A bivariate meta-Gaussian density for use in hydrology

Convenient bivariate densities found in the literature are often unsuitable for modeling hydrologic variates. They either constrain the range of association between variates, or fix the form of the marginal distributions. The bivariate meta-Gaussian density is constructed by embedding the normal quantile transform of each variate into the Gaussian law. The density can represent a full range of association between variates and admits arbitrarily specified marginal distributions. Modeling and estimation can be decomposed into i) independent analyses of the marginal distributions, and ii) investigation of the dependence structure. Both statistical and judgmental estimation procedures are possible. Some comparisons to recent applications of bivariate densities in the hydrologic literature motivate and illustrate the model.     

Geostatistical simulation of random fields with bivariate isofactorial distributions by adding mosaic models

This work deals with the geostatistical simulation of a family of stationary random field models with bivariate isofactorial distributions. Such models are defined as the sum of independent random fields with mosaic-type bivariate distributions and infinitely divisible univariate distributions. For practical applications, dead leaf tessellations are used since they provide a wide range of models and allow conditioning the realizations to a set of data via an iterative procedure (simulated annealing). The model parameters can be determined by comparing the data variogram and madogram, and enable to control the spatial connectivity of the extreme values in the realizations. An illustration to a forest dataset is presented, for which a negative binomial model is used to characterize the distribution of coniferous trees over a wooded area.     

Improving the prediction of extreme FPSO hawser tension,using another highly correlated hawser tension with a longer time record

The floating production storage and offloading unit (FPSO) is an offshore vessel that produces and stores crude oil prior to tanker transport. Robust prediction of extreme hawser tensions during the FPSO offloading operation is an important safety concern. Excessive hawser tension may occur during certain sea conditions, posing an operational risk. In this paper, the finite element method (FEM) software ANSYS AQWA has been employed to analyze vessel response due to hydrodynamic wave loads, acting on a specific FPSO vessel under actual sea conditions.In some practical situations, it would be useful to improve the accuracy of some statistical predictions based on a certain stochastic random process, given another synchronous highly correlated stochastic process that has been measured for a longer time, than the process of interest. In this paper, the issue of improving extreme value prediction has been addressed. In other words, an efficient transfer of information is necessary between two synchronous, highly correlated stochastic processes. Two such highly correlated FPSO hawser tension processes were simulated in order to test the efficiency of the proposed technique.     

Specifying a joint space- and time-lag using a bivariate Poisson distribution

Separate space- or time-lags have been considered regularly in data analyses; as space–time models are more recently being studied extensively in data analytic fashion, joint estimation of both lags has to be considered explicitly. This paper addresses this issue, taking into special consideration parametric parsimony together with specification richness; use of the bivariate Poisson frequency distribution is advocated and applied to an empirical case. The relation of this approach to random effects specifications is investigated. Data for Belgian regional products constitute the empirical case study.

Application of bivariate extreme value distribution to flood frequency analysis: a case study of Northwestern Mexico

In Mexico, poverty has forced people to live almost on the water of rivers. This situation along with the occurrence of floods is a serious problem for the local governments. In order to protect their lives and goods, it is very important to account with a mathematical tool that may reduce the uncertainties in computing the design events for different return periods. In this paper, the Logistic model for bivariate extreme value distribution with Weibull-2 and Mixed Weibull marginals is proposed for the case of flood frequency analysis. A procedure to estimate their parameters based on the maximum likelihood method is developed. A region in Northwestern Mexico with 16 gauging stations has been selected to apply the model and regional at-site quantiles were estimated. A significant improvement occurs, measured through the use of a goodness-of-fit test, when parameters are estimated using the bivariate distribution instead of its univariate counterpart. Results suggest that it is very important to consider the Mixed Weibull distribution and its bivariate option when analyzing floods generated by a␣mixture of two populations.     

