Responses of Water Level in China's Largest Freshwater Lake to the Meteorological Drought Index (SPEI) in the Past Five Decades

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Article Responses of Water Level in China’s Largest Freshwater Lake to the Meteorological Drought Index (SPEI) in the Past Five Decades

Ruonan Wang 1,2, Wenqi Peng 1, Xiaobo Liu 1,*, Wenqiang Wu 1, Xuekai Chen 1 and Shijie Zhang 1

1 State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research (IWHR), Beijing 100038, China; [email protected] (R.W.); [email protected] (W.P.); [email protected] (W.W.); [email protected] (X.C.); [email protected] (S.Z.) 2 College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China * Correspondence: [email protected]; Tel.: +86-010-6878-1897

Received: 22 December 2017; Accepted: 25 January 2018; Published: 1 February 2018

Abstract: Poyang Lake, which is the largest freshwater lake in China, is an important regional water resource and iconic ecosystem that has experienced a period of continuous low water level in recent years. In this paper, the Standardized Precipitation Evapotranspiration Index (SPEI) was applied to analyze the temporal variability and spatial distribution characteristics of meteorological drought over the Poyang Lake Basin during 1961–2015. In addition, correlation analysis was used to investigate the response relationship between lake level and meteorological drought in the basin. The main results showed that: (1) The decline of water level in Poyang Lake since 2000 has been dramatic, especially in autumn, when the downward speed reached 11.26 cm/day. (2) The meteorological drought in the Poyang Lake Basin has obvious seasonal characteristics, and drying tendencies in spring and autumn were relatively obvious. Following the 1960s, this basin entered a new drought period in the 2000s. (3) The results of correlation analysis showed that three- and six-month timescales were the optimum times for the lake level to respond to the SPEI in the Poyang Lake Basin. Seasonally, the correlation was best in winter and worst in autumn. Furthermore, the spatial distribution of correlations was: Hukou < Xingzi < Duchang < Wucheng < Tangyin < Kangshan. Overall, the results of this study quantiﬁed the response of lake level to meteorological drought in the context of climate change, and they provide a reliable scientiﬁc basis for water resource management in similar basins.

Keywords: lake level; drought characteristics; SPEI; spatial and temporal analysis; response; Poyang Lake Basin

1. Introduction Drought is one of the most common natural disasters in the world [1–3]. Statistics from the World Meteorological Organization show that meteorological disasters account for approximately 70% of all natural disasters and that half of them resulted from drought [4]. The annual global economic loss caused by drought is estimated to be as high as 6–8 billion dollars, which is far more than that caused by other meteorological disasters [2]. Because of the complex nature and widespread impacts of drought, the American Meteorological Society divided drought into four categories: (1) meteorological drought; (2) agricultural drought; (3) hydrological drought; and (4) socioeconomic drought [5]. Because climatic factors are the cause of drought disasters around the world, meteorological drought is considered to be the basis of the three other types of droughts [6]. With increasing global changes, the climate continues to become warmer, all kinds of extreme weather events have been frequent, and the impacts of drought on economic development and agricultural production have also been aggravated. These factors have

Water 2018, 10, 137; doi:10.3390/w10020137 www.mdpi.com/journal/water Water 2018, 10, 137 2 of 20 drawn the extensive attention of scholars at home and abroad [7]. China is located in the East Asian monsoon region. Due to its complex geographical conditions and climate change, China is one of the most climatically fragile areas in the world, and meteorological disasters occur there frequently [8,9]. Statistics indicate that drought disaster accounts for 50% of the affected area of China’s meteorological disasters, whereas flood disaster only accounts for 27.8% [10]. The effects of droughts are complex and difficult to grasp and forecast [11]. However, the proposal and development of drought indexes [12] has addressed this problem, thus facilitating the qualitative and quantitative measurement of drought events. Due to their advantages of easy access to information, flexible time scales and sensitive responses to drought monitoring, drought indexes based on ground climatic data are widely used around the world. Among the drought indexes, the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Index (SPI) are the most widely and profoundly used indexes [13–16]. The PDSI, which is one of the earliest drought indexes, was developed by Palmer in 1965; however, it has some shortcomings, such as a fixed time scale and complicated calculations [17]. The superior aspect of the SPI is its multi-time scale feature, which can be used to characterize different types of droughts [18]. However, the SPI only considers the influence of precipitation. In the context of global change, rising temperature has become one of the important factors aggravating drought processes [19]. Therefore, the objective characterization of drought conditions requires us to consider the joint effects of precipitation and temperature changes. For this purpose, Vicente-Serrano et al. (2010) [7] proposed a new meteorological drought index, the Standardized Precipitation Evapotranspiration Index (SPEI), which was based on the SPI. Because it takes into account both precipitation and temperature and integrates the advantages of the PDSI (i.e., its sensitivity to temperature changes) and the SPI (i.e., its simple calculation process and property of multiple timescales), the SPEI has become one of the most useful tools for monitoring drought processes [20–22]. The Poyang Lake Basin is one of the key areas of global water security and biodiversity conservation [23], and it is an important production base of grain, oil, cotton and aquatic products in China, feeding a population of up to 44 million [24,25]. The annual discharge from the Poyang Lake Basin is approximately 1457 × 108 m3, accounting for 5.3% of the national water resources and 15.7% of the discharge in the Yangtze River Basin [26]. However, due to the uneven distribution of seasonal precipitation, the Poyang Lake Basin has become one of the most flood- and drought-prone regions in China. Over the past several decades, both droughts and floods have occurred frequently in this basin, causing enormous damage to the environment and the agricultural development [19,27–29]. Poyang Lake, which is located in this basin, is the largest freshwater lake in China, and it is one of the first lakes in China to be included in the Ramsar Convention’s List of International Wetlands [30]. As the world’s largest bird conservation area and winter habitat for migratory birds, Poyang Lake wetland accommodates over 98% of the endangered Siberian Cranes (Grus leucogeranus) in the world, and the Carex cinerascens grown here provide unique feeding grounds and spawning beds for local fish [31]. However, this region is facing a series of problems, including enhanced recession in the lake’s water level, the deterioration of water quality and the degradation of wetland vegetation [25,32–35], which have greatly imperiled the lake’s wetland areas and their function as a winter habitat for migratory birds. The submerged “thousand-eye bridge”, which is located in the main navigation channel of Poyang Lake, is known as “the longest stone bridge inside a lake in China” (Figure1). Nevertheless, its frequent reveal in recent years also means that the water level recession in Poyang Lake has become more serious, which has raised widespread concerns about the ecological problems of Poyang Lake [36]. In addition, the enhanced recession in Poyang Lake has led to water supply and agricultural irrigation problems for the 12.4 million inhabitants of the surrounding region. Because this lake is a typical open water-carrying lake that naturally connects to the Yangtze River, the occurrence of droughts and floods in the lake is not only controlled by the discharge in the Yangtze River but also highly affected by the catchment inflow [37,38]. Water 2018, 10, 137 3 of 20

Water Although2018, 10, x FOR previous PEER REVIEW studies have indicated that the changes in the lake level of Poyang Lake3 of are 19 mainly affected by the comprehensive influences of the five rivers (Ganjiang, Fuhe, Xinjiang, Raohe, and Xiushui) and the Yangtze River, no direct research has been conducted to investigate how the lake level responds to meteorologicalmeteorological drought in the basin. Therefor Therefore,e, the main objectives of this study are: are: (1) (1) to to document document the the major major lake lake level level changes changes of ofPoyang Poyang Lake Lake over over the the last last five five decades; decades; (2) (2)to analyze to analyze the thespatial-temporal spatial-temporal evolution evolution of meteor of meteorologicalological drought drought over over the Poyang the Poyang Lake Lake Basin; Basin; and (3)and to (3) explore to explore the response the response relationship relationship between between the thelake lake level level and and meteorological meteorological drought drought index index in thisin this basin. basin.

Figure 1. The “thousand-eye bridge” built in the Ming Dynasty in the main navigation channel of Figure 1. The “thousand-eye bridge” built in the Ming Dynasty in the main navigation channel of Poyang Lake (source: http://dcx.jxnews.com.cn). Poyang Lake (source: http://dcx.jxnews.com.cn).

