Changes in Extreme Precipitation Across 30 Global River Basins

water

Article Changes in Extreme Precipitation across 30 Global River Basins

Xin Feng 1, Zhaoli Wang 1,2, Xushu Wu 1,2,* , Jiabo Yin 3, Shuni Qian 3 and Jie Zhan 4

1 School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China; [email protected] (X.F.); [email protected] (Z.W.) 2 State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510641, China 3 State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China; [email protected] (J.Y.); [email protected] (S.Q.) 4 China Water Resources Pearl River Planning Surveying & Designing Co., LTD., Guangzhou 510640, China; [email protected] * Correspondence: [email protected]

Received: 8 May 2020; Accepted: 25 May 2020; Published: 27 May 2020

Abstract: Extreme precipitation can cause disasters such as floods, landslides and crop destruction. A further study on extreme precipitation is essential for enabling reliable projections of future changes. In this study, the trends and frequency distribution changes in extreme precipitation across different major river basins around the world during 1960–2011 were examined based on two of the latest observational data sets respectively collected from 110,000 and 26,592 global meteorological stations. The results showed that approximately a quarter of basins have experienced statistically significant increase in maximum consecutive one-day, three-day and five-day precipitation (RX1day, RX3day and RX5day, respectively). In particular, dramatic increases were found in the recent decade for the Syr Darya River basin (SDR) and Amu Darya River basin (ADR) in the Middle East, while a decrease in RX3day and RX5day were seen over the Amur River basin in East Asia. One third of basins showed remarkable changes in frequency distributions of the three indices, and in most cases the distributions shifted toward larger amounts of extreme precipitation. Relative to the subperiod of 1960–1984, wider range of the three indices over SDR and ADR were detected for 1985–2011, indicating intensification along with larger fluctuations of extreme precipitation. However, some basins have frequency distributions shifting toward smaller amounts of RX3day and RX5day, such as the Columbia River basin and the Yellow River basin. The study has potential to provide the most up-to-date and comprehensive global picture of extreme precipitation, which help guide wiser public policies in future to mitigate the effects of these changes across global river basins.

Keywords: extreme precipitation; trend; frequency distribution change; global river basins

1. Introduction Flood, landslide and soil erosion triggered by extreme precipitation are among the major hazards that pose threats to society and the environment [1–7]. The understanding of extreme precipitation features is beneﬁcial for the forecasting and management of these hazards. However, extreme precipitation is becoming substantially more intense and unpredictable, with larger ﬂuctuations than the past largely due to climate change, and it always displays high spatiotemporal heterogeneity [8–11]. Exploring extreme precipitation behaviors across various regions enables a comprehensive understanding of how it changes in space and time under the changing environment [12,13].

Water 2020, 12, 1527; doi:10.3390/w12061527 www.mdpi.com/journal/water Water 2020, 12, 1527 2 of 12

