Assessing climate change impacts on river flows and environmental flow requirements at catchment...

ECOHYDROLOGYEcohydrol. 3, 28–40 (2010)Published online 15 February 2010 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/eco.92

Assessing climate change impacts on river flows andenvironmental flow requirements at catchment scale

Gulay Onusluel Gul,1* Dan Rosbjerg,2 Ali Gul,1 Maria Ondracek3 and Kobamelo Dikgola4

1 DEU, Department of Civil Engineering, Dokuz Eylul University, Tinaztepe Campus, 35160 Izmir, Turkey2 DTU, Department of Environmental Engineering, Technical University of Denmark, Miljovej, Bygning 113, 2800 Lyngby, Denmark

3 GEUS, Geological Survey of Denmark and Greenland, Voldgade 10, DK-1350 Copenhagen K, Denmark4 Department of Water Affairs, Ministry of Mineral Resources and Water Affairs, Gaborone, Botswana

ABSTRACT

The fourth assessment report of Intergovernmental Panel on Climate Change (IPCC) suggests studies that increase the spatialresolution to solve the scale mismatch between large-scale climatic models and the catchment scale while addressing climatechange impacts on aquatic ecosystems. Impacts occur mostly at the local scale, though potential changes in the hydrologicalcycle and eco-hydrological processes are more difficult to model and analyse at this level. The difficulty is even greater forstudies on lowland river systems, which require the modelling of hydrological processes in greater detail. In this study, theregional impacts of climate change on river flow and environmental flow requirement, which is a negotiated trade-off betweenwater uses, are analysed for a lowland catchment in Denmark through MIKE SHE/MIKE 11 coupling. The coupled modelpossesses an important capacity for simulating stream flows and groundwater head levels in a dynamic system. Although thesimulation results from different global circulation models (GCMs) indicate different responses in flows to the climate change,there are obvious deviations of the river flows and environmental flow potentials computed for all the scenario cases from theaverages of the base period with current conditions. Copyright 2010 John Wiley & Sons, Ltd.

KEY WORDS hydrological modelling; climate change; river flows; environmental flow requirements; model coupling

Received 16 May 2009; Accepted 26 August 2009

INTRODUCTION

Differentiation of climatic conditions from the past to thepresent is expected in many recent studies to have a pro-nounced impact on the capacity of freshwater systemsfor maintaining water supplies and sustaining aquatichabitats and ecosystem processes. The fourth assessmentreport of the Intergovernmental Panel on Climate Change(IPCC) prioritizes a number of research needs into thewater–climate interface mainly ‘to improve understand-ing and estimation, in quantitative terms, of climatechange impacts on freshwater resources and their man-agement’ (IPCC, 2007). The report also suggests studiesthat aim to increase the spatial resolution to solve thescale mismatch between large-scale climatic models andthe catchment scale and to address impacts of climatechange on aquatic ecosystems. Indeed, water resourceimpacts occur mostly at the local scale rather than atregional or larger scales, yet the large uncertainties inthe results from climate models hinder hydrological fore-casting in desired accuracy and precision at this level(IPCC, 1998), thus necessitating integration with hydro-logical models at catchment scale for better forecasts.

Climate change impacts on river flows in lowland riversystems were previously covered by a number of studiesin Europe. Arnell (1999) outlined the effects of climate

* Correspondence to: Gulay Onusluel Gul, Department of Civil Engineer-ing, Dokuz Eylul University, Tinaztepe Campus, 35160 Izmir, Turkey.E-mail: [email protected]

change on hydrological regimes at the continental scale inEurope by using a macro-scale hydrological model for thesimulations at a daily time step and under four climatechange scenarios. Limbrick et al. (2000) used a modelto assess the potential impacts of a number of HadleyCentre climate change scenarios on the hydrological flowregime of a catchment in the UK. Thodsen (2007) stud-ied the climate change influence on stream flow in Danishrivers, while Andersen et al. (2006) investigated the cli-mate change impacts on the hydrological conditions ofa lowland river basin, again in Denmark. These consti-tute only part of the studies that employed a numberof different hydrological models for assessing potentialchanges on river flows in a future world. Correspond-ingly, environmental flow requirements and related risksemerging from dramatic changes in river flows when allo-cating water for environmental needs were also consid-ered in several studies. Arthington et al. (2006) proposeda generic approach that incorporates essential aspects ofnatural flow variability shared across particular classesof rivers in an effort to bridge the gap between simplehydrological rules and more comprehensive environmen-tal flow assessments. Palmer et al. (2008) highlightedcertain serious problems, such as native biodiversity lossand risks to ecosystems from increased flooding or watershortages, that may arise from changes in global climateand water needs. Smakhtin and Eriyagama (2008) devel-oped a software package for global desktop assessmentof environmental flow requirements.

Copyright 2010 John Wiley & Sons, Ltd.

ASSESSING CLIMATE CHANGE IMPACTS ON RIVER FLOWS AND ENVIRONMENTAL FLOW REQUIREMENTS 29

Moreover, potential changes in the hydrological cycleand eco-hydrological processes under climate changeimpacts are more difficult to model and analyse at thelocal scale than temperature and precipitation changes ata larger level. The difficulty is even greater in studieson lowland river systems, where a detailed modelling ofthe interactions between shallow groundwater and surfacewaters is required. In applying the model, not only theheterogeneity of the interaction processes but also thetemporally and spatially variable impacts on the waterbalance are important (Cey et al., 1998; Langhoff et al.,2001).

