Modelling groundwater/surface water interaction in a managedriparian chalk valley wetland
A. R. House,1,2* J. R. Thompson,2 J. P. R. Sorensen,3 C. Roberts1 and M. C. Acreman11 Centre for Ecology and Hydrology, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, UK2 UCL Department of Geography, University College London, Gower Street, London WC1E 6BT, UK
3 British Geological Survey, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, UK
KEY WORDS wetlands; hydrological/hydraulic modelling; groundwater/surface water interaction; MIKE SHE; wetlandmanagement
Received 13 April 2015; Accepted 21 July 2015
INTRODUCTION
Wetlands are widely recognized as providing valuableenvironmental, cultural and economic functions andservices (Acreman et al., 2011). The European HabitatsDirective (EEC, 1992) lists groundwater (GW)-dependentwetland ecosystems as priority habitats that are particu-larly sensitive to environmental change. The need forsustainable wetland management is intensifying in theface of climate change as well as growing, and oftencompeting, demands for water (Baker et al., 2009; Maltbyand Acreman, 2011). The establishment and maintenanceof wetlands depend primarily on the hydrological regime(Mitsch and Gosselink, 2007), as it is a key control onvegetation (Baldwin et al., 2001; Wheeler et al., 2009),fauna (Ausden et al., 2001; McMenamin et al., 2008) and
biogeochemical cycling (McClain et al., 2003; Lischeidet al., 2007). Current and historical wetland managementpractices revolve around the maintenance of water levelsrequired for the conservation of desired species orcommunities, flood mitigation and arable or pastoralproductivity (Morris et al., 2008). An ability to predict theimpacts of modifications to wetlands’ hydrologicalregimes is therefore highly desirable. Models that canaccurately represent wetland hydrological processes haveenormous potential in the assessment of potentialdegradation to the ecological character of wetlands andtheir management (Acreman and Jose, 2000).In riparian wetlands, the water balance can incorporate a
significant measure of GW (Bravo et al., 2002; Krause andBronstert, 2005). This can be time dependent (Hunt et al.,1999), spatially heterogeneous (Hunt et al., 1996; Lowryet al., 2007; House et al., 2015) and influenced bytopographical, geological and climatic factors (Winter,1999; Sophocleous, 2002). The magnitude of the flux canexert strong controls upon the hydrological regime, nutrientstatus and species composition (Wheeler et al., 2009;
*Correspondence to: Andrew House, Centre for Ecology and Hydrology,Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, UK.E-mail: [email protected]
HYDROLOGICAL PROCESSESHydrol. Process. 30, 447–462 (2016)Published online 26 August 2015 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.10625
House et al., 2015). Thus, the impacts of abstraction,sustained low river flows, climate change or feedback fromwater management activities taking place within thecatchment could result in significant adverse impacts,particularly where wetlands are underlain by permeablegeology, such as chalk.Because of the complexity of process interactions in
these wetlands, quantifying a water balance through fieldobservations alone is often impractical. Comprehensivewetland studies have instead relied on simulation ofhydrological processes within fully integrated or coupledGW/surface water (SW) models (Refsgaard et al., 1998;Crowe et al., 2004; Thompson et al., 2004; Krause andBronstert, 2005; Thompson et al., 2009; Frei et al., 2010).However, these modelling studies often contain simpleinterpretations of the saturated zone through single-layerlithology (Refsgaard et al., 1998; Thompson et al., 2004,2009; Frei et al., 2010) or transfer functions (Krause andBronstert, 2005). Where applied to wetlands with morecomplex subsurface hydrogeological structures, processeshave been partially represented as boundary conditions(Crowe et al., 2004).In this study, the distributed hydrological model, MIKE
SHE, is applied to a riparian wetland in the chalklowlands of the UK. Understanding and modelling ofhydrological processes in the wetland are complicated byin-channel macrophyte growth and management, acompound geology and subtle GW/SW interactions.The numerical model is used to quantify the waterbalance, enhance understanding of the site’s hydrologicalfunctioning, identify some of the effects of currentmanagement practices and inform future managementschemes.