A variety of literatures have revealed extreme precipitation changes at different spatial scales. Alexander et al. (2006) examined global trends and probability distributions of extreme precipitation, showing that extreme precipitation has increased on the whole for the 20th century, but the probability distribution of maximum one-day precipitation (RX1day) does not exhibit remarkable changes [11]. Asadieh and Krakauer (2014) analyzed trends in global extreme precipitation and found that both observations and models show generally upward trends in extreme precipitation since the beginning of 1900s, and the changes for tropic regions are the largest [14]. Through using a global atmospheric model for forecasting of extreme precipitation, Akio et al. (2016) showed that RX1day and maximum five-day precipitation (RX5day) are increasing even though the mean precipitation is decreasing [15]. Shi and Durran (2016) held the viewpoint that, in mountainous regions, the sensitivity of extreme precipitation to global warming is lower than in oceanic regions or plains [16]. Zhang and Zhou (2019) stated that, for global monsoon regions, extreme precipitation is on the rise and its correlation with warming climate is distinctive [17]. Regionally, in North America, a study has revealed a strong relationship between extreme precipitation and hurricane activities based on a 25-year observational analysis [18]. Costa and Soares (2007) assessed the uncertainty of spatiotemporal interpolation of an extreme precipitation index using the southern region of continental Portugal as an example and made conclusions of a higher spatial continuity of extreme precipitation but a weaker relationship between altitude and the index in recent decades [19]. Vyshkvarkova and Voskresenskaya (2018) pointed out an abnormal phenomenon over the southern Russia where the wetting trend is negligible, not necessarily following the global overall upward trend of extreme precipitation [20]. A similar phenomenon is found for the Northwestern Highlands of Ethiopia, where evidence of increasing extreme precipitation is lacking [21]. Over eastern Asia, Gayoung et al. (2018) projected the average and extreme precipitation intensity in Korea in future and found an increased intensity for the future period 2021–2100 compared to the present [22]. For China, there are also many studies regarding extreme precipitation across different regions. For example, Wu et al. (2016) investigated extreme precipitation characteristics over 11 basins in China and revealed strengthened RX1day in the Liaohe River Basin (LB) together with strengthened RX5day in the Songhua River Basin [23]. Another example is the study of Wang et al. (2015) which explored seasonal extreme precipitation changes over the arid regions of northwest China [24]. In India, Gupta and Jain (2020) discovered that the annual total precipitation is decreasing, while extreme precipitation shows an opposite trend [25]. Although the aforementioned studies provide beneficial information on extreme precipitation features, most of them are regional and their conclusions may not completely agree because of the use of varying indices, data sets, methodologies and study periods. Compared to regional studies, global studies enable a direct comparison of extreme precipitation characteristics across different regions and can provide a large-scale picture of extreme precipitation characterization [26,27]. However, there are relatively few global-scale literatures at present and, more importantly, the existing studies seldom investigate extreme precipitation characteristics (e.g., frequency or probability distributions) from different subperiods, but rather, from the whole period of record, which hampers the formulation of integrated information regarding changing properties of extreme precipitation under the backdrop of climate change. Furthermore, there is no systematic study of extreme precipitation over various river basins worldwide. From the hydrological perspective, flood analyses rely on precipitation conditions within a basin rather than in a region, country, or continent [28]. The lack of sufficient knowledge on extreme precipitation over different basins across the globe has restricted governors or river managers from making flood adaptations and mitigation strategies appropriate to each basin. To overcome the shortage, we set out to reveal trends and frequency distribution changes in extreme precipitation across different major river basins globally. Our study aims to draw the most up-to-date and comprehensive global picture of extreme precipitation at the basin scale which would be beneficial for river flood managements over different basins around the world. Water 2020, 12, 1527 3 of 12

2. Materials and Methods

2.1. Sourced Data and Global Basins Daily precipitation data used in this study came from two sources; one is the data covering 1929–present generated from more than 110,000 meteorological stations around the world, which were obtained from the National Oceanic and Atmospheric Administration (https://www.ncei.noaa. gov/data/global-summary-of-the-day/archive/), and the other is the data for the period 1929–2017 obtained from the National Climate Data Center GSOD dataset produced by the National Centers for Environmental Information, covering 26,592 stations globally (https://www.ncdc.noaa.gov/). Most of the analyses in the current study were based on the first data set and the second data set was served as the complementary one; if there is no data from the first data set for a specific basin, or the data are less sufficient than the GSOD data set, then the GSOD data are used instead. Meanwhile, before the data were used, a quality control procedure was applied, including examination of internal consistency, and suspected and erroneous data. To reduce errors caused by insufficient sampling and record length, we chose 9181 stations with near-continuous (less than 10% of missing data each year) observations during 1960–2011 in which the missing data are minimum. There are a wide range of river basins around the world and we paid special attention to the top 60 basins ranked by their drainage areas. However, some of the basins suffer from insufficient meteorological stations (the ones selected from the quality control procedure). Finally, 30 major basins were selected which are in different continents. Details of the basins are listed in Table1.

Table 1. Thirty river basins from all over the globe used in this study.

