deriving drainage networks and catchment boundaries...

joint research centreEUROPEAN COMMISSION

DERIVING DRAINAGENETWORKS AND

CATCHMENT BOUNDARIESAT THE EUROPEAN SCALE

A New Approach Combining DigitalElevation Data and

Environmental Characteristics

Roberto Colombo, Jürgen Vogt and Francesca Bertolo

Euro-Landscape ProjectEnvironment and Geo-Information Unit (EGEO)

Space Applications Institute

SpaceApplicationsInstitute

EUROPEAN COMMISSIONJOINT RESEARCH CENTRE

EUR 19805 EN

Points of Contact:

Dr. Jürgen Vogt Dr. Sten FolvingSpace Applications Institute (SAI) Space Applications Institute (SAI)EC – Joint Research Centre (JRC EC – Joint Research Centre (JRC)

EGEO Unit, TP 262 EGEO Unit, TP 26221020 Ispra (VA), Italy 21020 Ispra (VA), ItalyTel: +39 0332 785481 Tel: +39 0332 785009Fax: +39 0332 789803 Fax: +39 0332 789803

Email: [email protected] Email: [email protected]

URL: http://www.egeo.sai.jrc.it

Euro-Landscape Catchment Characterisation and Modelling (CCM)

CoverCCM Digital Database ofDrainage Networks andCatchments for Italy,Vers. 1.0, October 2000

joint research centreEUROPEAN COMMISSION

DERIVING DRAINAGENETWORKS AND

CATCHMENT BOUNDARIESAT THE EUROPEAN SCALE

A New Approach Combining DigitalElevation Data and

Environmental Characteristics

Roberto Colombo, Jürgen Vogt and Francesca Bertolo

Euro-Landscape ProjectEnvironment and Geo-Information Unit (EGEO)

Space Applications Institute



EUR 19805 EN

LEGAL NOTICE

Neither the European Commission nor any personacting 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 Unionis available on the Internet.

It can of accessed through the Europa server(http://europa.eu.int).

European Communities, 2001Reproduction is authorised provided the source is acknowledged.

Printed in Italy

Cataloguing data can be found at the end of this publication

PREFACE

With the recently adopted Water Framework Directive (WFD) the European Union will makea significant step towards a more integrated management of its water resources through theEuropean-wide implementation of river basin management plans. The establishment of bothan efficient geographical information system and a related reporting system is considered tobe an essential support to this effort. The geographical information system on the one handshall provide a digital database of drainage networks, river basins and catchments, includinginformation on their physical and socio-economic characteristics as well as on theirgeographical relationships. The reporting system on the other hand shall provide efficientmeans for the collection, transfer, retrieval and analysis of measurements on water qualityand quantity. At the same time, these tools will be of use for the fulfilment of the reportingobligations of the European Environment Agency (EEA), by providing detailed geographicalinformation with respect to the measurement stations included in the so-called EuroWaternet-Waterbase and by providing a pan-European database of catchment boundaries andcharacteristics. The boundaries will serve as basic geographical reference units and thecatchment characteristics will provide vital information for various environmentalinvestigations.

In the frame of the EURO-LANDSCAPE project, the Space Applications Institute of theEuropean Commission’s Joint Research Centre is investigating methodologies for aEuropean-wide mapping and characterisation of river basins and catchments according tosurface characteristics, land cover dynamics and run-off conditions. In close contact with DGEnvironment, the EEA, Eurostat GISCO and the Member States, the CatchmentCharacterisation and Modelling (CCM) activity of EURO-LANDSCAPE is aiming to developa prototype of the required GIS. This report presents the work performed and the resultsobtained for the set-up of such a system for the territory of Italy, which has been studied as apilot case in order to test the feasibility of and the various possibilities for the implementationof a European-wide system.

When hydrologic analysis is performed on a continental scale, the determination of thechannel network is usually based on the application of a single area threshold for theinitialisation of drainage channels. Such an approach implicitly assumes homogeneousenvironmental and terrain characteristics for the whole study area. This assumption is,however, unrealistic when studying extended areas and certainly does not represent thespatial variability of landscapes on the European continent. As a consequence, a method forriver network delineation has been developed that is capable to reproduce differences indrainage density due to different landscapes and their environmental characteristics.

III

A variable area threshold is used to account for spatial variations in hydrologically relevantenvironmental parameters related to climate, morphology, vegetation cover, soils, andlithology. To do so, a landscape stratification reflecting a limited number of drainage densityclasses is proposed and for each landscape type the critical area threshold is defined by anobjective analysis of the relationship between local slope and contributing area.

The report is organised in six chapters. After an introduction in chapter one, chapters twoand three are dedicated to the state-of-the-art in the analysis of digital elevation data forhydrological purposes. Chapters four, five and six present the proposed methodology and theresults obtained for Italy. An extensive list of significant works published on the subjectconcludes the report.

Ispra, 5 March 2001 Jürgen VogtRoberto ColomboFrancesca Bertolo

DERIVING DRAINAGE NEWTORKS AND CATCHMENT BOUNDARIES AT THE EUROPEAN SCALE

IV

V

TABLE OF CONTENTS

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VII

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . VIII

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.1 Hydrological quantities based on Digital Elevation Data . . . . . . . . . . . . . . . . . 32.1.1 Stream burning and pit filling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.1.2 Flat area handling, slopes and flow direction . . . . . . . . . . . . . . . . . . . . . 52.1.3 Contributing area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.1.4 Channel initiation and channel network extraction . . . . . . . . . . . . . . . . 72.1.5 Basin extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.1.6 Software availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Drainage density, hillslope processes and environmental factors . . . . . . . . . . . 122.2.1 Drainage density and critical contributing area . . . . . . . . . . . . . . . . . . . 122.2.2 Drainage density and environmental factors . . . . . . . . . . . . . . . . . . . . . 12

2.2.2.1 Climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.2.2.2 Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.2.3 Geology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.2.4 Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.2.5 Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3. A Continental Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.1 Global datasets of channel networks and catchment boundaries . . . . . . . . . . . 163.2 Cell size influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.1 Outline of the Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2 Derivation of hydrologic quantities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.2.1 Drainage enforcing and pit filling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204.2.2 Flow direction, flat areas and contributing area . . . . . . . . . . . . . . . . . . . 21

4.3 Landscape characterisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.3.1 Climatic effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.3.2 Morphological effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244.3.3 Geological effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254.3.4 Soil effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264.3.5 Vegetation effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.3.6 Impermeable and impervious areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30


VI

4.4 Landscape drainage density index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314.5 Determination of the critical contributing area . . . . . . . . . . . . . . . . . . . . . . . . . 334.6 Channel network connection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.7 River basin extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

5. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

5.1 Extraction of the Italian river network and catchments . . . . . . . . . . . . . . . . . . . 38

5.2 Validation and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.2.1 Comparison to European-wide datasets . . . . . . . . . . . . . . . . . . . . . . . . . 425.2.2 Comparison to national datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.2.3 Comparison to local datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

6. Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

LIST OF FIGURES

Figure 1: The effect of different contributing area thresholds on channel network and drainage density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Figure 2: Drainage density, hillslope length and contributing area relationships . . . . . 12

Figure 3: Example of the contributing area map with GISCO 3 million riversoverlaid in blue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Figure 4: The effect of the algorithm used for flat area handling . . . . . . . . . . . . . . . . . 22

Figure 5: The Thornthwaite Precipitation Effectiveness Index for Italy . . . . . . . . . . . . 24

Figure 6: Morphological map of Italy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 7: Rock erodibility map of Italy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Figure 8: Soil transmissivity map of Italy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Figure 9: Mean yearly surface cover percentage for Italy . . . . . . . . . . . . . . . . . . . . . . . 30

Figure 10: Landscape Drainage Density Index, reclassified into five classes . . . . . . . . 33

Figure 11: Slope-area relationship for the different landscape classes . . . . . . . . . . . . . . 35

Figure 12: Drainage density in relation to the landscape classes . . . . . . . . . . . . . . . . . . 36

Figure 13: The effect of the connecting algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Figure 14: Principle of the drainage hierarchy (order) according to the Strahler system . . 38

Figure 15: Principle of the catchment hierarchy according to the Strahler system . . . . . 39

Figure 16: The final drainage network for Italy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Figure 17: Drainage network and 3rd order catchments of Italy . . . . . . . . . . . . . . . . . . . 40

Figure 18: Drainage network and 2nd order catchments of Sicily . . . . . . . . . . . . . . . . . 40

Figure 19: Overlay of CCM river network, vers1.0 (in blue) and Bartholomew, 1 M (in red) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Figure 20: Comparison of CCM and Bartholomew river networks through buffers of various widths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Figure 21: Location and size of the catchments used for detailed validation . . . . . . . . . 44

Figure 22: Drainage density derived from (a) the blue lines of 1:10,000 scale maps (in blue), and (b) the CCM river network (in green) . . . . . . . . . . . . . . 45

Figure 23: Comparison between blue lines, Bartholomew river network and the derived drainage network for the Val Chaivenna catchment (720 km2) . . . . 46

Figure 24: Comparison between blue lines, Bartholomew river network and the derived drainage network for the Esino catchment (1240 km2) . . . . . . . . . . 46

Figure 25: Comparison between blue lines, Bartholomew river network and the derived drainage network for the Simeto catchment (4230 km2) . . . . . . . . . 47

VII

LIST OF TABLES

Table 1: Climates corresponding to various ranges of the I index . . . . . . . . . . . . . . . . 13

Table 2: Digital elevation data grid cell sizes and typical scales of application . . . . . 17

Table 3: Monthly climatic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Table 4: Mean monthly climatic data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Table 5: Coding used to describe terrain morphology . . . . . . . . . . . . . . . . . . . . . . . . . 24

Table 6: Coding of rock erodibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Table 7: Classification of the parent material (Mat1) into rock erodibility classes . . . 26

Table 8: Texture classes of the European soil map . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Table 9: Coding of soil parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Table 10: Coding of saturated permeability (K) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Table 11: Coding of soil depth (D) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Table 12: Reclassification of the Corine Land Cover legend . . . . . . . . . . . . . . . . . . . . 29

Table 13: Monthly cover percentages (Cm) for different land cover types . . . . . . . . . . 30

Table 14: Factorial scoring system used in the definition of the Landscape Drainage Density Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Table 15: Classes of Landscape Drainage Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Table 16: Landscape classes, threshold areas and drainage density . . . . . . . . . . . . . . . 36


VIII

1. INTRODUCTION

Within the framework of its institutional activities, the Environment and Geo-Informationunit (EGEO) of the Space Applications Institute (SAI) is developing a comprehensivedatabase of drainage networks and catchment boundaries for the whole pan-Europeanterritory. This work is carried out by the Catchment Characterisation and Modelling (CCM)activity of the Euro-Landscape project as a support to the Directorate General Environment ofthe European Commission and to the European Environment Agency (EEA).

A database of drainage networks and catchment boundaries, including information ongeographical position and spatial relationships as well as on physical and socio-economiccharacteristics of the individual rivers and their catchments, is a necessary basis for theimplementation of various water-related policies and in particular of the recently adoptedWater Framework Directive. Catchments are an important functional entity for hydrologicaland landscape processes and a well-structured database with information on river andcatchment characteristics will be a major step forward for the development of environmentalpressure indicators and for supporting the distributed modelling of environmentally relevantprocesses. The scales for presenting such information should preferably range from 1:250,000to 1:1,000,000 and data should be sufficiently accurate to support the mentioned applications(Vogt et al. 1999).

The sheer extend of the area to be covered (Europe from the Mediterranean to northernScandinavia and from the Atlantic to the Ural mountains, covering some 11.5 million squarekilometres) requires the implementation of automatic tools for the derivation of the desiredinformation. EGEO is, therefore, developing and implementing algorithms for the automaticextraction of drainage networks and catchment boundaries from digital elevation data, takinginto consideration environmental variables such as climate, morphology, soils, geology andvegetation. The pilot study for Italy as presented in this report, has been implementedtogether with the Remote Sensing Department of the National Research Council of Italy(CNR-IRRS).

The problem of identifying the actual extent of drainage networks from digital elevation datahas been studied for many years and at a great variety of scales (e.g. Peucker and Douglas1975; Band 1986; O’Callaghan and Mark 1984; Tribe 1992; Jenson and Domingue 1988;Tarboton et al. 1991; Montgomery and Dietrich 1989; Roth et al. 1996; Rinaldo et al. 1995;O’Donnel et al. 1999), highlighting both the potential and the weaknesses of such anapproach. Major problems are, for example, related to the correct delineation of the drainagenetwork in flat areas such as the glacial plains in northern Europe or the floodplains of largerivers. However, such methodology is the only feasible approach to provide a consistent andwell-defined database over the entire European territory with largely variable environmentalconditions. The development of an adequate methodology and its implementation over theentire area are considered a major scientific and technical challenge.

