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Comparison of Floods Driven by Tropical Cyclones and Monsoons in the Southeastern Coastal Region of China

WEIWEI LU,HUIMIN LEI,WENCONG YANG,JINGJING YANG, AND DAWEN YANG State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

(Manuscript received 2 January 2020, in ﬁnal form 29 April 2020)

ABSTRACT

Increasing evidence indicates that changes have occurred in heavy precipitation associated with tropical cyclone (TC) and local monsoon (non-TC) systems in the southeastern coastal region of China over recent decades. This leads to the following questions: what are the differences between TC and non-TC flooding, and how do TC and non-TC flooding events change over time? We applied an identification procedure for TC and non-TC floods by linking flooding to rainfall. This method identified TC and non-TC rainfall–flood events by the TC rainfall ratio (percentage of TC rainfall to total rainfall for rainfall–flood events). Our results indicated that 1) the TC rainfall–flood events presented a faster runoff generation process associated with larger flood peaks and rainfall intensities but smaller rainfall volumes, compared to that of non-TC rainfall–flood events, and 2) the magnitude of TC floods exhibited a decreasing trend, similar to the trend in the amount and frequency of TC extreme precipitation. However, the frequency of TC floods did not present obvious changes. In addition, non-TC floods decreased in magnitude and frequency while non-TC extreme precipitation showed an increase. Our results identified significantly different characteristics between TC and non-TC flood events, thus emphasizing the importance of considering different mechanisms of floods to explore the physical drivers of runoff response. Also, our results indicated that significant decreases occurred in the magnitude, but not the frequency, of floods induced by TC from the western North Pacific, which is the most active ocean basin for TC activity, and thus can provide useful information for future studies on the global pattern of TC-induced flooding.

1. Introduction such as in the United States (Knight and Davis 2009; Kunkel et al. 2010; Barlow 2011), Australia (Lavender With increasing global mean temperature, more in- and Abbs 2013; Villarini and Denniston 2016), the tensified and frequently extreme precipitation events will Korean Peninsula (Kim et al. 2006), and China (Ren occur along the southeastern coastal region of China in et al. 2006, 2007; Chang et al. 2012; Su et al. 2015). the future (Guo et al. 2018). With socioeconomic devel- However, few researchers have focused on changes in opment, the number of people and the value of assets in coastal flood behavior (Villarini et al. 2014a; Aryal et al. the urban coastal regions have increased. Widespread 2018; Barth et al. 2018). Precipitation is a major driver torrential floods caused by intense extreme precipitation of flooding and hence of the associated losses (Pielke events contribute significantly to casualties and economic and Downton 2000; Hundecha and Merz 2012); how- costs. Therefore, analyzing the characteristics and vari- ever, observation evidence provided by some studies ability in floods is essential for designing strategies to (Small et al. 2006; Ivancic and Shaw 2015) reveals that adapt to potential changes in coastal flood risk. the upward trend in extreme rainfall amounts or in- Many previous studies have assessed variability in tensity is not always associated with increasing flood extreme precipitation in coastal areas around the globe, magnitudes. The possible mechanisms that contribute to decoupling between the trends in flood magnitude Supplemental information related to this paper is available and precipitation may be decreases in antecedent soil at the Journals Online website: https://doi.org/10.1175/JHM-D- moisture, earlier snowmelt, increases in canopy storage 20-0002.s1. capacity induced by higher temperatures and changing rainstorm characteristics (such as decreasing storm Corresponding author: Dawen Yang, [email protected] extent and shorter storm duration) (Sharma et al. 2018).

DOI: 10.1175/JHM-D-20-0002.1 Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/25/21 08:31 PM UTC 1590 JOURNAL OF HYDROMETEOROLOGY VOLUME 21

