Water Resources Modelling under Data Scarcity: Coupling MIKE ...

Water Resources Management (2006) 20: 567–590 DOI: 10.1007/s11269-006-3085-2

C

Springer 2006

Water Resources Modelling under Data Scarcity: Coupling MIKE BASIN and ASM Groundwater Model A. IRESON∗ , C. MAKROPOULOS and C. MAKSIMOVIC South Kensington Campus, Civil Engineering Building, Imperial College London, London, SW7 2AZ (∗ author for correspondence, e-mail: [email protected]) (Received: 31 August 2004; in final form: 1 September 2005) Abstract. The Water Framework Directive calls for strategic water resources planning at a catchment level, yet data and information are scarce in the areas where they are most needed: in the new EU Member States and Third Counties trying to assess the impact of EU environmental legislation in their water resources management policy. The research presented here proposes the coupling of a strategic scale water resources management simulation model (MIKE-Basin) and a finite difference groundwater model (ASM), as a tool to support decision making in data scarce environments. The models were applied in a particularly data scarce region, the Vrbas River basin, in Republic Srpska (RS) in Bosnia and Herzegovina (BiH) and the results are presented and discussed. It is argued that the approach adopted is valid and useful as an initial knowledge development and optioneering step, which can guide a national data collection exercise to support detailed modelling, and inform a strategic decision making process relevant to the application of the water framework directive. Key words: WFD, GIS, distributed modelling, strategic decision making, data scarcity

1. Introduction The objective of this study was to investigate the use of hydrological modelling software for data scarce catchments, in order to propose tools that could be used to support decision-making, specifically with regard to establishing structural and non-structural interventions required for conformity with the Water Framework Directive (WFD). The WFD was officially published on 22nd December 2000. All member states and candidate countries have to adapt their water management system to the requirements of the WFD and introduce participatory river basin management (Mostert, 2003). The Directive establishes a framework for the protection of all water bodies (including inland surface waters, transitional waters, coastal waters and groundwater) which (European Commission, 2003): – Prevents further deterioration of, protects and enhances the status of water resources. – Promotes a sustainable water use based on long-term protection of water resources

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– Aims at enhancing protection and improvement of the aquatic environment through specific measures for the progressive reduction of discharges, emissions and losses of priority substances and the cessation or phasing-out of discharges, emissions and losses of the priority hazardous substances Ensures the progressive reduction of pollution of groundwater and prevents its further pollution – Contributes to mitigating the effects of floods and droughts The key objective is to achieve good water status for all waters by 2015. The rationale for undertaking water resources studies on a basin scale rather than some smaller scale is based on the recognition that the water and land resources of a basin forms a unity and hence must be treated as such if future conflicts over water utilization are to be avoided. In order to support the proposed system of river basin management various analyses must be undertaken, for which the availability of hydrological data is of fundamental importance. The WFD recognises groundwater as an important resource, and therefore the management of both groundwater and surface water must be considered for a catchment. Previous studies have developed models for conjunctive representation of surface water and groundwater processes (e.g. Rozos et al., 2004). An alternative approach is to use separate models for surface water and groundwater, which are integrated by means of a GIS (e.g. Facchi et al., 2004). In this study it was desirable to use “off the shelf” software, and therefore the latter approach was adopted. The catchment modelling software Mike BASIN was selected to model the surface water, based on various case studies and literature. Furthermore, a groundwater model was available for the case study catchment, built in ASM software. The aim therefore, was to establish and document methodologies for using these software packages to model both surface water and groundwater quantity and quality. The use of these tools to model “what if scenarios”, to assess the impact of different activities in the catchment, is demonstrated. The models are integrated by means of a GIS (ArcView), to support efficient data management.

2. Integrating GIS and Catchment Models Models are invaluable tools for resource management. Models help resource managers develop a shared conceptual understanding of complex natural systems, allow testing of management scenarios, predict outcomes of high risk and high cost environmental manipulations, and set priorities. These are all essential components of developing regional catchment strategies and associated action plans. There will always be some degree of uncertainty because models are a simplification of reality. Uncertainties in model outputs can arise from conceptualisation of the processes modelled, quality and quantity of data, constraints of the modelling technology, and assumptions used in the scenarios tested (Caminiti, 2004). The development of spatially distributed hydrologic models has led to improved model forecasting at the cost of requiring more detailed spatial information.

