Simulating the Hydrology of Small Coastal Ecosystems in Conditions of Limited Data

Research Reports

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Research Report 77

Simulating the Hydrology of Small CoastalEcosystems in Conditions of Limited Data

V. U. Smakhtin, S. C. Piyankarage, P. Stanzel andE. Boelee

International Water Management InstituteP O Box 2075, Colombo, Sri Lanka

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The authors: Vladimir Smakhtin is a Principal Scientist (Eco-hydrology and Water Resources),Sujeewa Piyankarage is a Researcher (Water Quality) and Eline Boelee is an Irrigation andHealth Specialist, all of the International Water Management Institute (IWMI). Philipp Stanzelis an M.Sc. graduate of the University of Agricultural Sciences (BOKU), Vienna, Austria

(formerly an intern at IWMI).

Acknowledgements: The studies on Karagan Lagoon were carried out with the aid of a grantfrom the International Development Research Centre, Ottawa, Canada. Financial support forPhilipp Stanzel was provided by the University of Agricultural Sciences (BOKU), Vienna,Austria. Other studies described in the report are the products of IWMI research projects onimpacts of irrigated agriculture on wetland ecosystems. The authors gratefully acknowledgethe staff of the Embilipitiya IWMI field station for their assistance in data collection for theKaragan study. The Irrigation Department of Debarawewa and Tissamaharama, Bundala SaltCorporation, Weerawila Agricultural Research Station and Department of Wildlife Conservationof Sri Lanka are acknowledged for logistical support, provision of some hydrometeorologicaldata and assistance with setting up the monitoring programs in southern Sri Lanka. We thankDr Peter Droogers (formerly of IWMI) for his review of the modeling results of the Bundala ParkLagoons. Dr Paul Cowley (South African Institute for Aquatic Biodiversity, Grahamstown, SouthAfrica) and Dr. Trevor Harrison (Council for Scientific and Industrial Research, Durban, SouthAfrica) are gratefully acknowledged for the provision of data on estuarine mouth conditions.Thanks are also due to Mr R. Taylor (KwaZulu-Natal Wildlife, Durban, South Africa) and DrA. Whitfield (South African Institute for Aquatic Biodiversity, Grahamstown, South Africa) forvaluable comments during different stages of the South African estuarine study. The detailedreview of the entire report by Dr Hugh Turral (IWMI, Sri Lanka) is gratefully acknowledged,

as well as constructive comments from the anonymous external reviewer.

Smakhtin, V. U.; Piyankarage, S. C.; Stanzel, P.; Boelee, E. 2004. Simulating the hydrology ofsmall coastal ecosystems in conditions of limited data. Research Report 77. Colombo, SriLanka: International Water Management Institute (IWMI).

/ irrigation/ hydrology / water levels / coastal lagoons / reservoir model / water balance /temporarily closed-open estuaries / estuary mouth / daily stream flow time series / flowduration curves / spatial interpolation / Sri Lanka / South Africa /

ISSN 1026-0862ISBN 92-9090-538-7

Copyright © 2004, by IWMI. All rights reserved.

Please send inquiries and comments to: iwmi@cgiar.org

IWMI receives its principal funding from 58 governments, private foundations, andinternational and regional organizations known as the Consultative Group on Inter-national Agricultural Research (CGIAR). Support is also given by the Governmentsof Ghana, Pakistan, South Africa, Sri Lanka and Thailand.

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Contents

Summary v

Introduction 1

Evaluating Irrigation Impacts on the Hydrology of Karagan Lagoon 2

Simulating Hydrological Reference Condition of Bundala Lagoons 13

Simulating Inflow to and Mouth Conditions for Small Estuaries 18

Conclusions 25

Literature Cited 27

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Summary

Water resources and irrigation developments havedirect impacts on aquatic ecosystems. Theseimpacts need to be quantified and predicted tounderstand the environmental costs of waterprojects. The assessment of such impacts in mostdeveloping countries is often hampered by the lackof reliable observations on physiographic variablesused as inputs to simulation models. Complex andinformation-consuming models may not always beappropriate in such conditions, whereas simpler,pragmatic simulation approaches may perform wellwhilst their data requirements can be more easilysatisfied. This report illustrates the use of suchmethods to simulate the hydrology of several smallcoastal water bodies. Such water bodies arenormally not taken into account when developmentprojects are being planned and implemented.However, these water bodies often represent asource of livelihoods for local communities, mayhave a high recreational value and/or serve as ahabitat for rare species. Three examples areillustrated in this report. The first deals with theassessment of the impacts of a future irrigationscheme on a coastal lagoon in Southern Sri Lanka.The catchment upstream of the lagoon and thelagoon itself are simulated by water balancemodels, which operate with a weekly time step.The details of the upstream tank and paddysystems are ignored and large parts of the catch-ment are represented by dummy reservoirs to becommensurate with the level of input informationavailable. Several realistic scenarios describingirrigation development and lagoon management arethen defined and simulated. They include differentlevels of inflows from the proposed scheme into

the lagoon, envisaged upstream catchmentchanges associated with the scheme and someaspects of lagoon water level management. Thesecond example deals with coastal lagoons in SriLanka which are already receiving additionaldrainage flows from irrigation schemes. The goal inthis case is to establish and simulate the referencehydrological condition, which existed prior toirrigation development. This reference condition isnecessary to assess the present-day impacts onlagoon hydrology and to design a set of manage-ment measures to alleviate adverse impacts. Theexample illustrates how the combination of localknowledge and simple spatial interpolation methodsmay be used to simulate a daily time series ofreference water levels in the lagoon. The thirdexample focuses on temporarily closed/openungauged estuarine ecosystems along the eastcoast of South Africa. The duration of the closedand open phases of an estuarine mouth aredetermined by the interaction of river inflow andthe sea in the mouth region whilst the dynamicsof the mouth affects the structure and functioningof the estuarine biotic community. The reportdescribes methods for simulating the daily up-stream inflow time series to such estuaries andfor linking simulated hydrological data to theestuarine mouth state. It is concluded thatparsimonious methods can serve as a soundbasis for the quantification of impacts and genera-tion of required hydrological data of differenttypes. Such methods may also identify immediateresearch priorities, specify the requirements formonitoring networks and serve as the basis forsound management decisions.

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Simulating the Hydrology of Small CoastalEcosystems in Conditions of Limited Data

V. U. Smakhtin, S.C. Piyankarage, P. Stanzel and E. Boelee

This report highlights three case studies wherehydrological data generated using these pragmatictechniques were required. All three studies dealwith small coastal ecosystems. The first study isan evaluation of the ecological impact of anirrigation scheme extension at a coastal lagoon(Karagan Lewaya) in southern Sri Lanka. Thelagoon is likely to receive high quantities ofdrainage flows from the future scheme. This couldchange the natural water balance, deteriorate itswater quality and thereby the suitability of thelagoon as a habitat for migratory birds. It couldalso lead to flooding of adjacent settlements. Thestudy attempts to quantify the impacts on thelagoon’s water levels resulting from futuredevelopment scenarios.

The second study focuses on three coastallagoons (Bundala, Embilikala and Malala) insouthern Sri Lanka. Some of these lagoons arealready receiving drainage flows from upstreamirrigation schemes. There is concern thatcontinuous input of agricultural drainage into theselagoons will change their ecology and render themunsuitable for the existing aquatic species.Quantification of the impacts of drainage flowsmay only be possible if the condition of thelagoons prior to scheme implementation is known.The focus of the study is therefore the simulationof these reference hydrological conditions.

