亚马逊流域玛代拉河Santo Antonio鱼道设计与建造的启示

Santo Antnio过鱼设施位于亚马逊河最大支流玛代拉河下游,其设计规模宏大,在世界范围内鲜有,并且工程建设地所在区域水文环境和鱼类生物学特征等与我国长江较为类似,通过分析该过鱼设施,得出启示主要包括:(1)过鱼设施是为生物对象设计的水利工程,其设计和建设过程中必须由工程师和生物学家紧密合作;(2)吸引水流对过鱼设施的成败起关键性作用,虽然吸引水流系统需要较大投资并且运行过程中会损失大量的水能,但从确保过鱼设施效果而言是非常必要的;(3)物理模型对于鱼道进口位置、鱼道内部水力学特征的确定等具有重要参考价值,尤其是在1∶1局部模型内开展的生物学试验是细化和优化鱼道内部结构细节的有效方法;(4)卵石框(石笼)隔板的水池分隔方法兼具水池式鱼道与仿自然通道的双重特性,并且完工后还可根据鱼道的运行情况灵活调整,非常方便;(5)进口处采用较高的人字闸门,既可适应尾水变化,又可与吸引水流系统相配合,创造出有效的吸引水流,一举多得;(6)全部采用大功率灯泡式水轮机是保护下行鱼类的有效措施;(7)监测系统和公众教育等配套设施的建设非常必要,有利于改进鱼道运行方式和提高过鱼效率。

湄公河下游地下水域中的砷

通过对亚洲东南部多数冲积含水层的水进行调查，更进一步加强了人们对砷的认识，其调查结果使人们增加了对湄公河流域下游地下水水域中砷的关注。本文作者对此进行了新的研究并且回顾了许多以前小规模研究，提供了柬埔寨和越南Cuu Long三角洲含水层中砷的综合概况。天然砷一般起源于湄公河流域的下游，而不是起源于某一有地质特征的地区，而且发现浓度介于8ppm和16ppm（干重）之间的砷广泛分布于土壤中。与天然冲积层的砷相比，工业和农业用的砷是有限的。含水层地下水的砷浓度不低于10μgL^-1，但是，在离散的反常区域，砷的浓度在10-30μgL^-1也是常见的，有时也可能有暂时的砷异常，砷浓度可达到600μgL^-1，。最严重的是，洪积平原中的铁和富含有机质的沉积物容易受到洪水水位大的波动的影响，在氧化条件下．产生不稳定的毒砂。而一般情况下，地下水中高浓度的砷，由于受到吸附和解吸的袭夺作用，在还原和带轻微碱性的条件下，砷适宜释放。在水位埋深浅的含水层和100—200米较深的含水层中，地下水的砷浓度高。砷蔓延的过程没有明显的迹象，但是砷对健康有严重的局部危害，而且通过间接方式（诸如通过污染的稻米和水产品）摄取低浓度砷也存在着一定的风险。土壤中含砷几乎是普遍存在的，加之将来地下水抽取量的快速增加的可能性，这就要求在整个区域开发水资源时要持续保持警惕性。

Uncertainty Analysis of Wind-Wave Predictions in Lake Michigan

With all the improvement in wave and hydrodynamics numerical models, the question rises in our mind that how the accuracy of the forcing functions and their input can affect the results. In this paper, a commonly used numerical third-generation wave model, SWAN is applied to predict waves in Lake Michigan. Wind data are analyzed to determine wind variation frequency over Lake Michigan. Wave predictions uncertainty due to wind local effects are compared during a period where wind has a fairly constant speed and direction over the northern and southern basins. The study shows that despite model calibration in Lake Michigan area, the model deficiency arises from ignoring wind effects in small scales. Wave prediction also emphasizes that small scale turbulence in meteorological forces can increase prediction errors by 38%. Wave frequency and coherence analysis show that both models can predict the wave variation time scale with the same accuracy. Insufficient number of meteorological stations can result in neglecting local wind effects and discrepancies in current predictions. The uncertainty of wave numerical models due to input uncertainties and model principals should be taken into account for design risk factors.