2. Study Area and Data 2. Study Area and Data

2.1. Overview of the Study Area The Poyang Poyang Lake Lake Basin Basin (113°35 (113◦35′–118°290–118◦′29 E,0 24°29E, 24′◦–30°05290–30′◦ N)050 isN) located is located in the in middle the middle and lower and 4 2 regionslower regions of the Yangtze of the Yangtze River and River has anda drainage has a drainage area of 16.22 area × of 10 16.22 km ,× accounting104 km2, accountingfor 8.9% of the for Yangtze8.9% of theRiver Yangtze Basin and River 97 Basin.3% of and Jiangxi 97.3% Province of Jiangxi [39]. Province The basin [39 contains]. The basinfive main contains subbasins: ﬁve main the Ganjiang,subbasins: Fuhe, the Ganjiang, Xinjiang, Fuhe,Raohe, Xinjiang, and Xiushui Raohe, River and Basins Xiushui (Figure River 2). Basins Poyang (Figure Lake2 (115°49). Poyang′–116°46 Lake′ (115E, 28°24◦490′–116–29°46◦46′ 0N),E, 28which◦240–29 is ◦China’s460 N), whichlargestis freshwater China’s largest lake, freshwatermainly receives lake, mainlywater from receives these water five riversfrom thesebefore ﬁve discharging rivers before into dischargingthe Yangtze River into the at Hukou Yangtze [19]. River The at topography Hukou [19 in]. the The Poyang topography Lake Basinin the is Poyang quite complex, Lake Basin which is quite consists complex, of mountainous which consists areas, of mountainoussubordinate hills, areas, and subordinate alluvial plains. hills, Theand alluvialland use plains. across The the landbasin use is acrossdominated the basin by fo isrest, dominated farmland, by forest,grassland farmland, and water grassland bodies, and among water whichbodies, forest among area which accounts forest for area 63%, accounts cropland for 63%,for 26%, cropland and grassland for 26%, and and grassland water bodies and watereach for bodies 4%; theeach remaining for 4%; the areas remaining represent areas construction represent land construction and bare land [33]. and bareThe Poyang land [33 Lake]. The Basin Poyang is located Lake inBasin a subtropical is located humid in a subtropical monsoon humidclimate monsoon zone. The climate annual zone. precipitation The annual is 1620 precipitation mm, of which is 1620 42–53% mm, occursof which from 42–53% April occurs to June. from The April annual to June. average The annual temperature average is temperature 17.6 °C, and is the 17.6 coldest◦C, and and the coldesthottest monthsand hottest are January months and are July, January respec andtively. July, The respectively. annual potential The annual evapot potentialranspiration evapotranspiration is 1049 mm/year, is 1049with mm/year,the highest with rates the occurring highest from rates occurringMay to September. from May to September.

Water 2018, 10, 137 4 of 20 Water 2018, 10, x FOR PEER REVIEW 4 of 19

FigureFigure 2. 2.Locations Locations of ofthe the PoyangPoyang Lake Basin, meteorological meteorological and and gauging gauging stations. stations.

2.2. Data 2.2. Data The meteorological data used in this study include daily precipitation and temperature data at 23 Themeteorological meteorological stations data in used the Poyang in this studyLake includeBasin collected daily precipitation from 1961 to and 2015 temperature by the China data at 23 meteorological stations in the Poyang Lake Basin collected from 1961 to 2015 by the China

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Meteorological Data Sharing Service System. Daily lake level data obtained from actual measurements at six gauging stations, i.e., Hukou, Xingzi, Duchang, Wucheng, Tangyin, and Kangshan, covering the period from 1961 to 2015 (daily lake level data at Tangyin are from 1962 to 2015), were provided by the Jiangxi Hydrological Bureau, China. The lake level data were measured using Huanghai’s reference elevation system. The spatial distributions of the meteorological stations and gauging stations in the study area are shown in Figure2. In this study, areal monthly precipitation and temperature data from 1961 to 2015 were computed using the Thiessen polygon method [40,41] to obtain the SPEI series in the Poyang Lake Basin.

3. Methods

3.1. Standardized Precipitation Evapotranspiration Index (SPEI) The Standardized Precipitation Evapotranspiration Index (SPEI) was proposed based on the Standardized Precipitation Index (SPI) by Vicente-Serrano et al. [7]. This drought index has been widely used in many studies [20,42–44] because of its superior use of multiscale characteristics and consideration of temperature variations. The documentation and executable ﬁles are freely available at http://digital.csic.es/, and the speciﬁc calculation steps of the SPEI are described as follows: (1) Calculate potential evapotranspiration (PET) using the Tornthwaite method [45]. (2) Compute the monthly climatic water balance:

Di = Pi − PETi (1)

where Di is the difference between the monthly precipitation Pi and the monthly potential evapotranspiration PETi. The water profit or deficit series at different time scales can be constructed using the following formula:

k−1 k Dn = ∑ (Pn−i − PETn−i), n ≥ k (2) i=0 where k is the time scale (month) and n is the length of the data series. (3) Normalize the water balance. As there may be negative values in D series, the log-logistic distribution is selected by Vicente-Serrano et al. for standardizing the D series. The probability density function of a three-parameter log-logistic distributed variable is as:

−2 β x − γ β−1 x − γ β f (x) = ( ) [1 + ( ) ] (3) α α α where α, β, and γ are the scale, shape, and origin parameters, respectively, which can be obtained using the L-moment method. (4) Standardize the Log-Logistic distribution function, and then calculate the SPEI as follows:

" #−1 Z x α β F(x) = f (x)dt = 1 + (4) 0 x − γ

c + c W + c W2 q = − 0 1 2 ( = − ( − )) ≤ SPEI W 2 3 W 2 ln 1 P , P 0.5 (5) 1 + d1W + d2W + d3W c + c W + c W2 q = 0 1 2 − ( = − ( − )) > SPEI 2 3 W W 2 ln 1 P , P 0.5 (6) 1 + d1W + d2W + d3W where P is the probability of exceeding a determined D and P = 1 − F(x), and the constants in the Equations (5) and (6) are c0 = 2.515517, c1 = 0.802853, c2 = 0.010328, d1 = 1.432788, d2 = 0.189269, Water 2018, 10, 137 6 of 20

and d3 = 0.001308. The ﬁve classiﬁcations of meteorological drought based on the SPEI values are given in Table1[7].

Table 1. Drought classification defined for the Standardized Precipitation Evapotranspiration Index (SPEI).

Category SPEI Value No drought −0.5 < SPEI Light drought −1 < SPEI ≤ −0.5 Moderate drought −1.5 < SPEI ≤ −1 Severe drought −2 < SPEI ≤ −1.5 Extreme drought SPEI ≤ −2

In this study, different time-scale (1, 3, 6, and 12 months) SPEI values (i.e., SPEI-1, SPEI-3, SPEI-6, and SPEI-12, respectively) were calculated to quantify the monthly, seasonal, dry/wet seasonal, and annual variation characteristics of the meteorological drought in the Poyang Lake Basin and the responses of lake level to the meteorological index.

3.2. Mann–Kendall Trend Test The Mann–Kendall test is a rank-based non-parametric test [46,47] recommended by the World Meteorological Organization [48]. This method can test a linear or nonlinear trend [49,50], and has been widely used to assess the significance of monotonic trends in meteorological and hydrological data time series [11,51–53]. In this study, this method was applied to detect the variation trend of the SPEI series. In the Mann–Kendall test, the Z value is calculated as the critical value used to denote an increasing or decreasing trend. In this study, the significance level of the trend is set as 0.05, which has a corresponding Z value of 1.96. Accordingly, a value of Z > 0 denotes an increasing or upward trend, and a value of Z > 1.96 indicates that the increasing or upward trend is significant, and vice versa.

3.3. Accumulative Anomaly The accumulative anomaly is commonly used to identify the changing tendency of meteorological and hydrological data, such as sequential precipitation, evaporation, and runoff [54]. In this study, the accumulative anomaly method is used for analyzing the seasonal variation trend of lake level. For a discrete series xi, the accumulative anomaly (St) for data point xi can be expressed as:

t 1 n St = ∑ (xi − x), t = 1, 2, . . . , n, x = ∑ (7) i=1 n i=1 where x is the mean value of the series xi, and n is the number of discrete points. It should be noted that, to fully and reasonably analyze the changing characteristics of lake level in the Poyang Lake, some methods have been used in this study, such as accumulative anomaly, ﬁve-year moving average, linear regression, and so on.

