The Influence of Climate Change on Forest Fires in Yunnan Province, Southwest China Detected by GRACE Satellites

Abstract

Yunnan province in China has rich forest resources but high forest fire frequency. Therefore, a better understanding of the relationship between climate change and forest fires in this region is important for forest fire prevention. This study used the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage change (TWSC) data to analyze the influence of climate change on forest fires in the region during 2003–2016. To improve the accuracy and reliability of GRACE TWSC data, we used the generalized three-cornered hat (GTCH) and the least square method to fuse TWSC data from six GRACE solutions. The spatiotemporal variation of forest fires during 2003–2016 was investigated using burned area data. Then, the relationship between burned area and hydrological and climatic factors was analyzed. The results indicate that more than 90% of burned areas are located in northwestern and southern Yunnan (NW and S). On the seasonal scale, forest fires are mainly concentrated in January–April (dry season) and the burned area is negatively correlated with precipitation (correlation coefficient r = −0.83 (NW) and −0.51 (S)), relative humidity (r = −0.79 (NW) and −0.92 (S)), GRACE TWSC (r = −0.57 (NW) and −0.73 (S)) and evapotranspiration (r = −0.90 (NW) and −0.35 (S)). However, the burned area has no significant correlations with the above four factors on the interannual scale. The composite analysis suggests that the extreme climate affects precipitation, evapotranspiration and TWSC in this region, thereby changing water storage of the air in this region, leading to the formation of an environment prone to forest fires. Such conditions have led to an increase in the burned area in the above region. We also found that the difference between TWSC in high- and low-fire years is much greater than the precipitation in the same period. The above results show that GRACE satellites can detect the influence of climate change on forest fires in Yunnan province.

Impacts of the Western Pacific and Indian Ocean Warm Pools on Wildfires in Yunnan, Southwest China: Spatial Patterns With Interannual and Intraannual Variations

“…Studies have also shown a negative correlation of summer rainfall in Southwest China with the simultaneous size and temperature of IO warm pool, and a positive correlation with that of the WP warm pool (Cao et al., 2014; Ueda et al., 2015). As the precipitation in rainy season prevents fire occurrence in Yunnan (Cui et al., 2022), it indicates that the cold phase of IO warm pool, regularly occurring in summer for the northern hemisphere, should promote the rainfall in Southwest China (Figure 2b). This may result from the enhancement of the land‐sea thermal contrast and the IO monsoon, corresponding to the rainy season in this region (Gu et al., 2020; Ha et al., 2019).…”

“…Each record with the time and location of wildfire were used here, and 5,483 records in 18 years were collected. Undeniably, most ignition sources of wildfire were from human activities, but wildfire occurrence must dominantly be attributed to the air moisture and other climatic conditions in Yunnan (Cui et al., 2022; Ying et al., 2021). Obviously characterized by climatic conditions, with about 99% of the total fire number in winter and spring, there are indeed distinction on wildfire number between the locally practical fire alarming season (winter and spring, or December‐May) and nonfire alarming season (summer and autumn, or June‐November) across Yunnan (Figures 2a and 2b).…”

The Influence of Climate Change on Droughts and Floods in the Yangtze River Basin from 2003 to 2020

“…According to the relative covariance of different observation series by using the GCTH method, the different datasets were fused by taking a weighted average of them [ 36 ]. where and are the th observation time series and its corresponding weight, respectively.…”

Natural- and Human-Induced Influences on Terrestrial Water Storage Change in Sichuan, Southwest China from 2003 to 2020

“…According to the relative covariance of different datasets obtained by using the GTCH method, we fused the different datasets by taking a weighted average of them [45].…”