Effect of growing watershed imperviousness on hydrograph parameters and peak discharge

An increasing impervious area is quickly extending over the Wu‐Tu watershed due to the endless demands of the people. Generally, impervious paving is a major result of urbanization and more recently has had the potential to produce more enormous flood disasters than those of the past. In this study, 40 available rainfall–runoff events were chosen to calibrate the applicable parameters of the models and to determine the relationships between the impervious surfaces and the calibrated parameters. Model inputs came from the outcomes of the block kriging method and the non‐linear programming method. In the optimal process, the shuffled complex evolution method and three criteria were applied to compare the observed and simulated hydrographs. The tendencies of the variations of the parameters with their corresponding imperviousness were established through regression analysis. Ten cases were used to examine the established equations of the parameters and impervious covers. Finally, the design flood routines of various return periods were furnished through use of approaches containing a design storm, block kriging, the SCS model, and a rainfall‐runoff model with established functional relationships. These simulated flood hydrographs were used to compare and understand the past, present, and future hydrological conditions of the watershed studied. In the research results, the time to peak of flood hydrographs for various storms was diminished approximately from 11 h to 6 h in different decrements, whereas peak flow increased respectively from 127 m³ s^?1 to 629 m³ s^?1 for different storm intensities. In addition, this study provides a design diagram for the peak flow ratio to help engineers and designers to construct hydraulic structures efficiently and prevent possible damage to human life and property. Copyright © 2007 John Wiley & Sons, Ltd.     

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Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui River alluvial fan,Taiwan

This work evaluated the spatial variability and distribution of heterogeneous hydraulic conductivity (K) in the Choushui River alluvial fan in Taiwan, using ordinary kriging (OK) and mean and individual sequential Gaussian simulations (SGS). A baseline flow model constructed by upscaling parameters was inversely calibrated to determine the pumping and recharge rates. Simulated heads using different K realizations were then compared with historically measured heads. A global/local simulated error between simulated and measured heads was analysed to assess the different spatial variabilities of various estimated K distributions. The results of a MODFLOW simulation indicate that the OK realization had the smallest sum of absolute mean simulation errors (SAMSE) and the SGS realizations preserved the spatial variability of the measured K fields. Moreover, the SAMSE increases as the spatial variability of the K field increases. The OK realization yields small local simulation errors in the measured K field of moderate magnitude, whereas the SGS realizations have small local simulation errors in the measured K fields, with high and low values. The OK realization of K can be applied to perform a deterministic inverse calibration. The mean SGS method is suggested for constructing a K field when the application focuses on extreme values of estimated parameters and small calibration errors, such as in a simulation of contaminant transport in heterogeneous aquifers. The individual SGS realization is useful in stochastically assessing the spatial uncertainty of highly heterogeneous aquifers. Copyright © 2004 John Wiley & Sons, Ltd.     

Comparison between different kriging estimators

Six different geostatistical estimators (linear kriging, lognormal kriging, and disjunctive kriging, each with and without a nonbias, i.e., universality condition) were compared using data from a polymetallic deposit in Algeria. The differences between estimators with and without the nonbias condition were far more pronounced than between the different kriging methods. This highlights the importance of choosing an appropriate stationarity model for the data. The criterion concerning kriging weight of the mean in simple kriging, proposed by Remacre (1984, 1987) and Rivoirard (1984) was found to be helpful for determining blocks where the choice of the stationarity hypothesis was critical.     

Random vectors and spatial analysis by geostatistics for geotechnical applications

Geostatistics is extended to the spatial analysis of vector variables by defining the estimation variance and vector variogram in terms of the magnitude of difference vectors. Many random variables in geotechnology are in vectorial terms rather than scalars, and its structural analysis requires those sample variable interpolations to construct and characterize structural models. A better local estimator will result in greater quality of input models; geostatistics can provide such estimators: kriging estimators. The efficiency of geostatistics for vector variables is demonstrated in a case study of rock joint orientations in geological formations. The positive cross-validation encourages application of geostatistics to spatial analysis of random vectors in geoscience as well as various geotechnical fields including optimum site characterization, rock mechanics for mining and civil structures, cavability analysis of block cavings, petroleum engineering, and hydrologic and hydraulic modelings.     