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Incorporation of catchment models into a Geographical Information System (GIS) has improved matters by streamlining data input and providing better interpretation of model outputs (Pullar and Springer, 2000). A GIS is a tool for the management, query, visualisation and analysis of spatially referenced information (Burrough and McDonnell, 1998). It can: – Be used to preprocess information and validate its use in an environmental model – Be tightly coupled to an environmental model to provide an interactive system that allows decision-makers to quickly modify parameters and visualise the results of simulations. The level of system coupling between a model and a GIS has an impact on the reliability and ease of use of the system. Well integrated systems work as a coherent whole and still afford flexibility for modifying the modelling scenario. Three levels of integration have been identified: loose coupling, tight coupling, and fully integrated. These are explained as follows (Pullar and Springer, 2000): – Loose-coupled models – The systems are separate and interoperate by the user exchanging information through file exchange. This approach can prove to be successful and cost effective (e.g. Chowdary et al., 2003, Jain et al., 2004). – Tight-coupled models – One system provides a user interface for viewing and controlling the application, which may be built from several component programs. The GIS typically provides the shared interface to run and exchange data to a separate modelling program. – Fully integrated – The model is embedded as a component of the host GIS application (or GIS functionality is embedded into a host hydrological model). Integrating GIS and catchment models provides a tool to support integrated catchment management, defined as the co-ordinated planning and management of land, water and other environmental resources for their equitable, efficient and sustainable use at the catchment scale (Bathchelor, 1999). Such an approach was demonstrated by Jain et al. (2004), who developed a process oriented distributed rainfall runoff model which used a GIS to generate model inputs in terms of land use, slope, soil and rainfall. This allowed the model to handle catchment heterogeneity. The model was viewed as capable of predicting the runoff response to change in land use practise within the catchment, but in order to take full advantage of this model additional observations of the spatial patterns of runoff within the catchment were required.

3. Software 3.1.

ARCVIEW

The Geographical Information System (GIS) software ArcView, developed by ESRI, was used in this study. This combines the capabilities of mapping systems

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along with the ability to analyse geographic locations and the information linked to those locations. A powerful feature of ArcView GIS is the ability to carry out mathematical and logical operations on spatial data. Furthermore, tabular data from Arcview dBASE files can be created or manipulated using Microsoft Excel, which is useful in facilitating the integration of ArcView with other software. 3.2.

MIKE BASIN

MIKE BASIN, developed by DHI software, is an extension of ArcView, which uses GIS information as a basis of a water resources evaluation. Crucially, MIKE BASIN adds to ArcView the capability to deal with temporal data, in addition to the spatial data stored in the GIS. MIKE BASIN is a water resources management tool which is based on the basin-wide representation of water availability and potential users. Rivers and their main tributaries are represented mathematically by a network of branches and nodes. Nodes are point locations, where it is assumed that water enters or leaves the network through extractions, return flow and runoff. These may be confluences, diversions, locations where certain water activities occur (such as water offtake points for irrigation or a water supply), or important locations where model results are required. Rainfall-runoff modelling can be carried out in MIKE BASIN using the NAM model, a lumped, conceptual rainfall-runoff model suitable for modelling rainfallrunoff processes on the catchment scale. This simulates overland flow, interflow and baseflow as a function of the moisture content in each of four mutually interrelated storages: – – – –

Snow storage Surface storage Root zone storage Groundwater storage

An extended groundwater component allows drainage to or from neighbouring catchments. This is done using the parameter Carea , which represents the ratio between the groundwater catchment area and the topographic catchment area. Therefore, specifying Carea > 1 means that groundwater recharge area is larger than the surface water catchment. Also, an extended snow module allows accumulation of snow and occurance of snowmelt, based on a threshold temperature, and rate of snowmelt in millimetres per degree centigrade above the threshold temperature per day. A limitation with the rainfall runoff model in MIKE BASIN is that there is no facility to automatically calibrate parameters. There was therefore a need to utilise another simulation model from DHI, Mike 11. This is based on exactly the same rainfall-runoff model, i.e. NAM, but also has the facility to carry out automatic model calibration, based on the Shuffled Complex Evolution algorithm (Duan et al., 1992). This algorithm has been widely applied for the calibration of


