Observed Changes in Temperature and Precipitation Extremes Over the Yarlung Tsangpo River Basin During 1970–2017

atmosphere

Article Observed Changes in Temperature and Precipitation Extremes Over the Yarlung Tsangpo River Basin during 1970–2017

Chunyu Liu 1,2 , Yungang Li 1,2,* , Xuan Ji 1,2, Xian Luo 1,2 and Mengtao Zhu 1,2 1 Asian International Rivers Center, Yunnan University, Kunming 650091, China; [email protected] (C.L.); [email protected] (X.J.); [email protected] (X.L.); [email protected] (M.Z.) 2 Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Yunnan University, Kunming 650091, China * Correspondence: [email protected]; Tel.: +86-871-6503-4577

Received: 13 November 2019; Accepted: 12 December 2019; Published: 15 December 2019

Abstract: Twenty-five climate indices based on daily maximum and minimum temperature and precipitation at 15 meteorological stations were examined to investigate changes in temperature and precipitation extremes over the Yarlung Tsangpo River Basin (1970–2017). The trend-free prewhitening (TFPW) Mann–Kendall test and Pettitt’s test were used to identify trends and abrupt changes in the time series, respectively. The results showed widespread significant changes in extreme temperature indices associated with warming, most of which experienced abrupt changes in the 1990s. Increases in daily minimum and maximum temperature were detected, and the magnitude of daily minimum temperature change was greater than that of the daily maximum temperature, revealing an obvious decrease in the diurnal temperature range. Warm days and nights became more frequent, whereas fewer cold days and nights occurred. The frequency of frost and icing days decreased, while summer days and growing season length increased. Moreover, cold spell length shortened, whereas warm spell length increased. Additionally, changes in the precipitation extreme indices exhibited much less spatial coherence than the temperature indices. Spatially, mixed patterns of stations with positive and negative trends were found, and few trends in the precipitation extreme indices at individual stations were statistically significant. Generally, precipitation extreme indices showed a tendency toward wetter conditions, and the contribution of extreme precipitation to total precipitation has increased. However, no significant regional trends and abrupt changes were detected in total precipitation or in the frequency and duration of precipitation extremes.

Keywords: climate extremes; temperature; precipitation; trend analysis; Yarlung Tsangpo River Basin

1. Introduction

Global mean surface temperature rose by approximately 0.85 ◦C during 1880–2012, and warming is projected to continue because of the future emission of greenhouse gases [1,2]. In recent decades, observed climate data and climate models have shown that warming of the climate system leads to increases in extreme weather and climate events, such as heatwaves, cold spells, ﬂoods, droughts, and rainstorms [3–7]. There is general agreement that changes in the frequency or intensity of extreme weather and climate events would have a profound socioeconomic and environmental impact [5,8,9]. For instance, between 1998 and 2017, more than 526,000 people died worldwide and losses of US $3.47 trillion were incurred as the direct result of more than 11,500 extreme weather events [10]. Therefore, understanding changes in climate extremes within the context of global warming is of vital importance to policymakers.

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Temperature and precipitation extremes have been studied both on global [3,6,11,12] and on regional scales [13–17]. Previous studies have found widespread significant changes in temperature extremes associated with warming, however, spatial patterns of changes in extreme precipitation trends appear mixed on regional scales [3,9]. Therefore, regional assessment in various climatic and geographic regions is needed to understand the uncertainties in change trends of extreme climate events. To offer a generally applicable measure of climatic extremes, and to enable interregional comparison and analysis, the Expert Team on Climate Change Detection and Indices (ETCCDI) defined a set of extreme climate indices [18,19]. These indices have been applied widely to facilitate investigation of climate extremes based on observational climate records [13], satellite-based data [20,21], reanalysis data [22], and climate model output [23,24]. The Yarlung Tsangpo River Basin (YTRB), which is located in southern parts of the Tibetan Plateau (TP), is known as the highest great river in the world, with a mean elevation of >4000 m above sea level [25]. It contains vulnerable ecological environments and is sensitive to climate change [26]. For instance, the basin is typically covered by vast areas of snow, glaciers, permafrost, and seasonally frozen soil, all of which are highly sensitive to changes in temperature and precipitation. Thus, the regional hydrological process is expected to change within the context of global warming [27,28]. Moreover, extreme precipitation-induced natural disasters, such as floods, droughts, glacier debris flows, and landslides have become more serious and more frequent since the 1990s [29]. The YTRB is one of the main centers of human economic activity in Tibet [30]; however, it generally has high vulnerability to climate change and relatively low adaptive capacity. Therefore, understanding the variability of climate extremes in the YTRB is of great importance for the sustainable development of Tibet [31]. Many studies have performed analysis of climate extremes over the entire TP or specific localized regions, such as the eastern and central TP [32], western TP [33], southeastern Tibet [29], and the “three-river headwaters” region [34,35]. However, few studies have examined climate extremes over the YTRB [25,36]. Although some studies have analyzed the changes in both the mean temperature and mean precipitation over the YTRB [26,30,31,37], a more detailed analysis of temperature and precipitation extremes is required. Moreover, as climate data accumulate, the changes in climate extremes over the YTRB should be studied further. The objective of this study was to examine 25 climate indices (recommended by the ETCCDI) based on daily maximum and minimum temperature and precipitation at 15 meteorological stations to investigate the spatial and temporal changes in temperature and precipitation extremes over the YTRB during 1970–2017. In addition, the relationship between the extreme climate indices and Asian summer monsoon indices was analyzed. The findings of this study, which enhance the understanding of historical changes in temperature and precipitation extremes, represent a valuable reference regarding the formulation of policies for disaster mitigation and climate change adaptation in the YTRB.

2. Study Area and Data

2.1. Study Area The Yarlung Tsangpo River, also called the Brahmaputra River in India or Jamuna River in Bangladesh, originates from the Chamyungdung glacier at an elevation of 5200 m in central-southern parts of the TP and discharges into the Bay of Bengal after ﬂowing through Bangladesh (Figure1). The length of the river in China is approximately 2229 km with a drainage area of 2.4 105 km2 [25]. × The YTRB is characterized by low air temperature with a large diurnal temperature range but low intra-annual temperature range [38]. The annual air temperature range from 1960 to 2009 was 0.3 − to 8.8 ◦C[39]. Precipitation within the basin varies regionally. Annual precipitation in upstream, midstream, and downstream region was <300, 300–600, and >2000 mm, respectively [26]. Precipitation is mainly concentrated in the wet season (May–September). The main sources of water supplied Atmosphere 2019, 10, 815 3 of 19 Atmosphere 2020, 11, x FOR PEER REVIEW 3 of 19 suppliedto the river to arethe theriver melting are the of melting ice and of snow ice and on thesnow TP on and the precipitation TP and precipitation brought by brought the southwest by the southwestsummer monsoon. summer monsoon.

