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Project title

HydroDARE: Detection and Attribution of Change in Hydrological Series

Primary Funding Agency

Environmental Protection Agency

Co-Funding Organisation(s)

Met �ireann

Lead Organisation

Maynooth University (MU)

Lead Applicant

Conor Murphy

Project Abstract

HydroDARE seeks to advance approaches for the attribution of detected changes in hydrological series. Attribution is critical to sustainable catchment management, yet uncertianty exists among practitioners as to which approaches are best suited to different situations. Most attempts at attribution can be considered 'soft attribution' whereby only consistency or inconsistency with hypothesised drivers are established. Effective management requires progress towards 'hard attribution' where the magnitude of impact of drivers of change is quantified and statements of statistical confidence are established. Attribution is a complex scientific challenge given the oft times lack of data on critical drivers of change and can be subject to confirmation bias when a wide set of plausible drivers of change are not considered, or thier interactions are ignored. HydroDARE frames attribution within the framework of multiple working hypotheses to evaluate all plausible drivers of change in selected catchments. Effort is given to establishing quantitative estimates of different drivers at the catchment scale, including; land use change, arterial drainage and climate variability and change. Following this, three approaches to attribution are developed and evaluated, including; empirical approaches which use cutting edge methods of non-stationary and panel regression with plausible drivers of change as covariates to quantify the contribution of different and interacting drivers of change; simuation based approaches which use models to reconstruct hydrological series where observed drivers are omitted to quantify impacts, and; paired catchment approaches which rely on observations in similar catchments to evaluate the impacts of key drivers of change. Each approach to attribution differs in the level of experitise and data required on plausible drivers of change to match the real world context and needs. Insights and results from different approaches to attribution are compared and contrasted to distil recommendations for advancing attribution in different contexts and for integrating more confident understanding of change in hydroligical series into climate services.

Grant Approved

�302,804.05

Research Hub

Climate Change

Research Theme

n/a

Start Date

02/01/2023

Initial Projected Completion Date

01/01/2025