Spatial Distribution of Precipitation in Huang-Huai-Hai River Basin Between 1961 to 2016, China

International Journal of Environmental Research and Public Health

Article Spatial Distribution of Precipitation in Huang-Huai-Hai River Basin between 1961 to 2016, China

Yong Yuan 1, Denghua Yan 2,*, Zhe Yuan 3, Jun Yin 4 and Zhongnan Zhao 1

1 General Institute of Water Resources and Hydropower Planning and Design, Ministry of Water Resources, Beijing 100120, China; [email protected] (Y.Y.); [email protected] (Z.Z.) 2 Department of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, China 3 Changjiang River Scientiﬁc Research Institute, Changjiang Water Resources Commission of the Ministry of Water Resources of China, Wuhan 430010, China; [email protected] 4 Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China; [email protected] * Correspondence: [email protected]

Received: 19 July 2019; Accepted: 10 September 2019; Published: 13 September 2019

Abstract: The Huang-huai-hai River Basin is one of the most economically developed areas, but is also heavily impacted by drought and flood disasters. Research on the precipitation feature of the Huang-huai-hai River Basin is of great importance to the further discussion of the cause of flood disaster. Based on the selected meteorological stations of the study area from 1961–2016, the inverse distance weighting method was used to get daily precipitation grid data. Interannual variation of precipitation intensity and cover area of different precipitation classes was analyzed. The generalized extreme-value distribution method was used to analyze the spatial distribution of extreme precipitation. The results show that: (1) decrease of accumulated precipitation in light precipitation year and moderate precipitation year might be the reason why the precipitation in the whole basin decreased, but the coefficient of variation (CV) of different classes of precipitation and precipitation days does not change significantly; (2) since the cover area of precipitation > 50 mm and precipitation intensity both decreased, the extreme precipitation of the whole basin may be decreasing; (3) extreme precipitation mainly occurred in the loess plateau in the northeast of Huang-huai-hai River Basin, Dabieshan in the middle of Huang-huai-hai River Basin and other areas.

Keywords: Huang-huai-hai River Basin; precipitation classes; precipitation cover area; extreme precipitation; IDW (Inverse Distance Weighting); generalized extreme-value distribution

1. Introduction The Huang-huai-hai River Basin, which includes three larger rivers, the Yellow River, Huaihe River and Haihe River, is one of the most economically developed areas, but is also heavily impacted by flood disasters which have brought about great economic losses [1,2]. In 2013, economic losses in the Huang-huai-hai River Basin caused by flood was about 60 billion RMB [3]. Flood disaster has posed severe threat to the security of local residents. Precipitation is the main causing factor of flood. Many scholars have done research on the relationship between precipitation feature and flood [4–7]. Pielke presented a conceptual framework which assessed the role that variability in precipitation had in damaging flooding in the United States at national and regional levels [8]; Hartmann suggested that strong monsoon precipitation in the arid high mountainous regions played a major role in the 2010 floods in Pakistan [9]. Drought seriously restricts the economic and social development of the

Int. J. Environ. Res. Public Health 2019, 16, 3404; doi:10.3390/ijerph16183404 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2019, 16, 3404 2 of 11