The third study deals with the hydrology anddynamics of the mouths of small estuaries. Ituses data from the east coast of South Africa as

Introduction

Informed management decisions on waterresources allocation and irrigation development,and the assessment of the impacts of thesedevelopments can only be made if the processesand dynamics of aquatic ecosystems are properlyunderstood and quantified. Such quantification ishampered in most developing countries due to theacute lack of long-term, accurate and reliableobservations of hydrological processes andvariables such as rainfall, evaporation, river flowand water levels. Common problems associatedwith the available hydrological data include gaps inthe time series, short observation periods andinaccurate observations.

One conventional way of generatingrepresentative hydrological time series (e.g., forungauged sites) is through the use of deterministicrainfall-runoff models. The application of suchcomplex methods with their large datarequirements is not always appropriate in the data-poor regions (i.e., in most of the developingworld). Instead, the use of pragmatic techniquesof data generation may be more justified and/orequally successful. In order to be competitive withmore complex simulation methods, such“pragmatic” approaches need to provide resultswhich are suitably accurate, be simple andparsimonious, quick and easy to set up and run,and capable of generating hydrological time seriesfor ungauged catchments or water bodies fordifferent scenarios of catchment and/or waterresources development.

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an example. The state of an estuary mouth isgenerally one of the most important factorsgoverning the structure and functioning of theresident biotic community. It influencesinvertebrates and fish, which have a marine phasein their life cycle, many plant species, which aredependent on particular inundation regimes of theestuary, and certain water bird species, whichfavor tidal exposure of estuarine sediments. Nosystematic observations are currently beingconducted on the hydrology or mouth openingsand closures of South African estuaries. At thesame time, these small coastal systems oftenhave high conservation and/or recreational value.The duration of the closed and open phases of anestuary mouth depends on the interaction of riverrunoff, evaporation, seepage, floods and wave

over-wash events in the mouth region. As theinflow to an estuary reduces due to upstreamcatchment developments (such as irrigationwithdrawals), the mouth may close morefrequently. This could lead to a significant changein the status of an estuarine ecosystem. The studyexplores the ways of quantifying these processes,which are important for making informed decisionson catchment water allocations.

All three studies were conducted with verylimited hydrological data and are intended toprovide quantitative methods, which can work insuch conditions. All three studies also attemptedto bring more attention to the issue ofconservation and management of sensitive smallcoastal water bodies on which local communitiesoften depend for their livelihoods.

Evaluating Irrigation Impacts on the Hydrology of Karagan Lagoon

The Study Area and Problem

Karagan Lagoon is one of several coastalwetlands located in southern Sri Lanka (figure 1).These lagoons are transitional wetlands betweenfresh and salt water bodies and provide habitat formany animals and plants that are characteristic ofthe region. Karagan, for example, supports rare,vulnerable or endangered species or subspecies ofplants and animals, and hosts a variety ofwaterfowl (WCP 1994). However, unlike many ofits neighboring coastal wetlands, the lagoon hasbeen permanently separated from the sea as aresult of development activities in the region.

The maximum water surface area and depthof the lagoon are 3.2 km2 and 1.5 m, respectively.Both are subject to high seasonal and annualvariations. The lagoon is separated from the seaby the main coastal road and sand dunes in itswestern part and by Hambantota town in the east.

The upstream catchment area is 54 km2. Thetopography of the area is undulating in the north,with a gentle seaward slope to the flat southernparts of the catchment. Elevations range from 60m in the northeast to 1.5 m below sea level at thebottom of the Karagan Lagoon.

The upstream catchment area includes over10 small old tanks (farm dams) with a capacityless than 100,000 m3. Surface inflow to the lagooncomes from two sources. First, water releasedfrom the two most downstream tanks of theupstream tank cascade systems (TCSs) is usedto irrigate the downstream paddy fields and afterthat the return flows run through one majordrainage channel into the lagoon. These TCSs areknown by the names of their terminal (mostdownstream) tanks—Katu and Arabokka (figure 1).Runoff from the sub-catchments adjacent to thelagoon partially drains into this channel as welland partially into the lagoon directly.

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FIGURE 1.The study area showing the location of coastal lagoons and irrigation schemes in southern Sri Lanka.

The inflows from the sea into the lagoon atpresent are limited. Since 1970, the lagoon hashad no connection to the sea, except fortemporarily channels built through the sandbar forthe drainage of excess water during floods. Anemergency outlet channel through Hambantotaconstructed in 1998 has never been used.

Agricultural activities in the upstream area ofthe Karagan Lagoon are confined to the major(Maha) cultivation season due to the scarcity ofwater. To expand agricultural activities in this area,it was proposed to further develop the existingUdawalawe irrigation scheme (figure 1). Thedevelopment will include the extension of the leftbank main canal some 19 km further south. Thiswould ensure the irrigation of an additional 5,100ha and facilitate the settlement of almost 4,000families.

At present there is no connection between theKaragan catchment and Karagan Lagoon to theWalawe river basin and Udawalawe irrigationscheme. After the implementation of the irrigationextension project, a substantial amount ofdrainage water from irrigated areas will flow intothe Karagan Lagoon. The hydrology of both theupstream catchment area and the lagoon itself willchange. Possible adverse effects on the KaraganLagoon due to the implementation of the irrigationproject are changes in water and salinity levelsassociated with agricultural return flows.Consequently, the flora and fauna of the lagooncould be severely affected. An assessment ofhydrological changes (e.g., in water levels) is ofparamount importance for understanding andquantifying other associated environmentalimpacts.

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A Summary of the Simulation Approach

The catchment upstream of the Karagan Lagoonis effectively a combination of different units,which include sub-catchments, tanks and paddyfields (or other crop fields). Tanks are located atthe downstream ends of sub-catchments. Paddyfields are located downstream of tanks andupstream of the next catchment. There are anumber of units of each type, which makescatchment representation very complex. Anattempt was initially made to represent the waterflow between different units in full. The modeldeveloped included a detailed description of all theindividual components of the Karagan catchment(all the major tanks, sub-catchments and paddyfields). Individual physical and artificial processes

such as rainfall, evaporation, runoff, waterreleases for irrigation and spills were accountedfor. This was largely a conceptual exercise,carried out in order to understand the inputinformation requirements, which were necessaryfor the comprehensive simulation of this system.Stanzel et al (2002) have shown, amongst others,that practical application of such a model wouldnot be feasible in the near future due to thelimitations imposed by lack of input data.

A simplified catchment model, illustratedschematically in figure 2, was then developed inan attempt to reduce the input informationrequirements. The entire catchment of the twotank cascade systems with a total area of 36 km2

is simulated as one dummy catchment (“Kataracatchment”, which combines the names of Katu

FIGURE 2.A schematic representation of the components of the Karagan catchment in the hydrological model.

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and Arabokka TCSs). Paddy fields and smallertanks upstream are treated as parts of thecatchment area. Runoff from the “Katara”catchment is routed into one lumped reservoir,called “Katara tank”. This dummy reservoir poolsthe capacities of the two terminal tanks. Outflowfrom the reservoir is routed into a smaller“cascade catchment” which includes the cultivatedand non-cultivated areas eventually draining intothe Karagan Lagoon. The areas draining into thelagoon through minor streams and ditches, botheast and west of the “cascade catchment” area,are combined into one catchment, referred to as“direct catchment”. Runoff from both thesecatchments is an input to the Karagan Lagoonwater balance model.