STUDY AREA
Site description
The Centre for Ecology and Hydrology RiverLambourn Observatory (51.445°N, 1.384°W) contains~10 ha of riparian wetland adjoining a 600-m reach of theRiver Lambourn, Berkshire, UK (Figure 1). To the westof the river, the wetlands are divided by the WestbrookChannel into a northern meadow and a southern meadow.The wetland and River Lambourn are designated as a Siteof Special Scientific Interest and Special Area ofConservation owing to their importance as habitats forDesmoulin’s whorl snail (Vertigo moulinsiana), brooklamprey (Lampetra planeri) and bullhead (Cottus gobio).The wetland is also designated owing to the presence ofspecific habitats (Annex 1 habitat from EU HabitatDirective: Water courses of plain to montane levels withRanunculion fluitantis and Callitricho-Batrachion vege-tation) and terrestrial plant communities (MG8 vegetation
community of the UK National Vegetation Classification;Rodwell, 1991).The site is located 13 km downstream from the
ephemeral source of the River Lambourn at LynchWood, Lambourn (51.512°N, 1.529°W). The river drainsthe chalk of the Berkshire Downs and is characterized bya large baseflow component. At Shaw, the nearestgauging station 5 km downstream of the observatory,the river has a baseflow index of 0.96 and a meandischarge of 1.73m3 s!1 (Marsh and Hannaford, 2008).The wetland was managed as flood pastures and
water meadows until the middle to late 20th century(Everard, 2005). Maps dating to the 1880s show acorresponding network of predominantly linear conduits,sluices and aqueducts. Most of these channels havenaturally infilled and are absent from current maps,although the relic drainage network is still evident inthe topography. A single sluice gate remains, positionedapproximately halfway along the Westbrook andoperated by the local community. Grazing gradually
Figure 1. The Centre for Ecology and Hydrology River LambournObservatory showing the instrumentation network with chalk (C), gravel(G) and peat (P) piezometer locations, model domain and horizontal extent
came to an end on the water meadows from the mid-1960s to the 1980s. This is reflected in the currentprevalence of tall-herb fen vegetation communitiesthrough the site (House et al., 2015). There is somesuccession, with plant communities graded from swampand fen dominated by reed sweet grass (Glyceriamaxima) and lesser pond sedge (Carex acutiformis) inthe north to remnants of the MG8 community in thesouth. Current management is confined to the river,where instream macrophyte growth is cut backperiodically to maintain flood conveyance and lowerwater levels (Old et al., 2014).The site is underlain by the Seaford Chalk Formation,
dipping at 1–2° to the south-east (Allen et al., 2010) andcomprising a uniform soft to medium-hard chalk withfrequent flint nodules. The surface of the chalk has beenhighly weathered, in places, producing a low-permeability ‘putty chalk’ (Younger, 1989) up to 5mthick. River terrace deposits and alluvium up to 7m thickoverlie the chalk, consisting primarily of coarse gravelswith some sand (Chambers et al., 2014). These are thickerand more continuous in the north meadow, nearer to thecourse of the river. In the south, the gravels are morevariable and thin to the west, almost disappearing towardsthe south-west boundary of the site. In their lower layers,there is often a high proportion of reworked chalkmaterial. Above the gravels, a layer 0.4–2m thick consistspredominantly of peat (Chambers et al., 2014).
Conceptual model
A field campaign using three-dimensional (3D)electrical resistivity tomography (ERT) (Chamberset al., 2014), along with temperature, hydrochemistryand vegetation surveys (House et al., 2015), has enabledthe development of a conceptual model (Figure 2). Thepeat and gravels are considered to have good hydraulicconnectivity, with head boundaries at the RiverLambourn and Westbrook broadly controlling waterlevels across the wetland. A double-aquifer system ofgravels and chalk is mostly separated by a confining
layer of low-permeability putty chalk. Leakage occursbetween the gravels and chalk where the putty chalk isthin or absent, causing localized variations in waterlevels. These are concomitant with relic infilled channelsin the peat and occur mainly in the north meadow.
Site instrumentation
Piezometers were selected from a wider existing arrayinstalled in January 2012, based upon data availabilityand numbered 1–7 (Figure 1). These were supplementedby piezometers installed in May 2013 to target discreteareas of GW upwelling (locations 8–12; Figure 1). Waterlevels were monitored in pairs of piezometers installed inboth the peat (P) and gravel (G). Exceptions includelocations 11 and 12 with gravel piezometers only and 7and 10 with peat piezometers only. Gravel piezometerswere screened approximately 2.5–3.5m below groundlevel (bgl), whilst peat piezometers were screened acrossthe entire peat thickness. A chalk (C) piezometer is alsolocated at site 3, screened at 9.5–10.0m bgl.Peat and gravel GW heads were monitored every 5min
using either In Situ Level Troll® 500s or SWS Divers®installed to a consistent depth of 3m bgl in gravelpiezometers and to the base of the peat in peatpiezometers. GW heads are routinely checked bymanually dipping observed water levels to quality controllogged data susceptible to drift (Sorensen and Butcher,2011). Channel stage was observed monthly at sevenstage boards along the River Lambourn (L1 and L3–L7)and three in the Westbrook (W1–W3). River Lambournstage was also recorded every 5min at L2 using an SWSDiver®.Continuous 15-min averaged meteorological observa-
tions were logged using an automatic weather station. Airtemperature and relative humidity were recorded using aCS215™ sensor. An RM Young 03101™ cup anemometermeasured wind speed. Solar radiation was recordedwith a LP02™ pyranometer. Accumulated 15-minprecipitation was measured with a ARG100™ tipping-bucket rain gauge.
