Contribution of Tropical Cyclones to Precipitation Around Reclaimed Islands in the South China Sea

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Article Contribution of Tropical Cyclones to Precipitation around Reclaimed Islands in the South China Sea

Dongxu Yao 1,2, Xianfang Song 1,2,*, Lihu Yang 1,2,* and Ying Ma 1

1 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [email protected] (D.Y.); [email protected] (Y.M.) 2 Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China * Correspondence: [email protected] (X.S.); [email protected] (L.Y.); Tel.: +86-010-6488-9849 (X.S.); +86-010-6488-8266 (L.Y.) Received: 15 September 2020; Accepted: 2 November 2020; Published: 5 November 2020

Abstract: Tropical cyclones (TCs) play an important role in the precipitation of tropical oceans and islands. The temporal and spatial characteristics of precipitation have become more complex in recent years with climate change. Global warming tips the original water and energy balance in oceans and atmosphere, giving rise to extreme precipitation events. In this study, the monthly precipitation ratio method, spatial analysis, and correlation analysis were employed to detect variations in precipitation in the South China Sea (SCS). The results showed that the contribution of TCs was 5.9% to 10.1% in the rainy season and 7.9% to 16.8% in the dry season. The seven islands have the same annual variations in the precipitation contributed by TCs. An 800 km radius of interest was better for representing the contribution of TC-derived precipitation than a 500 km conventional radius around reclaimed islands in the SCS. Four track patterns of TCs were deﬁned. The order according to the primary pattern of contribution was I (26–85.3%) > IV (12.8–29.8%) > III (4.3–29%) > II (11.5–24%). The average distance between islands and TCs was 1163 and 712 km in the rainy and dry seasons, respectively. The average contribution was larger in La Niña than in El Niño periods. These results could be beneﬁcial for managing rainwater resources, especially the TC-induced precipitation in the reclaimed islands.

Keywords: tropical cyclones; precipitation; reclaimed islands; ENSO; South China Sea

1. Introduction Precipitation is a major natural source of fresh water for maintaining ecological balance and promoting the sustainable development of islands and reefs in the tropical Pacific. Total precipitation can be separated into tropical cyclone (TC) precipitation and non-TC precipitation (e.g., seasonal rainfall) [1,2]. TCs are important contributors to precipitation in the tropical marine areas and islands [3–5]. Such contribution varies significantly across different land and sea areas of the world. For instance, in Mexico, the highest contribution of TCs was 40% of total annual rainfall from 2001 to 2013 [6]. In the Mekong River Basin, the largest contribution of TC precipitation for 34 years was 12.4% [7]. On the Australian coast, the maximum percentage contribution of TC-induced precipitation was 55% [8]. At the global scale, TCs contributed to about 35–50% of annual precipitation [9]. Precipitation controls the formation and evolution of freshwater lenses through regulating recharge in reclaimed islands [10–14]. Therefore, the proportions of TC-induced precipitation are important. TCs are the dominant drivers for the occurrence of precipitation in the South China Sea (SCS), as the SCS is one of the origins of TCs and is frequently affected each year [15,16]. Available information indicates that the increased contribution of TC-induced precipitation to summer rainfall was 30% along the coastal regions in southern China [17]. The larger interannual variations of September and October

Water 2020, 12, 3108; doi:10.3390/w12113108 www.mdpi.com/journal/water Water 2020, 12, 3108 2 of 18 rainfall in Hainan for the period of 1965–2010 were in the wet years [18]. The frequency of TCs over the SCS is increasing, due to ever more TCs moving into the SCS from elsewhere [19], then leading to autumn precipitation in Central Vietnam [20]. Furthermore, because the rate of infiltration into the coral sands is higher than the precipitation rate, the surface water flow and freshwater lakes on the small coral reefs and reclaimed islands in the SCS are insufficient. In the dry season, a lack of rainfall recharge has adverse effects on freshwater lenses, even leading to extinction [21,22]. Consequently, TC-induced precipitation is affected by the frequency of TCs, with seasonal variations, thereby controlling the volumes of freshwater lenses. El Niño–Southern Oscillation (ENSO) and sea surface temperature (SST) have obvious impacts on TCs and precipitation [19]. El Niño events have been confirmed to trigger abnormal western North Pacific (WNP) cyclones in August 2016. The SST of the western Indian Ocean and tropical North Atlantic (TNA) also plays an important role in influencing southern China precipitation [23]. The warm SST anomalies in the northern SCS and the WNP over the past 50 years enhanced mean moisture transport into Taiwan, Hainan, and adjacent islands, which led to more seasonal rainfall over southern China [2,20,24]. In the cold TNA SST years during the period of 1977–2016, more TCs were generated over the SCS, as their genesis frequency and location were regulated by the TNA SST [25]. However, results from a regional climate modeling system show that the ENSO has less influence on TC activity in terms of interannual variation, although landfalling TCs in the WNP tend to be more intense [26]. In addition, some studies focus on the effects of different tracks. For example, the forming location and track patterns of TCs have a major impact on TC-induced precipitation [27]. However, they tend to ignore the attributes of the motion between islands and the TC’s center. Thus, although there have been many investigations on the ENSO and SST anomaly in the SCS region, few studies have considered the contribution of TCs to precipitation and the track pattern of TCs in the reclaimed islands. The scope of TC-induced precipitation was defined as a circle with an effective radius at the global or regional scale, which was made use of in investigating rainfall in the inner-core region (0–100 km) and outer region (100–300 km) in light of Tropical Rainfall Measuring Mission (TRMM) data [28]. Extreme precipitation was found more than 450 km away from the storm center [29], in accordance with many other studies [30–33], within the effective radius of 500 km. Although definitions of TCs based on a circle with an effective radius have been widely employed, the effective radii are different in other studies, ranging from 400 to 1000 km [34,35]. Hence, we set out to confirm the optimum effective radius around the region of reclaimed islands in the SCS. Spatial statistics and correlation-analysis methods were applied to examine the best-track data and correlation between TC-induced precipitation and ENSO. The study firstly describes the spatiotemporal variation of TCs and characteristics of precipitation together with ENSO, and then explores the monthly, seasonal, and interannual contribution of TCs to precipitation in the reclaimed islands. The major objectives of this study were therefore to (1) quantify annual and interannual features of TCs (intensity and tracks) and TC-induced precipitation rates, (2) determine the radius of TC-derived precipitation in reclaimed islands of the SCS, and (3) analyze the impacts of the track pattern of TCs and ENSO in terms of the contribution of TC-induced precipitation. This should help to clarify the contribution of TCs to precipitation and interfering factors in the reclaimed islands of the SCS. The findings of this study will be beneficial for deciding upon reasonable measures to supply freshwater to maintain the stability of freshwater lenses, especially in the dry season.

