changements climatiques et variations des

Hydrology for the Water Management of Large River Basins (Proceedings of the Vienna Symposium, August 1991). IAHS Publ. no. 201,1991.

CHANGEMENTS CLIMATIQUES ET VARIATIONS DES ECOULEMENTS EN AFRIQUE OCCIDENTALE ET CENTRALE, DU MENSUEL A L'INTERANNUEL

G. MAHE & J.C. OLIVRY Institut Français de Recherche Scientifique pour le Développement en Coopération (ORSTOM),BP504S, 34032 Montpellier Cedex 1, FRANCE

RESUME Les écoulements depuis la façade atlantique de l'Afrique sont analysés sur la période 1951-1989. La répartition régionale des volumes écoulés annuels et mensuels est quantifiée. Les apports sont globalement moyens durant la décennie 1950, largement excédentaires entre 1961 et 1970 et déficitaires depuis. Des disparités régionales existent, en particulier celles du couple Afrique occidentale-Afrique équatoriale, mais les séquences déficitaires de 1972-1973 et 1982-1984 apparaissent dans toutes les séries.L'apport annuel moyen à l'Atlantique du Sénégal au Congo est de 2660 milliards de m'.an"1. En 1962 l'apport supplémentaire est de 610 milliards (23%); en 1983 le déficit est de 900 milliards (34%). Un exemple d'étude de fleuve est fourni par l'analyse des débits de l'Ogooué (Gabon), dont 1'occurence et l'intensité des crues sont profondément modifiés depuis 1970, traduisant une modification climatique récente à Péquateur.

INTRODUCTION L'Afrique soumise au flux de mousson atlantique subit depuis plusieurs années une diminution de la pluviosité, comme le mettent en évidence les travaux de Lamb (1985) et Nicholson (1981) sur les précipitations, ou ceux d'Olivry (1987) sur les précipitations et les écoulements en Sénégambie, et de Mahé (1987) sur les apports hydriques au Golfe de Guinée. Dans la majeure partie des études, ce sont les précipitations qui sont utilisées pour décrire l'évolution climatique africaine. Mais les débits constituent également de très bons traceurs des variations climatiques car ils intègrent l'ensemble des phénomènes météorologiques et hydrogéologiques de grandes surfaces géographiques, et l'information climatique qu'elles contiennent est généralement plus rapidement accessible que celle des pluies. La connaissance des grandes variations climatiques mises en évidence par l'examen des séries de débits, le calcul des volumes d'eau écoulés ainsi que l'évolution mensuelle du régime des grands fleuves, intéresse de nombreux

163

164

G.Mahê&J.C.Olivty

domaines tels que les transports solides, les relations pêche-environnement (Binet, 1983) et l'aménagement.

LES DONNEES L'étude porte sur les écoulements de la façade atlantique de l'Afrique, principalement liés au flux de mousson d'été boréal. La surface drainée depuis le fleuve Sénégal jusqu'au fleuve Congo est de 6 850 000 km2; elle ne compte pas les parties de bassin désertiques ou subdésertiques qui ne restituent qu'un très faible volume d'eau. La surface d'étude est divisée en sept régions (Fig.l) regroupant des fleuves soumis à des conditions climatiques proches. Cette subdivision est inspirée des travaux de Janicot (1989) sur la variabilité spatio-temporelle des précipitations en Afrique de l'ouest.

10°O 0°

10°E

I

10 N

10 S

1000km

Fig. 1.

Situation des bassins-versants utilisés, groupés par régions. A à E: Afrique Occidentale, F et G: Afrique Equatoriale. A.SENEGAL FOUTA, B: GUINEE, C: NOBD-GOLFE, D: NIGER, E: ADAMAOUA, F: EQUATEUR, G: CONGO.

Les débits mensuels et annuels de 35 fleuves sont utilisés pour calculer les valeurs régionales; sur la période de référence de l'OMM 1951-1980, mais également sur les années récentes. Les données nous sont fournies par l'ORSTOM, EDF INTERNATIONAL, l'OMM, et de très nombreux services hydrologiques africains. Certaines valeurs manquantes sont reconstituées par corrélations linéaires entre stations proches, relations pluies-débits, ou également par interpolation linéaire entre débits journaliers (logiciel HYDROM (1985)).

165

Changements climatiques et variations des écoulements

Pour chaque fleuve, le calcul des volumes écoulés à l'Atlantique est effectué à partir des données d'une ou deux stations proches de l'exutoire ('). Quand les données aux exutoires ne sont pas disponibles, nous utilisons des corrélations avec des stations en amont ou des stations situées sur des fleuves ou des rivières voisins. Quand nous n'avons que des données de précipitations, nous transposons des relations pluies-debits établies pour des cours d'eaux soumis aux mêmes conditions hydroclimatologiques dans des régions de géomorphologie voisine. Certains grands cours d'eaux comme le Niger, traversent des régions aux climats très différents. Si les données manquent pour une section importante, il peut être difficile d'y reconstituer les caractéristiques des écoulements.

VARIATIONS INTERANMJELLES DES APPORTS Apports moyens interannuels Il arrive en année moyenne 2.660 milliards de m3 d'eau douce à l'Atlantique entre le Sénégal et le Congo, ce dernier en apportant plus de 50% à lui seul (Tableau 1). Le débit spécifique moyen est de 12.5 l.s'.km"2 sur le Congo (3.500.000 km2) et de 12.1 l.s'.km- 2 sur le reste de l'Afrique (3.350.000 km2). La lame d'eau écoulée moyenne est de 390 mm. Les débits spécifiques les plus forts ( > 2 0 l.tf'.km"2) sont enregistrés sur les côtes des Monts de Guinée, sur le delta du Niger et les massifs camerounais, et à l'équateur (principalement au Gabon). Variations décennales et annuelles (Tab.l, Fig.2) Décennie 1951-1960.1ues apports à l'Atlantique sont proches de la moyenne 1951-1980 (-5%). Les régions Equateur et Congo sont déficitaires (-3% et -17 %) alors que depuis l'Afrique occidentale (les cinq autres régions) les apports sont excédentaires, passant progressivement de +20% dans la région Sénégal-Fouta à + 5 % dans la Région Adamaoua. Cette graduation des apports est nettement visible sur la Fig.2 des hydraulicités régionales annuelles. Les hydraulicités de la région Sénégal-Fouta sont constamment supérieures à la normale; l'année 1958 y est particulièrement remarquable puisque le reste de l'Afrique est déficitaire. C'est tout le Sahel ouest-africain qui est plus arrosé cette année là, mais les débits du Niger, supérieurs à la normale à Niamey, sont affectés par les apports déficitaires de son affluent principal au Nigeria, la Bénoué, dont le bassin-versant est beaucoup plus méridional. Une situation identique se produit en 1967.

1

Les problèmes liés à la taille des bassins-versants sont decefait secondaires et tournent autour de la recherche et de la communication des données, éparpillées dans de nombreux organismes.

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G.Mahê&J.C.Olivry

TABLEAU 1 Caractéristiques hydrologiques régionales moyennes et décennales des sept régions. Région Période années

Débit m3.s'

Débit spécifique l.s'.km*

Lame d'eau mm

Volume d'apport annuel

Surface km2

Iff m'

319 297 180 148 265

133 125 76 62 111

1247 1228 934

227 224 170

1127

205

5.4 5.4 3.4 2.6 4.8

170 170 107 82 151

153 153 96.6 74.2 136

900000

6770 6690 5390 4270 6280

6.2 6.1 4.9 3.9 5.7

194 192 155 123 180

214 211 170 135 198

11000000

E

51-60 61-70 71-80 81-84 51-80

11970 12320 9800 7970 11400

33.2 34.2 27.2 22.1 31.7

1048 1079 858 697 1000

379 390 311 252 359

360000

F

51-60 61-70 71-80 81-85 51-80

8300 9380 7960 7650 8560

21.8 24.7 20.9 20.1 22.5

688 779 660 634 710

262 296 251 241 271

380000

G

51-60 61-70 71-80 81-88 51-80

36100 48300 41600 37700 43600

10.3 13.8 11.9 10.8 12.5

325 436 375 341 393

1148 1532 1313 1190 1380

3500000

A

51-60 61-70 71-80 81-89 51-80

4230 8396 2400 1955 3510

B

51-60 61-70 71-80 81-89 51-80

7200 7100 5400 pas 6500

C

51-60 61-70 71-80 81-88 51-80

4840 4840 3060 2350 4300

D

51-60 61-70 71-80 81-87 51-80

10.1 9.4 5.7 4.7 8.4 39.5 38.9 29.6 de données 35.7

420000

180000

Décennie 1961-1970. L'excédent d'apport est généralisé: 2.700 milliards de m3 supplémentaires ont été évacués vers l'Atlantique entre 1961 et 1970. Les

167

Changements climatiques et variations des écoulements

+

Fig. 2.

PAS DE DONNÉES

Hydraulicités 1951 à 1989. qu'au sud de apports de la plus humide.

HYDRAULICITÉS (H] : •

:>1.0S

Interflow

£>

Baseflow

190

M.Ottetal. COMPONENTS OF THE HYDROLOGICAL MODEL

Precipitation A rainfall field of high spatial and temporal resolution as obtained e.g. by radar rainfall measurements would be appropriate as input into the model of high resolution in time and space. At present precipitation input is derived from conventional raingauge measurements since no radar data are available for the Mosel River catchment, yet.

Evapotranspiration and Interception The retention of rainfall on the vegetation cover is simulated by an interception storage approach. The amount of intercepted water depends mainly on the type of vegetation, the season and the intensity and duration of rainfall. These parameters are taken into account by an interception storage model as developed by Meriam (1960):

A I = S U • ( 1 - e S m " ) + LAI • EL • te with AI Sw P LAI E; te

(1)

change in the amount of interception (mm) capacity of the interception storage (mm) precipitation (mm) leaf area index (-) evaporation rate (mm/h) duration of precipitation (h)

The variability of the vegetation type and its seasonal change in interception capacity is considered using the leaf area index with its annual variation. The value of S ^ can be derived from measured parameters (Hoyningen-Huene, 1983), after the landuse has been determined by satellite data. The interception storage is emptied by evaporation. The estimation of evaporation from plant and soil surfaces is based on daily evaporation rates. Evapotranspiration from the upper soil zone will be computed depending on the vegetation type and the moisture content of the zone.