Moreover, artificial facilities, such as constructed reservoirs, of 500 km is limited and some severe TC-induced flood can also contribute to inconsistency in changes in flood mag- events cannot be detected. For example, predecessor nitude and extreme precipitation (Batalla et al. 2004). rain events (PREs), which are typically located approxi- In the southeastern coastal region of China, precipi- mately 1000 km away from TCs were demonstrated to be tation is composed of TC rainfall caused by landfalling triggered by TCs (Galarneau et al. 2010). Since the dis- tropical cyclones in the western North Pacific Ocean tances between the TC and location of flooding were and monsoon (non-TC) rainfall associated with the East larger than 500 km, some severe flooding events caused Asian monsoon (Chou et al. 2009). In our study, TC by PREs over the midwestern United States (Rowe and and non-TC rainfall is defined as the situation in which Villarini 2013) cannot be detected by Villarini’s method rainfall in a catchment is dominantly caused by TC (Villarini and Smith 2010), thus underscoring the fact that and non-TC, respectively. When rainfall in a catchment TC identification based solely on the distance between is approximately equally caused by TC and non-TC, the gauge location and the TC center is not appropriate. this situation is called ‘‘mixed rainfall,’’ and the flood- Besides, the identification method of non-TC floods does ing caused by the mixed rainfall is called the ‘‘mixed not exist. flooding.’’ However, our objective is to compare the TC Due to these limitations, we improved the identifica- and non-TC flooding. Hence, the mixed flooding is not tion procedure of TC flood events by linking flooding to analyzed in our study. In southeastern coastal China, TC rainfall. The improved procedure can also extract non- rainfall accounts for 20%–40% of yearly total precipi- TC flood events. The main objective of this study is to tation (Ren et al. 2006) and non-TC rainfall constitutes compare the characteristics of TC and non-TC flood at least 50% of yearly total precipitation(Day et al. events and examine changes in TC and non-TC floods in 2018). TC extreme rainfall accounts for 50%–70% of the the southeastern coastal region of China. This study is total extreme precipitation volume (i.e., larger than the organized as follows. Section 2 presents the study area rainfall value of the 95th percentile) on an interannual and data. Section 3 details the methods used to compare scale (Wu et al. 2007; Chang et al. 2012). Although the TC and non-TC flooding. Section 4 describes the per- TC extreme precipitation has been analyzed in many centage of TC and non-TC extreme precipitation and previous studies (Chang et al. 2012; Su et al. 2015; Gu floods, the hydrographic differences between TC and et al. 2017; Zhang et al. 2018), only Chang et al. (2012) non-TC rainfall–flood events and the trends in TC and Su et al. (2015) calculated the trends in the TC and and non-TC extreme precipitation and floods. Finally, non-TC extreme precipitation amount and revealed that section 5 compares the results with studies in the the annual TC extreme precipitation amount showed southeastern coastal United States. a slight decline, but the annual non-TC extreme precipitation amount showed a large increase. However, 2. Study area and data the hydrographic differences between TC and non-TC flooding and variability in TC and non-TC flooding re- This study selected 19 catchments in areas where the main unexplored. ratio of the TC-induced rainfall to the total rainfall was When assessing the variability in TC and non-TC greater than 10% in the coastal region of southeastern flooding, TC and non-TC flood events must be identi- China, including the coastal area of the Chinese mainland fied. The identification method of TC floods was first and Hainan Island (Fig. 1) (defined as the region within proposed by Waylen (1991), who considered flood 1068–1208Eand188–328N), according to the analysis of events to be caused by a TC if the discharge station was spatiotemporal variation in typhoon rainfall over South within a 3-km-radius circle centered on the TC and the China (Lee et al. 2010). All 19 catchments are located in flood peak occurred within 7 days after the occurrence the humid subtropical monsoon climate zone. The mean of the TC. Later, many studies modified the circle radius annual rainfall ranges from 1200 to 2800 mm and heavy parameter in Waylen’s identification criteria (Villarini rainstorms are the major cause of floods in this region. and Smith 2010; Villarini et al. 2014a; Zhu et al. 2015). The rainfall in these catchments is produced by two dis- The circle radius parameter reflects the spatial extent of tinct systems: the East Asian monsoon and TCs in the rainfall associated with TCs (Villarini and Smith 2010, North Pacific Ocean. The ratio of the TC rainfall to 2013). This parameter is usually set to 500 km in studies the total rainfall ranges from 10% to 50% in this region. in the United States (Villarini and Smith 2010, 2013; The ratio on Hainan Island is higher than that in the Villarini et al. 2014a; Aryal et al. 2018; Zhu et al. 2015), coastal area of the Chinese mainland (Chang et al. 2012). and the rationality of the value was demonstrated by Nineteen hydrological stations are available within Kimball and Mulekar (2004), Dare et al. (2012), and the study catchments (Table 1). The daily river dis- Villarini et al. (2014b). However, the distance threshold charge data from 1960 to 2015 were collected from the

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FIG. 1. Study catchments with the hydrological stations.

