Abstract

Catchment-scale hydrologic response shows a wide range of temporal and spatial dynamics. Theoretically, it can be deconvolved into a sequence of impulse response functions derived from the application of a specific (linear or non-linear) transfer function to every single precipitation input. In doing so, each transfer function can be considered an independent transit time distribution (TTD) resulting from individual precipitation events. The shape of each event TTD is determined by a combination of catchment internal and external properties (topography, soil, climate), catchment states (terrestrial water storage), resulting flow paths (surface, subsurface) and the sequence of hydrologic forcing events (amount and timing of water and energy). Tracking a sequence of changing TTDs and combining the information that it provides can reveal valuable information about flow paths and hydrologic partitioning at catchment scales. Since the exact shape of the TTD is unknown as it depends on both internal and external catchment properties, we start out by tracking only one important descriptor of the TTD, its mean (MTT). Assuming an exponential TTD, a time series of MTTs would suffice to fully describe catchment response. The MTT is often considered to be stationary and no generally applicable method exists that allows for the tracking of temporal variations of MTT, especially in semi-arid catchments with large temporal variability in terrestrial water storage. We present a novel method to estimate varying MTT from stable isotope measurements. The approach is based on two principles. First, water leaving the catchment is a mixture of water with different transit times that entered the catchment at different locations. Therefore its final isotopic composition can be described by means of a transfer function that lags and disperses the input impulse in time (creating slow and quick components). Second, the chemical variability of the outflow in relation to the chemical variability of the inflow decreases with time (due to more mixing with older water components). Using the transient chemical variability as a tool to calibrate the transfer function model hands us the key to tracking variable MTT. Time series of δ2H in precipitation and stream water were recorded over a period of three years in a small semi-arid catchment in the Santa Catalina Mountains near Tucson, AZ. The variability within a thirty day moving window of the observed δ2H time series in stream flow is reproduced with an exponential transfer function model that uses the δ2H time series of precipitation as input. The model is calibrated for each time step by adjusting the MTT of the transfer function (with shorter MTT producing higher variability and vice versa). The MTTs of the resulting time series are used to create a TTD for each respective time step. When overlaying all TTDs, summing up the probabilities and normalizing, the resulting TTD is not necessarily an exponential function anymore. This catchment TTD can be utilized to make statements about the general response behavior of the catchment and hence serve as a catchment classification tool that incorporates catchment properties, states and flow paths.

Citation

Heidbuechel, I., Troch, P.A., Lyon, S.W. (2010): Tracking Varying Mean Transit Time in a Semi-Arid Catchment . AGU Fall Meeting (Poster) Abstract H11C-0813..