While protection of precious water resources is an important part of water management, restoration or remediation of contaminated water/groundwater has become a focus in recent years due to legacy of land uses. Point-source contamination of land and groundwater is easier to clean up than catchment-scale diffuse pollution. The EU Water Framework Directive (EC, 2000) has been an important regulation umbrella for an integrated protection and management of water resources, which needs various efforts including innovative modelling, in situ site investigations and proper water controls.
This issue of Proceedings of the Institution of Civil Engineers – Water Management features some interesting papers in an international context. The papers cover topics including remediation of contaminated soil and groundwater at the Olympic Park, east London; groundwater evolution of catchment scale aquifers supplying Beijing, the 2008 Olympics Games host city; innovative water flow modelling in India to provide a better handling of river flow data with wavelet neural network model; regional groundwater vulnerability assessment using geographical information systems (GIS) mapping and remote sensing to support decision-making; and a detailed study of the leakage from mains in the Caribbean. All show effective means of water management strategies to serve the needs of society.
Mead et al. (2012) present an interesting and timely briefing on the latest results of some components of contaminated groundwater and soil remediation in the 200 ha former industrial land which now makes up the Olympic Park. These mainly include identification of the contamination sources and pathways. Following the site investigation, remediation design and soil remediation phases on the Olympic Park, the focus was identified as the remediation of underlying contaminated groundwater. The aquifers had also been impacted by the downward migration of contamination from the overlying soil, following historic industrial activity in such an urban industrial area. The contamination of soil and groundwater required treatment by various suitable techniques to protect both the surrounding watercourses that dissect the park and the underlying groundwater aquifers from further impact, which is briefly outlined in this paper.
Another interesting area of study presented by Wu et al. (2012) is an assessment of the recharge processes and hydro-chemical evolution of multiple aquifers in Beijing, the host city of the 2008 Olympics, that uses groundwater as a major supply source. Based upon geological and hydrogeological surveys in the upper alluvial and proluvial fans of the Chaobai catchment, further sampling of precipitation, surface water and groundwater (from fractured bedrocks, shallow and deep alluvium sediments) springs were undertaken for analyses to support the hydrochemical and isotopic interpretations. Distinct groundwater recharge sources, flow paths and geochemical evolution histories were identified using cluster analysis to study the groundwater samples from multiple aquifers for potential links and relationships. Inverse geochemical modelling was also adopted to study the deep groundwater chemical evolution. Hydrochemical and isotopic data on D, T, 18O and 87Sr were further used to validate groundwater recharge, flow pathways and geochemical evolution. Supply induced groundwater cones of depression and deep alluvial groundwater may be partly derived from the deep fractured bedrock. The shallow alluvial groundwater within the central and southern areas of the alluvial and proluvial fan is recharged by local precipitation and agricultural irrigation, while the deep alluvial groundwater in the central and southern areas is sustained mainly by the lateral flows from the northern area or from the underlying fractured bedrock.
Quantitative assessment of the hydrological cycle is an important process in water management. Krishna et al. (2012) develop a new hybrid approach known as the wavelet neural network (WNN) model that couples wavelet and artificial neural network (ANN) models. It is developed and applied for time series data modelling of river flows. Time series of daily river flow of the Malaprabha River in Karnataka State, India were analysed by the WNN model. The time series data are decomposed into sub-series using a discrete wavelet transform and then an appropriate sub-series is used as input to the neural network for forecasting hydrological variables. The WNN model was compared with the standard ANN and auto-regressive (AR) models and was shown to provide a good fit with the observed data, especially the peak values during testing. The WNN model could also produce better results than the ANN and AR models in estimating hydrographs.
Farjad et al. (2012) present a study on groundwater intrinsic vulnerability assessment and risk mapping using a GIS and Drastic approach. Groundwater vulnerability mapping has been an important task in improving planning and decision-making processes in groundwater management. This case study generates groundwater vulnerability maps for the Izeh Plain in Iran using a modified Drastic model, GIS and remote sensing data. Sensitivity analysis was used to assign weight by showing the importance of each parameter in the Drastic model. The highest weight was assigned to the aquifer media factor and the lowest weight was assigned to the topography factor. The study shows that groundwater is highly vulnerable in the southwest part of the basin.
Quantification of leakage from mains is tricky; water companies acknowledge the high levels of non-revenue water in the Caribbean which is reflected in a study presented by Peters and Ben-Ephraim (2012). The paper reports a strategy adopted by Guyana Water Incorporated to reduce water leakage. This study uses a bottom-up real loss assessment to estimate customer night consumption and background leakage for the test district metered area (DMA) based on minimum night flow analyses. The results show that leakage may be more than 50% of the supply. However the results should be used with caution as water collection and storage during the night can lead to overestimation of recoverable losses. The paper suggests that testing should be carried out during the rainy season when there is an increased availability of supply and the DMAs should be refined for more accurate estimates.
