MULTISCALE RIVER ENVIRONMENT CLASSIFICATION FOR … · necting channel networks. Thus, ecoregions...

ABSTRACT: River Environment Classification (REC) is a new sys-tem for classifying river environments that is based on climate,topography, geology, and land cover factors that control spatial pat-terns in river ecosystems. REC builds on existing principles forenvironmental regionalization and introduces three specific addi-tions to the “ecoregion” approach. First, the REC assumes that eco-logical patterns are dependent on a range of factors and associatedlandscape scale processes, some of which may show significant vari-ation within an ecoregion. REC arranges the controlling factors in ahierarchy with each level defining the cause of ecological variationat a given characteristic scale. Second, REC assumes that ecologi-cal characteristics of rivers are responses to fluvial (i.e., hydrologi-cal and hydraulic) processes. Thus, REC uses a network of channelsand associated watersheds to classify specific sections of river.When mapped, REC has the form of a linear mosaic in which class-es change in the downstream direction as the integrated character-istics of the watershed change, producing longitudinal spatialpatterns that are typical of river ecosystems. Third, REC assignsindividual river sections to a class independently and objectivelyaccording to criteria that result in a geographically independentframework in which classes may show wide geographic dispersionrather than the geographically dependent schemes that result fromthe ecoregion approach. REC has been developed to provide a mul-tiscale spatial framework for river management and has been usedto map the rivers of New Zealand at a 1:50,000 mapping scale.(KEY TERMS: water resources planning; river environmental clas-sification; hierarchical classification; controlling factors; Geograph-ic Information Systems; river ecology.)

INTRODUCTION

Landscape classifications have been developed as atool for understanding ecosystem processes and resul-tant patterns at large scales. Such classificationshave been specifically promoted as spatial frame-works for environmental management activities such

as developing inventories, data interpretation, inter-polating information from specific sites to largerareas, strategic development of objectives and stan-dards, design of monitoring strategies, and evaluatingthe uniqueness of areas (e.g., Bailey, 1995; Klijn,1994; Griffith et al., 1999). River Environment Classi-fication (REC) is a new approach to classifying andmapping spatial patterns in river ecosystems. REChas been developed as a spatial framework for rivermanagement that employs a similar conceptual basisto other landscape classifications. REC, however, isbased on more explicit consideration of the fluvial pro-cesses that are the cause of patterns in river ecosys-tems at a range of spatial scales. The REC approachmay improve on the ability of existing landscape clas-sification methods such as ecoregions, to predict pat-terns in river ecosystems.

Landscape classifications are generally based onenvironmental factors that are assumed to largelydetermine (e.g., climate, topography, and geology) orreflect (e.g., land cover) patterns in organization andfunctioning of the associated ecosystems (Bailey,1995; Klijn and Udo de Haes, 1994). Thus they codify,in a simple way, understanding of ecosystem process-es at large scales that provide for and also constrainmanagement options (Christensen et al., 1996). Wedistinguish two types of commonly used approaches tolandscape classification: controlling factors and ecore-gions. Both of these classifications differ from simpletypologies in that each class can be geographicallylocated and mapped.

Controlling factor approaches to landscape classifi-cation provide a generalized hierarchical differentia-tion of the landscape based on a conceptual view of

1Paper No. 01251 of the Journal of the American Water Resources Association. Discussions are open until April 1, 2003.2R e s p e c t i v e l y, Scientist/Natural Resources Engineer and Scientist, NIWA, P.O. Box 8602, Riccarton, Christchurch, New Zealand

(E-Mail/Snelder: [email protected]).

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JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATIONVOL. 38, NO. 5 AMERICAN WATER RESOURCES ASSOCIATION OCTOBER 2002

MULTISCALE RIVER ENVIRONMENT CLASSIFICATIONFOR WATER RESOURCES MANAGEMENT1

Ton H. Snelder and Barry J. F. Biggs2

how ecosystems are organized. Classes are defined bysubdivision of factors into categories that are diagnos-tic (sensu Zonneveld, 1994) of particular characteris-tics (e.g., wet mountains and dry hills). In landscapeclassifications, classes are mapped by the geographiccoincidence of factor categories and, therefore, classi-fications appear as mosaics of spatially independentpolygons. Thus, controlling factor classifications aregeographically independent, describing similarecosystem types that may show wide geographic dis-persion (Detenbeck et al., 2000).

