A multiple distribution Markovian model for annual hydrologic time series

Nonlinear serial dependence and skewness of annual hydrologic time series {X _t } have been challenging the classical theory of Gaussian stochastic processes, particularly if the study of extremes (dry or wet years) is required as it is often the case. In this paper, a new and general model is proposed assuming that the geophysical system which is responsible forX _t can take different states and that this state process is modeled by a Markov chain. At each time,X _t is generated from a statistical distribution which depends on the state that has occurred. This model can preserve non-linear structures of serial dependence and it can produce a skewed marginal distribution ofX _t without any transformation. A successful application of this model to the study of annual rainfall at Fortaleza (Northeast of Brazil) is also presented.     

The Displacement and Strain Field of Three-dimensional Rheologic Model of Earthquake Preparation

Based on the three-dimensional elastic inclusion model proposed by Dobrovolskii, we developed a rheological inclusion model to study earthquake preparation processes. By using the Corresponding Principle in the theory of rheologic mechanics, we derived the analytic expressions of viscoelastic displacement U(r, t) , V(r, t) and W(r, t), normal strains ε_xx (r, t), ε_yy (r, t) and ε_zz (r, t) and the bulk strain θ (r, t) at an arbitrary point (x, y, z) in three directions of X axis, Y axis and Z axis produced by a three-dimensional inclusion in the semi-infinite rheologic medium defined by the standard linear rheologic model. Subsequent to the spatial-temporal variation of bulk strain being computed on the ground produced by such a spherical rheologic inclusion, interesting results are obtained, suggesting that the bulk strain produced by a hard inclusion change with time according to three stages (α, β, γ) with different characteristics, similar to that of geodetic deformation observations, but different with the results of a soft inclusion. These theoretical results can be used to explain the characteristics of spatial-temporal evolution, patterns, quadrant-distribution of earthquake precursors, the changeability, spontaneity and complexity of short-term and imminent-term precursors. It offers a theoretical base to build physical models for earthquake precursors and to predict the earthquakes.     

Seismic anomalies in the aftershock sequence of November 16, 2000, in Papua New Guinea

The study of seismic anomalies, related both to the temporal trend of aftershock sequences and to the temporal series of mainshocks, is important for an understanding of the physical processes relating to the existence and the characteristics of seismic precursors. The purpose of this work is to highlight some methodological aspects related to the observation of possible anomalies in the temporal decay of an aftershock sequence. It is realized by means of several parameters. We focused our work on an analysis of the Papua New Guinea seismic sequence that occurred on November 16, 2000. The magnitude of the mainshock is M = 8.2. The observed temporal series of shocks per day can be considered as a sum of a deterministic contribution and a stochastic contribution. If the decay can be modeled as a nonstationary Poisson process where the intensity function is equal to n(t) = K(t + c)^−p + K ₁, the number of aftershocks in a small time interval Δt is the mean value n(t)Δt, with a standard deviation σ = √n(t)Δt. We observe that there are some variations in seismicity that can be considered as seismic anomalies before the occurrence of a large aftershock. The data, checked according to completeness criteria, come from the website of the USGS NEIC data bank (). The text was submitted by the authors in English.     

