HydroDetect: The Identification and Assessment of Climate Change Indicators for an Irish Reference Network of River Flow Stations - an Overview

This paper provides an overview of key findings from the EPA funded HydroDetect project which establishes an Irish Reference Network (IRN) of river flow gauges for monitoring and detecting climate driven trends. The flow archive from 35 hydrometric stations has an average record length of 40 years and draws from the strengths of the existing national hydrometric network. Using criteria based on the quality of flow records and minimisation of artificial influences and land-use change, complimented by expert judgement, the IRN is a valuable resource facilitating more strategic monitoring of climate driven variability and change in hydrological indicators and enabling more confident attribution of detected trends. Here an analysis of trends in mean and high flows for stations in the IRN is presented, with the spatial distribution of trends across the network examined for the period 1976-2009. The following key findings emerge. While there is considerable evidence of change in the IRN, it is difficult at this point in time to attribute these to anthropogenic greenhouse gas induced climate change. Indeed some of the trends identified – decreases in shorter records in winter mean flows and increases in summer flows – are not consistent with expected changes as simulated by Global Climate Models. This should not be surprising given the large variability of river flows relative to climate change signals at this point. Trends in Irish river flows are strongly correlated with the winter North Atlantic Oscillation Index (NAOI). The sensitivity and response of the NAO to greenhouse gas forcing will have obvious implications for Irish hydrology; however the question remains open as to the impact that greenhouse gas forcing has had on recent behaviour of the NAO and how it is likely to respond to future forcing. While it remains challenging to identify anthropogenic climate change signals at the catchment scale due to large natural variability and therefore a low signal to noise ratio, there is high potential for identifying sentinel stations and indicators within the IRN for early detection of climate change signals. These findings heighten the importance of the IRN for monitoring and detecting climate change signals at the catchment scale, for tracking the emergence of signals relative to natural variability and for providing information, free from confounding factors, for validating output from climate change impact assessments and developing adaptation policies