and southern Japan.

**Figure 5.** Nine-point-filtered spatial distribution differences (strong SCSSM years minus weak SCSSM years) of (**a**) TCs track density, (**b**) average intensity, and (**c**) regional PDI over WNP basin during TC season. Dots mark areas statistically significant at the 90% confidence level. **Figure 5.** Nine-point-filtered spatial distribution differences (strong SCSSM years minus weak SCSSM years) of (**a**) TCs track density, (**b**) average intensity, and (**c**) regional PDI over WNP basin during TC season. Dots mark areas statistically significant at the 90% confidence level.

## **4. Possible Mechanisms**

#### The regional PDI spatial pattern (Figure 5c) can be well summarized as an integrated *4.1. Monsoon Trough*

result of track density and average intensity characteristics, which can effectively represent the destructive potential of TCs in different areas. In weak SCSSM years, TCs are generated close to the tropical Central Pacific, and there are more and stronger TCs activities in the east of 130° E, resulting in significantly higher regional PDI. After the development of these TCs over the warm-pool region, they hit the Philippine coast with violent intensities. However, since TCs moved into the SCS, owing to the weak SCSSM, deep convection is weaker than that in strong years, and cyclone intensity gradually weakened, resulting in low regional PDI in the SCS. In strong SCSSM years, the conditions are reversed and the opposite occurs. In these years, the East Asian continent, especially the southern coast of China, is required to defend against TCs that generate and intensify near the shore. **4. Possible Mechanisms**  *4.1. Monsoon Trough*  Previous studies have shown that the location of the Monsoon Trough (MT) has a controlling effect on the activity distribution of WNPTC [20,42,43]. Wu et al. [18] found that more TCs were generated in the WNP when the MT extended eastward. To verify the relationship between SCSSM intensity and monsoon trough pattern over the WNP, this study conducted a regression analysis on the 850 hPa circulation and OLR in WNP during the period from May to December based on the annual *ISM*. In both the strong and weak years, two MTs in the SCS and the Philippine Sea regions can be distinguished (as shown in Figure 6a,b). Compared to the weak years, the position of the SCS MT does not differ significantly in the strong years, but the intensity difference is remarkable, with stronger southwest winds and more intense convective activity. On the other hand, the intensity and pattern of the Philippine Sea MT undergo significant changes between the strong and weak years. In strong years, the Philippine Sea MT extends only to 140◦ E, with energy concentrated near the Philippine Islands due to the supplement of strong southwest moisture input from the SCS and the Western Pacific region, forming a block-shaped low OLR area. In the weak years of the SCSSM, the MT can extend eastward to nearly 155◦ E, at a lower latitude, with a more scattered distribution of low OLR values, and closer to the tropical central Pacific.

Previous studies have shown that the location of the Monsoon Trough (MT) has a

controlling effect on the activity distribution of WNPTC [20,42,43]. Wu et al. [18] found

study conducted a regression analysis on the 850 hPa circulation and OLR in WNP during the period from May to December based on the annual *ISM*. In both the strong and weak years, two MTs in the SCS and the Philippine Sea regions can be distinguished (as shown in Figure 6a,b). Compared to the weak years, the position of the SCS MT does not differ significantly in the strong years, but the intensity difference is remarkable, with stronger southwest winds and more intense convective activity. On the other hand, the intensity tropical central Pacific.

**Figure 6.** An 850 hPa horizontal wind field (streamline) and OLR (shade) during TC season (May to December) in (**a**) SCSSM strong years and (**b**) SCSSM weak years, respectively, regressed against the annual *ISM*. The regression elements have statistical significance at the 90% confidence level. The MT is denoted by a purple, thick-dashed line. The solid red dots represent the locations of TC genesis. The blue boxes represent four sub-regions: Area I (i.e., SCS, 3°–25° N, 105°–120° E), Area II (15°– 25° N, 120°–150° E), Area III (3°–15° N, 120°–150° E), and Area IV (3°–15° N, 150° E–180°). The bar charts (**c**) show the number of TCs genesis per year in each area during TC season, in which strong SCSSM years are in red and weak SCSSM years are in blue, with yellow shading indicating that the **Figure 6.** An 850 hPa horizontal wind field (streamline) and OLR (shade) during TC season (May to December) in (**a**) SCSSM strong years and (**b**) SCSSM weak years, respectively, regressed against the annual *ISM*. The regression elements have statistical significance at the 90% confidence level. The MT is denoted by a purple, thick-dashed line. The solid red dots represent the locations of TC genesis. The blue boxes represent four sub-regions: Area I (i.e., SCS, 3◦–25◦ N, 105◦–120◦ E), Area II (15◦–25◦ N, 120◦–150◦ E), Area III (3◦–15◦ N, 120◦–150◦ E), and Area IV (3◦–15◦ N, 150◦–180◦ E). The bar charts (**c**) show the number of TCs genesis per year in each area during TC season, in which strong SCSSM years are in red and weak SCSSM years are in blue, with yellow shading indicating that the differences are significant at 90% confidence level.

and pattern of the Philippine Sea MT undergo significant changes between the strong and weak years. In strong years, the Philippine Sea MT extends only to 140° E, with energy concentrated near the Philippine Islands due to the supplement of strong southwest moisture input from the SCS and the Western Pacific region, forming a block-shaped low OLR area. In the weak years of the SCSSM, the MT can extend eastward to nearly 155° E, at a lower latitude, with a more scattered distribution of low OLR values, and closer to the

differences are significant at 90% confidence level. As shown in Figure 6, based on the range of MT activity and TC genesis, four subregions are divided in the WNP: Area I (i.e., SCS, 3°–25° N, 105°–120° E), Area II (15°–25° N, 120°–150° E), Area III (3°–15° N, 120°–150° E), and Area IV (3°–15° N, 150° E–180°). The positions of TC genesis are closely related to the position of MT, and the number of TC genesis in the four regions differs significantly between the strong and weak years. In the SCSSM strong years, the SCS is affected by a stronger MT, resulting in a significantly higher number of TCs being generated. The same happens in Area II, the northern part of the Philippine Sea, where the number of TCs in strong years is more than twice as much as that in weak years, which is related to the northward MT. Conversely, in weak years, TCs tend to be generated at a lower latitude, in Areas III and IV. Especially in Area IV, during the weak years, the anomalous eastward expansion of MT results in more TCs generation, in contrast to the strong years. These composited patterns are consistent with As shown in Figure 6, based on the range of MT activity and TC genesis, four subregions are divided in the WNP: Area I (i.e., SCS, 3◦–25◦ N, 105◦–120◦ E), Area II (15◦–25◦ N, 120◦–150◦ E), Area III (3◦–15◦ N, 120◦–150◦ E), and Area IV (3◦–15◦ N, 150◦–180◦ E). The positions of TC genesis are closely related to the position of MT, and the number of TC genesis in the four regions differs significantly between the strong and weak years. In the SCSSM strong years, the SCS is affected by a stronger MT, resulting in a significantly higher number of TCs being generated. The same happens in Area II, the northern part of the Philippine Sea, where the number of TCs in strong years is more than twice as much as that in weak years, which is related to the northward MT. Conversely, in weak years, TCs tend to be generated at a lower latitude, in Areas III and IV. Especially in Area IV, during the weak years, the anomalous eastward expansion of MT results in more TCs generation, in contrast to the strong years. These composited patterns are consistent with previous studies. The MT is a background field for the growth of synoptic-scale wave disturbances [44], and it provides an environment for the enhancement of convective activity and TCs genesis [18–20], and thus anomalous MT activities controlling the variation in the frequency of TC formation in different regions.

previous studies. The MT is a background field for the growth of synoptic-scale wave disturbances [44], and it provides an environment for the enhancement of convective activity

#### *4.2. Subtropical High* edge of the subtropical high, which is well coupled with multi-scale systems including

*4.2. Subtropical High*

Climatologically, the MT over the Western Pacific extends along the southwestern edge of the subtropical high, which is well coupled with multi-scale systems including ENSO [45–47]. Before discussing the larger-scale ocean–atmosphere system, we examine the distribution of the Western Pacific subtropical high (WPSH) in anomalous SCSSM years. The patterns of the WPSH during TC season in the anomalous SCSSM years are shown in Figure 7. The WPSH intensity in strong SCSSM years is weak, the ridge latitude is northward, and the westward extension of the ridge point is eastward, which is in obvious contrast to the strong WPSH in the weak years, making it a contributor to the difference in destructive potential. The difference in ridge latitude is the most significant during the peak of the monsoon (JJA) (Figure 7b). In strong years, the WPSH ridge is northward, whereas the SCS MT and the convection over the northern part of the Philippine Sea are strong, which favors TC genesis over the SCS and Area II. On the contrary, in the weak years, when the WPSH ridge is southward, the TC formation condition of Area II turns worse due to the suppression of convection by the WPSH, so it can well explain the difference of Area II between the strong and weak years. ENSO [45–47]. Before discussing the larger-scale ocean–atmosphere system, we examine the distribution of the Western Pacific subtropical high (WPSH) in anomalous SCSSM years. The patterns of the WPSH during TC season in the anomalous SCSSM years are shown in Figure 7. The WPSH intensity in strong SCSSM years is weak, the ridge latitude is northward, and the westward extension of the ridge point is eastward, which is in obvious contrast to the strong WPSH in the weak years, making it a contributor to the difference in destructive potential. The difference in ridge latitude is the most significant during the peak of the monsoon (JJA) (Figure 7b). In strong years, the WPSH ridge is northward, whereas the SCS MT and the convection over the northern part of the Philippine Sea are strong, which favors TC genesis over the SCS and Area II. On the contrary, in the weak years, when the WPSH ridge is southward, the TC formation condition of Area II turns worse due to the suppression of convection by the WPSH, so it can well explain the difference of Area II between the strong and weak years.

and TCs genesis [18–20], and thus anomalous MT activities controlling the variation in the

Climatologically, the MT over the Western Pacific extends along the southwestern

*Atmosphere* **2023**, *14*, x FOR PEER REVIEW 10 of 18

frequency of TC formation in different regions.

**Figure 7.** (**a**) The patterns (unit: gpm) and (**b**) the ridge latitudes of Western Pacific subtropical high (WPSH) at 500 hPa over the WNP basin during TC season (May to December), in which strong SCSSM years are in red and weak SCSSM years are in blue, with yellow shading in (**b**) indicating that the differences are significant at 90% confidence level. **Figure 7.** (**a**) The patterns (unit: gpm) and (**b**) the ridge latitudes of Western Pacific subtropical high (WPSH) at 500 hPa over the WNP basin during TC season (May to December), in which strong SCSSM years are in red and weak SCSSM years are in blue, with yellow shading in (**b**) indicating that the differences are significant at 90% confidence level.

tion (ENSO) or IOD.

frequency of WNPTC.

utors to the WPSH variability [65–68].

The interannual variation of WPSH is modulated by tropical ocean and atmosphere interaction [48]. In order to understand the relationship between WPSH, SCSSM, and TCs activity, we need to turn our attention to a larger scale of the tropical Indo-Pacific Ocean. The above analysis shows that the SCSSM intensity is coupled with the MT and the WPSH, which indicates that its interannual variability is not only a response to regional synoptic-scale changes. As a central system in the Asian–Australian monsoon region, the

#### *4.3. Interaction of Tropical Indo-Pacific Ocean–Atmosphere System* anomalies of the SCSSM are closely responsive to the large-scale tropical Indo-Pacific

The interannual variation of WPSH is modulated by tropical ocean and atmosphere

interaction [48]. In order to understand the relationship between WPSH, SCSSM, and TCs activity, we need to turn our attention to a larger scale of the tropical Indo-Pacific Ocean.

*Atmosphere* **2023**, *14*, x FOR PEER REVIEW 11 of 18

*4.3. Interaction of Tropical Indo-Pacific Ocean–Atmosphere System*

The above analysis shows that the SCSSM intensity is coupled with the MT and the WPSH, which indicates that its interannual variability is not only a response to regional synoptic-scale changes. As a central system in the Asian–Australian monsoon region, the anomalies of the SCSSM are closely responsive to the large-scale tropical Indo-Pacific ocean– atmosphere system [49]. Studies have shown that there is a robust relationship between monsoon and sea surface temperature (SST) [23,50,51]. In terms of the SST anomaly (SSTA) during the TC season (May to December), the tropical Indo-Pacific Ocean shows tripolar distributions in anomalous SCSSM years (Figure 8), with a negative–positive–negative pattern in strong years and a positive–negative–positive pattern in weak years. In strong (weak) years, the Indian Ocean has an Indian Ocean Dipole (IOD) negative (positive) phase distribution, whereas the Central-Eastern Pacific Ocean has a La Niña-like (El Niño-like) pattern, and the Western Pacific warm-pool region is a large area of positive (negative) SSTA. This distribution further confirms the coupling relationship between the intensity of the SCSSM and Indo-Pacific pattern [52,53], such as El Niño-Southern Oscillation (ENSO) or IOD. ocean–atmosphere system [49]. Studies have shown that there is a robust relationship between monsoon and sea surface temperature (SST) [23,50,51]. In terms of the SST anomaly (SSTA) during the TC season (May to December), the tropical Indo-Pacific Ocean shows tripolar distributions in anomalous SCSSM years (Figure 8), with a negative–positive–negative pattern in strong years and a positive–negative–positive pattern in weak years. In strong (weak) years, the Indian Ocean has an Indian Ocean Dipole (IOD) negative (positive) phase distribution, whereas the Central-Eastern Pacific Ocean has a La Niña-like (El Niño-like) pattern, and the Western Pacific warm-pool region is a large area of positive (negative) SSTA. This distribution further confirms the coupling relationship between the intensity of the SCSSM and Indo-Pacific pattern [52,53], such as El Niño-Southern Oscilla-

**Figure 8.** Distribution of SSTA differences (strong years minus weak years) in the tropical Indo-Pacific Ocean during TC season (May to December) in anomalous SCSSM years. Dots mark areas that are statistically significant at the 90% confidence level. **Figure 8.** Distribution of SSTA differences (strong years minus weak years) in the tropical Indo-Pacific Ocean during TC season (May to December) in anomalous SCSSM years. Dots mark areas that are statistically significant at the 90% confidence level.

Numerous studies have described the activity of the WNPTC response to ENSO [23,54–60]: in El Niño years, there are more TCs in the eastern part of the WNP, and the TCs in El Niño years are stronger and longer-lived than in La Niña years. In addition to the influence of ENSO, the impact of the Indian Ocean mode on WNPTC has been emphasized in recent years. Zhan et al. [51] revealed that the influence of the East Indian Ocean SSTA on WNPTC has been significantly enhanced since the late 1970s. Zhou et al. [61] discovered a significant negative correlation between the number of TCs landfalling Numerous studies have described the activity of the WNPTC response to ENSO [23,54–60]: in El Niño years, there are more TCs in the eastern part of the WNP, and the TCs in El Niño years are stronger and longer-lived than in La Niña years. In addition to the influence of ENSO, the impact of the Indian Ocean mode on WNPTC has been emphasized in recent years. Zhan et al. [51] revealed that the influence of the East Indian Ocean SSTA on WNPTC has been significantly enhanced since the late 1970s. Zhou et al. [61] discovered a significant negative correlation between the number of TCs landfalling in China and the IOD. Liu et al. [62] proposed two joint modes of the Indo-Pacific SSTA, and the oscillations between them could reveal the interdecadal variation in the genesis frequency of WNPTC.

in China and the IOD. Liu et al. [62] proposed two joint modes of the Indo-Pacific SSTA, and the oscillations between them could reveal the interdecadal variation in the genesis Furthermore, tropical Indo-Pacific SSTA exerts a critical influence on the WPSH [63]. Previous studies have highlighted the impact of tropical SSTA on WPSH and demonstrated that El Niño is a key source of its variability [48,63,64]. Additionally, some mechanisms

nisms related to the Indian Ocean and the Maritime Continent are also important contrib-

Furthermore, tropical Indo-Pacific SSTA exerts a critical influence on the WPSH [63].

related to the Indian Ocean and the Maritime Continent are also important contributors to the WPSH variability [65–68]. spatial and temporal extent of TC activity and modulates the East Asian atmospheric circulation (including SCSSM), and plays a key role as a mediator in the strong relationship

Therefore, through the action of WPSH, the tropical Indo-Pacific SSTA controls the

Therefore, through the action of WPSH, the tropical Indo-Pacific SSTA controls the spatial and temporal extent of TC activity and modulates the East Asian atmospheric circulation (including SCSSM), and plays a key role as a mediator in the strong relationship between the SCSSM and WNPTC. between the SCSSM and WNPTC. As shown in Figure 9, the tripolar SSTA distribution leads to two anomalous Walker cells [69]. In strong (weak) SCSSM years, WPSH abnormally weakens (strengthens), and

As shown in Figure 9, the tripolar SSTA distribution leads to two anomalous Walker cells [69]. In strong (weak) SCSSM years, WPSH abnormally weakens (strengthens), and the Western Pacific is an anomalous ascending branch (descending branch) closely connected with the anomalous descending branches (ascending branches) over the Indian Ocean and Central-Eastern Pacific [66]. This means that in anomalous SCSSM years, the tropical Indo-Pacific air-sea systems are stably coupled. By combining the distributions of atmospheric circulation and related physical variables, the factors responsible for the differences in WNPTC activity in anomalous SCSSM years could be further understood. the Western Pacific is an anomalous ascending branch (descending branch) closely connected with the anomalous descending branches (ascending branches) over the Indian Ocean and Central-Eastern Pacific [66]. This means that in anomalous SCSSM years, the tropical Indo-Pacific air-sea systems are stably coupled. By combining the distributions of atmospheric circulation and related physical variables, the factors responsible for the differences in WNPTC activity in anomalous SCSSM years could be further understood.

*Atmosphere* **2023**, *14*, x FOR PEER REVIEW 12 of 18

**Figure 9.** Vertical section differences (strong years minus weak years) of equatorial (average of 5° S– 5° N) vertical velocity (shade) and latitudinal circulation (arrow) over the tropical Indo-Pacific Ocean during TC season (May to December) in anomalous SCSSM years. Shaded areas are statistically significant at the 90% confidence level. **Figure 9.** Vertical section differences (strong years minus weak years) of equatorial (average of 5 ◦ S–5◦ N) vertical velocity (shade) and latitudinal circulation (arrow) over the tropical Indo-Pacific Ocean during TC season (May to December) in anomalous SCSSM years. Shaded areas are statistically significant at the 90% confidence level.

In strong SCSSM years, anomalous eastward winds occur over the tropical Indian Ocean and anomalous westward winds occur over the tropical Pacific Ocean at the lower level, whereas the opposite is true at the upper level (Figure 10a,b), forming two Walker cells in their respective basins. They constitute a latitudinal wind anomalous convergence zone near the Western Pacific warm pool region (90–150° E), which also corresponds to the upward vertical motion anomalous zone (Figure 10c). This upward zone essentially controls the SCS to the Philippine Sea, which, together with the improved water vapor input from the SCSSM (Figure 10e), favors more TCs active in the region. Moreover, SCS is a zone of positive relative vorticity (Figure 10d), indicating active convection and enhanced monsoon trough, which also favors the generation and activity of the SCSTC. However, the opposite is true in the eastern part of WNP, with negative relative vorticity anomalies, anomalous downward vertical motion, and low water vapor, indicating that the dynamic and thermodynamic conditions are unfavorable for the generation of TCs in In strong SCSSM years, anomalous eastward winds occur over the tropical Indian Ocean and anomalous westward winds occur over the tropical Pacific Ocean at the lower level, whereas the opposite is true at the upper level (Figure 10a,b), forming two Walker cells in their respective basins. They constitute a latitudinal wind anomalous convergence zone near the Western Pacific warm pool region (90–150◦ E), which also corresponds to the upward vertical motion anomalous zone (Figure 10c). This upward zone essentially controls the SCS to the Philippine Sea, which, together with the improved water vapor input from the SCSSM (Figure 10e), favors more TCs active in the region. Moreover, SCS is a zone of positive relative vorticity (Figure 10d), indicating active convection and enhanced monsoon trough, which also favors the generation and activity of the SCSTC. However, the opposite is true in the eastern part of WNP, with negative relative vorticity anomalies, anomalous downward vertical motion, and low water vapor, indicating that the dynamic and thermodynamic conditions are unfavorable for the generation of TCs in this region. Therefore, this pattern explains the seesaw relationship between the spatial activity characteristics of TCs over the SCS and the eastern part of WNP during the SCSSM anomaly years, as shown in Figure 5.

this region. Therefore, this pattern explains the seesaw relationship between the spatial activity characteristics of TCs over the SCS and the eastern part of WNP during the SCSSM

anomaly years, as shown in Figure 5.

**Figure 10.** Distribution differences (strong years minus weak years) of (**a**) 850 hPa wind (arrow, unit: m/s); (**b**) 200 hPa wind (arrow, unit: m/s); (**c**) 500 hPa vertical velocity (shade, unit: 10<sup>−</sup>3 m/s); (**d**) 850 hPa relative vorticity (shade, unit: 10<sup>−</sup>6 s−1); (**e**) total column water vapor (shade, unit: kg/m2) over the tropical Indo-Pacific Ocean during TC season (May to December) in SCSSM anomalous years. Shaded areas in (**a**,**b**) and dotted areas in (**c**–**e**) are statistically significant at the 90% confidence level. **Figure 10.** Distribution differences (strong years minus weak years) of (**a**) 850 hPa wind (arrow, unit: m/s); (**b**) 200 hPa wind (arrow, unit: m/s); (**c**) 500 hPa vertical velocity (shade, unit: 10−<sup>3</sup> m/s); (**d**) 850 hPa relative vorticity (shade, unit: 10−<sup>6</sup> s −1 ); (**e**) total column water vapor (shade, unit: kg/m<sup>2</sup> ) over the tropical Indo-Pacific Ocean during TC season (May to December) in SCSSM anomalous years. Shaded areas in (**a**,**b**) and dotted areas in (**c**–**e**) are statistically significant at the 90% confidence level.

#### **5. Conclusions and Discussion 5. Conclusions and Discussion**

#### *5.1. Conclusions 5.1. Conclusions*

In this study, we investigate differences in TCs activity indicators between strong and weak anomalous SCSSM years and explore potential mechanisms that explain these differences. Our analysis highlights the impact of the tropical Indo-Pacific ocean–atmosphere In this study, we investigate differences in TCs activity indicators between strong and weak anomalous SCSSM years and explore potential mechanisms that explain these differences. Our analysis highlights the impact of the tropical Indo-Pacific ocean–atmosphere

interaction on the relationship between SCSSM intensity and TCs activity. Here are the main points of the study: interaction on the relationship between SCSSM intensity and TCs activity. Here are the main points of the study:

*Atmosphere* **2023**, *14*, x FOR PEER REVIEW 14 of 18

**Figure 11.** Mechanism diagram of the tropical Indo-Pacific ocean–atmosphere system and TCs activity characteristics in (**a**) strong and (**b**) weak SCSSM years. **Figure 11.** Mechanism diagram of the tropical Indo-Pacific ocean–atmosphere system and TCs activity characteristics in (**a**) strong and (**b**) weak SCSSM years.

#### *5.2. Discussion*

Although previous studies have investigated the impact of tropical oceans on WNPTC activity, there has been relatively little consideration of the role of the SCSSM. In this study, we have identified differences in WNPTC activity under different SCSSM intensity backgrounds, and have explained the relationship between the two through the coupled response of the Indo-Pacific SSTA pattern. This has improved our understanding of the influence of tropical ocean–atmosphere interactions, and emphasizes the crucial role of the SCSSM as an important signal amount. Although the Indo-Pacific system can explain the main co-variation between the SCSSM and WNPTC, the non-linearity of their connections makes it difficult to examine their direct relationship when controlling for Indo-Pacific SSTA effects. This is a limitation of our study, and we plan to validate the direct interaction between the two in a coupled ocean–atmosphere model in future research. With climate change causing uneven warming rates across ocean basins, it is possible that the relationship between SCSSM and TCs may also change [70]. Additionally, recent trends of TCs shifting towards land raise the question of whether the influence of the SCSSM on cyclones has increased [71]. These questions will be addressed in future research.

**Author Contributions:** Conceptualization, S.L.; methodology, S.L., J.X. and S.T.; formal analysis, S.L. and M.Z.; investigation, S.L.; writing—original draft, S.L.; writing—review & editing, S.L., J.X., S.T., M.Z. and Z.C.; visualization, S.L.; supervision, J.X. and S.T.; project administration, J.X. and Z.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is jointly supported by the National Key R&D Program of China (Grant No. 2019YFC1510002), the Major Program of the National Natural Science Foundation of China (Grant No. 72293604), and the Key Program of the National Natural Science Foundation of China (Grant No. 42130605).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data used in this study can be downloaded from the websites below: ERA5 reanalysis data: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5 -pressure-levels-monthly-means (accessed on 15 March 2022); HadISST1: https://www.metoffice. gov.uk/hadobs/hadisst/data/download.html (accessed on 21 March 2022); Outgoing Longwave Radiation (OLR) Climate Data Record: https://www.ncei.noaa.gov/products/climate-data-records/ outgoing-longwave-radiation-monthly (accessed on 25 March 2022); Best Track Archive for Climate Stewardship (IBTrACS): https://www.ncei.noaa.gov/products/international-best-track-archive? name=ib-v4-access (accessed on 7 March 2022).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Article* **Trends and Variability in Flood Magnitude: A Case Study of the Floods in the Qilian Mountains, Northwest China**

**Xueliang Wang 1,2,3,4, Rensheng Chen 1,\* , Kailu Li 1,2 , Yong Yang <sup>1</sup> , Junfeng Liu <sup>1</sup> , Zhangwen Liu <sup>1</sup> and Chuntan Han <sup>1</sup>**

**Abstract:** Analyzing trends in flood magnitude changes, and their underlying causes, under climate change, is a key challenge for the effective management of water resources in arid and semi-arid regions, particularly for inland rivers originating in the Qilian Mountains (QMs). Sen's slope estimator and the Mann–Kendall test were used to investigate the spatial and temporal trends in flood magnitude, based on the annual maximum peak discharge (AMPD) and Peaks Over Threshold magnitude (POT3M) flood series, of twelve typical rivers, from 1970 to 2021. The results showed that, in the AMPD series, 42% of the rivers had significantly decreasing trends, while 8% had significantly increasing trends; in the POT3M series, 25% of the rivers had significantly decreasing trends, while 8% had significantly increasing trends. The regional differences in the QMs from east to west were that, rivers in the eastern region (e.g., Gulang, Zamu, and Xiying rivers) showed significantly decreasing trends in the AMPD and POT3M series; most rivers in the central region had non-significant trends, while the Shule river in the western region showed a significantly increasing trend. Temperatures and precipitation showed a fluctuating increasing trend after 1987, which were the main factors contributing to the change in flood magnitude trends of the AMPD and POT3M flood series in the QMs. Regional differences in precipitation, precipitation intensity, and the ratio of glacial meltwater in the eastern, central and western regions, resulted in the differences in flood magnitude trends between the east and west.