Viscoelastic plastic interaction of tunnel support and strain-softening rock mass considering longitudinal effect

A simplified two-stage method was employed to provide an explicit solution for the time-dependent tunnel-rock interaction,considering the generalized Zhang-Zhu strength criterion.Additionally,a simplified mechanical model of the yielding support structure was established.The tunnel excavation is simplified to a two-stage process:the first stage is affected by the longitudinal effect,while the second stage is affected by rheological behavior.Two cases are considered:one is that the rigid support is constructed during the first stage,and the other is that constructed at the second stage.Distinguished by the support timing at the seconde stage,different kinds of the“yield-resist combination”support method are divided into three categories:“yield before resist”support,“yield-resist”support,and“control-yield-resist”support.Results show that the support reaction of“control-yield-resist”is much higher than that of“yield before resist”if the initial geostress is not very high,but the effect is not obvious on controlling the surrounding rock deformation.So,the“yield before resist”support is much more economical and practical when the ground stress is not very high.However,under high geostress condition,through applying relatively high support reaction actively to surrounding rock at the first stage,the“control-yield-resist”support is superior in controlling the deformation rate of surrounding rock.Therefore,in the high geostress environment,it is recommended to construct prestressed yielding anchor immediately after excavation,and then construct rigid support after the surrounding rock deformation reaches the predetermined deformation.

Soil water storage,yield,water productivity and transpiration efficiency of soybeans(Glyxine max L.Merr)as affected by soil surface management in lle-Ife,Nigeria

Rainfed agriculture has a high yield potential if rainfall and land resources are effectively used.In this study,conventional(NC)and six in-situ water conservation practices were used to cultivate Soybean in 2011 and 2012 in Ile-lfe,Nigeria.The conservation practices are:Tied ridge(TR),Soil bund(BD),Mulch(ML),Mulch plus Soil bund(MLBD),Tied ridge plus Mulch(TRML),Tied ridge plus Soil bund(TRBD).The practices were arranged in Randomised Complete Block Design with four replicates.Seasonal rainfall was 539 and 761 mm in 2011 and 2012,respectively.Seasonal soil water storage(SWS)ranged from 485 mm for NC to 517 mm for TRML in the two seasons.ML increased the SWS in the upper 30 cm of the soil by 17% while TR increased the soil water content in the lower 30-60 cm by 22% compared with NC.ML reduced soil temperature in the upper 30 cm between 2.2 and 2.9℃ compared with NC,TR and TRML.Seasonal crop evapotranspiration ranged between 432 mm for NC and 481 mm for BD in the seasons.Grain yield increased by 41.7% and 44.3% for BD and MLBD,respectively compared with NC.Water conservation practices increased water productivity for grain yield by 14.0-41.8% compared with NC.Similarly,it increased average seasonal transpiration efficiency by 15.3-32.5% compared with NC.These findings demonstrate that when there are fluctuations in rainfall,in-situ water conservation practices improve SWS,land,and water productivity and transpiration efficiency of Soybeans.

Deep learning-based prediction of effluent quality of a constructed wetland

Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands.However,the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction.To address this problem,in this study,we propose an approach based on multi-source data fusion that considers the following indicators:water quality indicators,water quantity indicators,and meteorological indicators.In this study,we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland:(1)multiple linear regression;(2)backpropagation neural network(BPNN);(3)genetic algorithm combined with the BPNN to solve the local minima problem;and(4)long short-term memory(LSTM)neural network to consider the influence of past results on the present.The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method,with a satisfactory R^(2).Additionally,given the huge fluctuation of different pollutant concentrations in the effluent,we used a moving average method to smooth the original data,which successfully improved the accuracy of traditional neural networks and hybrid neural networks.The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.

Abnormality monitoring model of cracks in concrete dams

The abnormality monitoring model (AMM) of cracks in concrete dams is established through integrating safety monitoring theories with abnormality diagnosis methods of cracks. In addition, emphasis is placed on the influence of crack depth on crack mouth opening displacement (CMOD). A linear hypothesis is proposed for the propagation process of cracks in concrete based on the fictitious crack model (FCM). Abnormality points are detected through testing methods of dynamical structure mutation and statistical model mutation. The solution of AMM is transformed into a global optimization problem, which is solved by the particle swarm optimization (PSO) method. Therefore, the AMM of cracks in concrete dams is established and solved completely. In the end of the paper, the proposed model is validated by a typical crack at the 105 m elevation of a concrete gravity arch dam.