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Hydrological drainage/river basins constitute highly heterogeneous systems of coupled natural and anthropogenic water and pollutant flows across political, national and international boundaries. These flows need to be appropriately understood, quantified and communicated to stakeholders, in order to appropriately guide environmental water system management. In this thesis, various uncertainties about water and pollutant flows in drainage/river basins and their implications for effective and efficient water pollution abatement are investigated, in particular for mine-related heavy metal loadings in the Swedish Dalälven River basin and for nitrogen loadings in the Swedish Norrström drainage basin. Economic cost-minimization modeling is used to investigate the implications of pollutant load uncertainties for the cost-efficiency of catchment-scale abatement of water pollution. Results indicate that effective and efficient pollution abatement requires explicit consideration of uncertainties about pollution sources, diffuse contributions of the subsurface water system to downstream pollutant observations in surface waters, and downstream effects of different possible measures to reduce water pollution. In many cases, downstream load abatement measures must be used, in addition to source abatement, in order to reduce not only expected, but also uncertainties around expected pollutant loads. Effective and efficient environmental management of water systems must generally also consider the entire catchments of these systems, rather than focusing only on discrete pollutant sources. The thesis presents some relatively simple, catchment-scale pollutant flow analysis tools that may be used to decrease uncertainties about unmonitored water and pollutant flows and subsurface pollutant accumulation-depletion and diffuse loading to downstream waters.

<p>Hydrological drainage/river basins constitute highly heterogeneous systems of coupled natural and anthropogenic water and pollutant flows across political, national and international boundaries. These flows need to be appropriately understood, quantified and communicated to stakeholders, in order to appropriately guide environmental water system management. In this thesis, various uncertainties about water and pollutant flows in drainage/river basins and their implications for effective and efficient water pollution abatement are investigated, in particular for mine-related heavy metal loadings in the Swedish Dalälven River basin and for nitrogen loadings in the Swedish Norrström drainage basin. Economic cost-minimization modeling is used to investigate the implications of pollutant load uncertainties for the cost-efficiency of catchment-scale abatement of water pollution.</p><p>Results indicate that effective and efficient pollution abatement requires explicit consideration of uncertainties about pollution sources, diffuse contributions of the subsurface water system to downstream pollutant observations in surface waters, and downstream effects of different possible measures to reduce water pollution. In many cases, downstream load abatement measures must be used, in addition to source abatement, in order to reduce not only expected, but also uncertainties around expected pollutant loads. Effective and efficient environmental management of water systems must generally also consider the entire catchments of these systems, rather than focusing only on discrete pollutant sources. The thesis presents some relatively simple, catchment-scale pollutant flow analysis tools that may be used to decrease uncertainties about unmonitored water and pollutant flows and subsurface pollutant accumulation-depletion and diffuse loading to downstream waters.</p>

The planet is currently experiencing a massive shift in the migration of people towards highly populous metropolitan regions which offer a better quality of life, which has resulted in rapid development and expansion. Meanwhile, the recent studies on climate change have shed light on precipitation events becoming increasingly wetter and intense. This rapid change in the land use patterns coupled with the climate change has increased the risk of flooding and puts the massive investment in the infrastructure, economy, and human life at a greater risk than ever before. This study aims to analyze the impacts of the stormwater infrastructure on the hydrology and hydraulics of highly urbanized environments. Traditional flood modeling approaches of independent hydrologic and hydraulic models have progressed into more complex models which can integrate the surface and sub-surface along with their interactions as the understanding of these physical processes and the availability of computational power has increased. A fully integrated hydro-systems model based on a distributed modeling approach is developed for a portion of the City of Minneapolis in Minnesota, USA which incorporates the surface hydraulics, stormwater infrastructure, vadose zone and a dynamic water table which realistically represents all the hydrologic and hydraulics processes. The result of this study shows the incorporation of the stormwater infrastructure in the integrated model leads to lower flood inundation areas, reduced vadose zone storage and lowered groundwater table for design flows as well as real events. The model displayed consistent results for the impact of stormwater infrastructure when tested across varied antecedent soil conditions. Ultimately this study proposes the implementation of a fully integrated hydro-systems modeling approach which link the hydrology and the hydraulics of the surface, sub-surface and stormwater infrastructure systems for a better representation of the flood hydrodynamics in urbanized regions.