This current study employs a technical reasoning to setup a model for analysing the variable impacts of potentialchanges in regional climatic conditions on the lowlandcatchment of the Havelse river system in Denmark. Themodel approach links the simulations on the MIKE SHEand MIKE 11 modelling systems so that water exchangebetween the two models becomes possible during thewhole simulation through a MIKE SHE/MIKE 11 cou-pling. The unsaturated flow is dynamically linked to thegroundwater and overland flow modules of the MIKESHE model to help quantify the effects of the unsaturatedzone component through verified model structures for thecatchment. The model set-up takes account of a completeset of river reaches as well as the hydraulic structures thatexist in the catchment and the recently updated geologicalinformation. Eco-hydrological impacts from regional cli-mate change, on the other hand, greatly depend on hydro-logical responses to the change. Like the scale problemin the changes of hydrological cycle, many of the mostsignificant impacts on freshwater ecosytems result fromhydrological changes at local scales of small catchmentsor drainage basins, which are again taken into accountpoorly and only as mean conditions by climate models.In this regard, it is a further objective of this study toassess environmental flows that will become necessaryto ensure a flow regime capable of sustaining aquatichabitats and ecosystem processes for the Havelse river

system, yet will require a negotiated trade-off betweendifferent water uses in the basin. Catchment-scale impactsfrom the potential change of climatic conditions in theregion are identified in the study according to the IPCCSRES A2 and B2 change scenarios.

DESCRIPTION OF THE STUDY AREA

The Havelse catchment system, which is located betweenthe coordinates of 12°0200700E—55°4805400N and12°2105900E—55°5804700N in the northern part of Zealandin Denmark, covers an area of ca 250 km2. The areaextends to Arresø Lake in the North and is delimited bythe Roskilde Fjord in the western direction (Figure 1).Rivers that form the system in the study are the centralHavelse River system, the northern Æbelholt, Lyngby and(partly) Pøle Rivers and the southern Græse River. Meanannual discharge is 0Ð5 m3 s�1 for the Havelse Riverfor the period 1947–1995 and 0Ð1 m3 s�1 for the GræseRiver for the period 1946–1995, with observed minimumflow rates of 0Ð08 and 0Ð06 m3 s�1, and maximum flowrates of 3Ð6 and 0Ð7 m3 s�1 for the Havelse and GræseRivers, respectively (HELCOM, 1998).

Over the entire catchment, the average elevation is23 m above the mean sea level with a maximum of78 m at the Eastern boundary, slightly decreasing towardsthe Roskilde Fjord. The catchment geology consists ofchalk and tertiary limestone deposits in the deepest layers,which are overlaid by meltwater sand and clayey till inthe upper layers. Soils are predominantly composed ofglaciofluvial sand and gravel, freshwater deposits, as wellas till, clayey and fine sandy soils (in the approximateproportions of 31, 26 and 36%, respectively). Accordingto the Corine land cover (CLC) classification of Europefor the year 2000 (EEA, 2007), agricultural activitiescover the largest part of the catchment area, whileother land covers include urban areas, industrial andcommercial units, mineral extraction sites (gravel pits),

Figure 1. Havelse catchment system and the river monitoring stations.

Copyright 2010 John Wiley & Sons, Ltd. Ecohydrol. 3, 28–40 (2010)DOI: 10.1002/eco

30 G. O. GUL ET AL.

artificial non-agricultural vegetated areas, pasture, forest,peat bogs and water bodies.

MATERIALS AND METHODS

MIKE SHE model

The MIKE SHE modelling tool, developed by a con-sortium of three European organizations: the Institute ofHydrology (UK), SOGREAH (France) and DHI Waterand Environment (Denmark), is a dynamic and physicallybased distributed modelling tool for describing the mainprocesses in the land phase of the hydrological cycle. Itsimulates water flow from rainfall to river flow througha set of processes that include snow melt, interception,overland flow, infiltration into soils, evapotranspirationfrom vegetation and subsurface flow in the saturated andunsaturated zones (Refsgaard, 1997; DHI, 2007). MIKESHE is directly linked to the MIKE 11 program for appli-cations ranging from simple routing of surface water tofully dynamic channel flow with dynamic flow controlstructures.

In MIKE SHE, the basin is horizontally representedby an orthogonal grid network and the equations for thestate variables are solved for individual grid elements.This requires defining a number of parameters and vari-ables for each element as input to the model. Based on thegridded structure, vertical variations are described withincolumns vertically formed at each horizontal grid ele-ment. Though the increased level of detail in describingthe processes and the distributed character of the modelitself are generally considered to be advantageous, inputdata requirements to feed into such models for obtainingvalid simulation results may become rather large whilemaking data preparation process more complex and timedemanding.

In the model, the Rutter accounting procedure (Rut-ter et al., 1971) in which the maximum storage capacityof vegetation canopy is accounted for is used for sim-ulating rainfall interception by leaves. MIKE SHE mayemploy either the Kristensen and Jensen (1975) methodor Two-Layer Water Balance model (Yan and Smith,1994) for computing actual evapotranspiration and actualsoil moisture in the root zone, by using potential evapo-transpiration (PET) rates as model input along with themaximum root depth (RD) and leaf area index (LAI) (theratio of total upper leaf surface of a crop divided by thesurface area of the land on which the crop grows) forvegetation types. For overland flow, simplified numericsolution of the St Venant’s equation in two dimensionsis used. Three options exist in MIKE SHE for sim-ulating water movement in unsaturated zone: (1) 1-DRichard’s equation that requires soil moisture release dataand a detailed discretization of the soil profile, (2) gravityflow or vertical unit gradient method, and (3) the simpletwo-layer water balance method. Kristensen and Jensenevapotranspiration method complies with the Richard’sequation or gravity flow method used for the unsaturatedzone modelling (Graham and Butts, 2005); 2-D and 3-D

Boussinesq equations are solved for ground water flowin the saturated zone.

