BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: Water scarcity in the Sevier River Basin in south‐central Utah has led water managers to seek advanced techniques for identifying optimal forecasting and management measures. To more efficiently use the limited quantity of water in the basin, better methods for control and forecasting are imperative. Basin scale management requires advanced forecasts of the availability of water. Information about long term water availability is important for decision making in terms of how much land to plant and what crops to grow; advanced daily predictions of streamflows and hydraulic characteristics of irrigation canals are of importance for managing water delivery and reservoir releases; and hourly forecasts of flows in tributary streams to account for diurnal fluctuations are vital to more precisely meet the day‐to‐day expectations of downstream farmers. A priori streamflow information and exogenous climate data have been used to predict future streamflows and required reservoir releases at different timescales. Data on snow water equivalent, sea surface temperatures, temperature, total solar radiation, and precipitation are fused by applying artificial neural networks to enhance long term and real time basin scale water management information. This approach has not previously been used in water resources management at the basin‐scale and could be valuable to water users in semi‐arid areas to more efficiently utilize and manage scarce water resources.     

GEOMORPHOLOGY OF STEEPLAND HEADWATERS: THE TRANSITION FROM HILLSLOPES TO CHANNELS1

Headwater streams comprise 60 to 80 percent of the cumulative length of river networks. In hilly to mountainous terrain, they reflect a mix of hillslope and channel processes because of their close proximity to sediment source areas. Their morphology is an assemblage of residual soils, landslide deposits, wood, boulders, thin patches of poorly sorted alluvium, and stretches of bedrock. Longitudinal profiles of these channels are strongly influenced by steps created by sediment deposits, large wood, and boulders. Due to the combination of small drainage area, stepped shallow gradient, large roughness elements, and cohesive sediments, headwater streams typically transport little sediment or coarse wood debris by fluvial processes. Consequently, headwaters act as sediment reservoirs for periods spanning decades to centuries. The accumulated sediment and wood may be episodically evacuated by debris flows, debris floods, or gully erosion and transported to larger channels. In mountain environments, these processes deliver significant amounts of materials that form riverine habitats in larger channels. In managed steepland forests, accelerated rates of landslides and debris flows resulting from the harvest of headwater forests have the potential to seriously impact the morphology of headwater streams and downstream resources.     

Status and fuzzy comprehensive assessment of combined heavy metal and organo-chlorine pesticide pollution in the Taihu Lake region of China

The status of combined heavy metal and organo-chlorine pesticide (OCPs; i.e. HCH and DDT) pollution was investigated and the soil environmental quality of the Taihu Lake watershed, one of the most developed regions in China, was evaluated using a fuzzy comprehensive assessment. Statistical analyses showed the presence of combined pollution in the soil. At many sampling sites, heavy metal concentrations were above corresponding background values, indicating the effects of extraneous pollutants. It has been over 20 years since China banned the use of OCPs, but they can still be found in soil samples of this region. HCH levels at all investigated sites were below the Chinese Environmental Quality Standard for Soils. Fuzzy comprehensive assessment showed that the overall soil quality in this region could be categorized as class I. Nevertheless, the high coefficients of variation for levels of DDT, Cd and Hg indicated the existence of some point-source pollution. Continuous monitoring and further studies of the region are recommended to prevent pollution of farmland from these sources.     

基于24Model的矿工怠工行为 管理模式架构及策略研究

为减少矿工怠工行为,基于24Model研究矿工怠工行为的管理模式架构及策略。首先,基于冰山模型理论分析矿工怠工行为的影响因素;其次,基于24Model和行为修正理论提出集怠工行为识别、行为原因分析、行为管理策略选择和行为修正评估于一体的矿工怠工行为管理模式架构;最后,提出了针对性的管理策略。对于提高煤矿企业矿工的工作绩效具有重要的理论意义和实践价值。     

基于生态系统的海洋管理:原则、实践和建议

基于生态系统的管理(ecosystem-based management, EBM)是一种得到海洋界广泛关注和普遍认可的管理理念,文章分析了EBM的内涵、原则,简要介绍了各海洋大国在海洋发展战略中基于生态系统的海洋管理实践,并简要分析了我国在基于生态系统的海洋管理方面的初步尝试.结合EBM所提倡的原则、方法以及我国国情,文章提出了为实施基于生态系统的海洋管理所急需开展的工作,包括开展海洋管理单元区划研究、制定科学的管理目标、建立和健全生态系统监测和评价系统、建立涉海机构和部门之间的有效合作机制以及扩展公众参与海洋管理的渠道等五个方面.     