Infiltration The infiltration is calculated for the pre-ponding and ponded state using the wellknown Green and Ampt equation as presented by Mein and Larson (1973) and later extended for the computation of infiltration during an unsteady rain (Chu, 1978):

191

Distributed hydrological model for flood forecasting

/(t) = i(t)

(2)

for pre-ponding conditions and f(t) = K. ( 1 + -±for ponding with f(t) i(t) Ks \j/ F A©

• AG )

(3)

conditions infiltration intensity (mm/h) precipitation intensity (mm/h) saturated hydraulic conductivity (mm/h) average suction at the wetting front (mm) infiltrated volume (mm) moisture deficit (-)

This approach represents the actual infiltration process in a simplified way based on physical measurable parameters (Rawls and Brakensiek, 1989). Tests of the model have shown satisfactory results with less computational requirements compared to other infiltration models.

Surface Flow Surface flow is computed as infiltration excess while the surface is under ponded conditions. Since input data for hydraulic approaches to model overland flow are hardly available and have to be estimated to a large extent, a conceptual technique was chosen.

Subsurface flow and baseflow Below the upper soil zone (i.e. the root zone) the infiltrating water quantity represents the input into the soil moisture storage from which interflow and groundwater recharge are generated. This lower soil zone is represented by a single reservoir with two outlets. The diversion into interflow and baseflow depends on the current water content of the storage, the volume of infiltrating water, a certain threshold value and the hydraulic conductivity (Boehme-Korn et al., 1981).

VERIFICATION OF THE MODEL The model as described above has been applied in a preliminary study to the upper part (18.5 km2) of the Nims catchment, a Mosel river tributary in Germany. The precipitation input is taken from the records of one recording rain gauge.

192

M. Ott et al.

Fig. 5 shows first results of the simulated hydrograph compared to the observed hydrograph of an event in June, 1984. The results are quite satisfying as far as the time to peak and the simulation of the peak flow are concerned. The dynamic behaviour of the observed hydrograph could not well be simulated by the model. This may be a result of the first trial approach treating the whole catchment as one 'hydrological unit', which leads to good results in estimating the amount of the runoff components but neglects the fact that some areas, e.g. the area adjacent to the river, produce runoff immediately while others contribute mainly to interflow. The next trials will use a more detailed structure of HSUs within the catchment, which should provide more realistic results.

Rainfall

Rain Gauge Pronsfeld

2.0-

1.0

0.0. 2.6.1984

3.6.1984

5.6.1984

6.6.1984

7.6.1984

5.6.1984

12»

12°>

12°>

12°°

12°°

12™

9.6.1984

time

12™1

River Gauge Giesdorf, Nims observed simulated

2.6.1984

3.6.1984

5.6.1984

6.6.1984

7.6.1984

12M

12 œ

12°°

12°°

12°°

Fig. 5.

8.6.1984 12oo

9.6.1984

time

12°°

Comparison between computed and observed hydrographs. Flood in June 1984, Nims catchment (18.5 km2), Germany.

CENTRAL QUESTIONS The most difficult problem caused by the large dimensions of the Mosel river basin was to cope with the enormous amount of data resulting from the high

193

Distributed hydrological model for flood forecasting

spatial resolution the model is based on. In order to achieve a reduction in effort of computation and data management the technique of aggregating hydrologically similar elements into 'units' and the application of a GIS are considered to be appropriate tools. The acquisition of data in the international Mosel river basin is, as far as the determination of the different types of land use is concerned, not a serious problem, since the land use can be derived from LANDSAT imagery. Digitized soil maps and elevation models of the different countries are all based on different sizes of grid elements; with the use of the GIS they are modified to grid elements of identical size. Precipitation and runoff data are being provided by the Federal Institute of Hydrology; so far only the data for the German part of the river basin were acquired. It is intended to apply the model with an hourly time step for flood forecasting, while the long-term version of the model, based on daily values, should indicate the hydrological effects of land use and climate changes.

CONCLUSIONS a)

b)

c) d) e) f)

g)

The structure of a mesoscale deterministic hydrological model of the distributed system type was described, a model which is still under development for implementation in the international Mosel river catchment. For the estimation of the model parameters satellite imagery of LandsatTM is used as well as a digital elevation model. Since this type of information provides a very high resolution in space it was necessary to aggregate the. small area elements to so-called 'hydrologically similar units'. First results of the application of a simplified version of this model were presented for the Nims river, a small tributary to the Mosel river. In the near future a more sophisticated version of the model will be developed and applied to larger sub-catchments of the Mosel river. Future work will also comprise long term simulation of the runoff process based on daily values and using a better évapotranspiration module. Later the model shall be applied in order to estimate the impact of landuse changes. Here changes of the agricultural use as well as the problem of forest diseases will be analyzed. In the long run it is intended to couple the Mosel river model with an atmospheric general circulation model (AGCM) provided by a meteorological institute. This coupling of the two models will allow to run the model with future scenarios of a changed climate. The impact of climate changes on hydrological processes can then be analyzed with the aid of the two coupled models.

ACKNOWLEDGMENTS This research project is being financed by the Federal Ministry of the Environment, Nature Conservation and Nuclear Safety/Federal Institute of the Environ-

M. Ott et al.

194

ment, Germany. We wish to thank the Federal Institute of Hydrology, Koblenz, for providing some of the required data.

REFERENCES Beven, K. (1989) Changing ideas in hydrology - The case of physically-based models. Journal of Hydrology 105, 157-172. Boehme-Korn, G., Hansel, N. & Schumann, A. (1981) Zwei Versionen des Modells ABHYP. Geod. Geophys. Veroeff. R. IV H.37. Burrough, P.A. (1989) Principles of geographical information systems for land resources assessment. Monographs on soil and resources survey No. 12, Oxford University Press, Oxford. Chu, T.C. (1978) Infiltration during an unsteady rain. Wafer Resources Research Vol.14, No. 3, 461-466. Finch, J.W. (1990) The contribution made by remotely sensed data to a study of groundwater recharge in a semi-arid environment. In: Proc. Int. Sym. on Remote Sensing and Water Resources, Enschede, the Netherlands, 573-577. Fortin, J.-P., Villeneuve, J.-P. and Bocquillon, C. (1990) Hydrotel, Physitel and Imatel: An integrated application of remote sensing and GIS to hydrological modeling on microcomputer. In: Proc. Int. Sym. on Remote Sensing and Water Resources, Enschede, the Netherlands, 793-803. Hoyningen-Huene, Frhr.v. (1983) Die Interzeption des Niederschlages in landwirtschaftlichen VûaazeBbesta&nden. DVWKSchriften Heft 57, 1-53. Hunt, E.R., Rock, B.N., Park, S.N. (1987) Measurement of leaf relative water content by infrared reflectance. Remote Sens. Environ., 22, 429-435. Knudsen, J., Thomsen, A. & Refsgaard, J.C. (1986) WATBAL - a semi-distributed , physically based hydrological modelling system. Proc. Nordic Hydrological Conference, Reykjavik, Iceland, 347-362. Marks, D., Dozier, J. & Frew, J. (1984) Automated basin delineation from digital elevation data. Geoprocessing, 2, 299-311. Mein, R.G., Larson, C.L. (1973) Modeling infiltration during a steady rain. Water Resources Research Vol. 9, No. 2, 384-394. Meriam, R.A. (1960) A note on the interception loss equation. / . Geophys. Res. 65,11 Neumann, P., Fett, W. and Schultz, G.A. (1990) A geographical information system as data base for distributed hydrological models. In: Proc. Int. Symp. on Remote Sensing and Water Resources, Enschede, the Netherlands, 781-791. Rawls, W. J., Brakensiek, D.L. (1989) Estimation of soil water retention and hydraulic properties. In: Unsaturated flow in hydrologie modeling, theory and practice, 275-300, H J . MorelSeytoux (ed.) Kluwer Academic Pub. Running, S.W. (1986) Remote sensing of coniferous forest leaf. Ecology 67, 273-276. Running, S.W. and Coughlan, J.C. (1988) A general model of forest ecosystem processes for regional applications. I. Hydrologie balance, canopy gas exchange and primary production processes. Ecol. Modelling 42, 125-154. Tucker, C.J. (1979) Red and photographic infrared linear combination for monitoring vegetation, Remote Sens. Environ., 8, 127-150.


THE ROLE OF BENCHMARK STATIONS IN WATER RESOURCES MANAGEMENT

B. J. STEWART, R. SRKANTHAN & J. W. CHATAWAY Hydrology Branch, Bureau of Meteorology, Melbourne, Victoria, Australia, 3000

A. J. HALL Snowy Mountains Hydro-Electric Authority, Cooma, New South Wales, Australia, 2630

ABSTRACT Once reliable estimates of changes in rates of precipitation and evaporation are available at a regional scale, as a result of increased greenhouse gases, it will be necessary to have in place the tools to measure the impact of these changes on the water resources of a region.In Australia, a network of benchmark streamflow gauging stations has been identified for such a purpose. The data recorded at these stations will be used to examine trends or changes in streamflow resulting from climate variability or change. Hydrological models, fitted to the recorded data, will be used to predict the impacts of climatic change on the water resources of a region.

INTRODUCTION There is growing international concern over the potential impact of man induced climate change on water resources (Stewart et al., 1990). Currently, the only reliable estimates of this impact are available from general circulation models (GCMs). GCMs are reasonably consistent in their estimates of global scale impacts, but differ widely in their predictions at a regional level (Schlesinger & Mitchell, 1987; Mitchell et al., 1989). While a significant effort is being placed in improving the output from GCMs, it is essential that hydrologists have the necessary tools available to enable the interpretation of the climatic change on water resources. This paper presents the background and information on the benchmark streamflow station network identified in Australia. Details of proposed analyses of the data collected at these stations are also provided.

STATION DEFINITION The Australian Water Resources Council (AWRC, 1989) has provided guidelines for the design and operation of surface water information systems. These guidelines include network design criteria and a classification of streamflow gauging stations. Within this classification system, benchmark stations are defined as selected primary stations with stable or protected catchments in which long 195

196

B. J. Stewart et al.

term variations are attributable to climatic features alone. Primary stations are base stations established to measure temporal variance of streamflow, but may not necessarily be on stable or protected catchments. Benchmark stations should not be subject to changes as a result of land use change. The benchmark stations were selected in cooperation with the State and Territory water agencies who operate the gauging stations. As far as possible, good quality long term stations were selected. However, with the development of the country, such sites become scarcer with time. The stations were selected to provide a set of stations representative of the various climatic and hydrological regimes found throughout Australia and fulfil the requirements as outlined above.