Figure 2. Conceptual vertical section through the River Lambourn Observatory, after House et al. (2015)
449MODELLING GW/SW INTERACTION IN A CHALK VALLEY WETLAND
MIKE SHE is an integrated modelling system thatsimulates the land-based phase of the hydrological cycle(Graham and Butts, 2005). Developed originally from theSystème Hydrologique Européen (Abbott et al., 1986a, b)by the Danish Hydraulic Institute (DHI), it has beenutilized for international river basins (Andersen et al.,2001; Stisen et al., 2008; Thompson et al., 2013, 2014),catchments with areas of hundreds to thousands of squarekilometres (Feyen et al., 2000; Huang et al., 2010; Singhet al., 2010, 2011), to small (<50-km2) catchmentsand individual wetlands (Refsgaard et al., 1998;Al-Khudhairy et al., 1999; Thompson et al., 2004;Thompson, 2012). Although often labelled as a deter-ministic, fully distributed and physically based model, thecomplexity of process representation may be varied toinclude empirical and semi-distributed methods. Themodel may thus be built iteratively in line with dataavailability and process understanding. This flexibility,along with a proven applicability for simulating wetlandhydrological systems (Refsgaard et al., 1998; Al-Khudhairy et al., 1999; Thompson et al., 2004; Staeset al., 2009; Thompson et al., 2009), underpins its use inthe current study.Model time step is adjusted automatically within MIKE
SHE dependent on precipitation and infiltration rates(DHI, 2009). The model domain was provided by the
River Lambourn Observatory formal boundary with atotal area of 10ha. This coincides with the perimeters ofthe wetland areas at the extents of the valley bottom. Agrid size of 1×1m was selected from a series of modelruns that showed little change in simulated GW heads forgrid sizes between 0.5× 0.5 and 10×10m (after Vázquezet al., 2002). The chosen resolution, which produces101689 grid cells within the model domain, provides agood balance between the representation of physicalcharacteristics of the site, such as topography, andcomputation time.Detailed topographic data were provided by a ground
survey combined with LiDAR. For the survey, differen-tial Global Positioning System (dGPS) was used togeoreference 2815 locations with an approximate gridresolution of 3× 3m. Dense scrub and watercourseboundaries confined the survey extent. To supplementdGPS coverage, 1×1m resolution LiDAR data wereresampled to the 1× 1m MIKE SHE grid (Figure 3).A single long-grass vegetation type was used to
represent land cover across the model domain in linewith the dominance of tall-herb fen at the site (Houseet al., 2015). Temporal variations in leaf area index androot zone depth, required for the interception andevapotranspiration (ET) modules, were taken from theliterature (Breuer et al., 2003) and an existing DHI (2009)vegetation properties file. The overland flow componentwas also uniformly distributed, with Manning’s nroughness coefficient employed as a calibration term.
Figure 3. (a) A 1m × 1mMIKESHE topographic grid of theRiver LambournObservatory and (b) extent and depth of surfacewater at peak flow (15/2/2014)
The one-dimensional form of the Richards equationused within the unsaturated zone module employed aspatially uniform soil profile comprising peat to a depth of1m. Values for the Van Genuchten (1980) expression ofthe soil moisture retention curve were obtained from theliterature (Letts et al., 2000). Infiltration rate and effectivesaturation were parameterized through calibration.The saturated zone was characterized as a four-layer
geological model with peat overlying gravels over adiscontinuous layer of putty chalk and chalk bedrockbeneath. The 3D finite-difference Darcy flow method wasemployed to calculate subsurface flow. Depths to thegravel–peat interface were taken from a manual probingsurvey of 2815 locations in conjunction with thetopographic survey (Chambers et al., 2014). Thegravel–chalk interface was derived from a 3.1-ha 3DERT survey of the meadows using a resistivity isosurfaceextended by trilinear interpolation from intrusive bore-holes where the interface could be identified in coreretrievals (Chambers et al., 2014). This was extended tothe edges of the model domain by bilinear interpolationwithin MIKE SHE. The horizontal extent of the puttychalk was also extracted from the resistivity model usinga representative range of resistivities (10–75Ωm) follow-ing Crook et al. (2008). Within the saturated zone set-up,this was specified as a 1-m layer with relatively lowhydraulic conductivity (1×10!10ms!1) at the top of thechalk bedrock. Gaps in the putty chalk were allocated thesame hydraulic conductivity as the chalk, taken from theliterature as 4.4×10!4ms!1 (Younger, 1989). Verticaland horizontal hydraulic conductivities of the peat andgravels were varied during model calibration.For the chalk aquifer, head boundaries were based on
observations from a piezometer at 3C (Figure 1).Observed values were adjusted to differences in eleva-tions at 50-m intervals along all sides of the modeldomain by linear interpolation. Gravel boundaries wereset to a constant flux gradient of 0.003 in the north andsouth, based upon observations from the piezometernetwork and following the topographic gradient. Theremaining boundaries, where the gravel thins to the valleyedge, were defined as zero flow. Zero-flow boundarieswere assigned around the peat and putty chalk layerswhere lateral flow was expected to be minimal owing tolow hydraulic conductivities.The river network was digitized in MIKE 11 from
Ordnance Survey MasterMap 1:1250 raster data. Thefully dynamic one-dimensional Saint-Venant equationswere used to describe channel flow with all MIKE 11branches specified as being coupled to MIKE SHE.Channel cross-sectional profiles applied to the networkwere based on dGPS surveys conducted at 44 locationsalong the Westbrook and 42 along the River Lambourn.Bank elevations were taken from the MIKE SHE
topographic grid with points across the cross sectionspecified as depths relative to the banks (Thompson et al.,2004). The channel bed was specified to be in full contactwith the saturated zone, so that exchange between theriver and aquifer was controlled by the hydraulicconductivity of the aquifer rather than river bed material.This was deemed appropriate owing to the nature of thechannel substrate and high baseflow index.Inflows for the upstream channel boundary, which were
specified as a mean 15-min discharge, were derived froma relationship between monthly measurements of dis-charge at L1 (Figure 1) using an electromagnetic flowmeter and the corresponding flow at the downstreamShaw gauging station. The downstream boundary was setto follow monthly stage observations at L7 (Figure 1)specified on a 15-min basis by linear interpolation.To account for instream macrophyte (weed) growth and
its cutting within the Lambourn, channel bed roughness(Manning’s n) was manipulated as a proxy because MIKE11 does not contain a method to represent such temporalchanges in instream vegetation explicitly. Weed cuts on1/5/2013, 16/7/2013, 21/5/2014 and 23/7/2014 signifiedrapid decreases in channel bed roughness, whichotherwise fluctuated in response to the growing season.A 15-min series of Manning’s n values was derived frommeasurements of cross-sectional geometry and stage at L1(Figure 1), energy slope between stage boards at L1 andL2 and the derived 15-min discharge at L1. This wasapplied as a multiplication factor to a fixed channelroughness at each time step.Meteorological data were supplied by the automatic
weather station installed in the south meadow. Thisprovided 15-min precipitation and potential ET calculatedusing the Penman–Monteith formula (Monteith, 1965).MIKE SHE calculates actual ET from these specifiedpotential rates and computed soil moisture in the rootzone using the Kristensen and Jensen (1975) method.
