A Transient Storage Model (TSM), which considers the storage exchange process that induces an abnormal mixing phenomenon, has been widely used to analyze solute transport in natural rivers. The primary step in applying TSM is a calibration of four key parameters: flow zone dispersion coefficient (Kf), main flow zone area (Af), storage zone area (As), and storage exchange rate (α); by fitting the measured Breakthrough Curves (BTCs). In this study, to overcome the costly tracer tests necessary for parameter calibration, two dimensionless empirical models were derived to estimate TSM parameters, using multi-gene genetic programming (MGGP) and principal components regression (PCR). A total of 128 datasets with complete variables from 14 published papers were chosen from an extensive meta-analysis and were applied to derivations. The performance comparison revealed that the MGGP-based equations yielded superior prediction results. According to TSM analysis of field experiment data from Cheongmi Creek, South Korea, although all assessed empirical equations produced acceptable BTCs, the MGGP model was superior to the other models in parameter values. The predicted BTCs obtained by the empirical models in some highly complicated reaches were biased due to misprediction of Af. Sensitivity analyses of MGGP models showed that the sinuosity is the most influential factor in Kf, while Af, As, and α, are more sensitive to U/U*. This study proves that the MGGP-based model can be used for economic TSM analysis, thus providing an alternative option to direct calibration and the inverse modeling initial parameters.

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INTRODUCTION Most of Solanaceae crops are very sensitive to quinclorac. The most sensitive tomato does not grow normal when it is grown with the irrigation water or soil containing quiclorac at 10 and 0 .1ppb, respectively. Because of this property quinclorac is not used in Korea and Japan inspite of excelllence of the herbicide for the transplanted and direct-seeded rice. Developer of this herbicide introduces

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One-dimensional solute transport modeling is fundamental to enhance understanding of river mixing mechanisms, and is useful in predicting solute concentration variation and fate in rivers. Motivated by the need of more adaptive and efficient model, an exact and efficient solution for simulating breakthrough curves that vary with non-Fickian transport in natural streams was presented, which was based on an existing implicit advection-dispersion equation that incorporates the storage effect. The solution for the Gaussian approximation with a shape-free boundary condition was derived using a routing procedure, and the storage effect was incorporated using a stochastic concept with a memory function. The proposed solution was validated by comparison with analytical and numerical solutions, and the results were efficient and exact. Its performance in simulating non-Fickian transport in streams was validated using field tracer data, and good agreement was achieved with 0.990 of R2. Despite the accurate reproduction of the overall breakthrough curves, considerable errors in their late-time behaviors were found depending upon the memory function formulae. One of the key results was that the proper formula for the memory function is inconsistent according to the data and optimal parameters. Therefore, to gain a deeper understanding of non-Fickian transport in natural streams, identifying the true memory function from the tracer data is required.

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