Hydrological Yearbooks published by the Information available period of tropical cyclone track data was from Center of Ministry of Water Resources and collected 1960 to 2015, and the temporal resolution was 6 h (Ying by the Tsinghua University Library. The daily river dis- et al. 2011). charge data in several years were missing, which may affect the trend detection. Hence, we applied the approach proposed by Slater and Villarini (2017) to an- 3. Methodology alyze the influence of missing data. The applied method a. Identification of TC and non-TC rainfall–runoff on the influence of missing data on trend detection is events provided in the Supplementary material. Digital elevation data with a resolution of 90 m were downloaded Figure 2 shows the three main steps in the identifica- from the Shuttle Radar Topography Mission (SRTM) tion procedure. First, based on the daily basin-average database (Jarvis et al. 2008). Daily precipitation data rainfall and runoff time series, we combined the POT from 1960 to 2015 were from the 0.25830.258 China approach (Smith 1984) with the HydRun package to Gauge-Based Daily Precipitation (CGDPA) product extract rainfall–flood events (named ‘‘POT rainfall– (Shen et al. 2014). The catchment boundaries were flood events’’ hereafter) with flood peaks that exceeded extracted from the DEM, and the daily basin-average a ‘‘threshold for flood events.’’ Subsequently, the ob- rainfall was calculated based on the catchment bound- jective synoptic analysis technique (OSAT) method, aries and CGDPA. To be consistent with the catchment proposed by Ren et al. (2006) was applied to separate resolution, the 0.25830.258 precipitation data were re- the gridded precipitation time series into TC rainfall and sampled to a 1-km grid using the nearest neighbor as- non-TC rainfall (section 2). Because of its convenience signment in ArcGIS Software. The basic information on and high accuracy, this method has been widely used in tropical cyclones, including the central location, maxi- China to distinguish TC precipitation (Wang et al. 2008; mum sustained wind and minimum sea level pressure, Chang et al. 2012; Yang et al. 2018; Wang et al. 2019). were collected from the Shanghai Typhoon Institute Finally, according to the ratio of TC rainfall to total (STI) of the China Meteorological Administration. The rainfall from the beginning of the rainfall event to the

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TABLE 1. Basic characteristics of the study catchments and hydrological stations.

Drainage Mean annual Data Region Catchment name Gauge station area (km2) rainfall (mm) length (years) Missing years Coastal area of Lin River Bozhiao 2475 1635 50 2000–05 China mainland Ou River Hecheng 13 500 1745 49 1999–2005 Jiao River Baita 3270 1949 54 2000, 2004 Huotong River Yangzhongban 2082 1616 54 2000, 2004 Min River Yongtai 4034 1649 50 1962, 1983–84, 1988, 2000, 2004 Jin River Shilong 5060 1650 44 1967–72, 2000–05 North River of Punan 8490 1800 50 2000–05 Jiulong River West River of Zhengdian 3419 1400 49 1985, 2000–05 Jiulong River Zeng River Qinlinzui 2866 2098 34 1960–63, 1988–2005 East River Heyuan 15 750 1750 34 1960–63, 1988–2005 Rong River Dongqiaoyuan 2016 1200 34 1960–63, 1988–2005 Moyang River Shuangjie 4345 2199 34 1960–63, 1986–2005 Jian River Huazhou 6151 1883 34 1960–63, 1988–2005 Nanliu River Changle 6645 1700 52 1998, 999, 2000, 2002 Qin River Luwu 1400 1658 52 1998, 1999, 2000, 2002 West River Ningming 4281 1370 52 1998, 1999, 2000, 2002 Hainan Island Nandu River Longtang 6841 1929 34 1960–63, 1988–2005 Wanquan River Jiaji 3236 2800 34 1960–63, 1988–2005 Changhua River Baoqiao 4634 1530 34 1960–63, 1988–2005

flood peak time in one POT rainfall–flood event, the an appropriate return threshold of 0.1 was set by TC-induced and non-TC-induced rainfall–flood events performing tests with values between 0.05 and 0.3 were identified among the POT rainfall–flood events according to Tang and Carey (2017), and it satisfied (section 3). the constraint condition. 3) The rainfall time series is separated into rainfall 1) EXTRACTION OF POT RAINFALL–FLOOD events by a rainless period whose duration exceeds EVENT the threshold of a rainless period (Fig. 3c), which is The POT approach and MATLAB toolbox for rainfall– set as 1 day. flood analysis named HydRun developed by Tang and 4) Rainfall events are attributed to the identified runoff Carey (2017) were combined to separate POT rainfall– events by finding the rainfall events that overlap on flood events based on daily basin-averaged rainfall and the search window of one runoff event (Fig. 3d). The runoff time series. This approach consists of four main search window parameter (n) was set to 2 days, which steps: base flow separation, runoff event identification, ensures that all identified runoff events have attrib- rainfall event identification, and rainfall to runoff events uted rainfall events. POT rainfall–flood events that attribution (Fig. 3). occur two times per year on average were selected. 1) Base flow separation separates streamflow time se- 2) SEPARATION OF TC AND NON-TC RAINFALL ries into quick and base flows (Fig. 3a). 2) A local-minimum method and return threshold are Based on the TC dataset and daily precipitation data used to identify runoff events based on quick flows with a resolution of 1 km, the OSAT method was applied (Fig. 3b). In this step, two parameters, namely, the to classify the gridded precipitation on each day as either peak threshold and return threshold, should be set TC or non-TC rainfall. This method is used because in HydRun. We set the peak threshold to make sure it considers two TC rainfall generation mechanisms that two flood events per year per average can be (TC circulation rainfall and remote rainfall) whereas chosen. The return threshold is set to satisfy the the widely used Villarini’s method (Villarini and constrain condition that two flood peaks of adjacent Smith 2010) of TC precipitation separation only con- flood events are larger than the logarithmic value of siders the TC circulation rainfall. In fact, TC rainfall is the drainage area and follow the guidelines for de- normally classified into two types: TC circulation rainfall termining flood flow frequency established by the and remote rainfall resulting from the interaction be- U.S. Water Resources Council (1981). In this study, tween the TC circulation and other weather systems

