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18 Computation of Hydrological Data for Design of Water Projects in Un gauged River Basins I. Papadakis I and G. A. Schult£ IConsulting Engineer, Werksstr. 15,45527 Hattingen, Gennany 2Ruhr University Bochum, Institute of Hydrology, Water Resources and Environmental Techniques, 44780 Bochum, Gennany 18.1 Introduction Socio-economical development on regional, national and international basis re- quires intensive investigations of water resources. Water differs from other re- sources by the time variability of the amount of water available for use at any given moment. Non-standard seasonal distribution of hydrometeorological pa- rameters, their fluctuations from region to region and from basin to basin make the planning of water projects difficult. Purposes to be served by water management projects may include: water supply, flood control, irrigation and drainage, hydropower, industrial use, water pollution monitoring, environmental protection, ground water etc. More than 1,5 billion people are living today on the Earth without any access to drinking-water and about 10 million deaths per year are registered as caused by waterborne diseases, flood disasters and famines following long drought periods. Furthermore, since the human population grows rapidly, there is extreme increase in water demand, mak- ing efficient management of the available water resources essential for human life. Planning of water projects requires knowledge of the quantity of water available at any given time unit. This can only be determined on the basis of adequate sets of hydrometeorological data for the area under consideration. It is well appreciated, that sets of hydrometeorogical data which extend back far enough to include typi- cal long-term fluctuations is a prerequisite for the successful planning and design of any water project. The minimum period of collecting records for reliable analy- sis is considered to be 20 to 30 years. If such data sets do not exist, or are not sufficiently long, or not accurate enough, the proposed water project may be either under-designed with accompanying risk of failure and consequent damage or may be over-designed which will render them uneconomic. For example, the estimated mean annual flow based on measured flow data observed in a short-term period, may be significantly different from that of flow data records over a long period of observation. The deviation may be positive or negative, depending on the cycle of the hydrometeorological phenomena to which the observed records belong (wet period, dry period, transition period). In the case of a multipurpose reservoir the negative deviation in the estimated mean annual flow would result in a smaller storage capacity than actually required. A smaller irrigated area and less hydro- electric-power production than expected would be the consequence. In the case of G. A. Schultz et al. (eds.), Remote Sensing in Hydrology and Water Management © Springer-Verlag Berlin Heidelberg 2000

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18 Computation of Hydrological Data for Design of Water Projects in Un gauged River Basins

I. Papadakis I and G. A. Schult£

IConsulting Engineer, Werksstr. 15,45527 Hattingen, Gennany 2Ruhr University Bochum, Institute of Hydrology, Water Resources and Environmental Techniques, 44780 Bochum, Gennany

18.1 Introduction

Socio-economical development on regional, national and international basis re­quires intensive investigations of water resources. Water differs from other re­sources by the time variability of the amount of water available for use at any given moment. Non-standard seasonal distribution of hydrometeorological pa­rameters, their fluctuations from region to region and from basin to basin make the planning of water projects difficult.

Purposes to be served by water management projects may include: water supply, flood control, irrigation and drainage, hydropower, industrial use, water pollution monitoring, environmental protection, ground water etc. More than 1,5 billion people are living today on the Earth without any access to drinking-water and about 10 million deaths per year are registered as caused by waterborne diseases, flood disasters and famines following long drought periods. Furthermore, since the human population grows rapidly, there is extreme increase in water demand, mak­ing efficient management of the available water resources essential for human life.

Planning of water projects requires knowledge of the quantity of water available at any given time unit. This can only be determined on the basis of adequate sets of hydrometeorological data for the area under consideration. It is well appreciated, that sets of hydrometeorogical data which extend back far enough to include typi­cal long-term fluctuations is a prerequisite for the successful planning and design of any water project. The minimum period of collecting records for reliable analy­sis is considered to be 20 to 30 years. If such data sets do not exist, or are not sufficiently long, or not accurate enough, the proposed water project may be either under-designed with accompanying risk of failure and consequent damage or may be over-designed which will render them uneconomic. For example, the estimated mean annual flow based on measured flow data observed in a short-term period, may be significantly different from that of flow data records over a long period of observation. The deviation may be positive or negative, depending on the cycle of the hydrometeorological phenomena to which the observed records belong (wet period, dry period, transition period). In the case of a multipurpose reservoir the negative deviation in the estimated mean annual flow would result in a smaller storage capacity than actually required. A smaller irrigated area and less hydro­electric-power production than expected would be the consequence. In the case of