Comparison of two techniques for applying disjunctive kriging: The Gaussian anamorphosis model versus the direct statistical inference of the bivariate distributions

Indicator cokriging is an alternative to disjunctive kriging for estimation of spatial distributions. One way to determine which of these techniques is more accurate for estimation of spatial distributions is to apply each to a particular type of data. A procedure is developed for evaluation of disjunctive kriging and indicator cokriging for such an application. Application of this procedure to earthquake ground motion data found disjunctive kriging to be at least as accurate as indicator cokriging for estimation of spatial distributions of peak horizontal acceleration. Indicator cokriging was superior for all other types of earthquake ground motion data.     

Trend-surface analysis

This paper is largely an exposition of the work of the French geostatistian G. Matheron and his school in English and at a simpler mathematical level. The probability theory on which it is based is essentially all contained in the references cited, most of which will be unfamiliar to mathematical geologists. The important method of universal kriging is explained intuitively. The genuine statistical problems, which have yet to be overcome, are pointed out.Research supported by the Office of Naval Research under contract NONR 4010(09) awarded to the Department of Statistics, The Johns Hopkins University. This paper in whole or in part may be reproduced for any purpose of the United States Government.     

Geostatistical analysis of field hydraulic conductivity in compacted clay

Hydraulic conductivity(K) of fractured or porous materials is associated intimately with water flow and chemical transport. Basic concepts imply uniform flux through a homogeneous cross-sectional area. If flow were to occur only through part of the area, actual rates could be considerably different. Because laboratory values ofK in compacted clays seldom agree with field estimates, questions arise as to what the true values ofK are and how they should be estimated. Hydraulic conductivity values were measured on a 10×25 m elevated bridge-like platform. A constant water level was maintained for 1 yr over a 0.3-m thick layer of compacted clay, and inflow and outflow rates were monitored using 10×25 grids of 0.3-m diameter infiltration rings and outflow drains subtending approximately 1×1 m blocks of compacted clay. Variography of inflow and outflow data established relationships between cores and blocks of clay, respectively. Because distributions of outflow rates were much less and bore little resemblance to the distributions of break-through rates based on tracer studies, presence of macropores and preferential flow through the macropores was suspected. Subsequently, probability kriging was applied to reevaluate distribution of flux rates and possible location of macropores. Sites exceeding a threshold outflow of 100×10^–9 m/s were classified as outliers and were assumed to probably contain a significant population of macropores. Different sampling schemes were examined. Variogram analysis of outflows with and without outliers suggested adequacy of sampling the site at 50 randomly chosen locations. Because of the potential contribution of macropores to pollutant transport and the practical necessity of extrapolating small plot values to larger areas, conditional simulations with and without outliers were carried out. Simulated scenarios based on all available data compared well with conditional simulations based on randomly chosen locations.This paper was presented at MGUS 87 Conference, Redwood City, California, 14 April 1987.     

Kriging in terms of projections

In the last few years, an increasing number of practical studies using so-called kriging estimation procedures have been published. Various terms, such as universal kriging, lognormal kriging, ordinary kriging, etc., are used to define different estimation procedures, leaving a certain confusion about what kriging really is. The object of this paper is to show what is the common backbone of all these estimation procedures, thus justifying the common name of kriging procedures. The word kriging (in French krigeage) is a concise and convenient term to designate the classical procedure of selecting, within agiven class of possible estimators, the estimator with a minimum estimation variance (i.e., the estimator which leads to a minimum variance of the resulting estimation error). This estimation variance can be seen as a squared distance between the unknown value and its estimator; the process of minimization of this distance can then be seen as the projection of the unknown value onto the space within which the search for an estimator is carried out.