International Conference on Headwater Control VI: Hydrology, Ecology and Water Resources in Headwaters. Bergen, Norway, 20–23 June 2005
The Changjiang Flood Forecasting Assistance Project Massimiliano Zappa1, Song Zhi Hong2, Michael F. Baumgartner3, Joachim Gurtz4, Bruno Schädler5 1.
Swiss Federal Institute WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland
2.
Changjiang Water Resources Commission, Bureau of Hydrology, Wuhan, PR China.
3.
MFB-GeoConsulting GmbH, CH-3254 Messen.
4.
Institute for Atmospheric and Climate Science, Swiss Federal Institute of Technology (ETH) Zürich, Winterthurerstr. 190, CH-8057 Zürich, Switzerland
5.
Swiss Federal Office for Water and Geology FOWG, CH-3003 Berne
Contact: Dr. Massimiliano Zappa,
[email protected]
Abstract Changjiang (Yangtze) is the largest river in PR China as well as one of the biggest in the world. There are abundant rainfalls with uneven distribution in space and time. Floods are mainly produced from April to October by storm rains. Flooding disasters have been frequent. The Changjiang Flood Forecasting Assistance Project started on August 1st 2003, aims to enhance the flood-forecasting standard for the middle part of Changjiang River by mean of distributed hydrological modelling and by flood monitoring through analysis of NOAH/AVHRR images. The project is assisted by the Swiss government through the Swiss Agency for Development and Cooperation and by the Changjiang Water Resources Commission (CWRC). A know-how and technology transfer from the Swiss partners to CWRC collaborators was completed. This includes training of CWRC staff in GIS, remote sensing and hydrological modelling as well as the installation of a satellite receiving station together with the required computer hardware and image processing and GIS software at CWRC in Wuhan (PRC). An operational spatially distributed flood forecasting model for the contributing area of the Three Gorges Dam has to be developed and is installed. The catchment of the Daning River (approx. 2000 km2) served as experimental basin. The forecasting system includes the online acquisition of remotely sensed data of the contributing area of the reservoir in the upstream part of the Three Gorges Dam for the management of flood situations and the parallel operation of a real-time spatially distributed flood forecasting model. This paper will give an overview on the implementation plan up to the end of March 2005 with special focus on the structure, functionality, parameterization and calibration of the distributed hydrological model in the Daning River for the periods 1990-1997 and 2000-2002. After implementation of the forecast system for the Daning River and training of the CWRC collaborators, the CWRC will be able to extend the application of the system for total directly contributing area of the Three Gorges Dam reservoir.
Introduction Changjiang (Yangtze) is the largest river in China as well as one of the biggest in the world. There are abundant rainfalls in this river basin with uneven distribution in space and time. Floods on the Changjiang River are mainly produced by storm rains. Heavy flooding disasters always occur in flood seasons lasting from April to October. Records show that since the year 1153, there have been eight times when at Yichang Station (area > 1 Mio. km2) the discharges exceeded 80’000 m3s-1, in which the biggest one was 105’000 m3s-1 in the year 1870. A minor flood in September 2004 caused more than eight deaths near Chongquing and generated discharges in a magnitude of 60’000 m3s-1 in Yichang (Figure 1). During August 2002 heavy floods occurred in Europe and Asia. As China was one of the most heavily struck countries in Asia, a project for the enhancement of flood forecasting for the Yangtze-river was granted by the Swiss Agency for Development and Cooperation (SDC). In the existing operational flood forecasting operated by the Bureau of Hydrology at Changjiang Water Resources Commission (CWRC Wuhan), the information source is almost single. The information consists mainly of measurements collected at river gauging stations, water level and rainfall measuring sites. There is hardly any information or data derived through Remote Sensing and the geographic information systems have not been used in the forecasting operations. Due to the limitation of observation times in a day, the information quantities are also limited. In the existing flood forecasting, information with spatial variation properties, such as areal precipitation, have been handled mainly by way of ‘single-point sampling’. As for the evaporation capacities in the river basin, it is always calculated approximately by way of using single point data to represent areal variations. If the information is uneven distributed in space, the above-mentioned handling methods will surely lead to reduced forecasting accuracy. The Changjiang flood forecasting project aims at enhancing flood forecasting capacity on the Changjiang River basin through improving the infrastructure for the receiving and processing of geographical and remote sensing information and data. The specific implementation content of the project included the setting up a remote sensing data receiving platform, the installation of a geo data processing chain and the evaluation of a distributed conceptual hydrological model, including implementation in the headwater test catchment “Daning” (2’000 km2) and in the directly contributing area of the “Three Gorges Reservoir” catchment (~45’000 km2).