2. Materials and Methods

2.1. Study Area

The study area is located in the SCS, at approximately 4–25◦ N and 100–125◦ E, and the major area of research covers seven reclaimed coral islands: Mischief Reef (MJ), Subi Reef (ZB), Fiery Cross Reef (YS), Caurteron Reef (HY), Gaven Reef (NX), Hughes Reef (DM), and Johnson South Reef (CG). Water 2020, 12, 3108 3 of 18 Water 2020, 12, x 3 of 17 The focus of the study is on the reclaimed coral islands, where freshwater lenses may exist (Figure1) The focus of the study is on the reclaimed coral islands, where freshwater lenses may exist (Figure 1) and will herein be referred to as the potential places for human survival and ecological water demand. and will herein be referred to as the potential places for human survival and ecological water The Spratly Islands belong to the tropical marine monsoon climate zone, which is characterized by a demand. The Spratly Islands belong to the tropical marine monsoon climate zone, which is dry season (January–May) and rainy season (June–December). characterized by a dry season (January–May) and rainy season (June–December).

Figure 1. MapMap showing showing reclaimed reclaimed islands in the SCS wi withth the tracks and grades of the TCs.

2.2. Data 2.2. Data The data of half-hourly precipitation (GPM IMERG Final Precipitation L3 Half Hourly The data of half-hourly precipitation (GPM IMERG Final Precipitation L3 Half Hourly 0.1° × 0.1° 0.1 0.1 V06) in the SCS during 2001–2018 were collected from Global Precipitation Measurement V06)◦ × in ◦the SCS during 2001–2018 were collected from Global Precipitation Measurement (https://gpm.nasa.gov/data/directory)[36]. We selected seven points to represent seven diﬀerent (https://gpm.nasa.gov/data/directory) [36]. We selected seven points to represent seven different reclaimed islands (Figure1) and obtained daily and monthly precipitation data by summing the reclaimed islands (Figure 1) and obtained daily and monthly precipitation data by summing the half- hourlyhalf-hourly meteorological meteorological data. data. The best-track data for each TC at 6 h time intervals from 2001 to 2018 were obtained from the Shanghai TyphoonTyphoon Institute Institute (STI) of the(STI) China of Meteorological the China Administration Meteorological (http: //TCdata.typhoon.Administration (http://org.cn/),TC includingdata.typhoon.org.cn/), the latitude–longitude including position, the latitude–longitude the maximum sustained position, surface the maximum wind speed sustained (WND), surfaceand the wind minimum speed central (WND), pressure. and the The minimum quality ofcentral the TC pressure. tracks was The controlled quality of using the TC the tracks method was of controlledYing et al. [using37]. Due the method to uncertainties of Ying et in al. tracking [37]. Due tropical to uncertainties depressions, in wetracking excluded tropical the contributiondepressions, weof tropical excluded depressions the contribution to precipitation of tropical in depressions this study, but to precipitation we showed the in spatialthis study, variation but we of showed tropical thedepressions spatial variation (Figure1 of). tropical depressions (Figure 1). ENSO phases werewere derived derived from from SST SST anomalies anomalies in in the the region region 5◦ N–55° N–5°◦ S, 170 S, ◦170°–120°–120◦ W( i.e.,W (i.e., Niño-3.4 Niño-), 3.4),which which is also is also the mostthe most important important region region for for WNP WNP TC TC activities activities [38 [38].]. MonthlyMonthly ENSOENSO data were obtained fromfrom the the Climate Climate Prediction Prediction Center Center of the National of theOceanic National and Oceanic Atmospheric andAdministration Atmospheric Administration(https://www.cpc.ncep.noaa.gov (https://www.cpc.ncep.noaa.gov).). In situ data were measured by using rain barrels on two islands (MJ (MJ island island and and YS YS island), island), including dailydaily andand monthlymonthly precipitation precipitation data. data. The The daily daily data data were were for for 24 24 May May to 11to 11 June June 2017 2017 on MJon MJisland, island, and and the the monthly monthly data data were were for Augustfor August 2015 2015 to July to July 2016 2016 on YSon island.YS island.

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2.3. Methods

2.3.1. The Monthly Precipitation Ratio (MPR)

The TC-induced precipitation (PTCs) ratio (TPR) was deﬁned as the ratio of summed TC-induced daily precipitation (PD) to monthly total precipitation (PTotal)[7]. The PTCs was deﬁned by the distance between the six-hourly interpolated tropical cyclone center and the island.

Pm = PTCs TPR = i 1 100% (1) PTotal ×

Xn PTotal = PD (2) j=1 where TPR is the PTCs ratio, m is the number of days of TCs, PTCs is TC-induced precipitation, PTotal is monthly total precipitation, and n is the days of a month.

2.3.2. Spatial Analysis and Statistics We used the Arc Toolbox including Data Management Tools and Analysis Tools to analyze the best-track data for each TC obtained at 6 h time intervals from 2001 to 2018. The TCs were also classiﬁed and summarized. All the spatial analytical processes were completed in ArcGIS 10.5 (ESRI, Redlands, CA, USA).

2.3.3. Correlation Analysis Pearson correlation coeﬃcient was used to assess correlation between precipitation and ENSO at the p < 0.01 and p < 0.05 levels (two-tailed) over the years. If the p value is less than 0.05, the result is considered statistically signiﬁcant and can be accepted. All the statistical analyses were performed using SPSS 22.0 (Statistical Package for the Social Sciences (IBM, Armonk, NY, USA)).

2.3.4. El Niño and La Niña Events In order to investigate the relationship between TC-induced precipitation and El Niño–Southern Oscillation (ENSO) phases, a classification of El Niño and La Niña events for the years 2001–2018 was used. The classification was based on the National Criterion GB/T 33666–2017 using three-month averages of the Niño-3.4 index [39]. When the index was 0.5 C( 0.5 C) for at least five months, ≥ ◦ ≤− ◦ an El Niño (La Niña) event was identified. According to this criterion, seven El Niño periods and eight La Niña periods were identified.

