The continuum of a stream channel, its permeable bed sediments and the adjacent groundwater is termed the stream corridor. The study of how water moves within the stream corridor is crucial to understanding the spatio-temporal evolution of solute transport and nutrient cycling affecting river water quality, and is thus essential for more efficient water resources management. In order to predict solute transport in stream corridors, mathematical models are widely used, but the estimation and interpretation of their parameters is difficult. This is because combinations of different parameter values can give the same model performance (identifiability problem). Several studies highlighted the strong non-identifiability of transient storage models (TSMs), which are among the most used phenomenological models to study water transport in the stream corridor. Despite the strong parameter interaction and non-identifiability, TSM studies often rely on calibrated parameter sets without testing parameter identifiability. However, even when identifiability is taken into account, the majority of work were unable to obtain identifiability of TSM parameters via the use of classic random-sampling approaches (see Bonanno et al., 2022). In this repository, I release the first version of GLaDY, a GLobal and DYnamic identifiability analysis, applied for solute breakthrough curves (BTCs). This analysis is iterative, meaning that a successive TSM iteration depends on the results of the identifiability analysis of the previous iteration. For further questions, please read Bonanno et al., 2022: Exploring tracer information in a small stream to improve parameter identifiability and enhance the process interpretation in transient storage models. Model description reported in: https://github.com/EnricoBon/BTC_analysis Enrico Bonanno, Nov 2022