THE ESTIMATION OF SIGNAL SPECTRA AND RELATED QUANTITIES BY MEANS OF THE MULTIPLE COHERENCE FUNCTION *

A seismic trace recorded with suitable gain control can be treated as a stationary time series. Each trace, χ_j(t), from a set of traces, can be broken down into two stationary components: a signal sequence, α_j(t) *s(t—τ_j), which correlates from trace to trace, and an incoherent noise sequence, n_j(t), which does not correlate from trace to trace. The model for a seismic trace used in this paper is thus χ_j(t) =α_j(t) * s(t—τ_j) +n_j(t) where the signal wavelet α_j(t), the lag (moveout) of the signal τ_j, and the noise sequence n_j(t) can vary in any manner from trace to trace. Given this model, a method for estimating the power spectra of the signal and incoherent noise components on each trace is presented. The method requires the calculation of the multiple coherence function γ_j(f) of each trace. γ_j(f) is the fraction of the power on traced at frequency f that can be predicted in a least-square error sense from all other traces. It is related to the signal-to-noise power ratio ρ_j(f) by where K_j(f) can be computed and is in general close to 1.0. The theory leading to this relation is given in an Appendix. Particular attention is paid to the statistical distributions of all estimated quantities. The statistical behaviour of cross-spectral and coherence estimates is complicated by the presence of bias as well as random deviations. Straightforward methods for removing this bias and setting up confidence limits, based on the principle of maximum likelihood and the Goodman distribution for the sample multiple coherence, are described. Actual field records differ from the assumed model mainly in having more than one correctable component, components other than the required sequence of reflections being lumped together as correlated noise. When more than one correlatable component is present, the estimate for the signal power spectrum obtained by the multiple coherence method is approximately the sum of the power spectra of the correlatable components. A further practical drawback to estimating spectra from seismic data is the limited number of degrees of freedom available. Usually at least one second of stationary data on each trace is needed to estimate the signal spectrum with an accuracy of about 10%. Examples using synthetic data are presented to illustrate the method.     

Yield and overflow from a finite stochastic reservoir

The paper discusses the overflow (spillage) and yield rates and the total overflow and total yield over a specified time from a finite discrete stochastic reservoir, in which the yieldY _t during the working interval (t,t+1) is a function of the storageZ _t at timet, the inflow sequence {X _t } being IID.The distribution vector of the spillage rate at timet is a telescoped version of the distribution of a certain Markovian variable whose transition matrix is derived. Formulae are given for the distribution of the total spillageW _h given suitable initial conditions, forh=1,2,3; and a simple expression derived forE(W _h ).     

Numerical simulations of transport of non-ergodic solute plumes in heterogeneous aquifers

Transport of non-ergodic solute plumes by steady-state groundwater flow with a uniform mean velocity, μ, were simulated with Monte Carlo approach in a two-dimensional heterogeneous and statistically isotropic aquifer whose transmissivity, T, is log-normally distributed with an exponential covariance. The ensemble averages of the second spatial moments of the plume about its center of mass, <S _{i i} (t)>, and the plume centroid covariance, R _{i i} (t) (i=1,2), were simulated for the variance of Y=log T, σ _Y ²=0.1, 0.5 and 1.0 and line sources normal or parallel to μ of three dimensionless lengths, 1, 5, and 10. For σ _Y ²=0.1, all simulated <S _{i i} (t)>−S _{i i} (0) and R _{i i} (t) agree well with the first-order theoretical values, where S _{i i} (0) are the initial values of S _{i i} (t). For σ _Y ²=0.5 and 1.0 and the line sources normal to μ, the simulated longitudinal moments, <S ₁₁(t)>−S ₁₁(0) and R ₁₁(t), agree well with the first-order theoretical results but the simulated transverse moments <S ₂₂(t)>−S ₂₂(0) and R ₂₂(t) are significantly larger than the first-order values. For the same two larger values of σ _Y ² but the line sources parallel to μ, the simulated <S ₁₁(t)>−S ₁₁(0) are larger than but the simulated R ₁₁ are smaller than the first-order values, and both simulated <S ₂₂(t)>−S ₂₂(0) and R ₂₂(t) stay larger than the first-order values. For a fixed value of σ _Y ², the summations of <S _{i i} (t)>−S _{i i} (0) and R _{i i} , i.e., X _{i i} (i=1,2), remain almost the same no matter what kind of source simulated. The simulated X ₁₁ are in good agreement with the first-order theory but the simulated X ₂₂ are significantly larger than the first-order values. The simulated X ₂₂, however, are in excellent agreement with a previous modeling result and both of them are very close to the values derived using Corrsin's conjecture. It is found that the transverse moments may be significantly underestimated if less accurate hydraulic head solutions are used and that the decreasing of <S ₂₂(t)>−S ₂₂(0) with time or a negative effective dispersivity, defined as , may happen in the case of a line source parallel to μ where σ _Y ² is small.     

