The introduction of “smart” tracer techniques in recent years has provided new ways to investigate sediment-water interactions and microbial activity in stream corridors. In this study, the formulation of the STIR model (Marion et al., 2008) is extended to represent the transport and transformation of Resazurin-Resorufin smart tracers, and an object-oriented toolbox, STIR-RST, is presented for model evaluation and calibration. STIR-RST allows different storage processes to be represented by specific residence time distributions (RTDs), with two possible arrangements of the storage zones: nested (in-series) or competing (in-parallel). The application of STIR-RST to field tracer data is demonstrated assuming two storage zones with exponential RTD. Results show that the assumption of two storage zones provides a better approximation of the observed BTCs compared to that of a single storage zone, at the cost of higher parameter uncertainty. Similar fits are obtained for nested and competing zone arrangements.

近年来，“智能”示踪剂技术的引入为研究河流走廊中的沉积物-水相互作用和微生物活动提供了新途径。在这项研究中，扩展了STIR模型的公式（Marion等，2008）以代表Resazurin-Resorufin智能示踪剂的运输和转化，并提供了一个面向对象的工具箱STIR-RST，用于模型评估和校准。STIR-RST允许通过特定的停留时间分布（RTD）来表示不同的存储过程，其中存储区域有两种可能的排列方式：嵌套（串联）或竞争（并联）。假定两个带指数RTD的存储区，证明了STIR-RST在现场示踪剂数据中的应用。结果表明，与单个存储区相比，两个存储区的假设提供了更好的观测BTC近似值，但代价是参数不确定性更高。对于嵌套区域和竞争区域安排，获得了类似的拟合。