Vegetated roofs are becoming more commonly deployed as a means of mitigating stormflow in urban areas. A greenroof performance comparison of stormwater runoff has yet to be conducted with controlled rain events and quantifiable antecedent soil moisture. This study aimed to investigate the rainwater management provided by various greenroof design schemes. Runoff retention, peak flow lagtime, conductivity, and pH of ten different small-scale greenroof schemes were observed and analyzed under repeatable rain simulations in a pilot-scale study. Sedum rupestre Angelina, Sedum hispanicum, Trifolium repens (white clover), Trifolium pratense (red clover), Vinca major (Big-Leaf Periwinkle), and Lolium multiflorum (ryegrass) were grown in the same type of soil media but separate 2′ × 2′ trays at depths of 5 cm and 14 cm to observe how soil depth and root zone development affects stormwater flow through for each plant type. Results showed that increased green roof soil depth improved water retention and runoff lagtime; the effect of plant type was mixed and inconclusive. Runoff conductivity test results depended primarily on soil depth and the existence or absence of vegetation. Testing results show that pH normalization provided by a greenroof does not depend significantly with the substrate depth.

No abstract

For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit http://www.usgs.gov or call 1-888-ASK-USGS For an overview of USGS information products, including maps, imagery, and publications, visit http://www.usgs.gov/pubprodTo order this and other USGS information products, visit http://store.usgs.gov Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.Suggested citation: Rounds, S.A., and Buccola, N.L., 2015, Improved algorithms in the CE-QUAL-W2 water-quality model for blending dam releases to meet downstream water-temperature targets: U.S. Geological Survey Open-File Report 2015-1027, 40 p., http://dx.doi.org/10.3133/ofr20151027. ISSN 2331-1258 iii AbstractWater-quality models allow water resource professionals to examine conditions under an almost unlimited variety of potential future scenarios. The two-dimensional (longitudinal, vertical) waterquality model CE-QUAL-W2, version 3.7, was enhanced and augmented with new features to help dam operators and managers explore and optimize potential solutions for temperature management downstream of thermally stratified reservoirs. Such temperature management often is accomplished by blending releases from multiple dam outlets that access water of different temperatures at different depths. The modified blending algorithm in version 3.7 of CE-QUAL-W2 allows the user to specify a time-series of target release temperatures, designate from 2 to 10 floating or fixed-elevation outlets for blending, impose minimum and maximum head and flow constraints for any blended outlet, and set priority designations for each outlet that allow the model to choose which outlets to use and how to balance releases among them. The modified model was tested with a variety of examples and against a previously calibrated model of Detroit Lake on the North Santiam River in northwestern Oregon, and the results compared well. These updates to the blending algorithms will allow more complicated damoperation scenarios to be evaluated somewhat automatically with the model, with decreased need for multiple model runs or preprocessing of model inputs to fully characterize the operational constraints.

No abstract

A one-dimensional, unsteady streamflow and temperature model (HEC-RAS) of the North Santiam and Santiam Rivers was developed by the U.S. Geological Survey to be used in conjunction with previously developed two-dimensional hydrodynamic water-quality models (CE-QUAL-W2) of Detroit and Big Cliff Lakes upstream of the study area. In conjunction with the output from the previously developed models, the HEC-RAS model can simulate streamflows and temperatures within acceptable limits (mean error [bias] near zero; typical streamflow errors less than 5 percent; typical water temperature errors less than 1.0 °C) for the length of the North Santiam River downstream of Big Cliff Dam under a series of potential future conditions in which dam structures and/or dam operations are modified to improve temperature conditions for threatened and endangered fish. Although a twodimensional (longitudinal, vertical) CE-QUAL-W2 model for the North Santiam and Santiam Rivers downstream of Big Cliff Dam exists, that model proved unstable under highly variable flow conditions. The one-dimensional HEC-RAS model documented in this report can better simulate cross-sectionalaveraged stream temperatures under a wide range of flow conditions. The model was calibrated using 2011 streamflow and temperature data. Measured data were used as boundary conditions when possible, although several lateral inflows and their associated water temperatures, including the South Santiam River, were estimated using statistical models. Streamflow results showed high accuracy during low-flow periods, but predictions were biased low during large storm events when unmodeled ephemeral tributaries contributed to the actual streamflow. Temperature results showed low annual bias against measured data at two locations on the North Santiam River and one location on the Santiam River. Mean absolute errors using 2011 hourly data ranged from 0.4 to 0.7 °C. Model results were checked against 2012 data and showed a positive bias at the Santiam River station (+0.6 ˚C). Annual mean absolute errors using 2012 hourly data ranged from 0.4 to 0.8 °C. Much of the error in temperature predictions resulted from the model's inability to accurately simulate the full range of diurnal fluctuations during the warmest months. Future iterations of the model could be improved by the collection and inclusion of additional streamflow and temperature data, especially near the mouth of the South Santiam River. Presently, the model is able to predict hourly and daily water temperatures under a wide variety of conditions with a typical error of 0.8 and 0.7 °C, respectively.