Earthquake Stability Analysis of Rock Slopes: a Case Study

Stability analysis of Surabhi landslide in the Dehradun and Tehri districts of Uttaranchal located in Mussoorie, India, has been simulated numerically using the distinct element method focusing on the weak zones (fracture). This is an active landslide on the main road toward the town centre, which was triggered after rainfall in July–August 1998. Understanding the behaviour of this landslide will be helpful for planning and implementing mitigation measures. The first stage of the study includes the total area of the landslide. The area identified as the zone of detachment is considered the most vulnerable part of the landslide. Ingress of water and increased pore pressures result in reduced mobilized effective frictional resistance, causing the top layer of the zone of detachment to start moving. The corresponding total volume of rock mass that is potentially unstable is estimated to 11.58 million m³. The second stage of this study includes a 2D model focussing only on the zone of detachment. The result of the analyses including both static and dynamic loading indicates that most of the total displacement observed in the slide model is due to the zone of detachment. The discontinuum modelling in the present study gives reasonable agreement with actual observations and has improved understanding of the stability of the slide slope.     

Empirical modeling of plate load test moduli of soil via gene expression programming

New empirical models were developed to predict the soil deformation moduli using gene expression programming (GEP). The principal soil deformation parameters formulated were secant (E_s) and reloading (E_r) moduli. The proposed models relate E_s and E_r obtained from plate load-settlement curves to the basic soil physical properties. The best GEP models were selected after developing and controlling several models with different combinations of the influencing parameters. The experimental database used for developing the models was established upon a series of plate load tests conducted on different soil types at depths of 1–24 m. To verify the applicability of the derived models, they were employed to estimate the soil moduli of a part of test results that were not included in the analysis. The external validation of the models was further verified using several statistical criteria recommended by researchers. A sensitivity analysis was carried out to determine the contributions of the parameters affecting E_s and E_r. The proposed models give precise estimates of the soil deformation moduli. The E_s prediction model provides considerably better results in comparison with the model developed for E_r. The simplified formulation for E_s significantly outperforms the empirical equations found in the literature. The derived models can reliably be employed for pre-design purposes.     

A hybrid computational approach to formulate soil deformation moduli obtained from PLT

In this study, new empirical equations were developed to predict the soil deformation moduli utilizing a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed models relate secant (E_s), unloading (E_u) and reloading (E_r) moduli obtained from plate load–settlement curves to the basic soil physical properties. Several models with different combinations of the influencing parameters were developed and checked to select the best GP/SA models. The database used for developing the models was established upon a series of plate load tests (PLT) conducted on different soil types at various depths. The validity of the models was tested using parts of the test results that were not included in the analysis. The validation of the models was further verified using several statistical criteria. A traditional GP analysis was performed to benchmark the GP/SA models. The contributions of the parameters affecting E_s, E_u and E_r were analyzed through a sensitivity analysis. The proposed models are able to estimate the soil deformation moduli with an acceptable degree of accuracy. The E_s prediction model has a remarkably better performance than the models developed for predicting E_u and E_r. The simplified formulations for E_s, E_u and E_r provide significantly better results than the GP-based models and empirical models found in the literature.     

Application of spectrum–area fractal model to identify of geochemical anomalies based on soil data in Kahang porphyry-type Cu deposit,Iran

The aim of this study is to identify geochemical anomalies using power spectrum–area (S–A) method based on the grade values of Cu, Mo and Au in 2709 soil samples collected from Kahang porphyry-type Cu deposit, Central Iran. S–A log–log plots indicated that there are three stages of Cu, Mo and Au enrichment. The third enrichment was considered as the main stage for the presence of Cu, Mo and Au at the concentrations above 416 ppm, 23 ppm and 71 ppb, respectively. Elemental anomalies are positively associated with monzo–granite–diorite and breccias units which are in the central and western parts of the deposit. The anomalies are located within the potassic, phyllic and argillic alteration types and also there is the positive correlation between the anomalies and nearing faults in the studied area. The results obtained via fractal model were interpreted accordingly to incorporate the information for the mineralized areas including detailed geological map, structural analysis and alterations. The results show that S–A multifractal modeling is applicable for anomalies delineation based on soil data.     

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most influential parameters from seven biophysical parameters including surface temperature (T _s), surface albedo (α), leaf area index (LAI), soil heat flux (G _o), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (R _n). T _s and G _o were found as the most effective parameters by this method. However, T _s, LAI, α, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be efficiently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study confirmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter.     

Detecting recent changes in the demographic parameters of drosophilid populations from western and central Africa

Previous genetic studies showing evidence of past demographic changes in African drosophilids suggested that these populations had strongly responded to Quaternary climate changes. We surveyed nine species of Zaprionus, a drosophilid genus mostly present in Africa, in forests located between southern Senegal and Gabon. The mitochondrial COI gene showed contrasted levels of sequence variation across species. Populations of the only cosmopolitan species of the genus, Z. indianus, and of its closely related sibling species, Z. africanus, are highly polymorphic and appear to have undergone a continuous population expansion beginning about 130,000 years ago. Five less variable species probably underwent a population expansion beginning only about 20,000–30,000 years ago. One of them, Z. taronus, was significantly structured between forest blocks. The last two species were nearly monomorphic, probably due to infection by Wolbachia. These results are similar to those obtained in three species from the melanogaster subgroup, and may be typical of the responses of African drosophilid populations to glacial cycles.     

