Civil & Environmental Engineering
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Item QUANTIFYING THE ADDED VALUE OF AGILE VIEWING RELATIVE TO NON-AGILE VIEWING TO INCREASE THE INFORMATION CONTENT OF SYNTHETIC SATELLITE RETRIEVALS(2022) McLaughlin, Colin; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Satellite sensors typically employ a “non-agile” viewing strategy in which the boresight angle between the sensor and the observed portion of Earth’s surface remains static throughout operation. With a non-agile viewing strategy, it is relatively straightforward to predict where observations will be collected in the future. However, non-agile viewing is limited because the sensor is unable to vary its boresight angle as a function of time. To mitigate this limitation, this project develops an algorithm to model agile viewing strategies to explore how adding agile pointing into a sensor platform can increase desired information content of satellite retrievals. The synthetic retrievals developed in this project are ultimately used in an observing system simulation experiment (OSSE) to determine how agile pointing has the potential to improve the characterization of global freshwater resources.Item Estimation of Terrestrial Water Storage in the Western United States Using Space-based Gravimetry, Ground-based Sensors, and Model-based Hydrologic Loading(2020) Yin, Gaohong; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Accurate estimation of terrestrial water storage (TWS) is critically important for the global hydrologic cycle and the Earth's climate system. The space-based Gravity Recovery and Climate Experiment (GRACE) mission and land surface models (LSMs) have provided valuable information in monitoring TWS changes. In recent years, geodetic measurements from the ground-based Global Positioning System (GPS) network have been increasingly used in hydrologic studies based on the elastic response of the Earth's surface to mass redistribution. All of these techniques have their own strengths and weaknesses in detecting TWS changes due to their unique uncertainties, error characteristics, and spatio-temporal resolutions. This dissertation investigated the potential of improving our knowledge in TWS changes via merging the information provided by ground-based GPS, GRACE, and LSMs. First, the vertical displacements derived from ground-based GPS, GRACE, and NASA Catchment Land Surface Model (Catchment) were compared to analyze the behavior and error characteristics of each data set. Afterwards, the ground-based GPS observations were merged into Catchment using a data assimilation (DA) framework in order to improve the accuracy of TWS estimates and mitigate hydrologic state uncertainty. To the best of our knowledge, this study is the first attempt to assimilate ground-based GPS observations into an advanced land surface model for the purpose of improving TWS estimates. TWS estimates provided by GPS DA were evaluated against GRACE TWS retrievals. GPS DA performance in estimating TWS constituent components (i.e., snow water equivalent and soil moisture) and hydrologic fluxes (i.e., runoff) were also examined using ground-based in situ measurements. GPS DA yielded encouraging results in terms of improving TWS estimates, especially during drought periods. Additionally, the findings suggest a multi-variate assimilation approach to merge both GRACE and ground-based GPS into the LSMs to further improve modeled TWS and its constituent components should be pursued as a new and novel research project.Item Uncertainty quantification of a radiative transfer model and a machine learning technique for use as observation operators in the assimilation of microwave observations into a land surface model to improve soil moisture and terrestrial snow(2020) Park, Jongmin; Forman, Barton A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Soil moisture and terrestrial snow mass are two important hydrological states needed to accurately quantify terrestrial water storage and streamflow. Soil moisture and terrestrial snow mass can be measured using ground-based instrument networks, estimated using advanced land surface models, and retrieved via satellite imagery. However, each method has its own inherent sources of error and uncertainty. This leads to the application of data assimilation to obtain optimal estimates of soil moisture and snow mass. Before conducting data assimilation (DA) experiments, this dissertation explored the use of two different observation operators within a DA framework: a L-band radiative transfer model (RTM) for soil moisture and support vector machine (SVM) regression for soil terrestrial snow mass. First, L-band brightness temperature (Tb) estimated from the RTM after being calibrated against multi-angular SMOS Tb's showed good performance in both ascending and descending overpasses across North America except in regions with sub-grid scale lakes and dense forest. Detailed analysis of RTM-derived L-band Tb in terms of soil hydraulic parameters and vegetation types