Numerical simulations of transport of non-ergodic solute plumes in heterogeneous aquifers

Transport of non-ergodic solute plumes by steady-state groundwater flow with a uniform mean velocity, μ, were simulated with Monte Carlo approach in a two-dimensional heterogeneous and statistically isotropic aquifer whose transmissivity, T, is log-normally distributed with an exponential covariance. The ensemble averages of the second spatial moments of the plume about its center of mass, <S _{i i} (t)>, and the plume centroid covariance, R _{i i} (t) (i=1,2), were simulated for the variance of Y=log T, σ _Y ²=0.1, 0.5 and 1.0 and line sources normal or parallel to μ of three dimensionless lengths, 1, 5, and 10. For σ _Y ²=0.1, all simulated <S _{i i} (t)>−S _{i i} (0) and R _{i i} (t) agree well with the first-order theoretical values, where S _{i i} (0) are the initial values of S _{i i} (t). For σ _Y ²=0.5 and 1.0 and the line sources normal to μ, the simulated longitudinal moments, <S ₁₁(t)>−S ₁₁(0) and R ₁₁(t), agree well with the first-order theoretical results but the simulated transverse moments <S ₂₂(t)>−S ₂₂(0) and R ₂₂(t) are significantly larger than the first-order values. For the same two larger values of σ _Y ² but the line sources parallel to μ, the simulated <S ₁₁(t)>−S ₁₁(0) are larger than but the simulated R ₁₁ are smaller than the first-order values, and both simulated <S ₂₂(t)>−S ₂₂(0) and R ₂₂(t) stay larger than the first-order values. For a fixed value of σ _Y ², the summations of <S _{i i} (t)>−S _{i i} (0) and R _{i i} , i.e., X _{i i} (i=1,2), remain almost the same no matter what kind of source simulated. The simulated X ₁₁ are in good agreement with the first-order theory but the simulated X ₂₂ are significantly larger than the first-order values. The simulated X ₂₂, however, are in excellent agreement with a previous modeling result and both of them are very close to the values derived using Corrsin's conjecture. It is found that the transverse moments may be significantly underestimated if less accurate hydraulic head solutions are used and that the decreasing of <S ₂₂(t)>−S ₂₂(0) with time or a negative effective dispersivity, defined as , may happen in the case of a line source parallel to μ where σ _Y ² is small.     

A multivariate gamma distribution arising from a Markov model

A Markov chain{X _t }, which has been useful for modelling in hydrology, can be specified by the Laplace transform (LT) of the conditional p.d.f. ofX _t+1 givenX _t =x _t , which is assumed to be of the exponential formH()exp{-G()x _t }. For appropriate choice ofH andG the marginal distribution ofX _t is the (univariate) gamma distribution. In this case, the joint p.d.f. ofX _t +1,...,X _t+n and its LT, are obtained, and this is extended to a seasonal version of the chain. A simple method of generating observations from these multivariate gamma distributions is noted, and the joint LT is applied to the problem of determining moments of weighted sums of such variables.     

Variations in the amplitude of the chandler wobble

It is shown that within the framework of the Kolmogorov model the “energy” of the pole E(t) = x ₁² + x ₂² can be interpreted as a Markovian process. The exact analytical expression has been obtained for the density of the conditional probability of the quantity E(t) and the problem of the first passage time of the process E(t) has been analyzed. It was shown that the available data on the swing of the function E(t) are not at variance with the Kolmogorov model and a short-period drop of the amplitude of the Chandler wobble in the early 20th century fits this model at Q = 50–200 too; values of Q > 350 are less reasonable.     

