Date of Award

June 2018

Degree Name

Master of Science (MS)

Department

Earth Sciences

Advisor(s)

Christa Kelleher

Keywords

Modeling, Stormwater, TIR, UAV

Subject Categories

Physical Sciences and Mathematics

Abstract

Stream temperature is an important metric for determining the health of a stream system, and derived from complex interactions between climate, weather, and local landscape characteristics. In urban areas, stream temperature is additionally influenced by impervious surfaces as well as stormwater infrastructure that translates water quickly to stream channels. Disentangling the impacts of spatial and temporal drivers of the stream temperature regime is therefore a complex task. To improve understanding of spatial and temporal variability in stream temperatures, we combined in situ stream temperature loggers with thermal infrared (TIR) imagery collected via unmanned aerial vehicles (UAV) along a 2.25 km section of creek in Syracuse, NY. TIR imagery was used document the heterogeneity of stream temperature as impacted by a natural spring and several stormwater inputs across the stream channel and down the length of a stream for three flights in May, June, and July of 2017. Thermal heterogeneities derived from stormwater culverts were observed to persist as far as 290 m downstream from their source depending on the time of year. Reach observations and weather station data were combined with TIR imagery to calibrate a deterministic stream temperature model using a modified version of HFLUX 3.0. Stream temperatures were simulated in HFLUX for a five-day period, after a two day warm up, surrounding monthly flights using different combinations of stormwater discharge and temperature. The use of two metrics derived from the TIR data, an ‘Initial Impact’ (on stream temperature) and ‘Effect Duration’, enabled spatial model calibration alongside temporal calibration to iButton observations at the end of the reach. Discrepancies between best models fits through space and best model fits through time establish that model approaches should incorporate errors in multiple dimensions. Overall, this study demonstrates that stormwater inputs in northeastern watersheds may cool mean stream temperatures, with effects that can persist for hundreds of meters downstream. Beyond the impact of stormwater, we also show that UAV-based TIR can be particularly useful for documenting these impacts when paired with in situ sensors. Finally, we find that calibrating models in multiple dimensions can more accurately simulate spatio-temporal hydrologic processes and mixing between urban water sources and the main channel.

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Open Access

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