Calibration and validation
A split-sample approach was used to calibrate andvalidate the model. Based on data availability, the periods1/2/2013–1/12/2013 and 1/12/2013–1/10/2014 were usedfor calibration and validation, respectively. Calibrationand validation were based on comparison betweenobserved and simulated GW heads for the network ofpiezometers in the wetland as well as channel stage fromthe stage boards installed within the Lambourn andWestbrook.In accordance with the literature (Refsgaard and
Storm, 1995), the number of calibration parameters wasminimized. Parameters adjusted during calibration in-cluded effective saturation and infiltration rate in theunsaturated zone, the vertical and horizontal hydraulic
451MODELLING GW/SW INTERACTION IN A CHALK VALLEY WETLAND
conductivity of the peat and gravel in the saturated zone,and Manning’s n roughness coefficient for overland flow.An automatic multiple-objective calibration was per-
formed based on the shuffled complex evolution method(Duan et al., 1992; Madsen, 2000, 2003). Equally weightedmodel performance statistics, the root mean square error(RMSE) and absolute value of the average error, wereaggregated into a single-objective function with a trans-formation that compensates for differences in magnitudes(Madsen, 2003). This provided the autocalibration routinewith a measure of convergence, evaluated at the modeltime step.Manual adjustment of calibration parameters to further
improve model performance was assessed using thePearson correlation coefficient (R), the Nash–Sutcliffecoefficient (R2) (Nash and Sutcliffe, 1970) and the RMSEof the deviation between observed and simulated GW andchannel water levels. A scheme adapted from Henriksenet al. (2008) was used to classify model performancebased on the values of these statistics.
RESULTS
Model calibration and validation
Final values for the seven calibration parametersindicate values of horizontal and vertical hydraulicconductivity of the peat are consistent with the scaledependence of peat hydraulic conductivity reported byBromley et al. (2004) (Table I). Higher values areobtained with increasing volumes owing to preferentialflow routes provided by features such as root holes andabandoned infilled ditches. Gravel hydraulic conductivityvalues are supported by similar measurements fromsuperficial deposits throughout the Thames basin (Brickerand Bloomfield, 2014). Model performance statistics forthe calibration period for all 20 piezometers and the tenstage boards show that, according to the classificationscheme, model performance is generally ‘very good’ to‘excellent’. Mean values for RMSE, R and R2 are 0.063m(very good), 0.92 (excellent) and 0.75 (very good),
respectively (Table II). Model results are notably betterfor the gravel GW heads compared with those in the peat.Out of 30 values for the gravel (10 piezometers×3statistics), 23 are classified as excellent with theremainder classed as very good. In contrast, 15 of the30 values for the peat piezometers are classed as excellentand 10 as very good. Four of the remaining five values areclassed as ‘fair’, whilst the R2 value for 2P (0.35) is‘poor’ (although the RMSE and R values are very goodand excellent, respectively). Model performance forchannel stage is predominantly very good (16 out of 30values) followed by excellent (10 values) although threeR2 values and one RMSE value are classified as only fair.
Table I. Calibrated parameter values
ParameterCalibratedvalue
Unsaturated zone effective saturation 0.93Unsaturated zone infiltration rate (ms!1) 2.30× 10!5
Manning’s n coefficient for overland flow (sm!1/3) 0.03
Table II. Model performance statistics for head elevations in peat (P)and gravel (G) piezometers and River Lambourn (L) and Westbrook(W) stage over the calibration (1/2/2013–1/12/2013) and validation
(1/12/2013–1/10/2014) periods
Observationsites
Calibration Validation
RMSE(m) R R2
RMSE(m) R R2
1G 0.020a 0.99a 0.98a 0.057b 0.98a 0.86a
1P 0.055b 0.98a 0.78b 0.037a 0.97a 0.92a
2G 0.026a 0.99a 0.97a 0.075b 0.97a 0.75b
2P 0.082b 0.87a 0.35d 0.059b 0.96a 0.67b
3G 0.067b 0.98a 0.78b 0.080b 0.98a 0.66b
3P 0.047a 0.96a 0.86a 0.070b 0.93a 0.57c
4G 0.047a 0.98a 0.90a 0.030a 0.98a 0.96a
4P 0.122c 0.92a 0.57c 0.106c 0.92a 0.70b
5G 0.059b 0.91a 0.82b 0.094b 0.87a 0.66b
5P 0.069b 0.86a 0.74b 0.099b 0.76b 0.46d
6G 0.076b 0.95a 0.74b 0.072b 0.92a 0.76b
6P 0.100b 0.92a 0.61c 0.082b 0.85a 0.70b
7P 0.095b 0.81b 0.63c 0.093b 0.85a 0.68b
8G 0.034a 0.99a 0.91a 0.079b 0.98a 0.82b
8P 0.029a 0.94a 0.80b 0.058b 0.89a 0.31d
9G 0.021a 0.99a 0.97a 0.056b 0.99a 0.85a
9P 0.034a 0.99a 0.88a 0.059b 0.96a 0.72b
10P 0.035a 0.98a 0.74b 0.034a 0.96a 0.87a
11G 0.036a 0.99a 0.89a 0.027a 0.99a 0.97a
12G 0.041a 0.97a 0.84b 0.088b 0.89a 0.72b
L1 0.078b 0.87a 0.71b 0.052b 0.97a 0.85a
L2 0.035a 0.97a 0.91a 0.027a 0.98a 0.92a
L3 0.086b 0.84b 0.69b 0.048a 0.97a 0.87a
L4 0.082b 0.84b 0.68b 0.062b 0.97a 0.79b