ID Name Continent Area (106 km2) Climate 1 Kolyma Asia 0.66 Semi-humid 2 Yukon North America 0.83 Semi-humid 3 Lena Asia 2.46 Semi-humid 4 Mackenzie North America 1.67 Semi-humid 5 Dnieper Europe 0.5 Humid 6 Volga Europe 1.37 Humid 7 Yenisei Asia 2.82 Humid 8 Ob Asia 3.11 Semi-humid 9 Saskatchewan River North America 0.38 Semi-humid 10 Don Asia 0.45 Semi-humid 11 Danube Europe 0.78 Humid 12 Amur Asia 2.13 Semi-humid 13 Columbia North America 0.71 Semi-humid 14 Saint Lawrence River North America 0.3 Semi-humid 15 Syr Darya River Asia 2.2 Arid 16 Amu darya Asia 4.65 Arid 17 Tarim River Asia 1.02 Arid 18 Huanghe Asia 0.84 Semi-arid 19 Colorado North America 0.81 Arid 20 Mississippi North America 1.32 Semi-humid 21 Yangtze Asia 1.84 Humid 22 Grand River North America 0.57 Semi-arid 23 Ganges Asia 1.54 Humid 24 Nile Africa 3.8 Semi-arid 25 Amazon South America 5.93 Humid 26 Great Artesian Basin Oceania 1.75 Arid 27 Orange River Africa 1.02 Semi-arid 28 Murray Oceania 0.96 Semi-arid 29 N.Dvina Asia 0.28 Humid 30 Zhujiang Asia 0.45 Humid Water 2020, 12, 1527 4 of 12

2.2. Extreme Precipitation Indices In this study, three indices were selected to characterize extreme precipitation, namely, annual maximum consecutive one-day, three-day, and five-day precipitation (RX1day, RX3day, and RX5day, respectively). RX1day is defined as the maximum daily precipitation in a year, and RX3day is defined as the maximum consecutive three-day precipitation in a year; RX5day is referred to as the maximum consecutive five-day precipitation in a year [20]. RX1day events usually represent extreme showers that can induce flash floods, while RX3day and RX5day events are more indicative of wet periods which can trigger high water levels in large-scale basins [20]. Moreover, these indices are considered suitable for characterizing extreme heavy precipitation that has devastating impacts on society and the environment, and is typically used to represent the probability of rare events during the design of infrastructure and in other applications. The three indices are now widely used for the analysis of extreme precipitation over different regions [29,30].

2.3. Linear Trend Analysis We employed the linear regression for the trend analyses. It is a relatively simple but robust trend analysis method that is commonly used [31–33]. The linear function can be given by:

y = ax + b + ε (1) where a is the regression coefficient signifying the slope of the trend, b is the constant and ε is the noise term. The Kolmogorov–Smirnov test was applied to determining the significance of trends (the 0.05 significance level) [34].

3. Results

3.1. Trend Figure1 shows the basins with significant trends in RX1day (insignificant trend results were not presented for simplicity). In North America, basin #14 has experienced significant increase in RX1day with a rate of 0.82 mm/year, and the maxima is found around the 1980s. In Europe, a significant upward trend is seen over basin #5, where the maxima of RX1day occurred around 2000s reaching to approximately 250 mm. However, for other European basins, no significant trend exists. It is noteworthy that basins #15 and #16 in the Middle East have encountered large fluctuations of RX1day since 2000, particularly in the recent years. These are tending toward increasing trends; the maxima occurred in 2010. The overall trends during 1960–2011 are 3.11 mm/year and 4.13 mm/year, respectively. By comparison, although RX1day over basin #8 also displays a significant upward trend, the slope is only 0.18 mm/year. For North and East Asia, significant upward trends are visible in basins #1 and #21. More specifically, the trend slope of basin #1 is larger than that of basin #21. Basin #27, located in South Africa, presents an increase in RX1day and the trend becomes steeper after 2000. Figure2 depicts the basins with significant trends in RX3day. Apparently, the trends of RX3day over basins #1, #5, #14, #15, #16 and #27 are similar to those of RX1day, which are significantly upward. Specifically, the interannual fluctuation patterns of RX1day and RX3day are similar in basins #14, #15 and #16. In addition, RX3day over basins #15 and #16 has increased dramatically in the most recent decade, and the maxima in basin #16 observed in 2011 exceeds 1000 mm. On the other hand, there are some basins where RX3day shows significant upward trends but RX1day does not, including basins #3 and #9, where the trend slopes are 0.83 mm/year and 1.72 mm/year, respectively. It is noted that RX3day in basin #12, located in East Asia, exhibits a significant downward trend during 1960–2011, different from those in the other basins; the changing slope is 1.05 mm/year with a p value of 0.021 − and the decreasing trend is more apparent after 1990 than before. Also note that some basins do not show significant trends in RX3day but RX1day, for example basins #8 and #21. Water 2020, 12, x FOR PEER REVIEW 5 of 12 Water 2020, 12, 1527 5 of 12 Water 2020, 12, x FOR PEER REVIEW 5 of 12

