Local magnitude scale for earthquakes in Turkey

Based on the earthquake event data accumulated by the Turkish National Seismic Network between 2007 and 2013, the local magnitude (Richter, Ml) scale is calibrated for Turkey and the close neighborhood. A total of 137 earthquakes (Mw?>?3.5) are used for the Ml inversion for the whole country. Three Ml scales, whole country, East, and West Turkey, are developed, and the scales also include the station correction terms. Since the scales for the two parts of the country are very similar, it is concluded that a single Ml scale is suitable for the whole country. Available data indicate the new scale to suffer from saturation beyond magnitude 6.5. For this data set, the horizontal amplitudes are on average larger than vertical amplitudes by a factor of 1.8. The recommendation made is to measure Ml amplitudes on the vertical channels and then add the logarithm scale factor to have a measure of maximum amplitude on the horizontal. The new Ml is compared to Mw from EMSC, and there is almost a 1:1 relationship, indicating that the new scale gives reliable magnitudes for Turkey.     

Conditional heavy-tail behavior with applications to precipitation and river flow extremes

This article deals with the right-tail behavior of a response distribution \(F_Y\) conditional on a regressor vector \({\mathbf {X}}={\mathbf {x}}\) restricted to the heavy-tailed case of Pareto-type conditional distributions \(F_Y(y|\ {\mathbf {x}})=P(Y\le y|\ {\mathbf {X}}={\mathbf {x}})\), with heaviness of the right tail characterized by the conditional extreme value index \(\gamma ({\mathbf {x}})>0\). We particularly focus on testing the hypothesis \({\mathscr {H}}_{0,tail}:\ \gamma ({\mathbf {x}})=\gamma _0\) of constant tail behavior for some \(\gamma _0>0\) and all possible \({\mathbf {x}}\). When considering \({\mathbf {x}}\) as a time index, the term trend analysis is commonly used. In the recent past several such trend analyses in extreme value data have been published, mostly focusing on time-varying modeling of location or scale parameters of the response distribution. In many such environmental studies a simple test against trend based on Kendall’s tau statistic is applied. This test is powerful when the center of the conditional distribution \(F_Y(y|{\mathbf {x}})\) changes monotonically in \({\mathbf {x}}\), for instance, in a simple location model \(\mu ({\mathbf {x}})=\mu _0+x\cdot \mu _1\), \({\mathbf {x}}=(1,x)'\), but the test is rather insensitive against monotonic tail behavior, say, \(\gamma ({\mathbf {x}})=\eta _0+x\cdot \eta _1\). This has to be considered, since for many environmental applications the main interest is on the tail rather than the center of a distribution. Our work is motivated by this problem and it is our goal to demonstrate the opportunities and the limits of detecting and estimating non-constant conditional heavy-tail behavior with regard to applications from hydrology. We present and compare four different procedures by simulations and illustrate our findings on real data from hydrology: weekly maxima of hourly precipitation from France and monthly maximal river flows from Germany.     

Research on progressive failure process of Baishuihe landslide based on Monte Carlo model

To the progressive landslide, development of the internal deformation and failure situation can’t be accurately reflected by the overall stability of coefficients and failure probability. But this problem can be solved by utilizing the principle of progressive failure by slices. Taking the warning area of Baishuihe landslide as an example, 5 days accumulated rainfall in different reappearing period is computed by Gumbel model. The failure probability of each slice is calculated by progressive failure principle, which is based on Monte Carlo model. The following results can be revealed through calculation: Overall stability and failure probability can’t reflect real situation of Baishuihe landslide warning area. Through building the calculation of progressive failure model of each slice, the stability of each part in the Baishuihe landslide warning area is quite different. Unstable region mainly lies in vicinity of the middle and posterior warning area. The front of the warning area remains stable. Deformation characteristics of the warning area are consistent with the investigation report. The scope of unstable area increased gradually with rainfall and the decline of reservoir water. Under 5 day’s accumulated rainfall of 50 years, the poor stable and unstable region reached 75 %, there is a large possibility of local deformation slip. Under the joint action of rainfall and reservoir water level, the warning area of Baishuihe landslide shows a progressive failure mode from top to bottom.     

Spectral simulation of vector random fields with stationary Gaussian increments in <Emphasis Type="Italic">d</Emphasis>-dimensional Euclidean spaces

This paper addresses the problem of simulating multivariate random fields with stationary Gaussian increments in a d-dimensional Euclidean space. To this end, one considers a spectral turning-bands algorithm, in which the simulated field is a mixture of basic random fields made of weighted cosine waves associated with random frequencies and random phases. The weights depend on the spectral density of the direct and cross variogram matrices of the desired random field for the specified frequencies. The algorithm is applied to synthetic examples corresponding to different spatial correlation models. The properties of these models and of the algorithm are discussed, highlighting its computational efficiency, accuracy and versatility.     