Regional hydrology is experiencing significant changes due to a combination of land use and land cover (LULC) transformations and the growing impact of climate change. As cities expand into what were once agricultural or forested areas, the hydrological characteristics of watersheds undergo substantial shifts, influencing stream flow and flood volumes. To better understand these changes, this study integrates two complementary approaches. The first utilizes the Cellular Automata–Markov (CA–Markov) model to project LULC changes, predicting substantial urban growth from 11.6% of total area in 2021 to 34.1% by 2050 and 44.2% by 2080. This urbanization correlates with increased peak discharge and runoff volume for a 100-year return period storm event, with peak discharge rising by 5% and 6.8%, and runoff volume by 8% and 13.3% by 2050 and 2080, respectively. The second approach employs the North American Regional Climate Change Assessment Program (NARCCAP) climate model to project future storm depths, with extreme climate scenarios suggesting an increase of up to 104% in storm depths, leading to a 37.72% rise in peak discharge and an 88.73% rise in flooding volume. To mitigate these impacts, Low Impact Development (LID) techniques such as Permeable Pavement, Green Roofs, and Bio-Retention Cells were evaluated for their effectiveness in reducing climate change-induced flood risks. Using the validated Personal Computer Storm Water Management Model (PCSWMM) to simulate rainfall-runoff scenarios, the results indicated that Permeable Pavement could reduce peak discharge by up to 28.57%, with Green Roofs and Bio-Retention Cells reducing peak discharge by up to 19.93% and 14.25%, respectively. These findings underscore the importance of implementing sustainable water management practices as part of urban planning to address the dual challenges of LULC changes and climate change-induced flooding, providing a path toward more resilient urban environments.

In the agricultural Midwest, subsurface drainage allows excess water to drain into agricultural channels, which flows into rivers and streams transporting excess nutrients downstream. The construction of an inset floodplain within agricultural channels enhances sedimentation of particulate nutrients and sediment, provides stable conditions for vegetation to establish, increases rates of microbial activity, and promotes denitrification. Sediments were collected from floodplains of two-stage channels and naturally forming floodplain benches in conventional channels to determine the effect of floodplain creation on carbon and nitrogen cycling. Denitrification rates were seasonally measured across the floodplain width using an unamended acetylene inhibition technique (DNFAIT). Composite respiration and denitrification rates were measured through sacrificial microcosms utilizing membrane inlet mass spectrometry (DNFMIMS). While the two-stage reach showed a significant increase in soil organic matter (two-way ANOVA, p < 0.001) and respiration rates (two-way ANOVA, p = 0.039), there was no effect on DNFMIMS rates (two-way ANOVA, p > 0.05). DNFAIT rates at the two-stage reach only showed an increase at locations closest to the channel (two-way ANOVA, p = 0.008). Nutrient processing rates were most dependent on local environmental conditions, particularly organic matter and sediment grain size. This suggests that site-specific conditions may dictate the impact of floodplain creation on water quality. However, because of the increase in biologically active surface area, the net effect on water quality is likely greater for the two-stage channels.

Urban populations are growing at unprecedented rates around the world, while simultaneously facing increasingly intense impacts of climate change, from sea level rise to extreme weather events. In the face of this concurrent urbanization and climate change, it is imperative that cities improve their resilience to a multitude of stressors. A key aspect of urban resilience to climate change is ensuring that there is enough drinking water available to service the city, especially given the projections of more frequent and intense droughts in some areas. However, the study of climate impacts on urban water resources is fairly nascent and many gaps remain. In this dissertation, I aim to begin to close some of those gaps by adopting an interdisciplinary approach to studying water availability. First, I focus on urban water supply, and in particular, reservoir operations. I employ a variety of methods, ranging from data science techniques to traditional hydrological models, to predict the reservoir levels under a variety of climate conditions. Following the analysis of water supply, I shift focus to urban water demand. Here, I include interconnected systems, such as electricity, to evaluate and characterize the impact of climate on water demand and the benefit of considering system interconnectivities. Additionally, I present an analysis on the projection of water and electricity demand into the future, based on representative concentration pathways of CO₂. Finally, I focus on the human dimension to the demand studies. By studying the social norms surrounding water conservation in urban areas, as well as the demographics, I built a predictive model to estimate monthly water consumption at the census tract-level. Through these interdisciplinary studies, I have made progress in filling knowledge gaps related to the impact of climate change on urban water resources, as well as the impact of people on these water resources.

