FIGURE 7 Maximum flooding in Viet Nam's Mekong Delta, 1994

Transcription

1 Figure 7 and Figure 8. The distribution of inflow and outflow in the Vietnamese Mekong Delta in the 1996's flood season is listed in Table 1 and shown in Figure 9. For four and a half months (16 July to 1 December) in 1996, a total volume of 352 thousand million m 3 entered into Viet Nam's Mekong Delta by two ways: through the Mekong and Bassac Rivers, 309 thousand million m 3 (88%); from the flooded areas in Cambodia, 43 thousand million m 3 (12%), in which 27 and 16 thousand million m 3 flowed into the Plain of Reeds and the Long Xuyen Quadrangle, respectively. Floodwaters enter the Plain of Reeds by two means: from the flooded area in Cambodia, 27.1 thousand million m 3 (87%); and from canals linked to the Mekong river such as the Hong Ngu, An Binh and Dong Tien, 4 thousand million m 3 (13%). Flood water in the Plain of Reeds drains out mainly into the Mekong River through many canals across national road No. 30 from An Phong to An Huu, 9.1 thousand million m 3 (29%) and across National Road No. 1 from An Huu to Long Dinli, 10.8 billion m 3 (34%). The remaining of 11.6 thousand million m 3 (37%) discharges into the two Vai Co rivers. FIGURE 7 Maximum flooding in Viet Nam's Mekong Delta, 1994

2 FIGURE 8 Maximum flooding in Viet Nam's Mekong Delta, 1996 FIGURE 9 Distribution of maximum discharge and total volume in the 1996 flood

3 TABLE flood season flow distribution in Vietnam's Mekong Delta (results from the model) Inflow/Outflow Inflow through Mekong and Bassac rivers Mekong at Tan Chau Bassac at Chau Doc Canals in Tu Thuong Inflow into Long Xuyen Quadrangle Across 7 bridges on Chau Doc-Tinh Bien road Through Tin Ve canal at Tinh Bien Through canals from Can Thao to Cai San Outflow from Long Xuyen Quadrangle Through canals from Ha Tien to Cai San Across Cai Sai road Inflow into Plain of Reeds Through canals from Hong Ngu to Tan Thanh Through canals from Cai Cai to Long Khot Through Hong Ngu, Dong Tien, An Binh canals Outflow from Plain of Reeds Across National Road No. 30 Across National Nighway No.1 Towards the East Vai Co River Through the West Vai Co River at Tan An Maximum flow Qmax (m 3 /s) Total volume W(10 6 m 3 ) Flood water enters the Long Xuyen quadrangle by three ways: from the flooded area in Cambodia across bridges along the Chau Doc Tinh Bien road, 11.6 thousand million m 3 (56%). through the Vinh Te canal, 4.1 thousand million m 3 (20%) from canals joining with the Hau river, 5.1 thousand million m 3 (24%). Flood water drains out the Long Xuyen Quadrangle mainly into the Gulf of Thailand thousand million m 3 (82%) - while only a small part flows into the South China Sea through the Cai San canal - or some 3.8 thousand million m 3 (18%). Sediment concentration in floodwater from Cambodia is lower than that in the mainstream. In the past few decades, the dredging of main canals and the expansion of secondary canals has had significant effect on irrigation and drainage. At the same time, such improvements also allow more floodwaters to flow into flood prone areas. Construction of new roads higher than flood levels also causes drainage problems. Consequently, earlier, higher and longer duration flooding is observed in the Plain of Reeds and the Long Xuyen Quadrangle. Flood control planning in the Mekong Delta Only short-term flood control in the Mekong Delta has been studied and presented in this paper. For long-term flood control, a comprehensive basinwide study with updated topographic and hydrological data at upstream locations as well as in the Mekong Deltais essential. Objectives of short-term flood control in the Mekong Delta The objectives of short-term flood control in the Mekong Delta are: to offer favourable conditions for economic development in order to achieve GDP growth rates of %, increase per caput income to US$ 900;

4 to achieve compulsory elementary education by the year 2000 and compulsory secondary education by the year 2010; to protect human life and property and to create favourable conditions for the redistribution of the population and labour force. Population in flood-prone areas is expected to exceed 10 million by the year 2000 and 12 million by the year 2010; to protect all infrastructure and create favourable conditions for further development. Transportation, communication and infrastructure development will be a focus. Existing roads will be upgraded and new roads will be built to realize a rural road network connecting all urban and densely populated areas. Navigation within each province and throughout the region will be improved. To give priority to protection of summer-autumn and winter-spring rice crops to meet increasing food demands and improve farmers income. Safe harvests will be ensured by strictly fixing planting calendars of summer-autumn and winter-spring crops. To protect hectares of orchard and annual industrial crops in shallowly flooded areas. About 18% to 20% of the total value of agricultural production is from fruit trees. To meet food requirements of 11 million tons from flood prone areas totalling million tons in the entire Mekong Delta by the year Annual growth of agricultural production is from 4.5% to 6.0%, while the value of non-rice crops is projected to rise from 22% in 1994 to 35 40% by To provide a suitable environment for supplying basic needs such as food, housing, and hygienic conditions. The environment of flood prone areas is to improve to a level sufficient to sustain rapid development. Flood control alternatives Figure 10 presents the short-term flood control planning in Viet Nam's Mekong Delta. In shallow flooded areas It is possible to apply small scale flood control measures for areas in the South of the Cai San, the Cai Tau Thuong and the Nguyen Van Tiep canals, and in the Tan Chau and the Cho Moi areas, with no negative effects on unprotected areas. However, it is necessary to improve draining capacities of all floodwater outlets. A system of embankments and culverts for full flood control will be built, primarily at secondary levels, to protect population centers, orchards, and annual industrial crops. In deeply flooded areas: For population protection: The rural population will be resettled into centres along roads at an elevation above flood levels. Raised floor or houses on piles will be built and public structures will be established in these population centers. In cities and urban centers, raised floors can be used or enclosed dikes to protect entire communities can be built. For protection of agricultural production: a. In the Plain of Reeds, the main strategy is to adapt to flooding conditions with restricted structural measures to stabilize existing double crop agriculture. Existing embankments and dikes will be reinforced to protect against floods until August for safe harvesting of the summer-autumn crop. Drainage outlets along National Highway 1 and National Road 30 will be enlarged to accelerate drainage from the Plain of Reeds into the Mekong River. b. In the Long Xuyen quadrangle, existing embankments will be strengthened to protect against floods until August as in the Plain of Reeds. Moreover, due to good drainage capacity toward the Gulf of Thailand, floodwater at the border can be diverted through the Vinh Te canal and the Tra Su-Tri Ton canal into the Gulf of Thailand. Such measures will reduce water depth in the Long Xuyen Quadrangle and allow water with higher sedimentation from the Bassac River to enter this area. A number of construction works are required: Sluices to replace seven bridges on the Chau Doc-Tinh Bien road. The Vinh Te Canal to be enlarged to appropriate size for drainage. Diversion weir to be built at the Vinh Te bridge.

