Streamflow monitoring in the Colorado River Basin (CRB) is essential to ensure diverse needs are met, especially during periods of drought or low flow. Existing stream gage networks, however, provide a limited record of past and current streamflow. Modeled streamflow products with more complete spatial and temporal coverage (including the National Water Model [NWM]), have primarily focused on flooding, rather than sustained drought or low flow conditions. Objectives of this study are to (1) evaluate historical performance of the NWM streamflow estimates (particularly with respect to droughts and seasonal low flows) and (2) identify characteristics relevant to model inputs and suitability for future applications. Comparisons of retrospective flows from the NWM to observed flows from the United States Geological Survey stream gage network over 22 years in the CRB reveal a tendency for underestimating low flow frequency, locations with low flows, and the number of years with low flows. We found model performance to be more accurate for the Upper CRB and at sites with higher precipitation, snow percent, baseflow index, and elevations. Underestimation of low flows and variable model performance has important implications for future applications: inaccurate evaluations of historical low flows and droughts, and less reliable performance outside of specific watershed/stream conditions. This highlights characteristics on which to focus future model development efforts.

Miller (2015) Reservoir water quality monitoring using remote sensing with seasonal models: case study of five central-Utah reservoirs, Lake and Reservoir Management, 31:3, 225-240,

Combined Sewer Overflow (CSO) infrastructure are conventionally designed based on historical climatedata. Yet, variability in rainfall intensities and patterns caused by climate change have a significant impact on the performance of an urban drainage system. Although rainwater harvesting (RWH) is a potential solution to manage stormwater in urban areas, its benefits in mitigating the climate change impacts on combined sewer networks have not been assessed yet. Hence, the goal of the present study was set to evaluate the effectiveness of RWH in alleviating the potential impacts of climate change on CSOs. To do so, first, future rainfall was achieved through the Coupled Model Intercomparison Project Phase 5 (CMIP5) based on modified historical record. Then, rainfall-runoff modeling was employed using the U.S. EPA Stormwater management model (SWMM) to study the response of CSO outfalls to future rainfall. The study site was the combined sewer network of the City of Toledo, Ohio. Results showed that under the maximum impact scenario in the near future, climate change might cause up to approximately 12-18% increase in CSOs occurrence, volume and duration in Toledo. However, an RWH plan with the capacity of 0.76 m 3 (200 Gallon) implemented on half on the buildings throughout the area, appeared to be able to mitigate the potential future impacts, and showed a remarkable controlling performance in the peak flow periods. This plan also met toilet flushing demands. Therefore, RWH can be considered as a feasible solution to mitigate future climate change impacts on CSOs and supply water demands.

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Abstract:This study demonstrates a number of methods for using field sampling and observed lake characteristics and patterns to improve techniques for development of algae remote sensing models and applications. As satellite and airborne sensors improve and their data are more readily available, applications of models to estimate water quality via remote sensing are becoming more practical for local water quality monitoring, particularly of surface algal conditions. Despite the increasing number of applications, there are significant concerns associated with remote sensing model development and application, several of which are addressed in this study. These concerns include: (1) selecting sensors which are suitable for the spatial and temporal variability in the water body; (2) determining appropriate uses of near-coincident data in empirical model calibration; and (3) recognizing potential limitations of remote sensing measurements which are biased toward surface and near-surface conditions. We address these issues in three lakes in the Great Salt Lake surface water system (namely the Great Salt Lake, Farmington Bay, and Utah Lake) through sampling at scales that are representative of commonly used sensors, repeated sampling, and sampling at both near-surface depths and throughout the water column. The variability across distances representative of the spatial resolutions of Landsat, SENTINEL-2 and MODIS sensors suggests that these sensors are appropriate for this lake system. We also use observed temporal variability in the system to evaluate sensors. These relationships proved to be complex, and observed temporal variability indicates the revisit time of Landsat may be problematic for detecting short events in some lakes, while it may be sufficient for other areas of the system with lower short-term variability. Temporal variability patterns in these lakes are also used to assess near-coincident data in empirical model development. Finally, relationships between the surface and water column conditions illustrate potential issues with near-surface remote sensing, particularly when there are events that cause mixing in the water column.