Uncertainty assessment of climate and land use changes scenarios for the Millbrook catchment - reservoir system simulated by the SWAT-SALMO

Nguyen, Hanh Hong; Recknagel, Friedrich; Meyer, Wayne

In this study, we analyse the uncertainty of eutrophication effects of ongoing environmental and climate changes on the Millbrook reservoir simulated by the model ensemble SWAT-SALMO. The semi-arid Millbrook catchment-reservoir system provides drinking water to the north-eastern region of Adelaide, South Australia. The Soil and Water Assessment Tool (SWAT) simulated flow as well as nitrate and phosphate loadings originating from the catchment before entering the reservoir. The lake model SALMO received the simulated nitrate and phosphate loadings as input and determined daily phosphate, nitrate, and chlorophyll -a concentrati ons in the reservoir. This integrated modelling framework was key for simulating complex scenarios on impacts of future climate and land use changes on the whole catchment -reservoir system. The uncertainty of simulation results has been taken into accoun t by complex statistical algorithms, including the Sequential Uncertainty Fitting (SUFI2) of the SWAT calibration wizard, and multi -objective parameter optimisation of SALMO by means of the Hybrid Evolutionary Algorithm (HEA). In view of the large number of data processing steps required for the integrated simulations, the uncertainty assessment focused on the five best simulations results from the SWAT to be utilised for the parameter optimisation of SALMO. The uncertainty of the model ensemble has been qu antified as envelope of the fifty best iterations of nitrate, phosphate, and chlorophyll -a concentrations based on daily time steps for a typical “dry” and a typical “wet” year. The synergized envelop was further used to compare with the results of predict ion of impacts of climate and land use changes on the Millbrook catchment - reservoir system. Overall, the estimation of uncertainty bound from the catchment -reservoir model ensemble may improve the credibility of the model predictions to be further considered in decision- making.


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