Climate Change and Water Resources in South Asia - Chapter 2 doc

Fourtypes of hydrologic model - empirical, water-balance, conceptual lumped-parameter andprocess-based distributed models - are used for hydrologic modeling.. A model is usually selected

Hydrologic Modeling Approaches for Climate Impact Assessment in South Asia

The hydrologic and water resources problems in South Asia are discussed in Chapter 1

It is anticipated that the problems will be exacerbated if basin-wide temperature andprecipitation would change due to climate change Quantification of possible changes inriver discharge (mean or peak) is achieved with the application of hydrologic models Fourtypes of hydrologic model - empirical, water-balance, conceptual lumped-parameter andprocess-based distributed models - are used for hydrologic modeling

A model is usually selected depending on the purpose of the application whichincludes: runoff-simulation; sediment transport and morphological changes; estimatingground water and changes in ground water volume; forecasting flood volume, depth andduration; assessing changes in land-use; and assessing impacts of changes in climate.Availability of data and resources are also governing factors in a model selection process.This chapter discusses the comparative advantages and limitations of various hydrologicmodels and their suitability for estimating changes in mean annual and mean peakdischarge under selected climate change scenarios for the river basins in South Asia Itexamines reduction of input variables for empirical modeling through the sensitivityanalysis of runoff to changes in temperature and precipitation This chapter also discussesapplication of hydrologic models in Bangladesh as a case study to assess climate changeimpacts

In planning for water resources and extreme events like floods and droughts, it is essential

to know the precipitation-runoff processes in the vegetation, land surface and soilcomponents of the hydrologic cycle These processes differ in arid, semi-arid and humidclimates Even within a single climate zone, physical processes can vary widely because ofthe diversity of vegetation, soils and microclimates

Hydrologic models describe these processes by partitioning the water among thevarious pathways of the hydrologic cycle (Dooge, 1992) Mathematically, hydrologic modelsincorporate a set of assumptions, equations and procedures intended to describe the

performance of a prototype (real-world) system (Linsley et al., 1988) Because of the

M MONIRUL QADER MIRZA

increase in computing capacity, complex mathematical descriptions of the physicalprocesses of the hydrologic cycle can now be incorporated into hydrologic models.However, because of variations in physical parameters and the limitations of ourknowledge and understanding about the complexity of the hydrological processes, no

‘hydrologic model’ is able to reproduce fully the prototype processes Accuracy of themodel is highly dependent on factors such as: adequacy of empirical, statistical andmathematical descriptions of the physical processes; the quantity and quality of input data;the extent of basin coverage; and the magnitude of variability in physical parameters.There are two main aims for using simulation modeling in hydrology The first is toexplore the implications of making certain assumptions about the nature of the real-worldsystem The second is to predict the behavior of the real-world system under a set ofnaturally occurring circumstances (Beven, 1989) In order to meet these aims, differenttypes of hydrologic models are required

There are four types of hydrologic models - empirical, water-balance, conceptuallumped-parameter and process-based distributed models The choice of model typedepends partly on the purpose of the application including: simulating runoff, sedimenttransport and morphological changes; estimating ground water and changes in groundwater volume; forecasting flood volume, depth and duration; assessing changes inland-use; and assessing impacts of changes in climate The choice of model also depends

on the availability of data and resources The various types of hydrologic models and theiradvantages and limitations are discussed below

2.2.1 EMPIRICAL MODELS

In hydrological modeling, empirical models are generally developed and used forprediction and estimation purposes These models do not explicitly consider the physicallaws governing the processes involving precipitation, temperature, vegetation and soils(Singh, 1988) However, they do implicitly incorporate the fundamental physical fact that,generally, variations in runoff tend to respond proportionally to the variations in climate.Empirical models are developed based on a ‘black box’ modeling approach whereempirical equations are used to relate runoff and rainfall, and only the input (rainfall) andoutput (runoff) have physical meanings Through statistical techniques, empirical modelsreflect only the relations between input and output for the climate and basin conditionsduring the time period for which they were developed These models provide a muchmore simplified view of reality, particularly when regression techniques are employed

