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Journal of Geographical Sciences >

2022 , Vol. 32 >Issue 1: 141 - 155

DOI: https://doi.org/10.1007/s11442-022-1940-6

Spatiotemporal variation and hotspots of climate change in the Yangtze River Watershed during 1958-2017

  • CHENG Guowei , 1 ,
  • LIU Yong 2 ,
  • CHEN Yan 3 ,
  • GAO Wei , 4
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  • 1. Institute of International Rivers and Eco-security, Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security, Yunnan University, Kunming 650091, China
  • 2. College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871, China
  • 3. State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy for Environmental Planning, Beijing 100012, China
  • 4. Institute of Environmental and Ecological Engineering, Guangdong University of Technology, Guangzhou 510006, China
*Gao Wei, E-mail:

Cheng Guowei, specialized in environment simulation and assessment. E-mail:

Received date: 2020-12-04

  Accepted date: 2021-08-12

  Online published: 2022-03-25

Supported by

Program for Guangdong Introducing Innovative and Entrepreneurial Teams(2019ZT08L213)

National Natural Science Foundation of China(41701631)

Guangdong Provincial Key Laboratory Project(2019B121203011)

Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)(GML2019ZD0403)

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Abstract

The Yangtze River Watershed in China is a climate change hotspot featuring strong spatial and temporal variability; hence, it poses a certain threat to social development. Identifying the characteristics of and regions vulnerable to climate change is significantly important for formulating adaptive countermeasures. However, with regard to the Yangtze River Watershed, there is currently a lack of research on these aspects from the perspective of natural and anthropogenic factors. To address this issue, in this study, based on the temperature and precipitation records from 717 meteorological stations, the RClimDex and random forest models were used to assess the spatiotemporal characteristics of climate change and identify mainly the natural and anthropogenic factors influencing climate change hotspots in the Yangtze River Watershed for the period 1958-2017. The results indicated a significant increasing trend in temperature, a trend of wet and dry polarization in the annual precipitation, and that the number of temperature indices with significant variations was 2.8 times greater than that of precipitation indices. Significant differences were also noted in the responses of the climate change characteristics of the sub-basins to anthropogenic and natural factors; the delta plain of the Yangtze River estuary exhibited the most significant climate changes, where 88.89% of the extreme climate indices varied considerably. Furthermore, the characteristics that were similar among the identified hotpots, including human activities (higher Gross Domestic Product and construction land proportions) and natural factors (high altitudes and large proportions of grassland and water bodies), were positively correlated with the rapid climate warming.

Key words： Yangtze River Watershed; climate change; RClimDex; Random Forest; anthropogenic and natural factors; hotspots

Cite this article

CHENG Guowei , LIU Yong , CHEN Yan , GAO Wei . Spatiotemporal variation and hotspots of climate change in the Yangtze River Watershed during 1958-2017[J]. Journal of Geographical Sciences, 2022 , 32(1) : 141 -155 . DOI: 10.1007/s11442-022-1940-6

1 Introduction

With rapid increase in global population and the socioeconomic developments, the threat posed by climate change to human survival and environmental security has become increasingly prominent (Duan et al., 2016; Salerno et al., 2018). According to China’s Climate Change Blue Book records (2018), since 1980, the highest global losses due to climate disasters were incurred in 2017, accounting for 93% of the total economic losses due to natural disasters. Therefore, the impact of extreme climates is considerably greater than that of average climates; furthermore, the changes in precipitation exhibit lower spatial consistency than the changes in temperature (Gebrechorkos et al., 2018). Most countries in the world have reported a clear warming trend from 1951 to 2017, especially at the middle and high latitudes of the Eurasian continent, where the strongest warming trend has been noted. Climate warming has primarily manifested as increments in the minimum temperatures (Zhang et al., 2019).
In recent years, the impact of individual factors (either natural or anthropogenic factors) on climate change has attracted considerable research attention (Stott, 2003; Zhai et al., 2018). Natural factors include atmospheric circulation, altitude, etc (Horton et al., 2015, Pepin et al., 2015). As revealed by the studies of Sun et al., (2009) and Shi et al., (2018), in China, the most extreme climate index changes were positively correlated with the Atlantic multidecadal oscillation and affected by the convection activity over the region of the maritime continent. Wang et al., (2014) compared climate change characteristics at high and low altitudes and reported that the warming trend in high-altitude areas was significantly greater than that in low altitude areas. Anthropogenic factors typically refer to urbanization and land use/cover change (LUCC), which have a strong correlation with climate change (Hamin and Gurran, 2009; Liu et al., 2016; Gogoi et al., 2019). For instance, in China, 51% of the extreme weather events that have occurred since the pre-industrial periods were caused by anthropogenic factors; the probability of more severe extreme events has doubled over the past 50 years (Chen and Sun, 2017). Therefore, a comprehensive consideration of anthropogenic and natural factors is necessary for gaining insight into the spatial and temporal heterogeneity of climate change.
As one of the regions with the strongest comprehensive strength and the most important strategic significance in China, the Yangtze River Watershed plays a vital role in China's ecological balance and security (Qu et al., 2018). The climate is undergoing significant changes in the Yangtze River Watershed (Su et al., 2006; Duan et al., 2019); this poses a certain threat to grain production, surface vegetation, and river hydrology (Ju et al., 2014; Cui et al., 2019; Chen et al., 2020). Some studies have suggested that the number of extreme climate events in the Yangtze River Watershed will continue to increase in the future (Xu et al., 2009). However, very few studies have investigated the correlation factors of climate change in the Yangtze River Watershed by focusing on the relationship between urbanization and daily precipitation and temperature (Yao et al., 2015). Furthermore, the relationship between natural factors and extreme climate changes has not been considered. We found that natural factors (such as elevation and water bodies) were correlated with the climate change in the Yangtze River Watershed and that this correlation can be positive or negative. To improve our understanding of climate change characteristics and to identify the most vulnerable areas in the Yangtze River Watershed, it is necessary to comprehensively consider a variety of natural and anthropogenic factors in order to analyze the spatiotemporal characteristics of climate change and its hotspots.
To this end, we obtained meteorological data from 717 stations in the Yangtze River Watershed for the period of 1958 to 2017, and the RClimDex model was used to calculate the climate change characteristics over the entire watershed and its sub-basins. Based on the Random Forest model, the natural and anthropogenic characteristics of regions with significant climate changes, which were likely to be vulnerable regions, were identified from the perspectives of elevation, slope, population density, Gross Domestic Product (GDP), and LUCC.

2 Materials and methods

2.1 Study area

The Yangtze River Watershed is located in the south of China (90°33′-122°25′E and 24°30′-35°45′N) (Figure 1a). It covers an area of approximately 1.8 million km2, accounting for 18.8% of the total land area of China. Affected by the East Asian monsoon, South Asian monsoon, terrain, and topography (Zhang et al., 2010), the climate of the Yangtze River Watershed is complex and diverse, with mean annual minimum temperature, maximum temperature, and precipitation being 12.18°C, 20.88°C, and 1182.11 mm, respectively (in 2015). The Yangtze River Watershed is adjacent to the Pacific Ocean in the east and the Qinghai-Tibet Plateau in the west, and the elevation varies between -140 and 7148 m (Figure 1c). The topography of the watershed is complex, and is divided into the Qinghai-Tibet Plateau, the Hengduan Mountains, the Yunnan-Guizhou Plateau, the Sichuan Basin, the Jiangnan Hills, and the middle-lower Yangtze Plains from the west to east. As of 2015, the areas of cultivated land, forest land, grassland, water body, construction land, and unused land in the Yangtze River Watershed accounted for 26.86%, 41.72%, 23.32%, 2.83%, 2.14% and 3.13% of the total land area, respectively (Figure 1b). Due to the rapid socioeconomic developments over recent years, the population density of the Yangtze River Watershed has reached 67,009.4 people/km2, and in 2015, the GDP per unit area was 20707 million RMB yuan/km2 (Figure S1).
Figure 1 Location of meteorological stations and sub-basins (a); SB1-45 denote the sub-basins. Land use and land cover change (LUCC) in 2015 (b); LUCC1 is cultivated land, LUCC2 is forest land, LUCC3 is grassland, LUCC4 is water body, LUCC5 is construction land, and LUCC6 is unused land. Study area elevation (c)
Figure S1 The GDP per unit area (a) and population density (b) of Yangtze River Watershed in 2015

