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Browsing by Author "Shortridge, Julie Elizabeth"

Now showing 1 - 9 of 9
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    Advancing Water Security and Environmental Sustainability Through Evaluation of Water Use From the Field to State-Wide Scale
    Sangha, Laljeet Singh (Virginia Tech, 2023-01-17)
    The United States (US) has experienced a surge in water shortages and droughts in recent times. Water shortages can result from population growth, climate change, inadequate water management policies, and the improper use of available technologies. The existing data and research on water use associated with water management policy structures are limited. Many states in the US follow strict regulations on water discharge into streams to enforce water quality standards; however, water withdrawal restrictions from streams are limited and inadequate in terms of water management at times of low flow. In states such as Virginia (VA), the Virginia Department of Environmental Quality (VDEQ) requires a Virginia Water Protection (VWP) permit for all water withdrawals from VA's surface waters. However, under certain provisions of VWP regulations, users are exempted from having a permit. Such permit exemptions exist in many states and present a severe challenge to water supply management. Chapter 2 compares the impact of permit exemptions on surface water availability and drought flows and compares these impacts to the relatively well-studied risks presented by dry climate change and demand growth in Virginia (VA). It was observed that in some regions, the impacts under the exempt user scenario were higher than those under the dry climate change scenario. In addition, water supply managers and government agencies use user-reported water withdrawal data to develop water management programs. Irrigated agriculture is the largest source of water consumption in the US. However, water-reporting regulations exempt users from withdrawing water for irrigation under a certain threshold. Moreover, as water is not metered, users often do not report their irrigation water use, resulting in considerable uncertainty about the impacts of irrigation withdrawals, which could potentially impact other water users, lead to water shortages or conflicts, and negatively impact stream ecology. Chapter 3 focuses on developing a novel methodology for quantifying unreported irrigation water withdrawals using publicly available USDA-Census and USDA-IWMS datasets. This method was used to evaluate the unreported water withdrawals in the VA. Finally, water use practices at the field level intersect with other environmental issues at a larger scale. For example, irrigation practices can influence nutrient uptake and transport at the field level. Insufficient water for irrigation, especially during critical growth stages, results in yield and economic losses and reduces agricultural productivity. However, excessive irrigation can lead to wasted water and energy as well as runoff and leaching of nutrients and agricultural chemicals. Therefore, the adoption of technological advancements at the field scale can reduce the amount of water needed to fulfill the needs while mitigating any nutrient impacts on the soil due to the excessive use of water. This is highly important when fertilizer prices are always high. Chapter 4 focuses on quantifying the impact of the use of short-term weather forecast data in irrigation scheduling on nutrient and water use efficiency in humid climates: experimental results for corn and cotton. It was found that irrigation scheduling using short-term weather forecast data is helpful for improving the nutrient and water use efficiency of corn. For cotton, nutrient and water use efficiency are highly influenced by irrigation and precipitation with respect to the growth stage.
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    Assessing Climatic Hazards in Coastal Socio-Ecological Systems using Complex System Approaches
    Nourali, Zahra (Virginia Tech, 2024-05-31)
    Coastal socio-ecological systems face unprecedented challenges due to climate change, with impacts encompassing long-term, chronic changes and short-term extreme events. These events will impact society in many ways and prompt human responses that are extremely challenging to predict. This dissertation employs complex systems methods of agent-based modeling and machine learning to simulate the interactions between climatic stressors such as increased flooding and extreme weather and socio-economic aspects of coastal human systems. Escalating sea-level rise and intensified flooding has the potential to prompt relocation from flood-prone coastal areas. This can reduce flood exposure but also disconnect people from their homes and communities, sever longstanding social ties, and lower the tax base leading to difficulties in providing government services. Chapter 2 demonstrates a stochastic agent-based model to simulate human relocation influenced by flooding events, particularly focusing on the responses of rural and urban communities in coastal Virginia and Maryland. The findings indicate that a stochastic, bottom-up social system simulator is able to replicate top-down population projections and provide a baseline for assessing the impact of increasingly intense flooding. Chapter 3 leverages this model to assess how incorporating heterogeneity in relocation decisions across socio-economic groups impacts flood-induced relocation patterns. The results demonstrate how this heterogeneity leads to a decrease in low-income households, yet a rise in the proportion of elderly individuals in flood-prone regions by the end of the simulation period. Flood-prone areas also exhibit distinct income clusters at the end of simulation time horizon compared to simulations with a homogenous relocation likelihood. Lastly, Chapter 4 explores relationships between extreme weather and agricultural losses in the Delmarva Peninsula. Existing research on climatic impacts to agriculture largely focuses on changes to major crop yields, providing limited insights into impacts on diverse regional agricultural systems where human management and adaptation play a large role. By comparing various multistep modeling configurations and machine learning techniques, this work demonstrates that machine learning methods can accurately simulate and predict agricultural losses across the complex agricultural landscape that exists on the Delmarva peninsula. The multistep configurations developed in this work are able to address data imbalance and improve models' capacity to classify and estimate damage occurrence, which depends on multiple geographical, seasonal, and climatic factors. Collectively, this work demonstrates the potential for advanced modeling techniques to accurately replicate and simulate the impacts of climate on complex socio-ecological systems, providing insights that can ultimately support coastal adaptation.