The details of the model algorithms arediscussed in Stanzel et al. (2002), but a summaryof the model is given below. The water balanceequation for the catchments, shown in Figure 2 is:

dV = P + S + X + IR + N – ET – R – D (1)

Where dV is the change in catchment storage,V, over one time step t. P is precipitation, S isseepage from an upstream tank, X is spill from anupstream tank, IR is water released (issued) froman upstream tank for irrigation, N is additional(new) inflow from the future irrigation scheme(applicable to “Katara” catchment and “direct”catchment), ET is evapotranspiration, R is runoffand D is deep percolation. The tank water balanceequation is similar to that for catchments butincludes seepage, spill and water issues asoutflow components (negative sign), and runoffinflow from an upstream catchment (positive sign).All components are in cubic meters.

Precipitation volumes are calculated bymultiplying the gauged rainfall by the catchmentarea. Seepage from the tank is calculated with aformula based on Darcy’s law, as a product oftank water depth, water surface area and a ratio ofhydraulic conductivity to distance of seepage(Stanzel et al. 2002). Evapotranspiration (ET) iscalculated using the formula:

ET = (ETo * KC * A)/1000 (2)

Where ETo is reference evapotranspiration(mm), A is catchment area (m²) and KC is a non-dimensional catchment crop factor. Referenceevapotranspiration (ETo) is calculated by theHargreaves formula, using minimum and maximumtemperature and radiation (Allen et al. 1998).

Irrigation releases (IR), runoff (R) andpercolation (D) terms are computed usingequations of the following generic form:

W = f*Vr V (3)

Where W is either IR, R or D, f are non-dimensional factors (model parameters which differfor different processes), Vr is a relative catchmentwater storage (a ratio of the current water storageV to the catchment storage capacity Vc).

Calculations of these three processes alsoinclude three threshold parameters. Runoff from acatchment is assumed to occur only if the currentcatchment storage exceeds a threshold minimumstorage parameter (Vmin). Calculation ofpercolation includes a threshold volume parameter(VD). The use of this threshold accounts for thefact that rainfall on dry soil leads to an increase insoil moisture, until the soil is saturated and waterstarts to percolate to lower layers. This percolatingwater does not contribute to the inflow intoKaragan Lagoon, but is used in the basin bycapillary rise, uptake by roots or is pumped bytube-wells. Percolation does therefore not occuruntil catchment storage exceeds VD.

Irrigation releases (IR) are computed in asimilar way to runoff and percolation. They areassumed to be proportional to “Katara” tank waterdepth and volume. Irrigation is assumed to startas soon as a water level threshold is exceeded.Irrigation will then continue for a maximum of 18weeks (which is the usual paddy irrigation periodin the area) or until the dead storage depth isreached. Land preparation requirements, croprequirements and rainfall are not taken into

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consideration in this form of simulation, because itis not possible without explicit representation ofpaddy fields.

The water balance of the lagoon itself isdescribed by the following equation:

dV = R +P – E – D – O (4)

Where dV is the water volume change in thelagoon over one time step, R is the total runofffrom both upstream catchment areas (“cascade”and “direct”), P is precipitation onto the maximumsurface area of the lagoon, E is evaporation fromthe current lagoon water surface, calculatedsimilar to equation 2. D is deep percolation and Ois outflow, which occurs only if the lagoon volumeexceeds its capacity and is calculated as thedifference between the two. All components arein m³.

Simulating Present Conditions

The models described above are relatively simpleand parsimonious. However, the scarcity ofavailable input data and observed informationimpose severe constraints on model calibrationand validation. Both models (described inequations 1 and 4) were set to operate with aweekly time step. Weekly precipitation data wereused as input to the models. Daily rainfall data foreleven years (1991-2001) from the nearestmeteorological station (Hambantota) were used toderive weekly rainfall.

Other input information, such as water levelsat the two terminal tanks and in the KaraganLagoon, as well as quantitative data on spillage,cropping and irrigation water releases werecollected during the monitoring program whichformed part of the study (Stanzel et al. 2002). Themonitoring period was however very short(September to December 2001) and was during anunusually dry year. It therefore allowed only somebasic data inputs for the model to be collected.Some model parameters (f factors, catchment

storage, etc.) were evaluated through a process of“soft calibration” (Stanzel et al. 2002). An attemptwas made to achieve in some years the waterlevels in a combined tank, which were highenough for irrigation water issues, or could lead tothe cases of spilling from real tanks (which werereported by local farmers). Some modelparameters were assigned values based onliterature sources. For example, for calculation ofevapotranspiration, values of crop factors and KC,were based on Allen et al. (1998). The naturalvegetation in the study area consists primarily ofdry scrub, which can be expected to have a lowKC, and trees, which have a higher KC.Proportions of the area covered by scrub andtrees could only be roughly estimated, and as afirst approximation, the Kc value of 1 wastherefore used on average. Evaporation from thelagoon water surface was calculated using anincreased Kc value of 1.15 to account for highwind speeds in the area (Allen et al. 1998; Stanzelet al. 2002). The simulated lagoon water volumeswere converted into water levels and water surfaceareas, using depth-area and depth-volumerelationships. These relationships were establishedusing available contour maps of the lagoon(Stanzel et al. 2002).

Because of the limitations in observed data,commonly used criteria of model performancecould not be used and only a visual comparison ofobserved and simulated water levels was possible.Figure 3 illustrates the observed and simulatedwater levels in the lagoon during the period ofwater-level monitoring. While the timing andpattern of the water-level fluctuations in the lagoonare reproduced correctly, the model seems tosimulate more variable water levels. However,longer observations on lagoon water levels(covering the entire water-level range) would beable to bring more clarity on the latter issue and,most likely, improve the simulation results. Apartfrom the inherent uncertainty associated with “softcalibration,” part of the differences may be causedby the use of Hambantota rainfall for the entire

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FIGURE 3.Observed and simulated water levels in Karagan Lagoon during the period of the monitoring program.

Karagan catchment, which ignores significantspatial variability of precipitation in the coastalzone (Stanzel et al. 2002).

A form of model validation could be made ifthe lagoon water levels are translated into water-spread areas and compared with the estimates ofwater-surface areas from available AdvancedSpaceborne Thermal Emission and ReflectionRadiometer (ASTER) satellite images. Theseimages have a resolution of 15 X 15 m. Thiscomparison is illustrated by figure 4, which showsthat the pattern of water-surface area fluctuationswas generally well reproduced by the model,regardless of the number of assumptions andsimplifications of the real processes that havebeen made.

Figure 5 shows the simulated lagoon waterlevels during the entire, 11-year long simulationperiod. The pattern of very low water levels insome summers and higher water levels in wetmaha seasons is reproduced in the simulated timeseries. The gradual fall of water levels from theflood water level of 1998 to zero in autumn 2001(confirmed through the discussions with local

farmers, fishermen and cattle owners) wasreproduced well by the model. A complete drying-up of the lagoon in autumn 2001 (actuallyrecorded) is also reflected in the simulated timeseries. This may be the only such case thatactually occurred over the period considered, asno quantitative confirmation of earlier dry cycles isavailable. The near-dry stages in lagoon regime inthe earlier parts of this period may therefore alsobe correctly simulated.

Scenario Formulation

The main sources of information for developingfuture scenarios of irrigation were engineeringreports of Nippon Koei (1999) and SAPI (2000).The design discharge for the left bank main canalat the head end of the extension area is 9.12m3s-1. If the canal is assumed to operate asplanned (full flow during the nine month cultivationperiod and 50 percent of full flow for 5 percent ofthe time in the three off-season months), theamount of water delivered to the head end wouldbe 216 million m3 (MCM). If more conservative

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FIGURE 5.Karagan Lagoon water levels simulated over a 11-year long period.

