Abnormality of the monsoon wind in the sea area along the southeasterncoast of China and the response of the northern areaof the South China Sea during the ENSO events

-Variations of monsoon wind field in the sea area along the southeastern coast of China during the ENSO events and its influence on the sea level and sea surface temperature (SST) are explored mainly on the basis of the data of monthly mean wind at 850 hPa and five coastal stations during 1973-1987. The results from the analyses of the data and theoretical estimation show that the southwest wind anomalies appeared in the study area during the events, and northeast wind anomalies occurred in general before the events. With the coastline of the area being parallel basically to the direction of the wind, an Ekman transport will result in an accumulation of the water near the coast or a departure of the water from the coast. As a result , the sea level and SST there will be affected markedly. During the events, southwest wind will intensify in the summer, and northeast wind will weaken in the winter. Their total effect is that a large negative anomaly of the sea level and SST will occur. The estimations indicate that the monsoon wind is stronger in the summer and weaker in the winter than the normal by 1-1. 5 m/s during the events, and this anomaly will cause a decrease of the sea level by 7-11 cm . Changes of the wind field, therefore, is mainly responsible for a large negative anomalies of the sea level and SST there during the ENSO events.

Interrelation between seismicity parameters and delimiting potential seismic sources in a seismic statistical region and its influence on seismic risk estimation

In the paper, we have discovered the abnormal area distribution features of maximum variation values of ground motion parameter uncertainty with different probabilities of exceedance in 50 years within the range of 100°-120°E, 29°-42°N for the purpose to solve the problem that abnormal areas of maximum variation values of ground motion parameter uncertainties emerge in a certain cities and towns caused by seismicity parameter uncertainty in a seismic statistical region in an inhomogeneous distribution model that considers tempo-spatial nonuniformity of seismic activity. And we have also approached the interrelation between the risk estimation uncertainty of a site caused by seismicity parameter uncertainty in a seismic statistical region and the delimitation of potential sources, as well as the reasons for forming abnormal areas. The results from the research indicate that the seismicity parameter uncertainty has unequal influence on the uncertainty of risk estimation at each site in a statistical region in the inhomogeneous distribution model, which relates to the scheme for delimiting potential sources. Abnormal areas of maximum variation values of ground motion parameter uncertainty often emerge in the potential sources of Mu greater than or equal 8 (Mu is upper limit of a potential source) and their vicinity. However, this kind of influence is equal in the homogeneous distribution model. The uncertainty of risk estimation of each site depends on its seat. Generally speaking, the sites located in the middle part of a statistical region are only related to the seismicity parameter uncertainty of the region, while the sites situated in or near the juncture of two or three statistical regions might be subject to the synthetic influences of seismicity parameter uncertainties of several statistical regions.

Estimating the Spatial Variation of Electricity Consumption Anomalies and the Influencing Factors

Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity consumption by customers,safe operation of power grids,and sustainable development of cities.However,current abnormal electricity consumption detection models do not consider the time dependence of time-series data and rely on a large number of training samples,and no study has analyzed the spatial distribution and influencing factors of abnormal electricity consumption in urban areas.In this study,we use the Seasonal-Trend decomposition procedure based on Loess(STL)based time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang,and analyze the relationship between spatial variation and urban functions through Geodetector.The results show that the degree of abnormal electricity consumption in urban areas is related to geographic location and has spatial heterogeneity,and the abnormal electricity users are mainly located in areas with highly mixed residential,commercial and entertainment functions in the city.The results obtained from this study can provide a reference basis and a theoretical foundation for the detection of abnormal electricity consumption by users and the arming of electricity theft devices in the power grid.