indicates DIs using intermediate timesteps are the best suited to evaluate drought in WRB.

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Figure 2. Pearson correlation coefficients between drought indices for various timesteps. Notes: Each Figure 2. Pearson correlation coefficients between drought indices for various timesteps. Notes: Each time step consists of three different indices (one pixel for each DI in the order of PN, Z-Score, and SPI). time step consists of three different indices (one pixel for each DI in the order of PN, Z-Score, and SPI). Table 4. Correlation coefficients between EDI and other DIs in different timesteps. DIs PN 1 Z-Score PN 0.32 SPI Z0.33 Score SPI 0.33 When DIs

Timestep Table 4. Correlation coefficients between EDI and other DIs in different timesteps. 1

0.32 0.37 0.41 0.48 0.53 0.57 0.61 2 0.41 30.47 0.52 4 0.575 0.63 60.68 0.33 0.37 0.420.41 0.48 0.60 0.530.660.57 0.33 0.47 0.54 0.73

2

3

8 9 10 11 Timestep 0.65 0.68 0.68 0.67 7 8 0.799 10 0.73 0.76 0.78 0.61 0.65 0.81 0.68 0.80 0.68 0.76 0.78

0.41

0.68

0.47

4

0.52

5

6

0.57

0.42 0.47 0.54 0.60 further calculating the

7

0.63

0.73

0.76

0.79

12

15

18

24

30

0.66 11 0.76 0.67 0.78

0.63 12 0.66 0.66 0.71

0.59 15 0.60 0.63 0.64

0.52 18 0.51 0.59 0.52

0.42 24 0.43 0.52 0.43

0.33 0.35 30 0.35 36 0.33 48 0.42 0.35 0.33 0.32 0.35

0.78

0.76

0.66

0.60

0.51

0.43

0.66 0.73coefficient 0.76 0.78 for0.81 0.78 all 0.71DIs0.64 average each0.80 DI with in

36

48

0.35

0.33

0.43 timestep, 0.35 0.32 a0.52 certain

When further calculating the averagecorrelations coefficient for for each each timestep. DI with all DIs in a certain timestep, DIs were found similar timestep-average Specifically, PN showed the DIs were found similar timestep-average correlations for each timestep. Specifically, PN showed the least correlation with other DIs with almost all timesteps. Z-Score with shorter timesteps (two-month least correlation with other DIs with almost all timesteps. Z-Score with shorter timesteps (two-month to six-month) had a better correlation with DIs, while SPI with intermediate and longer timesteps to six-month) to had a better correlated correlationbetter withwith DIs,other whileDIs. SPIWhile, with intermediate andbetter longer timesteps (seven-month 48-month) EDI is exhibiting relationship (seven-month to 48-month) correlated better with other DIs. While, EDI is exhibiting with others in most timesteps. More than 70% of the coefficients of EDI with other DIs larger thanbetter 0.50, relationship with others were in most timesteps. More thanmaking 70% ofEDI the to coefficients EDI with DI. other DIs and about 30% of which larger than 0.70, which be the bestofcorrelated largerTherefore, than 0.50,Z-Score4, and about 30% and of which thanas0.70, which making EDI to and be the best PN10, SPI24were werelarger selected the shorter, intermediate, longer correlated DI. timestep DIs, respectively, and a comparison was made between these DIs with EDI to investigate the Therefore, PN10, and SPI24 were selected as the shorter, intermediate, and longer proper timestepsZ-Score4, of the indices. timestep DIs, respectively, and a coefficients comparisonofwas betweenPN10, these SPI24, DIs with investigate Average values of correlation eachmade DI (Z-Score4, andEDI EDI)toagainst other the proper timesteps of the indices. DIs were calculated for each timestep (Figure 3). For example, the blue bar in the first timestep shows Average of correlation of each between DI (Z-Score4, PN10, SPI24, and EDI)between against the average of values three coefficient valuescoefficients (i.e., the coefficient EDI and PN1, the coefficient other DIs were calculated for each timestep (Figure 3). For example, the blue bar in the first timestep EDI and Z-Score1, and the coefficient between EDI and SPI1), and the first green bar represents the shows of three coefficient values (i.e., the coefficient between and EDI Z-Score1, and PN1, the the coefficient averagethe ofaverage three coefficient values (i.e., the coefficient between Z-Score4 coefficient between EDI and Z-Score1, and the coefficient between EDI and SPI1), and the first green bar between Z-Score4 and PN1, and the coefficient between Z-Score4 and SPI1), and so on. represents the average of three coefficient values (i.e., the coefficient between Z-Score4 and Z-Score1, As illustrated in Figure 3, EDI showed higher correlation with shorter timesteps than other DIs, the coefficient between Z-Score4 and PN1, and the coefficient between Z-Score4 and SPI1), and so on. except shorter timesteps DI (i.e., Z-Score4), which had significantly higher correlations than other As illustrated in Figure 3, EDI showed higher correlation with shorter timesteps than other DIs, indices only in timesteps shorter than five months. For intermediate timesteps DI (i.e., PN10), the except shorter DI EDI (i.e., only Z-Score4), hadand significantly correlations correlation wastimesteps higher than for 10-,which 11-, 36-, 48-monthhigher timesteps, and eventhan for other these indices only in timesteps shorter than five months. For intermediate timesteps DI (i.e., PN10), the timesteps the correlations were very similar and close to EDI. As for longer timestep DIs (i.e., SPI24), it correlation was higher than EDI only for 10-, 11-, 36-, and 48-month timesteps, and even for these performed well under the longer timesteps (longer than 18 months) and possessed higher correlations timesteps the correlations were verya similar and close to than EDI. other As forindices longer for timestep DIstimesteps, (i.e., SPI24), than EDI. Therefore, EDI indicated better performance different in it performed well under the longer timesteps (longer than 18 months) and possessed higher correlations than EDI. Therefore, EDI indicated a better performance than other indices for different

