How to assess climate change impact models: uncertainty analysis of streamflow statistics via approximate Bayesian computation (ABC)

Climate change impact models (CCIMs) suffer from inherent bias, uncertainty, and asynchronous observations in the baseline period. To overcome these challenges, this study introduces a methodology to assess CCIMs in the baseline period using the uncertainty analysis of streamflow statistics via the approximate Bayesian computation (ABC) post-processor, which infers the residual error model parameters based on summary statistics (signatures). As an illustrative case study, we analyzed the climate change projections of the fifth assessment report of the United Nations intergovernmental panel on climate change (AR5 - IPCC) of the monthly streamflow in the upper Oria catchment (Spain) with deterministic and probabilistic verification frameworks to assess the ABC post-processor outputs. In addition, the ABC post-processor is evaluated against the ensemble (reference method). The results show that the ABC post-processor outperformed the ensemble method in all verification metrics, and the ensemble method has reasonable reliability but exhibited poor sharpness. We suggest that the ensemble method should be complemented with the ABC post-processor for climate change impact studies.