FIGURE 4.Simulated lagoon water-surface areas compared with water-surface areas estimated from available remote sensingimages.

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assumptions on irrigation efficiencies are used,the water demand for the left bank extension areawould be 166 MCM (SAPI 2000). Furthermore, theamount of water that could be released to theextension area without causing water shortages inthe whole Udawalawe irrigation scheme equals 120MCM (SAPI 2000). To deliver 166 MCM of water,approximately 75 percent of the design dischargewill be sufficient, while for 120 MCM, only about55 percent of the design discharge is required.Based on these assumptions, three differentscenarios of irrigation scheme operation havebeen considered. They correspond to 100, 75 and50 percent of the design inflow (scenarios 1, 2and 3 in table 1).

All three scenarios consider the increase incatchment storage due to the construction of newtanks and the augmentation of the capacity ofsome existing ones. This increase was set to 20mm (Stanzel et al. 2002). The increase in paddycultivation area is expected to lead to higherstorage capacity in the basin. Similarly, areascovered with other crops may store more waterthan in what are currently “natural” areas. Thishigher storage capacity was taken into account byfurther increasing the catchment storageparameters by 30 mm. Finally, the crop factor KC

was also increased, to represent the higherevapotranspiration capacity of the newly cultivatedareas.

In all three cases, it is anticipated that theregime of water levels in the lagoon will changeand that water levels will generally increase due toincreased inflows associated with irrigation returnflows. One possible option for reducing the impactof increased flows to Karagan Lagoon is adrainage channel diverting drainage flows directlyto the sea before they reach the lagoon. Theeffect of such a diversion channel wasinvestigated in two additional sub-scenarios. Bothused the inflow calculated using the scenario of75 percent of design inflow—this is one of the twomost probable scenarios of future schemeoperation: the 75 and the 50 percent of designinflow (Meijer 2000). In these two sub-scenarios,75 and 100 percent of the total runoff from thecatchment was diverted to the sea (scenarios 4and 5 in table 1, respectively). Lagoon waterlevels can also be managed by opening or closingits outlet channel to the sea. It is very unlikely,that this channel will remain closed whenadditional drainage flows start reaching KaraganLagoon, as this may result in frequent flooding ofparts of Hambantota town. It was thereforegenerally assumed that the channel will be open inthe future scenarios outlined above (scenarios1–5). To illustrate the effects of not opening thechannel, the scenario of 75 percent of designinflow was also simulated but with the outlet tothe sea closed (scenario 6 in table 1).

TABLE 1.Future scenarios of irrigation inflows to Karagan Lagoon and the management of the lagoon.

Scenario Scenario Description

1 Inflow is 100% of the design capacity of the main canal

2 Inflow is 75% of the design capacity of the main canal

3 Inflow is 50% of the design capacity of the main canal

4 Inflow is 75% of the design capacity of the main canal. 75% of inflow is diverted to the sea beforereaching the lagoon

5 Inflow is 75% of the design capacity of the main canal. 100% of inflow is diverted to the sea beforereaching the lagoon

6 Inflow is 75% of the design capacity of the main canal. The outlet to the sea from the lagoon is closed

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Simulating the Impacts of Scenarios

Simulations show that all scenarios of futureirrigation inflow from the extended Udawalawescheme will have a significant impact on thehydrology of the Karagan Lagoon. The amount ofirrigation inflow is the most influential factor of thefuture scenarios, while changes in the runoffmodel parameters do not have a significant effect(Stanzel et al. 2002). The simulated water levelsof the three main scenarios (1, 2 and 3 in table 1)can be seen in figure 6. To aid interpretation onlythe period from 1997 to 2001 is shown. Thisperiod contains the wettest maha season of 1997and the very dry summer of 2001.

In the 100 percent scenario (scenario 1), waterlevels never drop below 1.60 m and outflow to thesea occurs during long periods. The periods ofoutflow are slightly shorter in the 75 percentscenario (scenario 2). The water level drops below1.40 m in dry periods. The 50 percent scenario(scenario 3 in table 1) results in significantly lowerwater levels of less than 1 m in dry periods.

Figure 7 compares the current conditionwith two scenarios of water diversion to the sea(scenarios 4 and 5 in table 1). The partialdiversion of 75 percent of the lagoon inflow tothe sea leads to no significant drop insimulated water levels. A complete diversion ofall lagoon inflow through the main drainagechannel leads to simulated water levels beingconsiderably lower than the water levels incurrent conditions. A complete drying up duringlonger periods, however, does not occur.

The scenario with a closed outlet (scenario6) shows that this is a practically impossibleoption (figure 8). The simulated water levels arealmost constantly above a level at which thehouses closest to the lagoon would be flooded.

Apart from the time series of water levelsin Karagan Lagoon, a useful means ofsummarizing the simulation results is a durationcurve of water levels. A duration curve is acumulative distribution of values in a timeseries. It shows the percentage of time that a

FIGURE 6.Lagoon water levels simulated using different future scenarios of irrigation development.

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FIGURE 7.Lagoon water levels simulated in different scenarios of diverting irrigation drainage water to the sea.

FIGURE 8.Simulated lagoon water levels in the scenario of a closed outlet to the sea.

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certain value (lagoon water level in this case) isequaled or exceeded. In other words, it provides ameasure of assurance and risk of exceedingcertain water levels and gives an indication of howfrequently a certain water level occurs in asimulated (or observed) time series. This analysismay point to various thresholds, which may needto be avoided (or maintained) from ecological,management or development perspectives. Theduration curves of water levels simulated by thedifferent scenarios are presented in Figure 9. Theyall are compared with the duration curverepresenting the simulated lagoon water levelsunder present conditions. The curves illustrate, forexample, that if 75 percent capacity inflow to the

lagoon is expected, the lagoon water levels willremain above 2 m for about 75 percent of the timethroughout the simulation period. If 50 percentcapacity inflow occurs, water levels of 2 m will beexceeded for 50 percent of the time. At present,this water level is reached or exceeded forapproximately about 7 percent of the time (figure9). Therefore, the most probable scenarios ofirrigation development will effectively convert theKaragan Lagoon into a perennial water body withrelatively minor water level fluctuations. Divertingsome of the inflows from the irrigation schemedirectly to the sea could provide a means ofmitigating these changes and recreating thenatural pattern of water level fluctuations.

FIGURE 9.Duration curves illustrating the variability of simulated Karagan Lagoon water levels under different scenarios of futureirrigation development.

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The Study Site and Problem

Bundala, Embilikala and Malala are the coastallagoons of the Bundala National Park. This park isa Ramsar wetland site and is located east of theKaragan Lagoon (figure 1). The Bundala Lagoonhas an estimated maximum water surface area of5.2 km2 (CEA 1993). It lies within a catchment ofapproximately 20 km2, although the boundaries ofthe catchment are not well defined. The two otherlagoons, Embilikala and Malala, are interconnected(figure 1) and have a total maximum surface areaof 10.8 km2 (CEA 1993). Being interconnected, thetwo lagoons effectively operate as one singlehydrological entity. All three lagoons are importantfeeding and resting sites for migratory andresident water birds and also serve as nurseriesfor shrimp, finfish, and a variety of other marineorganisms (Matsuno et al. 1998; Amerasingheet al. 2002).