Identification and assessment of water safety risk for groundwater recharge with reclaimed water in China

Groundwater recharge using reclaimed water has developed rapidly around the world to relieve the groundwater resource shortage and declining of the water table. Traditional water treatment systems are inefficient to remove all the types of contaminants, so it is urgent to identify the priority chemical substances (CSs) that deserve our first concern. In this study, we developed a method (EER method) to identify priority CSs in groundwater recharge by surface spreading and direct aquifer injection. Three stages were processed which were exposure assessment, effect assessment and ranking for identification of priority CSs. Fourteen cities in China were selected for data collected and 90 pollutants in reclaimed water samples were analyzed as the target pollutants for a case study. According to three stages, the 90 CSs studied were divided into five groups (primary control CSs and high, moderate and low and no risk control CSs). In the primary control CSs and high, moderate and low and no risk control CSs group there were 14, 18, 21, 21 and 16 CSs, respectively when groundwater recharged by surface spreading, while there were 15, 18, 21, 21 and 15 CSs when recharged by direct injection. This method provided an indicator of prioritizing the risk of 90 compounds in the reclaimed water for groundwater recharge.     

Integration of multiple soft data sets in MPS thru multinomial logistic regression: a case study of gas hydrates

A new approach is described to allow conditioning to both hard data (HD) and soft data for a patch- and distance-based multiple-point geostatistical simulation. The multinomial logistic regression is used to quantify the link between HD and soft data. The soft data is converted by the logistic regression classifier into as many probability fields as there are categories. The local category proportions are used and compared to the average category probabilities within the patch. The conditioning to HD is obtained using alternative training images and by imposing large relative weights to HD. The conditioning to soft data is obtained by measuring the probability–proportion patch distance. Both 2D and 3D cases are considered. Synthetic cases show that a stationary TI can generate non-stationary realizations reproducing the HD, keeping the texture indicated by the TI and following the trends identified in probability maps obtained from soft data. A real case study, the Mallik methane-hydrate field, shows perfect reproduction of HD while keeping a good reproduction of the TI texture and probability trends.     

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.     

Spectral density regression for bivariate extremes

We introduce a density regression model for the spectral density of a bivariate extreme value distribution, that allows us to assess how extremal dependence can change over a covariate. Inference is performed through a double kernel estimator, which can be seen as an extension of the Nadaraya–Watson estimator where the usual scalar responses are replaced by mean constrained densities on the unit interval. Numerical experiments with the methods illustrate their resilience in a variety of contexts of practical interest. An extreme temperature dataset is used to illustrate our methods.     

Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data

Producing accurate spatial predictions for wind power generation together with a quantification of uncertainties is required to plan and design optimal networks of wind farms. Toward this aim, we propose spatial models for predicting wind power generation at two different time scales: for annual average wind power generation, and for a high temporal resolution (typically wind power averages over 15-min time steps). In both cases, we use a spatial hierarchical statistical model in which spatial correlation is captured by a latent Gaussian field. We explore how such models can be handled with stochastic partial differential approximations of Matérn Gaussian fields together with Integrated Nested Laplace Approximations. We demonstrate the proposed methods on wind farm data from Western Denmark, and compare the results to those obtained with standard geostatistical methods. The results show that our method makes it possible to obtain fast and accurate predictions from posterior marginals for wind power generation. The proposed method is applicable in scientific areas as diverse as climatology, environmental sciences, earth sciences and epidemiology.     

Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model

The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extreme seasonal variation are not purely linear or nonlinear. To improve the forecasting accuracy of this phenomenon, a linear Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is combined with a nonlinear Artificial Neural Network (ANN) model. This new model is used to predict the monthly inflow to the Jamishan dam reservoir in West Iran. A comparison of the SARIMA and ANN models with the proposed hybrid model’s results is provided accordingly. More specifically, the models’ performance in forecasting base and flood flows is evaluated. The effect of changing the forecasting period length on the models’ accuracy is studied. The results of increasing the number of SARIMA model parameters up to five are investigated to achieve more accurate forecasting. The hybrid model predicts peak flood flows much better than the individual models, but SARIMA outperforms the other models in predicting base flow. The obtained results indicate that the hybrid model reduces the overall forecast error more than the ANN and SARIMA models. The coefficient of determination of the hybrid, ANN and SARIMA models were 0.72, 0.64 and 0.58, and the root mean squared error values were 1.02, 1.16 and 1.27 respectively, during the forecast period. Changing the forecasting length also indicated that these models can be used in the long term without increasing the forecast error.