Abstract:The State of Iowa, located in the Midwestern United States, has experienced an increased frequency of large floods in recent decades. After extreme flooding in the summer of 2008, the Iowa Flood Center (IFC) was established for advanced research and education specifically related to floods. IFC seeks to improve Iowa's flood hazard awareness through the development of easily accessible, high-quality mapping products. Mapping initiatives consist of two model development approaches: (1) statewide floodplain delineation using one-dimensional (1D) models, and (2) urban flood mapping using detailed one-dimensional/two-dimensional (2D) coupled models. The statewide floodplain project will benefit Iowans through the creation of a comprehensive set of floodplain maps developed under a single consistent methodology. These will be important tools in evaluating flood risk, regulating floodplains, and participating in the National Flood Insurance Program. Detailed urban flood analyses are used to develop inundation map libraries. These map libraries are meant to supplement National Weather Service river stage flood forecasts by providing a visual representation of potential flood extent according to predicted river stage at stream gage locations.

The execution of hydraulic models at large spatial scales has yielded a step change in our understanding of flood risk. Yet their necessary simplification through the use of coarsened terrain data results in an artificially smooth digital elevation model with diminished representation of flood defense structures. Current approaches in dealing with this, if anything is done at all, involve either employing incomplete inventories of flood defense information or making largely unsubstantiated assumptions about defense locations and standards based on socioeconomic data. Here, we introduce a novel solution for application at scale. The geomorphometric characteristics of defense structures are sampled, and these are fed into a probabilistic algorithm to identify hydraulically relevant features in the source digital elevation model. The elevation of these features is then preserved during the grid coarsening process. The method was shown to compare favorably to surveyed U.S. levee crest heights. When incorporated into a continental-scale hydrodynamic model based on LISFLOOD-FP and compared to local flood models in Iowa (USA), median correspondence was 69% for high-frequency floods and 80% for low-frequency floods, approaching the error inherent in quantifying extreme flows. However, improvements versus a model with no defenses were muted, and risk-based deviations between the local and continental models were large. When simulating an event on the Po River (Italy), built and tested with higher quality data, the method outperformed both undefended and even engineering-grade models. As such, particularly when employed alongside model components of commensurate quality, the method here generates improved-accuracy simulations of flood inundation. Plain Language SummaryTraditional flood risk assessments are carried out using computer models built with local data, but their spatial coverage is impaired by how expensive and time-consuming they are to produce. Recent advances in data availability, understanding of necessary physical process representation, and computational capacity have enabled hydraulic models of the entire globe to be built in an automated fashion at a fraction of the financial and human cost. However, their accuracy can be significantly impaired by a lack of information on flood defenses. As the model is built, elevation data are coarsened to reduce the number of calculations required to simulate flooding over such wide areas. This results in flood defense structures being smoothed out of the terrain information used in the model. Publicly available defense inventories are of insufficient coverage to ameliorate this issue. In this paper, a method is presented, which automatically detects levee-like features in high-resolution elevation data and accurately represents their heights during this necessary coarsening process. Simulating flood inundation over this "defended" topography results in high correspondence between local models and observations for test cases in the United States and Italy, with improve...

Various techniques exist to estimate stream nitrate loads when measured concentration data are sparse. The inherent uncertainty associated with load estimation, however, makes tracking progress toward water quality goals more difficult. We used high‐frequency, in situ nitrate sensors strategically deployed across the agricultural state of Iowa to evaluate 2016 stream concentrations at 60 sites and loads at 35 sites. The generated data, collected at an average of 225 days per site, show daily average nitrate‐N yields ranging from 12 to 198 g/ha, with annual yields as high as 53 kg/ha from the intensely drained Des Moines Lobe. Thirteen of the sites that capture water from 82.5% of Iowa's area show statewide nitrate‐N loading in 2016 totaled 477 million kg, or 41% of the load delivered to the Mississippi–Atchafalaya River Basin (MARB). Considering the substantial private and public investment being made to reduce nitrate loading in many states within the MARB, networks of continuous, in situ measurement devices as described here can inform efforts to track year‐to‐year changes in nitrate load related to weather and conservation implementation. Nitrate and other data from the sensor network described in this study are made publicly available in real time through the Iowa Water Quality Information System.

Jesse Piotrowski has also provided a great deal of technical support in the development of models and software. Brian Miller and Mark Wilson provided prompt computer and systems support. I would also like to thank my committee members, Drs. Witek Krajewski and A. Allen Bradley Jr. for their contributions and sharing of their expertise. Support of the Iowa Flood Center has made completion of this thesis possible. I would like to acknowledge my parents for their support and sacrifices. They have given much so that I may have an education. I would like thank my wife, Amanda, for her support and understanding during my late nights at the lab.

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