2.2 Description of data

2.3 Study methods

2.3.1 Extreme climate indices

An extreme climate value generally refers to a situation where a certain climate index exceeds a certain threshold (Karl et al., 1999). The RClimDex based on R language was developed by the Climate Research Branch of Meteorological Service of Canada, which could perform quality control on daily input data and provide a user-friendly interface to compute indices of climate extremes (http://etccdi.pacificclimate.org/software.shtml). This model computes all the 27 core indices (i.e., 16 temperature indices and 11 precipitation indices) recommended by the Expert Team for Climate Change Detection Monitoring and Indices (ETCCDMI), and the results can reflect the comprehensive climate change characteristics of an area of interest. Thus, the average daily precipitation and the maximum and minimum temperatures in the entire Yangtze River Watershed and its sub-basins were calculated and used as the input data for the RClimDex model. Subsequently, the 27 extreme climate indices from the model (Table S1) were used to analyze the climate change in this area of interest. The methods used for the quality control of the data included the following: (1) daily lowest temperature > daily highest temperature; (2) daily precipitation < 0 mm; (3) values greater than 3 times the standard deviation were defined as out of bound values, which were treated as the missing value (-99.9).
Table S1 Definition of the extreme climate indices
Index Indicator name Definition Unit
FD0 Frost days Annual count when TN (daily minimum)<0ºC Days
SU25 Summer days Annual count when TX (daily maximum)>25ºC Days
ID0 Ice days Annual count when TX (daily maximum)<0ºC Days
TR20 Tropical nights Annual count when TN (daily minimum)>20ºC Days
GSL Growing season Length Annual count between the first span of at least 6 days with daily
mean temperature >5℃ after winter and the first span after
summer of 6 days with a daily mean temperature <5℃		 Days
TXx Max Tmax Monthly maximum value of daily maximum temp ºC
TNx Max Tmin Monthly maximum value of daily minimum temp ºC
TXn Min Tmax Monthly minimum value of daily maximum temp ºC
TNn Min Tmin Monthly minimum value of daily minimum temp ºC
TN10p Cool nights Percentage of days when TN<10th percentile Days
TX10p Cool days Percentage of days when TX<10th percentile Days
TN90p Warm nights Percentage of days when TN>90th percentile Days
TX90p Warm days Percentage of days when TX>90th percentile Days
WSDI Warm spell duration indicator Annual count of days with at least 6 consecutive days
when TX>90th percentile		 Days
CSDI Cold spell duration indicator Annual count of days with at least 6 consecutive days when
TN<10th percentile		 Days
DTR Diurnal temperature range Monthly mean difference between TX and TN ºC
RX1day Max 1-day precipitation amount Monthly maximum 1-day precipitation Mm
RX5day Max 5-day precipitation amount Monthly maximum consecutive 5-day precipitation Mm
SDII Simple daily intensity index Annual total precipitation divided by the number of wet days
(defined as PRCP≥1.0mm) in the year		 Mm/day
R10 Number of heavy precipitation
days		 Annual count of days when PRCP≥10mm Days
Index Indicator name Definition Unit
R20 Number of very heavy precipitation days Annual count of days when PRCP≥20mm Days
R25 Heavy precipitation days Annual count of days when PRCP≥25mm Days
RR Daily precipitation A wet day is defined when RR ≥ 1 mm, and a dry day when RR < 1 mm Days
CDD Consecutive dry days Maximum number of consecutive days with RR<1mm Days
CWD Consecutive wet days Maximum number of consecutive days with RR≥1mm Days
R95p Very wet days Annual total PRCP when RR>95th percentile Mm
R99p Extremely wet days Annual total PRCP when RR>99th percentile Mm
PRCPTOT Annual total wet-day precipitation Annual total PRCP in wet days (RR≥1mm) Mm
Among them, the temperature indices included: frost days (FD0), summer days (SU25), ice days (ID0), tropical nights (TR20), growing season length (GSL), maximum temperature (TXx), maximum of the minimum temperature (TNx), minimum of the maximum temperature (TXn), minimum temperature (TNn), cool nights (TN10p), cool days (TX10p), warm nights (TN90p), warm days (TX90p), warm spell duration indicator (WSDI), cold spell duration indicator (CSDI), and the diurnal temperature range (DTR). The precipitation indices included: the maximum amount of precipitation in 1 day (RX1day) and over 5 days (RX5day); the simple daily intensity index (SDII); the number of days with precipitation ≥ 10 mm (R10) and the number of days with precipitation ≥ 20 mm (R20); the number of days with precipitation ≥ 25 mm (R25); the number of consecutive dry days (CDD), wet days (CWD), very wet days (R95p), and extremely wet days (R99p); and the annual total wet-day precipitation (PRCPTOT).

2.3.2 Response of climate change to relevant influencing factors

This study assessed the degree of the response of climate change to influencing factors by calculating regional climate change characteristics (CLCC), whereby a larger CLCC value indicates a larger response of climate change to impact factors. The calculation formula of the CLCC can be expressed as:
$CLCC = \frac{{NECI}}{{27}} \times 100\%,$
where CLCC denotes the degree of response of climate change to impact factors; NECI is the number of extreme indices that have changed significantly, and the denominator 27 is the total number of extreme climate indices recommended by ETCCDMI.

2.3.3 The Random Forest model

The Random Forest model was proposed by Breiman (2001) and has subsequently been widely used in various fields (Chen et al., 2018; Duan et al., 2020; Torbick et al., 2017). The Random Forest model is a collection of untrimmed classification or regression trees generated from the bootstrap samples of the training data and selecting random features in the decision tree induction method, and has the advantages of the general decision tree method in machine learning while avoiding the problem of excessive simulation (Svetnik et al., 2003). The Random Forest algorithm obtains the importance of the variable by calculating the mean of squared residuals (MSE) that would increase if the prediction variable was randomly permuted in the test set, and records it as %IncMSE (IMSE) (Cutler et al., 2004). The IMSE value of different independent variables from high to low indicates that the influence of the independent variable on the dependent variable is from strong to weak (Breiman, 2001). The model performance was quantified using the coefficient of determination (% var explained) (Liu et al., 2019). The importance assessment model was previously shown to be good at handling data containing the complicated interactions, and presented certain adaptability in the field of ecological environment (Tong et al., 2019). The impact of anthropogenic and natural factors on climate change was calculated using the random forest package in R (http://www.stat.berkeley.edu/users/breiman/RandomForests/cc_home.htm), and IMSE was calculated based on the regression trees.
IMSE was significantly different in the context of different climate indices, and we normalized the IMSE value of the Random Forest output for convenient analysis, as follows:
$IMSE{S_i} = \frac{{IMS{E_i} - IMS{E_{\min }}}}{{IMS{E_{\max }} - IMS{E_{min}}}},$
where IMSESi denotes the normalization result of the IMSE output value of the influencing factor i; IMSEi is the original output value of the IMSE of the influencing factor i; IMSEmax and IMSEmin are the maximum and minimum values, respectively, of the IMSE original output results of all influencing factors of the certain extreme climate indices.

3 Results and discussion

3.1 Climate change characteristics of the entire Yangtze River Watershed

The climate of the Yangtze River Watershed has been changing significantly, which may pose essential impacts on water resources, social and economic construction, ecological, and vegetation net primary productivity, etc. (Yang et al., 2007; Piao et al., 2010; Zhao et al., 2017). Among the 27 extreme climate indices, 19 have changed significantly from 1958-2017 (statistically significant at the 0.05 level), including 14 temperature indexes and five precipitation indices, accounting for 51.85% and 18.52% of their total number, respectively (Table 1). For the temperature indices, TXx, TXn, TNx, TNn, SU25, TR20, TX90p, TN90p, and WSDI increased significantly, and DTR, FD0, TX10p, TN10p, and CSDI decreased significantly. In the precipitation index, RX5day, SDII, R95p, R99p, and R10 increased significantly, while there were no significant declining indices.
Table 1 Temporal changes in extreme climate indices in the Yangtze River Watershed from 1958-2017
Temperature indices Changing rate per decade Precipitation indices Changing rate per decade
TXx (℃) 0.19 RX1day (mm) 0.36
TXn (℃) 0.28 RX5day (mm) 1.74
TNx (℃) 0.15 SDII (mm/day) 0.06
TNn (℃) 0.32 R95p (mm) 13.14
DTR (℃) -0.06 R99p (mm) 6.17
SU25 (days) 2.63 PRCPTOT (mm) 8.45
ID0 (days) -0.02 R10 (days) 1.04
TR20 (days) 2.57 R20 (days) 0.13
FD0 (days) -3.04 R25 (days) 0.02
TX10p (days) -1.5 CDD (days) -0.65
TX90p (days) 2.71 CWD (days) 0.87
TN10p (days) -3.65
TN90p (day) 4.29
WSDI (days) 1.77
CSDI (days) -1.56
GSL (days) 1.34

Notes: Trends marked in bold are statistically significant at the 0.05 level.

