Modelling groundwater flooding in a lowland karst catchment

Journal Pre-proofs Research papers Modelling Groundwater Flooding In A Lowland Karst Catchment Patrick Jerome Morrissey, Ted McCormack, Owen Naughton, Paul Meredith Johnston, Laurence William Gill PII: DOI: Reference:

S0022-1694(19)31096-0 https://doi.org/10.1016/j.jhydrol.2019.124361 HYDROL 124361

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

27 May 2019 12 November 2019 13 November 2019

Please cite this article as: Jerome Morrissey, P., McCormack, T., Naughton, O., Meredith Johnston, P., William Gill, L., Modelling Groundwater Flooding In A Lowland Karst Catchment, Journal of Hydrology (2019), doi: https:// doi.org/10.1016/j.jhydrol.2019.124361

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MODELLING GROUNDWATER FLOODING IN A LOWLAND

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KARST CATCHMENT

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(Re-submitted with revisions 09/08/2019)

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Patrick Morrissey, Ted McCormack, Owen Naughton, Paul Johnston and Laurence Gill

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Abstract

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Groundwater flooding is a phenomenon which has become recognised as a significant natural

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hazard in recent years. The Gort lowland karst catchment situated in south Co. Galway on the

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western coast of Ireland has experienced two extreme groundwater flood events in the past

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decade leading to considerable damage and disruption. Groundwater flooding in the

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catchment typically occurs following periods of sustained heavy rainfall when sufficient

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capacity is not available in the bedrock to store and convey water to the sea. The underground

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karst conduit system therefore surcharges to the ground surface through a system of

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estavelles and floods low-lying areas of ground known as turloughs (ephemeral lakes). A

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1D/2D pipe network model of the karst conduit system of the Gort lowland karst was developed

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in order to simulate the flooding mechanisms across the catchment as well as to assess flood

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alleviation options. The nature of the underground karstic connections in the system has been

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determined from a combination of available field data (dye tracing, water chemistry data etc.)

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and cross-frequency analysis on the turlough fluctuation time series data over several years.

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The availability of high accuracy LiDAR data of the catchment then allowed the flooding regime

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to be accurately simulated on the ground surface. The model was calibrated using historic

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continuous water level data for a number of turloughs in the catchment and then validated

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using historic peak spot flood levels. The model was then used to identify appropriate potential

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groundwater flood alleviation measures for the catchment. The impacts of such measures on

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both the salinity of Kinvara bay, through increased freshwater discharges, and eco-hydrology

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of the protected wetland habitats within the turloughs was also investigated. The study

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demonstrated that the measures proposed can be developed without inducing undesirable

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impacts to either salinity in Kinvara Bay (and thus mariculture) or to the protected turlough

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habitats. The study has also demonstrated the suitability and functionality of such karst models

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for examining groundwater flood management options and eco-hydrology in karst catchments.

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Keywords: Groundwater flooding, Karst modelling, groundwater flood alleviation, submarine

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groundwater discharge.

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Introduction

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Groundwater flooding has gained widespread recognition as a natural hazard in recent

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decades following extensive damage to property and infrastructure across Europe in the winter

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of 2000-2001 (Finch et al., 2004, Pinault et al., 2005, Hughes et al., 2011). Significant

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groundwater flooding also occurred in the UK at Oxford (2007) and at Berkshire Downs and

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Chilterns (2014) and in Galway, Ireland in 2009 & 2015/2016 (Naughton et al., 2017). Whilst

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groundwater flooding rarely poses a risk to human life, it typically causes damage and

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disruption over a long duration particularly when compared to fluvial flooding (Morris et al.,

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2008, Cobby et al., 2009).

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The main mechanisms by which groundwater flooding occurs can be summarised as:

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inundation of river floodplains caused by the saturation of alluvium deposits adjacent to a main

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river channel (Finch et al., 2004); groundwater rebound whereby the groundwater table rapidly

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rises following a reduction/cessation in abstraction from large aquifers causing flooding of

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subsurface structures such as basements or tunnels (Macdonald et al., 2014); and, increases

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in groundwater levels in bedrock aquifers which causes the water table to reach the ground

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surface and flood low-lying basins and valleys (Finch et al., 2004, Naughton et al., 2012). 2

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Groundwater flooding events in Ireland predominantly occurs within the lowland karstified

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limestone areas of the west of the country (Naughton et al., 2012, Naughton et al., 2018) via

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the third mechanism described above. The flooding is inherently linked to the underlying

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bedrock geology where extensive interactions between ground and surface waters

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predominate, with sinking and rising rivers/streams common with surface water features

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absent completely in many areas (Drew, 2008). The dominant drainage path for many areas

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of the catchment is through the karstified limestone bedrock; however, the limited storage

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within such secondary porosity dominated rocks, means that the fractures or conduits within

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the limestone are unable to drain the recharge fast enough during intense or prolonged rainfall,

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the result being surcharging of groundwater above the surface. This flood water is usually

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contained within low-lying topographic depressions known as turloughs, which represent the

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principal form of extensive, recurrent groundwater flooding in Ireland (Coxon, 1987a, Coxon,

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1987b). In Ireland, the most susceptible region to groundwater flooding is the Gort Lowlands

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in South Galway which is a lowland karst catchment covering an area of approximately 500km2

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(Naughton et al., 2018).

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Modelling karst systems is a complex task and has been the subject of continuing research

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globally (Hartmann, 2017). There is a broad range of different modelling approaches to

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simulate karst hydrology; however, these can be generally be grouped into three categories

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which are differentiated by their level of complexity, input data requirements and/or the

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accuracy of their output. Lumped or reservoir approaches provide a conceptual model

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designed to simulate the aquifer outflow (usually spring discharge) as well as water level

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variations within the karst conduit system (the saturated bedrock zone) (Kong-a-Siou et al.,

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2014, Hartmann, 2017). These models are usually implemented by representing the karst

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system, which includes both the epikarst and conduit zones, as well as the overlying subsoil

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(unsaturated zone), by a series of reservoirs. Each of these reservoirs contain different

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controlling parameters and represent different inflows and outflows within the karst system.

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Typically, different reservoirs are used to represent zones such as the fast and slow soil 3

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infiltration, the epikarst and the conduit zones (Fleury et al., 2009). Semi-distributed models

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aim to address this general simplification of the catchment by acknowledging that the

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variability of the spatial input information and the catchment properties have significant

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impacts on the hydrologic responses of the system. Semi-distributed models usually sub-

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divide the catchment into area averaged input data where the distributed properties such as

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the land cover, soil type and precipitation are lumped into discrete zones or points which aims

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to more accurately represent how the catchment operates. An example of a semi-distributed

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model is the VARKARST model which considers the spatial variability of karst system

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properties by distribution functions, representing the soil, epikarst and karst bedrock

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(Hartmann et al., 2013). The soil storage takes account of the soil moisture budget allowing

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the seasonal changes to be accounted for – overland runoff can also be included between

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model elements. This model was found to outperform a calibrated lumped reservoir routing

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model when both were applied to the same catchment (Hartmann, 2017). Mayaud et al. (2019)

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recently demonstrated the use of a semi-distributed pipe network model for simulating flooding

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in karst poljes in Slovenia (which are similar to Irish turloughs). Physically-based distributed

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models are capable of accounting for all of the spatial distribution of meteorological conditions

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(precipitation, evapotranspiration etc.) and physical parameters (e.g., soil saturation, land

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cover properties etc.) throughout the catchment and have the advantage of being an accurate

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representation of the entire catchment. In order to fully benefit from the added complexity of a

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fully distributed model, this approach requires a wealth of input data throughout the catchment,

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which is often not readily available. This data requirement together with the danger of over-

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parameterisation however, often leads to poor model calibration. In fact, it has been reported

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that a lumped model generally outperforms a fully distributed model except in rigorously

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studied catchments (Hartmann, 2017, Smith et al., 2019).

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Extensive flooding associated within the turloughs across the south Galway karst limestone

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lowlands of Ireland is known to have occurred on six occasions over the past 20 years (1989-

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1990, 1991, 1994, 1995, 2009 and 2015/2016). The flooding in 2009 was the most severe on 4

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record, until it was surpassed in many areas by the events of 2015. The two most recent flood

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events led to considerable damage, disruption and hardship for local residents and farmers.

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Over 24 km2 of land was flooded for up to 6 months with many residents and farms cut-off due

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to roads being impassable for extended periods. This study was therefore proposed with the

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aim to better understand the occurrence and behaviour of these flood events as well providing

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insight and guidance with respect to potential alleviation measures. A combination of limited

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records and detail of historic groundwater flooding coupled with a lack of data regarding flow

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within the karst bedrock renders the development of a karst model representing the system to

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be a challenging and unique task.