3. Results

3.1. Spatiotemporal Variation of TCs Table1 shows the proportions of TCs of six di ﬀerent grades: Tropical depression (TD), Tropical storm (TS), Severe tropical storm (STS), Typhoon (TY), Strong typhoon (STY), and super typhoon [27]. Obviously, TYs, STYs, and super typhoons accounted for only 13.5%. TSs and STSs accounted for 42.8%, and TDs, 36.9%. Therefore, approximately 80% of the TCs were equally or less intense than STSs. The best-track data for the maximum sustained surface wind speed (WND) of each TC collected at 6 h time intervals from 2001 to 2018 were analyzed. The spatial distribution of the TCs, along with intensity (Table1) and tracks, are shown in Figure1. TCs with six di ﬀerent intensities had complicated spatial distributions. Super typhoons were located in the northeast (lying north of 10◦ N and east of 108◦ E) of the reclaimed islands. The distribution range for TYs was more than 8◦ N and 106◦ E. The TCs around the reclaimed islands mainly consisted of TDs, TSs, and STSs. The TCs track almost covered the whole sea area above 5◦ N, and no TCs occurred in the tropics with a latitude lower than Water 2020, 12, 3108 5 of 18

Water 2020, 12, x 5 of 17 4◦ N. The TCs mainly moved from the eastern Paciﬁc Ocean to the western Eurasian continent and even land.Super typhoon (Super TY) 1.2 ≥51.0 6

The best-track dataTable for 1. theGrades maximum of TCs andsustained proportion surface over wind 18 years speed (2001–2018). (WND) of each TC collected at 6 h timeGrade intervals of TCs from 2001 to Proportion 2018 were (%) analyzed. Maximum The spatial Average distribution Wind Speed of (m the/s) TCs, Intensityalong with intensity (Table 1) and tracks, are shown in Figure 1. TCs with six different intensities had Tropical depression (TD) 36.9 10.8–17.1 1 complicatedTropical spatial storm distributions. (TS) Super 29.0 typhoons were located 17.2–24.4in the northeast (lying north of 2 10° N andSevere east of tropical 108° E) storm of the (STS) reclaimed islands. 13.8 The distribution range 24.5–32.6 for TYs was more than 8° 3 N and 106° E. TheTyphoon TCs around (TY) the reclaimed 13.5 islands mainly consisted 32.7–41.4of TDs, TSs, and STSs. The TCs 4 track almostStrong covered typhoon the (STY)whole sea area above 5.6 5° N, and no TCs occurred 41.5–50.9 in the tropics with a 5latitude Super typhoon (Super TY) 1.2 51.0 6 lower than 4° N. The TCs mainly moved from the eastern Pacific≥ Ocean to the western Eurasian continent and even land. FigureFigure 2 shows the the spatial spatial distribution distribution of of TCs TCs from from 2001 2001 to to2018. 2018. The The whole whole area area is divided is divided into into small squares (1 1 ) to analyze the numbers of TC centers in space. The squares with more small squares (1° × 1°)◦ to× analyze◦ the numbers of TC centers in space. The squares with more than 20 TCthan centers20 TC eachcenters were each located were locatedto the northeast to the northeast of the reclaimed of the reclaimed islands, islands, except except for two for squares two squares with 10with and 10 15 and TCs, 15 separately. TCs, separately. Squares Squares with more with morethan 10 than centers 10 centers each eachwere werelocated located north north of 10° of N, 10 and◦ N, aand square a square with witheven even32 was 32 located was located north north of 16° of N. 16 ◦ N.

FigureFigure 2. 2. SpatialSpatial distribution distribution of of TCs TCs over over 18 18 years years (2001–2018). (2001–2018).

TheThe TCsTCs werewere generally generally concentrated concentrated between between July July and October,and October, hardly hardly occurring occurring between between January Januaryand April. and At April. least oneAt least tropical one cyclone tropical occurred cyclone betweenoccurred July between and September July and September each year. Thereeach year. were Therefour TCs were in four February, TCs in theFebruary, least TC-prone the least TC-prone month over month the 18 over years the (2001–2018).18 years (2001–2018). September September was the wasmost the prone most month, prone inmonth, which in a totalwhich of a 46 total TCs of occurred 46 TCs occurred from 2001 from to 2018. 2001 There to 2018. were There rarely were more rarely than moreﬁve cyclones than five in cyclones a single in month a single (Figure month3a). (Figure 3a).

Water 2020, 12, 3108 6 of 18 Water 2020, 12, x 6 of 17

FigureFigure 3. ( a3.) Distributions(a) Distributions of TCs of inTCs di ﬀinerent different years years (2001–2018); (2001–2018); (b) average (b) average monthly monthly occurrence occurrence of TCs of in diTCsﬀerent in different stages. stages.

TheThe transformation transformation in averagein average monthly monthly numbers numbers of TCsof TCs was was found found in threein three diff differenterent stages stages that that everyevery seven seven years years was was divided divided from from 2001 2001 to 2018to 2018 (Figure (Figure3b). 3b). The The numbers numbers of TCs of TCs in the in the three three most most TC-proneTC-prone months months (July, (July, August, August, and an September)d September) were were five five times times those those in thein the four four least least TC-prone TC-prone monthsmonths (January, (January, February, February, March, March, and and April). April). The The frequency frequency of TCs of TCs in thein the first first stage stage was was 17% 17% higher higher thanthan that that in thein the third, third, while while that that in thein the second second was was the the same same as thatas that in thein the third. third. In theIn the rainy rainy season, season, thethe frequency frequency of of TCs TCs was was 15.4% 15.4% higher higher in in the the first first stage stage than than thatthat inin thethe third.third. In In the the dry dry season, season, the thenumber number of of TCs TCs waswas 30% higher inin thethe firstfirst stagestage than than that that in in the the third. third. Obviously, Obviously, the the frequency frequency of of TCsTCs increased increased owing owing to climateto climate change change in recentin recent years. years.

3.2.3.2. Characteristics Characteristics of Precipitationof Precipitation and and ENSO ENSO TheThe proportions proportions of of the the di differentﬀerent grades grades ofof precipitationprecipitation on on seven seven islands islands were were analyzed. analyzed. The Thestatistical statistical datadata for for the the 21 21 years showed thatthat daysdays ofof light light rain rain accounted accounted for for 51.9–53% 51.9–53% of of the the year; year; moderatelymoderately rainy rainy days, days, 8.2–9.2%; 8.2–9.2%; and heavily and heavily (or above) (or rainyabove) days, rainy less days, than 10%.less Thethan results 10%. indicatedThe results thatindicated light and that moderate light and rain moderate were similarly rain were dominant similarly in the dominant seven reclaimed in the seven islands. reclaimed Meanwhile, islands. daysMeanwhile, without rain days accounted without forrain 28.7–30.6% accounted of for the 28.7–30.6% whole year of (Figure the whole4a). Figureyear (Figure4b shows 4a). that Figure the 4b rateshows of contribution that the rate to precipitation of contribution was negativelyto precipitation correlated was with negatively the rainfall correlated intensity. with For example,the rainfall theintensity. contribution For ofexample, light rain the was contribution only 11.5–11.9%, of light thatrain ofwas moderate only 11.5–11.9%, rain was 16.1–17.9%,that of moderate and that rain of was heavy16.1–17.9%, (or above) and rain that was of heavy over 65%. (or above) It was rain therefore was over crucial 65%. to It quantify was therefore the contribution crucial to quantify of TCs to the rainfall,contribution as TCs usuallyof TCs to bring rainfall, extreme as TCs rainfall. usually bring extreme rainfall. Precipitation in the rainy season was higher than the mean precipitation at most times and conversely, that in the dry season was almost lower than the mean precipitation. From the point of developing trend, more and more precipitation has occurred in the rainy seasons in recent years, aggravating the unevenness of seasonal precipitation (Figure5a–g). The SST was intricately distributed in the grid consisting of months and years with El Niño and La Niña events alternatively occurring annually and monthly (Figure5h). The lowest temperature ( 1.7 C) occurred in January 2000, − ◦ and October and November 2010. The highest temperatures (2.4–2.6 ◦C) occurred in December 2015, January 2016, November 2015, and October 2015. Therefore, the most obvious anomalies occurred in the dry season.

