用叠加谱比法研究新疆地区Lg尾波Q值的区域性变化

运用叠加谱比法,对分布于新疆地区的5个台站所记录的51个地震数据进行分析,得出与各射线路径相对应的Lg尾波Q0(Lg尾波在1Hz处的Q)值。结果表明:Q0值呈现出明显的区域性变化特点,与构造活动关系紧密。在较为稳定的塔里木地台,Q0呈现出高值,在350～450之间。在南部构造活动较为激烈的昆仑褶皱系,Q0呈现出较低的值,约为200～250。在构造活动最为激烈的帕米尔地区,Q0值在170～200之间。在北部的天山褶皱系,Q0值约为220～270。准葛尔褶皱系比天山褶皱系的Q0值略高,约为260～290。     

Statistical identification of orographic effects in the regional analysis of extreme rainfall

Regional models of extreme rainfall must address the spatial variability induced by orographic obstacles. However, the proper detection of orographic effects often depends on the availability of a well‐designed rain gauge network. The aim of this study is to investigate a new method for identifying and characterizing the effects of orography on the spatial structure of extreme rainfall at the regional scale, including where rainfall data are lacking or fail to describe rainfall features thoroughly. We analyse the annual maxima of daily rainfall data in the Campania region, an orographically complex region in Southern Italy, and introduce a statistical procedure to identify spatial outliers in a low order statistic (namely the mean). The locations of these outliers are then compared with a pattern of orographic objects that has been a priori identified through the application of an automatic geomorphological procedure. The results show a direct and clear link between a particular set of orographic objects and a local increase in the spatial variability of extreme rainfall. This analysis allowed us to objectively identify areas where orography produces enhanced variability in extreme rainfall. It has direct implications for rain gauge network design criteria and has led to promising developments in the regional analysis of extreme rainfall. Copyright © 2015 John Wiley & Sons, Ltd.     

Discrete element method modeling of inherently anisotropic rocks under uniaxial compression loading

A new numerical approach is proposed in this study to model the mechanical behaviors of inherently anisotropic rocks in which the rock matrix is represented as bonded particle model, and the intrinsic anisotropy is imposed by replacing any parallel bonds dipping within a certain angle range with smooth‐joint contacts. A series of numerical models with β = 0°, 15°, 30°, 45°, 60°, 75°, and 90° are constructed and tested (β is defined as the angle between the normal of weak layers and the maximum principal stress direction). The effect of smooth‐joint parameters on the uniaxial compression strength and Young's modulus is investigated systematically. The simulation results reveal that the normal strength of smooth‐joint mainly affects the behaviors at high anisotropy angles (β > 45°), while the shear strength plays an important role at medium anisotropy angles (30°–75°). The normal stiffness controls the mechanical behaviors at low anisotropy angles. The angle range of parallel bonds being replaced plays an important role on defining the degree of anisotropy. Step‐by‐step procedures for the calibration of micro parameters are recommended. The numerical model is calibrated to reproduce the behaviors of different anisotropic rocks. Detailed analyses are conducted to investigate the brittle failure process by looking at stress‐strain behaviors, increment of micro cracks, initiation and propagation of fractures. Most of these responses agree well with previous experimental findings and can provide new insights into the micro mechanisms related to the anisotropic deformation and failure behaviors. The numerical approach is then applied to simulate the stress‐induced borehole breakouts in anisotropic rock formations at reduced scale. The effect of rock anisotropy and stress anisotropy can be captured. Copyright © 2015 John Wiley & Sons, Ltd.     

The use of the exponent K(q) function to delimit homogeneous regions in regional frequency analysis of extreme annual daily rainfall

The regional frequency analysis of extreme annual rainfall data is a useful methodology in hydrology to obtain certain quantile values when no long data series are available. The most crucial step in the analysis is the grouping of sites into homogeneous regions. This work presents a new grouping criterion based on some multifractal properties of rainfall data. For this purpose, a regional frequency analysis of extreme annual rainfall data from the Maule Region (Chile) has been performed. Daily rainfall data series of 53 available stations have been studied, and their empirical moments scaling exponent functions K(q) have been obtained. Two characteristics parameters of the K(q) functions (γ_max and K(0)) have been used to group the stations into three homogeneous regions. Only five sites have not been possible to include into any homogenous regions, being the local frequency analysis of extreme daily rainfall the most appropriate method to be used at these locations. Copyright © 2014 John Wiley & Sons, Ltd.     

