感潮河网降雨径流污染空间分析与模拟

为了探究茅洲河流域感潮河网面源污染空间分布特征和降雨径流污染规律,基于空间分析、统计分析与流域水动力-水质耦合模拟方法,对典型降雨情景下河网水质情况进行模拟分析,提出基于水质改善目标的生态补水点位空间布局优化策略.研究表明,层次聚类凝聚算法和K-均值法迭代组合可以较好地实现面源污染分级与分类;茅洲河各支流中,石岩渠、松岗河中上游等河道（段）由于面源污染负荷相对较高且缺乏生态补水,雨后水质恢复缓慢;基于补水总量不变原则,对生态补水方案进行局部优化,优化结果可使雨后受污染重点河道（段）水质恢复速度加快一倍以上,提高了流域水质的整体稳定性.研究结论可为进一步认识茅洲河流域水污染特征、实现流域水环境精细化管理提供支撑.     

Detection and visualization of storm hydrograph changes under urbanization: an impulse response approach

Urbanization often alters catchment storm responses, with a broad range of potentially significant environmental and engineering consequences. At a practical, site-specific management level, efficient and effective assessment and control of such downstream impacts requires a technical capability to rapidly identify development-induced storm hydrograph changes. The method should also speak specifically to alteration of internal watershed dynamics, require few resources to implement, and provide results that are intuitively accessible to all watershed stakeholders. In this short paper, we propose a potential method which might satisfy these criteria. Our emphasis lies upon the integration of existing concepts to provide tools for pragmatic, relatively low-cost environmental monitoring and management. The procedure involves calibration of rainfall-runoff time-series models in each of several successive time windows, which sample varying degrees of watershed urbanization. As implemented here, only precipitation and stream discharge or stage data are required. The readily generated unit impulse response functions of these time-series models might then provide a mathematically formal, yet visually based and intuitive, representation of changes in watershed storm response. Nominally, the empirical response functions capture such changes as soon as they occur, and the assessments of storm hydrograph alteration are independent of variability in meteorological forcing. We provide a preliminary example of how the technique may be applied using a low-order linear ARX model. The technique may offer a fresh perspective on such watershed management issues, and potentially also several advantages over existing approaches. Substantial further testing is required before attempting to apply the concept as a practical environmental management technique; some possible directions for additional work are suggested.     

On modelling the global copper mining rates,market supply,copper price and the end of copper reserves

The world supply and turnover of copper was modelled using simple empirical estimates and a COPPER systems dynamics model developed for this study. The model combines mining, trade markets, price mechanisms, population dynamics, use in society and waste as well as recycling, into a whole world system. The degree of sustainability and resource time horizon was estimated using four different methods including (1) burn-off rates, (2) peak discovery early warning, (3) Hubbert's production model, and (4) COPPER, a system dynamics model. The ultimately recoverable reserves (URR) have been estimated using different sources that converge around 2800 million tonne, where about 800 million tonne have already been mined, and 2000 million tonne remain. The different methods independently suggest peak copper mine production in the near future. The model was run for a longer period to cover all systems dynamics and delays. The peak production estimates are in a narrow window in time, from 2031 to 2042, with the best model estimate in 2034, or 21 years from the date of writing. In a longer perspective, taking into account price and recycling, the supply of copper to society is estimated to run out sometime after 2400. The outputs from all models put focus on the importance of copper recycling so that society can become more sustainable with respect to copper supply.     

Smart meters for enhanced water supply network modelling and infrastructure planning

To design water distribution network infrastructure, water utilities formulate daily demand profiles and peaking factors. However, traditional methods of developing such profiles and peaking factors, necessary to carry out water distribution network modelling, are often founded on a number of assumptions on how top-down bulk water consumption is attributed to customer connections and outdated demand information that does not reflect present consumption trends; meaning infrastructure is often unnecessarily overdesigned. The recent advent of high resolution smart water meters allows for a new novel methodology for using the continuous ‘big data’ generated by these meter fleets to create evidence-based water demand curves suitable for use in network models. To demonstrate the application of the developed method, high resolution water consumption data from households fitted with smart water meters were collected from the South East Queensland and Hervey Bay regions in Australia. Average day (AD), peak day (PD) and mean day maximum month (MDMM) demand curves, often used in water supply network modelling, were developed from the herein created methodology using both individual end-use level and hourly demand patterns from the smart meters. The resulting modelled water demand patterns for AD, PD and MDMM had morning and evening peaks occurring earlier and lower main peaks (AD: 12%; PD: 20%; MDMM: 33%) than the currently used demand profiles of the regions’ water utility. The paper concludes with a discussion on the implications of widespread smart water metering systems for enhanced water distribution infrastructure planning and management as well as the benefits to customers.