Huang-huai-hai River Basin due to water resource shortage, and some researchers have studied the drought feature and its impacts [10–12]. Yuan et al. shows that the Huang-huai-hai River Basin mainly experienced moderate drought and severe drought, and consecutive drought of several seasons often took place in the Ningxia plain [13]. However, all the research above is from temporal scale. As research and climate change continue, there is growing recognition that extreme precipitation (frequency, intensity and time) has great impact on flood disaster [14–16]. Norbiato et al. analyzed the relationship between extreme precipitation and flash flood on 29 August 2003 in the upper Tagliamento River Basin, Eastern Italian Alps [17]. Kwon et al. utilized a weather-state-based, stochastic multivariate model as a conditional probability model to simulate the natural precipitation field, so as to calculate design flood, and suggested that large-scale climatic patterns serve as a major driver of persistent year-to-year changes in precipitation probabilities [18]. A lot of methods analyzing extreme precipitation have also been brought out. For example, the Expert Team on Climate Change Detection and Indices (ETCCDI) suggested 27 core extreme indices based on daily temperature and precipitation, including maximum one-day precipitation, maximum five-day precipitation, tropical nights, and so on [19,20]. Xia et al. suggested that the precipitation in the upper and middle reaches of the Yellow River reduced obviously in spring and summer, but increased significantly in autumn, especially after 2000 [21]. For extreme precipitation in Huaihe River Basin, research shows that extreme precipitation did not have significant changing trend [22,23]. In the Haihe River Basin, Li et al. argued that light rain was the dominant precipitation, and the change of precipitation intensity was not significant [24]. However, the above-mentioned research usually analyzes extreme precipitation of meteorological stations and then draws extreme precipitation feature maps. There is a shortage of research on spatial distribution of extreme precipitation. Based on the precipitation data of the Huang-huai-hai River Basin, this research analyzed the variation character of intensity and cover area of different classes of precipitation in the study area. Besides this, spatial distribution of extreme precipitation was also analyzed. This research is expected to provide reference for the discussion of the reason for flood disaster in the Huang-huai-hai River Basin from two angles: Variation character of precipitation event and spatial non-uniformity of extreme precipitation.

2. Materials and Methods The total area of Huang-huai-hai River Basin is about 1.433 106 km2 with east-west length × of 2344.5 km and north-south width of 1323.6 km. The geographic scope of the study area is 95◦530~122◦600 E, 32◦100~43◦ N. The elevation of the Huang-huai-hai River Basin decreases from the west to the east in the form of three steps. The ﬁrst step is the Tibet Plateau with an average elevation of 4000 m; the second step mainly consists of plateau with an average elevation of 1000–2000 m; the third step mainly consists of low mountains, hills and plains with an elevation below 500 m, while the elevation of the North China Plain is less than 200 m. Precipitation of the Huang-huai-hai River Basin is mainly impacted by the intensity of paciﬁc monsoon and the forward and withdraw of rain belt, thus has an uneven spatial distribution and violent seasonal and interannual change. Annual average precipitation of the whole region is 471 mm to 864 mm. The population of the Huang-huai-hai River Basin is about 35% of China, and its GDP is about 32% that of China [25]. The Huang-huai-hai River Basin is an important agriculture production base of China, whose cultivated area and grain yield are about 20.4% and 23.6% of China, respectively [26].

2.1. Meteorological Data Source The meteorological data used in this research comes from China Meteorological Data Sharing Service System. Daily precipitation (1 January 1961–31 December 2016) with good consistency (there are less than 10 days from which data are absent) from 566 meteorological stations within and around the Huang-huai-hai River Basin was used in this research (Figure1). Based on the above-mentioned data, inverse distance weighting (IDW) [27] was used to do spatial interpolation so as to obtain daily Int. J. Environ. Res. Public Health 2019, 16, 3404 3 of 11

Int. J. Environ. Res. Public Health 2019, 16, x 3 of 11 precipitation grid data with the resolution of 5 km 5 km. The value of the estimated point is obtained × estimatedby weighted point averaging is obtained of the by distance weighted between averaging the estimatedof the distance point between and the samplethe estimated point. Thepoint closer and the sample point. point toThe be closer evaluated the sample is, the greaterpoint to the be weightevaluated it is is, given. the greater the weight it is given.

Figure 1. LocationLocation of of the the Huang-huai-hai Huang-huai-hai River Basin and the meteorological stations.

