Journal of Hydrology 412–413 (2012) 220–232

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Journal of Hydrology

journal homepage: www.elsevier .com/locate / jhydrol

Assessing the impact of future land-use changes on hydrological processesin the Elbow River watershed in southern Alberta, Canada

G.N. Wijesekara a,⇑, A. Gupta b, C. Valeo c, J.-G. Hasbani a, Y. Qiao d, P. Delaney d, D.J. Marceau a

a Department of Geomatics Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB, Canada T2N 1N4b Alberta Environment, Calgary, Deerfoot Square Building, 2938 11 Street, NE, Calgary, AB, Canada T2E 7L7c Department of Civil Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB, Canada T2N 1N4d DHI Water and Environment Canada, 150 Main St. Suite 303, Cambridge, ON, Canada N1R 6P9

a r t i c l e i n f o

Article history:Available online 23 April 2011

Keywords:Cellular automataPhysically-based distributed hydrologicalmodelingIntegrated modelingMIKE-SHELand-use change modeling

0022-1694/$ - see front matter � 2011 Elsevier B.V. Adoi:10.1016/j.jhydrol.2011.04.018

⇑ Corresponding author. Tel.: +1 4036687312.E-mail addresses: gnwijese@ucalgary.ca (G.N. Wije

(A. Gupta), valeo@ucalgary.ca (C. Valeo), jean-gabrieqiy@dhigroup.com (Y. Qiao), pad@dhigroup.com (P. Dy.ca (D.J. Marceau).

s u m m a r y

The Elbow River in southern Alberta, Canada is the source of the Glenmore reservoir, which providesdrinking water to the City of Calgary. Due to the rapid population growth in Calgary, the Elbow Riverwatershed (ERW) that covers about 1238 km2 has been under considerable pressure for land-use devel-opment over the last decade. This study was undertaken to assess the impact of potential land-usechanges over the next 20 years on the hydrological processes in ERW by combining a land-use cellularautomata (CA) model and the distributed physically-based MIKE-SHE/MIKE-11 hydrological model. TheCA model was calibrated using four land-use maps covering the period 1985–2001 and validatedagainst the maps of 2006 and 2010. Simulations of land-use changes were then performed from2006 to 2031 at a five year interval; land-use based parameters were extracted from the simulatedmaps and transferred to MIKE-SHE/MIKE-11. MIKE-SHE was calibrated for the period 1985–1990 andvalidated for the period 2000–2005. The Nash and Sutcliffe coefficients of efficiency calculated betweenobserved and simulated flow data for the calibration and validation periods are 0.56, 0.52, 0.79, and0.75 based on different hydrometric stations respectively, indicating an acceptable level of performanceof the model. Land-use changes analyzed for the period 2001–2031 reveal a 65% increase in built-upareas, 20% in rangeland/parkland, and 1% in agriculture along with a reduction of 28% in deciduous,and 6% in evergreen forest. As a result, the hydrological modeling indicates an increase of 7.3% in over-land flow, and a decrease of 1%, 13.2%, and 2.3% in total evapotranspiration, baseflow, and infiltrationrespectively along with a decrease of the total flow by 4%. These results reveal a potential significantnegative impact on the sustainability of ground/surface water supplies and groundwater storages inthe future in the watershed in addition to an increased risk of flashy floods. The study also revealedthat due to the complex hydrological regime existing in the study area, a comprehensive physically-based method is required to better represent the interaction between groundwater and surface water.The combined CA/MIKE-SHE models appear as a useful tool to assess the impact of land-use changes onthe hydrologic cycle and to better understand the connection among the hydrologic components in theElbow River watershed.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

Water is an essential component in our environment that hu-mans often take for granted, and forecasting its availability forthe next generation has become an essential task in planning andresource management for rapidly growing cities. Forecasting thespatial distribution of water availability requires hydrologic mod-

ll rights reserved.

sekara), anil.gupta@gov.ab.cal@hasbani.ca (J.-G. Hasbani),elaney), dmarceau@ucalgar-

eling of groundwater and surface water. In growing cities and sur-rounding areas, one of the primary factors that cause changes inwater resources is the constant evolution in land use. This transfor-mation of earth’s land surface has many consequences on biophys-ical systems at all scales ranging from local urban heat islands andalterations in stream flow patterns to altered patterns of globalatmospheric circulation and long-term extinction of species.Understanding the consequences of land-use change on hydrolog-ical processes, such as changes in water demand and supply fromaltered hydrological processes of infiltration, groundwater re-charge and runoff and integrating this understanding into theemerging focus on land-change science is a major need (DeFriesand Eshleman, 2004).

G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232 221

The Elbow River watershed is located in southern Alberta, Can-ada and covers approximately 1238 km2. 65% of the watershed islocated in the Kananaskis improvement district. The remainingarea is divided among the municipal district of Rocky View(20%), the Tsuu T’ina Nation (10%) and the City of Calgary (5%), afast growing City of over one million inhabitants (ERWP, 2010)(Fig. 1). The watershed is the source of the Glenmore reservoirwhich fulfills part of the drinking water supply to the City of Cal-gary – approximately supplying one in six Albertans. Due to the ra-pid population growth of the City of Calgary, this watershed hasbeen subjected to considerable pressure for development in thelast decade (City of Calgary, 2005). In addition, this area belongsto the Canada’s Western Prairie Provinces, which lie in the rainshadow of the Rocky Mountains, and as a result, are the driestareas of southern Canada. It is predicted that in the near future, cli-mate warming, through its effect on glaciers, snow packs and evap-oration, will combine with cyclic droughts and rapidly increasinghuman activity in the Western Prairie Provinces to cause a crisisin water availability in this area (Schindler and Donahue, 2006).Therefore, investigating the rapid changes in land-use in the ElbowRiver watershed and their impact on the land phase of the hydro-logical cycle is becoming a crucial issue.

Scientists have made several attempts to quantify the impact ofland-use change on hydrological processes (Calder et al., 1995;Gustard and Wesselink, 1993; Harbor, 1994; Thanapakpawinet al., 2006; Zimmermann et al., 2006). These methods mostly relyon either simple and lumped, or distributed and conceptual hydro-logical modeling. Harbor (1994) introduced a practical method ofestimating the impact of land use on surface runoff, groundwaterrecharge and wetland hydrology, based on a simple spreadsheet

Fig. 1. Location of t

analysis that can be used by planners. Zimmerman et al. (2006)and Calder et al. (1995) studied the impact of land clearing andconversion from forests to agriculture on soil properties, surfacerunoff and levels of lakes. Gustard and Wesselink (1993) applieda lumped conceptual model to Balquhidder catchments in UKbased on land-use change and found that with increasing affores-tation, the mean flow decreases, the flow duration curve shiftsdown, the annual minimum series decreases, and the storageneeded to maintain a given yield increases. Thanapakpawin et al.(2006) applied a distributed hydrology soil vegetation model tothe Mae Chaem river basin, in Thailand, to simulate forest-to-cropexpansion and crop-to-forest reversal scenarios based on land-cov-er transitions observed from 1989 to 2000. They found that theexpansion of highland crop fields is leading to slightly higher reg-ulated annual and wet-season water yields compared to a similarexpansion in the lowland–midland zone.