suggests the need for further improvement of RTM-derived Tb in regions with relatively large porosity, large wilting point, or grassland type vegetation. Secondly, a SVM regression technique was developed with explicit consideration of the first-order physics of photon scattering as a function of different training target sets, training window lengths, and delineation of snow wetness over snow-covered terrain. The overall results revealed that prediction accuracy of the SVM was strongly linked with the first-order physics of electromagnetic responses of different snow conditions. After careful evaluation of the observation operators, C-band backscatter observations over Western Colorado collected by Sentinel-1 were merged into an advanced land surface model using a SVM and a one-dimensional ensemble Kalman filter. In general, updated snow mass estimates using the Sentinel-1 DA framework showed modest improvements in comparison to ground-based measurements of snow water equivalent (SWE) and snow depth. These results motivate further application of the outlined assimilation schemes over larger regions in order to improve the characterization of the terrestrial hydrological cycle.Item MODELING URBAN FLOODING IN THE TIBER BRANCH WATERSHED, ELLICOTT CITY, MARYLAND, USING PCSWMM(2020) Walcott, Cadijah; Brubaker, Kaye; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Urban flooding — due to land cover change, inadequate drainage networks, and increased precipitation — exacerbates communities’ economic and social vul¬nerabilities. A detailed watershed model can help communities identify weak portions of the drainage network and design resolutions. This research details the development of a comprehensive model of the Tiber Branch Watershed in Ellicott City, Maryland, to reproduce observed depth in the Hudson Branch tributary using PCSWMM (a commercial version of the U.S. Environmental Protection Agency’s Storm Water Management Model). The 2,434.8-acre watershed comprises 8,821 PCSWMM objects, which were estimated from various raster and vector datasets. Without calibration, the model generally captures the timing and shape of the stage hydrographs but is less successful in simulating event magnitude and receives a R2 of 0.65 and SE/SY of 0.67 for the 43 selected events, collectively. Ultimately, model evaluation was not completed due to a lack of representative rainfall within the watershed.Item REMOVAL OF STORMWATER DISSOLVED ORGANIC NITROGEN MODEL COMPOUNDS THROUGH ADSORPTION AND BIOTRANSFORMATION(2019) Mohtadi, Mehrdad; Davis, Allen P.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bioretention systems are stormwater control measures designed to reduce nitrogen and phosphorus transferred by stormwater to water resources. They are, however, not effectively designed to remove dissolved organic nitrogen (DON). This study concentrated on improvement of bioretention design to remove stormwater DON. Batch adsorption of eight organic nitrogenous compounds onto several adsorbents showed that coal activated carbon (AC) could be a reliable adsorbent for removal of organic nitrogenous compounds such as pyrrole and N-acetyl-D-glucosamine (NAG). The adsorption capacity of pyrrole and NAG on coal AC were 0.4 mg N/g (at equilibrium concentration, Ce = 0.02 mg N/L) and 0.71 mg N/g (at Ce = 1 mg N/L), respectively. These eight nitrogenous compounds were also tested for continuous column adsorption on a media mixture of coal AC + quartz sand, and only pyrrole showed an appreciable adsorption performance; the breakthrough and exhaustion depths for pyrrole were 88 and 499 m, respectively, at the fixed superficial velocity of 61 cm/h and influent DON concentration of 1 mg N/L. Pyrrole adsorption was also minimally affected by superficial velocity (DON removal efficiency stayed > 91% for all tested superficial velocities, 7 to 489 cm/h). Because the adsorption process was successful for removal of only one (pyrrole) out of eight examined compounds, biological treatment was also investigated for removal of organic nitrogenous compounds. Biotransformation alongside adsorption demonstrated benefits such as ammonification of bio-recalcitrant organic nitrogen compounds, e.g., pyrrole, and bioregeneration of the adsorbent (coal AC). According to the results, ammonifiction might be considered as a possible reliable mechanism for stormwater DON removal at low temperatures > 4°C. Under intermittent wetting/draining conditions, the effluent DON was less than 0.1 mg N/L after the applied depth of 48 m, indicating that DON was successfully removed through simultaneous adsorption/ammonification, although generated ammonium in the effluent must be properly addressed. Overall, based on the results from the current study, some DON types were strongly adsorbed by adsorbents, e.g., adsorption of pyrrole on coal AC, some were more bioavailable, e.g., ammonification of leucine, and some were barely adsorbable and bioavailable, e.g., Aldrich humic acid on coal AC. Accordingly, both adsorption and biotransformation