排序方式： 共有4条查询结果，搜索用时 0 毫秒
1
1.
2.
The effects of rainfall and the El Niño Southern Oscillation (ENSO) on groundwater in a semi-arid basin of India were analyzed using Archimedean copulas considering 17 years of data for monsoon rainfall, post-monsoon groundwater level (PMGL) and ENSO Index. The evaluated dependence among these hydro-climatic variables revealed that PMGL-Rainfall and PMGL-ENSO Index pairs have significant dependence. Hence, these pairs were used for modeling dependence by employing four types of Archimedean copulas: Ali-Mikhail-Haq, Clayton, Gumbel-Hougaard, and Frank. For the copula modeling, the results of probability distributions fitting to these hydro-climatic variables indicated that the PMGL and rainfall time series are best represented by Weibull and lognormal distributions, respectively, while the non-parametric kernel-based normal distribution is the most suitable for the ENSO Index. Further, the PMGL-Rainfall pair is best modeled by the Clayton copula, and the PMGL-ENSO Index pair is best modeled by the Frank copula. The Clayton copula-based conditional probability of PMGL being less than or equal to its average value at a given mean rainfall is above 70% for 33% of the study area. In contrast, the spatial variation of the Frank copula-based probability of PMGL being less than or equal to its average value is 35–40% in 23% of the study area during El Niño phase, while it is below 15% in 35% of the area during the La Niña phase. This copula-based methodology can be applied under data-scarce conditions for exploring the impacts of rainfall and ENSO on groundwater at basin scales. 相似文献
3.
Caroline Avril Valérie Malavergne Razvan Caracas Brigitte Zanda Bruno Reynard Emeline Charon Ema Bobocioiu Fabrice Brunet Stephan Borensztajn Sylvain Pont Martine Tarrida François Guyot 《Meteoritics & planetary science》2013,48(8):1415-1426
Raman spectra were acquired on a series of natural and synthetic sulfide minerals, commonly found in enstatite meteorites: oldhamite (CaS), niningerite or keilite ((Mg,Fe)S), alabandite (MnS), troilite (FeS), and daubreelite (Cr2FeS4). Natural samples come from three enstatite chondrites, three aubrites, and one anomalous ungrouped enstatite meteorite. Synthetic samples range from pure endmembers (CaS, FeS, MgS) to complex solid solutions (Fe, Mg, Ca)S. The main Raman peaks are localized at 225, 285, 360, and 470 cm?1 for the Mg‐rich sulfides; at 185, 205, and 285 cm?1 for the Ca‐rich sulfides; at 250, 370, and 580 cm?1 for the Mn‐rich sulfides; at 255, 290, and 365 cm?1 for the Cr‐rich sulfides; and at 290 and 335 cm?1 for troilite with, occasionally, an extra peak at 240 cm?1. A peak at 160 cm?1 is present in all Raman spectra and cannot be used to discriminate between the different sulfide compositions. According to group theory, none of the cubic monosulfides oldhamite, niningerite, or alabandite should present first‐order Raman spectra because of their ideal rocksalt structure. The occurrence of broad Raman peaks is tentatively explained by local breaking of symmetry rules. Measurements compare well with the infrared frequencies calculated from first‐principles calculations. Raman spectra arise from activation of certain vibrational modes due to clustering in the solid solutions or to coupling with electronic transitions in semiconductor sulfides. 相似文献
4.
Costache Romulus Fontanine Iulia Corodescu Ema 《Central European Journal of Geosciences》2014,6(3):363-372
Sǎrǎ?el River basin, which is located in Curvature Subcarpahian area, has been facing an obvious increase in frequency of hydrological risk phenomena, associated with torrential events, during the last years. This trend is highly related to the increase in frequency of the extreme climatic phenomena and to the land use changes. The present study is aimed to highlight the spatial and quantitative changes occurred in surface runoff depth in Sǎrǎ?el catchment, between 1990–2006. This purpose was reached by estimating the surface runoff depth assignable to the average annual rainfall, by means of SCS-CN method, which was integrated into the GIS environment through the ArcCN-Runoff extension, for ArcGIS 10.1. In order to compute the surface runoff depth, by CN method, the land cover and the hydrological soil classes were introduced as vector (polygon data), while the curve number and the average annual rainfall were introduced as tables. After spatially modeling the surface runoff depth for the two years, the 1990 raster dataset was subtracted from the 2006 raster dataset, in order to highlight the changes in surface runoff depth. 相似文献
1