Climate Change Impact on Groundwater Levels: Ensemble Modelling of Extreme Values J

Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2 ` a Sciences Discussions Earth System Hydrology and ) are estimated. Three , and M. M. Escriv 1 100 − 21 T , L. P. Seaby 1 7836 7835 , L. Troldborg 1 , J. C. Refsgaard 1 erent methods. An integrated surface water/groundwater model is forced with pre- ff This discussion paper is/has been under review for the journal Hydrology and Earth System Sciences (HESS). Please refer to the corresponding final paper in HESS if available. Department of Hydrology, Geological Survey of Denmark and Greenland, Danish Road Directorate, Skanderborg, Denmark necessary either by dynamical downscalingstatistical to downscaling. Regional The Climate Models inherent (RCMs) uncertainty or in by the climate models (CMs) should Climate change adaptationof is infrastructure a development. more Infrastructuresto and such withstand extreme as more hydrological roads events. recognized Hydrological arebeen extreme component estimated events designed from have in commonly to historical data, planning be butestimates able the of evidence future of climatic a conditions changingtemperature climate should and implies be precipitation that used can be instead. generated Estimatesgrid by for Global resolutions the Climate of future Models typically (GCMs) 200 with in km. hydrological This models resolution (Fowler is et too al., coarse 2007), for thus further downscaling application to a more local scale is lower return periods. Uncertaintyto from around the 10 % extreme of value the statistics uncertainty only from the contribute three up sources. 1 Introduction di cipitation, temperature, and evapotranspiration fromcombinations. Extreme the value 18 analyses modelfrom are – performed a and on downscaling control the perioddraulic hydraulic (1991–2010) heads head to changes for the returnuncertainty future periods sources period of are for 21,statistics. evaluated; the Of 50 climate 18 these and sources, models, combinations. downscaling 100 downscaling yrand Hy- dominates 100 for and ( yr, the whereas extreme higher uncertainty return value periods from of climate 50 models and downscaling are similar for This paper presentsing a extreme first value attempt statisticsfuture on to period, predictions estimate from 2081–2100, future a arethree groundwater hydrological global represented model. climate levels by models Climate by projections and for six apply- from the regional nine climate combinations models, and of downscaled with two Abstract Hydrol. Earth Syst. Sci. Discuss.,www.hydrol-earth-syst-sci-discuss.net/9/7835/2012/ 9, 7835–7875, 2012 doi:10.5194/hessd-9-7835-2012 © Author(s) 2012. CC Attribution 3.0 License. Climate change impact on groundwater levels: ensemble modelling of extreme values J. Kidmose 1 Copenhagen, Denmark 2 Received: 3 June 2012 – Accepted: 4Correspondence June to: 2012 J. – Kidmose Published: ([email protected]) 25 June 2012 Published by Copernicus Publications on behalf of the European Geosciences Union. 5 20 25 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | et al., 2002; Scibek ff -year hydraulic heads for T 7838 7837 ) from four projections of future climate at a location 1 − ). The tool was developed to a building construction project at system in Toronto, Canada, Guo and Adams (1998a) compared cient for a given rain event, e.g. a 5 or 10 yr event. For exam- T 25 mm day ffi ff > return periods for an analytical expression, based on exponential ff erent drought indices were estimated with an above-threshold concept using a Gen- The lack of EVA for climate change studies of groundwater systems is concordant The use and concept of Extreme Value Analysis (EVA) are well known within the ff with the relatively fewflooding groundwater events. studies The describing area unusuallyflooding of high events groundwater in or flooding the groundwater received winter–springern increasing 2000–2001 Europe attention from (e.g. after chalk-aquifersand Tinch in Jackson, UK et 2011; and al., Hughes, North- flooding 2004; 2011) in but Pinault very a et few frequencygroundwater al., studies analysis flood have 2005; context. frequency dealt Morris One analysis witha et study method groundwater given (Najib al., to return et estimate period 2007; ( al., Upton 2008) developed a di eralised Pareto distribution. Sunyer etprecipitation al. events ( (2011) compared thejust distribution north of of extreme Copenhagen,tation, Denmark, 1979–2007. with distributions derived from observed precipi- methodology was used and spatialthresholds contour produced, maps for based return on periodsused data for from EVA the 36 wet statistics and rain to dry tensity gauges. develop Palynchuk with design and time, storms, Guo which (2008) rainfall, standardized conventional or is distribution storm developed of event from analysis, rainfall whereability depth in- actual duration density rainstorms frequencies functions. are of fitted EVABurke to has appropriate et also prob- al. been (2010)jections used applied of in EVA future climate to change precipitation calculate impact and drought studies. an indices observed for baseline UK, period. based Return on periods pro- for the Storm Water Management Modelriods (SWMM). In for Guo peak and discharge AdamsBordi (1998b), rates et return from al. pe- (2007) the used analytical theof Generalized extreme model Pareto values distribution and for for wet SWMM analysing anda return was dry periods Standardized compared. periods at Precipitation Sicily Index using precipitation for observations wetness and and dryness. A peak over threshold probability density functions of rainfall event characteristics, with return periods from volumes of runo climate model projections which are;Opposite (i) to water climate resources models assessment, andhighly analyses (ii) of relevant downscaling groundwater for methods. head roads extremesflooding in are and contact drainage or issues close can compromise to the groundwater use tables of sincehydrological the groundwater road. sciences. Thewithstand design or of handle urban andrainage extreme drainage water rain systems event, is are which su ple, means often at that an planned the urban to runo capacity for routing 2009). The general interest ofof these groundwater studies recharge is and waterare responding resources, adequate. groundwater where To levels the quantifications at knowledgeextreme seasonal of values time of the scales groundwater authors, heads noof future under reported groundwater future studies head climatic have extremes conditions. would focused The inherit on key estimates sources of uncertainty from the and therefore the response of thethe uncertainty hydrological for model these predictions. parameters Oneapproach should be way with shown to multiple in do climate this2009; change is Deque models via (e.g. and a Tebaldi Somot,related probabilistic et to 2010; modelling al., subsurface Sunyer 2005; water Smith has etrecent been et al., review considered al., 2011). by in around Allen The 200tions et impact studies with al. according of a (2011). to climate physically Only a basedand change a groundwater Allen, few flow 2006; of model van these (e.g. Roosmalen simulate Yoso et groundwater al., condi- 2007; Candela et al., 2009; Toews and Allen, drological climate change studies (Allenysed et al., the 2010). uncertainty Hawkins and cascadeThey Sutton for (2011) concluded anal- that projections relative of toclimate precipitation variability scenario and from uncertainty climate a model from uncertaintycentury. GCM emission For dominated, ensemble. scenarios, hydrological even at models, natural the precipitation end and of temperature the are 21st driving parameters carefully be considered because this is possibly the largest source of uncertainty in hy- 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ˚ 1 a − from , with C and 1 ff ◦ − ˚ a River Valley with max. monthly 1 − , respectively (Scharling 1 − ˚ a River at Silkeborg. In the ˚ a River tributaries form natural hy- . Highest precipitation is found at the 1 − and min. amount in April of 50 mm month 7840 7839 1 − ˚ a River system were used as drainage for the melting ˚ a River system flows through the city with a topography ranging from Below the Quaternary sediments, Oligocene and Miocene mica-clay, mica-sand and Toward northwest, north and east, smaller Guden The objectives of this study are to: (1) investigate the impacts of climate change et al., 2000). Average monthly temperature peaked in July and August with 15.2 lantic and the European continent. Atcoastal the Jutlandien to peninsula, in precipitation land varies from areasNorth-South with trending topographical around ridge 200 mm just yr tion vest at of Silkeborg. Silkeborg The during averagevalues the precipita- in period of November 1961–1990 of(Scharling was 101 mm et 903 month mm al., yr 2000,1998). with Average potential correction evapotranspiration factors for the frommax. same B and period min. type was in 546 shelter July mm yr and from December Allerup of 100 et and al., 4 mm month and eroded about2009). 