Constraining uncertainty in regional hydrologic impacts of climate change: nonstationarity in downscaling

Abstract

Regional impacts of climate change remain subject to large uncertainties accumulating from various sources, including those due to choice of General Circulation Models (GCMs), scenarios and downscaling methods. Objective constraints to reduce the uncertainty in regional predictions have proven elusive. In most studies to date the nature of the Downscaling Relationship (DSR) used for such regional predictions has been assumed to remain unchanged in a future climate. However, studies have shown that climate change may manifest in terms of changes in frequencies of occurrence of the leading modes of variability, and hence, stationarity of DSRs is not really a valid assumption in regional climate impact assessment. This work presents an uncertainty modeling framework where, in addition to GCM and scenario uncertainty, uncertainty in the nature of the DSR is explored by linking downscaling with changes in frequencies of such modes of natural variability. Future projections of the regional hydrologic variable obtained by training a Conditional Random Field (CRF) model on each natural cluster are combined using the weighted Dempster-Shafer (D-S) theory of evidence combination. Each projection is weighted with the future projected frequency of occurrence of that cluster (“cluster linking”) and scaled by the GCM performance with respect to the associated cluster for the present period (“frequency scaling”). The D-S theory was chosen for its ability to express beliefs in some hypotheses, describe uncertainty and ignorance in the system and give a quantitative measurement of belief and plausibility in results. The methodology is tested for predicting monsoon streamflow of the Mahanadi River at Hirakud Reservoir in Orissa, India. The results show an increasing probability of extreme, severe, and moderate droughts due to climate change. Significantly improved agreement between GCM predictions owing to cluster linking and frequency scaling is seen, suggesting that by linking regional impacts to natural regime frequencies, uncertainty in regional predictions can be realistically quantified. Additionally, by using a measure of GCM performance in simulating natural regimes, this uncertainty can be effectively constrained.