(Kirkby et al., 1987) The accuracy of models largely depends on the magnitude of error

inherent in the input and output data As the empirical models are developed with inputand output data within a certain range and time period, caution should be exercisedregarding the extension of the relationship for climate conditions different from those usedfor the development of the function (Leavesley, 1994) Models developed for a particularriver basin cannot be applied to a different basin Although empirical models are oftencriticized for these limitations, they are widely used compared to other models

Despite their limitations, empirical models have some distinct advantages over othertypes of hydrologic models For example, they are relatively easy to develop, require lessdata, can be calibrated simply, require fewer resources, and do not need a huge computingcapacity When other models cannot be developed or used because of the paucity of data,empirical models can be developed for various purposes In many situations, empiricalmodels can yield accurate results and can, therefore, serve a useful purpose in

24 H YDROLOGIC M ODELING A PPROACHES

decision-making (Singh, 1988) In hydrology, empirical models are generally useful inestimating the mean annual flood, monthly and annual mean discharge and bankfuldischarge (Garde and Kothyari, 1990; Kothyari and Garde, 1991; Mosley, 1979 and 1981;Schumm, 1969; Thomas, 1970; Rodda, 1969; Leopold and Millier, 1956; NaturalEnvironment Research Council (NERC), 1975; Beable and Mckerchar, 1982).

There are two important issues which need to be taken into account beforedeveloping an empirical model for estimating discharge and floods First, empirical modelsrequire very good spatial distribution of precipitation Ideally, this can be achieved byacquiring long-term records of precipitation for a large number of stations uniformlydistributed over a river basin, covering high and low elevations Similarly, long-term records

of temperature are also necessary if temperature effects are to be considered

Second, a fairly good record of discharge (or runoff) from downstream stations isneeded However, if there is any diversion of flows through the distributary (ies) or by anyother means in the upstream areas, this has to be taken into account depending on themagnitude of the diversion

2.2.2 WATER-BALANCE MODELS

Water-balance models were first developed by Thornthwaite (1948) in the 1940s and weresubsequently revised by Thornthwaite and Mather (1955) and by others Palmer (1965)used a water-balance model similar to that of the Thornthwaite model while developing anindex of meteorological drought Thomas (1981) presented an alternative water-balancemodel with several new features These water-balance models have very simple structuresand are characterized by a limited number of parameters This kind of model is essentially

a ‘book-keeping procedure,’ which uses the following fundamental equation to estimatethe balance between the precipitation (as rain and snowmelt), loss of water byevapo-transpiration, stream flow and recharge into the ground water:

where P is the precipitation, R is the runoff, G is ground water runoff, ∆S is the changes instorage (snow and soil water) and E is evapo-transpiration The typical structure of awater-balance model is shown in Figure 2.1

The models can be simple to complex depending on the details of each of thecomponents of the equation (2.1) Most water-balance models calculate direct runoff fromprecipitation and lagged runoff from the basin storage in the computation of the totalrunoff (R) The sensitivity and accuracy of water-balance models often depend on themethod of calculating potential evapo-transpiration (PET) Various PET-models areavailable among which Penman (1948), Thornthwaite (1948), Blaney and Criddle (1950),Monteith (1964), Priestley and Taylor (1972), and Hargreaves (1974) are important (seecited references for descriptions of these models) The selection of the PET model is largelydependent on the availability of sufficient climate data, which varies from place to place.Most models compute E as a function of potential ET and water available in soil storage(S) Various methods are in use for calculating E from the PET and soil moisture deficitrelationship, including linear, layered and exponential methods

One advantage of water-balance models is that they can potentially be used todetermine changes in seasonal snow storage and melt Within a water-balance model, thestorage and melting processes of snow are described by two types of model:energy-balance and temperature-index models

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Total Runoff

Fig 2.1 Typical structure of a water-balance model.