Unsaturated zone modelling with two-layer waterbalance method in MIKE SHE

The Two-Layer Water Balance model (Yan and Smith,1994), where the unsaturated zone is a simplified rep-resentation consisting of two layers, is best suited forareas with shallow water tables where actual evapotran-spiration is close to PET (DHI, 2007). As the methoddoes not account for the relationship between hydraulicconductivity and soil moisture content, it does not limitthe movement of water to the roots and assumes that, ifsufficient water is available in the root zone, the waterwill be available for evapotranspiration. The model hasspecific components for simulating canopy interception,ponding, infiltration, evapotranspiration and groundwaterrecharge. It calculates the average water content in theunsaturated zone composed by a root zone and a deeperzone below it and the amount of water that rechargesthe saturated zone whenever the unsaturated zone watercontent reaches its maximum. The method requires theRD and LAI parameters as well as the physical proper-ties of soils including constant infiltration capacity andsoil moisture contents at wilting point, field capacity andsaturation that belong to different soil types. Similar tothe Kristensen and Jensen model, evapotranspiration isextracted first from intercepted water by considering LAI,then ponded water and finally via transpiration from theroot zone based on average water content there (Grahamand Butts, 2005).

Coupling of MIKE SHE and MIKE 11 Models

MIKE 11 is a dynamic, one-dimensional modelling toolbased on the complete dynamic wave formulation of theSt Venant equations that is applicable for detailed design,management and operation of both simple and complexriver and channel systems (DHI, 2004). The modelprovides a simplified solution of the St Venant’s equationin one dimension for simulating channel flow, and allowsthe modelling of hydraulic systems that include structuressuch as weirs, gates, bridges and culverts.

MIKE SHE is fully integrated with the MIKE 11modelling system for rivers and channels so that waterexchange between the two models becomes possible dur-ing simultaneous runs in a dynamic modelling environ-ment. The water exchange takes place at coupled reachesdefined by the user in MIKE 11. MIKE SHE river linklocations, on the other hand, are automatically defined bythe model using the coordinates of MIKE 11 river points.

In a MIKE 11 set-up, the user should define the avail-able water exchange types either as (i) full contact inwhich the conductivity is only based on the aquifer mate-rial, or (ii) reduced contact (b) in which the conductivityis only based on the bed material or (iii) reduced contact(a) which considers both the conductivity of the river bedand the aquifer materials. MIKE 11 computes water lev-els at each node in the river network and flows (assumedconstant between the river nodes) at the points located



halfway between each node. Computed water levels arethen transferred to the MIKE SHE river links as inter-polated values between two river nodes, and overlandflow to each link is computed in MIKE SHE throughneighboring grid elements and river–aquifer exchange.These computed values are subsequently used at MIKE11 river nodes as lateral inflows or outflows for the nextcomputational step (DHI, 2004; Thompson et al., 2004).

Designating regional climate change impacts on riverand environmental flows

For assessing potential impacts from possible changesin regional weather and climate conditions, the Dan-ish Meteorological Institute (DMI) applies, among oth-ers, the results from the HIRHAM regional climatemodel (RCM), which is driven by the data fromthe ECHAM4/OPYC and HadAM3H global models.The ECHAM4/OPYC (European Center for Medium-Range Weather Forecasts Model, modified in HAM-burg/Isopycnal Ocean Model) that was developed by theMax Planck Institute for Meteorology in Hamburg, Ger-many, and HadAM3H that was developed by the UKHadley Centre are global circulation models (GCMs) toglobally perform present-day climate, paleo-climate sim-ulations and IPCC-scenario runs for the future and sup-ply global climate change datasets for regional models.HIRHAM that was developed jointly by DMI and theMax Planck Institute in Hamburg is an RCM to sim-ulate the regional climate variability and change withmain focus on regionally adapting the large-scale climatechanges from the global model and with main emphasison the simulation of extreme events such as heavy pre-cipitation and strong storms. Precipitation (mm/day), 2-mlevel temperature (K) and positive downward short-waveradiation (W m�2) time series data that can later be usedas direct or indirect input to any hydrologic model forsimulating changes in river flows are included in the vari-able set of the model. The model outputs considered inthe study to assess regional climatic changes are for boththe control period 1961–1990 and the selected scenarioperiod 2071–2100. SRES A2 scenario, which describesa very heterogeneous world in future with continuouslyincreasing global population, and B2 scenario in whichthe future emphasis is considered on local solutions toeconomic, social, and environmental sustainability, areboth used to interpret the differentiation of the futureconditions of river flow from those of the control period.