4. Results

4.1. Dryness Phenomenon in the Poyang Lake

4.1.1. Inner-Annual Variation Characteristics of Lake Level The Statistical results of the average lake level for each month in different decades at Xingzi station can be seen in Figure3. These data indicate that, over the past five decades, the minimum monthly average lake level generally appeared in January, with an average value of 7.12 m, whereas the maximum value appeared in July, with an average level of 15.87 m. As shown in Figure3, the monthly WaterWater2018 2018,,10 10,, 137 x FOR PEER REVIEW 77 ofof 2019 Water 2018, 10, x FOR PEER REVIEW 7 of 19 monthly average lake level in all decades had a similar single-peak characteristic, showing a gradual averageincreasemonthlylake from average level January lake in all tolevel decades July in and all had decadesa decrease a similar had from single-peaka similar July to single-peak December. characteristic, characteristic,Comparing showing the ashowing situations gradual a increase gradual during fromdifferentincrease January fromdecades to January July revealed and to aJuly decrease that and the a fromdecreaseaverage July lakefrom to December. le Julyvel toof December.each Comparing month Comparing was the situationsrelatively the situations duringhigh during different during the decades1990s,different especially revealed decades in thatrevealed July, the when average that the the monthly lakeaverage level averag lake of eachleevel level monthof waseach 17.47 was month relativelym. wasAdditionally, rela hightively during thehigh average during the 1990s, lake the especiallylevels1990s, inespecially different in July, whenin months July, the when during monthly the themonthly average 2000s levelaveragwerewas lowere level 17.47 than was m. those 17.47 Additionally, duringm. Additionally, other the average periods, the lake average which levels waslake in differentparticularlylevels inmonths different noticeable during months from the during 2000s September were the 2000s lower to November. were than thoselower duringthan those other during periods, other which periods, was particularly which was noticeableparticularlyThe seasonal from noticeable September variation from to trend September November. of the monthlyto November. average lake level at Xingzi station was obtained by usingTheThe the seasonal seasonalaccumulative variationvariation anomaly trend method. of the the monthly monthly As shown average average in Figure lake lake 4, level the level accumulative at at Xingzi Xingzi station station anomalies was was obtained obtainedin spring by by(Figureusing using the 4a), theaccumulative especially accumulative in anomaly March anomaly andmethod. April, method. As experi shown Asenced shown in Figure declines in Figure4, the in accumulative 4the, the 1960s accumulative and anomalies 1970s, anomalieswhile in spring they inshowed(Figure spring 4a),an (Figure upward especially4a), trend especially in March from inthe and March 1980s April, andto experi the April, 1990senced experienced and declines then decreased in declines the 1960s in the and 2000s. 1960s1970s, andThe while 1970s,overall they whilecharacteristicsshowed they an showed upward of the an trendaccumulative upward from trendthe anomaly 1980s from to thecurves the 1980s 1990s in summer to and the then 1990s and decreased andwinter then were in decreased the similar 2000s. (Figure in The the overall 2000s.4b,d), Theascharacteristics both overall of them characteristics of decreased the accumulative of slowly the accumulative anomalyin the 1960s curves anomalyand in 1970s, summer curves fluctuated and in summerwinter in the were and 1980s, similar winter increased (Figure were similar in 4b,d), the (Figure1990s,as both 4andb,d), of themthen as both decreaseddecreased of them decreasedslowlysignificantly in slowlythe 1960sin inthe thean 2000s.d 1960s 1970s, andIn fluctuatedautumn 1970s, fluctuated (Figure in the 1980s, in4c), the the 1980s,increased accumulative increased in the inanomalies1990s, the 1990s, and generally then and thendecreased presented decreased significantly an significantly upward in trend inthe the before2000s. 2000s. theIn In autumn2000s. autumn However, (Figure (Figure 4 4c),c),the the thedownward accumulative accumulative trend anomaliesduringanomalies 2000–2015 generallygenerally was presentedpresented more obvious anan upwardupward compared trendtrend with beforebefore those thethe of 2000s.2000s. otherHowever, However,seasons, and thethe the downwarddownward rate of decline trendtrend duringacceleratedduring 2000–20152000–2015 dramatically, waswas moremore especially obviousobvious in comparedcompared October. withThewith calculated thosethose ofof otherother rate seasons,seasons,of change andand of thethe rateratewater ofof decline leveldecline at acceleratedXingziaccelerated station dramatically,dramatically, revealed that especiallyespecially the velocity inin October.October. of wate TherThe level calculatedcalculated drawdown raterate in ofof October changechange during ofof thethe waterthewater 2000s levellevel was atat Xingzi11.26Xingzi cm/day, stationstation revealedwhichrevealed was thatthat much thethe velocityfastervelocity than ofof waterthewate multiyearrlevel leveldrawdown drawdown average value inin OctoberOctober of 7.72 duringcm/day.during thethe 2000s2000s waswas 11.2611.26 cm/day, cm/day, whichwhich waswas muchmuch fasterfaster thanthan thethe multiyearmultiyear averageaverage valuevalue ofof 7.727.72 cm/day.cm/day. 18 18 16 16 1960s 14 1960s 14 1970s 1970s 12 1980s 12 1980s

Lake level (m) level Lake 10 1990s

Lake level (m) level Lake 10 1990s 8 2000s 8 2000s 6 6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May JunMonth Jul Aug Sep Oct Nov Dec Month Figure 3. Inner-annual variations in lake level at Xingzi station. FigureFigure 3.3.Inner-annual Inner-annual variations variations in in lake lake level level at at Xingzi Xingzi station. station.

15 15 15 (a) Spring 15 (b) Summer (a) Spring 10 10 (b) Summer 10 10 5 5 5 5 0 0 0 0 -5 -5 -5 -5 -10 -10 -10 -10 Mar Apr May Jun Jul Aug Accumulative anomaly of lake level (m) level lake of anomaly Accumulative -15 (m) level lake of anomaly Accumulative -15 Mar Apr May Jun Jul Aug Accumulative anomaly of lake level (m) level lake of anomaly Accumulative -151960 1980 2000 2020 (m) level lake of anomaly Accumulative -151960 1970 1980 1990 2000 2010 2020 1960 1980Time 2000 2020 1960 1970 1980Time 1990 2000 2010 2020 Time Time Figure 4. Cont.

Water 2018, 10, 137 8 of 20 Water 2018, 10, x FOR PEER REVIEW 8 of 19

Water 2018, 10, x FOR PEER REVIEW 8 of 19

FigureFigure 4. 4. AccumulativeAccumulative anomaly anomaly curves curves of ofseasonal seasonal lake lake levels levels during during 1961–2015. 1961–2015. (a) Spring; (a) Spring; (b) Summer; (c) Autumn; (d) Winter. (bFigure) Summer; 4. Accumulative (c) Autumn; (anomalyd) Winter. curves of seasonal lake levels during 1961–2015. (a) Spring; (b) Summer; (c) Autumn; (d) Winter. 4.1.2.4.1.2. Inter-Annual Inter-Annual Variation Variation Characteristics Characteristics ofof LakeLake LevelLevel 4.1.2. Inter-Annual Variation Characteristics of Lake Level FigureFigure5 shows 5 shows the the ﬂuctuating fluctuating changes, changes, and and linearlinear andand ﬁve-yearfive-year moving moving average average trends trends of of the the annual average, maximum, and minimum water levels at the six gauging stations in Poyang Lake, annualFigure average, 5 shows maximum, the fluctuating and minimum changes, water and levels linear at and the five-year six gauging moving stations average in Poyang trends Lake,of the i.e., i.e., Hukou, Xingzi, Duchang, Wucheng, Tangyin and Kangshan. Hukou,annual Xingzi, average, Duchang, maximum, Wucheng, and minimum Tangyin water and levels Kangshan. at the six gauging stations in Poyang Lake, The analysis of linear trends reveals that the inter-annual variations of both the maximum and i.e.,The Hukou, analysis Xingzi, of linearDuchang, trends Wu revealscheng, Tangyin that the and inter-annual Kangshan. variations of both the maximum and average water levels at each gauging station presented declining trends. Meanwhile, although the averageThe water analysis levels of at linear each trends gauging reveals station that presented the inter-annual declining variations trends. Meanwhile, of both the although maximum the and linear linear trends of the minimum water levels at Hukou and Xingzi stations increased slightly, the inter- trendsaverage of thewater minimum levels at water each levelsgauging at station Hukou presented and Xingzi declining stations trends. increased Meanwhile, slightly, thealthough inter-annual the linearannual trends variations of the in minimum the minimum water wa levelster levels at Hukou of the and other Xingzi four stations stations increased showed different slightly, degreesthe inter- of variationsdecline. inIn theterms minimum of the five-year water levels moving of the values, other fourthe annual stations average showed water different levels degrees at all ofstations decline. Inannual terms variations of the five-year in the minimum moving values,water levels the annualof the other average four stations water levels showed at alldifferent stations degrees decreased of decline.decreased In obviouslyterms of theafter five-year 2003; the moving annual values,maximum the waterannual level average at each water gauging levels station at all exhibitedstations obviously after 2003; the annual maximum water level at each gauging station exhibited an obvious decreasedan obvious obviously upward trendafter 2003;before the 20 00,annual but dropped maximum distinctly water level from at 2000 each to gauging 2015; the station upward exhibited trend of upward trend before 2000, but dropped distinctly from 2000 to 2015; the upward trend of annual anannual obvious minimum upward water trend level before at 20Hu00,kou but was dropped obvious distinctly from 1961 from to 20002002, to before 2015; stabilizingthe upward after trend 2003, of minimum water level at Hukou was obvious from 1961 to 2002, before stabilizing after 2003, however, annualhowever, minimum the trends water at Xingzi, level at Duchang, Hukou was Wucheng, obvious Tangyin from 1961 and to Kangshan2002, before all stabilizing declined after after 2003. 2003, the trends at Xingzi, Duchang, Wucheng, Tangyin and Kangshan all declined after 2003. however,By analyzing the trends the at Xingzi,average, Duchang, maximum, Wucheng, and minimum Tangyin waterand Kangshan levels of alleach declined gauging after station 2003. for everyByBy analyzingyear, analyzing we can the the see average, average, that the maximum, maximum,lowest historical and and minimum minimum maximum water water water levels levels level of valueof each each gaugingat allgauging stations station station occurred for for every year,everyin we1972. year, can However, seewe thatcan see thethe that lowestlowest the historicallowestannual historical average maximum maximumwater water levels water level all valueappearedlevel value at all in stationsat 2011. all stations In occurred addition, occurred in the 1972. However,inextremely 1972. However, the low lowest water the annual levels lowest at average Xingzi,annual waterDuchang,average levels water Wucheng, all levels appeared Tanyin,all appeared in and 2011. Ka in Inngshan 2011. addition, Inoccurred addition, the extremelyin 2004the lowextremely(5.23 water m), levels2014 low (5.71water at Xingzi, m), levels 2015 Duchang,at (6.38 Xingzi, m), Duchang,2007 Wucheng, (7.82 Wucheng,m), Tanyin, and 2004 andTanyin, (10.11 Kangshan and m), Karespectively,ngshan occurred occurred inwhich 2004 indicatein (5.23 2004 m), 2014(5.23that (5.71 them), appearance m),2014 2015 (5.71 (6.38 m), times 2015 m), of 2007(6.38 extremely m), (7.82 2007 m),low (7.82 andwater m), 2004 level and (10.11 at2004 each m), (10.11 station respectively, m), all respectively, occurred which after which indicate 2000. indicate that the appearancethat the21 appearance times of extremelytimes of extremely low water low level water at eachlevel21 at station each station all occurred all occurred after 2000. after 2000. (a) (b) 21 21 18 (a) 18 (b)

