Monitoring and Evaluating the Influences of Class V Injection Wells on Urban Karst Hydrology

The response of a karst aquifer to storm events is often faster and more severe than that of a non-karst aquifer. This distinction is often problematic for planners and municipalities, because karst flooding does not typically occur along perennial water courses; thus, traditional flood management strategies are usually ineffective. The City of Bowling Green (CoBG), Kentucky is a representative example of an area plagued by karst flooding. The CoBG, is an urban karst area (UKA), that uses Class V Injection Wells to lessen the severity of flooding. The overall effectiveness, siting, and flooding impact of Injection Wells in UKA’s is lacking; their influence on groundwater is evident from decades of recurring problems in the form of flooding and groundwater contamination. This research examined Class V Injection Wells in the CoBG to determine how Injection Well siting, design, and performance influence urban karst hydrology. The study used high-resolution monitoring, as well as hydrologic modeling, to evaluate Injection Well and spring responses during storm and baseflow conditions. In evaluating the properties of the karst aquifer and the influences from the surrounding environment, a relationship was established between precipitation events, the drainage capacity of the Injection Wells, and the underlying karst system. Ultimately, the results from this research could be used to make sound data-driven policy recommendations and to inform stormwater management in UKAs

Fluvial Processes in Motion: Measuring Bank Erosion and Suspended Sediment Flux using Advanced Geomatic Methods and Machine Learning

Excessive erosion and fine sediment delivery to river corridors and receiving waters degrade aquatic habitat, add to nutrient loading, and impact infrastructure. Understanding the sources and movement of sediment within watersheds is critical for assessing ecosystem health and developing management plans to protect natural and human systems. As our changing climate continues to cause shifts in hydrological regimes (e.g., increased precipitation and streamflow in the northeast U.S.), the development of tools to better understand sediment dynamics takes on even greater importance. In this research, advanced geomatics and machine learning are applied to improve the (1) monitoring of streambank erosion, (2) understanding of event sediment dynamics, and (3) prediction of sediment loading using meteorological data as inputs. Streambank movement is an integral part of geomorphic changes along river corridors and also a significant source of fine sediment to receiving waters. Advances in unmanned aircraft systems (UAS) and photogrammetry provide opportunities for rapid and economical quantification of streambank erosion and deposition at variable scales. We assess the performance of UAS-based photogrammetry to capture streambank topography and quantify bank movement. UAS data were compared to terrestrial laser scanner (TLS) and GPS surveying from Vermont streambank sites that featured a variety of bank conditions and vegetation. Cross-sectional analysis of UAS and TLS data revealed that the UAS reliably captured the bank surface and was able to quantify the net change in bank area where movement occurred. Although it was necessary to consider overhanging bank profiles and vegetation, UAS-based photogrammetry showed significant promise for capturing bank topography and movement at fine resolutions in a flexible and efficient manner. This study also used a new machine-learning tool to improve the analysis of sediment dynamics using three years of high-resolution suspended sediment data collected in the Mad River watershed. A restricted Boltzmann machine (RBM), a type of artificial neural network (ANN), was used to classify individual storm events based on the visual hysteresis patterns present in the suspended sediment-discharge data. The work expanded the classification scheme typically used for hysteresis analysis. The results provided insights into the connectivity and sources of sediment within the Mad River watershed and its tributaries. A recurrent counterpropagation network (rCPN) was also developed to predict suspended sediment discharge at ungauged locations using only local meteorological data as inputs. The rCPN captured the nonlinear relationships between meteorological data and suspended sediment discharge, and outperformed the traditional sediment rating curve approach. The combination of machine-learning tools for analyzing storm-event dynamics and estimating loading at ungauged locations in a river network provides a robust method for estimating sediment production from catchments that informs watershed management

A New Machine-Learning Approach for Classifying Hysteresis in Suspended-Sediment Discharge Relationships Using High-Frequency Monitoring Data

Studying the hysteretic relationships embedded in high-frequency suspended-sediment concentration and river discharge data over 600+ storm events provides insight into the drivers and sources of riverine sediment during storm events. However, the literature to date remains limited to a simple visual classification system (linear, clockwise, counter-clockwise, and figure-eight patterns) or the collapse of hysteresis patterns to an index. This study leverages 3 years of suspended-sediment and discharge data to show proof-of-concept for automating the classification and assessment of event sediment dynamics using machine learning. Across all catchment sites, 600+ storm events were captured and classified into 14 hysteresis patterns. Event classification was automated using a restricted Boltzmann machine (RBM), a type of artificial neural network, trained on 2-D images of the suspended-sediment discharge (hysteresis) plots. Expansion of the hysteresis patterns to 14 classes allowed for new insight into drivers of the sediment-discharge event dynamics including spatial scale, antecedent conditions, hydrology, and rainfall. The probabilistic RBM correctly classified hysteresis patterns (to the exact class or next most similar class) 70% of the time. With increased availability of high-frequency sensor data, this approach can be used to inform watershed management efforts to identify sediment sources and reduce fine sediment export

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Soil Erosion

In the first section of this book on soil erosion, an introduction to the soil erosion problem is presented. In the first part of the second section, rainfall erosivity is estimated on the basis of pluviograph records and cumulative rainfall depths by means of empirical equations and machine learning methods. In the second part of the second section, a physically-based, hydrodynamic, finite element model is described for the computation of surface runoff and channel flows. In the first part of the third section, the soil erosion risk is assessed in two different basins. In the second part of the third section, the soil erosion risk management in a basin is evaluated, and the delimitation of the areas requiring priority planning is achieved

