排序方式： 共有32条查询结果，搜索用时 15 毫秒
1.
Anoop A. Krishnan P.K. Krishnakumar M. Rajagopalan 《Estuarine, Coastal and Shelf Science》2007,71(3-4):641-646
The incidence of a large scale Trichodesmium erythraeum bloom along the southwest coast of India (Arabian Sea) observed in May 2005 is reported. Around 4802 filaments of T. erythraeum ml−1 seawater was observed and a colony consisted of 3.6 × 105 cells. The bloom was predominant off Suratkal (12° 59′N and 74° 31′E) with a depth of about 47 m, covering an area of 7 km in length and 2 km width. The concentrations of Zinc, Cadmium, Lead, Copper, Nickel and Cobalt were determined in samples collected from the bloom and non-bloom sites using stripping voltammetry. The observed hydrographical and meteorological parameters were found to be favorable for the bloom. The concentrations of Zinc, Cadmium and Nickel were found to be higher at bloom stations, while the concentrations of Lead, Copper and Cobalt were found to be very low at bloom stations. Elevated concentrations of Cadmium and Cobalt were observed at Valappad mainly due to the decomposition of detrital material produced in the bloom. Statistically significant differences (P > 0.01) in metal concentrations between the bloom and non-bloom stations were not observed except for Copper. Metals such as Lead, Copper and Cobalt were removed from the seawater at all places where bloom was observed. Cadmium was found to be slowly released during the decaying process of the bloom. 相似文献
2.
B. Rajagopalan U. Lall D. G. Tarboton D. S. Bowles 《Stochastic Environmental Research and Risk Assessment (SERRA)》1997,11(1):65-93
A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the
original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought
of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate
conditional probability density function. This improves on the classical Bootstrap technique by generating values that have
not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation
is generated from the nonparametric wet/dry spell model as described in Lall et al. [1995]. A vector of other variables (solar
radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated
by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest.
An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided. 相似文献
3.
Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures 
下载免费PDF全文
下载免费PDF全文 Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km2 sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R2. Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R2) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R2 (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R2 scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R2 skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R2. The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
4.
Many have speculated about the presence of a stiff fluid in very early stage of the universe. Such a stiff fluid was first introduced by Zel’dovich. Recently the late acceleration of the universe was studied by taking bulk viscous stiff fluid as the dominant cosmic component, but the age predicted by such a model is less than the observed value. We consider a flat universe with viscous stiff fluid and decaying vacuum energy as the cosmic components and found that the model predicts a reasonable background evolution of the universe with de Sitter epoch as end phase of expansion. More over, the model also predicts a reasonable value for the age of the present universe. We also performed a dynamical system analysis of the model and found that the end de Sitter phase predicted by the model is stable. 相似文献
5.
Yeonsang Hwang Martyn P. Clark Balaji Rajagopalan 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(7):957-972
Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested.
The authors tested impact of uncertainty in daily precipitation on streamflow forecasts. In order to test the impact, a distributed
hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin
at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble streamflows. The uncertainty in model
inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations
in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences
in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider
range of streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time
lags between rainfall and runoff in this rainfall dominated basin. This ensemble streamflow generation framework was also
applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method. 相似文献
6.
7.
The influence of currents on the wave induced dynamic response of offshore structures with slender members is assessed by deriving appropriate load spectra. The analysis permits the implications of the modified Morison equation to be examined, and suggests that methods of equivalent linearization commonly employed may yield unconservative results. An alternative formulation is proposed, in which the equations of motion are linear in the structural kinematics but the non-linear dependence of drag force on fluid particle motions is retained. This leads to a load spectral density matrix expressed as a sum of terms, involving convolutions of the wave kinematics spectra. Results are obtained for a plane frame idealization of an offshore jacket structure. These demonstrate the strong influence of current, and the importance of retaining in the loading terms up to the third power of wave particle velocity. 相似文献
8.
9.
Balaji Rajagopalan Upmanu Lall David G. Tarboton 《Stochastic Hydrology and Hydraulics》1997,11(6):523-547
Kernel density estimators are useful building blocks for empirical statistical modeling of precipitation and other hydroclimatic
variables. Data driven estimates of the marginal probability density function of these variables (which may have discrete
or continuous arguments) provide a useful basis for Monte Carlo resampling and are also useful for posing and testing hypotheses
(e.g bimodality) as to the frequency distributions of the variable. In this paper, some issues related to the selection and
design of univariate kernel density estimators are reviewed. Some strategies for bandwidth and kernel selection are discussed
in an applied context and recommendations for parameter selection are offered. This paper complements the nonparametric wet/dry
spell resampling methodology presented in Lall et al. (1996). 相似文献
10.
O. P. Pandey Priyanka Tripathi G. Parthasarathy V. Rajagopalan B. Sreedhar 《Journal of the Geological Society of India》2014,83(6):599-612
Fluid driven metasomatism and mass transfer from the earth’s mantle have played an important role in the evolution of the lower continental crust in many geodynamically active areas. The epicentral region of the disastrous 1993 Killari earthquake (M 6.2), concealed below a thick suite of Deccan volcanics in central India, appear to be one such region. In connection with the study of seismotectonics of the earthquake prone Deccan volcanic region, we have carried out systematic and detailed geochemical and mineralogical investigation on core samples from the basement, obtained from the 617m deep KLR-1 borehole, drilled in the epicentral region of Killari. Our investigations indicate that the basement, concealed below 338m thick Deccan volcanics, is made up of CO2, Cl, FeO and CaO-rich, high density (2.82 g/cm3) — high velocity (avg. Vp: 6.2 km/s) moderately retrogressed upper amphibolite to granulite facies mid crustal rocks, which were subjected to pervasive Ca-metasomatism due to infiltration of mantle fluids. Graniticgneissic layer, typical of the upper crust, seems to be totally absent from this earthquake region. Chondrite normalized trace and rare earth elemental patterns display negative Eu anomalies together with LILE enrichment. Similarly, spider diagrams for incompatible elements show depletion in Zr, Hf, Y, Ta and Nb relative to the primitive mantle, indicating possible alterations of such relatively immobile elements at relatively high temperatures. Selective enrichment is also observed in transitional elements like Cu and Zn, indicating the possible role of chlorine in metal transport. The present study suggests that regional metasomatism beneath the Deccan Traps, which apparently alters the basic fabric of the rock during recrystallisation and makes it weak, may have a link with the nucleation of large earthquakes. 相似文献