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Materials and Methods

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Catchment Description

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The Gort lowlands catchment covers an area of approximately 500km2 in south Co. Galway

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on the western coast of Ireland. Three river systems discharge runoff from adjacent mountains

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to the lowland karst catchment and another smaller river drains to the catchment from the

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border of the Burren limestone pavement to the south. The surface hydrology of the Gort

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lowlands becomes extremely complex once these four rivers flow into the catchment due to

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the karstified nature of the limestone bedrock. The hydrogeology and surface hydrology of the

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Gort Lowlands are therefore closely linked due to the extensive bedrock karstification with

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water exchanged between the surface and subsurface in large volumes throughout the

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catchment through sinking streams (swallow holes), large springs and river risings. The entire

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catchment drains to a number of intertidal springs at Kinvara Bay through the karst limestone

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bedrock – see Figure 2. A comprehensive description of the catchment and its complex

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hydrogeology is given in Naughton et al. (2018).

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Model Setup & Calibration

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This present study created a new expanded pipe network model of the semi-distributive model

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of the complex karst system within the Gort Lowlands that had been developed over a 10 year

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period for five turloughs (Gill et al., 2013a; McCormack et al., 2014). The conceptual model

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was initially informed by extensive tracing studies previously carried out within the catchment

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(Southern Water Global, 1998). Flows within the main rivers, meteorology across the

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catchment and water hydrochemistry, allowed a deeper understanding of the system and how

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it operates. Time series analyses of the fluctuating turlough water levels were subsequently

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undertaken. This revealed additional hydraulic characteristics regarding the nature of the

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catchment (Gill et al. 2013b) such as whether the turloughs act as surcharge tanks or not; and

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whether a turlough is connected to the mainline system or is located offline. An additional 10

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turloughs and floodplains were added to the model and the model catchment was expanded

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from 95.8km2 to 159.2km2. An additional inflow from the Cloonteen River located to the south-

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west of the catchment was also added to the model which was identified as a lacunae during

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previous modelling efforts (Mccormack et al., 2014). Figure 2 below identifies the turloughs

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and floodplains contained within the model and the locations of the various river inputs.

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Figure 1 presents a schematic overview of all elements of the model development and

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calibration process undertaken within this study including the various data inputs. Each of the

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various data inputs and steps involved in the model development and calibration process are

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further described in the following sections.

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Figure 1 Schematic overview of the modelling process undertaken in the study – data inputs are

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shown shaded grey.

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Model Inputs

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Rainfall data were available from two tipping bucket ARG100 rain gauges which were

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previously installed at Kilchreest in the lowlands (70 mOD) and Francis gap in the Slieve

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Aughty mountains (elevation 250 mOD) (Gill et al., 2013a, Mccormack et al., 2014). The data

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were available intermittently between 2007 and 2016, however many gaps existed within the

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dataset due to periods of instrumentation failure or periods of in-operation. The data from other

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rain gauges in the area operated by Met Eireann were therefore also gathered in order to

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provide comparison with the two project gauges and to determine a means for obtaining a

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complete dataset. The Gort Derrybrien gauge presented the longest rainfall dataset within the

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catchment with data available from 1982 although rainfall was only available at daily

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frequency. In order to fill in missing gaps in the dataset from 2007 – 2018 data the Gort

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Derrybrien gauge was utilised and redistributed from daily to hourly using the Shannon and

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Athenry gauges as metrics. A correlation analysis indicated that Gort Derrybrien daily totals

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were moderately correlated with both the Shannon and Athenry gauges with Pearson

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Correlation coefficients of 0.60 and 0.55 respectively. The redistribution process involved

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taking the daily total at Gort Derrybrien and dividing it over each hour of the day in the same

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hourly proportion observed at Shannon on the same date. For days where rainfall occurred at

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Gort Derrybrien but not at Shannon, the Athenry guage was utilised. For a very small number

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of dates, rainfall occurred at Derrybrien but did not occur at either Shannon or Athenry and in

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this circumstance a uniform 24hour distribution was utilised. A sensitivity analysis was carried

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out comparing the simulated river hydrographs for a period when data was available at

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Kilchreest and Francis Gap using actual observations with Gort Derrybrien data redistributed

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using the three metrics described above and no appreciable change in either the timing or

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extent of hydrographs were observed. The redistributed data were therefore used to

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supplement the Kilchreest and Francis Gap datasets which were required for generating river

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inputs.

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River Rating Curves & Hydrometric Data

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The flows in the three main rivers draining the Slieve Aughty Mountains and the Cloonteen

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River were constantly measured at respective gauging stations operated by the Office of

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Public Works (OPW). These gauges consist of a pressure transducer embedded into the river,

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referenced against a staff gauge at the stream edge. Rating curves (i.e., flow against stage

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relationship) were previously developed for each of the three gauging stations on the rivers

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flowing off the Slieve Aughty Mountains (Gill et al., 2013a, Mccormack et al., 2014). A number

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of flow measurements were also taken on the Cloonteen River during 2017 and these

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measurements were supplemented using referenced data collected during the original Gort

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Flood Study (Southern Water Global, 1998) to allow a rating curve to be developed. A number

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of additional flow measurements were undertaken during 2017 and 2018 by the OPW and

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these measurements served to validate the existing rating curves but did not increase the

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range of gauged flows. 8

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Rainfall Runoff Models

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In order to fill any intermittent gaps in the river flows (which were used to calculate allogenic

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inflows to the main karst model), Rainfall-Runoff (RR) models were required. These models

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were developed on MIKE11-NAM (DHI Software) which is a single-catchment watershed

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lumped-parameter model for simulating rainfall-runoff and the hydrological cycle. Developing

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a RR model within MIKE11-NAM requires simple catchment descriptors with rainfall and

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potential evaporation as inputs that are then calibrated against a known discharge (runoff).

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Calibration is achieved by adjusting nine parameters relating to the surface-rootzone and

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groundwater within controlled bounds.

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The calibration period chosen for each river contained a minimum of one hydrological year

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which also contained a number of representative peaks in discharge/runoff. During calibration,

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initial parameters were selected based on previous work by others (Gill et al., 2013a, O’brien

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et al., 2013, Mccormack et al., 2014) and the model was run in auto-calibration mode. The

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model simulation results during calibration were checked for coefficient of optimisation (R2)

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and visually analysed for degree of agreement between simulated and observed runoff. The

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model parameters were then further adjusted to obtain the set of best fit model parameters

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which could simulate the observed runoff (in term of timings, peaks and total volume) with a

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high degree of agreement..

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Once an acceptable calibration was achieved, the model was then run for the period between

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2007 – 2016, for validation purposes (2010 – 2016 for the Cloonteen River – no logger was

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installed pre-2010).

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Turlough Water Level Profiles

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Water levels in five turloughs (Blackrock, Coy, Coole, Garyland & Caherglassaun) within the

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catchment were measured continuously by pressure transducers, as outlined by (Gill et al.,

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2013a, Mccormack et al., 2014). Data were available intermittently between 2007 and 2016, 9

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however many gaps existed within the dataset due to periods of instrumentation failure or

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periods of in-operation. Additional water level monitoring stations were installed at 8 locations

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within the catchment in 2016 by Geological Survey Ireland (GSI) with data available for some

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or all of the period between November 2016 and April 2018 for use within the model calibration

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and validation.

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In addition, water levels within turloughs were manually estimated for specific dates of interest

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using a combination of Sentinel-1 satellite imagery and LiDAR data. Sentinel-1 comprises two

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satellites equipped with C-band synthetic-aperture radar instruments and are operated by the

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European Space Agency under their Copernicus Programme. The extents of flooding at

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specific dates was visually identified using darker pixel shading within the various bands of

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the imagery and matched with a corresponding topographical contour to yield a flood

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elevation.

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Tide Level Monitoring

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Tide level data for Kinvara Bay were obtained from the Marine Institute of Ireland for the

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Galway Port tide gauge which is located c.15 km to the north. Tide levels were previously

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recorded at Kinvara Bay and compared to the record for Galway Bay during the same period

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of time and the levels were found to be comparable albeit with a slight shift of c.15 minutes in

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the magnitude of the tide level (Gill et al., 2013a, Mccormack et al., 2014).