Figure 1. LocationLocation of of the the study study area area an andd the the meteorological meteorological stations. 2.2. Data Source 2.2. Data Source Daily maximum and minimum temperature and precipitation data were obtained from the Daily maximum and minimum temperature and precipitation data were obtained from the National Meteorological Information Centre of China (http://data.cma.cn/). There are 23 meteorological National Meteorological Information Centre of China (http://data.cma.cn/). There are 23 stations in the YTRB and adjacent areas; however, most of these meteorological stations started meteorological stations in the YTRB and adjacent areas; however, most of these meteorological operations after 1970. For maximum spatial and temporal data coverage, 15 stations with data records stations started operations after 1970. For maximum spatial and temporal data coverage, 15 stations covering 1970–2017 were selected for analysis (Table1). The quality of the data was checked by the with data records covering 1970–2017 were selected for analysis (Table 1). The quality of the data was National Meteorological Information Centre based on a series method. Routine quality control and checked by the National Meteorological Information Centre based on a series method. Routine homogeneity assessment were further performed using the RClimDex (Version 1.0) and RHtestV3 quality control and homogeneity assessment were further performed using the RClimDex (Version programs, respectively (http://etccdi.paciﬁcclimate.org/index.shtml)[15]. 1.0) and RHtestV3 programs, respectively (http://etccdi.pacificclimate.org/index.shtml) [15]. Table 1. Meteorological stations used in this study. Table 1. Meteorological stations used in this study. ID Station Lat ( N) Lon ( E) Elevation (m) Study Periods ID Station Lat (° N) ◦ Lon (° E) ◦ Elevation (m) Study Periods 55437 55437Pulan Pulan 30.28 30.2881.25 81.25 4900 4900 1973–2017 1973–2017 55493 Dangxiong 30.48 91.1 4200 1970–2017 55493 Dangxiong 30.48 91.1 4200 1970–2017 55578 Rikaze 29.25 88.88 3836 1970–2017 55578 55585Rikaze Nimu 29.25 29.43 88.88 90.16 3836 3809 1973–2017 1970–2017 55585 55591Nimu Lasa 29.43 29.6690.16 91.13 3809 3648 1970–2017 1973–2017 55591 55598Lasa Zedang 29.66 29.2591.13 91.76 3648 3551 1970–2017 1970–2017 55598 55664Zedang Dingri 29.25 28.63 91.76 87.08 3551 4300 1970–2017 1970–2017 55680 Jiangzi 28.91 89.6 4040 1970–2017 55664 Dingri 28.63 87.08 4300 1970–2017 55690 Cuona 27.98 91.95 4280 1970–2017 55680 55696Jiangzi Longzi 28.91 28.41 89.6 92.46 4040 3860 1970–2017 1970–2017 55690 55773Cuona Pali 27.98 27.73 91.95 89.08 4280 4300 1970–2017 1970–2017 55696 56202Longzi Jiali 28.41 30.66 92.46 93.28 3860 4488 1970–2017 1970–2017 55773 56227Pali Bomi 27.73 29.8689.08 95.76 4300 2736 1970–2017 1970–2017 56312 Linzhi 29.66 94.33 2991 1970–2017 56202 Jiali 30.66 93.28 4488 1970–2017 56434 Chayu 28.65 97.46 2327 1970–2017 56227 Bomi 29.86 95.76 2736 1970–2017 56312 Linzhi 29.66 94.33 2991 1970–2017 56434The East AsianChayu subtropical monsoon 28.65 index 97.46 (EASMI) and South 2327 Asian subtropical 1970–2017 monsoon index (SASMI), developed by Li and Zeng [40], were used in this study (http://ljp.gcess.cn/dct/page/1). The EASMIThe East was Asian deﬁned subtropical as an area-averaged monsoon index seasonally (EASMI) (JJA) and dynamical South Asian normalized subtropical seasonality monsoon at index850 hPa within(SASMI), the Eastdeveloped Asian monsoon by Li domain and (10Zeng◦–40◦ N,[40], 110 ◦–140were◦ E). Similarly,used in thethis SASMI study was (http://ljp.gcess.cn/dct/page/1). The EASMI was defined as an area-averaged seasonally (JJA) dynamical normalized seasonality at 850 hPa within the East Asian monsoon domain (10°–40° N,

Atmosphere 2019, 10, 815 4 of 19 deﬁned as an area-averaged seasonally (JJAS) dynamical normalized seasonality at 850 hPa within the South Asian domain (5.0◦–22.5◦ N, 35.0◦–97.5◦ E).

3. Extreme Climate Indices and Methodology The 25 indices (recommended by the ETCCDI) selected for use in this study comprised 15 temperature indices and 10 precipitation indices. Detailed definitions of these indices are shown in Tables2 and3. The RClimDex program was used to calculate these indices following data quality control and homogeneity assessment. The Mann–Kendall (MK) test is one of the nonparametric tests widely used for trend analysis in hydrometeorological time series [15,41,42]. However, hydrometeorological time series may frequently display a statistically significant serial correlation. In such cases, the existence of serial correlation will increase the probability that the MK test detects a significant trend [43]. To eliminate the influence of a serial correlation occurring on the MK test, prewhitening has been applied in trend-detection studies of hydrometeorological time series [44]. In this study, a trend-free prewhitening (TFPW) procedure was used prior to applying a trend test. The slope of the trend was computed using the Theil–Sen regression estimator [45,46]. The detailed procedure of the TFPW Mann–Kendall (TFPW-MK) test can be found in the work of Yue et al. [41]. Moreover, the non-parametric Pettitt’s test was applied to assess the phenomenon of abrupt changes in the time series of the climatic data [47]. Pettitt’s test is the most commonly used test for change point detection because it is less sensitive to outliers and skewed distribution and is more sensitive to breaks in the middle of a time series [48].

Table 2. Deﬁnition of the 15 temperature extreme indices used in this study.

Classiﬁcation Abbreviation Index Deﬁnition Units

TNn Minimum Tmin Annual lowest TN ◦C

TNx Maximum Tmin Annual highest TN ◦C Absolute indices TXn Minimum Tmax Annual lowest TX C and DTR ◦ TXx Maximum Tmax Annual highest TX ◦C Diurnal Annual mean diﬀerence between TX DTR C temperature range and TN ◦ Percentage of days when TN < 10th TN10p Cold nights d percentile of 1971–2000 Percentage of days when TX < 10th TX10p Cold days d percentile of 1971–2000 Cooling indices FD Frost days Annual count when TN < 0 ◦C d

ID Ice days Annual count when TX < 0 ◦C d Annual count of days with at least 6 Cold spell duration CSDI consecutive days when TN d indicator < 10th percentile Percentage of days when TN > 90th TN90p Warm nights d percentile of 1971–2000 Percentage of days when TX > 90th TX90p Warm days d percentile of 1971–2000 Annual count between the ﬁrst span of at least 6 days with daily mean temperature Warming indices Growing season GSL >5 C after winter and the ﬁrst span after d length ◦ summer of 6 days with a daily mean temperature < 5 ◦C

SU25 Summer days Annual count when TX > 5 ◦C d Annual count of days with at least 6 Warm spell WSDI consecutive days when TX d duration indicator > 90th percentile Note: TN refers to daily minimum temperature; TX refers to daily maximum temperature. Atmosphere 2019, 10, 815 5 of 19

Table 3. Deﬁnition of the 10 precipitation extreme indices used in this study.

Classiﬁcation Abbreviation Index Deﬁnition Units Wet day PRCPTOT Annual total precipitation in wet days mm precipitation Simple daily SDII Average precipitation on wet days mm/d intensity index Maximum 1-d RX1d precipitation Annual maximum 1-day precipitation mm amount PRCPTOT and Maximum 5-d Annual maximum consecutive intensity indices RX5d precipitation mm 5-day precipitation amount Very wet day Annual total precipitation when RR >95th R95p mm precipitation percentile of 1971–2000 daily precipitation Annual total precipitation when RR > Extremely wet day R99p 99th percentile of 1971–2000 mm precipitation daily precipitation Number of heavy R10mm Annual count of days when RR > 10 mm d precipitation days Frequency indices Number of very R20mm heavy precipitation Annual count of days when RR > 20 mm d days Consecutive wet Maximum number of consecutive days CWD d days with RR > 1 mm Duration indices Consecutive dry Maximum number of consecutive days CDD d days with RR < 1 mm Note: RR refers to the daily precipitation amount.

In addition, trends and abrupt changes were determined for the entire region by performing regional averaging of the index time series. Regionally averaged anomaly series for each index was calculated as follows [49]: nt Rt = Σ (rs,t rs)/nt (1) s=1 − where Rt is the regionally averaged index at year t, rs,t is the index for station s at year t, rs is the mean from 1970 to 2017 at station s, and nt is the number of stations with data in year t.

4. Results

4.1. Temperature Indices The regional trends and the percentage of stations with positive, negative, and no trend in the extreme temperature indices during 1970–2017 are summarized in Table4.