An environmental flow (also-called ‘environmentalwater requirement’ or ‘environmental flow requirement’)is the water released into the river from a water stor-age or allocated from a natural flow with the intentionof providing adequate amount of water for maintainingaquatic habitats for freshwater animals and plants, sus-taining productivity and promoting connectivity. It can bedefined through four different approaches: look-up tables,desktop analysis, functional analysis and hydraulic habi-tat modelling. Among these, the desktop approach eitherfocuses on analysis of existing historical data or uses

the data from hydrological models (Acreman and Dun-bar, 2004). The total annual environmental flow at a sitecan be alternatively calculated through desktop analysisby using observed monthly flow data and a correspond-ing flow duration curve (FDC) provided that the monthlydata would carry sufficiently representative informationabout flow variability for any meaningful hydrologicalanalysis (Smakhtin and Anputhas, 2006). The softwarepackage called the Global Environmental Flow Calcula-tor (GEFC), which was jointly developed by the Inter-national Water Management Institute (IWMI) and theWater Systems Analysis Group of the University of NewHampshire, briefly uses the above-mentioned rationaleand provides a rapid desktop assessment of environmen-tal flows from observed monthly records. The objectiveecosystem conditions are described as any of the six envi-ronmental management classes in the software rangingfrom unmodified to critically modified conditions, andthe management class best suited for the river in ques-tion is to be selected in environmental flow calculations.For the sites where the environmental flow requirementsare to be calculated, the software first generates a period-of-record FDC that is represented by 17% points on theprobability (X) axis to ensure that the entire range of flowis adequately covered (IWMI, 2007). This reference FDCis then converted into the FDC of the agreed environmen-tal management class by lateral shift of a certain amountto the left along the probability (X) axis that correspondsto the 17 fixed percentage points. The resulting FDC canthen be converted into monthly time series of environ-mental flow by using a non-linear spatial interpolationprocedure. Some information about the GEFC techniquecan be obtained from Smakhtin and Anputhas (2006),while the details of the spatial interpolation procedurecan be accessed from Hughes and Smakhtin (1996).

Data availability

Major data in the study for uses either as model inputs orfor calibration and validation purposes comprise terraindata for extracting necessary catchment characteristics,land-cover data and vegetation characteristics requiredto model actual evapotranspiration; soil data for indicat-ing unsaturated zone component; meteorological data fordaily rainfall, temperature and PET; and runoff data forthe calibration of the model. Terrain data was obtained ina 100 ð 100 m resolution grid format from the Geolog-ical Survey of Denmark and Greenland (GEUS). Landcover information to define land uses in the catchmentarea in a compatible format with the model was extractedfrom the Corine land cover map of Europe for the year2000 (Figure 2a). Sixteen different types in total wereidentified as given in Table I by keeping land use def-initions relatively simple and without further detailingagricultural uses with respect to the types of agriculturalproduction.

Soil data, which was obtained from GEUS, indicatethree main soil types, each with more than 25% share inthe study area (Figure 2b and Table II). The required soil


32 G. O. GUL ET AL.

Figure 2. (a) Land use/cover map (based on CLC 2000 classification); and (b) soil map of the Havelse catchment system (Onusluel Gul andRosbjerg, 2009).

Table I. Land use/cover types and areas in the Havelse catchmentsystem.

Land use type Area (%)

Non-irrigated arable land 66Ð56Land principally occupied by

agriculture with significant areas ofnatural vegetation

9Ð82

Discontinuous urban fabric 9Ð17Broad-leaved forest 4Ð35Mixed forest 3Ð19Industrial or commercial units 1Ð11Green urban areas 1Ð11Peat bogs 0Ð91Sport and leisure facilities 0Ð90Complex cultivation patterns 0Ð62Pasture 0Ð55Inland marshes 0Ð54Coniferous forest 0Ð43Water bodies 0Ð33Continuous urban fabric 0Ð27Mineral extraction sites 0Ð14

parameters by the model were adapted from Usul (2001)and Ritzema (1994) by considering the correspondingsoil types in the study area and were then calibrated.The necessary vegetation parameters for different vegeta-tion types were determined on the basis of the dominantcharacteristic vegetation/land cover types in the basin.Data for the necessary meteorological parameters wereobtained from GEUS for the recording period betweenJanuary 1999 and December 2003 in different resolu-tions and for different meteorological grid elements. Forthe calibration of the model, discharge values observedat corresponding monitoring stations were obtained from

Table II. Soil types and areas in the Havelse catchment system.

Soil types Area (%)

Till, clayey and fine-sandy 36Ð42Glaciofluvial sand and gravel 31Ð00Freshwater deposits: peat, gyttja, clay silt

and sand26Ð38

Downwash sandy deposits 4Ð27Glaciolacustrine laminated clay, silt and

finesand0Ð94

Lakes 0Ð44Marine sand and clay 0Ð38Beach ridges consisting of gravel and

coarse sand on marine foreland0Ð17

GEUS with the observation period 1999–2003. Wastew-ater discharges data collected from the FrederiksborgCounty were used as boundary conditions in the MIKE11 model for hydraulic simulation of the rivers.

For climate change impact assessments on river andenvironmental flows in the catchment, necessary precipi-tation, temperature and solar radiation data (for potentialevapotranspiration calculations by using the Makkink for-mula (Makkink, 1957; Plauborg et al., 2002; Scharlingand Kern-Hansen, 2002)) from the HIRHAM RCM wereobtained from the data distribution website of the EU 5thFramework Program project, PRUDENCE (2001).

Application

A coupled MIKE SHE/MIKE 11 model was employedin this study for simulating Havelse catchment systemhydrology in adequate detail with an extensive geologicalset-up, river set-up elaborated with hydraulic structures



and small stream branches and a sufficient implication ofunsaturated zone component.

Model set-up

The new MIKE SHE model was built over a 100 ð100 m reference grid that entirely covers the study areaof 250 km2. For snow melt simulations that requiretemperature inputs, the two necessary parameters, degree-day factor and threshold melting temperature, were set to2 mm/day and 0 °C, respectively.