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FIG. 2. Schematic diagram of the identiﬁcation method.

outside of the TC region (Chen et al. 2010; Xing et al. 3) SEPARATION OF TC AND NON-TC 2016; Ren et al. 2006). TC circulation rainfall represents RAINFALL–FLOOD EVENTS the main rain shield of TCs, and remote rain was defined We first calculated the rainfall ratio for POT rainfall– as the precipitation occurring outside of the main body of flood events, which was defined as the ratio of TC the TC but having some physical relationship with the TC rainfall in the period T to total rainfall in the period (Xing et al. 2016). flood T (the definition of T is given in Fig. 4). Then, we In the OSAT method, the TC circulation rainfall was flood flood used k-means clustering (Visual Numerics 1997), an identified by determining whether the grid is within a unsupervised clustering technique to separate the POT D circle with a variable radius of 0 from the TC center. rainfall–flood events into three groups based on the TC remote rainfall was identified by checking whether rainfall ratio by assuming that the POT rainfall–flood the grid is within a circle with a variable radius of D1 events can be classified into three types generated by and belongs to the TC rain belts. TC rain belts were ‘‘non-TC rainfall,’’ ‘‘non-TC and TC rainfall (mixed),’’ obtained by determining independent rain belts by and ‘‘TC rainfall’’ (Vormoor et al. 2016). The events in grouping precipitation based on the spatial structure of the groups with the largest and smallest mean rainfall the gridded precipitation and then identifying TC rain ratio were classified as TC-induced and non-TC-induced belts from independent rain belts according to the distance rainfall–flood events, respectively. The events in the re- between the TC center and the weighted-precipitation maining group were considered mixed events and not center of the rain belts. The variable parameters D0 and analyzed in the trend analysis. D were set as piecewise functions of the TC maximum 1 b. Identification of the basin-average TC and wind speed rather than a fixed value (Table 2). The non-TC extreme precipitation piecewise functions of the two variable parameters were picked out from 12 alternative function schemes designed We calculated the daily basin-average rainfall, TC in Ren et al. (2007). rainfall, and non-TC rainfall time series separately by

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FIG. 3. Overview of the rainfall–runoff event extraction procedure. averaging the daily rainfall, TC rainfall, and non-TC to the daily basin-average extreme precipitation, TC ex- rainfall in all grids within the catchment, respectively. treme precipitation and non-TC extreme precipitation, the To extract daily basin-average extreme precipitation, we ratio of the daily basin-average TC extreme precipitation selected the 95th percentile of the nonzero daily basin- to the daily basin-average extreme precipitation was cal- average rainfall as the threshold for extreme precipita- culated. Based on this ratio, we used k-means clustering tion. Then the daily basin-average rainfall exceeding this to categorize the daily basin-average extreme precipitation threshold was selected as the daily basin-average ex- into three groups. The daily basin-average extreme pre- treme precipitation. Subsequently, the daily basin-average cipitation values in the groups with the largest and smallest TC extreme precipitation and non-TC extreme precip- mean ratios were classiﬁed as TC-induced and non- itation on the days with basin-average extreme precipi- TC-induced extreme precipitation, respectively. The re- tation were extracted from the basin-average TC rainfall maining extreme precipitation was considered mixed and non-TC rainfall time series, respectively. According rainfall and not analyzed in the trend analysis.

TABLE 2. Values of the parameters D0 and D1 as a function of the TC maximum wind speed; Dmin is the minimum distance between the TC center and the mainland of China, and Dmin 5 0 at landfall or on the land.

TC’s maximum sustained wind 21 speed (m s ) D0 (km) D1 (km)

TCs far away from China (Dmin . 300 km) u , 17.2 (tropical depression) 200 600 17.2 # u , 24.4 (tropical storm) 300 800 24.4 # u , 32.6 (strong tropical storm) 400 1000 u $ 32.6 (typhoon and above) 500 1100

TCs close to China (Dmin # 300 km) u , 17.2 (tropical depression) 300 800 u $ 17.2 (tropical storm and above) 500 1100