Figure 4. (a) Proportion of rainfall days by rainfall intensity; (b) rates of contribution of different intensities of rainfall to total annual rainfall.

Precipitation in the rainy season was higher than the mean precipitation at most times and conversely, that in the dry season was almost lower than the mean precipitation. From the point of developing trend, more and more precipitation has occurred in the rainy seasons in recent years,

Water 2020, 12, x 6 of 17

Figure 3. (a) Distributions of TCs in different years (2001–2018); (b) average monthly occurrence of TCs in different stages.

The transformation in average monthly numbers of TCs was found in three different stages that every seven years was divided from 2001 to 2018 (Figure 3b). The numbers of TCs in the three most TC-prone months (July, August, and September) were five times those in the four least TC-prone months (January, February, March, and April). The frequency of TCs in the first stage was 17% higher than that in the third, while that in the second was the same as that in the third. In the rainy season, the frequency of TCs was 15.4% higher in the first stage than that in the third. In the dry season, the number of TCs was 30% higher in the first stage than that in the third. Obviously, the frequency of TCs increased owing to climate change in recent years.

3.2. Characteristics of Precipitation and ENSO The proportions of the different grades of precipitation on seven islands were analyzed. The statistical data for the 21 years showed that days of light rain accounted for 51.9–53% of the year; moderately rainy days, 8.2–9.2%; and heavily (or above) rainy days, less than 10%. The results indicated that light and moderate rain were similarly dominant in the seven reclaimed islands. Meanwhile, days without rain accounted for 28.7–30.6% of the whole year (Figure 4a). Figure 4b shows that the rate of contribution to precipitation was negatively correlated with the rainfall intensity. For example, the contribution of light rain was only 11.5–11.9%, that of moderate rain was Water16.1–17.9%,2020, 12, 3108and that of heavy (or above) rain was over 65%. It was therefore crucial to quantify7 of the 18 contribution of TCs to rainfall, as TCs usually bring extreme rainfall.

Water 2020, 12, x 7 of 17 aggravating the unevenness of seasonal precipitation (Figure 5a–g). The SST was intricately distributed in the grid consisting of months and years with El Niño and La Niña events alternatively occurring annually and monthly (Figure 5h). The lowest temperature (−1.7 °C) occurred in January 2000, and October and November 2010. The highest temperatures (2.4–2.6 °C) occurred in December 2015,Figure FigureJanuary 4.4. ((a2016,)) ProportionProportion November ofof rainfallrainfall 2015, daysdays and bybyOctober rainfallrainfall intensity;2015.intensity; Therefore, ((bb)) ratesrates oftheof contributioncontribution most obvious ofof didifferent ﬀanomalieserent occurredintensitiesintensities in the ofof dry rainfallrainfall season. toto totaltotal annualannual rainfall.rainfall.

Figure 5. PrecipitationPrecipitation in rainy andand drydry seasonsseasons on on seven seven reclaimed reclaimed islands islands (a (–ag–);g); the the mean mean was was half half of ofthe the annual annual mean mean precipitation. precipitation. Monthly Monthly distribution distribution of of ENSO ENSO in in di differentﬀerent years years (h (),h black), black solid solid line line is +is5 +5C, °C, and and black black dotted dotted line line is is 5−5C. °C. ◦ − ◦ 3.3. Monthly and Seasonal Contribution of TC-Induced Precipitation The monthly precipitation can be decomposed into two components: TC-induced precipitation (PTCs) and non-TC-induced precipitation (PN). Figure 6 shows the average monthly (a–g) and seasonal (h) contribution of TCs to precipitation in the radii of 500 km and 800 km.

Water 2020, 12, 3108 8 of 18

3.3. Monthly and Seasonal Contribution of TC-Induced Precipitation The monthly precipitation can be decomposed into two components: TC-induced precipitation (PTCs) and non-TC-induced precipitation (PN). Figure6 shows the average monthly ( a–g) and seasonal (h) contribution of TCs to precipitation in the radii of 500 km and 800 km. Water 2020, 12, x 8 of 17

FigureFigure 6. 6. AverageAverage monthly monthly ( (aa––gg)) and and seasonal seasonal ( (hh)) contribution contribution of of TC-induced TC-induced precipitation. precipitation.

WhenWhen thethe radiusradius was was 800 800 km, km, all sevenall seven islands islands had two had peaks two inpeaks January in andJanuary December. and December. According Accordingto the months to the in months which thein which peaks the occurred, peaks occurr the sevened, the islands seven could islands be could separated be separated into two into groups: two groups:(I) DM, (I) NX, DM, ZB ,NX, and ZB, MJ; and (II) HY,MJ; (II) CG, HY, and CG, YS. and In Group YS. In I,Group the third I, the peak third in peak April in (15.4–39.8%) April (15.4–39.8%) was the wassame. the In same. Group In II,Group they II, had they the had same the third same peaks third in peaks March in (2.3–15%).March (2.3–15%). Furthermore, Furthermore, all of the all islands of the islandshad two had minimum two minimum contribution contribution rates in rates July in (0%) July and (0%) August and August (0%). In (0%). addition, In addition, when thewhen radius the radiuswas 500 was km, 500 although km, although all seven all seven islands islands had the had same the same trends trends as the as radius the radius of 800 of km, 800 allkm, had all athad least at least2 minimum 2 minimumcontribution contribution rates. rates. Obviously, Obviously, TC-induced TC-induced precipitation precipitation was underestimated was underestimated in the radiusin the radiusof 500 km,of 500 especially km, especially in the rainyin the season, rainy season, as the smalleras the smaller radius couldradius not could cover not all cover the TC-inducedall the TC- inducedprecipitation precipitation areas around areasthe around reclaimed the reclaimed islands. islands. TheThe rainy rainy season season (dry (dry season) season) contribution contribution was was th thee average average contribution contribution over over six six months months in in the the rainyrainy season season (dry (dry season). season). Figure Figure 6h6 hdisplays displays the the seasonal seasonal contribution contribution of TCs of TCs to precipitation to precipitation in the in radiithe radii of 500 of km 500 and km and800 km. 800 In km. the In radius the radius of 800 of km, 800 contribution km, contribution in the inrainy the rainyseason season ranged ranged from 5.9%from to 5.9% 10.1%, to 10.1%,while that while in thatthe dry in the season dry seasonranged rangedfrom 7.9% from to 7.9%16.8%. to When 16.8%. the When radius the was radius 500 km, was contribution500 km, contribution in the rainy in season the rainy was season 4.0% to was 7.5%, 4.0% and to 7.5%,that in and the thatdry inseason the dry was season 1.4% to was 9.2%. 1.4% The to contributions9.2%. The contributions of the rainyof and the dry rainy seasons and dry were, seasons respectively, were, respectively, underestimated underestimated by 1.9–2.6% by and 1.9–2.6% 6.5– 7.6%and 6.5–7.6%in the radius in the of radius 500 km of 500compared km compared to those to determined those determined in the inradius the radius of 800 of km. 800 However, km. However, the radiusthe radius of 500 of 500km kmwas was too too small small to tocontain contain all all the the TC-derived TC-derived precipitation precipitation around around the the islands; islands; consequently,consequently, the contributi contributionsons were underestimated.

