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.     

Estimation of extreme Component-wise Excess design realization: a hydrological application

The classic univariate risk measure in environmental sciences is the Return Period (RP). The RP is traditionally defined as “the average time elapsing between two successive realizations of a prescribed event”. The notion of design quantile related with RP is also of great importance. The design quantile represents the “value of the variable(s) characterizing the event associated with a given RP”. Since an individual risk may strongly be affected by the degree of dependence amongst all risks, the need for the provision of multivariate design quantiles has gained ground. In contrast to the univariate case, the design quantile definition in the multivariate setting presents certain difficulties. In particular, Salvadori, G., De Michele, C. and Durante F. define in the paper called “On the return period and design in a multivariate framework” (Hydrol Earth Syst Sci 15:3293–3305, 2011) the design realization as the vector that maximizes a weight function given that the risk vector belongs to a given critical layer of its joint multivariate distribution function. In this paper, we provide the explicit expression of the aforementioned multivariate risk measure in the Archimedean copula setting. Furthermore, this measure is estimated by using Extreme Value Theory techniques and the asymptotic normality of the proposed estimator is studied. The performance of our estimator is evaluated on simulated data. We conclude with an application on a real hydrological data-set.     

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.