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intensity, 10-day antecedent rainfall, and base flow at the beginning of the event: 1) The event peak time is the occurrence date of the flood peak in a rainfall–flood event and it represents the seasonality of flooding. To visualize the seasonality of TC and non-TC flooding, we calculated the percentage of floods per month and plotted the seasonality curves. 2) The event runoff coefficient is the ratio between the quick runoff volume (mm) and rainfall volume (mm) in a rainfall–flood event and it characterizes the por- tion of rainfall released from water storage as runoff. 3) The event time scale is the ratio between the quick runoff volume (mm) and the magnitude of flood peak 2 (mm day 1) and it characterizes the shape of the FIG. 4. Illustration of the time characteristics for a POT rainfall– hydrograph and the duration of an event, and is an flood event; t is the date of flood peak, t is the beginning peak start_rain indicator of catchment flashiness and the importance of rainfall, and Tflood is the time period from tstart_rain to tpeak. of fast runoff generation processes. A short event time scale shows that the fast runoff generation pro- c. Indicators describing the variability of TC and cess prevails. non-TC flooding 4) The event rise time characterizes the duration (day) We analyzed the variability of TC and non-TC flooding from the beginning of the rainfall event to the day based on two aspects: differences in event characteristics of the flood peak, and it is normalized by the total between TC and non-TC rainfall–flood events and long- duration (day) of the event and shows how fast the term changes in the magnitude and frequency of TC- peak is reached. A short event rise time represents a induced and non-TC-induced flooding and extreme high contribution of fast runoff to the runoff gener- precipitation. ation process. To describe the differences in the characteristics be- 5) The normalized event peak is the maximum total 2 tween TC and non-TC rainfall–flood events, nine indi- discharge (mm day 1) of the identified runoff event 2 cators of an rainfall–flood event were calculated based normalized by the long-term average flow (mm day 1) on the identified TC and non-TC rainfall–flood events derived from the observed discharge time series and according to the study by Tarasova et al. (2018) (Table 3). it represents the magnitude of the runoff event. These indicators were event peak time, event runoff 6) The rainfall volume is the sum of the rainfall volume coefficient, event time scale, event rise time, normalized (mm) in the period prior to the date of the flood peak event peak, rainfall volume and maximum rainfall in a rainfall–flood event.

TABLE 3. Rainfall–runoff event characteristics studied for all catchments.

Type Characteristics Remarks Runoff characteristics Event peak time (dimensionless) Occurrence date of flood peak in a rainfall–runoff event Event runoff coefficient (dimensionless) Ratio between the volumes of quick runoff and rainfall Event time scale (days) Ratio between quick runoff volume (mm) and peak 2 discharge (mm day 1) Event rise time (dimensionless) Duration (day) from the beginning of the rainfall event until the day of flood peak, normalized by the total duration (day) of the event Normalized peak discharge Maximum event flow normalized by the long-term (dimensionless) average flow Rainfall characteristics and Rainfall event volume (mm) Sum of rainfall volumes in the period prior to the date of antecedent wetness condition flood peak in a rainfall–runoff event 2 Maximum rainfall intensity (mm day 1) Maximum rainfall intensity in the period prior to the flood peak date during a rainfall–runoff event 10-day antecedent rainfall (mm) 10-day sum of rainfall before a rainfall–runoff event Base flow at the beginning of the event Base flow in the day preceding the runoff event 2 (mm day 1)

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TABLE 4. Extreme precipitation and ﬂood indicators studied for all catchments.

Extreme precipitation and flood indicators Remarks Extreme precipitation amount (mm) Annual basin-average rainfall above threshold: 95th percentile of the rainfall distribution Extreme precipitation frequency (day) Annual rainy days above threshold: 95th percentile of the rainfall distribution 2 Peak-over-threshold flood magnitude (m3 s 1) Discharge peaks above threshold; on average, 2 events per year Peak-over-threshold flood frequency (day) Annual number of discharge peaks above threshold; on average, 2 events per year

7) The maximum rainfall intensity is the maximum rainfall ratio threshold for TC flooding was larger than 0.56 and that 2 intensity (mm day 1) in the period prior to the date of for non-TC flooding was smaller than 0.3, and the ratio the flood peak in a rainfall–flood event. Both the rain- threshold for TC extreme precipitation was larger than 0.76 fall volume and maximum rainfall intensity describe the and that for non-TC extreme precipitation was smaller than rainfall characteristics in a rainfall–flood event. 0.3. Figure 6 shows the percentages of identified TC, non-TC 8) The 10-day antecedent rainfall is the sum of the and mixed extreme precipitation to total extreme precipi- rainfall volume (mm) over the 10 days prior to a tation. The percentages of TC extreme precipitation ranged rainfall–flood event. from 12% to 50%, while that of non-TC extreme precipi- 9) The base flow at the beginning of the event is the base tation ranged from 49% to 88%. The lowest percentage of 2 flow (mm day 1) on the day preceding the runoff TC extreme precipitation occurred in the coastal area of event. Both the 10-day antecedent rainfall and the the Chinese mainland, while thehighestvalueoccurredin base flow at the beginning of the event describe the Hainan Island. The average percentages of TC and non-TC antecedent wetness states. to extreme precipitation were 24% and 76%, respectively, and TC extreme precipitation accounted for a smaller The abovementioned metrics were calculated for both proportion of the total extreme precipitation than that TC and non-TC rainfall–flood events. Then the Wilcoxon of non-TC extreme precipitation in all catchments. test was used to examine the statistical significance of the With respect to flooding, the percentage of TC floods mean differences between the non-TC and TC metrics ranged from 9% to 47%, while that of non-TC floods ranged except for the event peak time at a significance level from 18% to 83% (Fig. 6). On average, the percentages of of 0.05. TC and non-TC floods were 28% and 53%, respectively. TC To detect long-term changes in the magnitude and floods occupied larger proportions in Hainan Island, with frequency of TC-induced and non-TC-induced flooding an average value of 45% of the total floods, while non-TC and extreme precipitation, four extreme precipitation floods dominated in the southeastern coast of the Chinese and flood indicators were calculated based on the iden- mainland, with an average value of 25% of the total floods. tified TC and non-TC rainfall–flood events and the The percentages of TC and non-TC floods were identified basin-average TC and non-TC extreme pre- closely related to the percentages of TC and non-TC cipitation (Table 4). The Mann–Kendall (MK) test (Mann 1945; Kendall 1975) was used to detect long-term trends in the magnitudes of flood peaks and extreme precipitation. Because the time series of the frequency of flood and extreme precipitation were discontinuous count values, we used a Poisson regression to evaluate the trends in the frequency, following the studies of Villarini and Smith (2013). The detected trends in four extreme precipitation and flood indicators were re- ported at the 5% significance level.