**Keywords:** trends and variability; flood magnitude; climate change; Qilian Mountains

## **1. Introduction**

River flooding is one of the most concerning natural disaster issues. According to the United Nations Office for Disaster Risk Reduction, floods have been the most frequent of all recorded natural disasters worldwide, accounting for 43% of all disasters, with global average annual losses estimated at USD 104 billion [1]. It is estimated that the losses are likely to increase in the future, with climate change, economic acceleration, and urbanization development [2,3]. The IPCC [4] has shown that climate change is already an undeniable phenomenon and that the occurrence of extreme flood events is also associated with rising temperatures, heavy precipitation, and an accelerated hydrological cycle at global and regional scales [5–7]. Catastrophic floods, primarily associated with climate change, have also attracted public attention, and have been the focus of much research [8–11]. Many recent flood events around the world have led to growing concern that flood disasters are becoming more frequent and severe [5,12–14].

The flood variability induced by extreme climate change, has become a very focal area of research in the past two decades. Many studies worldwide have focused their

**Citation:** Wang, X.; Chen, R.; Li, K.; Yang, Y.; Liu, J.; Liu, Z.; Han, C. Trends and Variability in Flood Magnitude: A Case Study of the Floods in the Qilian Mountains, Northwest China. *Atmosphere* **2023**, *14*, 557. https://doi.org/10.3390/ atmos14030557

Academic Editor: Haibo Liu

Received: 18 February 2023 Revised: 11 March 2023 Accepted: 13 March 2023 Published: 14 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

attention on the issue of fluvial flooding, mainly on the impact of changes in magnitude, frequency, and timing of flood events on a regional, continental, or global scale [5–7,12–14]. Recent studies have shown that not only has the changing climate shifted the timing of flooding in Europe, but the increased precipitation in autumn and winter has also led to increased flooding in the northwest, while decreased precipitation, warmer temperatures, and increased evapotranspiration have led to decreased flooding in the southeast [6,7]. Significant trends in flood magnitudes were found in the time series based on the annual maximum and Peaks Over Threshold, in Canada [14]. The trend of increasing magnitude and extent of floods is due to the correlation among the magnitude, extent of precipitation, soil moisture, and the shift in flood generation processes [15]. Some studies have shown that more frequent heavy precipitation and increased moisture in the catchment area, in the context of climate change, are expected to result in a greater risk of flooding in the future [11,16,17].

In China, a nationwide characterization of flood hazards, based on a dataset of 1120 hydrographic stations, has been presented [18], and changes in flood characteristics in the Yangtze and Pearl River basins in southern China have also been studied [19,20]. Some studies have also been carried out in arid and semi-arid regions of northwest China, such as the increase in the magnitude of floods in the Tarim River basin after the 1990s, especially in the case of high-latitude rivers [21]. Typical basins in the Tien Shan Mountains, such as the Tuoshikan River and the Kumalak River, have shown a significant increase in flood magnitude in response to climate change over the last 50 years [22,23]. The QMs, which are located in northwest China, are the origin of inland rivers in the Hexi Corridor, and an important region for the economic development of the Belt and Road Initiative. The Hexi Corridor, which is a typical arid and semi-arid area in northwest China, is divided into three sub-basins from east to west, namely the Shiyang River basin (SYRB), the Hei River basin (HRB), and the Shule River basin (SLRB) [24]. Wang et al. [25] have analyzed the trends in the frequency of floods in the QMs using the flood series of twelve rivers from 1970–2019, and the results show that the frequency of floods, mainly small floods in summer, is increasing, and medium and large floods are generally decreasing. There are differences between the eastern and western regions, with decreases in the east and increases in the west. However, temperature and precipitation in the northwest region have been increasing since 1987 [26,27], and the trend in flood magnitude of the 12 major rivers in the QMs is still unknown. The impact of climate change on river flood magnitude remains an important issue, due to the complex changes in precipitation, topography, and the hydrological cycle.

In this study, the impact of meteorological variables on the variability in flood magnitude in the QMs was comprehensively assessed, through flood information and meteorological data. The main objectives of this paper are (1) to assess the characteristics of the variability in AMPD and POT3M magnitude series at temporal scales, for twelve rivers in the QMs; (2) to identify regional differences in AMPD and POT3M magnitude series from east to west; and (3) to explore the causes of flood magnitude variability, including the analysis of meteorological variables in the QMs. Regional differences in precipitation, topography, and the hydrologic cycle have led to complex changes in increasing and decreasing flood magnitudes. Analyzing and exploring the changes in flood magnitude characteristics over the historical period is not only necessary for the scientific management of water resources, but also important for the social development and safety of people's property, in the middle and lower reaches. This finding will raise new concerns about the changes in flood magnitude in the QMs, northwest China, under climate change.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The QMs, whose geographical boundary is approximately 93.4◦–103.4◦ E and 35.8◦– 40.0◦ N, are located in northwest China, and consist of several parallel mountains and broad valleys (Figure 1). The elevation ranges from below 3000 m to above 5000 m, with

most peaks exceeding 4000 m [28]. There are large vertical differences in climate. Alpine areas are mainly subject to the coupling zone of three atmospheric circulation systems: westerly flow, the East Asian monsoon, and the Tibetan Plateau monsoon, and mean annual precipitation is 301.9 mm [29,30]. The northern part of the Qilian Mountains is the Hexi Corridor, which is geographically located between 92.4◦–104.2◦ E and 37.3◦–42.3◦ N, with a total area of 2.15 <sup>×</sup> <sup>10</sup><sup>5</sup> km<sup>2</sup> . The Hexi Corridor is a typical arid and semi-arid region in northwest China (Figure 1). The region is located in a narrow corridor extending from the east to the west, for more than 1000 km, and from the south to the north for 100–200 km [24]. The Hexi Corridor is the most important passage from northern China to Central Asia, and is an important part of the historical Silk Road. In this study, twelve rivers, which originate from the northern slopes of the QMs and end at the northern oasis of the Hexi Corridor, were selected as the study subject, and divided into three sub-basins from east to west. areas are mainly subject to the coupling zone of three atmospheric circulation systems: westerly flow, the East Asian monsoon, and the Tibetan Plateau monsoon, and mean annual precipitation is 301.9 mm [29,30]. The northern part of the Qilian Mountains is the Hexi Corridor, which is geographically located between 92.4°–104.2° E and 37.3°–42.3° N, with a total area of 2.15 × 105 km2. The Hexi Corridor is a typical arid and semi-arid region in northwest China (Figure 1). The region is located in a narrow corridor extending from the east to the west, for more than 1000 km, and from the south to the north for 100–200 km [24]. The Hexi Corridor is the most important passage from northern China to Central Asia, and is an important part of the historical Silk Road. In this study, twelve rivers, which originate from the northern slopes of the QMs and end at the northern oasis of the Hexi Corridor, were selected as the study subject, and divided into three sub-basins from east to west.

The QMs, whose geographical boundary is approximately 93.4°–103.4° E and 35.8°– 40.0° N, are located in northwest China, and consist of several parallel mountains and broad valleys (Figure 1). The elevation ranges from below 3000 m to above 5000 m, with most peaks exceeding 4000 m [28]. There are large vertical differences in climate. Alpine

*Atmosphere* **2023**, *14*, 557 3 of 18

**2. Materials and Methods**

*2.1. Study Area*

**Figure 1.** Location of the study area. (a−c) Denote Gulang, Zamu, and Xiying rivers in SYRB, (d−i) denote Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (j−l) denote Shiyou, Shule, and Dang rivers in SLRB, respectively. (1−5) Denote Wushaoling, Menyuan, Qilian, Yeniugou and Tuole meteorological stations, respectively. **Figure 1.** Location of the study area. (a–c) Denote Gulang, Zamu, and Xiying rivers in SYRB, (d–i) denote Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (j–l) denote Shiyou, Shule, and Dang rivers in SLRB, respectively. (1–5) Denote Wushaoling, Menyuan, Qilian, Yeniugou and Tuole meteorological stations, respectively.

#### *2.2. Data 2.2. Data*

The dataset for this study includes meteorological data and historical flood discharges from 1970 to 2021. The discharge data were selected for analysis from twelve rivers originating from the QMs (Figure 1, Table 1), the upstream areas of which are mostly located in the high-altitude mountainous regions of the QMs, with little disturbance from human activities and no hydraulic projects, such as reservoirs and dykes. Peak flood discharges were collected from the Hydrological Yearbook of the People's Republic of China. Meteorological data were obtained from the National Meteorological Center of China's The dataset for this study includes meteorological data and historical flood discharges from 1970 to 2021. The discharge data were selected for analysis from twelve rivers originating from the QMs (Figure 1, Table 1), the upstream areas of which are mostly located in the high-altitude mountainous regions of the QMs, with little disturbance from human activities and no hydraulic projects, such as reservoirs and dykes. Peak flood discharges were collected from the Hydrological Yearbook of the People's Republic of China. Meteorological data were obtained from the National Meteorological Center of China's Meteorological Administration. All flood data and meteorological data have been subjected to strict quality control (e.g., extreme value test), reasonableness checks, and a standard normal homogeneity test [31–33], and missing meteorological data for individual years have been interpolated with a gap-filling method, after correlation check analysis [34]. The general information on the hydrological stations is shown in Table 1, and the basic information on the meteorological stations is shown in Table 2.

**Table 1.** Basic information on hydrological stations/catchments.

**Table 2.** Basic information on meteorological stations in the QMs.

The continuously recorded AMPD and POT3M flood series were derived from hydrological stations originating from twelve rivers (i.e., three rivers in SYRB, six rivers in HRB, and three rivers in SLRB). Daily temperature and precipitation data from five meteorological stations were selected to analyze trends.

#### *2.3. Methods*

#### 2.3.1. Determination of Flood Independence in the POT3M Flood Series

In this study, the POT3M sampling method was also used to supplement the AMPD sampling, with information such as flood magnitude, and this method has a good application in inland river basins in arid regions. The independence of the flood peak was determined by Lang et al. [35].

$$\begin{cases} \quad \text{ $D > 5 + \log(A)$ }\\ \mathbb{Q}\_{\text{min}} < \frac{3}{4} \min(\mathbb{Q}\_1, \mathbb{Q}\_2) \end{cases} \tag{1}$$

where *D* denotes the flood duration between the two flood peaks; *A* denotes the catchment area in km<sup>2</sup> ; and *Q*<sup>1</sup> and *Q*<sup>2</sup> denote the magnitude of the two flood flows in m3/s, respectively.

#### 2.3.2. Test Methods for Trend and Abrupt Change Analysis

The non-parametric method is well suited to detecting trends and significance levels in hydrometeorological time series. In this study, the Sen's slope estimator [36] and the Mann–Kendall test (M–K test) [37,38], which are widely used to identify trends in hydrometeorological variables, were employed to explore trends in the flood and climate variables [39–42].

Sen's slope estimator is as follows.

$$\beta = median(\frac{\mathbf{x}\_j - \mathbf{x}\_k}{j - k}) \quad j > k \tag{2}$$

where the *β* symbol indicates whether a trend is positive or negative, while its value reflects the magnitude of the steepness of the trend, and *x<sup>j</sup>* and *x<sup>k</sup>* are the data values at times *j* and *k (j > k*).

Additionally, The M–K test statistic *S*, is calculated as:

$$S = \sum\_{i=1}^{n-1} \sum\_{j=i+1}^{n} \text{sgn}(\mathbf{x}(j) - \mathbf{x}(i)) \tag{3}$$

where

$$\text{sgn}(\mathbf{x}(j) - \mathbf{x}(i)) = \begin{cases} 1 & \text{if } \begin{array}{l} \text{if } \begin{array}{l} \mathbf{x}(j) - \mathbf{x}(i) > 0 \\ \text{if } \begin{array}{l} \mathbf{x}(j) - \mathbf{x}(i) = 0 \\ -1 \end{array} \end{cases} \end{cases} \tag{4}$$

A positive (negative) value of *S* indicates an increasing (decreasing) trend. When *n >* 8, the statistic *S* is approximately normally distributed, and its mean *E(S)* and variance *Var(S)* are identified as follows:

$$E(\mathcal{S}) = \mathbf{0} \tag{5}$$

$$Var(S) = \frac{1}{18} \left[ n(n-1)(2n+5) - \sum\_{i=1}^{m} t\_i(t\_i - 1)(2t\_i + 5) \right] \tag{6}$$

$$Z = \begin{cases} \frac{S - 1}{\sqrt{Var(S)}} & \text{if } \quad S > 0\\ 0 & \text{if } \quad S = 0\\ \frac{S + 1}{\sqrt{Var(S)}} & \text{if } \quad S < 0 \end{cases} \tag{7}$$

where *n* is the number of data points; *m* is the number of tied groups, and *t<sup>i</sup>* denotes the number of ties of extent *i*. The null hypothesis is rejected if the absolute value of *Z* is greater than the theoretical value Z1−*α*/2, where *α* is the statistical significance level. In this study, all trend results were evaluated at 90%, 95%, and 99% significance levels, respectively.

The sequential Mann–Kendall (SQ–MK) [43,44] test constructs the order series of the time series *X*.

$$s\_k = \sum\_{i=1}^k r\_i \quad (k = \text{2, 3}, \dots, n) \tag{8}$$

where *n* is the length of the time series *X*.

$$\mathbf{r}\_{i} = \begin{cases} +1 & \mathbf{x}\_{i} - \mathbf{x}\_{j} \\ 0 & \end{cases} \quad (j = 1, 2, \cdots, i) \tag{9}$$

Assuming that the time series *X* is random, define the statistic:

$$
\Delta UF = \frac{[s\_k - E(s\_k)]}{\sqrt{Var}(s\_k)} \quad (k = 1, 2, \cdots, n) \tag{10}
$$

where *UF*<sup>1</sup> = 0, *E*(*sk*) and *Var*(*sk*) are the mean and variance of *s<sup>k</sup>* , and *x*1, *x*2, · · · , *x<sup>n</sup>* are independent of each other, when they have the same continuous distribution, which can be deduced from the following equation:

$$E(s\_k) = \frac{n(n-1)}{4} \quad (2 \le k \le n) \tag{11}$$

$$Var(s\_k) = \frac{n(n-1)(2n+5)}{72} \quad (2 \le k \le n) \tag{12}$$

where *UF* is the standard normal distribution, which is the sequence calculated for the order of the time series *X* (*x*1, *x*2, · · · , *xn*).