Regarding temperature, the Yangtze River Watershed showed a warming trend, which was smaller than that of China (Yin and Sun, 2018). The study found that the increasing trend in the minimum temperature (0.32 ℃/decade, p < 0.05) in the basin was greater than that of the maximum temperature (0.19 ℃/decade, p < 0.05), which may be the reason for the decrease in the diurnal temperature range. The trends of night (TN90p, TN10p) and day indices (TX90p, TX10p) were asymmetric and the trend of night indices was approximately twice that of day indices, which was similar to a previous study (Cui et al., 2019). With the temperature increase, the warm spell duration (WSDI) was also increasing (1.77 days/decade, p < 0.05). Regarding precipitation, heavy precipitation in the watershed increased, whereas the annual total precipitation did not change significantly, and the annual precipitation showed a trend of wet and dry polarization. The heavy precipitation indices (R95p and R99p) were similar to those in southwest China at a similar latitude (Li et al., 2012; Liu et al., 2019; Qian et al., 2007). Under the background of concentrated precipitation, the contributions of R95p and R99p to PRCPTOT (R95p/PRCPTOT; R99p/PRCPTOT) increased by 0.98%/decade and 0.49%/decade (p < 0.05), respectively (Figure 2). Hence, if strong rainfall is not discharged in a timely manner, it may easily result in flood disasters (Ouyang et al., 2020).
Figure 2 Contribution of (a) very wet days (R95p) and (b) extremely wet days (R99p) to annual total wet-day precipitation (PRCPTOT) in the Yangtze River Watershed; slope of the regression line represents the interannual variations in the contributions

3.2 Climate change characteristics of the sub-basins

By calculating the CLCC of the sub-basins, this study showed significant differences in the degree of the response of climate change in sub-basins to natural and anthropogenic factors (Figure 3). The CLCC values of the sub-basins were between 22.22%-88.89%. The degree of the response of climate change in the upper and lower reaches of the Yangtze River to relevant influencing factors was higher than that in the middle reaches, especially SB17 (74.07%), SB18 (88.89%), and SB22 (74.07%) in the delta plain of the Yangtze River estuary and SB6 (74.07%) in the upstream. Classification of the sensitive areas of climate change based on Köppen climate classification found a similar result, whereby the southern Qinghai-Tibet Plateau, the Yunnan-Guizhou Plateau, the Sichuan Basin, and the middle-lower Yangtze Plains were identified as sensitive and more sensitive areas for climate change in a previous study (Li et al., 2018). Therefore, we should devote more energy to those areas when formulating measures to tackle climate change.
Figure 3 Degrees of responses of climate change to influencing factors in sub-basins
As shown in Figure 4, the directions of changes of the 27 extreme climate indices in the sub-basins differed, especially the precipitation indices. In the air temperature indices, TXx, TXn, TNx, TNn, TN90p, TX90p, TR20, SU25, WSDI, and GSL showed upward trends, TN10p, FD0, ID0, TX10p, and CSDI showed downward trends, DTR showed both upward and downward trends, however, only 3.81% of the sub-basins showed a downward trend. The changing trend of temperature indices of the Yangtze River Watershed was relatively consistent. For the precipitation indices, there were few sub-basins (< 50%) with changes, and these changes were inconsistent. RX1day and SDII showed upward trends, CWD showed a downward trend, and RX5day, R95p, R99p, PRCPTOT, R10, R20, R25, and CDD showed both upward and downward trends. In general, the majority of the sub-basins of the Yangtze River showed warming trends, with significant temperature increases, and few of the sub-basins had changes in precipitation, with the majority showing significantly increasing trends. The climate change characteristics of the sub-basins and the entire Yangtze River Watershed were basically the same.
Figure 4 Percentage of watersheds for each extreme climate index that was identified as experiencing an increase (IN), a decrease (DE), and no trend (NT)
Regarding climate change indices of the Yangtze River sub-basins, there were not only differences in the direction, but also in the magnitude (Figures 5, S2, and S3). Eleven extreme climate indices with changes in more than 50% of the sub-basins were selected to analyze the characteristics of climate indices changes in the sub-basins, all of which were temperature categories, including TXx, TXn, TNx, TNn, DTR, TN10p, TN90p, TX90p, FD0, TR20, and SU25. Among the increasing indices, TXx and TX90p had relatively large changes in the headwaters and the delta plain of the Yangtze River; TNx, TXn, TNn, and TR20 had relatively large changes in the middle and lower reaches of the Yangtze River; TN90p had relatively large changes in the headwaters, middle and lower reaches of the Yangtze River, and the sub-basins with a strong increasing trend of SU25 were scattered in the Yangtze River Watershed. The decreasing indices (TN10p, FD0) had stronger changing trends in the headwaters, middle and lower reaches of the Yangtze River. For DTR, and the sub-basins with decreasing trends were mainly in the middle and lower reaches, and only SB2 had an increasing trend. To summarize, the 11 climate indices of the delta plain of the Yangtze River estuary changed considerably, and this area was the hot spot of climate change in the Yangtze River Watershed.
Figure 5 Rate of change in the main extreme climate indices for the Yangtze River sub-basins from 1958 to 2017; slope is represented by k