4.1.1. Absolute Indices (TNn, TNx, TXn, and TXx) and Diurnal Temperature Range (DTR) Regional TNn, TNx, TXn, and TXx showed significant increasing trends during 1970–2017 with change rates of 0.48, 0.34, 0.35, and 0.22 ◦C/decade, respectively (Table4 and Figure2). A remarkable phenomenon was that the trend magnitude of TNn was larger than that of TXn, while the trend magnitude of TNx was greater than that of TXx, indicating a trend toward a smaller temperature difference between day and night. DTR presented a trend of a slight decrease with a rate of 0.06 ◦C/decade, which confirmed the reduction tendency in the diurnal temperature range (Table4 and Figure2). The spatial distributions of change trend for TNn, TNx, TXn, TXx, and DTR over the YTRB are shown in Figure3. For TNn, 60% of stations presented significant increasing trends, while only two stations (Longzi and Linzhi) had decreasing trends (Figure3a). All stations showed increasing trends of TNx and TXn, which were statistically significant for approximately 93% and 87% Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 19

FD −4.39 −9.64 to −0.83 0% (0%) 100% (87%) 0% ID −2.02 −8.66 to 0.00 0% (0%) 67% (27%) 33% CSDI −0.27 0.00 0% (0%) 0% (0%) 100% TN90p 3.75 1.94 to 6.22 100% (100%) 0% (0%) 0% AtmosphereTX90p 2019, 10,2.10 815 0.25 to 3.41 100% (93%) 0% (0%) 0%6 of 19 GSL 4.33 0.04 to 12.34 100% (27%) 0% (0%) 0% of stations,SU25 respectively1.23 (Figure0.003b,c). to 6.46 For TXx, 93% 47% of (47%) stations showed increasing 0% (0%) trends, while 53% 7% of stationsWSDI had no2.80 trend (Figure0.003d). to Around 5.00 73% of 80% stations (60%) showed decreasing 0% (0%) trends in DTR, 20%which were statistically signiﬁcant for 33% of stations (Figure3e). Note: Trends marked in bold are statistically significant at the 0.05 level.

Table 4. Trends per decade, change range, and percentage of stations with positive, negative, and no 4.1.1. Absolute Indices (TNn, TNx, TXn, and TXx) and Diurnal Temperature Range (DTR) trend for regional indices of temperature extremes. Regional TNn, TNx, TXn, and TXx showed significant increasing trends during 1970–2017 with Index Regional Trends Range Positive Trend (Significant) Negative Trend (Significant) No Trend change rates of 0.48, 0.34, 0.35, and 0.22 °C/decade, respectively (Table 4 and Figure 2). A remarkable TNn 0.48 0.14 to 0.91 87% (60%) 13% (0%) 0% − phenomenonTNx was0.34 that the trend0.18 tomagnitude 0.70 of 100%TNn (93%) was larger than that 0% (0%)of TXn, while the 0% trend magnitudeTXn of TNx0.35 was greater0.05 than to 0.60 that of TXx, 100% indicating (87%) a trend toward 0% (0%) a smaller temperature 0% TXx 0.22 0.00 to 0.57 93% (47%) 0% (0%) 7% differenceDTR between0.06 day and night.0.27 to 0.17DTR presented 20% a (7%) trend of a slight 73%decrease (33%) with a rate 7% of 0.06 − − TN10p 1.94 3.64 to 0.03 0% (0%) 100% (93%) 0% °C/decade, which− confirmed −the reduction− tendency in the diurnal temperature range (Table 4 and TX10p 1.66 2.14 to 0.55 0% (0%) 100% (93%) 0% − − − FigureFD 2). The spatial4.39 distributions9.64 to of0.83 change trend 0% for (0%) TNn, TNx, TXn, TXx, 100% (87%)and DTR over the 0% YTRB − − − ID 2.02 8.66 to 0.00 0% (0%) 67% (27%) 33% − − are shownCSDI in Figure0.27 3. For TNn, 0.0060% of stations presented 0% (0%) significant increasing 0% (0%) trends, while 100% only − twoTN90p stations (Longzi3.75 and Linzhi)1.94 tohad 6.22 decreasing 100% trends (100%) (Figure 3a). All 0%stations (0%) showed increasing 0% TX90p 2.10 0.25 to 3.41 100% (93%) 0% (0%) 0% trendsGSL of TNx and4.33 TXn, which0.04 towere 12.34 statistically 100% significant (27%) for approximately 0% (0%) 93% and 0% 87% of stations,SU25 respectively1.23 (Figure 3b,c).0.00 to 6.46For TXx, 93% 47% of (47%)stations showed increasing 0% (0%) trends, while 53% 7% of stationsWSDI had no trend2.80 (Figure 3d).0.00 toAround 5.00 73% of 80% stations (60%) showed decreasing 0% (0%) trends in DTR, 20% which were statistically significantNote: Trends for marked33% of in stations bold are statistically(Figure 3e). significant at the 0.05 level.

FigureFigure 2.2.Regionally Regionally averaged averaged anomaly anomaly series series of (aof) TNn,(a) TNn, (b) TNx, (b) TNx, (c) TXn, (c) (TXn,d) TXx, (d) and TXx, (e )and DTR (e) during DTR 1970–2017.during 1970–2017. The black The dashed black linedashed is the lin lineare is the trend linear and trend the solid and orangethe solid line orange from Locally line from Estimated Locally ScatterplotEstimated Scatterplot Smoothing Smoothing (LOESS). Z (LOESS). is the Mann–Kendall Z is the Mann–Kendall test statistic test and statistic S is the and Sen’s S is slope.the Sen’s slope.

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FigureFigure 3.3. SpatialSpatial distribution distribution of of change change trends trends for for (a) TNn,(a) TNn, (b) TNx, (b) TNx, (c) TXn, (c) (TXn,d) TXx, (d) and TXx, (e )and DTR (e) during DTR 1970–2017.during 1970–2017. Positive /negativePositive/negative trends are tr shownends are as up shown/down as triangles. up/down Triangle triangles. size isTriangle proportional size tois theproportional magnitude to of the the magnitude trend. Trends of the significant trend. Trends at the significant 0.05 level are at the shown 0.05 by level a black are shown box. by a black box. 4.1.2. Cooling Indices (TN10p, TX10p, FD, ID, and CSDI) 4.1.2.Cold Cooling nights Indices (TN10p), (TN10p, cold TX10p, days (TX10p), FD, ID, frostand CSDI) days (FD), ice days (ID), and cold spell duration indicator (CSDI) showed trends of decrease during 1970–2017 with change rates of 1.94, 1.66, 4.39, Cold nights (TN10p), cold days (TX10p), frost days (FD), ice days (ID), and −cold spell− duration− 2.02, and 0.27 d/decade, respectively, and the trends were statistically significant except for CSDI −indicator (CSDI)− showed trends of decrease during 1970–2017 with change rates of −1.94, −1.66, −4.39, (Table−2.02, 4and and −0.27 Figure d/decade,4). The respectively, magnitudes and of the the decrease trends were of nighttime-temperature-related statistically significant except indicesfor CSDI (TN10p)(Table 4 wereand Figure significantly 4). The higher magnitudes than daytime-temperature-related of the decrease of nighttime-temperature-related indices (TX10p). The spatialindices distributions(TN10p) were of significantly the change trendhigher for than TN10p, daytime-temperature-related TX10p, FD, ID, and CSDI indices over the(TX10p). YTRB The are shownspatial indistributions Figure5. Almost of the change all stations trend presented for TN10p, significant TX10p, FD, decreasing ID, and trendsCSDI over in TN10p, the YTRB TX10p, are shown and FD in (FigureFigure 55.a–c). Almost For ID,all stations 67% of stationspresented showed significant decreasing decreasing trends trends and the in stationsTN10p, withTX10p, significant and FD (Figure trends were5a–c). distributed For ID, 67% in of western, stations southern,showed decreasing and northern trends regions. and the Approximately stations with 33%significant of stations trends had were no trend;distributed these in were western, mainly southern, in midstream and northern and downstream regions. Approximately valley regions (Figure 33% of5 d).stations No obvious had no trendtrend; wasthese found were formainly CSDI in at midstream all stations and (Figure downstream5e). valley regions (Figure 5d). No obvious trend was found for CSDI at all stations (Figure 5e). 4.1.3. Warming Indices (TN90p, TX90p, GSL, SU25, and WSDI) Warm nights (TN90p), warm days (TX90p), growing season length (GSL), summer days (SU25), and warm spell duration indicator (WSDI) showed significant trends of increase during 1970–2017 with change rates of 3.75, 2.10, 4.33, 1.23, and 2.80 d/decade, respectively (Table4 and Figure6). The spatial distributions of the change trend for TN90p, TX90p, GSL, SU25, and WSDI over the YTRB are shown in Figure7. Almost all stations presented significant increasing trends in TN90p, TX90p, and GSL (Figure7a–c). For SU25, 47% of stations showed significant increasing trends, and these stations were distributed mainly along the valley of the midstream and downstream regions. The 53% of stations with no trend were located mainly in western, southern, and northern regions (Figure7d). For WSDI, approximately 60% of stations showed significant trends of increase and the stations with the largest trends were found mainly in midstream and upstream regions (Figure7e).