18 y = −0.0018x + 17.22 18 y = −0.0016x + 17.305 15 15 14.10m 13.95m y = −0.0018x + 17.22 y = −0.0016x + 17.305 15 15 12 13.95m 12 14.10m

12 12 y = −0.0117x + 11.737 Lake level level Lake (m) 9 y = −0.0064x + 11.093 level Lake (m) 9 9.07m 8.91 m y = −0.0117x + 11.737 Lake level level Lake (m) 9 4.02m y = −0.0064x + 11.093 level Lake (m) 9 6 6 8.91 m 9.07m 4.02m y = 0.0022x + 6.0589 6 y = 0.0173x + 4.8236 6 5.23m 3 3 y = 0.0022x + 6.0589 y = 0.0173x + 4.8236 5.23m

3 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 3 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 Year Year

1961 Max1964 1967 1970 1973 Mean1976 1979 1982 1985 Min1988 1991 1994 1997 5-year2000 2003 moving2006 2009 average2012 2015 Max1961 1964 1967 1970 Mean1973 1976 1979 1982 1985 Min1988 1991 1994 1997 5-year2000 2003 moving2006 2009 average2012 2015 Year Year Max Mean Min 5-year moving average Max Mean Min 5-year moving average

Figure 5. Cont.

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21 21 (d) (c) y = −0.0033x + 17.245 18 18

y = −0.0017x + 17.264 15 15 14.11m 14.2m

12 12 y = −0.0139x + 12.619 y = −0.0166x + 12.38 Lake level level Lake (m) Lake level level Lake (m) 9 9 9.86m 9.21m y = −0.0221x + 9.0798 6 y = −0.0174x + 7.9536 6 5.71m 6.38m 3 3 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 Year Year Max Mean Min 5-year moving average Max Mean Min 5-year moving average 21 21 (e) y = −0.0047x + 17.444 (f) 14.33m y = −0.0005x + 17.199 18 18

15 15 14.2m 11.93m

12 12 y = −0.0052x + 13.391 y = −0.0087x + 12.916 10.84m Lake level level Lake (m) 9 level Lake (m) 9 y = −0.005x + 10.865 10.11m y = −0.0135x + 9.9229

6 7.82m 6

3 3 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 Year Year Max Mean Min 5-year moving average Max Mean Min 5-year moving average

FigureFigure 5. 5.Hydrographs Hydrographs ofof maximum,maximum, minimum, and and mean mean lake lake level level at atsix six gauging gauging stations stations in the in the PoyangPoyang Lake: Lake: (a ()a) Hukou; Hukou; ( b(b)) Xingzi;Xingzi; ((cc)) Duchang; ( (dd)) Wucheng; Wucheng; (e (e) )Tangyin; Tangyin; and and (f ()f Kangshan.) Kangshan.

4.1.3. Temporal Statistics of Low Lake Level 4.1.3. Temporal Statistics of Low Lake Level The dry episode of Poyang Lake generally begins in August or September and ends in February The dry episode of Poyang Lake generally begins in August or September and ends in February or March of the following year [55]. According to the statistics for many years, once the water level orat March Xingzi of dropped the following below year10 m [duri55].ng According the water-receding to the statistics period, for Poyang many years,Lake entered once the a dry water state, level atand Xingzi once dropped it fell below below 8 m, 10 mthe during dry state the became water-receding more severe period, [56]. PoyangTherefore, Lake the entered onset times a dry and state, anddurations once it of fell periods below when 8 m, the lake dry level state was became below more 10 m severe and 8 [m56 at]. Xingzi Therefore, during the 1961–2015 onset times were and durationscounted in of this periods study when (Table the 2, Figure lake level 6). The was result belows indicated 10 m and that 8m the at onset Xingzi times during of different 1961–2015 grades were countedof low lake in this level, study i.e., (Table10 m and2, Figure 8 m, in6 ).Poyang The results Lake were indicated both in that advance, the onset and times their durations of different became grades oflonger low lake after level, the i.e.,year 10 2000. m and 8 m, in Poyang Lake were both in advance, and their durations became longerFirst, after thethe yearstatistics 2000. of the earliest appearance date when the water level first below 10 m in differentFirst, theperiods statistics indicated of the that earliest the dates appearance were concentrated date when from the September water level to November ﬁrst below during 10 m in differentthe 1960s–1990s; periods indicated however, that during the the dates 2000s, were the concentrated earliest date from was September22 August, which to November was 77 days, during 10 the 1960s–1990s;days, 55 days, however, and 28 duringdays earlier the 2000s, than those the earliest in the dateprevious was 22decades, August, respectively. which was Comparing 77 days, 10 the days, 55average days, and appearance 28 days earlierdate of thanthe water those level in the below previous 10 m decades,reveals that respectively. it always appeared Comparing in November the average appearanceduring different date of periods the water without level mu belowch fluctuation. 10 m reveals However, that it the always duration appeared when the inNovember water level duringwas differentbelow 10 periods m was withoutlongest in much the 2000s, ﬂuctuation. i.e., 142 However,days, which the was duration longer whenthan those the waterin previous level wasdecades below 10by m 22 was days, longest 14 days, in the35 days, 2000s, and i.e., 28 142days, days, respectively which was (Table longer 2, Figure than 6a). those in previous decades by 22 days, 14 days, 35 days, and 28 days, respectively (Table2, Figure6a).

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12/29 3/30 (a) (b) 2/28 11/29 1/29

10/30 12/30 Date Date 11/30 9/30 10/31 8/31 10/1

8/1 9/1 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 Year Year 1960s 1970s 1960s 1970s 1980s 1990s 1980s 1990s 2000s Onset time of 10m 2000s Onset time of 8m

Figure 6. 6. OnsetOnset times times of 10 of m 10 and mand 8 m 8during m during 1961–2015 1961–2015 at Xingzi at Xingzistation. station.(a) 10 m; ((ab)) 108 m. m; (b) 8 m.

Then, we examined the more severe dry condition in Poyang Lake, when the water level at XingziThen, wefell examinedbelow 8 m. theAs moreshown severe in Table dry 2 conditionand Figure in6b, Poyang except for Lake, the whenperiod the of waterthe 1970s, level when at Xingzi fell belowthe water 8 m. level As below shown 8 m in appeared Table2 and earliest Figure on 246b, Oc excepttober, the for earliest the period appearances of the 1970s, during when the periods the water levelof below the 1960s, 8 m appeared1980s and 1990s earliest all onoccurred 24 October, in November. the earliest However, appearances this time duringmoved forward the periods to 28 of the 1960s,September 1980s and during 1990s the all 2000s. occurred Similarly, in November. not only However,the earliest this date time of movedthis appearance forward but to 28also September the duringaverage the 2000s.date moved Similarly, forward not during only the2000–2015. earliest In date addition, of this the appearance duration lasted but for also 84 thedays, average which date was longer than before. moved forward during 2000–2015. In addition, the duration lasted for 84 days, which was longer than before.Table 2. Date statistics of the water level below 10 m and 8 m at Xingzi during different periods.