3.3 Natural and anthropogenic characteristics in climate change hotspots

Previous studies have suggested that human activities were more intensive in the part of warming regions of the Yangtze River Watershed (Yao et al., 2015; Wang et al., 2017), but they have not analyzed the natural characteristics in the regions from the perspective of quantitative methods (Wang et al., 2013). The natural factors have been recognized as significant impact factors on climate change (Zuo et al., 2011; Cai et al., 2017), which should not be ignored. Considering the obvious heterogeneity of physiographical features and human activities in the Yangtze River Watershed, and the part of sub-basins could not represent the whole watershed, we analyzed the natural (elevation, slope, forest land, grassland, water area, unused land) and anthropogenic (population density, GDP, and construction land, cultivated land) characteristics in the regions with significant climate change of the Yangtze River Watershed. In order to avoid the interaction of the correlation factors (Figure 6), we calculated the average elevation, slope, population density, GDP per unit area, and the proportion of the area of the LUCC1-6 in the sub-basins, then used them and the changes of the 11 extreme climate indices (TXx, TXn, TNx, TNn, DTR, TN10p, TN90p, TX90p, FD0, TR20, and SU25) with changes in more than 50% of the sub-basins as the input data (after removing outliers) for the Random Forest model to identify the main factors related to the extreme climate indices (Table 2). Combined the results of Random Forest with correlations between the main correlation factors and extreme climate indices, we could identify the natural and anthropogenic characteristics in the regions with significant change of the extreme climate indices.
Figure 6 Analysis of the related factors of climate change in the Yangtze River Watershed from 1958-2017 (a denotes significance at the 0.05 level; b denotes significance at the 0.01 level; blank value means no significant correlation)
Table 2 The importance of factors affecting climate change (after normalization)
DTR TXx TXn TNx TNn TN10p TN90p TX90p FD0 TR20 SU25
Elevation 0.74 0.63 1 1 0.78 1 1 0.83 0.77 0.44 0.74
Slope 0.36 0.49 0.60 0.19 0.45 0.24 0.06 0.81 0.48 0.61 0.09
Population 0.03 0.53 0.50 0.38 0.04 0.43 0.56 0.58 0.54 0.82 0.41
GDP 0.73 1 0.67 0 0.06 0 0 0.61 0.20 0.96 1
Cultivated land 0.47 0.17 0.36 0.21 0 0.11 0.21 0.15 0.63 0.21 0.53
Forest land 0.35 0.75 0.19 0.30 0.09 0.31 0.52 0.14 0.37 0.48 0
Grassland 0.06 0.74 0.05 0.77 0.24 0.38 0.65 1 0.34 0.37 0.04
Water body 0.06 0 0.28 0.14 1 0.74 0.11 0 1 0 0.42
Construction land 1 0.84 0.80 0.19 0.52 0.18 0.22 0.61 0.75 1 0.25
Unused land 0 0.51 0 0.37 0.34 0.33 0.09 0.01 0 0.16 0.31
The Random Forest results showed that, all the coefficients of determination were greater than 0 (2.04-51.3), the model was adapted to our study. The anthropogenic factors (GDP and construction land) were positively correlated with TXx, TR20, and SU25, which suggested that human activities were more intensive in the regions exhibiting a faster warming trend in the Yangtze River Watershed. This was mainly related to urbanization; the increasing concentration of carbon dioxide in the atmosphere due to the developments in the Yangtze River Watershed over the past 60 years was one of the causes of this warming (Yang et al., 2017; Sun et al., 2019). With regard to natural factors, the proportion of water bodies was positively and negatively correlated with TNn and FD0, respectively. All the correlations indicated that, in the Yangtze River Watershed, regions with a greater proportion of water bodies are more vulnerable to warming; a similar phenomenon was identified through studies on the impact of European lakes on climate change (Samuelsson et al., 2010). Water bodies have a large thermal inertia and can, therefore, release