Abstract

A method is described for the generation of multivariate stochastic climate sequences for the Berg and Breede Water Management Areas in the Western Cape province of South Africa. The sequences, based on joint modeling of precipitation and minimum and maximum daily temperatures, are conditioned on annualized data, the aim being to simulate realistic variability on annual to decadal time scales. A vector autoregressive (VAR) model is utilized for this purpose and reproduces well those statistical attributes, including intervariable correlation and serial autocorrelation in individual variables, most relevant for the regional climate in this setting. The sequences incorporate nonlinear climate change trends, inferred using an ensemble of global climate models from the Coupled Model Intercomparison Project (CMIP5). Subannual variability is simulated using a block resampling scheme based on the k-nearest-neighbor approach, preserving both temporal patterns and spatial correlations. Downscaling to a network of quinary-level catchments enables distributed runoff, streamflow, and crop simulations and the assessment and integration of impacts. Final output takes the form of daily sequences, structured for driving the ACRU agrohydrological model of the University of KwaZulu-Natal, South Africa.