A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server ..... implemented and for each landscape type a dedicated critical contributing area is derived. In order to ...
CCM River and Catchment Database Version 1.0 Jürgen Vogt Roberto Colombo, Maria Luisa Paracchini, Alfred de Jager, Pierre Soille
Agri-Environment Action Catchment Characterisation and Modelling (CCM) Institute for Environment and Sustainability EC Joint Research Centre 21020 Ispra (Varese) - Italy
EUR 20756 EN
June 2003
joint research centre EUROPEAN COMMISSION
European Commission
LEGAL NOTICE Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of the following information
A great deal of additional information on the European Union is available on the Internet. It can be accessed through the Europa server (http://europa.eu.int)
Cataloguing data can be found at the end of this publication
© European Communities, 2003 Reproduction is authorized provided the source is acknowledged.
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Table of Contents Table of Contents ............................................................................................................................. 3 Abstract ............................................................................................................................................ 6 1.
Introduction ............................................................................................................................. 6 1.1
User Requirements and Objectives of the Work ......................................................... 7
1.2
Potential Applications ................................................................................................. 7
2.
Mapping Area.......................................................................................................................... 7
3.
Projection System.................................................................................................................... 8
4.
Overview on the Methodology................................................................................................ 8
5.
Input data................................................................................................................................. 9
6.
7.
5.1
Digital Elevation Data ................................................................................................. 9
5.2
Inland Water Body Layer .......................................................................................... 10
5.3
Reference Rivers for Drainage Enforcement ............................................................ 12
5.4
Environmental Data Layers....................................................................................... 12
5.5
Reference Data for Validation................................................................................... 13
Landscape Stratification and Channel Threshold Definition ................................................ 13 6.1
Landscape Stratification ............................................................................................ 13
6.2
Slope contributing area analysis................................................................................ 14
River Network Extraction ..................................................................................................... 15 7.1
Drainage Enforcement in Flat Terrain (Stream Burning) ......................................... 15 7.1.1
Adaptive Burning ....................................................................................... 15
7.1.2
Forced Burning........................................................................................... 16
8.
Positioning of Eurowaternet Stations .................................................................................... 17
9.
Hydrological Coding System ................................................................................................ 20
10.
Data Validation...................................................................................................................... 21 10.1
10.2
River Network ........................................................................................................... 21 10.1.1
Official River Length ................................................................................. 21
10.1.2
Buffer analysis for Rivers........................................................................... 23
Catchments ................................................................................................................ 23 10.2.1
Official Catchment Size ............................................................................. 23
10.2.2
Buffer Analysis for Catchment Boundaries ............................................... 25
10.2.3
Catchment Geometry.................................................................................. 26
10.2.4
Eurowaternet Catchment Size .................................................................... 26
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11.
Important Database Characteristics....................................................................................... 27 11.1
Key Characteristics.................................................................................................... 27
11.2
Limitations................................................................................................................. 28
11.3
Known Problems ....................................................................................................... 28
11.4
Possible Improvements for Version 2.0 .................................................................... 28
12.
Data Distribution, Copyright and Disclaimer........................................................................ 29
13.
Further Information ............................................................................................................... 29
14.
References ............................................................................................................................. 30
List of Figures Figure 1:
Overview on the Methodology................................................................................... 8
Figure 2:
Processing Windows .................................................................................................. 9
Figure 3:
Grid–cell resolution of the original Digital Elevation Data ..................................... 10
Figure 4:
(a) Preparation of the inland lake layer and (b) example of the result for the area of Lago Maggiore, Lago di Lugano and Lago di Como. inland marshes (dark blue), inland lakes (light blue). ....................................................................... 11
Figure 5:
Estuaries: (a) the estuary of the river Seine in France as mapped in CORINE land cover and (b) after defining the primary drainage channel. ............................ 11
Figure 6:
Drainage Density classes for Europe (Dd: Drainage density)................................. 14
Figure 7:
Example of obligatory burning in rugged terrain of the Italian Alps: (a) original network , without obligatory burning (b) river stretches used for burning in yellow (c) resulting river network. ......................................................... 16
Figure 8:
Example of the CCM River Network for northern and central Italy........................ 17
Figure 9:
Search windows (coloured) for finding the station position. ................................... 18
Figure 10:
Station positioning: original position (blue), new position (red), not repositioned (green).................................................................................................. 19
Figure 11:
Principle of Pfafstetter coding of main rivers and tributaries .................................. 20
Figure 12:
Example of the Pfafstetter coding for the river Thames .......................................... 21
Figure 13:
Example of the under-estimation and over-estimation in the Kokemäenjoki catchment (Finland). Note that many small errors along the catchment perimeter are due to imperfect co-registration of the data with different scales and from different sources........................................................................................ 26
Figure 14:
Distribution of calculated drainage area (CCM) and reported drainage area (EWN) after repositioning........................................................................................ 27
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List of Tables Table 1:
Critical contributing area (CA) for each landscape class. ........................................ 14
Table 2:
Comparison of officially reported river length (REF) and CCM river length (CCM)). .................................................................................................................... 22
Table 3:
Results for different buffers .................................................................................... 23
Table 4:
Comparison of officially reported drainage area (Reference Area) and calculated drainage area (CCM Area) for 49 major river basins. ............................ 24
Table 5:
Buffer analysis with Spanish and Finish reference datasets..................................... 25
Table 6:
Number of items in the River and Catchment Database .......................................... 27
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Abstract The availability of digital data on rivers and lakes and their drainage basins (catchments), including information on the characteristics of these entities, is important for the analysis of environmental pressures and their impact on water resources. Recent policies, such as the Water Framework Directive require the setup of Geographical Information Systems on water bodies and drainage basins as a basis for the development of River Basin Management Plans and for the reporting to the European Commission. GIS tools allow for the combined analysis of digital elevation data and environmental parameters in order to derive at least part of the required information over extended areas. This report describes the development of a European-wide database of river networks and drainage basins. It presents an advanced methodology making use of medium resolution digital elevation data (250 m grid cell size) and information on climate, vegetation cover, relief, soils and lithology to derive river networks and catchments for the European continent. In order to model the spatial variability in drainage density, a landscape typology is implemented and for each landscape type a dedicated critical contributing area is derived. In order to comply with the needs of environmental monitoring, lakes and transitional waters are considered during river mapping and catchment delineation. With respect to the EEA’s monitoring needs, the Eurowaternet measuring stations are positioned and their corresponding catchments delineated. A dedicated algorithm has been developed to overcome problems of automatic river mapping in flat terrain.
1.
Introduction
The availability of digital data on rivers and lakes and their drainage basins (catchments), including information on the characteristics of these entities, is important for the analysis of environmental pressures and their impact on water resources. Recent policies, such as the Water Framework Directive require the setup of Geographical Information Systems on water bodies and drainage basins as a basis for the development of River Basin Management Plans and for the reporting to the European Commission. Also the European Environment Agency (EEA) is in need of such data for their monitoring activities, covering the whole European continent. In response to this need, the Catchment Characterisation and Modelling (CCM) activity of JRC’s EuroLandscape project (http://eurolandscape.jrc.it) developed a European-wide database of drainage networks and catchment boundaries. The resulting data layers should become part of the Eurostat-GISCO database. Under Framework Programme 6, CCM is now part of the Agri-Environment action of JRC (http://agrienv.jrc.it), which aims at the development of agrienvironmental indicators and at the quantification of the status and trends of European landscapes. Currently, the level of detail of available European river network and catchment data layers ranges from mapping scales of 1:10,000,000 to 1:1,000,000. As a consequence, the use of these data is restricted to small-scale studies, while the need for more detailed assessments (i.e. quantity, quality and trend of water resources, analysis of environmental pressures and impacts) is increasing. The automated extraction of topographic parameters, including valleys and drainage networks, from digital elevation models (DEMs) is recognized as a viable alternative
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to traditional surveys and manual evaluation of topographic maps, particularly as the quality and coverage of DEM data increases. CCM develops algorithms for DEM analysis and tries to achieve a mapping scale of 1:250,000 to 1:500,000, using highly automated data processing tools. This allows covering large areas, to repeat the processing when necessary and to extend the mapping area, if requested. This report is intended to accompany the data and to introduce the user of the database to the data and the underlying methodology. It also presents results from some first validation exercises. 1.1
User Requirements and Objectives of the Work
The requirements for a European-wide digital dataset of drainage networks have first been discussed as part of an expert meeting organised in July 1999 by the EuroLandscape project of JRC Ispra (Vogt et al. 1999). Subsequently, user requirements have been further discussed with the European Environment Agency and Eurostat-GISCO. Based on these discussions, the most important user requirements for such a dataset can be summarised as follows: • • • • • • • • 1.2
European or even pan-European coverage, homogeneous data with a consistently high quality across the whole area of interest, a fully connected and hierarchical network of rivers, a nested set of catchments according to the Strahler order of the river reaches, a link between the various types of water bodies (rivers, lakes, transitional waters) and their respective catchments, a set of catchment characteristics useful for the calculation of proxy pressure indicators, location of the Eurowaternet station network and identification of the drainage basin of each monitoring station, a level of detail corresponding to a nominal mapping scale of 1:250,000 to 1:500,000. Potential Applications
The presented database can be useful for a wide range of applications, including mapping, monitoring and modelling activities. A consistent database of river networks and catchment boundaries can support a variety of hydrological, ecosystem and climate models. Examples of such applications are the analysis of river discharge, sediment and nutrient transport or the analysis of biophysical attributes of drainage basins and the calculation of environmental pressure indicators.
2.
Mapping Area
The area covered with the current version 1.0 of the database extends from the Mediterranean to northern Scandinavia and from the Atlantic Ocean to roughly 38 degrees Eastern longitude. Its sheer extent asked for the development of automated tools. An important constraint for this large-area application was the availability of basic input data and the methodology developed, therefore, had to be based on data readily available over all or at least most of the study area.
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3.
Projection System
The projection system applied for data analysis is the Lambert Equal Area system, defined in metric units. The following system parameters apply:
4.
Radius of the sphere of reference:
6378388.0
Longitude of center of projection:
10 0 0.0
Latitude of center of projection:
52 0 0.0
False easting (meters):
0.0
False northing (meters):
0.0
Overview on the Methodology
Figure 1 provides an overview of the methodology implemented. The following main processing blocks can be identified: (1) modeling surface runoff as a basis for extracting the drainage network, including the pre-processing of the DEM (2) preparation of a landscape stratification reflecting drainage density, (3) preparation of additional information on water bodies, used to constrain the final river network, (4) data processing (i.e. extracting the river network), (5) ordering and coding of rivers, extraction of their catchments and calculation of catchment characteristics, and (6) data validation (not shown in the figure).
Modelling the Surface Runoff
Additional Information
Slope Slopeand and Flow FlowDirection Direction(D8, (D8,DDinfinf))
Accumulated Accumulated Runoff Runoff
Varying the Drainage Density
Lakes, Lakes,Lagoons, Lagoons, Coastline Coastline
Data Processing
Landscape Landscape Stratification Stratification
Critical Critical Contributing ContributingArea Area
Drainage DrainageNetwork Network with withvariable variableDd Dd Ordering, Coding, Extraction and Characterisation of Catchments
Figure 1: Overview on the Methodology
For reasons of data processing, Europe has been divided into 5 processing windows to be seen in Figure 2: (1) continental Europe, including the islands in the Mediterranean, (2) Scandinavia, (3) the British Isles and Ireland, (4) Iceland, and (5) Islands in the Atlantic Ocean.
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4 2
3 1
5
Figure 2: Processing Windows
5. 5.1
Input data Digital Elevation Data
Digital Elevation Models (DEMs) with grid-cell dimensions of 100 or 250 metres were merged in order to cover the European Union and the Accession Countries. For areas where DEMs at this resolution could not be acquired (e.g., Iceland, Russia), data from the HYDRO1K global digital elevation dataset (http://edcdaac.usgs.gov/gtopo30/hydro) with a 1000 m grid-cell resolution were used (Figure 3). DEMs where mostly acquired in geographic coordinates from different providers (e.g., Nigel Press Associates UK, Geosys F, National Mapping Agencies). In order to merge the different datasets, they were initially sub-sampled to a grid with a resolution of 0.001 decimal degrees, using a nearest neighbour algorithm. This grid-cell size represents a conservative value and allowed maintaining the integrity of elevation values. This grid-cell resolution corresponds to about 20 meters at the highest latitudes and to about 80 meters for the Mediterranean countries. All DEMs were then merged, giving priority to the DEMs with best quality. In cases were the quality of two datasets was comparable, the country border defined the extent of the dataset within the final mosaic. Data quality was visually checked by the operator. Cliff effects were sometimes observed along the boundaries between different DEMs. However, no filtering or edge matching techniques were required since the algorithm of river extraction resulted insensitive to them.
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Figure 3: Grid–cell resolution of the original Digital Elevation Data
Finally, the DEM mosaic at a grid resolution of 0.001 decimal degrees was projected into a Lambert Equal Area Azimuthal projection with a 250m grid-cell size, using a bilinear resampling technique. This projection system was chosen for its equal area property. For the case of islands close to the coast or marine straights narrower than 250 meters, the projection to a coarser resolution resulted in a topological degradation. This effect was mainly visible in Scandinavia, where the original DEM resolution was 100 meters. Therefore, in this area the continent was separated from the islands. Islands were then processed separately through the whole processing chain. In an ideal case every island would have to be processed individually. 5.2
Inland Water Body Layer
The preparation of an inland water body layer was necessary to ensure that the extracted river network is coherent with lakes and transitional waters. Transitional waters are defined as intertidal flats, coastal lagoons and estuaries. In addition, large internal drainage basins (without outlet to the sea) were considered when extracting the river network. From CORINE Land Cover (CLC) vector data (CEC, 1993) and SWISS Land Cover data (100 meter grid) the classes corresponding to inland marshes (code 35), peat bogs (code 36), salt marshes (code 37), salines (code 38), intertidal flats (code 39), water courses (code 40), water bodies (code 41), coastal lagoons (code 42), estuaries (code 43), and glaciers and perpetual snow (code 34) were selected. The dataset was further extended by merging it with the GISCO Lakes at 1:50,000 scale (HY Lake) and with PELCOM 1000 meter grid. CORINE Land Cover
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was the primary data set. Where CORINE Land Cover was not available, the others sources were used in order of decreasing quality (Figure 4).
CORINE 100m Inland marshes (CORINE) Inland lakes (CORINE/GISCO/PELCOM)
GISCO Lakes
PELCOM 1K
(a)
(b)
Figure 4: (a) Preparation of the inland lake layer and (b) example of the result for the area of Lago Maggiore, Lago di Lugano and Lago di Como. Inland marshes (dark blue), inland lakes (light blue).
Intertidal flats represent areas periodically submerged by seawater and they were considered as sea. In such cases the final river network stops at the landward edge of the intertidal flat. Lakes, inland marshes and coastal lagoons with no connection to the sea and larger than 8 grid-cells (0.5 km2), were used to force the drainage channels to flow through the water body along its centre line. These water bodies are often situated in flat areas and without such constraint the extracted river network could cross the water surface many times causing incoherence between river and water body. However, in some cases with errors in the DEM, this procedure did not fully perform. These cases need revision in a follow-up version. When coastal lagoons have an outlet to the sea they were considered as sea and the drainage channel stops at the edge of the lagoon. Also estuaries were considered as sea. In some cases they had to be modified with respect to the original CLC class. An example is given in Figure 5, for the case where an estuary has a bifurcation and the definition of a primary drainage path was necessary.
a)
b) Figure 5: Estuaries: (a) the estuary of the river Seine in France as mapped in CORINE land cover and (b) after defining the primary drainage channel.
Within a glacier no drainage line could start. Natural pits (internal drainage basins) were identified on basis of a priori knowledge. At the given scale only a few natural pits were
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considered, referring to lakes larger than 0.5 km2. They correspond to lakes Trasimeno and Fucino in Italy, lake Prispanko situated at the border between Albania and Macedonia, Neusiedler See in Austria and Myvatn lake in Iceland. 5.3
Reference Rivers for Drainage Enforcement
Drainage enforcement (also called river burning) is necessary in very flat terrain, where the DEM does not provide sufficient information for the location of the drainage channel. In these cases information from existing maps can be used to modify the DEM in such a way that an artificial canyon is created in the DEM. Since the problem mainly occurs downstream of large rivers, the GISCO river network at 1:3,000,000 scale (www.europa.eu.int/comm/eurostat) was used as the primary input for this procedure. It was first edited in order to remove lakes, double-lined streams, and artificial watercourses and a primary drainage path was chosen for braided river systems. It was then rasterised at a 250m grid-cell size. Where necessary, the GISCO river network was complemented with data from the Bartholomew river network at 1:1,000,000 scale (http://www.bartholomewmaps.com) and with information from CORINE Land Cover. This in order to ensure a correct flow pattern of the major tributaries in extended flat terrain (e.g., the plain of the river Po). 5.4
Environmental Data Layers
The following data layers were used to derive a landscape stratification reflecting the terrain aptitude to develop a certain drainage density: •
Mean annual precipitation (1975 – 1999) was used as the climate indicator. This information was derived from the meteorological database of the MARS project (Monitoring Agriculture by Remote Sensing), which are available on a 50 km grid for the whole of Europe (Van der Voet et al. 1994, Terres 2000).
•
The influence of the terrain morphology has been considered through the relative relief, defined as the maximum altitude difference in a moving window of 3 by 3 grid cells, calculated from the DEM.
•
The percentage of surface covered by vegetation was used in the analysis due to its effect on critical shear stress and thus its control on channel initiation. CORINE Land Cover data with a grid-cell size of 250 m were reclassified into 14 classes and monthly cover percentages were assigned to each class.
•
As a proxy indicator of saturated soil hydraulic conductivity, soil texture has been chosen as the main soil factor affecting drainage density. Soil texture was derived from the European soil map (ESBSC 1998) at a scale of 1:1,000,000.
•
From the European soil map the parent material corresponding to each soil mapping unit was extracted by deriving the dominant lithology. Based on these data a rock erodibility factor was calculated.
Through a weighted combination of these variables, a Landscape Drainage Density Index (LDDI) was then derived, which was classified into a few classes. The methodology is described in detail in Colombo et al. (2001) and Vogt et al. (2003), including references to the scientific background.
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5.5
Reference Data for Validation
Different reference data representing rivers and catchment boundaries have been used for validation purposes. •
A digital river dataset of Germany, provided by the German Federal Environment Agency (Umweltbundesamt,UBA);
•
Digital catchment boundaries for Spain, provided by the Spanish Centro de Estudios Hydrograficos; and
•
Digital catchment boundaries for Finland, provided by the Finnish Environment Institute.
Data for Germany, Spain and Finland were at a scale of approximately 1:50,000. In addition, the Bartholomew 1:1,000,000 digital river network (www.bartholomewmaps.com), the Eurowaternet station network of the EEA (Nixon et al. 1998) and the Eurostat-GISCO database (www.europa.eu.int/comm/eurostat), have been used for data validation. Finally published data on the size of large river basins and on the length of major rivers have been used as reference.
6.
Landscape Stratification and Channel Threshold Definition
Continental or global river networks and associated catchments are generally derived from digital elevation data by imposing a constant value for the minimum contributing area needed to form and maintain a channel. Moreover, the threshold area for channel initiation is usually specified arbitrarily although it is recognised that different threshold areas will result in substantially different channel networks for the same basin. This standard approach does not take into account the spatial variation in landscapes as well as the environmental factors driving channel initiation. To overcome this limitation river networks have been derived by developing a new method that combines a landscape stratification with the analysis of the log-log local slope vs contributing area as described in Colombo et al. (2001) and Vogt et al. (2003a; 2003b). 6.1
Landscape Stratification
The rationale for implementing a landscape stratification is to overcome the shortcomings of using a single contributing area threshold for the extraction of drainage channels over an extended and heterogeneous area. Europe has therefore been classified into different landscape types that reflect regions with variable aptitudes for developing drainage channels. This landscape stratification has been implemented using a parametric model that takes into account a set of five variables [annual rainfall, local relief, mean vegetation cover, soil transmissivity, and bedrock erodibility] that govern drainage density. Each variable was classified into three to seven classes, and its relationship with drainage density was evaluated (see Vogt et al., 2003a for a detailed discussion). Based on a weighted combination of these variables, a Landscape Drainage Density Index (LDDI) was then calculated. Based on this index, ten landscape classes have been defined. The resulting map is shown in Figure 6. The lack of data at the pan-European level hampered the implementation of the landscape stratification for the entire pan-European area (especially in the very eastern part of the study
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area). In areas where CA thresholds could not be defined on the basis of the described input data, a layer of relative relief derived from the HYDRO 1K DEM was used to get a first approximation. For these cases relative relief was computed as the difference between maximum and minimum elevation within a 3x3 moving window and then resampled to a 250m grid using a bilinear interpolation technique.
Drainage Density Classes High Dd
Low Dd
Figure 6:
6.2
Drainage Density classes for Europe (Dd: Drainage density)
Slope contributing area analysis
The LDDI was reclassified into 10 classes representing different landscapes and for each landscape type the log-log relationship between local slope and contributing area was analyzed. This step allowed defining a critical contributing area for each landscape type, which determines the density of the derived drainage network. Table 1 presents the critical contributing area for each of the ten classes. Table 1: Critical contributing area (CA) for each landscape class.
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Landscape Class
CA (km2)
1 2 3 4 5 6 7 8 9 10
1 3 6 15 20 30 35 50 60 80
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7.
River Network Extraction
Rivers have been extracted using a new algorithm described in Soille et al. (2003). This algorithm initially removes spurious pits by carving the digital elevation model, following the concepts of morphological image analysis (Soille, 2003). The carved DEM is then used as input to an adaptive drainage enforcement process that aims at creating a more precise flow direction path, especially in flat terrain. Due to the extent of the study area, flow direction was computed by using the traditional D8 algorithm and taking into account inland and transitional water bodies as well as the reference rivers. For all the water-related classes connected to the sea (intertidal flats, coastal lagoons and estuaries) the flow direction was stopped before reaching the coastline, while in all the other cases the flow path was connected with the coastline. The flow accumulation area was then computed by iteratively adding up the pixels along the downstream flow directions using the fast algorithm described by Soille and Gratin (1994). Basin delineation was done by automatically selecting outlet cells (river confluences, lake inflows, lake outflows, nodes along the coastline) and classifying the rivers according to the Strahler system. The highest order (8th) was found for the Danube and Rhone rivers while, for example, the Po, Rhine, Ebro, Duero and Garonne rivers belong to the 7th Strahler order. Finally, a vector database was generated from the river network and catchment grids. A further correction will be introduced in the case of sub-basins intersecting lakes. In test areas subbasins were recalculated so that the outlet of each basin lies along the lake perimeter. The tests showed that in alpine areas the overestimation of subcatchment areas without this correction could be as large as 10%. The procedure is planned for implementation in version 2.0. 7.1
Drainage Enforcement in Flat Terrain (Stream Burning)
7.1.1 Adaptive Burning An adaptive stream burning algorithm has been developed, which uses selected segments of the reference river network as input and, by an iterative process, defines the places where the reference network deviates substantially from the automatically detected river networks. While stream burning can itself create some artifacts, this procedure ensures that rivers are burned only where it is really necessary, preserving the topography of the carved DEM. It reduces coregistration problems that produce double streams and alleviates discrepancies in scale or generalization level between the DEM and external streamlines that may lead to the removal or creation of features such as meanders. In order to identify areas where burning of rivers was necessary, the river extraction algorithm was run without drainage enforcement in a first run, resulting in an unconstrained network. Through an automatic comparison with the reference river network, river stretches which deviated substantially in position from the mapped rivers (e.g., distance > 650 meters) were identified. For these stretches the DEM was modified by burning the reference rivers into the DEM (i.e. lowering the altitude values along the correct flow line by a certain amount, thus creating an artificial canyon). In a second run the algorithm for river extraction automatically followed the correct drainage line as a result of the specific topography. The actual amount of reference segments used to enforce flow direction, finally depends on the automatic comparison between an unconstrained river network derived in the first run and the reference river network. A first indication of the quality of the derived river network can be
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drawn from the ratio between the total length of the burned rivers and the total length of the automatically derived river network, expressed as a percentage. Over the entire area this value is about 6% (i.e. six percent of the final river network has undergone some modification due to burning). As expected, the spatial distribution of the automatically corrected errors is strongly related to the dominant relative relief. Errors decrease with increasing relief energy, from values of about 15 per cent in flat or almost flat areas to zero per cent in mountainous regions. 7.1.2
Forced Burning
In addition to the automatic adaptive burning, persistent errors have been corrected with a forced burning. In these cases, streams and crest lines (drainage divides) were digitised and used as obligatory input for the burning procedure. This option improves the performance of the algorithm in areas with noisy topography (i.e., DEM limitations). An example is given in Figure 7, where the digitalisation of two small river segments (yellow) allowed to correct some confluence errors, which were due to small errors in the original DEM. The mask with the river stretches used for obligatory burning can be edited and amended any time, thus improving the result as new errors have been detected.
a)
b)
c) Figure 7:
Example of obligatory burning in rugged terrain of the Italian Alps: (a) original network , without obligatory burning (b) river stretches used for burning in yellow (c) resulting river network.
As an example, the resulting river network for northern and central Italy is shown in Figure 8. Coastal lagoons are shown in light green. The red dots represent Eurowaternet measuring stations.
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Figure 8: Example of the CCM River Network for northern and central Italy.
8.
Positioning of Eurowaternet Stations
In accordance with the needs of the EEA, data from the Eurowaternet (EWN) station network of the EEA (Nixon et al. 1998, Boschet et al. 2000) have been positioned on the river network. Eurowaternet is an information and monitoring network designed by the European Topic Centre on Inland Waters (ETC/IW), that provides the EEA with information on the status and trend (quality and quantity) of Europe’s inland water resources. This information should also be related to environmental pressures and impacts. For the latter analysis, information on the size, position and characteristics of the drainage area for each station are, however, necessary. The monitoring stations are distributed along the river network and data are reported to the EEA from Member States and Accession Countries on a voluntarily basis. In total the EWN database contains 3490 river stations and 1249 lake stations. For version 1.0 of the river database, the positioning has been done for the river stations only. In addition, only those stations could be positioned, for which the coordinates of their location and the drained area were reported. This constraint reduced the available data to 2552 river stations. The positioning procedure is based on a comparison between calculated and reported size of the area drained by each station. Starting from the initial position as given in the EWN database, the procedure compares measured and calculated drained area and moves the location
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of a station to the most probable point along the river. The underlying assumption is that the coordinates of the stations have, in principle, a higher positional accuracy than the CCM river network and that small locational errors can be corrected using the catchment size as the controlling parameter. The most probable point on the river is defined by calculating the difference between the catchment area reported in the EWN database and the area drained by each cell along the river within a window centered on the station. The cell corresponding to the lowest difference in area is the one to which the station is repositioned. The procedure starts with a search window of 9 * 9 cells (approximately 1000m from the station) and only those stations for which the difference in area estimate is lower than 15% are retained. The rest of stations is used as input for a second run with a search window enlarged to 13 * 13 cells (approximately 1500m) around the original position. For those stations that still have a catchment error larger than 15% a third run is made, with a search window of 17 * 17 cells (approximately 2000m) (Figure 9). Note that the stations are not simply assigned on the basis of a nearest neighbour relationship to the rivers. This is important, especially since many stations are located close to river confluences with considerable ambiguities as to which river the station should be assigned. In these cases a simple nearest neighbour assignment could lead to frequent errors. An example for the repositioning is given in Figure 10.
original station position Figure 9:
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Search windows (coloured) for finding the station position.
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2 km Figure 10:
Eurowaternet Station positioning
With this methodology 68.7% of stations could be correctly assigned, the average moving distance is 1058 meters and the average difference between the catchment area estimated and the catchment area reported in the Eurowaternet database is 3.3 per cent. The remaining 31.3% of stations could not be automatically positioned due to different reasons: 1. the detail of the extracted river network may be insufficient, especially for stations monitoring small catchments in the headwater areas (i.e. the critical contributing area is larger than the area drained by the station), 2. the CCM river network may have positional errors, especially in flat terrain, 3. the EWN database may contain errors in the station position data, 4. in areas where the 1 km resolution Digital Elevation Model was used, the error in area estimate for the catchment may be too large.
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9.
Hydrological Coding System
In order to facilitate more advanced analysis techniques, the river and catchment database should include a structured hydrological coding system, which allows implementing queries on upstream-downstream relationships within the network (including the respective catchments). This issue has been discussed at length in the frame of the GIS Working Group under the Common Strategy for the Implementation of the Water Framework Directive. The “Guidance Document on Implementing the GIS Aspects of the Water Framework Directive” (Vogt, 2002) summarizes the most pertinent points and the review of hydrological feature coding systems of Britton (2002) gives a detailed analysis of the needs and possibilities. In line with the recommendations given in the above mentioned documents, the Pfafstetter coding system has partly been implemented in the current database. Pfafstetter codes can be used directly to determine if discharge in a sub-catchment impacts on a potentially downstream channel. In principle, this can be achieved without the need for specific GIS analysis. However, Pfafstetter codes are not able to cater completely for lakes and marine waters and further consideration is required in order to produce a system that adequately covers all waters in an integrated way (see Vogt, 2002 and Britton, 2002 for more details). The principle of Pfafstetter coding of the main tributaries is shown in Figure 11 and Figure 12 shows an example for the river Thames. Due to the complexity of the represented networks and due to the size of some of the largest river basins in Europe (e.g., Danube >800,000 km2), the Pfafstetter system could, however, not be fully implemented for version 1.0 of the database. Some problems still need to be resolved. The implemented codes, therefore, need to be seen as a test and should be taken with caution.
6
Sea
Sub-catchment code 46
5
code 4 6
6
2
4
4 3
Boundary of catchment code 4
8
2
1
Figure 11: Principle of Pfafstetter coding of main rivers and tributaries
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Figure 12: Example of the Pfafstetter coding for the river Thames
10. Data Validation Rivers and catchments were validated according to different procedures. In order to provide a precise evaluation of the European dataset, the results were qualitatively and quantitatively compared to a series of independent datasets such as existing digital river datasets, catchment boundaries and information on catchment size. The presented procedures are only a first step. A more systematic quality checking still needs to be performed. A first indication of the quality of the derived river network could be drawn from the ratio between the total length of reference rivers used in the burning procedure and the total length of the automatically derived river network, expressed as a percentage. Over the entire area this value is about 6%, which points to the quality of the extraction algorithm. Other validation procedures are detailed below. 10.1
River Network
10.1.1 Official River Length For many cases the CCM river length could be compared to reference data collected from published sources and official web pages of local water authorities (Table 2). In order to determine CCM river lengths, headwater cells were automatically selected from the river network, and for each starting node the flow length to the sea was computed. Since the longest flow length does not necessarily coincide with the official river source, a series of main headwaters was manually selected to report the vector length. In general, the extracted flow paths are underestimated with an error of about 10% with respect to the official values. While
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the inclusion of estuaries and deltas in the official values can be a source of discrepancy, the main reason for the underestimation is probably due to the generalisation caused by the gridcell size. Table 2: Comparison of officially reported river length (REF) and CCM river length (CCM). River
22
REF Length (km)
CCM Length (km)
Percent Deviation (%)
Alcantara
52
56
7,7
Sele
64
66
3,1
Esino
80
87
8,8
Magra
90
62
-31,1
Mannu
93
80
-14,0
Bradano
116
124
6,9
Liri
120
145
20,8
Flumendosa
125
128
2,4
Mira
130
102
-21,5
Vouga
136
142
4,4
Pescara
145
153
5,5
Warnow
149
119
-20,1
Llobregat
156
152
-2,6
Ombrone
160
125
-21,9
Sado
165
157
-4,8
Tagliamento
170
162
-4,7
Volturno
175
160
-8,6
Pinios
216
208
-3,7
Mondego
220
218
-0,9
Akheloos
221
220
-0,5
Arno
241
230
-4,6
Vardar
301
372
23,6
Minho
322
310
-3,7
Ems
335
277
-17,3
Segura
340
278
-18,2
Tevere
405
325
-19,8
Adige
410
404
-1,5
Dordogne
483
433
-10,4
Jucar
535
415
-22,4
Guadalquivir
602
547
-9,1
Garonne
650
456
-29,8
Po
676
634
-6,2
Duero
770
877
13,9
Senna
776
681
-12,2
Guadiana
778
796
2,3
J. Vogt et al.
River
REF Length (km)
CCM Length (km)
Percent Deviation (%)
Rodano
813
788
-3,1
Odra
908
844
-7,0
Tajo
925
932
0,8
Ebro
927
822
-11,3
Neman
930
918
-1,3
Loire
1012
927
-8,4
Wistla
1080
1046
-3,1
Elbe
1165
948
-18,6
Rhine
1326
1235
-6,9
Danube
2850
2932
2,9
Mean Absolute Error
10,1
Standard Deviation
11,9
10.1.2 Buffer analysis for Rivers With a buffering technique location errors with respect to the reference data were analysed. The analysis has been implemented for selected rivers in Germany, using the digital river dataset of the Federal Environment Agency (Umweltbundesamt, UBA) as reference. Only selected rivers could be used, since the level of detail of the UBA dataset corresponds to a mapping scale much larger than the CCM dataset. Three buffers of 250, 500 and 1000 meters width around the reference data were calculated. The percentage of the CCM rivers falling inside these buffers was then computed (Table 3). Table 3: Results for different buffers Buffer width (m)
CCM River Network included in the buffer (%)
250
50
500
87
1000
100
Table 3 shows that the derived data fall well within a buffer of 500 metres and completely within a buffer of 1000 meters around the detailed reference data. 10.2
Catchments
10.2.1 Official Catchment Size In this case, calculated catchment areas were compared with reference values collected from the above-mentioned sources as well as from published sources and official web pages of local water authorities (Table 4).
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Table 4: Comparison of officially reported drainage area (Reference Area) and calculated drainage area (CCM Area) for 49 major river basins. Reference Area (km2)
CCM Area (km2)
Esino
1,133
1,223
7,9
Flumendosa
1,826
1,815
-0,6
Gaula
3,080
3,629
17,8
Pescara
3,188
3,172
-0,5
Simeto
4,188
4,262
1,8
Neisse
4,403
4,413
0,2
Kyronjoki
4,923
5,023
2,0
Pinios
9,500
10,909
14,8
Pite
11,231
12,548
11,7
Skellefte
11,643
11,685
0,4
Adige
12,200
14,118
15,7
Marne
12,730
12,768
0,3
Shannon
14,007
13,804
-1,4
L. Vatten/Gota
15,438
15,167
-1,8
Tevere
17,374
17,990
3,5
Kalix
18,025
17,464
-3,1
Jucar
22,075
21,952
-0,6
Oulujoki
22,841
22,701
-0,6
Lule
25,118
25,147
0,1
Ljungan
26,621
27,054
1,6
Vindel
26,700
26,934
0,9
Kokemaen joki
27,046
26,798
-0,9
Moselle-Sarre
28,152
28,042
-0,4
Dala
28,853
29,224
1,3
Angerman
31,740
31,852
0,4
Kymijoki
37,159
36,511
-1,7
Torne
39,901
40,166
0,7
Glama
42,784
42,081
-1,6
Upper Tisza
49,449
48,060
-2,8
L. Vanern/Gota
49,937
48,555
-2,8
Kemijoki
51,127
50,838
-0,6
Garonne
56,000
56,118
0,2
Guadiana
66,800
67,035
0,4
Vuoksi
68,501
65,113
-4,9
Po
71,051
71,562
0,7
Dnestr
72,500
73,183
0,9
Seine
79,000
76,021
-3,8
Basin
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Deviation (%)
J. Vogt et al.
Basin
Reference Area (km2)
CCM Area (km2)
Deviation (%)
Tajo
81,000
71,135
-12,2
Ebro
86,000
85,717
-0,3
West Dvina
88,000
79,740
-9,4
Rodano
98,000
97,236
-0,8
Odra
119,000
119,266
0,2
Loire
121,000
115,426
-4,6
Elbe
143,300
134,680
-6,0
Rhine
185,000
161,251
-12,8
Vistla
194,000
191,510
-1,3
Dnepr Danube
500,000 805,000
495,008 797,160
-1,0 -1,0
Average Absolute Error
3.4
Standard Deviation
4.6
The comparison between derived catchment area and reference area shows a good correspondence, with a mean discrepancy of 3.4%. Some large errors might be due to administrative arrangements (e.g., the inclusion of small additional catchments into the river basin management area). 10.2.2 Buffer Analysis for Catchment Boundaries For this exercise digital data from the Spanish Centro de Estudios Hydrograficos and from the Finnish Environment Institute have been used as reference data. Three buffers of 250, 500 and 1000 meters width around the reference datasets were calculated. The percentage of the derived data falling inside these buffers was then computed (Table 5). Table 5: Buffer analysis with Spanish and Finish reference datasets Buffer Width (m)
Catchment perimeters included Catchment perimeter included in the buffer around the in the buffer around the Spanish dataset (%) Finish dataset (%)
250
29
47
500
54
64
1000
83
82
The relatively large errors found for the 250 metres buffer are to be seen in the light of the grid cell of 250 meters used for the analysis. A small error in co-registration of the data will lead to an error in this case. Due to the large area to be covered and the variety of data sources (scale, resolution, original projection), small co-registration errors are rather the rule than the
CCM River and Catchment Database, EUR 20756 EN
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exception. Also the large scale difference between the derived data and the reference data has to be considered. 10.2.3 Catchment Geometry In order to estimate the potential errors due to over- and under-estimation of the catchment area, an overlay analysis has been carried out for selected catchments in Finland and Spain. An example is shown in Figure 13. Corresponding catchments were automatically compared in order to produce a table of underestimation and over-estimation areas. The total error was computed by summing all the discrepant areas normalised by the catchment reference area. Errors included in a buffer strip of 500 metres along the catchment boundaries have not been considered in this procedure. This is to eliminate the errors due to co-registration problems and due to different scales of reference and simulated datasets. Underestimati on
Figure 13: Example of the under-estimation and over-estimation in the Kokemäenjoki catchment (Finland). Note that many small errors along the catchment perimeter are due to imperfect co-registration of the data with different scales and from different sources.
The average error found for a set of Finnish catchments, which are characterised by a low relief energy, was about 10%. It decreased to about 5% for the Spanish catchments characterised by more accentuated relief. 10.2.4 Eurowaternet Catchment Size A total of 2552 EWN river stations were available for validation purposes. For each station, information on its position (co-ordinates) as well as a series of attributes, including the drainage area, was available. Starting from the given station position, EWN stations have been automatically assigned to the grid cell along the derived river network, with the smallest difference between EWN and simulated drainage area. This assignment has been done through
26
J. Vogt et al.
an iterative procedure. Stations that could not be re-positioned with an error less than 15% in area estimate, were rejected (see section 8 for further information). Based on the rejection criterion, a subset of 1944 stations was repositioned and used for the drainage area comparison. The comparison of the EWN drainage areas with the drainage areas derived from the flow accumulation matrix resulted in an average deviation of 3.3%, with a standard deviation of 3.9% with the same proportion of over and underestimation errors. Fitting the EWN areas versus the derived areas, a strong linear relationship has been found with a coefficient of determination equal to 0.99 (see Figure 14). The result of this extended analysis shows a mean error of the same order of magnitude as the comparison between reference and derived basins as previously described (Table 4).
Difference in area estimation lt 15% 2
km 250000 y = 1.0052x - 4.2742 2 R = 0.9989 200000
EWN
150000
100000
50000
0 0
50000
100000
150000
2
250000 km
200000
CCM
Figure 14: Distribution of calculated drainage area (CCM) and reported drainage area (EWN) after repositioning
11. Important Database Characteristics The following points should be considered when using the database. 11.1
Key Characteristics
The current version 1.0 of the database roughly contains the following number of items: Table 6: Number of items in the River and Catchment Database
Region
Processing Window
Primary Catchments
River Segments
Continental Europe
1
235,000
170,000
Scandinavia
2
59,500
37,300
3 and 4
16,000
12,000
UK, Ireland & Iceland
CCM River and Catchment Database, EUR 20756 EN
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Note that the number of primary catchments is larger than the number of river segments. This is due to the fact that in version 1.0 small coastal drainage areas (without an identified drainage line) have not been re-grouped. They exist as small catchments. 11.2
Limitations
The river and catchment database presented is the result of a modeling exercise. The underlying grid has a resolution of 250 x 250 meters, which limits the level of detail to be resolved. It cannot replace large scale mapping and in particular, the database does not •
Contain artificial waterways,
•
Resolve small headwater creeks,
•
Give names for the rivers and lakes.
Some of these shortcomings may be alleviated in coming versions. 11.3
Known Problems
The following problems with the database have been noted: •
the two large coastal lagoons “Kurisches Haff” and “Frisches Haff” at the southern coast of the Baltic Sea have not been considered,
•
In the Netherlands some rivers are not correctly reported,
•
In the Hungarian plain some rivers are not correctly reported.
11.4
Possible Improvements for Version 2.0
The current version of the database will be distributed to interested users for testing and validation. In the light of the experiences and based on reported errors it is foreseen to prepare a second version of the database. Besides the correction of errors, the following issues could be considered for version 2.0:
28
•
Further study of the coding problem and full implementation of a coding system,
•
Naming the larger rivers, lakes and catchments
•
Calculation of a set of catchment characteristics and proxy pressure indicators
•
Recalculation of sub-basins along lake perimeters
•
Merging of small coastal drainage areas and small drainage areas along lake perimeters into larger entities.
J. Vogt et al.
12. Data Distribution, Copyright and Disclaimer The CCM River and Catchment data described in this report are distributed free of charge through a dedicated Internet portal. The copyright for the data, however, remains with JRC according to the following conditions: COPYRIGHT
The proprietary rights and copyright of the CCM River and Catchment data remain with the European Commission Joint Research Centre (JRC). Reports, articles, papers, scientific and non-scientific works of any form, including tables, maps, or any other kind of output, based in whole or in part on data supplied by JRC, must contain an acknowledgement of the form: CCM River and Catchment Database JRC/LMU © European Commission – JRC DISCLAIMER AND LEGAL STATEMENTS
The CCM River and Catchment data were created as part of JRC’s research activities. Although every care has been taken in preparing and testing the data, JRC cannot guarantee that the data are correct in all circumstances; neither does JRC accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. Being such, JRC will acknowledge receiving a notice from the user on the encountered problem. The JRC will not be responsible for any direct or indirect use which might be made of the data. The JRC does not provide any assistance or support in using the data.
13. Further Information Further information on the Catchment Characterisation and Modelling (CCM) activity, on the EuroLandscape project, and on the Agri-Environment action can be found on the following Internet pages: http://agrienv.jrc.it
CCM River and Catchment Database, EUR 20756 EN
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14. References Boschet, A.F., V. De Paepe, and T.J. Lack (2000): Inland Waters. Annual Topic Update. European Environment Agency, Topic Report 1/2000, Luxembourg (Office for Official Publications of the European Communities) 30p. Britton, P. (2002): Review of Existing River Coding Systems for River Basin Management and Reporting. (WFD GIS Working Group: European Coding Systems Task Group), October 2002, http://193.178.1.168/River_Coding_Review.htm. Colombo, R., J. V. Vogt, and F. Bertolo (2001): Deriving Drainage Networks and Catchment Boundaries at the European Scale. A New Approach Combining Digital Elevation Data and Environmental Characteristics.- EUR 19805 EN, CEC-JRC, Ispra, 58p. CEC (1993): CORINE Land Cover – Guide technique. EUR 12585 FR, Office for Official Publications of the European Communities, Luxembourg, 144 p. ESBSC, European Soil Bureau Scientific Committee (1998): Georeferenced Soil Data-Base for Europe, Manual of Procedures. EUR 16393 EN, EC-JRC, Ispra, 184p. Nixon, S., J. Grath, and J. Bogestrand (1998): Eurowaternet. The European Environment Agency’s Monitoring and Information Network for Inland Water Resources. European Environment Agency, Technical Report No. 7, Copenhagen (EEA) 47p. Soille, P.J., and C. Gratin (1994): An efficient algorithm for drainage network extraction on DEMs. Journal of Visual Communication and Image Representation 5: 181-189. Soille, P. (2003): Morphological Image Analysis. 2nd Edition, Springer-Verlag, Berlin, Heidelberg, New York. Soille, P., J.V. Vogt, and R. Colombo (2003): Carving and Adaptive Drainage Enforcement of Grid Digital Elevation Models. Water Resources Research, submitted. Terres, J.-M. (2000): The Crop Growth Monitoring System implemented by the JRC/ARIS unit for the information needs of the EC DG VI – Agriculture. In: Conese, C. and M.A. Falchi (Eds.): Proceedings of the 7th International Congress for Computer Technology in Agricultural Management and Risk Prevention, 15th – 18th November 1998, Florence, Italy (Supplemento agli Atti dei Georgofili, 2000), 225-230. Van der Voet, P., C.A. Van Diepen, and J. Oude Voshaar (1994): Spatial interpolation of daily meteorological data. A knowledge-based procedure for the region of the European Communities. Report 53.3, Winand Staring Centre for Integrated Land, Soil and Water Research, Wageningen, 105p. Vogt, J.V., P. Kennedy, and S. Folving (1999): Summary and Conclusions of the Expert Meeting on ‘European Watershed Characterisation and Modelling’. JRC-Ispra Technical Report, Ispra, 23 p. Vogt, J.V. (Ed.)(2002): Guidance Document on Implementing the GIS Elements of the Water Framework Directive. EUR 20544 EN, EC-JRC, Ispra, 166 p. Vogt, J.V., R. Colombo, M.L. Paracchini, P. Soille, A. de Jager, and S. Folving (2003a): A European Landscape Stratification Reflecting Drainage Density In: K. Helming & H. Wiggering (Eds.): Sustainable Development of Multifunctional Landscapes. Springer Verlag, Berlin, Heidelberg, New York, 95-110. Vogt, J.V., R. Colombo, and F. Bertolo (2003b): Deriving Drainage Networks and Catchment Boundaries. A New Approach Combining Digital Elevation Data and Environmental Characteristics. Geomorphology (in press, available on Science Direct).
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CCM River and Catchment Database, Version 1.0 Jürgen Vogt, Roberto Colombo, Maria Luisa Paracchini, Alfred de Jager, Pierre Soille
Agri-Environment – Catchment Characterisation and Modelling (CCM)
2003, 30 p.
EUR 20756 EN
© European Commission, DG Joint Research Centre (JRC), Institute for Environment and Sustainability (IES), 21020 Ispra (Va), Italy
The availability of digital data on rivers and lakes and their drainage basins (catchments) is important for the analysis of environmental pressures and their impact on water resources. Recent policies, such as the Water Framework Directive require the setup of Geographical Information Systems on water bodies and drainage basins as a basis for the development of River Basin Management Plans and for the reporting to the European Commission. GIS tools allow for the combined analysis of digital elevation data and environmental parameters in order to derive at least part of the required information over extended areas. This report describes the development of a European-wide database of river networks and drainage basins. It presents an advanced methodology making use of medium resolution digital elevation data (250 m grid cell size) and information on climate, vegetation cover, relief, soils and lithology to derive river networks and catchments for the European continent. In order to model the spatial variability in drainage density, a landscape typology is implemented and for each landscape type a dedicated critical contributing area is derived. In order to comply with the needs of environmental monitoring, lakes and transitional waters are considered during river mapping and catchment delineation. With respect to the EEA’s monitoring needs, the Eurowaternet measuring stations are positioned and their corresponding catchments delineated. A dedicated algorithm has been developed to overcome problems of automatic river mapping in flat terrain.
joint research centre EUROPEAN COMMISSION
The mission of the JRC is to provide customer-driven scientific and technical support for the conception, development, implementation and monitoring of EU policies. As a service of the European Commission, the JRC functions as a reference centre of science and technology for the Union. Close to the policymaking process, it serves the common interest of the Member States, while being independent of special interests whether private or national. The mission of the Institute for Environment and Sustainability is to provide scientific and technical support to EU policies for the protection of the environment contributing to sustainable development in Europe.
EUR 20756 EN