5 The T3, T4, T5, and T6 canals to be enlarged to improve drainage capacity. Floodwater outlets, including culverts, canals, and sluices will be installed for protection against saltwater intrusion along the Western coastline. All outlets along National Road 80 and along the Long Xuyen-Co To Nui Sap and Mac Can Dung roads to be enlarged. FIGURE 10 Short term flood control planning in the Mekong Delta of Vietnam Boundary conditions used in the model for analysing the effects of the above flood control alternatives are the upstream discharge data of the 1961 flood (as flood condition for design), tidal data in 1994 (as unfavorable drainage conditions) and 10% probability of rainfall.

6 In two cases of flooding (with and without flood control alternatives), comparisons of the model outputs showed that: In shallow flooded areas, full flood controls can be implemented in combination with irrigation, drainage, acidity leaching, sediment trapping, etc., to accelerate development. In the Plain of Reeds, there are no significant changes in water level and discharge. In the Long Xuyen Quadrangle, floodwaters with poor sediment flowing into the central portion of the Long Xuyen Quadrangle will be reduced from 14.3 billion m 3 to 2.7 billion m 3 while water with high sediment from the Bassac River will rise from 7.7 billion m 3 to 15.1 billion m 3. Water level in the central part of the Long Xuyen Quadrangle will be reduced 0.20 to 0.35 m. Conclusions and recommendations Annually, an area of 1.2 million to 1.8 million hectares in the Mekong Delta is flooded from 2 to 6 months. Flood water causes severe damage in terms of the loss of human life, production and infrastructure. Therefore, flood control planning is urgent in the Mekong Delta. From studies carried out in recent years, it can be concluded that full flood control in shallow flooded areas do not cause significant change in water flow regimes and brings high economic returns. Diverting floodwater to the Gulf of Thailand cannot entirely solve the problem, but flooding timing and duration can be controlled to ensure agricultural production. During , flood control activities will focus on protection for population centers along canals and roads. Key roads will be constructed and all flood water outlets will be enlarged. Small scale experimental cases with full flood control will be established to study alternatives in construction, management and operation. Short-term flood control planning has been studied in combination with irrigation and drainage planning. However, several issues are still in question. Flooding in the Mekong Delta has both advantages and disadvantages. How can flood control planning make full use of the advantages and limit disadvantages? Full protection is the desired goal, but it may lead to some negative effects, for example: major changes in water levels and flow regimes in the whole Mekong Delta, hindering sediment trapping, acidity leaching, and reducing fish production. Therefore, close cooperation between riparian countries through the Mekong River Commission is necessary in flood control planning. Mathematical models have now been used to compute various alternatives for short-term flood control in the Mekong Delta. In the next stages, effort will be made to improve models as essential tools in flood control planning and forecasting. A monitoring network is necessary to observe changes in flow regimes and to provide early identification of negative effects to the environment for studying mitigation measures. In the coming years, the pursuit of long-term flood control planning is necessary for a comprehensive solution to flood control and drainage challanges in the whole Mekong Delta. Flood forecasting and river modelling of the Mekong Basin Introduction The Mekong is ranked among the largest rivers of the world. The river drains an area of approximately km 2, covering parts of China, Myanmar, Thailand, Laos, Cambodia and Viet Nam (Figure 1). At Kratie, close to the upstream part of the Mekong Delta, the average annual discharge equals 437 billion m 3 /s, or an average discharge of around m 3 /s. Downstream of Kratie, the river enters the extremely flat and low lying Mekong Delta. This paper addresses the topic of floods in this river and its tributaries. In the Mekong, the ratio between 10% low flows and 10% high flood discharge is approximately 50. Years with severe floods

7 were 1961, 1966, 1971, 1978, 1984, 1991 and Despite the high discharges, it is not common for Mekong River floods to cause casualties. The principal problem from floods is damage to crops and infrastructure. In 1995, for example, severe floods caused substantial damage in the Vientiane Plain of Laos. During that monsoon, an area of approximately ha was flooded resulting in a damage estimated at US$21 million. In view of the frequency of the floods, a good forecasting system is a necessity to improve the preparedness of the population to floods and to support evacuation plans. Since 1970 the Mekong Secretariat (now called the Mekong River Commission Secretariat, or MRCS) has operated a flood forecasting system for the Mekong River during the flood prone months from July to October. Over the past decades many dikes were built along the Mekong River, in particular along the borders with Thailand. Secondary effects of these dikes are the increase in downstream flood levels as a result of the reduction in flood plain storage, the faster propagation of floods along the river and impeded drainage of tributaries, causing local floods. However, there are also other factors contributing to a reduction of flood levels. In the Mekong Basin many reservoirs have been built or are under construction, which store water from the rainy season for use during the dry season, either for hydro-electric power production and/or for irrigation water supply. Incidentally, such reservoirs may have a negative impact on flood levels as a result of changing lag times between peaks or the delay in conveyance of water from the watersheds. Adri Verwey, River Modelling Specialist, WL/Delft Hydraulics, Netherlands Mathematical simulation models can be very instrumental in evaluating the effects of reservoirs and their operation on the Mekong River floods. Flood forecasting models, in general, are of great help in improving the operation of reservoirs and avoiding unnecessary spilling of water. Mathematical models can also lead to an improved understanding of the flood phenomena and provide insight into the causes of flooding. In this manner, more appropriate measures can be taken to reduce flood damage. As an example, one might look at a country like Bangladesh, where in 1986 UNDP and the World Bank supported the creation of the Surface Water Simulation Modelling Centre (SWSMC). Currently this centre has a staff of 42 and is in charge of flood forecasting and flood control modelling for the country. At SWSMC flood forecasts are produced at 32 stations spread over the whole country. Many of the simulations made relate to the design of controlled flooding systems. The simulation models used and their supporting techniques have improved substantially over the past years. One important factor to this is the increase in computer speed and memory capacity. As a spinoff of this development, also many new technologies have emerged, which open up many new possibilities in flood modelling and in land and water development projects more in general.