The energy-balance models simulate the flow of mass and energy in the snow cover.The energy-balance approach for calculating snowmelt applies the law of conservation of

energy to a control volume The control volume has its lower boundary as the

snow-ground interface and its upper boundary as the snow-air interface The use of avolume allows the energy fluxes into the snow to be expressed as internal energy changes(Gray and Prowse, 1993) The energy balance model is physically or meteorologicallymore explicit than the temperature-index model It contains parameters that can be

extrapolated to a certain degree of confidence from weather maps or from regional climate

models (Kuhn, 1993) Various studies have used energy-balance models to estimate runofffrom snowmelt (Fitzharris and Grimmond, 1982; Granger and Gray, 1990; Gray and O’Neill,1974; and Gray and Landine, 1987) Details of an energy-balance model can be found inGray and Prowse (1993)

The second type of model is the temperature-index snowmelt model (Equation (2.2)).Despite its simplicity, the model is widely used in forecasting discharge in snow-coveredbasins Using monthly data, for example, Kwadijk (1993) applied a temperature-indexsnowmelt model in order to assess the impact of climate change on the Rhine River basinand found close fit between the simulated values and observed data While modeling the

effects of climate change on water resources in the Sacramento River basin in the USA,

Gleick (1987) found poor performance of a temperature-index snowmelt model usingmonthly data The temperature-index models for rain-free and rain conditions are asfollows:

(i) Rain-Free Condition

26 H YDROLOGIC M ODELING A PPROACHES

where M = snowmelt in mm

Mf = snowmelt factor

Ti = index temperature

Tb = base temperature (set as 0oC)

(ii) During Rain

For a rain event, the melt factor is modified as follows:

Mf = (0.74 + 0.007P) (Ti – Tb)

where P = precipitation (in mm)

Snowmelt is calculated by:

Overall, water-balance models incorporate soil-moisture characteristics of regions,allow monthly, seasonal, and annual estimates of hydrologic parameters, and use readilyavailable data on meteorological phenomena, soil, and vegetation characteristics Theycan often provide efficient estimates of surface runoff when compared to measured runoff,reliable evapo-transpiration estimates under many climate regimes, and estimates of groundwater discharge and recharge rates Typical data requirements are precipitation,temperature, sunshine hour, wind speed, information on characteristics of vegetation (whichmay include type of vegetation for estimating rooting depths), and soil (such as fieldcapacities and wilting points) While generally the water-balance models require hugeamounts of data, they can nevertheless be applied in reasonably large areas with sparsedata (Hare and Hay, 1971; Brash and Murray, 1980) For example, Hare and Hay (1971)

applied the Lettau’s (1969) empirical model to approximate precipitation in order to

analyze the anomalies in the large-scale annual water-balance over Northern North America.Brash and Murray (1980) estimated adjusted equilibrium precipitation from anenergy-balance equation The estimated precipitation was then used to estimate wateryield in the Taieri catchment in New Zealand and found to be very closely matched with themeasured data Note, however, that these energy balance techniques require reliable netradiation data, which are not readily available for the major river basins in South Asia

By integrating hydrologic advances with existing water-balance techniques, newinsights into hydrologic processes and environmental impacts can be gained for climateimpact assessments Furthermore, water-balance models are well suited to the currentgeneration of microcomputer software and hardware A number of water-balance modelshave been developed to assess the impact of climate change on river runoff and soilmoisture stress from wet to dry regions (Mather and Feddema, 1986; McCabe and Wolock,

1992; Thompson, 1992; Flaschka et al., 1987; McCabe and Ayers, 1989; Conway, 1993

and Kwadijk, 1993) These studies show various magnitudes of runoff and soil moisturesensitivities on monthly time-scales to possible changes in climate Overall, such studiesdemonstrate that the water-balance approach holds good potential for application in theriver basins of South Asia (subject to availability of the required data) in order to assesseffects of climate change on hydrology and water resources

