A. James Clark School of Engineering
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Item Estimating terrestrial water budget components across high mountain Asia using remote sensing, data assimilation, and machine learning(2021) Ahmad, Jawairia; Forman, Barton A.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Contemporary studies have predicted a vulnerable future for key water budget components across high mountain Asia (HMA) and the adjoining areas. Considering the regional population and its dependence on agrarian economies, it is imperative that efforts be channelized towards improving the estimation of the hydrologic cycle across HMA. In this study, data assimilation methods were employed to assimilate remotely-sensed observations into land surface models to improve snow mass, soil moisture, and runoff estimates. The NASA Land Information System was used to simulate the hydrologic cycle across HMA and the adjoining areas using the Noah-MP land surface model. In an effort to improve snow mass estimation, passive microwave brightness temperature spectral differences (∆Tb) from the Advanced Microwave Scanning Radiometer-2 (AMSR2) were assimilated into Noah-MP snow mass estimates. Support vector machine regression, a supervised machine learning technique, was used as the observation operator to map the geophysical states into the observed ∆Tb space. Evaluation of the assimilation routine highlighted the decrease in domain-wide snow mass bias. The assimilation framework proved to be more effective during the (dry) snow accumulation season resulting in decreased snow mass bias and RMSE at 76% and 58% of the comparative locations, respectively. Diagnostic metrics such as the innovation sequence were studied to assess the snow-related observation error characteristics of AMSR2 ∆Tb. To improve the spatiotemporal variability of modeled soil moisture estimates, Soil Moisture Active Passive (SMAP) soil moisture retrievals were assimilated into Noah-MP. Assimilation was carried out using bias corrected (via CDF-matching) and raw (without CDF-matching) SMAP retrievals. Comparison against in-situ soil moisture measurements across the Tibetan Plateau highlighted the improvement in modeled soil moisture with reductions in mean bias and RMSE by 8.4% and 9.4%, respectively, even though assimilation occurred during <10% of the total study period across the Tibetan Plateau. More importantly, SMAP retrieval assimilation corrected biases that were generated due to unmodeled hydrologic phenomenon (i.e., surface irrigation associated with agricultural production). Improvements in soil moisture translated into changes in the modeled evapotranspiration. Further, the improvement in fine-scale (0.05 degree) modeled soil moisture estimates by assimilating coarse-scale soil moisture retrievals (36 km) indicated the potential of the described methodology for soil moisture estimation over data scarce regions. Soil moisture assimilation also increased the gridded total runoff (particularly baseflow) and volumetric streamflow across irrigated areas; however, limited impact was noted in terms of volumetric streamflow along high-flow river tributaries. In this study, data assimilation was leveraged to advance contemporary land surface modeling of the terrestrial water budget components across HMA. The study objectives explored how assimilation systems could be used to improve critical geophysical state estimation for a better informed future of regional water resources.Item SEDIMENT SUSPENSION EVENTS FROM RIPPLE BEDS IN OSCILLATORY FLOW: EXPERIMENTS(2009) Knowles, Philip Leland; Kiger, Kenneth T; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)An experimental sediment flume is used to investigate sediment transport mechanics within an oscillatory turbulent boundary layer over a mobile sediment bed in the ripple bed regime. Two-phase PIV is utilized to simultaneously capture data from each phase, allowing examination of suspension mechanisms, carrier phase stresses, and to obtain statistics to describe the momentum exchange between the phases. The technique employs median filtering, as well as size and brightness criteria to separate and accurately identify each phase. Independent well-conditioned tests have been conducted to improve the algorithm to account for the imaging conditions encountered in the vicinity of a mobile bed in order to minimize cross-talk between the phases and allow quantification of the dispersed phase concentration. Results show that large-scale vortical structures are responsible for the ejection of sediment from the bed into the outer flow. These structures are a significant source of turbulent transport, but their overall contribution to the bed stress is small compared to the mean flow. Triple decomposition of the equations of motion show that long time averaged sediment flux is of similar magnitude to cyclic fluctuations and the time averaged flow consists of two counter rotating cells. Turbulent kinetic energy created at flow reversal advects over the sediment bed and keeps particles suspended in the flow. Calculation of the vertical particle drag, body force, and convection terms revels that at flow reversal the body force terms are larger than the drag causing the particles on average to settle. The particle convection terms are small compared to particle drag and body force terms. Some of the terms most significant in the particle drag are the fluctuating components indicating that the turbulence is keeping the particles suspended in the outer flow.