## 4 Conclusions  
Based on sea surface temperature and wind field data from 2000 to 2018, this paper divides upwelling waters in the central and western parts of the South China Sea into three regions: OU_1, OU_2, and OU_3, based on the different shoreline angles of the eastern coast of the Indochina Peninsula. The spatiotemporal distribution characteristics of upwelling in the central and western parts of the South China Sea were explored using the upwelling automatic detection method and EOF decomposition. The influence of the wind fields on upwelling in the central and western parts of the South China Sea was discussed using the  
Figure 8: Response time of the west-central South China Sea upwelling to changes in wind field characteristics after 2000–2018. upwelling intensity index based on wind field characteristics and correlation analysis. The main conclusions are as follows. The results of the upwelling detection showed that upwelling in the central and western waters of the South China Sea occurred from May to September, with the highest frequency in May and June (35.6% and 57.6%, respectively). In July and August, the monthly average upwelling frequency reached over 85%, with a high percentage of the upwelling areas concentrated in OU_2 and OU_1 (11-13.5\({}^{\circ}\) N). The monthly average upwelling frequency in September decreased to 59.8%, and the upwelling event entered the recession period. The trend line of upwelling intensity showed that the upwelling events were stronger in the OU_1 and OU_2 waters during the same period, with the intensity of upwelling gradually decreasing with increasing latitude. The variance contributions of the first three modes of the EOF decomposition were 69.46%, 8.34%, and 5.1%, respectively, with a cumulative explained variance of 82.9%. The zero contours of the spatial coefficient were located near the 109.5\({}^{\circ}\) E diameter line and shifted towards the eastern sea with increasing latitude. Upwelling events occurred in the range of 10.5-14.3\({}^{\circ}\) N, 111.2-113.8\({}^{\circ}\) E, which maintained an interannual variation cycle of 2-2.5 years, and the oscillation amplitude generally showed an increasing trend, which may be related to global warming factors. The effect of wind fields on the upwelling intensity center was divided into two zones. When the wind direction was in the range of 0\({}^{\circ}\) to 90\({}^{\circ}\), the radial wind speed decreased, the latitudinal wind speed increased, and the upwelling intensity center moved towards the high-latitude offshore direction. When the wind direction was in the range of 90\({}^{\circ}\) to 180\({}^{\circ}\), the radial wind speed increased, the latitudinal wind speed decreased, and the upwelling intensity center moved towards the low-latitude nearshore direction. The correlation analysis showed that the average response time of the west-central South China Sea upwelling to the local wind field was 38.9 h, and the response times of the OU_1, OU_2, and OU_3 seas to local wind field changes were 39.2 h, 63.4 h, and 14.2 h, respectively. This indicated a long response time in the central sea and a shortened response time in the upper and lower sides of the sea. The response time was shortened by 12.1 h (24.6 h) when the latitude decreased (increased) by 1. Overall, this study provided valuable insights into the spatiotemporal distribution characteristics of upwelling in the central and western parts of the South China Sea and the influence of wind fields on upwelling intensity. The findings could be useful for understanding the mechanisms driving the variability of upwelling in this region and for the development of more accurate models for predicting upwelling events. At the same time, we should also be aware that it is necessary to combine artificial intelligence to further study the upwelling events for higher spatiotemporal resolution data, providing new research directions for our subsequent studies [28; 29]. Conceptualization, J.S. and H.H. ; methodology, J.S. ; program, D.F. ; validation, B.X., Y.X.