AUSTRALIAN BENCHMARK STATION NETWORK In 1990, at the request of the Water Resources Management Committee of the Australian Water Resources Council, the Hydrology Branch of the Bureau of Meteorology identified, in cooperation with the major Australian water agencies,. a network of benchmark stations operated by the water agencies. This network consists of 85 streamflow stations (Table 1) spread across Australia (Fig. 1). in As can be seen in Table 1, a srnall number of stations have large catchment

120°

Fig. 1.

130°

140°

Locations of benchmark streamflow stations.

150°

197

Role of benchmark stations in water resources management

areas. Modelling of the hydrological characteristics of these catchments may be difficult, however, the length of recorded data at these sites was seen as a distinct advantage. For each benchmark station, associated climate and rainfall stations were identified from the existing Bureau of Meteorology networks. Three factors were taken into consideration when selecting the climate and rainfall stations. These were, the closeness of the station to the benchmark station, the length of record available and the quality of the available record. For each benchmark station at least one climate and one rainfall station were identified. In most instances more than one of each were identified. From Table 1 it can be seen that the length of recorded data at some stations is short. Hydrological models will be calibrated at these sites and where possible, longer term records simulated using rainfall data. It should be noted however, that the value of this network will be in the data collected into the future. While it may have been better to have begun the collection of data at an earlier date, short term past record is not a good reason to cease collection of data into the future. Table 1.

List of benchmark stations and some attributes.

Station Number

River Name

111105 113003 113004 118106 120204 125005 130503 138009 140002 145103 203002 204036 206001 208001 210022 212013 214003 215004 219001 221201 222213 224206 226220 227219 228207 233214 235205 238208 304089 304108

Bambinda Creek Nitchaga Creek Cochable Creek Alligator Creek Broken Blacks Creek Carnarvon Jinana Creek Tewah Creek Cainbable Creek Coopers Creek Cataract Creek Styx Barrington Allyn Megalong Creek Macquarie Rivulet Corang Rutherford Creek Conn Suggan Buggan Wonnangatta Loch Bass Bunyip Barwon Arkins Creek Jimmy Creek Nive Pine Tree Rivulet

Site Name The Boulders Upper Tully Powerline Allendale Credition Whitefords Wyseby Station Tagigan Road near Coops Corner good Dam Site Repetance Sandy Hill Jeogla Bobs Crossing Halton Narrow Neck Albion Park Hockeys Brown Mountain Weeragua Suggan Buggan Crooked River Noojee Loch Headworks Forrest Wyelongta Jimmy Creek Gowan Brae Lake Highway

Area (km2)

Year Opened

39 71 93 69 41 505 570 104 54 41 62 236 163 20 205 25.6 34.6 166 15.1 311 357 1096 97.1 52 41 17 89.8 22.5 185 19.4

1966 1949 1966 1974 1955 1973 1966 1974 1972 1962 1920 1952 1918 1944 1940 1968 1949 1924 1924 1922 1957 1953 1957 1966 1948 1955 • 1958 1950 1964 1969

198

B. J. Stewart et al. Table 1. (cont.) Station Number 307001 308003 308031 310009 312001 315006 401212 401216 401554 403218 406208 407214 408202 410094 410534 412093 420003 421034 503502 505517 507500 509503 513501 606002 609005 612005 612008 612011 616024 616065 701003 708009 709002 803003 806003 809310 809312 8110004 8140008 8140159 8150011 8150097 8200046 8210007 8210010 9030090 9070142 915001 927001 0060005 0070009 0280114

River Name Davey Franklin Collingwood Whyte Hellyer Forth Nariel Creek Big Tooma Dandongadale Campaspe Creswick Creek Avoca Jounama Creek Happy Jacks Naradhan Creek Belar Creek Slippery Creek Scott Creek North Para Hill Kanyaka Creek Rocky Weld Weenup Creek Stores Brook Bingham Tributary Salmon Brook Canning Canning Nokanena Brook Kanjenjie Creek Harding Fletcher Crystal Creek Ord Fletcher Creek East Baines Fergusson 17 Mile Creek Darwin River Dam East Finniss Deaf Adder Creek Magela Creek East Alligator Chambers Creek Mcarthur Mitchell Grass Jardine Trephina Creek Unca Creek Mclaren Creek

Site Name

Area (km2)

DS Crossing River Mt Fincham Track DS Alma River US Rocky Creek Guilford Junction US Lemonthyme Upper Nariel Jokers Creek above Tooma Res Matong North Ashbourne Clunes Amphitheatre abv JMama Pndg abv H/Jack Pndg Naradhan Warkton Dam Site Scotts Bottom Penrice near Andrews Old Kanyaka US Gorge Falls Wattle Block Mondelup Pool Mast View Ernies Catchment Salmon Catchment Scenic Drive Glen Eagle Woottachooka Fish pool Marmurrina Pool Dromedary Crystal Head Bedford Downs Frog Hollow Victoria Hwy Railway Bridge Waterfall View Dam Intake Tower Rum Jungle Coljon US Bwrbrd W/Hole 12deg 43mins S Wattle Hill Baileys Grave Richmond Telegraph Line Trephina Gorge Jervois Mine Stuart Hwy

686 1490 292.5 325 102 311 252 356 114 182 33.3 308 78 127 109 44 133 15.1 26.7 118 236 180 190 23.8 86.9 14.7 2.7 0.82 517 544 229 40.5 52.9 65.7 67.6 549 29.6 2432 1490 619 206 71 513 260 2384 89 4100 3 2500 422 13.3 414

Year Opened 1964 1953 1980 1960 1957 1962 1954 1934 1959 1962 1933 1943 1966 1967 1959 1973 1951 1954 1964 1977 1944 1973 1969 1982 1975 1972 1974 1974 1977 1950 1971 1973 1967 1967 1968 1967 1967 1963 1957 1962 1972 1960 1972 1977 1971 1973 1964 1968 1968 1967 1972 1964

199


Table 1. (cont.) Station Number 0290002 0290228 0290240

River Name Mary Anne Creek Morphett Creek Tennant Creek

Site Name Mary Anne Dam DS Stuart Hwy Old Telegrph Stn

Area (km2)

230 97

Year Opened 1981 1979 1972

PRESENTATION OF INFORMATION It is proposed to produce two different types of publication using the information and data from the above network. The first of these, which was nearing completion in December 1990, is a catalogue of information related to the network. The second will be a series of reports based on analysis of the data at five yearly intervals commencing in 1991. Additional studies using the data from the network are also proposed and these will be described later. Catalogue of information Information to be provided in this catalogue will include details of the benchmark station and its catchment, statistics which describe the climatic and hydrological characteristics of the catchment and a map showing the location of the catchment. Specific categories of information include: (a) site details (name, latitude, longitude, etc.); (b) catchment characteristics (area, levels, major landforms, vegetation, soils); (c) gauge station details (type, period and quality of record, rating curve); (d) climatic characteristics (monthly and annual means of maximum and minimum daily temperature, dew point at 9am, wind run, solar radiation, sunshine hours, rainfall, raindays and pan evaporation for the selected climate stations and rainfall and raindays for the selected rainfall stations); (e) streamflow characteristics (monthly and annual mean, coefficient of variation and coefficient of skewness); (f) catchment map (streamflow, climate, rainfall stations and catchment and basin boundaries). The main sources of information for this catalogue will be material currently available from the major Australian water agencies and publications on the nation's water resources, vegetation, soils etc. These will include: (a) the 7th edition of "Streamgauging Information, Australia" (AWRC, 1990); (b) the 1:250 000 and 1:100 000 Australian Map Series; (c) Australia's Vegetation in the 1780s, (AUSMAP, 1989); (d) Australia's Vegetation in the 1980s, (AUSMAP, 1989); (e) Australia's Soil Resources, (NATMAP, 1978); (f) various publications from the Australian water agencies; (g) the Bureau of Meteorology microfiche of data; (h) streamflow data provided by the Australian water agencies.


200

Five yearly analysis reports From 1991, it is proposed to produce, on a five yearly basis, analyses of the streamflow and climatic data for the above network. The analysis of this data will include the derivation of five year means and other statistics, and the presentation of the data as temporal plots (Graczyk et al., 1986). Both monthly and annual data will be analyzed. The time series data will also be (see instructions) tested for trend or jump in mean value using a similar approach to that recommended by World Meteorological Organization (WMO, 1988) and used by Srikanthan & Stewart (1991). The main aim of these publications will be to display the impact of climatic variability on the water resources of Australia and, should climatic change (greenhouse induced or otherwise) occur, provide a data base on which to evaluate the impacts of this change.

PROPOSED FUTURE ANALYSES The publications and analyses described above will provide a basis for the evaluation of climatic variability. In five to ten years time, general circulation models or nested macroscale/mesoscale models of the climate may be able to provide us with reliable predictions of the impacts of climate change on hydrological parameters such as rainfall, evaporation and soil moisture. Once calibrated and validated using the data from the above catchments, hydrological models will be used to estimate the impacts of the predicted changes on the water resources of the catchments and make assessments of the overall impacts of climate change on Australia's water resources. A hydrological model (which is yet to be determined) can therefore be fitted to a select number of the above catchments depending on the type, quantity and quality of data available and the study objectives. It is anticipated that all climatic regions in Australia will be represented within this set of catchments. Initially, a range of climate change scenarios may be applied to the models to test their sensitivity to a range of parameters.

APPLICATION TO LARGE RIVER BASINS Most large river basins will to some degree be disturbed by man's influence. Detecting the impact of climate change separate to this influence will be a difficult if not impossible task. To overcome this, it is necessary to establish a number of benchmark stations in large river basins and ensure that as far as possible, the different climatic and hydrological regimes are represented. The Murray-Darling River Basin in Australia covers an area of 1.1 million km2. Of the 85 streamflow benchmark stations, 12 are in the Murray-Darling River Basin. One problem to be faced in the application of the results of this study to the large river basins will be how to scale up from these smaller catchment. With this in mind, the selected catchments range in area and thus methods of scaling both up and down can be evaluated. As large river basins tend to support large populations and/or a high

201


percentage of agricultural activities, the impacts of climate change will be of great importance to water resources managers. The larger the river basin, the larger the potential range of impacts of climate change. As a result, within the one river basin, some regions may improve in water availability, while others may have less reliable resources. This may add a new dimension to the conflicting uses of water resources

CONCLUSIONS This paper describes the selection and proposed future usage of a network of benchmark streamflow gauging stations and related climate and rainfall stations in Australia. The data from these stations will be used to monitor and forecast the potential impacts of climate variability and change on the water resources of Australia. It is recommended that other countries, which have not established similar data collection sites, give consideration to selection or establishment of high quality stations for use as benchmark stations for monitoring the impacts of climate change. While the immediate value of "natural catchment" data may not be recognized, our ability to manage water resources in the future may depend heavily on their availability.

