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.     

Estimation of daily stream water temperatures with a Bayesian regression approach

Stream water temperature plays a significant role in aquatic ecosystems where it controls many important biological and physical processes. Reliable estimates of water temperature at the daily time step are critical in managing water resources. We developed a parsimonious piecewise Bayesian model for estimating daily stream water temperatures that account for temporal autocorrelation and both linear and nonlinear relationships with air temperature and discharge. The model was tested at 8 climatically different basins of the USA and at 34 sites within the mountainous Boise River Basin (Idaho, USA). The results show that the proposed model is robust with an average root mean square error of 1.25 °C and Nash–Sutcliffe coefficient of 0.92 over a 2‐year period. Our approach can be used to predict historic daily stream water temperatures in any location using observed daily stream temperature and regional air temperature data.     

Modeling the 2D behavior of dry‐stone retaining walls by a fully discrete element method

The dry‐stone retaining walls (DSRW) have been tipped as a promising solution for sustainable development. However, before recently, their behavior is relatively obscure. In this study, discrete element method (DEM) approach was applied to simulate the plane strain failure of these walls. A commercial DEM package (PFC2D™) was used throughout this study. The authors used a fully discrete approach; thus, both the wall and the backfill were modeled as discrete elements. The methodology for obtaining the micromechanical parameters was discussed in detail; this includes the three mechanical sub‐systems of DSRWs: wall, backfill and interface. The models were loaded progressively until failure, and then the results were compared with the full‐scale experimental results where the walls were loaded, respectively, with hydrostatic load and backfill. Despite its complexity and its intensive calculation time, DEM model can then be used to validate a more simplified approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches

River water temperature is a key physical variable controlling several chemical, biological and ecological processes. Its reliable prediction is a main issue in many environmental applications, which however is hampered by data scarcity, when using data‐demanding deterministic models, and modelling limitations, when using simpler statistical models. In this work we test a suite of models belonging to air2stream family, which are characterized by a hybrid formulation that combines a physical derivation of the key equation with a stochastic calibration of parameters. The air2stream models rely solely on air temperature and streamflow, and are of similar complexity as standard statistical models. The performances of the different versions of air2stream in predicting river water temperature are compared with those of the most common statistical models typically used in the literature. To this aim, a dataset of 38 Swiss rivers is used, which includes rivers classified into four different categories according to their hydrological characteristics: low‐land natural rivers, lake outlets, snow‐fed rivers and regulated rivers. The results of the analysis provide practical indications regarding the type of model that is most suitable to simulate river water temperature across different time scales (from daily to seasonal) and for different hydrological regimes. A model intercomparison exercise suggests that the family of air2stream hybrid models generally outperforms statistical models, while cross‐validation conducted over a 30‐year period indicates that they can be suitably adopted for long‐term analyses. Copyright © 2016 John Wiley & Sons, Ltd.     

Trends and multi‐annual variability of water temperatures in the river Danube,Serbia

The relationship between air (Ta) and water temperature (Tw) is very important because it shows how the temperature of a water body might respond to future changes in surface Ta. Mean monthly Tw records of three gauging stations (Bezdan, Bogojevo i Veliko Gradi?te) were analysed alongside mean monthly discharge (Q) for the same stations. Additionally, Ta series from two meteorological stations (Sombor and Veliko Gradi?te) were correlated with Tw variations over the period 1950–2012. Locally weighted scatter point smoothing (LOWESS) was used to investigate long‐term trends in the raw data, alongside the Mann–Kendall (MK) trend test. Trend significance was established using Yue–Pilon's pre‐whitening approaches to determine trends in climate data. Also, the rescaled adjusted partial sums (RAPS) method was used to detect dates of possible changes in the time series. Statistically significant warming trends were observed for annual and seasonal minimum and maximum Tw at all investigated sites. The strongest warming was observed at Bogojevo gauging station for seasonal maximum Tw, with +0.05 °C per year on average. RAPS established that the trend began in the 1980s. This behaviour is linked to climate patterns in the North and East Atlantic which determine the amount of heat advected onto mainland Europe. Statistically significant correlations were found for all Tw on an annual basis. Overall, the strongest correlations (p < 0.01) between Tw residuals and the North Atlantic Oscillation (NAO) were recorded for the winter period. These findings suggest possible predictability of Tw over seasonal time‐scales. Copyright © 2016 John Wiley & Sons, Ltd.     