1

As a first result, the current report presents a new technique, improving the extraction ofchannel networks from low-resolution digital elevation data (DEMs), covering large areassuch as the European continent. The developed technique is based on the stratification of thelandscape into a limited number of classes according to geophysical characteristicsdetermining drainage density. Subsequently, the area thresholds used for the initialisation ofdrainage channels are adjusted to the individual landscape classes. Adequate area thresholdsare derived from the analysis of the ‘local slope - contributing area’ relationship as computedfrom the DEM.

The methodology has been developed and tested for the territory of Italy and a first prototypedatabase for this country has been delivered to DG Environment, the EEA and EurostatGISCO in October 2000.

In chapter two the theory underlying such methodology is discussed on the basis of anextensive literature survey. In chapter three the challenges of a continental approach arehighlighted and in chapter four the developed methodology is described in detail. The resultsfor the Italian test case, including a first validation, are presented in chapter five and chaptersix summarises our conclusions.


2

2. THEORY

2.1 Hydrological Quantities based on Digital Elevation Data

Continued advances in the availability and quality of Digital Elevation Models (DEMs) havemade topographically based modelling approaches one of the most popular themes incatchment hydrology literature (Moore et al. 1991). Automated procedures are commonlyused to delineate basin geometry and to derive flow pathways from digital maps oftopography. Drainage networks and catchment and subcatchment boundaries are thencalculated for each grid cell by these routines, as well as the slope, aspect, flow direction,upstream contributing area, and various other hydrology-related indices.

This section is addressing the necessary steps to automatically extract the channel networkfrom a DEM.

2.1.1 Stream Burning and Pit Filling

When working with low resolution DEMs (e.g. 200 to 1000 meter grid cell size) it isgenerally observed that the automatically extracted channel network does not fully coincidewith the rivers mapped on topographic sheets and considerable problems usually result inareas with little or no relief. Other critical errors occur when a portion of the upstream part ofa river basin drains into the wrong downstream river. Such errors are due to the generalisationof the terrain form as a result of the low frequency sampling rate of the DEM.

The solution of such problems requires an iterative approach, including the editing of thestream network in order to eliminate stray streams, lakes and loops that confuse thedelineation process and that can result in a distortion of the watershed boundaries in areaswhere the DEM and the mapped streams are not completely consistent.

In order to avoid these inconsistencies, drainage channels can be forced along known riversas digitised from topographic maps. Such “burning-in” techniques have the great advantagethat the DEM-delineated streams match the mapped streams exactly and are especially usefulin coastal zones with very flat terrain and other locations where drainage is directed throughconstructed channels. It also helps to ensure that gauging stations and other features areprecisely located on the stream.

Different approaches exist for implementing a burn-in procedure. Maidment (1996) proposesto raise the land surface cells that are off the streams by an arbitrary elevation amount so thatthe streams delineated from the DEM exactly match the digitised streams. Such algorithm isimplemented in the CRWR-PrePro software, available at URL http://www.ce.utexas.edu/prof/ olivera/prepro/prepro.htm.

Tarboton (1997) developed a different method of burning-in without raising the elevationdata. He modifies the flow direction according to the courses of known (digitised) rivers and

3

thus forces the drainage along the known network. This methodology is implemented in theTARDEM software (http://www.engineering. usu.edu/dtarb/tardem.html).

Hutchinson (1989) developed a method for gridding elevation and streamline data withautomatic removal of spurious pits. A new DEM is constructed by resampling only necessarypoints, including relative minima along the location of streams (that is streams will sit in thebottom of valleys). This approach is implemented in ANUDEM (http://cres.anu.edu.au/ outputs/software.html) as well as in the ArcInfo Packages with the Topogrid function. Thegoal is to prepare a new matrix of elevation data, in which water can flow from each cell toanother without closed depressions (pits) interrupting the flow.

Typically DEMs have many local minima (depressions or pits). These pits are surrounded byhigher terrain and do not drain. Many of them are the result of errors in the DEM productionprocess, while only few are real (for example lakes and sinkholes in karstic landscapes). Pitsare often generated along meandering rivers or in narrow valleys where the width of thevalley bottom is smaller than the grid cell size of the DEM. The erroneous minima causedifficulties for algorithms, which simulate water flowing across the landscape and need to beremoved.

The process of removing spurious minima is commonly referred to as “pit filling” and it isthe process of changing the relative elevation of the DEM for selected areas. Generally, theelevations of the pixels within a pit are increased until they match the elevation of the lowestboundary pixel with a defined flow direction. This approach is commonly called a “flooding”approach, (O'Callaghan and Mark 1984; Jenson and Domingue 1988).

Soille and Gratin (1994) proposed an alternative approach based on principles taken from thefield of mathematical morphology. Their approach combines the pit filling and flow directionspecification into a single step. The result of applying the approach is a pit-free DEM havingno (strictly) flat areas.

Peckham (1995) implemented an improved flooding algorithm in the RiverTools Software(http://cires.colorado.edu/people/peckham.scott/RT.html). It is specifically designed to handlevery large DEMs. Fairfield and Leymarie (1991), among others, suggested to treat the pits asif they were real depressions, and to find the lowest point, from which water could flow outof the pit basin.

Martz and Garbrecht (1998), from the consideration that a pit can be produced also byelevation overestimation errors, proposed a “breaching” algorithm which simulates breachingof the outlet of closed depressions to eliminate or reduce those expected to have beenproduced by elevation overestimates. Obstruction breaching is particularly effective in DEMsof landscapes that have a low relief relative to the vertical resolution of the DEM becausesinks caused by flow path obstruction are more prevalent in these situations.


4

2.1.2 Flat Area Handling, Slopes and Flow Direction

Flat areas may exist both in the original and in the filled DEM. Flat areas by definition haveno defined flow direction and these areas require particular attention to assign the mostrealistic flow direction. The solution of the flow problem through flat areas is of great interestwhen the object of the study is a basin that includes a large floodplain, lakes or wetlands.

In the widely used algorithm of Jenson and Domingue (1988) flat area cells adjacent to othercells with a defined flow direction are identified in a first step. These flat area cells are thenassigned a flow direction, pointing to the nearest adjacent cell with a defined flow direction.This is repeated until all flat area cells are assigned a flow direction. This kind of approachconstrains the flow path to remain within the flat area and allows the possibility of multipleoutlets. However it often produces unrealistic, parallel flow patterns. As a consequence,procedures for a better definition of flow directions in critical areas have been the object ofseveral investigations.

Garbrecht and Martz (1997) and Martz and Garbrecht (1998) describe an algorithm, includedin the TOPAZ Software (http://duke.usask.ca/~martzl/topaz/) that is based on the recognitionof the fact that in homogeneous natural landscapes the drainage is generally towards lowerterrain while simultaneously being away from higher terrain. Such a drainage is achieved byimposing two gradients on the flat surface: one towards lower terrain which draws flow to thenearest downslope outlet, and a second which forces flow away from higher terrain.

Other researchers proposed to infer flow paths in flat areas from the surrounding topography.Tribe (1992) suggests defining a main flow path through the flat area and directing other flowpaths towards this main path.

Mackay and Band (1998) recently proposed to first identify flat features (e.g. lakes andrelatively flat areas) on the DEM for which slope tracking is likely to fail. Contiguous groupsof flat areas are formed into labelled regions by using local region growing. The labelledregions are then classified into water bodies or land areas using supervised classification ofremotely sensed imagery.

Other procedures were proposed by Nogami (1991), McCormack et al. (1993), Van Deursen(1995) and by Liang and Mackay (2000).

The earliest and simplest method for specifying flow directions is to assign flow from eachgrid cell to one of its eight neighbours, either adjacent or diagonally, in the direction withsteepest downward slope. This method, designated D8 (8 flow directions), was introduced byO'Callaghan and Mark (1984) and has been widely used. The D8 approach has disadvantagesarising from the discretization of flow into only one of eight possible directions, separated by45° angles, and thus representing convergent flow only. These disadvantages have motivatedthe development of other methods, comprising multiple flow direction methods, randomdirection methods and grid flow tube methods.

Some authors have proposed that flow must be partitioned between different pixels. Fairfieldand Leymarie (1991) proposed to introduce a stochastic rule in order to follow more closelythe aspect of the slope to avoid the fact that in the D8 algorithm the flow is discretized to only

THEORY

5


6

one of eight directions. The disadvantage of this new procedure is that the result is not exactlyreproducible because of its randomness.

Multiple flow directions were proposed by Quinn et al. (1991) and Freeman (1991). Theypropose to allocate flow to each lower neighbour in proportion to an exponent p of the slopewith an appropriate value of 1.1. These methods have the disadvantage that flow from a pixelis dispersed to all neighbouring pixels with lower elevation. Therefore the contributing areaof a pixel does not include any full pixel but instead is composed of portions of differentpixels and is discontinuous.

Lea (1992) developed an algorithm that uses the aspect associated with each pixel to specifyflow directions.

Costa-Cabral and Burges (1994) presented an elaborated set of procedures, which modeldownslope flow in two dimensions in well-defined flow tubes. Flow at any one point is in thedirection of maximal surface slope.

Desmet and Govers (1996) compared the performances of six routing algorithms of singleand multiple flow and developed a new flux decomposition algorithm.

Tarboton (1997) tries to reduce dispersion by dividing the flow between one or twodownslope pixels, as a special case of flow dispersion. The flow direction is called D∞, whichis a continuous quantity between 0 and 2π that is determined in the direction of the steepestdownwards slope on the eight triangular facets formed in a 3 x 3 grid cell window centred onthe grid cell of interest. The flow from each cell drains to one neighbour, if the angle fallsalong a cardinal (0, π/2, π, 3π/2) or diagonal (π/4, 3π/4, 5π/4, 7π/4) direction. If the angle isfalling between the direct angles to two adjacent neighbours, flow is proportioned betweenthe two neighbour grid cells. Proportions are defined according to the angles between theactual flow direction and the direction to each of the two grid cells.

2.1.3 Contributing Area

Once the flow direction is defined, the upslope contributing area is estimated for each gridcell. From the flow direction grid the flow accumulation grid is created, in which each cell isassigned a value equal to the number of cells that have water flowing to it. Such a proceduremay be executed for both single and multiple flow directions. Using the D∞ approach(Tarboton 1997), the upslope area of each grid cell is taken as its own area (one) plus the areafrom upslope neighbours that have some fraction draining to it.

A widely used method for calculating the upslope contributing area (counted in terms of thenumber of grid cells) is to use a recursive procedure based on the very efficient recursivealgorithm for single directions (Mark 1988).

On hillslopes flow and drainage area (A) need to be characterised per unit width. The specificcatchment area, a, is defined as the upslope area draining per unit contour width, b, (a = A/b)and has units of length (Moore et al. 1991). When working with gridded data, this impliesthat the grid cell size represents the unit contour width b.

THEORY

7

2.1.4 Channel Initiation and Channel Network Extraction

A variety of approaches exist for extracting the channel network using information extractedfrom DEMs and to be successful the topographic data must be sufficiently accurate.

The most common approach for extracting drainage networks is to consider a pixel as beingpart of the channel if its value of contributing area is larger than the defined contributing areathreshold (O'Callaghan and Mark 1984). Such an assumption implies the identification of anappropriate contributing area threshold for starting a drainage channel. A “reasonable”(constant) area is generally applied to the overall data, although such an application appearsunrealistic for large areas and/or for heterogeneous terrain. An arbitrary constant threshold,therefore, cannot reproduce the morphometric drainage properties and its fractal dimensions(Da Ros and Borga 1997; Bischetti et al. 1998; Helminger et al. 1993).

Figure 1 illustrates the dependence of the drainage density on the selected support areathreshold, as well as the difference in the resulting drainage networks.

Many investigators (e.g. Hack 1957; Flint 1974; Gupta and Waymire 1989; Tarboton et al.1989; Willgoose et al. 1991a), have discussed and reported a power law relationship between

Figure 1: The effect of different contributing area thresholds (As) on channel network and drainage density.


8

mean slope in the channel (S) and the contributing area (A) with a constant (c) and a scalingexponent (θ) ranging from 0.4 to 0.7.

(1)

This relationship between slope and area is explained in terms of catchment evolution modelsat the dynamic equilibrium between tectonic uplift, runoff and erosion processes (Kirkby1986; Willgoose et al. 1991a; Howard 1994; Tucker and Slingerland 1994, 1996, 1997; Ibbitet al. 1999).

