Some possibilities for multivariate analysis in hydrologic studies

Snyder, Willard M.

doi:10.1029/jz067i002p00721

Cited by 41 publications

(19 citation statements)

References 2 publications

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“…Hydrologists (32,36) Contact of clay-sized to silt-sized valley fill (Z) and underlying coarse-sand to granule-sized fill constitutes a proposed capillary barrier to flow in the unsaturated zone. Symbols QTal and Tt are defined in the legend to Fig.…”

Section: Transuranic Radioactive Waste Disposal In Sedan Cratermentioning

confidence: 99%

Radioactive Waste Disposal in Thick Unsaturated Zones

Winogard

1981

Science

123

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Portions of the Great Basin are undergoing crustal extension and have unsaturated zones as much as 600 meters thick. These areas contain multiple natural barriers capable of isolating solidified toxic wastes from the biosphere for tens of thousands to perhaps hundreds of thousands of years. An example of the potential utilization of such arid zone environments for toxic waste isolatic is the burial of transuranic radioactive wastes at relatively shallow depths (15 to 100 meters) in Sedan Crater, Yucca Flat, Nevada. The volume of this man-made crater is several times that of the projected volume of such wastes to the year 2000. Disposal in Sedan Crater could be accomplished at a savings on the order of $0.5 billion, in comparison with current schemes for burial of such wastes in mined repositories at depths of 600 to 900 meters, and with an apparently equal likelihood of waste isolation from the biosphere.

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Section: Transuranic Radioactive Waste Disposal In Sedan Cratermentioning

confidence: 99%

Radioactive Waste Disposal in Thick Unsaturated Zones

Winogard

1981

Science

123

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“…Multivariate approaches represent these events better than classical univariate tools. Snyder [1962] and Wong [1963] realized the first applications of multivariate analysis tools in hydrological analysis.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Multivariate L‐moment homogeneity test

Chebana

Ouarda

2007

Water Resources Research

115

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[1] Several types of hydrological events are described with multivariate characteristics (droughts, floods, rain storms, etc.). When carrying out a multivariate regional frequency analysis for these events it is important to jointly consider all these characteristics. The aim of this paper is to extend the statistical homogeneity test of Hosking and Wallis (1993) to the multivariate case. As a tool, multivariate L-moments are used to define the statistics and general copula models to describe the statistical behavior of dependent variables. The usefulness of the methodology is illustrated on flood events. Monte-Carlo simulations are also performed for a bivariate Gumbel logistic model with Gumbel marginal distributions. Results illustrate the power of the proposed multivariate L-moment homogeneity test to detect heterogeneity on the whole structure of the model and on the marginal distributions. In a bivariate flood setting, a comparison is carried out with the classical homogeneity test of Hosking and Wallis based on several types of regions.

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“…Multivariate analysis is simply a collection of procedures for analyzing the associations between two or more sets of data that have been collected on each object in one or more samples of object. Synder (1962) introduced some solutions, possibilities of multivariate statistics in hydrological modeling. Wong (1979) utilized a multivariate statistical technique component analysis in analyzing the effects of twelve basins and climatological parameters.…”

Section: Introductionmentioning

confidence: 99%

Application of principal component analysis in grouping geomorphic parameters of a watershed for hydrological modeling

Sharma¹,

Gajbhiye

Tignath

2014

Appl Water Sci

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Principal component analysis has been applied to 13 dimensionless geomorphic parameters on 8 subwatersheds of Kanhiya Nala watershed tributary of Tons River located in Part of Panna and Satna district of Madhya Pradesh, India, to group the parameters under different components based on significant correlations. Results of principal component analysis of 13 geomorphic parameters clearly reveal that some of these parameters are strongly correlated with the components but texture ratio and hypsometric integral do not show correlation with any of the component. So they have been screened out of analysis. The principal component loading matrix obtained using correlation matrix of eleven parameters reveals that first three components together account for 93.71 % of the total explained variance. Therefore, principal component loading is applied to get better correlation and clearly group the parameters in physically significant components. Based on the properties of the geomorphic parameters, three principal components were defined as drainage, slope or steepness and shape components. One parameter each from the significant components may form a set of independent parameters at a time in modeling the hydrologic responses such as runoff and sediment yield from small watersheds.

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Some possibilities for multivariate analysis in hydrologic studies

Cited by 41 publications

References 2 publications

Radioactive Waste Disposal in Thick Unsaturated Zones

Radioactive Waste Disposal in Thick Unsaturated Zones

Multivariate L‐moment homogeneity test

Application of principal component analysis in grouping geomorphic parameters of a watershed for hydrological modeling

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