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    Improved Environmental Characterization to Support Natural Resource Decision Making: (1) Distributed Soil Characterization, and (2) Treatment of Legacy Nutrients
    Buell, Elyce N. (Virginia Tech, 2022-09-27)
    Environmental concerns are becoming increasingly relevant during a period of hemorrhaging ecosystem goods and services. Restoring these would result in positive outcomes for public health and economic benefit. This thesis seeks to address two environmental concerns: (1) accurate soil mapping and (2) treatment of nitrogen to affect water quality change.The current method of soil mapping, SSURGO (USDA‐NRCS Soil survey), is often erroneous and misleading. Two studies in this dissertation are conducted to evaluate the potential that different resolution digital elevation models (DEMs) have to distribute soil characteristics successfully. These studies are conducted in southwest Virginia and western Vermont. The aforementioned studies evaluated 36 and 59 soil samples, respectively. Spatial characteristics, including slope, catchment area, and topographic wetness, are derived from several DEMs. In chapter 2, these characteristics are spatially compared, and we found that small resolution rasters result in narrow flow paths relative to coarser rasters. In chapter 3, we isolate the analysis to focus on resolution size, instead of a mix of both resolution size and generation method. This is done by recursively coarsening small rasters, deriving spatial attributes from said rasters and evaluating their potential to fit the soil characteristics of interest. Here we found that slopes generated from resolutions smaller than 11m were poor predictors of soil characteristics. Both chapters are finished by proposing and evaluating a soil map. Proposed regressions beat SSURGO in all investigated properties. Furthermore, proposed maps consistently beat out uninformed smallest resolution derived maps.Chesapeake bay water quality managers are struggling to achieve targets for nitrogen loading. This is in part due to the widespread presence of legacy nitrogen. Legacy nitrogen is an emerging issue, and springs exporting high levels of nitrogen are not uncommon in northern Virginia. This thesis explores, in part, a novel concept of treating large loads of nitrogen exported from a spring with a bioreactor. Bioreactors are a young science that most typically pair carbon heavy subterranean receptacles to agricultural drainage. This provides a location for nitrogen fixing bacteria to consume nitrate/nitrite, turning these into inert nitrogen gas. A spring fed bioreactor is studied for 10 months, and bioreactor conditions including influent and effluent nitrogen concentrations, bioreactor flow, and temperature are collected. A model driven by first order reaction equations is found to be most accurate with inputs of temperature and bioreactor age. The resulting marginal effects of these inputs were consistent with previously reported studies.
