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Seasonal Performance Of MRMS QPE In Urban Hydrologic Modeling Using EPA-SWMM
No full textAccurate rainfall estimation is essential for effective hydrologic modeling, particularly in urban environments where infrastructure must withstand extreme precipitation. This study evaluates the EPA Storm Water Management Model (EPA-SWMM) using MRMS RadarOnly Quantitative Precipitation Estimation (QPE) 1-hour Accumulation as the primary rainfall input for a watershed in Little Rock, Arkansas. The region was selected due to its urban character and the limited availability of radar-based hydrologic studies in Arkansas. 21 storms were analyzed from 2022-2025: 10 in Winter, 7 in Spring, and 4 in Summer. Storms were selected due to their effect on flow. The model was calibrated using February 15th, 2025 rainfall data due to its stratiform rainfall pattern and demonstration of typical hydrograph behavior and the rest were evaluated based on that calibration. Spring and winter storms yielded strong model performance, with average Nash-Sutcliffe Efficiency (NSE) values of 0.8075 and 0.8346, respectively. Summer storms showed greater variability (NSE = 0.6244), likely due to convective morphology and radar limitations. Percent Bias analysis revealed that winter storms tended to undervalue flow, while spring and summer storms overvalued. When outliers were omitted, winter and spring storms underrepresented peak flows consistent with literature on radar QPE underestimation however summer storms overestimated flow. Summer storms are too variable to be recommended for future stormwater analysis in Little Rock however winter storms can be recommended provided designers are aware of typical undervaluation amounts present in simulations. Future work should incorporate multi-sensor MRMS QPE products, refined subcatchment delineation, and field-based stream measurements to improve model accuracy
Thermal Desorption-Pyrolysis Gas Chromatography-Mass Spectrometry Method Development For The Determination Of Wood Preservatives
No full textA thermal desorption-pyrolysis gas chromatography with mass spectrometric detection (TD-Pyr-GC-MS) method was developed, enabling a comprehensive analysis of preservatives as well as native wood constituents in treated wood. The developed method is based on thermal desorption, i.e., extraction of wood preservatives, followed by pyrolysis to investigate changes in the occurrence of characteristic wood building blocks (e.g., guaiacol derivatives). We implemented a combined MS data acquisition (i.e., SITI) consisting of selected ion monitoring (SIM) and total ion current (TIC), allowing for low limits of detection of preservatives in SIM mode and TIC detection of any evolving species. The matrix detection limits were 0.3-20 ng, depending on the preservative, requiring only 100 µg sample size. The method quantified wood preservatives in aged wood samples in various states of deterioration in a range of 18-166 µg/g. A decline in the concentration of some of the target preservatives, like tebuconazole, in the environmentally weathered samples was observed as compared to those less aged, while some persisted, such as permethrin. We also noted changes in the occurrence of long-chain linear alkanes (C20–C25) used as preservative solvents, providing insights into the nature of wood decay as a result of long-term environmental exposure. Finally, the method provided elution profiles of wood extractives, i.e., products of native wood polymer (lignin) pyrolytic decomposition. The observed wood decay did not show a conclusive relationship with the level of wood extractives but occurred along with the gradual depletion of hydrocarbons (treatment solvent), most likely due to restriction in preservatives’ mobility within the wood
Control Strategy, Multi-Objective Optimization, And Economic Viability For Utility-Scale Wind-Solar-Based Hybrid Power Plant
No full textHybrid Power Plants (HPP) that combine wind power plants (WPP), solar power plants (SPP), and battery energy storage systems (BESS) have recently emerged to provide dispatchable renewable electricity, improve the stability of the power grid, maximize use of infrastructure assets, and minimize carbon releases. Nevertheless, regardless of their technical potential, the large-scale application of HPP still encounters many barriers, such as operational complexity, inadequate sizing, and financial viability. To mitigate these issues, this study incorporates three research questions. First, this study introduces a four-layer hierarchical control architecture for coordinating WPP, SPP, and BESS to ensure dispatchable power. Second, a multi-objective particle swarm optimization (MOPSO) is presented to identify the optimal system sizing in the context of uncertain wind and solar availability, maximizing the net present value (NPV), minimizing the loss of load probability (LOLP), and increasing efficiency. Finally, a comparative policy-driven economic assessment evaluates regulatory incentives such as investment tax credits (ITC), production tax credits (PTC), accelerated depreciation (AD), generation-based incentives (GBI), and capital subsidies in the U.S. and India to assess HPP financial feasibility. Simulation results show the efficiency of the proposed control structure for multi-technology coordination while reducing curtailment and maintaining BESS SOC. The optimal configuration (283 MW wind, 20 MW solar, 500 MWh battery) yielded a $165.2M NPV, 3.79% curtailment, and 8.26% LOLP. Policy analysis reveals HPPs require incentives to compete with LCOE: U.S. support was reduced by 12%, while Indian subsidies cut costs by 60%. This integrated methodology bridges simulation, economics, and policy, aiding developers, researchers, and policymakers in scaling HPPs across diverse regulatory landscapes
Tom Jones
No full textTom Jones at the Chester Fritz Performing Arts Centerhttps://commons.und.edu/performing-arts-photos/1073/thumbnail.jp
