Development of an ecohydrology framework for setting environmental flow standards for Irish rivers

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Resource or Project Abstract

The overarching goal of this research was to evaluate state-of-the-art environmental flow (EFlow) methodology to identify best practice, trial alternative approaches in catchments of contrasting hydrogeology, and provide recommendations to EPA for establishing a landscape-based framework for setting EFlow standards that will maintain ecological status and ecosystem services provided by Irish rivers. Our objectives were to: (1) characterise river flow regimes across a range of landscape settings in Ireland; (2) assess current abstraction pressures; (3) apply abstraction scenarios to identify alterations in hydrologic regime; and (4) identify information needs and research priorities. We identified five flow clusters using 34 metrics (representing magnitude, frequency of high/low pulses, duration of high/low flow periods, timing, and rate of change) generated from long-term daily flow records for 166 hydrometric stations. Flow clusters ranged from surface water (flashy) to groundwater (more stable) dominated; one cluster was lake-influenced. Cluster membership was related to elevation, precipitation, subsurface permeability and soil drainage. Agricultural activities appeared to influence the two weakest clusters with catchments characterised by high potential land drainage related to poorly-drained soils and channelisation that may have increased among-site variability in flow regime. Identification of reference sites representative of ?natural? flow in areas subject to centuries of alteration by agriculture may require a more appropriate definition of what is ?natural? in Ireland. A national abstraction geodatabase was collated from disparate datasets to assess current abstraction pressures. The geodatabase comprised over 4,600 surface water and groundwater abstraction points, which collectively abstract in excess of 5,382,000ám3 of water per day. Less than 5% of effective rainfall is abstracted from 90% of the catchments analysed. However more than 10% of the Q95 flow was abstracted in 49% of the catchments surveyed. These catchments would be classified as ?at risk? or ?probably at risk? of not achieving Water Framework Directive?s good status due to abstraction pressures based on the ratio of abstraction to low flow. An accurate national abstraction database is essential for future work assessing the impacts of abstractions on Irish rivers. To simulate the potential effects of abstraction on EFlow metrics, we applied 14 abstraction scenarios to daily flow records from stations representing each of the five flow clusters. Scenarios included proposed UK flow standards, proportional abstraction maintaining low flow, run-of-river hydroelectric power schemes (HEP), and abstraction rates representing the range quantified in the geodatabase above. Abstraction scenarios that maintain hands-off flow led to declines in maximum flow rates, which can disconnect rivers from riparian wetlands and floodplains, and in flashiness, which may reduce silt flux and favour organisms adapted to more stable flow conditions. HEP schemes, although local in effect as abstracted water is returned downstream, led to alterations across all flow metric groups including barriers to connectivity. Proportional flow abstraction had the largest effect on the high baseflow station while the flashiest station showed a muted response; flashier stations have higher inter-annual variability and, thus the signal due to abstraction may be swamped by long-term natural variability. Mid-high daily abstraction rates typical of Ireland increased days of zero flow and exacerbated summer low flow in four cases. Coincident with the scientific consensus, our results suggest that, when assessing the effect of abstraction on sensitive ecosystems, a holistic view of flow beyond just low flow assessment, be considered. We identified two critical data needs. First, an integrated national database of both abstraction and discharge with adequate temporal resolution and accurate designation of water source is essential. Second, long-term records in the hydrometric network should be maintained at a representative set of stations including more emphasis on underrepresented small catchments. A critical research need is to develop ecology ? flow relationships for assessing abstraction risk. Recommendations for abstraction licensing are to start simple, use data at hand in an integrated approach that prioritises assessment of rivers that are high status sites or where the qualifying interests may be sensitive to hydrological changes, are on the threshold between good and moderate status, and most at risk to cumulative abstraction. Combining flow assessment with hydromorphology assessment would acknowledge the close link between channel structure and flow regime. Abstraction licensing should remain flexible to adapt to future knowledge from research and monitoring efforts and future novel abstraction pressures and climate change.

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Contact Information for This Resource

Dr Ian Donohue
Trinity College Dublin
Assistant Professor,
School of Natural Sciences, Trinity College Dublin, Dublin 2, Dublin, Ireland
Telephone: +353 1 8961356

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Data, Files, Information Objects Related To This Project Resource

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Access Information For This Resource

SAFER-Data Display URL
Resource KeywordsEnvironmental flow; rivers; abstraction; flow regime types; long term flow data;
EPA/ERTDI/STRIVE Project Code2014-W-DS-21
EPA/ERTDI/STRIVE Project ThemeWater Quality
Resource Availability: Any User Can Download Files From This Resource
Limitations on the use of this ResourceAuthors should be credited
Number of Attached Files (Publicly and Openly Available for Download): 0
Project Start Date Monday 12th January 2015 (12-01-2015)
Earliest Recorded Date within any attached datasets or digital objects Monday 12th January 2015 (12-01-2015)
Most Recent Recorded Date within any attached datasets or digital objects Tuesday 15th December 2015 (15-12-2015)
Published on SAFERFriday 24th February 2017 (24-02-2017)
Date of Last EditFriday 24th February 2017 at 18:14:47 (24-02-2017)
Datasets or Files Updated On Friday 24th February 2017 at 18:12:53 (24-02-2017)

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Geographical and Spatial Information Related To This Resource

Description of Geographical Characteristics of This Project or Dataset
The EFlow project was a nation-wide study that relied on existing long term daily flow records from 166 stations and landscape features for those stations derived from a wide range of geospatial data sources.

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Supplementary Information About This Resource

In this section some supplementary information about this resource is outlined. Lineage information helps us to understand why this project was carried out, what policy or research requirements did it fulfil, etc. Lineage is important in understanding the rationale behind the carrying out of a project or the collection of a specific dataset etc. Links to web sites, applications, papers, etc are outlined to provide you with additional information or supplementary reading about the project or dataset

Lineage information about this project or dataset
The project was funded as a desk study by EPA to provide supporting information to maintain environmental flow under future abstraction standards under development in Ireland.
Supplementary Information
The study relied on existing datasets: (1) Flow data from EPA WISKI database and downloaded between April and July 2015 from the OPW website (; (2) geospatial data (see details in database) from EPA in-house datasets (included an intermediate LULC layer provided by Kevin Lydon); Met ╔ireann:; Geological Survey of Ireland:; and Teagasc

ArcGIS v 10.1 was used to generate landscape features for hydrometric station catchments from geospatial data; R was used for statistical analysis and graphics R Core Team. 2015. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL; The Indicators of Hydrologic Alteration software ( was used to generate flow metrics.

ArcGIS v 10.1 was used to generate summaries of geospatial features within catchment polygons and to develop maps of study sites.
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