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    Improving Predictions of Stormwater Quantity and Quality through the Application of Modeling and Data Analysis Techniques from National to Catchment Scales  
    Shahed Behrouz, Mina (Virginia Tech, 2022-06-30)
    Urbanization alters land cover by increases in impervious areas, resulting in large increases in runoff, sediment, and nutrient loadings downstream. These changes cause flooding, eutrophication, and harmful algal blooms. Stormwater control measures (SCMs) are used to address these concerns and are designed based on inflow loads. Thus, estimating nutrient and sediment loads from developed watersheds is vitally important for meeting the impacts of urbanization. Today, stormwater events are characterized mainly by watershed models using little, if any, actual field monitoring data. The simple event mean concentration (EMC) wash-off approach by land use is a common practice used by practitioners for estimating loads. Pollutants accumulate on surfaces during dry periods, making EMC a function of antecedent dry period (ADP). An EMC results from wash-off of accumulated pollutants from catchment surfaces during runoff events. However, it assumes concentration is constant across events from a particular land use and several studies found little to no correlation between constituent concentrations in stormwater and ADP. Build-up/wash-off equations were developed to account for variation of concentrations between events; however, the required parameters are difficult to estimate. This study applied machine learning approaches with a national dataset along with monitoring and modeling studies at watershed scales to improve predictions of stormwater quantity and quality. First, we obtained stormwater quality data from the National Stormwater Quality Database (NSQD), which is the largest data repository of stormwater quality data in the U.S., and used Bayesian Network Structure Learner (BNSL), a machine learning approach, to discover which climatological or catchment characteristics most significantly affect stormwater quality. Second, we developed and applied Random Forest (RF), a data-driven method, to predict nutrients and sediment EMCs in urban runoff. Third, we applied the Storm Water Management Model (SWMM), a widely used urban watershed model, to an urban watershed and assessed the best fit estimates of SWMM parameters and hydrological response of the watershed during dry and wet hydroclimatic conditions. Last, we conducted a monitoring and modeling study at a catchment scale and assessed the role of land use on stormwater quantity and quality to optimize and investigate the build-up/wash-off parameters for multiple urban land uses for nutrients and sediment. The results presented in this dissertation can help stakeholders, urban planners, and SCM designers improve estimates of nutrients and sediment loads and thus achieve more effective treatment of stormwater, better attain water quality goals, and protect downstream water bodies.
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    Integrating Machine Learning Into Process-Based Modeling to Predict Ammonia Losses From Stored Liquid Dairy Manure
    Genedy, Rana Ahmed Kheir (Virginia Tech, 2023-06-16)
    Storing manure on dairy farms is essential for maximizing its fertilizer value, reducing management costs, and minimizing potential environmental pollution challenges. However, ammonia loss through volatilization during storage remains a challenge. Quantifying these losses is necessary to inform decision-making processes to improve manure management, and design ammonia mitigation strategies. In 2003, the National Research Council recommended using process-based models to estimate emissions of pollutants, such as ammonia, from animal feeding operations. While much progress has been made to meet this call, still, their accuracy is limited because of the inadequate values of manure properties such as heat and mass transfer coefficients. Additionally, the process-based models lack realistic estimations for manure temperatures; they use ambient air temperature surrogates which was found to underestimate the atmospheric emissions during storage. This study uses machine learning algorithms' unique abilities to address some of the challenges of process-based modeling. Firstly, ammonia concentrations, manure temperature, and local meteorological factors were measured from three dairy farms with different manure management practices and storage types. This data was used to estimate the influence of manure characteristics and meteorological factors on the trend of ammonia emissions. Secondly, the data was subjected to four data-driven machine learning algorithms and a physics-informed neural network (PINN) to predict manure temperature. Finally, a deep-learning approach that combines process-based modeling and recurrent neural networks (LSTM) was introduced to estimate ammonia loss from dairy manure during storage. This method involves inverse problem-solving to estimate the heat and mass transfer coefficients for ammonia transport and emission from stored manure using the hyperparameters optimization tool, Optuna. Results show that ammonia flux patterns mirrored manure temperature closely compared to ambient air temperature, with wind speed and crust thickness significantly influencing ammonia emissions. The data-driven machine learning models used to estimate the ammonia emissions had a high predictive ability; however, their generalization accuracy was poor. However, the PINN model had superior generalization accuracy with R2 during the testing phase exceeded 0.70, in contrast to -0.03 and 0.66 for finite-elements heat transfer and data-driven neural network, respectively. In addition, optimizing the process-based model parameters has significantly improved performance. Finally, Physics-informed LSTM has the potential to replace conventional process-based models due to its computational efficiency and does not require extensive data collection. The outcomes of this study contribute to precision agriculture, specifically designing suitable on-farm strategies to minimize nutrient loss and greenhouse gas emissions during the manure storage periods.
