This document presents a sketch of the engineering and legal considerations necessary to implement a distributed storage flood mitigation system in Iowa. This document first presents the results of a simulation done to assess the advantages of active storage reservoirs over passive reservoirs for flood mitigation. Next, this paper considers how forecasts improve the operation of a single reservoir in preventing floods. After demonstrating the effectiveness of accurate forecasts on a single active storage reservoir, this thesis moves on to a discussion of distributed storage with the idea that the advantages of active reservoirs with accurate forecasting could be applied to the distributed storage system. The analysis of distributed storage begins with a determination of suitable locations for reservoirs in the Clear Creek Watershed, near Coralville, Iowa, using two separate algorithms. The first algorithm selected the reservoirs based on the highest average reservoir depth, while the second located reservoirs based on maximizing the storage in two specific travel bands within the watershed. This paper also discusses the results of a land cover analysis on the reservoirs, determining that, based on the land cover inundated, several reservoirs would cause too much damage to be practical. The ultimate goal of a distributed storage system is to use the reservoirs to protect an urban area from significant flood damage. For this thesis, the Clear Creek data were extrapolated to the Cedar River basin with the intention to evaluate the feasibility and gain a rough approximation of the requirements for a distributed storage system to protect Cedar Rapids. Discussion then centered on an approximation of the distributed storage system that could have prevented the catastrophic Flood of 2008 in Cedar Rapids. There is significant potential for a distributed storage system to be a cost effective way of protecting Cedar Rapids from future flooding on the scale of the Flood of 2008. However, more analysis is needed to more accurately determine the costs and benefits of a distributed storage system in the Cedar River basin. This paper also recommends that a large scale distributed storage system should be controlled by an entity be created within the Iowa Department of Natural Resources. A smaller distributed storage system could be managed by a soil and water conservation subdistrict. Iowa allows for condemnation of the land needed for the gate structures and the flowage easements necessary to build and operate a distributed storage system. Finally, this paper discusses the environmental law concerns with a distributed storage system, particularly the Clean Water Act requirement for a National Pollutant Discharge Elimination System permit.

Many communities are currently seeking to balance urban water needs with preservation of sensitive fish habitat. As part of that effort, CE-QUAL-W2, a hydrodynamic and temperature model, was developed for Chester Morse Lake and the lower Cedar River, WA. Chester Morse Lake is approximately 10 km long with a maximum depth at full pool of 40 m. The Cedar River model started immediately downstream of the Chester Morse dam and ended 21 km downstream at Landsburg, where drinking water is diverted for the City of Seattle. This water quality model was coupled with a fish habitat and bioenergetics model for bull trout and was calibrated to temperature data between 2005 and 2008. Bull trout fish bioenergetics parameters were provided by the USGS. The CE-QUAL-W2 model was found to be highly accurate in modeling temperature variation in the lake - at most locations having an average absolute mean error of between 0.5 and 0.8 oC. The Cedar River model had an average absolute mean error of 0.7oC. This tool is designed to allow managers and operators to estimate the impact to fish habitat and growth potential from various management decisions including extent of drawdown, timing/volume of flows, and various pumping operations. Future studies could include incorporating further water quality parameters such as nutrients, algae, and zooplankton as they relate to fish productivity.