8 FIGURE 1 Basin of Lower Mekong River

9 The MRCS flood forecasting centre Flood forecasts at MRCS are prepared on weekdays during the months July to October. Data are received from 22 rainfall stations and from 37 hydrologic stations between and hours daily. Water level forecasts are produced for the stations Chiang Saen, Luang Prabang, Chiang Khan, Vientiane, Nong Khai, Nakhon Phanom, Thakhek, Savannakhet, Mukdahan, Pakse and Kratie and are sent to the member countries by fax around midday. In alert situations the forecasts are also produced during the weekends. An example of the report is shown in Figure 2. Data received are transmitted via Fixed Frequency Radio Transmission. Apparently this system is quite frequently out of order at some stations. However, within the Improvement of Hydro-Met Project, funded by the Governments of Japan and Australia, the number of stations is being extended and/or rehabilitated. The improved system will still be based upon radio transmission of data. Flood forecasting at MRCS is based upon a SSARR model calibrated in It comprises the Mekong from Chiang Saen at Thailand's border with Myanmar to Kratie in Cambodia. The model consists of eight principal reaches, each of which has a number of watershed models attached to the nodes. Some of these watershed models also have channel routing components. The schematization is shown in Figure 3. At MRCS the probable rain depths are estimated from information received from the Thai Department of Meteorology. This information includes current rainfall data at ground stations and their forecasts. These forecasts are also based upon weather charts and ground radar imageries. FIGURE 2 Example of flood forecast form Mekong River Commission Kasatsuk Bridge, Rama 1 Road, BangKok 10330, Thailand Tel: (66-2) Fax: (66-2) mrc@mozart.inet.co.th To: Cambodia (855-23) , Lao PDR (856-21) , Thailand or , and Viet Nam (844) National Mekong Committee. Nongkhai (042) , UBon (045) and Mukdahan (042) Hydrology Centres From: Hydrology Unit, HRD and Environment Division, MRC Secretariat Subject Flood Forecast in 1997 Date: Tuesday, 05 August 1997 At Pakse, the water level is going down below the flood stage. ALERT: At Kratie and Kompong Cham, the water levels keep a firm rising and are approaching to the maximum levels of the last year (Kratie; 23.00m on Sep. 29. Kompong Cham 16.11m on Sep. 29). We should be careful of the flood wave observed on August 3 4 which is coming down to Kratie within a couple of days, and the contribution from the Sekong, SeSan and Sre-Pok basin. LOCATION Distance from the sea (km) Observd Rainfall (mm) Zero Gauge above MSL (m)

10 Flood Stage (m) Observed G.H.(m) Forecast Gauge Height (m) 06 Aug 07 Aug 08 Aug 09 Aug 10 Aug Chiang Saen 2, Luang Prabang 2,010 NR Chiang Khan 1, Vientiane 1, Nong Khai 1, Paksane

11 1, Nakhon Phanom 1, Thakhek 1, Savannakhet 1, Mukdahan 1, Ubon NR Khong Chiam

12 Pakse Strung Treng 668 n/a n/a Kratie 545 NR Kompong Cham 410 NR Phnom Penh (Bassac) NR P Penh Port (Tonle Sap) 325 n/a Phnom Penh (Mekong) 332 n/a n/a Tan Chau 220 n/a 0.000

13 4.20 n/a Chau Doc 200 n/a n/a Charge: /93/JPN/Line 53 Drafted: Tien/Manoroth Concurred: Tanaka Approved: Sok The modellers who estimate rainfall data during the lead period of the forecast also use 10-day forecasts based upon the Global Numerical Meteorological Model for reference. Data of further refined models are available from the Japan Meteorological Agency. Results of their Operational Numerical Weather Prediction Models cover the Mekong Basin in more detail and forecasts are made available through the internet in the form of bit maps. Expected daily rain depths are shown in eight classes on a logarithmic scale. Based upon experience, corrections for topographical deviations from the forecasted rain depths are entered into the average catchment rain depths provided to the SSARR model. As the rain infiltration processes were calibrated on the basis of 6-hourly rain depths, the daily depths are distributed over the day with assigned probabilistic weights of 0.2, 0.4, 0.3 and 0.1 respectively. Each forecast is based upon computations started four days ahead of the actual time of simulation. The simulation is initiated with measured discharges, overwriting the computed ones. Soil moisture data are maintained, which implies that the rainfall-runoff models are not being updated. Operation of the models is still based upon the manual editing of data in ASCII files. Data follow the old Fortran convention of formatted data input, which requires very careful checking of the position of digits and causes an unnecessary risk of mistakes. The models are still the same as those calibrated in the seventies. However, corrections are made for systematic errors in catchment runoffs as these have been determined during the years over which the model has been in operation. Model results are analysed carefully before issuing the forecasts. Computed discharges are converted into water levels via the known stage-discharge curves. Consistency is obtained with these data through the input of initial discharges converted from water levels by means of the same rating curves. Despite all these measures, the quality of the forecasts is not high. Although the one-day forecasts appear to produce acceptable results, the five-day forecasts at some stations give peak watér levels which are sometimes out by a half to one metre.