75 m The valleys into are possibly pre-quaternary backfilled with sediments re-deposited (Jørgensen Miocene2.2 sediments. and Sandersen, Hydrology The humid climate in Denmark is dominated by the weather systems of the North At- sediments). The terrace sand wereeast deposited of Silkeborg when and the the glacialice Guden to front the had Limfjord withdrawn and to laterA on geological to cross Kattegat section with is connection to shown the in North Fig. Atlantic 2. (Fig.quartz-sand 1). are found. These sedimentsEocene are marls observed are down found. toincluded In 80–100 in m the the b.s.l. eastern where geological model. part The of buried the valleys are modelled 6–8 area, km buried long, up valleys to are 1 km wide, confined aquifer with thicknesses up todeposited 50 m. during This retreat hydrogeological sand of unitiment, former is mostly properly ice in sheets, the althoughglacial lower till it part, and is sand evidencing perturbed are avalley, at not by more observed least clayey complex in 3 sed- the depositional erosional valley history. levels of and The Guden fluviatile sandy sediments are observed (terrace 18 K yr BP. Below this, coarser glacial sediments of sand and gravel form an upper un- eas. Thicknesses of these areWeichelian up glaciers, to when 35 m the and mostly main formed advance as was lodgement tills located below west of Silkeborg before crosses the river valleystructed at 6 the m location below of topography,The with the groundwater a city, level concrete and of bottom therefore theto and the shallow the vertical terrace road motorway. sheet aquifer level in piling is the walls. con- river valley is2.1 critically near Hydrogeology The near surface geology at Silkeborg is dominated by clayey tills in the upland ar- River, in a part of Silkeborg, where a new motorwaydrogeological is boundaries. planned. Toward westdivide a for topographical the upper heightdelineates hydrogeological forms the units a hydrological and groundwater model toward referred south to the as Guden the Silkeborg model. The motorway 2 Study area The study area is located at(Fig. the 1). city The of Silkeborg area inthe is the glacial central dominated retreat part of by of the deeply Jutland, Northsubsequent Denmark incised Guden East and valleys the formed Baltic during ice20 sheets melt to 16 95 o 000–18 m 000 a.s.l. yr (meters ago. The above sea level). The area in focus is just north of the Guden rise and thereby flooding. on extreme groundwater levels inuncertainty relation of to infrastructure extreme design;uncertainties groundwater and on level the (2) future estimates assess climate. the considering the key sources of a karstic aquifer in Southern France where heavy rainfall induced a groundwater table 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and h 100 m × ) with the Q , Figs. 3 and 4. 2 erent. The southern ff relation was established. Lake h / Q C (Scharling et al., 2000). These conditions ◦ 7842 7841 500 m numerical grid discretisation. The model 0.3 − × , 1990–1999 was also used to design an objective h ). During the latest model update (2005–2009), the voxel . Performance criteria and calibration parameters are sum- with a 500 Q 2 xyz ) observations and 66 time series of river discharge ( and h ect on surface and sub-surface hydrology by climate change (van h 10 m ( ff × 1000 × The model is bounded by the North See and Kattegat towards West and East, re- ) beyond periods with observations (1990–1995) a , mean error on h time-variant specified head with daily( time steps. In order to estimate lake water stage the coupled MikeShe –numerical Mike11 framework. grid The with model 3 wasThe vertical set top up layers most with and layer,clay a a layer in 100 model 1, the domain higher follows offor elevated the separate 103 areas. km terrace geological This and sand is numericalgeological in possible models, model. the because Layer with 2, the the river follows parameterization MikeShe theiments. valleys following glacial Boundary code and the sand conditions allows and glacial for layer theboundary 3 three the at numerical pre-quaternary layer layers sed- 1 are is di a lake, Silkeborg Langsø (A–B, Fig. 4) and was simulated as a release version can be found in Henriksen et al.3.1.2 (2003) and Højbjerg Local et model al. (2010). The geology illustrated inter Fig. model 2 was at used Silkeborg. for As the with local groundwater the and regional surface model, wa- the model was developed with spectively. Toward North and South theboundaries. model The is model bounded by wasgroundwater topographical calibrated head catchment transient ( forautomated the period parameter 2000–2003 optimiser with PESTservations, 2592 observations ver. of 11.8 mean (Doherty,function 2010). with 8 Besides weighted these criteriaQ ob- representing, water balance,marized transient error in on Table 1. Further detail on the DK-model and the calibration of the latest units. Geology was initially interpreted inof a 1000 voxel (volume pixel) framework withmodel cell was size superimposed by localmodel geological (Højbjerg models et based al., on 2010). the hydro-stratigraphic routing, and river flow. Numerical layering follows a hydro-stratigraphic model with 11 flow, evapotranspiration, flow in the unsaturated zone, the saturated zone with drainage 3.1 Hydrological models 3.1.1 Regional model The DK-model consistsland. of Figure 7 3 subareas showsmodel with the covers DK-model area 12 501 area km 5 5,is covering further setup the with referred the to middle MikeShe as part code the coupled of DK-model. with The Jut- the Mike11 code and describes overland the MikeShe code (Abbottto et evaluate al., the 1986; e Roosmalen Refsgaard et and al., Storm, 2007; 1995)National van has Water Roosmalen Resources been et Model, used was al., also used 2009; called to Stoll produce the BC’s et DK-model for al., a (Henriksen local 2011). et model The al., at Danish Silkeborg. 2003) The study applies EVA onthe model period predictions of of 2081 future to groundwaterthe 2100. levels planned representing The levels motorway are from extractedrepresenting an at a historic a integrated baseline groundwater-sensitive groundwater period part surface (1991–2010)are of and water compared. the Estimates model. future of period Results (2081–2100) groundwaterapproach, levels where are a large produced regional with modelfor a is a nested used local modelling to calculate model Boundaryruns at Conditions and (BC) Silkeborg. data Although processing,for this it the supplies approach the simulations doubles primary representing the the local number model future of with (Toews and model more Allan, realistic 2009). BC’s In recent studies result in recharge of groundwater aquifer during late autumn, winter and early3 spring. Methodology had a low in January and February of 5 5 25 15 20 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (1) 2 ) k ) from a , the root Q ff mean2 ME group receive Hobs × k w ( k 2 ) group receives the second m ff X ff + erent horizontal cell discretiza- 2 ff ) j MaxHDi MaxHDi means1 ˚ HTS a River toward east (G–A). The specified cient of 0.33 for the town area is derived in urbanized areas to streams. Paved areas × ffi ff 7844 7843 m Hobs w ( × m j w ect model predictions for several years. Groundwa- ( X j ff + mean1–3. The HTS 2 X ) l + 2 ) i mean3 ME HTS hobs × × i l w cient implies that one third of the precipitation for each time step is routed w ( ( i l ffi X X cient is used to describe direct runo = + ffi The area in focus is located in the City of Silkeborg and therefore a paved area Besides section C–D (Fig. 4), boundaries for layer 1 are smaller streams toward highest