Figure 1. Trends in maximum one-day precipitation (RX1day) for different river basins. The x-axis in Figure 1. Trends in maximum one-day precipitation (RX1day) for diﬀerent river basins. The x-axis in eachFigure panel 1. Trends shows in themaximum time and one-day the y-axis precipitation is the amount (RX1day) of forRX1day different for riverthe corresponding basins. The x-axis year. in each panel shows the time and the y-axis is the amount of RX1day for the corresponding year. Numbers Numberseach panel within shows basins the timesignify and basin the IDy-axis as listed is th ine amountTable 1. of RX1day for the corresponding year. within basins signify basin ID as listed in Table1. Numbers within basins signify basin ID as listed in Table 1.

Figure 2. Trends in maximum three-day precipitation (RX3day) for different river basins. The x-axis in Figure 2. Trends in maximum three-day precipitation (RX3day) for different river basins. The x-axis each panel shows the time and the y-axis is the amount of RX3day for the corresponding year. Numbers inFigure each 2.panel Trends shows in maximum the time andthree-day the y-axis precipitation is the amount (RX3day) of RX3day for different for the river corresponding basins. The x-axisyear. within basins signify basin ID as listed in Table1. Numbersin each panel within shows basins the signify time andbasin the ID y-axis as listed is thein Table amount 1. of RX3day for the corresponding year. FigureNumbers3 illustrates within basins significant signify basin trends ID as in listed RX5day in Table for some1. basins. The trends over #5, #12, #14, #15, andFigure #27 3 basinsillustrates are similarsignificant with trends RX3day in trends,RX5day with for some the one basins. over The basin trends #12 in over East #5, Asia #12, being #14, significantly#15, andFigure #27 3 downwardbasins illustrates are similar significant and the with others trends RX3day over in thetrendsRX5day remaining, with for somethe four one basins. basins over Thebasin being trends #12 significantly in over East #5, Asia upward.#12, being #14, Thesignificantly#15, largest and #27 trend downwardbasins is found are similar inand basin the with #27,others whereRX3day over RX5day thetrends remaining increases, with the four with one basins a over rate of basinbeing 3.70 mm#12significantly /inyear. East In Asia particular, upward. being basinThesignificantly largest #15 has trend downward faced is dramatically found and in the basi increasingothersn #27, over where RX3day the RX5dayremaining in recent increases four years, basins with being aa maximarate significantly of 3.70 observed mm/year. upward. in 2010 In particular,The largest basin trend #15 is foundhas faced in basi dramaticallyn #27, where increasing RX5day increasesRX3day inwith recent a rate years, of 3.70 with mm/year. a maxima In particular, basin #15 has faced dramatically increasing RX3day in recent years, with a maxima

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Water 2020, 12, x FOR PEER REVIEW 6 of 12 exceeding 700 mm. Compared to the patterns of RX1day, it can be found that, except for basin #12,observed both RX1dayin 2010 exceeding and RX5day 700 mm. over Compared basins #5, #14,to the #15 patterns and #27 of RX1day, display significantit can be found upward that, trends.except Apartfor basin from #12, the both above RX1day basins, and basins RX5day #20 andover #30 basins also #5, have #14, significant #15 and #27 increasing display RX5daysignificant with upward large interannualtrends. Apart fluctuations from the above for the basins, period basins of record, #20 and but #30 both also trends have are significant not steep increasing (2.10 mm/ yearRX5day and with 0.43 mmlarge/year interannual respectively). fluctuations for the period of record, but both trends are not steep (2.10 mm/year and 0.43 mm/year respectively).