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Figure 4. Regionally averaged anomaly series of (a) TN10p, (b) TX10p, (c) FD, (d) ID, and (e) CSDI FigureFigure 4. 4. Regionally Regionallyaveraged averaged anomalyanomaly seriesseries ofof ((a)a) TN10p,TN10p, ((b)b) TX10p, (c) (c) FD, (d) (d) ID, and (e) (e) CSDI CSDI during 1970–2017. The black dashed line is the linear trend and the solid orange line from Locally duringduring 1970–2017. 1970–2017. The The black black dashed dashed lin linee is is the the linear linear trend trend and and the the so solidlid orange orange line line from from Locally Locally Estimated Scatterplot Smoothing (LOESS). Z is the Mann–Kendall test statistic and S is the Sen’s slope. EstimatedEstimated Scatterplot Scatterplot Smoothing (LOESS). Z is the Ma Mann–Kendallnn–Kendall test statistic and S is is the the Sen’s Sen’s slope. slope.

Figure 5. SpatialSpatial distribution distribution of of change change trends trends for for (a) (a) TN10p, (b) (b) TX10p, TX10p, (c) (c) FD, FD, (d) (d) ID, and (e) (e) CSDI Figure 5. Spatial distribution of change trends for (a) TN10p, (b) TX10p, (c) FD, (d) ID, and (e) CSDI during 1970–2017.1970–2017. Positive Positive/negative/negative trends trends are shownare shown as up/ downas up/down triangles. tr Triangleiangles. size Triangle is proportional size is during 1970–2017. Positive/negative trends are shown as up/down triangles. Triangle size is proportionalto the magnitude to the of magnitude the trend. Trendsof the trend. signiﬁcant Trends at thesignificant 0.05 level at arethe shown0.05 level by aare black shown box. by a black proportional to the magnitude of the trend. Trends significant at the 0.05 level are shown by a black box. box.

4.1.3. Warming Indices (TN90p, TX90p, GSL, SU25, and WSDI) 4.1.3. Warming Indices (TN90p, TX90p, GSL, SU25, and WSDI) Warm nights (TN90p), warm days (TX90p), growing season length (GSL), summer days (SU25), Warm nights (TN90p), warm days (TX90p), growing season length (GSL), summer days (SU25), and warm spell duration indicator (WSDI) showed significant trends of increase during 1970–2017 and warm spell duration indicator (WSDI) showed significant trends of increase during 1970–2017 with change rates of 3.75, 2.10, 4.33, 1.23, and 2.80 d/decade, respectively (Table 4 and Figure 6). The with change rates of 3.75, 2.10, 4.33, 1.23, and 2.80 d/decade, respectively (Table 4 and Figure 6). The spatial distributions of the change trend for TN90p, TX90p, GSL, SU25, and WSDI over the YTRB are spatial distributions of the change trend for TN90p, TX90p, GSL, SU25, and WSDI over the YTRB are shown in Figure 7. Almost all stations presented significant increasing trends in TN90p, TX90p, and shown in Figure 7. Almost all stations presented significant increasing trends in TN90p, TX90p, and GSL (Figure 7a–c). For SU25, 47% of stations showed significant increasing trends, and these stations GSL (Figure 7a–c). For SU25, 47% of stations showed significant increasing trends, and these stations were distributed mainly along the valley of the midstream and downstream regions. The 53% of were distributed mainly along the valley of the midstream and downstream regions. The 53% of

Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 19 Atmosphere 2020, 11, x FOR PEER REVIEW 9 of 19 stations with no trend were located mainly in western, southern, and northern regions (Figure 7d). stations with no trend were located mainly in western, southern, and northern regions (Figure 7d). For WSDI, approximately 60% of stations showed significant trends of increase and the stations with AtmosphereFor WSDI,2019 approximately, 10, 815 60% of stations showed significant trends of increase and the stations9 with of 19 the largest trends were found mainly in midstream and upstream regions (Figure 7e). the largest trends were found mainly in midstream and upstream regions (Figure 7e).

Figure 6. Regionally averaged anomaly series of (a) TN90p, (b) TX90p, (c) GSL, (d) SU25, and (e) WSDI Figure 6. RegionallyRegionally averaged averaged anomaly series of (a) (a) TN TN90p,90p, ((b)b) TX90p, (c) (c) GSL, (d) (d) SU25, and (e) (e) WSDI during 1970–2017. The black dashed line is the linear trend and the solid orange line from Locally during 1970–2017. The The black black dashed dashed lin linee is is the the linear linear trend trend and the so solidlid orange line from Locally Estimated Scatterplot Smoothing (LOESS). Z is the Mann–Kendall test statistic and S is the Sen’s slope. Estimated Scatterplot Smoothing (LOESS). ZZ is the Mann–KendallMann–Kendall test statistic and S is the Sen’s slope.

Figure 7. SpatialSpatial distribution distribution of of change change trends trends for for (a ()a TN90p,) TN90p, (b) ( bTX90p,) TX90p, (c) (GSL,c) GSL, (d) (dSU25,) SU25, and and (e) Figure 7. Spatial distribution of change trends for (a) TN90p, (b) TX90p, (c) GSL, (d) SU25, and (e) (WSDIe) WSDI during during 1970–2017. 1970–2017. Positive/negative Positive/negative trends trends are are sh shownown as as up/down up/down triangles. triangles. Triangle Triangle size is WSDI during 1970–2017. Positive/negative trends are shown as up/down triangles. Triangle size is proportional toto thethe magnitude magnitude of of the the trend. trend. Trends Trends signiﬁcant significant at theat the 0.05 0.05 level level are shownare shown by a by black a black box. proportional to the magnitude of the trend. Trends significant at the 0.05 level are shown by a black box. 4.2. Precipitationbox. Indices 4.2. Precipitation Indices 4.2. PrecipitationThe regional Indices trends and number of stations with positive, negative, and no trend in terms of the precipitationThe regional indices trends during and 1970–2017 number of are stations summarized with positive, in Table negative,5. and no trend in terms of the The regional trends and number of stations with positive, negative, and no trend in terms of the precipitation indices during 1970–2017 are summarized in Table 5. precipitation indices during 1970–2017 are summarized in Table 5.

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Table 5. Trends per decade, change range, and percentage of stations with positive, negative, and no trend in regional precipitation extreme indices.