14 FIGURE 3 Schematization of the flood forecasting model The hydrodynamic model of the mekong In 1988 Delft Hydraulics was commissioned to conduct a study titled Scientific and Technical Assistance for Hydro-Meteorlogy and Mathematical Modelling with the following objectives: optimization of the hydro-meteorological network of the Lower Mekong Basin; implementation of a database management and data processing system; and development of a Master Model of the Lower Mekong River for simulation of flow and salinity intrusion. The resulting Master Model is a 1-D mathematical flow model of the Mekong River from Chiang Saen to the sea. The model was developed with the objective of becoming a key instrument for planning, analysis and design in the Mekong River Basin. In particular, it enables studies on the effects of natural and man-made interference's in the river basin. The Master Model was developed on the basis of Delft Hydraulic's WENDY package (further developed since into the software package called SOBEK).

15 The Master Model consists of three parts: the River Model for flow simulation in the reach Chiang Saen to Pakse; the Delta Tidal Model for flow and salinity intrusion simulation in the reach of the river from Phnom Penh to the sea; and the Delta Flood Model, covering the reach from Pakse to the sea. FIGURE 4 Verification of water levels simulated with WENDY at Mukdahan Despite the shortcomings of the maps providing topographic data in the flood plains, the models were caliberated satisfactorily. An example of the fit of water levels for a flood wave passing at Mukdahan is shown in Figure 4. In view of the fact that the calibration of this model focused on the fitting of discharges, the differences between computed and measured water levels are acceptable. At the time of the model development, there were still some problems in improving the quality of the hydrodynamic models. The developers of the Mekong River Model conclude that an accurate model development is hampered by: large changes in the discharge rating curves from year to year, leading to considerable deviations of actual ratings from the average rating curve applied to calibrate the model; and lack of data from tributaries, with only some 60 % of the catchment area between the model limits gauged.

16 However, since these observations were made the scope for further improvement of the models looks better. Since the calibration of the models, more reliable data have become available. River crosssections have been monitored through a FINADA sponsored river survey project. The cross-sections have been stored in a database and can be linked to the Mekong Master Model. The availability of discharge data from tributaries has improved since the start of the Hydro-Met Project. In addition, there is a considerable scope for further improvement as a result of emerging technologies, as discussed in the sequel. Emerging technologies Over various decades computer speed and storage capacities are increasing by 50% yearly or a factor of more than 50 over each decade. This simply means that what a computer does now in an hour, will be done in a minute ten years from now. Over twenty years, or half the professional lifetime of an engineer, the work done in one hour is reduced to one second only. There is no indication that there will be a slow down of this trend. Computer storage follows a similar trend. Whereas the PC had an internal memory of 640 Kb 10 years ago, it now has 32 Mb internal memory. This results in the development of technologies, which were unheard of or just in experimental phase 10 years ago. DGPS technology One of these areas of progress is the collection of topographic data. The combination of satellite technology and fast computer processing speed has opened up new possibilities for collecting flood plain levels on the basis of differential GPS systems (DGPS). The combination of laser beam scanning applied from a helicopter flying at approximately 100 m altitude, together with a DGPS in real-timekinetic-on-the-flight mode, has delivered digital terrain levels of flood plains in The Netherlands with an accuracy of 0.5 m. The laser altimetry method can also be applied from small planes flying at a m altitude. These planes can move at speeds of km/hour in order to allow a correct registration of their position. In one go, scans are made of a track of 400 m width. This implies the scanning of more than 100 km 2 during one hour. The number of points scanned is approximately 600 per ha. The accuracy of the vertical levels on the maps produced is 5 to 10 cm if powerful postprocessing software is used. In the scans, vegetation can be separated from the ground level, if the vegetation is somewhat permeable. Trees, for examples, are recognized and can be filtered from the surface level. The problem with paddy fields would be the somewhat unknown depth of water on various plots, as the laser beams would pass the vegetation, but are reflected at the water surface. Sampling at ground level would allow the removal of the systematic error, thus leaving only the standard deviation resulting from the variations in water depths at the fields. Technically the method is more or less proven technology. It is expected that by the year 2000 the complete area of the Netherlands has been resurveyed this way. However, the method is still rather costly at prices charged having an order of magnitude of US$5/ha. This is more than the unit price charged in Laos for conventional surveying. It is expected that these prices will go down as the initial investment costs are being recovered, possibly to levels of US$ 1 2/ha. The data collected can easily be processed in the form of a digital terrain model, which has big advantages both for modelling and for the general process of land and water development. The potential of this method is the possibility of collecting highly accurate information on flood plain topographies. The potential for model calibration is in the possibility to scan water levels along the river during a flood period and receive an accurate picture of water level variations all along the Mekong River. In other words, it is expected that this methodology will substantially improve the quality of hydrodynamic channel flow models, both in terms of the description of the topography and in terms of the calibration of the models.

17 FIGURE 5 Schematization of a biological neuron Artificial neural networks For extracting information from observed patterns new methodologies have come up with the further development of computational speed. Data mining techniques, such as the artificial neural networks (ANNs) enable the recognition of patterns which link the various sources of data. Contary to multiple regression techniques, the ANNs do not require prescribed functional relationships as input. The networks contain the flexibility to create both relations and their parameters as an integrated set of data. The idea stems from the way neurons function within the brains (Figure 5). These bio-logical neurons receive signals and pass these on to other neurons either as amplified or as dampened signals. This process is simulated by the simplified scheme shown by Figure 6, with amplification functions possibly defined by a sigmoid or logistic threshold function (Figure 7). Through this schematization it is possible to define quite non-linear processes. The potential of this technology has been proven in fields as different as hand written character recognition to stock exchange pattern recognition. In the fields of hydraulics and hydrology it has been applied to areas as diverse as rainfall-runoff modelling (Figure 8), to mathematical model emulation in system optimization, as well as to the establishment of rating curves in areas with backwaters.