A deviation from the previous approaches is the use of compre-hensive, physically-based, distributed hydrological models (Imet al., 2009; Niehoff et al., 2002; Oogathoo, 2006). Im et al.(2009) applied the MIKE-SHE model to investigate the watershedresponse to land-use changes within the Gyeongancheon wa-tershed in Korea. Using proportional changes in five land-use clas-ses derived from multi-temporal Landsat TM images, theyobserved an increase of total runoff (5.5%) and overland flow(24.8%), predominantly due to an increase (10%) of urbanization.Oogathoo (2006) also applied MIKE-SHE to the Canagagigue Creekwatershed located in the Grand River Basin, in Ontario, Canada totest the impact of various watershed management scenarios onhydrological processes by applying land-use increase/decreasepercentages (e.g., increase urbanization from 0.2% to 2%). Her

he study area.

222 G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232

results revealed that deforestation considerably increased the totalflow (11%) while with application of tile drainages, the high runoffpeaks decreased. Using a similar approach, Niehoff et al. (2002)generated land-use change scenarios by increasing the currentland-use composition by a percentage and investigated their im-pact on hydrological processes using a physically-based distrib-uted hydrological model (WaSiM-ETH). These authors concludedthat the influence of land-use conditions on storm-runoff genera-tion depends greatly on the rainfall event characteristics and onthe related spatial scale; they also noted that the influence ofland-use on storm-runoff generation is stronger for convectivestorm events.

Recent studies have demonstrated the potential of an integratedmodeling approach to evaluate the impact of land-use changes onwater resources (Bithell and Brasington, 2009; Fohrer et al., 2005;Lin et al., 2007). Lin et al. (2007) combined a spatially-explicit land-use change model (CLUE-s) and a distributed/lumped hydrologicalmodel developed by Haith and Shoemaker (1987) to evaluate theimpacts of land-use change scenarios on the hydrology and land-use patterns in the Wu-Tu watershed in Northern Taiwan. Theyfound that the impact on hydrological processes was very signifi-cant and influenced by cumulative land-use changes. They advo-cate that combining a spatially-explicit land-use simulationmodel with a hydrological model is an effective tool for evaluatingthe impact of land-use change on hydrological processes.

Following this approach, Bithell and Brasington (2009) coupledthree models representing society, ecology, and hydrology toinvestigate how demographic changes influence deforestation,which in turn affects the forest ecology, along with the streamhydrology and water availability. Their results revealed that asthe number of households increased from 3 to 337 in 200 yearsof simulation, the predicted storm response becomes progressivelyflashier. The expansion of agriculture and the loss of forest contrib-uted to a 4% increase in total evaporative losses, a 22% decrease inannual discharge and a 18% increase in the internal storage ofwater and loss to deep groundwater. Fohrer et al. (2005) evaluatedthe impact of different average agriculture field sizes produced bythe economic model ProLand on hydrological processes using theIOSWAT hydrological model in the German Aar watershed. Oneof the important findings of this study is that deforestation in favorof grassland and field crops impacts the time series of quick runoffcomponents and creates a potential risk for flood events.

These previous studies illustrate that in order to improve theassessment of the impact of future land-use changes on hydrolog-ical processes, it is essential to forecast reasonably well the possi-ble land-use changes at the individual cell level considering alldominant land uses in the area. Similarly, physical watershed char-acteristics that might be affected by land-use changes should alsobe investigated using a spatially-distributed, physically-basedhydrological model. In addition, processes of the entire hydrologi-cal cycle should be taken into consideration during this investiga-tion to obtain a more accurate and comprehensive understanding.Spatially-explicit land-use change models simulate localized land-use changes over time which exhibits significant changes in thelandscape patterns (White and Engelen, 2000). Furthermore, ahydrological model that operates at a spatially-distributed levelusing physical properties that are both land-use based (e.g., surfaceroughness) and non-land-use based (e.g., soil characteristics) willproduce more detailed and potentially more accurate results com-pared to a model that operates at lumped level (Refsgaard, 1996;Yang et al., 2000). Therefore, coupling these two modeling environ-ments (a spatially-explicit land-use change model and a physi-cally-based fully distributed hydrological model) is the focus ofthe current study.

The objective of this study is to assess the impact of land-usechanges on the hydrological processes of the Elbow River wa-

tershed. This is achieved by the coupling of two dynamic models:(1) a land-use cellular automata (CA) model applied to simulateland-use changes, and (2) a hydrological model, MIKE-SHE (com-bined with the MIKE-11 river model) to simulate the hydrologiccycle within the study area. CA models are remarkably effectiveat simulating spatial patterns and structures of the landscape. Un-like conventional land-use modeling techniques, they are spatiallyexplicit, able to capture a wide variety of dynamic spatial pro-cesses, highly adaptable, and relatively simple while demonstrat-ing a rich behavior (White and Engelen, 2000).

The land-use CA model selected for this study has been cali-brated, tested and successfully applied in the Elbow River wa-tershed in a previous study (Hasbani, 2008; Hasbani et al.,2011). Compared to other land-use change models such asCLUE-s, this CA allows a user to dynamically select the parame-ters and parameter values from an analysis of historical data tocreate conditional transition rules that are further automaticallytransformed into mathematical rules. This procedure provides adirect relationship and understanding of the factors, both inter-nal and external, that drive land-use changes in the study area.CLUE-s uses a stepwise logistic regression and a non-spatial de-mand module to calculate the area that changes before the mod-el spatially converts this area into land-use changes (Verburget al., 2002).

The MIKE-SHE flow model along with the MIKE-11 river modelis a comprehensive, deterministic, distributed, and physically-based modeling system capable of simulating all major processesin the land phase of the hydrologic cycle compared to other hydro-logical models (Sahoo et al., 2006). Furthermore, this model isbeing used by Alberta Environment for water related studies inthe province of Alberta, Canada. Compared to other well-knownhydrological models such as SWAT, MIKE-SHE uses physically-based equations (St. Venant equations) at the individual cell levelto simulate the overland flow, which is considered crucial in thecontext of our study. Further, the linkage of MIKE-SHE withMIKE-11 provides a fully integrated way to incorporate detailedchannel flow in the modeling (DHI, 2009) while the SWAT model,which is semi-distributed, simulates overland flow using the mod-ified SCS curve number method or the green ampt infiltrationmethod at the sub-basin level (Arnold et al., 1993).