Table 2. Date statistics of the water≤10 m level below 10 m and 8 m at Xingzi during≤8 m different periods. Period Earliest Date Mean Date Duration (Day) Earliest Date Mean Date Duration (Day) 1960s 7 November ≤ 2210 mNovember 120 22 November 13 December≤8 m 79 Period1970s 1 September 4 November 128 24 October 7 December 78 Earliest Date Mean Date Duration (Day) Earliest Date Mean Date Duration (Day) 1980s 16 October 23 November 107 25 November 12 December 65 1960s1990s 7 November 19 September 22 November 10 November 120 114 22 13 NovemberNovember 3 December 13 December 62 79 1970s 1 September 4 November 128 24 October 7 December 78 2000s 22 August 19 November 142 28 September 22 November 84 1980s 16 October 23 November 107 25 November 12 December 65 1990s 19 September 10 November 114 13 November 3 December 62 2000s4.2. Meteorological 22 August Drought 19 in November the Basin 142 28 September 22 November 84

4.2.1. Spatial-Temporal Trends of the SPEI 4.2. Meteorological Drought in the Basin In this study, the Mann–Kendall trend test was used to study the spatial and temporal trend 4.2.1.changes Spatial-Temporal of the SPEI series Trends in ofthe the Poyang SPEI Lake Basin. Here, we show the spatial variation trends of the annual and seasonal SPEI values in Figure 7, where the red “+” and black “−” denote the upward In this study, the Mann–Kendall trend test was used to study the spatial and temporal trend and downward trends representing wetter and drier meteorological conditions, respectively. For the changesannual of SPEI the SPEIvalues series (Figure in 7a), the 19 Poyang out of 23 Lake stations Basin. exhibited Here, non-significant we show the upward spatial variationtrends; only trends of thePoyang annual station and in seasonal the north, SPEI and valuesSuichuan, in Ganxia Figuren,7, and where Guangchang the red “+”statio andns in black the south “ − ”showed denote the upwardnon-significant and downward downward trends trends, representing which illustrate wetterd and that drier the Poyang meteorological Lake Basin conditions, showed a general respectively. For thewetter annual condition SPEI over values the (Figure past 557 a),years. 19 outAs shown of 23 stationsin Figure exhibited 7b–e, on a non-significant seasonal basis, the upward Poyang trends; onlyLake Poyang Basin station was generally in the north,characte andrized Suichuan, by non-significant Ganxian, downward and Guangchang trends in spring stations and autumn in the south showed(Figure non-significant 7b,d), which indicated downward that trends, drying whichtendencies illustrated dominated that the the entire Poyang basin Lake in these Basin two showed a generalseasons. wetter It should condition be noted over that the there past were 55 years. partial Asstations shown showing in Figure upward7b–e, trends on a seasonalin autumn basis, because of the uneven distributions of precipitation and temperature. Figure 7c,e shows that the the Poyang Lake Basin was generally characterized by non-significant downward trends in spring whole basin was generally characterized by upward trends in summer and winter. Thus, the wet and autumn (Figure7b,d), which indicated that drying tendencies dominated the entire basin in these tendency prevailed over the Poyang Lake Basin in these two seasons. Additionally, two stations, i.e., two seasons.Jingdezhen It and should Jinxian, be notedshowed that significant there were upward partial trends stations at the α showing = 0.05 level upward in summer. trends in autumn because of the uneven distributions of precipitation and temperature. Figure7c,e shows that the whole basin was generally characterized by upward trends in summer and winter. Thus, the wet tendency prevailed over the Poyang Lake Basin in these two seasons. Additionally, two stations, i.e., Jingdezhen and Jinxian, showed significant upward trends at the α = 0.05 level in summer. Water 2018, 10, x FOR PEER REVIEW 11 of 19

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Figure 7. Spatial variation trends of the annual and seasonal SPEI values in the Poyang Lake Basin. (a) Annual; (b) Spring; (c) Summer; (d) Autumn; (e) Winter. FigureFigure 7. 7.Spatial Spatial variation variation trends trendsof of thethe annualannual and seas seasonalonal SPEI SPEI values values in in the the Poyang Poyang Lake Lake Basin. Basin. 4.2.2.( aSpatial-Temporal)( Annual;a) Annual; (b )(b Spring;) Spring; Distribution (c ()c Summer;) Summer; Characteristics ( d(d)) Autumn; Autumn; ((ee)) Winter.ofWinter. the SPEI Considering that the spatial distribution of meteorological drought will change over time, the authors4.2.2.4.2.2. Spatial-Temporal Spatial-Temporaldivided the study Distribution Distribution period into Characteristics Characteristics five periods (i.e., ofof the the SPEI 1960s, 1970s, 1980s, 1990s, and 2000s) and calculatedConsideringConsidering the SPEI that thatvalues thethe on spatial a 12 -monthdistribution distribution timescale of of mete meteorologicalfororological each meteorological drought drought will station willchange change in over this time, overpaper. the time, By usingtheauthors authors the dividedInverse-distance divided the the study study periodweighting period into into five(IDW) five periods periods method (i.e., (i.e., the[57], the 1960s, 1960s,the spatial1970s, 1970s, 1980s, distributions 1980s, 1990s, 1990s, and andof 2000s) the 2000s) annual and and calculated the SPEI values on a 12-month timescale for each meteorological station in this paper. By averagecalculated SPEI the values SPEI values in different on a 12-month periods timescaleover the forbasin, each as meteorological obtained using station the ArcGIS in this (version paper. By 10.2.2, using using the Inverse-distance weighting (IDW) method [57], the spatial distributions of the annual Environmentalthe Inverse-distance Systems weighting Research (IDW) Institute, method Redlands, [57], the spatialCA, USA) distributions software ofplatform, the annual are averagepresented SPEI in average SPEI values in different periods over the basin, as obtained using the ArcGIS (version 10.2.2, Figurevalues in8. different periods over the basin, as obtained using the ArcGIS (version 10.2.2, Environmental Environmental Systems Research Institute, Redlands, CA, USA) software platform, are presented in SystemsFigure Research 8a shows Institute, that the Redlands, SPEI values CA, of USA) the enti softwarere basin platform, were generally are presented low during in Figure the8 .1960s and Figure 8. that theFigure meteorological8a shows that drought the SPEI condition values of in the the entire Poyang basin Lake were region generally was relatively low during severe the 1960scompared and to thoseFigure in other 8a shows subbasins. that the In SPEI the 1970s,values theof the over entiallre SPEIbasin valueswere generally of the whole low during basin the increased; 1960s and the thatthat the the meteorological meteorological drought drought condition condition in in the the Poyang Poyang Lake Lake region region was was relativelyrelatively severesevere comparedcompared to Poyang Lake region and the southern part of the Ganjiang River Basin exhibited the most obvious thoseto those in other in other subbasins. subbasins. In the In 1970s, the 1970s, the overall the over SPEIall valuesSPEI values of the of whole the whole basin basin increased; increased; the Poyang the increases (Figure 8b). In the 1980s, the watershed SPEI values decreased slightly, with the lowest level LakePoyang region Lake and region the southern and the partsouthern of the part Ganjiang of the Ganjiang River Basin River exhibited Basin ex thehibited most the obvious most obvious increases occurring in the Xinjiang River Basin in the northeastern part in the study area (Figure 8c). In the (Figureincreases8b).In (Figure the 1980s, 8b). In the the watershed 1980s, the SPEI watershed values SPEI decreased values slightly, decreased with slightly, the lowest with levelthe lowest occurring level in 1990s, the whole basin existed in a wet state (Figure 8d), the drought phenomenon was not obvious, theoccurring Xinjiang in River the BasinXinjiang in theRiver northeastern Basin in the part northeastern in the study part area in (Figurethe study8c). area In the(Figure 1990s, 8c). the In whole the and the SPEI values decreased from north to south. In addition, the SPEI values were relatively low basin1990s, existed the whole in a wet basin state existed (Figure in8 ad), wet the state drought (Figure phenomenon 8d), the drought was notphenomenon obvious, and was the not SPEI obvious, values indecreased andthe Ganjiangthe fromSPEI northvalues and Fuhe to decreased south. River In from addition,Basins. north During the to SPEIsouth. the values In2000s, addition, were the relativelySPEI the SPEI in Poyanglow valu ines the wereLake Ganjiang relativelyBasin anddropped low Fuhe againRiverin the Basins.(Figure Ganjiang During8e), andand the itsFuhe 2000s,spatial River the distribution Ba SPEIsins. in During Poyang was similarthe Lake 2000s, Basin to thatthe dropped SPEIduring inagain Poyangthe 1960s. (Figure Lake In8 general,e),Basin and dropped its the spatial SPEI valuesdistributionagain in (Figure the was basin 8e), similar decreased and toits thatspatial from during distribution south the to 1960s. north. was In similarIn general, particular, to thethat SPEI duringthe Poyang values the 1960s. inLake the Inregion basin general, decreased again the became SPEI from thesouthvalues region to north.in with the basin Inthe particular, lowest decreased SPEI the from values, Poyang south and to Lake north. the region SP InEI particular, values again became in the the Poyang Xiushu the region Lakei and region with Raohe theagain River lowest became Basins SPEI werevalues,the also region and relatively the with SPEI the low. values lowest in SPEI the Xiushuivalues, and and the Raohe SPEI River values Basins in the were Xiushu alsoi and relatively Raohe low. River Basins wereIn short,also relatively the the SPEI SPEI low. values values of of the the Poyang Poyang Lake Lake Basin Basin were were relatively relatively low low during during the the 1960s, 1960s, representativeIn short, of the a wet SPEI state values in the of 1990s,the Poyang and indicativeindica Laketive Basin of a were second relatively relative low drought during period the 1960s, during representative of a wet state in the 1990s, and indicative of a second relative drought period during the 2000s. the 2000s.