18 FIGURE 6 Example of the structure of an ANN FIGURE 7 The sigmoid or logistic threshold function

19 FIGURE 8 Example of rainfall-runoff results produced with an ANN In practical use, however, some observations have to be made. In the first place it is evident that the method only works if one tries to connect input signals to output signals, which also in the physical system show a clear dependence. For example, in a river catchment the level of the groundwater table is not just dependent on the current rainfall (input signal), but also to the antecedent rainfall. For this reason it is clear that either antecedent rainfall data have to be given as input signals, or the current groundwater level has to be entered through regressive definitions. In the second place it has to be stated that the development of the ANN goes through a calibration or training phase, just as the brains need some time to process information on what goes on around us and learn from it. However, whereas the intuitive brains are able to think beyond the limits of what has been learnt, the ANNs (so far) are not able to extrapolate and any attempt to do so is punished in the form of the likelihood to produce nonsense. In principle, this danger of extrapolation is much similar to the extrapolation of fitted curves, such as, for example, traditionally established rating curves used in hydrology. The conclusion on this technology is that it opens up many interesting possibilities in the field of flood problems, reservoir operation, water balance computations, rainfall forecasts and many others. The technology is extremely powerful under the condition that it is used with a lot of common sense. Hydrodynamic flow modelling in rivers The numerical description of river flow was developed in the 1970s and the 1980s and has been improved since primarily in terms of numerical robustness. This is of particular importance in flood forecasting, as one is dealing with extreme flow conditions. If a model suffers from numerical problems, it is exactly here that the risk of failure of simulations is highest. For this reason, robustness is a property that in the selection of numerical models for flood wave propagation simulation should get a very high priority. Improvements also stem from technological advances in other areas, such as data collection and emulation techniques. The progress in the applicability of hydrodynamic models lies mainly in the

20 progress of computer speed. In Vietnam, for example, nowadays large, detailed models of the Delta are run frequently to study salt intrusion in relation to various irrigation options, drainage problems, including the comparison of various alternatives etc. For optimization of systems, hydrodynamic models are currently only used in trial and error approaches. If many simulations are required, such as for on-line control of hydraulic systems, emulation techniques replacing the hydrodynamic models with, are being used. In such case, the ANN is trained on the basis of a selected number of simulations with an accurate hydrodynamic model. After this training, the ANN is applied to study a large number of alternatives and to compare the functioning of these. Here, again, it has to be stated that in such processes the ANN should not be used in extrapolation mode. In other words, it should not be used for cases for which it has not been trained. Potential improvement in reliability of flood forecasts The reliability of forecasts can be increased in various ways, such as: the improvement of rainfall forecasts; improved catchment modelling; improved channel routing; and improved model updating techniques. In addition, the current possibilities of user interfaces, data bases and GIS systems provide substantial scope for improvements in handling data entry and dissemination of the forecasts. More reliable forecasts are possible in the first place by improving the rainfall forecasts. For given meteorological conditions, rainfall forecasts can be made on the basis of various types of measurements, such as areal rainfall distributions, atmospheric pressure distributions, wind directions and vapour content. Radar measurements are useful, as well as satellite images. The problem is in making this information available at the forecasting centre and in extracting the correct information from such data. Another and more accessible source of data for precipitation forecasting is the weather maps. MRCS has recently introduced the practice of using the catchment rainfall from the areal rainfall forecasts produced by the Global Numerical Meteorological Model as a reference in rainfall forecasting. This method could be improved further through the calibration of which would establish relationships between catchment integrated rainfall from the weather forecast bit maps and the resulting catchment runoff. This approach is expected to replace the need for a much denser rain gauge network and its associated transmission system in the Mekong Basin This is particularly useful, as the installation of more rainfall gauges is not very practical in remote catchments in mountainous areas of the Mekong Basin. Any approach to flood forecasting which minimizes the need for ground stations should be given favourable consideration. Another improvement is based upon a re-calibration and possible replacement of the rainfall-runoff models for the Mekong subcatchments. Currently, the forecasting system uses eight subcatchments, for which rainfall-runoff simulation is made. This should be extended to the development of rainfallrunoff models for each individual main tributary, as was already attempted at the beginning of the eighties. Besides the SSARR model, a variety of other rainfall runoff models would be suitable, such as the Sacramento model, tank models etc. The upgrade of the forecasting system should include extension and re-calibration of sub-catchment models, based upon information collected at MRCS during the past decade. Further improvements are possible by replacing the SSARR channel routing model by a full hydrodynamic model. A hydrodynamic model is the only tool enabling flood forecasting in the flat areas of Cambodia and Vietnam. The calibration of the WENDY model, as part of the Mekong Master Model project finalized in 1991 has proven that such model can be developed with sufficient accuracy for the Mekong River, despite the shortcomings in accuracy of topographical data. The lack of

21 accuracy, in this case, was substituted with knowledge on the flood deformation characteristics and their relation to channel cross-section parameters. As discussed, there is now a good scope for further improvement of the hydrodynamic models. It is quite unfortunate that so far the hydrodynamic model was never incorporated into the forecasting system. A clear advantage of incorporating the existing hydrodynamic model in the forecasting system is the readily available possibility to extend the forecasting system to locations in Cambodia and Viet Nam as it includes the Tonle Sap River, the Great Lake and the main branches in the Mekong Delta. The principal reason to separate the model parts during their development in the period , at least the model parts 1 and 3, has been the lack of computational speed at that time. The various components were running on PC's with an Intel 386 processor. With the currently available Pentium processors combined models would be feasible and the forecasting system could easily be extended on the basis of one single model from Chiang Saen to the sea. A last element in improving the flood forecasts is an updating procedure, which handles uncertainties in input data. Currently, the updating is based upon a simple replacement of computed river discharges by measured ones in case of differences between both data sources. However, such procedure does not update the state of catchment storage and this is a deficiency that may contribute significantly to errors in forecasts. It is recommended to replace the updating method by a scientifically sounder approach, such as Kalman filtering. Capacity building at MRCS HU In 1994 a Mekong Hydrological Programme Review Mission (HRM) evaluated the Mekong Hydrology Programme (MHP) seeking donor assistance for the execution of various projects. The outcome was the recommendation to give priority to institutional strengthening of the Mekong Secretariat, both through capacity building and through the development of support software. After the signing of the new agreement on continued co-operation on the Mekong in 1995 and the formation of MRC, the recommendations were reviewed again in 1997 in the light of the new MRC mandates. This review was made by Prof. Bogardi, who also headed the 1994 HRM. The outcome was a revised report with a recommendation to GON to fund a project with institutional strengthening of MRCS and human resources development as the principal objectives, together with the recommendation to start the MHMP programme as a slightly modified and updated version of the HRM proposal of The MHMP programme proposed envisages the development of a framework within which various software packages already available at MRCS, or packages that will be acquired, are to be incorporated and connected in a consistent manner. The recommendations are a recognition of the need to develop an integrated set of tools, instead of the bits and pieces of software installed at MRCS until now. However, it would be advisable to combine such programme with well defined consultancy targets of the staff of MRCS. As an example, as part of the proposed institutional strengthening it would be advisable to upgrade the current forecasting system. Particularly useful elements of such a programme are on-the-job training programmes, where staff of MRCS works with a variety of specialists in various topics related to data collection, data storage and retrieval, data processing, flood forecasting, flood control, river morphology, environmental management, water resources management and many other. The on-the-job training must be a well planned part of the project and should be complemented by short seminars given by the visiting specialists prior to the start of the implementation work. Floods in subcatchments: example of the Vientiane Plain Laos is a mountainous country with a land area of km 2 and a population of nearly 5 million. Over 80% of the population lives in rural areas, with rice production as the principal source of income.

