Iowa first started seeing largescale changes to its landscape with the arrival of Europeans and ensuing conversion of forest and prairie to row crops and pasture and would see its landscape altered again in the early 1900s through the conversion of wetlands to row crops. Watersheds in Iowa, and the Midwest at large, have been drastically altered hydrologically—through land use change, tile drainage, digging of drainage ditches, and channelizing of meandering streams. Though drainage practices maximize arable land, they also induce higher flood peaks. Along with these practices, climate change also has the potential to increase flood peaks. Conservation practices —typically employed to reduce erosion and agricultural pollution—have been proposed to be used to reduce flood peaks, but little analysis has been done on their ability to do so at the watershed-scale. To quantify the impact implementing conservation practices at the watershed-scale has on flood peaks, a novel hydrologic model is run to simulate five conservation scenarios under both historic and increased precipitation continuously for seventeen years. The Generic Hydrologic Overland-Subsurface Toolkit (GHOST) is used to model the Boone River, an agriculturally dominated watershed in Iowa. The Boone River model is calibrated against the United States Geological Survey gaging station near the outlet of the watershed and achieves notable success. For the seventeen year study period from 2002 to 2018, calibration achieved a Nash Sutcliffe efficiency of 0.79, percent bias of -6.0 percent, and R2 of 0.80. To simulate the change from the baseline to a conservation practice, changes were made to the parameters of the baseline, calibrated model to reflect the effects of conservation practices. Scenarios run were the return of row crop acres to native vegetation, improved soil health via cover crops and no-till farming, distributed wetlands, conversion of river-adjacent row crop elements to native vegetation, and conversion of stream order one river-adjacent row crop elements to native vegetation. Results for the seventeen year study period show the average peak flow reductions simulated for the conservation scenarios are 82, 39, 9, 13, and 9 percent respectively for annual maximums and 75, 29, 10, 11, and 7.5 percent respectively for the peaks over the 2-year flood threshold. Of the five scenarios modeled, only native vegetation and cover crops were able to offset the increased precipitation anticipated from climate change.

On June 13, 2008, after many days of rain, the Cedar River flooded the city of Cedar Rapids. With a peak discharge of 139,987 cfs and at 19.12 feet above flood stage, the 2008 flood of Cedar Rapids was the largest flood in the city’s historic record. As rivers rose, the city had received forecasts of an incoming flood as early as June 8. Then, on June 12, it began to rain in Cedar Rapids. Finally, on June 13, 2008, the Middle Cedar crested at 31.12 feet. This thesis project modeled a variety of rainfall patterns on June 12, 2008, to determine the effect of varying rainfall intensity and location on the magnitude of the 2008 flood of Cedar Rapids. Using a method known as Stochastic Storm Transposition (SST), I overwrote precipitation data in a hydrologic model of the Middle Cedar Watershed with rainfall data extracted from specific storm events that occurred in the Upper Midwest. We used a physically-based, semi-distributed hydrologic model known as GHOST (Generic Hydrologic Overland-Subsurface Toolkit) developed by Marcela Politano at the University of Iowa. Traditionally, hydrologic modeling for watersheds has used design storms to create rainfall inputs in flood modeling. These design storms have uniform rainfall timing and accumulation patterns across a watershed and are determined by designated equations for a geographic region. In large watersheds such as the Middle Cedar (2,400 square miles), design storms are not physically realistic because of their uniformity. Additionally, design storms fail to capture unique storm patterns such as high intensity periods or the movement of a storm across a watershed. By implementing SST into GHOST, we used physically realistic storm events that have unique rainfall patterns and intensities within a designated return period. SST extracts rainfall data from real storm events and transposes the storm patterns onto watersheds to provide physically realistic rainfall data for hydrologic modeling. A tool called RainyDay, developed by Professor Daniel Wright at the University of Wisconsin, provided the storm transpositions used in this research. We assigned the storm transpositions return periods created by RainyDay, corresponding to their average transposed rainfall across the Middle Cedar Watershed. Replacing the June 12 rainfall with RainyDay’s two-year transposed storm events (average rain accumulation 1.8 inches) resulted in modeled flood peaks larger than the unaltered June 12 flood peak. Storm transpositions of 5-, 10-, and 2,000-year return periods showed even larger peaks, illustrating the potential for floods much larger than the 2008 flood. In addition to the analysis of flood magnitude in 2008, we modeled a set of storm transposition scenarios for a variety of soil-moisture conditions. The increased discharge levels in scenarios with high soil moisture emphasize the importance of initial conditions in flooding scenarios. Finally, we modeled the effect that two-year RainyDay storms would have had on the 2016 flood of Cedar Rapids had they occurred on the day before the peak. The two-year transpositions showed that with an impending flood crest smaller than the 2008 crest, several two-year RainyDay scenarios would have resulted in floods nearly equal in magnitude to the 2008 flood event. Our manipulation of the rainfall in the Middle Cedar Watershed on June 12, 2008, using the GHOST model provided the opportunity to re-examine the influence that a specific day of rainfall had on the 2008 flood of Cedar Rapids. The potential for higher flooding under conditions of repeated rainfall and high soil moisture illustrates the susceptibility of the Middle Cedar Watershed to future flood events under similar conditions. Applying SST in hydrologic modeling also provided an opportunity to model a variety of rainfall scenarios and to better understand watershed responses to nuanced and physically realistic rainfall patterns.