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Depth-Volume-Area Relationships

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Depth-area relationships for each of the turloughs and floodplains were computed using

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ArcGIS and/or Infoworks ICM software for use within the model. The data used to create these

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depth-area (and subsequently volume) relationships generally consisted of LiDAR mapping

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available for the catchment in the form of a Digital Elevation Model (DEM) with a grid spacing

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of 2 m and a vertical accuracy of +/- 0.15 m. This LiDAR was flown when some of the turloughs

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contained standing water and therefore accurate topographical data for the bottom of these 10

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basins was not available. Topographical survey data obtained from manual surveys carried

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out using a Trimble 4700 GPS system with a minimum accuracy of 0.01 m (horizontal and

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vertical direction) were available for a number of the turlough basins. In addition, bathymetric

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surveys were also undertaken where permanent water was present to accurately represent

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the base levels at these locations (Coole, Caherglassaun, Hawkhill and Newtown). These

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surveys were carried out using a remote controlled Acoustic Doppler Current Profiler (ADCP)

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integrated with the Global Positioning System (GPS) to record both vertical depth and

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horizontal position measurements, respectively. These topographical data were combined

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with the available LiDAR data in ArcGIS and a new integrated DEM was constructed using the

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Kriging method with a 2 m grid spacing and thus accurate depth-area relationships were

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generated for all turlough and floodplain locations.

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1D Pipe Network Model

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The pipe network was initially developed with reference to the previously developed model for

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five turloughs within the catchment by Gill et al. (2013a). A ground model was generated in

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the Infoworks ICM software using the DEM data discussed above. Each of the turloughs or

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floodplains were then represented by storage nodes with a depth-area relationship developed

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using this ground model. A total of 15 storage areas were added to the network, as shown on

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Figure 2. The initial connections between turloughs and storage areas was informed by the

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existing model, reviewing tracing studies available in the GSI karst database and statistical

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analysis of time series data collected at these new locations after Gill et al. (2013b). The initial

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network was adjusted and modified significantly, as the calibration process progressed. The

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calibration process was carried out by trial and error with pipe dimensions and the various

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controls varied in an iterative process. Rivers were represented using open channels with

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dimensions approximated from digital mapping and physical surveys. Overflow channels

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between basins were added as open channels with dimensions informed by digital mapping,

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physical surveys and maximum flood extents mapping. The catchment was divided into sub-

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catchments based on topography and these were connected to the pipe network using 11

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conduits and nodes with autogenic recharge applied to these sub-catchments using a Ground

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Infiltration Module with Infoworks ICM as described by Gill et al. (2013a).

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Whilst the 1D modelling approach described above is considered to be robust and performed

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very well in terms of matching flooded volumes and flood stages, the overland flow between

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turlough/floodplain basins was simplified for incorporation into the 1D environment. Thus,

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whilst the over-spilled volume of water between floodplains that occurred during extreme

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events could be quantified together with the timing of same, there was no capacity to simulate

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the overland flow paths (and accurately represent their associated velocities) and

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corresponding flooded extents outside storage basins. The only means to quantify these data

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is through the use of a 2D model. The InfoWorks ICM (integrated all source catchment

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modelling) software incorporates 1D-2D coupling capabilities, which are typically utilised to

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simulate fluvial flooding. Therefore, once the expanded 1D model was calibrated, it was

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modified with the addition of 2D zones for modelling capability of the surface overland flows.

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2D Overland – Pipe Network Model

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The development of the 2D overland flow model from the calibrated 1D model required first

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removing all storage nodes which represented turloughs and floodplains. In order to create

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the 2D aspect of the network, high resolution LiDAR data were used. A ground Digital

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Elevation Model (DEM) was created using a combination of LiDAR data, topographical data

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and bathymetric data. These data were combined ArcGIS (v10.4.1) and a new integrated DEM

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was constructed using the Kriging method with a 2 m grid spacing. A 2D mesh was then

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created using this ground model within the model network domain incorporating the full extents

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of the catchment where overland flooding and flow is known to occur. In order to gain

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efficiencies in model computation, terrain sensitive meshing was utilised whereby the

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resolution of the mesh is increased in areas where large variations in height occur. The

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maximum height variation allowed during the generation of the mesh was 0.25 m. In the

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interest of reducing run times many parameters (such as roughness coefficient) were kept 12

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constant across the 2D surface. The 1D pipe network (representing flow within the karst

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bedrock) was linked to this 2D mesh using 2D nodes. Flow between the 2D mesh towards a

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1D node (and vice-versa) was based on a standard head-discharge relationship. Modification

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of the original calibrated 1D model was kept to the minimum required to effectively incorporate

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these 2D elements with an aim of reducing the recalibration effort required.

314 315

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Figure 2: Sentinel Satellite Imagery (SAR) of the catchment under normal winter flood

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conditions (top) and following the flood of 2015/2016 (bottom) – flooded areas show up with

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darker shading based on the SAR imagery bandwidth. Turlough/floodplain nodes within the

14

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model are shown in red with proposed overland flood alleviation routes identified by blue

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arrows

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Results

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1D Model Calibration

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The calibration period was between 01/11/2016 and 31/03/2018 with continuous water level

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data available during this period for 12 of the 15 locations (no data at Ballyloughan, Corker

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and Ballylee) shown in Figure 2. As the primary objective of this model was for flooding

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purposes, the calibration process particularly focussed on matching the timing and magnitude

330

of the peak surface water levels. The calibration process was iterative with the catchment

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broken into two sections based on elevations and close frequency interactions between

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turloughs. The model calibration was checked for goodness of fit using the Nash-Sutcliffe

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(NSE) and Kling-Gupta (KGE) model efficiencies for both flood water stage (mOD) and

334

turlough volume (m3). A sample model calibration plot of observed flood levels against

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simulated flood levels for the Coole Lough is presented in Figure 3 below. Model efficiencies

336

ranging between 0.87 - 0.98 were achieved for water level (mOD) and between 0.77 – 0.99

337

for volume (m3) at all but two locations (see Table 1). Poorer model efficiencies (0.25 – 0.62)

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were achieved at the location of large underground river risings at Castletown and Kiltartan

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which display extremely flashy behaviour. This is due to the sharp interface at these locations

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between the pressurised pipe system and atmospheric flow conditions; representing the

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physical dynamics of such flashy karst features was an inherently challenging. However,

342

neither of these locations contribute significantly to the overall water balance of the catchment

343

and so did not affect the overall catchment flood statistics.

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Table 1: Summary statistics for the 1D model calibration

2016 - 2018 Calibration Period Model Efficiency Location

Nash-Sutcliffe (NSE)

Kling-Gupta (KGE)

Stage (m)

Volume (m3)

Stage (m)

Volume (m3)

Ballinduff

0.87

0.93

0.87

0.88

Blackrock

0.96

0.97

0.97

0.92

Caherglassaun

0.96

0.97

0.95

0.90

Cahermore

0.98

0.98

0.97

0.91

Castletown

0.98

0.85

0.88

0.62

Kiltartan

0.25

0.34

0.27

0.32

Coole

0.95

0.99

0.89

0.93

Coy

0.87

0.89

0.95

0.93

Garyland

0.98

0.99

0.97

0.95

Hawkhill

0.94

0.94

0.91

0.82

Mannagh

0.88

0.85

0.97

0.77

Newtown

0.93

0.99

0.88

0.97

346

Coole 14

NSE: KGE:

0.95 0.89

12

Stage (m)

10

8

6

4

2

347 348

Jan 2017

Apr 2017

Jul 2017

Observed Stage

Oct 2017

Simulated Stage

Figure 3: 1D Model calibration for Coole Lough (2016 – 2018) Blackrock 30

349

28 26

(m)

24 22

16

Jan 2018

350

1D Model Validation

351

Short Term Validation (2015 – 2016 Extreme Flood Event)

352

There were only two operational loggers in the study area during the 2015/2016 extreme flood

353

event located at Blackrock and Caherglassaun turloughs. Model simulation results for

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Blackrock Turlough during this event are presented in Figure 4. NSE and KGEs of between

355

0.95 – 0.99 were achieved at both locations for stage and 0.94 – 0.99 for volume (m3) during

356

the 2015/2016 flood event. In the absence of other calibration data for this extreme flood event,

357

georeferenced Synthetic Aperture Rader (SAR) satellite imagery were combined with LiDAR

358

contour mapping of the area to approximate the peak flood levels at other locations for

359

validation purposes. These data were further corroborated through comparison with historical

360

peak flood levels throughout the catchment which were surveyed for the 2015/2016 extreme

361

event as part of a Local Authority flood relief project (Ryan Hanley, 2018). The simulated peak

362

flood levels for the 2015/2016 flood event at all locations were found to match the actual

363

surveyed flood levels to within 0 – 3.6% level difference (0.1 – 0.4 m).

364 365 30

Blackrock

NSE: KGE:

28

0.98 0.95

26

Stage (m)

24 22 20 18 16 14 12 10 Oct 2015 Nov 2015 Dec 2015 Jan 2016 Feb 2016 Mar 2016 Apr 2016 May 2016 Jun 2016

366 367 368

Observed Stage

Simulated Stage

Blackrock Figure 4: 1D Model validation for Blackrock Turlough during the 2015/2016 flood event 30 28

369

26

Stage (m)

24 22 20 18 16

17

370

Medium Term Validation (2007 – 2016)

371

Model validation took place for the period between January 2007 to March 2018 covering the

372

entire period for which data were available at five locations. Goodness of fit statistics were

373

calculated for these combined datasets and are summarised in Figure 5 below; a sample

374

model calibration plot of observed flood levels against simulated flood levels for the

375

Caherglassaun Lough is also presented in Table 2. Model efficiencies ranging between 0.82

376

– 0.99 were achieved for this validation period.