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various conceptual rainfall-runoff models (Madsen, 2000). A demonstration copy of Mike 11 was available with Mike BASIN. This was unfortunately limited, in that it was not possible to account for inflows to the catchment other than from precipitation. None the less, it was possible to calibrate the parameters for the headwater subcatchment, where there is no contribution to the streamflow from the upstream river in our case study. The derived parameters were then applied to the remaining subcatchments, and model validation was carried out. As there were documented values of the runoff coefficient available, this parameter was taken out of the optimisation. Mike BASIN can simulate steady-state reactive processes of the most important substances affecting water quality, intended to give an overview of pollution sources and degradation processes. Substances are either treated as conservative or with first order decay, in which case the decay rate can be specified. Re-aeration from weirs can also be taken into account. Mike BASIN models point source pollution by using a water supply node. This is because the water supply node represents both abstraction of water for the needs of a community, and a return of waste water from the community. A node discharges waste at a load rate defined in flow and concentration time series files. Non-point source pollution can also be modelled, but was not considered in this study, due to lack of data. 3.3.

AQUIFER SIMULATION MODEL, ASM-6.0

ASM, Aquifer Simulation Model for Microsoft Windows, is a complete twodimensional groundwater flow and transport model. The model was originally produced as an educational tool in 1989, and since then has been continuously improved, to version 6.0, in 1998, which includes the capacity to model larger grids, and a user-friendly graphical user interface. The reason this particular software was chosen was because a model of the aquifer in our case study area, Lijevce Polje, the area in the floodplain of the confluence between the rivers Vrbas and Sava, already existed, and could be developed to include the specific features required. Furthermore, this software is in the public domain. ASM considers either confined, or unconfined aquifers, but not a combination of the two. In Lijevce Polje, the aquifer is confined, but there are also large areas where the water level in the aquifer is beneath the confining layer, where effectively the aquifer behaves as an unconfined aquifer. If the aquifer is modelled as a confined aquifer, the governing equations are based on transmissivity parameters, which are fixed because the saturated depth is fixed. In reality, when the water level in the aquifer drops below the confining layer, the saturated depth of the aquifer decreases, as does the transmissivity. Therefore strictly speaking the model is fundamentally flawed in this manner. However, if a steady-state model is used the groundwater levels do not change once the solution has converged. Therefore, in such a model the transmissivity is effectively fixed, meaning the basic

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assumptions are still valid, however the data used to define the model should be based on measured or calibrated transmissivity and not on measured hydraulic conductivity. This also means that only steady-state analysis can be carried out with this model. Contaminant transport through the aquifer can be modelled using a Random Walk Particle Tracking method. This method is based on representing the spatial distribution of some extensive quantity, such as the mass of a particular chemical constituent, by a large collection of particles. A simulation involves changing the various particle attributes according to a set of forces or other conditions determined by some interparticle relationship, space-dependent field property or boundary condition. In the case of a non-reactive dissolved chemical constituent, the particle is displaced in space, based on a deterministic component and a random component. The deterministic component represents advection-dispersion, and the random component accounts for small-scale dispersion, caused by small-scale heterogeneity, which cannot practically be measured (see also Tompson and Gelhar, 1990). In a groundwater transport model the particles represent the contaminant, and the spatial location of these particles is updated for each time step based on the velocity of the groundwater, and a random component. 3.4.

CASE STUDY

To investigate the applicability of the methodology and tools in a real world example, a case study in a particularly data scarce region was selected. The Vrbas catchment in Republic Srpska (RS) in Bosnia and Herzegovina (BiH) has severe problems of data scarcity, a situation that has come about for various reasons. Whilst in the past data monitoring schemes have been carried out, currently no data monitoring programmes are operational. Therefore, the objective of the case study was to carry out preliminary analyses by applying the methodology to historical data. Furthermore, this exercise should facilitate the improved design of appropriate monitoring programmes. The Vrbas catchment is described in the following section. The river Vrbas is a tributary of the river Sava, which in turn is the biggest tributary of the Danube. The Vrbas catchment is shown in Figure 1. The river Vrbas drains a part of the northern slopes of Mount Dinara. Its source is on the southern slope of Mount Varnica, at around 1530 m above sea level (m asl), it is around 235 km long, and drains into the Sava at about 90 m asl. The average slope is 6 m/km, which makes it very attractive for hydro-energy use. The river catchment is around 70 km wide and 150 km long, with an area of around 6385 km2 . The catchment has two distinct regions, the upper, southern mountainous region, and the lower, northern flat region, known as Lijevce Polje, which will be described in more detail. The climate of the catchment is humid. The average temperatures depend strongly on elevation, and average around 8–10 ◦ C in the south, and 16–17 ◦ C in the north. Average precipitation varies from around 800 mm/year in the north, to