Index Regional Trends Range Positive Trend (Significant) Negative Trend (Significant) No Trend PRCPTOT 7.33 17.80 to 20.27 80% (0%) 20% (0%) 0% − SDII 0.10 0.10 to 0.31 80% (13%) 13% (0%) 7% − RX1d 0.21 2.06 to 1.80 53% (7%) 47% (0%) 0% − AtmosphereRX5d 2020, 11, x FOR 0.69 PEER REVIEW1.77 to 2.44 53% (7%) 47% (0%) 0%10 of 19 − R95p 4.01 9.24 to 13.27 47% (7%) 40% (0%) 13% − R99p 0.92 0.60 to 0.00 0% (0%) 7% (0%) 93% − R10mmTable 5. Trends 0.37per decade, change0.85 to range, 1.26 and percentage 53% (7%) of stations with positive, 7% (0%) negative, and 40%no − R20mmtrend in regional 0.11 precipitation extreme 0.00 indices. 0% (0%) 0% (0%) 100% CWD 0.06 0.84 to 0.59 13% (0%) 13% (7%) 73% − − CDD 0.62Regional 6.10 to 5.87 47% (7%)Positive Trend Negative 47% (0%) Trend 7%No Index − Range TrendsNote: Trends marked in bold are statistically(Significant) significant at the 0.05 level.(Significant) Trend PRCPTOT 7.33 −17.80 to 20.27 80% (0%) 20% (0%) 0% 4.2.1. IntensitySDII Indices0.10 (SDII, RX1d,− RX5d,0.10 to R95p,0.31 and R99p) 80% and (13%) Wet Day Precipitation 13% (0%) (PRCPTOT) 7% RX1d 0.21 −2.06 to 1.80 53% (7%) 47% (0%) 0% WetRX5d day precipitation0.69 (PRCPTOT)−1.77 showed to 2.44 a positive trend 53% (7%) with a rate of 7.33 47% mm (0%)/decade; however, 0% the trendR95p was not statistically4.01 significant−9.24 to (Figure13.27 8a and Table47% (7%)5). Around 80% 40% of (0%) stations showed 13% increasingR99p trends, and0.92 these stations−0.60 were to 0.00 distributed mainly 0% (0%) in the middle 7% region.(0%) The 93%20% of stationsR10mm that showed a0.37 decreasing trend−0.85 were to 1.26 found mainly 53% in western(7%) and eastern 7% (0%) regions (Figure 40%9 a). Moreover,R20mm the annual simple0.11 daily intensity 0.00 index (SDII) was 0% found(0%) to present 0% a significant(0%) increasing 100% CWD −0.06 −0.84 to 0.59 13% (0%) 13% (7%) 73% trend with a rate of 0.10 mm/d per decade (Figure8b and Table5). Notably, 80% of stations showed CDD 0.62 −6.10 to 5.87 47% (7%) 47% (0%) 7% increasing trends in SDII, while significant positive trends were detected in 13% of stations (Figure9b). Note: Trends marked in bold are statistically significant at the 0.05 level.

FigureFigure 8. 8. RegionallyRegionally averaged averaged anomaly anomaly series series of of (a) (a) PR PRCPTOT,CPTOT, (b) (b) SDII, SDII, (c) (c) RX1d, RX1d, (d) (d) RX5d, RX5d, (e) (e) R95p, R95p, andand ((f)f) R99p duringduring 1970–2017.1970–2017. The The black black dashed dashed line line is theis the linear linear trend trend and and the solidthe solid orange orange line fromline fromLocally Locally Estimated Estimated Scatterplot Scatterplot Smoothing Smoothing (LOESS). (LOESS) Z is. Z the is Mann–Kendallthe Mann–Kendall test test statistic statistic and and S is S the is theSen’s Sen’s slope. slope.

4.2.1. Intensity Indices (SDII, RX1d, RX5d, R95p, and R99p) and Wet Day Precipitation (PRCPTOT) Wet day precipitation (PRCPTOT) showed a positive trend with a rate of 7.33 mm/decade; however, the trend was not statistically significant (Figure 8a and Table 5). Around 80% of stations showed increasing trends, and these stations were distributed mainly in the middle region. The 20% of stations that showed a decreasing trend were found mainly in western and eastern regions (Figure 9a). Moreover, the annual simple daily intensity index (SDII) was found to present a significant increasing trend with a rate of 0.10 mm/d per decade (Figure 8b and Table 5). Notably, 80% of stations showed increasing trends in SDII, while significant positive trends were detected in 13% of stations (Figure 9b).

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Figure 9. Spatial distribution of change trends for (a) (a) PRCPTOT, ((b)b) SDII, ((c)c) RX1d, (d) (d) RX5d, (e) (e) R95p, and ((f)f) R99pR99p duringduring 1970–2017.1970–2017. PositivePositive/negative/negative trendstrends are shown asas upup/down/down triangles.triangles. Triangle sizesize isis proportionalproportional toto thethe magnitudemagnitude of the trend.trend. Trends signiﬁcantsignificant atat thethe 0.05 level are shownshown by a black box.

The regionalregional averageaverage maximummaximum 1-day precipitationprecipitation amount (RX1d) and maximum 5-day precipitationprecipitation amount (RX5d) showed increasing trends of 0.21 and 0.69 mm/decade, mm/decade, respectively. respectively. However, thethe trends of RX1d and RX5d were not statistically significant significant (Figure 88c,dc,d andand TableTable5 ).5). TheThe changechange trendstrends ofof RX1dRX1d andand RX5dRX5d werewere spatiallyspatially heterogenousheterogenous (Figure(Figure9 9c,d),c,d), i.e.,i.e., 53%53% ofof stations stations showed increasingincreasing trendstrends inin RX1dRX1d andand RX5d,RX5d, whilewhile 47%47% ofof stationsstations showedshowed decreasingdecreasing trends.trends. Both veryvery wetwet day precipitation (R95p) and extremely wet day precipitation (R99p) exhibited insignificantinsignificant increasingincreasing trends,trends, thethe magnitudesmagnitudes of which were 4.01 and 0.92 mm/decade, mm/decade, respectively (Figure 88e,f,e,f, and TableTable5 ).5). AtAt thethe stationstation scale,scale, 47%47% ofof stationsstations showedshowed aa positivepositive trend,trend, whilewhile 40%40%of of stations stations showed a negativenegative trend inin R95pR95p (Figure(Figure9 9e).e). ItIt isis worthworth notingnoting thatthat thethe majoritymajority ofof stationsstations (93%)(93%) withinwithin thethe basinbasin showedshowed nono trendtrend inin R99pR99p (Figure(Figure9 f).9f). 4.2.2. Frequency Indices (R10mm and R20mm) and Duration Indices (CWD and CDD) 4.2.2. Frequency Indices (R10mm and R20mm) and Duration Indices (CWD and CDD) The number of heavy precipitation days (R10mm) and very heavy precipitation days (R20mm) The number of heavy precipitation days (R10mm) and very heavy precipitation days (R20mm) showed increasing trends with change rates of 0.37 and 0.11 mm/decade, respectively (Figure 10a,b, and showed increasing trends with change rates of 0.37 and 0.11 mm/decade, respectively (Figure 10a,b, Table5). For R10mm, 53% of stations showed positive trends, while 40% of stations had no change trend and Table 5). For R10mm, 53% of stations showed positive trends, while 40% of stations had no (Figure 11a). All stations presented no change in the trend of R20mm (Figure 11b). Consecutive wet change trend (Figure 11a). All stations presented no change in the trend of R20mm (Figure 11b). days (CWD) had an insignificant decreasing trend with a rate of 0.06 d/decade (Figure 10c and Table5). Consecutive wet days (CWD) had an insignificant decreasing trend with a rate of 0.06 d/decade Conversely, consecutive dry days (CDD) had a weak trend of increase with a rate of 0.62 d/decade (Figure 10c and Table 5). Conversely, consecutive dry days (CDD) had a weak trend of increase with (Figure 10d and Table5). Spatially, 73% of stations exhibited no change trend in CWD, and these a rate of 0.62 d/decade (Figure 10d and Table 5). Spatially, 73% of stations exhibited no change trend stations were distributed mainly in the upper and lower parts of the basin (Figure 11c). For CDD, in CWD, and these stations were distributed mainly in the upper and lower parts of the basin (Figure 47% of stations showed positive trends, while 47% of stations showed negative trends (Figure 11d). 11c). For CDD, 47% of stations showed positive trends, while 47% of stations showed negative trends Generally, at most stations, there was little change in the trends of precipitation frequency and duration (Figure 11d). Generally, at most stations, there was little change in the trends of precipitation indices compared with intensity indices. frequency and duration indices compared with intensity indices.