DEMs have been widely used to derive slope and contributing area relationships. Thisrelationship may be used to discriminate between different geomorphic process regimes andto infer adequate thresholds for channel initialisation (Tarboton et al. 1992; Dietrich et al.1993; Montgomery and Foufoula-Georgiou 1993; Montgomery and Dietrich 1994; Ijjàsz-Vàsquez and Bras 1995; Tucker and Bras 1998, Ibbit et al. 1999). The data plots of ‘localslope versus contributing area’ as presented in these studies show a series of inflection points,of which the significance has been object of discussions. The main inflection, whichcorresponds to the reversal point of the curve, generally marks the transition from diffusetransport processes on convex hillslopes to fluvial processes in concave valleys. Dependingon the environmental setting, the curve can, however, show a more complex pattern ofmultiple points, in which case a transition from landslide (gravity) type transport on hillslopesover debris flow dominated channels to alluvial transport in the channels is presented. In allcases, however, does the dominant and most obvious scaling trend represent channelized gridcells with alluvial transport in the main valley network. In these channels fluvial processesoccur (fluvial scaling trend) and the stream power law applies. For these grid cells a relationaccording to equation [1] with θ = 0.5 is generally assumed.

The physical basis for a constant area approach can be seen in an instability - processcompetition theory and is discussed in the studies of Gilbert (1909), Smith and Bretherton(1972), Kirkby (1980, 1986), Tarboton et al. (1991, 1992), Moglen et al. (1998), Howard(1997), and Tucker and Bras (1998). According to Tucker and Bras (1998) the instability -process competition theory is adequate for semiarid, low to moderate relief landscapes withpredominant Hortonian overland flow (spatial runoff generation and infiltration-excessdominated), sparse vegetation and loss of surface soils.

The model of Gilbert (1909) describes that a transition from convex to concave slope profilesreflects a transition in process dominance from creep to wash. It was quantified by Smith andBretherton (1972) in terms of a linear stability analysis showing that channelizationcorresponds to the transition from straight or convex hillslopes to concave valley slopes.The stability analysis was based on the view that valleys form where flow convergencecauses rill or gully excavation by runoff erosion to outpace infilling by diffusive processessuch as rain splash.

Under such a hypothesis a channel network extends up to the point where unstable fluvialsediment transport processes are replaced by stable diffusive processes and, according to thistheory, the fluvial scaling trend allows to quantify the value of a critical area at which bothfluvial and diffusive transport yield the same local slope under conditions of dynamicequilibrium (Kirkby 1986; Tarboton et al. 1992).

S cA= −θ

Tarboton et al. (1992) showed that the change in the slope in ‘local slope versus contributingarea’ plots, corresponds to the transition from hillslope to drainage channel and proposed touse the respective value of contributing area as the critical contributing area threshold forchannel initiation.

Although the procedure proposed by Tarboton et al. (1991, 1992) appears physically basedand useful for identifying the appropriate threshold for channelization, the hypothesis of themethod seems to be invalid when low resolution DEMs are involved (Montgomery andFoufoula-Georgiou 1993; Hussein and Schwartz 1997). The success of the method proposedby Tarboton et al. (1991, 1992) may rely on the pixel size being small enough to resolve theland surface at the hillslope scale.

An alternative view of the channelization process is that valley and channel formations arecontrolled by geomorphic thresholds. The hillslope-valley transition according this theoryoccurs where a selected geomorphic process threshold is regularly exceeded. The criticalcontributing area that is required to initiate a channel may then be estimated from differentmodels of channel initiation. A channel head could be associated with initiation of fluvialerosion, for example, an expectation that will not be met in landscapes where only sheet washerosion occurs.

In such models channel initiation processes may be controlled by several different processthresholds related to local environmental factors (climate, soils, slope, vegetation andgeology) that may be active in the same catchment and may influence landscape morphologyand drainage density. An overall view of the relation between hillslope processes anddrainage density can be found in Tucker and Bras (1998).

According to this theory, channelization can be controlled by a variety of thresholds such as,for example, thresholds for runoff generation by saturation overland flow (Montgomery andDietrich 1989; Ijjasz-Vasquez et al. 1992; Dietrich et al. 1993), thresholds for runoffgeneration by Hortonian overland flow (Prosser and Abernethy 1996), thresholds for slopestability (Montgomery and Dietrich 1989, 1994; Howard 1994; Tucker and Bras 1997), orthresholds runoff erosion (Montgomery and Dietrich 1989, 1992; Willgoose et al. 1991b;Rinaldo et al. 1995a, 1995b; Prosser and Dietrich 1995; Prosser and Abernethy 1996; Tuckerand Slingerland 1997; Howard 1994)

The theory of geomorphic thresholds is supported by field observations at channel heads thatshow also a relationship between contributing area and local slope, which supports theconcept of a channel initiation threshold (Montgomery and Dietrich 1989; Dietrich et al.1992; Prosser and Abernethy 1996; Bischetti et al. 1998).

Montgomery and Dietrich (1992), therefore, suggested a slope-dependent channelizationthreshold of the form:

(2)

where Ai is the cumulative area draining from cell i, At is the spatially variable threshold area,c is a factor which takes into account soil and climate, S is the local slope and φ= 2.

A A cSi t≥ = −φ

THEORY

9

In relatively small or homogeneous basins the channel network defined by either geomorphicthresholds or by area-slope dependent thresholds may not differ significantly. The differencesbetween the two methods, however, increase with basin size and environmental complexity(Montgomery and Foufoula-Georgiou 1993).

While the previously described theories allow to define channels by physical arguments,other methods have been proposed in the last years: one approach is based on the recognitionof individual DEM cells as valley cells and the subsequent definition of a drainage network.Such methods were proposed by Band (1986), Meisels et al. (1995), and Peucker andDouglas (1975) but generally they are efficient only when the drainage network is welldefined by the local surface properties derived from the DEM. The resultant network isalways discontinuous and the different valley segments need to be connected in order toderive the complete channel network.

Quinn et al. (1995) suggested to determine an optimum channel initiation threshold (CIT)based on the spatial pattern of topographic index and contributing area. The authors do notassume that CIT represents the real channel heads but it is estimated in agreement with theDEM resolution and with the assumptions of the TOPMODEL. Peckham (1998) proposed apruning strategy to remove all of the exterior links (or leaves) in the river network tree. Theexterior link-pruning scheme is then repeated one or more times to produce coarser river treesin a self-consistent manner.

Roth et al. (1996) described an interesting method for extracting a channel network based onthe combination of relative elevation of the catchment and its contributing area.

Fagherazzi et al. (1999) proposed a new method for extracting tidal channel networks byusing a combination of an elevation threshold and a curvature threshold.

2.1.5 Basin Extraction

For the delineation of river basins or catchments it is necessary to identify outlet points in thedrainage network. From the selected outlets all the cells upstream flowing to the specifiedoutlets are identified. The basic information is taken from the grids of flow direction and flowaccumulation and the drainage divides are defined by tracing the cells that drain to a selectedset of outlets (Jenson 1991). Outlets can be selected automatically or interactively.

An alternative method for automatically extracting river basins is proposed by Soille andAnsoult (1990) and it is based on the use of mathematical morphology.

A watershed delineation procedure for tidal environments and the analysis of networkproperties, including drainage density, has been proposed by Rinaldo et al. (1999).

There is only one method, which does not require a DEM; it is based on an automated rivernetwork overlay (Sekulin et al. 1992). This method needs a well-defined river networkdatabase, in which the basic unit is a river stretch (link) defined as the river length betweentwo nodes. Each cell of a grid is allocated to a river stretch using a “shortest distance”algorithm. Boundaries of hydrometric areas, coastlines, and boundaries of the catchment areaabove gauging stations can be used with a point-in-a-polygon algorithm to give added


10

THEORY

11

precision in the allocation phase. Grid cells are then accumulated upstream of river stretchesusing a “down-network-travel” technique. This procedure has been proposed in the ERICA(European Rivers and Catchments) project of the EEA (Flavin et al. 1998).

2.1.6 Software Availability

Over the last decades several software packages have been developed for landscape analysis.In the following we list a few, giving special attention to the problem of channel networkextraction:

a) TOPAZ is a software package for automated digital landscape analysis developed byGarbrecht and Martz (USDA-ARS) (http://grl.ars.usda.gov/overview.html).

b) TARDEM is a suite of programs for the analysis of Digital Elevation Data, developed byD. Tarboton (http://www.engineering.usu.edu/dtarb/).

c) TOPOG is a physically based, distributed hydrological model for catchments, developedjointly by CSIRO Land and Water and the Cooperative Research Centre for CatchmentHydrology (http://www.clw.csiro.au/topog/).

d) RiverTools is an extensive software toolkit written in IDL (Interactive Data Language)for digital terrain and river network analysis developed by S. Peckam(http://cires.colorado.edu/people/peckham.scott/RT.html).

e) ANUDEM, developed by M.F. Hutchinson, Centre for Resource and EnvironmentalStudies, The Australian National University, has been designed to produce accurate digitalelevation models with sensible drainage properties from comparatively small, but wellchosen, elevation and stream line data sets. (http://cres.anu.edu.au/outputs/software.html).

f) GOLEM is a numerical model that simulates the evolution of topography over geologictime scales. The model was developed by G. Tucker(http://www.mit.edu/people/gtucker/Golem/GolemMain.html).

g) TAPES (Terrain Analysis Programs for the Environmental Sciences) program tools weredeveloped by Ian Moore, Centre for Resource and Environmental Studies, The AustralianNational University (http://cres.anu. edu.au/software/tapes.html).

h) NERODE, a drainage basin simulation model, was developed by A. Howard(http://erode.evsc.virginia.edu/drainage.htm).

i) Landscape evolution models have been developed by J. Braun, Research School of EarthSciences, The Australian National University (http://rses.anu.edu.au/ ~jean).

k) CRWR-PrePro is an ArcView preprocessor for Hydrologic, Hydraulic and EnvironmentalModeling developed by D. Maidment and F. Olivera at the Center for Research in WaterResources, University of Texas at Austin(http://www.ce.utexas.edu/prof/olivera/prepro/prepro.htm).

l) MIKE BASIN is a GIS for River Basin Management, developed at the Danish HydraulicInstitute (http://www.dhisoftware. com/mikebasin/).

2.2 Drainage Density, Hillslope Processes and Environmental Factors

This section discusses the relationships between critical contributing area and drainagedensity as well as between environmental characteristics and drainage density. The aim is toshow how environmental factors such as climate, soils, geology, relief and vegetation can bemodelled in order to define homogeneous areas with respect to drainage density and howthese areas can be used to derive channel networks with a naturally varying drainage densityfrom a digital elevation model.

2.2.1 Drainage Density and Critical Contributing Area

Horton (1945) defined drainage density (Dd) as the sum of the lengths (L) of all streams in abasin divided by the basin area (A):

(3)

The relation between drainage density and upstream contributing source area (As) (see Figures 1 and 2), as used to extract the channel network in the process competitionmodel, is given by the following power law relationship (see Moglen et al. 1998):

(4)

Such formulation implies a strict relationship between drainage density and the variousfactors that control the source area. This relationship can be formalized:

Dd = f (climate, vegetation, topography, soil, rock) (5)

A relation between a nondimensional drainage density and a channel initiation number hasalso been derived in the study of Willgoose et al. (1991c).

2.2.2 Drainage Density and Environmental Factors

Tucker and Bras (1998) showed that the relationship between drainage density (Dd) andenvironmental factors such as climate, geology, relief and vegetation is depending on thenature of the geomorphic processes operating in a catchment.

Dd As∝ −0 5.

DdL

A

m

m=

2


12

Hillslope length = BA = 2B*LDd = L/A = 1/2BB = 1/2Dd

B B

L

Figure 2: Stream lenght (L), drainage density (Dd), hillslope length (B) and contributing area (A) relationships.

∞

Since each process may produce a different functional relationship between drainage densityand environmental factors, drainage density is actually controlled by a set of differentthresholds. If we want to derive information on the critical contributing area, it is, therefore,necessary to relate all relevant environmental factors with Dd.

2.2.2.1 Climate

Climate is usually expressed as a function of rainfall and most models predict a positivecorrelation between Dd and rainfall (Montgomery and Dietrich 1989; Tucker and Bras 1998),with a sensitivity depending on the dominant hillslope process in the catchment (Tucker andBras 1998).

The impact of climate on drainage density has been investigated by Rinaldo et al. (1995),using a mathematical model of landscape evolution. They showed drainage density at its peakduring wet periods that were equated with a low critical shear stress. Inversely, low Ddoccurred at high critical shear stress, associated with dry periods. Tucker and Slingerland(1997), however, argued that wet periods with low critical shear stress might have a differenteffect on drainage density due to a change in vegetation cover.