ACKNOWLEDGEMENTS This project is being carried out as part of the work program of the Water Resources Management Committee of the Australian Water Resources Council. The assistance and support of the following organisations are acknowledged: Water Resources Commission, Queensland; Department of Water Resources, New South Wales; Rural Water Commission, Victoria; Rivers and Water Supply, Tasmania; Engineering and Water Supply Department, South Australia; Water Authority of Western Australia; Northern Territory Power and Water Authority.

REFERENCES AUSMAP (1989) Australia Present Vegetation. Australian Surveying and Land Information Group, Canberra, AGPS. AUSMAP (1989) Australia Natural Vegetation. Australian Surveying and Land Information Group, Canberra, AGPS. Australian Water Resources Council (1989) Guidelines for the Design and Operation of Surface Water Information Systems. AWRC Water Management Series No. 18, Department of Primar Industries and Energy, Australian Government Publishing Service, Canberra. Australian Water Resources Council (1990) Stream gauging information, Australia. Seventh Edition, AWRC, Department of Primary Industries, Australia, Australian Government Publishing Service, Canberra. Graczyk, D. J., Krug, W. R. & Gebert, W. A. (1986) A history of annual streamflows from the 21 water-resources regions in the United States and Puerto Rico, 1951-83. Department of the Interior. U.S. Geological Survey. Open-File Report 86-128. Mitchell, J. F. B., Senior, C. A. & Ingram, W.J. (1989) COj and climate: missing feedback? Nature, & 341, 132-134. NATMAP (1978) Australia Soil Resources. Division of National Mapping, Canberra, AGPS. Schlesinger, M. E. & Mitchell, J. F. B. (1987) Climate model simulations of the equilibrium climatic


202

response to increased carbon dioxide. Reviews of Geophysics & 25, (4), 760-798. Stewart, B. J., Srikanthan, R. & Hall, A.J. (1990) Assessment of the potential impact of climate change on rainfall in the Murray-Darling basin, Australia. The Hydrological Basis for Water Resources Management. IAHS Publication No. 197. Srikanthan, R. & Stewart, B.J. (1991) Analysis of Australian rainfall data with respect to climate variability and change. Australian Meterological Magazine. In print. WMO (1988) Analysing long time series of hydrological data with respect to climate variability. WCAP-3, WMO/TD-No. 224.

Hydrology for the Water Management of Large River Basins (Proceedings of the Vienna Symposium, August 1991). IAHS Publ. no. 201, 1991.

CHANGE IN FLOOD REGIME OF LARGE RIVERS AS A CONSEQUENCE OF A SIGNIFICANT WATER DIVERSION : EXAMPLE OF THE HYDROPOWER PLANT GABCIKOVO ON THE RIVER DANUBE

A. SVOBODA Institute of Hydrology and Hydraulics, Slovak Academy of Sciences. Trnavska 32, 82651 Bratislava, Czechoslovakia

ABSTRACT The water diversion from the river into a diversion canal means a change of the hydrological regime both in the directly affected river reach and in the downstream sections. The change is particularly pronounced during floods as only that part of the flood wave will be attenuated that remains in the river whereas the rest passes the diversion canal and is solely lagged in time. As a consequence, downstream of the diversion, the original peak flows will be increased. In the paper a numerical solution to the problem is presented, which is based upon a hydrological runoff model of the system river - diversion canal, as well as the results of an analysis of the impact of the Gabcikovo power station on the Danube river flood regime downstream of Bratislava.

INTRODUCTION The system of structures Gabcikovo (WSG) on the river Danube downstream of Bratislava is a part of the originally planned international (Czechoslovakia Hungary) Gabcikovo - Nagymaros project (Fig.l). The WSG is the only part of it which, if completed, will change the flood regime of the Danube downstream of Bratislava. To a lesser extent, this change may be attributed to the creation of a reservoir upstream of the weir Hrusov - Dunakiliti (approximately 200 million m3) which the flood waves have to pass. The main cause is that the project is designed to divert up to 4890 m V into the diversion canal on which the hydropower plant Gabcikovo and two navigation locks are located (Hydroconsult, 1975a, 1975b). It was therefore necessary to determine peak discharges of various return periods for all characteristic sites of the WSG and downstream of it in order to assess the safety of existing and/or reconstructed flood protection structures (protective bunds) within the concerned reach of the Danube. These characteristic sites are : weir Hrusov-Dunakiliti (upstream,downstream), Gabcikovo power plant (canal), river Danube upstream of the diversion outlet (Palkovicovo), and downstream of it (Medvedov). The problem was solved by the method of mathematical modelling of runoff in the WSG system, using historical and

203

204

A. Svoboda

hypothetical flood hydrographs with peaks of various return periods as the system input (Bratislava). The used tool, a mathematical model is of a general nature and it can be used for solving similar problems related to flood regime changes in large river systems under man's intervention.

BUDAPEST

Fig. 1.

Schematic map of the water structure system Gabcikovo-Nagymaros.

In summary, the problem tackled was, how a diversion of large quantities of water influences the flood regime downstream of the outlet of the diversion canal and in the "abandoned" section of the river. When this problem is solved, operational rules can be developed for the flood control, in order not to exceed the safe capacity of the "abandoned" river section as well as of the section downstream of the diversion outlet. The problem of safety against floods is crucial for large structures in general, for this Czechoslovak - Hungarian project in the central part of the Danube with flood waters high above the terrain, in particular. .old

I

o o

river bed

I

reservoir

3

r

1

OBL OHR-OKA

Fig- 2.

Setup of the mathematical model.

205

Change in flood regime of large rivers

SOLUTION Numerical routing of the input hydrograph through the conceptual model shown in Fig. 2 is the base for determining of the flood hydrographs and their peaks in all characteristic system sites. The model consists of four components: (a) reservoir (b) flow diversion control (M) (c) diversion canal (d) "old" river channel. Components (a) and (d) are models of the same structure, series of equal nonlinear/linear reservoirs, (c) is a simple linear translation model, and (b) is a set of operating rules for allocating the reservoir output to the diversion canal and the "old" river bed. Components (a) and (d) are based on the river model NONLIN, described in detail by Svoboda (1988). Its description, as well as description of the data used for the model calibration would exceed the scope of this paper and therefore are not included here. Under the conditions given in this section of the Danube a proper calibration of the component (d) is decissive. This was done by trial-and -error using historical floods of the years 1977, 1980, and 1981 in order to reflect the latest hydraulic conditions of the river channel and of the active flood plain between Bratislava and Medvedov. Parameter of the (c) - component (time shift in the canal) is 6 hours, according to Hydroconsult (1975b). The same source was used to determine the parameters of the component (b) (operation rules of the weir and of the power plant Gabcikovo). The solution starts with the assumption that input hydrographs are known in Bratislava with peaks of a 100 - year flood (Q100), 1000 - year flood (Q1000), and of a 10000 -year flood (Q10000). Historical hydrograph exists only for Q100= 10600 m3s"' (1954), for the two others only peaks are available resulting from the statistical analysis (Q1000=13 000 m V , Q10000=15 000 m V ) . For the latter two, hypothetical hydrographs were constructed under the assumption of a triangular shape, with the respective peak, and with the base of the same length as that of the recorded 1954 - hydrograph (Q100). It was further assumed that, for floods with high return periods, the calculated peaks in all cross sections of the system would have the same return period as the input peak. In order to characterize the gradual change in flood regime during the construction period (gradual installation of the turbines each having the capacity of 500 mV 1 ), a number of model runs were performed with different rates of flows diverted into the canal (1000, 2000, 3000, 4000 and more mV 1 ). These runs helped also to answer the question to what extent it is reasonable to reduce the diverted flows in order not to exceed the safe flood capacity of the river bed downstream of the diversion canal. In connection with the recent investigations of ecological impacts of the project it is expected, that modifications in the operational rules of WSG will be needed to change the hydrological regime of the "old" river bed in order to meet the ecological requirements. However, during floods, the operation will have preference which will guarantee the safety of the structure and of the surrounding land.

206

A. Svoboda RESULTS

A summary of the results gained with the procedures described above is presented in Table 1. There are 100-, 1000-,and 10000- year peak discharges calculated for different values of the diverted flows (QKA = 1000,2000,3000, and 2: = 4000 m V ) were calculated for the following locations: QBL Bratislava QHR weir Hrusov-Dunakiliti upstream QKA. diverted flow into the canal QHR-QKA - weir Hrusov-Dunakiliti downstream QPA Palkovicovo (Danube upstream of the diversion outlet) QME Medvedov (Danube downstream of the diversion outlet) Table 1 Peak flows (m3/s) at critical sites of the WSG.