377 378 Caherglassaun 14

NSE: KGE:

0.97 0.98

12

Stage (m)

10

8

6

4

2 2008

379 380

2010

2012

2014

Observed Stage

2016

Simulated Stage

Figure 5: 1D Model validation for Caherglassaun Lough (2007 – 2018)

381

18

2018

382

Table 2: Summary statistics for medium term 1D model validation (2007 – 2018)

2007 - 2018 Validation Period Model Efficiency Location

Nash-Sutcliffe

Kling-Gupta

Stage (m)

Volume (m3)

Stage (m)

Volume (m3)

Blackrock

0.92

0.97

0.95

0.99

Caherglassaun

0.97

0.98

0.98

0.98

Coole

0.88

0.95

0.82

0.91

Coy

0.84

0.86

0.98

0.99

Garyland

0.94

0.93

0.96

0.89

383 384 385

Long-term Model Validation (1989 – 2018)

386

Daily rainfall data were available at a Met Eireann operated rain gauge within the catchment

387

with uninterrupted records extending back to 1988. Three historical large flood events

388

occurred within the catchment in the early 1990’s and spot flood level data were available at

389

Blackrock and Coole (Macdermot, 1995, G.S.I., 1992) during these events. Flood level data

390

were also acquired for the period 1999 – 2003 from a study carried out for an adjoining road

391

construction project. Utilising the full rainfall record and rainfall-runoff models for the four river

392

inputs, a long-term model validation simulation was carried out for the period 1989 – 2007.

393

The model accurately predicted the extreme flood events of 1989/1990 and 1994/1995 to

394

within 0.15% (0.4 m) as summarised in Table 3.

19

395

Table 3: Summary statistics for long-term 1D model validation (1989 – 2007)

1989 - 2018 Validation Period Spot Level Validation

Blackrock Turlough

Lough Coole

Actual Stage

Simulated

Actual Stage

Simulated

(mOD)

Stage (mOD)

(mOD)

Stage (mOD)

1989 – 1990

27.75

27.54

12.54

12.69

1994 – 1995

28.9

28.47

13.5

13.36

1996 – 1997

21.56

22.1

9.79

9.55

2002

23.84

24.01

-

-

Flood Event

396 397 398

Simulation of spring discharge to Kinvara Bay (Submarine Groundwater Discharge)

399

A number of previous studies have focussed on quantifying the Submarine Groundwater

400

Discharge (SGD) to Kinvara Bay. Drew (2008) suggested a spring discharge ranging between

401

5 – 30 m3/s while Cave and Henry (2011) suggested outflows of between 14 – 96 m3/s. More

402

recent numerical modelling approaches by Gill et al. (2013a) and Mccormack et al. (2014)

403

estimated the average outflow to Kinvara West to be approximately 10 m3/s with a maximum

404

outflow of 16.6 m3/s for the periods 2007-2009 and 2010-2013.

405 406

The calibrated model allowed the outflow to Kinvara West Springs to be quantified for the

407

catchment. The average spring discharge from the catchment found during the period 2007 –

408

2018 was 12.32 m3/s with a peak discharge of 35.81 m3/s occurring during the extreme flood

409

event of 2015/2016. Observed negative flow values indicate tidal intrusion into the karst

410

network during low flow periods occurring at high tides – and the pressure transfer of this tidal

411

signal has been picked up by level loggers in Caherglassaun turlough 5 km inland at low lake

412

stages. Whist the peak values differ from previous numerical modelling estimates, which is

413

expected due to the current simulation including two extreme flood events, the average values 20

414

are generally of a similar order of magnitude given that the current model accounts for a larger

415

catchment area and includes for more turlough/flood storage areas. The simulated 90th

416

percentile discharge indicates that outflow at Kinvara West springs rarely exceeds 23.4 m3/s.

417 418

2D Model Results

419

The 2D model scenario simulation was computationally expensive particularly given the size

420

of the 2D zone (62.5 km2) and the number of mesh elements (>1x106) required, with

421

simulations of 6 month duration (a typical extreme flood period) taking up to 10 days to

422

complete. Initially efficiencies were achieved in computation time through the use of high

423

specification Graphics Processing Unit (GPU) Cards for computation of the 2D element of the

424

simulation, reducing a 6 month simulation duration to approximately 72 hours. A calibrated

425

model was eventually achieved, but did not perform as well as the 1D model in terms of

426

efficiency for stage or volume over the validation period (particularly at lower levels). For the

427

2015/16 flood event, model error in peak simulated flood levels (mOD) ranged between 0.29

428

– 8.23% (avg. 2.55%) which represented an actual error in flood levels of 0.08 – 1.41m (avg.

429

0.42m). The 2D model did however, facilitate simulation of the overland flood path between

430

floodplains; an example of same is shown in Figure 6(a) which demonstrates how the model

431

correctly simulated the overland flow which occurred between Blackrock turlough through

432

Skehanagh village to Ballylee during the 2015/2016 extreme flood event. The overland flood

433

flow path matched very well with what was mapped for this flood event by Geological Survey

434

of Ireland (GSI) – shown in red on Figure 6(a). Similarly, the overland flow paths simulated

435

between Lough Coole and Caherglassaun turlough and then on towards Cahermore turlough

436

were in good agreement with what actually occurred during the flood event – see Figure 6(b).

437

The final 2D flood extents for the 2015/16 flood event together with the historical maximum

438

flood extents mapped by the GSI are shown in Figure 6 below. Spot flow measurements taken

439

during the overland flow event on the 7th of January 2016 estimated a discharge of 5.15 m3/s

440

between Cahermore and Kinvara. The 2D simulation predicted a discharge of 5.44 m3/s at the

21

441

same time and date, which was the only verifiable overland flood flow measurement with which

442

to validate the model other than mapped flood extents and spot flood levels.

443

444 445

Figure 6: Results of the 2D simulations compared with GSI mapped flood extents for the 2015/16

446

flood event shown on shaded relief mapping. Inserts (a) & (b) show model performance for

447

overland flow-paths between selected basins.

22

448

Model Application

449

Flood Alleviation Simulations

450

The volume of water stored across the system within turlough and floodplain basins during

451

extreme flood events is extremely large – for example, the total flooded volume during the

452

2015/2016 flood event was c.92 Mm3. In addition, some or all of these basins are overtopped

453

during extreme events indicating that the maximum possible storage within the system has

454

been exceeded. It has therefore been suggested previously (Southern Water Global, 1997,

455

Southern Water Global, 1998) that the only viable way to lower peak flood levels is through

456

the provision of a series of engineered controlled overflow channels allowing water to drain

457

directly to the sea. The option of slowing the flow into the lowlands by provision of control

458

structures and temporary storage areas in the adjacent mountains was deemed economically

459

and environmentally impractical and would introduce a larger risk from catastrophic failure of

460

such structures.

461 462

Achieving a reduction in flood levels at any one location within the catchment is complicated

463

by the high degree of hydraulic connectivity within the underground karst system. A measure

464

which might be targeted at one specific location at the lower end of the catchment would be

465

ineffective as the overall hydraulic gradient within the system would simply cause water from

466

another slightly higher elevation to flow towards that location. Similarly, at the higher end of

467

the catchment displacing water further down the system would improve the situation at that

468

location whilst exacerbating flooding further down the system. For this reason a holistic

469

approach was taken to develop a solution that sequentially moves water down the system

470

towards the sea in a controlled manner. The calibrated 1D pipe network model was utilised to

471

investigate the effectiveness and practicality of such flood alleviation measures. The modelling

472

approach involved engineered channels being added to the model between turloughs and

473

floodplains at known overflow locations in an incremental manner from the bottom of the

474

system – i.e. starting with the final channel to the sea – back inland. Channels (OL-1 – OL-6)

23

475

with 1:1 side slopes and a Manning’s n roughness value of 0.05 (equivalent to a stony/grassed

476

winding channel) were incrementally added at the locations shown in Figure 1 to achieve the

477

required reductions in peak flood levels at all locations. The sensitivity of the Manning’s

478

roughness values were also investigated (ranging between 0.025 – 0.1) determine the

479

influence of channel type on the required channel dimensions. Over 100 different scenarios

480

combining different channel properties were simulated for the period 2007 – 2018, with the

481

final optimal solution summarised in Table 4.