Manning’s M bed resistance (m1/3 s�1), which isthe reciprocal of Manning’s n coefficient (s m�1/3),and detention storage (mm) are the two parametersrequired for finite difference solutions in overland flowcomputations. As the M value gets higher, water is routedfaster to the nearest river reach resulting in higher runoff.When M values become lower, more water percolatesinto the ground. Prior to any calibration, M was initiallyset to 3 m1/3 s�1 in the study. Detention storage, on theother hand, which has an effect to increase infiltrationby delaying surface flow (USACE, 1994) since it adds asmall pressure head to the wetting front, was first set to10 mm and then calibrated.

Unsaturated zone was considered using a two-layerwater balance model, in combination with an addition-ally activated evapotranspiration model, which was basedon PET and soil moisture content in the root zone com-partment of the two-layer unsaturated zone. For simu-lating processes in the unsaturated zone, four parame-ters are to be defined for each soil type in the studyarea. These parameters are soil water content at satura-tion (�sat) which is the maximum water content in soil;soil water content at field capacity (�FC), which is thewater content of soil at which vertical flow becomesnegligible; soil water content at the field wilting point(�WP), which is the lowest water content in the soil sothat plants can extract; and infiltration rate (Kinf), whichrepresents the saturated hydraulic conductivity of soil.Appropriate initial values for these parameters includedin the calibration set were taken for each soil typefrom the manual prepared for the Food and Agricul-ture Organization of the United Nations (FAO, 1985)

and the web pages of Natural Resources ConservationService (NRCS) of the US Department of Agriculture(http://www.mo10.nrcs.usda.gov/references/guides/properties/awcrange.html). The bypass option was acti-vated for the unsaturated zone computations as a corre-sponding bypass fraction, which was also calibrated.

Geological description necessary for saturated zonecomputations is based on the definition provided by Hen-riksen et al. (2003), but with some modifications. Thelargest changes from this earlier study are the new toplayer, where the horizontal extents for each top soil groupis created from the recent soil map and a new tertiary claylayer adapted from recently updated soil surveys. Thehydraulic parameters such as the hydraulic conductivitiesand storage coefficients for the geological layers includ-ing clay, chalk and lime stone, and geological lensesincluding four sand layers, were not subject to calibra-tion. Table III shows the geological layers and lenses withtheir corresponding initial parameter estimates derivedfrom Slater et al. (2000); Henriksen et al. (2003); Son-nenborg et al. (2003) and Hefting et al. (2006). Verticalsaturated hydraulic conductivity (Kv) is a parameter thatmakes cumulative infiltration through the soil surfacelarger while decreasing runoff and flattening peaks asit gets higher. Decreasing values of Kv causes higherrunoff since it makes less contribution to the aquifer.Horizontal saturated hydraulic conductivity (Kh) affectsbase flow in addition to flow peaks. As Kh decreases, adelay occurs in the flow reaching the stream. In contrast,water drains quickly and affects the base flow in the longterm in case of no rain for an extended period of time(Sahoo et al., 2006). As given in Table III, vertical andhorizontal hydraulic conductivities were set to calibrationparameters.

In order to account for the drainage flow (tile drainsand ditches) in MIKE SHE, the drainage component inthe groundwater module was included. Drainage outflowcomputation under the saturated zone component isbased on the linear reservoir concept and depends ondrainage depth, drainage time constant and drainagezones. Drainage zones (or drainage codes) were used in agrid form in the study to define the drain flow producing

Table III. Geological layers and lenses in the study area with corresponding initial parameter estimates.

Geological layersand lenses

Kh

(m s�1)Kv

(m s�1)Specific

yieldStorage

coefficient (m�1)

Geological layers Top (till, clayey and finesandy soil)

1Ð45 ð 10�5 1Ð45 ð 10�7 0Ð06 0Ð0001

Clay 1Ð9 ð 10�8 1Ð9 ð 10�9 0Ð06 0Ð0001Tertiary clay 1 ð 10�5 1 ð 10�6 0Ð06 0Ð0001Chalk and limestone Distributed Distributed 0Ð20 0Ð0001

Geological lenses Top (glaciofluvial sandand gravel)

1 ð 10�5 1 ð 10�6 0Ð20 0Ð0001

Top (freshwater deposits) 1 ð 10�5 1 ð 10�6 0Ð20 0Ð0001Sand1 1Ð35 ð 10�4 1Ð35 ð 10�5 0Ð26 0Ð0001Sand2 1Ð35 ð 10�4 1Ð35 ð 10�5 0Ð26 0Ð0001Sand3 1Ð35 ð 10�4 1Ð35 ð 10�5 0Ð26 0Ð0001Sand4 1Ð35 ð 10�4 1Ð35 ð 10�5 0Ð26 0Ð0001


34 G. O. GUL ET AL.

locations and link them to the locations receiving theflow. Drainage depth below the soil surface and drainagetime constant that affect the peak and recession of theriver runoff were initially set to 1 m and 7 ð 10�8 s�1,respectively, and were then subjected to calibration.

The drainage network recently validated by a seriesof surveys in the basin was used to define a full setof river reaches when performing hydraulic simulationsin MIKE 11. The hydraulic structures that include fourculverts (two on the Buresø channel, and two alongthe Lynge and Pøle streams) and a control structure(on the Lyngby stream) were included in the hydraulicmodel set-up for the basin as well as the cross sectionsof river reaches. Variable distances between the crosssections are around 50 cm in the vicinity of the hydraulicstructures and in places where cross sections substantiallychange to allow detailed assessments on these points.A boundary data file was generated taking account ofthe topographical boundary, inflow boundary values andwaste water inputs as point sources and water levelsfor Arresø Lake and Roskilde Fjord, while definingthe hydrodynamic parameters with their default values.Uniform values of M (Manning’s roughness coefficient)and the river leakage coefficient were assumed to beconstant with the respective values of 30 m1/3 s�1 and5 ð 10�7 s�1 for all rivers.