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Correlation Coefficient

general. The best correlation of EDI was observed with the timesteps between 8 to 12 months, with the timesteps, in general. best correlation of EDI was observed with the timesteps between 8 to 12 average coefficients of The 0.71–0.76. months, with the average coefficients of 0.71–0.76.

1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00

EDI

1

Z-Score4

2

3

4

PN10

5

6

SPI24

7 8 9 10 11 12 15 18 24 30 36 48 Timestep (months)

Figure 3. Average correlation correlation coefficient coefficient of Figure 3. Average of representative representative DIs DIs with with short short (Z-Score4), (Z-Score4), intermediate intermediate (PN10), and long timesteps (SPI24) against other DIs for each timestep. (PN10), and long timesteps (SPI24) against other DIs for each timestep.

3.2. Evaluation of Drought Classes Indicated by DIs 3.2. Evaluation of Drought Classes Indicated by DIs 3.2.1. Comparison of PN with EDI 3.2.1. Comparison of PN with EDI The comparison of droughts identified by PN and EDI is shown in Figure 4a. The frequency of The comparison of droughts identified byand PNmild and droughts EDI is shown in was, Figure 4a. The frequency of identified severe droughts, moderate droughts, by EDI respectively, 2.5%, 9.3%, identified droughts, and mild droughtsHowever, by EDI was, respectively, 2.5%, and 76.8% severe during droughts, 1960–2010,moderate and no extreme drought was detected. the identified results of 9.3%, and 76.8% during 1960–2010, and no extreme drought was detected. However, the identified PN was very different, and the disparity became larger when the timestep increased. In general, the results of PN was different, and the disparity became larger than whenthat theoftimestep increased. In total percentage ofvery droughts indicated by EDI (88.6%) was higher PN for any timestep. general, the total percentage of droughts indicated by EDI (88.6%) was higher than that of PN for any Using the PN index, the basin was always (about 40%) in a normal or wet condition during 1960–2010. timestep. Using the PN index, the basin was always (about 40%) in severe a normal or wet condition during For PN with shorter timesteps (1–7 months), extreme (1.4–21.7%), (4.7–10.6%), and moderate 1960–2010. For PN withwere shorter timesteps (1–7 months), extreme (1.4–21.7%), (4.7–10.6%), and droughts (17.7–25.1%) overestimated when compared with EDI, but mildsevere droughts (10.7–24.9%) moderate droughts (17.7–25.1%) were overestimated when compared with EDI, but mild droughts had been underestimated. As the timestep went longer, less moderate droughts and more mild (10.7–24.9%) had been underestimated. As the timestep went longer, less moderate droughts and droughts were detected. more mild droughts were detected. 3.2.2. Comparison of Z-Score with EDI 3.2.2. Comparison of Z-Score with EDI The Z-Score had a better consistency with EDI in drought identification than PN did (Figure 4b). The had aofbetter consistency EDI in drought identification (Figure 4b). Based onZ-Score the results the Z-Score withwith various timesteps, mild droughtthan wasPN thedid most frequent Based on the results of the Z-Score with various timesteps, mild drought was the most frequent drought class between 1960 and 2010. The Z-Score with intermediate timesteps matched droughts drought between 1960 and 2010. The Z-Score intermediate timesteps matched identifiedclass using EDI most closely. For example, the with frequency of extreme drought, severedroughts drought, identified using EDI most closely. For example, the frequency of extreme drought, severe drought, moderate drought, and mild drought identified by Z-Score9 was 0%, 1.99%, 9.27%, and 76.66%, moderate drought, and mild drought by Z-Score9 was 0%, 1.99%,detected 9.27%, by and 76.66%, respectively, similar to frequencies of 0%,identified 2.34%, 9.36%, and 76.76%, respectively, Z-Score15. respectively, to frequencies 0%,frequencies 2.34%, 9.36%, and 76.76%, respectively, detected by ZFurther, thesesimilar values were very close of to the identified using EDI. For Z-Scores with shorter Score15. Further, these values were very close to the frequencies identified using EDI. For Z-Scores timesteps, moderate droughts and normal conditions were underestimated when compared with EDI, with shorter in timesteps, and normal conditions underestimated when but droughts extreme, moderate severe, anddroughts mild drought conditions had beenwere overestimated. In addition, compared with buttimesteps droughtsalso in overestimated extreme, severe, mild drought conditions the Z-Scores withEDI, longer theand frequency of extreme droughts,had but been they overestimated. In addition, the Z-Scores with longer timesteps also overestimated the frequency of performed very well in the identification of mild droughts. Therefore, longer timesteps in general extreme droughts, but they performed veryby well in thetimesteps. identification of mild droughts. Therefore, generated more reliable Z-Score than those shorter However, intermediate timesteps longer timesteps in general generated more reliable Z-Score than those by shorter timesteps. matched EDI best. However, intermediate timesteps matched EDI best.