Figure 3. Trends in maximum five-day precipitation (RX5day) for different river basins. The x-axis in Figure 3. Trends in maximum five-day precipitation (RX5day) for different river basins. The x-axis in each panel shows the time and the y-axis is the amount of RX5day for the corresponding year. Numbers each panel shows the time and the y-axis is the amount of RX5day for the corresponding year. within basins signify basin ID as listed in Table1. Numbers within basins signify basin ID as listed in Table 1. 3.2. Frequency Distribution Changes 3.2. Frequency Distribution Changes To explore changes in frequency distributions of RX1day, RX3day, and RX5day, the data were split intoTo explore two equal changes subperiods: in frequency 1960–1984 distributions and 1985–2011. of RX1day, Figure RX3day,4 presents and the RX5day, basins where the data distinct were changessplit into (either two equal the shapessubperiods: of distributions 1960–1984 and for 1985–201 the two subperiods1. Figure 4 presents differ substantially, the basins where or the distinct peaks ofchanges two distribution (either the curvesshapes showof distributions a visible horizontal for the two distance) subperiods in frequencydiffer substantially, distributions or the of RX1daypeaks of duringtwo distribution 1960–1984 andcurves 1985–2011 show a werevisible found, horizontal from which distance) it can in be frequency seen that, indistributions most cases, of significant RX1day distributionduring 1960–1984 changes and exist 1985–2011 in the basins were wherefound, significant from which trends it can in be RX1day seen that, are in found. most Correspondingly,cases, significant RX1daydistribution exhibits changes strengthening exist in trends the duringbasins thewhere subperiod significant of 1985–2011 trends with in increasingRX1day frequenciesare found. ofCorrespondingly, larger amount, and RX1day this is particularlyexhibits strengthening true for basins trends #1, #5 during and #8. the Note subperiod that basin of #18 1985–2011 shows visible with changesincreasing in thefrequencies frequency of distributionslarger amount, between and this 1960–1984 is particularly and 1985–2011, true for basins although #1, #5 its and trend #8. is Note not statisticallythat basin #18 significant shows visible (Figure changes1). It is also in the noteworthy frequency that distributions the distribution between changes 1960–1984 over basinsand 1985– #15 and2011, #16 although in the Middle its trend East is and not that statistically over basin signif #27 inicant South (Figure Africa 1). all It show is also a larger noteworthy range of RX1daythat the indistribution 1985–2011 changes compared over to thebasins other #15 subperiod. and #16 in In th particular,e Middle RX1day East and over that basins over #15basin and #27 #16 in for South the 1985–2011Africa all show period a larger can be range larger of than RX1day 300 mmin 1985–20 and, in11 some compared cases, to it the can other reach subperiod. 600 mm or In even particular, larger, whereasRX1day thatover for basins the 1960–1984 #15 and #16 period for isthe generally 1985–2011 lower period than can 300 mm.be larger As for than basin 300 #27, mm RX1day and, in during some thecases, latter it can subperiod reach 600 can mm be largeror even than larger, 400 mm wherea buts is that generally for the lower 1960–1984 than 400 period mm is during generally 1960–1984. lower than 300 mm. As for basin #27, RX1day during the latter subperiod can be larger than 400 mm but is generally lower than 400 mm during 1960–1984.

