River runoff evaluation for ungauged watersheds by SWAP model. 2. Application of methods of physiographic similarity and spatial geostatistics

The efficiency of the methods of spatial proximity and geostatistics, as well as physico-geographic similarity, is studied as applied to the evaluation of the key model parameters of ungauged watersheds to be used in river runoff calculation by SWAP model. The target geographic objects were 323 experimental watersheds of MOPEX project. The quality of model parameter estimates and reproduction of river runoff hydrographs was analyzed in the case of the use of different similarity methods, and the order of decisions to be made was developed for the problem of river runoff calculation from an ungauged watershed for the entire area under study.     

Reproduction of secondary data in projection pursuit transformation

A challenge when working with multivariate data in a geostatistical context is that the data are rarely Gaussian. Multivariate distributions may include nonlinear features, clustering, long tails, functional boundaries, spikes, and heteroskedasticity. Multivariate transformations account for such features so that they are reproduced in geostatistical models. Projection pursuit as developed for high dimensional data exploration can also be used to transform a multivariate distribution into a multivariate Gaussian distribution with an identity covariance matrix. Its application within a geostatistical modeling context is called the projection pursuit multivariate transform (PPMT). An approach to incorporate exhaustive secondary variables in the PPMT is introduced. With this approach the PPMT can incorporate any number of secondary variables with any number of primary variables. A necessary alteration to the approach to make this numerically practical was the implementation of a continuous probability estimator that relies on Bernstein polynomials for the transformation that takes place in the projections. Stopping criteria were updated to incorporate a bootstrap t test that compares data sampled from a multivariate Gaussian distribution with the data undergoing transformation.     

Fast and direct nonparametric procedures in the L-moment homogeneity test

Regional frequency analysis is an important tool to properly estimate hydrological characteristics at ungauged or partially gauged sites in order to prevent hydrological disasters. The delineation of homogeneous groups of sites is an important first step in order to transfer information and obtain accurate quantile estimates at the target site. The Hosking–Wallis homogeneity test is usually used to test the homogeneity of the selected sites. Despite its usefulness and good power, it presents some drawbacks including the subjective choice of a parametric distribution for the data and a poorly justified rejection threshold. The present paper addresses these drawbacks by integrating nonparametric procedures in the L-moment homogeneity test. To assess the rejection threshold, three resampling methods (permutation, bootstrap and Pólya resampling) are considered. Results indicate that permutation and bootstrap methods perform better than the parametric Hosking–Wallis test in terms of power as well as in time and procedure simplicity. A real-world case study shows that the nonparametric tests agree with the HW test concerning the homogeneity of the volume and the bivariate case while they disagree for the peak case, but that the assumptions of the HW test are not well respected.     

Impact of sensor measurement error on sensor positioning in water quality monitoring networks

This paper studies the impact of sensor measurement error on designing a water quality monitoring network for a river system, and shows that robust sensor locations can be obtained when an optimization algorithm is combined with a statistical process control (SPC) method. Specifically, we develop a possible probabilistic model of sensor measurement error and the measurement error model is embedded into a simulation model of a river system. An optimization algorithm is used to find the optimal sensor locations that minimize the expected time until a spill detection in the presence of a constraint on the probability of detecting a spill. The experimental results show that the optimal sensor locations are highly sensitive to the variability of measurement error and false alarm rates are often unacceptably high. An SPC method is useful in finding thresholds that guarantee a false alarm rate no more than a pre-specified target level, and an optimization algorithm combined with the thresholds finds a robust sensor network.     

Seasonal copula models for the analysis of glacier discharge at King George Island,Antarctica

Modelling glacier discharge is an important issue in hydrology and climate research. Glaciers represent a fundamental water resource when melting of ice and snow contributes to runoff. Glaciers are also studied as natural global warming sensors. GLACKMA association has implemented one of their Pilot Experimental Catchment areas at the King George Island in the Antarctica which records values of the liquid discharge from Collins glacier. In this paper, we propose the use of time-varying copula models for analyzing the relationship between air temperature and glacier discharge, which is clearly non constant and non linear through time. A seasonal copula model is defined where both the marginal and copula parameters vary periodically along time following a seasonal dynamic. Full Bayesian inference is performed such that the marginal and copula parameters are estimated in a one single step, in contrast with the usual two-step approach. Bayesian prediction and model selection is also carried out for the proposed model such that Bayesian credible intervals can be obtained for the conditional glacier discharge given a value of the temperature at any given time point. The proposed methodology is illustrated using the GLACKMA real data where there is, in addition, a hydrological year of missing discharge data which were not possible to measure accurately due to problems in the sounding.     