Validating the ability of the discontinuous deformation analysis method to model normal P‐wave propagation across rock fractures

The effects of fractures on wave propagation problems are increasingly abstracting the attention of scholars and engineers in rock engineering field. This study aims to fully validate the ability of discontinuous deformation analysis (DDA) to model normal P‐wave propagation across rock fractures. The effects of a single fracture and multiple parallel fractures are all tested. The results indicate that DDA can accurately reflect the fracture effects, including the fractures stiffness, the fracture spacing and the fracture number, and the effects of incident wave frequency on one‐dimensional P‐wave propagation problems. Thus, DDA is able to deal well with normal incident P‐wave propagation problems. Copyright © 2017 John Wiley & Sons, Ltd.     

Implicit integration under mixed controls of a breakage model for unsaturated crushable soils

This paper discusses a series of stress point algorithms for a breakage model for unsaturated granular soils. Such model is characterized by highly nonlinear coupling terms introduced by breakage‐dependent hydro‐mechanical energy potentials. To integrate accurately and efficiently its constitutive equations, specific algorithms have been formulated using a backward Euler scheme. In particular, because implementation and verification of unsaturated soil models often require the use of mixed controls, the incorporation of various hydro‐mechanical conditions has been tackled. First, it is shown that the degree of saturation can be replaced with suction in the constitutive equations through a partial Legendre transformation of the energy potentials, thus changing the thermomechanical state variables and enabling a straightforward implementation of a different control mode. Then, to accommodate more complex control scenarios without redefining the energy potentials, a hybrid strategy has been used, combining the return mapping scheme with linearized constraints. It is shown that this linearization strategy guarantees similar levels of accuracy compared with a conventional strain–suction‐controlled implicit integration. In addition, it is shown that the use of linearized constraints offers the possibility to use the same framework to integrate a variety of control conditions (e.g., net stress and/or water‐content control). The convergence profiles indicate that both schemes preserve the advantages of implicit integration, that is, asymptotic quadratic convergence and unconditional stability. Finally, the performance of the two implicit schemes has been compared with that of an explicit algorithm with automatic sub‐stepping and error control, showing that for the selected breakage model, implicit integration leads to a significant reduction of the computational cost. Such features support the use of the proposed hybrid scheme also in other modeling contexts, especially when strongly nonlinear models have to be implemented and/or validated by using non‐standard hydro‐mechanical control conditions. Copyright © 2015 John Wiley & Sons, Ltd.     

Development of a soil moisture‐based distributed hydrologic model for determining hydrologically based critical source areas

A simple grid cell‐based distributed hydrologic model was developed to provide spatial information on hydrologic components for determining hydrologically based critical source areas. The model represents the critical process (soil moisture variation) to run‐off generation accounting for both local and global water balance. In this way, it simulates both infiltration excess run‐off and saturation excess run‐off. The model was tested by multisite and multivariable evaluation on the 50‐km² Little River Experimental Watershed I in Georgia, U.S. and 2 smaller nested subwatersheds. Water balance, hydrograph, and soil moisture were simulated and compared to observed data. For streamflow calibration, the daily Nash‐Sutcliffe coefficient was 0.78 at the watershed outlet and 0.56 and 0.75 at the 2 nested subwatersheds. For the validation period, the Nash‐Sutcliffe coefficients were 0.79 at the watershed outlet and 0.85 and 0.83 at the 2 subwatersheds. The per cent bias was less than 15% for all sites. For soil moisture, the model also predicted the rising and declining trends at 4 of the 5 measurement sites. The spatial distribution of surface run‐off simulated by the model was mainly controlled by local characteristics (precipitation, soil properties, and land cover) on dry days and by global watershed characteristics (relative position within the watershed and hydrologic connectivity) on wet days when saturation excess run‐off was simulated. The spatial details of run‐off generation and travel time along flow paths provided by the model are helpful for watershed managers to further identify critical source areas of non‐point source pollution and develop best management practices.     