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Figure 1. The Vrbas catchment.

around 1500 mm/year in the south. The average volume of precipitation that falls on the catchment is 6.95 ∗ 109 m3 . Average potential evaporation is 700–750 mm, and in the summer months this exceeds precipitation. Almost half of the average precipitation returns to the atmosphere by evaporation, and the average annual runoff is equivalent to 600 mm/year. The average runoff coefficient is estimated as 0.59 (Vodoprivereda BiH and Energoinvest, 1989). 3.5.

THE UPPER CATCHMENT

The upper region of the catchment comprises of hills and mountains. There is an extensive network of streams, the most significant being the rivers Pliva, Ugar, Crna and Vrbanja, all of which drain Karstic Plateaus at the top of mountains, and are

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tributaries of the Vrbas. The region also contains springs that are suitable for water supplies, the most significant being the springs of the rivers Pliva and Janja. The geology is composed of rocks originating from the Mesozoic and Tertiary periods. In the south western area there are thick layers of karstic limestone. In this area there is an extensive underground network of branches and reservoirs. The rest of the upper-catchment is largely impermeable, meaning there are more developed surface water streams, and less springs.

3.6.

THE LOWER CATCHMENT

– LIJEVCE POLJE

Lijevce Polje is located in the North of the Vrbas catchment, in the floodplain of the river Sava. The area of this region is around 370 km2 . It is surrounded by the mountains Kozara and Prosara to the west, the river Vrbas to the south and east, and the river Sava to the north. The aquifer’s main source of recharge is the river Vrbas, which is at a higher altitude. The aquifer drains to the river Sava and the Bardaca wetlands. The field is an alluvial plateau, with altitude in the range of 125 m in the south to 90 m in the north, with slopes of up to 5%. The aquifer is composed of a layer of sandy gravel, whose thickness varies between 5–30 m. It has a high conductivity of around 7 ∗ 10−3 m/s. Overlying this aquifer is a layer of sandy clay, with thickness between 0.5–4 m. The clay has low permeability, less than 10−6 m/s. This therefore acts as a confining layer, and also protects the aquifer from infiltration of pollutants. Finally, there is a thin layer of fertile topsoil overlying the clay layer. Note that there are perched sandy-clay layers located in the gravel layer, 6–10 m below the surface. This can give the false impression of two separate aquifers. However, due to the patchy, localised nature of these layers, and the fact that they are well beneath the water level, they do not affect the global pattern of groundwater flow, and the aquifer effectively behaves as a normal confined aquifer. Furthermore, it should be noticed that in southern area of the aquifer, the water level is not generally sufficiently high to reach the confining layers. In this area, the aquifer behaves as an unconfined aquifer, with a water table. The aquifer in the Lijevce Polje is an important water resource, for drinking water and for irrigation. Large numbers of small wells in the area serve as water supplies for small rural properties and farms. Large pumping wells are used to abstract water for water supplies to the larger communities, such as the of town Bosanska Gradiska, where groundwater is pumped into a large water tower reservoir, and the town Laktasi. There also is a large sprinkler irrigation system near Nova Topola that uses groundwater. Therefore, the groundwater quality in this region is very important, and ensuring it is of a standard suitable for drinking is imperative. Currently there are several major point sources of pollution which threaten the long term quality of the aquifer. These include leaky sewer systems, abandoned gravel pits which are used as illicit landfills, buried fuel tanks, etc.


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4. Model Descriptions 4.1.