G. A. Schultz et al. (eds.), Remote Sensing in Hydrology and Water Management© Springer-Verlag Berlin Heidelberg 2000

402 I. Papadakis and G.A. Schultz

positive deviation in the estimated mean annual flow, all structures would be over­estimated. Dam height would be higher than necessary, the reservoir would very seldom be filled up to the required water level. The consequence would be that the structures necessary for water power production such as the power station, con­duits etc. would be much larger in size than required. The power production would be below the planned target, and some machines would then remain idle or work with low efficiency. For the planned irrigation schemes and water supply networks there would be insufficient water to irrigate and supply the whole area and the channels and other structures would be of disproportionate size.

Often the design of water management systems in many countries in the devel­oping world suffers from inadequate or non-available sets of hydrometeorological data. If hydrometeorological records are available, they are either too short or, if they are sufficiently long, they include numerous gaps or discontinuities which make these useless for further analysis (Gyau-Boakye, 1993; Gyau-Boakye and Schultz 1994). The consequence is that very often water projects in these coun­tries have to be designed under circumstances which are insufficiently representa­tive for the project area. Instead of calculation of design data based on detailed knowledge of the hydrological behaviour of the area under consideration, data are prepared based on formulas, the validity of which has been proven for entirely different areas.

Different mathematical-statistical and deterministic methods have been devel­oped in the past which enable the designer to estimate missing data, generate syn­thetic sequences and derive estimates for design purposes (Gyau-Boakye, 1993). The success of these methods, however, depends primarily on the length of the available data considered for the identification of the system in question since computation of systems reliability depends on long time series.

The remote sensing technology provides very useful methods of monitoring and identifying many hydrological parameters and variables which are necessary for the estimation of the project reliability. With the aid of remote sensing techniques, relevant hydrological data can be obtained in a short time, at periodic intervals and covering extended areas. Satellite imagery has been used in the fields of meteorol­ogy, watershed management, hydrologic modelling etc. Mathematical techniques in a multidimensional feature space allow an accurate land use classification as well as estimation of vegetation indices (Su, 1996). Changes in land use can be quantified on the basis of multi-spectral and multi-temporal satellite imagery. Also the identification of the potential location of dams in river valleys can be sup­ported by remote sensing. A number of scientists have already used digital spectral data from meteorological geostationary satellite systems to estimate precipitation quantities with success (e.g Arkin 1979; Griffith 1987; Huygen, 1989; Stout et al. 1979; Sorooshian 1997). Furthermore, the estimation of runoff values for flood forecasting, irrigation and other purposes may be based on remote sensing (Hardy et al. 1989, Rott et aI., 1986; Tiwari et al. 1991; Kite, 1991; Koren et al. 1994, 1995).

For more than 20 years now, different satellite systems have monitored the globe and will continue to do so, collecting a large amount of data in different spectral

18 Computation of Hydrological Data 403

bands. These data are stored and are available for further analysis at any time. On the basis of the capabilities of remote sensing briefly mentioned above and taking into consideration the availability of long-term satellite data, a method is presented here by which historical hydrological data, particularly rainfall and runoff values can be generated on the basis of multi-spectral and multi-temporal satellite im­agery for the period when satellite information is available.

18.2 General Approach

Planning and design of water management systems (e.g. multipurpose reservoirs) with small risk of failure requires long-term series of hydrometeorological data. In most countries of the developing world observed hydrological data contain either many gaps or are not available at all. Thus hydrologists are urgently seeking new ways of augmenting their conventional data supplies. Satellite remote sensing is being explored as one possible answer to the data acquisition problem.

The general approach presented here is as follows (Papadakis 1994): At the be­ginning of the project planning after the feasibility study, if hydrological data are not available, a hydrological network has to be installed which collects hydrome­teorological data over usually two to three years. Thus on the basis of the collected data and the information obtained from satellite imagery over the same period of time, a mathematical model can be developed which connects the observed hy­drometeorological data with data obtained from satellite imagery. The parameters of the mathematical model can be calibrated on the basis of simultaneous satellite data and ground truth. After the calibration it becomes possible to reconstruct historical river flows with the aid of the mathematical model on the basis of the satellite data alone for the period of time for which relevant satellite information exists. This way the very short series of hydrological data (collected during the planning period) can be extended considerably into the past, thus allowing an estimate of the future performance of the water project. It is expected that the technique presented here will become a valuable planning aid for design with short-term series of hydrological data. Figure 18.1 gives an indication of the basic principles of the method.

