APPLICATION OF SAR-DATA FOR FLOOD MODELLING IN SOUTHERN GERMANY
Bach, H. (1), Lampart, G. (1), Ludwig, R. (2), Mauser, W. (2), Strasser, G. (2) , Taschner, S. (2)
(1) VISTA Remote Sensing in Geosciences GmbH Luisenstr.45, 80333 München, Germany Tel. +49-89-52389802, Fax +49-89-52389804 Email:
[email protected],
[email protected] (2) Institute for Geography, University of Munich Luisenstr.37, D-80333 Munich, Germany Tel. +49 89 2180 6676 , Fax +49 89 2180 6675 Email:
[email protected],
[email protected]
ABSTRACT The application of an Integrated Flood Forecast System (IFFS) in a watershed in Southern Germany is presented. IFFS uses synergistically microwave and optical remote sensing data to model runoff. As a first data source an interferometrically derived elevation model of the watershed is used. From this, information layers on watershed boundaries, subwatershed definition, slope and flow paths are derived. These layers are used to automatically generate the hydrological structure of the runoff model. Since the hydrological structure is not changing rapidly, the analyses of one pair of ERS-tandem scenes is sufficient. Additionally land use information from classification of TM data is applied to characterise the watershed. The second SAR derived data source for flood modelling is the surface soil moisture. Soil moisture information is of relevance for runoff modelling because it determines the partitioning of rainfall into surface runoff and infiltration. After the ERS-SAR data were radiometrically and geometrically corrected using DEM information, surface soil moisture was derived under crops using a semi-empirical model, which corrects the influence of the soil texture and the canopy contribution to the SAR signal. This information is then used to describe the moisture conditions of the watershed prior to a flood. A validation run of the Integrated Flood Forecast model in the Ammer watershed (700 sqkm) in Southern Bavaria is performed. As test-case an extreme flood which was the most severe since 150 years was selected. Four different rainfall inputs are used and the resulting hydrographs compared. Planned applications and extensions of the methodology in an operational flood forecast system in South Germany are described. For such an operational application the use of SAR data with a high temporal resolution, which could be provided by ENVISAT ASAR data wide-swath mode, is mandatory.
1 INTRODUCTION Hydrological modelling for flood forecast is well understood and widely applied. These models, which are used in practice, are driven by the rainfall as input. Through hydrological parameterisation of the watershed and the river network, the rainfall is transformed into runoff. These so-called rainfall-runoff models need the spatial characterisation of the land surface concerning parameters relevant for runoff formation. Static information or information with low temporal frequency can be gathered by mapping (e.g. soil type map) or using remote sensing data (e.g. land cover type).
However, land surface parameters that are required in these models and temporally highly variable, like soil moisture and snow properties, can not be measured yet in a spatially distributed way and with the needed temporal frequency (approx. 3 days). The improvement of operational flood forecast systems at this stage of development requires the improvement of spatial input parameters. Remote sensing data have a large potential to contribute to this. SAR-data together with optical data can be used for a better spatial determination of soil moisture and snow properties with sufficient temporal frequency required for runoff modelling. How this information is used for flood modelling in Southern Germany is demonstrated in this paper and the potential of ENVISAT towards this topic is addressed.