Figure 4. 4. Drought Drought frequency frequency identified identified by by PNs PNs vs. vs. EDI EDI (a), (a), Z-Scores Z-Scores vs. vs. EDI EDI (b) (b) and and SPIs SPIs vs. vs. EDI EDI (c) (c) in in the the WRB WRB during during 1960–2010. 1960–2010. Figure

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Compared to PN and Z-Score indices, SPI is more consistent with EDI in drought estimation 3.2.3. 4c) Comparison of SPIgood with EDI (Figure with relatively performances for almost all timesteps. Again, estimations by SPI using intermediate timesteps were more reliable than other timesteps. SPI using shorter timesteps showed an Compared to PN and Z-Score indices, SPI is more consistent with EDI in drought estimation exact estimation of mild droughts normal-wetfor conditions astimesteps. EDI did. However, the identification (Figure 4c) with relatively goodand performances almost all Again, estimations by SPI of other drought types was poor, with moderate droughts being underestimated while extreme droughts using intermediate timesteps were more reliable than other timesteps. SPI using shorter timesteps and severean droughts were overestimated. Generally, SPI withconditions longer timesteps displayed a similar showed exact estimation of mild droughts and normal-wet as EDI did. However, the estimation with that with shorter timesteps. Interestingly, SPI24 greatly overestimated moderate identification of other drought types was poor, with moderate droughts being underestimated while droughts, underestimated droughts compared Generally, with EDI. SPI with longer timesteps extreme but droughts and severemild droughts werewhen overestimated. Based ona the aboveestimation analysis, PN showed higher variability with timesteps than Z-Score and SPI. displayed similar with that with shorter timesteps. Interestingly, SPI24 greatly This index was also founddroughts, to be the but leastunderestimated matched DI compared with when the others. If PNwith is the only DI overestimated moderate mild droughts compared EDI. Based on the above analysis, PN showed higher variability with timesteps than Z-Score and SPI.SPI choice in this basin, shorter timesteps (1–3 months) are recommended. In contrast, the Z-Score and This index also found to bewith the least DI compared withvariability the others.was If PNslight is theamong only DIthe yielded morewas consistent results EDI.matched Especially with SPI, the choice in this basin, (1–3 recommended. In contrast,inthe Z-Score and different timesteps. Forshorter either timesteps the Z-Score or months) SPI, DIs are with intermediate timesteps general provided SPI yielded more consistent results with EDI. Especially with SPI, the variability was slight among the best matches to values generated using EDI. the different timesteps. For either the Z-Score or SPI, DIs with intermediate timesteps in general the best matches to values generated using EDI. 3.3.provided Effectiveness of DIs in Detecting Historical Droughts is known that there were four significant droughts in Keshan, i.e., from autumn 1999 to spring 3.3.ItEffectiveness of DIs in Detecting Historical Droughts 2000, autumn 2000 to spring 2001, autumn 2002 to spring 2003, and autumn 2004 to spring 2005 [39]. It is known that there were four significant droughts i.e.,as from 1999 to spring Therefore, we enrolled the period from September 1999 in to Keshan, May 2001 theautumn representative drought 2000, autumn 2000 to spring 2001, autumn 2002 to spring 2003, and autumn 2004 to spring 2005 [39]. period in Keshan, and the DIs were calculated based on the data of Keshan station during this period. Therefore, we enrolled the period from September 1999 to May 2001 as the representative drought Based on the prior analysis among DIs, only EDI, and Z-Score and SPI in intermediate timesteps (9, 12, period in Keshan, and the DIs were calculated based on the data of Keshan station during this period. and 15 months) were selected to identify how DIs coordinated with the historical deficiency conditions Based on the prior analysis among DIs, only EDI, and Z-Score and SPI in intermediate timesteps (9, of monthly and multi-monthly precipitation. 12, and 15 months) were selected to identify how DIs coordinated with the historical deficiency Actual monthly precipitation (precipitation received in a particular month), average monthly conditions of monthly and multi-monthly precipitation. precipitation for a particular month received during the period), multi-monthly average Actual(average monthlyvalue precipitation (precipitation instudying a particular month), average monthly precipitation (average value of precipitation in particular preceding months),period), and moving cumulative precipitation (average value for a particular month during the studying multi-monthly precipitation was employed to evaluate a variety of precipitation. average precipitation (average value of precipitation in particular preceding months), and moving When comparing the indices andtoprecipitation variables in 9-month timestep, recurring cumulative precipitation was employed evaluate a variety of precipitation. deficitsWhen between monthly and averagevariables monthlyin precipitation were observed comparing theprecipitation indices and precipitation 9-month timestep, recurring (Figure deficits 5). The largest deficit happened from September to November 1999 and in June 2000, with monthly between monthly precipitation and average monthly precipitation were observed (Figure 5). The precipitation not even reaching a half amount of long-term1999 average for each The deficit largest deficit happened from September tothe November and value in June 2000,month. with monthly not even reaching a half amount of with the long-term average value for each month. The of precipitation the moving cumulative precipitation compared multi-monthly average precipitation declined deficit offrom the moving cumulative precipitation comparedawith multi-monthly average precipitation gradually September 1999 to March 2000, implying mitigation of drought conditions, but then declinedfrom gradually September 1999 March 2000, implying a mitigation of drought conditions, increased Aprilfrom to June in 2000, as to drought conditions took hold again. However, the deficit but thenfrom increased fromto April June and in 2000, drought conditions took hold again. However, stabilized July 2000 Mayto2001, thenasdecreased from March to May 2001, after whichthe time deficit stabilized from July 2000 to May 2001, and then decreased from March to May 2001, after drought conditions ceased. which time drought conditions ceased.