Calibration and validation analyses

As it was not possible to obtain all required parame-ters from field measurements, the standard split sampleapproach (Klemes, 1986) was applied in the study formanual calibration and validations. The available dataperiod (i.e. 1 January 1999–31 December 2003) wasdivided in that way into two sets to be able to carryout daily calibration for the period 1 January 1999–31December 2001, while reserving the remaining for vali-dation.

The calibration process covered the numerous param-eters of MIKE-SHE and MIKE 11, which includehydraulic conductivities for the geological layers in thesaturated zone, the Manning’s roughness coefficient,detention storage for overland flow simulation, drainagelevel, drainage time constant in MIKE-SHE as well asfour unsaturated zone parameters which are soil watercontent at saturation, at field capacity, at the field wiltingpoint and the infiltration rate of each soil type.

The correlation coefficient (R), which is an index of thedegree of linear relationship between observed and simu-lated data (Moriasi et al., 2007), and Nash–Sutcliffe sim-ulation efficiency (E) (Nash and Sutcliffe, 1970), whichshows the deviations between simulated and observedvalues relative to the scattering of observations, wereemployed together with visual graphical comparisons toevaluate the model results for discharge values fromcalibration and validation processes. The model predic-tions improve as the E values approach 1; for smallervalues, the simulations are not considered acceptable.Previous studies on various river systems in Denmark

computed E values which mainly varied between 0Ð5and 0Ð95 (Henriksen et al., 2003). The root mean-squared(RMS) residual performance criterion was used to evalu-ate the simulated groundwater head elevations against theobserved values of the same period. Head elevations anddaily observed stream flow values measured on the riversin the catchment were mainly used to assess the modelperformance for simulating groundwater and river flowvariations. Table IV gives the final values of the modelparameters from the manual calibrations.

Climate change impact assessment on flows

While assessing regional climate change impacts onlocal river and environmental flows, long-term aver-ages of monthly precipitation, temperature and potentialevapotranspiration were first evaluated for the periods1961–1990 and 1961–2003. The differences between theaverages that correspond to both periods were then cal-culated to show the variation for the non-overlappingperiod 1991–2003. This difference indicates the degreeof necessary subtraction from the total change computedbetween the HIRHAM model results of the control period1961–1990 and of the scenario period 2071–2100. Inthis way, it is secured that any detected change, whichwould later be considered when assessing the changesin hydrological conditions, would purely reflect the netvariation from the present-day climatic conditions of theperiod 1991–2003. The net changes in the averages ofthree climatic variables were correspondingly computedas in the following equations:

P�%� D f [�PMS � PMC�/PMC]

�[�POB � POC�/POC]g ð 100 �1�

T�°C� D �TMS � TMC� � �TOB � TOC� �2�

PET�%� D f [�PETMS � PETMC�/PETMC]

�[�PETOB � PETOC�/PETOC]g

ð 100 �3�

where P, T and PET, respectively, denote precipita-tion, 2-m level temperature and potential evapotranspi-ration; P, T and PET represent the net changesfor each month of an average year between the baseperiod 1961–2003 and the scenario period 2071–2100;and the sub-indices MS, MC, OB and OC, respectively,represent the model scenario period 2071–2100, modelcontrol period 1961–1990 for the RCM-modeled values,observed base period 1961–2003 and the observed con-trol period 1961–1990 for the observed values. Afterreflecting the net changes calculated in this way to allclimatic variables, a different model run for the scenariosimulation period of the same hypothetical length fol-lowed the previous model run for the base simulationperiod 1999–2003 to evaluate resulting changes in flows.Although the temporal patterns of wet and dry spells maybe altered with climatic change (Xu et al., 2005), theapproach only uses climatic inputs amplified or reducedby a certain degree without modifying the temporal pat-tern much.



Table IV. Model calibration parameters with their corresponding values from manual calibrations.

Overland flowparameters

Manning’s M value (m1/3 s�1) 2

Detention storage (mm) 50

Unsaturated zoneparameters

Glaciofluvial sandand gravel

�sat 0Ð24

�FC 0Ð12�WP 0Ð04Kinf (m s�1) 1Ð35 ð 10�5

Freshwater deposits: peat, gyttja, clay, silt and sand �sat 0Ð7�FC 0Ð4�WP 0Ð25Kinf (m s�1) 6Ð5 ð 10�5

Till, clayey and fine sandy �sat 0Ð72�FC 0Ð35�WP 0Ð22Kinf (m s�1) 1 ð 10�6

Downwash sandy deposits �sat 0Ð36�FC 0Ð18�WP 0Ð04Kinf (m s�1) 3 ð 10�6

Bypass fraction (for all soil types) Byp 0

Saturated zoneparameters

Geological layers Top clay Kh (m s�1) 1 ð 10�5

Kv (m s�1) 1 ð 10�6

Sy 0Ð06Ss (1 m�1) 0Ð0001

Tertiary clay Kh (m s�1) 5 ð 10�6

Kv (m s�1) 5 ð 10�7

Sy 0Ð1Ss 0Ð0001

Geological lenses Top (sand) Kh (m s�1) 1Ð35 ð 10�4

Kv (m s�1) 1Ð35 ð 10�5

Sy 0Ð26Ss (1 m�1) 0Ð0001

Top (fresh water deposits) Kh (m s�1) 6Ð5 ð 10�4

Kv (m s�1) 6Ð5 ð 10�5

Sy 0Ð44Ss (1 m�1) 0Ð0001

Drainage Level Zdr (m) �0Ð60Time constant Cdr (1 s�1) 2 ð 10�7

As the flow data is required on a monthly basis bythe previously described desktop approach, daily simu-lation results generated by MIKE SHE were convertedto monthly time series, and environmental flows weredetermined again on a monthly basis for the base and sce-nario simulation periods. In the light of available knowl-edge on current management practices in the catchmentarea, largely natural conditions with limited modificationon river flows through water supply schemes or irriga-tion developments were assumed to continue also in thefuture. In this way, a high ecological potential in theslightly modified but still ecologically important riverswas assumed to persist over the years. The changes inthe long-term monthly averages of environmental flowwere then identified for the base and scenario simulationperiods correspondingly.