The purpose of this study was to evaluate variations of the Natural Resources Conservation Service (NRCS) curve number (CN) method for estimating near real-time runoff for naturalized flow, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. The CN method is an empirical method for calculating surface runoff which has been tested on various systems over a period of several years. Many of the findings of previous studies indicate the need to develop variations of this method to account for regional and seasonal changes in weather patterns and land cover that might affect runoff. This study seeks to address these issues, as well as the inherent spatial variability of rainfall, in order to develop a means of predicting runoff in near real-time for water resource management. In the past, raingauge networks have provided data for hydrologic models. However, these networks are generally unable to provide data in real-time or capture the spatial variability associated with rainfall. Radar networks, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS), which are widely available and continue to improve in quality and resolution, can accomplish these tasks. In general, a statistical comparison of the raingauge and NEXRAD data, where both were available, shows that the radar data is as representative of observed rainfall as raingauge data. In this study, watersheds of mostly homogenous land cover and naturalized flow were used as study areas. Findings indicate that the use of a dry antecedent moisture condition CN value and an initial abstraction (Ia) coefficient of 0.1 produced statistically significant results for eight out of the ten watersheds tested. The urban watershed used in this study produced more significant results with the use of the traditional 0.2 Ia coefficient. The predicted results before and during the growing season, in general, more closely agreed with the observed runoff than those after the growing season. The overall results can be further improved by altering the CN values to account for seasonal vegetation changes, conducting field verification of land cover condition, and using bias-corrected NEXRAD rainfall data.

This study focuses on developing and evaluating efficient and effective parameter calibration and uncertainty methods for hydrologic modeling. Five single objective optimization algorithms and six multi-objective optimization algorithms were tested for automatic parameter calibration of the SWAT model. A new multi-objective optimization method (Multi-objective Particle Swarm and Optimization & Genetic Algorithms) that combines the strengths of different optimization algorithms was proposed. Based on the evaluation of the performances of different algorithms on three test cases, the new method consistently performed better than or close to the other algorithms. In order to save efforts of running the computationally intensive SWAT model, support vector machine (SVM) was used as a surrogate to approximate the behavior of SWAT. It was illustrated that combining SVM with Particle Swarm and Optimization can save efforts for parameter calibration of SWAT. Further, SVM was used as a surrogate to implement parameter uncertainty analysis fo SWAT. The results show that SVM helped save more than 50% of runs of the computationally intensive SWAT model The effect of model structure on the uncertainty estimation of streamflow simulation was examined through applying SWAT and Neural Network models. The 95% uncertainty intervals estimated by SWAT only include 20% of the observed data, while Neural Networks include more than 70%. This indicates the model structure is an important source of uncertainty of hydrologic modeling and needs to be evaluated carefully. Further exploitation of the effect of different treatments of the uncertainties of model structures on hydrologic modeling was conducted through applying four types of Bayesian Neural Networks. By considering uncertainty associated with model structure, the Bayesian Neural Networks can provide more reasonable quantification of the uncertainty of streamflow simulation. This study stresses the need for improving understanding and quantifying methods of different uncertainty sources for effective estimation of uncertainty of hydrologic simulation.