QBL

QHR

10600 10600 10600 10600

10078 10078 10078 10078

4000 3000 2000 1000

6078 7078 8078 9078

5492 6430 7369 8311

9492 9430 9369 9311

9256

13000 13000 13000 13000

12609 12609 12609 12609

max.4240 3000 2000 1000

8369 9609 10609 11609

7225 8337 9225 10176

11465 11337 11225 11179

11100

15000 15000 Q10000 15000 15000

14561 14561 14561 14561

max.4890 3000 2000 1000

9671 11561 1256 13561

8297 9794 10698 11604

13038 12794 12698 12604

12514

Q100

Q1000

QKA

QHR-QKA

QPA

QME

ME1*

* Peak flows QME (Medvedov) without diversion (QKA—0)

The results show the apparent change in peak flows, as a function of the magnitude of diverted flows - rise of peaks in the "old" river bed and their reduction downstream of the canal outlet, with the decrease of the diverted flows and vice versa. Apparently, the lowest peak at the critical site - downstream of the canal outlet, can be expected (for a given input) when routing the whole flood through the "old" river bed. However, the results indicate, that only slightly higher peaks at this site were determined when diverting approximately 1000 m3s"1 into the canal. This is important for energy production during floods. As an example of the computer runs, the 1954 - flood and its routing with and without diverting of the flood flows is shown in Fig. 3. The knowledge how the flood regime will change and develop along the

Change in flood regime of large rivers

207

reach has a significant impact on the design of structures in the "old" river bed, on the improvement of the regime of water levels in the bed, in the system of arms, and on the groundwater levels in the adjacent region. The results indicate, that the flood peaks along the structure depend not only upon the magnitude of the diverted flows and of the peak of the input hydrograph, but also upon its shape. Results presented in Table 1 were derived from inputs similar in shape to the 1954 flood. For inputs with a slower rise the calculated peaks will be higher, for a steeper rise they will be lower than those in Table 1. Computer runs with hypothetical inputs of different shapes were performed too. These results are not presented in this paper, they can be found in the report of Svoboda (1989). x105

"

9 - 2 0 July 1

Fig. 3.

r——~-r100

1954

1 200

< 1 ,. ,, , 3C time (nrs)

Routing of the 1954 flood between Bratislava and Medvedov with and without diversion

CONCLUSIONS The general conclusions which can be derived from the performed investigations are as follows: (a) diversion of water from the river during floods induces an increase of the peak downstream of the diversion outlet, depending upon the rate of diverted flows; (b) there exists a limit to diverted flows which will not substantially influence the flood peak downstream of the diversion outlet, as can be seen from Table 1; (c) besides the rate of diverted flows and the peak of the input hydrograph also its shape as well as the flow prior to the flood will influence the flood regime downstream;

A. Svoboda (d)

208

with respect to (c), the importance of a forecast of the input flows increases, for proper and timely control of flood flows downstream by operation of the diversion structure.

REFERENCES Hydroconsult (1975a) Sustava vodnych diel Gabcikovo-Nagymaros,cast V.-4.2"Vodna bllancia". Project document,Bratislava,Czechoslovakia. Hydroconsult (1975b) Sustava vodnych diel Gabcikovo-Nagymaros,cast V.-4.22 "Hydrologicke podklady, zmeneny stav pri vystavbe a po vystavbe, I. permanentny stav ", (System of water structures Gabcikovo-Nagymaros, part 4.22 Hydrological data, changed regime during and after construction, La-permanent regime. Project document, Bratislava. Czeschoslovakia. Svoboda A. (1988) River model NONLIN. HOMS component.Reference centre for HOMS.WMOGeneva.Switzerland. Svoboda A. (1989) Odtokovy rezim Dunaja v podmienkach uvadzania VD Gabcikovo do prevadzky,sprava etapy "Zmeny priebehu povodni v useku ovplyvnenon VD Gabcikovo", Report R-05-531-139-01.05, Research institute for water management, Bratislava, Czechoslovakia.

Hydrology for the Water Management of Large River Basins/Proceedings Vienna Symposium, August 1991). IAHS Publ. no. 201, 1991.

of the

PREDICTION OF RIVER RUNOFF CHANGES DUE TO HYDROPOWER DEVELOPMENT ON THE DANUBE AT GABCIKOVO

J. SZOLGAY Institute of Hydrology and Hydraulics Slovak Academy of Sciences, Trnavska 32,826 SI Bratislava, Czechoslovakia Now at Slovak. Technical University, Dept. of Civil Engineering, Radlinskeho 11, 813 68 Bratislava

ABSTRACT The impact of the planned future operation rules of the Gabcikovo diversion type peak hydropower station on the runoff regime of the River Danube downstream from the weir in Hrusov has been studied. A multilinear flow routing model based on the state space representation of the Kalinin-Miljukov cascade was developed and calibrated. A model of the operation rules of the power station (8 Kaplan type turbines of 500 m3/s capacity) has been embedded into the routing procedures. Eighty-five years of daily operation of the system have been simulated using flow data from the period 1901-1985. Basic statistical characteristics of natural and influenced flow conditions were evaluated and compared.

INTRODUCTION Serious attempts of the CSFR and Hungary to utilize the hydro-energetical potential of the Danube on the joint border reach started in 1952. After reviewing several project alternatives, the presently questioned agreement to build a system of two power plants was signed in 1977. Fig. 1 shows the schematic representation of the joint project. The first power station is designed for peak operation with a weir at Hrusov (seven 24 m broad segments with the capacity of 15000 m ¥ ) and a fiat reservoir with the volume of 197 10s m3. The water is diverted to the power station at Gabcikovo through a 16.9 km long artificial navigation channel with capacity of 4000 m3s"'. Power head at the plant varies from 16 to 21.5 m. The Nagymaros station was designed 115 km downstream to operate continuously in order to attenuate the peaks generated by the operation of Gabcikovo. During recent years a gradual but significant shift has become apparent in societal values and norms from an automatic acceptance of economic growth for its own sake toward a deeper concern and a better understanding of the environmental social and political consequences of this growth. This trend is supported by the recent political changes in both countries. Consequently the Gabcikovo - Nagymaros project has become a controver209

210

/. Szolgay

sial environmental and political issue not only between but also within the two countries. Hungary refused to build the Nagymaros plant and stopped construction work on the almost finished Gabcikovo plant. Influence of the project on the environment is intensively studied.

Fig. 1.

Study site location and schematic representation of the Gabcikovo power plant.

GOALS, METHODS AND DATA The alluvial land under the impact of the system consists of a valuable agricultural area, huge groundwater resources and unique forested flood plain ecosystems directly connected to the river. Among other things, it became necessary to evaluate the long-term effects of the originally planned project parameters on the runoff regime and groundwater levels downstream from the weir. The expected changes of the river regime will depend on the operation modes of hydraulic and energetic structures and on future hydraulic properties of the studied river reach. The possibilities of using analytical approaches to estimate them are limited to simple scenarios. It became necessary to develop a methodology for the detailed simulation of the system operation using mathematical models. The problem was broken down into following parts: (a) routing of flows through the natural river bed and the designed reservoir from Bratislava (cross section A -nearest gauging station) to Hrusov (cross section B);

211

Danube runoff changes due to hydropower development

(b) (c)

simulation of the operation rules of the scheme at the weir; routing of flows through the old river bed across the study site (cross section C) to the confluence of the Danube with the outlet channel (cross section E); (d) analysis of simulated flows at locations B,C,E and in the diversion channel (cross section D). Eighty-five years of mean daily flows from the period 1901 to 1985 served as input data. For comparison the predicted uninfluenced hydrological conditions had to be modeled too. The resulting time series represent a dynamic boundary condition for follow-up studies evaluating the influence of the project on the soilwater-plant system of adjacent areas and on the behaviour of diverse aquatic ecosystems.

FLOW ROUTING MODEL The well-developed flood plains with meandering river arms significantly contribute to the attenuation of flood peaks in the modeled river reach. To account for this with sufficient accuracy and to avoid difficulties arising from the use of the complete Saint Venant equations (complicated hydraulics of the flood plain, decades of simulation etc.) the multilinear conceptual approach has been selected. Its attractiveness results, as described in detail in Kundzewicz (1984), from the possibility of accounting for nonlinear effects by means of convenient linear system techniques. The multilinear model consists of an algorithm for dividing the input I into a sum of elementary signals ll,I2>---1L aa^ °f a ^ °f distinct linear submodels driven by the respective elementary inputs. The response Q of the (in general nonlinear) multilinear model is the sum of the responses Q 1 ,Q 2 ,...Q n of the submodels to the corresponding elementary input signals. In the present study the so called time distribution scheme for input division was adopted. The elementary inputs are consecutive portions of the input staying within specified nonoverlapping intervals. For details see Kundzewicz (1984) and Becker & Kundzewicz (1987). Each linear submodel consists of a series of n linear reservoirs with storage coefficient k. To account for lateral inflow or water withdrawal along the reach, each reservoir is allowed for external input I{ in addition to the inflow from the upper one. Inputs are considered constant during the sampling interval (a;a+l) of length T. Then, as shown in Szollosi-Nagy (1982) for one input and in Szolgay (1982) for n external inputs into the cascade, the governing equations of the model can be written in the following discrete form: S(a+1) = F(a+l,a)S(a) + G(a+l,a)I(a)

(1)

Q(a +1) = H(a + l)S(a + 1)

(2)

where S and Q are (n*l) vectors of reservoir volumes and outflows respectively, H is an (l*n) vector, which equals (0,0,0,..,1/k). The elements of the (n*n) transition matrices F and G are defined as:

212

/. Szolgay i-j

T

-17k

e for i greater or equal to j

Vfi i\ —

(3)

(i-j)

(i-j)! k i-j a/i i\ — v ? . f=0

f

T

-T/k

e for i greater or equal to j

(4)

f-i

f! k

and are equal to 0 elsewhere. In the simulation two linear submodels were used. The first one routed flows lower than the bankfull discharge (3000 mV 1 ), the second was activated when the discharges were in the out bank range. Both models were calibrated by trial and error for the two subreaches from Bratislava to Hrusov and from there to the confluence of the Danube and the outlet channel. Two sets of selected recorded events with peak flows lower or greater than the bankfull discharge were used in the calibration. The same multilinear approach was employed for the routing through the future reservoir. The volume - discharge relationship of the designed reservoir was approximated by two linear relationships. They defined the storage coefficients and the threshold for the two sub-models.

MODEL OF THE OPERATING RULES Water will be withdrawn from the river by several users (agriculture for irrigation schemes, industry, fishery etc.). The aquifer fed by the river will be increasingly exploited for drinking water supply. Losses due to seepage to deep layers, for evaporation, through navigation locks etc. had to be considered too. The withdrawal rate will exhibit considerable seasonal variation between 100 and 300 m3s"1. Therefore the following water balance model has been chosen. The expected requirements of the users based on data from the inter-governmental agreement on the project were estimated and tabulated as 10 day averages in a wet, dry and normal year respectively. These values were used in the simulation to account for seasonal and year to year variation of the real needs. The main operating principles are set on governmental levels in the project documents. According to them, the following rules can be set up: (a) all discharges lower than the operating capacity of the diversion channel (4000 mV1) are fully used for peak power generation, only 50 mV 1 sanitary flow is allowed for in the old river bed; (b) during flood situations the flow rate in the diversion channel can be higher than 4000 m V according to these rules: (i) 100 years flood 4240 mV 1 (ii) 1000 years flood 4890 m V (iii) 10000 years flood 5270 m ¥ (c) excess water in flood situations will be released to the old river bed.

