Seasonal and Spatial Variability of Microparticles in Snowpits on the Tibetan Plateau, China

The work presents microparticle concentrations in snowpits from the East Rongbuk Glacier on Mt. Qomolangma (Everest) (ER) (28.02°N, 86.96°E, 6536 m a.s.l.), the Zhadang Glacier on Mt. Nyainqentanglha (NQ) (30.47°N, 90.65°E, 5800m a.s.l.), and the Guoqu Glacier on Mt. Geladaindong (GL) (33.95°N, 91.28°E, 5823m a.s.l.) over the Tibetan Plateau (TP). Variations of microparticle and major ions (e.g. Mg2+, Ca2+) concentrations in snowpits show that the values of the microparticles and ions in the non-monsoon seasons are much higher than those in the monsoon seasons. Annual flux of microparticle deposition at ER is lower than those at NQ and GL, which could be attributed to the long distance away from the possible dust source regions as well as the elevation for ER higher than the others. Compared with other remote areas, microparticle concentrations in the southern TP are much lower than those in the northern TP, but still much higher than those in Greenland and Antarctica. The seasonal and spatial microparticle variations are clearly related to the variations of atmospheric circulation according to the air mass 5-day backward trajectory analyses of HYSPLIT Model. Resultingly, the high microparticle values in snow are mainly attributed to the westerlies and the strong dust storm outbreaks on the TP, while the monsoon circulation brings great amount of precipitation from the Indian Ocean, thus reducing in the aerosol concentrations.

The Study on Apparent Stress before and after the Minxian M_S6.6 Earthquake on July 22,2013

Based on the waveform data observed by the regional seismic network of Gansu Province,we calculated the apparent stress of 422 earthquakes with M_L≥ 2. 0 occurring in the surrounding area of the Minxian earthquake from January 2010 to July 2014 and obtained the temporal and spatial variation of apparent stress before and after the Minxian earthquake. Results show that( 1) the high value of apparent stress of earthquakes with M_L≥4. 0 was concentrated in the epicenter area before the Minxian earthquake while that of earthquakes with M_L< 4. 0 was not;( 2) Apparent stress around the epicenter area showed an obvious increasing process before the Minxian earthquake and the increasing process has continued after the main shock,which means that this study area is still in the danger of strong earthquakes.

Neural Network Ensemble Residual Kriging Application for Spatial Variability of Soil Properties

High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN)ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.