The Park currently experiences adverseimpacts on its environment, of which the mostimportant are those associated with irrigatedagriculture (Kirindi Oya–Badagiriya irrigationsystem) immediately upstream of the parkboundary and the lagoons. Since 1989 drainagewater from the upstream irrigated area (figure 1) ofabout 26 km2 has flowed into the Embilikala andMalala lagoons. The drainage flow component ismixed with the runoff generated from rainfall withinthe same area. As a result of extra drainageinflow, water levels in the Embilikala-Malalasystem often fluctuate over a wider range than inpre-development conditions. In order to preventoccasional flooding of irrigated land upstream, theEmbilikala-Malala lagoon system is breachedregularly (at least once a year) by local residents.This breaching causes the lagoon system toempty almost completely in approximately 6-7days while the breached outlet (mouth) staysopen. After mouth closure, the lagoons quickly fill

up again due to the continuous upstream inflowdominated by irrigation drainage water.

The Bundala lagoon has not been impacted todate by irrigation drainage flows and its hydrologyis still driven by natural physical processes. As aresult, the fluctuation of water levels in Embilikalaand Malala lagoons is currently very different fromthat of observed Bundala water levels. Figure 10illustrates the observed variability of water levelsin the three lagoons during a period of 19 monthsin 1998-2000 (Piyankarage 2002). The water levelfluctuations in Embilikala and Malala lagoons aresimilar to each other, and show larger fluctuationsdue to mouth opening and closure than withinBundala lagoon.

There is concern that further increases infreshwater input to the lagoons would render themunsuitable to existing aquatic species (Matsuno etal. 1998). One of the major impacts of irrigationwater could be the raising of water levels in thelagoons that could make feeding sites unavailablefor many water birds. Also, the increasedfreshwater inputs to the lagoons may effectivelyconvert these rare brackish coastal ecosystemsinto freshwater ones and have a profound impacton the associated biota.

The analysis of the hydrological regimes ofthe lagoons is important for quantifying thelagoons’ past and present hydrological conditionsand for improving the understanding of thelinkages between hydrological characteristics ofthe lagoons and their aquatic life. Prior to theestablishment of the upstream irrigation scheme,the three lagoons were likely to have had a verysimilar temporal pattern of water-level fluctuations.They have similar ranges of depths and are allclose to each other. Therefore it is reasonable toexpect that they are subject to similar patterns ofrainfall and evaporation. This, in turn, implies thatBundala lagoon (which has not been affected byirrigation inflows), may be considered as a

Simulating Hydrological Reference Condition of Bundala Lagoons

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‘reference wetland’. If its water level regime isquantified, it could be ‘transferred’ to the other twolagoons.

Simulation Approach

The focus has been on simulating the hydrologicalconditions which could have existed in theEmbilikala and Malala lagoons prior to theimplementation of the upstream irrigation systems.Such reference conditions will reflect the pre-development status of the lagoons and allow thequantification of hydrological changes in thelagoons to be made.

Smakhtin and Piyankarage (2003) simulatedthe water levels in Bundala lagoon using a simplelagoon water balance model, similar to thatdeveloped for Karagan Lagoon described in theprevious section, but operating on a daily timestep. The model was however simplified evenmore compared to the Karagan case. The areathat was likely to contribute to the lagoon waterbalance (‘active catchment area’) wasapproximated by the maximum water surface areaof the lagoon. This assumption effectively implies

that lateral inflow to the lagoon was very limitedand its major part occurred predominantly fromthe area surrounding the lagoon itself. Theassumption was based on the fact that thetopography of the Bundala catchment is flat andno clear drainage network exists. Thisassumption allowed two components of the waterbalance (rainfall on the lagoon surface and lateralrunoff) to be simulated as one lumped inflow. Noseparate simulations were therefore carried outfor runoff inflows from the upstream catchment.

The simulation period was again determinedby the length of the input rainfall data at thenearest rain gauge (11 years of observations inHambantota town). The model reproduced thegeneral pattern of observed water-levelfluctuations correctly (figure 11). However, theduration and occurrence of the dry phases in thelagoon hydrological cycle (which are presentaccording to local residents) were notsatisfactorily reproduced due to multipleproblems with input data (Smakhtin andPiyankarage 2003). It was therefore important torevisit the simulations in an attempt to simulatethe dry periods in the lagoon hydrological cycle.

FIGURE 10.Observed water levels in Embilikala, Malala and Bundala lagoons.

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Adjustments were made to carry out thesimulation through a spatial interpolation approachdeveloped by Hughes and Smakhtin (1996),Smakhtin (1999) and Smakhtin and Masse (2000).The technique transfers a hydrological time seriesfrom a source site (a site with data) to adestination site (normally an ungauged site, wherethe simulation of a hydrological variable, such asdischarge, is intended). A key component of thistechnique is a flow duration curve (FDC)—agraphical summary of stream flow variability at asite, illustrating the relationship between flowmagnitude and its frequency of occurrence. Themain assumption of the spatial interpolationtechnique, in its original form, is that flowsoccurring simultaneously at sites in reasonablyclose proximity to each other correspond to similarpercentage points on their respective FDCs. Thesource and the destination site FDCs arerepresented by tables of discharge valuescorresponding to 17 fixed percentage points (0.01,0.1, 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95,99, 99.9 and 99.99%). These tables are generatedmonthly for the whole year. Following the mainassumption of the algorithm, the core of the

computational procedure includes the estimation ofthe percentage point for each day’s flow at thesource site and the identification of flow for thesame percentage point from the destination site’sFDC (figure 12). The discharge tables are used to“locate” the flows on corresponding curves andlog-interpolation is used between 17 fixedpercentage points.

Similarly, the approach may be used forsimulation of other hydrological variables (e.g.,water levels). In the context of the present study,the main assumption will be that reference dailywater levels in Bundala Lagoon and the dailywater levels for the Embilikala-Malala lagoonsystem in natural, pre-development conditions,correspond to similar probabilities on theirrespective duration curves of water levels. Aduration curve of water levels for Bundala lagoonmay be calculated from water levels simulated bySmakhtin and Piyankarage (2003). In the contextof the spatial-interpolation approach, BundalaLagoon becomes the source site, from where theinformation will be transferred. Embilikala-Malalalagoon system becomes the destination site towhich the information will be transferred as it is

FIGURE 11.Water levels in Bundala Lagoon: observed and simulated using a daily water balance model.

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effectively ungauged (no observations of waterlevels representative of pre-developmentconditions are available).

The spatial interpolation approach naturallyrequires the establishment of a duration curve forthe ungauged destination sites prior to thesimulation of the actual time series. To establish aduration curve at the destination site, a briefsurvey of the local residents was carried out.These interviews suggested that before theimplementation of the upstream Kirindi Oya–Badagiriya irrigation scheme in 1989, Embilikalaand Malala lagoons used to dry up every year. Nosuggestions were made about the length of thedry phase. Residents said that Bundala Lagoondries up about once in two years for at least onemonth. On this basis, it can be assumed that atleast for one month a year (or approximately 10%of the time on average in one year) the Embilikalaand Malala lagoons may have been dry. Some

sources suggest that Embilikala and Malalalagoons now fluctuate frequently in the range of1.0 to 2.2 m (Jayawardena 1993) whilstobservations indicate that manual breaching isdone at water levels of 1.9-2.2 m (figure 10).

Based on the above information, three waterlevels with corresponding probabilities ofoccurrence may be specified: 2.2 m—almostnever exceeded (0.01% of the time), 1 m as alikely median (exceeded 50% of the time) and 0 m(dry lagoon) which occurs 10% of the time(exceeded 90% of the time). The median value of1 m has been approximated from the observedwater levels in Bundala Lagoon (figure 10) as thisis the only source of quantitative informationavailable. Water levels at other intermediatepercentage points (e.g., 5, 10, 20%, etc.) may befound by means of interpolation between the threeestablished points thereby completing the durationcurve for the destination site.