213


SIMULATION RESULTS Basic statistical characteristics of the predicted time series (mean X, coefficient of variation CV, skewness coefficient CS, 1st order autocorrelation coefficient R l were computed for mean annual, monthly and daily discharges at sites B,C,D,E before and after completion of the scheme. Flow duration curves and runoff volumes for different periods of the year were evaluated and compared too. Corresponding water level time series have been determined using steady state discharge rating curves for groundwater level and soil moisture fluctuation studies (For details see Benetin & Soltesz (1990)) Results of the analysis for the old river bed are extensively treated in Szolgay (1990) in connection with efforts to prevent the drying of the flood plain forest. In the following the overall changes in the discharge regime will be discussed.

50 Fig. 2.

100

150

200

250

300

Time (days)

350

Simulated hydrograph of the power plant operation.

Simulated hydrograph of the plant operation in a wet year in Fig. 2 illustrates the character of the expected changes. The diversion channel will overtake the role of the Danube, while this degrades to an overflow channel carrying only sanitary discharges most of the time. Table 1 contains the statistical characteristics describing this process on annual level. The overall changes in the mean annual flow of the Danube at section E will be reduced to the 118 m 3 s 4 drop in the mean. Other flow parameTable 1.

Basic characteristics of mean annual flows.

Site

B before

X (m3s') CV CS Rl

2045 0.17 0.34 0.09

B after 82 0.72 4.43 0.001

C before 2045 0.17 0.36 0.09

C after 82 0.72 4.46 0.01

D 1845 0.17 0.15 0.1

E after 1927 0.18 0.34 0.09

214

/. Szolgay

ters remain unchanged. The changes in the old river bed will however be dramatic, the mean drops by one order of magnitude, variability and skewness will increase to values far beyond of the experience in our geographical conditions. Annual flows in the channel will be less skewed and will resemble the natural regime. The same type of behaviour can be observed in Table 2, which contains characteristics of the time series of mean monthly flows. Fig. 3 shows the corresponding autocorrelation functions. As expected, the seasonality of monthly flows will be destroyed in the old river and reinstalled downstream of the confluence with the outlet channel. The flat low volume reservoir is unable to change the autocorrelation pattern. Although the autocorrelation function suggests a completely random behaviour in the old river after completion, values of month to previous month correlation coefficients in Table 3 show high dependence between flows in May and June, and not negligible dependence between June and Table 2.

Basic characteristics of mean monthly flows.

Site

B before

CV CS Rl

B after 1.91 11.06 0.28

0.4 1.02 0.62

Table 3.

C after

C before 0.4 1.02 0.62

D

1.88 10.92 0.29

0.42 1.07 0.61

0.39 0.55 0.64

Month to previous month correlation.

Month

NOV

DEC

JAN

FEB MAR APR

MAY JUN

JUL AUG

SEP

B before B after D E after

.56 -.03 .57 .56

.61 .00 .61 .61

.41 .00 .43 .41

.32 .06 .34 .32

.63 .12 .64 .63

.41 .23 .45 .42

.53 .04 .57 .52

Fig. 3.

E after

.29 -.06 .34 .29

.45 .01 .51 .45

.62 .71 .60 .62

.53 .26 .57 .53

Autocorrelation functions of mean monthly flows.

OCT

.47 -.01 .50 .47

215


July, July and August. This results from the release of excess water caused by the typical flood regime of the Danube, which is fed by Alpine snow melt water in the early summer. Flows in the channel will preserve the observed cyclicity with slight changes in the mean and skewness resulting from the upper threshold of flows.

Table 4.

Predicted statistics of the mean monthly flow in June.

Site

B before

X (mV) CV CS

2840 0.28 2.28

B after

130 2.81 7.50

C before

2828 0.28 2.18

Cafter

129 2.76 7.52

D

2478 0.25 0.44

Table 5.

Predicted statistics of the mean monthly flow of November.

Site

B before

X fmV'j CV CS

1460 0.33 0.92

B after

S3 0.36 7.43

C before

1456 0.32 0.87

Cafter

D

53 0.33 7.16

1289 0.37 0.89

E after

2578 0.28 1.1

E after

1342 0.36 0.92

Tables 4 and 5 illustrate the expected regime in a wet and dry month. The difference in the mean between sections B and E in the wet June of 262 nrV1 is caused by increased water use for irrigation in comparison with the dry November with a withdrawal of only 118 m V . Other computed statistics fit into the

150 200 Duration (days) P;'ig.4.

Flow duration curves of mean daily flows.

216

/. Szolgay

picture described above. Fig. 4 shows flow duration curves of mean daily flows at three sites. Overall changes are expected to be negligible again and are caused by water withdrawal and attenuation effect of the flood plains. Sanitary flow in the old river will be exceeded only for 18 days on the average, mean annual flow for 15 days instead of 150 as before. Flooding of the flood plain forest which occurs at discharges higher than 3000 mV 1 and is essential for its survival will be reduced from 49 days to a minimum of less than one day as the long term average. Table 6.

Basic characteristics of mean daily maximum and minimum flows.

Site

B before

X (m's-'j CV CS X (m3s') CV CS

5330 0.26 0.80 887 0.17 -0.08

B after

1392 0.93 1.14 50 0.0 0.0

C before

5065 0.26 0.95 887 0.17 -0.04

Cafter

D

1257 0.97 1.22 50 0.0 0.0

3891 0.07 -3.28 748 0.20 -0.01

E after

5148 0.26 0.77 798 0.18 -0.01

The regime of floods will exhibit interesting changes between sections B and E, which are listed in Table 6 for mean daily maximum discharges. (Note the negative value of skewness in the channel, which results from the truncation of flows at the upper end.) Up to now the flood peaks have been decreasing downstream due to the attenuation effect of the flood plains (the mean annual flood decreases from 5370 to 5065 mV 1 ). In the future an increase from 5065 to 5148 mV 1 must be expected despite the water withdrawal from the reservoir. This is due to several reasons: (a) events with a peak lower than 4000 mV 1 will be routed through the diversion channel almost without attenuation; (b) a portion of events with peaks above 4000 mV 1 (ranging from 4000 to 5270 m3s"') will not be attenuated in the diversion channel. It will be superimposed on flows from the old river downstream from the confluence; (c) from the fact that the bankfull discharge of the old Danube is 3000 mV 1 , it follows, that flows with peaks between 4000 and 7000 m V will be routed without flooding the flood plains. In turn the design floods will increase, which makes changes in safety measures necessary to protect the most valuable agricultural land in the country. Table 7 shows estimates the magnitude of such increase based on simulated mean daily maximum flows and log normal distribution of peak flows . Changes in the low flow regime will be less pronounced, since in the typical low flow seasons (late summer and early autumn, whiter) the water requirements of the diverse users are minimal. As a result the mean minimum daily flow is expected to decrease by 89 mV 1 , while other parameters remain stable.

217 Table 7.

Predicted design floods for various probabilities

Site

10 1 0.1 0.01


% % % %

ofexceedance.

B before

C before

D

E after

7142 9 303 11 287 13 234

6 803 8 886 10 801 12 685

4000 4240 4890 5270

6 932 9 077 11054 13 000

CONCLUSIONS The main goal of this paper was to present a simulation methodology for quantitative predictions of effects of man-made interventions into river runoff. A combination of relatively simple concepts has proven to be capable of simulating relatively complicated hydrological and hydraulic conditions. A simulation run with data from 1987 combined with field measurements of water levels at the various sites showed an accuracy of the predicted levels within the limits of ten centimetres (Benetin & Soltesz 1990). The possibility of expressing conceptual model parameters in terms of hydraulic characteristics offers further potential in areas with no observations (except situations with backwater effects since the applied model types do not account for a downstream boundary condition). Simulation results have quantified the significant intervention into the natural hydrological regime between the intake and outlet of the diversion channel caused by the planned operation rules. The variability and skewness of flows at all investigated aggregation levels would dramatically increase coupled with a decrease in their range. Predicted parameter values are far beyond observed ones in our hydrological conditions. Truncation of flows would fully destroy seasonality. The overall influence on the river regime will be negligible downstream from the scheme (note however that mean daily flow will be composed of two peaks in non flood situations) with the exception of the interesting new flood regime. The predicted increase of design floods will make the réévaluation of existing and planned safety measures necessary. This along with the results of follow up studies are clearly indicating the need for new operation strategies which would allow for conservation of nature at the cost of energy production. The presented methodology allows testing of the effects of new scenarios on the river regime.

REFERENCES Becker, A., Kundzewicz, Z. (1987) Nonlinear flood routing with multilinear models. Water Resour. Res. 23 (6), 1043-1048. Benetin, J.,Soitesz, A. (1990) Metoda prognozovania vodneho rezimu pod v pririecnom pasme (Prediction of the soil water regime in a river zone.) Vodohospodarsky casopis 38 (6) in print. Kundzewicz, Z. (1984) Multilinear flood routing. Acta Geophysica Polonica 32 (4), 419-445.

/. Szolgay

218

Szollosi-Nagy, A. (1982) The discretization of the continuous linear cascade by means of state space analysis. / Hydrol. 58 ,223-236. Szolgay, J. (1982) Prispevok k diskretizacii spojitych linearnych modelov transformacie povodnovej vlny (On the discretization of linear continuous flood routing models). Vodohospodarsky casopis 30 (2), 141-154. Szolgay, J. (1990) Zmeny vybranych charakteristik hydrologickeho rezimu opusteneho koryta Dunaja vplyvom planovanej prevadzky VD Gabcikovo (Prediction of river runoff changes due to hydropower station operation in the old Danube). Vodohospodarsky Casopis 38 (2),148-164.


THE "ACRU" MODELLING SYSTEM FOR LARGE CATCHMENT WATER RESOURCES MANAGEMENT

K. C. TARBOTON & R. E. SCHULZE Department of Agricultural Engineering, University of Natal, Pietermaritzburg, South Africa

ABSTRACT The ACRU agrohydrological modelling system was applied to the critical runoff producing Midmar subcatchment of the Mgeni river basin, which supports the water needs of more than three million people and supplies water to industry and agriculture producing 20% of South Africa's Gross National Product. Projections that the water resources of the Mgeni will be completely utilized by the year 2005, together with increasing urban, industrial and agricultural development make management of the Mgeni's water resources imperative. Verification of the ACRU modelling system on gauged subcatchments within the Midmar catchment is presented, then two scenarios are used to assess the impacts of agricultural development, in the form of increased afforestation and the proliferation of farm dams, on the catchment water resources. The potential of ACRU for use by managers and planners in the reconciliation of increasing and varied demands on limited water resources is illustrated in its ability to assess objectively the impacts of potential development scenarios.

