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The paucity of readily available demographic, economic, and water consumption data at household levels has limited the application of disaggregate water demand models. This research develops regression-based water demand models capable of forecasting single-family residential water demands within individual census tracts at a bimonthly time-step. The regression models are estimated using 12 years of demographic, weather, economic, and metered bimonthly water consumption data associated with over 100 unique census tracts in Seattle, Washington. In general, the three regression methods perform well in replicating total single-family water consumption in the study region. Two regression models, a fixed effects model and a random effects model, provide better estimates of water demand within individual census tracts. Improved water demand forecasts at the spatial scale of census tracts provide policy makers and planners information useful for managing water resources. These proposed approaches allow examination of spatially distributed demands within systems, identify the value of targeted conservation and infrastructure development, and improve understanding of the variables impacting demand in heterogeneous areas. The coefficient estimates developed in this research are appropriate for use in spatially disaggregate urban simulation models. CE Database subject headings: Municipal water; Water demand; Forecasting; Planning; Residential location; Seasonal variations. Author keywords: Municipal water; Water demand; Forecasting; Regression; Planning. DOI: 10.1061/(ASCE)WR.1943-5452.0000003

The preponderance of evidence in the scientific community supports the premise that global climate is changing. The precise impacts of climate change on natural and man-made systems remain less certain. Municipal water supplies, particularly those that rely on summer snowmelt to augment storage capacity, may experience significant changes in their flow regimes in the future. This paper presents an evaluation of climate change impacts on a water resource system using a three-stage modeling approach: General circulation models (GCMs) to simulate global climate, basin scale hydrology models, and water resource system simulation models. This approach is applied to two river basins in the Puget Sound Region of the Pacific Northwest: The Cedar and South Fork Tolt Rivers, which are the principal sources of water for the Seattle metropolitan region. The greatest source of uncertainty associated with climate change impacts arises from the range of future scenarios produced by GCMs. This uncertainty is addressed by incorporating multiple climate models at every stage of the process and using the values produced to generate an ensemble average that quantifies the most likely impact. The ensemble average is characterized by an envelope of uncertainty based on the range and spread of the individual GCM ensemble members. Hydrologic impacts of climate change such as alteration of historic streamflow patterns and snow accumulation are explored, as well as impacts to the water supply system's yield. DOI: 10.1061/(ASCE)0733-9496(2008)134:3(239) CE Database subject headings: Climatic changes; Municipal water; Uncertainty principles; Water resources; Water yield; Washington; Estimation.

This paper describes the development of the knowledge base expert system denoted as Sewer Cataloging, Retrieval and Prioritization System. This computer support system prioritizes sewer pipeline inspections used to target critical areas within a sewer drainage system. This system addresses a growing need of municipalities. The sewer infrastructure of many cities is in a state of disrepair due to budgetary constraints, a history of neglect and, often most importantly, a lack of critical information about the aging and complex system of sewers that convey wastewater for 75% of the population. The knowledge base was assembled with input from a national group of experts from both the public and private sectors. Input from the experts assesses the overall need to inspect based on both the line's consequence and likelihood of failure. In turn, consequence and likelihood of failure are based on six mechanisms describing failure and two mechanisms predicting the impact of failure. Prioritization is accomplished using a Bayesian belief network that allows the uncertainty of the experts' beliefs to be propagated through the decision process. The knowledge base is evaluated with a series of case studies and is shown to be effective at mimicking the knowledge of experts. DOI: 10.1061/(ASCE)0733-9496(2002)128:2(121) CE Database keywords: Expert systems; Sewers; Inspection.

This article discusses the water resource situation in the Puget Sound area in Washington State. Growth, water use, and environmental conditions in the region indicate that water resources are to be shared. The Spada Lake seems to be important for the development and management of water resources in the region.

This paper presents a Bayesian Stochastic Dynamic Programming (BSDP) model to investigate the value of seasonal flow forecasts in hydropower generation. The proposed BSDP framework generates monthly operating policies for the Skagit Hydropower System (SHS), which supplies energy to the Seattle metropolitan area. The objective function maximizes the total benefits resulting from energy produced by the SHS and its interchange with the Bonneville Power Administration. The BSDP-derived operating policies for the SHS are simulated using historical monthly inflows, as well as seasonal flow forecasts during 60 years from January 1929 through December 1988. Performance of the BSDP model is compared with alternative stochastic dynamic programming models. To illustrate the potential advantage of using the seasonal flow forecasts and other hydrologic information, the sensitivity of SHS operation is evaluated by varying (1) the reservoir capacity; (2) the energy demand; and (3) the energy price. The simulation results demonstrate that including the seasonal forecasts is beneficial to SHS operation.