2.2. Method Method To do do summation summation of of daily daily precipitation precipitation grid grid data data in order in order to get to yearly get yearly precipitation precipitation grid data. grid Yearlydata. Yearlyprecipitation precipitation and andrainy rainy days days of ofHuang-huai-hai Huang-huai-hai River River Basin Basin during during 1961–2016 1961–2016 were calculated. BasedBased onon annual annual precipitation precipitation and and rainy rainy days, days, CV was CV calculated was calculated according according to the following to the followingequation [ 28equation,29]. [28,29]. σ CV = 100 (1) σµ × CV = × 100 (1) where, CV is coefficient of variation; σ is standardμ deviation; µ is average vale. Besides this, the slope of liner regression equation of precipitation and rainy days of each grid where, CV is coefficient of variation; σ is standard deviation; μ is average vale. were calculated according to the following equation [30]. Besides this, the slope of liner regression equation of precipitation and rainy days of each grid were calculated according to the following equationPn [30]. Pn Pn n (i Ki) i Ki × i=1 × − i=1 i=1 Slope = nnn (2) niKiK××−()n n 2 P 2 ii( P ) iiin===111i i = × = − = Slope i nn1 i 1 (2) ni×−22() i If Slope > 0, it means the precipitation/rainy days were increasing during the study period; while ii==11 Slope < 0 means they were decreasing. Slope is the link-line between the first year and the last year,If it Slope also reflects> 0, it means the general the precipitation/rainy changing trend days of the were average increasing precipitation during/ rainythe study days period; in each while grid Slopecalculated < 0 means by liner they regression were decreasing. equation Slope during is the studylink-line period. betweenn stands the first for year total and years, the and lastK year,i is the it alsoprecipitation reflects /rainythe general days in changingith year. trend of the average precipitation/rainy days in each grid calculatedAccording by liner to theregression precipitation equation intensity during classification the study period. standard n stands raised for by total China’s years, Meteorological and Ki is the precipitation/rainyAdministration [31 ],days precipitation in ith year.of the Huang-huai-hai River Basin is classified into 5 grades: light rain,According mild rain, heavyto the rain, precipitation very heavy rainintensity and extreme classification precipitation standard (Table 1).raised Based onby theChina’s daily Meteorologicalgridded precipitation Administration data, yearly [31], precipitation, precipitation of rainy the days,Huang-huai-hai precipitation River intensity Basin is (divide classified annual into 5precipitation grades: light by rain, rainy mild days), rain, ratio heavy of precipitation, rain, very heavy multi-year rain and average extreme precipitation, precipitation ratio (Table of rainy 1). Based days onand the the daily whole gridded number precipitation of days in a year data, (365 yearly days orpr 366ecipitation, days) and rainy cover days, area precipitation of precipitation intensity of each (divideclass during annual the precipitation study period by were rainy all days), calculated. ratio of precipitation, multi-year average precipitation, ratio Generalizedof rainy daysExtreme-Value and the whole number (GEV) is of widely days in used a year in (365 the days research or 366 on days) extreme and cover precipitation area of precipitationvariation [32 ,of33 ].each Total class extreme during precipitationthe study period would were obtain all calculated. exceeding special threshold (95th and Generalized Extreme-Value (GEV) is widely used in the research on extreme precipitation variation [32,33]. Total extreme precipitation would obtain exceeding special threshold (95th and 99th percentile was threshold in this article) by GEV to study spatial pattern of extreme

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99thInt. J. Environ. percentile Res. Public was thresholdHealth 2019, in16,this x article) by GEV to study spatial pattern of extreme precipitation.4 of 11 The 95th percentile and 99th percentile of the precipitation in grid data were calculated by GEV distributionprecipitation. to The get a95th corresponding percentile thresholdand 99th map.percentile Based of on the thresholdprecipitation map, in total grid precipitation data were whichcalculated exceeds by GEV the distribution threshold during to get thea correspon study periodding threshold was calculated; map. Based by dividing on the threshold this amount map, of precipitationtotal precipitation by multi-year which exceeds average the precipitation, threshold duri weng can the get study the standardizedperiod was calculated; extreme precipitation by dividing distributionthis amount map. of precipitation by multi-year average precipitation, we can get the standardized extreme precipitation distribution map. Table 1. Grade of precipitation [31] (unit: mm). Table 1. Grade of precipitation [31] (unit: mm). Light Moderate Heavy Very Heavy Extreme Precipitation Precipitation Precipitation Precipitation Precipitation Light Moderate Heavy Very Heavy Extreme PrecipitationPrecipitation P24 < 10 10PrecipitationP24 < 25 25PrecipitationP24 < 50 50PrecipitationP24 < 100 Precipitation 100 P24 ≤ ≤ ≤ ≤ Precipitation P24 < 10 Note: 10 P24 ≤ P24 means < 25 total precipitation 25 ≤ P24 of< 50 24 h. 50 ≤ P24 < 100 100 ≤ P24 Note: P24 means total precipitation of 24 h.

3. Results

3.1. Interannual Variat Variationion of Precipitation Multi-year averageaverage precipitationprecipitation of of the the Huang-huai-hai Huang-huai-hai River River Basin Basin is shownis shown in Figurein Figure2a. In2a. the In grid,the grid, the maximumthe maximum precipitation precipitation occurs occurs in the southin the ofsouth the Huang-huai-hai of the Huang-huai-hai River Basin River with Basin 1375 with mm, while1375 mm, the minimumwhile the precipitationminimum precipitation occurs in the occurs north in of the Huang-huai-hainorth of the Huang-huai-hai River Basin with River 134 Basin mm. Thewith maximum 134 mm. The precipitation maximum is aboutprecipitation 10 times is the about minimum 10 times precipitation. the minimum It can precipitation. be seen here It that can thebe precipitationseen here that of the Huang-huai-hai precipitation Riverof Huang-huai-h Basin showsai a River decreasing Basin trendshows on a the decreasing whole, which trend is on about the 0.71 mm/annually, as seen in Figure3. −whole, which is about −0.71 mm/annually, as seen in Figure 3.

(a) (b)

Figure 2.2.( a)( Multi-yeara) Multi-year average average precipitation; precipitation; (b) Coe ﬃ(cientb) Coefficient variance of precipitationvariance of inprecipitation Huang-huai-hai in RiverHuang-huai-hai Basin. River Basin.

By calculating CV (coeﬃcient of variation) of precipitation spatial change in Huang-huai-hai River Basin (Figure2b), it can be determined that the maximum CV is 0.39 and the minimum CV is 0.09. The region where CV is larger than 0.25 is mainly located in the Hetao Plain of the north, North China Plain of the east and upper reaches of the Huaihe River of the south. Table2 shows that the minimum CV of decadal precipitation occurs in mild rain with about 0.3, while the maximum CV happens to very heavy rain with about 2.3. The minimum CV of decadal rainy days occurs in light rain with about 0.3, while the maximum CV also occurs in heavy rain with about 2.3 (Table3).

Figure 3. Interannual variation of precipitation in Huang-huai-hai River Basin.

Int. J. Environ. Res. Public Health 2019, 16, x 4 of 11 precipitation. The 95th percentile and 99th percentile of the precipitation in grid data were calculated by GEV distribution to get a corresponding threshold map. Based on the threshold map, total precipitation which exceeds the threshold during the study period was calculated; by dividing this amount of precipitation by multi-year average precipitation, we can get the standardized extreme precipitation distribution map.

Table 1. Grade of precipitation [31] (unit: mm).

Light Moderate Heavy Very Heavy Extreme Precipitation Precipitation Precipitation Precipitation Precipitation Precipitation P24 < 10 10 ≤ P24 < 25 25 ≤ P24 < 50 50 ≤ P24 < 100 100 ≤ P24 Note: P24 means total precipitation of 24 h.

3. Results

3.1. Interannual Variation of Precipitation Multi-year average precipitation of the Huang-huai-hai River Basin is shown in Figure 2a. In the grid, the maximum precipitation occurs in the south of the Huang-huai-hai River Basin with 1375 mm, while the minimum precipitation occurs in the north of the Huang-huai-hai River Basin with 134 mm. The maximum precipitation is about 10 times the minimum precipitation. It can be seen here that the precipitation of Huang-huai-hai River Basin shows a decreasing trend on the whole, which is about −0.71 mm/annually, as seen in Figure 3.

(a) (b)

Int. J. Environ.Figure Res.2. Public(a) Multi-year Health 2019 , 16average, 3404 precipitation; (b) Coefficient variance of precipitation in 5 of 11 Huang-huai-hai River Basin.