WaterWater2020 2020,,12 12,, 1527x FOR PEER REVIEW 77 ofof 1212

Figure 4. Changes in frequency distributions of RX1day for different river basins. The green and pinkFigure shadings 4. Changes in each in frequency panel represent distributions the distributions of RX1day for for different the period river of basins. 1960-1984 The andgreen 1985-2011, and pink respectively.shadings in Theeach x-axis panel in repr eachesent panel the shows distributions amount of for RX1day the period and the of y-axis1960-1984 is the and corresponding 1985-2011, frequencyrespectively. distribution The x-axis density. in each panel shows amount of RX1day and the y-axis is the corresponding frequency distribution density. When looking into the frequency distribution changes in RX3day, as shown in Figure5, it is found that theWhen basins looking with distinctinto the distributionfrequency distribution changes generally changes encountered in RX3day, significant as shown trends in Figure in RX3day, 5, it is suchfound as that basins the #1, basins #5, #15 with and distinct #16 (Figure distribution2). More changes specifically, generally a strengthening encountered of RX3day significant is remarkable trends in inRX3day, basins such #1, #14 as basins and #15 #1, during #5, #15 1985–2011and #16 (Figure relative 2). toMore 1960–1984. specifically, Larger a strengthening range of RX3day of RX3day can be is foundremarkable in basins in basins #5 and #1, #16, #14 respectivelyand #15 during located 1985–2011 in Europe relative and to the 1960–1984. Middle East;Larger during range 1985–2011,of RX3day RX3daycan be found larger in than basins 300 #5 mm and occurred #16, respectively in some located cases for in bothEurope the and basins, the Middle whereas East; such during a situation 1985– never2011, RX3day occurred larger for the than period 300 mm of 1960–1984. occurred in Combining some cases with for both the results the basins, from whereas Figure2, such there a are situation some basinsnever occurred dominated for bythe significant period of 1960–1984. trends in RX3day, Combining but thewith frequency the results distributions from Figure between2, there are the some two subperiodsbasins dominated do not changeby significant remarkably, trends for in example,RX3day, basinbut the #3 frequency in North Asia distributions and basin between #12 in East the Asia. two Contrarily,subperiods basins do not #18 change and #21remarkably, present distinct for example, changes ba insin frequency #3 in North distributions. Asia and basin Specifically, #12 in East RX3day Asia. hasContrarily, shifted towardsbasins #18 a smaller and #21 amount present in basindistinct #18, changes while the in frequencyfrequency distributiondistributions. becomes Specifically, more dispersiveRX3day has indicating shifted towards increased a smaller frequency amount of RX3day in basin larger #18, thanwhile 450 the mm frequency and smaller distribution than 350 becomes mm in themore latter dispersive subperiod indicating than the increased former subperiod. frequency of RX3day larger than 450 mm and smaller than 350 mm Figurein the latter6 shows subperiod the distinct than the frequency former distributionsubperiod. changes in RX5day. Note that prominent changesFigure exist 6 inshows basins the #5, distinct #14, #15 frequency and #27 where distribu significanttion changes trends inare RX5day. diagnosed Note (Figure that prominent3), while basinschanges #1, exist #9, #13in basins and #16 #5, are#14, examined #15 and #27 to havewhere visible significant changes trends in frequencyare diagnosed distributions (Figure 3), but while the trendsbasins are#1, not#9, statistically#13 and #16 significant. are examined In addition, to have visible basins #12,changes #20 andin frequency #30 all show distributions significant but trends the intrends RX5day, are not but statistically their frequency significant. distributions In addition, do not basins seemingly #12, #20 follow and #30 such all show changes. significant Remarkable trends frequencyin RX5day, distribution but their frequency changes toward distributions larger amounts do not seemingly of RX5day arefollow found such to basinschanges. #1, Remarkable #5, #14 and #27frequency basins, distribution respectively changes located in toward North larger Asia, Europe, amount Norths of RX5day America are and found South to basins Africa. #1, For #5, basin #14 #13,and RX5day#27 basins, shows respectively the opposite located situation; in North namely, Asia, it shiftsEurope, towards North a America smaller amount, and South indicating Africa. decreasedFor basin RX5day.#13, RX5day In the shows Middle the East, opposite it is found situation; that both namely basins, it #15 shifts and towards #16 are characterized a smaller amount, by larger indicating range of RX5daydecreased during RX5day. 1985–2011 In the relativeMiddle toEast, the it period is found 1960–1984, that both which basins is #15 quite and di ff#16erent are from characterized other basins. by Specifically,larger range RX5day of RX5day larger during than 4001985–2011 mm is observed relative to in the some period cases 1960–1984, for both of thewhich basins, is quite however different this doesfrom notother occur basins. during Specifically, the former RX5day subperiod. larger When than looking400 mm moreis observed closely in at some the results, cases for basins both #9 of and the #27basins, also however have a larger this rangedoes not of RX5dayoccur during during the the former latter subperiodsubperiod. to Wh someen extent;looking amounts more closely larger at than the 600results, mm basins occurred #9 inand basin #27 #9also basin have during a larger 1985–2011 range of rather RX5day than during 1960–1984, the latter and amounts subperiod larger to some than extent; amounts larger than 600 mm occurred in basin #9 basin during 1985–2011 rather than 1960–

Water 2020, 12, x FOR PEER REVIEW 8 of 12 Water 2020, 12, 1527 8 of 12 Water1984, 2020 and, 12 amounts, x FOR PEER larger REVIEW than 1000 mm are observed over basin #27 for the latter subperiod, whereas8 of 12 amounts during the former subperiod are mainly smaller than 1000 mm. 10001984, mm and are amounts observed larger over than basin 1000 #27 mm for are the observed latter subperiod, over basin whereas#27 for the amounts latter subperiod, during thewhereas former subperiodamounts are during mainly the smallerformer subperiod than 1000 are mm. mainly smaller than 1000 mm.