The inverse order of the time series *X* (*xn*, *xn*−1, · · · , *x*1) is used to calculate the statistic *UB*, for the inverse series of the time series *X*.

In this paper, given a significance level of *α* = 0.05 (95% significance level), then the critical value *U*0.05 = ±1.96, and the two statistic series curves of *UF* and *UB*, and the two straight lines of ±1.96, are plotted on a single graph. If *UF* and *UB* intersect between the critical values, then the intersection point corresponds to the time at which the mutation of time series *X* begins.

#### 2.3.3. Meteorological Variables

Six temperature and precipitation indices were employed to analyze the meteorological variables and to explore the changes in temperature and precipitation (Table 3).

**Table 3.** Definitions of the six temperature and precipitation indices used in this study.

#### **3. Results**

#### *3.1. Flood Independence Analysis*

Flood independence analysis is a prerequisite for determining trends in the AMPD and POT3M flood series. In order to make the assumption of independence of the sampled flood peaks reasonable, the flood independence criterion method proposed by Lang et al. [35] was used, while the POT3M sampling method, applied in the arid and semi-arid regions of northwest China, was considered [21,23]. For the AMPD and POT3M flood series of the 12 rivers in the study, all sampled flood peaks comply with the two conditions of the flood independence criterion method, in terms of duration, *D,* and intermediate minimum flow, *Q*min, and thus flood independence was valid.

## *3.2. Trends in AMPD Series*

The AMPD series of twelve rivers in the QMs were tested using Sen's slope estimator and the M–K test, and the results are shown in Figure 2 and Table 4. Five rivers exhibited significantly decreasing trends, while one river showed a significantly increasing trend, and six rivers had non-significant trends. In SYRB (a–c rivers), Gulang, Zamu, and Xiying rivers showed decreasing trends in AMPD at 99%, 95%, and 95% significance levels, respectively. In HRB (d–i rivers), five rivers (Babao, Hei, Liyuan, Hongshui, and Taolai rivers) showed non-significant trends, while Dazhuma River showed a decreasing trend, at the significance level of 99%. However, in SLRB (j–l rivers), Shiyou River showed a decreasing trend at the significance level of 90% and Shule River exhibited an increasing trend at the significance level of 99%, while Dang River had a non-significant trend. The trend line of AMPD for the twelve rivers is shown in Figure 2, where the evolutionary trend in historical floods can be seen. Although the AMPD trends are different among the eastern, central, and western parts of the QMs, most of the rivers presented obvious decreasing trends from 1970 to 2021, especially in the eastern and central regions.

**Figure 2.** Trends of AMPD for 12 representative rivers in the QM region, northwest China. (**a**−**c**) Gulang, Zamu, and Xiying rivers in SYRB, (**d**−**i**) Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (**j**−**l**) Shiyou, Shule, and Dang rivers in SLRB, respectively. **Figure 2.** Trends of AMPD for 12 representative rivers in the QM region, northwest China. (**a**–**c**) Gulang, Zamu, and Xiying rivers in SYRB, (**d**–**i**) Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (**j**–**l**) Shiyou, Shule, and Dang rivers in SLRB, respectively.

spectively. In HRB (d–i rivers), five rivers (Babao, Hei, Liyuan, Hongshui, and Taolai rivers) showed non-significant trends, while Dazhuma River showed a decreasing trend, at the significance level of 99%. However, in SLRB (j–l rivers), Shiyou River showed a decreasing trend at the significance level of 90% and Shule River exhibited an increasing trend at the significance level of 99%, while Dang River had a non-significant trend. The trend line of AMPD for the twelve rivers is shown in Figure 2, where the evolutionary trend in historical floods can be seen. Although the AMPD trends are different among the eastern, central, and western parts of the QMs, most of the rivers presented obvious de-

creasing trends from 1970 to 2021, especially in the eastern and central regions.

**Table 4.** Results of trend analysis of AMPD and POT3M series, in the QM region, northwest China.

In Table 4, the M–K test for the AMPD and POT3M series detected trends at 90%, 95%, and 99% significance levels, respectively; S means statistically significant at the significance level shown in parentheses; NS means not significant; "↑" and "↓" mean increasing and decreasing trend. (a–l) Denote: Gulang, Zamu, Xiying, Babao, Hei, Dazhuma, Liyuan, Hongshui, Taolai, Shiyou, Shule, and Dang rivers, respectively. The corresponding hydrological stations for the (a–l) rivers are Gulang, Zamusi, Jiutiaoling, Qilian, Zhamashike, Wafangcheng, Sunan, Xindi, Jiayuguan, Yumen, Changmabao, and Dangchengwan, respectively.

#### *3.3. Abrupt Behavior for Changes in AMPD Series*

The twelve rivers, that originate at high-altitude in the QMs, are mainly recharged by precipitation, snow, and glacial meltwater and have specific, typical flood generation mechanisms. Heavy precipitation and the melting of snow and ice water, due to increased temperatures, are the main factors in flood generation. As a result, the magnitude of floods has fluctuated over the 50 years of observation, but the structure is stable. The results of the SQ-MK test for twelve representative rivers are shown in Figure 3. Six of the twelve rivers exceeded the critical value of 1.96, for which α = 0.05 in the significance test. Thus, the AMPD series showed abrupt changes. Among them, in the eastern region of QMs, the SQ-MK test values of the Gulang and Xiying rivers continued to decrease and break the critical values during the period 1970–2021, but the SQ-MK test values of the Zamu River fluctuated steadily. In the central region, the change points of the Babao and Dazhuma rivers occurred in 1989 and 2020, with the significance level of 95%. However, the Hei, Liyuan, Hongshui, and Taolai rivers all showed stable fluctuation from 1970 to 2021, without breaking the critical values. In the western region of the QMs, the SQ-MK test values of the Shiyou, Shule, and Dang rivers behaved differently. Shiyou River showed an increasing trend from 1970 to 1984 and broke the critical value in 1983, followed by a continuous decreasing trend from 1985–2021. Though it had a decreasing trend from 1970 to 1986, Shule River showed a continuously increasing trend after 1987, bypassing the critical value of 1.96, to a value of 3.47. From 1970 to 2021, Dang River did not break through the critical value and presented a slightly increasing trend. In Figure 3, most of the change points can be seen after the mid-1990s, whereas significant trends occurred in SYRB (a and c), HRB (f), and SLRB (k). *Atmosphere* **2023**, *14*, 557 9 of 18

**Figure 3.** Trends of SQ–MK test in AMPD for 12 representative rivers in the QM region, northwest China. (**a**−**c**) Denote Gulang, Zamu, and Xiying rivers in SYRB, (**d**−**i**) denote Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (**j**−**l**) denote Shiyou, Shule, and Dang rivers in SLRB, **Figure 3.** Trends of SQ–MK test in AMPD for 12 representative rivers in the QM region, northwest China. (**a**–**c**) Denote Gulang, Zamu, and Xiying rivers in SYRB, (**d**–**i**) denote Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (**j**–**l**) denote Shiyou, Shule, and Dang rivers in SLRB, respectively.

series and the statistical results are presented in Figure 4 and Table 4. Significantly increasing and decreasing trends of the POT3M series were detected at 90%, 95%, and 99% significance levels, respectively. The results demonstrate that Shule River in SLRB displayed a significantly increasing trend, at the significance level of 99%, while the Gulang and Xiying rivers in SYRB, and Shiyou River in SLRB showed decreasing trends, at the significance levels of 99%, 95%, and 90%, respectively. It should be noted again, that non-significant trends were found in the Zamu, Babao, Hei, Dazhuma, Liyuan, Hongshui, Taolai, and Dang rivers. Compared to the AMPD series, more reliable conclusions could be drawn from these series, because samplings of the POT3M series provided more flood information. By comparing the trends of the POT3M series and AMPD series in Table 4 in parallel, the trends of increasing and decreasing are consistent for most of the rivers, with some differences in the Zamu and Dazhuma rivers, which are mainly caused by the dif-

respectively.

*3.4. Trends in POT3M Flood Series*

ferent flood information of the sample selection.

respectively.

period of 1970–2021.

#### *3.4. Trends in POT3M Flood Series*

Twelve rivers in the QMs were selected to calculate the trends of the POT3M flood series and the statistical results are presented in Figure 4 and Table 4. Significantly increasing and decreasing trends of the POT3M series were detected at 90%, 95%, and 99% significance levels, respectively. The results demonstrate that Shule River in SLRB displayed a significantly increasing trend, at the significance level of 99%, while the Gulang and Xiying rivers in SYRB, and Shiyou River in SLRB showed decreasing trends, at the significance levels of 99%, 95%, and 90%, respectively. It should be noted again, that non-significant trends were found in the Zamu, Babao, Hei, Dazhuma, Liyuan, Hongshui, Taolai, and Dang rivers. Compared to the AMPD series, more reliable conclusions could be drawn from these series, because samplings of the POT3M series provided more flood information. By comparing the trends of the POT3M series and AMPD series in Table 4 in parallel, the trends of increasing and decreasing are consistent for most of the rivers, with some differences in the Zamu and Dazhuma rivers, which are mainly caused by the different flood information of the sample selection. *Atmosphere* **2023**, *14*, 557 10 of 18

**Figure 4.** Trends of POT3M flood series for 12 representative rivers in the QM region, northwest China. (**a**−**c**) Denote Gulang, Zamu, and Xiying rivers in SYRB, (**d**−**i**) denote Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (**j**−**l**) denote Shiyou, Shule, and Dang rivers in SLRB, **Figure 4.** Trends of POT3M flood series for 12 representative rivers in the QM region, northwest China. (**a**–**c**) Denote Gulang, Zamu, and Xiying rivers in SYRB, (**d**–**i**) denote Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (**j**–**l**) denote Shiyou, Shule, and Dang rivers in SLRB, respectively.

series showed an overall decreasing trend from 1970 to 2021, with different significance levels. In the central HRB (d–i rivers), there were minor differences in the trends of the AMPD and POT3M series. In the trend in the AMPD series, five of the six rivers were nonsignificant trends, except for the Dazhuma River, which exhibited a decreasing trend at the significance level of 99%. However, in the trend in the POT3M series, all six rivers showed non-significant trends. Among the western SLRB (j–l rivers), Shiyou River displayed a decreasing trend, at the significance level of 90%, in both the AMPD and POT3M series, Shule River showed an increasing trend at the significance level of 99% in both the AMPD and POT3M series, whereas Dang River had a non-significant trend during the

According to the regional comparative analysis of the different flood series from the flood observation dataset, the flood magnitude trends for twelve rivers in the QMs are both increasing and decreasing in the eastern, central, and western regions, as shown in

*3.5. Trends of Flood Magnitude in the Eastern, Central, and Western Regions*

#### *3.5. Trends of Flood Magnitude in the Eastern, Central, and Western Regions*

According to the regional comparative analysis of the different flood series from the flood observation dataset, the flood magnitude trends for twelve rivers in the QMs are both increasing and decreasing in the eastern, central, and western regions, as shown in Figure 5 and Table 4. In the eastern SYRB (a–c rivers), the results of the AMPD and POT3M series showed an overall decreasing trend from 1970 to 2021, with different significance levels. In the central HRB (d–i rivers), there were minor differences in the trends of the AMPD and POT3M series. In the trend in the AMPD series, five of the six rivers were nonsignificant trends, except for the Dazhuma River, which exhibited a decreasing trend at the significance level of 99%. However, in the trend in the POT3M series, all six rivers showed non-significant trends. Among the western SLRB (j–l rivers), Shiyou River displayed a decreasing trend, at the significance level of 90%, in both the AMPD and POT3M series, Shule River showed an increasing trend at the significance level of 99% in both the AMPD and POT3M series, whereas Dang River had a non-significant trend during the period of 1970–2021. *Atmosphere* **2023**, *14*, 557 11 of 18

**Figure 5.** Trends in AMPD and POT3M flood series for 12 representative rivers in the QM region, northwest China. (a−c) Denote Gulang, Zamu, and Xiying rivers in SYRB, (d−i) denote Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (j−l) denote Shiyou, Shule, and Dang rivers in SLRB, respectively. A big arrow indicates a statistically increasing trend (red) and decreasing trend (black), at the significance level of 99%, a medium arrow indicates a statistically increasing trend (red) and decreasing trend (black), at the significance level of 95%, a small arrow indicates a statistically increasing trend (red) and decreasing trend (black), at the significance level of 90%. No marked symbol at stations represents a non-significant trend. **Figure 5.** Trends in AMPD and POT3M flood series for 12 representative rivers in the QM region, northwest China. (a–c) Denote Gulang, Zamu, and Xiying rivers in SYRB, (d–i) denote Babao, Hei, Dazhuma, Liyuan, Hongshui, and Taolai rivers in HRB, (j–l) denote Shiyou, Shule, and Dang rivers in SLRB, respectively. A big arrow indicates a statistically increasing trend (red) and decreasing trend (black), at the significance level of 99%, a medium arrow indicates a statistically increasing trend (red) and decreasing trend (black), at the significance level of 95%, a small arrow indicates a statistically increasing trend (red) and decreasing trend (black), at the significance level of 90%. No marked symbol at stations represents a non-significant trend.