482 483

Table 4: Optimal overflow channel properties derived from the flood simulations

Channel ID

Base Width

Manning’s

Gradient

Upstream Invert

Downstream Invert

(m)

n

(m/m)

Level (mOD)

Level (mOD)

OL-1

2

0.035

0.003

27

20

OL-2

2

0.035

0.001

16.5

16

OL-3

4

0.035

0.001

16.5

16

OL-4

5

0.035

0.002

10

9

OL-5

3

0.035

0.008

10

6

OL-6

6

0.035

0.001

10

3.5

484 485 486

As expected, the optimal solution is ultimately controlled by the width and roughness of

487

channel OL-6 with channel OL-4 also of high importance from a conveyance perspective.

488

Choosing a lower Manning’s n roughness allowed narrower channels to be incorporated,

489

however, in reality considering constructability and cost, a value of 0.035 is reasonable

490

representing a relatively straight channel with some short grass/weeds present – avoiding

491

lined channels altogether. The optimal solution resulted in an average reduction in peak flood

492

volumes of 1.1 m with a total volume of flood water redirected overland to the sea at Kinvara

493

(instead of through the underground conduits) of 48.6 Mm3. Whilst peak flood levels at nearly 24

494

all turloughs were reduced, the overall length of inundation was very similar due to the

495

overflows only activating at higher levels – refer to Figure 8 for sample flood hydrograph at

496

Caherglassaun. A summary of the flood levels at all locations within the model extents before

497

and after the optimal solution was incorporated in the model (for the 2015/2016 extreme flood

498

event) are given in Figure 7 below.

499

30 Actual flood level Simulated flood level - optimal solution

28 26

Stage (m)

24 22 20 18 16 14 12 10

f

uf nd

gh

na

an

n

ill

kh

w

nd

w

la

to

w

lli

Ba

M

Ha

Ne

ry

ss

e or

la

rg

e

ol

he

rm

au n

un

ha ug

e

lo

he

ar

G

Co

Ca

Ca

lly

Ba

k oc

le

lly

Ba

y

Co

kr

ac

Bl

500 501

Figure 7: Peak flood levels before and after optimal flood alleviation options added to the model

502

for the 2015/2016 flood event

25

Caherglassaun 14

12

Stage (m)

10

8

6

4

2 Oct 2015 Nov 2015 Dec 2015 Jan 2016 Feb 2016 Mar 2016 Apr 2016 May 2016 Jun 2016

503

Observed Stage

Simulated Stage (Flood Allievation)

504

Figure 8: Flood hydrograph at Caherglassaun turlough before and after optimal flood alleviation

505

channels added to the model for the 2015/2016 flood event

506 507

Impacts to Salinity in Kinvara Bay

508

Kinvara Bay has been reported to be a site of important commercial shellfish value and

509

designated as shellfish waters under the Shellfish Waters Directive (Cave and Henry, 2011).

510

Sudden exposure to low salinity water may affect the quality of the shellfish stock which have

511

been reported to cease feeding and have reduced growth rates during these conditions

512

(Riisgård, 2012). The salinity of Kinvara Bay must therefore be considered when implementing

513

any solution to groundwater flooding within the catchment given that the addition of large

514

volumes of fresh water to the bay, over a relatively short time, could potentially adversely

515

impact commercial shellfish production.

516 517

Figure 9(a) below illustrates the simulated SGD to Kinvara Bay from the linked conduit karst

518

network together with the overland discharge which briefly occurred during the peak of the

519

2015/2016 extreme flood event. The addition of the optimal flood alleviation solution outlined 26

520

above would alter these outflows as shown in Figure 9(b). The main change to the discharge

521

profile to Kinvara Bay occurs during the period 14/12/15 to 11/01/16 when the average daily

522

discharge volume increases from 0.11 to 0.15 Mm3 which equates to an average increase of

523

32% in daily discharge to the bay. The peak discharge during the simulation with no flood

524

alleviation occurred on the 10/01/2016 (refer to Figure 9(c)) when a combined outflow of 38.17

525

m3/s discharged to the bay – discharge values are shown as daily averages in Figure 9(c). In

526

comparison, the peak discharge during the simulation incorporating flood alleviation channels

527

occurred on the 04/01/2016 (6 days earlier) and was almost 10 m3/s higher at 47.52 m3/s.

528

27

529 (a) Calibrated simulation - no flood alleviation

40

Flow (m 3 /s)

30

Simulated Spring Discharge Simulated Overland Discharge

20 10 0 -10

Jul 2015

Flow /s) (m33/s) Flow (m

Jan 2016

Apr 2016

(b) Simulation withsimulation flood alleviation (optimal solution) (a) Calibrated - no flood alleviation

40 40 30 30

Oct 2015

Simulated Spring Spring Discharge Discharge Simulated Simulated Overland Overland Discharge Discharge Simulated

20 20 10 10 0 0 -10

Jul 2015

Oct 2015

Jan 2016

Apr 2016

530 (c) Simulated total discharge to the bay

50

Flow (m 3 /s)

40

Simulated existing conditions Flood alleviation incorporated

30 20 10 0

Jul 2015

Oct 2015

Jan 2016

Apr 2016

531 532

Figure 9: Simulated outflows to Kinvara Bay before and after flood alleviation options added to

533

the model for the 2015/2016 flood event – average daily discharge shown in Plot (c).

534 535

In order to demonstrate the impacts of such a discharge on the overall salinity of Kinvara Bay

536

the proportional change in volume of freshwater versus the total volume of the bay during high

537

and low tide was used to assess the potential net dilution. Whilst it is acknowledged that a

538

more complicated mixing model of Kinvara Bay would more accurately predict the impacts of

539

such additional freshwater discharge (incorporating hydrodynamics within the bay such as 28

540

currents, mixing and additional deep springs), this more simple calculation demonstrates the

541

broader impact from such an intervention. In addition, the relative complexity (and associated

542

margin for error) and absence of long-term historical salinity data for calibration purposes

543

renders the accurate development of such a spatially distributed mixing model very

544

challenging.

545 546

Bathymetry and LiDAR data for the seabed of Kinvara Bay, was obtained from Geological

547

Survey of Ireland and the Marine Institute with a spatial resolution of 5 m. The data were

548

processed using spatial analyst in ArcGIS (ESRI) and combined with tidal level data to

549

accurately represent the volume of the bay at low and high tide during the period between

550

01/12/15 to 31/01/16, found to be 0.85 Mm3 and 15.84 Mm3 respectively (average 5.46Mm3).

551

Previous studies (O'toole, 1990). Mccormack et al. (2014) highlighted that the bay is not well

552

mixed at its head where a film of freshwater was found to exist at the top of the water column

553

which dissipated with distance out into the bay (c.2 km) compared to outer Kinvara Bay which

554

has been shown to be well mixed. In addition, Smith and Cave (2012) suggested that land

555

geometry at the mouth of Kinvara Bay likely traps a portion of ebb water which may in fact re-

556

enter Kinvara Bay on the next flood tide. The assumption of a well-mixed bay allows for the

557

application of more simplistic modelling approach and is considered reasonable for most of

558

the shellfish farming locations which are located in the deeper areas of the bay closer to the

559

mouth. In reality, complex dynamics likely occur within the bay itself with freshwater lens likely

560

present (Mccormack et al., 2014) and re-entry of partially mixed freshwater contributing to the

561

modelling complexity which would be required for a full bay mixing model.

562 563

Given the assumption that Kinvara Bay is well mixed, the approach outlined by Barber and

564

Wearing (2004) which consists a simplified 1D pollution flushing model, was employed for

565

predicting the concentration of continuously released pollutants into a tidal embayment. This

566

model has previously been successfully applied to a small bay with submarine freshwater

567

discharge along the Irish coast by Schuler et al. (2018). Barber and Wearing (2004) proposed 29

568

that the pollutant concentration, C, and volume of water, V, within the bay satisfy the following

569

equations during ebb and flood tides:

570 571 𝑑 𝑑𝑉 𝑑𝐶 (𝐶𝑉) = 𝐶 + 𝑉 = 𝑄𝐶 𝑑𝑡 𝑑𝑡 𝑑𝑡

Eqn. 1 - ebb tide interval

𝑑 𝑑𝑉 𝑑𝐶 (𝐶𝑉) = 𝐶 + 𝑉 = 0 𝑑𝑡 𝑑𝑡 𝑑𝑡

Eqn. 2 - flood tide interval

572 573

574 575 576 577

and also for both flood and ebb tide intervals: 𝑑𝑉 = 𝑄 + 𝑄𝑓 𝑑𝑡

Eqn. 3

578 579

where Q represents the discharge through the entrance of Kinvara bay (Q > 0 on the flood

580

tide, Q < 0 on the ebb tide) and Qf is the steady freshwater discharge into the bay from the

581

karst springs.