Figure 1: Discharge of the Yangtze at the gauge Yichang. Left: Low flow in 28. February 2004 (< 4’000 m3s-1). Right: Flood (> 60’000 m3s-1) 7. September 2004.
Module 1: Remote sensing and other Geo-data Remote sensing (RS) can have a wide coverage and can collect data quickly and dynamically. Regular or irregular observations by making use of the RS technology can acquire dynamic information about actual surface patterns. The geographic information system can be used to analyze and manage various kinds of information needed for flood forecasting models. In particular, it has a strong capacity in the management of spatial data, expanding the traditional single-point data calculation to three-dimensional spatial calculation. This will be of great significance in enhancing flood-forecasting standard for the Changjiang River. For flood forecasting and flood monitoring, geo-data are a basic requirement (Schmugge et al. 2002). RS is an excellent tool to derive actual information on the Earth’s surface. For flood forecasting using raster-based geo-data can directly be used as model input. The minimum spatial resolution required for model input is a grid of 1 km x 1 km. Following input is of interest for the model: vegetation, geology, topography, land cover, soil properties and snow cover. Part of this information can be derived from RS data. Flood monitoring as the second area of interest depends mainly on remote sensing data for time series analyses of large and remote regions. Remote sensing data allow the determination of the extent of water bodies and flood areas. Analyzing flooding events on long-term basis enables statistical calculations and to derive flood hazards maps. In addition to remote sensing data, GIS data as basin boundaries, rivers, lakes, etc. are required. The following datasets were found to be of importance for the project: - NOAA-AVHRR (Advanced Very High Resolution Radiometer) scenes, 1km pixel size, to get acquainted with this type of data; land-use map derived from AVHRR data for the Three Gorges region; 1km x 1km pixel size - Land-use map based on AVHRR data for the Three Gorges region (Joint Research Center, Ispra, Italy) ; 1km x 1km pixel size - Landsat-TM data for the entire Three Gorges region; 30m x 30m pixel size - stereo ASTER data for the entire Three Gorges region; 15m x 15m pixel size; deriving a 90m DEM from stereo data (Figure 6) - QuickBird scene for the Three Gorges Dam Site; 60cm x 60cm pixel size. For accessing RS data on a regular basis it was decided to install a NOAA-AVHRR receiving station. Having direct access to NOAA-AVHRR has several advantages: AVHRR data are available on a daily basis; the large Field-of-View of AVHRR data allows receiving data for a large area; the spatial resolution for flood monitoring is sufficient; the data can be received free of charge. For setting up a workflow for flood forecasting and monitoring means also to define the necessary infrastructure needed. As mentioned above, a NOAA-AVHRR satellite receiving station plays a fundamental role. Consequently, a server-based computer infrastructure with focus on satellite image processing and GIS analyses is needed (see Figure 2). ERDAS Imagine (http://www.leica-geosystems.com/) and ArcGIS (http://www.esri.com/) were chosen as image processing and GIS tools. After the evaluation of the providers, the complete computer infrastructure was setup and tested in Switzerland. All necessary software including the operating system, image processing, GIS, graphics tools, the hydrological model was installed on the systems. After successful Factory-Acceptance-Tests, the infrastructure was shipped to China. After the successful installation, with a Site-Acceptance-Test, the infrastructure was handed over to the Chinese partners. A main product was setup of a database of spatial information with a grid resolution of 90x90 m2. This database allows the distributed application of the installed flood forecasting system and the determination of the spatial characteristics of the catchments within the Three Gorges Area.
