Quantifying vertical streambed fluxes and streambed thermal properties using heat as a tracer during extreme hydrologic events

As a natural tracer, heat has some remarkable advantages over competing test methods and has become a popular and widely used tool for quantifying streambed fluxes. In recent decades, extreme hydrological events have become intense and frequent, resulting in irregular or complex temperature changes at the streamstreambed interface. During extreme hydrological events, most existing analytical models with periodic changing top boundary conditions are inapplicable. In this study, we present a heat-based analytical model that accounts for the arbitrary top boundary and initial conditions to estimate time-variant vertical streambed fluxes (VSFs) and streambed effective thermal diffusivity using temperature-time series from streambed sediments during extreme hydrologic events. The particle swarm optimization is used to estimate the time-variant VSFs. The Morris global sensitivity analysis method is used to test the impacts of the input parameters on heat transport in the streambed. Results show that the output temperature data is most sensitive to VSFs, followed by the volumetric heat capacity of the streambed, thermal conductivity and thermal dispersivity. The performance of the new model is tested using a numerical model with time-variant VSFs associated with extreme hydraulic events such as hurricanes and dam-controlled river conditions. Results demonstrate that despite oscillations in the estimated VSFs and fluctuations being stronger when the VSFs begin to reverse and at the peak of the VSFs, the new model of this study captures changes in VSFs under extreme hydraulic events. In addition, the frequency domain model, LPMLEn, performs satisfactorily in estimating VSFs in hurricane conditions. Application to field data demonstrates that the temperature distribution can be effectively remodeled based on the estimated VSFs and streambed effective thermal diffusivity. Both the present model and LPMLEn model could be considered as alternatives to the traditional isotope-based method in estimating VSFs during extreme hydrologic events.