2 THE INTEGRATED FLOOD FORECAST SYSTEM IFFS An Integrated Flood Forecast System (IFFS) was developed which uses remote sensing data to a wide extend [1]. IFFS consists of two parts, which are summarised in Fig. 1. The first part of the system describes the watershed and is assumed to be temporally static. The second part of IFFS consists of the modelling of the dynamic reaction of discharge on a rainfall input. The hydrological model used for the translation of rainfall into runoff is a modified version of the SCS method TR-20 [2]. This model is routinely used in hydrological practice. It has been modified to allow remote sensing inputs in an automated way using GIS tools. Examples of the methodology will be given for the Ammer catchment, situated South-West of Munich in the Alpine Foreland. As prime remote sensed data source an interferometrically derived elevation model is used to determine topographic information on the watershed. The phase information of a tandem pair of ERS SAR RAW data serves as basis. The processed digital elevation model (DEM) has a spatial resolution of 30 m and a relative vertical accuracy of 10 m (Fig. 2, left). This is sufficient for the extraction of flow patterns in hilly and mountainous regions to generate the hydrological structure of the runoff model (Fig. 2, centre and right). Additionally, optical satellite data (LANDSAT-TM) are classified to deliver the land cover distribution [3]. Together with a soil map, the watershed is then classified into hydrologic relevant classes of water storage capacity, so called CN-values. (see Fig. 3). In addition to remote sensing data, only information on the reservoirs (storage volume, discharge curve) is required for this static setup of the hydrological model. For the dynamic part of the system, which deals with a specific flood event, rainfall information is required as driving variable. Different options are possible to generate this information. Meteorological station data can be interpolated, but also remote sensing data can be used (METEOSAT, weather radar). Numerical Weather Prediction (NWP) models allow further to forecast the rainfall. Within the dynamic part of IFFS (Fig. 1, bottom), also soil moisture information is of relevance for runoff modelling because it determines the extent of saturation of the watershed and thereby the partitioning of rainfall into surface runoff and infiltration. The same amount of rainfall, which normally does not lead to a significant increase in water level, can cause a severe flood, if the soil has already been filled with water and the storage capacity is close to zero. Because information on the actual soil moisture distribution is normally missing, it is estimated in hydrological models by utilising an antecedent precipitation index, that is derived from rainfall measurements of the preceding days, or a moisture index, that is derived from actual discharge values. However, this parameter can not reflect adequately the full temporal and spatial variability of soil moisture. Large errors in flood forecast may occur as a result. Therefore SAR data are used to derive soil moisture distributions, which have the potential to improve the antecedent moisture characterisation of the watershed. With this information, the actual water storage capacity of the soil is determined as input into the rainfall-runoff model.
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Fig. 1. Methodology of the Integrated Flood Forecast System IFFS. In the upper part the data sources and methodologies for the definition of the static description of the watershed are illustrated. Input of rainfall data of various sources and soil moisture information prior to the rainfall event from SAR lead to the runoff output.
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Fig. 2. SAR derived spatial inputs for IFFS using GIS tools; left: interferometric elevation model of the Ammercatchment, centre: extracted flow paths, right: extracted subwatersheds
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Fig. 3. Spatial inputs for IFFS using optical satellite data and GIS tools, left: landuse of the Ammer-catchment, centre: soil map, right: CN-values parameterising the water holding capacity of each pixel The methodology for deriving the surface soil moisture distribution from ERS SAR images will be described shortly. Since the backscattering signal of SAR images of mountainous and hilly regions is strongly influenced by the terrain, special attention must be paid to compensate these terrain effects before any other interpretation. A method for georeferencing and correcting of illumination effects in SAR images of mountainous areas is applied to the ERS images acquired shortly before a flood period. The correction uses the local incidence angles of each pixel and applies a cosinus correction using a spatial weighted resampling method, which is based on energy conservation [4].
In a next step, the geometrically and illumination corrected ERS images were used for the derivation of soil moisture information prior to a flood. The basis of the approach for surface soil moisture determination is an algorithm developed for ERS-data [5] and already successfully used in a series of applications ranging from small scale, field based hydrology [6] to moisture distributions in medium sized watersheds [7]. The algorithm is based on the empirical compensation of the influence of vegetation and soil-surface scattering (represented by land-use, biomass and soil-type), organic matter content and density of soils (represented by soil-type) on the backscattering signal. This model works well for annual vegetation if all the ancillary data is available with the appropriate accuracy [8]. The necessary information on land cover was gathered through the classification of optical remote sensing data. The classification was also used to determine the forested areas and settlements, where soil moisture can not be determined with C-band SAR data.