Roof Installation
Satelliteantenna
GPS
Indoor Installation
220V AC Motor Controler Unit
LAN CWRC
220V AC
220V AC Antenna Interface Unit
220V AC
Internet
Antenna Control PC
Gateway 10.6.1.94 DNS 10.6.1.15 ; 10.6.1.30 Subnet Mask 255.255.255.224
Flood Monitoring Office
220V AC
2met! Workstation Dell Precision 650 FLOMON14
Switch I
Switch II WS Modelling Dell Dimension 8300 DELL No H9PCV0J FLOMON13 10.6.1.93
Office PC Dell Dimension 8300 FLOMON20 10.6.1.87
Ethernet
220V AC
APC 1500VA Server Dell PowerEdge 2600 DELL No JDG311J FLOMON00 10.6.1.76
Printer C8110A HP Business 2600/dn HP No SG3CG67864 FLOMON60
220V AC
220V AC
WS Photogrammetry 1 Dell Precision 650 DELL No HSP911J FLOMON10 10.6.1.74
WS Photogrammetry 2 Dell Precision 650 DELL No FSP911J FLOMON11 10.6.1.73
220V AC
WS GIS Dell Precision 450 DELL No H7D911J FLOMON12 10.6.1.82
Figure 2: Processing chain for the management of remotely sensed data for flood forecasting and monitoring (© MfB-GeoConsulting). The dataset consists of a digital elevation model, a land use map, a map of the flow accumulation and flow directions, and a map of the soil classes. The soil map relies on the “FAO Soil Map of the World” (FAO-UNESCO 1988). Soil depths and other hydrological relevant soil characteristics have been downscaled according to the local distribution of an index based on elevation, land-use and slope.
Module 2: Implementation of a flood forecast model The spatially distributed hydrological model PREVAH (Precipitation-RunoffEVApotranspiration-HRU related model) has been selected for runoff predictions (Gurtz et al. 1999). PREVAH has been originally developed with the intent of improving the understanding of the spatial and temporal variability of hydrological processes in catchments with complex topography. The spatial discretization of PREVAH relies on the aggregation of gridded spatial information into so-called hydrologic response units HRUs. For full information on the model physics, structure and parameterizations we refer to previous work with PREVAH (Gurtz et al. 1999 and 2003, Zappa 2002, Verbunt et al. 2005). The model is forced by interpolated values of observed climatic variables. Six meteorological variables are required: precipitation, air temperature, global radiation, relative sunshine duration, wind speed and relative humidity. For use in the Three Gorges area the model has been extended to a complete system for data preprocessing, assimilation and use for real-time flood forecasts. The main tasks included the development of a user friendly graphic user interface, the connection to the real time database of rainfall and discharge at CWRC and the development of tools for the management of both meteorological and hydrological information. Figure 3 shows the workflow for the assimilation, pre-processing, processing and post-processing of data within the PREVAH-Modelling-System.
