Catering for Uncertainty in a Conceptual Rainfall Runoff Model: Model Preparation for Climate Change Impact Assessment and the Application of GLUE using Latin Hypercube Sampling
Changes in Irish climate may pose a number of obstacles for water resource management. There is a need to approach this problem using the catchment as the basic unit of analysis. The application of a lumped conceptual rainfall-runoff model for simulating beyond a baseline calibration set is a major challenge for climate change impact assessment. This is due in no small part to the limitations associated with the use of these models, with uncertainty in model output being associated with model structure and the non-uniqueness of optimised parameter sets. In this paper, HYSIM, an “off-the-shelf” conceptual rainfall runoff model using data on a daily time-step is applied to a suite of catchments throughout Ireland in preparation for use with downscaled climate data. Uncertainties relating to process parameter calibration due to parameter interaction and equifinality are highlighted. In an attempt to improve the reliability of model output the generalised likelihood uncertainty estimation (GLUE) framework is adopted to analyse the uncertainty in model output derived from parametric sources. Traditionally this approach has been applied using Monte Carlo random sampling (MCRS). However, when using an “off-the-shelf” type model, source code may not be available and it may not be feasible to run the model for large MCRS samples without user intervention. In order to make the propagation of uncertainty through the model more efficient, input parameter sets are generated using Latin Hypercube sampling (LHS). A number of acceptable parameter sets are generated and uncertainty bounds are constructed for each time step using the 5th and 95th percentile at each temporal interval. These uncertainty bounds will be used to quantify the uncertainty in simulations carried out beyond the baseline calibration period as they include the error derived from data measurement, model structure, and parameterisation