Water 2020, 12, 3108 9 of 18

3.4. Interannual Contribution of TC-induced Precipitation Figure7 displays the interannual contribution of TCs to precipitation and SST during 2001–2018. The interannual contribution of TC-derived precipitation included the seven islands with the radii of 800 km (Figure7b) and 500 km (Figure7c). Figure7a shows the SST anomalies for the 18-year period. The highest SST anomaly was 1.5 C in 2015, and the lowest was 1.2 C in 1999. Two records (in ◦ − ◦ 2002 and 2015) were higher than +0.5 ◦C, and three records (in 2007, 2008, and 2011) were lower than 0.5 C. The seven islands have the same annual variations in the precipitation contributed by TCs. − ◦ When the radius was 800 km (Figure7b), the four highest contributions of TC-induced precipitation were almost zero in 2007, 2011, 2013, and 2017. When the radius was 500 km (Figure8c), the highest contributions were 2–6% lower for the same years. The trends in the precipitation contributed by TCs were similar, but the contribution rate was clearly lower, by 2–6%, when the radius was 500 km than when it was 800 km. Hence, the contribution rate was underestimated with the 500 km radius. When the SST was >0.5 ◦C, the contribution rate was close to zero, and when the SST ﬂuctuated around zero, the contribution rate was the highest. Water 2020, 12, x 9 of 17 However, the two records for 2010 could not be explained, because using the mean SST meant that the almostdistribution zero ofin SST2007, within 2011, or 2013, across and years 2017. could When not the be analyzed.radius was Therefore, 500 km the(Figure relationship 8c), the betweenhighest TCscontributions and ENSO were according 2–6% lower to El Niñofor the and same La Niñayears. event is discussed in Section 4.3.

Figure 7. Annual contribution of TC TCss and SST during 2001–2018. ( a)) SST anomalies (dotted lines are ±0.50.5 °C);◦C); ( (bb)) annual annual contribution contribution of of TCs TCs during 2001–2018 (when (when the distance between the tropical ± cyclone center and island is 800 km); (c) annual contribution of TCs during 2001–2018 (when the distance between the tropical cyclonecyclone center and island is 500 km).

The trends in the precipitation contributed by TCs were similar, but the contribution rate was clearly lower, by 2–6%, when the radius was 500 km than when it was 800 km. Hence, the contribution rate was underestimated with the 500 km radius. When the SST was >0.5 °C, the contribution rate was close to zero, and when the SST fluctuated around zero, the contribution rate was the highest. However, the two records for 2010 could not be explained, because using the mean SST meant that the distribution of SST within or across years could not be analyzed. Therefore, the relationship between TCs and ENSO according to El Niño and La Niña event is discussed in Section 4.3.

4. Discussion

4.1. Influence of Radius on TC-Derived Precipitation The radius defined by the distance between the six-hourly interpolated tropical cyclone center and island varied between months and years. For instance, the TC tracks were closer to Hainan island in the dry than in the wet years in September and October in 1965–2010 [18]. Figure 8 shows the distance between the tropical cyclone center and island. Taking CG as an example, the three nearest months were January (mean, 450 km), February (mean, 595 km), and March (mean, 420 km), while the four farthest months were June (mean, 1258 km), July (mean, 1390 km), August (mean, 1420 km), and September (mean, 1342 km). The average radii were 1163 km and 712 km in the rainy and dry seasons, respectively. The radius was larger than 500 km as we considered both the precipitation originated from rainband and the pre-precipitation. In a word, we think it was the contribution of TC to total precipitation as long as the rainfall was caused by TC. Moreover, the differences in radius led to two groups divided according to interannual contribution of TC-induced precipitation. There are two possible explanations for the inaccuracy when using the 500 km radius in our study area. Firstly, the radius of 500 km was defined in a static climatological view, so it varied between different regions

Water 2020, 12, x 10 of 17

[34]. Secondly, preliminary work mainly considered the landfall TCs but neglected the initial stage Waterof tropical2020, 12 ,cyclone 3108 motion at sea [8]. The optimal radius for defining TC-induced precipitation10 ofwill 18 be critical for improving the forecast of the contribution of TC-induced precipitation in the future.

FigureFigure 8.8. MonthlyMonthly distributiondistribution ofof radiiradii (distances(distances betweenbetween tropicaltropical cyclonecyclone centercenter andand island)island) inin thethe periodperiod 2001–2018.2001–2018.

4. DiscussionThe numbers of TCs at different distances were variable because of the continuous movement of TCs. Although the number of events increased beyond 800 km, it rained on the ocean instead of 4.1. Influence of Radius on TC-Derived Precipitation producing effective rainfall on the island, resulting in the contribution of TC-induced precipitation on theThe islands radius decreasing defined by as the calculated distance between by formulas the six-hourly (1) and (2). interpolated The number tropical of TCs cyclone was less center than and 100 islandwhen variedthe distance between was months less than and 500 years. km. ForWhen instance, the distance the TC was tracks larger were than closer 500 tokm, Hainan the increase island inin theTCs dry approximately than in the wet followed years in a Septemberquadratic curve and October (Figure in9). 1965–2010 [18]. Figure8 shows the distance between the tropical cyclone center and island. Taking CG as an example, the three nearest months were January (mean, 450 km), February (mean, 595 km), and March (mean, 420 km), while the four farthest months were June (mean, 1258 km), July (mean, 1390 km), August (mean, 1420 km), and September (mean, 1342 km). The average radii were 1163 km and 712 km in the rainy and dry seasons, respectively. The radius was larger than 500 km as we considered both the precipitation originated from rainband and the pre-precipitation. In a word, we think it was the contribution of TC to total precipitation as long as the rainfall was caused by TC. Moreover, the differences in radius led to two groups divided according to interannual contribution of TC-induced precipitation. There are two possible explanations for the inaccuracy when using the 500 km radius in our study area. Firstly, the radius of 500 km was defined in a static climatological view, so it varied between different regions [34]. Secondly, preliminary work mainly considered the landfall TCs but neglected the initial stage of tropical cyclone motion at sea [8]. The optimal radius for defining TC-induced precipitation will be critical for improving the forecast of the contribution of TC-induced precipitation in the future. The numbers of TCs at different distances were variable because of the continuous movement of TCs. AlthoughFigure the 9. Numbers number of TCs events at different increased distances beyond (between 800 km, TCs’ it rainedcenters and on theislands). ocean instead of producing effective rainfall on the island, resulting in the contribution of TC-induced precipitation on the islands decreasing as calculated by Formulas (1) and (2). The number of TCs was less than 100 when the distance was less than 500 km. When the distance was larger than 500 km, the increase in TCs approximately followed a quadratic curve (Figure9). Water 2020, 12, x 10 of 17