A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate

The purpose of this study is to evaluate and to compare the results of multivariate (logical regression) and bivariate (landslide susceptibility) methods in Geographical Information System (GIS) based landslide susceptibility assessment procedures. In order to achieve this goal the Asarsuyu catchment in NW Turkey was selected as a test zone because of its well-known landslide occurrences interfering with the E-5 highway mountain pass.Two methods were applied to the test zone and two separate susceptibility maps were produced. Following this a two-fold comparison scheme was implemented. Both methods were compared by the Seed Cell Area Indexes (SCAI) and by the spatial locations of the resultant susceptibility pixels.It was found that both of the methods converge in 80% of the area; however, the weighting algorithm in the bivariate technique (landslide susceptibility method) had some severe deficiencies, as the resultant hazard classes in overweighed areas did not converge with the factual landslide inventory map. The result of the multivariate technique (logical regression) was more sensitive to the different local features of the test zone and it resulted in more accurate and homogeneous susceptibility maps.     

Copula-based mixed models for bivariate rainfall data: an empirical study in regression perspective

A comprehensive parametric approach to study the probability distribution of rainfall data at scales of hydrologic interest (e.g. from few minutes up to daily) requires the use of mixed distributions with a discrete part accounting for the occurrence of rain and a continuous one for the rainfall amount. In particular, when a bivariate vector (X, Y) is considered (e.g. simultaneous observations from two rainfall stations or from two instruments such as radar and rain gauge), it is necessary to resort to a bivariate mixed model. A quite flexible mixed distribution can be defined by using a 2-copula and four marginals, obtaining a bivariate copula-based mixed model. Such a distribution is able to correctly describe the intermittent nature of rainfall and the dependence structure of the variables. Furthermore, without loss of generality and with gain of parsimony this model can be simplified by some transformations of the marginals. The main goals of this work are: (1) to empirically explore the behaviour of the parameters of marginal transformations as a function of time scale and inter-gauge distance, by analysing data from a network of rain gauges; (2) to compare the properties of the regression curves associated to the copula-based mixed model with those derived from the model simplified by transformations of the marginals. The results from the investigation of transformations’ parameters are in agreement with the expected theoretical dependence on inter-gauge distance, and show dependence on time scale. The analysis on the regression curves points out that: (1) a copula-based mixed model involves regression curves quite close to some non-parametric models; (2) the performance of the parametric regression decreases in the same cases in which non-parametric regression shows some instability; (3) the copula-based mixed model and its simplified version show similar behaviour in term of regression for mid-low values of rainfall. An erratum to this article can be found at     

A bivariate pareto model for drought

Univariate Pareto distributions have been so widely used in hydrology. It seems however that bivariate or multivariate Pareto distributions have not yet found applications in hydrology, especially with respect to drought. In this note, a drought application is described by assuming a bivariate Pareto model for the joint distribution drought durations and drought severity in the State of Nebraska. Based on this model, exact distributions are derived for the inter arrival time, magnitude and the proportion of droughts. Estimates of 2, 5, 10, 20, 50 and 100 year return periods are derived for the three variables, drought duration, drought severity and the pairwise combinations: (drought duration, drought severity), (inter arrival time of drought, proportion of drought) and (drought duration, drought magnitude). These return period estimates could have an important role in hydrology, for example, with respect to measures of vegetation water stress for plants in water-controlled ecosystems.     

Improving extreme wind speed prediction for North Sea offshore oil and gas fields

The paper adresses accuracy improving of statistical prediction, extracted from a shorter stochastic process, using the information provided by another synchronous highly correlated stochastic process that has been measured for a longer time. As an example, the specific issue of improving extreme wind speed prediction has been addressed. For this purpose, an efficient transfer of information is necessary between two synchronous, highly correlated stochastic processes. To illustrate the efficiency of the proposed technique, two time series of measured wind speed data from North sea oil and gas fields were used.     

中国省级以上开发区产业集聚的多尺度分析

从"区域-省域-地(市)"3个空间尺度入手,分析中国开发区产业集聚特征,结果表明:①中国开发区总体上以技术密集型为主导,装备制造、电子信息、生物医药产业优势明显.但东中西部差异巨大,技术密集型、劳动密集型、资本密集型产业分别占据地区主导地位.②开发区产业集聚度随尺度缩小而扩大,城市群、中心城市是其最重要的空间载体.同时...