This project has been undertaken in collaboration with AlbertaEnvironment and the DHI Water and Environment Canada in Cam-bridge Ontario, developer of MIKE-SHE/MIKE-11. It is expected thatthe integrated modeling system being developed will help plan-ners to improve land-use management and water resource alloca-tion in the context of urban growth, land-use intensification andclimate change in the Elbow River watershed.

2. Methods

This section first describes the main characteristics of the ElbowRiver watershed and the datasets that are needed for the modeling.The implementation of the land-use CA model is then presentedfollowed by a detailed description of the Elbow River WatershedHydrology Model (ERWHM) that was developed, including its con-figuration, calibration and validation. This section ends with a pre-sentation on how the two model setups (land-use CA and ERWHM)were coupled through land-use based parameters and how com-bined simulations were run to assess the impact of land-usechanges on the hydrological processes of the watershed.

2.1. Study area and datasets

The Elbow River watershed is characterized by a complexhydrological regime where there is a considerable groundwater –

Fig. 2. Land-use map of the watershed for the year 2010, alluvial aquifer and location of the hydrometric stations.

G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232 223

surface water interaction along the river mainly through the allu-vial aquifer located in the north-east portion of the watershed(Fig. 2). The average annual precipitation for the entire watershedis approximately 600–700 mm. The Elbow River drops just over akilometer in elevation between the head of the river at 2100 mabove sea level and where it enters the Bow River at 1060 m abovesea level. The mean discharge at the inlet of the Glenmore reservoirlocated in the east end of the watershed is about 12 m3 s�1. Thealluvial aquifer covers approximately 5% of the watershed and iscomposed primarily of sand and gravel in various proportions. Itis generally very permeable and hydraulically connected to the El-bow River and is characterized by significantly higher hydraulicconductivity than the adjacent uplands (ERWP, 2010).

In terms of land use, urban areas are concentrated in the north-east part of the watershed and cover 5.9% of the entire watershed(Fig. 2). Agriculture and rangeland/parkland represent respectively16.7% and 6.2% while evergreen and deciduous forests respectivelycover 34% and 10% of the watershed. Recent clear-cuts can be ob-served in about 1.8% of the entire watershed.

For the development of the CA model, a set of historical land-use maps generated from Landsat Thematic Mapper imagery ac-quired during the summers of 1985, 1992, 1996, 2001, 2006 and2010 at the spatial resolution of 30 m were used. These maps con-tain nine dominant classes: water, road, rock, evergreen forest,deciduous forest, agriculture, rangeland/parkland, built-up, andclear-cut. The quality of these maps was assessed through fieldverification and the use of third-party maps (from Google) along

with expert knowledge. An in-house computer program was ap-plied to detect and correct minor spatial–temporal inconsistenciesin the historical maps due to classification and georeference errors.The datasets required for the setup of MIKE-SHE/MIKE-11 werecollected from field surveys, scientific literature, and online datasources. Their description can be found in Table 1.

2.2. The land-use CA model

A CA is a dynamic simulation model that represents space as amatrix of regularly arranged cells with each cell having its ownstate, such as its land use, defined by a numerical value. The modelincorporates transition rules that decide the next state of the cur-rent cell considering the values of the cells within its local or ex-tended neighborhood and some additional constraints that canbe incorporated in the model. The state of each cell evolvesthrough discrete time steps with transition rules applied to all cellsiteratively (White and Engelen 2000). Over the recent past, CAmodeling has been found remarkably effective in the simulationof land-use/land-cover changes (Almeida et al., 2008; Barredoet al., 2003; Li et al., 2003; Ménard and Marceau, 2007; Puliafito,2006; Santé et al., 2010; Shen et al., 2009; Stevens et al., 2007)and are increasingly used to guide decision making in spatial plan-ning (Jenerette and Wu, 2001; Li and Yeh, 2000).

The raster-based land-use CA model used in this study has thefollowing design details. The neighborhood configurationapproximates a circle around a center cell and comprises several

Table 1Dataset collection and preparation.

Data Description of data gathering and processing

LAI – leaf area index A LAI value was calculated for each land-use class for each month of the year for the period September 2000–September2001 based on satellite derived spatially distributed LAI and scientific literature (with comparison to standard LAI valuesbased on types of vegetation) (LPDAAC, 2009; Myneni et al., 2003; Wijesekara and Marceau, 2009; Scurlock et al., 2001;Zeng, 2001)

Root depth (RD) Since information on specific crops that are harvested in the agricultural areas in the Elbow River watershed wereunavailable, the four most commonly harvested crops (wheat, barley, canola, and tame hay) in southern Alberta and theiraverage RD values were obtained from the literature. The temporal changes of the root depth for the agricultural areas andthe root depth values for the rest of the vegetation classes were also obtained from the literature (Allen et al., 1998; HeritageCommunity Foundation, 2002; Kim et al., 2005; Task Committee on Hydrology Handbook, 1996)

Manning number M (inversion ofstandard Manning’s n)

Each land-use class was given a value for the Manning’s M roughness coefficient based on the literature (Wijesekara andMarceau 2009). They are: water: 25.04, road: 76.9, rock: 40.0, evergreen: 10.0, deciduous: 10.0, agriculture: 28.57,rangeland/parkland: 33.33, built-up: 90.9, and clear-cut: 90.9

Soil structure Two different soil classes were derived based on the land-use maps. Average soil properties were derived using soildatabases available from the province of Alberta (AgraSID, CanSIS). Different saturated hydraulic conductivity (Ks) valueswere assigned based on land use. Appropriate values were assigned to Ks during the calibration

Groundwater table The spatially-distributed static ground water table was derived using the Groundwater Information System Databaseprepared by the Groundwater Information Centre, Government of Alberta

Climate data Climate data were acquired from the Agroclimatic Atlas of Alberta which includes climate data from over 1200 stations inAlberta and from about 1400 stations bordering Alberta and British Columbia, Saskatchewan, Yukon, Northwest Territories,Nunavut and the United States. Climate data (temperature, precipitation, reference evapotranspiration) were calculated foreach township of Alberta using an interpolation procedure

Topography A digital elevation model (DEM) hydrologically corrected was obtained from Alberta Environment at a spatial resolution of25 m

Cross sections of the river network 16 surveyed cross sections done by Golder Associates (2008) were used along the main Elbow River; the remaining of thecross sections in the middle section of the Elbow River and its branches were digitized from the DEM. A total of 141 crosssections were used to represent the whole river network. It is assumed that the digitized cross sections from thehydrologically corrected DEM adequately represent the river bathymetry for this study

Water flow/level The data on water flow (discharge hydrographs) were obtained for ten hydrometric stations. Data from two selectedhydrometric stations were used for the calibration of MIKE-SHE based on their spatial location and their availability for thesimulation time period

Table 2Land-use transitions considered during the simulations.

From To

Evergreen AgricultureDeciduous AgricultureEvergreen Built-upDeciduous Built-upAgriculture Built-upRangeland/parkland Built-upRangeland/parkland AgricultureAgriculture Rangeland/parkland

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concentric neighborhood rings. The influence of the neighboringcells on the central cell is constant within each ring, but differs be-tween the rings. The modeler can choose the desired number andsize of concentric neighborhood rings around a cell. In additionto the influence of the cells located within the local and extendedneighborhoods, four external driving factors were considered asparameters in the transition rules, namely the distance to the Cal-gary City center, the distance to a main road, the distance to a mainriver, and the ground slope.

The transition rules are dynamically extracted during the cali-bration of the model using the set of historical maps. For each typeof land-use change that is under consideration, all cells that havechanged state in the historical maps are identified along with thenumber of cells of a particular state in their neighborhood andthe values of each driving factor. Frequency histograms are gener-ated to display this information, which is then used by the modelerto identify the significant parameters and parameter values to beincluded in the conditional transition rules. As an example, a con-ditional rule can take the form of ‘‘If [distance to a main road is be-tween 0 and 427 m] and [. . .] then the central cell changes fromEvergreen to Built-Up’’. These conditional rules are then automat-ically converted into mathematical rules; the mean and standarddeviation of the defined ranges of values on the frequency histo-grams are computed to become the coefficients of the parametersof the mathematical transition rules. Using the coefficients of eachtransition rule, a resemblance index (RI) is calculated using Eq. (1),which quantitatively describes the similarity between the neigh-borhood content of a cell at the time of the simulation and theneighborhood contents that have been used to generate the valuesof the parameters of the transition rule.

RI ¼Xm

i¼1

jni � �xijri

ð1Þ

where m is the number of layers (corresponding to the number ofdriving factors plus the number of land-use classes multiplied by

the number of neighborhood rings), ni is the value in layer i, �xi isthe mean value for layer i in the transition rule, and ri is the stan-dard deviation for layer i in the transition rule. Eight land-use statetransitions as described in Table 2 were considered during theextraction of the transition rules from the historical data.

Using the mathematical transition rules that are created duringthe calibration, the simulation of land-use maps corresponding tofuture time instances is implemented. For each time step, theneighborhood composition of every cell is read and the level of cor-respondence with the parameters of the transition rules is com-puted. The cells having the highest level of correspondence basedon user-specified constraints and the influence of each rule aresubjected to change state. Decision on which cell should be associ-ated to each type of land-use change is made by recursively sortingthe type of land-use changes and selecting the cell having thesmallest RI value. Once the required number of cells associatedto each type of land-use change is met or when no more cellscan be assigned, the model generates the new land-use map andupdates the statistics that correspond to the percentage of cellsassociated to each rule and each type of change. If the numbersof cells associated to each rule and each type of land-use changeis different than the numbers found from the historical data andprevious time steps, a correction is applied at the next time step(Hasbani et al., 2011).

G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232 225

Sensitivity analyses were carried out to evaluate the influenceof the cell size, neighborhood configuration, and external drivingfactors on simulation results. Tests were also carried out to inves-tigate whether the method of selecting the ranges of value fromthe frequency histograms affects the outcomes of the simulations.Details regarding the sensitivity analyses and their interpretationcan be found in Hasbani et al. (2011). In summary, it revealed thatthe best simulation outcomes were obtained with a cell size of60 m, a neighborhood configuration defined by three rings ofrespectively 5, 9 and 17 cells, corresponding to 300, 540 and1020 m respectively, a selection of the ranges of values from thefrequency histograms concentrated around the mode, and theuse of all four external driving factors. This overall configurationwas therefore selected for the simulation of future land-use mapswith the CA model.

The quality of the calibration of the CA model was evaluated bycomparing the simulated land-use maps of 2006 and 2010 with thereference maps of the corresponding years using the standard Kap-pa statistics. An overall Kappa of 0.89 and 0.81 was obtainedrespectively for the years 2006 and 2010, indicating a high levelof agreement between the maps being compared. The decrease ofvalue for the year 2010 is largely explained by the fact that consid-erable urban development occurred in the eastern part of the wa-tershed between 2006 and 2010 and that this information was nottaken into account in the model calibration. Similarly, relativelylarge clear-cut areas appeared only after 2006 in the watershed.While such measures of agreement are not perfect indicators ofthe quality of the simulation outcomes, the CA model was consid-ered sufficiently well calibrated for the purpose of this study.

2.3. The Elbow River Watershed Hydrology Model (ERWHM)

The Elbow River Watershed Hydrology Model (ERWHM) wasconstructed using the MIKE-SHE and MIKE-11 model environ-ments developed by DHI (2009) to simulate the hydrological pro-cesses in the Elbow River watershed. MIKE-SHE is a numericalhydrologic model that simulates all the major components of theland-based phases of the hydrologic cycle. These components in-clude snowmelt, evapotranspiration (ET), overland flow, unsatu-rated flow, and groundwater flow. For each of these processes,MIKE-SHE offers several different approaches which range fromsimple, lumped, and conceptual to advanced, distributed, andphysically based.

A conceptual model was first built by combining the most suit-able approaches in order to meet the requirement of the studywhile considering computational and data availability constraints.The purpose of building a conceptual model is to implement a sys-tematic approach for the selection of appropriate data and meth-ods to simplify the model configuration based on acceptableassumptions for the particular focus of the study (Refsgaard,1997). Specific requirements such as a suitable scale at which thedesired level of spatial details is maintained, computational timeand power, and data availability were also considered in the con-ceptual model. In this study, the surface water components (over-land flow and channel flow) were considered as the governingcomponents in the simulation of the impact of land-use changeson the hydrological processes of the watershed. Therefore, thesecomponents were given prominence when selecting the most com-prehensive methods to be used for the simulation.

The spatial boundary of ERWHM was set to match the boundaryof the Elbow River watershed. The boundary was delineated fromthe DEM (Table 1). The model domain was spatially discretizedusing 100 m grid cells. The climate data that were used are contin-uous daily records for each of the 29 townships located within theElbow River watershed for the period of 1961–2005. These dailyrecords include minimum and maximum temperature, total

precipitation, and calculated potential evapotranspiration. Theconceptual model that was used for the study is described below.Details about the governing equations and the selected methodsfor the simulation of hydrological processes are included in Appen-dix A.

The ERWHM uses a finite difference method for simulatingoverland flow (Appendix A). Each grid element representing thewatershed contains a unique set of physical properties that gov-erns the changes of the overland flow. Physically-based valueswere assigned for surface roughness to each land use as describedin Table 1. A uniform value was set for detention storage for thewhole watershed.

The method selected in the snowmelt module in the ERWHM isthe modified degree-day method, whereby the rate of melting in-creases as the air temperature increases. Air temperature, whichvaries considerably throughout the watershed over time, is themost important input parameter in determining the ability of themodel to predict snow accumulation and melt. Compared to mod-els based on the energy balance method, this method requires lessdata and is less computationally intensive.

The unsaturated flow component of the ERWHM uses a two-layer water balance method that functions in conjunction withthe ET component of the model (Appendix A). This simplifiedmethod was selected to simulate ET and unsaturated flow mainlydue to the unavailability of detailed soil characteristics and geolog-ical data required in more detailed methods. It is assumed that thesoil structure is independent of land-use change throughout thesimulation period. The use of the two-layer water balance ap-proach considerably reduces computational time compared tothe use of a more comprehensive method when run in combinationwith the physical based overland flow for the entire Elbow Riverwatershed. Furthermore, it supports the integration of distributedsoil conductivity based on different land uses in a simplified way,thereby limiting the infiltration within built-up areas. In the con-figuration of the ERWHM, two uniform soil types were created todistinguish built-up land-use and the other land uses. This wasdone mainly to adjust the amount of infiltration based on pavedareas.

The linear reservoir method was selected to simulate saturatedzone flow considering the data availability, parameter estimation,and computational requirements (Appendix A). In most cases, thesubsurface flow is simulated reasonably using this method, andthis approach has been primarily developed to provide a reliable,efficient instrument for the assessment of water balance and sim-ulation of runoff for ungauged catchments, the prediction of hydro-logical effects of land-use changes, and flood prediction (DHI,2009). This method was also selected to ensure that the overallsimulations of MIKE-SHE could be completed within a reasonableperiod of time when applied to the entire watershed at the spatialresolution of 100 m.

A one-dimensional hydrodynamic model, MIKE-11, was used toanalyze water movement in the channel flow in the current study(Appendix A). The river network was generated based on the DEMusing the river network digitizing technique. The network consistsof the main Elbow River running from the Elbow Lake to the Glen-more reservoir and to the east end of the Elbow River watershed, inaddition to eight main branches of the Elbow River. Most of theelevation differences between the surveyed points of the riverbanks and the DEM, and the elevation differences at connectingpoints of the main river and tributaries were adjusted. Both sur-veyed cross sections by Golder Associates (2008) and digitizedcross sections from the DEM were used along the river streams.Boundary conditions for all unconnected ends of the river brancheswere set to ‘closed’ (i.e. no-flow boundaries). The downstreamboundary of the model was selected as the relationship betweenflow vs. water level. The surface roughness coefficient (Manning’s

226 G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232

M value) of the river bed was kept uniform and constant for the en-tire river network.

A sensitivity analysis of the ERWHM was conducted for theparameters: saturated hydraulic conductivity, ET surface depth,degree day coefficient, detention storage, surface roughness, andtime constants for interflow and baseflow. The model calibrationand validation was then carried out using observed discharge datameasured at three hydrometric stations along the main river chan-nel (05BJ004, 05BJ006, 05BJ010) (Fig. 2) and using the total waterbalance as an indicator. The performance of the ERWHM was eval-uated using the Nash and Sutcliffe efficiency (Nash and Sutcliffe1970) (NSE) calculated using Eq. (A.10) between simulated and ob-served flow hydrographs using averaged monthly output data. Thecalibration period was selected as 1985–1990 while the validationperiod was selected as 2000–2005. The land-use maps of 1985 and2001 were used during the periods 1985–1990 and 2000–2005,respectively. During the calibration and validation, NSE was calcu-lated for the years 1990 and 2005 and for the periods 1985–1990and 2000–2005. As an additional statistical comparison, the corre-lation coefficient was also calculated (Eq. (A.10)) between observedand simulated flow data for the above calibration and validationperiods.

During the calibration it was found out that the simulatedhydrographs underestimated the water flow in the river system.This was mainly due to two problems. The head water to the ElbowRiver main channel was not included in the ERWHM due to dataunavailability. Furthermore, past studies done in the Elbow Riverwatershed showed that in the upstream part of the river, the bankstorage accounts for 73% of the actual groundwater discharge dur-ing a normal baseflow recession (Meyboom, 1961). The simpler lin-ear reservoir method selected to simulate groundwater has asimple conceptual layout representing the hydro-geology of thewatershed in the ERWHM and therefore was too simple to repre-sent complex groundwater-surface interactions through alluvialaquifer (DHI, 2009). To compensate for this problem, a constant-head inflow boundary condition was added to MIKE-11 channelmodel as the boundary inflow. The most fitting values of inflowfor an improved model NSE were derived during the calibration.

2.4. Simulating the impact of land-use changes on hydrologicalprocesses

To evaluate the impact of land-use changes on the hydrologicalprocesses of the watershed, future possible land-use changes weresimulated with the land-use CA model. The initial map used for thesimulations was the 2001 map and simulations were carried outfor the years 2011, 2016, 2021, 2026, and 2031. A local constraintto forbid new built-up development within the Tsuu T’ina Nationreserve was applied for all simulations. A global constraint to limitthe number of new built-up cells at each time step was also ap-plied based on the historical and forecasted population trends ob-tained from Calgary Economic Development (2010).

Land-use based parameters were created for the ERWHM usingthe simulated maps. Assigning the corresponding parameter valuesto each land use, spatially distributed maps of surface roughness(Manning’s M) and saturated hydraulic conductivity were created.Furthermore, the spatial distribution of vegetation properties ofeach land use (LAI, RD) was changed by interpreting the vegetativecover changes observed in the simulated maps. The number of cat-egories and the spatial distribution of the land-use based parame-ters correspond to the number of land-use classes and their spatialdistribution. For example, the surface roughness expressed in Man-ning’s M associated with built-up has the value of 90.9, while thesurface roughness of evergreen vegetation is 10.0. Their spatial dis-tribution in the ERWHM is the same as the distribution of built-upand evergreen in the land-use maps. Therefore, the changes of land

use over time in the watershed affect the spatial distribution of theland-use based parameters of the model and consequently somehydrological processes in the watershed. For example, with theexpansion of built-up areas, the spatial distribution of the surfaceroughness corresponding to built-up increases, which leads to in-creased total surface runoff. Areas of less infiltration capacity in-creases as the built-up areas expand, affecting the spatialdistribution of saturated hydraulic conductivity. With the changeof land use, the spatial distribution of vegetation properties suchas LAI and RD also changes. For example, with the reduction of for-ested areas, the number of cells having LAI and RD values associ-ated with forested areas is decreased in the model domain. As aresult, the total transpiration by the roots and canopy evaporationdefined by LAI and RD within this land use is also reduced.

