Relative information from thermal infrared imagery via unoccupied aerial vehicle informs simulations and spatially-distributed assessments of stream temperature.

Stream temperature is a measure of water quality that reflects the balance of atmospheric heat exchange at the air-water interface and gains or losses of water along a stream reach. In urban areas, stormwater sewers deliver water with varying magnitude and temperature to streams at variable timescales. Understanding the impacts of stormwater through space and time is therefore difficult to do with conventional approaches like in situ sensors. To study the impacts of stormwater on creek water temperatures, we combined in situ water temperature observations with thermal infrared (TIR) imagery collected via unoccupied aerial vehicle (UAV). Imagery was collected in May, June, and July of 2017. As ongoing work with UAV-based TIR suggests that this imagery is prone to poor accuracy, we focused on creating several data products beyond absolute water temperatures that can be used to assess temporal and spatial water temperature variations. In particular, TIR data products were used to extract the length of the observed stormwater plume as well as the width of the creek cross-section impacted by stormwater. From these values, we conclude that relatively narrow stormwater plumes affecting a small fraction of creek width can alter creek water temperatures for considerable distances downstream. We also applied TIR data to constrain results of a deterministic stream temperature model (HFLUX 3.0) that simulates the physical processes affecting stream heat exchanges. Stormwater plume lengths obtained from TIR imagery were used to refine spatially-distributed simulations, demonstrating that relative temperature information obtained from UAV imagery can provide useful calibration targets for stream temperature models. Overall, our work demonstrates the added value of UAV datasets for understanding urban stream temperatures, calibrating water quality models, and for modeling and monitoring of the impact of spatially explicit hydrologic processes on stream temperature.

Improved Vertical Streambed Flux Estimation Using Multiple Diurnal Temperature Methods in Series.

Analytical solutions that use diurnal temperature signals to estimate vertical fluxes between groundwater and surface water based on either amplitude ratios (Ar ) or phase shifts (Δϕ) produce results that rarely agree. Analytical solutions that simultaneously utilize Ar and Δϕ within a single solution have more recently been derived, decreasing uncertainty in flux estimates in some applications. Benefits of combined (Ar Δϕ) methods also include that thermal diffusivity and sensor spacing can be calculated. However, poor identification of either Ar or Δϕ from raw temperature signals can lead to erratic parameter estimates from Ar Δϕ methods. An add-on program for VFLUX 2 is presented to address this issue. Using thermal diffusivity selected from an Ar Δϕ method during a reliable time period, fluxes are recalculated using an Ar method. This approach maximizes the benefits of the Ar and Ar Δϕ methods. Additionally, sensor spacing calculations can be used to identify periods with unreliable flux estimates, or to assess streambed scour. Using synthetic and field examples, the use of these solutions in series was particularly useful for gaining conditions where fluxes exceeded 1 m/d.