Characterizing and classifying urban watersheds with compositional and structural attributes

Current land-use classifications used to assess urbanization effects on stream water quality date back to the 1980s when limited information was available to characterize watershed attributes that mediate non-point source pollution. With high resolution remote sensing and widely used GIS tools, there has been a vast increase in the availability and precision of geospatial data of built environments. In this study, we leverage geospatial data to expand the characterization of developed landscapes and create a typology that allows us to better understand the impact of complex developed landscapes across the rural to urban gradient. We assess the ability of the developed landscape typology to reveal patterns in stream water chemistry previously undetected by traditional land-cover based classification. We examine the distribution of land-cover, infrastructure, topography and geology across 3876 National Hydrography Dataset Plus catchments in the Piedmont region of North Carolina, USA. From this dataset, we generate metrics to evaluate the abundance, density and position of landscape features relative to streams, catchment outlets and topographic wetness metrics. While impervious surfaces are a key distinguishing feature of the urban landscape, sanitary infrastructure, population density and geology are better predictors of baseflow stream water chemistry. Unsupervised clustering was used to generate a distinct developed landscape typology based on the expanded, high-resolution landscape feature information. Using stream chemistry data from 37 developed headwater catchments, we compared the baseflow water chemistry grouped by traditional land-cover based classes of urbanization (rural, low, medium and high density) to our composition and structure-based classification (a nine-class typology). The typology based on 22 metrics of developed landscape composition and structure explained over 50% of the variation in NO₃⁻-N, TDN, DOC, Cl⁻, and Br⁻ concentration, while the ISC-based classification only significantly explained 23% of the variation in TDN. These results demonstrate the importance of infrastructure, population and geology in defining developed landscapes and improving discrete classes for water management.     

Spatiotemporal estimation of snow depth using point data from snow stakes,digital terrain models,and satellite data

Snow availability in Alpine catchments plays an important role in water resources management. In this paper, we propose a method for an optimal estimation of snow depth (areal extension and thickness) in Alpine systems from point data and satellite observations by using significant explanatory variables deduced from a digital terrain model. It is intended to be a parsimonious approach that may complement physical‐based methodologies. Different techniques (multiple regression, multicriteria analysis, and kriging) are integrated to address the following issues: We identify the explanatory variables that could be helpful on the basis of a critical review of the scientific literature. We study the relationship between ground observations and explanatory variables using a systematic procedure for a complete multiple regression analysis. Multiple regression models are calibrated combining all suggested model structures and explanatory variables. We also propose an evaluation of the models (using indices to analyze the goodness of fit) and select the best approaches (models and variables) on the basis of multicriteria analysis. Estimation of the snow depth is performed with the selected regression models. The residual estimation is improved by applying kriging in cases with spatial correlation. The final estimate is obtained by combining regression and kriging results, and constraining the snow domain in accordance with satellite data. The method is illustrated using the case study of the Sierra Nevada mountain range (Southern Spain). A cross‐validation experiment has confirmed the efficiency of the proposed procedure. Finally, although it is not the scope of this work, the snow depth is used to asses a first estimation of snow water equivalent resources.     

Three dimensional numerical simulation of residential building on shrink–swell soils in response to climatic conditions

Shrink–swell soils can cause distresses in buildings, and every year, the economic loss associated with this problem is huge. This paper presents a comprehensive system for simulating the soil–foundation–building system and its response to daily weather conditions. Weather data include rainfall, solar radiation, air temperature, relative humidity, and wind speed, all of which are readily available from a local weather station or the Internet. These data are used to determine simulation flux boundary conditions. Different methods are proposed to simulate different boundary conditions: bare soil, trees, and vegetation. A coupled hydro‐mechanical stress analysis is used to simulate the volume change of shrink–swell soils due to both mechanical stress and water content variations. Coupled hydro‐mechanical stress‐jointed elements are used to simulate the interaction between the soil and the slab, and general shell elements are used to simulate structural behavior. All the models are combined into one finite element program to predict the entire system's behavior. This paper first described the theory for the simulations. A site in Arlington, Texas, is then selected to demonstrate the application of the proposed system. Simulation results are shown, and a comparison between measured and predicted movements for four footings in Arlington, Texas, over a 2‐year period is presented. Finally, a three‐dimensional simulation is made for a virtual residential building on shrink–swell soils to identify the influence of various factors. Copyright © 2015 John Wiley & Sons, Ltd.