库水位变化对库岸边坡稳定性的影响

在假定坡体孔隙水水位为水平线且不考虑渗透作用影响的基础上,基于极限平衡法考察了水位上升及下降的快慢对边坡安全系数的影响。对比计算表明:在水位缓慢变化即坡体内外水位线等高的条件下,边坡的安全系数随着水位坡高比的增大先略减小后急剧增大,且在水位坡高比为0.3处取得最小值,在边坡完全淹没于水中时取得最大值。当边坡完全淹没于水中后,水位高于坡顶的多少对边坡安全系数没有影响;在水位骤降或陡升条件下,相同库水位对应的边坡安全系数基本上均小于水位缓慢变化情况下的安全系数,故工程实际中无论是排水还是蓄水,都应尽量保持水位缓慢变化,这样才能使边坡处于较安全的状态。     

Soil–rock-forming processes and engineering mechanical properties of the ancient gravel stratum near Nanjing, China

The ancient gravel stratum near Nanjing, China, is a major stratum of Cenozoic age along the middle–lower reaches of the Yangtze River. This paper first presents a typical geological profile and material composition of the ancient gravel stratum, and then discusses its geologic origin, soil–rock-forming processes, engineering mechanical properties, and their relations. These analyses are useful for city planning, geotechnical engineering, construction, and characterization of the geological environment in the Nanjing area.     

填海挤淤爆破对马迹山港城门头景点稳定性影响分析

在分析水下爆破规律性的基础上,确认了在马迹山港挤淤爆破中地震波对城门头景点的稳定性起主要的影响,并对其传播机理及影响机制进行了分析.对岩体裂隙进行的网络模拟结果揭示了爆破点与城门头景点之间岩体裂隙发育的宏观特征,对爆破震动的影响进行了分析,计算了爆破在景点处所引起的波速,得出挤淤爆破不会对城门头景点构成破坏性影响的结论.     

燕山造山带构造变形格局研究及相关问题

燕山造山带具有极其复杂的地质结构构造，其复杂性在现有公开出版的地质图件中未能有效地反映出来。从事地学研究的科研人员，以公开出版的地质图件为基础所衍生的相关分析图件和由此作出的结论与野外实际地质情况往往相去甚远。本文作者近年来所做基础地质调查成果显示，基础地质调查成果的缺陷成为制约国家地学科研发展的严重障碍。影响基础地质调查成果质量提高的原因很多，但较为重要的是国家对基础地学科研资金的政策性投入存在问题。     

Discussion on influence of cavern’s span on surrounding rock classification

The influence of cavern’s span on surrounding rock classification is getting more and more recognition along with large span underground working’s increasingly coming forth. The authors analyse the timbering expense of cavern in jointed rock mass, the radius of plastic loosened zone and the size effect of macroscopical mechanical parameters of rock mass. Based on the complexion of depressing of surrounding rock’s stability due to increment of cavern’s span, the authors pinpoint attaching importance to the influence of cavern’s span on surrounding rock classification; and suggest reckoning the factor of cavern’s span in surrounding rock classification scientifically by studying the size effect of rock mass’s physico-mechanical parameters.     

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

Evaluation of NLDAS‐2 evapotranspiration against tower flux site observations

The North American Land Data Assimilation System project phase 2 (NLDAS‐2) has run four land surface models for a 30‐year (1979–2008) retrospective period. Land surface evapotranspiration (ET) is one of the most important model outputs from NLDAS‐2 for investigating land–atmosphere interaction or to monitor agricultural drought. Here, we evaluate hourly ET using in situ observations over the Southern Great Plains (Atmospheric Radiation Measurement/Cloud and Radiation Testbed network) for 1 January 1997–30 September 1999 and daily ET u‐sing in situ observations at the AmeriFlux network over the conterminous USA for an 8‐year period (2000–2007). The NLDAS‐2 models compare well against observations, with the National Centers for Environmental Prediction's Noah land surface model performing best, followed, in order, by the Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic models. Daily evaluation across the AmeriFlux network shows that for all models, performance depends on season and vegetation type; they do better in spring and fall than in winter or summer and better for deciduous broadleaf forest and grasslands than for croplands or evergreen needleleaf forest. Copyright © 2014 John Wiley & Sons, Ltd.     

Piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events on the Loess Plateau,China

Establishing a universal watershed‐scale erosion and sediment yield prediction model represents a frontier field in erosion and soil/water conservation. The research presented here was conducted on the Chabagou watershed, which is located in the first sub‐region of the hill‐gully area of the Loess Plateau, China. A back‐propagation artificial neural model for watershed‐scale erosion and sediment yield was established, with the accuracy of the model, then compared with that of multiple linear regression. The sensitivity degree of various factors to erosion and sediment yield was quantitatively analysed using the default factor test. On the basis of the sensitive factors and the fractal information dimension, the piecewise prediction model for erosion and sediment yield of individual rainfall events was established and further verified. The results revealed the back‐propagation artificial neural network model to perform better than the multiple linear regression model in terms of predicting the erosion modulus, with the former able to effectively characterize dynamic changes in sediment yield under comprehensive factor conditions. The sensitivity of runoff erosion power and runoff depth to the erosion and sediment yield associated with individual rainfall events was found to be related to the complexity of surface topography. The characteristics of such a hydrological response are thus closely related to topography. When the fractal information dimension is greater than the topographic threshold, the accuracy of prediction using runoff erosion power is higher than that of using runoff depth. In contrast, when the fractal information dimension is smaller than the topographic threshold, the accuracy of prediction using runoff depth is higher than that of using runoff erosion power. The developed piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events, which introduces runoff erosion power and runoff depth using the fractal information dimension as a boundary, can be considered feasible and reliable and has a high prediction accuracy. Copyright © 2013 John Wiley & Sons, Ltd.