SURFACE WATER MODEL DESCRIPTION

A Digital Elevation Model (DEM) was generated for this project by digitising isoline maps, and including some field measurements. This provides an appropriate resolution of elevation data over the catchment, except in the relatively flat lowland area of Lijevce Polje. This area is the least important in generating stream flow, so the lack of elevation data was not considered a problem for modelling purposes. The DEM is shown in Figure 2, with the main precipitation, evaporation and streamflow

Figure 2. The digital elevation model, showing hydrological measurement stations.

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gauging stations. The River Vrbas is shown, from its source in the south to its confluence with the Sava in the north. The two main tributaries, the Pliva to the west and the Vrbanja to the east are also shown. In order to run a rainfall runoff model, rainfall, evaporation, temperature (to account for snow) and streamflow time series data are necessary. The data set that was used was for the years 1971 to 1972, the only period for which all required time series data were available with daily time steps. Also during that period no major anthropogenic changes in the catchment had taken place (e.g. building dams). In order to attain a simple, functional model of the catchment, no tributaries were modelled, but their corresponding catchment area was assumed to contribute to the Vrbas directly as overland-flow, inter-flow and base-flow. The river was traced and the catchment delineated (such that the watershed of each sub-catchment were defined), based on the Digital Elevation Model. Initially, due to the low resolution of data in the northern, flat floodplain area, the river was not traced correctly in this region. To amend this the DEM was modified by ‘burning’ the digitised channel into it, so that the river would follow the correct path. The path of this channel was created based on the river path as shown on a map of the region. The delineated catchment is shown in Figure 3. It can be seen that the south western, and western areas do not apparently contribute to the catchment runoff. This is true in terms of overland flow, however they may have a significant contribution from karstic springs under the mountains. Rainfall was applied to each sub-catchment using weighted average rainfall data from the observation stations, using a Thiessen polygon technique. Evaporation and temperature time series data were applied to sub-catchments using data from the gauging station nearest to the sub-catchment, because this data tends to vary less between stations than the precipitation data. Observed streamflow time series data was associated with the representative catchment node. It was found from the observed streamflow data, on a daily timescale, that there was no significant delay in the flow peaks along the river Vrbas, and therefore it was assumed that there is no significant delay or smoothing of the hydrograph between nodes. Some water quality parameters for the river Vrbas were available for the year 1971, from the Faculty of Technology at the University of Banja Luka. In order to model water quality, the flow velocity is required for each branch of the river. This was calculated based on the river width, the stage-discharge relationship, and the discharge time series. For the river Vrbas, the stage – discharge data was available for two gauging stations, which were used to estimate a stage-discharge relationship in the form Q = a.Hb, where Q = discharge (m3 /s), H = stage (m) and a and b are constants, to be input into MIKE BASIN. Water supply nodes were used to represent point sources of pollution. This routes water into the catchment node at a rate specified in a time series file. Quality parameters are input as concentrations in this flow. The data that was available was sufficient to demonstrate the use of the pointsource pollution modelling capabilities of the software, but not sufficient to calibrate the rate coefficients, and hence predict realistic downstream concentrations.


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Figure 3. The delineated catchment.

4.2.

GROUNDWATER MODEL DESCRIPTION

The groundwater model used in this study was based entirely on work carried out previously for the University of Banja Luka (Jandric, 2003). In this study, various parameters were optimised to calibrate the model. The calibration criterion was the simulated head compared to the observed head from various boreholes in the southern region of the aquifer. Unfortunately there was no observed data for the northern region of the aquifer. The simulation tended to underestimate the water table by an average of around 3 m. This result is far from ideal, but there was no sufficient data to attempt to improve it. In this section, all parameters that were subject to optimisation in the previous study will be described. A steady-state analysis, using average annual data, was used.

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Figure 4. Groundwater model boundary conditions.

The base and top of the confined aquifer were generated by interpolating point measurements from borehole logs in the region. The elevation of the base of the aquifer was in the range of 60–129 m, and the top was in the range of 81–130 m. The aquifer thickness ranged from 36 m in the central and north-eastern areas to 1 m in the southern and western areas. Three types of hydrogeological boundary condition were used in the model, as shown in Figure 4. A zero-flux boundary is used to represent the impermeable rock to the west of the aquifer. A specified flow boundary condition represents an inflow to the aquifer from karstic rock in the north-west area of the aquifer. The flow rate on this boundary was subject to optimisation in the previous study, which gave the value of 0.0002 m2 /s, expressed in terms of flow per unit depth. Finally, the rivers Vrbas to the south and east, Sava to the north, Jablanica to the north west, as well as the canal and lakes in the north east, were all modelled with mixed boundary conditions, with the flow through the boundary defined by a leakage rate. This allows flow to occur into the aquifer from the river, or out of the aquifer into the river, depending on the hydraulic head in the aquifer, and the stage in the river. Recharge to the aquifer is mainly from the river Vrbas, which is taken into account through the use of this boundary condition. A further small amount of recharge was specified from infiltration of rainfall through the confining layer, which was uniform over the entire aquifer. A value of 1.4 ∗ 10−10 m/year was found


