Thesis
Estimating stream temperature through nonlinear regression and equilibrium temperature models
Washington State University
Master of Science (MS), Washington State University
05/2020
DOI:
https://doi.org/10.7273/000004096
Handle:
https://hdl.handle.net/2376/125036
Abstract
The temperature of a particular body of water can be telling of its health. There lies the importance of assessing the impact of climate change on stream temperature. General Circulation Models (GCMs) such as those represented by the Coupled Model Intercomparison Project (CMIP) offer understanding of atmospheric and oceanic parameters under various climate scenarios. The aforementioned models, however, do not offer the same level of information for stream temperatures. It is for this reason that previous studies have made an effort to assess the air and stream temperature relationship through the use of a nonlinear regression S-curve model. This S-curve model was applied to 306 sites around the US in an effort to better understand how model parameters and performance differed among different climate types. An equilibrium temperature model was built that could estimate stream temperatures based on air temperature, shortwave solar radiation, relative humidity, and windspeed. Stream temperature was estimated at six different sites along the Columbia River through the use of the equilibrium temperature model. Temperature estimates obtained through the equilibrium temperature and S-curve models were compared. A higher resulting NSC was observed for all sites under the equilibrium temperature model, showing better model fit, and an overall improvement over the S-curve model.
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Details
- Title
- Estimating stream temperature through nonlinear regression and equilibrium temperature models
- Creators
- Emmanuel Rendon Aguilar
- Contributors
- Yonas Demissie (Advisor) - Washington State University, Department of Civil and Environmental Engineering
- Awarding Institution
- Washington State University
- Academic Unit
- Department of Civil and Environmental Engineering
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Identifiers
- 99900890787001842
- Language
- English
- Resource Type
- Thesis