Climate informed non-stationary simulation of daily streamflow – a comparison of three stochastic models

For optimal planning of reservoir design and management, long series or many realizations of daily streamflow are required. Stochastic streamflow models can provide such data. However, considering future changes in climate in these models is challenging. The objective of this study is to compare three non-stationary stochastic models with respect to their performance in simulating daily streamflow for current and future climate conditions. This comparison relies on two non-parametric approaches, namely the k-nn Bootstrap and Simulated Annealing optimization as well as a parametric model working in the frequency domain.

All models are run under different experiments: (1) with observed climate from the German Weather Service for a reference and pseudo-future period and (2) with future climate simulations using data from climate models. The simulations from the three models are evaluated for general flow statistics considering current climate and observed changes for a pseudo future. As an additional reference, the HBV rainfall - runoff model driven by observed climate and climate model data is used. The testing of the methods is carried out for some mesoscale catchments in the Harz Mountains comprising streamflow gauges with long daily records. The ability of the stochastic models to simulate the changes for the pseudo future will be the core test for their applicability under changing climate conditions. The results are also expected to demonstrate advantages, disadvantages and limitations of the three methods.