4. Results a. Percentages of TC and non-TC extreme precipitation and ﬂooding

Figure 5 shows the ratio threshold for TC and non-TC FIG. 5. Ratio threshold for non-TC and TC ﬂood/extreme extreme precipitation and ﬂooding. In all catchments, the precipitation.

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FIG. 6. Percentage of identiﬁed (a) TC, (b) non-TC, and (c) mixed extreme precipitation and POT ﬂood events.

extreme precipitation among the catchments (Spearman’s events per month in the coastal area of the Chinese rank correlation coefficient between the contribution of mainland and Hainan Island are shown in Fig. 7. The TC rainfall and flood events was equal to 0.91, while that highest percentages of TC floods appeared in August in of non-TC rainfall and floods was equal to 0.9 at the 5% the coastal area of the Chinese mainland. On Hainan significance level). The mean percentage of TC floods Island, however, the highest values appeared in was significantly larger than that of TC extreme pre- October. The percentages of TC floods were larger than cipitation, whereas that of non-TC floods was significantly 10% from June to October in both the coastal area of the smaller than that of non-TC extreme precipitation. In Chinese mainland and Hainan Island. The highest per- addition, the percentage of mixed events was nearly zero centages of non-TC floods occurred in July in the coastal for extreme precipitation but was much higher for floods area of the Chinese mainland and in October on Hainan than extreme precipitation. This finding suggests that Island. Non-TC floods accounted for more than 10% daily extreme precipitation in the whole catchment be- of the events from May–August in the coastal area of longed to a single type of extreme precipitation, but the Chinese mainland, and from August–November on many flood events that lasted for several days belonged Hainan Island. to the compound effects of both rainfall types. Figure 8 shows comparisons of the remaining eight indicators between TC and non-TC rainfall–flood events b. Hydrographic characteristics of TC and non-TC over the southeastern coastal China. Our results showed rainfall–flood events obvious differences between TC and non-TC rainfall– The catchments on Hainan Island had a different flood events. The runoff in TC rainfall–flood events pattern from those in the coastal area of the Chinese exhibited a significantly smaller event time scale [12 (63%) mainland. The percentages of TC and non-TC flood of the stations; 12 means the number of stations showing

