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 examinati...

Urban regions are complex and dynamic with many external and internal factors driving growth, movement, and composition. Forecasting regional characteristics important for long-term planning is inherently difficult for such settings, but quantitative estimates of the changes in urban environments are necessary for engineers and planners. This study examines how population growth, land use, pricing policy, and climate change affect residential water demands in the Puget Sound region. A spatially disaggregate water demand model is coupled with an advanced urban simulation model (UrbanSim) to generate demands at a detailed spatial resolution over a 30 year planning horizon. A baseline scenario is compared with output from UrbanSim for three different planning scenarios.

Identification of changes in hydrological extremes plays an important role in water resources management. This study investigates various properties in hydrological extremes including annual maximum daily flow (high flow) and annual minimum 7-day flow (low flow) in 252 unimpaired streamflow gauges in the continental United States. Three statistical methodologies are performed: the nonparametric Mann-Kendall test is used to evaluate temporal trends; the non-parametric change point Pettitt test, to identify abrupt shifts; and the quantile perturbation method, to assess temporal variability. In the results of trend analysis, low flow exhibited some spatial patterns compared to high flow. In addition, low flow has increased over 64 years (1950–2013) in many stations. The Pettitt test indicates that low flows have experienced more significant abrupt changes compared to high flow. The results of the quantile perturbation method confirm that many significant increasing trends obtained during the 64 years...

Evidence from ecological studies suggests that the alteration of river flows downstream of reservoirs can threaten native aquatic ecosystems and the services they offer. Innovative revisions to water management practices are required to improve the health of aquatic species while maintaining the benefits from current infrastructure projects. The impacts of individual reservoir operations on ecosystem vitality are often masked by the uncoordinated and compounding influences of several impoundments upstream, undermining the examination of environmental impacts from particular reservoirs in a large watershed system. This paper presents a large-scale optimization model that investigates the value of coordinated reservoir management practices for ecological benefits in a large watershed with several major reservoir systems operating for a range of management objectives. An application of the model is presented for the Connecticut River watershed, the largest river basin in New England and one of the mo...

Climate change is likely to impact precipitation as well as snow accumulation and melt in the Northeastern and Upper Midwest United States, ultimately affecting the quantity and seasonal distribution of streamflow. The objective of this study is to analyze seasonality of long-term daily annual maximum streamflow (AMF) records and its changes for 158 sites in Northeastern and Upper Midwest Unites States. A comprehensive circular statistical approach comprising a kernel density method was used to assess the seasonality of AMF. Temporal changes were analyzed by separating the AMF records into two 30-year sub-periods (1951–1980 and 1981–2010). Results for temporal change in seasonality showed mixed pattern/trend across the stations. While for majority of stations, the distribution of AMF timing is strongly unimodal (concentrated around spring season) for the period 1951–1980, the seasonal modes have weakened during the period 1981–2010 for several stations along the coastal region with simultaneous emergence of multiple modes indicating changes of seasonality therein. The fresh statistical approach based on non-parametric circular density estimates reduces some of the limitations of previous studies to detect and model event timing distributions with multiple seasons and addresses issues of non-stationarity in the data records of extreme events.

Analysis of daily streamflow is of interest to river restoration and conservation efforts in many regions in the world. However, the paucity of stream-gauging stations presents significant challenges in making flow predictions. In this study, two process-based rainfall-runoff models that differ in complexity were used to estimate a reliable simulation of the daily streamflow hydrograph using 15 subbasins in the Deerfield River Basin, a major tributary to the Connecticut River Watershed. Catchment characteristics were employed across the study area, and regional regression equations were developed that correlate these physical and climate characteristics with the parameters of the rainfall-runoff models. The regression-based regionalization approach had a higher degree of accuracy when compared with simpler regionalization approaches, with an average normalized RMS error (NRMSE) value of 0.26 compared with 0.42 and 0.32 for a spatial proximity method and a naïve-mean method, respectively. In addition, the more complex rainfall-runoff model performed better than the less complex model with Kling-Gupta efficiency (KGE) values of 0.78 and 0.68, respectively, suggesting that model discretization may play a significant role in hydrologic model accuracy. These findings support a viable framework for addressing water resource management at a small-catchment level. In addition, this study may contribute to regionalization of rainfall-runoff model parameters for ungauged basins in the northeastern U.S. region.