22 Only approximately 9% of the country is suitable for agricultural production. As this limitation puts much strain on the population living in the mountainous areas, the practice of slash-and-burn is increasing, with a decreasing number of years left between successive use of the land for cultivation. This practice is a highly damaging cause of deforestation and erosion. Laos is one of the poorest countries of Asia, with a gross national product of approximately US$ 260 per caput per annum. The cultivable areas of Laos are mainly situated along the banks of the Mekong River. The level of protection against such floods, so far, is low. Floods are a yearly returning threat to the farmers cultivating their crops in the vicinity of the Mekong River. One of the most densely populated areas of the country is the Vientiane Plain, located North of the capital Vientiane, between the Nam Ngum I Reservoir (Figure 9) and the confluence of the Nam Ngum and Mekong Rivers. The area has a population of approximately inhabitants and is one of the principal rice producing areas of Laos. This area was severely flooded in In the past, the Vientiane Plain was frequently flooded, a situation which improved after the construction of the Nam Ngum Reservoir in However, a large part of the area is still threatened by floods. The extent of flood damage varies from year to year. The principal problem of floods is the restriction the farmers feel in selecting high yielding rice varieties. Consequently, a sustainable agricultural development of the area and a reliable food supply to the growing population of the Vientiane Plain is highly dependent on an improved flood control. FIGURE 9 The Nam Ngum I catchment

23 The extent of 1995 flood damage was studied in large detail with the assistance of FAO. This study has led to the preparation of a flood depth map of the Vientiane Plain. The map, which is available at the MAF-DOI office, shows flood depths of 2 5 metres and in some depressions up to 8 metres. The flooded area shown is approximately ha. The map clearly shows that there is hardly any flow from the Mekong into the Vientiane Plain, except, possibly, through back flow into the Nam Ngum. It should be noted that the accuracy of the flood maps is limited, due to the lack of reliable topographic data of the Vientiane Plain. The underlying topographic maps date from 1960 and have a scale of 1: Levels, however, are not satisfactorily shown, as only 10 meter contour lines and a number of spot levels are given. The preparation of the flood maps was based upon interviews with the local population and the estimated flood depths at all spots investigated were plotted on the 1: scale topographic maps. In the same project, the flood damage was assessed, resulting in an estimated loss to assets and agricultural production of US$ 21 million. For the flood several possible causes have to be mentioned: high discharge from the Nam Ngum reservoir, which during the 1995 flood had a maximum inflow of m 3 /s and a maximum outflow of m 3 /s. The turbines passed 472 m 3 /s that day, whereas 1949 m 3 /s left the reservoir via the spillway at a reservoir level of m above mean sea-level (masl). The catchment area upstream of the dam is km 2. The PMF for the dam has been estimated at m 3 /s at a reservoir level of masl; high discharge from the Nam Lik river, which joins the Nam Ngum river just downstream of the Nam Ngum dam site with a catchment area of km 2 ; additional local rainfall on the Vientiane Plain and the remaining part of the Lower Nam Ngum catchment, which has an area of km 2 of the total km 2 of the complete Nam Ngum catchment; and high Mekong River levels, which impede drainage from the Vientiane Plain via the Nam Ngum River. One of the factors that influenced the severity of the floods may have been the delayed opening of the Nam Ngum I spillway gates. So far, reservoir operation is only based upon the optimization of hydroelectric energy production. Yearly energy yield has an export value of US$ 20 million, partly as base energy supply and partly as peak energy. The higher priced peak power contracts make it interesting to keep the end of the monsoon reservoir levels as high as possible. The export earnings gained from the hydropower production makes it difficult to give a balanced priority to the conjunctive use of the reservoir for flood control purposes. So far, a thorough evaluation of the role the reservoir operation has played on the generation of the flood damage has not been carried out to sufficient depth, simply due to a lack of understanding of the overall functioning of the system. Hydropower and flood regulation Hydro-electric power is an important export product of Laos. The exploitable potential of hydropower generation in Laos is MW. Currently, only approximately 2% of this potential has been developed. However, the further development of the potential is expected to accelerate, as GOL has been signing contracts for the delivery of electricity to Thailand (1 500 MW by the year 2000) and Viet Nam (1 500 to MW by the year 2010). In addition, the domestic energy consumption is growing at a rate of 8 to 10 percent annually. Currently, the total installed hydropower capacity is 203 MW. The largest hydropower plant is Nam Ngum I, with an installed capacity of 150 MW. Of this, 30 MW was installed in 1971, working from the start at the full supply level of masl. The plant was extended in 1978 with the installation of an additional 80 MW. The system was completed in 1984 by adding another unit of 40 MW.