L5 0.072b 0.86a 0.72b 0.049a 0.94a 0.86a
L6 0.075b 0.88a 0.64c 0.061b 0.98a 0.85a
L7 0.064b 0.88a 0.73b 0.032a 0.98a 0.96a
W1 0.078b 0.85a 0.67b 0.047a 0.97a 0.86a
W2 0.110c 0.87a 0.63c 0.101c 0.87a 0.73b
W3 0.086b 0.93a 0.63c 0.069b 0.98a 0.81b
Model performance indicators are adapted from Henriksen et al. (2008).RMSE, root mean square error. Performance indicators are as follows:a Excellent: RMSE< 0.05, R> 0.85, R2> 0.85.b Very good: RMSE = 0.10–0.05, R = 0.65–0.85, R2 = 0.65–0.85.c Fair: RMSE = 0.15–0.10, R = 0.50–0.65, R2 = 0.50–0.65.d Poor: RMSE = 0.20–0.15, R = 0.20–0.50, R2 = 0.20–0.50.e Very poor: RMSE> 0.20, R< 0.20, R2< 0.20.
Performance for the validation period is in general verysimilar to that of the calibration period (Table II). Themean values for RMSE, R and R2 are 0.063m (verygood), 0.94 (excellent) and 0.77 (very good), respectively(Table II). For the gravel piezometers, 16 of the statisticsare classified as excellent with the remainder being verygood. Slightly more of the statistics have a higher valuefor the calibration period than for the validation (16:10with four unchanged). For the peat piezometers, an equalnumber (13) of the statistics are classed as excellent orvery good with two each being classified as fair or poor.Performance as indicated by these statistics is improvedfor the validation period for 12 and reduced for 17, but ingeneral changes are small in magnitude (with one valueremaining the same). The previous classification of R2 aspoor for P2 is replaced by very good, whilst the samestatistics for 5P and 8P, which for the calibration periodwere classed as very good, are now poor. A markedimprovement in the model’s ability to simulate channelstage for the validation period is evident with 19 of the30 statistics having higher values for later periods (tenlower and one unchanged). Performance is predominant-ly classified as excellent (22 statistics). With the
exception of RMSE for W2 (fair), the others areclassified as very good.The generally excellent or very good performance of
the model in terms of reproducing the observed gravelGW head elevations are shown throughout both thecalibration and validation periods (Figure 4). Thesimulated heads clearly display the seasonal rise and fallobserved throughout the site as well as the impacts ofindividual rain events. In addition, the effects of the weedcuts in the form of the subsequent rapid declines in GWhead are clearly simulated by the model, suggesting goodrepresentation of the exchange between the river and theunderlying gravels. Some overprediction towards the endof the validation period is noticeable at 1G, 2G and 5G(Figure 4). GW heads at some locations with upwelling(8G, 9G and 12G) are underpredicted during the period ofhigh head elevation at the beginning of 2014 although at11G this overestimate of head elevation is not apparent.Weaker performance is apparent at 3G and 6G, withunderprediction notable during periods of high head.As noted previously, model performance for the peat
GW head elevations is inferior to the gravels (Figure 5).The impacts of many of the individual rain events are
Figure 4. Observed and simulated groundwater head elevations (mAOD) in gravel (G) piezometers at locations 1–6, 8, 9, 11 and 12
453MODELLING GW/SW INTERACTION IN A CHALK VALLEY WETLAND
simulated as are the rapid declines in level associated withthe weed cuts in the River Lambourn. Model performancetends to be better at low head elevations. Nonetheless,observed peat GW head at locations P1–P7 shows sharphead increases throughout these periods of low elevationsthat, although evident, are of smaller magnitude in themodel results. Relatively weak performance is noticeableat 4P and 5P throughout both the calibration andvalidation periods, and at 8P in the latter. In contrast,despite the issues discussed earlier, relatively goodperformance is achieved at the other locations, especially1P, 2P, 9P and 10P, although there is a generaloverprediction during periods of high head.Observed and simulated channel stages correspond
well on the whole although, with the exception of L2,observations are not as frequent as those for gravel andpeat GW heads (Figure 6). At L2, generally goodagreement between observed and simulated river levelsin the Lambourn is obtained. Elsewhere, there is a generalunderprediction of stage during the validation periodalthough, as previously reported, the model performancestatistics are generally classified as very good to excellent.The weakest performance is for W2, especially through
the validation period, with the simulated water levelsoften falling well outside the observed stage. The modelclearly simulates the rapid drops in stage due to the fourweed cuts that drive the resulting declines reported atthese times in the peat and gravel whilst the increasesover the beginning of 2014 are also represented.
