Assessing the impact of mixing assumptions on the estimation of streamwater mean residence time

Catchment streamwater mean residence time (T_mr) is an important descriptor of hydrological systems, reflecting their storage and flow pathway properties. T_mr is typically inferred from the composition of stable water isotopes (oxygen-18 and deuterium) in observed rainfall and discharge. Currently, lumped parameter models based on convolution and sinewave functions are usually used for tracer simulation. These traditional models are based on simplistic assumptions that are often known to be unrealistic, in particular, steady flow conditions, linearity, complete mixing and others. However, the effect of these assumptions on T_mr estimation is seldom evaluated. In this article, we build a conceptual model that overcomes several assumptions made in traditional mixing models. Using data from the experimental Maimai catchment (New Zealand), we compare a complete-mixing (CM) model, where rainfall water is assumed to mix completely and instantaneously with the total catchment storage, with a partial-mixing (PM) model, where the tracer input is divided between an ‘active’ and a ‘dead’ storage compartment. We show that the inferred distribution of T_mr is strongly dependent on the treatment of mixing processes and flow pathways. The CM model returns estimates of T_mr that are well identifiable and are in general agreement with previous studies of the Maimai catchment. On the other hand, the PM model—motivated by a priori catchment insights—provides T_mr estimates that appear exceedingly large and highly uncertain. This suggests that water isotope composition measurements in rainfall and discharge alone may be insufficient for inferring T_mr. Given our model hypothesis, we also analysed the effect of different controls on T_mr. It was found that T_mr is controlled primarily by the storage properties of the catchment, rather than by the speed of streamflow response. This provides guidance on the type of information necessary to improve T_mr estimation. Copyright © 2010 John Wiley & Sons, Ltd.