Figure 5. Changes of precipitation and drought indices for the nine-month timestep in Keshan station Figure 5. Changes of precipitation and drought indices for the nine-month timestep in Keshan station from September from September1999 1999totoMay May2001. 2001.

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Compared EDI, temporal changes of SPI9 and Z-Score9 generally performed better in Water 2019, 11, xwith FOR PEER REVIEW 10 of 17 tracking the deficits shown with moving cumulative precipitation and multi-monthly average Compared withwere EDI, temporal changes of SPI9 and Z-Score9EDI generally performed in precipitation. There two critical differences between and the other better two indices. tracking the deficits shown with moving cumulative precipitation and multi-monthly average The first one was observed from February to June 2000, when SPI9 and Z-Score9 showed an precipitation. There were two critical differences between EDI and the other two indices. The first increase-decrease-increase change while EDI was initially stable, but then declined. This shows that one was observed from February to June 2000, when SPI9 and Z-Score9 showed an increase-decreaseEDI did not capture the deficit signaled thebut moving cumulative precipitation multi-monthly increase change while EDI was initially by stable, then declined. This shows that EDIand did not capture average precipitation from February to March 2000, and also responded inversely to the decline in the deficit signaled by the moving cumulative precipitation and multi-monthly average precipitation deficit in June 2000.toThe other difference was found duringtoFebruary toinMarch when from February March 2000, and also responded inversely the decline deficit2001, in June 2000.SPI9 The and othershowed difference foundinduring to March 2001, SPI9 andwhich Z-Score9 an an Z-Score9 anwas increase deficitFebruary conditions but EDI waswhen decreasing, alsoshowed implicated increase in deficit conditions but EDIduring was decreasing, which also implicated an inverse response to inverse response to the deficit decline this period. the deficit decline this period. The scatter plotsduring of drought indices and precipitation deficit in the nine-month timestep were The scatter plots of drought indices and precipitation deficit in the nine-month timestep were shown in Figure 6. DIs ((i.e., Z-Score9, SPI9, and EDI)) did not reflect the deficit between monthly shown in Figure 6. DIs ((i.e., Z-Score9, SPI9, and EDI)) did not reflect the deficit between monthly precipitation and average monthly precipitation very well, and2 r2 was only 0.037, 0.10, and 0.12 precipitation and average monthly precipitation very well, and r was only 0.037, 0.10, and 0.12 respectively. However, DIs had a statistically close relationship with the deficit between moving respectively. However, DIs had a statistically close relationship with the deficit between moving cumulative precipitation and multi-monthly precipitation nine-month timestep cumulative precipitation and multi-monthlyaverage average precipitation forfor thethe nine-month timestep with with 2 0.997,0.997, 0.55,0.55, and and 0.320.32 values of of r .r2. values