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Figure 5. Spatial variation of transmissivity in the aquifer.

in the previous study by calibration. The distribution of transmissivity is shown in Figure 5. The values range from 0.02–0.4 m2 /s, equivalent to 1728–34560 m2 /day. These values were found by calibration in the previous study, and as such are based on the response of the aquifer rather than its perceived saturated depth and hydraulic conductivity. Therefore the model assumption that the aquifer is confined, and hence transmissivity values are fixed, is valid. The effective porosity was assumed to be 0.25, and was uniform over the entire aquifer. In the original model, no account was given to groundwater abstractions through wells, even though there are significant abstractions from this aquifer. One important example is the well used for the water supply to the town Bosanska Gradiska, with a population of approximately 11,000. A conservative estimate of demand of 200 l/person/day was used and a well was included in the model, to quantify the effect of such an abstraction. This is less than 1% of the total water budget and the predicted

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drawdown was 0.163 m, which makes a visible difference to the head distribution only on a very small scale. Therefore, the effect of wells on the overall groundwater flow was deemed negligible. For the groundwater quality investigation, a number of potential sources of contamination were identified, and random walk particle tracking was used to establish the pathway of contaminants from these perceived sources. No degradation or retardation were modelled, and the source was assumed to have a continuous loading rate. The resulting contaminant plume would originate from the source and propagate to the edge of the aquifer, becoming increasingly dispersed. In terms of modelling contaminant concentration this technique is not at all realistic, as it assumes there is no retardation or decay. However, this technique does allow the identification of potential receptors downstream of contaminant sources. This has the advantage over simply using streamlines in that it attempts to account for transverse dispersion. 4.3.

MODEL INTEGRATION WITH GIS

The surface water model, in MIKE BASIN, is already fully integrated into the ArcView GIS software. The input and output data files for ASM software are stored in ASCII format, in a matrix structure. Therefore, it was possible to write a simple programme to format these files such that they could be read by the GIS, thus providing loose integration between the groundwater model and GIS.

5. What-if Scenarios The combined catchment model developed was used a basis for a series of what-if scenarios. As insufficient data were available to attempt to forecast actual outcomes, a sensitivity analysis approach was employed. Two specific scenarios were investigated: 1. The effect of dams on the groundwater. There are a number of proposed dams on the river Vrbas, three of which are within the area of the aquifer. Dams raise the upstream water level in the river. The river is separated from the aquifer by a layer with a relatively low permeability, so the river level determines the hydraulic gradient through this layer, and hence the recharge to/discharge from the aquifer. The objective therefore is to investigate the effect of these dams on the groundwater in the region, and specifically whether the local increase of the water table could cause flooding. This was be modelled by simply modifying the specified head in the mixed boundary condition for the river Vrbas. No backwater effects were accounted for in this simplistic simulation. 2. River bed lowering When a dam is built on a river, the sediment load in the flow downstream of the dam tends to decrease. This then leads to increased erosion in the river, and as a result the riverbed is lowered. This can be modelled


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by simply lowering both the specified head and the base of the leakage node boundary conditions for the river Vrbas. Both should be lowered by the same amount, so that the river is assumed to still have the same stage, and hence flow. The approach to investigating these effects was to incrementally lower these boundary conditions for the entire length of the Vrbas.

