UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item SEA-SURFACE TEMPERATURE BASED STATISTICAL PREDICTION OF THE SOUTH ASIAN SUMMER MONSOON RAINFALL DISTRIBUTION(2019) Sengupta, Agniv; Nigam, Sumant; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The South Asian summer monsoon brings copious amounts of rainfall accounting for over 70% of the annual rainfall over India. Summer monsoon predictions have drawn considerable public/policy attention lately as South Asia has become a resource-stressed and densely populated region. This environmental backdrop and the livelihood concerns of a billion-plus people generate the demand for more accurate monsoon predictions. The prediction skill, however, has remained marginal and stagnant for several decades despite advances in the representation of physical processes, numerical model resolution, and data assimilation techniques, leading to the following key question: what is the potential predictability of summer monsoon rainfall at lead times of one month to a season? This dissertation examines the role of influential climate system components with large thermal inertia and reliable long-term observational records, like sea-surface temperature (SST) in forecasting the seasonal distribution of South Asian monsoon rainfall. First, an evolution-centric SST analysis is conducted in the global oceans using the extended-Empirical Orthogonal Function technique to uncover the recurrent modes of spatiotemporal variability and their potential inter-basin linkages. A statistical forecast model is next developed using these extracted modes of SST variability as predictors. Assessment of the forecasting system’s long-term performance from reconstruction and hindcasting over an independent verification period demonstrates high forecast skill over core monsoon regions – the Indo-Gangetic Plain and southern peninsular India, indicating prospects for improved seasonal predictions. The influence of SSTs on the northeast winter monsoon is subsequently investigated, especially, its evolution, interannual variability and the El Niño–Southern Oscillation (ENSO) influence. Key findings from this study include evidence of increased rainfall over southeastern peninsular India and Sri Lanka (generated by an off-equatorial anticyclonic circulation centered over the Bay of Bengal) during El Niño winters. This dissertation provides the first quantitative assessment of the potential predictability of summer monsoon rainfall anomalies – the maximum predictable summer rainfall signal (amount, distribution) over South Asia from prior SST information – at various seasonal leads, and notably, at SST-mode resolution. The improved skill of the SST-based statistical forecast establishes the bar – an evaluative benchmark – for the dynamical prediction of summer monsoon rainfall.Item Release and runoff/infiltration removal of Escherichia coli, enterococci, and total coliforms from land-applied dairy cattle manure(2014) Blaustein, Ryan Andrew; Hill, Robert L; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Simulating the rainfall-induced release of indicator bacteria from manure is essential to microbial fate and transport modeling with regard to water quality and food safety. Experiments were conducted to determine the effects of rainfall intensity, surface slope, and scale on the release of Escherichia coli, enterococci, and total coliforms from land-applied dairy manure. Rainfall intensity did not affect bacterial release dependencies on rainfall depth, but it did have a significant effect on the post-rainfall quantities of indicator bacteria in soil. While bacterial concentrations were evenly released into runoff and infiltration, the surface slope controlled the partitioning of total released bacterial loads. The proportion of E. coli released from manure exceeded enterococci, especially with infiltration flow. Scale had strong, inverse effects on the recovery of land-applied bacteria with runoff. These results will be used to improve microbial fate and transport models, critical for risk assessment of microbial contamination in the environment.Item Factors Affecting Fungicide Performance when Targeting Dollar Spot Disease in Creeping Bentgrass(2009) Pigati, Ray L.; Dernoeden, Peter H; Plant Science and Landscape Architecture (PSLA); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Creeping bentgrass (Agrostis stolonifera) is commonly grown on golf course fairways and dollar spot (Sclerotinia homoeocarpa) is perhaps the most chronically severe disease of bentgrass. Field studies were conducted to: a) determine the influence of simulated rainfall and two mowing timings (AM and PM) on the performance of four fungicides, and b) to assess the effects of two fungicide spray volumes (468 and 935 L water ha-1) and application timings (AM and PM) on dollar spot control in creeping bentgrass. Fungicide effectiveness generally was reduced by simulated rain imposed about 30 minutes after application. Boscalid and chlorothalonil were most and least rain-safe; respectively, and propiconazole and iprodione were intermediate in rain-safeness. Fungicide performance was improved by mowing in the AM prior to fungicide application. A tank-mix of chlorothalonil + propiconazole was unaffected by spray volume or application timing, but the performance of chlorothalonil and propiconazole applied separately was inconclusive.