我国流域水生态完整性评价方法构建

流域水生态完整性评价是指通过对水生态系统中不同水生态指标(生物和非生物)的监测以及由数学方法综合形成的综合评价指数,来反映水生态系统完整性状况。近年来,世界各国水环境管理政策发生了变化,开始强调生态保护,重视水体的生态质量。中国现行的常规理化监测指标(如COD、氨氮、BOD5)很难满足水环境管理的需求,难以全面准确地反映水环境质量变化的趋势。因此,在借鉴欧美发达国家流域水生态完整性评价方法的基础上,结合中国目前监测现状以及流域水环境管理需求,构建了包括物理生境指标、理化指标、水生生物指标在内的流域水生态完整性监测与评价方法,以期为中国流域水质目标管理技术体系的业务化运行提供可资借鉴的技术支撑,实现从单一的化学指标监测转向综合的水生态系统监测,实现流域水生态完整性的监测与评价。     

基于精细化管理的环境物联网发展趋势

环境物联网运用智能传感器等设备感知生态环境的变化,监测环境质量、监控企业的排污行为,通过环境数据的采集、传输、处理、整合,辅助环境管理决策。近年来环境物联网得到长足发展,但现有的环境物联网体系无法满足多种任务和应用场景的需求,主要问题体现在数据质量和精准度缺乏保证,数据传输能力有限,数据共享还未打通,数据分析应用能力还有待提升。笔者系统分析了环境物联网感知层、传输层和应用层的现状与问题,较全面地研究了物联网与大数据技术的最新进展,提出了环境物联网的发展方向以解决上述问题,为实现精细化管理与科学决策提供科学参考。     

船舶油污染防治的30年

1967年，油轮Torrey Canyon和以后发生的Amoco Cadiz、Exxon Valdez等溢油事件，震动了石油、航运等企业以及海洋环境保护部门。从1969年开始的溢油会议，交流油污染防治经验和成果，到1999年的30年，可以看出船舶污染防治的梗概。文中对此作了综合评述，并说明目前状况和今后的发展。     

模糊综合评价法在疏散时间影响因素研究中的应用

人员疏散是一个复杂的运动过程,人员疏散时间受众多因素影响,具有一定的模糊性和随机性。该文将人员安全疏散作为一个系统进行分析,从疏散场所、疏散人员以及疏散环境三个角度分析影响人员疏散时间的因素,构建出人员疏散时间的评价指标体系。结合模糊综合评价法的基本原理和步骤,建立人员疏散时间影响因素的三级模糊综合评价模型,并应用该模型对某高层建筑火灾中影响人员疏散时间的因素进行模糊综合评价。     

SWMM 模型在城市不透水区地表径流模拟中的参数识别与验证

为了研究城市不透水下垫面的降雨径流过程和污染负荷,以屋面为例,选择径流管理模型SWMM,采用独立场次实测数据,应用基于不确定性分析的HSY算法和Monte Carlo采样方法对模型中的水文水力和水质参数进行识别和验证.结果表明,地表不透水区径流模型中主要包含6个关键参数,分别为不透水区初损填洼深度(S-imperv)、不透水区曼宁系数(N-imperv),指数累积方程中的最大可能累积值(max buildup)、累积常数(rate constant),指数冲刷方程中的冲刷系数(coefficient)和冲刷指数 (exponent).水文水力参数的识别可以最小二乘法偏差作为目标函数,水质参数的识别可以场次污染负荷和污染物峰值浓度作为目标函数.参数识别结果为N-imperv0.012～0.025, S-imperv 0～0.7, max buildup 15～30, rate constant0.2～0.8, coefficient0.01～ 0.05, exponent1.0～1.2.参数的区域灵敏度由大到小排序为 coefficient、S-imperv、N-imperv、max buildup、exponent、rate constant.识别后的参数可以通过模型验证,但是在模拟一些雨型特殊的降雨径流污染物浓度曲线时,仍然存在一定的困难.