Figure 5. Changes in frequency distributions of maximum three-day precipitation RX3day for Figuredifferent 5. Changes river basins. in frequency The green distributions and pink shadings of maximum in each three-day panel represent precipitation the di RX3daystributions for for diﬀ theerent riverFigureperiod basins. of5. 1960–1984Changes The green inand and frequency 1985–2011, pink shadings distributions respectively. in each of The panel maximum x-ax representis in eachthree-day the panel distributions precipitationshows amount for RX3day the of RX3day period for of 1960–1984differentand the y-axis andriver 1985–2011, isbasins. the corresponding The respectively. green and frequency pink The shadings x-axis distribution in in eacheach density. panelpanel showsrepresent amount the distributions of RX3day for and the the y-axisperiod is the of 1960–1984 corresponding and 1985–2011, frequency respectively. distribution The density. x-axis in each panel shows amount of RX3day and the y-axis is the corresponding frequency distribution density.

Figure 6. Changes in frequency distributions of maximum five-day precipitation RX5day for different riverFigure basins. 6. Changes The green in frequency and pink distributions shadings in of each maximum panel represent five-day precipitation the distributions RX5day for for the different period of 1960–1984river basins. and The 1985–2011, green and respectively. pink shadings The in x-axis each panel in each represent panel showsthe distributions amount of for RX5day the period and of the y-axisFigure1960–1984 is the 6. Changes corresponding and 1985–2011, in frequency frequencyrespectively. distributions distribution The x-axis of maximum density.in each five-daypanel shows precipitation amount of RX5day RX5day for and different the y- riveraxis is basins. the corresponding The green and freq pinkuency shadings distribution in each density. panel represent the distributions for the period of 4. Discussion 1960–1984 and 1985–2011, respectively. The x-axis in each panel shows amount of RX5day and the y- Analysesaxis is the of corresponding extreme precipitation frequency distribution trends and density. its frequency distribution changes, established using a 52-year span of data (1960–2011) gathered from 30 major basins around the world, have revealed a significant rise of RX1day, RX3day and RX5day over some basins, in accord with previous studies [35–38]. In particular, basins #15 and #16 in the Middle East show dramatic increase in Water 2020, 12, 1527 9 of 12