Figure 6. Scatter plots of drought indices(i.e., (i.e., Z-Score9, Z-Score9, SPI9, and deficit between monthly Figure 6. Scatter plots of drought indices SPI9,and andEDI) EDI) and deficit between monthly precipitation and average monthly precipitation (the row) first and row)deficit and between deficit between precipitation and average monthly precipitation (the first movingmoving cumulative cumulative and average multi-monthly average(the precipitation (thefor second row) for thetimestep nineprecipitation andprecipitation multi-monthly precipitation second row) the nine-month in month timestep in Keshan station from September 1999 to May 2001. Keshan station from September 1999 to May 2001.

For 12-month the 12-month timestep,multi-monthly multi-monthly average was relatively highhigh in every For the timestep, averageprecipitation precipitation was relatively in every month compared to the moving cumulative precipitation at Keshan station (Figure 7). The gap month compared to the moving cumulative precipitation at Keshan station (Figure 7). The gap between between these two variables was relatively stable from September 1999 to June 2000, and then became these two variables was relatively stable from September 1999 to June 2000, and then became larger larger in July and August, which led to more severe drought conditions. From September 2000, it in July andtoAugust, led to more asevere drought began decreasewhich again and reached stable level untilconditions. May 2001. From September 2000, it began to decrease again andofreached a stable until were May consistent 2001. Changes EDI, SPI12, and level Z-Score12 with the water deficits indicated by Changes of EDI, SPI12, and Z-Score12 were consistent with the water deficits indicated by moving moving cumulative and multi-monthly average precipitation totals. To be specific, SPI12 and Zcumulative and multi-monthly average precipitation totals. To be specific, SPI12 and Z-Score12 exhibited a similar pattern, with changing lines almost coinciding with each other. EDI deviated from

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Score12 exhibited a similar pattern, with changing lines almost coinciding with each other. EDI Score12 exhibited a similar pattern, with changing lines toalmost coinciding with each other. EDI deviated from these twoways. indices in two ways. From May 2000, EDI other these two indices in two From May to June 2000, EDIJune decreased butdecreased the otherbut twothe remained deviated from these indices in two ways. From May to Junemonthly 2000, EDI decreased but two remained mostlytwo stable. In the meantime, the gap between precipitation andthe its other longmostly stable. In the meantime, the gap between monthly precipitation and its long-term average two mostly stable. gapthe between monthly precipitation and its longtermremained average increased from In 14 the mmmeantime, to 56 mm, the while gap between cumulative precipitation and increased from 14 mm to 56 mm, while the gap between cumulative precipitation and multi-monthly term average increased from 14 mm to 56 mm, while the gap between cumulative precipitation and multi-monthly average precipitation remained mostly unchanged. EDI also deviated from the other average precipitation remained mostly unchanged. EDI also deviated from the from otherthe indices in multi-monthly EDI also deviated other indices in June average and Julyprecipitation of 2000. EDIremained increasedmostly but theunchanged. other two decreased. Monthly precipitation June and July of 2000. EDI increased but the other two decreased. Monthly precipitation deficits indices Juneimproving and July of 2000. EDI increased but the otheraverage, two decreased. deficits in were and reached 87% of its long-term but the Monthly deficit inprecipitation the moving were improving and reached 87% of its long-term average, but the deficit in the moving cumulative deficits wereprecipitation improving and multi-monthly reached 87% ofaverage its long-term average, the deficit in62the moving cumulative precipitation wasbut increasing from mm to 122 precipitation and multi-monthly average precipitation was increasing from 62 mm to 122 mm. cumulative precipitation and multi-monthly average precipitation was increasing from 62 mm to 122 mm. mm.

Figure 7. 7. Changes ofofprecipitation for 12-month 12-monthtimestep timestepininKeshan Keshan station from Figure Changes precipitationand anddrought drought indices indices for station from September 1999 to May 2001. Figure 7. Changes of precipitation and drought indices for 12-month timestep in Keshan station from September 1999 to May 2001. September 1999 to May 2001.