weight because a good fit ofObj the annual hydraulic head variation is sought. zone and unsaturated zone because ofcharge their or sensitivity water toward balance (non-available) related river data. dis- For the same reason, observation data could not highest weight. Furthermore, this groupbration only period contain opposite observations Hobs from the actual cali- Selection of calibration parametersdata. was It limited was by not the possible available to type calibrate of parameters observation describing surface runo ments for the periodweighted groups 2010–2011. considering The mean error objectiveon for function maximum daily hydraulic amplitude (Eq. head for 1) measurements,The hydraulic error was weighing heads, defined of and with groups errorhydraulic heads was 5 on in done mean the terrace heads according aquifer. Thus (1990–2010). to observations the in the modelling HTS purpose of predicting outside this. The modelup was and run 2005–2011 for thecause as period of calibration 1990–2011 large period. with groundwaterbecause The 1990–2005 extractions the warm in as initial the warm up early conditionster period 1990’s extraction a is in in relatively the theconsist long terrace terrace of be- aquifer aquifer a and has numbermeasurements been of from steady the head the Danish measurements last nationalor from borehole 15 a yr. two archive few Observation (Jupiter), measurement categorizes: often dating data (i) with from historical a 1990–2010. (ii) single head Time series of daily head measure- Calibration of the model focused on thewater critical zone flooding for the (Fig. motorway regardingPEST 2). ground- vers. 11.8 Optimization (Doherty, 2010). PEST of optimizationgraphical is model user not interface available parameters within and the was setup MikeShe of done PEST inversely and optimization with was therefore performed 3.1.3 Silkeborg model calibration local model receives head levels from the same 500 mcoe cell from the regional model. are illustrated in Fig.from an 1. estimate The that chosenwhereas one the coe third rest is of covered the byarea recreational town areas coe area (grass/forest). In is the model covereddirectly the by to paved pavement the or closest stream, buildings whereas the rest will be available for infiltration. are observed and sectionboundary F–B for is layer 3 thereforeDaily defined (B–F) head as is levels are a openvertically simulated no-flow for aligned by with BC. exchange layer the The via 3 regionaltion’s in remaining a between model the transient the local for model. model, specified which, The 500 head m one di and of BC. 100 the m, layers involve that is several boundary cells in the elevation model. Section C–D followsany a topographical connecting low stream. with The smallstream ponds section sections but probably and without is drains therefore towardterminates simulated the as toward southern a the no-flow or River BC.lustrated Valleys northern The surrounding in glacial Fig. the sand 2) in model and layer (e.g. thereforesediments 2 a at defining no-flow the BC layer valley is slope used 3groundwater for il- systems this crosses layer. in The the pre-quaternary areassouthern model with and boundary eastern coarse model and sediments, boundary, only interacts mica the and fine-grained with pre-quaternary quartz regional sediments sand. At the river discharge station 21.109Fig. (Resenbro, 4) Danish just downstream monitoring of programme, the location lake. A, west and north (B–C,head elevations D–G) used and to simulate at these Guden boundaries are adopted from a detailed digital stage observations were received from the Silkeborg municipality and flow ( 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ), T erent from erent dou- ff ff ), temperature ( , relies on a nation- P P erent methods for bias ff was catch corrected with P 10 km grid discretisation for pre- × 10 km size for ) as one order of magnitude higher. cients for the lake, the southern BC, v × K ffi 7846 7845 , and 10 p erence between these two gamma distributions E ff ), were obtained for the period 1991–2010 (base- p E and T ects of average climate factors such as groundwater recharge and ff 20 km size for × Delta Change (DC)method. – The DC key principle is is the that the simplest future and climate the is described most by common the historical downscaling precipitation projected for the futureues period projected 2081–2100 and for averagethe the January val- climate control models period aremonthly 1991–2010. not precipitation This used is implies directly, used.cussing only that on DC the results e is change from well inaverage groundwater proven projected heads and average (van well Roosmalen2011). et suited al., for 2007; studies van fo- Roosmalen etDistribution al., Based Scaling (DBS)rects – the DBS outputs is from the acorrection climate so-called model parameters direct and (Piani method only et uses which al., observed cor- data 2010). to In estimate the DBS method the climate model for the future periodcan preserve are the then projected corrected changesthe in projected by mean changes using values, in this the otherbetter DBS correction. statistical suited can for properties While extreme also and values. the preserve is DC therefore theoretically climate data corrected by monthlyprojections, change e.g. factors derived daily from precipitation theprecipitation values climate for model for January January 1991 2081 multiplied consist by of the observed ratio between average January data and the observedble data gamma in distributions. the Therepresents di control the period correction are made fitted to by two the di DBS and the climate model simulations ) was tied to vertical hydraulic conductivity ( – – h K More information on the DCvided and by DBS Seaby methodologies et and al. their (2012), implementation who is pro- documented that the DBS is able to correct direct put from the RCMscipitation, have temperature been and transferred to potentialcorrection have a evaporation been 10 and applied: two di combinations (Table 2) for the period 1991–2010 (control period) and 2081–2100. Out- been made for Europe withsion many scenario. combinations of In GCMs the and present RCMs study for we the A1B have emis- used data from nine of these GCM-RCM grid values by Stisen etmodel al. which (2012). mainly The uses catch wind correction speed model to is correct a measured3.3 spatially daily distributed rainfall. Climate change projections 3.3.1 Ensemble of climate models representingIn future the weather ENSEMBLES project (Christensen et al., 2009) future climate projections have and potential evapotranspiration ( line period) in a gridgrid-values, format 20 from The Danish Metrologicalwide Institute. network Calculation of of climate areal stations.tions The can methodology be used found in fora Scharling making dynamic et the correction al. grid model (2000). interpola- originally Grid developed values by of Allerup et al. (1998) but applied on support this assumption, thus thereason parameter was for not the included insensitivity in is aaquifer final and likely calibration. the due The lake. to Observed awater hydraulic low heads stage in fluctuations hydraulic the contact at terrace the between aquifer lake the are (the terrace di reason for3.2 this is discussed Climatic later). baseline data Daily climatic data for the hydrological models, i.e. precipitation ( trast, hydraulic conductivities for thehydraulic heads 5 and geological therefore units selected were for( calibration. sensitive Horizontal to hydraulic the conductivity observed Furthermore, it was assumedwould that be leakage sensitive coe to head variations in the terrace aquifer. Initial calibrations did not support calibration of specific yield and specific storage for the saturated zone. In con- 5 5 15 10 25 20 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | (3) (2) year return interval is calculated with 95 % T 7848 7847 erences, i.e. climate change impacts, for simulated 2 ff / 1 ] 2 T K ∞ · ME with all its observations in the terrace sand has more 1.1 ) in the upper tail of distribution (Eq. 2, Gumbel, 1958). + < x < x T are found by a maximum likelihood method and the standard error K · β − ∞ , ) and 1.14 β − α + x ( α [1 e · − e n x s √ = ) = x The reason for selection of the mean dataset and the dataset with the 95 % limit was Model predictions of (five-day-average) hydraulic head in