Figure 3. Interannual variation of precipitationprecipitation in Huang-huai-hai River Basin.

Table 2. Interannual variation of CV of precipitation.

1961–1969 1970–1979 1980–1989 1990–1999 2000–2009 2010–2016 Light 0.44 0.45 0.46 0.45 0.45 0.45 Moderate 0.33 0.32 0.32 0.32 0.33 0.32 Heavy 0.53 0.53 0.56 0.53 0.53 0.54 Very heavy 1.00 0.97 1.03 1.03 1.02 1.01 Extreme 2.29 2.33 2.42 2.22 2.35 2.32

Table 3. Interannual variation of CV of rainy days.

1961–1969 1970–1979 1980–1989 1990–1999 2000–2009 2010–2016 Light 0.29 0.29 0.30 0.30 0.28 0.29 Moderate 0.41 0.41 0.45 0.41 0.43 0.42 Heavy 0.66 0.68 0.71 0.69 0.67 0.68 Very Heavy 1.12 1.09 1.16 1.16 1.14 1.14 Extreme 2.33 2.33 2.48 2.25 2.40 2.36

Figure4 shows the slope of precipitation and rainy days. Figure4a shows that there is a precipitation belt in the middle of the Huang-huai-hai River Basin which has an obvious decreasing trend. On the other hand, precipitation of the Huaihe River Basin in the south of the study area has an obvious increasing trend. For rainy days, decreasing trends mainly take place in the east and south part (Figure4b). Precipitation in the Huaihe River Basin of the south of the study area increases while the rainy days decrease. Thus, it can be speculated that the precipitation intensity of this area may increase, and the probability of extreme precipitation occurrence may also increase. Influenced by monsoon, the Huaihe River Basin has always been one of the regions which is heavily affected by flood. The precipitation intensity and frequency of extreme precipitation are also comparatively large [34,35]. Int. J. Environ. Res. Public Health 2019, 16, x 5 of 11

By calculating CV (coefficient of variation) of precipitation spatial change in Huang-huai-hai River Basin (Figure 2b), it can be determined that the maximum CV is 0.39 and the minimum CV is 0.09. The region where CV is larger than 0.25 is mainly located in the Hetao Plain of the north, North China Plain of the east and upper reaches of the Huaihe River of the south. Table 2 shows that the minimum CV of decadal precipitation occurs in mild rain with about 0.3, while the maximum CV happens to very heavy rain with about 2.3. The minimum CV of decadal rainy days occurs in light rain with about 0.3, while the maximum CV also occurs in heavy rain with about 2.3 (Table 3).

Table 2. Interannual variation of CV of precipitation.

Table 3. Interannual variation of CV of rainy days.

Figure 4 shows the slope of precipitation and rainy days. Figure 4a shows that there is a precipitation belt in the middle of the Huang-huai-hai River Basin which has an obvious decreasing trend. On the other hand, precipitation of the Huaihe River Basin in the south of the study area has an obvious increasing trend. For rainy days, decreasing trends mainly take place in the east and south part (Figure 4b). Precipitation in the Huaihe River Basin of the south of the study area increases while the rainy days decrease. Thus, it can be speculated that the precipitation intensity of this area may increase, and the probability of extreme precipitation occurrence may also increase. Influenced by monsoon, the Huaihe River Basin has always been one of the regions which is heavily Int. J. Environ.affected Res.by Publicflood. Health The2019 precipitation, 16, 3404 intensity and frequency of extreme precipitation are also 6 of 11 comparatively large [34,35].

(a) (b)

FigureFigure 4. 4.( a()a) Slope Slope ofof precipitation; (b ()b rainy) rainy days. days.