#### **4. Discussion 4. Discussion**

#### *4.1. Attribution of Climate Change to Variability in Flood Magnitude 4.1. Attribution of Climate Change to Variability in Flood Magnitude*

The results of this study indicate that the flood magnitude of the QMs changed significantly from east to west during the period 1970–2021. The linear trends of the AMPD and POT3M series show a decreasing trend in flood magnitude for SYRB in the east (e.g., Gulang, Zamu, and Xiying rivers), a slight fluctuation for six rivers in HRB in the central part, and an increasing trend, with larger values, for SLRB in the west (e.g., Shule River). The AMPD and POT3M series differ slightly, in that the AMPD series shows more rivers with a decreasing trend (e.g., Zamu and Dazhuma rivers), as shown in Figures 2, 4, and 5, and Table 4. A recent study has shown that climate change in the Qilian Mountains region has led to variations in the frequency of floods of different levels, with a decrease in SYRB in the east, a slight fluctuation in HRB in the center, and an increase in SLRB in the west, and the main factors causing those changes in flood frequency in the QMs are heavy rainfall, abnormal warming, and accelerated glacial melting [25]. Even changes in river discharge in arid and semi-arid regions of northwest China are associated with climate change [21–23,45]. In the past decades, e.g., Shi, et al. [27] have confirmed that the climate of arid and semi-arid regions in northwest China changed from warm-dry to warm-wet The results of this study indicate that the flood magnitude of the QMs changed significantly from east to west during the period 1970–2021. The linear trends of the AMPD and POT3M series show a decreasing trend in flood magnitude for SYRB in the east (e.g., Gulang, Zamu, and Xiying rivers), a slight fluctuation for six rivers in HRB in the central part, and an increasing trend, with larger values, for SLRB in the west (e.g., Shule River). The AMPD and POT3M series differ slightly, in that the AMPD series shows more rivers with a decreasing trend (e.g., Zamu and Dazhuma rivers), as shown in Figures 2, 4 and 5, and Table 4. A recent study has shown that climate change in the Qilian Mountains region has led to variations in the frequency of floods of different levels, with a decrease in SYRB in the east, a slight fluctuation in HRB in the center, and an increase in SLRB in the west, and the main factors causing those changes in flood frequency in the QMs are heavy rainfall, abnormal warming, and accelerated glacial melting [25]. Even changes in river discharge in arid and semi-arid regions of northwest China are associated with climate change [21–23,45]. In the past decades, e.g., Shi, et al. [27] have confirmed that the climate of arid and semi-arid regions in northwest China changed from warm-dry to warm-wet during 1961–2003, and Chen, et al. [26] also found an increasing trend in temperature and

during 1961–2003, and Chen, et al. [26] also found an increasing trend in temperature and precipitation in arid northwest China during 1960–2015, and precipitation started to rise

Twelve rivers, originating in the high-altitude mountainous regions of the QMs, have a special and typical flooding mechanism, mainly influenced by heavy precipitation and the melting of snow and ice water, due to rising temperatures. Owing to the complex topography of the QMs, the high altitude, and the inconvenient access, only five national meteorological stations are located at high-altitude in the mountains, while the rest of the meteorological stations are located in the piedmont and oasis plain areas. In this study, five meteorological stations in high-altitude mountainous areas, were selected to calculate trends using daily precipitation and temperature data for the period 1970–2021. To better explore the meteorological factors affecting the variation in flood magnitude in the QMs, temperature and precipitation trends at five meteorological stations were analyzed, by defining six temperature and precipitation indices (Figures 6 and 7). Over the past 52 years, temperature and precipitation in SYRB, HRB, and SLRB have generally increased to different degrees (Figures 6 and 7). Although the only five meteorological stations in

precipitation in arid northwest China during 1960–2015, and precipitation started to rise sharply after 1987. Climate change may cause high temperatures and heavy precipitation, which in turn lead to changes in flooding [46–48].

Twelve rivers, originating in the high-altitude mountainous regions of the QMs, have a special and typical flooding mechanism, mainly influenced by heavy precipitation and the melting of snow and ice water, due to rising temperatures. Owing to the complex topography of the QMs, the high altitude, and the inconvenient access, only five national meteorological stations are located at high-altitude in the mountains, while the rest of the meteorological stations are located in the piedmont and oasis plain areas. In this study, five meteorological stations in high-altitude mountainous areas, were selected to calculate trends using daily precipitation and temperature data for the period 1970–2021. To better explore the meteorological factors affecting the variation in flood magnitude in the QMs, temperature and precipitation trends at five meteorological stations were analyzed, by defining six temperature and precipitation indices (Figures 6 and 7). Over the past 52 years, temperature and precipitation in SYRB, HRB, and SLRB have generally increased to different degrees (Figures 6 and 7). Although the only five meteorological stations in the high-altitude mountainous regions do not correspond to analyzing the temperature and precipitation change patterns of the twelve rivers one by one, it can be found that the trend varies from region to region. In terms of the causes of variation in flood magnitude generated in the twelve rivers, it is mainly influenced by temperature, heavy precipitation, substratum, and topography, but in high-altitude areas of the QMs, heavy precipitation is the main factor causing the variation in flood magnitude. According to the analysis of the data from five meteorological stations, temperature and precipitation have generally increased at different rates in SYRB, HRB, and SLRB over the past 52 years (Figures 6 and 7), however, the magnitudes of the AMPD and POT3M series have increased or decreased in different rivers (e.g., Gulang, Zamu, Xiying, Dazhuma, Shiyou, and Shule rivers), the possible causes are the regional differences of heavy precipitation in the upper reaches of the rivers, which are responsible for the changes in flood magnitude.

Among high-latitude rivers in northwest and southwest China, similar results of increasing and decreasing trends in the magnitude of river floods due to climate change, dominated by changes in temperature and precipitation, have been obtained in the Tien Shan Mountains, Aksu, Tarim, and Lancang–Mekong river basins [21–23,49]. In Central Asia, climate change is leading to an increased risk of flooding and landslides, and the likelihood of glacial lake outburst flooding is expected to increase with rising temperatures and an increase in the number of glacial lakes [50]. Similar studies can be compared in Europe, increases and decreases in temperature, evaporation, and precipitation in different regions have led to increased flooding in the northwest and decreased flooding in the south and east. Flood magnitude trends in different regions of Europe range from an increase of 11% to a decrease of 23%, per decade [6]. Even in Canada and North America, the trend of increasing and decreasing flood magnitude in different regions over the past decades has been identified by researchers [14]. Despite the spatial heterogeneity of the observed records of temperature, precipitation, and flooding in different regions, it is reasonable that the variation in flood magnitudes in the high-altitude mountains of the QMs found in our study, is mainly attributed to climate change.

Table 3.

the high-altitude mountainous regions do not correspond to analyzing the temperature and precipitation change patterns of the twelve rivers one by one, it can be found that the trend varies from region to region. In terms of the causes of variation in flood magnitude generated in the twelve rivers, it is mainly influenced by temperature, heavy precipitation, substratum, and topography, but in high-altitude areas of the QMs, heavy precipitation is the main factor causing the variation in flood magnitude. According to the analysis of the data from five meteorological stations, temperature and precipitation have generally increased at different rates in SYRB, HRB, and SLRB over the past 52 years (Figures 6 and 7), however, the magnitudes of the AMPD and POT3M series have increased or decreased in different rivers (e.g., Gulang, Zamu, Xiying, Dazhuma, Shiyou, and Shule rivers), the

the rivers, which are responsible for the changes in flood magnitude.

**Figure 6.** Temporal variations in temperature indices at meteorological stations in the QM region. The stations are, WSL: Wushaoling; MY: Menyuan; QL: Qilian; YNG: Yeniugou; TL: Tuole. (**a**−**c**) WSL; (**d**−**f**) MY; (**g**−**i**) QL; (**j**−**l**) YNG; (**m**−**o**) TL. Definitions of these temperature indices are given in **Figure 6.** Temporal variations in temperature indices at meteorological stations in the QM region. The stations are, WSL: Wushaoling; MY: Menyuan; QL: Qilian; YNG: Yeniugou; TL: Tuole. (**a**–**c**) WSL; (**d**–**f**) MY; (**g**–**i**) QL; (**j**–**l**) YNG; (**m**–**o**) TL. Definitions of these temperature indices are given in Table 3.

in Table 3.

## *4.2. Regional Differences in the Evolution of Flood Magnitude*

Among high-latitude rivers in northwest and southwest China, similar results of increasing and decreasing trends in the magnitude of river floods due to climate change, dominated by changes in temperature and precipitation, have been obtained in the Tien Shan Mountains, Aksu, Tarim, and Lancang–Mekong river basins [21–23,49]. In Central Asia, climate change is leading to an increased risk of flooding and landslides, and the likelihood of glacial lake outburst flooding is expected to increase with rising temperatures and an increase in the number of glacial lakes [50]. Similar studies can be compared in Europe, increases and decreases in temperature, evaporation, and precipitation in different regions have led to increased flooding in the northwest and decreased flooding in The long-term variability in the AMPD and POT3M flood series highlights the regional differences in understanding the evolution of flood magnitude in the QMs. Analysis of the datasets shows a clear regional pattern of flooding trends across the QMs (Figures 2, 4 and 5), with a decreasing trend in SYRB in the east, a slight fluctuation for six rivers in HRB in the central part, and an increasing trend with larger values in SLRB in the west (e.g., Shule River). In arid and semi-arid regions, for the twelve inland rivers originating in the QMs, regional variations in flood magnitudes are mainly caused by heavy precipitation, abnormal warming, and accelerated glacial melting. Regional differences in precipitation and precipitation intensity, river catchment, underlying surface, and topography are the main drivers of flood magnitude variability. Nevertheless, the climate shows great variability with altitude, and most precipitation events in mountainous regions are

found in our study, is mainly attributed to climate change.

the south and east. Flood magnitude trends in different regions of Europe range from an increase of 11% to a decrease of 23%, per decade [6]. Even in Canada and North America,

observed records of temperature, precipitation, and flooding in different regions, it is reasonable that the variation in flood magnitudes in the high-altitude mountains of the QMs

influenced by three main sources of water vapor; namely, the East Asian monsoon, the Tibetan Plateau monsoon, and the westerly circulation [29]. The SYRB in the east, HRB in the center, and SLRB in the west are mainly influenced by the combination of the East Asian monsoon, the Tibetan Plateau monsoon, and the westerly circulation, respectively. The enhanced westerly circulation and increased water vapor, are the main factors leading to the increased precipitation in the QMs [30,51]. Different climate types result in different regional precipitation and precipitation intensities, causing differences in flood magnitude trends in the eastern, central, and western regions. In addition, due to the different glacier coverage areas and reserves in the eastern, central and western regions of the QMs, (Table 5), the eastern part is the smallest, the central part is larger, and the western part is the largest. The glaciers in the east are retreating rapidly, while the central and western regions are retreating more slowly in turn [52]. Due to the differences in glacier area and glacial meltwater ratios in the upper reaches of the twelve rivers in three sub-basins, the three rivers in the eastern SYRB have the smallest glacial meltwater ratios, whereas the three rivers in the western SLRB have the largest (Table 5) [53,54]. Differences in precipitation and the proportion of glacial meltwater, due to temperature increases in the east and west regions are the main reasons for the spatial patterns of flood magnitude variation in those three main regions (Figures 2, 4 and 5).

**Table 5.** Glacier area and percentage of glacier meltwater runoff in this study [25,54].

The trend comparison of the AMPD and POT3M series, shows that in the eastern part of the QMs, three rivers (e.g., Gulang, Zamu, and Xiying rivers) showed an overall decreasing trend, at the significance level of 99% or 95%. In the central region, most of the rivers showed non-significant trends, except for minor differences in individual rivers. For example, in the AMPD series, Dazhuma River exhibited a decreasing trend at the significance level of 99%. In the western region, Shule River displayed a significant increasing trend at the significance level of 99%, whereas Shiyou River was decreasing at the significance level of 90%, and Dang River had a non-significant trend. Similar results for regional differences in flood magnitude trends can also be found for the Tien Shan Mountains, and Aksu and Tarim rivers, in northwest China, as well as in Europe and North America [6,14,21–23].

#### **5. Conclusions**

In this study, flood events of twelve inland rivers in the QMs, northwest China, were examined, using Sen's slope estimator and the M–K test, for the continuously recorded AMPD and POT3M flood series, from 1970 to 2021. Through analyzing the trends, abrupt changes, and causes of flood magnitude changes, the main findings can be summarized as follows:

1. The evolution of the flood magnitude of the twelve rivers originating in the QMs over the last 52 years, has been mainly influenced by the gradual increase in temperature and precipitation. The main factors causing abrupt changes in flood magnitude are heavy precipitation events and anomalous warming.

2. The trend analysis of the AMPD and POT3M magnitude series of the twelve rivers, for the period of 1970–2021, shows that the spatial distribution of flood magnitude changes is different. The AMPD and POT3M series show a decreasing trend in SYRB in the east (e.g., Gulang, Zamu, and Xiying rivers), a slight fluctuation for six rivers in HRB in the central region, and an increasing trend, with larger significance values, in SLRB in the west (e.g., Shule River), especially since 1987.

In the future, research on the formation process of flood events in arid and semiarid regions should be strengthened, including elements such as meteorological variables related to flood frequency, the timing of flood peak occurrence, and flood magnitude. These findings can suggest solutions for water resources management and influence decisions for adaptation to climate change.

**Author Contributions:** Conceptualization, X.W.; data curation, C.H.; funding acquisition, R.C.; investigation, K.L.; methodology, X.W. and Y.Y.; project administration, J.L.; resources, R.C.; software, X.W. and Z.L.; supervision, R.C.; validation, K.L.; writing—original draft, X.W.; Writing—review and editing, Z.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2019YFC1510505, the National Natural Science Foundation of China, grant number 42171145, the Natural Science Foundation of Gansu Province, China, grant number 21JR7RA043, the Qinghai Key R&D and Transformation Program, grant number 2020-SF-146, and the open research fund of the National Cryosphere Desert Data Center, grant number 2021kf09.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Meteorological data were collected by the National Climate Center of the China Meteorological Administration and are available on http://data.cma.cn/ under request (accessed on 10 October 2022). Flood data were collected by the Bureau of Hydrology and Water Resources of Gansu Province, China, and are available under request.