FIGURE 12.Illustration of spatial interpolation algorithm.

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Analyzing the Impact

Figure 13 illustrates the simulated water-level timeseries, which may be interpreted as either a water-level time series for Bundala Lagoon in currentconditions, or as a water-level time series for theEmbilikala-Malala system, which could haveexisted prior to the implementation of the irrigationscheme. The observed water levels in Embilikala-Malala lagoon system for a 19-month period during1998-2000 are also shown in order to illustrate thesubstantial departure of present and probable pastwater-level variations.

Figure 14 shows duration curves of waterlevels in the Embilikala-Malala lagoon system.The curve based on daily observations during the19-month monitoring period, reflects the variationof impacted water levels. The other curve isbased on daily water-level time series simulatedusing the spatial interpolation approach discussedabove (reference water-level time series).Comparison of the curves allows the changes

brought to the system by additional drainage flowsto be quantified. For instance, it can be seen thatalthough the simulated water levels are generallylower than those observed throughout the wholerange of probabilities, the major differences occurat probabilities of over 50 percent. This indicatesthat low water levels in the lagoons are currentlyhigher than they used to be before theimplementation of the upstream irrigation scheme.The proportion of time when the lagoon is dry wasalso greater in the pre-irrigation conditions than atpresent. For example, a water level that isequaled or exceeded for 70 percent of the time inthe observed times series is approximately 2.3times higher than the corresponding water level inthe reference time series. A water level of 1 m iscurrently occurring as often as 80 percent of thetime compared to 50 percent of the time in thesimulated time series. This type of analysis maysimilarly allow for the quantification of changes inother (e.g., ecologically meaningful) water levels inthese lagoons.

FIGURE 13.Simulated reference water levels for Bundala Park lagoons and observed water levels in the Embilikala-Malalalagoon system.

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Simulating Inflow to and Mouth Conditions for Small Estuaries

FIGURE 14.Duration curves of water levels in Embilikala-Malala lagoon system based on observed and simulated data.

The Study Problem

This example focuses on small estuarinesystems, which are widespread in different partsof the world. For illustrative purposes, however,specific cases from the South African coast lineare considered. Many estuaries along SouthAfrican’s coast may be classified as temporarilyclosed systems. Such systems are also known as“temporarily open”, “blind” or “lagoonal” estuaries(e.g., Day 1981a; Whitfield 1992). The state of anestuary mouth is probably the single-mostimportant factor determining the estuarine salinityregime and governing the structure and functioningof the resident biotic community. This appliesparticularly to many invertebrates and fish whichhave a marine phase in their life cycle, but forwhich estuaries often serve as nursery areas. It

also influences many plant species, which aredependent on particular inundation regimesprevailing within the estuary. Similarly, certainwater bird species favor tidal exposure ofestuarine sediments whereas others benefit fromprolonged closure of an estuary mouth.

Temporarily closed or open estuaries andlagoons are blocked off from the sea for varyinglengths of time by a sand bar, which forms at theestuarine mouth (figure 15). The duration of theclosed and open phases are determined by theinteraction of river runoff, evaporation, seepageand wave over-wash events in the mouth region.Unlike the coastal lagoons in Sri Lanka featured inthe earlier sections of this report, which representan example of more static water bodies withlimited interaction with the sea, the dynamics ofmany small estuaries is driven by river inflow.

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The closed phase normally occurs during periodsof low inflow, whereas floods breach the mouthand scour an estuary.

Small estuaries in South Africa usually havesmall river catchments (10–500 km2) and streamflow that is seldom measured, either in thecatchment or at the mouth. Observations onestuary mouth conditions are very scarce,inconsistent and fragmented between differenthistorical periods, institutions or individuals. Formost South African estuaries, no documentedobservations on changing mouth conditions areavailable. The lack of continuous observations onfreshwater inflow and mouth condition precludesthe development of tools for quantification ofestuarine dynamics.

FIGURE 15.Aerial view of the temporarily closed Umgababa estuary in KwaZulu–Natal Province, South Africa.

The approach described below highlights thecombined use of simple modeling tools, whichhave been described in the previous sections.However, due to the nature of the problem anddominating processes in this case, the focus ismade on integrating both the spatial interpolationmethod and a simple reservoir water-balancemodel. The first is used to simulate inflow toestuaries whilst the second indicates the openingsand closures of the estuarine mouth.

Simulating Inflow to Estuaries

The most appropriate time resolution for thedescription of physical processes in smallestuarine systems is 1 day. The spatial

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interpolation approach, which operates with a dailytime step, has been described in the previoussection of this report (in the study of Bundalalagoons). More details of this method are availablefrom Hughes and Smakhtin (1996), Smakhtin etal. (1997) and Smakhtin and Masse (2000). Thisapproach can also be used to transfer theavailable daily rainfall time series at some sourcesite(s) into stream flow time series at thedestination site of interest (the destination site inthe context of this study is represented by anestuarine mouth). Since all or most of theestuaries along the South African coast (andsimilar systems elsewhere in the world) areunlikely to be gauged, no suitable source flowgauge(s), with observed records, can be identified.The use of more readily available rainfall recordsprovides a potential solution. These records willfirst need to be converted into some continuousfunction of rainfall, to allow spatial interpolation.This is necessary because daily rainfall itself is avery discrete process and rainfall duration curvesare normally very steep as it rains for only for asmall percentage of the time on average during ayear. Smakhtin and Masse (2000) suggested theuse of a current precipitation index (CPI) for thispurpose. The CPI reflects the catchment wetnessand accounts for current daily precipitation inputand the exponential depletion of catchmentmoisture content during a period of no rainfall.

CPIt = CPIt-1 K + Rt (5)

Where CPIt

is the CPI (mm) for day t, Rt isthe precipitation (mm) for day t and K is therecession coefficient. On any day with no rain(Rt= 0) the CPI is equal to the CPI of the previousday multiplied by K. If it rains on any day, thedaily rainfall depth is added to the CPI on thatday. The recession coefficient, K, normally variesfrom 0.85 to 0.98 (Smakhtin and Masse 2000) anda standard value of 0.9 was found to be suitablefor most applications.

Once a continuous daily CPI time series isgenerated for a source rainfall site, the required

CPI duration curve may also be established. Itmay then be used in the spatial interpolationalgorithm as a substitute for the source flows, andrepresents a source time series, which allows adestination site duration curve to be converted intoactual destination site discharges. In the contextof this study, these discharges are inflows to anestuary. The major assumption of the algorithm inthis case becomes that both the CPIs occurring atrainfall site(s) in reasonably close proximity to anestuary and estuarine inflows, themselvescorrespond to similar percentage points on theirrespective duration curves. The layout of thecomputational procedure outlined initially byHughes and Smakhtin (1996) remains the sameand similar to that presented in figure 12.

As the estuary mouth is also ungauged, it isnecessary to calculate its FDC prior to thegeneration of the actual daily stream flow timeseries. In a South African context, daily FDCsmay be derived from FDCs based on coarser,monthly time step stream flow data. The latterhave been simulated (Midgley et al. 1994) for alarge number of small and normally ungaugedincremental drainage subdivisions known asquaternary catchments. These data are currentlywidely used in South Africa for a variety ofengineering and environmental applications(Smakhtin et al. 1997; Hughes and Hannart 2003).The average catchment area of a quaternarycatchment is around 650 km2 and the total numberof catchments is close to 2,000. Coastalquaternary catchments may include severalstreams and, consequently, estuaries. For suchstreams of sub-quaternary size, the simulatedmonthly-flow time series may simply beapportioned based on the ratio of a sub-quaternarycatchment to the total quaternary catchment area.