3.2. Interannual3.2. Interannual Variation Variation of Precipitationof Precipitation Intensity Intensity for Each Grade Grade Int. J. Environ. Res. Public Health 2019, 16, x 6 of 11 RatioRatio between between precipitation precipitation of each of each grade grade and and total total precipitation precipitation of a of whole a whole year year is shown is shown in Figure in 5. Figure 5. It can be seen that multiannual average ratio (the same below) between light rain and total It can betotal seen precipitation that multiannual of a whole average year ratiois about (the 30%, same also below) with an between decreasing light trend; rain the and ratio total between precipitation precipitation of a whole year is about 27%, with a decreasing trend; the ratio between mild rain and of a wholeheavy year rain is aboutand total 27%, precipitation with a decreasing of a whole trend; year is theabout ratio 23%, between with a slightly mild rain decreasing and total trend; precipitation the ratio between very heavy rain and total precipitation of a whole year is about 14%, with no of a whole significant year is changing about 30%, trend; also the ratio with between an decreasing extreme rain trend; andthe total ratio precipitation between of heavya whole rain year and is total precipitationabout of 5%, a wholealso with year no issignificant about 23%, changing with atrend. slightly Figure decreasing 6 shows the trend; ratio thebetween ratio rainy between days veryand heavy rain andthe total total precipitation number of days of in a wholea year (365 year days is aboutor 366 days). 14%, withIt can nobe seen significant from Figure changing 6 that rainy trend; days the ratio betweenfor extreme light rain rain is andabout total 19% precipitation of the total number of a whole of days year in a isyear, about with 5%, an alsodecreasing with no trend; significant rainy days changing trend. Figurefor mild6 shows rain is theabout ratio 3% betweenof the total rainy number days of da andys thein a totalyear, numberwith a decreasing of days trend; in a year rainy (365 days days or for heavy rain is about 1% of the total number of days in a year, with a slightly decreasing trend; 366 days). It can be seen from Figure6 that rainy days for light rain is about 19% of the total number of rainy days for very heavy rain is about 0.3% of the total number of days in a year, with no significant days in achanging year, with trend; an decreasingrainy days for trend; extreme rainy rain days is about for 0.06% mild rainof the is total about number 3% of ofthe days total in a numberyear, also of days in a year,with with a adecreasing decreasing trend. trend; Considering rainy days the forsituation heavy that rain the is precipitation about 1% of has the been total decreasing number ofeach days in a year, withyear a slightly in the study decreasing area, as trend;shown rainyin Figure days 3, forthe verydecrease heavy of precipitation rain is about and 0.3% rainy of thedays total for light number of days in arain year, and with mild no rain significant may be the changing cause of the trend; decrease rainy of dayswhole for precipitation extreme rain in the is study about area. 0.06% of the total number of daysFigure in a7 year,shows also the withinterannual a decreasing variation trend. of precipitation Considering intensity the situation of each grade. that the It can precipitation be seen has that the intensity of light rain and mild rain has an increasing trend; the intensity of heavy rain and been decreasing each year in the study area, as shown in Figure3, the decrease of precipitation and rainy very heavy rain has no significant changing trend; the intensity of extreme rain has a decreasing days fortrend. light rainThe results and mild have rain also may been be presented the cause by ofothers the decrease[36,37]. of whole precipitation in the study area.

Figure 5.FigureRatio 5. Ratio between between precipitation precipitation of of each each gradegrade and and total total precipitation precipitation of a whole of a whole year. year.

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Int. J. Environ. Res. Public Health 2019, 16, x 7 of 11

FigureFigure 6. Ratio 6. Ratio between between rainy rainy days days of of each each grade grade andand the total number number of of days days in ina year. a year.

Figure7 shows the interannual variation of precipitation intensity of each grade. It can be seen that the intensity of light rain and mild rain has an increasing trend; the intensity of heavy rain and very heavy rain has no signiﬁcant changing trend; the intensity of extreme rain has a decreasing trend. The results have also been presented by others [36,37]. Figure 6. Ratio between rainy days of each grade and the total number of days in a year.

Figure 7. Interannual variation of precipitation intensity of each grade.

3.3. Interannual Variation of Precipitation Cover Area for Each Grade

FigureFigure 7. Interannual 7. Interannual variation variation of of precipitation precipitation intensity intensity of of each each grade. grade.