3 THE WHITSUN FLOOD IN THE AMMER CATCHMENT The static model setup was created for the Ammer catchment and successfully tested with a standard flood event, which happens statistically once every 5 years [1]. During Whitsun 1999 an extreme flood occurred which led to a bicentennial flood. This means that only once during 200 years such a severe flood must be expected. Actually this flood was the highest since data collection started in the region. The question was whether even such an extreme situation can be modelled correctly with IFFS and whether the ERS based soil moisture characterisation prior to the Whitsun flood works successfully. Fortunately, two days before the heavy rainfall during Whitsun started ERS-SAR images were acquired over the watershed. This allows the moisture characterisation of the watershed prior to the flood. The ERS image used after terrain correction is illustrated in Fig. 4 (left). The pixel-wise soil moisture distribution from 18 May 1999 is shown in the centre of Fig. 4. The white areas correspond to forested and build up areas, where the algorithm can not be applied. Soil moisture values vary between 40 and 60 Vol. % (saturation). For the flood model it is sufficient to quantify the soil moisture in 3 classes representing dry ( 48 Vol%) moisture conditions. The result of this classification for each subwatershed is illustrated in the right part of Fig. 4. It is obvious that the largest part of the watershed is determined to be wet. Precipitation falling on these areas will directly contribute to surface runoff.
< 30 Vol% 30-48 Vol% > 48 Vol.%
Terrain corrected backscatter intensities
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Soil moisture distribution calculated for the subcatchments
Fig. 4. Determination of the soil moisture distribution prior to the Whitsun flood 1999; left: terrain corrected backscatter intensities, centre: soil moisture distribution grouped in 3 classes (dry, normal, wet); right: soil moisture classes for the sub-watersheds
The rainfall distribution during Whitsun 1999 was determined for the test-case using 4 different approaches, as illustrated in Fig. 5: 1) It is interpolated from meteorological station data; this is the standard option in most hydrological applications. 2) METEOSAT analyses were applied to improve the spatial fields from 1) using the CLOUD temperatures. 3) Weather radar provided fields of rainfall with high spatial resolution; their quantitative accuracy in terms of rainfall rates is however limited. 4) Numerical Weather Prediction (NWP) model results of the SWISS Model (SM) was provided to forecast the rainfall of the next 48 hours. This enables to increase the forecast time of the flood model, but of course also brings an uncertainty to the runoff simulation.
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Fig. 5. Sources of rainfall information for flood modelling of the Whitsun flood in the Ammer catchment: interpolation of rain gages without (upper left) and with (upper right) METEOSAT cloud images; lower left: Numerical weather prediction of the SWISS Model (SM); lower right: weather radar
4 RESULTS Based on the rainfall information of Fig. 5, the discharge in the Ammer catchment was modelled. Fig. 6 shows the modelled runoff using the 4 rainfall options. In all 4 cases the soil moisture conditions were derived from the ERS observations. The modelled hydrograph is compared to the measured discharge. Both coincide very well in height as well as shape when using interpolated station data, which is the standard hydrological method. This result was achieved without any calibration of the rainfall-runoff model and shows that IFFS works well even for an extreme flood event. The agreement between the hydrographs could even be improved when integrating METEOSAT cloud images to the rainfall interpolation. The weather prediction model resulted in too high rainfall values compared to measurements of precipitation. Therefore also the modelled runoff is too high. This demonstrates that further research and development is required to apply NWP model results in hydrological models of medium sized watersheds. Despite this poor result using the NWP rainfall, the potential of such a procedure is high, since it is the only option when the forecast time needs to be increased. Disappointing are the results using the weather radar. The rainfall fields from radar could not reflect the station measurements. Therefore the modelled runoff differs substantially from the modelled discharge.
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Fig. 6. Comparison of modelled and measured hydrographs for the Whitsun flood using 4 different rainfall inputs. In summary, the IFFS model proved again to be applicable without special calibration. IFFS produced good results using data from rain gauges even for an extreme flood. For the Whitsun event, METEOSAT images could even improve runoff modelling through the adjustment of the spatial interpolation of rainfall data according to the cloud temperatures. If a larger forecast time is required, NWP models can be used with the disadvantage of reduced accuracy.