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    Projecting Planning-Related Climate Impact Drivers for Appalachian Public Health Support
    Larsson, Natalie Anne (Virginia Tech, 2024-07-10)
    Climate change is impacting the intensity, duration, and frequency of climatic events. With climate change comes a multitude of adverse conditions, including extreme heat events, changes in disease patterns, and increased likelihood and frequency of natural disasters, including in places previously not exposed to such conditions. Human health has foundations in the environment; therefore, these adverse climatic conditions are directly linked to human health. Rural communities in Appalachia are likely to experience negative consequences of climate change more severely due to unique geomorphology and sociopolitical realities of the region. Non-governmental organizations (NGOs) throughout the Appalachian region are currently working to build resilience and prepare for potential adverse effects from climate change. To aid in this process, projections of future climate scenarios are needed to understand possible situations and adequately prepare. In partnership with Ohio University and West Virginia University, this study aims to characterize potential future climatic scenarios from publicly-available global climate models (GCMs) and prepare information to share with Appalachian communities. Climate model information for this analysis was obtained from NASA's Coupled Model Intercomparison Project (CMIP6). All code for data processing and analysis was prepared using the open-source R programming language to support reproducibility. To confirm that models can accurately simulate Appalachian climatic conditions, CMIP6 hindcast simulations for precipitation and maximum temperature were compared to observed weather records from NOAA. Climate models over and underestimated average precipitation values depending on location, while models consistently underestimated extreme precipitation values, simulated by total five-day precipitation. For temperature, climate models consistently underestimated average and extreme high temperature indicators. For Appalachian region projections, three towns of interest (one for each state involved in the study: Virginia, West Virginia, and Ohio) were selected based on current community resilience efforts. In these locations, mid-century (2040 – 2064) and end-of-century (2075 – 2099) projections for precipitation and temperature were summarized under a low emissions scenario and a high emissions scenario. Increases in precipitation and temperature were observed under average and extreme scenarios; these increases were noticeably more extreme under higher emissions scenarios. These trends are consistent with other studies and climate science consensus. When compared to hindcast values, observed average precipitation values were overestimated and underestimated, while observed extreme precipitation indices, average temperatures, and heat wave indices were underestimated by GCMs. Context with observed data is important to understanding model accuracy for the Appalachian region. GCMs are a useful tool to project potential future climate scenarios at specific locations in the Appalachian region, though model data is best used to communicate general trends rather than as inputs for other physical models.
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    Survey of Groundwater Wells in the United States
    Miller, Alexandra Leigh (Virginia Tech, 2023-06-29)
    Groundwater wells are critical infrastructure with significant impacts on the environment, water availability, and economy. However, comprehensive data on the purposes, locations, depths, and construction of these wells are only collected by individual states. We have compiled a nationwide dataset of groundwater wells throughout the United States. The tabular dataset consists of all groundwater well data obtained from the states, containing over nine million records. A subset of this dataset was created that excludes wells located outside of the reported county or state, with over eight million records. Our dataset represents all known groundwater well locational data that states could release. The data made available by these datasets can serve as a critical tool for refining our understanding of how groundwater is accessed and used throughout the United States, and how it impacts different industries.
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    A Systematic Evaluation of Climate Services and Decision Support Tools for Climate Change Adaptation
    Jahan, Momtaz (Virginia Tech, 2021-01-28)
    Climate services, often refers as decision support tools, are developed to provide information with a view to aid in decision making and policy planning for adaptation due to climate variability and change. This study investigated different publicly available climate services and decision support tools based on previously proposed evaluation framework. This evaluation framework originally consists of four design elements which are divided into nine evaluation metrics for this study. These evaluation metrics are: identification of decision making context, discussion of the role of climate information in decision making, discussion of non-climatic factors, uncertainty of the data presented, accessibility of information, discussion on the development process, sustainability/ ongoing process, discussion of funding sources, and evaluation of the tool through survey, modeling or contingent valuation method. Tools were then given "High", "Medium", and "Low" score for each of the criterion. A total of 19 tools were evaluation for this study. Tools performed relatively well in "characteristics, tailoring, and communication of the climate information" and "governance, process, and structure of the climate service" whereas they got average scores in "problem identification and the decision-making context" and "value of the service provided". Additionally, four case study evaluation of tools showed detail evaluation of how the tools performed against each of the criterion. The results of this study showed the relative strengths and weakness of the evaluated tools which can be used to improve existing climate services to aid in adaptation decision needs for climate change. This will also help in better decision making and policy planning for different sectors impacted by the changing climate.