Based onon the correlation it was was further furtherfound foundthat thatZ-Score12 Z-Score12 and SPI12 Based the correlationanalysis analysisshown shown Figure Figure 8, 8, it and SPI12 2 = 0.005). While they showed diddid have a weak relationship with monthly precipitation variability (r 2 Based on the correlation analysis shown Figure 8, it was further found that Z-Score12 and SPI12 have a weak relationship with monthly precipitation variability (r = 0.005). While they showed a significant with between moving precipitation andmulti-monthly multi-monthly did have a correlation weak relationship with monthly precipitation variabilityprecipitation (r2 = 0.005). While they showed a significant correlation withdeficit deficit between moving cumulative cumulative and 2 aaverage significant correlation with between moving cumulative precipitation and multi-monthly average precipitation, andand thethe r deficit was 0.998 andand 0.992, respectively. Correlation between EDIEDI and and water precipitation, r2 was 0.998 0.992, respectively. Correlation between 2 was 0.998 and 0.992, respectively. Correlation between EDI and average precipitation, and the r deficit indicated by moving cumulative and multi-monthly average precipitation in 12-month timestep water deficit indicated by moving cumulative and multi-monthly average precipitation in 12-month 2 was water bythan moving cumulative and precipitation multi-monthly averagebut precipitation timestep wasindicated also better its relationship with monthly precipitation change, thein r212-month was 0.47, is was alsodeficit better than its relationship with monthly change, the rbut 0.47, which was also better than its relationship monthly precipitation change, but the r2 was 0.47, which is not strong astwo the indices other two indiceswith were. nottimestep as strong asas the other were. which is not as strong as the other two indices were.

Figure 8. Scatter plots of drought indices (i.e., Z-Score12, SPI12, and EDI) and deficit between monthly Figure 8. 8. Scatter plots ofofdrought indices (i.e., Z-Score12,SPI12, SPI12, andEDI) EDI)and anddeficit deficit between monthly Figure Scatter plots drought indicesprecipitation (i.e., Z-Score12, precipitation and average monthly (the first and row) and deficit between monthly moving precipitation andand average monthly precipitation (the first deficit moving cumulative precipitation average monthly precipitation (therow) firstand row) andbetween deficit between moving precipitation and multi-monthly average precipitation (the second row) for 12-month timestep in Keshan station from September 1999 to May 2001.


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As shown in Figure 9, moving cumulative precipitation was larger than multi-monthly average precipitation in 15-month timestep during September and October in 1999, and then went shorter As shown in Figure 9, moving cumulative precipitation was larger than average and below the multi-monthly average precipitation. The deficit was 31 mm to multi-monthly 80 mm from November precipitation in 15-month timestep during September and October in 1999, and then went shorter 1999 to May 2000 and became larger from June 2000 until getting a peak value of 186 mmand in below the multi-monthly averageit precipitation. deficit 31 mm 80 mmDecember from November November 2000. Subsequently, declined againThe to 125 mmwas to 142 mmto during 2000 to 1999 May to May 2000 and became larger from June 2000 until getting a peak value of 186 mm in November 2000. 2001. Subsequently, declined again to 125cumulative mm to 142 mm during December May 2001.experienced Z-Score15it and SPI15 tracked precipitation changes,2000 and togenerally Z-Score15 and SPI15 tracked cumulative and generally decrease-increase-decrease-increase fluctuationsprecipitation during the changes, entire drought period. experienced The indices decrease-increase-decrease-increase fluctuations during the entire drought period. The indices detected a normal-wet condition in September 1999, and then decreased indicating a mild detected drought aduring normal-wet condition in May September 1999,June and 2000, then decreased indicating mild −1.0 drought duringa November 1999 to 2000. From their values dropped abelow indicating November 1999 to May 2000. From June 2000, their valuesatdropped below −1.0 indicating a moderate moderate drought. However, EDI performed differently times. The index detected a mild drought drought. However,1999 EDI performed differently times. Thefurther, index detected a mild droughtdrought during during September to May 2000 and thenatdecreased indicating a moderate September 1999 October to May 2000 and then decreased further, indicating moderate except in mild July, except in July, to December in 2000, and in May in 2001,a when EDIdrought only performed October December in 2000, and in May in 2001, when EDI only performed mild drought conditions. droughtto conditions.

Figure 9. Changes of precipitation and drought indices for 15-month timestep in Keshan station from Figure 9. Changes of precipitation and drought indices for 15-month timestep in Keshan station from September 1999 to May 2001. September 1999 to May 2001.