582 583

For the purposes of the development of this dynamic salinity model for Kinvara Bay, the salinity

584

of the sea water was considered as a conservative pollutant which varies with the exchange

585

of saline water with Galway Bay and the outflow of freshwater from Kinvara springs over the

586

tidal

587

LiDAR/Bathymetric for the bay and was then used to compute the changing volume of the bay

588

at each hourly time step using the tidal level (Marine Institute buoy at Galway port). Similarly,

589

the simulated freshwater discharge to the bay, together with any overland discharges during

590

peak flood events, were extracted from the calibrated pipe network model. Cave and Henry

591

(2011) carried out salinity measurements at the outlet of Kinvara Bay and found that the

592

salinity of the top 10 – 15 m of seawater at high tide was 33.5 ±0.3 Practical Salinity Units

593

(PSU). The salinity of the incoming freshwater was assumed to be zero PSU. A script was

594

then developed in MATLAB which calculated the direction of the tide for each time step and

595

then solved Eqns 1 – 3 accordingly. Salinity values within the bay at the beginning of each

period.

A

stage-volume

curve

for

30

Kinvara

Bay

was

established

utilising

596

time step were updated from the previous time step. As suggested by Smith and Cave (2012),

597

an allowance was made for some re-entry to the bay of less saline water from the previous

598

ebb tide with the next flood tide. The salinity of the water entering the bay during the flood tide

599

was partitioned between saline seawater and less saline water as determined by the last time

600

step of the previous ebb tide. In the absence of any actual previous quantification, the

601

proportion of return flow was assumed to be 0.1. The script was implemented between

602

1/6/2015 to 31/05/2016 in order to include the extreme flood event of December 2015-2016 –

603

an initialisation period of 12 months had previously been implemented in order to determine

604

initial conditions at the beginning of the simulation period. The resulting time-series of salinity

605

values during high and low tide for summer and winter months and the impact of the large

606

freshwater discharges during the flood event (see Figure 10) was compared with observed

607

data collected during previous studies (Cave and Henry, 2011, Smith and Cave, 2012,

608

Mccormack et al., 2014) and was found to show good agreement. For example, following the

609

extreme flood event of November 2009 (similar to the 2015/2016 flood event), Smith and Cave

610

(2012) reported a salinity low in Kinvara Bay of 17 PSU which compares with the proposed

611

salinity model which predicted a salinity low of 16.35 PSU following the 2015/2016 flood event.

612

The script was then run again using the modified simulated time-series for freshwater

613

discharges to the bay following the inclusion of the flood alleviation options outlined above.

614

The resulting time-series of salinity within Kinvara Bay was compared with the scenario without

615

the flood alleviation options (see Figure 10) which reveals only a very marginal change. For

616

example, the minimum salinity value predicted in the bay (16.68 PSU) was actually similar to

617

what was predicted to have actually occurred (16.35 PSU). A small downward shift in salinity

618

values in the bay during low and high tide was observed in the period 10/12/15 – 01/02/16

619

however the magnitude of this shift was small with a maximum observed difference in salinity

620

between the two time-series of 1.03 PSU. In fact, the salinity of the bay was actually shifted

621

upwards following the inclusion of the flood alleviation measures in the period 14/03/16 –

622

10/05/16 due to the tailing off in freshwater discharges following larger overland releases at

623

an earlier date. An examination of the summary statistics for the two salinity profiles further 31

624

illustrates the marginal changes caused by the inclusion of overland flood alleviation

625

discharges (see Figure 10). The European Communities (Quality of Shellfish Waters)

626

Regulations (2006) specify guideline values for salinity in shellfish waters of between 12 – 38

627

PSU (Smith and Cave, 2012). The results of the dynamic salinity model therefore suggest that

628

the provision of flood alleviation as outlined in this study would not have a significant adverse

629

impact on existing salinity profiles within the bay during extreme flood events. These findings

630

must be cautioned given the underlying assumption of a well-mixed bay, which has been

631

shown not to be valid closer to the head of the bay. The model also does not take account of

632

potential additional freshwater discharges from other catchments or indeed other unknown

633

freshwater discharges further out in the bay.

634

32

90 30 80

Salinity

60 20 50

15

Flow (m 3 /s)

70

25

40

30 10

Nov 30, 2015

635

Dec 28, 2015

Jan 11, 2016

Jan 25, 2016

20

35

120

30

100

80

Salinity

25

20

60

Kinvara Bay Salinity Kinvara Bay Salinity (flood alleviation) Bay discharge Bay discharge (flood alleviation)

Flow (m 3 /s)

636

Dec 14, 2015

40 15

20 10 0 5

Jul 2015

Oct 2015

Jan 2016

Apr 2016

Figure 10: Simulated time-series of salinity within Kinvara Bay before and after flood alleviation measures are incorporated with simulated freshwater outflows from the karst model shown on the secondary y-axis; insert provides expanded section of the plot for the period Nov15 – Jan16

33

637

Implications for protected groundwater dependent terrestrial ecosystems (GWDTEs)

638

The ecology of turloughs is unique and complex and directly related to the periodic inundation

639

of the basins for varying periods (Goodwillie, 1992, Goodwillie and Reynolds, 2003, Sheehy

640

Skeffington et al., 2006, Moran et al., 2008, Porst and Irvine, 2009, Naughton et al., 2012).

641

Hence, the potential impacts of any flood alleviation measures on the eco-hydrology of these

642

GWDTEs also needs to considered, given they are protected under the European Habitats

643

Directive. The largest turlough within the study catchment, Lough Coole, has been shown to

644

be an integral control for levels within the entire catchment (Gill et al., 2013b) and so the

645

potential ecological impacts of the optimum flood alleviation measures have been examined

646

for this turlough, as a representative wetland. The long-term simulated water levels at Lough

647

Coole in the period 1988 – 2018 demonstrated that peak winter flood levels generally range

648

between 8 to 11.5 mOD with the frequency of more extreme flood peaks tending to increase

649

after 2009. Figure 11 demonstrates the impact that the optimum flood alleviation measures

650

would have on peak flood level and duration in the validated period 2007 – 2018 at Coole.

651

14

12

Stage (m)

10

8

6

4

2 2008

2010

2012

2014

Simulated Stage (existing)

652

2016

2018

Simulated Stage (flood alleviation)

653

Figure 11: Simulated stage at Coole turlough before and after incorporating optimum flood

654

alleviation measures

34

655 656

During this period, six flood events would have been significantly impacted with the flood

657

alleviation channels with peak flood levels reduced by up to 1.8 m. The resulting recessions

658

are also typically altered to reduce the overall length of time the basin is flooded and depth-

659

duration curves are given in Figure 12. Figure 12(a) shows the total number of days that each

660

flood level in the basin was exceeded in the simulation period, indicating a small downward

661

shift in the curve when the optimum flood alleviation measures are included which increases

662

for water levels above a stage of 9 m. The number of days for which each flood level was

663

exceeded was averaged annually and a similar plot produced (Figure 12(b)) with the addition

664

of a similar plot for the full 1988 – 2018 simulation (dashed black line). Again, the average

665

number of days exceeding the higher stages is reduced in the period 2007 – 2018 but

666

interestingly almost the entire curve for the period 1988 – 2018 is lower than both scenarios.

667

This would appear to indicate that the period 2007 -2018 included higher flood events which

668

may highlight a changing climatological trend. For example, when comparing the annually

669

averaged flooded durations in the validated period 2007 – 2018, the flood alleviation measures

670

indicate an annual reduction of flooded levels in excess of 12 m of 17.55 days (a 68%

671

reduction). However, when the flood alleviation scenario averages are compared with the long

672

term averages for the period 1988 – 2018 the difference drops to 7.18 days (a 46% reduction).

673

Similarly, for durations of levels exceeding 11 m there was an average 13.4 day reduction

674

which compares to 3.8 days when using the long term simulated averages.

675

35

16

(a)

(b)

14

14

12

12

10

10

Stage above (m)

Stage above (m)

16

8

6

6

4

4

2

2

0

0

1000

2000

3000

0

4000

Simulated (2007-2018)

0

100

200

300

400

No. of days (yearly average)

No. of days (total)

676

8

Simulated (flood alleviation - 2007-2018)

Long term average (1988 - 2018)

677

Figure 12: Flooded duration plot for Coole turlough with and without flood alleviation measures

678

included over an 11 year period (2007 to 2018). The y-axis shows the level (mOD) which is

679

exceeded for the corresponding number of days on the x-axis

680 681

From an ecological perspective, the wetland habitats which exist at these higher zones of the

682

basins are limited due to the relatively infrequent flooding cycles which they are exposed to

683

(Irvine et al., 2018). Generally the presence of an established the tree line around the

684

perimeter of the turlough basis identifies the normal annual inundation level (Waldren, 2015,

685

Irvine et al., 2018). For the above example at Coole turlough, flood levels above 12 mOD

686

extends well into the established tree line indicated how infrequently these events occur.