RESULTS AND DISCUSSION

As a result of numerous calibration trials, the efficiencyindicators for the finally agreed simulation with

comparably higher degrees of fit and a satisfactoryagreement between the observed and simulated hydro-graphs at different monitoring locations in the catchmentwere calculated as seen in Table V. In the table arealso given the efficiency indicators for the period 1 Jan-uary 2002–31 December 2003 reserved for validatingthe results. Here, the inadequate simulation efficiencythat came out with the Pøle station might have resultedfrom the omission of the hydraulic structure over thecorresponding river branch in the study due to lack ofnecessary information.

Figure 3 shows the observed and simulated hydro-graphs overlaid in the model calibration and valida-tion periods to allow graphical comparisons for thefour stations with higher simulation efficiencies from theTable V. Besides the graphical consistency between theobserved and the modelled time series, the efficiencyindicators R and E both give indication of a propermodel set-up and acceptable modelling performance.The degree of conformity was additionally questionedby comparing the indicative measures of simulation


36 G. O. GUL ET AL.

Table V. Simulation efficiency indicators computed for the stations.

Calibration results Validation results

E R E R

Æbelholt, 49Ð17 0Ð64 0Ð91 0Ð75 0Ð93Pøle, 49Ð07 �1Ð64 0Ð88 �0Ð14 0Ð84Lyngby, 49Ð19 0Ð79 0Ð94 0Ð29 0Ð64Græse, 52Ð07 �0Ð77 0Ð85 0Ð49 0Ð86Græse, 52Ð52 0Ð54 0Ð83 0Ð53 0Ð83Havelse, 52Ð08 0Ð78 0Ð90 0Ð66 0Ð87Kollerød, 52Ð22 0Ð26 0Ð72 0Ð53 0Ð76

Figure 3. Calibration and validation results on river flows at (a) Æbelholt, (b) Lyngby, (c) Havelse and (d) Græse Stations.

efficiency to the model performance criteria, which werealready considered adequate in a number of earlier stud-ies from Denmark (Henriksen et al., 2003; Kuntsche,2006).

The results from the synchronous simulations ongroundwater head levels within both the calibration andvalidation periods confirm general model efficiency alsofor simulating groundwater table changes and dynamicinteractions between surface waters and groundwater.Figure 4 shows the simulation and calibration results forthe four sample locations, which are quite evenly dis-tributed in the catchment to represent a wider ground-water system, and which resulted in comparably highervalues of the Root Mean Squared Error (RMSE) effi-ciency indicator for the calibration and validation periods.The indicator values are lower than the critical limit of4 m defined by Henriksen et al. (2008) to show an excel-lent modelling performance. Together with the simulation

results on river flows, there is also enough indicationof the usefulness of the model set-up in assessing localclimate change impacts on river flows at the selectedmonitoring stations and environmental flows calculatedfrom them through the desktop approach.

Table VI gives the net changes in the long-termmonthly averages of certain climatic variables whichwere calculated between the base simulation conditionsand the A2 and B2 scenario conditions of the scenarioperiod by using the observations and the outcomes of theselected RCM HIRHAM. The values show that HIRHAMresults based on the HadAM3H GCM data indicate amore shifted period of precipitation decreases in summermonths than the results based on the ECHAM4/OPYCglobal data. Monthly temperature increases are quitedifferent for the study area between the A2 scenariosmodeled by considering different global data from thetwo different GCMs, while average monthly PET changes



Figure 4. Calibration and validation results on groundwater head levels at the wells entitled as (a) 193Ð249, (b) 187Ð72, (c) 187Ð1261 and (d) 192Ð75.

Table VI. Net changes in regional climatic conditions [precipitation (P), temperature (T) and potential evapotranspiration (PET)]between the base period conditions and the conditions of the scenario period 2071–2100 for the study area.

Months J F M A M J J A S O N D

Had AM3H A2 P (%) 35Ð5 28Ð2 14Ð5 4Ð0 24Ð8 36Ð0 �3Ð7 �17Ð3 �12Ð3 �0Ð2 20Ð2 31Ð6T (°C) 3Ð6 3Ð3 3Ð1 3Ð1 3Ð0 3Ð2 3Ð2 3Ð8 4Ð2 4Ð2 3Ð8 3Ð9

PET (%) �5Ð3 �2Ð8 2Ð8 2Ð4 1Ð4 �0Ð9 2Ð8 7Ð7 12Ð1 12Ð5 9Ð1 �2Ð3ECHAM4/OPYC A2 P (%) 74Ð2 90Ð8 39Ð3 18Ð2 �20Ð9 �16Ð7 �15Ð8 �35Ð0 �7Ð4 7Ð9 45Ð9 53Ð0