Scatter plots were also produced for DIs with respectively monthly and multi-monthly Scatter plots were also produced for DIs with respectively monthly and multi-monthly precipitation deficits in 15-month timestep Figure 10. Similar with the other timesteps, Z-Score15 and precipitation deficits in 15-month timestep Figure 10. Similar with the other timesteps, Z-Score15 and SPI15 showed a weak relationship with deficit between monthly precipitation and average monthly SPI15 showed a weak relationship with deficit between monthly precipitation and average monthly precipitation, the r22 was 0.0084 and 0.0089, respectively. While they correlated quite well with the precipitation, the r was 0.0084 and 0.0089, respectively. While they correlated quite well with the deficit between moving cumulative precipitation and multi-monthly average precipitation in 15-month deficit between moving cumulative precipitation and multi-monthly average precipitation in 15timestep, with r2 of both2 of them higher than 0.95. The r2 of EDI 2and water deficit indicated by moving month timestep, with r of both of them higher than 0.95. The r of EDI and water deficit indicated by cumulative and multi-monthly average precipitation in 15-month timestep was higher than that in the moving cumulative and multi-monthly average precipitation in 15-month timestep was higher than nine-month timestep but lower than the 12-month timestep. that in the nine-month timestep but lower than the 12-month timestep. Based on the above results using different timesteps (nine-month, 12-month, and 15-month), SPI, Z-Score, and EDI picked up the precipitation deficit and can be used to demonstrate actual drought conditions. SPI and Z-Scores were more sensitive to the changes of the deficit between moving cumulative precipitation and multi-monthly average precipitation, while EDI tracked the variation in monthly precipitation deficit more closely. Compared with other timesteps, SPI and Z-Scores best responded to deficits in cumulative precipitation and more closely tracked EDI when a 12-month timestep was used.


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Figure 10. Scatter plots of drought indices (i.e., Z-Score15, SPI15, and EDI) and deficit between monthly Figure 10. Scatter plots of drought indices (i.e., Z-Score15, SPI15, and EDI) and deficit between precipitation and average monthly precipitation (the first row) and deficit between moving cumulative monthly precipitation and average monthly precipitation (the first row) and deficit between moving precipitation and multi-monthly average precipitation (the second row) for the 15-month timestep in cumulative precipitation and multi-monthly average precipitation (the second row) for the 15-month Keshan station from September 1999 to May 2001. timestep in Keshan station from September 1999 to May 2001.

In terms of drought severity, SPI indicated more severe drought conditions than the Z-Score and Based on the above results using different timesteps (nine-month, 12-month, and 15-month), SPI, EDI from September 1999 to May 2001, except for the 12-month time step, which was almost the same Z-Score, and EDI picked up the precipitation deficit and can be used to demonstrate actual drought as Z-Score12. The frequency of drought severity conditions detected by DIs in Keshan is demonstrated conditions. SPI and Z-Scores were more sensitive to the changes of the deficit between moving in Table 5. The extreme drought and severe drought detected by SPI9 occurred in May and June 2000, cumulative precipitation and multi-monthly average precipitation, while EDI tracked the variation respectively. The two severe droughts detected by Z-Score12 and SPI12 occurred in August 2000, in monthly precipitation deficit more closely. Compared with other timesteps, SPI and Z-Scores best while the severe drought was detected in November 2000 using SPI15. The cumulative precipitation responded to deficits in cumulative precipitation and more closely tracked EDI when a 12-month respectively achieved 49.19%, 53.18%, 60.81%, and 67.55% of its corresponding multi-monthly average timestep was used. in each particular month. Overall, Z-Score12 and SPI12 was more consistent with drought severity In terms of drought severity, SPI indicated more severe drought conditions than the Z-Score and according to actual conditions. EDI from September 1999 to May 2001, except for the 12-month time step, which was almost the same as Z-Score12. The frequency of ofdrought severity conditions DIs in Keshan is Table 5. Drought frequency (%) detected by DIs in Keshan duringdetected Septemberby 1999 to May 2001. demonstrated in Table 5. The extreme drought and severe drought detected by SPI9 occurred in May EDI Z-Score9 SPI9 detected Z-Score12 SPI12 and Z-Score15 SPI15 in and June 2000, respectively. The two severe droughts by Z-Score12 SPI12 occurred August 2000, while the severe drought was detected in November 2000 using SPI15. The cumulative Extreme drought 0.00 0.00 4.76 0.00 0.00 0.00 0.00 Severe drought 4.76 0.00 53.18%, 4.76 60.81%,4.76 4.76 0.00 4.76 precipitation respectively achieved 49.19%, and 67.55% of its corresponding multiModerate drought 23.81 33.33 28.57 42.86 42.86 42.86 52.38 monthly average in each particular month. Overall, Z-Score12 and SPI12 was more consistent with Mild drought 71.43 66.67 61.90 52.38 52.38 52.38 38.10 drought severity according to actual conditions. Normal-wet 0.00 0.00 0.00 0.00 0.00 4.76 4.76 Condition Table 5. Drought frequency (%) of detected by DIs in Keshan during September 1999 to May 2001.