To assess the impact of land-use changes over the hydrologicalprocesses, simulations were conducted with the ERWHM usingland-use based parameters extracted from the maps of 1992,1996, 2001, 2006, 2010,2011, 2016, 2021, 2026, and 2031. Thenon-land-use based data (i.e., river channel, topography, ground-water table) and parameters (i.e., snow melt parameters, ET sur-face depth, etc.) were kept unchanged. The climate data (dailyprecipitation, reference ET, daily temperature) used for these sim-ulations were the same as the data used within the validation per-iod (i.e., 2000–2005). The output frequency of each hydrologicalprocess was set to 24 h. After each simulation, the total water bal-ance error, total surface runoff, total ET, total infiltration, total riverflow and baseflow were derived and tabulated. The initial condi-tion for snow storage was set at 0 mm and each simulation wasstarted from the month of September. A global initial conditionwas set for the river flow based on field measurements. A simula-tion period of 5 years was used in MIKE-SHE/MIKE-11 model to al-low the river condition to become stable following the initialconditions.

3. Results and discussion

This section first describes the performance of the ERWHM, fol-lowed by the results obtained with the land-use CA model up tothe year 2031 and the impact of land-use changes on the hydrolog-ical processes of the watershed.

The sensitivity analysis conducted for the ERWHM showed thatthe distributed surface roughness highly affected both the totalwater balance error and the goodness-of-fit (NSE). Saturatedhydraulic conductivity, ET surface depth, degree day coefficient,detention storage, surface roughness of the river bed, and timeconstants for interflow and baseflow affected the goodness-of-fitto varying degrees. Based on the sensitivity analysis, the two mainparameters that were adjusted during the calibration were the sat-urated hydraulic conductivity (Ks) (1e�012 m/s for urban andclear-cut, 8e�008 m/s for the remaining area) and the surfaceroughness for the river bed (Manning’s M: 15). The remainingparameters were assigned the default values and physical valuesappropriate to the Elbow River watershed. The global average of8e�008 m/s was determined for Ks based on the sensitivity analy-sis that was carried out with changes of various other parameterssuch as detention storage. This value was found to be optimum interms of the amount of surface runoff, infiltration and simulatedriver flow. A low value of 1e�012 m/s was assumed for mainlythe urban land areas to allow a minimal amount of infiltrationdue to the paved environment existing in developed landscapes.This was also determined based on the sensitivity of Ks towardsthe generation of infiltration flow. The Manning’s M value of 15for the river bed was recognized as optimum after the head-waterand bank storage flow was compensated by the constant flowintroduced. Even though surface roughness values derived from

G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232 227

field measurements for the Elbow River watershed were not avail-able for comparison, a field survey done by Limerinos (1970) instreams in California State, United States, shows the range of Man-ning’s M of river streams can vary from 10 to 50. Therefore, the va-lue of 15 was considered a reasonable value for the current setup ofthe ERWHM. The total water balance error during all model runswas less than 1% and considered as an adequate model perfor-mance. With the introduction of constant level water inflow atthe head of the Elbow River, the NSE calculated on monthly inter-val improved from 0.02 and �0.56 to 0.56 and 0.52 during the per-iod 1985–1990 (for the hydrometric stations 05BJ004 and05BJ006); it improved from 0.53 and 0.54 to 0.79 and 0.75 duringthe period 2000–2005 (for the hydrometric stations 05BJ004 and05BJ010). The calculated NSE and correlation coefficient values atdifferent hydrometric stations based on monthly flow data arelisted in Table 3.

Table 3Calibration and validation results. The NSE and correlation coefficient were ca05BJ006, and 05BJ010. N/A means that no observed data were available.

Calibration/validation period NSE

05BJ004 05BJ006

September 1985–September 1990 0.56 0.52September 1989–September 1990 0.69 0.62September 2000–September 2005 0.79 N/ASeptember 2004–September 2005 0.94 N/A

Fig. 3. Comparison of simulated and observed flow data at the hydrometric station 05BJ0and 1990 (d).

The comparison of simulated and observed hydrographs (Fig. 3)shows that the trends of flow changes are reasonably simulated forthe calibration and validation periods. A higher performance is ob-served for the years 1990 and 2005 and the period of 2000–2005.According to model evaluation guidelines mentioned by Moriasiet al. (2007), a NSE above 0.5 for a monthly time step in hydrolog-ical modeling is considered satisfactory. Furthermore, a compari-son of observed and simulated flow data based on correlationcoefficients shows values greater than 0.7 for both the calibrationand validation periods using monthly data. Therefore, the perfor-mance of the ERWHM as configured was considered sufficient toevaluate the impact of land-use changes on hydrological processes.

The objective of adding constant head water to the river flowwas to compensate for the low simulated flow in the river so thatthe performance of the ERWHM is sufficient to run simulations. Bydoing so, the pattern of the hydrograph was not modified but the

lculated using observed discharge at the hydrometric stations 05BJ004,

Correlation coefficient

05BJ010 05BJ004 05BJ006 05BJ010

N/A 0.76 0.52 N/AN/A 0.89 0.62 N/A0.75 0.90 N/A 0.750.86 0.97 N/A 0.86

04 during the periods 2000–2005 (a) and 1985–1990 (c), and for the years 2005 (b),

228 G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232

magnitude of the flow was elevated to the level of the observedhydrograph. When the same constant head flow was applied dur-ing the validation period, the ERWHM showed improved correla-tion between the simulated and the observed data. A separatestudy (DHI Water and Environment, 2010) which was carried outbased on a physical based distributed module to simulate thegroundwater flow and surface–groundwater interaction, showedbetter performance in simulating the baseflow contribution tothe main river flow. This shows that the use of a more comprehen-

Fig. 4. Historical and simulated lan

Fig. 5. Simulated land-use

sive method to simulate the groundwater is capable of simulatingthe bank storage flow contribution to the streams. However, in theabove study, due to high computational intensity, the spatial reso-lution used in the model was reduced to 200 m. Since this modelsetup was not optimized to capture localized land-use changes ofthe Elbow River watershed, it was not used in the current study.We believe that the use of a physically-based and distributedmethod for simulating the groundwater at the scale of 100 mwould provide a better approach for simulating the complex

d-use trends in the study area.

map for the year 2031.