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2.2.3 CONCEPTUAL LUMPED-PARAMETER MODELS

Conceptual lumped-parameter models are developed based on approximations orsimplifications of physical laws These models embody a series of functions which areconsidered to describe the relevant catchment processes The algorithms are usuallysimplified by the use of empirical relations in order to speed the solution and to adapt themodel to cope with the point-to-point variations in the hydrologic processes within the

catchment (Crawford and Linsley, 1968; Boughton, 1968; Linsley et al., 1988; Leavesley,

1994) They contain parameters, some of which may have direct physical significance andcan, therefore, be estimated by using concurrent observations on input and output Somewidely-used models of this category are: the Sacramento Soil Moisture Accounting model

(Burnash et al., 1973), the Institute of Royal Meteorology Belgium (IRMB) model (Bultot

and Dupriez, 1976), the HBV model (Bergstorm, 1976), the Hydrologic SimulationProgram-FORTRAN (HSPF) model (USEPA, 1984), the Erosion Productivity Impact

Calculator (EPIC) model (Williams et al., 1984) and the MODHYDROLOG (Chiew and

McMahon, 1993) A schematic diagram of a conceptual lumped-parameter model(MODHYDROLOG) is shown in Figure 2.2

In the conceptual lumped-parameter models, the vertical and lateral movement ofwater with respect to time is incorporated Variations in respect of space are ignored Thevertical processes of water movement include interception storage and evaporation,infiltration, soil-moisture storage, evapo-transpiration, percolation to ground waterstorage, snow-pack accumulation and melt, and capillary rise The horizontal processesinclude surface runoff, interflow, ground water flow, and stream flow Components of thevertical and lateral processes are integrated The model development starts with thevertical processes Interception storage is assumed and calibrated usually by trial anderror Empirical algorithms are used for calculating the evaporation from the surfacestorage

For calculating infiltration calculation, two methods are in practice First, themaximum infiltration rate is assumed from the field observations and then the infiltrationrate is expressed as a function of soil storage (Boughton, 1968) Second, some prominentinfiltration models, such as Green-Ampt (1911), Philip (1957 and 1969) and Holtan (1961),can be used directly For example, the Hydrologic Engineering Center’s HEC-1 modeluses the Green-Ampt and Holtan’s infiltration models One of the important limitations ofusing these models is the need to estimate a number of parameters, some of which have to

be estimated either from laboratory experiments or from field observations

The other vertical and horizontal components that need to be developed areevapo-transpiration, percolation and base flow Evapo-transpiration is usually calculated

as a function of soil moisture storage, soil moisture storage capacity and potentialevapo-transpiration (Chiew and McMahon, 1993) A constant is used to calculate thepercolation to ground water storage Another constant is used to estimate the base flowfrom the ground water storage The base flow constant is usually determined by calibratingthe estimated flows with the observed values

Lumped-parameter models have some distinct advantages They do not necessarilyrequire direct use of mathematical equations of physical processes and they take intoaccount more physical processes than water-balance models They also have been shown

to be capable of making acceptable estimates of stream flow, evapo-transpiration, soilmoisture deficits, and storage changes, including changes in ground water storage, forsmaller river basins

28 H YDROLOGIC M ODELING A PPROACHES

Fig 2.2 Schematic representation of the MODHYDROLOG daily rainfall-runoff model Source: Courtesy of Chiew and McMahon, 1994.