INTRODUCTION The 4387 km2 Mgeni catchment located on the east coast of South Africa (Fig. 1) supplies water to 3.6 million people and supports industry and agriculture producing 20% of South Africa's Gross National Product. Unique problems faced in this catchment include exceedingly rapid urban and agricultural development, water resources which are close to being completely utilized and a lack of knowledge of how development and land cover changes impact water resources. It has been predicted that the population in the area presently supplied could increase to between 9 and 12 million by the year 2025 (Home Glasson Partners, 1989) and that the water resources of the Mgeni catchment will be fully utilized by the year 2005. Competition for water from the increasing population concomitant with increased agricultural, urban and industrial development make effective water resource management within the Mgeni catchment imperative. 219

K. C. Tarboton & R. E. Schube

Fig. 1.

220

Mgeni Catchment Location.

Agricultural development, predominantly in the upper reaches of the Mgeni catchment, impacts the water available to the large urban supply reservoirs while urban development, predominantly in the lower reaches of the catchment, increases the demand on the reservoirs. In this paper the Agricultural Catchments Research Unit (ACRU) modelling system is applied to the upper reaches of the Mgeni catchment in order to assess impacts of likely agricultural developments on water resources. Information obtained from hydrological simulation provides water resources managers with objective answers on how agricultural development, in the form of land cover changes, impacts water resources, thereby enhancing their ability to manage catchment water resources. Developed in the Department of Agricultural Engineering at the University of Natal, Pietermaritzburg, South Africa, ACRU is a multipurpose daily soil water budgeting model capable of simulating runoff, reservoir storages, sediment yield, irrigation demand and supply, land use impacts and yields for various crops. ACRU is a physically based conceptual modelling system that idealizes and conceptualizes physical processes in the sequences that they would occur in nature. The ability of ACRU to evaluate the influence of the above broad range of processes on catchment response makes it an effective aid for decision-making when assessing various impacts on water resources. As a pilot study to simulating water resources throughout the Mgeni catchment, the catchment upstream of the Midmar dam (Fig.l) was chosen for investigation since a substantial portion of the mean annual runoff (MAR) of the entire Mgeni catchment is generated within this area and, being subject to little industrial development it provides good quality water (Breen, Akhurst & Walmsley, 1985). Agricultural development, however, in the form of land cover

221

The "ACRU" modelling system

changes and the construction of numerous small farm dams continues. It was shown by Maaren & Moolman (1985) that there was a significant reduction in streamflow with time due to the construction of small reservoirs within the catchment, with this effect being accentuated during dry years. A further practice causing land cover change is the afforestation of land previously uncultivated or under annual crops. In South Africa the Forestry Council in its Strategic Forestry Development Plan (1989) called for an increase in the area under forestry by some 380 km2.year'1 from 1990 until the year 2010. This implies that there is likely to be a doubling of the present area under forestry over the next 20 years, much of it in catchments such as Midmar with a MAP generally exceeding 850 mm. In this paper the ACRU agrohydrological modelling system is evaluated for its ability to simulate streamflow from the rural catchments upstream of the Midmar dam in the Mgeni catchment and used to assess the impacts of increased afforestation and the proliferation of farm dams on the catchment water resources. Information on present land cover and the number of farm dams within the Midmar catchment is given and the effect on water resources that these dams have already had, is shown by simulating the potential streamflow without the dams. Implications of land use change in the form of increased afforestation are shown by investigating the local and regional effects on streamflow, of a doubling of the area under forestry in the Midmar catchment.

ACRU MODELLING SYSTEM Structure The ACRU agrohydrological modelling system (Schulze 1989) is structured on daily multi-layer soil water budgeting (Fig.2). Rainfall and/or irrigation not abstracted as interception by the vegetation canopy or stored on the surface is partitioned into stormflow and effective rainfall that enters the topsoil horizon. Saturated drainage from the topsoil to subsoil horizon takes place when soil water in the topsoil exceeds field capacity. Similarly, when the soil water in the subsoil horizon exceeds field capacity, drainage to the intermediate and groundwater stores occurs, from which baseflow is generated. Unsaturated soil water redistribution between the two horizons occurs according to their relative soil water contents. Runoff comprises stormflow, in the form of quickflow, delayed stormflow and baseflow. Total evaporation takes place simultaneously from previously intercepted water as well as from the soil horizons in the form of soil evaporation and plant transpiration, depending on plant growth stage, root distributions and horizon water contents. The nature of this model implies that the model is not a parameters fitting or optimizing model but parameters are replaced by variables estimated entirely from physical features of the catchment. ACRU has been designed as a multilevel model with multiple options available in many of its routines depending on the level of sophistication of available input data or the type of output required. An important option in areas of complex land cover and soils is that ACRU can operate either at a point or as a lumped or as a semi-distributed cell type model.

K. C. Tarboton & R. E. Schulze

Fig. 2.

222

General structure of the ACRU agrohydrological modelling system.

In distributed mode each subcatchment can generate individually requested and different output. To facilitate land cover or management changes over time, be they gradual changes such as forest growth or expanding urbanization, or abrupt changes such as clear-felling or reservoir construction, a dynamic time series dependent input option is available.

Model input Model input includes information on catchment location/position, daily rainfall, evaporation, details on soils, vegetation/land cover, irrigation (including scheduling) and reservoir dimensions. To alleviate the problem of simulating either

223


distributed or complex catchments requiring extensive input information, inputs to ACRU are by way of an expert system type program-interface called the menubuilder, which leads the user interactively through alternative decision paths. Decision support is by means of interactive data and information input whereby the menubuilder prompts the user for information, gives instructive explanation via a help facility and supplies default values where required. For example, soil input decision support offers two levels of sophistication. At the lower level, only soil texture and depth need be estimated with relevant hydrological soil properties being read from default decision support tables while at the higher level, porosity, field capacity, wilting point and drainage response rates between soil layers as well as texture and depth would be requested as inputs for each layer. Similar decision support at two levels of sophistication is also available for other inputs. A series of screening tests and error traps check input for validity and acceptable range. Depending on the severity of the input error either a warning or an error massage is issued requesting re-entry of the information.

Model output Output options at daily, monthly or annual levels include simulation of the hydrological soil water budget, runoff, reservour status, sediment yield, crop yield and irrigation demand and supply. The soil water budget output shows rainfall, interception, effective rainfall, soil water contents in each layer and fluxes between layers. Runoff components for example, which can be output include daily storm and basefiows, design flows, peak discharges with an option for extreme value distribution analysis. Graphical output options are available for some of these components. Reservoir status output includes details on overflows, seepage, abstractions and inter-basin transfers. Crop yield models incorporated in ACRU at present include maize, wheat, sugarcane and primary productivity models. Irrigation output includes details on scheduling methods, irrigation requirements and losses. When observed data are available a statistical summary can be requested to compare observed and simulated values for various outputs including streamflow, total evaporation and soil water status.

APPLICATION OF ACRU TO MIDMAR CATCHMENT The ACRU modelling system was applied to initially the 912 km2 Midmar catchment, disaggregated into 19 subcatchments as shown in Fig. 3 to obtain a base simulation using present land cover information and to evaluate its performance in terms of simulated and observed streamflow and the correlation between these values. The impacts of agricultural developments were then evaluated using two development scenarios. To assess the impacts of farm dams on water resources all present reservoirs upstream of the Midmar dam were hypothetically removed for simulation under scenario 1. Areas under irrigation remained unchanged but irrigation was extracted from streamflow rather than from reservoirs.

K. C. Tarboton & R. E. Schulze

Fig. 3.

224

Midmar suhcatchment discretization and system layout.

Impacts of increased afforestation were assessed in scenario 2 by assuming that the subcatchments 1, 6, 8 and 12 were completely afforested to Eucalyptus grandis except for the wetlands and areas under reservoirs. This was equivalent to an increase in afforestation of 11 881 ha over and above the existing 11 845 ha of forestry within the Midmar catchment, or a 100% increase (i.e. a doubling) in afforestation. Daily rainfall from 6 stations was used with a subcatchment rainfall adjustment factor based on its median annual rainfall compared with that of the rainfall station. Present physiographic, land cover, reservoir and irrigation information used as inputs for the base simulation are shown for each subcatchment in Table 1. Within the land use groups represented in Table 1, grassland was subdivided into different categories according to its grazing condition, with different hydrological properties of interception, leaf area index, and root distribution for each category. Similarly forestry was subdivided into indigenous forest and the major commercially grown species, Eucalyptus grandis, Pinus patula and Acacia mearnsii. Streamflow gauging weirs at the outlets of subcatchments 6 and 12, namely weirs U2H007 and U2H013 respectively, with observed daily streamflow data enabled comparison of observed with simulated streamflow data.

RESULTS AND DISCUSSION Nomenclature In considering the results the following nomenclature was used. Streamflow refers to the total water flowing out of a particular subcatchment, and consists of runoff generated from that subcatchment plus overflow, seepage and controlled discharge from any reservoirs which may be found within the subcatchment plus

225 Table 1.


Pysiographic, rainfall and present land cover information for the Midmar catchment.

Subcatchment Median Mean Land cover Number Area annual altitude Grassland Wetland Forestry Dryland Irrigated Urban rainfall agric. agric. (km2) (mm) (m.a.s.l) (%) (%) (%) (%) (%) (%)

Dams

(%)

No

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

38 111 61 53 55 29 30 40 29 86 93 25 31 27 46 57 29 16 56

1031 928 947 1026 966 967 891 887 981 1016 1011 957 1016 966 932 903 839 842 841

1151 1454 1378 1254 1409 1214 1848 1779 1712 1530 1430 1207 1287 1142 1150 1298 1279 1236 1089

64 67 64 45 63 70 81 64 85 81 72 67 50 38 48 67 50 73 54

0 1 0 0 0 0 14 6 0 0 0 0 0 7 0 0 0 0 0

9 9 9 32 20 9 0 1 1 11 24 10 21 30 7 18 15 7 5

6 9 12 12 15 16 0 15 6 5 2 18 21 16 34 11 4 0 30

18 13 13 73 1 4 0 12 4 2 2 5 7 8 11 4 12 0 6

0 0 1 3 0 0 0 0 0 0 0 0 0 0 >.5 >.5 19 20 5

3 1 1 1 1 1 5 2 4 1 >.5 >.5 1 1 >.5 >.5 >.5 0 >.5

29 56 55 44 40 22 13 6 16 42 19 3 15 23 24 9 12 1 30

Total

912

952

1328

64

1

13

12

7

2

1

459

the streamiiow contribution of all upstream subcatchments. Runoff refers to the water produced from a particular subcatchment and consists of stormflow and baseflow. When there are reservoirs within a subcatchment a portion or all of the runoff (depending on the spatial location of reservoirs within the subcatchment), is routed through the reservoirs before it contributes towards the streamflow.

