During dry periods, streamflow acts as a critical source of water supply and sustains a wide variety of aquatic plants and animal species. Streamflow in the non-rainy periods, known as recession flow, is the water from previous rainfall events which is released from various subsurface storages into the streams. Accurate modelling of recession flows remains a challenge due to a limited understanding of the mechanisms of water release from subsurface storages and difficulties in the parameterization of these processes in hydrologic models. The spatial variability in subsurface characteristics combined with temporal variability in the water stored in different subsurface storages can contribute to changes in recession flow characteristics across events. In this study, we model the catchment as a set of parallel buckets with different storage discharge relationships. Assuming, a linear storage–discharge relationship for each bucket, we develop an inverse methodology for quantifying the contributions of the buckets during recession events in which inverse Laplace transform is applied to streamflow data during recession periods.