Complexity analysis of precipitation using the Lempel–Ziv algorithm and a multi-scaling approach: a case study in Jilin province,China

Precipitation is an important part of the hydrologic cycle, and its complexity is closely related to surface runoff and changing groundwater dynamics, which in turn influences the accuracy of precipitation forecasts. In this study, we used the Lempel–Ziv algorithm (LZA) and a multi-scaling approach to assess precipitation complexity for 1958–2011 by analyzing time series data from 28 gauging stations located throughout Jilin province, China. The spatial distribution of normalized precipitation complexity was measured by LZA, a symbolic dynamics algorithm, and by a multi-scaling approach, which is described by fractals. In addition, the advantages and limitations of these two methods were investigated. The results indicate that both methods are applicable and consistent for calculating precipitation complexity, and that the degree of relief is a primary factor controlling precipitation complexity in the mountainous area; in the plain terrain, however, the prominent influencing factor is climate.     

A stochastic methodology for risk assessment of a large earthquake when a long time has elapsed

We propose a stochastic methodology for risk assessment of a large earthquake when a long time has elapsed from the last large seismic event. We state an approximate probability distribution for the occurrence time of the next large earthquake, by knowing that the last large seismic event occurred a long time ago. We prove that, under reasonable conditions, such a distribution is exponential with a rate depending on the asymptotic slope of the cumulative intensity function corresponding to a nonhomogeneous Poisson process. As it is not possible to obtain an empirical cumulative distribution function of the waiting time for the next large earthquake, an estimator of its cumulative distribution function based on existing data is derived. We conduct a simulation study for detecting scenario in which the proposed methodology would perform well. Finally, a real-world data analysis is carried out to illustrate its potential applications, including a homogeneity test for the times between earthquakes.     

Modelling larval dispersal of the king scallop (Pecten maximus) in the English Channel: examples from the bay of Saint-Brieuc and the bay of Seine

The king scallop (Pecten maximus) is one of the most important benthic species of the English Channel as it constitutes the first fishery in terms of landings in this area. To support strategies of spatial fishery management, we develop a high-resolution biophysical model to study scallop dispersal in two bays along the French coasts of the English Channel (i.e. the bay of Saint-Brieuc and the bay of Seine) and to quantify the relative roles of local hydrodynamic processes, temperature-dependent planktonic larval duration (PLD) and active swimming behaviour (SB). The two bays are chosen for three reasons: (1) the distribution of the scallop stocks in these areas is well known from annual scallop stock surveys, (2) these two bays harbour important fisheries and (3) scallops in these two areas present some differences in terms of reproductive cycle and spawning duration. The English Channel currents and temperature are simulated for 10 years (2000–2010) with the MARS-3D code and then used by the Lagrangian module of MARS-3D to model the transport. Results were analysed in terms of larval distribution at settlement and connectivity rates. While larval transport in the two bays depended both on the tidal residual circulation and the wind-induced currents, the relative role of these two hydrodynamic processes varied among bays. In the bay of Saint-Brieuc, the main patterns of larval dispersal were due to tides, the wind being only a source of variability in the extent of larval patch and the local retention rate. Conversely, in the bay of Seine, wind-induced currents altered both the direction and the extent of larval transport. The main effect of a variable PLD in relation to the thermal history of each larva was to reduce the spread of dispersal and consequently increase the local retention by about 10 % on average. Although swimming behaviour could influence larval dispersal during the first days of the PLD when larvae are mainly located in surface waters, it has a minor role on larval distribution at settlement and retention rates. The analysis of the connectivity between subpopulations within each bay allows identifying the main sources of larvae which depend on both the characteristics of local hydrodynamics and the spatial heterogeneity in the reproductive outputs.     

Global stability of the ballooning mode in a cylindrical model

Ballooning disturbances in a finite-pressure plasma in a curvilinear magnetic field are described by the system of coupled equations for the Alfvén and slow magnetosonic modes. In contrast to most previous works that locally analyzed the stability of small-scale disturbances using the dispersion relationship, a global analysis outside a WKB approximation but within a simple cylindrical geometry, when magnetic field lines are circles with constant curvature, is performed in the present work. This model is relatively simple; nevertheless, it has the singularities necessary for the formation of the ballooning mode: field curvature and non-uniform thermal plasma pressure. If the disturbance finite radial extent is taken into account, the instability threshold increases as compared to a WKB approximation. The simplified model used in this work made it possible to consider the pattern of unstable disturbances at arbitrary values of the azimuthal wavenumber (k _y ). Azimuthally large-scale disturbances can also be unstable, although the increment increases with decreasing azimuthal scale and reaches saturation when the scales are of the order of the pressure nonuniformity dimension.