T (°C) 9Ð5 11Ð2 11Ð4 9Ð2 8Ð1 7Ð6 5Ð3 4Ð8 5Ð0 6Ð7 7Ð7 8Ð3PET (%) 6Ð1 12Ð3 16Ð1 9Ð8 8Ð8 7Ð8 14Ð4 18Ð0 12Ð9 16Ð3 9Ð1 17Ð2

B2 P (%) 21Ð2 37Ð0 35Ð9 11Ð3 �5Ð9 �1Ð2 �1Ð8 �19Ð5 20Ð5 17Ð4 12Ð9 30Ð3T (°C) 4Ð1 5Ð3 5Ð0 2Ð8 3Ð0 3Ð1 3Ð1 3Ð3 3Ð5 3Ð9 3Ð8 3Ð5

PET (%) 2Ð6 9Ð6 6Ð6 10Ð3 5Ð5 �0Ð7 8Ð4 11Ð2 13Ð0 9Ð6 5Ð0 6Ð6

have, on the other hand, variable patterns in all scenariocases.

Despite the average precipitation that starts decreasingin May for the ECHAM4/OPYC A2 and B2 scenariosand in July for the HadAM3H A2 scenario, the simu-lated river flows still show slight increases in the averagesof the same months, possibly due to delayed river flowresponses to the changing precipitation in the lowlandcatchment study area (Figure 5). A similar late, but oppo-site response with still decreasing river flows against theprecipitation increases that start in the autumn can beobserved in all cases until December. Overall simulationsof the net changes between the flows of the base and sce-nario simulation periods indicate increases in river flowsup to 40% for the ECHAM4/OPYC B2 and HadAM3HA2 and even to 90% under the ECHAM4/OPYC A2 con-ditions. The decrease rates almost reach the levels of 20

and 35%, respectively, for the ECHAM4/OPYC B2 andA2 scenarios, while staying relatively constant with onlya 6% change in the HadAM3H A2 scenario.

Environmental flow requirements show distinct behav-iors between the northern and southern parts of the inves-tigated catchment. The flows computed with the simula-tion results of the ECHAM4/OPYC A2 scenario do notdivert much from the computed environmental flows ofthe base period at the two stations located in the north.However, the difference from the base period increasesto the south. For the Græse station, which is locatedin the very south, for example, all three scenarios giveyearly average values of around 3Ð4 m3 s�1, while thesame value calculated from the observations of the baseperiod is 2Ð9 m3 s�1. As a result of the general increasesof river flows estimated in all climate change scenarios,the quantities that would be available in the river system


38 G. O. GUL ET AL.

Figure 5. Deviations of river flows and corresponding environmental water allocation requirements from the base period values in three differentscenarios.

seem to allow ascending allocations for environmentalpurposes from the river flows in a future period.

CONCLUSIONS

Hydrological modelling of lowland river systems mayrequire detailed modelling through coupled hydrologi-cal and hydraulic models to achieve more realistic flow

estimates. Indeed, a delayed or immediate response ofa river system to the changes in meteorological inputs,which may have been amplified through the impacts ofclimate change, depends very much on catchment topog-raphy and morphology. Without overshading any pre-vious methodological application or transcending otheralternative modelling tools that can be experimented forsimilar case studies, the MIKE SHE/MIKE 11 dynamicand interactive modelling platform can be considered as



effective in such cases to tackle this sort of problemsas long as the studies that employ such coupled modelspay special attention to calibration and validation pro-cesses. The results also show that MIKE SHE/MIKE11 coupling provides an important capacity for simulat-ing stream flows and groundwater head levels especiallywhen the unsaturated zone component is included and theentire catchment is analysed as a dynamic system.

Although the actual lengths of the simulation periods(i.e. the base period 1999–2003 with current conditionsand the future simulation period of the same length underclimate change effects) employed in the study for sim-ulating river flows and correspondingly computing envi-ronmental flows may be considered short from an hydro-logical point of view, the results still remain indicativeof the estimated changes in average flow conditions for arepresentative year with similar periodical characteristics.

While the scenario estimates mostly show clear devi-ations from the observed averages, the response of riverflows to the changes in climate events varies between thedifferent GCMs investigated in the study. However, a typ-ical delayed response of flows to any immediate monthlychange (either increase or decrease) in precipitation isobvious for all the scenario cases. The simulation resultson river flows may provide insights into any follow-upflood study that aims to assess how the floodplains in thecatchment react to the considerable changes in the flowregime as a result of climate change.

While the overall increases in the capacity of environ-mental flows to be supplied at different locations of thecatchment may be considered, at first sight, as potentiallypositive effect of a changing climate, further studies thattarget ecological resilience in the study area are necessaryto follow such hydrological assessments. This is mainlynecessary to figure out whether any excessive change inthe available quantities of water may bring about changesin physical environment or biota, eventually having sig-nificant deleterious effects on the composition, resilienceor productivity of natural and managed ecosystems.

ACKNOWLEDGEMENTS

This work was carried out as a part of the research on‘Implementation and Use of SWAT and MIKE SHE Mod-els for Danish and Turkish Catchments’, supported bya Post Doctoral Grant from the Scientific and Techno-logical Research Council of Turkey (TUBITAK). Theauthors would like to thank the Geological Survey ofDenmark and Greenland (GEUS) for providing relevantdata and consultancy; Danish Hydraulic Institute (DHI)for providing the MIKE SHE model; and Dr VladimirSmakhtin from the IWMI for providing the Global Envi-ronmental Flow Calculator (GEFC) software package fordesktop rapid assessment of environmental flows. Dataon RCM results which were utilized in local climatechange impact assessments were provided through thePRUDENCE data archive, funded by the EU throughcontract EVK2-CT2001-00132.

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