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10 (53%) and 5 (26%) of the stations presented significant increases in the non-TC extreme precipitation frequency and total precipitation frequency, respectively, and no station showed a significant decreasing trend. The TC extreme precipitation frequency decreased significantly, with an average significant trend 2 of approximately 20.003 days yr 1. The non-TC and total extreme precipitation frequencies increased significantly, with average significant trends of 0.005 and 2 0.003 days yr 1, respectively. The impact of missing years on the trend analysis of flood magnitude and frequency is shown in Table S1 in the online supplemental material. Table S1 shows that the trend values of flood magnitude in 15 (79%) of the stations and flood frequency in 13 (68%) of the stations were reliable. Table 6 shows the trends in the magnitude and frequency of the TC, non-TC, and total floods. The FIG. 7. Percentage of numbers of TC and non-TC floods per month in the coastal area of the Chinese mainland and Hainan unreliable trend values due to the missing years are Island. marked in Table 6. The results show that 3 (16%) of the stations exhibited reliably significant decreasing trends significantly smaller values and 63% represents the per- in the magnitude of TC floods, only 2 (11%) of the sta- centage of these stations], larger normalized peak dis- tions showed reliably significant decreasing trends in the charge [10 (52%) of the stations], and shorter event rise magnitude of non-TC floods, and 1 (5%) of the stations time [8 (42%) of the stations] than that in non-TC rainfall– showed reliably significant decreasing trends in the mag- flood events. Furthermore, the rainfall in TC rainfall–flood nitude of total floods. No stations showed a significant in- events was characterized by a larger maximum rainfall creasing trend in the magnitude of the TC floods. intensity [15 (79%) of the stations] but a smaller rainfall Additionally, none of the stations showed significant event volume [8 (42%) of the stations] than that in non-TC trends in the frequency of the TC floods, 2 (11%) sta- rainfall–flood events. However, the event runoff coeffi- tions reflected reliably significant decreasing trends in cient and antecedent wetness condition indicators, in- the frequency of the non-TC floods, while 4 (21%) of the cluding the 10-day antecedent rainfall and base flow at the stations reflected reliably significant decreasing trends in beginning of the event, exhibited no obvious differences the frequency of the total floods. between TC and non-TC rainfall–flood events. c. Long-term trends in magnitude and frequency of 5. Discussion TC and non-TC extreme precipitation and floods This study found that the percentage of TC floods was The trends in the TC, non-TC, total extreme precipita- significantly larger than that of TC to extreme precipi- tion amounts and frequencies are given in Table 5.With tation in southeastern coastal China. This finding is respect to the trends of extreme precipitation amount, 4 different from the pattern observed in the southeastern stations (21%) exhibited significant decreasing trends in United States, where the percentage of TC floods was TC extreme precipitation amount, and no station showed lower than that of extreme precipitation (Aryal et al. a significant increasing trend. In addition, 10 (53%) and 2018). In southeastern coastal China, non-TC extreme 7 (37%) of the stations reflected statistically significant precipitation frequently occurred from April to July prior increasing trends in the non-TC extreme precipitation to TC precipitation season (Su et al. 2015), and it may amount and total precipitation amount, respectively, and have increased the soil water storage. Therefore, in ad- no station showed a significant decreasing trend. The TC dition to flooding events caused by TC extreme precipi- extreme precipitation amount significantly decreased by tation from July to September, additional TC flooding also 2 an average of 20.15 mm yr 1. Non-TC extreme precipi- occurred because of moderate TC precipitation on par- tation showed significant positive trends, with an amount tially saturated areas due to the impact of high antecedent of 1.98 mm on average and 1.79 mm in total per year. soil water (Su et al. 2015). In contrast, in the eastern Furthermore, 2 (11%) of the stations exhibited a sig- United States, extreme precipitation mainly occurs in the nificant decrease in TC extreme precipitation frequency, summer season (from June to September) in association and no station presented a significant increase. Moreover, with frequent TCs (Nogueira and Keim 2011); however,

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FIG. 8. Boxplots with whiskers from minimum to maximum showing the non-TC and TC rainfall–flood event characteristics for all catchments. The boxes show the interquartile ranges of the values, and the short bold lines show the medians of the values. The asterisks indicate distributions with a statistically different median (Wilcoxon test, p value , 0.05). Outliers are not presented. most of the flooding (as much as 80%) occurs in the cold southeastern coastal China, which is in inconsistent with season (from October to March) and is associated with the pattern in the southeastern United States, where winter–spring extratropical cyclone systems (Villarini and increasing trends in TC extreme precipitation amount Smith 2010; Villarini 2016)ratherthanTCs. and frequency occurred but obvious changes in TC flood Our study also revealed the consistency of the de- magnitude and frequency did not occur (Knight and crease in TC floods (flood magnitude) and TC extreme Davis 2009; Kunkel et al. 2010; Aryal et al. 2018). The precipitation (precipitation amount and frequency) in pattern difference may be due to that the relatively wet

TABLE 5. Trends in the magnitude and frequency of TC, non-TC and total extreme precipitation. The numbers in bold indicate that the trend of values is signiﬁcant at the 95% conﬁdence level.

2 2 Extreme precipitation amount (mm yr 1) Frequency of extreme precipitation (days yr 1) Gauge station TC Non-TC Total TC Non-TC Total Bozhiao 1.454 3.162 4.394 0.008 0.012 0.011 Hecheng 20.019 2.953 2.466 20.003 0.007 0.004 Baita 20.256 3.163 2.773 20.003 0.008 0.005 Yangzhongban 0.244 4.300 4.575 0.001 0.009 0.008 Yongtai 20.205 0.690 0.402 0.000 0.001 0.001 Shilong 0.091 0.912 1.037 0.001 0.002 0.001 Punan 20.097 2.505 2.336 0.000 0.007 0.006 Zhengdian 20.027 2.266 2.316 20.001 0.007 0.005 Qinlinzui 20.452 0.318 20.202 20.013 0.000 20.002 Heyuan 21.222 2.194 0.906 20.017 0.007 0.003 Dongqiaoyuan 22.770 2.153 20.614 20.017 0.006 0.000 Shuangjie 21.729 1.367 20.054 20.011 0.004 0.000 Huazhou 20.515 1.464 0.883 20.003 0.003 0.002 Changle 20.128 0.759 0.921 20.001 0.001 0.001 Luwu 0.553 0.616 1.096 0.007 0.000 0.001 Ningming 0.754 0.287 0.977 0.003 0.002 0.002 Longtang 0.932 3.837 4.503 0.000 0.011 0.005 Jiaji 20.614 2.035 1.512 20.005 0.004 0.000 Baoqiao 1.105 2.662 3.807 20.003 0.011 0.004

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TABLE 6. Trends in the magnitude and frequency of TC, non-TC and total floods. The numbers in bold indicate that the trend of values is significant at the 95% confidence level. Asterisks indicate that the trend value may be not reliable due to the missing years according to the criteria (Slater and Villarini 2017).