5 OUTLOOK Since the methodology presented here proved to be successful, it will be further developed to integrate multisensoral data of the ENVISAT satellite. The goal is to apply remotely sensed information on soil moisture and snow properties within an operational flood forecast system in Southern Germany. Especially information on snow wetness will be determined with ASAR on ENVISAT. Information on the land cover type, which is essential for the ASAR analyses, will be gathered from MERIS observations. Additional information on the snow covered area will be collected both with AATSR and MERIS. These spatial information is required for a better performance of the runoff model. The improvements that are addressed in the next years will therefore consist of 3 steps: 1) The actual surface soil moisture distribution will be derived from ASAR measurements with the help of land cover information derived from MERIS. 2) the actual snow cover distribution will be derived from MERIS and AATSR data. 3) The actual distribution of wet snow will be derived from ASAR data. ad 1) ENVISAT ASAR in wide swath mode will allow the observation of the soil moisture distribution with a sufficient spatial and good temporal resolution. To achieve this, the methodologies developed for ERS-SAR must however be transferred and extended to data from ENVISAT-ASAR. ad 2) Since many severe floods are caused by a coincidence of heavy rainfall and snow melt, the observation of the snow covered area is also of great importance for the runoff process. For mapping the snow covered areas MERIS (VIS, NIR) and AATSR (SWIR) data will be applied. ad 3) For flood modelling not only the extent of the snow cover is essential. It is even more relevant to know whether the snow is already melting (and so contributing to runoff) or still frozen (and so has the potential to retain water in the snow cover and maybe even reduce runoff). Since for dry snow the backscattering coefficient vary from -6 to -1 dB, while wet snow with liquid water content has backscatter coefficients between -19 and -13 dB, SAR images can be used to separate areas with liquid water in the snow cover from areas without liquid water content [9, 10]. Consequently the spatial and temporal location of the transition from wet to dry snow zone can be monitored with a time series of SAR images.
ACKNOWLEDGEMENTS The results presented here were partly funded by the European Commission within the project RAPHAEL coordinated by the University of Brescia (Runoff and Atmospheric Processes for flood HAzard forEcasting and controL, ENV4CT97-0552). The “Bayerisches Landesamt für Wasserwirtschaft” in Munich gratefully provided hydrological data. Thanks to Peter Binder from SMI (Swiss Meteorological Institute) for providing the SM model rainfall results. Martin Hagen from DLR is gratefully acknowledged for the collection and provision of radar based rainfall data. ERS-SAR data were provided by ESA.
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[5] Mauser, W., Rombach, M., Bach, H., Dermircan, A., Kellndorfer, J. (1995): Determination of spatial and temporal soil-moisture development using multi-temporal ERS-1 data, SPIE Vol. 2314, pp.502-515 [6] Rombach, M. & Mauser, W. (1997): Multi-annual analysis of ERS surface soil moisture measurements of different land uses, Proc. of the 3rd ERS Symp. ESA-SP-414, pp.27-34 [7] Oppelt, N., Schneider, K., Mauser W. (1998): Mesoscale soil moisture patterns derived from ERS data. Proceedings of the EUROPTO-SPIE conference Barcelona, SPIE Vol. 3499, pp. 41-51 [8] Mauser, W., Stolz, R., Schneider, K., Bach, H. (2000): Comparison of ERS-SAR data soil moisture distributions with SVAT model results, Proc. ESA ERS-ENVISAT-Symposium, Goteborg, this issue [9] NAGLER, T. Rott H., Glendinning G., (1998) : SAR-based Snow Cover Retrievals for Runoff Modelling ; In : Proceedings of 2nd International Workshop on Retrievals of Bio-&Geo-physical Parameters from SAR Data for Land Applications, ESTEC, Noordwijk, Netherlands, pp. 511-518 [10] NAGLER, T. ; ROTT, H.; (1998): SAR Tools for Snowmelt Modelling in Project HydAlp; In: Proc. IGARSS’98, Seattle (WA) 6-10 July 1998, pp. 1521-1523