heat continuously even at low temperatures. Furthermore, in the Yangtze River Watershed, forest land was mainly converted to grasslands, which led to a decrease in the surface evapotranspiration and a corresponding increase in air temperature (Wang et al., 2015). As indicated by the results, grasslands, as the main relevance factor for TX90P, were mainly distributed in the upper reaches, which exhibited a warming trend. This warming can be amplified through snow-albedo and cloud-radiation feedback at high-elevation regions (Liu et al., 2009). Furthermore, elevation was the main relevance factor for TNx, TNn, TN10P, and TN90P; elevation was negatively correlated with the increasing trends of TNx and TNn and positively correlated with the increasing trends of TN10p and TN90p. This is in line with the results of previous studies on the temperature change characteristics of other regions at high altitudes (Li et al., 2012; Li et al., 2020; Wang et al., 2014). In summary, in the Yangtze River Watershed, regions with rapid warming are characterized by intense human activities, large proportions of water bodies or grasslands, and high altitudes. Thus, to address the climate change in the Yangtze River Watershed, relevant management departments should formulate reasonable regional planning measures, such as restricting development areas and deforestation.
The findings described here inform the natural and anthropogenic characteristics in the regions with significant climate change of the Yangtze River Watershed, however there are also some limitations. Due to limitations in the data, we did not consider the characteristics of monsoon and ocean current in the climate change hotspots, and the further analysis is needed. Despite this limitation, this study provides a scientific reference (i.e., the important relevant factors of climate change) for managers to identify hotspots and formulate measures to address climate change. These results are expected to help better understand the influence of natural and anthropogenic factors on regional climate and facilitate future land planning.

4 Conclusions

Based on observation data from 1958 to 2017, which were obtained from 717 meteorological stations, this study analyzed the climate change characteristics of the Yangtze River Watershed and their relationship with climate change hotspots; these results are significantly important for protecting the environment and facilitating sustainable social developments.
The climate of the Yangtze River Watershed varies considerably under the influences of anthropogenic and natural factors, and the degree of climate change in the upper and lower reaches was greater than that in the middle reaches, especially at the delta plain of the Yangtze River estuary. Furthermore, the watershed exhibited a clear trend of increase in temperature, where the night indices exceeded the day indices. In addition, heavy precipitation increased and tended to be centralized. The change in temperature was more evident than the change in precipitation. Human activities (higher GDP and construction land proportion) and natural factors (high altitudes and large proportions of grassland and water bodies) were the main correlation factors for TXx, TR20, SU25, TXn, TNx, TNn, TN10p, TN90p, TX90p, and FD0; this was particularly evident in regions that were more vulnerable to climate warming. Based on these results, management departments should devote additional efforts for formulating measures to tackle climate change in the Yangtze River Watershed, particularly for regions more susceptible to significant climate changes.
Figure S2 Changing rate of the rest extreme temperature indices in the Yangtze River sub-basins from 1958-2017
Figure S3 Changing rate of the extreme precipitation indices in the Yangtze River sub-basins from 1958-2017

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