2 2 2 Flood magnitude (m3 s 1 yr 1) Flood frequency (days yr 1) Gauge station TC Non-TC Total TC Non-TC Total Bozhiao 20.250 210.333 20.366 0.002 20.014 20.008 Hecheng 283.724 222.546 230.104 20.003* 20.016* 20.014* Baita 222.402 0.161 25.941 20.001 20.007 20.008 Yangzhongban 214.154 14.320 2.221 20.015 20.020 20.022 Yongtai 212.362 5.909 1.086 20.007 20.029 20.021 Shilong 243.569 212.113 219.635 20.015* 20.013* 20.015* Punan 242.782 25.912 213.091 20.021 20.009 20.014 Zhengdian 0.552 1.528 20.599 20.004 20.012 20.010 Qinlinzui 9.595* 3.516* 8.788* 20.016* 20.009* 20.008* Heyuan 19.577* 24.303* 2.210* 0.010* 20.012* 20.010* Dongqiaoyuan 0.504* 225.583* 219.197* 20.005 0.010 20.001 Shuangjie 211.247 10.034 4.810 0.006 20.002 20.001 Huazhou 26.477 21.887 21.930 0.013 20.003 0.002 Changle 7.921 5.477 7.121 0.024 0.006 0.008 Luwu 215.936 24.103 26.378 0.000 20.009 20.006 Ningming 24.096 0.820 21.004 0.011 0.005 0.005 Longtang 14.270 226.224 10.762 0.002 0.018 0.004 Jiaji 28.543 21.947 0.883 20.007* 20.016* 20.013* Baoqiao 211.654* 290.656* 248.648* 20.025* 20.030* 20.023* soil moisture condition during the TC flood season in thus to provide insights into the different physical drivers of southeastern coastal China (Suon et al. 2019) but rela- TC and non-TC flood response (Blöschl et al. 2013). Such tively dry conditions in the southeastern United States work can aid in the interpretations of the heterogeneity of (Gao et al. 2006). Catchments with a wet soil moisture flood generation processes and facilitate a better under- condition have a limited capacity to regulate the runoff standing of how they can be accurately captured by hy- response to rainfall. The decreases in extreme precipi- drological models (Fenicia et al. 2008; Rogger et al. 2013). tation directly lead to decreases in flooding in south- This study separated TC and non-TC floods and an- eastern coastal China. Conversely, increases in extreme alyzed the trends in TC and non-TC floods. Since floods precipitation may not necessarily result in increasing associated with different generation mechanisms may flooding in the southeastern United States during the TC have different magnitudes and thus different frequency flood season (Bennett et al. 2018). distributions, future studies can focus on deriving com- Moreover, our study showed inconsistency in the trend pound distributions that combine TC and non-TC directions between the flooding (decreasing flood magni- floods, which can reduce uncertainty when designing tude and frequency) and extreme precipitation (increasing floods for flood frequency analyses (Fischer et al. 2016; precipitation amount and frequency) events for the non- Yan et al.2019). However, after dividing floods into TC types in southeastern coastal China. This inconsistency various types, the length of the flood time series for each may be attributed to regulation by the high-density res- type is limited, which leads to uncertainty in the flood ervoirs in southeastern China. Before the non-TC rainy frequency analysis. Also, future studies can attribute season, the large storage capacity per area enables reser- trends in floods, which has been a topic of high interest voirs to reduce the flood magnitude when floods occur (Merz et al. 2012; Slater et al. 2015). The attribution (Yang and Lu 2014; Gao et al. 2012). Unfortunately, of flood trends requires assumptions about possible previous studies have not revealed the trends in the drivers. Separation of TC and non-TC floods can pro- magnitude and frequency of non-TC flood events in the vide a better physical basis on which to select the pos- southeastern United States, although an increasing trend sible drivers that are responsible for floods. in the non-TC extreme precipitation (precipitation amount and frequency) has been revealed (Bishop et al. 2019). 6. Conclusions In this study, we found significant different characteristics between the TC and non-TC flood events. In the This study evaluated the different hydrographic char- future, the linkage between event characteristics and acteristics between non-TC and TC rainfall–flood events, climate or landscape features can be further studied, and analyzed the long-term trends in the frequency and

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