The approach presented here consists of three consecutive MODULS (Fig. 18.2).

IS.2.1 MODUL I: Satellite system, data processing

1. Satellite system

For the choice of a satellite system suitable for the problem on hand, the following criteria should be considered:

Time resolution of the satellite system. On the one hand, for high accuracy a good resolution in time is required, while on the other hand it may not be neces­sary to process too many data since, for most planning purposes, monthly data are sufficient. Geostationary satellites such as Meteosat and GOES-W/E etc. have a

404 I. Papadakis and G.A. Schultz

CAUBAATION' GROUND TRUTH

MATHEM, MODEL

Q • f!.Sattll.Dura)

Fig. 18.1. Generation of historical river flows on the basis of long-term satellite data and short­term ground truth

repetition rate of 30 minutes, while polar orbiting satellites, such as NOAA!AVHRR produce only two images per day. Four images per day are avail­able if two NOAA! A VHRR satellite systems are considered.

Satellite systems such as Landsat TM, Spot and ERS-I with repetition cycles of 16, 26 and 35 days, respectively, are not suitable since the temporal resolution is not appropriate for highly dynamic hydrometeorological processes such as rainfall

MODULI Satellite System Data processing

MODULII Rainfall estimation based on

satellite data

MODULIII Generation of historical runoff

values via a rainfall runoff model

Fig. 18.2. The technique for estimation of historical flow time series on the basis of satellite imagery

18 Computation of Hydrological Data 405

from convective clouds.

Spatial resolution. The resolution of the data in space is correlated with the area of the river basin under consideration. Geostationary satellite systems have a spa­tial resolution between 2.5 km by 2.5 km and 8.0 km by 8 km, while the spatial resolution of available polar orbiting satellite systems lies between 10m by 10m and 1.0 km by 1.0 km, or even larger. The unfavourable spatial resolution of Me­teosat and GOES implies that these systems can be considered only for larger catchment areas (>2.500 km2).

Spectral resolution. GOES, NOAN A VHRR, Meteosat and other meteorological satellite systems transmit their images in the visible (0.4 J.Ull- 1.1 J.Ull) and thermal infrared band (10.5 J.Ull- 12.5 /lm) through the "atmospheric windows". Further­more, Meteosat transmits images in the water vapour absorption band (5.1 J.Ull -7.1 J.Ull).

On the basis of brightness characteristics of satellite images taken in the visible spectral band or temperature characteristics taken in the thermal infrared band it is possible to distinguish precipitating clouds from those without precipitation (Bar­rett and Martin, 1981). Non-precipitating high, thin ice cirrus clouds are very transparent (low brightness) in the visible spectral region, while in the thermal infrared region they are very bright (very cold). Furthermore, it is well known that two convective precipitating cloud cells of comparable size yield different pre­cipitation amounts, depending on the mean relative humidity of the cells' environ­ment. A mean relative humidity is defined as the arithmetic mean of the relative humidity values in different atmospheric layers.

Using the water vapour spectral band of the Meteosat satellite system it is possi­ble to obtain information on the humidity conditions of atmospheric layers situated above a certain level. For a given temperature profile an increase in humidity leads to a decrease of the transmittance towards space. For example, under tropical at­mospheric conditions the 600 mb-Ievel should be considered as the lower limit of the layer while the 300 mb-Ievel corresponds to the upper limit of the layer of the relevant water vapour contribution function (Poc et aI., 1983). An existing rela­tionship between radiance in the water vapour channel and mean relative humidity can be used for the assessment of the state of the humidity in the near environment of convective clouds.