RX1day, RX3day and RX5day for the recent years. This implies that flood risk as well as other extreme precipitation-induced hazards may increase over these basins. The countries in the Middle East should, therefore, make more hazard mitigation measures to avoid potential increased losses resulting from strengthened extreme precipitation. However, all these do not indicate that RX1day, RX3day and RX5day are strengthening in all global major river basins, as can be seen from the exception of basin #12 in East Asia, where RX3day and RX5day show declines for the period of record. On the other hand, we show remarkable changes in the frequency distributions of extreme precipitation over some basins, such as the RX1day, RX3day and RX5day over basin #1, the RX1day and RX5day over basin #5 and the RX3day and RX5day over basin #14, all of which shift toward larger amount suggesting intensified extreme precipitation at local scales. However, some basins display changes toward smaller amounts of extreme precipitation, such as the RX3day over basin #18 and the RX5day over basin #13. In addition, we found a larger range of extreme precipitation over basins #15 and #16 for 1985–2011 relative to 1960–1984. Therefore, attention should be paid to the apparent changing features of extreme precipitation for these basins and we suggest that the responsible stakeholders and administrations should consider the necessity of modifying current flood strategies to more feasibly cope with the extremes. The intensification of RX1day, RX3day and RX5day across a majority of the basins in our study is largely due to global warming and climate change. Higher temperature may enhance evaporation and increase the moisture content in the atmosphere, leading to an increase in extreme precipitation [39,40]. As further illustrated in a previous study, against the background of global warming, extreme precipitation is more sensitive to climate change and the relationship between extreme precipitation and temperature is closer than the past [41]. Another study has demonstrated that global observed RX1day has increased by about 5.73 mm on the whole in the past 110 years, approximately 10%/K since the 1900s [42]. The El Niño-Southern Oscillation (ENSO) is the Earth’s strongest source of year-to-year climate variability, which has an impact on regional climate regimes over many regions, including the central Asia [43]. Under climate change, ENSO is expected to exhibit larger fluctuations [44], and might result in larger variability of extreme precipitation over basins #15 and #16 as illustrated in Figures1–3. Apart from the global temperature rise, we argued that changes in extreme precipitation could be also partly impacted by local topography and geography [10,28]. In our study, the trends and frequency distribution changes in extreme precipitation over basins #5 and #10 situated in Europe are different (one is significant but the other is not), although the two basins are considered adjacent to each other and the controlled climates are possibly similar (as the drainage areas are relatively small). To some extent, the topography difference between these two basins is partly responsible for the local various trends and distribution changes in extreme precipitation. Furthermore, the location of mountain chains with respect to dominant wind direction is another possible factor causing extreme precipitation changes. For example, although basin #12 is located in East Asia facing the western Pacific, the Changbai Mountains along the east edge of the basin could block water vapor transport from the western Pacific to the basin, and possibly contribute to the downward trend in extreme precipitation. It should be noted that the meteorological stations employed in this study are mostly located in North America, Eurasia and other economically developed regions. In South America and Africa, however, there are relatively few stations available for our analysis. Such an unavoidable problem also exists in previous studies [45,46] and some attempted to solve the problem by gridding the station data, although the gridded data precision in regions with sparse stations might be compromised [47,48]. We only selected two major river basins in South America and Africa, i.e., the Amazon and Nile basins regardless of other basins such as the Congo and Parana basins. However, we found no significant trends or changes regarding RX1day, RX3day and RX5day in either the Amazon or Nile basin. Taye and Willems (2012) found that extreme precipitation in the Blue Nile basin, one of the major source basins of the Nile, shows a particular variation pattern [49]. The 1980s had statistically significant negative anomalies compared to the basic period of 1964–2009, and the 1960s–1970s and 1990s–2000s had less significant positive anomalies; however, no consistent trend exists after then [49]. The inconsistent Water 2020, 12, 1527 10 of 12 trends in their study somewhat support our findings. For the Amazon basin, Da Silva et al. (2019) stated that most of the extreme precipitation indices presented insignificant trends in the Amazon basin, again in line with our results [50]. Therefore, although the data for South America and Africa are limited in the current study, the statistical results in these regions are reasonable and convincing. Overall, our study could contribute to the scientific community as well as local public policymakers, by providing a better understanding and more details on changes in extreme precipitation. These insights can support wiser public policies to mitigate the effects of these changes. Future work should also be conducted regarding the possible drivers of extreme precipitation across different river basins.

5. Conclusions In this study, the trends and frequency distribution changes in RX1day, RX3day and RX5day over 30 river basins around the world during 1960–2011 were examined, and the main findings can be summarized below. (1) Approximately a quarter of the basins showed significant upward trends in RX1day, RX3day and RX5day for the period of record, particularly for basins #15 and #16 in the Middle East, where dramatic increases were observed in the recent decade. In contrast, basin #12 in East Asia has experienced significant declines in RX3day and RX5day. (2) Remarkable changes in the frequency distributions of RX1day, RX3day and RX5day are found in one third of the basins, such as basins #1, #5 and #14, where the indices have shifted toward larger amounts, suggesting intensification of extreme precipitation. Larger ranges of RX1day, RX3day and RX5day over basins #15 and #16 are also found for 1985–2011 relative to 1960–1984, suggesting that extreme precipitation became more extreme in recent decades. In addition, some basins have experienced diminished extreme precipitation, such as RX3day over basin #18 and RX5day over basin #13. Our study is expected to provide a better understanding and more details on changes in extreme precipitation across different river basins, contributing to the scientific community and policymakers in terms of river flood managements and environment protection.

Author Contributions: X.F. wrote the original draft. Z.W. provided conceptualization. X.W. provided supervision and directed the study. J.Y. provided data curation and interpretation. S.Q. And J.Z. designed figures and tables. All authors have read and agreed to the published version of the manuscript. Funding: The research is financially supported by the China Postdoctoral Science Foundation (2019M662919), the National Natural Science Foundation of China (51879107, 51709117), the Guangdong Basic and Applied Basic Research Foundation (2019A1515111144), and the Water Resource Science and Technology Innovation Program of Guangdong Province (2020-18). Acknowledgments: The authors wish to express their gratitude to all authors of the numerous technical reports used for this paper. Conflicts of Interest: The authors declare no conflict of interest.

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