the upper aquifer from 20 ( T than half of the total weight in the objective function (Table 1). At areas of the model with 4 Results 4.1 Model calibration The calibrated model shows a distribution(Fig. of 5). mean error This with is bestpart fit not of in surprising the the terrace because model sand a and majority HTS of observations are located in this thing. The baseline simulation results werethe generated same by 9 9 models climate withDC models input method. data instead from of one model with theits observed relevance for data the motorway as design, with where the the lower extreme values are unimportant. hydraulic heads of the futurethe and baseline the period, present simulated periodima with from at each observation each of zone. data, the (i) wereThus DC The resulting members subtracted maxima in and from a for the the dataset resultsand with sorted max- baseline 9 again simulated series from hydraulic of highest head 20 toues annual (at lowest. and maxima values each of changes of upper between the 95distributions future % 20 were confidence zones). then limits From fitted of this the toThe mean dataset both procedure were val- the for calculated. mean the Gumbel dataset DBS method and was the the upper same 95 as % for dataset. the (ii) DC method except for one Gumbel distribution. zones along the motorwaynual were extracted maxima from for the theproaches baseline were 20 and yr used ensemble to were runs. analyse then An- di found and sorted according to value. Two ap- The calculated 95 % confidence limits will be referred to as the error bound for the s can be estimated. Within hydrology theapproximates double events exponential ( or Gumbel distribution often F Parameters of the estimate of the extremeconfidence value limits with as: a Extreme Value Analysis (EVA) wasfrom applied the to hydrological predictions model. of EVAor focuses future highest on groundwater percentile the levels of tail values ofthe in the selected a extreme distribution, dataset. values e.g. A and the suitable from lowest probability this values distribution corresponding is to fitted given to return periods Applying a hydrological model developedditions for present involves a conditions number to of simulateare assumptions. future assumed Calibration con- parameters constant and throughout model thewill structure 21st most century. likely Land change use butextraction and how is agricultural and assumed practice to to whichbaseline be degree model the is run, same uncertain. applying as Futurethe the climatic groundwater constant average pumping observations for value from from the 1991–2010, 2003–2010. period is 2003–2010. also The 3.4 run with Extreme value analysis For the present studycovering the we local have model area extracted in climate Silkeborg. model3.3.2 results from the Climate change 10 km simulation with grid groundwater models data from all RCMs so that they reproduce extreme precipitation in the control period. 5 5 25 15 20 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , respectively. cient in the ur- ffi 100 T , evapotranspiration ff ), respectively. At zone 100 T erence is present between the two ff estimate for the DC, and 26 % for the 100 T 7850 7849 shows the same trend as temperature which is not erence for extreme values by using the DC or DBS p ff E for Pre-quaternary sand and clay appears to be a bit narrow h values between DC and DBS estimates is then 141 % at zone K 100 for the lowest of the two pre-quaternary sand units is close the value T h K erence of ff seems to be in the upper end of expected. The range between A likely reason is the transition between the river valley deposits and the upland h DBS. At zone 50, these numberson are 31 the and mean 28 %. of Besides Gumbel extreme distributions values based of the DC and DBS ensemble, Gumbel distributions methodology, with higher changes forcur the with DBS 0.48 method. and At 1.1650 m zone lower for 34, changes the are high DC seen changes and withThe oc- 0.23 DBS di and 100 0.37 yr m event for ( 34 the DC and and 61 % DBS atto zone 50. be Zones more with sensitive higherGumbel changes to distributions of are the extreme similar downscaling hydraulic forbounds heads both method. seem are ensembles. The For 31 % instance, calculated higher at error zone (or bound 34 lower) the than for error the the EVA was performed for therameters for 20 each zone. zones Figure at 8values the shows for Gumbel motorway zone distributions 34 with for and the estimation 50 changetracted of in with by extreme results Gumbel where the pa- the baselinehighest mean and of result. lowest the changes The climate between ensembletwo two is the sub- zones future zones and clearly are baseline display period. selected the Furthermore, because di the they represent ensemble are wider than inthroughout the the year DC with ensemble.Temperatures highest are relativeintervals, predicted increase well during to winter. separated increase The from narrow confidence future the period. observed Predicted 1991–2010, future indicatesurprising a because clear of trend its direct for correlation. the 4.3 Analysis of extreme groundwater levels November, December, and January.age) The predicts distribution a basedJanuary, decrease scaling from in (ensemble March February aver- todisagree and May, most from and during July in thesimilar to November summer from October and and October and December. in to an The May. September, increase Except climate whereas for in models predictions November, are ranges more of predictions in the DBS observations from February to June, a decrease June to October and an increase in Fig. 7. Predictions of futureThe precipitation DC are the method most (ensemble varying average) climate variable predicts (Fig. future 7). precipitation similar to baseline seem to evidence thatunits, only e.g. a sand small layers lithologicalexemplified by dominated di by fine-sand,of and the pre-quaternary clays clay. layers by silt. This4.2 is also Climate change parameters Results from the DC and DBSthe climate baseline ensembles period are compared (1991–2010) with for observations precipitation, from temperature and evapotranspiration, Fig. 6. This is likelyK because of a small number of observations in this clayand unit confidence and limits the utterly overlap, especiallythe because clay of (2.5 the orders wide of confidence magnitude). limit Boreholes of penetrating the pre-quaternary deposits A smaller error formodel this of component this of sizebanized and the zone, discretisation. where objective One the function hydrology explanationthe might is MikeShe could code be somewhat be accounts dominated expected for the by paved forand areas, location paved besides a unsaturated areas. in surface Although zone runo an flow, ur- theban physical zone description to seems simulate observed totivities groundwater be for fluctuations. the insu Optimized glacial hydraulic units conduc- values (terrace and sand, the glacial sand, 95 % glacial confidence clay) are limits within are expected relatively narrow except for the glacial clay, north vest) high mean errors are seen. glacial sand and clay.coarse The to model resolve variation discretization inon and hydraulic max. heads seasonal heterogeneous amplitude, in geology which this in are area. some too Figure areas 5b in also the shows terrace aquifer error is up to 0.6 m. high topographical gradients (e.g. where the motorway leaves the river valley toward 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , (4) 0.51; { would be 21 are similar at T 100 total uncertainty T event. T StandardDeviation + } erences for 0.60 m. ecting the estimation of future ff ff = 2 · 0.29; 0.68 { at zone 34 of 0.81 m for the DC ensemble 0.68))/2) are the uncertainties related to climate mod- − 100 7852 7851 in Table 3 represents half of the 95 % con- 0.25 m. T erence multiplied with 2, averaged over the two ff = ± 2 EVA would correspond to only two standard deviations EVA 2 σ σ · (1.06 21 + T + erence between mean ensemble and upper 95 % en- and ff erent methods. Thirdly, estimation of extreme values from ff 0.29) 0.19)/4) ((StandardDeviation · − + erent return periods, while downscaling uncertainty increases 2 downscaling ff σ + downscaling 0.07 σ + , 1.33 m. We argue, however, that the two downscaling methods are not = 0.16 would be 4 of 1.64 m for the DBS ensemble, which are significantly higher than the + 2 climatemodel /2) 21 } σ T would be (((0.51 climatemodel 100 q σ T 21 resent a central estimate,derestimate the the variance DC of isunderestimate the known the future extreme to climate values data. only andof The hence preserve the DC provide uncertainty the is an interval. mean estimate therefore Assumingtical likely at that but mean the the to value, un- while DBS low the estimate end DC representsdownscaling represents the uncertainty the statis- lower for 95 % confidenceor estimate, 0.67 the m. downscaling methods. For example, atT zone 30 the climate model uncertainty for Downscaling – ifthe the uncertainty two could strictlythe downscaling standard be methods deviation considered were is asDC calculated assumed four and from standard equally DBS the deviations,for estimate). likely two where For known example, random1.06 at variables zone (the 30equally the likely. downscaling While uncertainty we have no reason to assume that the DBS does not rep- Climate models – thesemble represents di half of theuncertainty 95 is % estimated confidence as interval. this Hence di the climate model Extreme Value Analysisfidence – interval. the Hence,quantified the as uncertaintymethods the related and to average the the oftiplied two Gumbel with the climate distribution 2. values error((0.07 For is (mean instance, from and at each upper zone 95 of 30 % the the ensemble) EVA mul- two uncertainty for downscaling = , can be assessed by: – – – From Fig. 10 it is seen that the uncertainty from climate models and downscaling We will characterise the uncertainty from these three sources as the interval between Results illustrated in Fig. 9 contain a total total is almost constant for di with higher return period (also see Figs. 9 and 10). σ where els, downscaling and extremecomponents value with analysis. downscaling Figure calculated 10the figure as shows are the two calculated three as standard the uncertainty deviations. average of The the results 20methods zones in are for the each two dominating sources of uncertainty. Climate modelling uncertainty Assuming that the threeσ sources of uncertainty are independent the the simulated groundwater levels involves uncertaintytribution, related and to this fitting uncertainty thevalues. Gumbel is dis- described by error bounds on thethe estimated extreme upper anddeviation lower of 95 an % estimated confidence value. Based values, on equivalent the results to shown four in Table times 3 we the find: standard 4.4 Uncertainties of future extreme groundwaterThe levels results illustrate severalextreme sources groundwater levels. of First of uncertainty all,ing. a the In estimation of this the study futurecombinations it climate of is is global challeng- handled and byare regional downscaled applying using climate an two models. di ensemble Secondly, of climate climate model projections results from 9 shown in Fig. 9. and a values of 0.48 and 1.16zone for 50, the Table 3. mean ensemble. The di based on calculated upper 95 % confidence limits of the ensemble extreme values are 5 5 20 25 15 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 100 T ect the aquifer ff ). The underlying objective; to estimate at zone 32 is the one 100 T 100 T 7854 7853 erent from the three dimensional, physically based ff ect extreme groundwater levels. The high conductivity of ff erences between the studies are seen. First of all, Najib et al. (2008) ff infrastructure design Extreme value analysis for groundwater systems in a future climate is to the knowl- The modest climate change impact at the investigated aquifer is a result of site spe- years representing the last 20 yr of the 21th century and not a representation of the sation toward widely distributedmodels observations. as The global general reservoir models discussion versusMODFLOW more for models physically and based is against models beyond as the MikeSheulation scope or of of future this conditions paper. one Nevertheless,maybe in could argue respect more that to robust a sim- for physicallyphysical based simulations system parameterisation with is is changing described with climaticclimate some input, confidence. change because Thirdly, impacts the at in present least study theT includes the EVA for hydraulic heads.next This 100 leads yr with to today’s an climate estimation as of shown by Najib et al. (2008). tion data. This is fundamentallygroundwater – di surface water modelling usedscription in in the present Najib study. et Theis non-physical al. de- done (2008) fits locally observed towardmodel, data local through very observations, the well, objective because whereas parameterisation function, calibration attempts of to a make 3-D the groundwater best overall parameteri- by estimating theThree 100 yr major event di atinvestigate a a dual given or site tripleup is porosity to carbonate the 90 aquifer m, same with whereasobserved hydraulic as variation the head and terrace in variations is sand of theFrance. relative aquifer present Secondly, homogeneous in Najib compared study. Silkeborg to et onlyby the al. have a aquifer a global (2008) in few reservoir Southern reconstruct metersof model hydraulic parameters with of is heads a done non-physically used based for for individual parameterisation. the sites Calibration with EVA observed hydraulic head and precipita- edge of the authorsbeen not made presented to in use thestudy an by literature EVA Najib methodology before. et within As al.ysis the (2008) noticed, and introduced area attempts estimate a of have hydraulic methodology groundwater heads toimplement flooding. for perform flood 100 The flood yr hazard frequency events assessment anal- ( at a groundwater dominated hydrological regime present study. to the potential rateobviously of very recharge. site-specific This and relationextreme therefore, between groundwater the aquifer levels potential recharge/discharge isthe is impact also study of very is climate site-specific.Changing change the One land for anthropogenic aspect use influence not andrecharge/discharge on development considered relation of the in and the hydrological drainagetems thereby systems and system extreme land in could groundwater use the a levels. willthe future. be Drainage groundwater part sys- system of (Holman future adaptation et measures al., and 2012). include This feedback to is not taken into account in the and lakes with aels relatively quickly low will responsedrainage be time, system, the reduced. implying elevation of that With theto higher drains a drainage will groundwater of confine good the lev- groundwater levels. aquifer connectivityconnecting In which contrary aquifers between reduces and the the the extremethe events, aquifer unsaturated increased situation zone and recharge at will from Silkeborg the tend it to seems that amplify the extreme potential events. rate At of drainage is high compared climate is modest. Theevents extreme (zone value analysis 32). shows Thisprediction changes estimate with of the up is 9 to basedbased climate 1.7 on on m model. the for the A mean more upper value, likely and 95 gives % a confidence 1.2 m limitcific change. of conditions. the Two interactingconductivity of groundwater the conditions, aquifer, a drainagethe aquifer and will the remove hydraulic groundwater towards hydraulic boundaries as drains, streams 5.1 Climate change impacts on extreme groundwater levels in relation to The development of extreme groundwater levels from today’s climate toward future 5 Discussion 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | to 10 T events ff is estimated ff ect the prediction. estimations (0.65– ff estimates and stan- T 100 T volumes are not sensitive to ff erent downscaling methods is very ff ects prediction of extreme runo ff 7856 7855 estimates using an annual maximum serie methodology, a 100 T cult to compare the relative size of uncertainty sources found in ffi erent ff estimate of hydraulic head. This is maybe not surprising, because the cult and related with high uncertainty when estimations of extreme values 100 T ffi (Fig. 10). This uncertainty is a result of uncertainty in estimation of parameter and a The findings from the Silkeborg case are in principle site-specific. The estimated The lack of studies investigating extreme groundwater conditions under future cli- Uncertainty on the Gumbel prediction ranges between 0.21 and 0.39 m from 21 100 in a trustworthy manner.groundwater One fluctuations model in limitation the is terracecilitate aquifer. a the Further better model-ability replication model to of developmentthis these simulate should again relative should fa- annual low be fluctuations supported observed byorder more in to than the one simulate aquifer year fluctuations and of on detailedother a head limitation, measurement seasonal, and in annual, probably the and most interization, crucial, annual drainage is system perspective. the An- development future and changes otherthe of anthropogenic land-use, hydrological introduced urban- system. changes on are investigated. changes for future extreme groundwater levels arefor the a aquifer result at of Silkeborg, the the hydrogeologicalhydrological climatological setup model’s changes projected ability for to this simulate region, and the the natural, but also the highly urbanized area DC and DBS methods.ciated Graham with et the al. choice (2007)study, of conclude the climate that choice model large of and uncertaintyand downscaling more is seasonal methods important asso- dynamics, a whereas in thedownscaling relation prediction method. to of the In runo present thisrelevant context when dealing testing with of extreme hydrologicalby di events. Allen A groundwater et recharge al. study (2010)ticularly also di conclude that downscaling from climate models can be par- mate makes itthis di study. General comparisonother areas can of hydrology. nevertheless One study bepresent supporting investigations made the is uncertainty to distribution Grahamwith impact found et in a studies the al. combination within (2007), of where GCM’s, RCM’s future and river two runo downscaling methods equivalent to the peak over threshold methodology, bothof with moments, parameter the estimationsmethod. maximum using These the likelihood six combinations method method of methodsdard and gave deviations very for the the similar estimate probability ofone weighted hydraulic method. head moment and In this climate justify changethe the current studies climate use it conditions of is do only critical notassumption used honour to in the select extreme stationarity too value20 long condition, analysis. yr periods which Our period results is because the suggest uncertainty an that dueuncertainties underlying when to due choosing the to extreme a climate value models analysis and is downscaling much smaller methods. than the values in the Gumbelcould distribution be with reduced by theto selecting EVA extreme a uncertaintylimited value longer data, analysis period andto that than parameter hence we 20 yr. estimation have it Another methods, not uncertaintycompared selection addressed six related of in di extreme the values present etc. study Najib is et related al. (2008) opposite downscaling uncertainty it is0.67 stable m, around Fig. 0.65 10). m for InT all other words, the uncertaintychanges from climate applied models through is the thelated DC same and future for DBS period a methods ofheads are 2081–2100. are uniform On simulated throughout by the the a other complex simu- model, hand, where estimates many of processesT a extreme hydraulic The largest uncertainty for theResults extreme clearly groundwater levels show is that thebution when downscaling for dealing method. groundwater with head the predictions,the upper the uncertainty. choice part Uncertainty of for per downscaling climate thousand method models of is dominates also the substantial distri- for the predictions but 5.2 Uncertainty of extreme groundwater level estimates 5 5 25 15 20 10 25 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | events 100 − , 2010. 21 T doi:10.1029/2009wr008932 7858 7857 erences in the prediction of extreme groundwater lev- ff volume, Water Resour. Res., 34, 3421–3431, 1998a. ff The Danish Road Directorate is thanked for funding the study. ce Hadley Centre, UK, 47–58, 2009. ffi erent GCMs, Water Resour. Res., 46, W00f03, ff bilistic model, 1. Runo bilistic model, 2. Peak discharge rate, Water Resour. Res., 34, 3433–3443, 1998b. mark Numerical Computing, 2010. ies: recent advances in27, downscaling 1547–1578, techniques 2007. for hydrological modeling, Int. J. Climatol., ogy from an ensemblecase study of on regional the climate Lule River models, basin, model Climatic Change, scales 81, and 293–307, linking 2007. methods – a the ensembles regional climate experiments, Clim. Res., 44, 195–209, 2010. regional climate model ensemblesChange for and Europe its [Research Impacts: ThemeMet Summary 3]. O ENSEMBLES: of Climate research and results from the ENSEMBLES project, climate and management scenarios onjorca, groundwater Spain), resources and J. associated Hydrol., wetland 376, (Ma- 510–527, 2009. in Sicily, Theor. Appl. Climatol., 87, 61–71, 2007. projections of change in the future, J. Hydrol., 388, 131–143, 2010. Meterological Institute, Technical report, 98-10, 1998. di Guo, Y. and Adams, B. J.: Hydrologic analysis of urban catchments with event-based proba- Guo, Y. and Adams, B. J.: Hydrologic analysis of urban catchments with event-based proba- Gumbel, E. J.: Statistics of extremes, Columbia University Press, 1958. Graham, L. P., Andreasson, J., and Carlsson, B.: Assessing climate change impacts on hydrol- Fowler, H. J., Blenkinsop, S., and Tebaldi, C.: Linking climate change modeling to impact stud- Doherty, D.: PEST, Model-independent parameter estimation, User manual, 5th Edn., Water- Deque, M. and Somot, S.: Weighted frequency distributions express modeling uncertainties in Christensen, J. H., Rummukainen, M., and Lenderink, G.: Formulation of very-high-resolution Candela, L., von Igel, W., Javier Elorza, F., and Aronica, G.: Impact assessment of combined Burke, E. B., Perry, R. H. J., and Brown, S. J.: An extreme value analysis of UK drought and Bordi, I., Fraedrich, K., Petitta, M., and Sutera, A.: Extreme value analysis of wet and dry periods Allerup, P., Madsen, H., and Vejen, F.: Standard values of precipitation corrections, Danish Allen, D. M., Cannon, A. J., Toews, M. W., and Scibek, J.: Variability in simulated recharge using References estimates of 0.87 m of0.39 uncertainty m from from downscaling, extreme 0.66 value m statistics.during from Compared a climate to 100 models, the yr and estimates event ofhigh. (0.23–1.22 m), groundwater levels the uncertainties from the threeAcknowledgements. sources are very val. Even with this assumption themodel downscaling uncertainty uncertainty still and dominatesuncertainty over uncertainty climate is from considered the incertainty extreme this from simplistic the value three way, statistics. sources, downscaling climatefor If accounts models 11 for for % downscaling 32 57 (for %, % and estimation of extreme of values un- statistics a 100 yr event). These uncertainty contributions come from els. The variation for0.23 a and 100 yr 0.51 event m usingThese compared the results to DC emphasise 0.37 method the toing shows importance 1.22 hydrological a m of extreme variation values for downscaling for between the methodology anot DBS future when considered climate. method estimat- as The equally (mean two possible, downscalingadvantage ensamble). because methods over the are a DBS DC methodologyargue methodology most that for likely most downscaling weight has should an of probablyand be extreme put events. we on Therefore, the use results we the with the DC DBS methodology method as an indicator of the lower end of the confidence inter- Changes of extreme groundwater levelsare found modest. in For this a studysamble, 100 DBS). in yr The terms event, used of a downscalingScaling, variation methods, demonstrate of Delta large Change 0.37 and di to Distribution 1.22 m Based is estimated (mean en- 6 Conclusions 5 5 30 25 20 15 10 15 20 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , , 2006. ences in climate ff doi:10.5194/hess-15-21- www.begravede-dale.dk ects of climate change and its erent regional climate models ff ff doi:10.1029/2005wr4742 7860 7859 , 2005. , 2009. doi:10.1029/2004wr003169 , 2011. erent downscaling methods, Hydrol. Earth Syst. Sci., 15, 21–38, ff ing using statisticalHydrol. methods Process., of 25, 1949–1963, hydrograph 2011. classification and lumpedchange parameter impacts on models, groundwater and571, stream discharge 2007. in Denmark, Vadose Zone J., 5, 554– semi-arid region using a scenario ofdoi:10.1088/1748-9326/4/3/035003 modelled climate change, Environ. Res. Lett., 4, 035003, regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192, 2010. Toronto, Canada, J. Hydrol., 348, 335–345, 2008. dominated hydrological system inW05001, the context of climate changes, Water Resour. Res., 41, of regional climate change:J. A Climate, Bayesian 18, approach 1524–1540, to 2005. the analysis of multimodelin ensembles, a chalk catchment in southern England, Hydrol. Process., 18, 959–971, 2004. mination in carbonate aquifers: Groundwater2008. flood frequency analysis, J. Hydrol., 352, 1–15, 2011 Henriksen, H. J.: On theical importance modelling of at appropriate mid rain-gauge to catch high correction latitudes, for to hydrolog- beand submitted, 2012. statistical downscaling methodsAtmos. Res., for 130, extreme 119–128, rainfall 2011. estimation under climate change, last access: 19 June 2012. Geol. Hydroge., 40, 203–211, 2007. ensembles o climate models, J. Ame. Stat. Assoc., 104,climate 97–116, change 2009. on groundwater related hydrologicaldi fluxes: a multi-model approach including 2009, GEUS særudgivelse ISBN: 978-87-7871-259-2, available at: groundwater levels, Water Resour. Res., 42, W11405, H.: Downscaling and uncertainty in climate projections for Denmark, to be submitted, 2012. S. B.: Model setupDenmark and Greenland calibration investigations of report, 2010/78, Middle 2010. Jutland, DK-model2009, Geologicaluncertainty Survey on of UK Chalkprojections, groundwater J. resources Hydrol., from 399, an 12–28, ensemble 2011. of global climate model Meteorological Institute, Ministry of Transport, Technical report 00-11, 2000. assessing the impacts of climate change on groundwater, Hydrogeol.England, J., 20, Journal 1–4, of 2012. Flood Risk Management, 4, 143–155, 2011. and the way forward?, Hydrogeol. J., 14, 637–647, 2006. B.: Methodology for construction, calibrationfor Denmark, and J. validation Hydrol., of 280, a 52–71, national 2003. hydrological model cipitation change, Clim. Dynam., 37, 407–418, 2011. Van Roosmalen, L., Christensen, B. S. B., and Sonnenborg, T. O.: Regional di Upton, K. A. and Jackson, C. R.: Simulation of the spatio-temporal extent of groundwater flood- Toews, M. W. and Allen, D. M.: Simulated response of groundwater to predicted recharge in a Piani, C., Haerter, J. O., and Coppola, E.: Statistical bias correction for daily precipitation in Tinch, J. W., Bradford, R. B., and Hudson, J. A.: The spatial distribution of groundwater flooding Pinault, J. L., Amraoui, N., and Golaz, C.: Groundwater-induced flooding in macropore- Tebaldi, C., Smith, R. L., Nychka, D., and Mearns, L. O.: Quantifying uncertainty in projections Palynchuk, B. and Guo, Y.: Treshold analysis of rainstorm depth and duration statistics at Najib, K., Jourde, H., and Pistre, S.: A methodology for extreme groundwater surge predeter- Stisen, S., Højberg, A. L., Troldborg, L., Refsgaard, J. C., Christensen, B. S. B., Olsen,Sunyer, M., M. and A., Madsen, H., and Ang, P. H.: A comparison of di Stoll, S., Hendricks Franssen, H. J., Butts, M., and Kinzelbach, W.: Analysis of the impact of Morris, S. E., Cobby, D., and Parkes, A.: Towards groundwater flood risk mapping, Q. J. Eng. Smith, L., Tebaldi, C., Nychka, D., and Mearns L. O.: Bayesian modelling of uncertainty in Jørgensen, F. and Sandersen, P.:Kortlægning af begravede dale i Danmark – opdatering 2007– Scibek, J. and Allen, D. M.: ModeledSeaby, L. impacts P., Refsgaard, J. of C., predicted Sonnenborg, T. O., climate Stisen, change S., Christensen, on J. H., recharge and and Jensen, K. Højbjerg, A. L., Nyegaard, P., Stisen, S., Troldborg, L.,Jackson, Ondracek, C. R., M., Meister, R., and and Prudhomme, Christensen, C.: Modelling B. the e Scharling, M.: Climate grid – Denmark, norms 1961–90, month and annual values, Danish Hughes, A. G.: Flood risk from groundwater: examples from a Chalk catchment in southern Holman, I. P., Allen, D. M., Cuthbert, M. O., and Goderniaux, P.: Towards best practice for Holman, I. P.: Climate change impacts on groundwater recharge-uncertainty, shortcomings, Henriksen, J. H., Troldborg, L., Nyegaard, P.,Sonnenborg, T. O., Refsgaard, J. C., and Madsen, Hawkins, E. and Sutton, R.: The potential to narrow uncertainty in projections of regional pre- 5 5 30 25 30 20 15 25 10 20 15 10 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Obs. m − i Observationperiod Weight, No. w 7862 7861 Error of maximum annual amplitude of hydraulic head (daily data) 2010–2011 5 33 ff Definition of groups in the objective function. mean3 Error of average hydraulic head for the 1990–2010, in layer 3 1990–2010 1 61 mean1mean2 Error of average hydraulic head Error for of the average 1990–2010, hydraulic in head layer for 1 the 1990–2010, in layer 2 1990–2010 1990–2010 1 1 20 29 , I., Hiscock, K. M., and Conway, D.: Simulation of the impacts of climate change on ME Mean error of timeMaxHDi series of hydraulic head (daily data) 2010–2011 10 33 ff groundwater resources intions, eastern 193, England, 325–344, 2002. Geological Society, London, Special Publica- of Hydrological SimulationsDistribution-Based of Scaling, Vadose Climate Zone J., Change 10, 136–150, Using 2011. Perturbation of Observations and GroupHTS Definition HTS Hobs Hobs Hobs Table 1. Yuso Van Roosmalen, L., Sonnenborg, T. O., Jensen, K. H., and Christensen, J. H.: Comparison 5 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ) 100 ± T 50 T ) and 100 ( 50 ± T 50 T ), 50 ( 21 T ± ´ eorologiques, France ´ et 21 T ± 7864 7863 21 T ± ) in m for return intervals of 20 ( ± 21 T ´ ´ eorologiques, France eorologiques, France M ´ ´ et et M and Nansen Center, Norway and Nansen Center, NorwayMeteorology, Germany Meteorology, GermanyMeteorology, Germany HydrologicalMeteorology, Germany Institute, Sweden Meteorology, Germany Theoretical Physics, Italy Meteorological Institute, The Netherlands Meteorology, Germany Hydrological Institute, Sweden Global Climate Model (GCM)Model name – institutionM Regional Climate Model (RCM) Model name – institution ± Mean ensemble Upper 95% ensemble Mean ensemble DC DBS DC DBS DC DBS erent zones along the motorway. 21 ff T Estimated climate induced increases in extreme groundwater table and associated Climate model ensemble, combinations of GCM and RCM. 3031 0.2932 0.37 0.0733 0.38 0.1034 0.36 0.10 0.6835 0.10 0.35 0.94 0.1636 0.36 0.10 0.95 0.2237 0.35 0.10 0.92 0.22 0.5138 0.36 0.10 0.21 0.90 0.71 0.0739 0.35 0.10 0.92 0.21 0.72 0.1040 1.06 0.35 0.10 0.89 0.21 0.70 0.1041 1.43 0.19 0.10 0.34 0.91 0.21 0.10 0.6842 1.46 0.23 0.32 0.10 0.86 0.21 0.70 0.1043 1.41 0.23 0.34 0.33 0.09 0.87 0.20 0.68 0.1044 0.22 1.37 0.44 0.09 0.31 0.09 0.20 0.83 0.69 0.1045 1.41 0.22 0.45 0.13 0.29 0.08 0.78 0.19 0.65 0.1046 1.36 0.22 0.43 0.13 0.79 0.27 0.08 0.79 0.18 0.67 0.1047 1.38 0.21 0.13 0.42 1.08 0.20 0.07 0.28 0.73 0.18 0.10 0.6348 1.30 0.22 0.43 0.13 1.10 0.27 0.28 0.07 0.68 0.17 0.59 0.0949 1.33 0.20 0.42 0.13 1.06 0.28 0.26 0.07 0.62 0.16 0.59 0.0850 0.21 1.26 0.43 0.13 0.27 1.04 0.23 0.07 0.14 0.62 0.55 0.08 1.18 0.20 0.42 0.13 1.07 0.26 0.17 0.06 0.63 0.14 0.51 0.07 1.19 0.19 0.42 0.13 1.03 0.27 0.05 0.58 0.14 0.48 0.07 1.09 0.19 0.13 0.41 1.05 0.26 0.46 0.13 0.07 0.48 1.02 0.18 0.38 0.12 0.99 0.26 0.28 0.11 0.49 0.07 0.93 0.16 0.39 0.11 1.01 0.25 0.07 0.45 0.07 0.15 0.93 0.36 0.11 0.25 0.96 0.37 0.07 0.94 0.15 0.34 0.10 0.91 0.24 0.26 0.06 0.85 0.15 0.32 0.10 0.91 0.23 0.05 0.67 0.14 0.09 0.33 0.85 0.23 0.40 0.11 0.34 0.09 0.79 0.21 0.07 0.31 0.09 0.72 0.20 0.27 0.09 0.18 0.72 0.20 0.08 0.73 0.18 0.06 0.67 0.18 0.53 0.17 0.33 0.13 0.09 Zone ARPEGE-DMI ARPEGE – Centre National de Recherche HIRHAM5 – Danish Meteorological Institute BCM-DMIBCM-SMHI BCM –ECHAM-DMI Bjerknes Centre for Climate Research BCM – Bjerknes Centre HIRHAM5 forECHAM-ICTP Climate – Research Danish ECHAM Meteorological – Institute Max Planck Institut RCA3 for – SwedishECHAM-KNMI ECHAM Meteorological – and Max Planck Institut for ECHAMECHAM-MPI – Max Planck Institut forECHAM-SMHI ECHAM – Max Planck HIRHAM5 Institut – for Danish ECHAM Meteorological – Institute Max Planck Institut REGCM3 for - International Centre for RACHMO2 - Royal Netherlands REMO - Max Planck Institute for RCA3 – Swedish Meteorological and Model name ARPEGE-CNRM ARPEGE – Centre National de Recherche RM5.1 - Centre National de Recherche Gumbel (EVA) error bounds ( Table 3. years for di Table 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | ˚ a ± 100 ¹ T The motorway (a) ± Kattegat 100 T Limfjord a) River