should be considered to enhance stormwater DON removal as much as possible.Item Predicting Low Probability Streamflow Using Lidar Data and Hydraulic Geometry(2019) Mardones, Javier; Brubaker, Kaye L; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Predicting stream flow is essential for safe and economic planning and design of hydraulic structures. This study uses the observed channel cross-section from LiDAR data and physical concepts of shear stress to estimate bankfull discharge (Qbf). Assuming that Qbf is the median of the annual peak flow distribution, a 2-parameter Extreme Value Type I distribution was fitted to predict discharge to a 200-year return period. The method was compared with gauged sites in low-order streams (less than 90-meter bankfull width) resulting in SE/SY=1.31 for Qbf and SE/SY=1.90 for the 200-year return period discharge; model precision is poor. However, the relative bias (-15\% to +15\%) demonstrates that on average results are similar to gauged data. Relationships between flow and channel geometry assure a quick way to estimate stream data and can serve as a tool used prior to applying conventional hydrologic methods such as flow routing and regional regression equations.Item WATER-ENERGY-CLIMATE NEXUS: INTERDEPENDANCIES AND TRADEOFFS, AND IMPLICATIONS FOR STRATEGIC RESOURCE PLANNING(2017) Liu, Lu; Forman, Barton; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The water-energy nexus has been an active area of research in recent decades and has been explored in many different directions pertaining to its core. It is imperative to manage water and energy in a holistic approach as there are critical interconnections between the two systems. Climate change is an intrinsic environmental variable that has vital implications for the study of water-energy nexus, and hence, the term water-energy-climate nexus is used throughout the dissertation in reference to the interdependencies and tradeoffs between these systems. This dissertation is composed of three research studies under the domain of the water-energy-climate nexus, and they are interconnected through the intrinsic linkages among the three systems. The first study deals with the vulnerability of U.S. thermoelectric power plants to climate change. Findings suggest that the impact of climate change is lower than in previous estimates due to the inclusion of a spatially-disaggregated representation of environmental regulations and provisional variances that temporarily relieve power plants from permit requirements. This study highlights the significance of accounting for legal constructs and underscores the effects of provisional variances along with environmental requirements. The second study demonstrates the adaptation measures taken by the U.S. energy system in the face of constraints on water availability. Results show that water availability constraints may cause substantial capital stock turnover and result in non-negligible economic costs for the western U.S. This work emphasizes the need to integrate water availability constraints into electricity capacity planning and highlights the state-level challenges to facilitate regional strategic resource planning. The last study assesses the potential of surface reservoir expansion for major river basins around the world as an adaption measure to secure a reliable water supply. Results suggest that conservation zones and future human migration will have a substantial, heterogeneous impact on the maximum amount of reservoir storage that can be expanded worldwide. Findings from this study highlight the importance of incorporating human development, land-use activities, and climate change drivers when quantifying available surface water yields and reservoir expansion potential. This dissertation takes an integrated holistic approach to examine water and energy system interrelationships, and assesses the role of climate change in reshaping the interconnectivity. The three studies are tied in to each other by identifying some of the challenges the society is facing in the water-energy-climate nexus (first study) and providing a few possible solutions in both energy supply (second study) and water supply (third study) sector. Novelty of this dissertation includes but not limited to 1) explicit representation of state-level environmental regulations pertaining to power plant operations in the U.S. 2) integrated approach that captures the interactions of energy system with other sectors of the economy; and 3) global assessment of reservoir capacity expansion potential with consideration of multiple constraints. General conclusions, along with further details, provide insights for sustainable resource planning and future research directions.Item ASSIMILATION OF PASSIVE MICROWAVE BRIGHTNESS TEMPERATURES FOR SNOW WATER EQUIVALENT ESTIMATION USING THE NASA CATCHMENT LAND SURFACE MODEL AND MACHINE LEARNING ALGORITHMS IN NORTH AMERICA(2017) Xue, Yuan; Forman, Barton A.