The method of establishing daily FDCs frommonthly data is based on the premise that for anystream, the variability of daily flows is higher thanthat of monthly flows. In a high-flow month,maximum daily average discharges are higherthan the average discharge for that month. In alow-flow month, minimum daily average discharges

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are usually lower than the monthly average flow.The general implication for FDCs is that dailydischarges are higher than monthly discharges inthe area of low probabilities of exceedence andlower than monthly discharges in the area of highprobabilities. At the same time, a strongrelationship exists between 1-day and 1-monthflows of the same exceedence levels. Smakhtin(2000) explored these relationships forapproximately 200 gauged rivers in South Africaand suggested linear regression equations andstep-by-step procedures to calculate a 1-day FDCfrom a 1-month FDC. This procedure could beapplied at any site, where monthly flow data areavailable (or may be simulated) and was alsoused to calculate 1-day FDCs at the estuarinemouth site. This calculation completes theacquisition of components necessary to generatea continuous daily stream flow time series for anestuary using a spatial interpolation algorithm.

Estuarine Mouth Condition Records versus theVariability of Upstream Inflow

Only a few estuaries in South Africa whereobservations on mouth condition were conducted inthe past have been identified. The required recordswere acquired from different sources for these sites.However, only a few sets of these data were usabledue to their poor quality and/or short records.Different individual estuaries display very differentpatterns of temporal variability in mouth condition.Figures 16 and 17 display daily inflow to an estuarysimulated as described above and the mouthphases for two different estuaries. An estuary openmouth phase is represented in these figures by adummy non-zero constant (1 or 5) and a closedmouth phase is represented by zero.

The first estuary, Vungu (figure 16), is anexample of a non-responsive system. The estuaryis located 142 km southwest of the coastal city ofDurban in the KwaZulu-Natal Province of SouthAfrica. The upstream catchment is approximately120 km2 (Begg 1978), and the estuarine lagoon is

about 175 m long with a maximum width of 125 mand an average surface area of 1.13 X 10-2 km2. Afew weirs impound the stream upstream of theestuary, but their total capacity (about 2% ofnatural mean annual runoff) is negligible comparedto the natural mean annual runoff (MAR) at theestuary mouth. The estuary lagoon is more than2 m deep and the mouth is open for most of theyear. Mouth closure occurs for short periodsduring the dry season, but during wet cycles theestuary, and others like it, may remain open forthe entire year. The variability in daily inflow doesnot lead to multiple closures because the flow isusually sufficient to prevent the mouth fromclosing.

The second estuary, Little Manzimtoti (figure17), is an example of a much more sensitivesystem. The Little Manzimtoti river is locatedclose to the Vungu estuary and drains acatchment of approximately 15 km2. The S-shapedlagoon is about 0.8 km long, with a maximumwidth of 45 m and an average surface area of1.5–10-2 km2. The estuary is subject to siltationdue to upstream catchment developments and theentire lagoon is shallow (mean depth less then1.0 m). The estuary opens and closes frequentlyas the flow increases or decreases. As figure 17illustrates, even small increases in river flow maycause a mouth opening event and/or increase theduration of the open mouth phase. However, inestuaries such as this, not all mouth behaviormay be explained by the variability in river flow,since sea effects (e.g., tidal exchange andsandbar overtopping events) may also play a role(e.g., recorded short-lived open mouth phasesshortly after day 50, just before day 200 andaround day 300 on figure 17). The sea processeseffectively have impacts on all estuaries, but to adifferent extent.

The examples above illustrate the range ofvariability in estuarine mouth conditions. Theydemonstrate that the pattern of observed mouthphases in estuaries is often strongly linked to thesimulated inflows.

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FIGURE 17.Example time series of simulated daily inflow and observed daily mouth condition in the Little Manzimtoti estuary.

FIGURE 16.Example time series of simulated daily inflow and observed daily mouth condition in the Vungu estuary.

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Simulating Estuarine Mouth Conditions

An estuary which is blocked off from the sea by asandbar effectively represents a reservoir wherethe sandbar serves as a dam wall. This analogy isused here as the basis for simulating the openingsand closures of an estuarine mouth. The“estuarine reservoir” water balance equation iseffectively the same as in equation 4, but thepercolation term (D) includes the seepage throughthe sand bar and bed of the lagoon. It is hereafterreferred to throughout this section as seepage.The outflow term (O) in equation 4 is the outflowfrom an estuary into the sea, when the estuary is‘full’. The description of the inflow from the seainto the estuary was not considered at this stageand it has therefore been assumed that mouthopenings are driven exclusively by flow from theinflowing stream. The inflow from the sea refers tomarine overtopping into the estuary and not toseepage from the sea. The latter is likely to beinsignificant if it occurs at all (A.Whitfield,personal communication). The model forms part ofa comprehensive hydrological modeling computerpackage, which incorporates other models aswells as data pre- and post-processing and displayroutines. The use of this model in the context ofreservoir operation has been described by Hughesand Ziervogel (1998).

In the absence of observed stream flowrecords, the inflow time series may be simulatedusing the spatial interpolation algorithm describedpreviously. Daily rainfall time series may be takenfrom the closet rain gauge to the estuary underconsideration. Normally at least one rain gaugewith data may be found within a reasonablevicinity of any estuary, even in remote areas.Information on regional mean monthly evaporationvalues in South Africa is available from Midgley etal. (1994). Daily evaporation values have beenapproximated by the division of monthlyevaporation values by the number of days in amonth. Daily rainfall and evaporation valuesmultiplied by the estuarine water surface area form

the components of the estuarine water balance.Seepage from an estuary may represent asubstantial component of an estuarine waterbalance. It is, perhaps the least studied processin small temporarily closed South Africanestuaries. In the absence of detailed informationon this process and in the context of the modelused, a seepage component is approximated asthe total annual water abstraction from an estuarybased on synthetic monthly data from Midgley etal. (1994). This annual total is then distributedbetween calendar months of the year inproportions based on the monthly distribution ofinflow. The daily values are calculated by thedivision of monthly volumes by the number ofdays in a month.

Outflow from an estuary is assumed to occurif the volume of water stored within it exceeds theestuarine reservoir capacity (a model parameter),and, consequently, the reservoir water levelexceeds the “full supply level”. The water level(H, m) at the end of each time interval isestimated using a depth—volume relationship. Theoutflow from an estuary is calculated using thefollowing equation:

O = h1.5 * K * W * 86400 (6)

Where h is the depth above the full level of anestuary, averaged over the start and end of thetime interval and determined from the depth-volume relationship, K and W are the estuarine‘spillway’ coefficient and width respectively (modelparameters). In the context of the current study,the actual outflow volume is not of primaryimportance. What is more important is the factthat on certain days outflow does occur. On dayswhen the estuary ‘spills’, the estuarine mouth isassumed to be open. On days when no ‘spillage’occurs, the estuarine mouth is closed.

The model parameters which define thecharacteristics of the estuarine reservoir may beestimated from available literature sources (e.g.,Begg 1978; Day 1981a), which often list such

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variables as length, maximum and minimum widthand maximum, minimum, or mean depth of anestuarine lagoon. Alternative sources may includemaps or aerial photographs, and databasesavailable from regional environmental or natureconservation agencies and departments. Someparameters may be approximated during the stageof model calibration in cases when suchcalibration is possible (when at least limited butaccurate observed flow data exist close to theestuarine mouth or where observations ofestuarine water level are available).