ERDAS / ArcGIS Raw Hydrometeorological Data
Figure 3: Workflow of the PREVAH-Modelling System and its interfaces for data assimilation and visualization. Model core
Pre-processes spatial information
Visualization of calibration results
Assimilates hydrometeorological data
Visualizes and edits maps
Interpolates meteorological information
„Standalone-GIS Tool“
Generates soil maps from FAO-soil classes
WEB-Training and documentation
Visualizes model resultsas Line PLOT
The management of digital representations of spatially distributed physiogeographical information is an important requirement for the development, parameterization and initialization of GIS-based spatially distributed hydrological models. A standalone tool with basic GIS functions has been developed to allow the management of spatial data. Among others the tool allows the assimilation of remotely sensed information processed by ERDAS and ArcGIS. A tool has been developed to easily produce all of the spatial information needed for running PREVAH in a single step. The tool is able to delineate hydrological catchments (both headwater and in-between catchments), performs a detailed topographical analysis (Schulla 1997), derive hydrological response units based on user-specified criteria (Gurtz et al. 1999) and create tables and input files suitable for the hydrological model. The assimilation and management of hydrometeorological data is the second important prerequisite for the distributed application hydrological models. An interface allowing the assimilation and verification of hydrometeorological data is integrated in the system. This interface provides a link between hydrometeorological data and the spatial interpolation tool of the PREVAH-Modelling-System. The interface is designed for the stepwise assimilation of raw meteorological information from a network of stations and makes a simple plausibility/acceptability test of the data. A station attribute table is maintained. This governs the data-flow of a simple database of hydrometeorological information, which is connected to a tool allowing spatial interpolation of the data. Such tool extracts the hydro meteorological information and offers different methods for the selection of hydrometeorological stations. The software creates elevation dependent regressions of the analyzed variables. Different approaches (among others elevation dependent regression, Kriging and inverse distance weighting) are
available for the spatial interpolation of the hydrometeorological data. All of this is specified for the creation of meteorological tables as required by the core of the hydrological modelling system. The model core assimilates the spatial and hydrometeorological information and computes the complete hydrological cycle of a catchment. A monitored calibration routine based on objective functions is integrated (Sonderegger 2004). The linear efficiency score after Nash and Sutcliffe (1970), the logarithmic efficiency score (Schulla 1997) and the volumetric deviation of observed and simulated runoff are building the basics for the derivation of an objective score system to asses the goodness-of-fit between simulated and observed discharge. Three scores are proposed to assign a integral goodness-of-fit to each model run and eventually determine the best model run during calibration. Further tools have been developed to allow the visualization of maps, hydrographs and calibration results. The flood forecast version of the model core (Figure 4) has been provided to CWRC with a direct link to their database of rain and runoff gauges. More than 70 gauges (see Figure 6) provide the current rainfall four times per day at 2:00, 8:00, 14:00 and 20:00. Thus, up to 4 forecasts per day can be computed by the system. Since no other meteorological data can be provided in real-time, the forecast system uses climatologies of temperature, radiation and relative humidity for the period 1990-2002 instead. Local weather forecast for the Three Gorges Area is derived by the meteorological section of CWRC and can be interactively loaded into the hydrological forecast system. A quantitative precipitation forecast by means of a numerical weather prediction model is not yet operational at CWRC.
Figure 4: Operational flood forecast for the test catchment Daning using the PREVAH-Modelling System.
Module 3: Model calibration for the test catchment Daning Several questions have been addressed during the calibration of the hydrological model (Refsgaard 1997) for the test catchment Daning. Well-based suggestions about the most suitable precipitation interpolation method and grid size for the rainfall/runoff simulation of the regional scale study area in central China have been target of particular investigations (Sonderegger 2004). Model runs were driven by interpolated meteorological data at time resolution of one day. The calibration period was 1990-1993. Two verification periods were chosen: 1994-1997 and 2000-2002. The choice of the precipitation interpolation method is, due to the scarcity of the meteorological stations in the study area, of major influence on the rainfall/runoff model results (Susong et al. 1999, Ninyerola et al. 2000). The suitability of different precipitation interpolation methods was verified by comparing simulated runoff with the corresponding observed value (Table 1, Figure 5). Results with Ordinary Kriging were superior to the results attained by Inverse Distance Weighting interpolation. As for the study area no stable variogram could be achieved, the best fitting variogram model could not be derived from the variogram plot but had to be selected by comparing the goodness of the resulting rainfall/runoff modeling for each variogram model under investigation (Table 1). The spherical model proved to be the most suitable variogram model, whereas differences in general, and especially to the Gaussian model, were small. Several approaches with elevation detrending (e.g. detrended inverse distance weighting detrended ordinary Kriging) were tested for the investigated area, but all model results attained by elevation detrended precipitation interpolation methods proved to be extremely unstable (Table 1). This instability can probably be explained by the enormous altitudinal extrapolation arising from the absence of meteorological stations at higher elevation. The scarcity of meteorological stations and their doubtful elevation precision seem to impede the use of elevation detrended interpolation methods for the study area. The identification of most suitable grid size for the rainfall/runoff simulation has been an unresolved problem for hydrologists in form of scale problems for decades (Vazquez et al. 2002, Booij 2003). In this study area this subject has arisen due to the enormous heterogeneity of the quality of the input-data. The model was calibrated for grid resolution of 270, 450, 630, 810 and 990 meters. For grid sizes above 810 meters resolution a slight decrease in the goodness-of-fit results could be recognized. For further work in the project area a grid size of 630 meters resolution was finally chosen. Table 1: Model calibration and verification for the Daning catchment and evaluation of different methods for precipitation interpolation. Values represent an integral goodness-of-fit measure after application of different objective functions (Sonderegger 2004). Interpolation Variogram Calibration Verification Verification Method
(1990-1993) (1994-1997) (2000-2002)
Kriging
Gaussian
0.743
0.393
0.501
Kriging
Exponential
0.755
0.383
0.443
Kriging
Spherical
0.701
0.383
0.524
Kriging
Combination
0.741
0.365
0.471
Detended Kriging
Gaussian
0.624
0.018
-
Detended Kriging IDW
Spherical -
0.609 0.65
0.044 0.337
0.37
80
Observation
-1 Discharge [mm d ]
Simulation
60 40 20 0 1-Jan-90
1-Jan-92
31-Dec-93
31-Dec-95
30-Dec-97
Figure 5: Observed and simulated daily discharge hydrograph for the Daning catchment within the 8-year period 1990-1997. Module 4: Training and know how transfer A fundamental aspect of the project was the training of local specialists. At CWRC, remote sensing technologies didn’t exist and also the use of GIS was limited. Therefore, an important step was to select local specialists and provide them with the necessary know how for the use of these tools. In a first step, a training course took place in Wuhan leading to the selection of four persons. These specialists were then sent to ITC (International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands) four a six months training in the application of remote sensing and GIS technologies in hydrology. After a successful completion at ITC, an additional 14-day training course took place in Switzerland focusing on the project-relevant fields (forecasting and monitoring). The Swiss-lead training was (and will be) continued with courses in Wuhan. Within this training, the definition of the workflow for flood forecasting and monitoring has been setup. In parallel a second group of CWRC collaborators has been trained in catchment hydrology and use of the PREVAH-Modelling-System during two-week course held in Switzerland. A second two-week training in the use of the real-time flood forecasting version of the system has been held in Wuhan (China). For this purpose a web-based training platform was developed and will be continuously extended and updated to provide further support beyond the end of the project. This part of the project was very important to allow further extension of the use of the installed system in other sub-catchments of the Three Gorges Area and possibly later in other further upstream areas of Changjiang. The local hydrologist and remote sensing specialists are now ready to use the system independently. Conclusions and Outlook The presented project set the basics to provide the CWRC with a complete infrastructure and related know-how for setting up a flood forecast and monitoring system. During the last mission for hydrological training, the flood forecasting system has been extended to four other sub-catchments of the directly contributing area of the Three Gorges Dam reservoir (Figure 5). CWRC is going to test the system operationally during the flood season 2005.
At the same time the local personnel, which participated to the project training courses, will try to add further sub-catchments to the operational version of the model and moving on that way towards the declared final goal of the project, which is the installation of a system able to improve the forecasting standard within the Three Gorges area. For the Swiss counterpart, the project means a great progress setting up a portable, spatially distributed real-time forecasting system which can easily be transferred to other areas with similar data availability. The minimal system requirements are the availability of a GIS database with relevant spatial information in metric coordinates (e.g. UTM) consisting of a digital elevation model, a digital land-use map and a digital soil map (e.g, FAO-UNESCO 1998). The hydro-meteorological database should contain at least 6 years of quality-checked runoff data of the target catchment(s) and 6 years of quality-checked daily meteorological data for at least 3 stations close to area under investigation. The station co-ordinates (in the same coordinate system as the spatial data) and attributes (e.g. elevation) must be well known. This was one of the most limiting factors during the Changjiang Flood Forecasting Assistance Project since accurate information on the elevation and location of the stations (Latitude and Longitude in Degrees/Minutes only) was not available. Based on our experiences, the concept of coordinate-systems and transformation was one of the most critical points for a successful collaboration with CWRC. Another critical point was the data availability. No temperature data were available within the Three-Gorges Area and only three fully equipped meteorological stations were available in a meaningful distance from the catchment. This might limit the improvement of the flood forecasting accuracy. CWRC plans for 2005 to introduce a quantitative precipitation forecast based on the MM5 numerical weather prediction model. An eventual follow up of the project should include the task of coupling the real-time version of the PREVAH-Modelling system with the forecasts provided by MM5.