The purpose of this study was to evaluate variations of the Natural Resources Conservation Service (NRCS) curve number (CN) method for estimating near real-time runoff for naturalized flow, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. The CN method is an empirical method for calculating surface runoff which has been tested on various systems over a period of several years. Many of the findings of previous studies indicate the need to develop variations of this method to account for regional and seasonal changes in weather patterns and land cover that might affect runoff. This study seeks to address these issues, as well as the inherent spatial variability of rainfall, in order to develop a means of predicting runoff in near real-time for water resource management. In the past, raingauge networks have provided data for hydrologic models. However, these networks are generally unable to provide data in real-time or capture the spatial variability associated with rainfall. Radar networks, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS), which are widely available and continue to improve in quality and resolution, can accomplish these tasks. In general, a statistical comparison of the raingauge and NEXRAD data, where both were available, shows that the radar data is as representative of observed rainfall as raingauge data. In this study, watersheds of mostly homogenous land cover and naturalized flow were used as study areas. Findings indicate that the use of a dry antecedent moisture condition CN value and an initial abstraction (Ia) coefficient of 0.1 produced statistically significant results for eight out of the ten watersheds tested. The urban watershed used in this study produced more significant results with the use of the traditional 0.2 Ia coefficient. The predicted results before and during the growing season, in general, more closely agreed with the observed runoff than those after the growing season. The overall results can be further improved by altering the CN values to account for seasonal vegetation changes, conducting field verification of land cover condition, and using bias-corrected NEXRAD rainfall data.

Scientific studies have suggested an increase in the frequency and intensity of flooding. The research presented herein is focused on a small watershed, which has experienced intense flooding of a downstream, urbanized area. For emergency response and preparedness, it is pertinent to have the ability to predict intensity and peak flows of a flood. The Town of Dyer, Indiana has been severely impacted by flooding in the last twenty years. A 37.6 square mile watershed begins in a rural section of Illinois with tributaries draining into Plum Creek. The creek crosses into Indiana and becomes Hart Ditch, a straight, narrow, deep channel through the urbanized Town of Dyer. A HEC-HMS hydrologic model was used and calibrated based on USGS gage data. Storm events ranging from short, high intensity to long, intermittent precipitation provided a vast representation of possible scenarios within the watershed. The hydrologic model was paired with an unsteady HEC-RAS hydraulic model to allow for different lateral inflows to the creek providing variations of flow. A comparison between upstream and downstream stream gage readings was utilized to create a working model that predicts downstream water surface elevations for previous real-time storm and hypothetical storms. These conditions were analyzed by two stream gages and a correlation between the two gages was developed. This correlation was used to predict downstream water surface elevations. The correlation was also used to determine the time to crest based on readings at the upstream gage for many different storm events. The ability to know downstream water surface elevations for real-time storm events allows a window of time to implement emergency response in areas where flooding is imminent. The downstream area of concern has known flood elevations that represent various damage levels.