Although lumped-parameter models are widely used, they have a number oflimitations These include: (1) the equations of a lumped-parameter model can only beapproximate representations of the real world and must introduce some error arising fromthe model structure; (2) spatial heterogeneities in system responses may not be wellreproduced by catchment-averaged parameters (Sharma and Luxmore, 1979; Freeze, 1980);(3) the accuracy with which a model can be calibrated or validated is very dependent on

the observations of both inputs and outputs (Ibbit, 1972; Hornberger et al., 1985) Since

input variables, particularly evapo-transpiration estimates, may be subject to considerableuncertainty; (4) there is a great danger of over-parameterization if attempts are made tosimulate all hydrological processes thought to be relevant and to fit those parameters by

optimization against an observed discharge record (Hornberger et al., 1985), so three to

five parameters should be sufficient to reproduce most of the information in a hydrologicalrecord; and (5) the calibrated parameters of such models may be expected to show adegree of interdependence, so that equally good results may be obtained with different sets

M M Q M IRZA 29

of parameter values, even though a model has only a small number of parameters(Ibbitt and O’Donnell, 1971; Pickup, 1977; and Sorooshian and Gupta, 1983).

Another potential disadvantage is that the use of lumped-parameter rainfall-runoffmodels depends essentially on the availability of sufficiently long meteorological andhydrological records for their calibration Such records are not always available Theircalibration also involves a significant element of curve fitting, thus making any physicalinterpretation of the f itted parameter values extremely diff icult There are otherlimitations, too Because of their inherent structure, these models also make very little use

of contour, soil, and vegetation maps, or of the increasing body of information related tosoil physics and plant physiology These models are not suitable for predicting the effects

of land-use changes on the hydrological regime of a catchment, particularly when only apart of the catchment is affected

In the case of the lumped models, parameter values are highly dependent on both themodel structure and the period of calibration (Beven and O’Connell, 1982) Therefore, aswith other hydrologic models, it is not advisable to extrapolate events that are outside theconditions over which the model parameters are estimated

2.2.4 PHYSICALLY-BASED DISTRIBUTED MODELS

Neither the empirical nor the lumped models are capable of addressing the physicalprocesses of the basin which control the basin response, as they do not account for thespatial distribution of basin parameters This limitation prompted the development ofphysically-based models aimed at improving the understanding of catchment processes Aschematic diagram of the Système Hydrologique Européen (SHE) distributed model isshown in Figure 2.3

Fig 2.3 Schematic representation of the SHE model Source: Adapted from Abbot et al., 1986.

30 H YDROLOGIC M ODELING A PPROACHES

Physically-based distributed models require descriptive equations for the hydrologicalprocesses involved (Freeze and Harlan, 1969) The equations on which distributed modelsare developed generally involve one or more space coordinates They thus have thecapability of forecasting the spatial pattern of hydrologic conditions within a catchment aswell as the simple outflows and bulk storage volumes In general, the descriptive equationsare non-linear differential equations that cannot be solved analytically for cases of practicalinterest Therefore, for simplification, some empirical discretization is made Indeed, thecomplexities of hydrological systems are such that all the model components ultimatelyrely on an empirical relationship.

As discussed by Freeze and Harlan (1969), the development of a computational model

to simulate physical processes is carried out by: (1) defining a physical system isolating aregion of consideration with simplified boundaries and neglecting all physical processesnon-essential to the phenomenon being studied; (2) representing the idealized andsimplified physical system by a mathematical model, including governing differentialequations and boundary/initial conditions; (3) converting the mathematical model into anumerical model using one of the numerical methodologies (finite difference, finiteelement, boundary element, and characteristics methods) which is most appropriate to theproblem; and (4) writing a computer code based on the selected computational algorithm

to obtain numerical results in still graphic or animated form In other words, beforethe computational model is developed, numerous idealizations, simplifications,approximations and discretizations have to be made

Regarding calibration of the physically-based model, the theoretical idea is that themodel has the potential to estimate parameter values by field measurements withouthaving to carry out parameter optimization as required by the simpler models of the lumped,

conceptual type (Abbott et al., 1986) But in reality, the situation is different Such an ideal

situation requires comprehensive field data covering all parameters and a model discretization

to an appropriate scale (Refsgaard et al., 1992) For example, the SHE model was applied

to the Wye catchment in England and in six small catchments in the Narmada basin in India

(Bathurst, 1986 and Refsgaard et al., 1992) In these catchments, during the application,

optimizations were carried out because of inadequate representation of the hydrologicalprocesses, insufficient data, and the possible difference in scale between the measurement

and the model grid scale (Bathurst, 1986 and Refsgaard et al., 1992).