Quantitative assessment of short‐term rainfall forecasts from radar nowcasts and MM5 forecasts

Short‐term Quantitative Precipitation Forecasts (QPFs) can be achieved from numerical weather prediction (NWP) models or radar nowcasting, that is the extrapolation of the precipitation at a future time from consecutive radar scans. Hybrid forecasts obtained by merging rainfall forecasts from radar nowcasting and NWP models are potentially more skilful than either radar nowcasts or NWP rainfall forecasts alone. This paper provides an assessment of deterministic and probabilistic high‐resolution QPFs achieved by implementing the Short‐term Ensemble Prediction System developed by the UK Met Office. Both radar nowcasts and hybrid forecasts have been performed. The results show that the performance of both deterministic nowcasts and deterministic hybrid forecasts decreases with increasing rainfall intensity and spatial resolution. The results also show that the blending with the NWP forecasts improves the performance of the forecasting system. Probabilistic hybrid forecasts have been obtained through the modelling of a stochastic noise component to produce a number of equally likely ensemble members, and the comparative assessment of deterministic and probabilistic hybrid forecasts shows that the probabilistic forecasting system is characterised by a higher discrimination accuracy than the deterministic one. Copyright © 2011 John Wiley & Sons, Ltd.     

Random model of turbulent diffusion and latent parameters of the process

Zusammenfassung Wir studieren die Folgen eines Modells der unter der Voraussetzung konstruierten turbulenten Diffusion, dass die zuf?llige FunktionX(X ₀,t) eine Markowsche Funktion ist und dass der Mittelwert der Konzentration von der Beimengung der zweiten Kolmogorowschen Gleichung entspricht. Das Verlassen dieser Vorstellung führt zur Schlussfolgerung, dass man den Zustand des „Mikroobjekts” (Teilchens) für jedent-Wert als eine Klasse von Gr?ssenwerten betrachten kann, die es gleichzeitig prinzipiell m?glich ist mit beliebiger Genauigkeit zu messen. Eine Abwendung von der statistischen Relation der Unbestimmtheit wird durch die Existenz von verborgenen Parametern bei den diffusions- und Turbulenzprozessen verursacht. ihr Vorkommen ruft jedoch keinen übergang zum Kausalmodell des Prozesses hervor; die FunktionX(X ₀,t) (Lagrangesche Charakteristik vom Fliessen einer unkompressibeln Flüssigkeit) bleibt immer eine zuf?llige Funktion. Die Klasse der m?glichen Wahrscheinlichkeitswerte des übergangsp bildet jedoch keinen Markowschen Prozess im entsprechenden Phasenraum.

---

---

Address: Boční II, Praha 4-Spořilov.     

Modeling non-stationary extreme waves using a point process approach and wavelets

In the present paper a statistical model for extreme value analysis is developed, considering seasonality. The model is applied to significant wave height data from the N. Aegean Sea. To build this model, a non-stationary point process is used, which incorporates apart from a time varying threshold and harmonic functions with a period of one year, a component μ _w(t) estimated through the wavelet transform. The wavelet transform has a dual role in the present study. It detects the significant “periodicities” of the signal by means of the wavelet global and scale-averaged power spectra and then is used to reconstruct the part of the time series, μ _w(t), represented by these significant features. A number of candidate models, which incorporate μ _w(t) in their location and scale parameters are tried. To avoid overparameterisation, an automatic model selection procedure based on the Akaike information criterion is carried out. The best obtained model is graphically evaluated by means of diagnostic plots. Finally, “aggregated” return levels with return periods of 20, 50 and 100 years, as well as time-dependent quantiles are estimated, combining the results of the wavelet analysis and the Poisson process model, identifying a significant reduction in return level estimation uncertainty, compared to more simple non-stationary models.     

