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Spectral densities and coherence of NM hydrology; NM decomposition analysis; Quantifying science education


Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/21085

Spectral densities and coherence of NM hydrology; NM decomposition analysis; Quantifying science education

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dc.contributor.author Tichy, Jennifer L.
dc.date.accessioned 2012-08-28T18:19:10Z
dc.date.available 2012-08-28T18:19:10Z
dc.date.issued 2012-08-28
dc.date.submitted July 2012
dc.identifier.uri http://hdl.handle.net/1928/21085
dc.description.abstract Chapter 1: River discharge and montane snowpack play a central role in controlling processes at all levels of ecological organization, so the identification of dominant hydrological periods is an important issue in relating the physical system to the biological system. To quantitatively study the Rio Grande discharge along with the closely related snowpack, we determine the spectral densities as a function of frequency in addition to coherence estimates. This is quality-controlled through a signal to noise ratio, which also allows us to create a transfer function to model the relationship between river discharge and snowpack. By using frequency domain techniques, it becomes apparent that there are in fact three signal peaks at distinct frequencies (every 365 days, every 182 days, and every 122 days) for the riparian ecosystem of the Rio Grande in New Mexico, USA. By generalizing power spectral estimates, seasonal variation can be quantified (spring snowmelt in addition to summer monsoons) through spectrograms, yielding further amplitude estimates across a large band of frequencies of interest. Chapter 2: To find events in both river discharge data and snow water equivalent data for the Rio Grande, New Mexico, an event detector is created through moving averages (short term average over long term average, STA/LTA). Additionally, a cross-correlation is performed on the data from the United States Geological Survey and the National Resource Conservation Service (1981-2010) to see the relationship between the river discharge data and the snow water equivalent data. Using lag times calculated from the cross correlation, we differences in peak times from discharge and snowmelt indicates a shift in snowmelt to earlier in the spring. Because of the high correlation between snow water equivalent and river discharge, plus the results of the cross-correlation, it is found that the peak in snowmelt is occurring earlier in the year and that the time lag between peak snowpack and peak river discharge is decreasing, meaning that the snowpack is generally melting faster. One implication resulting from hydrologic changes such as these is the adaptation of aquatic and terrestrial species depending on the system for survival. Chapter 3: The temperature and moisture responses of heterotrophic soil respiration are crucial for a reliable prediction of carbon dynamics with respect to climatic change. Despite numerous studies regarding temperature and moisture effects on soil decomposition, open questions remain regarding local ecosystem response to large-scale changes in climate (i.e. If annual average soil temperature increases, will faster decay alter net ecosystem carbon exchanges?). The objective of this study was to analyze the influence of different soil and temperature response functions on decomposition rates over a time, depth, and spatial continuum, using data obtained from the USDA NRCS. According to the CENTURY soil decomposition model, decomposition was predominantly defined by temperature controls, but soil moisture provides the pulse-like activity of decomposition across the state of New Mexico. This study suggests that as the local climate changes, so will decomposition rates, which ultimately affects levels of carbon sequestration. Current measurements are on a daily time-step, but more frequent data collection could identify diel variability. Chapter 4: In order to study the idea of teaching mainstream students how to be scientists, a literature review is performed and the data are quantitatively analyzed. The data are compared across the educational research papers and similar statistical results are then categorized and analyzed for similarities in the papers utilized. It is found that similarities in how the data are collected groups the normalized data to an extent, but the quantitative analysis finds that a more scientific approach needs to be applied to all research fields, minimizing subjective results and maximizing objective results for a given research study. A conclusion is drawn that all students need to be taught the scientific process and need to understand the importance of questioning facts and the need to critically, quantitatively, study data. en_US
dc.language.iso en_US en_US
dc.subject spectral densities en_US
dc.subject coherence en_US
dc.subject transfer function en_US
dc.subject decomposition en_US
dc.subject.lcsh Environmental indicators--New Mexico.
dc.subject.lcsh Environmental indicators--New Mexico.
dc.subject.lcsh Climatic changes--Environmental aspects--New Mexico.
dc.subject.lcsh Snow--New Mexico--Measurement.
dc.subject.lcsh Runoff--New Mexico--Measurement.
dc.subject.lcsh Soil temperature--New Mexico.
dc.subject.lcsh Soil moisture--New Mexico.
dc.subject.lcsh Carbon sequestration--New Mexico.
dc.subject.lcsh Observation (Scientific method)--Study and teaching.
dc.subject.lcsh Critical thinking--Study and teaching.
dc.title Spectral densities and coherence of NM hydrology; NM decomposition analysis; Quantifying science education en_US
dc.type Dissertation en_US
dc.description.degree Biology en_US
dc.description.level Doctoral en_US
dc.description.department University of New Mexico. Biology Dept. en_US
dc.description.advisor Sinsabaugh, Robert
dc.description.committee-member Litvak, Marcy
dc.description.committee-member Watkins, Kathryn
dc.description.committee-member Wearing, Helen

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