Parameter optimisation and uncertainty assessment for large-scale streamflow simulation with the LISFLOOD model

This work addresses the calibration of the distributed rainfall-runoff model LIS-FLOOD and, in particular, the realistic quantification of parameter uncertainty and its effect on the prediction of river discharges for large European catchments. LIS-FLOOD is driven by meteorological input data and simulates river discharge in large drainage basins as a function of spatial information on topography, soils and land cover. Even though LIS-FLOOD is physically based to a certain extent, some processes are only represented in a lumped conceptual way. As a result, some parameters tack physical basis and cannot be directly inferred from quantities that can be measured. In the current LIS-FLOOD version five parameters need to be determined by calibration. We employ the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimisation algorithm to automatically calibrate the model. against daily discharge observations. The resulting posterior parameter distribution reflects the uncertainty about the model parameters after taking into account the discharge observations, and forms the basis for making probabitistic flow predictions. To overcome the computational burden the optimisation has been implemented using parallel. computing. As an illustrative example, we demonstrate the methodology for the Meuse catchment upstream of Borgharen, covering approximately 21,000 km(2). Results demonstrate the capabilities of the SCEM-UA algorithm to efficiently evolve to the target posterior distribution and to identify, except for the lower groundwater zone time constant.