Gudenå

North Sea North

20 70 35 35 65 100

70 85

40 70 65

60 35

65 Lakes Gudenå River Motorway stations 5m Topography Motorway 45

Gudenå River Valley River Gudenå

30 7866 7865 80 Denmark, and location of Silkeborg and Guden 70

80 80 (b) 100

T 40 70

70 70 "

" 20

" 65

" 45

" 40

" 55 "

" 45

" 40 "

" 55 ± "

70 "

" 35 "

" 70 "

" 30 "

65 60

50 55

45 30 30 60

60 20 65 65 Km

± 60

60 70

70 30 0.5

30 DC DBS DC DBS DC DBS

Upper 95% ensemble Mean ensemble Upper 95% ensemble 65 40

55 35 50

65 0 20

a) Silkeborg b) Denmark 30 70 70 40 Continued. Location of Silkeborg in Denmark (right) and the new motorway (left). 3031 0.5632 0.09 0.7833 0.79 0.1234 0.77 0.13 1.1935 0.75 0.13 0.24 1.5936 0.77 0.12 1.62 0.2837 0.75 0.13 1.56 0.29 0.3838 0.12 0.76 1.52 0.28 0.11 0.5039 0.72 0.13 1.56 0.27 0.51 0.1540 0.88 0.73 0.12 1.51 0.28 0.49 0.1541 0.23 1.20 0.70 0.12 0.27 1.53 0.48 0.1542 1.22 0.32 0.65 0.11 1.44 0.27 0.49 0.1543 1.18 0.32 0.60 0.10 0.65 1.47 0.26 0.48 0.1544 1.16 0.31 0.11 0.84 0.60 0.10 1.40 0.26 0.15 0.4945 1.18 0.30 0.85 0.14 0.56 0.09 1.31 0.25 0.47 0.1546 1.15 0.31 0.82 0.15 1.29 0.52 0.09 0.24 1.32 0.48 0.1547 0.30 1.17 0.81 0.15 0.28 1.71 0.53 0.08 1.22 0.24 0.46 0.1548 1.10 0.31 0.82 0.15 1.74 0.33 0.54 0.09 1.13 0.22 0.43 0.1449 1.12 0.29 0.80 0.15 1.68 0.34 0.09 0.50 1.03 0.21 0.13 0.4350 1.07 0.29 0.15 0.82 1.64 0.32 0.41 0.08 1.03 0.19 0.41 0.13 1.01 0.28 0.77 0.15 1.68 0.32 0.29 0.07 1.05 0.19 0.38 0.12 0.26 1.01 0.79 0.14 1.62 0.32 0.06 0.19 0.95 0.36 0.11 0.94 0.26 0.75 0.14 0.31 1.65 0.75 0.18 0.37 0.11 0.87 0.25 0.69 0.13 1.56 0.32 0.45 0.14 0.38 0.11 0.80 0.23 0.12 0.69 1.58 0.30 0.09 0.11 0.35 0.80 0.21 0.64 0.12 1.51 0.31 0.31 0.10 0.81 0.21 0.60 0.11 1.42 0.29 0.23 0.09 0.21 0.74 0.56 0.10 0.28 1.42 0.07 0.59 0.20 0.57 0.10 1.31 0.28 0.37 0.16 0.58 0.10 1.22 0.26 0.10 0.10 0.53 1.11 0.24 0.45 0.10 1.12 0.22 0.32 0.09 1.13 0.22 0.07 0.22 1.03 0.81 0.21 0.49 0.17 0.11 Zone River. Fig. 1. stretch where construction willbelow be surface. below Zones present are groundshaded used level for polygons with groundwater indicate a head road paved analyses surface (Motorway ares. around stations). 6 The m grey Table 3. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 0 A’ m 0 5 ± 4 Km A’ 25 0 A 0 0 4 River discharge stations Rivers Silkeborg model tz sand 0 # 0 y y 0 0 5 3 race sand er T Glacial sand Glacial cla Mica cla Mica and quar 0 # 0 # 0 # 0 # 0 # 0 0 0 # 0 3 0 # 0 # 0 # 0 # 0 0 # 0 # 0 # 0 0 # 5 0 # 2 0 # 0 0 # # 0 # 0 # 0 # 0 # 0 # 0 0 # 0 # # 0 # 0 # 0 0 # 0 # # 0 # 0 # 0 0 # 0 0 # # 0 # # 0 0 0 # # # 0 0 # 0 0 # # 7868 7867 0 0 # # 0 # 0 0 # 0 0 # # 0 # 0 2 0 0 # 0 # # 0 # 0 # 0 # 0 # 0 0 # # 0 0 # # 0 0 0 # # # 0 # 0 # 0 0 # # 0 0 # # 0 0 # # 0 0 # 0 # 0 # # 0 0 0 # # 0 # # 0 # 0 # 0 # 0 0 # # 0 # 0 0 0 # # # 0 0 # # 0 0 0 # # 0 # 0 # # 0 0 # 0 # 0 0 # # 0 0 # 0 # 0 # 5 0 # 0 0 # # 0 1 # 0 # 0 # 0 0 # # 0 # 0 # 0 # 0 # 0 # 0 # 0 0 # # 0 0 # # 0 # 0 # 0 0 # 0 0 # 0 # 0 # 0 1 0 # 0 0 # # 0 # 0 0 # # 0 # 0 # 0 0 # 0 # 0 # 0 0 # 0 # 5 0 # 0 # 0 # 0 # 0 # 0 # 0 # 0 # 0 # 0 # 0 # 0 A 0 5 0 0 0 0 0 0 0 0 - m.a.s.l 7 6 5 4 3 2 1 1 - Setup of nested model approach with the Regional DK-model area 5. Geological cross section along the planned motorway. Location of cross section is show Fig. 3. Fig. 2. in upper right corner together with Silkeborg model boundary; see later Figs. 3 and 4. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | mean1– ≤ 1 1 - 2 2 - 5 5 - 10 > 10 HTS_ME "" )) " ) ) and mean error of ≤ 0.05 0.05 - 0.20 0.20 - 0.50 0.50 - 1.00 > 1.00 " ) "" )) ﬀ "" )) " ) " ) " ) " ) " ) " ) " ) HTS_Diff " ) " ) " ) " ) " ) " ) " ) ≤ 1 1 - 2 2 - 5 5 - 10 > 10 " ) maxHDi " ) ^_ ^_ ^_ ^_ ^_ Hobs_3 " ) " ) " ) "" )) ≤ 1 1 - 2 2 - 5 5 - 10 > 10 """ ))) "" )) "" )) # # # # # * * * * * Hobs_2 " ) " ) ≤ 1 1 - 2 2 - 5 5 - 10 > 10 ME). %, %, %, %, %, Legend Hobs_1 Error for individual groups in the objective funktion [m] ^_ 7870 7869 ^_ %, ^_ ^_ # * ^_ ^_ ^_ ^_ ^_ ^_ ^_ # * Mean error of hydraulic head layer 1–3 (Hobs # * ^_ ^_ %, # * %, # * ^_ ^_ %, # * ^_ ^_ ^_ (a) ^_ ^_ # * ^_ ^_ # * # * # * ^_ # * # * ^_ # * ^_ ^_ ^_ ^_ # * # * # * # * ^_ # * # * ^_ ^_ ^_ %, ^_ # * # * # * ^_ ^_ ^_ ^_ ^_ # * # * # * ^_ # * ^_ %, %, # * %, %, ^_ . ^_ ^_ %, %, %, %, ^_ %, ^_ %, 2 %, ^_ # * ^_ ^_ ^_ ^_ ^_ ^_ %, %, %, b) %, # * ^_ ^_ ^_ ^_ ^_ ^_ ^_ ^_ ^_ a) b) Local model setup. Model boundary (section A-G), Mike11 river network, and topogra- Model error of hydraulic head. Error of max. seasonal amplitude of hydraulic head (HTS (b) Fig. 5. 2). hydraulic head with time series observations (HTS Fig. 4. phy. Model area is 103 km Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 3 3 3 3 - - - - 10 ×10 ×10 × 7 0 07×10 5 3 . . . 2.31 1 1 1 Terrace sand 5 4 5 5 - - - - ×10 ×10 7 0 ), and temperature from the 19×10 2 0 . . . p 4.36×10 1 2 5 E Mica sand 2 4 4 3 5 - - - - ×10 ×10 2 0 49×10 5 0 . . . 1 8.34×10 3 5 Mica sand 1 or temperature. p E 5 8 4 6 - - - - 7872 7871 ×10 ×10 9 0 9 0 . . Mica clay 4.51×10 1.99×10 2 1 3 4 3 4 - - - - ×10 ×10 1 3 61×10 8 0 . . . 1 2.10×10 5 1 Glacial sand 4 5 2 10 - - - ×10 ×10 0 4 18×10 0 2 . 30×10 . . . 1 1 1 1 Glacial clay m i -4 -5 -6 -7 -8 -9 -2 -3 t t i p n i o

×10 ×10 ×10 ×10 ×10 ×10 ×10 ×10

1 1 1 1 1 1 1 1

s / m Up 95% Low 95% Optimized values of hydraulic conductivity, initial parameters values, and calculated Monthly average for precipitation, evapotranspiration ( climate models forvalues the are period calculated from 2081–2100 thetively. Red, and 9 blue observed and ensemble grey members for background forreduced, bars 1991–2010. the illustrate increased months DC or Average where and unchanged ensemble the precipitation, DBS ensemble ensembles, average show respec- Fig. 7. Fig. 6. 95 % conﬁdence limits by PEST. Geological units are shown in Fig. 2. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 1 0.0 e obs) e n li e s a B

(DC e obs) e in 0.1 l e s ba

- error bounds for Gumbel distr. error

C x. GW head a Return period [yr] period Return sim. m l r and Lower a Probability for yearly exceedence yearly for Probability e u p n p Gumbel distr. (D U An 1 1 10 100 0.1 0.5 0.3 0.1 - 1 DBS ) e 0.0 in l e 7874 7873 s a B

- DBS ) e (DBS in l e s ba

error bounds for Gumbel distr. error x. GW head a m l sim. a 0.1 u n Gumbel distr. (DBS - Gumbel and Lower Upper An Return period [yr] period Return Probability for yearly exceedence yearly for Probability Zone 34 Zone 50 Zone 1 1 10 100

0.1 1.5 1.3 1.1 0.9 0.7 0.5 0.3 0.1 - Change (future - baseline) [m] baseline) - (future Change Gumbel distributions for zone 34 and 50 at the motorway calculated from mean of Gumbel distributions for zone 34 and 50 at the motorway calculated from upper 95 % Fig. 9. limit of ensembles. Distributionsdelta and change associated (DC) error data. In boundsmarked the marked with same with way red. results blue Values using are usedheads the based (future) to distribution – based on parameterise annual scaling the (DBS) max. Gumbel are hydraulic distribution, heads annual (baseline), max. are shown hydraulic as red and blue dots. Fig. 8. ensembles. Distributions and associatedchange error (DC) bounds data. markedmarked In with with the red. blue same Values are usedheads way based (future) to results – parameterise on annual the using delta max. Gumbel hydraulic the distribution, heads distribution annual (baseline), max. are based shown hydraulic scaling as red (DBS) and are blue dots. Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | l

t Contribution to uncertainty uncertainty to Contribution o T % % % % 0% 0% 80 60 40 20 10 0 10 EVA 90 80 g n Downscaling EVA 70 Climate models li a c s n 60 w 7875 Do 50 40 Return Period, T [yr] Return Period, ls e 30 d o m

20 e t a im l 10 C

1.2 1.0 0.8 0.6 0.4 0.2 0.0 Hydraulic head [m] head Hydraulic Propagation of uncertainty for estimation of future extreme groundwater levels. Un- Fig. 10. certainty from Climate models,values downscaling (left and axis) Gumbel and distribution percentage are contribution (right shown axis) with with absolute background colouring.