The distributed nature of physically-based models offers some advantages over othertypes of models For example, they are capable of forecasting the effects of land-use changes,the effects of spatially variable inputs and outputs, the movement of pollutants andsediments, and the hydrological response of ungauged catchments Regarding land-usechanges in a catchment, deforestation rarely takes place abruptly over a complete basin;

it is more common for piecemeal changes to take place over a considerable period of time

In a distributed model the effects of such changes can be examined in their correct spatialcontext

It is clear from the above discussion that physically-based models require much moreinformation than their empirical, water-balance or lumped-conceptual counterparts Thus,calibration and validation emerge as major tasks Extensive field measurements requirehuge amounts of resources and time, and computing capacities are high Finally, despitethe greater effort required to parameterize, validate and run physically-based models, thesimulated results often provide only slightly better (or sometimes worse) correspondencewith measured values than lumped-conceptual models (Beven, 1987; Logue, 1990; and

Wilcox et al., 1990) Perhaps this results from the equations used to describe the physical

variability and the high degree of temporal and spatial variability of critical input

M M Q M IRZA 31

parameters Ironically, the description of physical variability is presumed to be a strengthfor physically-based models (Beven, 1985; Bathurst and O’Connell, 1992).

Regarding extrapolation of physically-based distributed models, Beven and O’Connell(1982) mentioned that, because of the physical basis of the model parameters, themeasured parameters’ values might be extrapolated to other locations or time periods.However, response of the physical parameters at other locations or other time periods maynot be same Therefore, physically-based distributed models also have limitationsregarding extrapolation Comparisons of various hydrologic models are tabulated in

Table 2.1

In this section, the advantages and limitations of various hydrologic models have beendiscussed In the next section, the applicability of some hydrologic models for assessingthe impact of climate change on water resources is discussed

2.3 ADVANTAGES AND LIMITATIONS OF HYDROLOGIC MODELS IN

CLIMATE CHANGE APPLICATION

A number of studies have been carried out to assess the impacts of climate changes usingempirical, water-balance and lumped-parameter models (Revelle and Waggoner, 1983;

Mather and Feddema, 1986; McCabe et al., 1990; McCabe and Wolock, 1992; Thompson, 1992; Flaschka et al., 1987; MaCabe and Ayers, 1989; Conway, 1993; and

Kwadijk, 1993) All these studies used monthly precipitation and temperature time-seriesdata for the assessment Models were calibrated to the observed data and then validatedagainst the other observed dataset in order to assess the capacity of the model to generatecurrent hydrological output (for example, runoff) Finally, the models were used to predictthe possible effect of future climate change on water resources Most of the models usedGCM-based and hypothetical climate scenarios for sensitivity analysis

In the applications noted above, the model parameters were estimated from thecurrent climate as a basis for predicting future conditions This is one of the majorlimitations of modeling the effects of climate change The behavior of physicalparameters of a catchment is not necessarily stationary overtime For example, mostpedological processes operate over a very long time-scale, but changes in organic mattercontent and soil structure may become apparent over a time-scale of less than 10 years(Climate Change Impact Review Group (CCIRG), 1991) Higher temperatures andincreased rainfall would lead to a loss of soil organic matter and hence a decrease in ability

of the soil to hold moisture; higher temperatures would also encourage clayey soil toshrink and crack, thus assisting the passage of water into and through the soil profile(CCIRG, 1991) Another issue is the response of vegetation to climate changes Forexample, Idso and Brazel (1984) estimated that plant evapo-transpiration may be decreased

by one-third for a doubling of carbon-dioxide due to partial stomatal closure in plants,increasing their water use efficiency and conserving soil moisture for increased runoff

to rivers and streams Thus, as CO2 concentrations change over time, so might therelationships between climate and hydrology Indeed, Dooge (1992) suggested thatresearch should not be used to develop more complex models until the issue of the