FigureFigure 8. Spatial-temporal8.Spatial-temporal Spatial-temporal distribution distribution distribution of ofof the thethe SPEI SPEI onon a a 12-month 12-month timescaletimescale timescale in in in the the the Poyang Poyang Poyang Lake Lake Lake Basin. Basin. Basin. (a) (1960s; a) 1960s; (b )( b 1970s;) 1970s; (c )(c 1980s;) 1980s; (d (d) ) 1990s; 1990s; ( e(e)) 2000s. 2000s.

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4.2.3.4.2.3. Drought Drought Frequency Frequency TheThe statistical resultsresults indicateindicate that that there there were were 192 192 months months during during the periodthe period of 1961–2015 of 1961–2015 in which in whichmeteorological meteorological drought drought (SPEI (SPEI≤ −0.5) ≤ −0.5) occurred occurred in in the the Poyang Poyang Lake Lake Basin. Basin. AmongAmong these drought drought months,months, light light drought drought occurred occurred in in 116 116 months, months, moderate moderate drought drought occurred occurred in in 61 61 months, months, and and severe severe droughtdrought occurredoccurred inin 15 15 months. months. Moreover, Moreover, from from a decadal a decadal point ofpoint view, of the view, total the occurrence total occurrence frequency frequencyof meteorological of meteorological drought in thedrought past 55 in years the haspast experienced 55 years has a repeated experienced “down-up-down-up” a repeated “down-up- process down-up”(Figure9a). process By the 2000s,(Figure the 9a). frequency By the 2000s, reached the 28.1%,frequency of which reached the 28.1%, occurrence of which frequency the occurrence of severe frequencydrought reached of severe the drought maximum reache valued the of maximum all decades, value i.e., of 3.6%. all decades, As shown i.e., in3.6%. Figure As 9shownb, based in Figure on the 9b,annual based SPEI, on the the annual frequency SPEI, of meteorologicalthe frequency of drought meteorological in the entire drought basin in was the 27.3%, entire of basin which was moderate 27.3%, ofdrought which wasmoderate dominant, drought accounting was do forminant, 14.5%. accounting On a seasonal for 14.5%. basis, On the highesta seasonal frequency basis, the was highest 34.5%, frequencywhich occurred was 34.5%, in autumn, which followed occurred by in 27.8% autumn, in winter. followed Although by 27.8% the frequenciesin winter. Although of spring andthe frequenciessummer droughts of spring were and both summer 25.5%, droughts there was were one both year in25.5%, which there extreme was one drought year occurredin which inextreme spring, droughtwith a frequency occurred ofin 1.8%.spring, with a frequency of 1.8%.

40% 40% (a) (b)

30% 30%

20% 20%

10% 10% Meteorological drought frequency drought Meteorological Meteorological drought frequency drought Meteorological 0% 0% 1960s 1970s 1980s 1990s 2000s Annual Spring Summer Autumn Winter Period Time Light drought Moderate drought Severe drought Light drought Moderate drought Severe drought Extreme drought

FigureFigure 9. 9. CumulativeCumulative frequencies frequencies of of the the meteorological meteorological drought drought in in the the Poyang Poyang Lake Lake Basin Basin over over the the periodperiod of of 1961–2015. 1961–2015. ( (aa)) Based Based on on the the Decadal Decadal SPEI; SPEI; ( (bb)) Based Based on on the the annual annual and and seasonal seasonal SPEI. SPEI.

To further analyze the spatial distribution variations of meteorological drought occurrence To further analyze the spatial distribution variations of meteorological drought occurrence frequency, the Inversed Distance Weight (IDW) method was applied to interpolate the drought frequency, the Inversed Distance Weight (IDW) method was applied to interpolate the drought characteristics of 23 meteorological stations to the Poyang Lake Basin using ArcGIS. Figure 10 characteristics of 23 meteorological stations to the Poyang Lake Basin using ArcGIS. Figure 10 illustrates illustrates the different spatial patterns of drought occurrence frequency. From an annual the different spatial patterns of drought occurrence frequency. From an annual perspective, the drought perspective, the drought frequency ranged from 27.3 to 38.2%, with the highest frequency occurring frequency ranged from 27.3 to 38.2%, with the highest frequency occurring in Yichun, Guangchang in Yichun, Guangchang (which is located in the central part of the entire basin), and Yushan (which (which is located in the central part of the entire basin), and Yushan (which is located in the northeast). is located in the northeast). Comparing the meteorological drought conditions of the subbasins Comparing the meteorological drought conditions of the subbasins revealed that the drought frequency revealed that the drought frequency in the Xinjiang River Basin reached 34.6%, which was higher in the Xinjiang River Basin reached 34.6%, which was higher than those of other subbasins; the Fuhe than those of other subbasins; the Fuhe River Basin had the second-highest frequency of 33.6%, and River Basin had the second-highest frequency of 33.6%, and the Xiushui River Basin had the lowest the Xiushui River Basin had the lowest frequency of 31.1% (Figure 10a). On a seasonal basis (Figure frequency of 31.1% (Figure 10a). On a seasonal basis (Figure 10b–e), over the years, autumn drought 10b–e), over the years, autumn drought has most frequently occurred in the Poyang Lake Basin (29.1– has most frequently occurred in the Poyang Lake Basin (29.1–43.6%) and has had the largest coverage, 43.6%) and has had the largest coverage, mainly in the northeastern region of the whole basin, mainly in the northeastern region of the whole basin, namely, the northern part of the Ganjiang River namely, the northern part of the Ganjiang River Basin, the Poyang Lake region and the Xiushui River Basin, the Poyang Lake region and the Xiushui River Basin (Figure 10d). Spring drought had the Basin (Figure 10d). Spring drought had the second-highest frequency of 27.3–36.1%; although its second-highest frequency of 27.3–36.1%; although its coverage decreased, the northeastern region of coverage decreased, the northeastern region of the entire basin remained the main area of occurrence the entire basin remained the main area of occurrence (Figure 10b). Finally, the meteorological drought (Figure 10b). Finally, the meteorological drought frequencies of the entire basin in summer and frequencies of the entire basin in summer and winter were relatively low (Figure 10c,e). winter were relatively low (Figure 10c,e).