2. Data processing

In order to adapt the spectral data obtained from the satellite imagery for hydro­logical purposes a number of preparatory efforts has to be made:

conversion of the geographical coordinates of the catchment area under con­sideration into image pixel coordinates;

normalization of the satellite images in the visible spectral band for sun-angle if the visible information is to be used;

406 I. Papadakis and G.A. Schultz

conversion of the digital counts of the thennal infrared and water vapour im­ages into temperature and mean relative humidity in the upper troposphere re­spectively;

atmospheric corrections are not necessary since it has been shown (Rott et. al. 1986) by means of a atmospheric simulation model "LOWTRAN - 5" (Knei­zys et. al. 1980) that atmospheric correction is important only for low levels in the atmosphere. Levels below 700 hPa are of main influence for atmospheric transmittance. However, since for rainfall estimation purposes only higher cloud levels are of interest, the radiance of the thermal infrared channel can be converted into temperature without considering atmospheric effects. Uncer­tainties about cloud emissivity cause larger errors than the effects of atmos­pheric transmittance.

For image processing techniques applicable here see Chap. 3.

18.2.2 MODUL II: Assessment of the monthly area precipitation on the basis of multi-temporal satellite imagery

A number of scientists have already used data from geostationary satellites to compute precipitation from convective Clouds (e.g. Arkin 1979; Griffith et aI., 1978; Hardy et ai. 1989; Sorooshian 1997 and others). Some of their schemes are, however, based on the individual tracking of cloud entities throughout their life­times. These methods require substantial computing capacity and are applicable only over limited areas in space and time periods. For the reconstruction of long hydrometeorological time series the method for deriving rainfall estimates has to be relatively simple. Arkin (1979) has shown that the fractional cloud cover, which is colder than a certain cloud top threshold temperature, is proportional to the accumulated rainfall amount below (Fig. 18.3).

Hardy et ai. (1989) used the duration of cold cloud (CCD) as an indication of rain and the rainfall total within a time period.

There are, however, some limitations in the Arkin and Hardy approach. The threshold cloud top temperature is the only one parameter taken into account, although two cloud cells with the same cloud top temperature and with comparable sizes produce different rainfall amounts depending on the state of their environ­mental humidity (KrUger and Schultz, 1982). Clouds in a moist environment pro­duce considerably more rain than those in a relatively dry environment. The influ­ence of the humidity on the rainfall intensity is also emphasized by Adler and Mack (1984). The mean relative humidity in the upper troposphere obtained by the water vapour channel of the satellite system Meteosat can be used as an indicator for the humidity state of the environment of clouds or cloud systems.

The principles of the techniques mentioned above for estimating rainfall from temperature of cloud tops are to associate rainfall with cold (high) clouds assum­ing that these are the tops of active convective cells or convective systems.

Figure 18.4 shows examples of the correlation between rainfall rate and cloud top height (i.e. cloud top temperature) for various regions in North America.

18 Computation of Hydrological Data 407

0.9

0.8

C 0.7 II> ·0 0.6 :e II> 0 0.5 u c:

0.4 .2 n; ! 0.3 0 u 0.2

0.1

0 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Cloud Height Threshold [km]

Fig. 18.3. Correlation between rainfall accumulation and fraction of area covered by clouds above various height (temperature) thresholds (acc. to Arkin, 1979)

1000 Florida (Summer) (Gagin and Lopenz, 1981)

- East Coast (Summer) [Konrad, 1977]

:c Midwest (Summer) [Wilk /

E 100 and Dooley, 1980) ~~,

,

.§. - Midwest (Spring) [Negri and Adler!, 1981 ]

~ " ~ /

nI "" / a:: ,

~ ,

c: 10 / ·ii a::

/

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Cloud Top Hight [km]

Fig. 18.4. Maximum rainfall rate as a function of cloud top height in various regions of North America (acc. to Adler et al. 1984)

408 I. Papadakis and G.A. Schultz

The application of the technique requires that various parameter values and the mathematical approach have to be specified prior to computations:

time resolution of the images (given by sensor system, may be chosen as mul­tiples thereof)

spatial and time resolution for area precipitation computations

cloud top temperature threshold value relevant for precipitation occurrence

mathematical relationship between satellite information and area precipitation

The choice of the above factors depends on the requirements of the user. Based on the technique of Arkin the determination of the Fractional Cloud

Cover Index (FCCI) may be carried out in the following two consecutive steps, nanlely:

Step I: Identification of the cloud cell areas which are colder than a certain threshold cloud top temperature value, covering the river basin under consideration either totally or partially in the thermal infrared spectral band.

Step 2: Addition of all pixels for every cloud cell as obtained in Step 1 within the river basin under consideration.