**Acknowledgments:** We are very grateful for the support of the National Meteorological Center of China's Meteorological Administration on meteorological data and the Hydrological Station of Gansu Province regarding the experimental flood data. We would like to thank the editor and four anonymous reviewers for their insightful and constructive comments that greatly improved the paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Article* **Climate Change, Land Use, and Vegetation Evolution in the Upper Huai River Basin**

**Abel Girma 1,2,\* , Denghua Yan 3,4,\*, Kun Wang 3,4, Hailu Birara <sup>2</sup> , Mohammed Gedefaw <sup>2</sup> , Dorjsuren Batsuren 1,5 , Asaminew Abiyu <sup>1</sup> , Tianlin Qin <sup>3</sup> , Temesgen Mekonen <sup>2</sup> and Amanuel Abate <sup>2</sup>**

**Abstract:** Land-use/land-cover change and climate change have changed the spatial–temporal distribution of water resources. The Huai River Basin shows the spatial and temporal changes of climate from 1960 to 2016 and land-use/land-cover changes from 1995 to 2014. Thus, this study aims to investigate climate change, land use, and vegetation evolution in the Upper Huai River Basin. The Mann–Kendall test (MK), Innovative Trend Analysis Method (ITAM), and Sen's slope estimator test were used to detect climate change trends. The land-use/land-cover change was also examined using a transformation matrix and Normalized Difference Vegetation Index (NDVI). The results of this study revealed that precipitation has shown a slightly decreasing trend during the past 56 years. However, the air temperature has increased by 1.2 ◦C. The artificial and natural vegetation and wetland were decreased by 12,097 km<sup>2</sup> , 3207 km<sup>2</sup> , and 641 km<sup>2</sup> , respectively. On the other hand, resident construction land and artificial water bodies increased by 2277 km<sup>2</sup> and 3691 km<sup>2</sup> , respectively. This indicates that the land cover has significantly changed during the past 30 years. The findings of this study will have implications for predicting the water resources safety and eco-environment of The Huai River Basin. The spatial distribution showed an uneven change in the Huai River Basin. Together, we suggested that the variability of water resources availability in the Huai River Basin was mainly attributed to climate variability, while land use change plays a key role in the sub-basins, which experienced dramatic changes in land use.

**Keywords:** climate change; land cover/land use; vegetation; NDVI; Huai River Basin

#### **1. Introduction**

Climate change has a significant influence on vegetation and water resources in river basins across the globe [1–3]. The production of large amounts of greenhouse gases will exacerbate global temperature rise [4]. Land-use/land-cover (LULC) change is linked with climate change because of its diverse environmental impacts [5]. It is also an important driving force of environmental changes across all spatial and temporal scales. LULC change contributes significantly to earth atmosphere interactions, forest fragmentations, and loss of biodiversity [6]. It is also one of the factors contributing to climate change. However, the disorderly expansion of urban construction land and the massive loss of ecological land has restricted the sustainable development of the overall ecological environment [4].

In the past decades, water resource shortages have become increasingly severe in many parts of the world, associated with the impacts of climate variability and population expansion. To study the impacts of climate change, the Haui River Basin is a suitable area.

**Citation:** Girma, A.; Yan, D.; Wang, K.; Birara, H.; Gedefaw, M.; Batsuren, D.; Abiyu, A.; Qin, T.; Mekonen, T.; Abate, A. Climate Change, Land Use, and Vegetation Evolution in the Upper Huai River Basin. *Atmosphere* **2023**, *14*, 512. https://doi.org/ 10.3390/atmos14030512

Academic Editor: Haibo Liu

Received: 4 February 2023 Revised: 27 February 2023 Accepted: 28 February 2023 Published: 7 March 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Climate variables include precipitation, sunshine hours, air temperature, cloudiness, pressure, rainfall/snow, and wind velocity humidity. These metrological variables interrelate directly or indirectly and significantly influence the environment and living entities [6–8]. Land surface temperature is an imperative ecological factor, and its warming tendency affects the topsoil [9].

The rate of biochemical and plant growth is significantly influenced by soil temperature [6]. Temperature and precipitation influences other variables that have a direct impact on vegetation growth during the concurrent year [10]. Climate change can greatly affect land cover by altering vegetation [5–9]. Changes in vegetation cover alter the strength of the sink and may turn it into a source of green house gasses as a result of land processses such as deforestation [11]. The most used vegetation quantity index derived from remotesensing resources is the normalized difference vegetation index (NDVI) [11]. The NDVI is a compelling index for monitoring vegetation, soil, and water [1,12].

Land-use/land-cover change (LUCC) is the major content of research on world climate and environmental change [13]. It has a huge impact on resources, environment, biodiversity, artificial water environment changes, and economic and social development [14]. Land-use change serves as the ideal starting point for coupled systems about human– environment research in land systems, and the core content of the LUCC dynamic change process has gradually become the focus of scholars. In the past, land-use investigation has merely recognized various land-use scales and structures, however, this method is unable to detect the evolution of the different vegetation types. Lousia employed the TM imagery to analyze land-use/land-cover changes between 1990 to 2004 in Manica province, Mozambique. Ma Xiaoxue et al. [9] explicitly analyzed the characteristics and driving mechanism of land use of the Qinhuai river basin; they proposed the main driving mechanisms of land use in this basin were climate change, economic development, population growth, and the improvement of people's living standards [15]. Wang Xiulan and Zhang Jin et al. organized relevant methods to research the impacts of change of different land use/cover; they presented the meaning and significance of these models of land resources quantity change, land-use degree change, and land demand forecast in the study of land-use change [16–18].

Changes in land cover may occur as a result of the shift in vegetation cover. The determination of land-use and land-cover change is a potent tool for comprehending and evaluating the crucial connection between socioeconomic activities and natural processes [19]. Specifically, it is essential to examine the changes in the different land use/land cover in naturally sensitive areas [20]. The arid and semi-arid regions of central Asia are the most sensitive areas to climate change [21–23]. The Huai River Basin could be the largest demonstrative of these regions [24,25]. Climate change has a significant impact on the Huai River, with water quality being the most sensitive parameter [26]. In recent decades, water resources shortages have become increasingly severe in many parts of the world, associated with the impacts of climate variability and population expansion. Additionally, changes in hydrological cycles may attribute to the change in climate and land uses [27].

The Huai River Basin is spatially all-embracing, with prominent environmental gradients principally determined by temperature and precipitation on wide-ranging scales. Hence, the Huai River Basin is an ideal place to observe the changing aspects of landscape structures and biological communities [21]. Changes in the hydroclimate can also alter lake conditions, land cover, and vegetation cover [22–29]. The main aim of the study is to investigate spatiotemporal trends of climate changes during 1960 to 2016 and examine the vegetation change as well as land-use/land-cover changes during 1985 to 2014 in the Huai River Basin. The main objectives of this are (i) to identify historical climatic trends with land use change, (ii) to determine the extent to which observed trends in land-use/cover change can be reproduced, and (iii) to determine the extent to which trends in vegetation cover change in the region under study.

#### **2. Study Area 2. Study Area**

The study basin is found between 111◦560 E~109◦150 E and 30◦570 N~37◦500 N with area coverage of 207 × 105 km [24]. The mean annual precipitation and temperature of the basin is 883 mm and 11~16 ◦C, respectively. The average annual surface evaporation is between 600~1500 mm [23]. The basin is known to experience water shortages (Figure 1). The study basin is found between 111°56′ E~109°15′ E and 30°57′ N~37°50′ N with area coverage of 207 × 105 km [24]. The mean annual precipitation and temperature of the basin is 883 mm and 11~16 °C, respectively. The average annual surface evaporation is between 600~1500 mm [23]. The basin is known to experience water shortages (Figure 1).

2014 in the Huai River Basin. The main objectives of this are (i) to identify historical climatic trends with land use change, (ii) to determine the extent to which observed trends in land-use/cover change can be reproduced, and (iii) to determine the extent to which

*Atmosphere* **2023**, *14*, x FOR PEER REVIEW 3 of 17

trends in vegetation cover change in the region under study.

**Figure 1.** Location map of the Huai River Basin and drainage. **Figure 1.** Location map of the Huai River Basin and drainage.

#### **3. Data Source 3. Data Source**

#### *3.1. Meteorological Data 3.1. Meteorological Data*

Climate data were gathered for this study from six distinct representatives meteorological and water gauge stations located throughout the Huai River Basin (Table 1). The duration of the study was from 1960 to 2016. The basic information of the representative stations are presented in (Table 1). Climate data were gathered for this study from six distinct representatives meteorological and water gauge stations located throughout the Huai River Basin (Table 1). The duration of the study was from 1960 to 2016. The basic information of the representative stations are presented in (Table 1).

**Table 1.** List of representative Meteorological stations' information. **Table 1.** List of representative Meteorological stations' information.

### *3.2. Data on Land Cover*

For validation, the topographic map, the soil map, the ecological landscape potential map, the forest map, and the vegetation map were selected. From 1982 to 2014, Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper (ETM+) maps with a 30 m resolution were used to classify land cover. The National Geomatics Center of China in 2014 described the Global Land covers dataset (GlobeLand30) product, from which the study area's satellite data for land covers were derived. The name, code, and definition of land cover classification are presented in Table 2. Over the past 30 years, the land cover spatial pattern data were interpreted using all Landsat TM and ETM + images downloaded from USGS (http://landsat.usgs.gov/ accessed on 1 September 2019).

> farmland, high coverage grassland, and forest land.

> > fixed sand, and beach. Medium

residential areas, etc. Inferior

scattered vegetation. Subalternation

Good

**Table 2.** Gray scale value and vegetation coverage equivalent distribution table.

#### *3.3. Vegetation Data*

Level 3 139~155 15%~30% Grassland, the middle plain has

Level 4 128~138 5%~15% Forest land, desert grassland, and

Level 5 below 128 below 5% Artificial water areas, desert,

Level 2 156~190 30%~60%

The MODIS NDVI product (MOD13Q1), which was obtained from NASA's land processes distributed active archive center, served as the source for the normalized difference vegetation index (NDVI) data. The MODIS surface reflectance values from the red band (610–680 nm) and the near-infrared band (780–890 nm) was used to calculate the NDVI after they were adjusted for molecular scattering, ozone absorption, and aerosols [30]. The MOD13 Q1 products, which include 12 scientific data sets with a 16-day temporal sampling period and a spatial resolution of 250 m × 250 m, were derived from the most recent version.

#### **4. Methods**

#### *4.1. Climate Variable Analysis*

4.1.1. Innovative Trend Analysis Method (ITAM)

ITAM divides dataseries into two equivalent sub-series and categorizes them in increasing order [6,15,31–34]. The *X*-axis (x\_i:i = 1, 2, 3, . . . , n/2) and the *Y*-axis (x\_j:j = n/2 +1, n/2 + 2, . . . , n) were then used to arrange the two sections on a coordinate system. There is no trend if time-series data are collected on a scattered plot along a 1:1 (45◦ ) straight line. When data points accumulate above the 1:1 straight line, the trend is upward, and when data points accumulate below it, the trend is downward. The difference in mean values between x I and x j might reflect how far a data series is trending (Figure 2).

**Figure 2.** Paradigmatic illustrations of the ITA method. **Figure 2.** Paradigmatic illustrations of the ITA method.

#### 4.1.2. Mann–Kendall (M–K) Test Method 4.1.2. Mann–Kendall (M–K) Test Method

The Mann–Kendall (M–K) test is used to detect the trends of time series data [2,35]. The test statistics "S" is equated as follows: The Mann–Kendall (M–K) test is used to detect the trends of time series data [2,35]. The test statistics "S" is equated as follows:

$$S = \sum\_{i=1}^{n-1} \sum\_{j=i+1}^{n} \text{sgn}\left(\mathbf{x}\_{j} - \mathbf{x}\_{i}\right) \tag{1}$$

(1)

(2)

(4)

$$\text{sgn}(\mathbf{x}\_{j} - \mathbf{x}\_{i}) = \begin{cases} +1 \, \text{if } \begin{pmatrix} \mathbf{x}\_{j} - \mathbf{x}\_{i} \end{pmatrix} > \mathbf{0} \\\ \mathbf{0} \, \text{if } \begin{pmatrix} \mathbf{x}\_{j} - \mathbf{x}\_{i} \end{pmatrix} = \mathbf{0} \\\ -1 \, \text{if } \begin{pmatrix} \mathbf{x}\_{j} - \mathbf{x}\_{i} \end{pmatrix} < \mathbf{0} \end{cases} \tag{2}$$

where and represents the data points in periods and . While the amount of data series is larger than or equivalent to 10 ( ≥ 10), the M–K test is then categorized by a standard distribution with the mean () = 0 and variance (), which is given as follows [35]: where *x<sup>j</sup>* and *x<sup>i</sup>* represents the data points in periods *j* and *i*. While the amount of data series is larger than or equivalent to 10 (*n* ≥ 10), the M–K test is then categorized by a standard distribution with the mean *E*(*S*) = 0 and variance *Var*(*S*), which is given as follows [35]:

$$E(\mathcal{S}) = \mathbf{0} \tag{3}$$

$$Var(S) = \frac{n(n-1)(2n+5) - \sum\_{k=1}^{m} t\_k(t\_k - 1)(2t\_k + 5)}{18} \tag{4}$$

where m is the number of the tied groups in the time series, and *t<sup>k</sup>* is the number of ties in the *k*th tied group. From this the test, the *Z* statistics is obtained using approximation as follows:

$$Z = \begin{cases} \frac{s-1}{\delta} & \text{if } \mathcal{S} > 0\\ 0, & \text{if } \mathcal{S} = 0\\ \frac{s+1}{\delta} & \text{if } \mathcal{S} < 0 \end{cases} \tag{5}$$

#### 4.1.3. Sen's Slope Estimator Test

The slope *Q<sup>i</sup>* between two data points is given by the equation [2,35].

$$Q\_i = \frac{\mathbf{x}\_j - \mathbf{x}\_k}{j - k}, \text{ for } i = 1, 2, \dots, N \tag{6}$$

where *x<sup>j</sup>* and *x<sup>k</sup>* are data points at time *j* and (*j* > *k*), respectively.