Variance Decomposition of Forecasted Water Budget and Sediment Processes under Changing Climate in Fluvial and Fluviokarst Systems

Variance decomposition is the partitioning of different factors affecting the variance structure of a response variable. The present research focuses on future streamflow and sediment transport processes projections as the response variables. The authors propose using numerous climate factors and hydrological modeling factors that can cause any response variable to vary from historic to future conditions in any given watershed system. The climate modeling factors include global climate model, downscaling method, emission scenario, project phase, bias correction. The hydrological modeling factor includes hydrological model parametrization, and meteorological variable inclusion in the analysis. This research uses a wide spectrum of data, including climate data of precipitation and temperature from GCM results, and observations of meteorological data, streamflow and spring flow data, and sediment yield data. This research focuses on employing an off-the-shelf hydrological model and developing different numerical models (using MATLAB) for simulating sediment transport processes and water movement in an epigenetic karst system. With regards to variance decomposition, the approach is to use a mixed statistical method of linear and nonlinear analysis by means of analysis of variance (ANOVA) and artificial neural networks (ANN) respectively. All the computational tools that will be used to perform the statistics are provided by SPSS software. Two study sites are considered in this work including South Elkhorn watershed and Cave Run watershed. South Elkhorn watershed is a fluvial system and is located in Lexington, Kentucky, USA. This system is characterized as a wet, temperate region in the central and eastern United States where a change in the climate is projected. The mean streamflow, extreme streamflow, and sediment processes forecast are investigated in this watershed. Royal Spring watershed is a fluviokarst system and is adjacent to the South Elkhorn watershed. In this watershed we investigate the water pathway connectivity as well as the impact of climate change on the mean annual spring flow and streamflow. Analysis of variance results indicate that the difference in forecast and hindcast mean streamflow predictions is a function of GCM type, climate model project phase, and downscaling approach. Predicted average monthly change in streamflow tends to follow precipitation changes and result in a net increase in the average annual precipitation and streamflow by 10% and 11%, respectively, when comparing historical period (1980-2000) to the future period (2045-2065). Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima method versus peak over threshold method. However, it was dependent all climate modeling factors. Ensemble projections forecast an increase of streamflow maxima of 51% for 100-year streamflow event. Hydrologic model parameterization was the greatest source of variance impacting forecasted sediment transport variables. Hydrologic inputs from climate change including forecasted precipitation, temperature, relative humidity, solar radiation and wind speed all impacted sediment transport. Ensemble average forecasts sediment yield to increase by 14% for the Upper South Elkhorn watershed. The numerical model of the Cave Run/ Royal Spring watershed suggests 30 to 45% of surface stream discharge originates from in-stream swallet reversal and hillside springs. Also, the hydrology of the floviokarst system might be altered by the impact of climate change where an increase in the surface flow and spring flow is projected to be 8.8% and 12.2%, respectively. The results show that the change in pathway connectivity is important on seasonal bases and follows the seasonal change in precipitations

A Predictive Flood Model for Urban Karst Groundwater Systems

Urban karst environments are often plagued by groundwater flooding, which occurs when water rises from the subsurface to the surface through the underlying caves and other karst features. The heterogeneity and interconnectedness of karst systems often makes them very unpredictable, especially during intense storm events; urbanization exacerbates the problem with the addition of many impervious surfaces. Residents in such areas are frequently disturbed and financially burdened by the effects of karst groundwater flooding. The Federal Emergency Management Agency (FEMA) offers limited protection to citizens living near flood-prone areas as they primarily focus on the areas near surface bodies of water. The City of Bowling Green, Kentucky is one of the largest cities in the United States built entirely upon karst and experiences frequent, unpredictable groundwater flooding making it the ideal study area for this project. This research attempted to aid the flooding problem in Bowling Green, by laying the framework for the creation of a predictive flood model in the Lost River Karst Aquifer, in Bowling Green, KY. The model was created primarily by analyzing relationships between precipitation and antecedent moisture conditions of the aquifer using effective precipitation and antecedent water levels as a proxy. High-resolution, spatiotemporal data monitoring of several hydrometeorological parameters to ensure accuracy of the model. The results from this study provide a stable and validated methodology to create a predictive flood model for karst environments that could potentially allow residents to better prepare for rain events and offers additional information on the storage and response times of a large karst aquifer

Advancing Urban Flood Resilience With Smart Water Infrastructure

Advances in wireless communications and low-power electronics are enabling a new generation of smart water systems that will employ real-time sensing and control to solve our most pressing water challenges. In a future characterized by these systems, networks of sensors will detect and communicate flood events at the neighborhood scale to improve disaster response. Meanwhile, wirelessly-controlled valves and pumps will coordinate reservoir releases to halt combined sewer overflows and restore water quality in urban streams. While these technologies promise to transform the field of water resources engineering, considerable knowledge gaps remain with regards to how smart water systems should be designed and operated. This dissertation presents foundational work towards building the smart water systems of the future, with a particular focus on applications to urban flooding. First, I introduce a first-of-its-kind embedded platform for real-time sensing and control of stormwater systems that will enable emergency managers to detect and respond to urban flood events in real-time. Next, I introduce new methods for hydrologic data assimilation that will enable real-time geolocation of floods and water quality hazards. Finally, I present theoretical contributions to the problem of controller placement in hydraulic networks that will help guide the design of future decentralized flood control systems. Taken together, these contributions pave the way for adaptive stormwater infrastructure that will mitigate the impacts of urban flooding through real-time response.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163144/1/mdbartos_1.pd