EDI Z-Score9 Z-Score12 SPI12 Several researchers had addressed droughtsSPI9 conditions in northeast ChinaZ-Score15 [40,41]. SongSPI15 et al. [42] Extreme 0.00 4.76 0.00 in this0.00 indicated thatdrought precipitation is a key0.00 factor affecting drought area, and0.00 proposed0.00 an index Severe drought 4.76 0.00 4.76 4.76 4.76 0.00 4.76 particularly suitable for detecting, monitoring, and exploring spring droughts in the Songnen Plain. Moderate drought 23.81 33.33 28.57 42.86 52.38 Therefore, we selected several precipitation-based indices to42.86 evaluate the drought42.86 condition in the WRB Mild drought Normal-wet Condition

71.43

66.67

61.90

52.38

52.38

52.38

38.10

0.00

0.00

0.00

0.00

0.00

4.76

4.76

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and assessed their performances of DIs in drought monitoring. Our results proved that it was necessary to investigate various indices to develop a reliable drought monitoring system. This was consistent with previous studies [17,43,44], which indicated that considering more than one DI for drought studies can help investigate how well they cohere with each other and examine the sensitivity and accuracy of DIs. Montaseri and Amirataee [45] used seven meteorological drought indices to monitor drought characteristics in 12 diverse parts of the world endowed with various climatic conditions and found that the application of SPI can result in higher relative advantages to undertake a comprehensive and accurate analysis. Our study agrees with this study and also found SPI was the most reliable drought indicator among the DIs. The performance of SPI may vary when compared with different indices. However, this conclusion will be true for a meteorological drought monitoring which is based solely on rainfall, and also where rainfall can define drought conditions by its own [17,45,46]. In this study, performance of DIs in different timesteps varied a great deal, and DIs overall exhibited the best coherence in intermediate timesteps. This result showed that timestep selection is as important as a particular DI in drought assessment. These conclusions were supported by previous studies [28,29,47]. Therefore, we recommended that various indices should be investigated in drought monitoring considering intermediate timesteps to develop a reliable drought monitoring system. All types of drought originate from the deficiency of precipitation and thus many drought indices incorporate the precipitation component in various forms (e.g., SPI, precipitation percentile, decile), especially for a comprehensive drought assessment [14,17,19]. However, the relationship between precipitation and drought needs to be further investigated as other studies indicated that high temperature can also contribute to the quick onset of drought [48,49]. Furthermore, according to the Fifth Assessment Report of the IPCC [50], most ecosystems will be impacted to a greater extent by the climatic extremes in future because most of the global climate models predicted more extremes in the climates such as multi-year droughts. Droughts monitoring and evaluation for ecosystems are becoming more urgent [24,26,27]. Therefore, future studies will need to focus on ecosystem droughts based on multiple drought indices, including remote-sensing indices. 4. Conclusions Comparisons of numerical values of selected DIs using diverse timesteps and their relative applicability in drought detection for a particular period (September 1999 to May 2001) were conducted in the WRB. The results showed that timestep selection is as important as a particular DI in drought assessment. DIs with intermediate timesteps (7–18 months) were better correlated to each other and showed a better conformity. SPI was the most reliable drought indicator in the WRB, especially with timesteps of 10–12 months. Although inferior to SPI, the Z-Score was also a useful index, especially because of its simple calculation. EDI was found to be of high value and should be used with DIs because of its timestep-independence and high coherence with other DIs. However, PN was not recommended for use in determining drought in the WRB due to its poor performance in comparisons of drought class indications and, if used, should be limited to short timesteps if (less than three months). Based in the application of the DIs during an historical drought period, Z-Score and SPI were found to be more sensitive to multi-monthly cumulative precipitation changes with respect to monthly precipitation changes, while EDI can provide reasonable accuracy if only monthly precipitation data is used. DIs overall exhibited the best coherence using the 12-month timestep. These results indicate that additional studies should be conducted with various indices for different timesteps to reliably identify drought conditions, especially in combination with intermediate timesteps. Author Contributions: All authors contributed to the design and development of this manuscript. H.L., W.L., and G.Z. provided important advice on the concept of structuring of the manuscript. F.L. carried out the formal analysis and prepared the original draft of the manuscript. J.-C.K. made great contributions to editing the manuscript. Funding: The research was jointly funded by the National Natural Science Foundation of China (No. 41701020), the Key Program of Science and Technology Development Plan of Jilin Province (No. 20170520086JH and No.

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20180101078JC), the Open Foundation of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (No. 2016490611), and the NSFC-NRF Scientific Cooperation Program (No. 51711540299). Acknowledgments: The authors are grateful to Christopher Martin Swarzenski for editing the manuscript prior to submission. Conflicts of Interest: The authors declare no conflict of interest.

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