IMPROVED FLOOD FORECASTING AND THE POSSIBLE ROLE OF SATELLITE IMAGES

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1 IMPROVED FLOOD FORECASTING AND THE POSSIBLE ROLE OF SATELLITE IMAGES C.J.M. Vermeulen, H.J. Huizinga, H.J. Barneveld (HKV Consultants, The Netherlands), J. Silander (SYKE, Finland) ABSTRACT In the FloodMan project (Near real time flood forecasting, warning and management) the possibilities are investigated to improve flood forecasting and management in lowland rivers. In the first phase of the project data-assimilation routines have been developed for updating the one-dimensional hydrodynamic model of the Rhine river using in-situ data. In the second phase of the project the possible role of satellite images will be addressed. Artificially produced flood maps are combined with digital elevation maps so as to estimate river water levels. Ideas are developed for using remote sensing and calculated flood maps or flood areas directly in the data-assimilation process. First results for the pilot stretch along the German Rhine are presented, as well as some examples for the use of SAR-data for the Finnish pilot basin. INTRODUCTION FloodMan is a research project co-funded by the European Commission, 5th Framework program, Energy, Environment and Sustainable development. Key Action, Improved Flood Forecasting. Contract no. EVG1-CT The overall goal of the project is to develop, demonstrate, and validate an information system for cost effective flood forecasting and management using EO data, in particular spaceborne SAR data, hydrological and hydraulic models, and in-situ data. The prototype system will provide near real-time information on the flood event, better flood predictions, and improve best practices for management of rivers and their catchments, including hydropower production planning. In Work Programme 2 of FloodMan possibilities are assessed to improve flood forecasts in river basins using field data and satellite information. Hydrological and hydraulic models are further improved and coupled. In addition data-assimilation routines are developed and implemented. Research on the use of satellite information (flood maps and soil moisture) for the automatic updating of the models is underway. In the first phase of the project data-assimilation routines have been developed for updating the one-dimensional hydrodynamic model of the Rhine river using measured water levels and discharges. Sensitivity analyses with the model were carried out in which input and model parameters were varied. Based on our preliminary results the most realistic and sensitive model parameters (only parameters in the input-file were considered, so as to make the method model independent) were assessed and those will be varied in the data-assimilation. Also the weighting coefficients for these parameters in the data assimilation process were determined. For the HBV model of the Kemijoki basin more details were incorporated so as to enable comparison with satellite data. 1

2 In the second phase of the project the possible role of information from remote sensing will be addressed. In order to guarantee the availability of remote sensing flood maps under all weather conditions, the feasibility of using radar images (SAR) for this purpose is investigated. Although the research on the production of flood maps from SAR-images is still underway, some preliminary exercises have been carried out, based on synthetic flood maps. With these, the sensitivity of measured water levels for uncertainties in the flood contour is assessed. Furthermore ideas are developed for using satellite images of flood maps or flooded areas directly in the data-assimilation process. TEST SITES For the development of the model instrumentation two test-sites are assigned: 1. Rhine river, Germany 2. Kemijoki basin, Finland. 1. Rhine river Germany The River Rhine from gauging station Andernach to gauging station Düsseldorf was selected (130 km). This river reach is modelled with a one-dimensional hydrodynamical model (SOBEK). For the inflow of the Sieg river (downstream of Bonn) a hydrological model (HBV) of the basin (2862 km²) has been analyzed and coupled to the one-dimensional Rhine model. The rainfall-runoff model consists of four sub basins. For flood routing of the discharge of these four basins through the river Sieg a water transport model (WTM) is used. Figure 1: Pilot for the Rhine river. For the other tributaries the distance between the gauges of the tributaries and the confluence into the river Rhine has been taken into account (hydrographs of the tributaries are extended by area weighted factors and shifted by a time lag). 2. Kemijoki area, Finland. For the Kemijoki basin a hydrological model (HBV) has been used for several years. The model consists of numerous sub-basins with an average area of about 100 km 2. The model simulates the rainfall-runoff process including snow melt. 2