Moglen et al. (1998) showed the importance of vegetation as a limiting factor on Dd and theexistence of two distinct regimes of behaviour in drainage density. In desert and semi-desertconditions (annual precipitation less than about 250 mm) the drainage density increases withprecipitation, while in humid conditions (annual precipitation exceeding 250 mm) thedrainage density decreases with precipitation. The model of Moglen et al. (1998) states thatsuch a dependency is associated with the degree of vegetation cover and that withoutvegetation Dd is simply positively related to precipitation. Such theoretical results supportfield observations from previous studies in which vegetation outweighs precipitation inhumid climates, producing a decrease in drainage density (Morisawa 1985).

Perhaps the most exhaustive study on the effect of climate on drainage density wasundertaken by Melton (1957). The study concluded with an inverse relationship between theThornthwaite Precipitation Effectiveness Index (I) and drainage density. The I index definedby Thornthwaite (1931) is:

(6)

where Ti is the average monthly temperature in degrees Fahrenheit, and di is the precipitationin month i, in inches. The I index is a measure of climate, as indicated in Table 1.

Id

Ti

ii

=−

=

∑115101

12 1 11

.

THEORY

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Climatic Regime Precipitation Effectiveness Index

Rainforest >= 128

Forest 64 – 127

Grassland 32 – 63

Steppe 16 – 31

Desert <= 15

Table 1: Climates correspondingto various ranges of the I index.


14

The dependency of the spatial and temporal distribution of Dd on climate is also correlated tocreep diffusivity. A higher diffusivity tends to decrease Dd under a humid climate and toincrease Dd under an arid climate (Tucker and Slingerland 1997).

2.2.2.2 Morphology

Slope steepness, slope form and local relief are the main morphological factors controllingdrainage density.

The relationship between Dd (expressed as source area) and slope has been analysed byKirkby (1987), predicting a positive relationship for semiarid (infiltration excess dominated)climates and an inverse relationship for humid (saturation dominated) climates. An inverserelation between source area and local valley slope has been documented by fieldobservations (Montgomery and Dietrich 1992; Dietrich et al. 1992). For an idealisedlandscape, channel initiation on steep hillslopes shows a positive relationship between valleygradient at the channel head and drainage density (Montgomery and Dietrich 1989).

From a theoretical point of view the relationship between Dd and relief predicts a positivecorrelation for Hortonian, creep-dominated landscapes and an inverse correlation forlandslide-dominated ones. Strahler (1964) noted that low Dd is favoured where relief is lowand high Dd is favoured in mountainous relief. Roth et al. (1996) introduced the relativeelevation in the channel initiation model and derived an inverse correlation between drainagedensity (expressed as contributing area) and relief. Kirkby (1980, 1993) predicted that under ahumid climate Dd should decrease with increasing relief, while under a semiarid climate Ddmight be independent of relief.

For humid landscapes, dominated by saturation-excess runoff, and/or in high-relief landscapes,dominated by simple threshold landsliding, the Tucker and Bras (1998) analysis showed aninverse correlation between relief and drainage density, while in semiarid landscapes with lowrelief, dominated by Horton overland flow, the sign of the predicted relationship is positive.Other relationships between drainage density and morphological factors are reported inSchumm (1956) Schumm et al. (1987), Oguchi (1997) and Howard (1997).

2.2.2.3 Geology

Under homogeneous climatic conditions, drainage density is related to bedrock geology(Hadley and Schumm 1961). Strahler (1964) and Morisawa (1985) qualitatively describe therelation between the resistance of the rocks and the drainage density, explaining that resistantrocks determine low drainage density.

A relation between drainage density and bedrock erodibility was developed in a landscapeevolution model by Tucker and Slingerland (1997), taking into account the bedrock channelincision with respect to the lithological gradient of slope.

2.2.2.4 Soils

Strahler (1964) and Morisawa (1985) observed that drainage density is inversely related tothe hydraulic conductivity of the underlying soil. Also Montgomery and Dietrich (1992),

THEORY

15

modelling drainage density through channel initiation factors on steep hillslopes, showed aninverse relation between saturated hydraulic conductivity and drainage density.

Taking into account the integrated soil transmissivity, defined as the product of soil permeabilityand soil depth, it has been argued that different process thresholds controlling channel initiationshow a dependence on transmissivity (Dietrich et al. 1992; Tucker and Bras 1998).

Moreover, soil transmissivity and soil erodibility, combined with vegetation cover, are thefactors that influence the land surface resistance to erosion by surface flow (erosionthreshold) and are inversely related to the drainage density (Rinaldo et al. 1995b; Tucker andSlingerland 1997).

2.2.2.5 Vegetation

Vegetation has an influence both on the climate and on the soil characteristics. Vegetationoutweighs precipitation in humid climates, resulting in a decline in drainage density withincreasing precipitation (Moglen et al. 1998). While Rinaldo et al. (1995b) assigned humidand wet climates to high drainage densities as observed in Tucker and Slingerland (1997),others argue that a decrease in vegetation cover means a decrease in surface resistance andcritical shear stress, which result in an increase of drainage density (Montgomery andDietrich 1989; Willgoose et al. 1991b; Tucker and Slingerland 1997; Prosser and Dietrich1995). Kirkby (1999) showed analytically that drainage density decreases with land cover.

If vegetation cover is simply related with drainage density, field observations show that lowdrainage density is generally favoured under dense vegetation cover (Strahler 1964;Morisawa 1985).

The effect of the vegetation cover percentage on critical shear stress and its control onchannel initiation has been quantified in the studies of Tucker et al. (1997, 1999) and Fosteret al. (1995).


16

3. A CONTINENTAL APPROACH

3.1 Global Datasets of Channel Networks and Catchment Boundaries

At continental scale the constant area approach is the most common method used for theextraction of channel networks. Generally, a constant area threshold is applied for alllandscapes in a study area (Hutchinson and Dowling 1991; Verdin and Jenson 1996; Grahamet al. 1999; O’Donnel et al. 1999).

The HYDRO1K package, for example, includes global channel networks extracted from a 1 km grid cell size DEM with an area threshold set at 1000 km2 (Verdin and Jenson 1996, seehttp://edcwww.cr.usgs.gov/landdaac/gtopo30/ hydro.html).

Also the TerrainBase 5’ Global DTM provides rivers extracted from a 5’ DEM (approx. 9 kmgrid cell size) with an area threshold of 50,000 km2 (Graham et al. 1999, seehttp://www.ngdc.noaa.gov/seg/). A revised area-corrected version of the study conducted byGraham et al. (1999) was realised by Comanor et al. (2000). It is available athttp://www.hydro.washington.edu.

A digital atlas of the world water balance and a terrain analysis for global runoff routing canbe found at:http://www.ce.utexas.edu/prof/maidment/atlas/atlas.htm andhttp://www.ce.utexas.edu /prof/olivera/NSFGlobal/NSFGlobal.htm.

A continental hydrological analysis has been conducted by Hutchinson and Dowling (1991) forAustralia, using a 1/40-degree of latitude and longitude DEM (approx. 2.5 km grid cell size).

The following list gives the URLs of different global DEMs of the USGS, which can bedownloaded free of charge:

USGS 7.5-Minute Digital Elevation Data:http://edcwww.cr.usgs.gov/glis/hyper/guide/7_min_dem as of March 1998.

USGS 1-Degree Digital Elevation Data:http://edcwww.cr.usgs.gov/glis/hyper/guide/1_dgr_dem as of March 1998.

USGS Metadata for GCIP Reference Data Set (GREDS):http://nsdi.usgs.gov/nsdi/wais/water/gcip.html as of March 1998.

USGS Global 30 Arc-Second Digital Elevation Data:http://edcwww.cr.usgs.gov/landdaac/gtopo30/gtopo30.html as of March 1998.

3.2 Cell Size Influence

Scale and grid cell size influence the extraction of the channel network to a point where thesame method produces different results for the same area. In general, the grid cell sizedependency is highlighted by the inability to accurately reproduce drainage features that are

at the same scale as the spatial resolution of the DEM. For meandering channels, this resultsin shorter channel lengths and for networks with high drainage density, it leads to channel anddrainage area aggregation. In these situations, the number of channels, the size of directdrainage areas and the network pattern may depart considerably from reference values (Wangand Yin 1998). Garbrecht and Martz (1994) presented a sensitivity analysis on drainage properties extractedfrom DEMs of increasing grid cell size and for several hypothetical network configurations.On the basis of these results they found that a DEM should have a grid cell area of less than5% of the network reference area in order to reproduce important drainage features with anaccuracy of about 10%. The network reference area is the mean subcatchment area drainingdirectly into the channel links of the network.

Table 2 shows different grid cell sizes of digital elevation data and their typical range ofapplication. For the calculation of this table it has been assumed that a typical catchment areacontains 5000 DEM cells and the region of application is made up of 200 of thesecatchments.

Under these assumptions, it results that DEMs with 1" and 3" grid cell sizes are suitable fordelineating watersheds and stream networks within urban areas or in small river basins.DEMs with 15" grid cell size are suitable for regional studies, while a 30" grid cell size ismore appropriate for continental scale studies, and a grid cell size of 5' is best suited for ananalysis covering the whole globe.

Maidment (1996) proposed a “thousand-million” rule as a rough guide: taking the area of theregion to be analysed and dividing it by one million should give the appropriate cell size touse; multiplication of the cell size chosen by one thousand will give the minimum drainagearea of watersheds that should be delineated from this DEM.

Different studies highlighted the influence of the DEM grid cell size on the accuracy of thehydrological and geomorphic properties extracted. Zhang and Montgomery (1994) analysedthe effect of the DEM grid cell size on the derived topographic index (ln(a/tanβ)) andsuggested that the most appropriate grid cell size for topographically driven hydrologicmodels is somewhat finer than the hillslope scale identifiable in the field, resulting in a valueof about 10 m grid cell size. Quinn et al. (1995) showed that a grid cell size of 100 meters isinappropriate for the application of the topographic index. If the grid cell size is too large

A CONTINENTAL APPROACH

17

Geographic Linear Cell Watershed Region Typical Source MapsCell Size Size (m) Area (km2) Area (km2) Application

1" 30 5 1000 Urban watersheds 1:24,000

3" 90 40 8000 Rural watersheds 1:250,000

15" 460 1000 200,000 River basins

30" 930 4000 900,000 Nations

3' 5.600 150,000 30,000,000 Continents

5' 9.300 400,000 90,000,000 Global

Table 2: Digital Elevation Data Grid Cell Sizes and Typical Scales of Application (Maidment 1996).

(ranging from 30 to 90 m), many topographic features such as hollows, low-order channels,and hillslopes will not be resolved.

According to Wolock and Price (1994) it should, however, not be concluded that coarseresolution DEMs are inappropriate for topographic models such as TOPMODEL, since thewater table configuration (reflected by ln(a/tanβ)) may be smoother than the land surfacetopography and may be related more accurately to a coarse resolution DEM (1:250,000) thanto a fine resolution (1:24,000) DEM. Moreover, it is important that a subbasin should have anarea greater than 5 km2 to be representative in terms of topographic characteristics andadequate for the representation of hydrologic processes (Wolock 1995). Wolock and McCabe(2000) showed that DEM resolution affects computed values of topographic characteristics.

An important observation, useful for large area applications with coarse resolution DEMs, isthe fact that slope-area relationships can be determined with great reliability using lowresolution DEMs (Walker and Wilgoose 1998).

Various studies highlighted the importance of different area thresholds used in the extractionof channel networks and showed that the extent of the stream network and the length of theoverland flow path strongly influence hydrological modelling results (White and Running1994; Da Ros and Borga 1997, Gandolfi and Bischetti 1997).


18

19

4. METHODOLOGY

4.1 Outline of the Procedure

In this chapter the methodology developed for the derivation of the drainage network and forthe subsequent delineation of catchments of different order is discussed in detail. Themethodology has been tested and validated for the territory of Italy.

The methodology for the derivation of the drainage network is based on the combination ofdigital elevation data and environmental parameters in a GIS environment, leading to thedelineation of areas of homogeneous drainage density. To this end, the following data havebeen used:

• A Digital Elevation Model with a grid cell size of 250m, coming from the ItalianGeological Survey;

• The European Soil Database, ESB (European Soil Bureau) (1:1,000,000 scale);

• CORINE Land Cover data (1:100,000 scale);

• Climatic Data form the MARS Database for Europe (50 km grid).

It should be clear that only large-scale geomorphic processes can be derived from these dataand that they are adequate for small-scale applications only.