Hydrologic and hydraulic modeling are used to assess watershed function at different spatial and temporal scales. Many tools have been developed to make these types of models more accessible to use and model results easier to interpret. One tool that makes hydrologic models more accessible in a geographic information system (GIS) is the Automated Geospatial Watershed Assessment tool (AGWA); the GIS enables the development of spatially variable model inputs and model results for a variety of applications. Two major applications of AGWA are for rangeland watershed assessments and post-wildfire rapid watershed assessments. Each of these applications have primarily utilized the Kinematic Runoff and Erosion model (KINEROS2) which is accessible in AGWA. Two new tools were developed which work within the existing AGWA/KINEROS2 framework in ArcGIS to enhance rangeland and post-wildfire watershed assessments. The Storage Characterization Tool, was developed to work with high-resolution topographic data to characterize existing stock ponds so these features can easily be incorporated into AGWA/KINEROS2 for rangeland hydrologic analysis. The second tool simulates reach scale flood inundation (the Inundation Tool) utilizing AGWA/KINEROS2 outputs and local channel properties for Hydrologic Engineering Center (HEC-2) hydraulic calculations to compute flood inundation in post-wildfire environments. Both tools have been validated using multiple datasets and desired applications were outlined so that the tools are properly used.

The slope-area method is a commonly used and widely accepted technique for estimating peak flood flows in rivers where direct discharge measurements could not be obtained during the flood. The method makes multiple assumptions to simplify calculations which include assuming uniform flow conditions between surveyed cross sections, and that losses of energy in the reach occur only due to bank friction. Even though flows in nature do not always exhibit these simplified conditions, this method has been proven to provide adequate results when compared to direct measurements and thus, has become the go-to approach. To conduct a slope-area computation, the hydrologist needs to make multiple assumptions in the field based on experience, judgment, and published resources as guides. One of these assumptions is determining where to locate cross sections for the slope-area computation such that they sufficiently represent the cross-sectional area and slope of the channel. Traditional methods suggest to place the cross sections at breaks in the water surface slope. This research focuses on the variability of results of computed discharge values when cross sections are located at many different locations in the reach. What has been found is that many combinations of cross sections in the reach, including sections not located at the breaks in water surface slope, produce similar results when compared to the traditional methods. In fact, 121 of these combinations of cross sections produce peak discharge calculations within plus or minus five percent of the traditional methodology. What also was found was that variability in channel geometry goes unnoticed when using the traditional locating method, and losses due to expansion and contraction of flow area at locations which would not have been traditionally surveyed are occurring at multiple cross sections in the reach. The results suggest that reaches be evaluated for changes in geometry and not overlooked, so that the changes in shape, and subsequent losses in energy, be considered in the computation.

Water security is defined as a combination of water for achieving our goals as a society, and an acceptable level of water-related risks. Hydrologic modeling can be used to predict streamflow and aid in the decision-making process with the goal of attaining water security. Developed countries usually have their own hydrologic models; however, developing countries often lack hydrologic models due to factors such as the maintenance, computational costs, and technical capacity needed to run models. A global streamflow prediction system (GSPS) would help decrease vulnerabilities in developing countries and fill gaps in areas where no local models exist by providing extensive results that can be filtered for specific locations. The development of a GSPS has been deemed a grand challenge of the hydrologic community. To this end, many scientists and engineers have started to develop large-scale systems to an acceptable degree of success. Renowned models like the Global Flood Awareness System (GloFAS), the US National Water Model (NWM), and NASA's Land Assimilation System (LDAS) are proof that our ability to model large areas has improved remarkably. Even so, during this evolution the hydrologic community has started to realize that having a large-scale forecasting system does not make it immediately useful. New hydroinformatic challenges have surfaced that prevent these models from reaching their full potential. I have divided these challenges in four main categories: big data, data communication, adoption, and validation. I present a description of the background leading to the development of a GSPS including existing models, and the components needed to create an operational system. A case study with the NWM is also presented where I address the big data and data communication challenges by developing cyberinfrastructure and accessibility tools such as web applications and services. Finally, I used the GloFAS-RAPID model to create a forecasting system covering Africa, North America, South America, and South Asia using a service-oriented approach that includes the development of web applications, and services for providing improved data accessibility, and helping address adoption and validation challenges. I have developed customized services in collaboration with countries that include Argentina, Bangladesh, Colombia, Peru, Nepal, and the Dominican Republic. I also conducted validation tests to ensure that results are acceptable. Overall, a model-agnostic approach to operationalize a GSPS and provide meaningful results at the local level is provided with the potential to allow decision makers to focus on solving some of the most pressing water-related issues we face as a society.

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