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Figure 10. Regionally averaged anomaly series of (a) R10mm, (b) R20mm, (c) CWD, and (d) CDD FigureFigure 10.10. Regionally averaged anomaly series of ((aa) R10mm,R10mm, ((b)b) R20mm, ((c)c) CWD,CWD, andand ((d)d) CDDCDD during 1970–2017. The black dashed line is the linear trend and the solid orange line from Locally duringduring 1970–2017.1970–2017. The black dashed lin linee is the linear trend and the solidsolid orange line from LocallyLocally Estimated Scatterplot Smoothing (LOESS). Z is the Mann–Kendall test statistic and S is the Sen’s slope. EstimatedEstimated ScatterplotScatterplot SmoothingSmoothing (LOESS).(LOESS). ZZ isis thethe Mann–KendallMann–Kendall testtest statisticstatistic andand SS isis the Sen’s slope.

Figure 11. Spatial distribution of change trends for (a) R10mm, (b) R20mm, (c) CWD, and (d) CDD Figure 11. Spatial distribution of change trends for (a) R10mm, (b) R20mm, (c) CWD, and (d) CDD duringFigure 1970–2017.11. Spatial Positivedistribution/negative of change trends trends are shown for as(a) up R10mm,/downtriangles. (b) R20mm, Triangle (c) CWD, size is and proportional (d) CDD during 1970–2017. Positive/negative trends are shown as up/down triangles. Triangle size is toduring the magnitude 1970–2017. of thePositive/negative trend. Trends significanttrends are at theshown 0.05 levelas up/down are shown triangles. by a black Triangle box. size is proportional to the magnitude of the trend. Trends significant at the 0.05 level are shown by a black proportional to the magnitude of the trend. Trends significant at the 0.05 level are shown by a black box. 4.2.3.box. Proportion of Heavy Precipitation in Total Precipitation 4.2.3.From Proportion 1970 toof 2017,Heavy very Precipitation wet day in precipitation Total Precipitation (R95p) accounted for an average of 20.53% of4.2.3. the Proportion total precipitation of Heavy amountPrecipitation (range: in Total 13.45% Precipitation to 26.77%). The R95p/PRCPTOT presented a From 1970 to 2017, very wet day precipitation (R95p) accounted for an average of 20.53% of the fluctuatingFrom 1970 increase to 2017, before very 2000, wet followed day precipitation by a rapid (R95p) upward accounted trend thereafter for an (Figureaverage 12 ofa). 20.53% In addition, of the total precipitation amount (range: 13.45% to 26.77%). The R95p/PRCPTOT presented a fluctuating thetotal average precipitation contribution amount of extremely(range: 13.45% wet day to precipitation26.77%). The (R99p) R95p/PRCPTOT was 6.37% (range:presented 1.83% a fluctuating to 12.79%). increase before 2000, followed by a rapid upward trend thereafter (Figure 12a). In addition, the Theincrease R99p /beforePRCPTOT 2000, series followed showed by aatransition rapid upward from atrend weak thereafter trend of decrease (Figure during 12a). In 1970–1999 addition, to the an average contribution of extremely wet day precipitation (R99p) was 6.37% (range: 1.83% to 12.79%). increasingaverage contribution trend during of extremely 2000–2017 wet (Figure day 12precipitb). Overall,ation (R99p) the contribution was 6.37% of(range: heavy 1.83% precipitation to 12.79%). to The R99p/PRCPTOT series showed a transition from a weak trend of decrease during 1970–1999 to totalThe R99p/PRCPTOT precipitation amount series increasedshowed a from transition 1970 tofrom 2017; a weak the trend trend magnitude of decrease of during the R95p 1970–1999/PRCPTOT to an increasing trend during 2000–2017 (Figure 12b). Overall, the contribution of heavy precipitation andan increasing R99p/PRCPTOT trend during series was2000–2017 0.45% /(Figuredecade 12b). and 0.23%Overall,/decade, the contribution respectively. of Theseheavy resultsprecipitation are in to total precipitation amount increased from 1970 to 2017; the trend magnitude of the R95p/PRCPTOT accordto total withprecipitation previous amount findings increased in southwestern from 1970 China to 2017; [50– the52]. trend Notably, magnitude the contributions of the R95p/PRCPTOT of R95p and and R99p/PRCPTOT series was 0.45%/decade and 0.23%/decade, respectively. These results are in R99pand R99p/PRCPTOT to total precipitation series amount was 0.45%/decade have increased and rapidly 0.23%/decade, since the respectively. 2000s. These results are in accord with previous findings in southwestern China [50–52]. Notably, the contributions of R95p and accord with previous findings in southwestern China [50–52]. Notably, the contributions of R95p and R99p to total precipitation amount have increased rapidly since the 2000s. R99p to total precipitation amount have increased rapidly since the 2000s.