24 Collection of data just upstream of the Nam Ngum dam site started in The hydrometric station was abandoned during the filling of the reservoir. Since 1971 the recorded reservoir outflows have been filed. Lahmeyer International converted the outflowing discharges into a series of inflowing discharges based upon the recorded reservoir levels and the reservoir geometry. Mean monthly discharges are reported to be reliable. A lower accuracy must be attached to the mean daily inflows generated. The area of the Nam Ngum I reservoir is approximately 370 km 2 at the level of 212 masl, which is nearly the same as the area of the Vientiane Plain flooded in In a very approximate way this leads to the conclusion that every additional meter of flood storage depth created in the reservoir, leads to a one meter reduction in flood depth on the Vientiane Plain. Of course, one must be very careful with such a conclusion, as the reduced flood depths also lead to reduced drainage capacities towards the Mekong River, so the effect of creating flood retention volume in the reservoir might be less than expected. In the Vientiane Plain the situation is in fact even more complex, as an important contribution to floods is given by the discharges from the Nam Lik river. Moreover, floods are aggravated by the contribution of local rainfall. Such a complex system can only be studied thoroughly through simulations based upon a hydrodynamic modelling package and assuming that for such model development data of a reasonable quality are available. Flood forecasting and simulation modelling for the Vientiane Plain Although the existence of the reservoir is most likely beneficial to flood control, a modified operation might have prevented a substantial part of the damage. Such statements, however, can only be supported with the development of a thorough knowledge of the flood system through simulation of various scenarios by means of a hydrodynamic flood simulation model. The need for the development of this understanding is felt both in the Ministry of Industry and Handicrafts (MIH - Electricité du Laos) and in the Ministry of Agriculture and Forestry (MAF - Department of Irrigation). There appears to be a clear willingness to cooperate on this issue. The development of the flood simulation model will have the following components: institutional arrangements detailing of a ToR financing appointment of a consultant acquisition of the suitable data processing and modelling tools hydrological data collection topographic survey of the Vientiane Plain model calibration and simulations, and capacity building in Laos The institutional arrangement requires the consensus of MIH and MAF on the establishment of a Flood Modelling Centre. One possibility might be to create the Centre at the Lao National Mekong Committee (LNMC) in Vientiane, with additional staffing provided by MIH and MAF. Currently, LNMC has a total staff of 11 of which: 3 irrigation engineers, 2 hydrologists, 1 civil engineer, 2 technicians and 3 in the administration. It is foreseen to extend the technical staff with 3 more members, funded by GOL. Training of this new and/or detached staff would have to get a high priority. Part of this training should be on-the-job training programmes under the supervision of international consultants. A close cooperation with MRCS would be possible and recommended. The ToR would focus on the need to generate the understanding of the behaviour of physical and partly controlled process of flood wave propagation through the Vientiane Plain. The model would enable the study of various flood control mechanisms, including the construction of flood protection works, reservoir operation options. It would include a tool for the optimization of hydropower

25 production and flood control. Preferably and if feasible, it should include rainfall-runoff modelling of the complete Nam Ngum catchment in order to support such reservoir optimization. The model should be extended to include flood forecasting along the lines described above. Full advantage of this model use and minimum losses in energy production could be achieved when the model would be complemented with a flood forecasting system for Nam Ngum I reservoir. If based on the same concepts as proposed for the Mekong flood forecasting system, the reservoir inflow forecasting system would not require the (impossible) installation of additional rain gauges in remote upstream locations. The total package of modelling support, therefore, would include the following model components: flood prediction model of the Vientiane Plain, for the study of the effects of flood propagation through the Plain as a result of the controlled and/or uncontrolled upstream discharges, Mekong levels and the flood control works which could be constructed in the Plain. The tools should preferably be those already in use at MRCS; rainfall-runoff models of the catchments of the Nam Lik river and the Nam Ngum river upstream of the reservoir; a flood forecasting model for the same catchments, set up along the lines described above; a reservoir operation optimization component, based upon a global optimization technique, such as a genetic algorithm approach. The set of tools would support the following types of studies: further develop the understanding of the flood mechanism of the Vientiane Plain. This would also allow for a comparison of the floods occurring with and without the reservoir or the routing of other historic floods, such as the 1996 event; compare various options of controlled flooding of the Vientiane Plain and prioritize these in terms of various options of protecting parts of the flood plain, e.g. construction of low dikes around the higher elevated parts, creation of storage areas etc.; optimize the control of the spillway gates of Nam Ngum I by using historic records, possibly complemented with records generated through the used of the rainfall-runoff models fed with historic rains; optimization of reservoir operation on the basis of real time control by implementing the flood forecasting model; Apart from its function of supporting flood control studies, the modelling project should be seen as a necessary preceding action to support a Master Plan Study defining a staged development of the Vientiane Plain. Such development would require studies on partial flood control and possibly include controlled flooding concepts. Such developments can no longer be based upon an interative approach, without using the informatics and modelling support available nowadays. The Master Plan would be a logical follow-up to the Nam Ngum River Basic Management project, announced in 1996 by ADB. One of the major problems encountered in setting up the modelling tools is the lack of accurate topographic data of the Vientiane Plain. The 10 m contour lines and the incidental spot levels are by no means sufficient to represent the storage and conveyance components of the system. Additional surveying is expensive. For the purpose of modelling, land level information on a grid of at least one point per ha would be required. Moreover, level and position of all sorts of dikes and roads in the area would have to be collected. This last information is rather easy to collect, especially with the current availability of DGPS. Of late, these DGPS instruments can be mounted on a car or a motorbike and even in a back pack, which allows for travelling along roads and dikes crests. By continuous recording or by a stop-and-go method, the position can be stored continuously in terms of x-y-z co-ordinates. The method allows for an accuracy in the vertical level of a few centimetres. Total cost of the preparation of a digital terrain model of the Vientiane Plain for modelling purpose would be of the order of US$ , depending on how easy it is to get full access to the terrain. The data collected could be further processed to support agricultural development studies. However, for the combination with these studies the more