The example used to illustrate the modelingapproach described above is the temporarilyclosed Umgababa estuary in KwaZulu-NatalProvince (figure 15). The Umgababa River, some35 km south of Durban, is about 15 km long anddrains a coastal belt catchment of approximately37 km2. The S-shaped lagoon is about 3 km longwith an estimated surface area of 17.6 X 10-2 km2.It gradually widens from a small stream 20 macross to a maximum width of 150 m (Day 1981b)and has an average depth of about 1.5 m for mostof its length. After heavy rains, the water levelrises until the sandbar is breached and theestuary becomes tidal until the bar builds upagain. The Umgababa estuary is one of the mostimportant on the Natal south coast, and hasconsiderable conservation potential. The estuaryappears to have preserved its currentenvironmental character and has an abundance ofestuarine life (Begg 1978).

Daily rainfall data from the nearest station(Illovo Mill), located upstream of the estuary wasused as input to the model. Figure 18 shows an

extract from the time series of simulated dailyoutflow from Umgababa estuary and observedmouth conditions. The observed data (1988 –1995) suggests that the estuary is open forapproximately 31.6% of the time on averagethroughout a year (although the lengths of openphases vary from year to year). The quality ofthese data is however highly questionable andthere are multiple periods of missing data,particularly in the latter half of the record. Thesimulation (carried out for the same period)suggests that the estuary is open for 28.5% of thetime, which is close to the value obtained fromobserved data.

Some observed open mouth phases do not,however, coincide with periods of outflow fromthe model. In the simulated series there arefewer (13 cases in the whole record) but longer(mean of 50 days) events of the open mouthphase compared to the observed data (28 casesand a mean of 23 days, respectively). Themodel, however, cannot and has not beenproperly calibrated against the observed data, asthe observed record was very short andinaccurate, as mentioned above. Modelparameters have been derived from publishedsources (Begg 1978; Day 1981a; Midgley et al.1994). At the same time, the example indicatesthat even under conditions of very scarce anduncertain data, the pattern of mouth statusdynamics could be reproduced by this or a similarpragmatic model and further improved if additionalfield data on estuarine characteristics, mouthconditions and flow (even obtained throughoccasional measurements) become available.

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Conclusions

This report illustrates how simple modelingapproaches can be used to generate, analyze andpresent information for natural hydrologicalsystems in cases where information is extremelyscarce and where the availability of data isunlikely to improve quickly. Measured time seriesare, for example, normally not available for smallcoastal water bodies. The methods described inthis report are capable of producing long-termsimulated time series of required hydrologicalvariables (e.g., discharge, water levels, volumes,water surface areas and mouth conditions).

The simulation methods employed in thisreport include water balance equations and anumber of assumptions about the physicalprocesses, system structure and characteristics.They require much less input information andparameters to operate, compared to morecomplex, distributed, information-consuming andlabor-intensive techniques.

Such modeling approaches also have toemploy simple, and often, unconventional ways ofdescribing certain processes or components ofnatural systems, which have not yet been studiedand which may remain unmeasured due to limitedbudgets and time constraints. For example, in theKaragan study the multiple interactions betweentanks, crop fields and sub-catchments have beensuccessfully described by a few lumped dummyreservoirs. Similarly, in the Bundala study resultsof surveys of local residents have been used toderive a hydrological tool (a water level durationcurve), which was then used to generate therequired hydrological time series.

The study illustrates that simple simulationtechniques may be used for a variety of tasks, fromthe assessment of the future impacts of irrigation andbasin development on natural ecosystems, to theestablishment of past, reference hydrological regimes,against which to measure the current impacts.

FIGURE 18.Simulated daily outflow and observed mouth condition in the Umgababa estuary.

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The methods presented and illustrated in thereport are not limited to any specific region. Theexamples described focus on small coastal waterbodies, but the techniques could be used inprinciple for any ungauged natural coastal systemor river/stream.

Some of the methods presented in the reporthave undergone development through newapplications. This applies to the spatialinterpolation of observed time series. Although itis already widely used in hydrological practice forgeneration of flows, it has been applied here forthe first time to generate lagoon water volumesand levels. Similarly, an unconventionalinterpretation of an estuary as a reservoir which‘spills’ into the sea when the estuary is full, allowsthe estuarine mouth closures and openings to besimulated without major modifications to the waterbalance modeling framework.

The outputs simulated by these methods mayhave many other applications and spin-offs. Forexample, continuous long-term records of lagoonvolumes and water-surface areas may be used incalculating salt concentrations and the dynamicsof flooding. Simulations of inflows to estuariesunder conditions of different upstream catchmentdevelopment may facilitate the assessment ofchanges in the estuarine type (e.g., they could beused to assess whether an estuary might closecompletely under planned irrigation withdrawal froman inflowing stream). The analysis of water levels

using a duration curve allows a summary ofhydrological variability to be presented and maysuggest ways of linking hydrology with ecology(e.g., changes in the probabilities—assurances—of water levels may be related to recorded orestimated losses of aquatic biodiversity).

Even modeling methods as parsimoniousand pragmatic as those illustrated in the report,may point to further research needs. Forexample, it is clear from the above examplesthat more experimental data are required toproperly quantify such components aspercolation and seepage in coastal ecosystems.Therefore, continuous observations of waterlevels have been established in Karagan Lagoonand its catchment in Sri Lanka. Establishingcontinuous observations on estuarine mouthcondition for at least a few small representativeestuaries of different types in South Africa (andother regions, where such systems representimportant natural ecosystems) would also helpconsiderably.

Finally, the simulated time series may formthe basis for a transparent discussion on mattersrelated to impacts (current or future) of irrigationdevelopment. The models may be runinteractively at specialist and/or stakeholderworkshops in order to illustrate the hydrologicalimpacts of scenarios or to quantitativelyinterpret the perceptions about current or futuredevelopment plans.

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Allen, R.G.; Pereira, L.J.; Raes, D.; Smith, M. 1998. Crop evapotranspiration: Guidelines for computing crop waterrequirements. FAO Irrigation and Drainage Paper 56. Rome, Italy: Food and Agriculture Organization of the UnitedNations (FAO).

Amerasinghe, F.P.; Matsuno, Y.; De Silva, R.I.; Piyankarage, S.C.; Bambaradeniya, C.N.B.; Mallawatanthri, A. 2002.Impact of irrigation on water quality, fish and avifauna of three coastal lagoons in southern Sri Lanka. In The AsianWetlands: Bringing Partnerships into Good Wetlands Practices, eds., Ahyaudin Ali, Che Salmah Md. Rawi, MashhorMansor, Reiko Nakamura, Sundari Ramakrishna and Taej Mundkur. Proceedings of the Asian Wetland Sympo-sium, Penang, Malaysia, 27-30 August 2001.

Begg, G. 1978. The estuaries of Natal. Natal town and regional planning report. Vol. 41. , Pietermaritzburg, SouthAfrica: L. Backhouse. 657 pp.

CEA (Central Environment Authority). 1993. Bundala National Park. Wetland site report and conservation manage-ment plan. Colombo, Sri Lanka: Central Environment Authority / Euroconsult, Ministry of the Environment andParliamentary Affairs, Sri Lanka.

Day, J.H. 1981a The nature, origin and classification of estuaries. In Estuarine ecology with particular reference tosouthern Africa, ed., J.H. Day. Cape Town, South Africa: A.A. Balkema. p. 1-6

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