687

Frequent visits to the area over many years have identified sections of very mature established

688

tree lines (>100 years old) which have died out following the extreme flood of 2009 and

689

2015/16. Whilst the analysis does predict minor changes to the annual averaged flooded

690

duration for flood levels below 11 mOD, the average annual changes are small (<3 days) and

691

if compared with the long term simulated averages (1988 – 2018) there is almost no difference 36

692

observed. It is therefore conservatively concluded that the ecology of the turloughs will

693

experience a minimal impact should the optimum flood alleviation measures be implemented

694

and may in fact benefit from the protection afforded to habitats at higher elevations not capable

695

of surviving under flood waters for extended periods.

696

Conclusion

697

This study demonstrated the successful development of a 1D/2D pipe network model of a

698

large karst catchment which includes 15 turloughs and floodplains for simulating groundwater

699

flooding. The addition of the 2D aspect of the model coupled with the 1D pipe network, which

700

is a novel achievement for a karst groundwater system, has been shown to successfully

701

simulate overland flooding between basins that are overtopped during extreme flood events.

702

The calibrated model has been used to predict SGD to Kinvara Bay over a long-term period

703

providing further insight into the catchment hydrology. The peak SGD estimated by the model

704

to Kinvara Bay from the catchment during an extreme flood event was 35.8 m3/s.

705 706

Only marginal impacts on salinity within Kinvara Bay are predicted with the introduction

707

overland flood relief channels resulting in accelerated water transfer to the bay with a peak

708

reduction in salinity of 1.03 PSU predicted during an extreme flood event. Similarly, no

709

significant ecological impact on the protected wetland habitats within the turlough basins would

710

result with a yearly average reduction in inundation duration of below what is considered to be

711

a “normal” flooding range of less than 3 days. An apparent upward shift in the flooded duration

712

curves in the catchment over the last decade, which may be an indicator of changing climate,

713

was identified and presents an opportunity for further study.

714 715

This study has demonstrated the practical applications for simulating groundwater flood

716

alleviation measures of such models with an optimum suite of flood alleviation measures for

717

the catchment successfully demonstrated. The capability of such models for groundwater

37

718

flood management and related eco-hydrology could be utilised in other conduit dominated

719

karst systems, especially those featuring ephermeral lakes such poljes.

720

Acknowledgements

721

This work was carried out as part of the scientific project “GWFlood: Groundwater Flood

722

Monitoring, Modelling and Mapping”, funded by Geological Survey Ireland, and also

723

represents outputs from research funded by the Office of Public Works and the Irish Research

724

Council. The authors would like to thank the Irish Meteorological Service (Met Eireann) for the

725

provision of rainfall data, Galway County Council for the provision of aerial photography and

726

GIS data, and the Office of Public Works for the provision of LIDAR, hydrometric and aerial

727

photography data. The authors would also like to acknowledge the generous assistance of the

728

support staff at Innovyze Ltd., particularly Mr. Andrew Chapman.

729 730 731 732

38

733

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Barber, R. W. & Wearing, M. J. 2004. A Simplified Model for Predicting the Pollution Exchange Coefficient of Small Tidal Embayments. Water, Air and Soil Pollution: Focus, 4, 87100. Cave, R. R. & Henry, T. 2011. Intertidal and submarine groundwater discharge on the west coast of Ireland. Estuarine, Coastal and Shelf Science, 92, 415-423. Cobby, D., S.E, M., Parkes, A. & Robinson, V. 2009. Groundwater flood risk management: Advances towards meeting the requirements of the EU floods directive. Coxon, C. E. 1987a. An Examination of the Characteristics of Turloughs, using Muitivariate Statistical Techniques. Irish Geography, 20, 24-42. Coxon, C. E. 1987b. The spatial distribution of turloughs. Irish Geography, 20, 11-23. Drew, D. P. 2008. Hydrogeology of lowland karst in Ireland. Quarterly Journal of Engineering Geology and Hydrogeology, 41, 61-72. Finch, J. W., Bradford, R. B. & Hudson, J. A. 2004. The spatial distribution of groundwater flooding in a chalk catchment in southern England. Hydrological Processes, 18, 959971. Fleury, P., Ladouche, B., Conroux, Y., Jourde, H. & Dörfliger, N. 2009. Modelling the hydrologic functions of a karst aquifer under active water management - The Lez spring. Journal of Hydrology, 365, 235-243. G.S.I. 1992. A Report on the Flooding in the Gort-Ardrahan Area. Occasional Reports. Geological Survey Ireland, Beggars Bush, Dublin, Ireland. Gill, L. W., Naughton, O. & Johnston, P. M. 2013a. Modeling a network of turloughs in lowland karst. Water Resources Research, 49, 3487-3503. Gill, L. W., Naughton, O., Johnston, P. M., Basu, B. & Ghosh, B. 2013b. Characterisation of hydrogeological connections in a lowland karst network using time series analysis of water levels in ephemeral groundwater-fed lakes (turloughs). Journal of Hydrology, 499, 289-302. Goodwillie, R. 1992. Turloughs over 10 hectares: Vegetation Survey and Evaluation. Unpublished Report for the National Parks and Wildlife Services. Office of Public Works, Dublin. Goodwillie, R. & Reynolds, J., D., 2003. Turloughs. In: Otte, M.L. (Ed.), Wetlands of Ireland: Distribution, Ecology, Uses and Economic Value. pp 130–134. Hartmann, A. 2017. Experiences in calibrating and evaluating lumped karst hydrological models. Geological Society, London, Special Publications, 466. Hartmann, A., Barberá, J., Lange, J., Andreo, B. & Weiler, M. 2013. Progress in the hydrologic simulation of time variant recharge areas of karst systems - Exemplified at a karst spring in Southern Spain. Advances in Water Resources, 54, 149-160. Hughes, A. G., Vounaki, T., Peach, D. W., Ireson, A. M., Jackson, C. R., Butler, A. P., Bloomfield, J. P., Finch, J. & Wheater, H. S. 2011. Flood risk from groundwater: examples from a Chalk catchment in southern England. Journal of Flood Risk Management, 4, 143-155. Irvine, K., Coxon, C., Gill, L., Kimberley, S. & Waldren, S. 2018. Turloughs (Ireland). In: FINLAYSON, C. M., MILTON, G. R., PRENTICE, R. C. & DAVIDSON, N. C. (eds.) The Wetland Book: II: Distribution, Description, and Conservation. Dordrecht: Springer Netherlands. Kong-a-Siou, L., Fleury, P., Johannet, A., Borrell Estupina, V., Pistre, S. & Dörfliger, N. 2014. Performance and complementarity of two systemic models (reservoir and neural networks) used to simulate spring discharge and piezometry for a karst aquifer. Journal of Environmental Hydrology, 519, 3178-3192. Macdermot, C. 1995. Water levels in the Gort Area 1994 - 1995. Occasional Reports. Geological Survey Ireland, Beggars Bush, Dublin, Ireland.