All data were transformed into a Lambert Azimuthal projection with the followingparameters:

Spheroid : International 1909 Major axis : 6,370,997 metersRadius of the sphere of reference : 6,370,997 metersLongitude of center of the projection : 9°0’0’’Latitude of center of the projection : 48°0’0’’

In order to avoid errors in flat terrain (mainly in the Po valley and in some major floodplains),a burning-in procedure was performed, using the major rivers as given in the GISCO 3million (WPEU3M) river network layer for Europe. The DEM was then filled by a floodingapproach and slope, flow direction and contributing area were computed using single andmultiple flow algorithms.

To overcome problems related to the use of a single constant area threshold for alllandscapes, a landscape stratification was implemented first.

To do so, the landscape was characterised according to different environmental factorsgoverning drainage density: climate, morphology, soils, geology and vegetation cover.

Using the slope-area relationship, the critical contributing area was then derived for eachindividual landscape class. The channel network was thus extracted by using different sourceareas, merged and connected with a dedicated algorithm.

Rivers were finally vectorised and catchments were delineated according to Strahler orders.

4.2 Derivation of Hydrologic Quantities

The primary hydrologic quantities to be derived from a DEM are the local slope, the flowdirection and the contributing area. All necessary operations for the calculation of thesequantities were executed using the TARDEM software, a suite of programs for the analysis ofdigital elevation data, developed by David Tarboton (Tarboton 1997). In a first step drainageenforcing along digitised river channels was performed and spurious pits were removed. In asecond step the hydrologic quantities were computed.

4.2.1 Drainage Enforcing and Pit Filling

In flat terrain automatically extracted streams often do not coincide with mapped streams. Forthis reason a drainage enforcement algorithm was applied to enforce drainage along knownriver channels in flat areas. This step produces a grid of flow directions only incorrespondence to the delineated river network. The GISCO 3 million river network was firstedited in order to ensure a fully connected drainage network (e.g. through lakes) with well-defined and consistent flow directions, and to remove double-lined streams and artificialwater courses. It was then converted to an EPA style river reach file in ‘shape file’ format.Care was taken to identify one and only one drainage path for each delineated stream. Wherenecessary, the database was densified, adding digitised rivers from other data sources.

In the TARDEM software the river flow enforcement direction is named FDR and anassociated index grid is named FDRP. Serious problems were encountered during theproduction of the FDR grid file. Sometimes it showed lost pixels or cut-off meanders whenpixel resolution was too coarse with respect to the delineated meander, or it produced areverse stream flow direction when the elevation of some downstream cells was greater thanthe elevation of upstream cells. For such reasons GISCO and FDR files had to be correctedseveral times.

All pits were assumed to be artefacts and they were eliminated using a ‘flooding’ approach(Jenson and Domingue 1988), raising the elevation of each pit grid cell within the DEM tothe elevation of the lowest pour point on the perimeter of the pit.

The river flow enforcement direction grid (FDR) was used to guarantee that pits are filledconsistently with the drainage direction along existing streams. The output grids are the filledDEM (FEL) and a new flow direction grid with enforced rivers (FDRN). This process is themost time consuming of all processing steps and takes considerable time to execute.

An example of the resulting river network is shown in the Figure 3.


20

4.2.2 Flow Direction, Flat Areas and Contributing Area

Flow directions were assigned with the D8 method (O’Callaghan and Mark 1984). D8 flowdirections and local slopes were derived from the filled elevation data file and the enforcedflow direction grid.

In the case where flat surfaces, such as level valley floors or plateaux at drainage divides,were produced by the previous pit-filling, further rectifications were required to ensure anunambiguous downslope drainage at every location in the DEM. This was achieved through arelief imposition algorithm, which takes into consideration the rising and falling topographysurrounding the flat surfaces to generate a realistic, topographically consistent and convergentdrainage over those surfaces. Thus, in flat areas, flow directions were assigned away fromhigher ground and towards lower terrain using the method of Garbrecht and Martz (1997). Insuch a way arbitrary drainage direction assignment is minimised, since the topographysurrounding a flat surface controls the relief imposition and drainage determination. Anexample of such application is shown in figure 4.

Upslope contributing area (counted in terms of the number of grid cells) was calculated forsingle flow directions using a recursive procedure based on the recursive algorithm proposedby Mark (1988). The upslope area of each grid cell was taken as its own area (one) plus thearea from upslope neighbours that drain to it. Edge contamination checking was disabled inorder to avoid problems at the boundary of the area covered.

While the D8 method was used for deriving the channel network, the D∞ method was usedfor the analysis of the relationship between slope and contributing area (Tarboton 1997). Thereason to apply D∞ arises from the necessity to determine the most appropriate threshold forstarting a drainage channel. A theoretically justified procedure is, in fact, to look for a breakin the plot of local slope versus contributing area. In this context the D8 approach has adisadvantage arising from the discretization of flow into only one of eight possible directions.

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Figure 3: Example of thecontributing area map withGISCO 3 million rivers overlaidin blue. The figure demonstratesthe fit between GISCO rivers andthe derived drainage network inflat areas (here an example fromthe Po valley)


22

The D∞ method is, therefore, preferable for the computation of flow directions on hillslopeswhere the D8 method results in a significant bias.

At this point all the maps necessary to automatically extract the channel network are ready,only the identification of the appropriate threshold is missing.

4.3 Landscape Characterisation

At this point the main question is how to decide on an appropriate threshold for extracting thechannel network.

In relatively small or homogeneous basins the channel network defined by geomorphicthresholds and by a process competition approach may not differ significantly, although thedifference between the two methods increases with basin size and environmental complexity.Using geomorphic methods the threshold is variable in space and the drainage density thatresults is greater in steep slopes, as observed in real world (Roth et al. 1996; Montgomeryand Foufoula-Georgiou 1993).

Geomorphic models, however, need many and accurate data for their application and processknowledge is required. Process competition models appear more appropriate and are mostlyused for continental scale studies (Hutchinson and Dowling 1991; Verdin and Jenson 1996;Graham et al. 1999; O’Donnel et al. 1999). However, with a constant contributing areathreshold, equal for all European landscapes, the model will certainly produce unrealistic results.

The decision to apply a process competition model, therefore, implies the necessity to definedifferent support area thresholds for different European landscapes. As a consequence, thereis a need to define “homogeneous areas”, and hence different support area thresholds, toovercome problems related to the complexity of the European landscape. In this studyhomogeneous areas were defined on the basis of environmental factors influencing channelinitiation and, therefore, governing drainage density.

The link between drainage density (Dd) and upstream contributing source area, is governedby a power law relationship as defined in Moglen et al. (1998) (see equation 4, p.12).

According to Strahler (1964) and Morisawa (1985) it was assumed that low drainage densityis favored in regions of highly resistant or highly permeable subsoil material, under densevegetation cover and when relief is low, while high drainage density is favored in regions ofweak or impermeable subsurface materials, sparse vegetation cover and mountainous terrain.

Figure 4: The effect of thealgorithm used for flat areahandling

a: standard D8 algorithm b: improved algorithm

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Thus, the usual input parameters of geomorphic models are considered to individuatehomogeneous areas in terms of similar drainage density, even though the channel network isderived following the principles of the process competition - instability theory. In thefollowing the individual environmental parameters are discussed in more detail.

4.3.1 Climatic Effect

The Thornthwaite Precipitation Effectiveness Index (I) as defined by Thornthwaite (1931)was used as one factor predicting drainage density (see equation 6, p.13). Climate data fromthe European Database of the MARS project (50 km grid cell size) were used to estimate theindex. Although the database is referring to a 50 by 50 km grid, the following analysis wasperformed for a 250m grid. Meteorological data are stored in the database as grid points withvalues derived from an interpolation of station data within a maximum distance from the gridpoint. The Italian area includes 121 grid points. A data series of daily data running from 1975to 1997 was used for this study.

For each grid point average monthly minimum and maximum temperatures and monthlyprecipitation were calculated for all years available and stored in an intermediate table (seetable 3). Longterm mean monthly temperature and mean monthly rainfall were determinedfrom the monthly values in a second step (see table 4).

From table four the Thornthwaite Precipitation Effectiveness Index was calculated for eachgrid cell and a map as shown in figure 5 was produced.

Point Month Year Tmax Tmin Rain Rain days

12014 01 87 xxx xxx xxx xxx



. . . . . . .

. . . . . . .

. . . . . . .







. . . . . . .

. . . . . . .


. . . . . . .

Point Month Mean T Mean R MeanRain days

12014 01 xxx xxx xxx



. . . . .

. . . . .

. . . . .





. . . . .




. . . . .

. . . . .

Table 3: Monthly climatic data. Table 4: Mean monthly climatic data.

4.3.2 Morphological Effect

The influence of the morphology on drainage density has been considered by definingdifferent relief classes based on the relation between slope steepness and relative heightdifference. These parameters have been selected on the basis of their influence on drainagedensity (Montgomery and Dietrich 1989, Roth et al. 1996).

The two parameters were derived from the original DEM, using neighbourhood operators(ESRI 1994) working on a moving window of 3x3 grid cells, in order to represent a smoothedmorphology and thus obtaining a general trend comparable with the working scale.

Table 5 shows how the two factors were combined to obtain different relief classes accordingto table 5 as proposed by van Zuidam and van Zuidam-Cancelado (1979).

An AML code was used to calculate relief classes. In cases where slope steepness and relativeheight difference resulted in different relief classes, grid cells were assigned according to the


24

Figure 5: The Thornthwaite PrecipitationEffectiveness Index for Italy.

No. Relief class (topography) Slope steepness (%) Relative heightdifference (m)

1 Flat or almost flat 0 – 2 < 5

2 Undulating/gently sloping 3 – 7 5 –50

3 Undulating – rolling/sloping 8 – 13 25 – 75

4 Rolling – hilly/moderately steep 14 – 20 50 – 200

5 Hilly – steeply dissected/steep 21 – 55 200 – 500

6 Steeply dissected - mountainous/very steep 56 – 140 500 – 1000

7 Mountainous/extremely steep > 140 > 1000

Table 5: Coding used to describe terrain morphology.

TPE Index

16-31

32-63

64-127

>128

No Data

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slope class. The classes were finallyregrouped into 4 new classes (1; 2/3; 4/5;6/7) and an 8x8 majority filter was used toreduce the salt and pepper effect. The finalresult is shown in figure 6 with a grid-cellsize of 250m x 250m.

4.3.3 Geological Effect

The role played by the structure of theunderlying rock type was reduced to theeffect of the lithological type. In addition,the rock erodibility scale as proposed byGisotti (1983) was adopted.

From the Soil Map (Soil Database forEurope, European Soil Bureau, 1:1,000,000scale) the parent material (mat1 field) foreach Soil Mapping Unit (SMU) wasextracted by deriving the dominant lithology (> 50% in terms of Soil Typology Unit (STU)areas). An AML code to derive the dominant parent material for each SMU was developed.

The rock erodibility scale was originally divided into six classes. The main lithological typesand associated erodibility classes are listed in table 6.

Based on the dominant parent material (mat1 field) the rock erodibility was extractedaccording the Table 7.

Classes were finally reduced to five by re-grouping classes 3 and 4 into one medium class.The resulting map is shown in figure 7.

Figure 6: Morphological map of Italy.

Figure 7: Rock erodibility map of Italy.

Table 6: Coding of rock erodibility.

# Rock Main lithological typeerodibility

1 Very low Igneous, metamorphic

2 Low Calcareous

3 Medium low Sandy and loamy, pyroclastic

4 Medium high Meta-clayey, flysh

5 High Clayey materials

6 Very high Unconsolidated clastic

Rock Erodibilitywater, town

very low

low

medium

high

No Data

Morphological Class

flat areas

undulating/sloping

hilly/steep

mountaionous/very steep

4.3.4 Soil Effect

The different process thresholds controlling channel initiation show a dependence on theintegrated soil transmissivity (see Dietrich et al. 1992; Tucker and Bras 1998). For thelandscape stratification the uniform soil transmissivity has been chosen as the main soil factoraffecting the drainage density. Soil transmissivity (T) was calculated as the product ofsaturated permeability (K) and the soil depth (D):

(7)

Information regarding soil permeability and depth were derived from the European Soil Map.Saturated permeability for each SMU was calculated from the dominant texture class (text1field). As for the lithology, also soil texture and soil depth were derived as dominant for eachSMU, if they occupied more than 50% in terms of STU areas.

The European Soil Map gives texture classes as shown in table 8.

Through the textural class, the soil types were associated with typical values for soilparameters as used in well-known soil models (see, for example, Morgan et al. 1984; Fosteret al. 1995; ANSWER and TOPOG user manuals).

T K D m day= x [ 2 ]


26

Table 7: Classification of the parent material (Mat1) into rock erodibility classes.

