The occurrence of droughts has become more frequent, and their intensity has increased in mainland China. With the aim of better understanding the influence of climate background on drought events in this region, we analyzed the role of the drought-related factors and extreme climate in the formation of droughts by investigating the relationship between the drought severity index (denoted as GRACE-DSI) based on the terrestrial water storage changes (TWSCs) derived from Gravity Recovery and Climate Experiment (GRACE) time-variable gravity fields and drought-related factors/extreme climate. The results show that GRACE-DSI was consistent with the self-calibrating Palmer Drought Severity Index in mainland China, especially for the subtropical monsoon climate, with a correlation of 0.72. Precipitation (PPT) and evapotranspiration (ET) are the main factors causing drought events. However, they play different roles under different climate settings. The regions under temperate monsoon climate and subtropical monsoon climate were more impacted by PPT, while ET played a leading role in the regions under temperate continental climate and plateau mountain climate. Moreover, El Niño–Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) events mainly caused abnormalities in PPT and ET by affecting the strength of monsoons (East Asian and Indian monsoon) and regional highs (Subtropical High, Siberian High, Central Asian High, etc.). As a result, the various affected regions were prone to droughts during ENSO or NAO events, which disturbed the normal operation of atmospheric circulation in different ways. The results of this study are valuable in the efforts to understand the formation mechanism of drought events in mainland China.
The mass changes in the Earth’s surface internally derived from the Gravity Recovery and Climate Experiment (GRACE) and the GRACE Follow-On (GRACE-FO) missions have played an important role in the research of various geophysical phenomena. However, the one-year data gap between these two missions has broken the continuity of this geophysical research. In order to assess the feasibility of using the Swarm time-variable gravity field (TVGF) to bridge the data gap, we compared Swarm with the GRACE/GRACE-FO models in terms of model accuracy, observation noise and inverted terrestrial water storage change (TWSC). The results of the comparison showed that the difference between the degree-error root mean square (RMS) of the two models is small, within degree 10. The correlation between the spherical harmonic coefficients of the two models is also relatively high, below degree 17. The observation noise values of GRACE/GRACE-FO are smaller than those of Swarm. Therefore, the latter model requires a larger filter radius to lower these noise levels. According to the correlation coefficients and the time series map of TWSC in the Amazon River basin, the results of GRACE and Swarm are similar. In addition, the TWSC signals were further analyzed. The long-term trend changes of TWSC derived from GRACE/GRACE-FO and the International Combination Service for Time-variable Gravity Fields (COST-G)-Swarm over the period from December 2013 to May 2020 were −0.72 and −1.05 cm/year, respectively. The annual amplitudes of two models are 15.65 and 15.39 cm, respectively. The corresponding annual phases are −1.36 and −1.33 rad, respectively. Our results verified that the Swarm TVGF has the potential to extract TWSC signals in the Amazon River basin and can serve as a complement to GRACE/GRACE-FO data for detecting TWSC in local areas.
Accurate quantification of drought characteristics helps to achieve an objective and comprehensive analysis of drought events and to achieve early warning of drought and disaster loss assessment. In our study, a drought characterization approach based on drought severity index derived from Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) data was used to quantify drought characteristics. In order to improve drought detection capability, we used the local drought data as calibration criteria to improve the accuracy of the drought characterization approach to determine the onset of drought. Additionally, the local precipitation data was used to test drought severity determined by the calibrated drought characterization approach. Results show that the drought event probability of detection (POD) of this approach in the four study regions increased by 61.29%, 25%, 94.29%, and 66.86%, respectively, after calibration. We used the calibrated approach to detect the drought events in Mainland China (MC) during 2016 and 2019. The results show that CAR of the four study regions is 100.00%, 92.31%, 100.00%, and 100.00%. Additionally, the precipitation anomaly index (PAI) data was used to evaluate the severity of drought from 2002 to 2020 determined by the calibrated approach. The results indicate that both have a strong similar spatial distribution. Our analysis demonstrates that the proposed approach can serve a useful tool for drought monitoring and characterization.
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