HydroDARE seeks to develop and evaluate approaches for attributing detected changes in hydrological series. Attribution is central to sustainable catchment management and climate change adaptation.

The project brings together state of the art approaches to attribution to evaluate their applicability in different contexts and situations of differing data availability. HydroDARE frames attribution in the context of multiple working hypotheses of drivers of detected change to ensure all plausible drivers are considered. The project then develops datasets on key drivers (including land use change, drainage, water use, climate variability and change) with three approaches to attribution developed:

1) Empirical approaches and the use of non-stationary regression to detect and attribute change;

2) Simulation based approaches to attribution;

3) Use of paired catchments and reference networks for attribution.Each approach has different levels of complexity, necessary expertise and data requirements. By applying approaches to the same catchment sample HydroDARE will evaluate insights and conclusions offered by each and develop guidance and recommendations to advance and consolidate attribution science and inform integration into catchment management and climate services for hydrology.


Overview of HydroDARE project structure and work packages. The underlying image represents the complexity of attribution at the catchment scale through external drivers (climate) and internal confounding factors (through for example, land use change, urbanisation, impoundments, and typical human activities).

HydroDARE is a two year project led by Prof. Conor Murphy and Dr. Niamh Cahill and funded by the Environmental Protection Agency (EPA). Please get in contact with us if you have any queries or would like to know more.

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