When there is only single datum in each time, then *N* = *n*(*n*−1) 2 ; n are a number of time periods. However, if there are many data in each year, then *N* < *n*(*n*−1) 2 ; n total number of observations [36]. The *N* values of slope estimator are arranged from smallest to biggest. Then, the median of slope (β) is computed as follows:

$$\mathcal{B} = \begin{cases} \mathcal{Q}[(N+1)/2] & \text{when N is odd} \\ \mathcal{Q}[(N/2) + \mathcal{Q}(N+2)/(2)/(2)] & \text{when N is even} \end{cases} \tag{7}$$

### *4.2. Analysis of Land Use and Land Cover*

The overlaying operation was used to perform spatial analysis, which revealed how land use and land cover changed over time and established a connection between the two. A land cover transformation map was obtained and utilized for transformation matrix analysis by intersecting the two land cover/land use maps (1985 and 2014). The magnitudes of land cover shifts were calculated as [12].

$$CA = TA(t\_2) - TA(t\_1)\_\prime \tag{8}$$

$$\mathbb{C}E = \left[ \mathbb{C}A / TA(t\_1) \right] \* 100,\tag{9}$$

where: t\_1 and t\_2 represent the beginning and end, while TA, CA, and CE represent the total area, changed area, and extent of change, respectively. The Kappa coefficient (Kappa) was also calculated. The agreement between user-assigned ratings and the predefined producer ratings is measured by kappa. The difference between the amount of agreement that is present (the "observed" agreement) and the amount of agreement that would be expected to be present by chance alone (the "expected" agreement) is the basis of the calculation [37].

$$K = P(A) - P(E)/1 - P(E) \tag{10}$$

$$P(A) = \frac{(A+D)}{N} \,\,\,\,\,\,\tag{11}$$

$$P(E) = \left(\frac{A1}{N}\right) \* \left(\frac{B1}{N}\right) + \left(\frac{A2}{N}\right) \* \left(\frac{B2}{N}\right)'\tag{12}$$

where *K* is the Kappa coefficient, *P*(*A*) is the number of times the *K* raters agree, and is the number of times the *K* rates are expected to agree only by chance, *A* and *D* are unchanged categories, *A*1 and *B*1 are subject's categories, and *N* is the change of results. ArcGIS 10.2 and the land-use transfer matrix were used to investigate a comprehensive land-use dynamics degree [38] of the Huai River Basin.

$$\text{LCC} = \frac{\sum\_{i=1}^{n} VLU\_{i-j}}{\sum\_{i=1}^{n} LUI\_i \ T} \times 100\text{\%} \tag{13}$$

where LC is the total land-use dynamic degree, Lui is the type of area, Lui−j is the total value of an area in land-use type I to J, and T is the amount of time that has passed since the last monitoring. LC is the land-use dynamic change rate when T is set for years.

We can generate five groups of transfer matrixes for various land-use types using five years of land-use data—1985–1990, 1990–2000, 2000–2005, 2005–2014, and 1985–2014—to comprehend the basin's transfer situation.

#### *4.3. Vegetation Analysis*

One of the most widely used vegetation indices of plant biomass and activity is the normalized difference vegetation index (NDVI) which is used to denote a shift in an area's vegetative greenness [1,39].

$$NDVI = \frac{(NIR - RED)}{(NIR + RED)} \,\prime \tag{14}$$

where the electromagnetic spectrum's near-infrared (NIR) and red (RED) channels correspond to bands 2 and 1 of the MODIS (MOD13Q1) product, respectively. We used a linear regression model based on MODIS data to spread GIMMS data until 2014 due to the different spatial resolutions of GIMMS and MODIS. In addition, we utilized the same time data to check outspread data. Afterward, the NDVI average correlation coefficient reached greater than 90% in the Huai River Basin. The land-use type and the NDVI gray-scale value were additionally examined (Table 1) to determine vegetation coverage evolution tendency.

#### **5. Results and Discussions**

#### *5.1. Trends of Observed Climate Changes*

In the study region, positive and negative tendencies are present by the M–K test estimator, and annual mean precipitation, and display temporal variations (Figure 3). This is in good agreement with studies and annotations from various parts of China [8,24]. Since precipitation tends to decrease across the study basin, the anticipated decrease in precipitation determination will probably result in a decrease in water accessibility in the years to come [8]. *Atmosphere* **2023**, *14*, x FOR PEER REVIEW 8 of 17

**Figure 3.** Spatially distribution land-use/land-cover map of the Huaihe River Basin. **Figure 3.** Spatially distribution land-use/land-cover map of the Huaihe River Basin.

The hydrological series and the supply of water resources for ecological units and society can be disrupted by a decrease in precipitation during the wet season [40]. The summer is the most precipitous time of year in the study area, contributing nearly 49.3%

begins in December and lasts through February (winter), accounts for approximately 7.3% of the total precipitation. Winter is more susceptible to the occurrence of persistent drought events, as revealed by the consequences of light precipitation concentration [30]. In China, a variety of trend exploration studies have been piloted at various spatiotemporal measures, yielding a wide range of results with various trend test parameters. Although they discovered a statistically significant upward trend in temperature, the

The rise in temperature is amongst the indices of global climate transformation. Globally, mean air temperature has increased by 0.85 °C from 1880 onwards, which is expected to move along in the near future [4,5,7,40]. The global large inland water bodies temperature has been promptly heating ever since 1980, at the rate of 0.05 ± 0.012 °C/year and by the maximum rate of 0.1 ± 0.011 °C/year [41]. An abrupt upward trend of average annual air temperature was detected in the upper reaches of the Huai River Basin via 1.2 °C or 0.021 °C/year during the deliberated chronological period from 1960 to 2016 (Figure 4b). It is nearly twice as fast as the global average heating rate (0.012 °C/year) [42]. The basin's average annual temperature was found to be 15.5 °C. From 1990 onwards, a sig-

situation regarding precipitation varied.

nificant rise in temperature was observed.

The hydrological series and the supply of water resources for ecological units and society can be disrupted by a decrease in precipitation during the wet season [40]. The summer is the most precipitous time of year in the study area, contributing nearly 49.3% of the total precipitation, indicating a high intensity of precipitation and snowfall. Additionally, stream flow is at its best during this time of year. The squat rainy season, which begins in December and lasts through February (winter), accounts for approximately 7.3% of the total precipitation. Winter is more susceptible to the occurrence of persistent drought events, as revealed by the consequences of light precipitation concentration [30]. In China, a variety of trend exploration studies have been piloted at various spatiotemporal measures, yielding a wide range of results with various trend test parameters. Although they discovered a statistically significant upward trend in temperature, the situation regarding precipitation varied.

The rise in temperature is amongst the indices of global climate transformation. Globally, mean air temperature has increased by 0.85 ◦C from 1880 onwards, which is expected to move along in the near future [4,5,7,40]. The global large inland water bodies temperature has been promptly heating ever since 1980, at the rate of 0.05 ± 0.012 ◦C/year and by the maximum rate of 0.1 ± 0.011 ◦C/year [41]. An abrupt upward trend of average annual air temperature was detected in the upper reaches of the Huai River Basin via 1.2 ◦C or 0.021 ◦C/year during the deliberated chronological period from 1960 to 2016 (Figure 4b). It is nearly twice as fast as the global average heating rate (0.012 ◦C/year) [42]. The basin's average annual temperature was found to be 15.5 ◦C. From 1990 onwards, a significant rise in temperature was observed. *Atmosphere* **2023**, *14*, x FOR PEER REVIEW 9 of 17

**Figure 4.** (**a**) Trends of annual temperature. (**b**) Trends of annual precipitation. **Figure 4.** (**a**) Trends of annual temperature. (**b**) Trends of annual precipitation.

The cumulative upward trend of air temperature could be the outcome of global warming, which is due to the "built-up heat island", greenhouse influence, and long-standing climate inconsistency [6–9]. In the present study, the temperature during the summer season was higher. Similarly, Gu et al. [43]. found a higher magnitude of air temperature with an increasing trend in the summer seasons, which was superior to the other periods (Figure 5). The cumulative upward trend of air temperature could be the outcome of global warming, which is due to the "built-up heat island", greenhouse influence, and longstanding climate inconsistency [6–9]. In the present study, the temperature during the summer season was higher. Similarly, Gu et al. [43]. found a higher magnitude of air temperature with an increasing trend in the summer seasons, which was superior to the other periods (Figure 5).

> y = 0.0184x + 14.9 R² = 0.3313

**Figure 5.** Mean annual precipitation and temperature of the Huai River Basin (1960–2016).

The M–K trend detection test result discovered that the annual mean temperatures have been considerably increasing throughout the period. The general increment in mean annual temperature in the study region then is principally indorsed to a rise in the minimum temperature. Increased temperatures in winter and spring will affect the precipitation phase and, as a consequence, the snow/precipitation ratio and the volume of water stored in snow cover will be changed. Therefore, the hydrology of rivers in the

**13 13.5 14 14.5 15 15.5 16 16.5 17 17.5 18**

(mm) °C

**Temperature**

°**C**

We may be able to comprehend the synoptic temporal change in the study area's land cover types through the use of satellite data, which can make concurrent, synoptic,

**1988**

**1990**

**1992**

**1994**

**1996**

**1998**

**2000**

**2002**

**2004**

**2006**

**2008**

**2010**

**2012**

**2014**

**2016**

Northern Hemisphere is sensitive to climate change [44].

*5.2. Change Detection and Classification of Land Cover*

and repetitive annotations [45].

y = -0.6399x + 1015.4 R² = 0.0025

year

**precipitation(mm)**

**1960**

**1962**

**1964**

**1966**

**1968**

**1970**

**1972**

**1974**

**1976**

**1978**

**1980**

**1982**

**1984**

**1986**

other periods (Figure 5).

(**a**) (**b**)

**Figure 4.** (**a**) Trends of annual temperature. (**b**) Trends of annual precipitation.

The cumulative upward trend of air temperature could be the outcome of global warming, which is due to the "built-up heat island", greenhouse influence, and long-standing climate inconsistency [6–9]. In the present study, the temperature during the summer season was higher. Similarly, Gu et al. [43]. found a higher magnitude of air temperature with an increasing trend in the summer seasons, which was superior to the

**Figure 5.** Mean annual precipitation and temperature of the Huai River Basin (1960–2016). **Figure 5.** Mean annual precipitation and temperature of the Huai River Basin (1960–2016).

The M–K trend detection test result discovered that the annual mean temperatures have been considerably increasing throughout the period. The general increment in mean annual temperature in the study region then is principally indorsed to a rise in the minimum temperature. Increased temperatures in winter and spring will affect the precipitation phase and, as a consequence, the snow/precipitation ratio and the volume of water stored in snow cover will be changed. Therefore, the hydrology of rivers in the Northern Hemisphere is sensitive to climate change [44]. The M–K trend detection test result discovered that the annual mean temperatures have been considerably increasing throughout the period. The general increment in mean annual temperature in the study region then is principally indorsed to a rise in the minimum temperature. Increased temperatures in winter and spring will affect the precipitation phase and, as a consequence, the snow/precipitation ratio and the volume of water stored in snow cover will be changed. Therefore, the hydrology of rivers in the Northern Hemisphere is sensitive to climate change [44].

#### *5.2. Change Detection and Classification of Land Cover 5.2. Change Detection and Classification of Land Cover*

We may be able to comprehend the synoptic temporal change in the study area's land cover types through the use of satellite data, which can make concurrent, synoptic, and repetitive annotations [45]. We may be able to comprehend the synoptic temporal change in the study area's land cover types through the use of satellite data, which can make concurrent, synoptic, and repetitive annotations [45].

A maximum-likelihood classifier and the classification scheme in Table 2, have made five classification maps of the Huai River Basin. With substantial precision, the study region landscape was classified into 65 land-use classes (Figure 6).

In the present study, the artificial and natural vegetation, as well as the wetland, showed a decreasing trend of 12,097 km<sup>2</sup> , 3207 km<sup>2</sup> , and 641 km<sup>2</sup> , respectively, during the period 1985–2014. Meanwhile, the area of resident construction land and artificial water showed an increasing trend by 2277 km<sup>2</sup> and 3691 km<sup>2</sup> , respectively. However, the other areas only displayed a small change (Figure 7).

It should be noted that the artificial water bodies experienced the majority of the area's greatest changes and urban land during the past 30 years. The dynamic degree was 9.2% and 6.4%, respectively. The area for wetland artificial vegetation and natural vegetation was diminished, and the shifting ratio was inferior. From the comprehensive land-use dynamic degree of the whole study years, the degree of intervention to land use by human activities was prevalent from 2005–2014. The dynamic degree is approximately 0.5% from 1985–2014, which implies that about 4.5% of the land use type has been transformed throughout this period every year (Table 3). In the course of the early 21st century, the population of the Huai River Basin had a high rate of growth; throughout this phase, the per capita possession of land resources was small, which caused a shift in land use to some extent (Figure 7).

A maximum-likelihood classifier and the classification scheme in Table 2, have made five classification maps of the Huai River Basin. With substantial precision, the

study region landscape was classified into 65 land-use classes (Figure 6).

**Figure 6.** Study area map of land-use type by spatial transformation variation. **Figure 6.** Study area map of land-use type by spatial transformation variation.

**Figure 7.** Land-use/land-cover transformations from 1985 to 2014. **Figure 7.** Land-use/land-cover transformations from 1985 to 2014.