“antitranspirant effect” of higher atmospheric CO2 enrichment is effectively resolved.Which type of model should be chosen for assessing changes in runoff from scenarios

of climate change? Empirical models can be applied successfully if the processes are

ignored and the objective is limited to predicting runoff or discharge on monthly or annualtime-scales Empirical models require less data than the other models The modelperformance during the calibration and validation period is highly dependent on goodspatial and temporal coverage of the input data

32 H YDROLOGIC M ODELING A PPROACHES

Water-balance models are equally suitable for monthly and annual time-scales, but

require more data Calibration and validation are relatively more time and resource

consuming as compared to empirical models Conceptual lumped-parameter models need

more data than either empirical or water-balance models These models can be applied atshorter time-scales (say on a daily or hourly basis), a distinct advantage over empirical andwater-balance models However, calibration and validation procedures for these models

are much more complicated Physically-based distributed models require a vast amount

of data, which are often impracticable to collect These models need laboratoryexperimentation to estimate parameter values Calibration and validation procedures aremuch more complex than for other models and computing (and other resource) demandsare higher Finally, physically-based models may not necessarily improve the accuracy ofoutputs compared to other models

This last point is particularly important to consider when choosing between complexand simpler models For example, while applying the SHE model in the Narmada basin in

India, Refsgaard et al (1992) concluded that the simulated results of the rainfall-runoff

were of the same degree of accuracy as would have been expected with similarhydrological models of the lumped-parameter type They concluded that the resultsobtained in the Narmada basin do not justify the application of an advanced model, such asthe SHE, where the objective is limited to rainfall-runoff modeling

2.4 APPLICATION OF HYDROLOGIC MODELS FOR CLIMATE CHANGE

IMPACT ASSESSMENT IN BANGLADESH

Based on the comparative advantages, limitations, and suitability of various hydrologicmodels with respect to research purposes, data availability, scale and resources, Mirza(1997) applied a suite of empirical model and MIKE 11-GIS hydrodynamic model for:(1) determining the sensitivity of mean annual and mean peak river discharges in the Ganges,Brahmaputra and Meghna (GBM) basins (Fig 2.4) in Bangladesh to future climate changes;and (2) estimating the consequent changes in flood magnitude, depth and extent

Fig 2.4 The Ganges, Brahmaputra and Megna basins.

M M Q M IRZA 35

Broadly, the objectives of the application were centered on relationships betweenprecipitation, temperature and discharge, and the time-scale of concern was annual Based

on the above discussion the use of lumped-parameter or process-based distributed models

for these purposes was not found practicable Note that complex hydrologic modelsgenerally operate over small time intervals, such as an hour or day Consequently, the fieldmeasurement, determination, estimation and optimization procedures can be onerous Forthis reason, complex models are usually more suitable for smaller, more manageablecatchments For example, in the application of the SHE model to the small Wye catchment,

it was still necessary to specify about 2,400 parameter values (Beven, 1989) Bycomparison, the combined GBM basins are approximately 1.75 million sq km in area Forthese river basins, the number of parameter values that would need to be specified using

the same modeling approach is unmanageably large The water-balance approach had a

good potential for application in the GBM basins in order to determine the effects ofclimate change on annual discharge and flooding in Bangladesh But the use of thewater-balance approach was hindered by the lack of adequate hydro-meteorological(radiation, wind speed and humidity) and land-use data Although the water-balance

approach has been employed successfully in smaller basins with sparse data, as

discussed in Section 2.2, the shear size and geographical diversity of the GBM basins(1.75 million sq km) dictates against its use and it would probably create moreuncertainties than it would resolve Therefore, given the time and resources available,

Mirza (1997) decided to apply a simpler empirical approach in combination with the