G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232 229

groundwater-surface interactions existing within the study area,which will produce a greater insight about the dynamics ofgroundwater levels and storages due to the impact of land-usechanges. The study on the integration of a comprehensive ground-water model within MIKE SHE/MIKE 11 will be presented in a sub-sequent publication.

When the CA model is used to simulate land-use changes from2001 to 2031, the land-use trends indicate a regular decrease ofdeciduous and evergreen and a regular increase of agriculture,rangeland/parkland, and built-up areas (Fig. 4). This is in accor-dance with the historical trends since the model assumes thatchanges likely to occur in the future will not vary greatly comparedto those that happened in the past. A map of these changes revealthat the built-up expands in the eastern part of the watershedadjacent to the City of Calgary, mostly at the expanse of agricul-ture, which moves further west (Fig. 5). The forecasted land-usechanges from 2001 to 2031 show an increase of 65%, 20%, and 1%in built-up, rangeland/parkland, and agriculture, which corre-sponds to 5%, 8%, and 20% of the entire watershed respectively.

Table 4The amount of evapotranspiration (ET), overland flow (OL), baseflow (BF) andinfiltration in storage depth (mm) from 1992 to 2031.

Year of land-use change ET(mm)

OL(mm)

BF(mm)

Infiltration(mm)

1992 1696 343 483 5621996 1699 342 481 5592001 1698 345 479 5562006 1694 350 476 5532010 1692 367 459 5342011 1693 360 467 5432016 1690 364 466 5422021 1686 367 466 5422026 1683 368 416 5432031 1681 370 416 543

Fig. 6. Variations of evapotranspiration (ET), baseflow (BF), overland flow

As a result of urbanization and further agricultural development,significant deforestation has occurred in both evergreen (6%) anddeciduous (28%) vegetation, which corresponds to 47%, and 19%of the entire watershed. As a comparison, within the past 20 years(1992–2010), there was an increase of 28% and 89% in rangeland/parkland and built-up areas, and a decrease of 8%, 20%, and 6% inevergreen, deciduous, and agriculture respectively.

Table 4 lists the total amount of ET, OL, BF and the infiltration instorage depth (mm) while Fig. 6 shows the variations of eachhydrological process as the land-use changes from 1992 to 2031.Using these extracted land-use based parameters, the impact onthe hydrological processes within the past 20 years (between1992 and 2010) shows an increase of 7.0% in overland flow (OL),and a decrease of 0.23%, 5.0%, and 5.0% in total ET, baseflow, andinfiltration respectively. With the change of land use between2001 and 2031, the results indicate an increase of 7.3% in overlandflow (OL), and a decrease of 1%, 13.2%, and 2.3% in total ET, base-flow, and infiltration respectively. The analysis of river flow inthe river stream shows a decrease of 4% in total flow. The compar-ison between Table 4 and Fig. 4 indicates that the above changesoccur as the urbanization and agriculture expands over time alongwith the deforestation due to the expansion of urbanization andagriculture.

4. Conclusion and work in progress

This study was conducted to assess the impact of land-usechanges on different hydrological processes of the Elbow River wa-tershed in southern Alberta. A land-use cellular automata modelwas linked to the physically-based distributed hydrological modelMIKE-SHE/MIKE-11. Considering five dominant land-uses in thewatershed, land-use maps were simulated for the years 2011,2016, 2021, 2026, and 2031, taking into account historical land-use changes, external driving factors and a constraint applied tolimit new land developments based on forecasted population

(OL), and infiltration as the land-use changes from 1992 to 2031.

230 G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232

growth. Considering the physical parameters of the localized land-use changes, the entire cycle of hydrological processes was thensimulated using physical methods based on the land-use basedparameters extracted from the simulated land-use maps.

The study demonstrates that both historical and forecastedland-use changes progressively increase the impact on the hydro-logical processes in the watershed. The results show that due tothe expansion of built-up and agriculture and the reduction ofnet capacity of water retention on surface as a result, the totalsurface runoff to the river increases creating less infiltration(Fig. 6). The augmentation of surface runoff over time increasesthe risk of occurrences of flash floods in the area in a rainfallevent of high magnitude. With reduced groundwater rechargecaused by less infiltration, the baseflow of the river systemdiminishes over time and lowers the total flow of the Elbow Riv-er. This might have a negative impact on the total water supply tothe Glenmore reservoir reducing the total supply of drinkingwater to the City of Calgary from the reservoir. The reduction oftotal annual flow of the river and groundwater will further affectthe existing and new surface water and groundwater extractions,respectively. The hydrological impact over the past 20 years (be-tween 1992 and 2010) shows a more significant reduction of totalinfiltration than between 2001 and 2031 (Fig. 6), which may bedue to contiguous larger patches of built-up areas that appearin 2010 (Fig. 2) compared to 2031 (Fig. 5), even though the num-ber of built-up cells forecasted in 2031 is greater. This indicatesthat, compared to a more fragmented landscape, the same areacharacterized by contiguous patches with low water retentioncapacity can more effectively reduce infiltration, thus allowingmore water to runoff to streams in close proximity, as indicatedby significantly high total overland flow based on the 2010land-use map (Fig. 6). The reduction in ET, BF, and infiltrationalong with significant growth in OL due to land-use changes thatappear in 2010 (Fig. 6) are due to the rapid urban growth from2006 to 2010 that is not reflected in the simulation of futureland-use changes.

This study provides a comprehensive picture of the watershedby incorporating the dominant land-use transitions and their im-pact on the land phase of the hydrological processes. This is animprovement over the use of a single scenario such as an increaseof deforestation, and over the use of a lumped model as the mod-eling based on localized changes tends to provide a more accuratepicture.

Further work is in progress to improve the setup of the twomodels. Additional constraints and transition rules could be imple-mented in the CA model to better capture certain aspects of thedynamics of the watershed, such as the deforestation. Work isbeing carried out to integrate a physically-based comprehensivegroundwater model and improve the calibration of the ERWHM.It is expected that this will lead to a better representation of thesurface–groundwater interactions in the watershed and the effectof bank storage in baseflow of the river network. Furthermore,snow surveyed data are also being integrated in the calibrationof the ERWHM. Finally an interface will be built to automaticallyextract and input the land-use parameters extracted from themaps simulated by the CA model and required for MIKE-SHE to en-sure that the two models can operate with minimum manualintervention.

5. Role of the funding source

Funding was received to carry out this research from AlbertaEnvironment, from a NSERC Discovery grant awarded to D. Mar-ceau, and from GEOIDE (project PIV-32), the Canadian Networkof Centers of Excellence in Geomatics.

Acknowledgements

This project was initiated in collaboration with the AlbertaEnvironment who provided financial support to G.N. Wijesekara.We thank Cheng Zhang from the University of Calgary for produc-ing the historical land-use maps used in the project. Additionalfunding was provided by a NSERC Discovery grant awarded to D.Marceau and by GEOIDE (project PIV-32), the Canadian Networkof Centers of Excellence in Geomatics.

