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Model simulation of Cl- and δ18O in stream waters has been investigated as a means of improving interpretation of catchment-scale hydrological processes. The procedure has been evaluated for a small upland catchment which is one of the UK Environmental Change Network sites. Precipitation and stream samples have been analysed for hydrochemical determinands since the mid 1990s and, since November 2004, measurement of δ18Q has also been undertaken. A conceptual hydrological model STREAM (STorage REsidence times And Mixing) was applied to the catchment to simulate the hydrology and responses of Cl and δ18O Results from model simulations confirmed that the catchment generally behaves as a well-mixed system. Tile feasibility of flow contributions from a deep groundwater source and infiltration excess runoff was examined, in addition to the apparently dominant shallow groundwater response. The ability to estimate mean residence times and draw strong conclusions about catchment processes was limited by the range of uncertainties in the experimental data and modelling. Integration of the tracer data in the model was found to be of value for probing model sensitivities and developing hypotheses that inform the design of further field experimentation. In this way, the modelling provides key feedback within a catchment learning framework. [ABSTRACT FROM AUTHOR]

To predict rice-farming pesticide concentrations in river water with imprecise model inputs for screening-level analysis, a basin-scale runoff model was developed. The Monte Carlo method was applied to create estimates of input data regarding agricultural work schedules and parameters for pesticide decomposition and sorption in solids and water. The prediction accuracy of the model was evaluated when used with non-optimised pesticide parameters; the model was calibrated using hydrological data alone without reference to observed pesticide concentration data. Overall, predictions for the pesticide concentrations were successful within order-of-magnitude accuracy. The pesticide rankings according to the predicted concentration roughly agreed with those observed. The success of screening-level analysis indicates that the model prediction can help in selection of pesticides to be monitored and in determining the monitoring schedule for the river basin. [ABSTRACT FROM AUTHOR]