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Colorado State University hosts conferences and events from all disciplines across campus. These digital collections include materials presented at various conferences and events held at CSU.

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Browsing Conference Proceedings and Events by Author "Allen, Sarah M., author"

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    A conceptual framework for the use of machine learning for the synthesis of stream discharge - gage height rating curves
    (Colorado State University. Libraries, 2016) Allen, Sarah M., author; Emerman, Steven H., author; Murdock, Thomas H., author; Tulley, Skyler K., author; Colorado State University, publisher
    The objective of this research is to use machine learning for the synthesis of stream discharge – gage height rating curves from easily measurable hydrogeologic parameters. A machine learning algorithm would require as input a compilation of relevant hydrogeologic parameters for each gaging station. Since such a compilation does not yet exist, the first step has been to create a conceptual framework that identifies the relevant hydrogeologic parameters that would need to be compiled. Frequent reverse flow or flood waves preclude the existence of a rating curve (unique relationship between gage height and discharge). If a rating curve exists, then a stable channel has a power-law rating curve. Deviations from the power-law curve result from deposition (power-starvation) or scouring (sediment-starvation), which could occur at the high or low range of discharge or both. The eight types of deviation (including no deviation) from the power-law curve can be regarded as eight functional forms of rating curves, which can be represented as lines, parabolas or cubic polynomials on plots of the Z-scores of the logarithms of gage height and discharge. Rating curves can be classified into the eight types based on the hydrogeologic criteria of (1) stream slope (2) relative erodibility of the stream banks (3) distance to the nearest upstream and downstream confluences with relatively significant discharge. USGS gaging stations in Utah were chosen randomly until each of the eight types of rating curves was found. The first example of each type was shown to be consistent with the corresponding hydrogeologic criteria.
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    ItemOpen Access
    Use of the Manning equation for predicting the discharge of high-gradient canals and natural streams
    (Colorado State University. Libraries, 2018) Ostraff, Ashley A., author; Emerman, Steven H., author; Udy, Nicholas D., author; Allen, Sarah M., author; Rakotoarisaona, Henintsoa, author; Gherasim, Janelle, author; Stallings, Alison M., author; Saldivar, Jeremy N., author; Larsen, Kenneth L., author; Abbott, Morgan, author; Colorado State University, publisher
    The Manning Equation is used to predict stream or canal discharge from hydraulic radius, slope of the water surface, and a Manning roughness coefficient. Jarrett (1984) proposed that, for high-gradient streams (S > 0.002), the Manning roughness coefficient could be predicted from the hydraulic radius and the slope alone. The objective of this study was to develop separate empirical formulae, depending upon climate and stream bank lithology, for predicting the Manning roughness coefficient for high-gradient canals and natural streams from hydraulic radius and slope. The objective was addressed by separating the database used by Jarrett (1984) according to stream bank lithology, and by carrying out new measurements of the Manning roughness coefficient at nine high-gradient stream sites with crystalline (igneous and metamorphic) banks and two high-gradient stream sites with carbonate banks in Haiti, nine high-gradient stream sites with carbonate banks in Utah, and 14 high-gradient canals in Utah. The data were used to develop empirical formulae for predicting the Manning roughness coefficient for (1) continental climate, clastic stream bank (2) tropical climate, crystalline stream bank (3) continental/tropical climate, carbonate stream bank (4) continental climate, earthen canal with grassy bank. The Manning roughness coefficient was a negative function of hydraulic radius for the first case and a positive function for the other cases, suggesting that the increase in turbulent resistance is caused by the roughness of the sediment in the first case, but by the increase in the Reynolds number, which is proportional to the depth, in the other cases.