The result of this procedure consists of a discrete Fractional Cloud Cover Index (FCCI) for each image having values between 0 and I representing the fraction of area (or relative area) of the total river basin under consideration, for which a certain information (Cloud top temperature) relevant for precipitation may be obtained in one or more spectral bands of a satellite system. The relative area is expressed by the number of relevant satellite image pixels divided by the total number of satellite image pixels within the river basin.

The AFCC (Accumulated Fractional Cloud Cover) which is of interest for the hydrological computations (rainfall estimation) is simply the sum of the FCCI's for a given time interval (e.g. a day or a month).

In order to estimate the rainfall in a time interval, an empirical relationship of best fit may be determined using the least-squares method. This means that differ­ent numerical values depending upon the radiance, e.g. the threshold cloud top temperature (from the thermal infrared spectral band) have to be varied such that an optimum agreement between observed rainfall and spectral information in the same time interval is achieved. The mathematical function P = f(AFCC) is derived specifically for different climatic regions. For the test catchment in West Africa the equation is shown in Sect. 18.3.2.1.

18 Computation of Hydrological Data 409

18.2.3 MODUL III: Estimation of runoff values

The fmal goal of the approach discussed here is the estimation of historical river flow data on the basis of satellite infonnation alone. This requires a mathematical transformation into runoff values of the rainfall information obtained from the satellite imagery by means of a simple parametric rainfall runoff model. A major constraint to the application of general rainfall runoff models is the non­availability of data required. More sophisticated physically-based distributed mod­els require large quantities of data which are usually not available in the countries of the developing world. Therefore the most complex model is not necessarily best for all purposes, particularly where input data are sparse or nonrepresentative.

A simple model which can be applied under tropical environments is described by Higgins (1981). This model relates runoff to rainfall by employing a single equation and a single constraint and was used for estimation of monthly data. The equation assumes that runoff in period i consists of a portion 7i of the rainfall in period i, a smaller proportion 7i-1 of the rainfall in the previous period i-I, and so on, to a total of n periods. Provision is made for a time lag of T periods before frrst runoff appears after rainfall. The portions 7i are related to each other through an exponential decay and are limited such that the simulated runoff as a portion of rainfall is equal to the observed long-term runoff/rainfall ratio A. The model is mathematically described by the following equation:

where: Yi : runoff in period i Xi: rainfall in period i T: lag between rainfall and runoff n: number of time periods contributing to Yi

<Xi: error component ofYi not explained by the model v, b: recession parameters

The parameters v and b of the recession are related by:

bO- T +b l- T +b2- T + ... +b n- 1- T =A/v

where: A: the observed long-term runoff/rainfall ratio

(18.1)

(18.2)

Since b can be estimated from v or vice versa, using the equations above, there are only three independent parameters to be evaluated, namely, n, v (or b), and T. T will be equal to zero except in cases of unusual catchment shape or soil conditions or when very short time periods are applied.

The model applied has to be calibrated with the aid of the measured runoff and simultaneous rainfall data collected during the planning period of e.g. two or three years to produce the best overall fit. After calibration it is possible to compute

4lO I. Papadakis and G.A. Schultz

historical river flows on the basis of satellite information alone. In order to find out how much satellite information is really required in order to meet the hydrological goal, a sensitivity analysis has to be done by which the amount of satellite data to be used can be optimized.

18.3 Application

In order to show, how the approach described in Sect. 18.2 functions in practice an example will be presented.

Due to the fact that the approach discussed here is mainly meant for planning of water management systems in countries of the developing world, it seems appro­priate to give an example from the tropics. On the basis of an existing cooperation between the Institute of Hydrology, Water Management and Environmental Tech­niques of the Ruhr-University Bochum and the Water Resources Research Unit, CSIR, in Ghana, West Africa, the Tano river basin in Ghana was chosen.

IS.3.1 Study area and data used

The Tano river basin comprises a catchment area of about 16.000 km2, in which daily records from 23 raingauges for the period of 1983-1984 are available. The raingauge density is 1 gauge per 700 km2 and their distribution over the catchment is reasonably uniform. Additionally measured monthly runoff values and monthly rainfall for the periods from 1969 - 1972, 1975 - 1976 and 0711984 - 0411985 are also available. The observed daily rainfall data are accumulated in monthly values, since for planning purposes of water management systems monthly data are usu­ally adequate. The required monthly area precipitation is computed from the avail­able point measurements with the aid of the reciprocal distance weighting method.