3 METHODOLOGY For the test site of the Rhine river a model instrumentation is developed for operational flood forecasting. New forecasts are made on a regular basis, e.g. once a day. Based on new measured data (precipitation, measured flow at Andernach) the actual state is calculated using HBV (flow into the Sieg), WTM to calculate the total inflow of the Sieg into the Rhine and SOBEK for water flow in the Rhine. A data-assimilation procedure, using actual measured water levels at Bonn and Köln, is developed and applied to improve the SOBEK calculation. In the future the filtered calculation result for SOBEK is used for the data-assimilation of WTM which in turn provides the input for the data-assimilation in HBV. The data-assimilation procedure requires no changes in the computer code of SOBEK, WTM and HBV. The data-assimilation procedure implemented for SOBEK takes into account the uncertainty of the parameters involved (lateral discharge and bottom roughness) and the measurement uncertainty. The data-assimilations algorithm compares calculated and observed water levels. Based on the differences (small) changes are made to the parameters (bottom roughness in the three sections of the Rhine river and the lateral inflow from the Rhine tributaries). The changes are made in respect to the uncertainty of the parameters and measured data. The procedure is repeated until the desired accuracy is obtained. Figure 2 illustrates the dataassimilation procedure for in-situ data. Σ Figure 2: Schematic lay-out of the data-assimilation procedure for in-situ data. Flood maps of the Rhine river are determined based on the calculated water levels and a Digital Elevation Model (DEM). These flood maps will be compared to the satellite images of the flooded area. The data-assimilation procedure is similar to the above described dataassimilation procedure. 3

4 For the test site of the Kemijoki basin in Finland the hydrological model was extended with a storage component for surface water. The surface water is an indicator for the (maximum) discharge at the basin outlet. The total surface water are processed from satellite images and compared with the calculated surface water. DATA-ASSIMILATION USING IN-SITU DATA At the confluence of the river Sieg and the river Rhine near Menden, the water of the Sieg flows into the Rhine. A part of the Rhine, from Andernach (upstream) to Düsseldorf (downstream) is modelled with SOBEK. In this reach water levels are measured in between at Bonn and Cologne. A sensitivity analysis has been carried out based on the parameters bottom roughness, lateral discharges into the Rhine (the rivers Ahr, Sieg Aft and Wupper) and groundwater storage during floods. In all cases 10 to 20 percent variations in these parameters are studied. Bed roughness For the bottom roughness the main channel, the bank section and the floodplain are distinguished. Changes in bottom roughness are made uniformly over the three identified branches (Andernach to Bonn, Bonn to Cologne and Cologne to Düsseldorf). The effects of changes to bed roughness in the bank section or the floodplain are small compared to changes to the roughness in the main channel. A change of 1% in the roughness of the main channel leads to water level changes at the upstream gauging station of the branch of around 5 cm. The effects are similar for each of the three above mentioned branches. Also backwater curves that affect the upstream water levels are calculated. Lateral inflow The effects of the river Sieg on the top water levels are linear; a factor of 2 in the discharge of the river Sieg leads to a water level difference in the river Rhine of approximately cm. The effect is almost constant along the river stretch. A linear interpolation gives an accuracy of around 2 cm. Time effects however should not be ignored: two days before the peak discharge of the Rhine occurs the effect is about 50% stronger. Two days after the peak, the effect is reduced with a factor 2. Groundwater storage The groundwater parameters don t show any significant influence. Only small variations of some centimetres are found for the peak discharge. In Table 1 the results of the sensitivity analysis for the SOBEK model are given. In this, also the uncertainty of the parameters is indicated. Table 1: Parameter Influence Uncertainty Roughness in main channel large moderate Roughness in bank section moderate moderate Roughness floodplain moderate moderate Lateral discharges moderate large Groundwater small moderate/large Results for the sensitivity analysis of hydraulic model. 4