Water balance
The modelled monthly water balance is summarized sothat SW represents net outflow (channel and overlandoutflow minus inflow), whilst GW represents net inflow(GW inflow minus outflow) and baseflow the exchangebetween channel and gravels with negative valuessignifying loss to gravels (Table III). The degree towhich SW and GW dominate the water balance isapparent, with SW and GW comprising 44.2% and 43.4%of total flow (5849.4mm), respectively. Precipitation andET are of secondary importance in transferring water intoand out of the site and constitute 6.3% and 5.7% of totalflow, respectively. GW, SW, precipitation and ET are,perhaps unsurprisingly, interlinked, with associatedincreases and decreases in the first two terms reflecting
Figure 5. Observed and simulated groundwater head elevations (mAOD) in peat (P) piezometers at locations 1–10
changes in the balance between the second two. Forexample, the winter flood period from December 2013 toFebruary 2014 is marked by the increase in water withinGW and SW, with both peaking in January 2014, themonth with the largest precipitation input. Althoughstorage components are small annually (changes in SWand GW storage), there is obvious temporal variability,with monthly storage changes often as significant asprecipitation and ET. Increases in ET over the spring andsummer periods, when precipitation is relatively low,correspond to periods of reduced water storage.Selected principal components of the water balance
over the full simulation period, with GW split by verticaldirection and geological layer, are shown in Figure 7. Thecorrespondence between increases in SW flux and rainfallevents is clear. In addition, the influence of the weed cutson surface fluxes is clearly demonstrated. The highestpeaks occur at the time of the weed cuts in 2013 whenrainfall inputs are low or absent and signify the flush ofSW within the floodplain to channels and in turn to theriver in response to the fall in channel stage.Upward flows of GW between layers are consistently
higher than downward flows (Figure 7). The latter are
generally in line with SW flux, although peak responses aremuted in the gravels. However, in the month before each ofthe weed cuts in 2013, downward flows increase gradually.These increases are mirrored by marked decreases inupward flows, especially between the chalk and gravels.Upward exchanges from the gravels increase sharply withrainfall events and the weed cuts yet follow the generalpattern of upward chalk GW fluxes. Flow upwards fromthe chalk displays an inverse pattern, with rapid increasesduring and immediately after weed cuts, yet decreases inassociation with rainfall events. Post-weed cut flows fromthe gravels to the peat are maintained at a higher level,corresponding to an increased upward flow from the chalk.However, the 23/7/2014 weed cut has comparativelyminimal impact. Aside from the conspicuous human-induced effects due to the weed cuts, strong seasonality isnoticeable, with volumes of GW exchanges gradingbetween winter wet periods and summer dry spells.
Surface water flooding and groundwater upwelling
The extent and depth of simulated SW flooding atperiods of high flow correspond closely with topography
Figure 6. Observed and simulated channel stages for the River Lambourn (L) and Westbrook (W)
455MODELLING GW/SW INTERACTION IN A CHALK VALLEY WETLAND
(Figure 3). Shallow relic channels from the historicalwater meadow system are apparent as areas of relativelydeep flooding. Elsewhere, much of the flooding appearslinked to the main channel system. This is especially thecase for areas adjacent to the Westbrook as it flowsthrough the centre of the site and towards the RiverLambourn in the south-east. However, in the northmeadow and south-east section of the south meadow,areas of flooding not directly linked to the main channelsystem still occur.Simulated gravel and peat head gradients show an
overall resemblance evident in both wet and dry periods(Figure 8). GW mounding can be seen to occur in boththe northern meadow and the northern part of the southmeadow around the Westbrook. A discrete area ofparticularly high GW head is simulated towards the northof the site. These elevated heads in the north meadow areconcomitant with locations where putty chalk is absent atthe interface between the chalk and gravel aquifers(Figure 1). There are additional areas of higher head andhence steeper local head gradients to the centre east whichare especially noticeable in the gravels (Figure 8a and b).Small-scale head variations in the gravels in line with theLambourn are evident during the high peak (Figure 8b).Gradients in the peat appear influenced by the topographyof the relic drainage network during this peak (Figure 8d),yet less so at low levels (Figure 8c). In general, headelevations follow the topographic gradient in line with the
valley at peak elevations (Figure 8b and d). However,there is a shift in the direction of GW flow from towardsthe south to the south-west as head elevations drop(Figure 8a and c).
DISCUSSION
Model performance
Although the model generally simulates conditionsvery well across the River Lambourn Observatory, thereare clearly spatial and temporal disparities in modelperformance. The superior representation of water levelsin particular areas and within the different geologicallayers highlights the influence of heterogeneity instructure and process at the site scale. Such results alsounderline the importance of robust field survey andmonitoring approaches at spatial resolutions that aresufficient to incorporate this heterogeneity.Model performance is inferior within the peats. This is
especially true when water levels are high and could bedue to the inability of MIKE SHE to representcompressible, anisotropic soils, instead defining thehydraulic properties of each geological unit as beingtemporally and spatially constant. Indeed, incorporationof the effects of soil deformation into hydrologicalmodels has, with a few exceptions (Camporese et al.,2006), been generally overlooked. However, peat
Table III. Simulated monthly and total water balance for the Centre for Ecology and Hydrology River Lambourn Observatory
All values in millimetres.P, precipitation; ET, evapotranspiration; I, interception storage; SW, net surface water outflow; SWS, change in surface water storage; GW, netgroundwater inflow; GWS, change in groundwater storage; B, baseflow.