6. Results 6.1.

THE SURFACE WATER MODEL

The calibration result for the headwater sub-catchment, using the demonstration version of MIKE 11 is shown in Figure 6. The main flaw with this is that in the springtime the model fails to simulate the high flows. It was noted that the river Pliva, a tributary in the south west of the catchment, gets much of its flow from karstic springs. This means the area to the south west of the catchment (i.e. outside the delineated sub-catchments shown in Figure 6.), which doesn’t contribute any overland flow directly, contributes to the catchment water balance by draining some proportion of the rainfall into karstic conduits flowing under the watershed and emerging as springs. This explainaton is indeed backed up by field observations. As these karstic plateaus are at a high altitude, they would be particularly susceptible to the effects of snow storage. It follows then that these karstic springs mainly contribute in the spring months, when there is a large amount of snowmelt. This could explain the observed spring peaks, in 1971. Karstic aquifers often contain open conduit flow paths with hydraulic characteristics similar to surface streams rather than groundwater (White, 2002). Therefore, it is not sufficient to treat this contribution as groundwater flow using the extended groundwater components in the NAM rainfall-runoff model. It is suggested that further work should attempt to quantify this contribution by means of a conceptual network link between this region, and the Vrbas. This would require a full version of Mike 11 to perform the calibration, as inputs to the catchment other than precipitation must be considered. Furthermore, rainfall, evaporation and temperature data from the top of the mountains would be required. Further research is required to establish how effective this suggested methodology might be. The rainfall-runoff model was then run for the rest of the sub-catchments, using the optimised parameters. Figure 7 shows the result for the lower sub-catchment, which gives an indication of the overall catchment response. The results are considered satisfactory in view of the severe shortages in data and the various model uncertainties discussed. The main issues are the peak in the spring and the fact that the late autumn peak underestimates the runoff magnitude. The assumption of the method employed is that the rainfall-runoff parameters are homogeneous throughout the entire catchment, which would seem unlikely in reality. However, comparing the results for each sub-catchment seems to indicate that this assumption could indeed be valid.

Figure 6. Calibration of headwater sub-catchment for 1971.

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Figure 7. The observed and simulated outflow from the lowermost sub-catchment.


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Figure 8. The Nitrate concentration in the River Vrbas.

Whilst a calibration of the water quality rate coefficients was not possible, a demonstrative simulation was run, using arbitrary coefficients. The result for degradation of Nitrate concentration in the river between two points, Banja Luka upstream and Delibasino Selo downstream, in the river is shown in Figure 8. 6.2.

THE GROUNDWATER MODEL

Using the ASM water budget calculator a summary of flows into and out of the catchment was produced, which gave an overall mass balance error of 0%. In order to compare this with other data it was converted to units of m3 /year, and expressed in the following terms: – – – – –

Recharge to aquifer from the river Vrbas 6.33 ∗ 107 m3 /year Recharge to aquifer from infiltration through clay +1.86 ∗ 106 m3 /year Recharge to aquifer from seepage from karstic rock +3.63 ∗ 107 m3 /year Total recharge into aquifer = 1.01 ∗ 108 m3 /year Groundwater flow out of aquifer, to rivers and lakes −1.01 ∗ 108 m3 /year

Comparing these volumes to the average precipitation, 6.95 ∗ 109 m3 /year, it is estimated that around 1.5 % of the total catchment water budget (i.e. precipitation) goes towards recharging the aquifer. The modelled head distribution is shown in Figure 9, which is taken from the ASM software output display. Measurements are in meters above sea level. This shows the heads are high in the south, and decrease in a fairly uniform pattern from south to north. The karstic rocks to the northwest have a significant effect


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Figure 9. The distribution of hydraulic head in the aquifer.

on the groundwater, causing a local increase in head. However, as there were no observation wells in this region, it is hard to know if these results are reliable. Hydraulic head data from the groundwater model modified to account for the two ”what if” scenarios, was imported into the GIS, so that the spatial calculation facilities of the GIS software could be exploited to present the results in a more informative manner. The first scenario modelled was the effect of dams on the groundwater distribution. Figure 10 shows the increase in heads as a result of including the dams. The maximum increase is 9.77 m, which occurs at the location of the Laktasi dam. The head is increased over the entire aquifer as a result of the dams, as a result of increased recharge from the river, caused by an increased head gradient though the confining layer under the reservoirs. From comparing the water budget before and after the dams were modelled, the recharge from the Vrbas increases by 33.65% to 2.68 m3 /s, whilst the overall water budget increases by 21% to 3.89 m3 /s. That is to say, 21% more water passes through the aquifer as a result of the dams. The second scenario modelled was the effect of lowering the river. The river Vrbas was lowered in 1 m increments, and the resulting head distribution was calculated. The change in heads relative to the initial head values is shown in Figure 11. The effect is not uniform as a result of the different leakage rates along

