Using High Resolution Tracer Data to Constrain Storage and Flux Estimates in a Spatially Distributed Rainfall-runoff Model

Abstract:

Models simulating both stream flow and conservative tracers can provide a more realistic representation of flow paths, storage distributions and mixing processes that is advantageous for many predictions. Conceptual models with such integration have provided useful insights, but tend to be lumped and thus crude representations of catchment processes. Using tracers to aid spatially-distributed models has considerable potential to improve the conceptualisation of the dynamics of internal hydrological stores and fluxes. Here, we examine the strengths and weaknesses of a data-driven, spatially-distributed tracer-aided rainfall-runoff model. The model structure allows the assessment of the effect of landscape properties on the routing and mixing of water and tracers. The model was applied to an experimental site (3.2 km²) in the Scottish Highlands with a unique tracer data set; 4 years of daily isotope ratios in stream water and precipitation were available, as well as 2 years of weekly soil and ground water isotopes. The model evolved from an empirically-based, lumped tracer-aided model previously developed for the catchment. The best model runs were selected from Monte Carlo simulations based on a dual calibration criterion that included objective functions for both stream water isotopes and discharge at the outlet. Model results were also tested against observed spatially-distributed soil water isotope data. Model performance for both criteria was good and the model could reproduce the variable isotope signals in steeper hillslopes where storage was low and damped isotope responses in valley bottom cells with high storage. The model also allows us to estimate the age distributions of internal water fluxes and stream flow and has substantially improved spatial and temporal dynamics of process representation. This gives a more robust framework for projecting the effects of environmental change.