Because of the influence of various storage zones, solute transport in a stream channel cannot be interpreted by advection and dispersion processes alone. Thus, in recent decades, various models that incorporate storage-zone effects have been proposed to characterize anomalous storage mechanisms. The validity of these models has been predominantly demonstrated using tracer breakthrough curves measured in surface flow. However, the storage effect is usually less influential on the breakthrough curve behavior than in-stream flow dynamics. Thus, for model validation, the tracer behavior within the storage zone should be investigated separately. The present study aims to quantify the time-dependent storage effect, herein termed the net retention time distribution (NRTD), from tracer measurements in the flow zone using a deconvolution technique with manipulation in the Fourier domain. The results demonstrated that the deconvolved NRTDs successfully represented the temporal behavior of the tracer in the storage zones without significant distortion in the primary information of the observed breakthrough curves. Using estimates of NRTD, we evaluated the simulations of the transient storage model, and found that the storage effects were underestimated as much as an average 18.5% in this study. The deconvolved NRTDs were also utilized to predict the biodegradation losses of organic chemicals flowing along a stream at confidence intervals.

Sediment transport load monitoring is important in civil and environmental engineering fields. Monitoring the total load is difficult, especially because of the cost of the bed load transport measurement. This study proposes estimation models for the suspended load to total load ratio (Fsus) using dimensionless hydro-morphological variables. Two prominent variable combinations were identified using the recursive feature elimination procedure of support vector regression (SVR): (1) W/h, d*, Reh, Frd, and Rew and (2) Reh, Fr, and Frd. The explicit interactions between Fsus and the two combinations were revealed by two modern symbolic regression methods: multi-gene genetic programming and Operon. The five-variable SVR model showed the best performance (R2=0.7722). The target dataset was clustered by applying a self-organizing map and Gaussian mixture model. Through these steps, Reh and Frd are determined as the two most influential variables. Subsequently, the one-at-a-time sensitivity of the input variables of the empirical models was investigated. By referring to the clustering and sensitivity analyses, this study provides physical insights into Fsus controlling relationships. For example, Fsus is proportional to Reh and is inversely related to Frd. The empirical models developed in this study are applicable in practice and easy to implement in other real-time surrogate suspended-sediment monitoring methods, because they only require basic measurable hydro-morphological variables, such as velocity, depth, width, and mean bed material grain size.