26 accurate and flexible method of airborne laser altimetry is to be preferred. The cost of this process will most likely be two to three times higher. Detailing of a ToR for a complete modelling project would require a separate mission. A rough estimate of the budget required is US$0.8 million for consultancy input, transfer of tools, training programmes and the additional collection of data. The study component of the project would provide Laos with a pilot investigation, which could be replicated at other flood-prone areas. Capacity building has to be an important element of the project. Laos has a strong need for capacity building. Flood control: example of Bangladesh Flood control in the flood plains of the Mekong Basin has already been applied on a substantial scale in Thailand. The primary reason for flood control is the protection of agricultural production. In larger river systems, with an often rather predictable time of arrival of the flood peak, the concept of controlled flooding has been introduced. Controlled flooding implies that flooding will be allowed, though at a lower frequency and at a time suiting better the cropping pattern. The principle behind it is the creation of a delay of the flood, so that usually the crops can be harvested before the area gets inundated. Controlled flooding implies that in the case of extreme floods the waves still find storage for their dampening and show propagation. The unsetady flow equations describing the propagation of flood waves show us that the travel time of flood waves is a linear function of the storage available. Taking storage away makes the flood waves travel faster. The dampening of a flood wave peak is a quadratic function of the storage, due to the fact that slower travelling flood waves have a smaller length for a given wave period. It is primarily this smaller wave length along the river that leads to the increased dampening. An interesting example of comprehensive flood control is the Flood Action Plan (FAP) of Bangladesh. On the basis of above principles, an analysis was made for the whole country regarding suitable measures against floods. In Bangladesh there are three principal causes of flooding: floods caused by the effects of atmospheric depressions passing over the Bay of Bengal. These floods are very severe, can only be forecast with relatively short lead times and may cause many victims. Given the nature of the floods in this country, coastal defence works are too costly to cope with this problem; floods caused by the flood waves coming down from the Himalayan mountains and propagating via the Ganges and the Brahmaputra Rivers. In some years these flood peaks coincide and cause severe flooding. The lead time in forecasting, however, is much higher than for the coastal floods and the number of victims is usually small. Dikes are often attractive investments to improve the agricultural production by reducing damage and by encouraging the farmers to plant higher yielding rice varieties; flash floods of local origin, due to the high local rainfall intensities and depths. One important difference in relation to other parts of the world where flood control measures were introduced is that Bangladesh has a very controlled approach to flood mitigation works. Whereas in the past, many areas of the world developed their flood control works on the basis of trial and error, the approach in Bangladesh has been much more planned, with design options continuously checked on the basis of model simulations. Bangladesh experienced one of the most catastrophic river floods in 1988, immediately after the already high flood of the 1987 monsoon. The damage of the 1987 flood had hardly been repaired when most of the results of these efforts was lost again. UNDP, World Bank and various donor countries joined efforts to launch a Flood Action Plan, with a budget of US$ 150 million. Of this fund, US$ 55 million would be directed to pilot projects for testing approaches, river bank protection and flood plain management.

27 In terms of planning of projects the country was at that moment already prepared, as in March 1987 the National Water Plan (NWP) had been concluded at the Master Plan Organization (MPO). The NWP had assembled a substantial amount of data and other information, developed a range of planning models and analytical tools and had recommended strategies and programmes. Many of these had already been adopted by the government and donor organizations. One of the tools that had been developed at MPO was a suite of surface water simulation models. This project, funded by UNDP and executed under the supervision of the World Bank by the Danish Hydraulic Institute (DHI), had already produced a general model of the main river system in Bangladesh and a regional model of the South East Region. One of the objectives of the project had been the development of such regional models for the simulation of the effects and control of various flood control alternatives. Another important objective of the project had been the development of local expertise in the use and development of such models. The project, therefore, had a clear capacity building component with the following elements: lecture programme organized at the Bangladesh University of Engineering and Technology; participation in a specialized training programme at the consultant's home office; participation in some courses abroad; on-the-job training under the guidance of expatriate specialists. Especially this last component of the project has been very useful and has partly explained the success of the group which, initiated in 1986, now has a staff of 42 local engineers and is in charge of all modelling support to water control and management projects in Bangladesh. It also is in charge of executing all modelling work related to flood forecasting in the country. Bibliography Bogardi, J.J., Report on the Review Mission of the Mekong Hydrology Programme, MRCS, Bangkok. Cunge, J.A., Holly, F.M. and A. Verwey. Reprinted 1994 Practical Aspects of Computational River Hydraulics, Iowa Institute of Hydraulic Research. DANIDA Revised Proposal for Flood Forecasting and Effective Warning Dissemination in the Lower Mekong Basin. Delft Hydraulics Network Optimization in the Mekong Basin, Final Report. Delft Hydraulics Mekong Master Model, The Mekong Secretariat. Hasan, M.R Preparation of Flood Loss Prevention and Management Plan, Technical Report on Hydrology and Field Data Collection, FAO, Rome. Hasan, M.R Preparation of a Comprehensive Flood Loss Prevention and Management Plan for the Agricultural Sector, Report on Flood Plain Mapping and Flood Loss Prevention and Management, FAO, Rome. Minns, A.W Artificial Neural Networks as Subsymbolic Process Descriptors, Ph.D. Thesis, IHE - Balkema, Delft - Rotterdam. MRCS The Mekong Hydrology Model Package (Basinwide). Somboune Manolom, Hydropower and the Environment, Lao PDR, International Energy.

28 Reservoir management and options for flood control Effects of reservoirs on floods The purpose of a reservoir is usually to store water in the wet season and to increase downstream flows in the dry season to maximize hydropower benefits to cover downstream water demands to improve year-round navigation to reduce flood damages to prevent a river from falling dry during droughts Every storage reservoir provides downstream flood control, whatever the purpose(s) for which the reservoir was built. Reservoirs have a backwater effect which can worsen flooding along the river immediately upstream of the reservoir. With time the backwater effect becomes more severe due to the deposition of sediment at the tail-end of the reservoir. (See Figure 1.) Low return-period floods, such as the annual flood, are often completely absorbed by the reservoir, without any downstream flooding. FIGURE 1 Effect of reservoirs on floods, upstream and downstream of dam Engelbertus Oud, Project Manager, and. Terence Muir, Senior Hydrologist, Lahmeyer International, Frankfurt, Germany and Vientiane People living downstreams of the dam become accustomed used to the new conditions and are often led to believe that floods are a thing of the past. They start to encroach upon the flood plains, build houses and cultivate land, all in a false sense of security. If downstream flooding then occurs, the flood damage for the same rate of flow is much higher than before.