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    Variation and Change in Daily Precipitation Extremes Across the United States Since the Mid-20th Century
    Marston, Michael Lee (Virginia Tech, 2020-06-19)
    Research indicates a warming global climate leads to change in the spatial and temporal characteristics of precipitation. Although precipitation is inherently variable through time and space, for some water-sensitive stakeholders, the evenness with which precipitation is distributed through a time interval rivals the importance of total precipitation amount and frequency within that period. This study uses a relatively new approach of analyzing inequity in the temporal distribution of precipitation to examine the recent historical record of precipitation across the United States. The Gini coefficient (GC), which has been commonly used in the field of economics to measure wealth distribution, was used here to assess inequity in the temporal distribution of daily precipitation through seasonal and annual timeframes. Additionally, the Lorenz asymmetry coefficient (LAC) was used to assess the magnitude of daily precipitation events (light, heavy) associated with inequity in the temporal distribution of precipitation. The concept of using these two metrics together to quantify changes in the character with which precipitation occurs across a time interval has yet to be documented for areas within the United States. Therefore, this study expands upon previous research of long-term hydroclimatic change and variability by illustrating the combined ability of these two relatively under-utilized metrics, the GC and the LAC, to enhance quantification of recent change in the characteristics of the temporal distribution of daily precipitation across the United States. The first element of the research presented here is demonstration of the utility of the GC and LAC metrics using data from the physically diverse mid-Atlantic sub-region of the United States. This research used station-level daily precipitation data to compute historic time series of intra-annual and intra-seasonal precipitation amount, precipitation frequency, GC, LAC, variance (V), and interquartile range (IQR). The results of this portion of the research show that when compared to other simpler measures of characterizing variability (i.e., V and IQR), the GC is relatively robust to both the number of days with precipitation and the total precipitation received in a temporal increment (i.e., season or year). The research expanded in scale to the continental United States, requiring data integration to a regional level to facilitate data analysis and physical understanding. The analysis used gridded seasonal means (1981 – 2010) of four precipitation characteristics: precipitation amount, precipitation frequency, GC, and LAC to delineate regions of homogenous precipitation characteristics. To accomplish this, a multi-step regionalization technique was employed. Specifically, the historic seasonal means were subjected to a Principal Components Analysis (PCA), and the resulting component scores were subjected to several cluster analysis techniques. The average linkage clustering technique produced the most logical clustering solution, indicating that 15 regions of homogenous precipitation exist within the contiguous United States. It is argued that the regions better serve hydroclimatic analyses than the nine climate regions designated by the United States National Centers for Environmental Information (NCEI). The third element of the research integrates the first two research elements in study of recent United States hydroclimate variability and change. For the 15 United States hydroclimate regions, regionally averaged water year time series (1949 – 2018) of precipitation amount, precipitation frequency, GC, and LAC were computed using in-situ precipitation data gathered from the NCEI's Global Historical Climatology Network (GHCN)-Daily database. The time series of all precipitation characteristics for each region were then subjected to the nonparametric Mann-Kendall trend test to assess the significance of each trend, and the Sen's slope estimator was used to quantify the magnitude of the trend. Time series that characterize two key atmospheric characteristics, total column water vapor and static stability, were also computed for each region. For most of the 15 study regions, water year total precipitation and precipitation frequency increased through the latter half of the 20th century. The largest magnitude of change in water year total precipitation and precipitation frequency occurred in the time series of regions located within the eastern and northern portions of the contiguous United States. Results also show that inequity in the temporal distribution of water year precipitation increased through the 70-year study period for most of the 15 study regions. Combined, these results indicate that days with light and heavy precipitation are becoming more prevalent at the expense of days with moderate precipitation. Furthermore, variability in the time series of some precipitation characteristics for several regions coincide with variability in the atmospheric variables that characterize total column water vapor and static stability, however the dominant driver of hydroclimatic change across the contiguous United States remains elusive.