Geochemistry and zircon U-Pb geochronology of Aligoodarz granitoid complex, Sanandaj-Sirjan Zone, Iran

The Aligoodarz granitoid complex (AGC) is located in the Sanandaj-Sirjan Zone (SSZ), western Iran and consists of quartz-diorites, granodiorites and subordinate granites. Whole rock major and trace element data mostly define linear trends on Harker diagrams suggesting a cogenetic origin of the different rock types. (⁸⁷Sr/⁸⁶Sr)_i and εNd_t ratios are in the ranges 0.7074-0.7110 and −3.56 to −5.50, respectively. The trace elements and Sr-Nd isotopic composition suggest that the granitoids from the AGC are similar to crustal derived I-type granitoids of continental arcs. The whole rock suite was produced by assimilation and fractional crystallization starting from a melt with intermediate composition likely possessing a mantle component. In situ zircon U-Pb data on the granites with LA-ICP-MS yield a crystallization age of ∼165 Ma. Inherited grains spanning in age from ∼180 Ma up to 2027 Ma were also found and confirm that assimilation of country rock has occurred.Chemical and chronological data on the AGC were compared with those available for other granitoid complexes of the central SSZ (e.g., Dehno, Boroujerd and Alvand). The comparison reveals that in spite of the different origins that have been proposed, all these granitoid complexes are likely genetically related. They share many chemical features and are derived from crustal melts with minor differences. Alvand granites have the most peculiar compositions most likely related to the presence of abundant pelitic component. All these intrusions are coeval and reveal the presence of an extensive magmatic activity in the central sector of the SSZ during middle Jurassic.     

Temporal structures of the North Atlantic Oscillation and its impact on the regional climate variability

In this study, the temporal structure of the variation of North Atlantic Oscillation （NAO） and its impact on regional climate variability are analyzed using various datasets. The results show that blocking formations in the Atlantic region are sensitive to the phase of the NAO. Sixty-seven percent more winter blocking days are observed during the negative phase compared to the positive phase of the NAO. The average length of blocking during the negative phase is about 11 days, which is nearly twice as long as the 6-day length observed during the positive phase of the NAO. The NAO-related differences in blocking frequency and persistence are associated with changes in the distribution of the surface air temperature anomaly, which, to a large extent, is determined by the phase of the NAO. The distribution of regional cloud amount is also sensitive to the phase of the NAO. For the negative phase, the cloud amounts are significant, positive anomalies in the convective zone in the Tropics and much less cloudiness in the mid latitudes. But for the positive phase of the NAO, the cloud amount is much higher in the mid-latitude storm track region. In the whole Atlantic region, the cloud amount shows a decrease with the increase of surface air temperature. These results suggest that there may be a negative feedback between the cloud amount and the surface air t.emperature in the Atlantic region.     

The impact of el Nino‐Southern oscillation on the temperature field over Canada: Research note

Abstract

The impact of the two phases of El Niño‐Southern Oscillation (ENSO), namely El Niño and La Niña, on the surface and lower tropospheric temperature fields over Canada is documented. Gridded surface temperature data for 91 years (1900–1990) and 500–1000 hPa thickness data for 49 years (1946–1994) have been analyzed statistically in the context of El Niño, La Niña and normal years.

Using a composite analysis, the present study conclusively demonstrates that significant positive surface temperature anomalies spread eastward from the west coast of Canada to the Labrador coast from the late fall to early spring (November through May) following the onset of El Niño episodes. The accompanying temperatures in the lower troposphere show a transition from the Pacific/North American (PNA) pattern to the Tropical/Northern Hemisphere (TNH) pattern over the North American sector during the same period. Conversely, significant negative surface temperature anomalies spread southeastward from the Yukon and extend into the upper Great Lakes region by the winter season following the onset of La Niña episodes. Furthermore, the lower tropospheric temperatures show a negatively‐phased PNA‐like pattern in early winter which weakens considerably by May of the following year. Thus, while western Canadian surface temperatures are influenced during both phases of ENSO, eastern Canadian surface temperature effects are found during the El Niño phase only. The impact of ENSO on the Canadian surface temperatures is the strongest during the winter season and nearly disappears by spring (April and May). The largest positive (negative) anomalies are found to be centred over two separate regions, one over the Yukon and the other just west of Hudson Bay in the El Niño (La Niña) years. Over western Canada, mean wintertime temperature distribution of the El Niño (La Niña) years is found to be shifted towards warmer (colder) values relative to the distribution of the normal years.

This study suggests the possibility of developing a long‐range forecasting technique for Canada using ENSO related indices.     

Non-stationary extreme value analysis in a changing climate

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.