In West Africa, where the test catchment is located, rainfall intensity and distri­bution in space is dependent on the migration of the ITDZ (Inter Tropical Discon­tinuity Zone). The ITDZ is the front where two air masses of different origin and characteristics converge (Tropical Continental Air Mass and Tropical Maritime Air Mass). The migration of the ITDZ gives rise to two well-defined seasons (wet and dry). The months July and August are dry although the ITDZ is farthest north (wet season) which means that the atmospheric water vapour is high. This situation is defmed as a climatic anomaly over West Africa. Especially for the Tano river basin two main seasons are defined, namely the wet season in which the whole catchment area under consideration is influenced only by maritime air masses excluding the months July and August (short dry season) and the dry or relatively dry season in which the catchment area is influenced mostly by continental air masses (Ojo, 1977). Figure 18.5 shows the location of the catchment in a semi-arid area in Ghana, West Africa.

18 Computation of HydroJogicaJ Data 411

Fig. 18.5. Location of the Tano river basin in Ghana, West Africa. MeteosatVIS Image

18.3.2 Assessment of the monthly area precipitation with the aid of multi-temporal B2-Meteosat satellite imagery

Most of the existing cloud indexing techniques are based on a threshold value of a certain cloud top temperature determined from the thermal infrared spectral band. Griffith et al. (1978) states e.g. that precipitating cloud systems usually have a top black body temperature of less than -20°C and a brightness of more than 80 DC (Digital Counts) in the visible spectral band. Arkin (1979) gives a threshold value for cloud top temperature of precipitating cloud systems with 235 K (-38°C). In our case for the Tano river catchment sensitivity analyses showed that a threshold value of -20°C provides optimal results. The technique presented here uses the IR spectral band from the Meteosat in order to estimate the monthly area precipita­tion.

Since there is a trade-off between required model accuracy and amount and costs of the data acquisition and processing, a compromise has been made in the tech­nique presented here. The so-called B2 Meteosat data were used which contain only every sixth pixel in a line and every sixth line in all three Meteosat spectral channels. The time resolution was chosen to be 3 hours. This way the data costs

412 I. Papadakis and G.A. Schultz

were reduced significantly without much loss of accuracy as a sensitivity analysis has shown.

The relationship between AFCC (Accumulated Fractional Cloud Cover) and monthly area precipitation. Three-hourly Fractional Cloud Cover Indices (FCCI) were determined as a function ofa chosen threshold cloud top temperature (-20°C in this case). The AFCC which is of interest for the computation of the monthly area precipitation is simply the sum of the three-hourly FCCI for the whole month. Colour Plate 18.A shows the cloud development over the Tano River basin, Ghana, West Africa by means of two successive Meteosat images of the IR spec­tral channel for a rainfall event in August 1984.

To estimate the monthly area precipitation, the empirical relationship of best fit is determined using the least-squares method. Different relationships with numeri­cal values depending upon the radiance and specific local climatological condi­tions, e.g. the threshold cloud top temperature (from the thermal infrared spectral band) and the migration of the ITDZ (Inter-Tropical Discontinuity Zone), were varied in order to fmd out an optimum agreement between observed monthly rain­fall and spectral information from Meteosat. The AFCC and the monthly area precipitation for the test catchment in Ghana are related by:

wet season

P = 10 1.4 . AFCC~13 s

(18.3)

dry season

P = 2.03 . AFCC\:2 s

(18.4)

where: P: Monthly area precipitation AFCC: Accumulated Fractional Cloud Cover Ts: Threshold value of the cloud top temperature (here -20°C)

The equations described above and developed in the calibration procedure with the aid of simultaneous satellite data (AFCC) and ground truth (observed precipita­tion) may be used for reconstruction of historical monthly area precipitation in the catchment area on the basis of satellite data alone. As can be seen from Fig. 18.6 the agreement between observed and computed monthly area precipitation values is reasonably good.

Moreover, according to a sensitivity analysis of the time resolution, two satellite images per day taken at 20 and 23 GMT (Greenwich Meridian Time) are enough to estimate monthly area precipitation for hydrological purposes in the Tano river basin without significant loss in accuracy as compared to the use of all 48 images per day.