[34]. Secondly, preliminary work mainly considered the landfall TCs but neglected the initial stage of tropical cyclone motion at sea [8]. The optimal radius for defining TC-induced precipitation will be critical for improving the forecast of the contribution of TC-induced precipitation in the future.

Figure 8. Monthly distribution of radii (distances between tropical cyclone center and island) in the period 2001–2018.

The numbers of TCs at different distances were variable because of the continuous movement of TCs. Although the number of events increased beyond 800 km, it rained on the ocean instead of producing effective rainfall on the island, resulting in the contribution of TC-induced precipitation on the islands decreasing as calculated by formulas (1) and (2). The number of TCs was less than 100 when the distance was less than 500 km. When the distance was larger than 500 km, the increase in Water 2020, 12, 3108 11 of 18 TCs approximately followed a quadratic curve (Figure 9).

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4.2. Track PatternsFigure of Figure9. TCs Numbers 9. Numbers of TCs of TCs at different at diﬀerent distances distances (between (between TCs’ TCs’ centers centers and islands). and islands). According4.2. Track to Patterns the relationship of TCs between the position of the tropical cyclone and its sphere of

interest and Accordingthe motion to the process relationship of the between TCs, the four position types of of the track tropical modes cyclone were and its defined sphere of (Figure interest 10): (I) both theand initial the motionand terminal process ofpoints the TCs, were four inside types ofthe track sphere modes of were interest; deﬁned (II) (Figure the initial 10): (I) point both thewas inside initial and terminal points were inside the sphere of interest; (II) the initial point was inside but the but the terminal point was outside the sphere of interest; (III) the initial point was outside but the terminal point was outside the sphere of interest; (III) the initial point was outside but the terminal terminalpoint point was was inside inside the sphere the sphere of interest; of interest; (IV) both the(IV) initial both and the terminal initial pointsand terminal were outside points the sphere were outside the sphereof interest.of interest.

FigureFigure 10. 10.MapMap of of tropical tropical cyclonecyclone track track patterns. patterns.

The influenceThe influence of these of these four four types types of ofpatterns patterns on precipitationprecipitation was was different: different: pattern pattern (I) remained (I) remained unchanged inside as the radius increased, so it could maintain a continuous contribution to precipitation; unchanged inside as the radius increased, so it could maintain a continuous contribution to pattern (II) moved away from the island as the radius increased; pattern (III) remained close to the precipitation;island aspattern the radius (II) increased; moved away and pattern from (IV) the was isla ablend toas convert the radius to pattern increase (I) as thed; radiuspattern increased. (III) remained close to Therefore,the island the as four the di radiusfferent typesincreased; of tropical and cyclone pattern track (IV) patterns was able could to lead convert to significant to pattern impact (I) as the radius increased.between distance Therefore, and precipitation the four different as shown in type Figures of 11 tropical. cyclone track patterns could lead to significant impact between distance and precipitation as shown in Figure 11.

Figure 11. Numbers of TCs in four track patterns. (a) 500 km radius; (b) 800 km radius.

Water 2020, 12, x 11 of 17

4.2. Track Patterns of TCs According to the relationship between the position of the tropical cyclone and its sphere of interest and the motion process of the TCs, four types of track modes were defined (Figure 10): (I) both the initial and terminal points were inside the sphere of interest; (II) the initial point was inside but the terminal point was outside the sphere of interest; (III) the initial point was outside but the terminal point was inside the sphere of interest; (IV) both the initial and terminal points were outside the sphere of interest.

Water 2020, 12, 3108 12 of 18

Figure 11 shows the numbers of TCs in four track patterns with radii of 500 and 800 km over the 18 years. Track patterns II and IV represented the greatest numbers. Track pattern I existed only in HY and YS when the radius was 500 km (Figure 11a), while it did not exist only in MJ when the radius was 800 km (Figure 11b). TCs of trackFigure pattern 10. Map II occurred of tropical 5–14 cyclone times track in the patterns. seven islands within the radius of 800 km, but that number was about half within the radius of 500 km. For instance, there were 14 TCs of track patternThe influence II (the most)of these in ZBfour and types 5 (the of patterns least) in HYon precipitation when the radius was wasdifferent:800 km pattern, while (I) there remained were 7unchanged and 1, respectively, inside as when the theradius radius increased, was 500 km.so it Track could pattern maintain III happened a continuous 5–9 times contribution within the to radiusprecipitation; of 800 km patternand 1–2(II) timesmoved in away that offrom 500 the km. isla Similarly,nd as the trackradius pattern increase IVd; occurred pattern (III) 21–35 remained times withinclose to800 the km islandand 19–25as the timesradius within increased; 500 km.and patte In brief,rn (IV) a larger was able radius to enablesconvert to the pattern more accurate(I) as the quantiﬁcationradius increased. of TCs Therefore, of the four the trackfour patterns,different type owings of to tropical the average cyclone distance track betweenpatterns thecould TCs lead and to islandssignificant being impact approximately between distance 1000 km, and as shown precipitation in Figure as8 shown. in Figure 11.