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Macdonald, A. M., Lapworth, D. J., Hughes, A. G., Auton, C. A., Maurice, L., Finlayson, A. & Gooddy, D. C. 2014. Groundwater, flooding and hydrological functioning in the Findhorn floodplain, Scotland. Hydrology Research, 45, 755-773. Mayaud, C., Gabrovšek, F., Blatnik, M., Kogovšek, B., Petrič, M. & Ravbar, N. 2019. Understanding flooding in poljes: A modelling perspective. Journal of Hydrology, 575, 874-889. Mccormack, T., Gill, L. W., Naughton, O. & Johnston, P. M. 2014. Quantification of submarine/intertidal groundwater discharge and nutrient loading from a lowland karst catchment. Journal of Hydrology, 519, 2318-2330. Moran, J., Kelly, S., Sheehy Skeffington, M. & Gormally, M. 2008. The use of GIS techniques to quantify the hydrological regime of a karst wetland (Skealoghan turlough) in Ireland. Applied Vegetation Science, 11, 25-36. Morris, J., Bailey, A. P., Lawson, C. S., Leeds-Harrison, P. B., Alsop, D. & Vivash, R. 2008. The economic dimensions of integrating flood management and agri-environment through washland creation: A case from Somerset, England. Journal of Environmental Management, 88, 372-381. Naughton, O., Gill, L. W., Johnston, P. M., Morrissey, P. J., Regan, S., Mccormack, T. & Drew, D. 2018. The hydrogeology of the Gort Lowlands. Irish Journal of Earth Sciences, 36, 1-20. Naughton, O., Johnston, P. M. & Gill, L. W. 2012. Groundwater flooding in Irish karst: The hydrological characterisation of ephemeral lakes (turloughs). Journal of Hydrology, 470-471, 82-97. Naughton, O., Johnston, P. M., Mccormack, T. & Gill, L. W. 2017. Groundwater flood risk mapping and management: examples from a lowland karst catchment in Ireland. Journal of Flood Risk Management, 10, 53-64. O'toole, M. 1990. A survey of some coastal zones in North Co. Clare and South Co. Galway in relation to development of inter-tidal shellfish culture. . In: MHARA, B. I. (ed.). O’brien, R. J., Misstear, B. D., Gill, L. W., Deakin, J. L. & Flynn, R. 2013. Developing an integrated hydrograph separation and lumped modelling approach to quantifying hydrological pathways in Irish river catchments. Journal of Hydrology, 486, 259-270. Pinault, J.-L., Amraoui, N. & Golaz, C. 2005. Groundwater-induced flooding in macroporedominated hydrological system in the context of climate changes. Water Resources Research, 41. Porst, G. & Irvine, K. 2009. Implications of the spatial variability of macroinvertebrate communities for monitoring of ephemeral lakes. An example from turloughs. Riisgård, H. U. 2012. Effect of Salinity on Growth of Mussels, Mytilus edulis , with Special Reference to Great Belt (Denmark). Open Journal of Marine Science, 02, 167-176. Ryan Hanley 2018. South Galway (Gort Lowlands) Flood Relief Scheme - Surveyed Historic Flood Levels. Draft Report (unpublished). Schuler, P., Duran, L., Mccormack, T. & Gill, L. 2018. Submarine and intertidal groundwater discharge through a complex multi-level karst conduit aquifer. Hydrogeology Journal, 26, 2629-2647. Sheehy Skeffington, M., Moran, J., O Connor, Á., Regan, E., Coxon, C., Scott, N. E. & Gormally, M. 2006. Turloughs – Ireland’s unique wetland habitat. Smith, A. M. & Cave, R. R. 2012. Influence of fresh water, nutrients and DOC in two submarine-groundwater-fed estuaries on the west of Ireland. Science of The Total Environment, 438, 260-270. Smith, M., Koren, V., Reed, S., Zhang, Z., Seo, D.-J., Moreda, F. & Cui, Z. 2019. The Distributed Model Intercomparison Project: Phase 2. Southern Water Global, J. O. D. 1997. Addendum to Interim Factual Report. Report Addendum, Volume 1. Southern Water Global, J. O. D. 1998. An Investigation of the flooding problems in the GortArdrahan Area of South Galway. Final Report, Volume 1.

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Waldren, S. 2015. Turlough Hydrology, Ecology and Conservation. Unpublished Report. National Parks & Wildlife Services, Department of Arts, Heritage and the Gaeltacht, Dublin, Ireland.

Modelling Groundwater Flooding in a Lowland Karst Catchment

842 843 844 845 846 847 848 849 850 851 852

1

853

Highlights:

Patrick Jerome Morrissey1, Ted McCormack2, Owen Naughton2, Paul Meredith Johnston1 and Laurence William Gill1 Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, University of Dublin, Museum Building, College Green, Dublin 2, Ireland 2 Geological Survey of Ireland, Beggars Bush, Haddington Road, Dublin 4, Ireland

Corresponding Author: Patrick Jerome Morrissey, Email Address: [email protected]

854 855



Groundwater flooding in Irish lowland karst has been accurately simulated

856



Spring discharge from the lowland karst to the sea has been estimated

857



Groundwater flood alleviation options have been simulated

858



Impacts of flood alleviation on the salinity of Kinvara Bay has been assessed

859



Impacts of flood alleviation on eco-hydrology of the turloughs was assessed

860 861

MODELLING GROUNDWATER FLOODING IN A LOWLAND

862

KARST CATCHMENT

863 864

Patrick Morrissey, Ted McCormack, Owen Naughton, Paul Johnston and Laurence Gill

865 866

Abstract

867

Groundwater flooding is a phenomenon which has become recognised as a significant natural

868

hazard in recent years. The Gort lowland karst catchment situated in south Co. Galway on the

41

869

western coast of Ireland has experienced two extreme groundwater flood events in the past

870

decade leading to considerable damage and disruption. Groundwater flooding in the

871

catchment typically occurs following periods of sustained heavy rainfall when sufficient

872

capacity is not available in the bedrock to store and convey water to the sea. The underground

873

karst conduit system therefore surcharges to the ground surface through a system of

874

estavelles and floods low-lying areas of ground known as turloughs (ephemeral lakes). A

875

1D/2D pipe network model of the karst conduit system of the Gort lowland karst was developed

876

in order to simulate the flooding mechanisms across the catchment as well as to assess flood

877

alleviation options. The nature of the underground karstic connections in the system has been

878

determined from a combination of available field data (dye tracing, water chemistry data etc.)

879

and cross-frequency analysis on the turlough fluctuation time series data over several years.

880

The availability of high accuracy LiDAR data of the catchment then allowed the flooding regime

881

to be accurately simulated on the ground surface. The model was calibrated using historic

882

continuous water level data for a number of turloughs in the catchment and then validated

883

using historic peak spot flood levels. The model was then used to identify appropriate potential

884

groundwater flood alleviation measures for the catchment. The impacts of such measures on

885

both the salinity of Kinvara bay, through increased freshwater discharges, and eco-hydrology

886

of the protected wetland habitats within the turloughs was also investigated. The study

887

demonstrated that the measures proposed can be developed without inducing undesirable

888

impacts to either salinity in Kinvara Bay (and thus mariculture) or to the protected turlough

889

habitats. The study has also demonstrated the suitability and functionality of such karst models

890

for examining groundwater flood management options and eco-hydrology in karst catchments.

891 892 893

CRediT author statement

894

Patrick

895

Investigation, Simulating, Writing- Original draft preparation. Ted McCormack: , Data curation,

Morrissey:

Conceptualization,

Methodology,

42

Data

curation,

Visualization,

896

Reviewing. Owen Naughton: Reviewing. Paul Johnston: Conceptualization, Reviewing.

897

Laurence Gill: Supervision, Conceptualization, Reviewing and Editing.

898 899

Declaration of interests

900 901

☒ The authors declare that they have no known competing financial interests or personal

902

relationships that could have appeared to influence the work reported in this paper.

903 904 905 906

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

907 908 909 910 911 912

Table 5: Summary statistics for the 1D model calibration

2016 - 2018 Calibration Period Model Efficiency Location

Nash-Sutcliffe (NSE)

Kling-Gupta (KGE)

Stage (m)

Volume (m3)

Stage (m)

Volume (m3)

Ballinduff

0.87

0.93

0.87

0.88

Blackrock

0.96

0.97

0.97

0.92

Caherglassaun

0.96

0.97

0.95

0.90

43

Cahermore

0.98

0.98

0.97

0.91

Castletown

0.98

0.85

0.88

0.62

Kiltartan

0.25

0.34

0.27

0.32

Coole

0.95

0.99

0.89

0.93

Coy

0.87

0.89

0.95

0.93

Garyland

0.98

0.99

0.97

0.95

Hawkhill

0.94

0.94

0.91

0.82

Mannagh

0.88

0.85

0.97

0.77

Newtown

0.93

0.99

0.88

0.97

913 914 915 916

Table 6: Summary statistics for medium term 1D model validation (2007 – 2018)

2007 - 2018 Validation Period Model Efficiency Location

Nash-Sutcliffe

Kling-Gupta

Stage (m)

Volume (m3)

Stage (m)

Volume (m3)

Blackrock

0.92

0.97

0.95

0.99

Caherglassaun

0.97

0.98

0.98

0.98

Coole

0.88

0.95

0.82

0.91

Coy

0.84

0.86

0.98

0.99

Garyland

0.94

0.93

0.96

0.89

917 918 919 920

44

921

Table 7: Summary statistics for long-term 1D model validation (1989 – 2007)

1989 - 2018 Validation Period Spot Level Validation

Blackrock Turlough

Lough Coole

Actual Stage

Simulated

Actual Stage

Simulated

(mOD)

Stage (mOD)

(mOD)

Stage (mOD)

1989 – 1990

27.75

27.54

12.54

12.69

1994 – 1995

28.9

28.47

13.5

13.36

1996 – 1997

21.56

22.1

9.79

9.55

2002

23.84

24.01

-

-

Flood Event

922 923 924

Table 8: Optimal overflow channel properties derived from the flood simulations

Channel ID

Base Width

Manning’s

Gradient

Upstream Invert

Downstream Invert

(m)

n

(m/m)

Level (mOD)

Level (mOD)

OL-1

2

0.035

0.003

27

20

OL-2

2

0.035

0.001

16.5

16

OL-3

4

0.035

0.001

16.5

16

OL-4

5

0.035

0.002

10

9

OL-5

3

0.035

0.008

10

6

OL-6

6

0.035

0.001

10

3.5

925 926 927

45