# Rock erodibility Mat1 (European Soil DB, v.1.0)

1 Very low 454, 455, 456, 700, 709, 710, 711, 712, 720, 721, 722, 723, 730, 731, 732, 800, 810,820, 821, 822, 823, 824

2 Low 200, 210, 211, 212, 213, 214, 215, 215, 217, 218

3 Medium low 220, 240, 250, 450, 451, 452, 453, 457 600, 610, 620

4 Medium high 630, 640, 740, 741, 742, 743, 744, 745, 749, 750

5 High 230, 231, 232, 233, 234, 300, 310, 311, 312, 313, 314, 319, 320, 321, 322, 323,324, 330, 331, 332, 333, 340, 350, 500, 510, 511, 512, 513, 530

6 Very high 100, 110, 111, 112, 113, 120, 130, 131, 140, 150, 400, 410, 411, 412, 413, 414,419, 420, 421, 422, 429, 430, 431, 440, 441, 442, 514, 520, 521, 522, 523, 910

Class Description Fraction (%)

0 No texture Peat soils

1 Coarse 18 ≤ clay and > 65 sand

2 Medium 18 ≤ clay < 35 and 15 sand,or 18 ≤ clay and 15 ≤ sand < 65

3 Medium fine < 35 clay and < 15 sand

4 Fine 35 ≤ clay < 60

5 Very fine ≥ 60 clay

Table 8: Texture classes of theEuropean soil map.

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Class Soil Map Detailed K (m/day) τc (bare) γs (g/cm3) MS (w/w) HSGTexture Texture

1 Coarse Sand > 1.0 2.1 1.50 0.08 A

1 Coarse Loamy sand > 1.0 A

2 Medium Sandy Loam 0.5 – 5.0 2.5 1.20 0.28 A

2 Medium Loam 0.5 – 1.0 3.3 1.30 0.20 B

2 Medium Sandy Clay Loam 0.25 – 0.75 C

3 Medium fine Silty Loam 0.50 – 2.0 3.5 1.30 0.25 B

3 Medium fine Silty Clay Loam 0.10 – 0.50 3.20 C/D

4 Fine Clay Loam 0.10 – 0.25 4.7 1.30 0.40 C

4 Fine Sandy Clay 0.10 – 0.25 C/D

4 Fine Silty Clay 0.05 – 0.20 4.8 0.30 D

5 Very fine Clay 0.01 – 0.20 2.9 1.10 0.45 D

Table 9: Coding of soil parameters.

Table 10: Coding of saturated permeability (K).

Class Soil Map K (m/day) τc (bare) τc (100% veg) γs (g/cm3) MS (w/w) HSGTexture

1 Coarse 1.50 2.20 102.2 1.50 0.10 A

2 Medium 0.75 3.90 103.9 1.40 0.20 B

3 Medium fine 0.50 3.50 103.5 1.35 0.25 B

4 Fine 0.20 4.20 104.2 1.30 0.35 C

5 Very fine 0.05 2.90 102.9 1.20 0.45 D

Table 9 reports such typical values for selected soil parameters for each class (K: saturatedhydraulic conductivity; τc: critical shear stress; γs: soil specific weight; MS: volumetric soilmoisture content at saturation; HSG: hydrologic soil group).

In order to use the European soil map, the 11 texture classes as represented in table 9 had tobe reclassified according to the five texture classes of the map, assigning representativevalues of saturated hydraulic conductivity (K). Table 10 shows the result of this exercise.Besides the values for K, the table also shows recommended values of different soilparameters as assigned to these texture classes.

When no information on texture was reported in the European soil map the Hydrological SoilGroup was determined on basis of the parent material, which was then converted to theappropriate soil permeability.

The soil depth was derived from the field roo by taking into account the average value asreported in the Table 11.

Finally, the map of uniform soil transmissivity was obtained by multiplying the K map withthe D map (Figure 8).

4.3.5 Vegetation Effect

The percentage surface cover was used as vegetation parameter since its effect on the criticalshear stress and thus its control on channel initiation has been demonstrated (Tucker et al.1997, 1999; Foster et al. 1995). To do so the CORINE Land Cover (1:100,000 scale) with agrid-cell size of 250 m was reclassified according table 12.

Subsequently, monthly cover percentages were assigned to each new class using the resultsobtained by Kirkby (1999) at the European level. The corresponding values are given inTable 13.


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roo Soil Map Depth (cm) Average Depth (D) (m)

1 > 80 1.0

2 60 - 80 0.7

3 40 - 60 0.5

4 20 - 40 0.3

5 0 - 20 0.1

Table 11: Coding of soil depth (D).

Figure 8: Soil transmissivity map of Italy.

Soil Transmissivity

water, town

< 1m2/hr

1 - 3

3 - 6

> 6 m2/hr

No Data

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Table 12: Reclassification of the Corine Land Cover legend.

Code Class CORINE Land Cover class Reclassification1.1.1 1 Continuous urban fabric 11.1.2 2 Discontinuous urban fabric 11.2.1 3 Industrial or commercial units 11.2.2 4 Road and rail networks and associated land 11.2.3 5 Port Areas 11.2.4 6 Airports 11.3.1 7 Mineral extraction sites 11.3.2 8 Dump sites 11.3.3 9 Construction sites 11.4.1 10 Green urban areas 11.4.2 11 Sport and leisure facilities 12.1.1 12 Non-irrigated arable land 22.1.2 13 Permanently irrigated land 22.1.3 14 Rice fields 22.2.1 15 Vineyards 32.2.2 16 Fruit trees and berry plantations 32.2.3 17 Olive groves 32.3.1 18 Pastures 42.4.1 19 Annual crops associated with permanent crops 52.4.2 20 Complex cultivation patterns 52.4.3 21 Principally agriculture, with sign. natural vegetation 52.4.4 22 Agro-forestry areas 53.1.1 23 Broad-leaved forest 63.1.2 24 Coniferous forest 63.1.3 25 Mixed forest 63.2.1 26 Natural grassland 73.2.2 27 Moors and heathland 73.2.3 28 Sclerophyllous vegetation 73.2.4 29 Transitional woodland-scrub 73.3.1 30 Beaches, dunes, sands 83.3.2 31 Bare rocks 83.3.3 32 Sparsely vegetated areas 93.3.4 33 Burnt areas 93.3.5 34 Glaciers and perpetual snow 104.1.1 35 Inland marshes 114.1.2 36 Peat bogs 114.2.1 37 Salt marshes 114.2.2 38 Salines 114.2.3 39 Intertidal flats 115.1.1 40 Water courses 125.1.2 41 Water bodies 135.2.1 42 Coastal lagoons 145.2.2 43 Estuaries 145.2.3 44 Sea and ocean 14

A yearly average surface cover (V) has then been derived from the monthly surface cover accordingto equation (8). This value has been used as a representative value for each land use class.

(8)

The map of the resulting mean yearly surface cover percentages is shown in figure 9.

4.3.6 Impermeable and Impervious Areas

Areas for which channel initiation could be considered as extremely improbable were notincluded in the stratification procedure. Such areas correspond at the classes 1, 11, 12, 13 and14 of the reclassified Corine Land Cover (see Table 12, last column). These areas can only becrossed by the river network and it is considered as unrealistic that channels start from them.

VCm

m

==

∑ 121

12


30

Table 13: Monthly cover percentages (Cm) for different land cover types.

# Class Land Cover Type J F M A M J J A S O N D

1 2 Arable 10 10 10 20 50 80 100 100 50 0 0 10

2 4 Pasture 100 100 100 100 100 100 100 100 100 100 100 100

3 3 Permanent vineyards, 30 30 30 40 50 60 60 60 60 40 30 30tree crops, etc.

4 6 Forest 100 100 100 100 100 100 100 100 100 100 100 100

5 5,7 Heterogeneous 50 50 50 60 70 80 90 90 60 50 45 45

6 8, 9 Natural degraded 20 20 20 20 20 20 20 20 20 20 20 20

7 1, 8,10, Rock, urban, -- -- -- -- -- -- -- -- -- -- -- --11, 12, wetland, etc.13, 14

Figure 9: Mean yearly surface cover percentage for Italy.

Surface Cover

< 11%

11 - 24

24 - 45

45 - 80

> 80%

No Data

4.4 Landscape Drainage Density Index

A scoring system was finally developed in order to compute a Landscape Drainage DensityIndex, which ranks the previously described environmental factors. Such factorial scoringsystems provide a powerful technique in environmental analysis and have been successfullyapplied, for example, in soil erosion mapping (see Giordano et al. 1991; van Zuidam and vanZuidam Cancelado 1978; Morgan 1993). Factorial scoring is a relatively simple procedure,which can be applied at a variety of scales. The methodology is to divide the area into sectorsand to score the various environmental factors for each sector separately. In our case thosevariables are considered, which influence drainage density. Individual factor ratings are thencombined and a drainage density class is assigned on basis on the total score. To do so, eachenvironmental factor has been evaluated with respect to its relationship with drainage densityas described in previous studies.

The choice to apply a simple scoring system is justified on the basis that we are not deriving aphysical value of drainage density but rather defining areas with specific environmentalconditions. These areas reflect the complex evolution of the landscape in an open process-response system, where energy and matter are inputs and water and sediments are one type ofoutput, which is reflected in the pattern and density of the drainage network (e.g. Schumm1977; Morisawa 1985; Gregory and Walling 1968). As a consequence, our landscapecharacterisation needs to be able to reflect the complex system of environmental factorsinfluencing drainage density. Taking into account this landscape characterisation, we are ableto extract the drainage network using different support area thresholds for each landscapetype, thus overcoming the problems related to the classical approach, which considers thedrainage network as a result of the topographical surface only.

Improved automatic extraction techniques for drainage channels have also been presented byOliveri (1998) and Garcia Lopez and Camarasa (1999). These techniques have, however,been applied at the scale of single catchments and the drainage connecting problem has notbeen encountered.

The environmental factor maps were reclassified according to Table 14, using the intervalsproposed by Thornthwaite (1931), van Zuidam and van Zuidam-Cancelado (1978), andGisotti (1983). For soil transmissivity we used ad hoc intervals defined on the basis of naturalbreaks of the soil transmissivity map. The score for each class was defined on the basis ofestablished relationships between each environmental parameter and drainage density.

An inverse power relationship was associated with the climate interval (Melton 1957), thusestablishing highest drainage densities for desert environments and lowest drainage densitiesfor humid forests.

Although in theory relationships between drainage density and relief are depending on thedominant hillslope process (see Tucker and Bras 1998), giving problems in defining a simplescoring system, the role of relief has been assumed as having a positive relationship withdrainage density, thus giving a major role to slope steepness.

Vegetation cover percentage and uniform soil transmissivity were ranked decreasingly withdrainage density, which is justified on qualitative observations (Strahler 1957; Morisawa1985). Their effects, combined with soil and rock erodibilities, control the erosionalresistance of the surfaces, which is expressed in terms of critical shear stress (Tucker and

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Slingerland 1996, 1997). It has been shown that critical shear stress is one of the main factorscontrolling channel initiation by overland flow, allowing to define a critical contributing areaand thus drainage density (Dietrich et al. 1992, 1993; Montgomery and Foufoula-Georgiou1993; Prosser and Dietrich 1995; Prosser and Abernethy 1996), although such a model cannotbe well adapted for a specific environment and climate (Prosser and Soufi 1998). For thisreason a strong weighting factor has been assigned to soil influence and vegetation cover.

The influence of resistance to bedrock channel erosion has been qualitatively observed andanalyzed for example in Tucker and Slingerland (1996). Based on their results, a linearrelationship between drainage density and rock erodibility has been implemented in theparametric model.


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Code Environmental Factor Description Score

Precipitation Effectiveness Index (I) [-]1 >= 128 Humid forest 12 64 – 127 Forest 23 32 – 63 Grassland 34 16 – 31 Steppe 45 <= 15 Desert 8

Slope steepness [%] and Relative height difference (S) [m] 1 0 – 2; < 5 Flat or almost flat 12 3 – 13; 5 –75 Undulating sloping 23 14 – 20; 50 – 200 Rolling hilly steep 34 21 – 55; 200 – 500 Hilly very steep 45 > 56 > 500 Mountainous extremely steep 8

Vegetation Cover (V) [%]1 <10 No cover 162 11 - 25 Scarce 93 26 – 50 Moderate 64 51 – 75 High 25 > 75 Very high 1

Rock erodibility (R) [-]1 Very low 12 Low 23 Medium 34 High 45 Very high 5

Transmissivity (T) [m2/day]1 < 1.0 Very low 162 1.1 – 3.0 Low 93 3.1 – 6.0 Medium 64 6.1 – 9.0 High 25 > 9.1 Very high 1

Table 14: Factorial scoring system used in the definition of the Landscape Drainage Density Index.