Figure 11. Numbers of TCs in four track patterns. (a) 500 km radius; (b) 800 km radius. Figure 11. Numbers of TCs in four track patterns. (a) 500 km radius; (b) 800 km radius. Figure 12 shows the contributions of TC precipitation for the four track patterns within the 800 km Figure 11 shows the numbers of TCs in four track patterns with radii of 500 and 800 km over the radius. Type I occurred once on six of the islands (YS, ZB, NX, CG, HY, and DM) and was absent on MJ 18 years. Track patterns II and IV represented the greatest numbers. Track pattern I existed only in island. The average contribution of type I was 26–85.3%, and the maximum contribution occurred in HY and YS when the radius was 500 km (Figure 11a), while it did not exist only in MJ when the radius YS. The average contribution of type II reached 11.5–24%, and the largest contribution was 88.5% on YS. was 800 km (Figure 11b). TCs of track pattern II occurred 5–14 times in the seven islands within the For type III, the average contribution was 4.3–29%, and the maximum contribution was 51.2% on CG. radius of 800 km, but that number was about half within the radius of 500 km. For instance, there For type IV, the average contribution was 12.8–29.8%, and the highest contribution was 86.8% in MJ. were 14 TCs of track pattern II (the most) in ZB and 5 (the least) in HY when the radius was 800 km, The islands could be divided into three categories based on the orders of the contribution rates. The while there were 7 and 1, respectively, when the radius was 500 km. Track pattern III happened 5–9 ﬁrst primary order was I (26–85.3%) > IV (12.8–29.8%) > III (4.3–29%) > II (11.5–24%), represented by HY, ZB, DM. The second order was I > IV > II > III, including YS and NX. The contribution rate for type I was the highest, due to almost all the moisture carried by the TCs falling into the sphere of interest (the circular region within a radius of 800 km). The third order was III > IV > II > I, represented by MJ. Water 2020, 12, x 12 of 17

times within the radius of 800 km and 1–2 times in that of 500 km. Similarly, track pattern IV occurred 21–35 times within 800 km and 19–25 times within 500 km. In brief, a larger radius enables the more accurate quantification of TCs of the four track patterns, owing to the average distance between the TCs and islands being approximately 1000 km, as shown in Figure 8. Figure 12 shows the contributions of TC precipitation for the four track patterns within the 800 km radius. Type I occurred once on six of the islands (YS, ZB, NX, CG, HY, and DM) and was absent on MJ island. The average contribution of type I was 26–85.3%, and the maximum contribution occurred in YS. The average contribution of type II reached 11.5–24%, and the largest contribution was 88.5% on YS. For type III, the average contribution was 4.3–29%, and the maximum contribution was 51.2% on CG. For type IV, the average contribution was 12.8–29.8%, and the highest contribution was 86.8% in MJ. The islands could be divided into three categories based on the orders of the contribution rates. The first primary order was I (26–85.3%) > IV (12.8–29.8%) > III (4.3–29%) > II (11.5– 24%), represented by HY, ZB, DM. The second order was I > IV > II > III, including YS and NX. The contribution rate for type I was the highest, due to almost all the moisture carried by the TCs falling Water into2020 ,the12, 3108sphere of interest (the circular region within a radius of 800 km). The third order was III > IV13 of 18 > II > I, represented by MJ.

FigureFigure 12. 12.Monthly Monthly contribution contribution of TC precipitationprecipitation for for the the four four track track patterns. patterns.

4.3. Impact4.3. Impact of ENSO of ENSO on on TC-Derived TC-Derived Precipitation Precipitation AfterAfter separating separating tropical tropical cyclone cyclone precipitation, precipitation, the relationship the relationship between between TC-derived TC-derived precipitation andprecipitation ENSO was and analyzed. ENSO was The analyzed. Pearson The correlation Pearson correlation coefficients coefficients for the for ENSO the ENSO and and TC-induced TC- precipitationinduced contributionprecipitation contribution are shown inare Table shown2. The in coeTableffi cients2. The for coefficients TC-induced forprecipitation TC-induced and precipitation and ENSO were all negative, but all the islands did not show significant correlation at ENSO were all negative, but all the islands did not show significant correlation at the 0.01 level with the 0.01 level with ENSO events for 2001–2018. The contributions of TC-induced precipitation among ENSOthe events seven islands for 2001–2018. positively The correlated contributions with one of anot TC-inducedher at the 0.01 precipitation level; in other among words, the TC-induced seven islands positivelyprecipitation correlated was withmainly one consistent another in at the the region 0.01 level;of the inreclaimed other words, islands. TC-induced precipitation was mainly consistent in the region of the reclaimed islands. The TC-induced precipitation contributions were obviously more increased in La Niña events than in El Niño events with the alternation of warm and cold SST leading to variations in TCs (Table3). The peak of SST was the SST at the maximum absolute value of fluctuation. There were six El Niño and eight La Niña events in our study. The largest TC-induced precipitation contributions in El Niño events was in MJ. In the La Niña events, the contributions of TC-induced precipitation were notably higher, and the maximum was 72.3%, in the period of July 2007 to June 2008. In the neutral period, the range of TC-induced precipitation contribution was between La Niña events and El Niño events. The results are similar to those of previous studies in other regions, in that the rainfall was positively correlated with La Niña [40–42]. However, in the seven islands, the TC-induced precipitation contributions exhibited significant spatial variations, as the TC-induced precipitation was asymmetric. The potential physical mechanisms underlying the correlation between TC precipitation and ENSO simply were the reflection in the atmosphere of changes in ocean circulation caused by abnormal sea temperature. Oceanic temperature and ocean current changes will influence the atmosphere and lead to variations of TC precipitation. For example, during the La Niña event, the abnormally cold water is in the central and eastern Pacific Ocean. The southeast trade wind blows the sun-heated seawater to the western Pacific Ocean. The temperature of the sea in the western Pacific Ocean increases, the pressure drops, and humid air accumulates to form the TC precipitation. However, the physical mechanisms between TC precipitation and ENSO were complicated. In a word, ENSO significantly affected the TC-induced precipitation, presenting a challenge for the accuracy of forecasting in the future. Water 2020, 12, 3108 14 of 18

Table 2. Correlation coeﬃcients for ENSO and TC-induced precipitation.

ENSO HY YS ZB NX CG DM MJ ENSO 1 HY 0.39 1 − YS 0.36 0.97 ** 1 − ZB 0.27 0.72 ** 0.75 ** 1 − NX 0.16 0.82 ** 0.83 ** 0.88 ** 1 − CG 0.36 0.94 ** 0.95 ** 0.86 ** 0.93 ** 1 − DM 0.36 0.96 ** 0.94 ** 0.84 ** 0.94 ** 0.99 ** 1 − MJ 0.27 0.71 ** 0.67 ** 0.92 ** 0.85 ** 0.79 ** 0.81 ** 1 − ** Signiﬁcant at 0.01 level.

Table 3. ENSO and TC-induced precipitation contribution.

