Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
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 give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item WARM SEASON HYDROLOGIC PROCESSES IN A BOREAL FOREST HILLSLOPE AND CATCHMENT, NEWFOUNDLAND(2020) Talbot-Wendlandt, Haley; Prestegaard, Karen; Geology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Prior investigations into boreal forest ecosystems have examined hydrological processes on plot scales, examining factors such as precipitation, soil characteristics, tree rooting depths, evapotranspiration, infiltration, and groundwater, or on the catchment scale, investigating factors such as stream discharge and water chemistry. In this study, I examine hydrological processes at both plot and catchment scales, with the goal of understanding how rooting depths influence evapotranspiration (ET) and the effects of ET on catchment discharge and water chemistry. Evapotranspiration was found to influence seasonal and diurnal fluctuations in groundwater table, stream discharge, and stream electrical conductivity. Tree rooting depths were shallow, primarily within O and Ae soil horizons, suggesting that these trees intercept infiltrating water, reducing summer groundwater recharge. Stream electrical conductivity increased with cumulative ET. Summer streamflow minima coincided with hillslope groundwater minima. Stream depth and conductivity exhibited similar diurnal patterns, suggesting variations in groundwater contributions and opportunities for future research.Item Parameterized and Machine Learning Methods for Estimating Evapotranspiration from Satellite Data(2019) Carter, Corinne Minette; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The studies in this dissertation present evaluation of and improvement to parametric and machine learning regression methods for estimating evapotranspiration from remote sensing. It includes three main parts. The first part is an assessment of parametric regression methods for obtaining evapotranspiration from vegetation index and other variables. It was found that including more variables tends to improve results, but the form of the regression formula does not make a large difference. Algorithm performance is not as good for wetland and agricultural sites as for other land cover types. Re-training of algorithms for those surface type results in some improvement. The second part consists of an evaluation of ten machine learning techniques for retrieval of evapotranspiration from surface radiation and several other variables. It is found that the best results are obtainable using all available input variables to train the bootstrap aggregation tree, random kernel, and two- and three- hidden layer neural network algorithms. Performance is again found to be weaker for wetland and agricultural surface types than for other surface types. However, separate training of the machine learning algorithms with data from those surface types does not significantly improve performance. The third part consists of further refinement to the machine learning algorithms and application of the bootstrap aggregation tree method to generate evapotranspiration maps of the continental United States for 2012. It is found that separating snow and non-snow data points improves performance. Performance for all tested algorithms was similar against the validation data set, but best for the bootstrap aggregation tree using an independent test data set. Monthly mean maps of the continental United States are generated for the drought year 2012 using the bootstrap aggregation tree. Evapotranspiration levels are lower than those shown in comparison data sets for the growing season in the eastern United States, resulting from a low bias at high evapotranspiration values. Retraining with the training data set weighted towards higher evapotranspiration values reduces this discrepancy but does not eliminate it. It is clear that machine learning evapotranspiration algorithm results have a significant dependence on training data set composition.Item Assessing evapotranspiration rates of a Mid-Atlantic red maple riparian wetland using sap flow sensors.(2005-04-13) Renz, Jennifer Theresa; Momen, Bahram; Plant Science and Landscape Architecture (PSLA); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Riparian forests are unique due to increased exposure of trees to winds and radiation and the subsequent effects on the quality and quantity of water discharge from the system. Since "edge effects" can enhance evapotranspiration (ET) of exposed trees, ET rates of a first-order red maple riparian wetland were assessed with thermal dissipation probes during the 2002 growing season to address: a) if edge trees transpire more water daily than interior trees, b) correlations among sap flow rates and energy balance-derived estimates, c) variations in ecosystem ET estimates based on 6 scaling variables, and d) diurnal correlations between maximum sap flow rates and streamflow losses. Results from this study indicate that: a) edge trees transpire more water daily than interior trees during early summer, b) choice of scaling variable affects estimation of ecosystem ET rates, and c) maximum sap flow rates correlate with streamflow losses diurnally under specific environmental conditions.