18 Computation of Hydrological Data 413

250

c:: 0 200 !

r--~~eaSUred I

O:;omputed I 'c, ~ .- .r:: a: c 150 ... 0 Il.::!i "'-~ E

r-::- -

« E 100 ~-.r:: C 0 50 ::!i

0 J1Irrt Ji'l WL Jfl M J J A SON 0 J F M A M J J A SON 0

1993 1994 ~~r--------------•• ~~------------------------~.

Validat ion Calibration

Fig. 18.6. Comparison between observed and computed monthly rainfall on the basis of one spectral channel (thermal infrared), Tano river catchment, Ghana, Westafrica

18.3.3 Rainfall - Runoff Model

The task of the rainfall-runoff model is to convert the time series of monthly pre­cipitation values derived from remote sensing data into monthly runoff volumes.

In the case of surface runoff from the Tano river basin, an accurate application of a hydrological approach would require a detailed topographical, soil and geo­logical survey as well as determination of many other parameters. In order to avoid these difficulties, the model described by Higgins (1981) was applied here. Ac­cording to Higgins wet tropical environments allow the use of relatively simple rainfall-runoff relationships. Because rain occurs regularly throughout the year (migration of the ITCZ) , soil moisture levels are seldom far from field capacity, and actual evapotranspiration approximates potential evapotranspiration. Related to this, soil moisture deficiencies are likely to be rare and of short duration when they do occur. This simplifies the soil water movement process. Time lags between the occurrence of rainfall and the appearance of surface flow are short, because the high rainfall intensities regularly exceed the maximum infiltration rates of the soil profile below the surface.

The objective is to solve the problem of calibrating the parameters of the model for the river basin under consideration. The data of rainfall and corresponding runoff observed from 1962 - 1972 are used. To estimate the monthly runoff values, the model parameters of best fit are determined using the least-squares method. The observed rainfall and runoff values are related by the equation given in Sect. 18.2.3:

414 I. Papadakis and G.A. Schultz

wet season (months: J, J, A, 0, N)

Q . =vp· +v·b·P· I +v·b 2 .p. 2 1 I 1- 1-

with: v=0.112 b=0.507 T=O P j= precipitation in time interval i.

dry season (J, F, M, A, M, S, D):

Q . =vp· +v·b·P· I +vb 2 .p. 2 +vb 3 .p. 3 1 1 J- 1- 1-

with: v=0.063 b=0.582 T=O P j= precipitation in time interval i.

(18.5)

(18.6)

Consequently, the rainfall based on satellite spectral information is transformed into monthly runoff for the period when both satellite data and runoff values were available, namely from 07.1983 until 04.1985. The resulting fit is shown in Fig. 18.7 and is found to be satisfactory. This means that the technique is suitable for practical applications in data scarce tropical areas.

18.4 Further Applications

Not only design of water management systems but also river management (moni­toring, forecasting and simulation of flows) requires adequate hydrometeorologi­cal data. Because of the frequent lack of such data, remote sensing information may be used to obtain better temporal and spatial resolution of relevant data over large river basins.

Based on remote sensing data, a technique was developed by Koren et al. (1995) to monitor, forecast, and simulate (MFS) flows along the Nile river, where no relevant data were available. The principle of this technique is shown in the flow chart of Fig. 18.8.

The MFS System consists of three subsystems: the Primary Data User System (PDUS) which provides the continuous input of Meteosat satellite data, the Nile Forecasting System (NFS) which resides on workstation and the High Aswan Dam (HAD) Decision Support system. The hydroclimate data base consists of ob­served hydrometeorological data and Meteosat raw imagery data. The Preproces­sor Component of the NFS converts incoming raw data into precipitation esti­mates required by hydrologic models. Because very little observed precipitation data in the Nile basin are available, satellite estimates become a major source of rainfall data. A unique Hybrid Climatological Technique (Schaake and Green­Newby, 1993) was developed which uses Cold Cloud Duration (CCD) data for different temperature thresholds. CCD means the number of hours anyone Pixel

18 Computation of Hydrological Data 415

250

U GI

,,!!J. 200

.E. <II GI ::I 150 iii > ~ § 100

cr:: >-:i: .. c: o :!i

50

_ Runoff with satetl. based I rainfal l (8 images/day) I

"""'*- Runoff with satell. based I rainfall (2 images/day)