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Figure 10.10.Spatial Spatial distribution distribution of meteorologicalof meteorological drought drought frequency. frequency. (a) Annual; (a) Annual; (b) Spring; (b) (cSpring;) Summer; (c) Figure 10. Spatial distribution of meteorological drought frequency. (a) Annual; (b) Spring; (c) (Summer;d) Autumn; (d) (Autumn;e) Winter. (e) Winter. Summer; (d) Autumn; (e) Winter. 4.3. Correlation Analysis between Meteorological Drought and Lake Level 4.3.4.3. Correlation Correlation Analysis Analysis between between Meteorological Meteorological Drought Drought and and Lake Lake Level Level To explore how the water level of Poyang Lake has responded to the meteorological drought ToTo explore explore how how the the water water level level of of Poyang Poyang Lake Lake has has responded responded to to the the meteorological meteorological drought drought index (SPEI) over the past five decades, this study analyzed the correlations between the monthly indexindex (SPEI) (SPEI) over over the the past past five five decades, decades, this this study study analyzed analyzed the the correlations correlations between between the the monthly monthly average water level at each gauging station and the SPEI values at different (1-, 3-, 6-, and 12-month) averageaverage water water level level at at each each gauging gauging station station and and the the SPEI SPEI values values at at different different (1-, (1-, 3-, 3-, 6-, 6-, and and 12-month) 12-month) timescales during 1961–2015. Almost all of the correlation coefficients passed the significant timescalestimescales during during 1961–2015. 1961–2015. Almost Almost all of all the correlationof the correlation coefficients coefficients passed the passed significant the correlationsignificant correlation test of 0.01, that is, the water level of Poyang Lake exhibited significant correlations with testcorrelation of 0.01, that test is, of the 0.01, water that levelis, the of water Poyang level Lake of exhibitedPoyang Lake significant exhibited correlations significant with correlations the variations with the variations in the SPEI series. inthe the variations SPEI series. in the SPEI series. The temporal distribution characteristics of the influence of meteorological drought on the water TheThe temporal temporal distribution distribution characteristics characteristics of of the the influence influence of of meteorological meteorological drought drought on on the the water water level in Poyang Lake were analyzed in this study by calculating the correlation coefficients between levellevel in in Poyang Poyang Lake Lake were were analyzed analyzed in in this this study study by by calculating calculating the the correlation correlation coefficients coefficients between between the monthly average water level of Xingzi station and the SPEI values at 1-, 3-, 6-, and 12-month thethe monthly monthly average average water water level level of of Xingzi Xingzi station station and and the the SPEI SPEI values values at at 1-, 1-, 3-, 3-, 6-, 6-, and and 12-month 12-month timescales. As Figure 11 shows, from a temporal perspective, the lake level was closely related to the timescales.timescales. As As Figure Figure 11 11 shows, shows, from from a a temporal temporal perspective, perspective, the the lake lake level level was was closely closely related related to to the the SPEI at three- and six-month timescales. The maximum correlation coefficients at three- and six- SPEISPEI at at three- three- and and six-month six-month timescales. timescales. The The maximum maximum correlation correlation coefficients coefficients at three- at andthree- six-month and six- month timescales were 0.81 and 0.82, respectively, and the minimum correlation coefficients were timescalesmonth timescales were 0.81 were and 0.82,0.81 and respectively, 0.82, respectively, and the minimum and the minimum correlation correlation coefficients coefficients were both 0.28.were both 0.28. However, the correlations between the lake level and the SPEI values at 1- and 12- month However,both 0.28. the However, correlations the correlations between the between lake level the and lake the level SPEI and values the SPEI at 1- andvalues 12- at month 1- and timescales 12- month timescales were relatively poor. The maximum correlation coefficients at 1- and 12-month timescales weretimescales relatively were poor. relatively The maximum poor. The correlation maximum coefficientscorrelation coefficients at 1- and 12-month at 1- and timescales 12-month weretimescales 0.61 were 0.61 and 0.68, respectively, and the minimum correlation coefficients were only 0.14 and 0.37, andwere 0.68, 0.61 respectively, and 0.68, respectively, and the minimum and the correlation minimum coefficients correlation were coefficients only 0.14 were and only 0.37, 0.14 respectively. and 0.37, respectively. Additionally, the correlations varied greatly in different months, as higher correlations Additionally,respectively. theAdditionally, correlations the varied correlations greatly varied in different greatly months, in different as higher months, correlations as higher were correlations mainly were mainly concentrated from January to March. concentratedwere mainly from concentrated January tofrom March. January to March. On a seasonal basis, the correlation between lake level and the SPEI was best in winter, with a OnOn a a seasonal seasonal basis, basis, the the correlation correlation between between lake lake level level and and the the SPEI SPEI was was best best in in winter, winter, with with a a maximum of 0.74 and a minimum of 0.44, which was followed by summer. The correlation in autumn maximummaximum of of 0.74 0.74 and and a a minimum minimum of of 0.44, 0.44, which which was was followed followed by by summer. summer. The The correlation correlation in in autumn autumn was the worst, with a maximum of 0.27 and a minimum of 0.16 (Figure 12). waswas the the worst, worst, with with a a maximum maximum of of 0.27 0.27 and and a a minimum minimum of of 0.16 0.16 (Figure (Figure 12 12).).

Figure 11. Correlation coefficients between the monthly lake level at Xingzi station and the SPEI Figure 11. Correlation coefficients between the monthly lake level at Xingzi station and the SPEI Figurevalues 11.at differentCorrelation timescales. coefﬁcients between the monthly lake level at Xingzi station and the SPEI values atvalues different at different timescales. timescales. To further analyze the spatial distribution characteristics of the response between the water level To further analyze the spatial distribution characteristics of the response between the water level in Poyang Lake and the meteorological drought in the entire basin, using the SPEI data series at a in Poyang Lake and the meteorological drought in the entire basin, using the SPEI data series at a

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Water 2018, 10, x FOR PEER REVIEW 14 of 19 Water 2018To further, 10, x FOR analyze PEER REVIEW the spatial distribution characteristics of the response between the water14 levelof 19 in Poyang Lake and the meteorological drought in the entire basin, using the SPEI data series at a three-month timescale as an example, the authors calculated the correlation coefficient between the three-monththree-month timescale timescale as as an an example, example, the the authors authors calculated calculated the the correlation correlation coefficient coefficient between between the the SPEI values and the monthly average water level at each gauging station in Poyang Lake. Figure 13 SPEISPEI values values and and the the monthly monthly average average water water level level at at each gauging station in in Poyang Lake. Figure Figure 1313 demonstrates that the correlation coefficients increased from north to south, as the correlation at demonstratesdemonstrates thatthat thethe correlation correlation coefficients coefficients increased increased from from north north to south, to south, as the as correlation the correlation at Hukou at Hukou was the worst at 0.24–0.64. However, Kangshan station, which was far from the lake outlet, Hukouwas the was worst the at worst 0.24–0.64. at 0.24–0.64. However, However, Kangshan Kangshan station, station, which was which far was from far the from lake the outlet, lake showed outlet, showed the best correlation, with a maximum of 0.78 and a minimum of 0.33. Thus, the spatial showedthe best correlation,the best correlation, with a maximum with a maximum of 0.78 and of a 0.78 minimum and a ofminimum 0.33. Thus, of the0.33. spatial Thus, distributionthe spatial distribution regularity of the correlation between the SPEI and the monthly average lake level was distributionregularity of regularity the correlation of the betweencorrelation the between SPEI and the the SPEI monthly and the average monthly lake average level was lake basically level was as basically as follows: Hukou < Xingzi < Duchang < Wucheng < Tangyin < Kangshan. Comparing the basicallyfollows: Hukouas follows: < Xingzi Hukou < Duchang< Xingzi < Duchang Wucheng < < Wucheng Tangyin << Kangshan.Tangyin < Kangshan. Comparing Comparing the correlation the correlation coefficients of different seasons clearly shows that this correlation was still best in winter correlationcoefficients coefficients of different of seasons different clearly seasons shows clearly that shows this correlation that this correlation was still best was in still winter best (0.64–0.78), in winter (0.64–0.78), while it remained the worst in autumn (0.24–0.33). (0.64–0.78),while it remained while it the remained worst in the autumn worst (0.24–0.33).in autumn (0.24–0.33).

Figure 12. Seasonal characteristics of the correlations between the lake level at Xingzi station and the FigureFigure 12. 12. SeasonalSeasonal characteristics characteristics of of the the correlations correlations be betweentween the the lake lake level level at at Xingzi Xingzi station station and and the the SPEI values at different timescales. SPEISPEI values values at at different different timescales.

Figure 13. Spatial distribution of of the correlations between the the lake level at each gauging station and Figure 13. Spatial distribution of the correlations between the lake level at each gauging station and thethe SPEI SPEI values values at at a a 3-month 3-month timescale. timescale. the SPEI values at a 3-month timescale.

5.5. Discussion Discussion ThisThis studystudy demonstrates demonstrates the the water water level level in Poyang in Poyang Lake hasLake experienced has experienced a dramatic a anddramatic prolonged and prolongedreduction afterreduction the year after 2000 the by year analyzing 2000 by the analyzin long-termg the records long-term of water records level of data; water the level results data; of thisthe resultsstudy are of consistentthis study withare consistent those of recent with studiesthose of [58 recent–60]. Moreover,studies [58–60]. compared Moreover, with previous compared research, with previousthis paper research, not only usedthis paper more completenot only lakeused level more data complete covering lake six gauginglevel data stations covering and extendedsix gauging the stationsstudy time and series, extended but also the putstudy forward time series, some newbut ﬁndings.also put forward For example, some the new decline findings. of the For lake’s example, water thelevel decline since theof the year lake’s 2000 water has been level dramatic, since the especially year 2000 in has autumn; been dramatic, the appearance especially times in autumn; of extremely the appearancelow water level times at of the extremely six gauging low stations water level all occurred at the six after gauging 2000. Instations addition, all occurred the main innovationafter 2000. In of addition, the main innovation of this paper is combining the lake level in Poyang Lake with the meteorological drought index (SPEI), exploring the spatial-temporal response relationships between them by correlation analysis.