3.3. Interannual Variation of Precipitation Cover Area for Each Grade

Int. J. Environ. Res. Public Health 2019, 16, 3404 8 of 11

3.3.Int. J. Interannual Environ. Res. VariationPublic Health of 2019 Precipitation, 16, x Cover Area for Each Grade 8 of 11

InterannualInterannual variation variation of of precipitation precipitation cover cover area area for for each each grade grade is is shown shown in in Figure Figure8 and 8 and Table Table4. From4. From Figure Figure8 it 8 can it can be be seen seen that that light light rain rain coverscovers the whole whole study study area, area, with with an an area area of about of about 1440 1440× 103 km1033; kmmild3; mildrain covers rain covers an area an areaof about of about 1425 1425 × 103 km103, kmwith3, withan increasing an increasing trend; trend; the heavy the heavy rain, × × rain,very veryheavy heavy rain rainand extreme and extreme rain cover rain cover an area an of area about of about 1127 × 1127 103 km103, 3565km ×3 ,10 5653 km310 and3 km 1103 and× 103 × × 110km3 respectively,103 km3 respectively, all with a all decreasing with a decreasing trend. Table trend. 4 shows Table4 thatshows the that precipitation the precipitation cover area cover of area each × ofgrade each basically grade basically all reached all reached the top the in topthe in1990s. the 1990s. Considering Considering that the that intensity the intensity of extreme of extreme rain has rain a hasdecreasing a decreasing trend trend (Figure (Figure 7), the7), theextreme extreme precipitat precipitationion in the in the whole whole study study area area may may decrease decrease on on the thewhole, whole, which which is similar is similar to tothe the research research done done by byZhang Zhang et etal. al.[38]. [38 ].

FigureFigure 8. 8.Interannual Interannual variation variation of of precipitation precipitation cover cover area area for for each each grade. grade.

Table 4. Interannual variation of precipitation cover area for each grade. Table 4. Interannual variation of precipitation cover area for each grade.

1961–19691961–1969 1970–1979 1970–1979 1980–19891980–1989 1990–19991990–1999 2000–20092000–2009 2010–20162010–2016 LightLight 1434.03 1434.03 1434.03 1434.03 1434.031434.03 1434.031434.03 1434.031434.03 1434.031434.03 ModerateModerate 1419.63 1419.63 1426.77 1426.77 1420.551420.55 1431.261431.26 1426.071426.07 1425.011425.01 HeavyHeavy 1135.75 1135.75 1138.30 1138.30 1099.901099.90 1143.211143.21 1121.081121.08 1127.231127.23 VeryVery Heavy 576.56 576.56 596.13 596.13 544.98544.98 552.14552.14 558.15558.15 565.08565.08 Extreme 113.71 107.63 98.41 120.46 109.39 109.83 Extreme 113.71 107.63 98.41 120.46 109.39 109.83

3.4.3.4. Spatial Spatial Distribution Distribution of of Extreme Extreme Precipitation Precipitation TheThe 95th95th percentilepercentile andand 99th99th percentilepercentile ofof thethe extreme extreme precipitation precipitation thresholdthreshold areare shown shown inin FigureFigure9. 9. It canIt can be seenbe seen that thethat threshold the threshold in both in pictures both pictures is in the is shape in the of aboutshape ﬁve of belts,about increasing five belts, fromincreasing the northeast from the to northeast the southwest. to the southwest. If we assume If we that assume the area that where the area the where standardized the standardized extreme precipitationextreme precipitation accumulation, accumulation, as seen in Figureas seen 10 in, isFigure larger 10, than is 0.4larger (i.e., than the blue0.4 (i.e., part the in Figure blue part 10) isin theFigure region 10) su isﬀ eringthe region from extreme suffering precipitation, from extreme the extremeprecipitation, precipitation the extreme is most precipitation severe in the is Loess most severe in the Loess Plateau of the northeast, Dabieshan area of the mid Shandong peninsula of the east, middle and lower reaches of the Huaihe River Basin of the south.