Appendix A

A.1. Two-dimensional finite difference method to simulate overlandflow

This method solves a two-dimensional diffusive wave approx-imation of the Saint Venant equations to calculate the surfaceflow in the x- and y-directions and the water depths for each gridcell of the model domain. Using the diffusive wave approxima-tions of Saint Venant equations, the last three terms of themomentum equations are ignored to reduce the fully dynamicequations’ complexity. Losses due to local and convective acceler-ation and lateral inflows perpendicular to the flow direction areignored. Using the conservation of mass and the momentumequations, the following equations are derived to determine thedischarge per unit length along the cell boundary in the x- andy-directions respectively.

uh ¼ Kx �@z@x

� �1=2

h5=3 ðA:1Þ

vh ¼ Ky �@z@y

� �1=2

h5=3 ðA:2Þ

where h is the flow depth above ground surface (m), uh and vh rep-resent discharge per unit length along the cell boundary in the x-and y-directions, respectively [m2s�1], Kx and Ky are Manning Mor Stickler coefficient in the x- and y-directions, respectively. UsingEqs. (A.1) and (A.2), the flow across any boundary between grid cellsis given by:

Q ¼ KDxDx1=2 ðZU � ZDÞ1=2h5=3

u ðA:3Þ

where K is the appropriate strickler coefficient, hu is the depth ofwater that can freely flow into the next cell, and Zu and ZD arethe maximum and minimum water levels, respectively (mm).

A.2. Two-layer water balance method

This method uses a simple mass-balance approach to representthe unsaturated zone, and accounts for interception storagechanges, surface ponding, and water content in the root zone, infil-tration, evapotranspiration, and groundwater recharge. The ETmodule in MIKE-SHE uses meteorological, vegetation-basedparameters such as LAI and RD and soil moisture to simulate ET.It simulates evaporation from interception storage in the canopy,evaporation from the soil surface, transpiration of water by plantroots based on soil moisture in the unsaturated zone, and transpi-ration from groundwater if the rooting depth exceeds the thicknessof the unsaturated zone. This method assumes that if sufficientwater is available in the root zone, it is available for evaporation.The calculation of ET proceeds using a top down approach. Thismethod calculates ET from the canopy (Ecan), ponded water (Epon),unsaturated zone (Euz), and saturated zone (Esz) consecutively. Thetotal ET is calculated as the total of all (Eq. (A.4))

ETa ¼ Ecan þ Epon þ Euz þ Esz ðA:4Þ

G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232 231

If the average water content calculated exceeds the maximumwater content of the unsaturated zone, the groundwater rechargeis produced. The infiltration is determined based on the followingequation: Infiltration = minimum (ponded water, saturated conduc-tivity x duration of a time step, (water content at saturation – actualwater content) � layer depth) Eq. (A.6).

A.3. Linear reservoir method

In the linear reservoir method, the entire catchment is subdi-vided into a number of sub-catchments; within each sub-catch-ment, the saturated zone is represented by a series ofinterdependent, shallow interflow reservoirs, plus a number ofseparate, deep groundwater reservoirs that contribute to thestream baseflow (Fig. A.1).

The linear reservoir method is based on the linear reservoir the-ory in which the storage of the reservoir is linearly related to theoutput by a storage constant or time constant (Eq. (A.7)).

S ¼ kQ ðA:6Þ

In the context of our study, the linear reservoir method routes thewater to the river as interflow and baseflow (Fig. A.1: QIriver andQB) through the appropriate river links. The water being infiltratedfrom the unsaturated zone may either contribute to the baseflow ormove laterally as interflow towards the stream, and hence, withinthe linear reservoir model, the interflow reservoirs have two outlets(Fig. A.1). The interflow reservoirs in the linear reservoir methodwere constructed based on the alluvial aquifer and the river net-work. Combined areas of river channel along with the alluvial aqui-fer with an added buffer were configured as one interflow reservoirand the rest of the area as the second reservoir. Two baseflow res-ervoirs were configured based on the upper and lower sub-basins ofthe watershed. A sensitivity analysis carried out based on the timeconstants for the interflow and baseflow reservoirs showed that theperformance of the ERWHM was least sensitive to these parame-ters. Based on this analysis, the standard values of these parameterswere not changed during the calibration.

A.4. MIKE-11: Fully dynamic solution of Saint Venant equations

Within MIKE-11, the surface water flow along channels can berepresented by fully dynamic, diffusive, or kinematic approxima-

Fig. A1. Model structure of the linear reservoir method

tions of the Saint Venant equations. In this study, a fully dynamicsolution of Saint Venant equations was used to simulate surfacewater along river channels in order to accurately calculate the ex-change flow between the channels and the overland flow. Further-more, this method can be used to analyze the sustainability ofwater availability in the Glenmore reservoir by accurately simulat-ing water levels in the channels and in the reservoir, an elementthat will be explored in future work.

The governing equations used in the above selected method arethe vertically integrated equations of conservation of continuityand momentum, which are the Saint Venant equations (Eqs. (A.8)and (A.9)).

@Q@xþ @A@t¼ q ðA:7Þ

@Q@tþ@ a Q2

A

� �@x

þ gA@h@xþ gQ jQ j

C2AR¼ 0 ðA:8Þ

where Q, is the discharge; A is the flow area; q is the lateral inflow; his the stage above datum; C is the Chezy resistance coefficient; R isthe hydraulic or resistance radius; ais the momentum distributioncoefficient.

The numerical solution of these equations is based on the impli-cit finite difference scheme developed by Abbott and Ionescu(1967). In this scheme, the governing equations are transformedinto a set of implicit finite difference equations and are appliedin a computational grid consisting of alternating points of the dis-charge, Q and water level h, and are computed at each time step.

A.5. Statistical equations used for NSE and correlation coefficient

NSEðR2iÞ ¼ 1�P

tðObsi;t � Calci;tÞ2PtðObsi;t � ObsiÞ2

ðA:9Þ

Correlation Co:ðRiÞ ¼P

tðCalci;t � Calci;tÞ � ðObsi;t � Obsi;tÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPtðCalci;t � Calci;tÞ2 �

PtðObsi;t � Obsi;tÞ2

q

ðA:10Þ

Calci,t is the simulated flow at time t, at location i, Obsi,t is the ob-served flow at time t, at location i, Calci;t – is the mean simulatedflow, Obsi;t – is the mean observed flow.

in MIKE-SHE for the saturated zone (DHI, 2009).

232 G.N. Wijesekara et al. / Journal of Hydrology 412–413 (2012) 220–232

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