Soil water content vertical profiles under natural conditions: matching of experiments and simulations by a conceptual model

The prediction of soil moisture content, θ, as a function of depth, z, and time, t, is of fundamental importance for applications in many hydrological processes. The main objective of this paper is to provide an approach to solve this problem at a local scale in soils with vegetation. The matching of soil moisture vertical profiles observed under natural conditions in grassy plots and their simulations by a conceptual model is presented. Experimental measurements were performed in a plot located in Central Italy, complete with hydrometeorological sensors specifically set up and equipped with a time domain reflectometry system providing the water content, θ_e(z, t). A conceptual model framework earlier proposed for two‐layered soil vertical profiles was modified and adopted for simulations. The changes concern the incorporation of evapotranspiration, the reduction of the original model for applications also to homogeneous soil vertical profiles, and a correction for the differences existing between assumed and observed initial moisture contents. In the model calibration, it was found that the effects of vegetation could be represented adequately by a fictitious soil vertical profile with a more permeable upper layer of saturated hydraulic conductivity, K_s, independent of time. Then, for the validation events, the model simulations in the stages of both infiltration and redistribution/evapotranspiration reproduced appropriately θ_e(z, t) with typical values of root mean square error in the range 0.0017–0.0657. Similar results were obtained by applying the modified two‐layered model for simulations of experimental data observed in three other plots located in Northern Italy and Germany. For all four vegetated sites, the two‐layer profile better matched the experimental data than the assumption of a homogeneous profile. Thus, the conceptual approach based on a two‐layered scheme for representing θ(z, t) in soils with vegetation appears to be appropriate for many hydrological applications. Copyright © 2013 John Wiley & Sons, Ltd.     

The 20th September 1999 Chi-Chi Earthquake (Taiwan): a case of study for its aftershock seismic sequence

The purpose of this work is to highlight some methodological aspects related to the observation of possible anomalies in the temporal decay of aftershocks temporal series following a mainshock with magnitude M ≥ 7.0. In this paper we present the results for the Taiwan seismic sequence started on 20 September 1999 (M = 7.7) by tuning some seismic parameters that show considerable variations during the aftershock decay process. In here we also present the results obtained using a fractal approach for the seismic sequence. Earthquakes belong to a class of phenomena known as multifractals. In general it is important to define the fractal dimension D, but sometimes is not useful if we are describing a natural phenomenon; so it is necessary to define D ₀ called box-counting dimension and D ₂ called correlation dimension, usually D ₀ ≥ D ₂. In the elaborations of the fractal dimensions, for this sequence, we have obtained values lesser than 1, with a greater tendency of aftershocks to clusterize in time before a large aftershock. This is coherent with the possible existence of seismic anomalies, that could occur before the large aftershock. We also report the results obtained by using the delta/sigma method described firstly in [Caccamo et al., 2005] and later applied to different seismic sequence. The observed temporal series of the aftershocks per day can be considered as a sum of a deterministic and a stochastic contribution. If the decay can be modeled as a non-stationary Poissonian process, the number of aftershocks in a small time interval Δt is the mean value n(t) Δt, with a standard deviation (δ = √n(t)Δt. Investigating both aftershock behavior and a wide spectrum of parameters may find the key to explain better the mechanism of seismicity as a whole.     

Long-term forecasting of the extreme eruptions of Etna

Until now, the widely used predictive long-term hazard models of the most intense natural phenomena do not yield predictions other than the mean return period. To achieve less uncertain estimates, an alternative model is proposed. The analysis of the eruptions of Etna from 1868 to 1993 shows that the time interval D_t between consecutive large eruptions is clearly random, whereas the total volume of tephra ejected during D_t is roughly constant. Taking into account the above evidence, the model yields significant long-term forecasts both of the time and of the intensity of future large eruptions. The large volume of tephra ejected by the volcano since the last intense eruption in 1980 allows the model to estimate that the next large eruption will not to take place soon.     