Figure 5: Real-time rain gauges network (dots) within the Three Gorges Area. The colored areas indicate the sub-catchments that are currently ready for operational flood forecast with the PREVAH-Modelling-System.
As a final consideration, we would like to point out that training efforts must be continued for at least one more year to reach a sustainable situation of the project, since it was noticed that the Chinese partner were very interested in including new technologies in their flood forecasting setup, but need still some support in order to successfully use the new and modern tools for flood forecasting. This is a well known effect in such cooperation projects and can only be overcome by joint efforts in adapting the workflow to the local context.
Acknowledgements The Humanitarian Aid of the Swiss Agency for Development and Cooperation SDC founded the project in the framework of its prevention efforts. We thank the CWRC organization for the close collaboration and for being so generous to us during our missions in China. We thank Andreas Götz of FOWG for helping us in the first contacts to the Chinese partners. Christof Sonderegger is acknowledged for the redaction of his thesis on the calibration and verification of PREVAH for the Daning catchment. Daniel Viviroli was a key contributor in the development of the hydrological modelling system. References Booij, M.J., 2003. Determination and integration of appropriate spatial scales for river basin modelling. Hydrological Processes, 17(13): 2581-2598. FAO-UNESCO, 1988. Soil Map Of World. World Soil Resources Report 60, FAO, Roma. Gurtz, J., Baltensweiler, A. and Lang, H., 1999. Spatially distributed hydrotope-based modelling of evapotranspiration and runoff in mountainous basins. Hydrological Processes, 13(17): 2751-2768. Gurtz, J. et al., 2003. A comparative study in modelling runoff and its components in two mountainous catchments. Hydrological Processes, 17(2): 297-311. Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models (1), a discussion of principles. Journal of Hydrology, 10: 282-290. Ninyerola, M., Pons, X. and Roure, J.M., 2000. A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. International Journal of Climatology, 20(14): 1823-1841. Refsgaard, J.C., 1997. Parameterisation, calibration and validation of distributed hydrological models. Journal of Hydrology, 198(1-4): 69-97. Schmugge, T.J., Kustas, W.P., Ritchie, J.C., Jackson, T.J. and Rango, A., 2002. Remote sensing in hydrology. Advances in Water Resources, 25(8-12): 1367-1385. Schulla, J., 1997. Hydrologische Modellierung von Flussgebieten zur Abschätzung der Folgen von Klimaänderungen, Zürich. Sonderegger, C., 2004. Rainfall/Runoff Modelling of a Sub-Satchment of the Yangtze in China, Diploma Thesis at the ETH and University Zürich, 103 pp. Susong, D., Marks, D. and Garen, D., 1999. Methods for developing time-series climate surfaces to drive topographically distributed energy- and water-balance models. Hydrological Processes, 13(12-13): 2003-2021. Vazquez, R.F., Feyen, L., Feyen, J. and Refsgaard, J.C., 2002. Effect of grid size on effective parameters and model performance of the MIKE-SHE code. Hydrological Processes, 16(2): 355-372. Verbunt, M., Zappa, M., Gurtz, J. and Kaufmann, P., 2005. Verification of a coupled hydrometeorological modelling approach for alpine tributaries in the Rhine basin. Journal of Hydrology (submitted). Zappa, M., 2002. Multiple-response verification of a distributed hydrological model at different spatial scales, Dissertation No. 14895, ETH Zurich.