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Figure 12.12. ((a)a) RatioRatio ofof very very wet wet day day precipitation precipitation (R95p) (R95p) to totalto total precipitation; precipitation; (b) ratio(b) ratio of extremely of extremely wet daywet precipitationday precipitation (R99p) (R99p) to total to total precipitation. precipitation. Green Green dashed dashed line isline the is linear the linear trend trend during during 1970–1999, 1970– and1999, the and red the dashed red dashed line is line the linearis the linear trend duringtrend during 2000–2017. 2000–2017. 4.3. Abrupt Change Analysis 4.3. Abrupt Change Analysis Pettitt’s test is used to detect abrupt changes in the temperature and precipitation extreme indices, Pettitt’s test is used to detect abrupt changes in the temperature and precipitation extreme the results are shown in Table6. According to Table6, during the last 48 years, most of the extreme indices, the results are shown in Table 6. According to Table 6, during the last 48 years, most of the temperature indices displayed abrupt changes. The absolute indices (TNn, TNx, and TXn) showed extreme temperature indices displayed abrupt changes. The absolute indices (TNn, TNx, and TXn) significant abrupt changes in the 1990s, when it transformed from a relatively cold period to a relatively showed significant abrupt changes in the 1990s, when it transformed from a relatively cold period to warm period. The DTR presented a distinct decrease from 1988. Most of the cold and warm extremes a relatively warm period. The DTR presented a distinct decrease from 1988. Most of the cold and exhibited abrupt changes in the late 1990s, with the exception of SU25, which showed an abrupt warm extremes exhibited abrupt changes in the late 1990s, with the exception of SU25, which showed change from a period with few summer days to a period with more summer days in 2004. Overall, an abrupt change from a period with few summer days to a period with more summer days in 2004. the temperature extremes experienced abrupt changes in the 1990s. However, there were no significant Overall, the temperature extremes experienced abrupt changes in the 1990s. However, there were no abrupt changes in the precipitation extreme indices. significant abrupt changes in the precipitation extreme indices. Table 6. Pettitt’s test results of the temperature and precipitation extreme indices. Table 6. Pettitt’s test results of the temperature and precipitation extreme indices. Index Change Year U * p Index Change Year U * p IndexTNn Change 1997 Year 387U * <0.01p SU25Index 2004Change Yea 316r U * 0.01 p TNnTNx 1997 1995 459 387 <0.01<0.01 WSDISU25 1997 2004 438 316< 0.01 0.01 TNxTXn 1995 1997 295459 0.02<0.01 PRCPTOTWSDI 19871997 154438 0.57 <0.01 TXnTXx 1997 1992 208295 0.200.02 SDIIPRCPTOT 20061987 206154 0.21 0.57 TXxDTR 1992 1988 279208 0.030.20 RX1dSDII 20062006 161206 0.50 0.21 DTRTN10p 1988 1997 548 279 <0.010.03 RX5dRX1d 20062006 151161 0.60 0.50 TN10pTX10p 1997 1997 522 548 <0.01<0.01 R95pRX5d 2006 2006 143151 0.67 0.60 FD 1997 503 <0.01 R99p 2006 175 0.39 TX10p 1997 522 <0.01 R95p 2006 143 0.67 ID 1998 434 <0.01 R10mm 1987 192 0.28 FD 1997 503 <0.01 R99p 2006 175 0.39 CSDI 1998 268 0.04 R20mm 1983 157 0.54 ID 1998 434 <0.01 R10mm 1987 192 0.28 TN90p 1997 546 <0.01 CWD 2004 207 0.21 CSDITX90p 1998 1997 466 268 <0.010.04 CDDR20mm 19771983 138157 0.73 0.54 TN90pGSL 1997 1997 407 546 <0.01<0.01 CWD 2004 207 0.21 TX90p Note: U*1997 and p represent the 466 maximum <0.01 Pettitt’s statistics,CDD and the significance 1977 level, respectively.138 0.73 GSL 1997 407 <0.01 Note: U* and p represent the maximum Pettitt’s statistics, and the significance level, respectively. 5. Discussion 5. Discussion 5.1. Correlation Analysis of Extreme Temperature and Precipitation Indices 5.1. Correlation Analysis of Extreme Temperature and Precipitation Indices The Pearson correlation coefficients among the different temperature indices are shown in FigureThe 13 a.Pearson Positive correlation relationships coefficients were found among among the the different absolute temperature indices (TNn, indices TNx, TXn,are shown and TXx), in andFigure TNn 13a. exhibited Positive therelationships strongest correlationwere found with amon TXng the with absolute a correlation indices coe(TNn,fficient TNx, of TXn, 0.85 and (p < TXx),0.01). Positiveand TNn relationships exhibited the were strongest also detectedcorrelation among with TXn thewarming with a co indicesrrelation (TN90p, coefficient TX90p, of 0.85 GSL, (p <0.01). SU25, andPositive WSDI), relationships and the highest were also correlation detected coe amongfficients the were warming between indices TX90p (TN90p, and WSDI TX90p, (0.91, GSL,p < SU25,0.01). Forand theWSDI), cooling and indices the highest (TN10p, correlation TX10p, FD, coeffi ID,cients and CSDI), were significantbetween TX positive90p and correlations WSDI (0.91, were p <0.01). found withFor the correlation cooling coeindicesfficients (TN10p, of >0.45 TX10p, (p < FD,0.01). ID, The and TN10p CSDI), and significant FD indices positive presented correlations the strongest were relationshipfound with withcorrelation a correlation coefficients coeffi cientof >0.45 of 0.86 (p (<0.01).p < 0.01). The In TN10p addition, and DTR FD hadindices a weak presented correlation the withstrongest the other relationship indices. with It is noteworthya correlation that coefficient the warming of 0.86 indices (p <0.01). were In found addition, correlated DTR had negatively a weak correlation with the other indices. It is noteworthy that the warming indices were found correlated negatively with the cooling indices. All the above findings indicate that the occurrence of extreme

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Atmosphere 2020, 11, x FOR PEER REVIEW 14 of 19 with the cooling indices. All the above findings indicate that the occurrence of extreme warm events warm events has been increasing, while that of extreme cold events has been decreasing with a has been increasing, while that of extreme cold events has been decreasing with a definite warming definite warming trend reflected in the absolute indices. trend reflected in the absolute indices.

FigureFigure 13. 13. PearsonPearson correlation correlation analysis analysis of of (a ()a )temperature temperature and and (b (b) )precipitation precipitation indices. indices. Previous studies have indicated that the annual total precipitation is well correlated with extreme Previous studies have indicated that the annual total precipitation is well correlated with precipitation [29,32,34,53]. Figure 13b indicates a strong correlation between total precipitation and extreme precipitation [29,32,34,53]. Figure 13b indicates a strong correlation between total each of the precipitation indices (except CDD), of which R95p, R10mm, and R20mm showed the best precipitation and each of the precipitation indices (except CDD), of which R95p, R10mm, and R20mm correlation with total precipitation with a correlation coefficient of >0.70 (p < 0.01). The SDII was also showed the best correlation with total precipitation with a correlation coefficient of >0.70 (p <0.01). found well correlated with the extreme precipitation indices (except CWD and CDD). As expected, The SDII was also found well correlated with the extreme precipitation indices (except CWD and CDD has a negative relationship with each of the other extreme precipitation indices, although none of CDD). As expected, CDD has a negative relationship with each of the other extreme precipitation the correlations found were statistically significant. Therefore, the extreme precipitation indices used indices, although none of the correlations found were statistically significant. Therefore, the extreme in this work could be used to reflect the changes in total precipitation. precipitation indices used in this work could be used to reflect the changes in total precipitation. 5.2. Comparison with Results of Other Studies 5.2. Comparison with Results of Other Studies For the temperature extremes, trend analysis showed the absolute indices (TNn, TNx, TXn, and For the temperature extremes, trend analysis showed the absolute indices (TNn, TNx, TXn, and TXx) and the warming indices (TN90p, TX90p, GSL, SU25, and WSDI) had significant increasing trends, TXx) and the warming indices (TN90p, TX90p, GSL, SU25, and WSDI) had significant increasing while the cooling indices (TN10p, TX10p, FD, and ID) exhibited significant decreasing trends. This trends, while the cooling indices (TN10p, TX10p, FD, and ID) exhibited significant decreasing trends. finding is consistent with studies in adjacent regions, e.g., the western Qinghai–Tibet Plateau [33], This finding is consistent with studies in adjacent regions, e.g., the western Qinghai–Tibet Plateau central and eastern parts of the Qinghai–Tibet Plateau [32], three-river headwaters region [34,35], [33], central and eastern parts of the Qinghai–Tibet Plateau [32], three-river headwaters region [34,35], Southwest China [50], and China as a whole [13] (Table7). In addition, the overall warming trends Southwest China [50], and China as a whole [13] (Table 7). In addition, the overall warming trends in the extreme indices based on nighttime temperature (TN10p, TN90p, TNn, and TNx) were found in the extreme indices based on nighttime temperature (TN10p, TN90p, TNn, and TNx) were found greater than those based on daytime temperatures (TX10p, TX90p, TXn, and TXx), indicating an greater than those based on daytime temperatures (TX10p, TX90p, TXn, and TXx), indicating an obvious decrease in DTR, consistent with other regions worldwide [3,13,54,55]. Wintertime indices obvious decrease in DTR, consistent with other regions worldwide [3,13,54,55]. Wintertime indices (FD, ID, TNn, and TXn) are likely to increase/decrease more quickly than summertime indices (GSL, (FD, ID, TNn, and TXn) are likely to increase/decrease more quickly than summertime indices (GSL, SU25, TNx, and TXx) because the magnitude of warming in winter is greater than in summer [35]. SU25, TNx, and TXx) because the magnitude of warming in winter is greater than in summer [35]. For the precipitation extremes, both PRCPTOT and SDII showed increasing trends, indicating a For the precipitation extremes, both PRCPTOT and SDII showed increasing trends, indicating a tendency toward wetter climate conditions over the YTRB, consistent with previous studies in adjacent tendency toward wetter climate conditions over the YTRB, consistent with previous studies in regions and China as a whole (Table7). Notably, CDD tended to become prolonged and CWD tended adjacent regions and China as a whole (Table 7). Notably, CDD tended to become prolonged and to become shortened, while RX1d, RX5d, R95p, R99p, R10 mm, and R20 mm all increased, which CWD tended to become shortened, while RX1d, RX5d, R95p, R99p, R10 mm, and R20 mm all suggests that increased precipitation intensity is concentrated on extreme precipitation events. This increased, which suggests that increased precipitation intensity is concentrated on extreme was also reflected by the increase in the contribution of extreme precipitation to total precipitation precipitation events. This was also reflected by the increase in the contribution of extreme during 1970–2017 (Figure 12). precipitation to total precipitation during 1970–2017 (Figure 12).