Finally, all environmental scores were combined by simple addition in order to determine thelandscape drainage density index for each grid-cell:

(9)

From the total score (ranging from 5 to 53) five drainage density classes were definedaccording to table 15.

The resulting map has been filtered, using a 3x3 majority filter in order to remove singlespurious grid-cells (Figure 10).

In the next step an optimum contributing area threshold needed to be defined for eachdrainage density class.

4.5 Determination of the Critical Contributing Area

The critical contributing area was derived for each landscape drainage density class byanalyzing the local slope-area relationship derived from DEM data.

A variety of approaches exist for extracting the channel network from DEMs. In order tosuccessfully implement such approaches the underlying topographic data must be sufficientlyaccurate. However, it has been shown that the slope-area relationship can be determined withgreat reliability even from low resolution DEMs (Walker and Wilgoose 1998).

LDd I S R T VIndex = + + + +

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Table 15: Classes of Landscape Drainage Density.

Landscape Description Total ScoreDrainage Density

Class

I Very low < 15

II Low 16 – 25

III Medium 26 –35

IV High 36 – 45

V Very high > 45

Figure 10: Landscape Drainage DensityIndex, reclassified into five classes.

Drainage Density

very low

low

medium

high

very high

No Data

At lower DEM resolutions it is appropriate to assume that runoff is generated uniformly and thatthe geomorphologic response with respect to channel initiation is spatially homogeneous. Underthese conditions the critical contributing area can be used to represent the complex interaction offactors such as geology, vegetation, soils and topography, which control the initiation andmaintenance of a channel network. At regional scales, where the conditions of uniformity maynot be assumed, it is better to allow that the critical contributing area varies between differentgeomorphic regions and to include physical parameters that underlie the spatial variability of thegeomorphology (Martz and Garbrecht 1995).

Based on these considerations an approach of parameterisation of the different environmentalfactors governing drainage density has been implemented. This is considered adequate,especially since the low resolution DEM used in this study is not able to discriminate thechannel head at the inflection point and is inadequate to be used for deriving informationabout geomorphic processes (Zhang and Montgomery 1994).

Local slope and contributing area were estimated from the DEM by using the D∞ method(Tarboton 1997) in order to have a better representation of such hydrologic quantities. Thelog-log diagrams of local slope and contributing area of each of the 5 landscape classes wereplotted and, after a binning procedure of at least 1500 values of contributing area for eachbin, the diagrams were analysed (Figure 11). While figure 11a allows to distinguish threeregimes, only two regimes are can be distinguished in diagramms 11b to 11e. These regimesare characterised by the presence of distinct slopes or scaling lines, with the right-handscaling line being characterised by a steeper slope. A red line marks the limit between theseregimes. Whilst we can assume that the right-hand zone belongs to the fluvial regimecontaining the main fluvial network, the left-hand zone appears to be the result of severalundistinguished geomorphic processes.

According to the slope-area threshold criterion the channelized grid-cells are those located onthe right-hand side of our diagrams, representing a slope of about –0.5. The process regimesrepresented by the points situated on the left-hand side of the red line are difficult to explain.The typical break points to be expected at that scale are impossible to define since theminimum DEM resolution (62,500m2 per grid cell) is greater than the channel head sourcearea. As a consequence, and in order to guarantee that a grid-cell is belonging to the fluvialnetwork, we decided to extract a drainage channel when the contributing area is greater thanthe value defined by the inflection point separating undistinguished geomorphic processes(left part) from fluvial processes (right part). In this way we ensure that the main drainagechannels are included in our drainage network.

From the diagrams plotted in figure 11 we identified those points where the inflection of thescaling line is marked and where the fluvial scaling line started with a coefficient of about –0.5.

After the derivation of the corresponding minimum contributing area for each class as definedby the inflection point, the D8 flow direction grid was used for the extraction of the channelnetwork in order to overcome problems related to drainage lines bifurcating downstream.

The fact to find widely differing minimum contributing areas (from 3 km2 to 700 km2) is afirst indication of the adequacy of the stratification procedure. In Figure 12 and Table 16 theresulting relation between minimum contributing area and drainage density is presented.


34

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Figure 11: Slope-area relationship for the different landscape classes

a) Class I: support area = 700 km2, b) Class II: support area = 100 km2, c) Class III: support area = 50 km2, d) Class IV: support area = 9 km2, e) Class V: support area = 3 km2

a b

c d

e

When low resolution DEMs are used for the extraction of drainage channels, the outlinedprocedure allows to define an appropriate contributing area for starting a drainage channelcoherently with the spatial resolution of the DEM. From a physical point of view suchmethod can be considered as a conservative method based on the instability/processcompetition theory. By varying the minimum contributing areas as a function of a landscapestratification, which reflects the environmental factors governing drainage density and themain geomorphic processes, we believe to be able to derive the drainage network with highconfidence.

The underlying idea, therefore, is to find support areas that change with respect to thedifferent environments, reflecting the condition of the main geomorphic processes anddescribing the hillslope threshold, even though a procedure of constant area thresholds isapplied.

The five fluvial networks extracted in this way were finally merged into one and channelsstarting in urban areas, marshes, ponds and tidal zones (Corine Classes 1 to 11; 35 to 39; 42to 44) were eliminated. The assumption being that no rivers can start from those.


36

Landscape Classes

Dra

inag

e D

ensi

ty (

km/k

m2 )

Figure 12: Drainage density inrelation to the landscape classes.

Table 16: Landscape classes, threshold areas and drainage density.

Landscape Drainage Density Class Threshold Area (km2) Drainage Density (km/km2)

I 700 0.20

II 100 0.22

III 50 0.26

IV 10 0.30

V 3 0.35

4.6 Channel Network Connection

The initial merging of the river networks extracted with different area thresholds produces anunconnected river network with flow interruptions at class boundaries. However, this is nomajor problem, since the real problem in channel network extraction is to find the start of ariver and initial river cells are obviously marked.

To connect the channel network a pixel growing algorithm was developed (Baraldi and Colombo2000). The basic condition of the algorithm is the identification of so-called seed pixels, whichmark the start of a drainage channel. A seed pixel is identified based on a series of conditions.After all seed pixels are marked, the algorithm defines the drainage network on the basis of theaccumulation and flow direction matrices. An example of the unconnected and the connectedriver network for the area north of the Ligurian coast is given in figure 13.

4.7 River Basin Extraction

Catchment boundaries are delimited using an algorithm based on the calculation ofcontributing upslope areas for each grid cell (Jenson 1991). The method depends on the flowdirection and flow accumulation matrices and drainage divides are defined by tracing thecells that drain to a selected set of ‘pour points’ or outlets. A catchment is delineated byidentifying all cells flowing to a specified outlet.

In order to automise this procedure, it was necessary to identify the outlet points in thedrainage network by developing and appropriate AML procedure.

Finally each river segment was classified according the Strahler order and all the sub-catchments were delineated. Results of this procedure are presented in chapter five.

METHODOLOGY

37

Figure 13: The effect of the connecting algorithm.Left: the unconnected channel network; Right: the connected channel network

5. RESULTS

5.1 Extraction of the Italian River Network and Catchments

The outlined methodology has been applied to the entire Italian territory, extracting thedrainage network based on a landscape stratification and variable thresholds for the minimumcontributing area. The procedure lead to a well-connected and coherent network as presentedin figure 16.

The new method allows to extract the channel network taking into account the variability ofthe different environmental factors acting on the landscape. The drainage pattern and thedrainage density of each basin are depending on the distribution of and interrelation betweendifferent environmental factors such as climate, morphology, soils, geology and vegetation.As such, the developed methodology allows to reproduce the natural variation in drainagedensity. Drainage density does not necessarily assume highest values in mountainous regionsand lowest values in gently sloping areas, as would be predicted by a slope dependentmethod, for example. It is rather modelled according to a combination of several factorsacting together. Gently sloping agricultural areas with moderate rainfall, but characterised bya relatively sparse vegetation cover and lots of impermeable surfaces may have a greaterdrainage density than hilly terrain with heavier rainfall, but characterised by a goodpermeability of the soil and underground and a dense forest cover.

All rivers were finally classified according the Strahler system (small rivers have the lowestorder) and sub-catchments were derived for the different orders. Figures 14 and 15 show theprinciple, while figures 16 to 18 show some results for Italy.


38

Figure 14: Principle of thedrainage hierarchy (order)according to the Strahler system. Lowest order (smallest) streamsare shown in red. Example ofNorthern Sardinia.

RESULTS

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Figure 16: The final drainagenetwork for Italy.

3rd Order Catchments 5th Order Catchments

Figure 15: Principle of the catchment hierarchy according to the Strahler system. Smallest catchments represent the lowest order. Example of Northern Sardinia.

0 100 200 300 400 500 Kilometers

River Network of ItalyEuropean Commission - DG Joint Research Centre - Space Applications Institute (EC - JRC - SAI)

Environment and Geo-Information Unit (EGEO)EuroLandscape Project - Catchment Characterisation and Modelling (CCM)

Data Elaboration:J. Vogt, R. Colombo, F. Bertolo

Version: 1.0. October 2000


40

Figure 17: Drainage Network and3rd order Catchments of Italy.

Figure 18: Drainage Network and2nd Order Catchments of Sicily.

3rd Order Catchments of ItalyEuropean Commission - DG Joint Research CentreSpace Applications Institute (EC - JRC - SAI)




2nd Order Catchments of SicilyEuropean Commission - DG Joint Research CentreSpace Applications Institute (EC - JRC - SAI)




0 100 200 300 400 500 Kilometers

0 50 100 150 Kilometers

5.2 Validation and Discussion

The final result of an automatic delineation of rivers and catchments is depending on both themethodology used and the primary input data. Each one represents a potential source oferrors, which needs to be considered and evaluated in detail. In order to ensure a high qualityof the product, various validation procedures need to be considered. The different sources oferrors and a first validation of the data are presented in this section.

The low resolution DEM is the first source of error. The grid-cell size determines theminimum area resolved on the ground, which in turn determines the maximum positionalaccuracy (i.e. the exact position of a river is limited by the grid-cell size). As a consequence, ameandering river system cannot be resolved, if the radius of the meander is in the order ofmagnitude of the grid-cell dimension.

Even though the stream burning procedure allows to force rivers along known drainage lines,which is necessary in flat terrain, in some cases a doubling of the river may result, due to aslight mis-registration between burn-coverage and DEM. In such a situation, the burningprocedure will force a river along the digitised line, while the automatic extraction procedurewill derive another river course according to the topography represented in the DEM. This isnot necessarily a projection problem, but may be related to the coarse resolution of the DEM.The problem has been overcome by editing the burn-coverage and deleting all river reachesin areas where the DEM represents sufficient relief to define the course of the river. In thisway ambiguities have been avoided.

The most crucial point, however, is the determination of the extent of the channel network,which depends on the accuracy of the contributing area threshold. Although the relationshipbetween local slope and contributing area can be defined with good accuracy from a lowresolution DEM (Walker and Wilgoose 1998), and even though we have computed thisrelationship using a multiple flow algorithm (Tarboton 1997), the identification of the startingpoint of the fluvial scaling line is not always evident. As a consequence, it is difficult todetermine a highly accurate critical support area for each class. However, we coulddemonstrate that the differences between the thresholds of the different landscape types arevery large with respect to the uncertainty on the exact value (see table 16). The value to beused for the final product, therefore, needs to be fine-tuned by an iterative procedure,comparing the result with validation data sets.

The evaluation of results from catchment delineation and network extraction procedures isusually done by visual comparison with existing maps. It is, however, difficult toquantitatively evaluate the results in such a way. As a consequence, quantitative comparisonsare often made by comparing the size of a sample of the derived catchments with their size asgiven in other sources (Miller and Morrice 1996; Graham et al. 1999). Such comparison,however, remains of limited value, especially with regard to the position of the rivers.

In order to provide a more precise evaluation of a European dataset, the drainage network andcatchment boundaries can be quantitatively and qualitatively compared to a series ofindependent datasets, including for example:

RESULTS

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A: European-wide datasets:(a) the Bartholomew river network (1:1 million scale),(b) the position of the stations of the EuroWaternet station database,(c) the size of the catchments draining to the EuroWaternet stations, and(d) the Erica catchment boundaries (1:1 million scale).

B: National datasets:(a) digitised river networks and catchment boundaries, and(b) oro-hydrographical maps at scales 1:100,000 to 1:250,000.

C: Local datasets:(a) river networks and catchment boundaries digitised from maps at scales ranging

1:10,000 – 1:100,000.