Water 2018, 10, 137 15 of 20 this paper is combining the lake level in Poyang Lake with the meteorological drought index (SPEI), exploring the spatial-temporal response relationships between them by correlation analysis.

5.1. Analysis of the Driving Forces of the Poyang Lake Dryness It is well-known that the dryness problem in Poyang Lake has had a great impact on the water supply and ecosystem health in this region. To date, many scholars have attempted to explore the causes of the water level reduction in the Poyang Lake since 2000. Zhao et al. [61] argued that the intra- and inter-annual variations in water level in Poyang Lake exhibit significant correlations with the inflows from the five rivers. Min et al. [62] argued that the main reasons for the reduction of the lake’s water level were the changes in precipitation in the basin and inflows from the five rivers. Liu et al. [63] analyzed the phenomenon and causes of the frequent extreme drought events in the Poyang Lake region during 2000–2010 on the basis of water balance in the basin. Their results also showed that the precipitation deficit in the basin was the basic factor leading to drought in the Poyang Lake region and that the increase in evapotranspiration in the basin enhanced the degree of drought; however, the contribution of outflow discharge to the Poyang Lake droughts was less than that of precipitation in the basin. Moreover, Guo et al. [64] predicted, based on climate change in the Poyang Lake Basin, that the precipitation and runoff in the Poyang Lake Basin will continue to decrease in the second half of the year over the next decade or so. Coupled with the combined effect of the Three Gorges Dam, the dryness problem of Poyang Lake may continue to intensify in the next ten years. This conclusion should attract increased attention from all sectors of the community. In contrast, some other scholars have obtained different conclusions. Ye et al. [65] indicated that the Yangtze River discharge has a greater impact on the annual lake level variation than the lake’s catchment inflow and that the advance of the Poyang Lake dry season has been primarily driven by climate change in the Yangtze River Basin since the 1990s and was further aggravated by the Three Gorges Dam in the 2000s. Mei et al. [66] quantified the average contributions of the regulation of the Three Gorges Dam, precipitation changes, and human activities in the Poyang Lake Basin to the water level reduction in Poyang Lake as 56.3%, 39.1%, and 4.6%, respectively. In addition to the comprehensive effects of the five rivers, the Yangtze River, and water conservancy projects, there are some other factors that affect the lake’s water level. For instance, Jiangxi Province implemented the water-control strategies of returning farmland to the lake and transmigrating and establishing towns after an extreme flood disaster in 1998, which resulted in an increase in the lake area and a consequent drop in water level [67]. In addition, large amounts of sand extraction in Poyang Lake since 2003 have resulted in variations in the lake terrain and caused the outflow channel of the lake to become wider and deeper, thus allowing the lake to drain quickly and reach a lower water level [68]. However, Mei et al. [66] argued that the contribution of sand extraction to the reduction of the lake’s water level since 2000 may be overestimated. In addition, Ye et al. [65] also argued that the specific effects of changes in lake terrain conditions on the lake’s water level require further study. In summary, the abovementioned data demonstrate that the factors affecting the water level of Poyang Lake are numerous and complicated, and further research is needed in the future. In this study, relationships between the Poyang Lake dryness and climate change in the entire basin has been analyzed from a new research perspective based on the meteorological drought index (SPEI). From the results of this paper, it can be seen that there were some similar characteristics between water level in the Poyang Lake and the SPEI series, especially on seasonal and decadal bases. The authors further discussed the specific response relationship between them in this paper.

5.2. Responses of the Lake Level to the SPEI The response relationship between lake level and the meteorological drought index (SPEI) was analyzed in this paper. From a temporal perspective, the lake level was closely related to the SPEI at three- and six-month timescales, with maximum correlations of 0.81 and 0.82, respectively. This may indicate that the response of water level in Poyang Lake to meteorological drought requires a certain amount of time and that three or six months are precisely the optimum time for the lake level to respond Water 2018, 10, 137 16 of 20 to a decrease in precipitation and an increase in temperature. It is not true that, when precipitation begins to decrease or temperature increases on a short timescale, such as one month, the water level in Poyang Lake will drop. In addition, if the timescale is too long (12 months), the response of the water level to climate change in some months will be attenuated, which does not favor the ability of the lake level to capture the meteorological drought. From a seasonal perspective, this research indicated that the correlation between lake level and SPEI was best in winter and worst in autumn. This could indicate that, in winter, the inflows from the five rivers, which is affected by the dry-wet condition in the basin, is the main influence factor of the lake level. However, in autumn, the influence of inflows from the five rivers is weakened, and the river–lake relationship between the Yangtze River and the Poyang Lake is more closely related to the changes in lake level, especially when the impoundment of the Three Gorges Dam from September to October during the 2000s. Moreover, the correlation between lake level and the SPEI followed a pattern of spatial distribution: Hukou < Xingzi < Duchang < Wucheng < Tangyin < Kangshan. This is not particularly surprising, given the relationships between Poyang Lake, the Yangtze River, and the five rivers (Ganjiang, Fuhe, Xinjiang, Raohe, and Xiushui) in the Poyang Lake Basin. Hukou station is located at the junction of the Yangtze River and Poyang Lake. The water level at this station is strongly affected by the interactions between the Yangtze River and the lake; therefore, its correlation with climate change in the Poyang Lake Basin is relatively worse. In addition, some previous studies [69,70] have argued that the closer distance to the lake outlet, the more vulnerable to the river–lake relationship. However, the other stations are located relatively far from the lake outlet and closer to the estuaries where the lake receives water from the five rivers in the basin, which results in their better correlation with the meteorological drought index of the basin.

6. Conclusions This study used the Poyang Lake Basin as a research area and analyzed the enhanced reduction of water level after the year 2000 in the Poyang Lake and the spatial and temporal characteristics of meteorological drought over the entire basin using multiple methods, including the SPEI, Mann–Kendall trend test, accumulative anomaly, and moving average. The main conclusions are as follows:

1. Compared to past decades, the dryness in Poyang Lake has become more dramatic since 2000. In addition, during the 2000s, the lake level clearly decreased in autumn, with a speed of 11.26 cm/day. The annual average, maximum, and minimum lake levels showed decreasing trends during the past decade. Moreover, the occurrences of the different grades of low lake level (10 m, 8 m) in the Poyang Lake both moved forward, and their durations were also prolonged. 2. The meteorological drought in the Poyang Lake Basin showed obviously seasonal characteristics over time; drying tendencies were apparent in spring and autumn, which will undoubtedly make it more difficult for the region to achieve drought resistance. In addition, the worsening meteorological drought in autumn and spring may lead to severe agricultural drought in the Poyang Lake Basin. Furthermore, the spatial distribution of SPEI values in the 2000s showed that the Poyang Lake Basin has entered a second relative drought period. Moreover, seasonal meteorological droughts have also occurred frequently in previous decades, especially in autumn (34.5%). 3. There was a signiﬁcant correlation between the water level of Poyang Lake and the meteorological drought index (SPEI), and the three- and six-month timescales were the optimum times for the lake level to respond to climate changes in the basin. Seasonally, the correlation between lake level and SPEI was best in winter, with a maximum correlation of 0.74, and worst in autumn. Furthermore, the correlation coefﬁcients increased from north to south, namely, the spatial distribution of correlations between the SPEI and lake level was: Hukou < Xingzi < Duchang < Wucheng < Tangyin < Kangshan. Water 2018, 10, 137 17 of 20

In summary, the results of this study could provide a scientiﬁc basis and support for future drought relief work and could facilitate the implementation of reasonable and effective water resource management in river basins. In addition, given that many factors affect the changes in water level in Poyang Lake, the variation tendency of the lake level under the inﬂuence of single or multiple factors and its mechanism of action will be the focus of future research.

Acknowledgments: This work is supported by the National Natural Science Foundation of China (51479219 and 51439007), National Basic Research Program of China (2016YFC0401701), Program for Innovative Research Team of IWHR (WE0145B592017), and Special Project for Basic Research of IWHR (WE0145B532017 and WE0145C032017). Author Contributions: This research was carried out in collaboration among all authors. Xiaobo Liu provided the writing ideas for the study; Ruonan Wang completed the statistical analysis and wrote the paper; Xiaobo Liu and Wenqi Peng provided overall guidance; and Wenqiang Wu, Xuekai Chen, and Shijie Zhang supervised the study and provided scientific advice on the manuscript. Conflicts of Interest: The authors declare no conflict of interest.

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