In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)-LSTM network, the proposed model has higher forecast accuracy.

With the warming of the global climate, the mass loss of the Greenland ice sheet is intensifying, having a profound impact on the rising of the global sea level. Here, we used Gravity Recovery and Climate Experiment (GRACE) RL06 data to retrieve the time series variations of ice sheet mass in Greenland from January 2003 to December 2015. Meanwhile, the spatial changes of ice sheet mass and its relationship with land surface temperature are studied by means of Theil–Sen median trend analysis, the Mann–Kendall (MK) test, empirical orthogonal function (EOF) analysis, and wavelet transform analysis. The results showed: (1) in terms of time, we found that the total mass of ice sheet decreases steadily at a speed of −195 ± 21 Gt/yr and an acceleration of −11 ± 2 Gt/yr2 from 2003 to 2015. This mass loss was relatively stable in the two years after 2012, and then continued a decreasing trend; (2) in terms of space, the mass loss areas of the Greenland ice sheet mainly concentrates in the southeastern, southwestern, and northwestern regions, and the southeastern region mass losses have a maximum rate of more than 27 cm/yr (equivalent water height), while the northeastern region show a minimum rate of less than 3 cm/yr, showing significant changes as a whole. In addition, using spatial distribution and the time coefficients of the first two models obtained by EOF decomposition, ice sheet quality in the southeastern and northwestern regions of Greenland show different significant changes in different periods from 2003 to 2015, while the other regions showed relatively stable changes; (3) in terms of driving factors temperature, there is an anti-phase relationship between ice sheet mass change and land surface temperature by the mean XWT-based semblance value of −0.34 in a significant oscillation period variation of 12 months. Meanwhile, XWT-based semblance values have the largest relative change in 2005 and 2012, and the smallest relative change in 2009 and 2010, indicating that the influence of land surface temperature on ice sheet mass significantly varies in different years.

Sunspot number is an important parameter for presenting the intensity of solar activity. Based on the sunspot number series, which has been replaced by a new improved version since 2015, we confirm that the sunspot number has significant variations at 11-year and 112-year periods. e sunspot number has also increased from 1700 to 2016 with 0.08 annual increments on the basis of wavelet analysis and least-square fitting. We further confirm that global temperatures are remarkable in 22-year and 64-year cycles. e result of wavelet transform coherence (WTC) analysis suggests that solar activity has a positive lag effect on global temperatures in the period band of 22 years with a 3-year lag. However, the linearly increasing global temperature has hampered WTC analysis since 1960. Aiming to solve this problem, we apply wavelet decomposition and cross correlation to determine whether the aforementioned lag effect in the period band of 22 years has a 2-year lag rather than a 3-year lag. We find that the 22-year magnetic field solar cycle plays a greater role in global climate change than the 11-year sunspot cycle. In addition, we notice that the solar activity is not a representation of the driving force of the upward trend of global temperature after the industrial age.e Granger causality test results demonstrate that the phenomenon of the global warming is caused by excessive CO 2 emissions.

We investigated the effect of mass loading (atmospheric, oceanic and hydrological loading (AOH)) on Global Positioning System (GPS) height time series from 30 GPS stations in the Eurasian plate. Wavelet coherence (WTC) was employed to inspect the correlation and the time-variable relative phase between the two signals in the time–frequency domain. The results of the WTC-based semblance analysis indicated that the annual fluctuations in the two signals for most sites are physically related. The phase asynchrony at the annual time scale between GPS heights and AOH displacements indicated that the annual oscillation in GPS heights is due to a combination of mass loading signals and systematic errors (AOH modelling errors, geophysical effects and/or GPS system errors). Moreover, we discuss the impacts of AOH corrections on GPS noise estimation. The results showed that not all sites have an improved velocity uncertainty due to the increased amplitude of noise and/or the decreased spectral index after AOH corrections. Therefore, the posterior mass loading model correction is potentially feasible but not sufficient.

The surface displacement caused by hydrological loading makes an important contribution to the non-linear crustal movement observed at the International Global Navigation Satellite System Service (IGS) stations. In this paper, the amplitude, correlation, and root mean square (RMS) of the vertical displacement time series signals of 47 IGS stations are used to analyze which data of Gravity Recovery and Climate Experiment (GRACE) or Global Land Data Assimilation System (GLDAS) can better reflect the hydrological load effect in Europe. The results show that in Europe, the hydrological load effect calculated based on GRACE data is more accurate than that of GLDAS, which has not been reported before. Then, the relationship between the GPS height and GRACE load deformation in terms of annually-oscillating signals, correlation, and phase is analyzed by using singular spectrum analysis, the Pearson correlation coefficient, and wavelet coherence (WTC). It was found that GPS and GRACE agree at some stations (e.g., BOR1 and ZIMM), while they differ significantly in amplitude and phase at other stations (e.g., KIRU and NOT1), indicating that not all GRACE-derived displacements of IGS stations can clearly explain their nonlinear motion. The correlation coefficients between GPS and GRACE are higher than 0.7 at 85 % of stations. Amongst them, the values are obviously greater than 0.8 (e.g., ZIMM and LAMA) around inland areas and high mountains, and even less than 0.6 (e.g., ANKR and KIRU) along the coast of the Mediterranean ocean, which more precisely shows that the hydrological load effect has obvious spatial and regional characteristics compared with previous studies. In addition, the relative phase of the WTC solution is basically consistent under non-detrend and detrend, which shows that the relative phase difference of each station is only related to the nonlinear movement and not to the linear trend caused by the tectonic deformation. Finally, we study the influence of GRACE hydrological load on the RMS of GPS height, which is reduced by 24.60 % on average, and the reduction rate distribution of the RMS is in good agreement with the spatial distribution of the correlation coefficient.