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Figure 10. The head increase as a result of the dams.

the river. For example, in the middle of the river Vrbas (on the east boundary) the head in the aquifer is reduced more than the surrounding areas. The effect of river lowering is therefore, to reduce the head in the aquifer, reduce the recharge from the river, and reduce the overall water budget of the aquifer. For the investigation of groundwater quality, contaminant plumes were generated from a number of potential point sources, using the random walk particle tracking technique. These sources were identified in a previous study by Trifkovic (2001). A typical result is shown in Figure 12, of a contaminant plume coming from a pig farm. The plume can be used to identify potential receptors and develop risk maps. 7. Conclusions A surface water model was created, calibrated and validated using Mike BASIN. There was scope to improve the rainfall-runoff model calibration. It was suggested

Figure 11. The reduction in head as the river Vrbas is lowered.


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Figure 12. The contaminant plume from a pig farm.

that the software Mike 11 be obtained, and the conceptual model be modified to include a contribution of runoff from karstic springs in the south of the catchment, similar to the approach that was used by Jørgensen, 2002. Further research should endeavour to use the suggested methodology to produce a reliable rainfall-runoff model. A reliable rainfall-runoff model would allow streamflow to be predicted for extreme floods and droughts as well as form the basis of groundwater and surfacewater quality models, to predict worst-case pollution risk scenarios. The water quality modelling capabilities of Mike BASIN for point source pollution were successfully demonstrated. There was need for more data in order to be able to calibrate the parameters, and thus decrease the associated uncertainty. It was suggested that a study be carried out to identify all pollution sources in the catchment, and incorporate these into the GIS. Based on this study a water quality monitoring scheme should be designed. Future work should also aim to use available data to produce a fully representative surface water quality model for the Vrbas catchment, using Mike BASIN. Two possible scenarios were modelled separately using the groundwater model of the Lijevce Polje region. The first scenario was the effect of three dams being built on the water level in the river Vrbas, the main source of recharge to the aquifer.


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This was performed by modifying the boundary condition representing the river. As a result, the recharge to the aquifer increased by 21%, and the groundwater head in the aquifer increased both locally (maximum of 9 m) and globally. It is therefore suggested that this is a highly significant modification. The second scenario was riverbed lowering, as a result of increasing the erosion of the riverbed, caused by the construction of upstream dams. This was again modelled by modifying the boundary condition representing the river Vrbas. This led to a reduction in recharge to the aquifer, and a reduction in the groundwater head. The implications of this could be wells drying up, which would be extremely serious in a region where a large proportion of the population relies on groundwater, either from small hand pumped wells, or from large water supply wells. These investigations highlighted the sensitivity of the groundwater to the stage in the river. Therefore, it was hypothesised that the seasonal variation of the groundwater, as a response to the seasonal variation of stream flow, would be significant, and should be accounted for. This should ideally be done with a transient model, but could also be done by using a number of steady state models driven by differing seasonal data. Random walk particle tracking was used to simulate transport of contaminants, from perceived sources in the aquifer. It was suggested that this method be used to simulate plumes from all point sources in the aquifer, in order to design a groundwater-monitoring scheme. The issue of model integration was addressed so that the results from both the groundwater and surface water models could be processed and presented in the GIS software. Mike BASIN software is already tightly integrated with ArcView GIS. The ASM groundwater model was loosely integrated, by means of a simple program to exchange data beween ASM and ArcView formats. It is suggested that this loose integration technique is easy to implement and yields acceptable results, particularly when data scarcity prohibits the use of more detailed models. The methodology presented in this paper demonstrates how GIS based tools can provide an effective, integrated approach to environmental catchment modelling in data scarce situations. However, it must be understood that there are limitations to this approach and even a good GIS model cannot help if a bare minimum of data are not available. Acknowledgements The authors wish to thank Professor Demetris Koutsoyiannis for his very useful comments in reviewing this paper. References Batchelor, C., 1999, ‘Improving water use efficiency as part of integrated catchment management’, Agricultural Water Management 40, 249–263.

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