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Snow is a critical component in the global energy and hydrologic cycle. It is important to know the mass of snow because it serves as the dominant source of drinking water for more than one billion people worldwide. To accurately estimate the depth of snow and mass of water within a snow pack across regional or continental scales is a challenge, especially in the presence of dense vegetations since direct quantification of SWE is complicated by spatial and temporal variability. To overcome some of the limitations encountered by traditional SWE retrieval algorithms or radiative transfer-based snow emission models, this study explores the use of a well-trained support vector machine to merge an advanced land surface model within a variant of radiance emission (i.e., brightness temperature) assimilation experiments. In general, modest improvements in snow depth, and SWE predictability were witnessed as a result of the assimilation procedure over snow-covered terrain in North America when compared against available snow products as well as ground-based observations. These preliminary findings are encouraging and suggest the potential for global-scale snow estimation via the proposed assimilation procedure.Item HYDROLOGIC RESPONSE OF A SUBURBAN WATERSHED TO CLIMATE MODELS(2017) Xiang, Zhongrun; Brubaker, Kaye L.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Non-Point Source (NPS) pollution is an important issue in the Chesapeake Bay areas of the northeastern U.S. The TMDLs established by the Environmental Protection Agency requires a reduction in sediments, nitrogen and phosphorus by preset amounts, by 2025. One approach to meeting these requirements is to implement Best Management Practices (BMPs) for NPS pollution control. BMPs are most effective when implemented on areas named Critical Source Areas (CSAs) that contribute excessively to the pollutant load relative to their spatial extent. Studies have shown that climate change may have significant influence on the hydrology and water quality variables, and can therefore influence CSA identification in the future. In this study, six climate models were used for the evaluation of the hydrologic response of a suburban watershed in Maryland. The Soil and Water Assessment Tool (SWAT) was used for the model development, driven by the future climate from six models in four scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Surface runoff, total suspended solids, total nitrogen and total phosphorus at the watershed outlet and on-land were assessed for two time horizons, mid-century and end-century. The simulations showed a significant increase of yields in all variables both in-stream and on-land among all models/scenarios/periods. CSAs identified using a relative threshold (eg. Top 20% of HRUs) did not vary markedly as climate was changed. However, CSAs identified using a fixed threshold increased substantially in area under future climate. Overall, results demonstrate the potential impacts of climate change on watershed hydrology across six models, and suggest that CSA identification based on relative threshold is most robust against future variability.Item Modeling Flood Stage-Duration-Frequency: A Risk Assessment of Critical Infrastructure in the Tidal Potomac(2016) Feng, Yilu; Brubaker, Kaye L; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The service of a critical infrastructure, such as a municipal wastewater treatment plant (MWWTP), is taken for granted until a flood or another low frequency, high consequence crisis brings its fragility to attention. The unique aspects of the MWWTP call for a method to quantify the flood stage-duration-frequency relationship. By developing a bivariate joint distribution model of flood stage and duration, this study adds a second dimension, time, into flood risk studies. A new parameter, inter-event time, is developed to further illustrate the effect of event separation on the frequency assessment. The method is tested on riverine, estuary and tidal sites in the Mid-Atlantic region. Equipment damage functions are characterized by linear and step damage models. The Expected Annual Damage (EAD) of the underground equipment is further estimated by the parametric joint distribution model, which is a function of both flood stage and duration, demonstrating the application of the bivariate model in risk assessment. Flood likelihood may alter due to climate change. A sensitivity analysis method is developed to assess future flood risk by estimating flood frequency under conditions of higher sea level and stream flow response to increased precipitation intensity. Scenarios based on steady and unsteady flow analysis are generated for current climate, future climate within this century, and future climate beyond this century, consistent with the WWTP planning horizons. The spatial extent of flood risk is visualized by inundation mapping and GIS-Assisted Risk Register (GARR). This research will help the stakeholders of the critical infrastructure be aware of the flood risk, vulnerability, and the inherent uncertainty.