Effects of variations of river stage and hydraulic conductivity on temporal scaling of groundwater levels: numerical simulations

It was found in previous studies that groundwater levels may fluctuate as a temporal fractal. In this study numerical simulations of groundwater level fluctuations in an unconfined aquifer near a river were conducted to investigate the effects of aquifer heterogeneity and river stage variations on the fractal behavior of the water levels, h(t). Groundwater recharge was taken to be a white-noise process. The aquifer heterogeneity was simulated with a second-order stationary field of hydraulic conductivity (K) with an exponential variogram model. The results showed that groundwater levels fluctuate as a temporal fractal in both homogeneous and heterogeneous aquifers as long as K is less than 10 m/d. Most aquifers may indeed act as a fractal filter which takes a random non-fractal recharge inputs and produces a fractal responses of groundwater level fluctuations. A crossover in temporal scaling of h(t) may appear in more permeable aquifers. Fluctuations of the groundwater level in a homogeneous aquifer are dominated by the recharge process when the river stage is constant or by the river stage variations when the river stage varies in highly permeable aquifers. Heterogeneity plays an important role in the temporal scaling of h(t) in more permeable aquifers: the stronger the heterogeneity, the stronger the temporal scaling of h(t).     

A shot noise model for the generation of soil moisture data

An attempt is made to estimate the expected contribution of rainfall to soil moisture during the irrigation season. Effective rainfall and evapotranspiration are the parameters considered in the water balance carried out in the root zone. Rainfall occurrence is simulated by a Poisson process whereas evapotranspiration is described by a simple deterministic function of potential evapotranspiration and soil moisture in the root zone. Using the theory of shot noise models a closed form solution is derived from the expected soil moisture in the root zone at the end of the time interval (0,t]. For illustration purposes the proposed model is applied to a series of data from Mikra meteorological station in Greece.List of symbols x change in water storage in the root zone during the time interval t - X infiltrated rainfall of thei th storm event - ET actual evapotranspiration during thej th day - Poisson rate - number of storm events in (0,t] - t _r duration of rainfall - t _b interarrival time - h _i rainfall depth of thei th storm event - i _m mean rainfall intensity - i(t) instantaneous rainfall intensity - x(0),x(t) available soil moisture in the root zone at time 0 andt, respectively - PET potential evapotranspiration rate - x _F available soil moisture in the root zone at field capacity - soil moisture depletion rate (=PET/x _F ) - w impulse shape of filtered Poisson processes - E[·] mean value - S _i time of thei th rainfall event - N(t) time of storm events in (0,t] - estimated standard deviation The following symbols were used in this paper     

Yield and overflow from a finite stochastic reservoir

The paper discusses the overflow (spillage) and yield rates and the total overflow and total yield over a specified time from a finite discrete stochastic reservoir, in which the yieldY _t during the working interval (t,t+1) is a function of the storageZ _t at timet, the inflow sequence {X _t } being IID.The distribution vector of the spillage rate at timet is a telescoped version of the distribution of a certain Markovian variable whose transition matrix is derived. Formulae are given for the distribution of the total spillageW _h given suitable initial conditions, forh=1,2,3; and a simple expression derived forE(W _h ).The distribution of the yield rateY _t is a trivial modification of the storage distribution. As for the total yieldR _t =Y ₁+...+Y _t , it is shown that the bivariate sequence {R _t ,Z _t } is first-order Markovian, whereZ _t is the storage at timet. The transition matrix of this process is obtained and the method of evaluating the marginal distribution of the total yieldR _t is exemplified.     

Seismic hazard assessment in central Ionian Islands area (Greece) based on stress release models

The long-term probabilistic seismic hazard of central Ionian Islands (Greece) is studied through the application of stress release models. In order to identify statistically distinct regions, the study area is divided into two subareas, namely Kefalonia and Lefkada, on the basis of seismotectonic properties. Previous results evidenced the existence of stress transfer and interaction between the Kefalonia and Lefkada fault segments. For the consideration of stress transfer and interaction, the linked stress release model is applied. A new model is proposed, where the hazard rate function in terms of X(t) has the form of the Weibull distribution. The fitted models are evaluated through residual analysis and the best of them is selected through the Akaike information criterion. Based on AIC, the results demonstrate that the simple stress release model fits the Ionian data better than the non-homogeneous Poisson and the Weibull models. Finally, the thinning simulation method is applied in order to produce simulated data and proceed to forecasting.