According to the ＂jacking-up＂ theory, which relates the cause of earthquakes to outer core convection ascension bodies, the crust will gradually recover after an earthquake. In such cases, the crust is stretched, the underground temperature is reduced, precipitation decreases, and drought occurs. In this paper, precipitation is compared with ground temperature and seismic data to determine the spatial and temporal relationship between earthquakes and subsequent droughts. Our objective is to develop a new method of drought prediction. With a few exceptions in location, the analysis of the first drought to occur after the Ms 〉 7 earthquakes in China's Mainland and the adjacent areas since 1950 shows that droughts tended to occur in regions near earthquake epicenters and in the eastern regions of the epicenters at the same latitude within six months after the earthquakes. In addition, and the differences between the starting time of the earthquakes and the droughts nearly share the same probability of 0 to 6 months. After careful analysis of 34 Ms 〉 6.5 earthquakes occurring in western China from 1980 to 2011, we determined that a second drought tends to occur approximately six months following the first drought, indicating a quasi-half-year period. Moreover, the duration of the quasi-half-year fluctuation increases with the magnitude of earthquake, at approximately 2.5 years for Ms 6.5 earthquake and approximately 5 years for Ms 8 earthquake.
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation （CNOP） in a land surface model （LSM） using long-term enhanced field observations at Tongyu station in Jilin Province,China,combined with a sophisticated LSM （common land model,CoLM）.Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project （CEOP） that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments：First,a single-parameter optimization was performed,while the second and third experiments performed triple-and six-parameter optimizations,respectively.Notable improvements in simulating sensible heat flux （SH）,latent heat flux （LH）,soil temperature （TS）,and moisture （MS） at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.