Int. J. Environ. Res. Public Health 2019, 16, 3404 9 of 11

Plateau of the northeast, Dabieshan area of the mid Shandong peninsula of the east, middle and lower reaches of the Huaihe River Basin of the south. Int. J. Environ. Res. Public Health 2019, 16, x 9 of 11

(a) (b)

Figure 9. The 95th percentile and 99th percentile of the extreme precipitation threshold. ( a) The 95th percentile; (b)) thethe 99th99th percentile.percentile.

(a) (b)

Figure 10. The standardized extreme precipitation accumu accumulationlation larger than the 95th percentile and 99th percentile. ( a)) The 95th percentile; ( b) the 99th percentile.

4. Conclusions Conclusions Based on the data of meteorological stations in the Huang-huai-hai River Basin, the amount of precipitation and the temporal-spatial distribution of each precipitation grade grade (light (light rain, rain, mild mild rain, rain, heavy rain, very heavy rain and extreme rain) of the study area during 1961–2016 were analyzed via daily interpolated meteorological data.data. Main conclusions are as follows: (1) The precipitation of the Huang-huai-hai Rive Riverr Basin showed a decreasing trend; the amount of precipitation of each grade and CV for rainy days did not change much; Huai-he River Basin in the south of the studystudy areaarea isis atat highhigh riskrisk ofof extremeextreme precipitation.precipitation. ConsideringConsidering serious shortage of water resources and a high degree of water resourcesresources development and utilization, the reduction of precipitation would aggravate eeffectsﬀects ofof droughtdrought inin thisthis basin.basin. (2) The ratio between the light/mild light/mild precipitat precipitationion amount and the total precipitation amount, and the ratio between the light rain/mild rain/mild rainy days and the total number of days are comparatively big, but they both have decreasing trends, which co coulduld be the main reason for the decrease of total precipitation amount.amount. BecauseBecause of of the the special special underlying underlying surface surface in thein the basin, basin, short-duration, short-duration, light light and andmoderate moderate precipitation precipitation usually usually have have no runo no runoffﬀ observed. observed. (3) The cover area of the heavy rain, very heavy rain and extreme rain all showed a decreasing trend. ConsideringConsidering the the decreasing decreasing trend trend of theof extremethe extreme rain intensity,rain intensity, the extreme the extreme precipitation precipitation amount amountin the whole in the study whole area study may area also may decrease. also decrease. (4) In the Huang-huai-hai River Basin, extreme precipitation, which analyzed by generalized extreme-value (GEV), was most severe in Loess Plateau of the northeast, Dabieshan area of the mid-Shandong peninsula of the east, middle and lower reaches of the Huaihe River Basin of the south. These areas are high-risk regions of flood disaster. The above-mentioned conclusions are of great importance to reveal the variation of precipitation, cause of drought/flood and coping measures. Since the area of the Huang-huai-hai

Int. J. Environ. Res. Public Health 2019, 16, 3404 10 of 11

(4) In the Huang-huai-hai River Basin, extreme precipitation, which analyzed by generalized extreme-value (GEV), was most severe in Loess Plateau of the northeast, Dabieshan area of the mid-Shandong peninsula of the east, middle and lower reaches of the Huaihe River Basin of the south. These areas are high-risk regions of ﬂood disaster. The above-mentioned conclusions are of great importance to reveal the variation of precipitation, cause of drought/ﬂood and coping measures. Since the area of the Huang-huai-hai River Basin is big and the duration and cover area of extreme precipitation is small, the precision for the extreme precipitation of this research might need further improvement.

Author Contributions: Conceptualization, Y.Y.; D.Y.; methodology, Z.Y.; J.Y.; resources, Z.Z.; writing—Original draft, Y.Y.; Z.Y.; writing—Review and editing, J.Y.; Z.Z.; supervision: D.Y. Funding: This research was funded by National Science Fund for Distinguished Young Scholars (No. 51725905), National Key Research and Development Project (No.2017YFC1502404), National Natural Science Foundation of China (No. 51709008) and Natural Science Foundation of Hubei Province (No. 2018CFB655). Conﬂicts of Interest: The authors declare no conﬂict of interest.

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