MIKE 11-GIS hydrodynamic model It is worth noting that simple empirical models havealready been developed and applied successfully for similar purposes in the Himalayanregion (Khosla, 1994; Garde and Kothyari, 1990 and Kothyari and Garde, 1991).Following sub-sections describe the processes involved in empirical models and simulatingtheir results in the MIKE 11-GIS hydrodynamic model

2.4.1 THE RELATIVE SENSITIVITY OF RUNOFF TO PRECIPITATION AND

TEMPERATURE

What is the relative importance of precipitation and temperature in affecting runoff in theGanges, Brahmaputra and Meghna basins? Precipitation and temperature (as it affectsevaporation and transpiration) are the principal climate driving forces in generating runoff.Therefore, runoff is mainly sensitive to these two meteorological inputs

The sensitivity of runoff to temperature and precipitation changes can beapproximately determined by using the water-balance equation (see equation (2.1)) Inmany cases it is assumed that the catchment is watertight and that no inflow or outflow ofground water occurs On an annual basis it is often assumed that no change in storagetakes place from year to year Therefore, equation (2.1) reduces to:

In equation (2.3), E cannot be determined directly However, if the annual open-waterevaporation in any place is known or determined using the PET model of Penman (1948),

E can be estimated by the following empirical model (Pike, 1964):

36 H YDROLOGIC M ODELING A PPROACHES

The equation for the future runoff under the climate change can be written as:

The percentage change from the present day runoff can be determined using thefollowing expression:

In order to estimate the sensitivity of runoff to temperature and precipitation, onestation has been selected from each of the river basins - the Ganges, Brahmaputra and

Meghna These stations are New Delhi, Gauhati and Sylhet For the sensitivity analysis an

approximately 5% change in precipitation is associated with each degree change in globalmean temperature, in accordance with global estimates from GCM simulations(IPCC, 1990), in order to select the range of precipitation change The sensitivity of runoffhas been calculated applying equations (2.6), (2.7) and (2.8)

The results of the sensitivity analysis are presented in Figures 2.5a, 2.5b, and 2.5c

In general, the gentle slopes of equal percentage change lines show that runoff change ismore sensitive to precipitation change than to temperature change The results also showthat, in percentage terms, runoff is more sensitive to precipitation and temperature changes

in relatively dry stations than wet stations As an example, in the case of the New Delhistation (a drier station) no change in temperature and a 4% increase in precipitation changesrunoff by +11%, while for the Gauhati and Sylhet (the wetter stations) the changes inrunoff are +6% and +8%, respectively In the extreme case, a 5oC increase in temperatureand a 20% increase in precipitation could increase runoff by 29% at the New Delhi station,whereas for Gauhati and Sylhet stations the expected changes are 22% and 21%,respectively

2.4.2 THE EMPIRICAL MODEL DEVELOPMENT PROCESS

The sensitivity analysis in preceding sub-section shows that runoff in the Ganges,Brahmaputra and Meghna River basins appears to be much more sensitive to changes inprecipitation than to changes in temperature Based on this analysis, it was decided to useonly precipitation as the independent variable in developing empirical models for the threeriver basins

2.4.2.1 STEP I: DATA IDENTIFICATION AND ACQUISITION

The sensitivity analysis described above has facilitated the reduction of variables byexcluding temperature The main data requirements are identified as annual precipitation,annual mean discharge and annual peak discharge Annual precipitation can be derivedfrom daily or monthly records, or by directly using annual totals depending on availability.Similarly, annual mean discharge can be calculated from the daily observations or monthlymean values Peak discharge will be the highest daily observed value in a year

M M Q M IRZA 37

Fig 2.5 Sensitivity of runoff to temperature and precipitation changes in the: (a) Ganges basin (New Delhi), (b) Brahmaputra basin (Gauhati) and (c) Meghna basin (Sylhet).

(a)

(b)

38 H YDROLOGIC M ODELING A PPROACHES

(c)