In the present study, the artificial and natural vegetation, as well as the wetland,

It should be noted that the artificial water bodies experienced the majority of the area's greatest changes and urban land during the past 30 years. The dynamic degree was 9.2% and 6.4%, respectively. The area for wetland artificial vegetation and natural vegetation was diminished, and the shifting ratio was inferior. From the comprehensive land-use dynamic degree of the whole study years, the degree of intervention to land use by human activities was prevalent from 2005–2014. The dynamic degree is approximately 0.5% from 1985–2014, which implies that about 4.5% of the land use type has been transformed throughout this period every year (Table 3). In the course of the early 21st century, the population of the Huai River Basin had a high rate of growth; throughout this phase, the per capita possession of land resources was small, which caused a shift in

**Type 1985–1990 1990–2000 2000–2005 2005–2014 1985–2014**

(AV) −0.56 0.16 0.21 −1.04 −1.32

(NV) 0.16 0.05 −1.09 −1.28 −2.38

(AW) 3.03 2.94 4.54 2.78 9.15 Wetland (W) −0.71 0.31 −1.15 0.23 −1.44

0.44 0.14 0.33 0.57 0.46

tion land (RCL) 3.45 0.76 −2.13 7.04 6.41 Others (O) 2.18 −1.57 −7.47 11.55 0.21

, and 641 km<sup>2</sup>

, respectively, during the

,respectively. However, the other

, 3207 km<sup>2</sup>

period 1985–2014. Meanwhile, the area of resident construction land and artificial water

**Table 3.** Land-use/land-cover dynamics of the Huai River Basin from 1985 to 2014 (%).

showed a decreasing trend of 12,097 km<sup>2</sup>

land use to some extent (Figure 7).

**Land-Use/Cover**

Artificial Vegetation

Natural Vegetation

Artificial water area

Resident construc-

(LC) Comprehensive land-use dynamic degree

showed an increasing trend by 2277 km2 and 3691 km<sup>2</sup>

areas only displayed a small change (Figure 7).

RCL and AW bodies are formed from more than one third of artificial vegetation. More than 30% of Nevada is converted into additional land uses, such as empty land and sand. The leading causes for the alterations are hasty population growth, urbanization, ecological deterioration, and high intensity of socio-economic activities in the study area [46].

Natural factors are yet another significant cause of land-cover shift. The components of the land cover, for instance, may be impacted directly or indirectly by the effects of climate change. The overall pattern of regional vegetation was characterized by the shifting of various forest types, whereas the distribution of forest boundaries may not have changed as clearly [25]. When the climate changes rapidly, fast-growing pioneer communities expand rapidly and require a significant amount of migration distance. As a result, it is essential to define the size of changes in land cover (Table 4).

**Table 4.** The Huai River Basin's land-use and land-cover transformation matrix from 1985 to 2014 (in percent).

#### *5.3. NDVI*

MODIS vegetation indices produced on 16-day intervals and at multiple spatial resolutions provide consistent spatial and temporal comparisons of vegetation canopy greenness, a composite property of leaf area, chlorophyll, and canopy structure. Two vegetation indices were derived from atmospherically corrected reflectance in the red, near-infrared, and blue wave bands—the normalized difference vegetation index (NDVI). The strength of global NDVI data is their high temporal information content [1,47]. The common compositing time of 8–14 days provides at least 25–30 global NDVI datasets per year [2]. This ensures consistency with the historical and climate applications of NOAA's AVHRR NDVI time series record, reducing variations in the canopy and soil and increasing sensitivity in dense vegetation conditions. The two products better describe the global range of states and processes of vegetation [47]. As a result, the NDVI was used to determine the vegetation cover (Figure 8). *Atmosphere* **2023**, *14*, x FOR PEER REVIEW 13 of 17

**Figure 8.** NDVI—the spatial distribution map of the Huai River Basin vegetation. precipitation throughout the summer season [44], and to some extent, harsh climatic **Figure 8.** NDVI—the spatial distribution map of the Huai River Basin vegetation.

The pixel's NDVI values ranged from −1 to 1. The highest NDVI values were greater than 0.6, indicating the healthiest or richest vegetation. The normalized

trarily, during 2000, the vegetation coverage had significantly declined, and the percentage of preeminent coverage area accounted for only about 8.7% of the study region. This was principally because the total mean annual precipitation significantly declined [44]. For the period from 2005–2014, the area of preeminent vegetation coverage declined by 30%; on the other hand, the area of lower vegetation coverage NDVI < 0.4 increased by about 75%. The major reason for the vegetation boost was the prevalence of adequate

The pixel's NDVI values ranged from −1 to 1. The highest NDVI values were greater than 0.6, indicating the healthiest or richest vegetation. The normalized difference vegetation index had a significant difference in the study region. This is due to the prevalence of sufficient precipitation during the period from 1985 onwards. Contrarily, during 2000, the vegetation coverage had significantly declined, and the percentage of preeminent coverage area accounted for only about 8.7% of the study region. This was principally because the total mean annual precipitation significantly declined [44]. For the period from 2005–2014, the area of preeminent vegetation coverage declined by 30%; on the other hand, the area of lower vegetation coverage NDVI < 0.4 increased by about 75%. The major reason for the vegetation boost was the prevalence of adequate precipitation throughout the summer season [44], and to some extent, harsh climatic circumstances were reduced during this period that generated advantageous situations for vegetation growth. On the contrary, urbanization leads to a scattered configuration of vegetation coverage in the study region. *Atmosphere* **2023**, *14*, x FOR PEER REVIEW 14 of 17 circumstances were reduced during this period that generated advantageous situations for vegetation growth. On the contrary, urbanization leads to a scattered configuration of vegetation coverage in the study region. Temperature and precipitation may influence the dynamics of vegetation in the

> Temperature and precipitation may influence the dynamics of vegetation in the Huai River Basin. Changes in the global carbon and hydrology cycle, as well as feedback on climate change, could be caused by vegetation dynamics [25]. One of the factors that alter the ecosystem, water system circulation, and surface water supply balance is the cover of vegetation. There may be a number of factors connected to the change in the Huai River's vegetation cover. This indicates a significant degree of climate change throughout the basin due to the coincidence of overlaps with climatic variations of the current weather. It is obvious that cultivated land areas are growing bigger and areas of vegetation are becoming smaller. This suggests that human activity may have altered the river basin's land cover. Huai River Basin. Changes in the global carbon and hydrology cycle, as well as feedback on climate change, could be caused by vegetation dynamics [25]. One of the factors that alter the ecosystem, water system circulation, and surface water supply balance is the cover of vegetation. There may be a number of factors connected to the change in the Huai River's vegetation cover. This indicates a significant degree of climate change throughout the basin due to the coincidence of overlaps with climatic variations of the current weather. It is obvious that cultivated land areas are growing bigger and areas of vegetation are becoming smaller. This suggests that human activity may have altered the river basin's land cover.

#### *5.4. Implication of Climate Change over Land Cover/Use of Huai River Basin 5.4. Implication of Climate Change over Land Cover/Use of Huai River Basin*

In the Huai Basin, precipitation has decreased significantly over the past 56 years. The basin's temperature has varied by approximately 1.2 ◦C over the same period (Figure 9). The most changed parts of the land covers in the Huai River Basin are the artificial vegetation, natural vegetation, and wetland, which showed a decrease of 12,098, 3028, and 640 km<sup>2</sup> , respectively. This could be due to the increasing temperature and an overall decline pattern of precipitation in the study basin. On the other hand, the rest of the other land transformation type is related to human activities. In the Huai Basin, precipitation has decreased significantly over the past 56 years. The basin's temperature has varied by approximately 1.2 °C over the same period (Figure 9). The most changed parts of the land covers in the Huai River Basin are the artificial vegetation, natural vegetation, and wetland, which showed a decrease of 12,098, 3028, and 640 km<sup>2</sup> , respectively. This could be due to the increasing temperature and an overall decline pattern of precipitation in the study basin. On the other hand, the rest of the other land transformation type is related to human activities.

**Figure 9.** The changes in land cover from 1985 to 2014 are depicted in the diagram. Gains and losses are indicated by arrows. Herein: Pink is a result of human activity, red is a result of temperature rise, yellow is a result of humans and nature, and blue is a result of other natural factors. **Figure 9.** The changes in land cover from 1985 to 2014 are depicted in the diagram. Gains and losses are indicated by arrows. Herein: Pink is a result of human activity, red is a result of temperature rise, yellow is a result of humans and nature, and blue is a result of other natural factors.

In this region, climate change may alter the land cover [48]. Large mountains and river basins have different levels of vegetation cover. This demonstrates that the moun-

of shifting land cover as a result of intense human activity and global warming. Along the river valley, there is also a large amount of agricultural land. In addition, extensive human activity in the region has altered the vegetation cover of the Huai River Basin (Figure 10). The vegetation cover in the river basin will change if there are many people working there [49]. Artificial surfaces and cultivated land have increased as a result of

In this region, climate change may alter the land cover [48]. Large mountains and river basins have different levels of vegetation cover. This demonstrates that the mountain and river valley vegetation cover changed between 1985 and 2014 [49]. Additionally, the 2007 average annual temperature overlap was the highest (Figure 10). This is a result of shifting land cover as a result of intense human activity and global warming. Along the river valley, there is also a large amount of agricultural land. In addition, extensive human activity in the region has altered the vegetation cover of the Huai River Basin (Figure 10). The vegetation cover in the river basin will change if there are many people working there [49]. Artificial surfaces and cultivated land have increased as a result of intense human activity. Thus, this region's vegetation and land cover may be significantly affected by climate change and human activity. *Atmosphere* **2023**, *14*, x FOR PEER REVIEW 15 of 17 intense human activity. Thus, this region's vegetation and land cover may be significantly affected by climate change and human activity.

**Figure 10.** Correlation between precipitation and temperature. **Figure 10.** Correlation between precipitation and temperature.

#### **6. Conclusions 6. Conclusions**

stations.

We assessed the Huai River Basin's climate changes between 1960 and 2016, land use and land cover, and vegetation between 1985 and 2014 using remote sensing, classification of land use and land cover, and detection of vegetation. We assessed the Huai River Basin's climate changes between 1960 and 2016, land use and land cover, and vegetation between 1985 and 2014 using remote sensing, classification of land use and land cover, and detection of vegetation.

The study area's precipitation slightly changed between 1960 and 2016. On the other hand, the temperature often went up by 1.2 °C, leading to a significant increase over the past 40 years in the semi-arid central Asian region. The study area's precipitation slightly changed between 1960 and 2016. On the other hand, the temperature often went up by 1.2 ◦C, leading to a significant increase over the past 40 years in the semi-arid central Asian region.

Artificial vegetation, natural vegetation, and wetland changed the most in the Huai River Basin with a decreasing trend. Construction land for homes significantly increased. The Huai River Basin's land cover and land use have changed slightly over time, both in quantity and quality. Artificial vegetation, natural vegetation, and wetland changed the most in the Huai River Basin with a decreasing trend. Construction land for homes significantly increased. The Huai River Basin's land cover and land use have changed slightly over time, both in quantity and quality.

There was a clear shift in the vegetation cover in the river basin and mountainous regions. Between 1985 and 2014, the vegetation cover and land cover changed significantly. In the study area, these changes coincide with the process of climate change and human activity. Furthermore, the increase in run-off may also have a wider implication for increasing soil erosion and sedimentation. Thus, curving the trends of LULC towards increasing vegetation cover is very important to reduce wet season flow and surface run-off. Therefore, the concerned entities should take appropriates measures for the sustainable management of land as well as water resources. There was a clear shift in the vegetation cover in the river basin and mountainous regions. Between 1985 and 2014, the vegetation cover and land cover changed significantly. In the study area, these changes coincide with the process of climate change and human activity. Furthermore, the increase in run-off may also have a wider implication for increasing soil erosion and sedimentation. Thus, curving the trends of LULC towards increasing vegetation cover is very important to reduce wet season flow and surface run-off. Therefore, the concerned entities should take appropriates measures for the sustainable management of land as well as water resources.

Scientific research on the causes of land-cover change and its potential effects on the hydrological and ecological systems of the Huai River Basin is essential in the near future. Due to the limitation of data, this study only considers six stations for this analysis. Therefore, detail study should be conducted in the future by considering more sample Scientific research on the causes of land-cover change and its potential effects on the hydrological and ecological systems of the Huai River Basin is essential in the near future. Due to the limitation of data, this study only considers six stations for this analysis. Therefore, detail study should be conducted in the future by considering more sample stations.

**Author Contributions:** Conceptualization, A.G., D.Y., H.B., K.W., D.B., A.B. and T.Q., methodology, A.G., D.Y., D.B. and M.G., associated software, D.Y., D.B. and M.G., investigation, D.Y., H.B.,

D.Y., K.W., A.B. and M.G., writing review and editing, H.B., T.M. and A.A. All authors have read

**Funding:** This research was supported by the National Science Fund Project (Grant No. 52130907;

52109043) and the Five Major Excellent Talent Programs of IWHR (WR0199A012021).

and agreed to the published version of the manuscript.

**Institutional Review Board Statement:** Not applicable.

**Author Contributions:** Conceptualization, A.G., D.Y., H.B., K.W., D.B.; M.G and T.Q., methodology, A.G., D.Y., D.B. and M.G., associated software, D.Y., D.B. and M.G., investigation, D.Y., H.B., K.W., T.M., A.A. (Asa-minew Abiyu) and A.A. (Amanuel Abate) data curation, A.G. and D.Y.; Formal analysis, A.G., D.Y., T.M., D.B., T.Q. and A.A., writing original draft preparation, A.G., D.Y., K.W. and M.G., writing review and editing, H.B., T.M. and A.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the National Science Fund Project (Grant No. 52130907; 52109043) and the Five Major Excellent Talent Programs of IWHR (WR0199A012021).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data will be obtained uopon the request of the corresponding authors.

**Acknowledgments:** The researchers thank the National Key Research and Development Project (Grant No. 2016YFA0601503). The individuals who contributed data to this study are appreciated by the authors. We are also grateful to the China Institute of Water Resources and Hydropower Research (IWHR) for their financial assistance.

**Conflicts of Interest:** The authors declared no conflict of interest.

#### **References**