Tracking the methodological evolution of climate change projections for UK river flows

Abstract

Much research has been carried out on the possible impacts of climate change for UK river flows. Catchment and national-scale studies since the early 1990s are here categorized into four modelling approaches: “top-down” GCM (Global Climate Model)-driven and probabilistic approaches and “bottom-up” stylised and scenario-neutral approaches. Early studies followed a stylised approach with a small number of model experiments focused on system sensitivity. GCM-driven approaches dominate since the mid-1990s and are scenario-led and “top-down”, but which incur the cascade of uncertainty which results in a large amount of information that may not be conducive to decision-making. The emergence of probabilistic projections aims to incorporate probabilistic information in navigating climate model uncertainty but remained “top-down” with challenges over its practical use for water resources planning. The scenario-neutral approach has clear roots in the early stylised approach with the aim to explore plausible futures beyond climate model projections and system sensitivity. A synthesis of studies employing each approach shows that the magnitude and sign of change in different hydrological variables remain uncertain between different regions of the UK. Comparison between studies is difficult due to their methodological differences and consequently different choices along the impact modelling chain, and with a notable geographic bias in catchment selection in southeast England. Major limitations for each approach include barriers to decision-making from wide uncertainty ranges, limited consideration of high-impact outcomes, and challenges in their application in water resources planning. These challenges represent priorities for future research using new “hybrid” approaches to produce complementary information to “top down” projections within a more “bottom-up” framework. Exploratory modelling, robust decision-making and storylines are examples of new approaches that have emerged. Key to the emerging approaches identified is a need to combine different modelling approaches to tackle different sources of uncertainty according to the intended aims of individual applications.