Table 7. Trends of temperature and precipitation indices from this study and other sources.

Eastern Three- The TP and Its Western Southwestern Indices and River China YTRB Surroundings TP China Central Headwaters

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Table 7. Trends of temperature and precipitation indices from this study and other sources.

TP and Its Eastern and Western Three-River Southwestern Indices The YTRB China Surroundings Central TP TP Headwaters China TNn 0.48 1.04 - 0.63 0.49 0.29 0.58 TNx 0.34 0.36 - 0.38 0.29 0.17 0.28 TXn 0.35 0.10 - 0.67 0.26 0.13 0.32 TXx 0.22 0.30 - 0.15 0.39 0.11 0.17 DTR 0.06 - 0.20 0.20 0.11 0.18 - − − − − − TN10p 1.94 - 2.38 4.92 4.63 0.37 2.00 − − − − − − TX10p 1.66 - 0.85 2.84 2.47 0.13 0.80 − − − − − − FD 4.39 4.06 4.32 5.69 4.28 0.29 3.90 − − − − − − − ID 2.02 2.03 2.46 7.74 4.68 0.09 2.10 − − − − − − − CSDI 0.27 - - 2.55 -- 0.80 − − − TN90p 3.75 - 2.54 4.00 4.30 0.36 3.30 TX90p 2.10 - 1.26 3.43 3.05 0.22 1.70 GSL 4.33 - 4.25 4.35 3.94 0.12 3.40 SU25 1.23 2.59 - 0.42 1.20 - 1.90 WSDI 2.80 -- 3.31 -- 3.00 PRCPTOT 7.33 6.98 6.66 0.47 8.33 0.03 0.30 SDII 0.10 0.08 0.03 0.01 0.02 0.03 0.70 RX1d 0.21 0.45 0.27 0.37 0.16 0.05 - − RX5d 0.69 0.50 0.08 1.25 0.44 0.03 - − − R95p 4.01 3.24 1.28 0.48 3.83 0.04 2.10 R99p 0.92 1.96 1.09 0.41 1.90 0.05 - R10mm 0.37 0.27 0.23 0.06 0.16 0.00 - R20mm 0.11 0.07 - - 0.04 0.00 - CWD 0.06 0.02 0.07 0.17 0.16 0.08 0.20 − − − − − CDD 0.62 0.87 4.64 0.52 2.06 0.05 3.50 − − − − − Data sources: TP: Temperature (1963–2015) [56], precipitation (1975–2014) [52]; Eastern and central TP (1961–2005) [32]; Western TP (1973–2011) [33]; three-river headwaters: Temperature (1961–2016) [35], precipitation (1960–2012) [34]; Southwestern China (1961–2008) [50]; China (1961–2010) [13].

5.3. Relationship between Extreme Precipitation Changes and Monsoon Activities Previous studies reported that the Asian summer monsoon affects precipitation in southern regions of the TP [29,53]. As shown in Table8, the South Asian summer monsoon index (SASMI) has a positive correlation with PRCPTOT and R10 mm, indicating that the South Asian summer monsoon has considerable influence on precipitation variation. The South Asian monsoon affects lower-latitude areas in southeastern parts of the Qinghai–Tibet Plateau, bringing abundant water vapor along the valley of the YTRB [30]. For instance, in those years in which the South Asian summer monsoon was prevalent, the water vapor content transported from the Indian Ocean increased significantly and the occurrence of heavy precipitation increased. Moreover, the East Asian summer monsoon index (EASMI) showed significant negative correlation with the extreme precipitation indices (except CDD), probably because a strengthened East Asian summer monsoon limits the northward extension of the South Asian summer monsoon, while an enhanced South Asian summer monsoon limits the westward extension of the East Asian summer monsoon. The significant positive correlation between the EASMI and CDD also indicates that strengthening of the East Asian monsoon will limit the amount of water vapor transported by the South Asian monsoon, resulting in a persistent precipitation deficit. In recent decades, the weakening of the Asian monsoon circulation has been confirmed by numerous studies [50,57,58]. The decline in the strength of the Asian monsoonal circulation has contributed to severe rainfall anomalies [50,51,59].

Table 8. Correlation coeﬃcients between extreme precipitation indices and the East Asian summer monsoon index (EASMI) and South Asian summer monsoon index (SASMI).

Indices PRCPTOT SDII RX1d RX5d R95p R99p R10mm R20mm CWD CDD EASMI 0.32 0.28 0.25 0.19 0.23 0.25 0.31 0.27 0.30 0.28 − − − − − − − − − SASMI 0.21 0.11 0.01 0.04 0.09 0.07 0.24 0.09 0.08 0.14 − Note: Trends marked in bold are statistically signiﬁcant at the 0.05 level. Atmosphere 2019, 10, 815 16 of 19

6. Conclusions This study analyzed the change of temperature and precipitation extremes over the YTRB during 1970–2017. Temperature extremes showed widespread significant changes associated with warming, i.e., the majority of stations showed statistically significant trends for most temperature indices. The absolute indices (TNn, TNx, TXn, and TXx) exhibited significant trends of increase with rates of 0.48, 0.34, 0.35, and 0.22 ◦C/decade, respectively. The magnitudes of the change of the daily minimum temperature indices were greater than the daily maximum temperature, indicating an obvious decrease in DTR ( 0.06 C/decade). Warming indices (TN90p, TX90p, GSL, SU25, and WSDI) − ◦ showed a significant increase of 3.75, 2.10, 4.33, 1.23, and 2.80 d/decade, respectively. Meanwhile, cooling indices (TN10p, TX10p, FD, and ID) showed a significant decrease of 1.94, 1.66, 4.39, and − − − 2.02 d/decade, respectively. Moreover, the warming magnitudes of most of the indices were more − evident after the 1990s than before. In addition, change in the extreme precipitation indices exhibited much less spatial coherence than that of the temperature indices. Spatially, mixed patterns of the station with increasing and decreasing trends were found, and few trends in the extreme precipitation indices at individual stations were statistically significant. Precipitation extreme indices showed a tendency toward wetter conditions, and the contribution of extreme precipitation to total precipitation has increased. On average, CDD tended to become prolonged and CWD tended to become shortened, while RX1d, RX5d, R95p, R99p, R10 mm, and R20 mm all increased, which suggests that significant increase in SDII is concentrated on extreme precipitation events. However, no significant regional trends and abrupt changes were detected in total precipitation or in the frequency and duration of precipitation extremes. One of the most significant potential consequences of climate change may be alterations in regional hydrological cycles and subsequent changes in river flow regimes. Previous studies indicated the glaciers in the YTRB had retreated severely during the last decades [60], and runoff from the glacial melt has been increasing in response to climatic warming [26,61], which increased water availability in the downstream. However, the long-term effects of glacier retreat will be a decrease in regional water supplies, particularly during the dry season [62]. Furthermore, changes in precipitation extremes may have important implications for water-related hazard risk management. Recent temporal fluctuations in precipitation extremes indicated a greater degree of likelihood of flood risk as well as dry events, which pose major challenges to agriculture, water, and other sectors. The midstream region of the YTRB is the main center of human economic activity in Tibet. Given the increase in socioeconomic vulnerability to climate extremes, it is critical to improve the climate risk management strategies in the basin.

Author Contributions: Conceptualization, Y.L.; software, C.L.; data analysis, C.L. and M.Z.; writing—original draft preparation, C.L.; writing—review and editing, Y.L., X.J., and X.L. Funding: This research was funded by the National Natural Science Foundation of China, grant number 41661144044. Acknowledgments: We are very grateful to the editors and anonymous reviewers for their valuable comments, which greatly improved the quality of the paper. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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