3 resultados para 1852 and 1881 Canadian Census
em Indian Institute of Science - Bangalore - Índia
Resumo:
Geologic evidence along the northern part of the 2004 Aceh-Andaman rupture suggests that this region generated as many as five tsunamis in the prior 2000years. We identify this evidence by drawing analogy with geologic records of land-level change and the tsunami in 2004 from the Andaman and Nicobar Islands (A&N). These analogs include subsided mangrove swamps, uplifted coral terraces, liquefaction, and organic soils coated by sand and coral rubble. The pre-2004 evidence varies in potency, and materials dated provide limiting ages on inferred tsunamis. The earliest tsunamis occurred between the second and sixth centuries A.D., evidenced by coral debris of the southern Car Nicobar Island. A subsequent tsunami, probably in the range A.D. 770-1040, is inferred from deposits both in A&N and on the Indian subcontinent. It is the strongest candidate for a 2004-caliber earthquake in the past 2000years. A&N also contain tsunami deposits from A.D. 1250 to 1450 that probably match those previously reported from Sumatra and Thailand, and which likely date to the 1390s or 1450s if correlated with well-dated coral uplift offshore Sumatra. Thus, age data from A&N suggest that within the uncertainties in estimating relative sizes of paleo-earthquakes and tsunamis, the 1000year interval can be divided in half by the earthquake or earthquakes of A.D. 1250-1450 of magnitude >8.0 and consequent tsunamis. Unlike the transoceanic tsunamis generated by full or partial rupture of the subduction interface, the A&N geology further provides evidence for the smaller-sized historical tsunamis of 1762 and 1881, which may have been damaging locally.
Resumo:
Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.
Resumo:
In this letter, we submit our comment on the following recently published papers by Kalidas Das: (1) ``Influence of chemical reaction and viscous dissipation on MHD mixed convection flow,'' Journal of Mechanical Science and Technology 28 (5) (2014) 1881-1885; and (2) ``Cu-water nanofluid flow and heat transfer over a shrinking sheet,'' Journal of Mechanical Science and Technology 28 (12) (2014) 5089-5094. The authors attempt to present the similarity solutions in both papers. We comment that the similarity transformations considered in Refs. 1, 2] are incorrect. Thus, the results presented by Kalidas Das lead to invalid conclusions.