Base simulation evaluation Selected statistics for daily and monthly streamflow simulation over the 16 years from 1971 to 1986 using present land cover information are shown in Table 2. At both gauging weirs U2H007 and U2H013 the total simulated streamflow was 11% less than the observed streamflow for the simulation period. Totals of monthly and daily observed and simulated streamflows for U2H013 are different because of the way the model handles missing data. If observed streamflow data are missing for a particular day, that day is omitted from the statistical analysis for purposes of assessing daily model performance while the entire month is omitted from the monthly analysis of performance. Good correlation between observed and simulated streamflows is shown by the high correlation coefficients (Table 2) for both daily and monthly simulations at both gauging points. Accor-

226

K. C. Tarboton & R. E. Schulze Table 2.

Satistics of performance of ACRU for daily and monthly totals of daily streamflow simulation.

Statistic

Total observed streamflow (mm) Total simulated streamflow (mm) Mean observed streamflow (mm) Mean simulated streamflow (mm) Correlation coefficient Students' 't' value Regression coefficient Base constant for regression (mm) Variance of observed values (mm) Variance of simulated values (mm) Coefficient of determination Coefficient of efficiency

Daily simulation U2H007 U2H013

2461.08 2170.71 0.42 0.37 0.75 87.21 0.64 0.10 0.59 0.42 0.57 0.55

3993.61 3561.47 0.68 0.61 0.76 87.89 0.83 0.05 1.17 1.38 0.57 0.96

Monthly simulation U2H007 U2H013

2461.08 2170.71 13.83 12.20 0.88 24.84 0.64 3.29 439.07 234.25 0.78 0.76

3945.97 3527.80 20.77 18.57 0.92 31.12 0.86 0.75 656.28 576.70 0.84 0.90

ding to Students' V test correlation is significant at the 99.5 percentile level in all cases. The coefficients of determination and efficiency show a slight systematic error in the underestimation of the streamflow at gauging weir U2H007 while no systematic error is detected at U2H013. Fig. 4(a) shows the close correlation between observed and simulated annual streamflows over the 16 year simulation period. Plots of daily observed and simulated streamflows at weir U2H013 for January 1986 and monthly values for the year 1985 are shown in Fig. 4 (b) and (c). The underestimation of observed streamflow, revealed in the statistics, can be seen in the daily plot which also shows the observed streamflow peak to lag the simulated streamflow peak indicating that simulated catchment response time is less than actual response time. Research to improve routing routines to rectify this effect is presently being undertaken. Use of current land cover data which reflects greater agricultural development and demands on water resources than in 1971 could be the cause for the underestimation in streamflow. By using the dynamic input file option which caters for changing land cover this problem could be rectified. It should be noted that ACRU is a not a parameter optimizing model and that input variables are changed only when there are physical reasons for doing so. This implies that the model is transferable to other catchments in which differences in variables are due to catchment specific physical characteristics. The potential of ACRU to assess water resources is shown in Fig. 4(d) by the frequency analysis, which shows percentiles of non-exceedance, of simulated streamflow into the Midmar dam. For example, the eightieth percentile represented by the line labelled 80 % shows the monthly streamflow into the Midmar dam that one could expect not to be exceeded 80% of the time or conversely to be exceeded on average once in every 5 years, for each month of the year. This

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241 Distributed parameter models for the impact of human disturbance TABLE 1. Model Performance Tests for the Amazon/Tocantins and Zambezi Rivers. K

cf

20.0 25.0 20.0 25.0 20.0

0.9 0.9 0.8 1.0 1.0

f

Index of Agreement,dM

Mean % Error

0.65 0.75 0.65 0.75 0.65

AMAZON 0.823 0.819 0.815 0.799 0.793

16 15 16 17 18

r

ZAMBEZI Precipitation Adjustment 25.0 0.6 0.75 25.0 0.8 0.75 25.0 0.7 0.75 20.0 0.6 0.65 25.0 0.9 0.75

0.817 0.814 0.813 0.810 0.809

45 45 46 45 47

0.811 0.805 0.801 0.800 0.799

47 47 48 50 47

0.816 0.816 0.816 0.814 0.812

46 44 45 45 45

Potential ET Adjustment 25.0 25.0 25.0 30.0 20.0

0.6 0.7 0.8 1.1 0.6

0.75 0.75 0.75 0.85 0.65

Water Capacity Adjustment 25.0 25.0 25.01 30.0 20.0

1.4 0.9 .2 0.9 1.1

0.75 0.65 0.75 0.75 0.65

the 20 to 25 range tabulated for the Amazon and within the theoretical range predicted by Vorôsmarty et al (1989). Furthermore, for both the precipitation and potential ET-adjusted scenarios, the flood initiation parameter (cf) is in nearly all cases below 1.0 and the flooding ratio (rf) is between 0.65 and 0.85. The best Amazonian analogues varied between 0.8 and 1.0 and between 0.65 to 0.75 for cf and rf, respectively. For adjusted PET, one of the top five parameter sets for the Zambezi (K = 25, cf = 0.9, rf = 75) appeared as well among the preferred parameter sets for the Amazon. The scenarios in which the available water capacity was adjusted showed a similarly small range in K values (20 to 30), but convey a different picture of floodplain inundation. Initiation of flooding appears delayed (cf from 0.9 to 1.4), although once flooding occurs, flooding ratios are similar to those of the other scenarios. In light of the consistent results from the precipitation and ET scenarios, and the rather large water capacity corrections required, it appears that less confidence should be placed in the results of this final scenario.

242

C. J. Vôrôsmarty et al.

MONTHLY DISCHARGE LIVINGSTONE a). P r e c i p i t a t i o n a d j u s t m e n t K = 2 5 . 0 / c f = 0.6/rf = 0.75

b). ET a d j u s t m e n t

c).Water capacity a d j u s t m e n t

K=25.0/cf =0.6/rf = 0.75

K=25.0/cf=1.4/rf =0.75

500

ITEZHI - TEZHI 1500

.A : /

< I m 2000

J F M A M J J A S O N D MONTH

Fig. 4.



Monthly timeseries predicted by linked WBM/WTM and corrected biophysical data sets at three discharge monitoring stations. Adjustments were applied at the subbasin level for: a), precipitation, b). évapotranspiration, and c). soil water capacity.

From these tests, it is reasonable to conclude that K values from 20 to 30, cf from 0.6 to 1.0, and rf from 0.65 to 0.85 can adequately simulate flow dynamics in both the Amazon and Zambezi river systems. The fluvial transport

243 Distributed parameter models for the impact of human disturbance parameters were cast to resemble parameters with clear physical analogues. Demonstrating a fairly narrow range in both river systems is an important finding since it confirms that the dynamics river systems follow general rules with respect to flow velocity, river geometry and discharge (Leopold et al 1964).

SUMMARY AND CONCLUSIONS In this paper we presented a simple water balance and fluvial transport model for the Zambezi River system in southeastern Africa. The model was used to examine key features of the catchment's surface hydrology including landscape runoff and river discharge. Observed river discharge was used to check water balance calculations. The unadjusted WBM produced large errors in regional runoff. Three sources of error were identified, associated with precipitation, potential évapotranspiration and available water capacity. The problems encountered in defining an accurate water budget highlight the difficulty in correctly characterizing even the most basic of climatic and biophysical variables in the Tropics. There is no substitute for accurate observed discharge when making such water balance estimates. With improvements in the water budget and clearly identified areas of floodplain inundation, timeseries of predicted flow showed a reasonable correspondence to observed data in the Zambezi River system. Results were compared to earlier work on the Amazon River. The optimal parameter sets identified for the Zambezi bore a clear resemblance to those of the Amazon/Tocantins River system. To the degree that the Zambezi and Amazon/Tocantins Rivers represent large tropical river systems, the parameter sets identified as part of this study may have wide application in the Tropics. This hypothesis assumes that errors in water balance are minimized and selective floodplain inundation can be ascertained. Obviously, more tests are required, but the capacity of similar parameters to successfully capture the dynamics of river flow in so disparate a set of river systems as the Amazon and the Zambezi is encouraging. Identifying universal parameter sets is also important because global assessments will require simulation of poorly-studied tropical rivers in which flow data are often incomplete or non-existent. Finally, it would be interesting to explore to what degree such parameter sets can successfully simulate rivers in other regions of the globe. This study has presented a calibrated model representing long-term climatic conditions, natural land cover and unregulated river flows. The challenges of optimizing contemporary water resources use in the Zambezi requires further work. There are a series of useful experiments that could be performed with the WBM/WTM to address the management implications of a hydrologically-altered drainage basin. Since the WBM/WTM is a distributed parameter model, scenarios could be cast which imbed the various disturbances within the simulated drainage basin. For climate change, inputs from the current generation of General Circulation Models (GCM's) could be gridded to 1/2 degree spatial scale and analyzed in the context of the WBM/WTM, for example under a 2X C0 2 atmosphere. For land use effects, contemporary and potential future distributions of agricultural and urban landscapes could be mapped and the

C. J. Vôrôsmarty et al.

244

resulting water balance and river flow alterations noted. For water resources management, key reservoir attributes and operating rules could be applied to specific grid locations to determine the historic and future impacts of river regulation. Performing such experiments becomes increasingly important as the hydrologie community strives to understand the complexities of global change in the coming decades.

ACKNOWLEDGEMENTS This work has been supported in part by the National Aeronautics and Space Administration, Grant NAGW-714, NAGW-1888 and Contract NA55-30558, and by the US Environmental Protection Agency, Grant No. CR816278010.

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