-+- Runoff with measured rainfall I I

-+-Observed runoff I

o L-____________ ~~------------------~------~

J A SON D J F M A M J J A SON D J F M A Months

Fig. 1S.7. Observed and estimated runoff values on the basis of measured rainfall and rainfall based on satellite imagery for the period July 1983 until April 1985 (acc. to Papadakis. 1994), Tano river, Ghana, Westafrica

I PDUS I I USE R I

PREPRO CESS ORS 61 ASSIM ILATOR II D ETE RMINISTIC I CALIBRAT ION

FORE A T COMPO ENT

SYST M WAT ER HILLSLOPE C HANNEL

BALANCE r- ROUTI G ~ ROUTI G

J J I STOCHA · DIV ERSION I RESERVO I R J

STIC MOD EL

J

HYDROCLIMATE DATA BASE I I GIS J I

Fig.IS.S. The MFS system (after Koren; et ai. (1995))

416 1. Papadakis and G.A. Schultz

(5x5 km resolution) passes a designated threshold for one day. Analogue gridded fields are created using raingage data only. Observed daily precipitation is inter­polated. The Forecast Component consists of hydrologic models and software to produce flow and stage hydro graphs based on inputs supplied by the Preprocessor. The User Interface displays satellite imagery data, gridded data, and time series data; it also serves as the interface between the forecaster and the NFS software, data base and models. The results gained by this technique were used for moni­toring and forecasting purposes in the Nile river system.

18.5 Summary and Discussion

Since it is not possible to measure runoff directly with the aid of remote sensing information an indirect approach is suggested which uses remote sensing data for the estimation of rainfall and transform it into runoff with the aid of a rainfall­runoff-model. This way, a long time series of monthly river flows can be generated which allows the estimation of the expected future performance of a planned water project in terms of reliability indices.

The approach presented here shows that the use of multi-temporal satellite im­agery allows the estimation of monthly area precipitation values essential for the transformation into runoff values by means of a rainfall runoff model.

The application of the approach presented here, however, is limited. Catchment characteristics, the rainfall type, the area extension of the catchment and the geo­graphical transferability form the main constraints.

The relationship between rainfall and runoff depends mainly on the hydrological behaviour of the catchment area. Therefore, the reconstruction of historical river flows based on a rainfall runoff model calibrated with the aid of a short observed flow record has to take into consideration possible changes in the catchment char­acteristics, such as residential areas, land use, etc. These changes could be quanti­fied using satellite systems such as NOAA! A VHRR, Landsat or SPOT.

The use of spectral information obtained from the visible, the thermal infrared and the water vapour spectral region allows estimation only of convective rainfall events. This means that only regions with mainly convective rainfall activity can be contemplated. Such regions are concentrated around the Equator.

There is a relationship between the spatial resolution of satellite data and the extension of the catchment area under consideration. By using geostationary satel­lite systems only areas greater than 10 000 km2 can be modelled adequately.

The relationship between spectral cloud information and rainfall is empirical and is valid only for the area in question. Previous investigations (Wylie, 1979; Griffith et al. 1981; Adler and Mack, 1984) have shown that the direct transfer to other geographical regions is not possible without further analysis for the specific area. However, the use of the water vapour channel seems promising for over­coming this problem.

Additional spectral information such as passive microwave data could contribute to removing the limitations mentioned above, because the rainfall and passive microwave information have a direct physical relationship to each other. The dis-

18 Computation of Hydrological Data 417

advantage, however, is mainly the large size of the receiving antenna required for the reception of the weak passive microwave radiance emitted. Furthermore, the spatial resolution of the microwave information depends on the size of the receiv­ing antenna, which means that in the future the equipment for the reception of passive microwave information must be considerably improved. New systems (TRMM) can fulfil the requirements for hydrological purposes. The TRMM (Tropical Rainfall Measuring Mission, a joint scientific satellite project between NASA and the NASDA (the Japanese Space Agency)) payload contains five in­struments. It includes the Precipitation Radar (PR), a TRMM Microwave Imager (TMI), a Visible and Infrared Scanner (VIRS), a Cloud and Earth Radiant Energy System (CERES), and a Lightning Imaging Sensor (LIS). The main objective of TRMM is to monitor and document the rainfall and energy release at monthly time scales over 500x500 km areas. TRMM was launched on November, 27, 1997. This means that it will take many years until a long time series of data is available as required for the purpose of planning water projects as discussed in this chapter.

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