基于RS与GIS的茂县地区干旱河谷范围变化监测

以遥感(RS)和地理信息系统(GIS)为平台,选取茂县地区两期Landsat卫星影像,利用ENVI对影像数据进行监督分类,提取区域内植被覆盖图。利用Arc GIS结合茂县地区河谷分布图,界定出茂县地区两个时期内干旱河谷的分布范围。结果表明:整个茂县地区干旱河谷的范围呈缩减趋势,2005年干旱河谷面积457 619 400 m2,2015年干旱河谷面积为381 971 125 m~2,干旱河谷范围缩减了75 648 275 m~2。准确地界定出干旱河谷的范围变化,对指导川西地区生态环境综合治理以及西部广大山区的生态环境可持续发展战略的实施具有重要意义。     

A microscale model for air pollutant dispersion simulation in urban areas: Presentation of the model and performance over a single building

A microscale air pollutant dispersion model system is developed for emergency response purposes. The model includes a diagnostic wind field model to simulate the wind field and a random-walk air pollutant dispersion model to simulate the pollutant concentration through consideration of the influence of urban buildings. Numerical experiments are designed to evaluate the model's performance, using CEDVAL(Compilation of Experimental Data for Validation of Microscale Dispersion Models) wind tunnel experiment data, including wind fields and air pollutant dispersion around a single building. The results show that the wind model can reproduce the vortexes triggered by urban buildings and the dispersion model simulates the pollutant concentration around buildings well. Typically, the simulation errors come from the determination of the key zones around a building or building cluster. This model has the potential for multiple applications; for example, the prediction of air pollutant dispersion and the evaluation of environmental impacts in emergency situations; urban planning scenarios;and the assessment of microscale air quality in urban areas.     

基于贝叶斯模型的中国未来气温变化预估及不确定性分析

利用第5次耦合模式比较计划(CMIP5)中35个全球气候模式历史模拟与RCP4.5预估结果,通过贝叶斯模型平均(Bayesian Model Averaging,BMA)对中国气温进行多模式集合研究,给出了中国未来气温变化预估及其不确定性的时空分布。结果表明,中国21世纪冬夏将持续升温,且升温具有冬季高于夏季,北方高于南方的特点。初期(2016—2035年)北方有很大可能(80%)升温超过0.7℃,南方升温相同幅度的概率则超过50%;中期(2046—2065年)北方和南方升温超过1.5℃的概率分别为80%和50%;末期(2081—2100年),北方(南方)有80%(50%)的可能的升温超过2℃。气温预估的不确定性研究发现,无论冬夏,21世纪不同时期升温相对较弱的塔里木盆地、青藏高原南侧和中国东南地区为不确定性低值区,基本低于0.6℃,对应可信度较高,如21世纪初期信噪比超过4;而不确定性的高值区则主要分布在新疆北部、东北平原北部和青藏高原东南侧等升温相对较大的地区,普遍高于1℃,对应可信度较低,如初期信噪比低于2.5。此外,基于信噪比对比发现除青藏高原东部外,其他区域夏季预估的可信度均高于冬季,21世纪末期高于初期,且空间分布特征一致。     

哈密地区空中水汽及增水潜力分析

采用哈密地区6站1975—2014年逐日地面水汽压和降水量资料,计算了哈密各站的大气可降水量、有效空中水资源量、自然降水产出率和人工增水潜力值,并分析了各量的时空分布特征。结果表明:哈密地区年平均整层大气可降水量为2560~4327 mm,年均有效空中水资源量约为232~828 mm,占整层大气可降水量的1/4~1/10;年均自然降水产出率在9%~28%,自然降水产出率与降水量成正比关系。哈密地区的年人工增水潜力理论计算值在844~2399 mm之间,潜力值在夏季最大,巴里坤和伊吾明显多于其它区域。     

干侵入对我国东部一次强飑线过程的作用分析

利用NCEP/NCAR再分析资料、雷达资料、FY2E卫星资料等,对2014年7月30日至31日发生在我国东部的一次强飑线天气过程进行综合分析,并着重分析了干侵入对此次飑线过程的作用。结果表明：中层阶梯槽引导高层干冷空气向下入侵后叠加在低层暖湿气流之上,增强了大气不稳定,不稳定能量累积;低空切变线促使不稳定能量释放,是此次飑线的重要触发机制。对干侵入的分析结果表明：此次飑线过程中,干侵入来源于高空槽后下沉气流,干侵入底部风场调整使得飑线在移动过程中发生了转向。在飑线发展过程中,干侵入范围扩大、强度增强;干侵入使飑线发生区域上空空气变冷、变干,增强了大气不稳定。干侵入底部的低相当位温空气与飑线内部高相当位温空气混合是飑线长时间维持的重要原因。