Studies on wind environment around high buildings in urban areas

High buildings or architectural complex in urban areas remarkably distort the urban surface wind fields. As the air flow approaches,local strong wind may appear around the buildings. The strong wind makes the pedestrians on sidewalks, entrances and terrace very uncomfortable and causes the pedestrian level wind environment problem. In this studies, hot-wire wind measurement, wind scouring in wind tunnel and numerical computation were carried out to evaluate the wind environment of tall buildings in the prevailing flow conditions in Beijing areas. The results obtained by three techniques were compared and mutually verified. The conclusions drawn from three approaches agree with each other. Also the advantages and limitations of each method were analyzed. It is suggested that the combination of different techniques may produce better assessment of wind environment around high buildings.     

Model identification for hydrological forecasting under uncertainty

Methods for the identification of models for hydrological forecasting have to consider the specific nature of these models and the uncertainties present in the modeling process. Current approaches fail to fully incorporate these two aspects. In this paper we review the nature of hydrological models and the consequences of this nature for the task of model identification. We then continue to discuss the history (“The need for more POWER‘’), the current state (“Learning from other fields”) and the future (“Towards a general framework”) of model identification. The discussion closes with a list of desirable features for an identification framework under uncertainty and open research questions in need of answers before such a framework can be implemented.     

Stochastic Modelling of the Impact of Flood Protection Measures Along the River Waal in the Netherlands

River flooding is a problem of international interest. In the past few years many countries suffered from severe floods. A large part of the Netherlands is below sea level and river levels. The Dutch flood defences along the river Rhine are designed for water levels with a probability of exceedance of 1/1250 per year. These water levels are computed with a hydrodynamic model using a deterministic bed level and a deterministic design discharge. Traditionally, the safety against flooding in the Netherlands is obtained by building and reinforcing dikes. Recently, a new policy was proposed to cope with increasing design discharges in the Rhine and Meuse rivers. This policy is known as the Room for the River (RfR) policy, in which a reduction of flood levels is achieved by measures creating space for the river, such as dike replacement, side channels and floodplain lowering. As compared with dike reinforcement, these measures may have a stronger impact on flow and sediment transport fields, probably leading to stronger morphological effects. As a result of the latter the flood conveyance capacity may decrease over time. An a priori judgement of safety against flooding on the basis of an increased conveyance capacity of the river can be quite misleading. Therefore, the determination of design water levels using a fixed-bed hydrodynamic model may not be justified and the use of a mobile-bed approach may be more appropriate. This problem is addressed in this paper, using a case study of the river Waal (one of the Rhine branches in the Netherlands). The morphological response of the river Waal to a flood protection measure (floodplain lowering in combination with summer levee removal) is analysed. The effect of this measure is subject to various sources of uncertainty. Monte Carlo simulations are applied to calculate the impact of uncertainties in the river discharge on the bed levels. The impact of the “uncertain” morphological response on design flood level predictions is analysed for three phenomena, viz. the impact of the spatial morphological variation over years, the impact of the seasonal morphological variation and the impact of the morphological variability around bifurcation points. The impact of seasonal morphological variations turns out to be negligible, but the other two phenomena appear to have each an appreciable impact (order of magnitude 0.05–0.1 m) on the computed design water levels. We have to note however, that other sources of uncertainty (e.g. uncertainty in hydraulic roughness predictor), which may be of influence, are not taken into consideration. In fact, the present investigation is limited to the sensitivity of the design water levels to uncertainties in the predicted bed level.     

浙江江山石鼓水库除险加固工程技术措施

根据浙江江山石鼓水库大坝的结构构造特点、水库地质条件以及水库病险原因与状况，制定出一套科学完善的病险水库除险加固工程技术措施和施工操作规程，对病险水库进行了全面彻底治理，并取得良好效果。详细介绍了工程病险状况，除险加固方案设计，施工工艺及操作规程，加固效果检测及评价等。     

固沙林庇护区内降尘特征的初步观测

采用野外定位实测法,连续两年对科尔沁沙地24龄人工固沙杨树(Populussimonii)林庇护区内4～6月份及强沙尘暴事件中的降尘特征进行了观测研究。结果表明:(1)林地庇护区内4、5月份的降尘量较多,分别为273和437kg/hm2,6月份的降尘量较少,为171kg/hm2。(2)林地中央的滞尘效应在风蚀季节和强沙尘暴天气过程中十分显著。(3)林地庇护区内的降尘中粒径<0.02mm颗粒含量占60.7%,降尘中的全C、全N和速效P含量分别高达1.676%、0.163%和210.66mg/kg,这对风沙土表层土壤的细化和养分的积累具有重要的生态学意义。     

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.     

The multi-component Hekla Ö Tephra,Iceland: a complex widespread mid-Holocene tephra layer

Large Plinian eruptions from Hekla volcano, Iceland, produce compositionally zoned tephra used as key markers in tephrochronology. However, spatial variations in chemical composition of a tephra layer may complicate its identification. An example is the 5950–6180 cal a bp Hekla Ö tephra layer, which shows compositional spread from rhyolite, dacite and andesite to basalt. In soil sections north of Hekla, the SiO₂ content of the tephra glass reaches 76 wt% in the lowest unit of the Hekla Ö deposit and decreases to 62–63 wt% in the uppermost unit. Intermingled within the whole deposit are basalt tephra grains having 46–47 wt% SiO₂. The composition of the basalt glass includes primitive basalt and a more evolved basalt (MgO >6 and <6 wt%, respectively). Together with literature data, the Hekla Ö tephra and the so-called T-Tephra/Hekla-T are most likely from contemporaneous eruptions of different vents on the Hekla volcanic system, forming a single important marker tephra (Hekla ÖT) deposited over 80% of Iceland. Identification is complicated by its spatial compositional heterogeneity, such as systematic decrease in SiO₂ content from the east to the west of Hekla volcano. Consequently, an individual tephra layer from a large explosive eruption can have different composition at different locations. © 2020 John Wiley & Sons, Ltd.     

Ocean current estimation using a Multi-Model Ensemble Kalman Filter during the Grand Lagrangian Deployment experiment (GLAD)

In the summer and fall of 2012, during the GLAD experiment in the Gulf of Mexico, the Consortium for Advanced Research on Transport of Hydrocarbon in the Environment (CARTHE) used several ocean models to assist the deployment of more than 300 surface drifters. The Navy Coastal Ocean Model (NCOM) at 1 km and 3 km resolutions, the US Navy operational NCOM at 3 km resolution (AMSEAS), and two versions of the Hybrid Coordinates Ocean Model (HYCOM) set at 4 km were running daily and delivering 72-h range forecasts. They all assimilated remote sensing and local profile data but they were not assimilating the drifter’s observations. This work presents a non-intrusive methodology named Multi-Model Ensemble Kalman Filter that allows assimilating the local drifter data into such a set of models, to produce improved ocean currents forecasts. The filter is to be used when several modeling systems or ensembles are available and/or observations are not entirely handled by the operational data assimilation process. It allows using generic in situ measurements over short time windows to improve the predictability of local ocean dynamics and associated high-resolution parameters of interest for which a forward model exists (e.g. oil spill plumes). Results can be used for operational applications or to derive enhanced background fields for other data assimilation systems, thus providing an expedite method to non-intrusively assimilate local observations of variables with complex operators. Results for the GLAD experiment show the method can improve water velocity predictions along the observed drifter trajectories, hence enhancing the skills of the models to predict individual trajectories.     

Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method

Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd.