catchtment delination and characterisation

CATCHMENT

DELINEATION AND CHARACTERISATION

A Review

by

Francesca Bertolo

C atchm ent C haracterisa tion & M odelling

C atchm ent C haracter isation & M od elling

A n A ctiv ity o f th e E uroL an dscape P ro jec t Space A pp lica tions Institu te (SA I)

A n A ctiv ity o f th e E uroL an dscape P ro jec t Space A pp lica tions Institu te (SA I)

Catchment Characterisation and ModellingEuroLandscape Project

Space Applications Institute, Joint Research CentreIspra (Va), Italy

April 2000

Space ApplicationsInstitute

Environment &Geo-Information Unit

DG Joint Research CentreEuropean Commission

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JRC SAI EGEO

For further information concerning the Catchment Characterisation and Modellingactivity or the EuroLandscape project you may contact:

Dr. Jrgen Vogt, JRC-SAI-EGEO, email: [email protected] orDr. Sten Folving, JRC-SAI-EGEO, email: [email protected].

Or visit our website on URL: http://www.egeo.sai.jrc.it

Catchment Delineation and Characterisation iii

EuroLandscape CCM, April 2000

Table of Contents

Preface v

1. Introduction ..................................................................................................12. Digital Elevation Models ..............................................................................23. Catchment Delineation .................................................................................5

3.1 Defining Drainage From Raster Datasets ........................................6

3.2 The Problem of Flat Areas . ...........................................................11

3.3 The Channel Source Definition .....................................................12

3.4 About Errors ..................................................................................15

4. River and Catchment Ordering ...................................................................165. Catchment Characterisation ........................................................................196. Final Considerations ....................................................................................227. References ..................................................................................................24

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Catchment Delineation and Characterisation v


Preface

Economic and environmental sustainability is one of the major goals of European policy.One of the basic pre-requisites to meet these goals is a sound knowledge of the differentprocesses underlying economic and environmental evolution in the European territory. Thedocumentation of the current situation and the study of relevant processes are, therefore,important issues for European institutions such as, for example, the Directorate GeneralEnvironment (DG ENV), the European Environmental Agency (EEA), the EuropeanStatistical Office (EUROSTAT) or the Joint Research Centre (JRC) of the EuropeanCommission.

In the frame of the 5th Framework Programme on Research and TechnologicalDevelopment, the Environment and Geo-Information (EGEO) Unit of JRCs SpaceApplications Institute (SAI) is aiming to construct such fundamental knowledge andinformation through the implementation of the EuroLandscape project. EuroLandscape(Geo-Information for Development and Environmental Monitoring) is aiming at assessing,mapping and monitoring the European Environment, with special emphasis on thesustainable management of natural resources, including forests, grasslands and waterresources.

The catchment, as a basic physical entity of the landscape, has gained increasing attentionin this context. Most processes related to the movement and quality of water are best studiedat the catchment or sub-catchment scale and many associated processes such as soilerosion, mass movements, sediment transport, or land cover changes are strongly linked tothis spatial reference unit. The EEA, therefore, intends to set-up a comprehensive databaseof catchment boundaries and river networks for the whole pan-European territory. Suchdatabase should include information on the topology and ordering of both the catchmentsand river stretches in order to allow for a detailed analysis of data on water quality andquantity, which are collected through the EuroWaternet network of measurement stations.This network is established by the EEA with the support of the national water authorities.Eurostats sections for Geographic Information (GISCO) and for Water Statistics areadditional customers for the use and dissemination of such information. Together with theplanned physical and socio-economic characterisation of the mapped catchments, theinformation will also be useful for the hydrological, geomorphological and socio-economicmodelling communities in order to put the results of a wide variety of models into a widergeographical context.

Within EuroLandscape it is the Catchment Characterisation and Modelling (CCM) activityto implement the catchment related work. CCM aims at a comprehensive mapping andcharacterisation of catchments in Europe and at a subsequent modelling of key processes ina set of representative catchments. This work is implemented at SAI, in close collaborationwith the EEA and with a network of experts in a variety of institutions throughout Europe.

This report has been written as a preparation to the European-wide mapping of catchmentsand drainage networks. In parallel, different algorithms have been tested for selectedregions in Europe.

Ispra, April 2000 Jrgen VogtEuroLandscape CCM Leader

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Catchment Delineation and Characterisation 1


1. Introduction

The Catchment Characterisation and Modelling (CCM) activity of theEuroLandscape project is aiming at a European-wide mapping of catchments anddrainage networks. The derived catchments shall then be characterised and classifiedaccording to surface characteristics, land cover dynamics and run-off conditions. Itis in this context that this report on the state-of-the-art in catchment mapping andcharacterisation has been prepared. In particular two aspects have been investigated:how to automatically extract and map catchments and drainage networks fromdigital elevation models (DEMs) and whether examples of catchment classificationsat regional level have been described in the literature.

The availability of a DEM with adequate spatial resolution and covering the wholearea of interest is a basic requirement to achieve the goals of CCM. It is evident thatthe quality of the DEM is of high importance and that considerable attention has tobe paid to the errors that are introduced by the terrain model itself. This is related tothe problem of scale: does the geometric resolution of the DEM have any influenceon the precision of catchment boundaries and drainage channels and which degree oferror is introduced by using a given DEM? In connection to that, it is important tohighlight that the final database of European catchments will be the core of aGeographic Information System (GIS). Since the propagation of errors plays asignificant role in GIS analysis it is important to have a clear understanding of howto deal with them. Procedures have, therefore, to be implemented for evaluating thereliability of both the input data and the final results (Chapter 2).

A further step relates to the available algorithms for delineating catchments and rivernetworks. It is not only important to know how to analyse the DEM, but also tounderstand the limitations of each algorithm. The method that ideally fits the needsof the EuroLandscape project must be computationally robust and efficient, andmust be able to cope with the most important problems that this kind of analysispresents: DEM inherent altitude errors and the difficulty to recognise the pathwaysof water in flat areas. Another interesting problem is the definition of the riversource area; in fact there is an intrinsic difficulty to define where exactly water startsto form a river channel. Finally, there is a need for a theory to help in taking adecision on the minimum source area that can be mapped from medium resolutionDEMs (Chapter 3).

Some attention needs to be given to the fact that, at the end, a large database has tobe managed. Within this database the geographic relationships between differentriver networks or different catchments must be defined through an adequatenomenclature or coding system. This nomenclature or coding system is necessaryfor analysing the associated information (e.g. water quality, water quantity,sensitivity to flooding) in a geographical context and for understanding theenvironmental implications of political decisions. A specific chapter, therefore, is

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devoted to investigate existing coding systems and to understand if it is possible toimplement a similar system at the European level (Chapter 4).

Finally, the question of catchment characterisation and classification involves aproblem of scale and of data availability. In this framework is important tounderstand which are the most important parameters that characterise the landscape,the hydrological response and the environmental behaviour of a catchment atdifferent scales, since CCM aims to obtain a database through which it will bepossible:

a) to derive a catchment typology and to select a set of representative catchments;b) to aggregate catchments to identify homogeneous zones or groups of

catchment units at the pan-European scale;c) to extrapolate model results from representative catchments to the entire pan-

European region.

Simultaneously, it is important to consider which data are available or canreasonably be derived for the whole European territory (Chapter 5).

2. Digital Elevation Models

Raster DEMs can be derived directly from stereo-photos or from satellite imagerysuch as stereoscopic SPOT images, but are generally derived by interpolation ofscattered point elevation data, of contour lines, or of Triangulated IrregularNetworks (TIN).

The main limitation of a regular gridded DEM appears to be the fixed grid cell size.Such a DEM cannot always accurately describe the topography, especially inlandscapes with varying complexity. Errors are introduced by the interpolationprocedure, whatever method is used and, in general, these errors are spatially auto-correlated. Moreover, in order to save memory, gridded elevation data are oftenrounded to the nearest meter. In regions with gentle slopes this creates flat areaswith abrupt changes in altitude (similar to stairs).

All of these errors produce artefacts, such as pits and hummocks, that do notcorrespond to real landscape features and they affect the derived quantities such asslope-gradient and slope-aspect. While slope-gradient has about the same degree oferror as the original elevation data, slope-aspect errors are usually amplified duringcalculation (Isaacson and Ripple, 1990; Bolstard and Stowe, 1994; Giles andFranklin, 1996; Desmet, 1997).



These drawbacks have a clear impact for the extraction of the channel network andfor the catchment delineation, which are generally based on slope gradient and slopeaspect.

For most hydrological applications, the vertical resolution of a DEM is consideredsatisfactory if the ratio of the average drop per pixel and the vertical resolution isgreater than unity. The average drop per pixel is defined as the elevation between apixel and the next in steepest descent (Thieken et al., 1999, Walker and Willgoose,1999).

Scale and grid cell size influence the extraction of the channel network to a pointwhere the same method produces different results for the same area. In general, thegrid cell size dependency is introduced by the inability to accurately reproducedrainage features that are at the same scale as the spatial resolution of the DEM. Formeandering channels, this results in shorter channel lengths and for networks withhigh drainage density, it leads to channel and drainage area aggregation. In thesesituations, the number of channels, the size of direct drainage areas and the networkpattern may depart considerably from the initial reference values (Wang and Yin,1998). Garbrecht and Martz (1994) presented a sensitivity analysis on drainageproperties extracted from DEMs of increasing cell size and for several hypotheticalnetwork configurations. On the basis of these results they found that a DEM shouldhave a grid cell area of less than 5% of the network reference area in order toreproduce important drainage features with an accuracy of about 10%. The networkreference area is the mean area draining directly into the channel links of thenetwork.

The underlying data source used for deriving the DEM is a crucial factor. For thisreason, the aggregation of an accurate DEM is considered better than using a DEMderived from maps at a lower scale (Thieken et al., 1999; Wolock and Price, 1994,Walker and Wilgoose 1999).

Different studies highlighted that the importance of these issues is due to the factthat the extent of the stream network and the length of the overland flow pathstrongly influence hydrological modelling results (Wolock and Price, 1994; Zhangand Montgomery, 1994; White and Running, 1994; Thieken et al., 1999).

Hussein and Schwartz (1997) presented a systematic strategy for improving thequality of a DEM by including additional digital information on the geometry of thestream network. The approach is based on a theory developed by Hutchinson (1988,1989) for creating digital elevation models by combining point elevation data and/orcontour lines with a stream network. A unique feature of Hutchinsons approach isan automatic drainage enforcement algorithm, which attempts to removespurious sinks in order to create a depressionless DEM. In addition, the algorithmalso modifies the elevation of grid points that conflict with a downstream decreasein stream elevation. Control over the removal of spurious sinks and conflictingelevation points is achieved using two tolerances: the first controls the maximum

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difference between a sink considered for removal and the nearest pour point (i.e., thelocal topographic minimum controlling a basin area). The second tolerance controlsthe maximum allowable modification of grid points that conflict with the streamnetwork. The method of Hutchinson is incorporated in Arc/Info (ESRI) as a functioncalled Topogrid.

To correct the errors derived from elevation round-off, Nelson and Jones (1994)proposed a smoothing filter. The weights of the filter matrix are determined using aninverse distance squared function, so that the elevations of cells nearest to the centralcell will influence the result more than those of cells that are further away. A finalconstraint is placed on the calculated elevation so that it is not adjusted by more thanone half the elevation resolution, to ensure that the terrain model is smoothedwithout any loss of the original accuracy.

Only a few DEMs are available covering the whole pan-European area. One of themis the so-called GTOPO30. It is a global DEM that was released by the USGS EROSData Center in 1996 (http://edcwww.cr.usgs.gov/landdaac/gtopo30/gtopo30.html).The elevation values in GTOPO30 are regularly spaced at 30-arc seconds, whichcorresponds to approximately one kilometre. They are derived from eight sources ofelevation information, including both vector and raster data sets. For most of Eurasiathe data source is the Digital Terrain Elevation Data (DTED) produced by theNational Imagery and Mapping Agency (NIMA, formerly the Defense MappingAgency), which is a raster topographic database with a horizontal grid spacing of 3-arc seconds (approximately 90 meters). The generalization of the high-resolutiondata to the 30-arc seconds horizontal grid spacing was conducted by calculating themedian value of the 100 full resolution cells corresponding to each cell of the newDEM. For this reason the accuracy of the values obtained is the same as in theoriginal data set. The full resolution 3-arc seconds DTED has a vertical accuracy of 30 meters linear error at the 90% confidence level, which correspond to a RMSEof 18 meters. To ensure that the DEM is able to reproduce the correct movement ofwater across its surface, the DEM is processed to remove elevation anomalies thatcan interfere with hydrologically correct flow (Verdin and Jenson, 1996).

Another high resolution DEM of Europe is distributed by GAF mbH, Germany,according to a sales agreement with GEOSYS/MONA PRO Visual Media, France.The MONA PRO DEM covers 22 countries in Europe and is available with the gridcell sizes of 75 m, 100 m and 250 m. The altitude precision (according to GEOSYS)is about 3,5 5 m in relatively flat terrain, and 12 15 m in very steep mountains(GAF Products and Services, http://www.gaf.de/gaf04.htm).

The X-SAR/SRTM Shuttle Radar Topography Mission flown on the Space Shuttlein February 2000 will result in another potentially interesting DEM. This jointmission of the US National Imagery and Mapping Agency (NIMA), the US NationalAeronautics and Space Administration (NASA), the German Aerospace Centre(DLR) and the Italian Space Agency (ASI) had the objective to use C-band and X-band interferometric synthetic aperture radars (IFSARs) to acquire topographic data



over 80% of Earths land mass (between 60 degrees North and 56 degrees South)during the 11-day Shuttle mission. Within 18 months after the mission the data willbe processed into digital topographic data with a 30 x 30 m spatial sampling rate.The expected accuracy of the resulting data is given as 16 m absolute vertical heightaccuracy, 10 m relative vertical height accuracy and 20 m absolute horizontalcircular accuracy at a 90% confidence level. Worldwide data will be distributed at aspatial resolution of around 100 m. When available, the X-SAR/SRTM DEM will bethe first continuous high-resolution product, which has not been mosaiced from dataderived from differing sensors, formats and dates. (http://www.jpl.nasa.gov/srtm/;http://www.dlr.de/srtm/).

Higher resolution DEMs could also be collected from national databases. Althoughthese DEMs are generally of high quality they are usually very expensive. Inaddition, it is generally difficult to join adjacent DEMs that come from differentsources and have been produced in different ways. Finally, with increasing spatialresolution there is an increasing need of storing capacity and of computationalpower. Clearly, these problems limit the use of such data within the CCM project. Ahomogeneous DEM, covering the whole area of interest should be the preferredoption.

3. Catchment Delineation

The last years saw a general recognition of the catchment or the drainage basin asthe most significant surface unit in environmental studies. Traditionally catchmentboundaries have been manually derived from topographic maps, a labour-intensiveactivity. This limitation has changed after the introduction of Digital ElevationModels (DEMs).

Even though methods for delineating catchment boundaries and flow paths fromcontour lines (Moore and Grayson, 1991) and triangulated irregular networks (Joneset al., 1990; Palacios-Velez and Cuevas-Renaud, 1986) provide reliable results, theyrequire extensive data storage and computation time. Grid cell elevation modelshave advantages for their computational efficiency and the availability oftopographic databases (Sabbagh et al., 1994). Therefore, they have seen widespreadapplication for analysing hydrological problems.

There is only one method, which does not require a DEM; it is based on anautomated river network overlay (Sekulin et al., 1992). This approach needs a well-defined river network database, in which the basic unit is a river stretch (link)defined as the river length between two nodes. Each cell of a grid is allocated toriver stretches using a shortest distance algorithm. Boundaries of hydrometricareas, coastlines, and boundaries of the catchment area above the gauging stationscan be used with a point-in-a-polygon algorithm to give added precision in theallocation phase. Grid cells are then accumulated upstream of river stretches using a

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down-network travel technique. This procedure has been proposed in the ERICA(European Rivers and Catchments) project of the EEA (Flavin, 1998).

The catchment delineation is a two-step procedure: drainage patterns have to berecognized before the boundaries between different catchments can be inferred.

3.1 Defining Drainage Networks From Raster Datasets

Three main approaches to the automated recognition of valleys and drainage linesfrom raster DEM can be identified (Tribe, 1992):

A. The recognition of individual DEM cells as valley cells, where a cell isclassified as a valley if some of the cells neighbours are higher;

B. The assignment of drainage directions to each DEM cell and the use of thisinformation for the derivation of a drainage network;

C. Two-step methods based on a combination of approaches A and B.

They will be described in the following sections. Finally, a few remarks concerningthe scale properties and fractal geometry of drainage networks will be given insection D.

A. The recognition of individual DEM cells as valley cells

In the first approach individual DEM cells are identified as valley cells bycomparing the heights of each neighbour of the cell in turn with that of the cell. Themethods of comparison are based on different concepts:

graphs constructed from the elevations of cells neighbours (If the cell representsa valley cell this graph will conform to a particular configuration);

the connectivity number and the coefficient of curvature are calculated from thecells neighbours;

comparison of the elevations of the neighbour cells in predefined directions(Peucker and Douglas, 1975).

The methods described produce noise and valleys, which extend too far up-valleybecause they are based on the concept of higher than. Higher could be only 1 m,meaning that any cell representing a hardly discernible or a local depression can beclassified as part of the valley network. Generally the network is discontinuous andsome procedure is needed to connect the different valley segments and finally toderive the channel network by thinning.



B. The assignment of drainage directions to each DEM cell.

Mark (1984) and OCallaghan and Mark (1984) noted the discontinuities producedby the previous methods and proposed an algorithm to produce a continuousnetwork. The algorithm, known as the D8 algorithm, is based on the flow of waterover terrain along lines of steepest slope and is a computerized version of a manualmethod of catchment area measurement (Speight, 1968). Each cell is considered todrain to whichever of its eight neighbours has the steepest downslope from it. This isnot always the lowest neighbour, since the height differences for the four diagonalhalf-neighbours of a point must be divided by the grid spacing multiplied by thesquare root of two. Initially, each cell may be considered to produce a unit quantityof runoff; this runoff is then carried downslope in accordance with drainagedirections of the grid cells. Then, whenever the runoff in a cell exceeds somethreshold, the cell is considered to be part of the drainage network. The principallimitation in the method is that each cell of the DEM has to have a drainagedirection assigned to it.

The D8 algorithm appears in many works, and in particular in Jenson and Domingue(1988) on which the ARC/Info tools for catchment delineation are based. Thebiggest drawback of this method is the fact that it represents only convergent flow.To overcome this limitation in flow direction assignment different approaches havebeen proposed.

Fairfield and Leymarie (1991) proposed to introduce a stochastic rule in order tofollow more closely the aspect of the slope to avoid the fact that in the D8 algorithmthe flow is discretized to only one of eight directions, separated by 45. Thedisadvantage of this new procedure is that the result is not exactly reproduciblebecause of its randomness.

Some authors have proposed that flow must be partitioned between different pixels.Freeman (1991) allocates flow to each lower neighbour in proportion to an exponentp of the slope. According to his results, a value of p = 1.1 is appropriate. Thesemethods have the disadvantage that flow from a pixel is dispersed to allneighbouring pixels with lower elevation. Therefore the contributing area of a pixeldoes not include any full pixel but instead is composed of portions of different pixelsand is discontinuous.

Costa-Cabral and Burges (1994) oppose to most current models because of theirpoint source representation of flow generation and the resulting one-dimensionalrepresentation of flow paths; as an alternative they presented an elaborate set ofprocedures which model downslope flow in two dimensions in well-defined flowtubes. Flow at one point is in the direction of maximal surface slope. For planarpixels, if the flow direction is parallel to the grid orientation, the exit portion of theboundary is a single full boundary segment. If the flow direction is not parallel to thegrid orientation, the exit portion of the boundary consists of two full adjacentboundary segments.

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Tarboton (1997) tries to reduce dispersion by dividing the flow between one or twodownslope pixels. The flow direction is a continuous quantity between 0 and 2 thatis determined in the direction of the steepest downwards slope on the eighttriangular facets formed in a 3 x 3 pixel window centred on the pixel of interest.Where the direction does not follow one of the cardinal or diagonal directions, theupslope area is calculated by distributing the flow from a pixel between the twodownslope pixels according to how close the flow angle is to the direction of thepixel centre. Unresolved flow directions, in flat areas or depressions, are resolvediteratively by making them flow toward a neighbour of equal elevation that has aflow direction resolved. A suite of programs for the Analysis of Digital ElevationData (TARDEM) from David Tarboton is freely distributed(http://www.engineering.usu.edu/dtarb/).

For all of those methods depressions (pits) cause serious problems, but also in flatareas the assignment of a flow direction is not obvious. While closed depressionsand flat areas in a DEM may represent real landscape features, they are more oftenartefacts that arise from errors in the input data, interpolation procedures, and thelimited horizontal and vertical resolution of the DEM (Mark, 1984; Jenson andDomingue, 1988; Tribe, 1992; Martz and Garbrecht, 1998; Zhang and Montgomery,1994).

The correction of spurious pits has been conducted principally with two strategies.

The first strategy attempts to remove depressions by smoothing the DEM data(Mark, 1984). Objections to this method are threefold:

It does not distinguish between natural and spurious pits. It fails to remove all spurious pits, in particular deeper ones. A loss of significant information is evident after smoothing.

The second strategy is to fill depressions by increasing the values of cells in eachdepression to the value of the cell on the depression boundary with the lowest value(Jenson and Domingue, 1988). First, the algorithm finds the pits outflow point: thatcell on the pits boundary where water would flow out of the pit if it were filled withwater. Then the heights of all cells in the pit, lower than the outflow, are changed tothe height of the outflow. This creates a flat area over which drainage directions canbe assigned.

Fairfield and Leymarie (1991), among others, suggested to treat the pits as if theywere real depressions, and to find the lowest point, the pass, from which water couldflow out of the pit basin. This is achieved changing the directions of flow on thepath between the pass and the low point of the pit. Water is figuratively made toclimb up the side of the basin, which is unrealistic, but the DEM is not changed.



Martz and Garbrecht (1998), from the consideration that a pit can be produced alsoby elevation overestimation errors, proposed a breaching algorithm whichsimulates breaching of the outlet of closed depressions to eliminate or reduce thoseexpected to have been produced by elevation overestimate. It evaluates the localoutlet of each closed depression in a DEM to determine whether the elevation of oneor two cells at the outlet could be lowered to eliminate or reduce the size of thedepression without reversing the direction of overland flow across the outlet.

C. Two-step methods

Several researchers have developed two-step procedures for extracting channelnetworks. Start cells or disconnected valley segments are first identified using amethod similar to, or based on the concept of higher than, and these are thengrown, usually down the line of steepest slope, to give continuous drainage lines(Band, 1986; Riazanoff et al., 1992; Lammers and Band, 1990; Skidmore, 1990;Yoeli, 1984; Smith et al., 1990).

Yoeli (1984) proposed a valley line finding algorithm that, first of all, findselevation minima on the DEM using a spline curve. A continuous search for the nextlower neighbour is then conducted in a sampling square of twice the grid intervalaround the current last point of the valley line. Valley lines start from the highestminimum which, at this stage does not lie in an existing valley line, and finish whenone of three possible situations is reached: joining another valley line; flowing into alake or sea; reaching the edge of the DEM.

Band (1986) in a first step marks convex- and concave-upward points as ridge andstream points, respectively, then the procedure searches for segment ends; newpixels are added downstream to the segments using a maximum descent algorithmuntil another stream segment is encountered. The final result is obtained by thinningthe image to single pixel-width lines. Bands stream network and sub-catchmentextraction algorithms, along with the production of topological codes describingtheir structures have been released as part of GRASS, a US Army Corps ofEngineers public domain GIS (http://www.geog.uni-hannover.de/grass/).

Smith et al. (1990) describe a two-step method based on a procedure first proposedby Haralick (1983). A cubic surface is fitted to the neighbourhood of each pixel. Apixel is defined as a valley pixel if the first directional derivative has zero crossingsin a direction in which the second-directional derivative has positive extreme values.In a second step the procedure applies knowledge about drainage networks tointegrate these probable valley pixels into a network of single-pixel-width linessatisfying the constraints imposed by a binary tree model.

Riazanoff et al. (1992) and Chorowicz et al. (1992) first identify saddles, points thatdivide two groups of pixels in a neighbourhood which have higher elevation than thesaddle. In their method these researchers use two layers: one is the DEM and the

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second is a virtual image of the network represented by segments. From each saddle,a segment is initialised in all the possible directions (generally not more than two);segments are grown in the virtual image in the direction of maximum slope, untilanother segment, or another saddle, or the border of the DEM is reached. Theresulting network is very dense.

Meisels et al. (1995) propose a two-stage algorithm of multilevel skeletonisation ofa DEM, followed by a process of enumeration, mainly to eliminate loops in theextracted drainage network. Depressions are filled to the surrounding elevation levelbefore the extraction of the drainage network. The main algorithm extracts pixelsthat lie on high curvature contours starting from pixels of maximal elevation,elevation-level by elevation-level; the selection is based on a condition for a largeenough number of higher elevation pixels in the immediate neighbourhood of a pixelbelonging to the elevation currently being processed. The second stage uses acomplementary local condition of connectivity and connects all the pixels of theflow path.

In general, these methods suffer from the same problems as the methods describedin the first section; they are effective only when the drainage network is well definedby the local surface properties, which can be derived from the DEM.

D. Scale properties and fractal geometry of channel networks

In the context of the studies on scale properties of channel networks, severalresearchers have been interested in the fractal geometry of individual streams andchannel networks as a whole (Hjelmfelt, 1988; Tarboton et al., 1988; LaBarbera andRosso, 1989). The fractal nature of river networks manifests itself in two ways: onthe one hand, the plane pattern of an individual watercourse has fractal geometryand, on the other hand, fractal properties are also characteristics of the branchedpattern of river networks (Da Ros and Borga, 1997). As a conclusion from all theseworks, Roth et al. (1996) propose a global approach to the problem of drainagenetwork identification. All empirical evidence and the theoretical description of thehydrodynamical and morphological conditions, which are expected to hold in thestreams constituting the effective drainage network, can be expressed in the form:

AS = constant (1)

Where A is the drainage area, S is the local slope and is a constant ranging from0.44 to 0.5. The practical application of this relation for the extraction of the channelnetwork is often limited by the low accuracy of the elevation data; in particular theevaluation of the local slope is a critical issue. The resulting network is often formedof non-connected streams and therefore this approach is only used as a theoreticalbasis for the design of filtering procedures.



Another approach, which LaBarbera and Roth propose, starts from the analysis ofthe altimetric properties of the area that contribute to a given site. Under thehypothesis of self-similarity of stream slopes, as described by the Hortonian sloperatio, the relation Sl Sa can be assumed between the local slope, Sl, and the averageslope in the subcatchment draining in the point being studied, Sa. Introducing:

Ha = (1/n)i (Hi-Hs) (2)

as the relative elevation of the subcatchment draining in the selected point, weobtain that:

Sa Ha/L (3)

where n is the number of pixels in the subcatchment, Hi is the elevation of a pixel inthe subcatchment, Hs is the elevation in the site studied and L is a linear measure ofthe subcatchment size. Introducing the fractal dimension of single rivers, d, arelation with the contributing area has been defined (Rosso et al., 1991):

L Ad/2 (4)

Ha Ad/2 Sl (5)

Equation 5 provides a link between the relative elevation of the subcatchment at agiven site, Ha, the contributing area, A, the fractal dimension of single rivers, d, andthe local slope, Sl. Moreover, the structure on the right-hand side of this equation isthe same as the general equation 1. The application of this approach leads to a well-connected and coherent network; the spatial variation of the drainage density is wellreproduced with a high drainage density in the mountain regions that tends todecrease towards the alluvial areas, characterized by low slope values, and a verylow density in the plains.

3.2 The Problem of Flat Areas.

In a DEM, there is a well-known difficulty to discriminate between flat areasdrained by incised channels and truly flat areas that carry water as sheet flow. It is ageneral problem that follows from the horizontal resolution of the DEM: it is clearthat when the size of a drainage feature is much smaller than the grid cell size of theDEM, the channels cannot be captured by the DEM. However assigning drainagedirections to cells where there are two or more possible choices, and to flat areas, isa general problem for all the methods described previously.

Generally, following Jenson and Domingue (1988), flat area cells adjacent to othercells with a defined flow direction are identified. These flat area cells are thenassigned a flow direction, pointing to the nearest adjacent cell with a defined flowdirection. This is repeated until all flat area cells are assigned a flow direction. This


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kind of approach constrains the flow path to remain within the flat area and allowsthe possibility of multiple outlets. However it often produces unrealistic, parallelflow patterns.

Other researchers proposed to infer flow paths in flat areas from the surroundingtopography. Tribe (1992) suggests defining a main flow path through the flat areaand directing other flow paths towards this main path. Unfortunately, because themain flow path is defined along the shortest path between the inflow and the nearestoutlet, it is possible for the main flow path to pass through areas of higher elevationto reach the outlet.

Garbrecht and Martz (1997) and Martz and Garbrecht (1998) describe an algorithm,which is a core component of the freely available TOPAZ (TOpographicPArameteriZation) landscape analysis tool (http://duke.usask.ca/~martzl/topaz/).The method is based on the recognition that in homogeneous natural landscapes thedrainage is generally towards lower terrain while simultaneously being away fromhigher terrain. Such a drainage is achieved by imposing two gradients on the flatsurface: one towards lower terrain which draws flow to the nearest downslopeoutlet, and a second which forces flow away from higher terrain.

Mackay and Band (1998) recently proposed to first identify flat features (e.g. lakesand relatively flat areas) on the DEM for which slope tracking is likely to fail.Contiguous groups of flat areas are formed into labelled regions by using localregion growing. The labelled regions are then classified into water bodies or landareas using supervised classification of remotely sensed imagery. Different cell-based algorithms can be associated with each class of flat feature: for lakes the goalis to deliver the total upslope area contributed to the lake and the lake itself to thelake outlet, for other flat areas it is necessary to optimise the slope threshold neededto define the flow path (Band, 1986). Since actual flow of water through a lakerequires additional information, a lake boundary-following procedure is used: eachgrid cell along the boundary acts as a depression point for catchments that drain intoit. The contributing area assigned to the lake outlet cell is the total area along theboundary plus the area of the unmarked cells in the interior of the lake; this retainsthe topology of the land-lake features, but eliminates cell-to-cell flow within the lakeitself.

3.3 The Channel Source Definition.

The delineation of catchments is an important aspect in hydrological modelling andis closely related to the definition of channel networks. Physically basedhydrological models must distinguish between overland runoff and water that flowsin channels. On the other hand a catchment outlet must be on a river course. Itappears clearly that the correct delineation of a catchment is closely related to thecorrect extraction of its channel network.



Hydro-physically, the channel network represents those points at which runoff issufficiently concentrated for fluvial processes to dominate over slope processes(Mark, 1984). This mechanism is basically driven by the topography; therefore adifferent kind of information is needed to define the conditions of this transition,which introduces an indetermination in the methods available.

The correct automatic derivation of the channel network is an unsolved problem.Field investigations showed that the source area above the channel head decreaseswith an increasing local valley gradient of the slopes, except in locations wherebedrock properties control the channel head locations (Montgomery and Foufoula-Georgiou, 1993). Although steeper channel heads are, at least, partially controlledby slope instability, landslide instability alone is not sufficient for canalisation.Critical sheer stress is a dominant control factor on the extent of the channel networkwhen saturation overland flow is significant, but it is difficult to quantify this in thefield.

The most common method of extracting channel networks from DEMs is to specifya critical support area that defines the minimum drainage area required to initiate achannel (Mark, 1984; Band, 1986; Jenson and Domingue, 1988). In practice, thisthreshold value is often selected on the basis of visual similarity between theextracted network and the blue lines depicted on topographic maps. Tarboton et al.(1989, 1992) propose a method to find the critical value of the contributing areafrom the scaling diagram of slope versus contributing area for individual grid cellswithin a catchment. An example is given in figure 1.

1.E-03

1.E-02

1.E-01

1.E+001.E+04 1.E+05 1.E+06 1.E+07 1.E+08 1.E+09 1.E+10

- 0.5

At = 5 Km2 = 80 pixels

Contributing Area (m2)

Loca

l slop

e (m/

m)

Fluvial scaling line

AS

5.0 ADd

Hillslopeprocesses

Fluvial process(Channelized pixels are

considered those with A > At)

Figure 1: Diagram of local slope versus contributing area, derived for the Pocatchment in northern Italy from a DEM with 250 m grid-cell size.


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The theoretical argument for using an area-threshold is based on the hypothesis thatthe channel network extends up to the point where unstable fluvial sedimenttransport processes change to stable diffusive hill-slope processes. Severalresearchers showed that the change of dominance from hill-slope to fluvialprocesses is equivalent to a change in the slope versus contributing area diagramfrom a positive-gradient (dS/dA > 0) at small values of the contributing area, to anegative-gradient (dS/dA < 0) at larger values of the contributing area. Tarboton etal. (1989, 1992) propose to use the size of the contributing area that corresponds tothe change in the scaling response, as the critical support area for finding theextension of the channel network. The break in slope-area scaling, however, seldomgoes from a positive to a negative gradient as predicted by the theory, but appearsinstead as an inflection from a low to a high negative gradient in the slope-areadiagram (see figure 1).

Montgomery and Dietrich (1988, 1992) demonstrate that channel heads lie at atransition between channelled and unchannelled portions of the landscape but thatfor any given slope the size of the source area may vary by as much as an order ofmagnitude. In other words the threshold area is not constant in a basin but it is afunction of the local valley slope (the slope immediately upstream of the channelsource in the unchannelled valley). Their research has identified an empiricalrelationship (power law) between threshold area and slope:

Ath = CS- (6)

where C and are constants empirically determined from field data and S is thelocal valley slope. Identification of an appropriate value for C is a major impedimentfor implementing this model, as this parameter should vary with both rainfall andcritical shear stress of the ground surface, the latter reflecting both soil propertiesand the type and density of vegetation cover (Montgomery and Foufoula-Georgiou,1993).

Using a slope-dependent threshold, drainage density is greater in steeper portions ofa catchment, which generally corresponds to the situation found in naturallandscapes. For the case of a catchment with little spatial variability in slope, theconstant threshold and the slope-dependent threshold methods converge and predictsimilar channel networks for the same mean source area. (Montgomery andFoufoula-Georgiou, 1993). However the area-threshold and the slope-area-thresholdcriteria need not to be conflicting and they may be even combined in a singleframework, or be considered as a representation of processes over differentgeomorphic time scales (Ijjasz-Vasquez and Bras, 1995).

The general conclusion of the majority of works on the source area location is thateven limited field data collection on the drainage area-slope relation for channelheads, is the best method for determining appropriate values of parameters definingchannel network extent (Montgomery and Foufoula-Georgiou, 1993; Helmlinger etal., 1993).



All the analyses described above refer to DEMs of maximum 30 m grid cell size; itis stated several times in those works that the correct solution to the question of thechannel initiation needs high resolution DEMs, able to depict the landscape in aconsistent way.

At lower resolution it is more appropriate to analyse the problem from another pointof view. The fundamental assumption is that runoff is generated uniformly over thebasin and that the geomorphologic or erosional response to that runoff is spatiallyhomogeneous regarding channel initiation. Under these conditions the thresholdcontributing area can be used to represent the complex interaction of factors such asgeology, vegetation, soils and topography, which control the initiation andmaintenance of a channel network. At a regional level, where the conditions ofuniformity may not be assumed, it is better, either to allow the thresholdcontributing area to vary between different geomorphic regions, or to includephysical parameters that underlie the spatial variability of the geomorphology(Martz and Garbrecht, 1995).

The importance of the selection of the threshold area is related to the high impactthat this value has on the morphometric properties (such as drainage density, lengthof drainage paths, statistics of external and internal links) and scaling properties(such as Hortons ratios and fractal dimensions) of a channel network (Helmlinger etal., 1993; Moussa and Bocquillon, 1996; Da Ros and Borga, 1997).

3.4 About Errors.

As stated frequently before, the final result of the automatic delineation of thecatchments is influenced by many sources of errors. Selecting a DEM of goodquality and paying attention to the algorithm used can minimize some of thepossible errors a priori. Other errors need the definition of some criteria that help tolimit their influences. Finally, some errors are inherent to all the procedures andneed to be clearly evaluated and quantified.

Generally, the evaluation of the results of catchment delineation and networkextraction procedures is made by visual comparison with existing vectors. It is,however, difficult to quantitatively evaluate the results. Generally, quantitativecomparisons are made on the total area included in the catchment boundaries.

Miller and Morrice (1996) proposed to evaluate the reliability of the catchmentboundaries using the rate of change of height and aspect in the neighbourhood of theborderlines. In order to assess the sensitivity of the boundary location to errors in theDEM, they proposed to add an error with normal distribution and fixed standarddeviation to the elevation values and to quantify the areas that would be re-allocatedbetween catchments. It is supposed that the sensitivity to DEM errors is best


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represented by the area of land that is subject to re-allocation as a proportion of thecatchment area.

4. River and Catchment Ordering.

In order to construct a geographical database of all the catchments of Europe, acrucial point is to define a system for the codification of the catchments and relatedchannel networks.

This is a necessary step in order to maintain the topology of the hydrological systemand is useful to facilitate the access to the database. A unique code that identifieseach catchment and its river network is necessary for the structure of a database. Butit is also important for describing the relationships that exist within a hydrologicalregion, or between mainstreams and tributaries, or between headwaters, drainagesystem and outlet, etc.

Each country has its own system of coding the national river network. Generally thenetwork itself influences the coding system through characteristics such as thedrainage density, the shape of the drainage system (i.e. coastal drainage or no seaoutlet), or the mean drained area. It is difficult to find a method that fits to all thepossible combinations that arise on a continent.

Another relevant aspect is the number of digits (alphanumeric elements) that isnecessary to completely describe a catchment: at a national level this is lessimportant than at a continental scale where thousands of catchments have to becoded.

In the United States the Water Resources Division of the US Geological Survey hasits Hydrologic Units System (Seaber et al., 1987), which divides the Nation in 21major geographic areas, or regions, composed of 222 sub-regions. A sub-regionincludes the area drained by a river system, a reach of a river and its tributaries inthat reach, a closed basin, or a group of streams forming a coastal drainage area. Thethird level of classification subdivides many of the sub-regions into accounting unitsand, furthermore, the fourth level in cataloguing units, which are the smallestelements in the hierarchy of hydrologic units. An eight-digit code uniquely identifieseach of the four levels of classification within four two-digit fields. The first twodigits identify the region, the next two digits the sub-region, the next two digits theaccounting unit and the last two digits the cataloguing unit.

In France the Environment Ministry has defined a coding system in 1991, which isbased on a code of eight digits. The first four digits indicate the hydrographic zone,which refers to a hydrological classification of the country in four levels: regions (1stlevel), sectors, sub-sectors and zones. There are six regions that represent the mostimportant river basins of the country. Each region is subdivided in not more than ten



sectors, each sector in not more than ten sub-sectors that have not more than tenzones inside. The next three digits (5, 6, 7) refer to a single entity which can beone out of five different inland waters: river, tributary, artificial channel, lake,wetland, or coastline. The last digit refers to the class of inland water. An additionalparameter, the kilometric point (pk), defines the position of a specific point along orin the border of an entity. It is used to concatenate each entity with the neighbouringentities and to describe changes in the entity, like different regulations for water useor for fishing, different fish species, or different owners, etc.

A different approach is used in the Baltic Sea Region GIS, Maps and StatisticalDatabase developed by United Nations Environmental Program - Global ResourceInformation Database (UNEP/GRID) Arendal (Norway) in collaboration withInstitutes in Sweden (http://www.grida.no/prog/norbal/baltic/ welcome.htm). In thisdatabase there are only 81 sub-basins, in total forming the seven major catchmentsthat define the Baltic Sea drainage area. The sub-basins all have an outlet to the seaor are coastal drainage areas; portions of the Baltic Sea are considered sub-basins.Therefore the sub-basins are numbered sequentially in clock-wise order beginningfrom the northern catchment (Bothnian Bay); jumping in the numbering scheme canoccur when a portion of sea is encountered: a number ending by nine is alwaysassigned to it. An additional parameter is linked to each sub-basin indicating themajor catchment it belongs to.

The Catchment Database for Sub-equatorial Africa (Verheust and Johnson, 1998) isanother example of a regional database. The catchment delineation for sub-equatorial Africa was done in 1997 based on a digital elevation model and the riverlayer of the Digital Chart of the World. The database was developed for users whodo not have access to high-end GIS packages and can be used as a standalonecellular database or in combination with simple mapping packages. The databaseholds 1157 catchment polygons; the ordering was done manually and namesassigned using a map of Central and Southern Africa from J. Bartholomew & SonsLTD at a 1:5,000,000 scale (1988). At the same time, a parameter was added to eachpolygon, which identifies the next catchment downstream. With a repetitive linkingof this parameter to the catchment identifier a downstream sequence is generatedand stored in a related database. Based on the created downstream sequence anupstream sequence was also generated and stored in another related database. Thefirst record of this sequence refers to the outlet (ocean, internal basin, or edge basin)and the second refers to the lowest catchment, which drains to the outlet. This lowestcatchment is the outlet area of the principal river of the network, and gives the nameto a megabasin. Two other parameters associated to each catchment complete thedescription of the topology of the drainage system in this database: the level,indicating the number of catchments that separate it from the outlet, and themegabasin name.

Verdin and Verdin (1999) propose a reference system that at once uniquelyidentifies and indicates the spatial nature of a hydrographic basin, with the aim tohave a simple and globally applicable method of coding. The system proposed is


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founded upon concepts first articulated by Otto Pfafstetter, an engineer within theDepartamento Nacional de Obras de Saneamento, a civil works agency of thefederal government of Brazil (Pfafstetter, 1989 as cited in Verdin and Verdin, 1999).This system is designed to exploit features of the base-10 numbering system: theordinal nature of digit values indicates the relative upstream/downstream position,while the binary trait of being alternately odd and even indicates the possibility of astream to be on or off the main channel. In order to apply the method to a river, it isnecessary to distinguish between the main stem and tributaries: the main stem isdefined here as the watercourse that drains the greater area. The area drained by atributary is called basin while the area directly drained by the reach of the main stemlying between two tributaries is called inter-basin.

Subdivision of the area drained by a major river into coded basins and inter-basins isbased on the convention to increase ordinal values from downstream to upstream,and to assign odd digits to inter-basins and even digits to basins. A zero digit isreserved for areas of closed drainage. There will be a large number of candidatetributary basins to be delineated, but only four digital values are available per level:the four tributaries with the greatest drainage areas are assigned a value. However,the value assigned to a basin or inter-basin depends on its topological position andnot its area. Values are assigned in the order in which they are encountered along theriver course from the outlet to the source. Any basin or inter-basin can be furthersubdivided by simply applying the same rules to its internal area adding a digit foreach level of sub-division. The appeal of the approach stems from its economy ofdigits, the topological information that the digits carry, and its global applicability.Simple rules to check digits with tests of odd or even, and less than orgreater than, can quickly isolate areas of interest for a particular investigation.

A coding system for the territory of the European Union has been proposed in theERICA (European Rivers and Catchments) project of the EEA (Flavin et al., 1998).The aim of this coding system is to provide explicit information as to areas drainingto a given sea/ocean or coastal stretch, identification of all areas above or below agiven point, and the size of a catchment. This result is achieved through a code thathas: Two digits to identify the sea that the catchment drains to; Three digits to identify the coastal position of the area drained; A series of two digits for nested catchments; A single character indicating the catchment size.

For the first two digits of the code the International Hydrographic Bureau system(1953) is used. This is a widely used system of coding for seas and oceans.

The second group of digits, the marine border code, can assume two kinds of values:an even number indicates a true river outlet to the sea or the border between twooceans (water-water boundary); an odd number indicates a stretch of coast betweentwo river outlets or two oceans (water-land boundary). The 499 (total number ofeven numbers between 1 and 999) most significant rivers are numbered sequentially



from north to south and from west to east. To identify the most significant rivers it isproposed to use the catchment area or, if this is not available, the total stream lengthdraining to each mouth.

A drainage area can be subdivided in smaller drainage areas according to thebifurcating structure of rivers. At each level of subdivision the 49 (the total numberof even numbers between 1 and 99) most significant tributaries are numberedsequentially from the outlet to the source. The inter-catchment areas betweensignificant tributaries will take the odd numbers. To identify the most significanttributaries the drained area can be used or, if this is not available, the total streamlength. For each level of subdivision two digits are added. This subdivisionprocedure can apply also in inter-catchment areas and coastal drainage areas.

The last element in the code is a character, the area band code, which indicates thesize of the catchment essential for comparing like-with-like at a European scale.This code refers to ranges of size to be defined.

5. Catchment Characterisation.

The Catchment Characterisation part of CCM aims at a European-widecharacterisation and classification of catchments according to surface characteristics,land cover dynamics and run-off conditions. Subsequently, run-off, soil erosion andsediment dynamics will be modelled for selected (representative) catchments inorder to assess the risk of land degradation and the needs for environmentalprotection. Within this context, emphasis is placed on characterising the landscapeand modelling landscape processes. As such the approach extends beyond moretraditional hydrological characterisation and modelling and should provide the basisfor:

1. a comprehensive catchment classification and thus the production of typologiesand the selection of a set of representative catchments;

2. the aggregation of catchments to identify homogeneous zones or groups ofcatchment units at the pan-European scale;

3. the extrapolation of model results from representative catchments to the entirepan-European region.

Catchment characterisation, therefore, should be based on a set of fundamentalmultipurpose parameters capable of representing both the diversity and the keyfeatures of the pan-European landscape.

Two different aspects need to be investigated to achieve these proposed results. Onone hand, there is the classification or the way to aggregate catchments based on a


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set of characteristics. On the other hand, there is the necessity to understand what isrepresentative.

Classification is defined as the ordering of objects into groups or sets on the basisof their similarities or relationships; the subsequent allocation or assignment ofobjects into the pre-established classification system is called identification(Sokal, 1974). A major contribution of a land classification to resource studies on aregional scale is that spatial patterns become evident. The relationship betweenterrain features and physical and biological processes are almost alwayscharacterized by spatial patterns. A land classification system may be built either ontaxonomic site classification or regionalisation. The taxonomic approach seeks todelineate zones by grouping sites with similar properties, while a regionalisationapproach subdivides land hierarchically into land units on the basis of presumptiverules. The inherent differences between these two approaches can be simply reducedto whether an inductive process or a deductive process is applied. In theregionalisation approach, land classification systems have been set-up based onlogical rules and the methodology subsequently tailored to fit principles. Analyticalregionalisation is basically a mapping procedure and often implies knowledge of thecauses of similarities and differences between the objects studied.

For long time hydrologists have been striving for the perfect model that can beapplied to all catchments. This has apparently led to inefficiencies and over-parameterisations in rainfall-runoff modelling, giving rise to some questioning aboutthe value of complex, spatially distributed models for hydrologic predictions(Grayson et al., 1992).

As an alternative, theories that would enable to transfer hydrologic information andunderstanding from one catchment to another have been searched. Flood estimationin ungauged catchments, for example, is quite often carried out by a regionaltransfer of rainfall-runoff and/or flood frequency data from one catchment toanother. Success in such regionalisations depends crucially upon defining theconditions under which two catchments may be considered to be hydrologicallysimilar. Definitions of similarity used in the past have been based onphysioclimatic characterizations without explicit recognition of the environmentalcontrols on runoff generation. An alternative point of view suggest that, ifcatchments can be classified in terms of the similarity of their runoff generationresponses, specialised models can be developed to suit the actual mechanisms ofrunoff generation that operate on a given catchment. Similarity considerations canhelp in guiding the design of field experiments and the interpretation of data fromsuch field studies (Larsen et al., 1994).

The theoretical concepts of similarity were first proposed by Sivapalan et al. (1987)to provide a framework for a comparison of runoff generation responses fromcatchments with different characteristics and of different sizes. By recasting theequations of a rainfall-runoff model into dimensionless forms, they were able toidentify five dimensionless similarity parameters as potential measure of hydrologic



similarity. These parameters are based on field-measured soil and topographicproperties such as the saturated hydraulic conductivity at the soil surface, thethickness of the capillarity fringe above the water table, mean pore size, saturatedand residual soil moisture content and a topographic index (Larsen et al., 1994).

In this general framework the following studies are interesting, because theydescribe what kind of work has been carried out until today and how close theinternational research is to the aims of the EuroLandscape project.

Huang and Ferng (1990) used a taxonomic approach to classify land unitsobjectively into homogeneous zones for water quality management purposes. Theframework for the classification is divided into two parts: catchment classificationand land process classification. In order to reveal interrelations among the spatialpatterns of both land and water characteristics, as well as human factors, catchmentclassification is subdivided in three parts: drainage basin morphometry, assimilativecapacity of stream and water quality management. The purpose of the catchmentclassification with particular reference to basin morphometry is to describe thespatial pattern of terrain characteristics and to analyse their relevance for waterquality management. The classification based on the assimilative capacityemphasises the potential influence of land characteristics on the resilience andstability of surface water. The water quality management classification representsthe final product of the catchment classification scheme, which not only includesfactors of land characteristics related to water quality, but also incorporates therelative importance of each catchment for human society, specifically the designatedstream classes and water quality standards.

In practice, variables assumed to affect stream quality and quantity were measuredin each of 78 catchments of the study area, the Tanshui River Basin in northernTaiwan. Variables were selected only if they could be readily measured or derivedfrom published maps. The selected variables are: location, area, basin configuration,elevation, slope, surface roughness, parent material, texture, drainage, streamnumber, stream length, stream frequency, stream density, bifurcation ratio, stream-bed slope, water quality standards, precipitation, evapotranspiration, land use of thecatchment and in particular of the riparian zones. The framework of theclassification proposed in this research is of a multivariate nature consisting in athree-step procedure utilising both clustering and ordination techniques for each ofthe three catchment classifications described previously, each time with a specificsubset of variables.

Civco et al. (1995) described the effective catchment characterization project as aprocedure that typically follows four basic steps. The first step is to establish a mainobjective; the spatial extent and degree of characterization depend on that objective.The second step is to perform a general search to ascertain what data are available,followed by acquiring general data. The third step is to select the catchments using amulti-step process in which the first selection is based on general data, andsubsequent selections are based on more specific data. The fourth step is the


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catchment characterization itself. They also highlighted that GIS is an ideal tool inthis kind of applications: scientists must work with data such land use and landcover, geology, soils, hydrography and topography, and this usually involvesintegrating multiple, diverse data sources and types.

Lent et al. (1998) described another example of multivariate analysis for aclassification of small order catchments of the Quabbin Reservoir basin,Massachusetts (USA), related to water quality. Effective management of waterquality in a large drainage basin that is comprised of numerous small catchmentsmay require aggregation of them into a small number of sub-basins on the basis oflandscapes features that affect the quantity and quality of water. Meaningfulcatchment aggregation is predicated on identifying the hydrologic andbiogeochemical processes that are most important in controlling stream waterchemistry and in understanding how those processes are distributed amongcatchments throughout the large drainage basin.

Based on eleven stream water chemical constituents a first classification of 15gauged catchments was conducted. With principal components analysis (PCA) themedian water quality concentrations were reduced to three principal components,then the extent to which these 15 stations were related to a principal component wasmeasured with the so-called stations scores of the three principal components. PC1and PC2 scores varied geographically from west to east, indicating that there weresystematic spatial variations in water chemistry. This grouping based on geographiclocation formed the basis for a preliminary classification of catchments and threegroups were defined: the west, central, and east sub-basin. In a second stage analysisof variance and multiple comparisons of means were used to test for significantdifferences among sub-basins in the magnitude of the stream water variables and in24 landscape attributes (slopes, bedrock, soils, land-use, roads lengths and detailedwetland data). These showed that differences in stream water chemistry wereconsistent with variations in landscape attributes. Then multiple regression analysiswas used to develop relations between stream water chemistry, defined by thestations scores from the PCA, to selected landscape attributes. The analysis resultedin three significant equations, one for each of the three principal components.

6. Final Considerations

Several topics have been investigated in this review. Not in all cases it was possibleto find a clear answer to the questions formulated at the beginning. A few pointscould, however, be highlighted that will be useful in the Catchment Characterisationand Modelling activity. The most pertinent conclusions are the following:

(1) There is a need for some method to quantify errors. It is clear that a raster DEMis a partial representation of the landscape and that slope is the derived parameter,which suffers most from that partiality. As a consequence, the delineation of



catchment boundaries suffers from a high degree of uncertainty, independently fromthe quality and the geometric resolution of the underlying DEM and independentlyfrom the method used to define drainage pathways. There is not clear indication ofhow to quantify this error.

(2) Considering the necessary spatial resolution of the DEM and the extended areato be covered, there are only few DEMs available. The USGS GTOPO30 DEM hasbeen widely used and it proved to be of good quality in the European part. However,its resolution is too coarse to achieve the targeted final map scale of 1:250.000.Buying commercially available DEMs for the whole area is not affordable at thismoment in time. A possible alternative could be the DEM resulting from the STRMmission. However, neither the distribution modalities nor the price policy have yetbeen defined.

(3) As can be seen from the large number of publications, there has been a lot ofinterest in river network delineation in the last years. This aspect is much morerelevant than the delineation of the catchment boundaries. Technically it is arelatively simple problem to define catchment boundaries once the drainagechannels are identified. For this reason, the report gives most attention to thetechniques to model the flow paths.

Several methods have been described and the different aspects of the problem havebeen evaluated: convergent flow, divergent flow, non-point diffusion, undefinedflow direction, flat area problem, etc. From this point of view the choice of anappropriate algorithm is difficult. However, many of these algorithms are notavailable in ready to use software packages or require too large computationalefforts and, therefore, are not adequate to handle large DEMs.

(4) The correct definition of source areas is still an open problem. Considerablediscussions have been conducted on this topic, but generally there is no solutionapplicable to DEMs of medium resolution. The problem is that the theories on thechannel initialisation are generally based on studies conducted on the field scale andworking with DEMs of high and very high resolution. The source area size isgenerally smaller than the grid size of a medium or low resolution DEM. In general,a quick solution is to select an arbitrary size of the source area and to adjust ititeratively in order to approximate the extent of mapped river networks, which serveas a reference. At the European scale it is difficult to obtain a complete andcomprehensive river network to use as a reference. Therefore, there is a clear needfor some technique to bridge the gap between the theories and the available gridresolution.

(5) The final coding system seems to be technically complicated by the largenumber of catchments involved, but there exist many examples at regional level thatcan be helpful. In the framework of this project it has been decided to aim at aminimum catchment area of around 500 to 1000 km2. Considering that the Europeanarea to be mapped extends from the Mediterranean to northern Scandinavia and


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from Portugal to the Urals and corresponds to a surface of about 10.2 106 km2(Stanners and Bourdeau, 1995: 19), this will result in an approximate number of10,000 to 20,000 catchments.

7. References

A. Cited in the Text

Band, L. E. (1986): Topographic partition of watersheds with Digital Elevation Models - WaterResources Research, 22, pp.15-24.

Bolstard, P. V.; Stowe, T. (1994): An evaluation of DEM accuracy: elevation, slope and aspect -Photogr. Eng. and Rem. Sens., 60, pp.1327-1332.

Chorowicz, J.; Ichoku, C.; Riazanoff, S.; Kim, Y.; Cervelle, B. (1992): A combined algorithm forautomated drainage network extraction - Water Resources Research, 28, pp.1293-1302.

Civco, D. L.; Garcia, A. R.; Warner, G. S. (1995): Key steps to effective watershedcharacterisation - GIS World, 8, pp.62-67.

Costa-Cabral, M. C.; Burges, S. J. (1994): Digital elevation model networks (DEMON): a Modelof flow over hillslopes for computation of contributing and dispersal areas - Water ResourcesResearch, 30, pp.1681-1692.

DaRos, D.; Borga, M. (1997): Use of digital elevation model data for the derivation of thegeomorfological istantaneous unit hydrograph - Hydrological Processes, 11, pp.13-33.

Desmet, P. J. J. (1997): Effects of interpolation errors on the analysis of DEMs - Earth SurfaceProcesses and Landforms, 22, pp.563-580.

Fairfield, J.; Laymarie, P. (1991): Drainage Networks from Grid Digital Elevation Models - WaterResources Research, 27, pp.709-717.

Flavin, R. W.; Andrews, A. J.; Kronvang, B.; Mller-Wohlfeil, D.; Demuth, S.; Birkenmayer,A. (1998): ERICA, European Rivers and Catchments (European Environment Agency)Copenhagen.

Freeman, T. G. (1991): Calculating catchment area with divergent flow based on a regular grid -Computers & Geosciences, 17, pp.413-422.

Garbrecht, J.; Martz, L. (1994): Grid size dependency of Parameters extracted from digitalelevation Models - Computers & Geosciences, 20, pp.85-87.

Garbrecht, J.; Martz, L. W. (1997): The assignment of drainage direction over flat surfaces inraster digital elevation models - Journal of Hydrology, 193, pp.204-213.

Giles, P. T.; Franklin, S. E. (1996): Comparison of derivative topographic surfaces of a DEMgenerated from stereoscopic SPOT images with field measurements - PhotogrammetricEngineering & Remote Sensing, 62, pp.1165-1171.

Grayson, R.B.; Moore, I.D.; McMahon, T.A. (1992): Physically based hydrologic modelling, 2, Isthe concept realistic? - Water Resources Research, 28, pp.2659-2666.

Haralick, R.M. (1983): Ridge and valley on digital images Computer Vision, Graphics, and Imageprocessing, 22, pp.28-38.

Helmlinger, K. R.; Kumar, P.; Foufoula-Georgiou, E. (1993): On the Use of Digital ElevationModel Data for Hortonian and Fractal Analyses of Channel Networks - Water ResourcesResearch, 29, pp.2599-2613.

Hjelmfelt, A.T. (1988): Fractals and the river-length catchment-ratio - Water Resources Bulletin, 24,pp.455-459.

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Moussa, R.; Bocquillon, C. (1996): Fractal analyses of tree-like channel networks from digitalelevation model data - Journal of Hydrology, 187, pp.157-172.

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B. Further Reading

Arnell, N. W. (1999): A simple water balance model for the simulation of streanflow over a largegeographic domain - Journal of Hydrology, 217, pp.314-335.

Bailey, R. G.; Pfister, R. D.; Henderson, J. A. (1978): Nature of land and resources classification -A review - Journal of Forestry, 76, pp.650-655.

Becker, A.; Braun, P. (1999): Disaggregation, aggregation and spatial scaling in hydrologicalmodelling - Journal of Hydrology, 217, pp.239-252.

Benosky, C. P.; Merry, C. J. (1995): Automatic extraction of watershed characteristics using spatialanalysis techniques with application to groundwater mapping - Journal of Hydrology, 173,pp.145-163.

Bischetti, G. B.; Gandolfi, C.; Whelan M.J. (1998): The definition of stream channel head locationusing digital elevation data - Proc. HeadWater'98: Hydrology, Water resources and Ecologyin Headwaters. Meran/Merano, Italy, pp. 545-552.

Blaszczynski, J. S. (1997): Landform Characterization with Geographic Information Systems -Photogrammetric Engineering & Remote Sensing, 63, pp.183-191.

Carpenter, T. M.; Sperfslage, J. A.; Georgakakos, K. P.; Sweeney, T.; Fread, D. L. (1999):National threshold runoff estimation utilizing GIS in support of operational flash floodwarning systems - Journal of Hydrology, 224, pp.21-44.

Cialella, A. T.; Dubayah, R.; Lawrence, W.; Levine, E. (1997): Predicting Soil Drainage ClassUsing Remotely Sensed and Digital Elevation Data - Photogrammetric Engineering & RemoteSensing, 63, pp.171-178.

Dawes, W. R.; Short, D. (1994): The significance of topology for modeling the surface hydrology offluvial landscapes - Water Resources Research, 30, pp.1045-1055.

DeVantier, B. A.; Feldman, A. D. (1993): Review of GIS applications in hydrologic modelling -Journal of Water Resources Planning and Management, 119, pp.246-261.

Dietrich, W. E.; Wilson, C. J.; Montgomery, D. R.; McKean, J. (1993): Analysis of erosionthresholds, channel networks, and landscape morphology using a digital terrain model -Journal of Geology, 101, pp.259-278.

Dikau, R. (1989): The application of a digital relief model to landform analysis in geomorphology -In: Raper, J. (ed): Three dimensional applications in GIS. (Taylor & Francis), pp. 51-77.

Eash, D. A. (1994): A geographic information system procedure to quantify drainage-basincharacteristics - Water Resources Bulletin, 30, pp.1-8.

Engman, E. T. (1995): The use of remote sensing data in watershed research - Journal of Soil &Water Conservation, 50, pp.438-440.

Fels, J.E.; Matson, K.C. (1996): A cognitively-based approach for hydrogeomorphic landclassification using digital elevation models - Proc.Third Int. Conference/Workshop onIntegrating GIS and Environmental Modeling. Santa Fe, NM, USA, January 21-26, 1996.National Center for Geographic Information and Analysis, Santa Barbara, CA, USA.http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/main.html.


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Fernandez, B. L.; Pizarro, G. P. (1996): Statistical estimation of

catchtment delination and characterisation

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