hydraulic conductivities may vary over relatively shortdistances by several orders of magnitude (Kneale, 1987;Bragg, 1991; Bromley et al., 2004), seasonally by up toan order of magnitude (Price, 2003; Kettridge et al.,2013), and with depth by several orders of magnitude(Clymo, 2004; Baird et al., 2008). The effectiveness ofapplying rigid soil theory to peat soils has therefore beenquestioned (Brown and Ingram, 1988; Baird and Gaffney,1994). Price (2003) found that saturated hydraulicconductivity was highly correlated to water table depth,with increases of up to two orders of magnitude observedafter a 0.5-m rise in water table elevation. Peatliquefaction with saturation and the associated increasein hydraulic conductivity could explain the overestima-tion of head in the MIKE SHE model when waterelevations are high, particularly in areas of GW upwelling(8P–10P).Occurrences of silt, sand and gravel within the peat
(Allen et al., 2010) could also contribute to localvariations in hydraulic conductivity. The presence of
these small-scale variations in substrate characteristics isdifficult to establish in the field yet at the applied modelgrid resolution could have a significant impact onsimulated GW flow and levels. Variations in the alluvialcomposition may account for the poorer performance incertain areas, for example, at 5P. Additionally, the peatpiezometers of the pre-existing array (1P–7P) wereinstalled with the slotted screen extending above groundlevel. The sharp observed responses in head duringperiods of low water levels may be a reflection of directinflux of water from the surface during rain events. Theexaggerated plateau of high head elevation at 4P may thusbe due to SW above the open level of the piezometer. Incontrast, at locations 8P–10P, where bentonite was usedto seal new piezometers with closed screens above groundlevel, event peak head elevations match well. Theseinstrumental issues could therefore produce misleadingresults, where otherwise the model could be performingeffectively. The influx of SW into piezometers, though,has not been directly observed and may otherwise be a
Figure 7. Detailed components of the water balance: (a) precipitation, (b) surface water outflow, (c) downward groundwater flow between geologicallayers and (d) upward groundwater flow between geological layers
457MODELLING GW/SW INTERACTION IN A CHALK VALLEY WETLAND
result of substrate variations and peat compressibility.Differences in magnitude between observed and simulatedevent peaks vary noticeably by location, whilst thetimings match well. Hence, measurements from thesepiezometers were not excluded from this study. Gravelhead elevations are generally simulated very well by the
model. Where deviations occur, they fall into two groups:locations where the model underestimates levels (3G,6G–9G and 12G) and locations where levels areoverestimated towards the end of the simulation period(1G, 2G and 5G). The ERT survey revealed significantbraided structures in the gravels (Chambers et al., 2014).
Figure 8. Head elevations in (a) gravels at the lowest head elevation (12/12/2013), (b) gravels at the highest head elevation (15/2/2014), (c) peat at thelowest head elevation (12/12/2013) and (d) peat at the highest head elevation (12/2/2014)
These suggest large differences in gravel porosity acrossthe site, which would cause localized and depth-dependent variations in hydraulic conductivity, as couldquantities of reworked chalk in lower levels (Allen et al.,2010). Although the features could help explain theunderestimation at high heads, the overpredicted gravelhead elevations are more problematic and may be due toinadequacies in the boundary conditions.Discrepancies between simulated and observed channel
stages may in part be due to discrete changes in channelbed roughness. The growth and distribution of instreamvegetation is affected by many factors, amongst whichchannel morphology, bed material and adjacent condi-tions will contribute. Localized effects of macrophytegrowth on bed roughness are not accounted for within themodel; instead, a uniform resistance factor is appliedthroughout the MIKE 11 river model. An unmonitoredsluice gate located just upstream of W2 could additionallyaccount for the poor representation of channel stage atthis location. Local residents adjust the control structurein order to maintain the aesthetics of a pool feature, andthe times when the sluice is open or closed areunfortunately not recorded.
Groundwater/surface water interaction
Surface water and GW are inextricably linked andcrucial to processes in the wetland. The channels, gravelsand peat are hydraulically connected. Model results showthat gravel waters provide a significant contribution to thesite, supporting earlier findings of research in theLambourn (Grapes et al., 2006; Abesser et al., 2008).Channel stage acts as a head boundary and controls broadwater levels within both the gravels and peats. It alsoinfluences responses in GW flow from the chalk aquifer.Chalk GW is an important source of water into theLambourn Observatory, discharging into the gravelaquifer and wetland through gaps in the putty chalk andresulting in locally elevated heads. Rapid reductions inhead from weed cutting in the River Lambourn drawwater up from the chalk, increasing the rate of upwardGW flow. Conversely, increased stage resulting fromstorm events raises head elevations within the gravels andpeats, inhibiting upwelling from the chalk GW. Theinflux of SW into the gravels at high stage drives theincreases in gravel head.The longer-term trend of SW outflow follows the
seasonal pattern of GW inflow. When heads in the chalkare high, so are levels in all components of the system.This reflects larger-scale catchment processes and theposition of the site in a chalk valley bottom with a GW-fed river. Surface flooding is a combination of seepagefrom upwelling GW and overbank flow routed from thechannels by the relic drainage network. The simulated
areas of GW mounding in the north meadow andassociated flooding support earlier findings in the field(House et al., 2015). To the east, steeper head gradientscorrespond with the mouth of a dry valley. However, thecause of the high heads around the Westbrook is lessclear. The area does, however, fall beyond the extents ofthe detailed topographical and geological surveys, withaccess limited by dense vegetation. It is difficult to assesswhether the results are from a real or interpolated featureand highlight the importance of high-resolution field data.