5 Based on influence and uncertainty of the parameters involved the bottom roughness in the main channel and the lateral discharges are selected as parameters for the data-assimilation. The sensitivity analysis also provides information on the relative changes in the parameters needed to minimize the differences between model and measurements. The data-assimilation procedure described in the previous section is applied tot the flood of December In Figure 3 the forecast for location Cologne (Köln) is given for December 23 up to December 31, using measured data up to December 23. The blue line depicts the difference between forecast and measured water levels without data-assimilation, the red line the differences with data-assimilation (data-assimilation on measured data up to December 23 of course). Sobek with and without data-assimilation (Köln) 0.2 difference measurement and Sobek water level measured difference measurement and Sobek assimilated water level differences Dec 24-Dec 25-Dec 26-Dec 27-Dec 28-Dec 29-Dec 30-Dec 31-Dec 40 Figure 3: Water level forecast with and without data-assimilation. The data-assimilation improves the water level forecast. Since Andernach to Bonn is a relatively short stretch, the data-assimilation accounts only for the improved results in the first two days. The small differences between forecast and measured data thereafter are due to the perfect forecast for the input at Andernach. ROLE OF SATELLITE DATA FOR IMPROVED FLOOD FORECASTING IN RIVERS Kemijoki area, Finland In the hydrological HBV model for the Kemijoki basin in Finland a surface water storage component was introduced so as to simulate lakes and ponds in the basin area. It was found that the maximum storage in lakes (more than 6000 lakes larger than 1 hectare) is reached 1-2 days in advance of the maximum discharge at the basin outlet. When good quality data on the surface water storage is available, the accuracy of the discharge forecast can be improved. 5

6 The routines to convert observed flood extents to surface water volume were tested on 1/3 of the total upper Kemijoki basin of 27,424 km 2. For flood periods in 1995 and 1998 the relation between observed flood extents (from satellite images, for example see Figure 4) and simulated surface water volume were calibrated. These relations are applied in dataassimilation routines to update the model. The procedure will be further tested in the 2004 spring floods. Figure 4: Example flood map from satellite data (May 27 th 1995). 6

7 The method enables the production of course scale flood maps (1: and over) for remote areas prior to an exceptional flood using hydrological models. Furthermore first tests have been carried out for determining flood maps from satellite images (radar and optical). A preliminary result is shown in Figure 5. The figure shows that the deviations between observed in situ and observed from space are still quite considerable. Based on this results it could be concluded that at present only high resolution optical satellite images and airphotographs are applicable for flood mapping in narrow river stretches (up to 100 m wide). In addition to the flood maps satellite images (SAR) can provide information on the soil moisture of the top layer (about 10 cm). The hydrological model for the Kemijoki is adapted in such a way that also the soil moisture in this top layer is simulated. Satellite information on soil moisture will be used for data-assimilation on this adapted model. Figure 5: The River Kyronjoki is located in the area of the West Finland Regional Environment Centre. The flood plain in Iskala embankment area during the flood of 2000 Maanmittauslaitos lupa nro 7/MYY/03. Rhine river, Germany Flood maps from EO data could play an important role for improved flood forecasting. Flood extent as well as flood levels (deduced from combining flood maps and DEM) could be additional sources of information. For the Rhine river there are no suitable EO data available for the available historic flood periods. Therefore it was decided to use synthetic flood maps for the study on the possible role of EO-data for improved flood forecasting. These synthetic flood maps are generated using calculated water levels and a DEM. 7

8 Figure 6: Example of a synthetic flood map. In a first exercise, the flood maps where used to derive the water levels (per km river). Since the synthetic flood map is produced using the calculated water levels in the first place, the synthetic flood map is a perfect measurement of the calculated water levels. The results are presented in figure absolute difference between original en deduced waterlevels absolute difference (cm) Distance from Andernach (~km) Figure 7: Absolute difference between original and deduced water level for December 24, 1993, using a perfect flood map. The figure shows several stretches in the river where the water level is poorly estimated, even using the perfect flood map. These errors are due to canalization of the river: on these stretches there is no unambiguous relation between water level and flood map. The exercise proves that flood maps only provide information on selected river stretches. In order to assess the effect of inaccuracies in EO flood maps on the deduced water levels, the synthetic flood maps were disturbed in two ways: random added flooded cells adjacent to the cells already flooded (inaccuracy in classification) and a shift in location of the flooded cells (inaccuracy in geo-referencing). The inaccurate flood maps were then used in the opposite procedure to derive water levels for the flooded areas along the river. 8

