Statistical modelling approach for integrating probabilistic climate projections with the river flow data

To address some of the key challenges arising due to the uncertain climate change, paper proposes suits of novel stochastic modelling techniques, capable of integrating probabilistic climate information within the synthetic streamflow series generation procedure. To achieve this, a thorough statistical analysis of the climate information, available from the UK daily rainfall data, part of the Met Office Integrated Data Archive System (MIDAS), the UK Climate Projections (UKCP09) and the daily streamflow time series of the case study river (the Dee, of the UK) has been conducted. The model is first calibrated, using the historic flow and climate timeseries, and then simulated with the probabilistic climate projections available from UKCP09. Stochastic modeling techniques used are mainly the Hidden Markov Models (HMM) combined with the Generalised Pareto (GP) distribution and Probability Distribution models. Potential impacts of the climate change on the statistical characteristics of the river flow data has been explored.