Management implications
Results of the field monitoring programme, which arereplicated by the model results, demonstrate that instreamweed cutting has profound effects upon the wetland’shydrological processes. Wetland plant species andcommunities have preferences to certain water levelsand depths to GW (Elkington et al., 1991; Newbold andMountford, 1997; Gowing et al., 2002; Wheeler andBrooks, 2004; Wheeler et al., 2009). The species-poorswamps prevalent at the Lambourn Observatory reflectthe duration and magnitude of drops in GW levels fromweed cutting (Old et al., 2014). However, the unaccount-ed effects of GW upwelling in locally raising heads andmaintaining areas of standing water may be vital to thepromotion of certain species.The degree to which water sources interact will affect
plant species distribution through the available nutrientbudget. Previous hydrochemical analysis has shown thatchalk GW upwelling into the peat contains highconcentrations of NO3 and SO4 and low P concentrations(House et al., 2015). Elsewhere, the peat containsreducing waters low in NO3 and SO4, yet high in P.These different chemical environments were found topromote distinct plant species. High concentrations ofnitrate supported localized growth of greater tussocksedge (Carex paniculata) within surrounding fen com-munities in higher phosphate waters. Artificial loweringof water levels will promote aerobic conditions within thepeat, whilst increases in GW contributions will causefurther changes in the chemical environment. Suchchanges could have localized ecological effects that arenot accounted for by generalized site managementpractices. Aerobic conditions can also lead to peatoxidization and release of CO2, whereas wet peat maygenerate methane; hence, soil water levels are thereforeimportant for managing greenhouse gas emissions fromwetlands (Acreman et al., 2011).The meadows of the Lambourn Observatory once
supported breeding snipe (Gallinago gallinago) (Everard,2005), but these are no longer present, and a generaldecline in the UK and Europe has been linked to losses inlowland wet grassland (Ausden et al., 2001). The species
459MODELLING GW/SW INTERACTION IN A CHALK VALLEY WETLAND
breeds in wet areas where the softer ground allows it toeasily forage for food (Smart et al., 2008). Waterloggedfeatures support a higher biomass of surface-active andaerial invertebrates (Plum, 2005; Eglington et al., 2010),and the wading birds that feed on them (Milsom et al.,2000; Ausden et al., 2001; Milsom et al., 2002).Consistently high GW levels therefore benefit snipe andother wading birds and are also considered essential forDesmoulin’s whorl snail, the species that contributes tothe site’s scientific and nature conservation status(Tattersfield and McInnes, 2003). Site hydrology is aprincipal factor in determining the distribution of thesnail, with optimum conditions found where water levelsremain above ground level year-round. A surveyundertaken in 2012 showed a reduced presence of thesnail, suggesting a gradual decline since the 1990s(Natural England, 2012). The roles of GW upwelling,channel stage and the relic surface drainage network inthe distribution of water levels are thus importantconsiderations for conservation management.
CONCLUSIONS
The potential of MIKE SHE to model wetlands with acomplex subsurface architecture has been demonstrated forthe River Lambourn Observatory, a lowland riparianwetland in a chalk valley bottom of south-east UK. Findingssupport a conceptual model, with hydrological processes inthe wetland dominated by the interaction between GW andSW. Channel stage provides head boundaries for broadwater levels across the wetland, whilst areas of upwellingfrom the chalk aquifer control discrete head elevations. Arelic surface drainage network confines flooding extents androutes seepage to the main channels.Model performance is generally very good. Results are
consistent with field observations and follow short-termresponses to hydrological and management events, aswell as the longer-term seasonal trend. The impact ofinstream weed cutting is well represented and affectswater levels throughout the site. The interaction betweenSW and GW is also markedly affected by weed cutting.This influences head variations across the wetland, theproportional contribution of each water source and themaintenance of areas of standing water. The waterbalance will dictate species composition and distributionthrough the controlling influences of water levels and thenutrient budget. The findings demonstrate the necessity toconsider not only SW but also GW in site managementschemes. Indeed, separation of the terms is counterpro-ductive in such applications. This is especially importantwhen balancing the promotion of desired species and theirassociated habitats for conservation with productivity andflood risk mitigation.
The MIKE SHE model of the Lambourn Observatorymay be used to investigate the hydrological effects ofenvironmental changes, whether from alterations toclimate, GW abstraction, channel morphology or vegeta-tion management. Representative boundary conditionscould be obtained through links to existing regional GWmodels (e.g. Jackson et al., 2011), although differences inmodel grid resolution would need to be addressed.Further application of ecological indices to model
results will allow assessment of the ecological sensitivityof the wetland to environmental changes (e.g. Thompsonet al., 2009). Specific water-level requirements of plantsand animals in the wetland, and environmental flows inthe channels, could be linked to species maintenance orsuccession. The addition of a nutrient or contaminanttransport module would further aid impact assessment forparticular species and communities. The MIKE SHEmodel of the Lambourn Observatory therefore representsan essential tool for understanding the wetland ecosystemand its response to change and for developing manage-ment approaches.
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