ENSO TC-Induced Precipitation Contribution (%) Event Time length Peak HY YS ZB NX CG DM MJ 2002.06–2003.02 1.3 0 0 0 0 0 0 0 2004.07–2005.02 0.7 0.9 1.4 34.7 7.8 1.5 1.2 36.1 2006.09–2007.01 0.9 0 0.1 0.1 0 0 0 0 El Niño 2009.07–2010.03 1.6 0 0 0.6 0 0 0 0 2014.11–2016.05 2.6 1.4 9.1 13.6 31.1 7.9 9.0 16.8 2018.10–2018.12 0.9 3.1 11.0 25.9 17.3 15.9 9.5 1.2 2001.01–2001.02 0.7 0 0 0 0 0 0 0 − 2005.11–2006.03 0.8 0 0 0 0 0 0 0 − 2007.07–2008.06 1.6 44.7 72.3 56.2 46.1 48.8 42.1 41.9 − 2008.11–2009.03 0.8 0.6 7.3 6.2 1.5 1.3 0.2 0.1 La Niña − 2010.06–2011.05 1.7 0 0 0 0 0 0 0 − 2011.07–2012.03 1.1 3.9 2.3 43.8 31.1 20.1 19.2 47.3 − 2016.08–2016.12 0.7 0 0 0 0 0 0 0 − 2017.10–2018.03 1 57.9 61.3 44.4 48.8 48.8 50.3 51.2 − 2001.03–2002.05 0.4 0 0 28.5 3.3 0.2 1.9 0.3 2003.03–2004.06 0.4 0 0 14.8 9.0 3.0 3.3 3.8 2005.03–2005.10 0.4 1.6 11.5 16.6 20.5 8.7 15.2 8.1 2006.04–2006.08 1.7 0 0 22.3 0.0 0 0 0 − 2007.02–2007.06 0.4 0 0 0 0 0 0 0 − 2008.07–2008.10 0.4 0 0 0 0 0 0 0 − Neutral 2009.04–2009.06 0.4 4.1 2.0 2.1 3.4 5.6 12.5 3.7 2010.04–2010.05 0.4 0 0 0 0 0 0 0 2011.06–2011.06 0.4 0 0 0 0 0 0 0 − 2012.04–2014.10 0.4 64.7 85.1 50.2 56.2 79.9 71.8 46.9 ± 2016.06–2016.07 0.3 0 0 0 0 0 0 0 − 2017.01–2017.09 0.4 0 0 0 0 0 0 0 ± 2018.04–2018.09 0.4 0 0 0 0 0 0 0 ± Water 2020, 12, x 14 of 17

2006.04–2006.08 −1.7 0 0 22.3 0.0 0 0 0 2007.02–2007.06 −0.4 0 0 0 0 0 0 0 2008.07–2008.10 −0.4 0 0 0 0 0 0 0 2009.04–2009.06 0.4 4.1 2.0 2.1 3.4 5.6 12.5 3.7 2010.04–2010.05 0.4 0 0 0 0 0 0 0 2011.06–2011.06 −0.4 0 0 0 0 0 0 0 2012.04–2014.10 ±0.4 64.7 85.1 50.2 56.2 79.9 71.8 46.9 2016.06–2016.07 −0.3 0 0 0 0 0 0 0 2017.01–2017.09 ±0.4 0 0 0 0 0 0 0 Water 2020, 12, 3108 2018.04–2018.09 ±0.4 0 0 0 0 0 0 0 15 of 18

4.4. Uncertainty Analysis 4.4. Uncertainty Analysis Based on in situ data from two rain stations, we analyzed the differences in precipitation betweenBased the on in insitu situ and data IMERG from twodata rain (Figure stations, 13). The we analyzedin situ and the IMERG differences data inby precipitation month were betweensimilar, exceptthe in situfor and September, IMERG data October, (Figure and13). TheJune. in situOver andall, IMERG the IMERG data by data month for were precipitation similar, except were for overestimatesSeptember, October, compared and June.to the Overall, in situ data, the IMERG but the data increasing for precipitation and decreasing were overestimates trends were consistent compared (Figureto the in 13a). situ data,This error but the was increasing mainly caused and decreasing by the IMERG trends measurements were consistent [36]. (Figure Additionally, 13a). This errorthe daily was precipitationmainly caused was by compared the IMERG between measurements the in situ [36 and]. Additionally, IMERG data, the the daily error precipitation in the daily data was obviously compared beingbetween more the difficult in situ andto determine IMERG data,(Figure the 13b). error However, in the daily the data error obviously was still acceptable being more except difficult for toa fewdetermine points. (Figure It was perhaps13b). However, caused theby random error was error still acceptableand systematic except error for ain few the points.IMERG It data. was perhapsBriefly, althoughcaused by there random were error some and errors systematic in the error IMERG in the data, IMERG they data. could Briefly, still although reflect the there precipitation were some characteristicserrors in the IMERG of the data,studythey area, could especially still reflect in the the absence precipitation of actual characteristics daily precipitation of the studylong-term area, seriesespecially (Figure in the 13c). absence of actual daily precipitation long-term series (Figure 13c).

FigureFigure 13. InIn situ andand IMERGIMERG datadata of of monthly monthly precipitation precipitation in in YS YS (a )(a and) and daily daily precipitation precipitation in MJin MJ (b); (theb); boxthe box plots plots indicate indicate the ditheﬀ erencesdifferences between between the inthe situ in situ and and IMERG IMERG data data (c). (c).

5. Conclusions Based on the 6 h precipitation and the best-track data for each TC collected at 6 h time intervals from 2001 to 2018, the contribution of TCs to precipitation around the reclaimed islands in the South China Sea was estimated. The ENSO phases were derived from SST anomalies of Niño-3.4. This paper describes the monthly, seasonal, and annual contributions of TCs and the impacts of ENSO, TC track patterns, and the radius of interest on the contributions around the reclaimed islands in the South China Sea. We conclude the following points. Water 2020, 12, 3108 16 of 18

The contribution of TCs was 5.9 to 10.1% in the rainy season and 7.9 to 16.8% in the dry season. This was mainly affected by the influence of the radius on the TC-derived precipitation. The seven islands have the same annual variations in the precipitation contributed by TCs. An 800 km radius (the distance between the hourly interpolated tropical cyclone center and island) of interest was better for representing the contribution of TC-induced precipitation than a 500 km conventional radius around the reclaimed islands in the South China Sea. According to the relationship between the position of the tropical cyclone center and spheres of interest in the motional process of the TCs, four types of track patterns were defined. The order of the primary patterns by contribution was I (26–85.3%) > IV (12.8–29.8%) > III (4.3–29%) > II (11.5–24%). The average distances between the tropical cyclone center and island were 1163 and 712 km in the rainy and dry seasons, respectively. The relationship between TC-derived precipitation and ENSO indicates that average TCs contribute more during La Niña than El Niño periods around the reclaimed islands in the South China Sea. The results could be beneficial for managing rainwater resources, especially the TC-induced precipitation in the reclaimed islands, and supplying freshwater to maintain the stability of freshwater lenses in the dry season.

Author Contributions: Conceptualization, D.Y., X.S., and L.Y.; methodology, D.Y.; formal analysis, D.Y., Y.M., and L.Y.; resources, D.Y., X.S., and L.Y.; data curation, D.Y.; writing—original draft preparation, D.Y.; writing—review and editing, D.Y., X.S., L.Y., and Y.M.; visualization, D.Y.; supervision, X.S. and L.Y.; project administration, D.Y., X.S., and L.Y.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA13010303. Acknowledgments: The authors acknowledge contributions from all members of the project team. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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