In recent years, many droughts have happened over mainland Australia, especially the two severe prolonged droughts, from 2006 to 2009 and 2018 to 2020, resulting in serious water scarcity. Therefore, using the Total Storage Deficit Index (TSDI) from the Gravity Recovery and Climate Experiment (GRACE), we analyzed the two severe prolonged droughts from the perspective of the affected area, spatial evolution, frequency, severity and drought driving factors. The results show that the affected area of Drought 2006–2009 ranged from 57% to 95%, and that of Drought 2018–2020 ranged from 45% to 95%. Drought 2006–2009 took its rise in southeastern Australia and gradually spread to the central part. Drought 2018–2020 originated in the southwest corner of the Northern Territory and northern New South Wales, and gradually expanded to Western Australia and the whole New South Wales respectively. During Drought 2006–2009, Victoria suffered drought all months, including 59% mild drought and 41% moderate drought, North Territory had the highest drought severity of 44.26 and Victoria ranked the second high with the severity of 35.51 (cm months). For Drought 2018–2020, Northern Territory was also dominated by drought all months, including 92% mild drought and 8% moderate drought, the drought severities were in North Territory and Western Australia with 52.19 and 31.44 (cm months), respectively. Finally, the correlation coefficients between the two droughts and Indo-Pacific climate variability including El Niño-Southern Oscillation and Indian Ocean Dipole (IOD) are computed. By comparing the correlation coefficients of Drought 2018–2020 with Drought 2006–2009, we find that the impact of the El Niño on the hydrological drought becomes weaker while IOD is stronger, and the role of Southern Oscillation on droughts is diverse with the quite different spatial patterns. The results from Fourier analysis confirm that the two hydrological droughts are all related to Indo-Pacific climate variability but with slightly different driving mechanisms.

SUMMARY: Gravity Recovery and Climate Experiment (GRACE) Spherical Harmonics (SH) solutions are usually provided together with the corresponding formal errors, however, all multichannel singular spectrum analysis (MSSA) approaches neglect the formal errors of SH coefficients in processing the GRACE time-series. In this contribution, we propose a weighted MSSA to directly process the incomplete time-series of GRACE monthly gravity models by taking the formal errors into account. A 14-yr time-series of Release 06 (RL06) monthly gravity field models from the Center for Space Research (CSR) truncated to degree and order 60 for the period April 2002 to August 2016 is analysed by weighted MSSA, the results are compared with those from the improved MSSA that directly processes the incomplete time-series without considering the formal errors. The first 10 principal components derived by using the weighted MSSA can capture the variance in total up to 82.05 per cent, higher than 80.19 per cent by the improved MSSA. All the fitting errors of GRACE SH coefficients by the weighted MSSA are smaller than those by the improved MSSA, which indicates that weighted MSSA can extract more geophysical signals than the improved MSSA approach. To evaluate the efficiency of the weighted MSSA to eliminate noise, the ratios of the latitude weighted RMS of signals over lands and oceans are computed. Based on all the available months of GRACE solutions, the mean RMS ratio of signals over lands relative to oceans derived by the weighted MSSA is 5.04, higher than 4.96 by the improved MSSA. Therefore, we can conclude that the proposed weighted MSSA can suppress noise more efficiently and extract more geophysical signals from the GRACE time-series, which highlights the necessity of considering formal errors and the effectiveness of the weighted MSSA in post-processing the GRACE monthly gravity field models.

The monitoring of Poyang Lake water area and storage changes using remote sensing and satellite gravimetry techniques is valuable for maintaining regional water resource security and addressing the challenges of global climate change. In this study, remote sensing datasets from Landsat images (Landsat 5, 7, 8 and 9) and three Gravity Recovery and Climate Experiment (GRACE) and Gravity Follow-on (GRACE-FO) mascon solutions were jointly used to evaluate the water area and storage changes in response to global and regional climate changes. The results showed that seasonal characteristics existed in the terrestrial water storage (TWS) and water area changes of Poyang Lake, with nearly no significant long-term trend, for the period from April 2002 to December 2022. Poyang Lake exhibited the largest water area in June and July every year and then demonstrated a downward trend, with relatively smaller water areas in January and November, confirmed by the estimated TWS changes. For the flood (August 2010) and drought (September 2022) events, the water area changes are 3032 km² and 813.18 km², with those estimated TWS changes 17.37 cm and −17.46 cm, respectively. The maximum and minimum Poyang Lake area differences exceeded 2700 km². The estimated terrestrial water storage changes in Poyang Lake derived from the three GRACE/GRACE-FO mascon solutions agreed well, with all correlation coefficients higher than 0.92. There was a significant positive correlation higher than 0.75 between the area and TWS changes derived from the two independent monitoring techniques. Therefore, it is reasonable to conclude that combined remote sensing with satellite gravimetric techniques can better interpret the response of Poyang Lake to climate change from the aspects of water area and TWS changes more efficiently.

This study utilizes 27 years of sea level anomaly (SLA) data obtained from satellite altimetry to investigate spatial–temporal variations in the South China Sea (SCS). The local mean decomposition (LMD) method is applied to decompose the sea level data into three components: high-frequency, low-frequency, and trend components. By removing the influence of high-frequency components, multiple time series of regular sea level changes with significant physical significance are obtained. The results indicate that the average multi-year SLA is 50.16 mm, with a linear trend of 3.91 ± 0.12 mm/a. The wavelet analysis method was employed to examine the significant annual and 1.5-year periodic signals in the SCS SLA series. At the seasonal scale, the sea level rise in coastal areas during autumn and winter surpasses that of spring and summer. Moreover, there are generally opposing spatial distributions between spring and autumn, as well as between summer and winter. The linear trends in multi-year SLA for the four seasons are 3.70 ± 0.13 mm/a, 3.66 ± 0.16 mm/a, 3.49 ± 0.16 mm/a, and 3.74 ± 0.33 mm/a, respectively. The causes of SCS sea level change are examined in relation to phenomena such as monsoons, the Kuroshio Current, and El Niño–Southern Oscillation (ENSO). Based on the empirical orthogonal function (EOF) analysis of SCS SLA, the contributions of the first three modes of variance are determined to be 34.09%, 28.84%, and 8.40%, respectively. The temporal coefficients and spatial distribution characteristics of these modes confirm their associations with ENSO, monsoons, and the double-gyre structure of SCS sea surface temperature. For instance, ENSO impacts SCS sea level change through atmospheric circulation, predominantly affecting the region between 116° E and 120° E longitude, and 14° N and 20° N latitude.