9 Difference (cm) Distance from Andernach (~km) Figure 8: Differences between original and deduced water level for December 22, 1993, using a shifted flood map. The disturbed flood maps where used to determine the water level sat each river section. The results are presented in figure 8 for the shifted flood map and figure 9 for the noise added flood map Difference (cm) Distance from Andernach (~km) Figure 9: Differences between original and deduced water level for December 22, 1993, using a noise added flood map. In case of the shifted flood maps, representing an error in the geo-referencing of the EO-data, the water levels are poorly estimated (differences up to 5 meter!). The example shows the importance of an accurate geo-referencing of the satellite image. For the noise added flood maps far better results are derived, but still results in large errors. The error in noise added flood maps is strictly negative because flooded cells are added, therefore the inaccurate flood maps is larger then the original flood map. 9

10 In both cases the results are unsatisfactory and unusable for practical purposes. Next the procedure was adapted to estimate the water levels of areas instead of river sections. For this, areas of one kilometre length where used. Figure 10 presents the results. In this exercise only the water level of 1 kilometre river length at 5, 10, 15 etc. kilometre from Andernach where calculated from the noise added flood maps Difference (cm) Distance from Andernach (~km) Figure 10: Differences between original and deduced water level for December 22, 1993, using the area method on the noise added flood map. Although only a first approach to the area method is used, the preliminary results for the areamethod are better then the river section method, but both are unsuitable for flood forecasting. Further research is needed. The area method should be optimised with respect to the length of the river (now only one kilometre length was used) and also the river characteristics should be used (selecting the best areas in the river). Furthermore, using areas instead of rivers sections introduces the problem of which location in the river is represented by the derived water level for the area used? CONCLUSIONS In the pilot for the Rhine river a model instrumentation is developed. Using data-assimilation based on in-situ measured water levels the accuracy of the water level forecasts is improved. Since only a relatively small stretch of the river is modelled the forecast horizon is 2 days, forecasts more than 2 days ahead are almost completely determined by the flow forecast for Andernach (Andernach makes up 90 percent of the total flow). The data-assimilation based on measured in-situ data works well on the hydraulic model. Refinement of hydrological schematisations (increased number of sub basins) and the structure of the flood routing module is in progress. To be followed by data-assimilation options for the model instrumentation. The data-assimilation of EO data to the hydraulic model is not yet implemented, due to lack of accurate satellite data of the German river. 10

11 The role of EO-data for flood forecasting is studied. The first results indicate accurate georeferenced EO-data are a prerequisite for data-assimilation. Furthermore the water levels from flood maps should be derived using an area method, basically averaging the water levels over a stretch of the river. Application of EO-data is difficult when river banks are steep (e.g. levees). Both for the satellite images of flood maps and of flooded areas, the resolution and accuracy of EO flood maps and DEM should be very high to determine water levels with sufficient accuracy (approximately 10 centimetre). Further work is focussed on the following subjects: For the Rhine river data-assimilation will be implemented on the hydrological model (both HBV and WTM). Part of this activity is a feasibility study to incorporate EO data of soil moisture. Furthermore, in anticipation of improved EO images of the Rhine flood maps the data-assimilation of synthetic EO flood maps is further investigated. Desk top experiments with these synthetic flood maps will result in data-assimilation algorithms for EO data to 1D hydraulic models and provide detailed requirements for the EO data. For the Kemijoki area the added value of EO data of soil moisture (only in the about top 10 cm layer of the soil) will be studied. The EO data is compared with the present simulation of soil moisture (of about 1 meter top layer of the soil). Therefore the hydrological model is modified by dividing the soil moisture storage into two separate storages: (1) the top layer of soil and (2) the lower layer. A preliminary version of the top layer moisture simulation has been implemented, but not yet tested with observation data. However, first simulations have shown that the simulated soil surface layer moisture fluctuates more rapid than the simulated moisture of lower (about 1 meter) layer of soil. 11