A nonstationary extreme value distribution for analysing the cessation of karst spring discharge

The effects of climate change and population growth in recent decades are leading us to consider their combined and potentially extreme consequences, particularly regarding hydrological processes, which can be modeled using a generalized extreme value (GEV) distribution. Most of the GEV models were based on a stationary assumption for hydrological processes, in contrast to the nonstationary reality due to climate change and human activities. In this paper, we present the nonstationary generalized extreme value (NSGEV) distribution and use it to investigate the risk of Niangziguan Springs discharge decreasing to zero. Rather than assuming the location, scale, and shape parameters to be constant as one might do for a stationary GEV distribution analysis, the NSGEV approach can reflect the dynamic processes by defining the GEV parameters as functions of time. Because most of the GEV model is designed to evaluate maxima (e.g. flooding, represented by positive numbers), and spring discharge cessation is a ?minima’, we deduced an NSGEV model for minima by applying opposite numbers, i.e. negative instead of positive numbers. The results of the model application to Niangziguan Springs showed that the probability of zero discharge at Niangziguan Springs will be 1/80 in 2025, and 1/10 in 2030. After 2025, the rate of decrease in spring discharge will accelerate, and the probability that Niangziguan Springs will cease flowing will dramatically increase. The NSGEV model is a robust method for analysing karst spring discharge. Copyright © 2013 John Wiley & Sons, Ltd.     

An analytical solution for deforming twin‐parallel tunnels in an elastic half plane

The interaction between twin‐parallel tunnels affects the tunnelling‐induced ground deformation, which may endanger the nearby structures. In this paper, an analytical solution is presented for problems in determining displacements and stresses around deforming twin‐parallel tunnels in an elastic half plane, on the basis of complex variable theory. As an example, a uniform radial displacement was assumed as the boundary condition for each of the two tunnels. Special attention was paid to the effects of tunnel depth and spacing between the two tunnels on the surface movement to gain deep insight into the effect of the interaction between twin‐parallel tunnels using the proposed analytical approach. It is revealed that the influence of twin tunnel interaction on surface movements diminishes with both the increase of the tunnel depth and the spacing between the two tunnels. The presented analytical solution manifests that, similar to most of the existing numerical results, the principle of superposition can be applied to determine ground deformation of twin‐parallel tunnels with a certain large depth and spacing; otherwise, the interaction effect between the two tunnels should be taken into account for predicting reliable ground movement. Copyright © 2014 John Wiley & Sons, Ltd.     

Landform‐oriented flow‐routing algorithm for the dual‐structure loess terrain based on digital elevation models

The loess landform in the Loess Plateau of China is with typical dual structure, namely, the upper smooth positive terrain and the lower cliffy negative terrain (P–N terrain for short). Obvious differences in their morphological feature, geomorphological mechanism, and hydrological process could be found in the both areas. Based on the differences, a flow‐routing algorithm that separately addresses the dual‐structure terrain would be necessary to encompass this spatial variation in their hydrological behaviour. This paper proposes a mixed flow‐routing algorithm to address aforementioned problems. First, the loess landform surface is divided into P–N terrains based on digital elevation models. Then, specific catchment area is calculated with the new algorithm to simulate the water flows in both positive and negative terrain areas. The mixed algorithm consists of the multiple flow‐routing algorithm (multiple‐flow direction) for positive areas and the D8 algorithm for negative areas, respectively. The approach is validated in two typical geomorphologic areas with low hills and dense gullies in the northern Shaanxi Loess Plateau. Four indices are used to examine the results, which show that the new algorithm is more suitable for loess terrain in simulating the spatial distribution of water accumulation, as well as in modeling the flow characteristics of the true surface by considering the morphological structures of the terrain. Copyright © 2013 John Wiley & Sons, Ltd.     

Damage and Acoustic Emission Characteristics of Combined Rock Mass with Anchor Under Different Inclination Angles and Combination Modes

Geotechnical and Geological Engineering - Soft and hard rock assemblages and anchor solids have important effects on rock mechanics properties under different inclinations and assemblages, so it is...     

Frequency analysis of nonstationary annual maximum flood series using the time‐varying two‐component mixture distributions

The most popular practice for analysing nonstationarity of flood series is to use a fixed single‐type probability distribution incorporated with the time‐varying moments. However, the type of probability distribution could be both complex because of distinct flood populations and time‐varying under changing environments. To allow the investigation of this complex nature, the time‐varying two‐component mixture distributions (TTMD) method is proposed in this study by considering the time variations of not only the moments of its component distributions but also the weighting coefficients. Having identified the existence of mixed flood populations based on circular statistics, the proposed TTMD was applied to model the annual maximum flood series of two stations in the Weihe River basin, with the model parameters calibrated by the meta‐heuristic maximum likelihood method. The performance of TTMD was evaluated by different diagnostic plots and indexes and compared with stationary single‐type distributions, stationary mixture distributions and time‐varying single‐type distributions. The results highlighted the advantages of TTMD with physically‐based covariates for both stations. Besides, the optimal TTMD models were considered to be capable of settling the issue of nonstationarity and capturing the mixed flood populations satisfactorily. Copyright © 2016 John Wiley & Sons, Ltd.