5.2.1 Comparison to European-wide Datasets

In this category a comparison with the Bartholomew river network at 1:1,000,000 scale hasbeen done. This dataset is widely accepted as one of the most accurate at the given scale. Inorder to compare the two datasets, they have been (a) superimposed for a qualitativeevaluation and (b) the percentage of Bartholomew rivers falling within a buffer of varyingwidth around the CCM rivers has been calculated. In other words, we calculate how much of


42

Figure 19: Overlay of CCM rivernetwork, Vers1.0 (in blue) andBartholomew, 1 M (in red).

the Bartholomew river network has been traced correctly. Figure 19 shows the result of asimple superposition of the two datasets.

This figure highlights that the coincidence between the two datasets is varying according toregions (or landscape classes) and as such demonstrates that the chosen contributing areathresholds for the individual classes are not always optimal. While in some landscape classesthe two datasets are in good agreement, Bartholomew rivers start earlier in other classes (seenin red on figure 19). As a consequence area thresholds need to be fine-tuned for version twoof the dataset.

The quantitative comparison, using buffers of 250 m, 500 m and 1000 m around the CCMriver network highlights the problem (see figure 20). While for the 1000 m buffer almost 75%of the Bartholomew rivers are traced, this value falls to 38 % for the 250 m buffer.

There are three reasons for the relatively low coincidence when using narrow buffers:

(1) The thresholds for some classes have been overestimated and as a consequence CCMrivers are too short (start too late) in many cases. This error is easily to be corrected.

(2) The projections of both datasets are not fully identical (e.g. a small difference in the radiusof the sphere of reference can cause large differences for the narrow buffers)

(3) Also Bartholomew does not represent the truth. Generalisations and errors are present andtherefore differences between the two datasets must exist. While Bartholomew has beendigitised from maps of various scales, the CCM river network is extracted from a DEM of

RESULTS

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Figure 20: Comparison of CCM and Bartholomew river networks through buffers of various widths. (a) The principle: the 250 m buffer around the CCM network for a selected catchment and the Bartholomewriver network in blue; (b) Results for the different buffers for the whole of Italy.

(a) (b)

250m grid-cell size. As a consequence, the length of the Bartholomew rivers will dependon subjective judgement of the operator performing the digitalisation, for example withrespect to the choice of the main reach. The positional accuracy of the CCM rivernetwork, on the other hand, is limited to the size of the underlying grid-cell. The use of abuffer of 250 m width, therefore, requires an exact co-registration of the two datasets.

The section on local datasets will highlight some of these validation problems in more detail.

5.2.2 Comparison to National Datasets

In the course of this study a validation with national datasets has not been performed, sincesuch a dataset was not available at the time of data analysis.

5.2.3 Comparison to Local Datasets

Based on available data from local catchment datasets, which had been digitised at 1:10,000scale, detailed comparisons could be made with respect to the positional accuracy of riversand the drainage density. The catchments studied are the Esino, Val Chiavenna and Simeto


44

Figure 21: Location and size ofthe catchments used for detailedvalidation.

catchments. They represent Alpine, Appeninian and Sicilian catchment types. Their locationis shown in figure 21.

Based on the digitised river networks of the selected catchments, the drainage densitycalculated (a) from the local databases and (b) from the CCM database could be compared.Due to the large difference in scale (1:10,000 and 1:250,000) the absolute values between thetwo data sets are differing largely (see figure 22). This was expected, since the drainagedensity is dependent on the mapping scale. However, the general tendency (lower drainagedensity in the Esino catchment as compared to the Val Chiavenna catchment) and the order ofmagnitude of the difference between the two catchments have been preserved.

In the next step, the CCM channel network was compared to the Bartholomew network andto the blue lines derived from the 1:10,000 scale maps. Figures 23 to 25 show the result foreach of the selected catchments.

All three figures show a good agreement between the derived channel network and the bluelines, much better than the agreement observed between the blue lines and the Bartholomewriver network. The channel network extracted from the 250 m DEM is in all cases denser thanthe Bartholomew network, which was to be expected due to the difference in scale. Moreimportant, however, the figures demonstrate that the DEM derived network is more accuratethan the Bartholomew network in most cases. The fact that the Bartholomew network hasmost probably been digitised from maps at smaller scales and the related generalisations andpositional shifts of river reaches can be seen. The DEM derived network, on the other side,coincides very well with the blue network on the maps. Problems can be seen in flat areas incase when no burning procedure has been used.

In summary, the validation procedure has shown that the developed methodology is capableto derive drainage networks and associated drainage boundaries with high accuracy fromDEMs of 250 m grid-cell size. The quantitative comparison to an established database such asBartholomew allows to fine-tune the thresholds for the minimum contributing areas and tooptimise the result. The absolute values of the coincidence need, however, to be interpretedwith care, since the detailed analysis of selected catchments has shown that also the

RESULTS

45

Figure 22: Drainage densityderived from:(a) the blue lines of 1:10,000 scalemaps (in blue), and (b) the CCMriver network (in green).

Extracted

Blue lines

Dd

(km

/km

2 )


46

Figure 23: Comparison between blue lines, Bartholomew river network and the derived drainage network forthe Val Chaivenna catchment (720 km2).

Figure 24: Comparison between blue lines, Bartholomew river network and the derived drainage network forthe Esino catchment (1240 km2).

Bartholomew database cannot represent the truth. This is due to the well-known problemsrelated to the generalisation of information, but also to problems of the projection ofunderlying maps, their accuracy and to the subjective influence of the digitising operator.Similar considerations apply naturally to the DEM, which underlines the need to carefullyprepare the DEMs in terms of accuracy of the projection and data quality. The combined useof medium resolution DEMs (e.g. 100 to 250 m grid-cell size) and environmental data,proved to be a good basis for the derivation of river networks and catchment boundaries at acontinental scale.

RESULTS

47

Figure 25: Comparison between blue lines, Bartholomew river network and the derived drainage network forthe Simeto catchment (4230 km2).

6. SUMMARY AND CONCLUSIONS

In the course of this study we developed a new approach to derive drainage networks andcatchment boundaries by combining digital elevation data and information on environmentalconditions such as climate, morphology, soils, geology and vegetation cover. Particularattention was paid to the fact that the methodology is well adapted to analyse large areas,using medium resolution DEMs and environmental information at appropriate scales.

As a test, the methodology has been applied to the Italian territory before starting animplementation at the pan-European scale.

The technique is based on the process competition theory, which assumes that channelizationrepresents a transition in the dominant transport process. This theory is based on thehypothesis that a valley topography forms when flow convergence causes rill or gullyexcavation by runoff erosion to outpace infilling by diffusive processes. This assumptionimplies that channel heads correspond to the spatial transition from convex to concave slopeprofiles and requires to determine the effective distance from the divide to the transition point(expressed in terms of valley head source area) where fluvial transport becomes dominantover diffusive transport. The channel network is, therefore, delineated by assuming anupstream critical contributing area. This critical contributing area is usually assumed to beconstant for the whole catchment.

The theory has been the object of many critics and discussions, especially since the nature ofthe channel initiation is not always explainable by means of a minimum contributing arearequired for a channel to form. Other theories of channel initiation have hence beendeveloped, assuming that channel formation is controlled by a variety of geomorphicthresholds. The calculation of these thresholds is, however, often complicated and requires amultitude of input data, usually not available for extended areas.

To overcome the problem related to the assumption that the development of a channelnetwork is fully reflected through a critical contributing area, often assumed to be constanteven under varying environmental conditions, a landscape characterisation procedure hasbeen implemented. In fact, the European continent is made up of a mixture of differing andvery complex landscapes. This situation makes it obviously unrealistic to assume a singlecritical contributing area for the whole territory. As a consequence, a landscapecharacterization was implemented, based on environmental conditions such as climate,morphology, soils, geology and vegetation cover. The parameters used have been selected onthe basis that they have an important influence on drainage density and pattern. As anoutcome, five major landscape classes were defined on the basis of a factorial scoring of theindividual environmental parameters.

Using a multiple flow algorithm improved for its performance in flat areas and implementinga stream burning procedure, the local slope and the contributing area were computed for thewhole Italian territory, based on a DEM with a grid-cell size of 250 x 250 meters.


48

In the next step an appropriate threshold for the critical contributing area had to be definedfor each landscape class. The analysis of the slope-area relationship for each class proved tobe a reliable means to do so. Contrary to other hydrologic and geomorphic relationships, thisslope-area relationship can be determined with great reliability even from coarse resolutionDEMs. The underlying theory, however, is based on studies conducted at the field scale andwith high resolution DEMs, able to reproduce the spatial effect of the various hill slopeprocesses. Most of the described inflection points in the plot of local slope versuscontributing area are, therefore, not to be determined when the analysis is based on a mediumor even low resolution DEM. The only inflection point that shows a good stability at suchresolutions is related to the starting of the fluvial scaling line. The slope or scaling coefficientof this line is generally falling in the range from -0.4 to -0.7, indicating the existence of themain valley network. We, therefore, choose this inflection point for the determination of theminimum contributing area for each landscape class. The study has shown that this methodallows to define an appropriate contributing area for determining a drainage network incoherence with the scale of application.

Since the definition of different landscape classes and the use of an individual threshold foreach class results in an unconnected river network, a simple and efficient connectingalgorithm had to be developed. This algorithm takes account of the flow direction and flowaccumulation grids and connects individual parts of the rivers to a coherent and consistentdrainage network over the whole territory.

Based on the coding of the resulting rivers according to the Strahler system, a drainagehierarchy has been developed and six nested levels of catchments have been derived.

The landscape stratification, the drainage network and the catchment boundaries were finallyvalidated against a set of independent data layers, ranging from the Bartholomew rivernetwork at a 1:1,000,000 scale to drainage networks digitised for individual catchments frommaps at scales of 1:10,000. The results of this validation proved the high quality of the finalproduct and allowed to discuss the advantages and disadvantages of the individual data sets.

The described method allows to derive a high quality drainage network over extended areas,taking into account the variability of the environmental conditions acting on the landscapeand the formation of a drainage network. Using this approach the natural spatial variation ofthe drainage density is reproduced.

The results from the Italian case study suggest that the use of digital elevation data with agrid-cell size of 250x 250 meters is appropriate for mapping scales ranging from 1:300,000 to1:500,000.

SUMMARY AND CONCLUSIONS

49

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Acknowledgements

The implementation of the described algorithm would not have been possible without theavailability of the required data and without the help of various colleagues. We like to thankthe European Soil Bureau and especially Mr. Luca Montanarella for providing the soil datanecessary for the completion of this exercise. The MARS project of SAI has kindly providedthe climatic data. We acknowledge the essential support provided by Andrea Baraldi in thedevelopment of the algorithm for river connection. The expertise of Maria-Luisa Paracchinihas been very useful in solving the manifold problems of data handling and analysis. Finally,thanks are due to Alessandro Brivio (CNRS-IRRS Milano), Niels Thyssen (EEA), PamelaKennedy and Sten Folving (JRC-SAI) their continuous support of this activity.


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In the frame of the EuroLandscape project, the Space Applications Institute (SAI) of theJoint Research Centre (JRC) of the European Commission is working towards thecreation of a pan-European database of catchment boundaries and river networks.Together with the physical and socio-economic characterisation of the mappedcatchments, this database will serve for the analysis of data on water quality andquantity as well as for the modelling of hydrological and landscape processesthroughout Europe. Particular support will be given to the EuroWaternet of theEuropean Environmental Agency (EEA) and to the implementation of the WaterFramework Directive of the European Commission, DG Environment.

This report describes the development of a novel methodology for the derivation ofdrainage networks and catchment boundaries from digital elevation data andenvironmental characteristics such as climate, topography, lithology, soils, andvegetation cover. The developed methodology has been tested and validated for thecase of the Italian territory. It will now be implemented for the derivation of relevantinformation for the whole pan-European area.

European Commission

EUR 19805 – Deriving Drainage Newtorks and Catchment Boundaries at theEuropean Scale.

R. Colombo, J. Vogt and F. Bertolo

Luxembourg: Office for Official Publications of the European Communities

2001 – 58 pp. – 21.0 x 29.7 cm

Environment and quality of life series

The mission of the JRC is to provide customer-driven scientific and technical support for theconception, development, implementation and monitoring of EU policies. As a service of theEuropean Commission, the JRC functions as a reference centre of science and technology for theUnion. Close to the policy-making process, it serves the common interest of the Member States, whilebeing independent of special interests whether private or national.


The primary mission of SAI is to develop and promote the use of space-derived data and geo-spatialdata from other sources in the service of EU policies, especially those relating to agriculture, fisheries,transport and anti-fraud. SAI also seeks to make the best use of information from space systems, tomaximise the return from European investments in space and to help the Union reinforce its role ininternational action on the environment and sustainable development.


deriving drainage networks and catchment boundaries...

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