Spatial interpolation techniques play an important role in hydrology as many point observations need to be interpolated to create continuous surfaces. Despite the availability of several tools and methods for interpolating data, <a>not all of them work consistently for hydrologic applications</a><a>. One of the techniques, Laplace Equation, which is used in hydrology for creating flownets, has rarely been used for interpolating hydrology data</a>. The objective of this study is to examine the efficiency of Laplace formulation (LF) in interpolating hydrologic data and compare it wih other widely used methods such as the inverse distance weighting (IDW), natural neighbor, and kriging. Comparison is performed quantitatively for using root mean square error (RMSE), visually for creating reasonable surfaces and computationally for ease of operation and speed. Data related to surface elevation, river bathymetry, precipitation, temperature, and soil moisture data are used for different areas in the United States. RMSE results show that LF performs better than IDW and is comparable to other methods for accuracy. LF is easy to use as it requires fewer input parameters compared to IDW and Kriging. Computationally, LF is comparable to other methods in terms of speed when the datasets are not large. Overall, LF offers an robust alternative to existing methods for interpolating various hydrology data. Further work is required to improve its computational efficiency with large data size and find out the effects of different cell size.

<p>Rivers are constantly undergoing change due to erosion and sedimentation along their banks. Although these processes generally occur gradually, flood events can significantly accelerate river migration, creating a risk for human life and infrastructure. As a result, it is important to identify river reaches that are prone to channel migration and determine the extent of migration. However, detailed information about river migration across entire river networks is not readily available. This study seeks to develop a Python-based tool that can generate river migration rasters across large watersheds using Landsat imagery. The methodology involves extracting the centerlines of river features in Landsat imagery using the Modified Normalized Difference Water Index (MNDWI) and the Skeletonize function available in the scikit-image library, followed by the application of the Particle Image Velocimetry (PIV) algorithm to compute the river channel migration. The PIV algorithm generates a set of migration rasters that are analyzed to extract the long-term migration of each of the reaches. The tool also creates intermediate outputs, such as the MNDWI raster, binary land-water raster, and skeletonized river centerlines, which can be further analyzed to gain insights into the river's behavior. The methodology is implemented in the Wabash and Lower Mississippi River Basins, and the tool's effectiveness is validated against manual measurements of the river migration available for the Wabash Basin. In addition, this study analyzes the correlation between long-term migration and various factors, such as reach sinuosity, drainage area, geology, and streamflow. The results of the analysis show that drainage area is highly correlated with river migration. The correlation results are compared with the prior literature, thereby serving to validate the developed framework. This framework has the potential to aid decision-makers and policymakers in identifying the long-term patterns of river channel migration, facilitating their efforts to plan for infrastructure resilience. By utilizing this methodology, river managers and other stakeholders can gain insights into river migration across large watersheds and identify areas that require further monitoring and management.</p>

<p dir="ltr">Recent high-water levels in Lake Michigan caused extensive shoreline changes along the Indiana coastline. To evaluate recent shoreline changes of the Indiana coastline along Lake Michigan, topographic LiDAR surveys available for the years 2008, 2012, 2013, 2018, 2020, and 2022 were analyzed. This study included LiDAR data of over 400 cross-shore transects, generated at 100 m spacing. Beach profiles were generated to detect the shoreline position and quantify beach width and nearshore volume change. The analysis revealed accretion of both shoreline and beach width from 2008 to 2013 during a low water level period. The beach was rebuilt with a median increased value of 4 m. On the contrary, the shoreline eroded during increasing and high-water periods. Both shoreline and beach width receded with median values of 41 m and 32 m respectively during the period of water level increase from 2013 to 2020. Consequently, the beach profiles lost a median sand volume of 21.6 m³/m. Overall, the Indiana shoreline moved with a median of 18 m landward from 2008 to 2022. However, there was a large amount of spatial variability in the shoreline changes. The shoreline movement varied spatially between 63 m recession to 29 m accretion. Similarly, beach profiles showed a loss of median sand volume of 10 m³/m. The volume change ranged from 918 m³/m loss to 296 m³/m accumulation varying spatially along the shoreline. The largest sand loss was experienced at the downdrift of Michigan city harbor near Mt. Baldy. In addition to the spatial variation, the recession also varied slightly with shoreline type. The natural and hardened beaches were mostly recessional. The recession along the hardened shoreline was influenced by the timing of construction and its proximity to inland areas. Buffered beaches, characterized by a swath of vegetation or dunes, experienced the least erosion.</p>