Controlling factor approaches have been used todevelop classifications of river environments. A signif-icant difference between terrestrial and river ecosys-tems, however, is the recognition of the watershed asa fundamental spatial unit determining processes inrivers. This has led to controlling factor approachesthat attempt to discriminate patterns within water-sheds (e.g., Lotspeich and Platts, 1982; Frissell et al.,1986). These classifications consider that watershedsare contained within homogeneous land type or regionclasses based on climatic, topographic, geologic, andvegetation factors. The classes attempt to define pat-terns within watersheds that are controlled by local-scale factors. Geographically independent classes aredelineated using morphological features that areassumed to control processes at a variety of nestedspatial scales within the watershed.

The second approach to river classification, com-monly called ecoregions (Omernik, 1987), describepatterns among watersheds. Ecoregion classificationuses understanding of large-scale ecosystem patternsto develop a mosaic of regions within which water-sheds can be considered homogeneous. However, astrict hierarchy of controlling factors is not adhered toin defining ecoregions. Rather, the factors are variedwithin a classification level to make use of whicheveris considered to best distinguish each ecoregion (Bryceet al., 1999). For example, topographic characteristicsmay be used to define a mountain ecoregion, whereasdifferences in soils or vegetation may be used to dif-ferentiate ecoregions in a valley. This classificationprocedure is a “gestalt” in which classes and bound-aries are selected that appear to be the best determi-nants of the character of river water quality and biotabased on subjective judgments of experts (Bailey,1995). Ecoregions are geographically dependent clas-sifications where classes form discrete units in geo-graphic space that are not repeated across theclassified area (Detenbeck et al., 2000).

Ecoregions have been widely promoted as a frame-work for water resources management (e.g., Omernikand Griffith, 1991; Griffith et al., 1999). A numbero f shortcomings of the ecoregion approach, however,can be identified. First, the gestalt classification pro-cedure means that the functional links between the

factors and characteristics are not explicit. Second,the mosaic of spatially independent polygons does notrepresent the configuration of watersheds and con-necting channel networks. Thus, ecoregions areunable to represent longitudinal gradients, which arerecognized as typical of spatial patterns in riverecosystems such as the river continuum concept (Van-note et al., 1980). This is perhaps a major factor limit-ing the effectiveness of these classifications indiscriminating biotic composition of rivers (see Biggset al., 1990; Hawkins and Vinson, 2000).

A specific benefit of controlling factor approaches isthe ability to vary the grain (sensu O'Neill et al., 1987;Wiens, 1989) of classifications by altering the levelthat is used to define and map classes at differentspatial scales. Both controlling factor and ecoregionapproaches to river classification, however, have limi-tations on changing the grain size. This causes diffi-culties when attempting to apply such classificationsto water management because watersheds of largerivers generally comprise a heterogeneous mix of fac-tors (Griffith et al., 1999) whose spatial distributioncan be independent of watershed boundaries (Bryceet al., 1999). For example, there are often large varia-tions in climate, topography, and geology withinwatersheds (Biggs et al., 1990). This means thatecoregions are only effective for classifying parts ofthe channel network whose watersheds are endoge-nous to the classifying ecoregion, effectively streamsbetween first and third order (Griffith et al., 1 9 9 9 ) .Parts of the channel network whose watersheds areexogenous to the immediate ecoregion are not correct-ly classified. On the other hand, controlling factorclassifications that differentiate the channel networkwithin a watershed using morphological features canonly assume comparability of classes when water-sheds are wholly contained within identical land typeor region. These existing approaches to river classifi-cation are, therefore, restricted in their ability tomove between grain sizes while retaining their coher-ence.

In this article we present a new approach to theclassification of river environments. The River Envi-ronment Classification (REC) is based on a controllingfactors approach and uses understanding of fluvialprocesses to classify river environments and delineateclasses. The aim of the REC is to discriminate pat-terns of instream physical components of river ecosys-tems (e.g., hydrological variables, temperatureregimes, geomorphologic features, physical habitat,water chemistry) at a range of scales and allow for theintegration of landscape and local-scale process in thedefinition of classes. These physically defined classesare expected to discriminate specific ecosystem prop-erties (e.g., biological communities and water quality).We start by outlining a conceptual framework for

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applying controlling factor principles to classificationof river environments and then describe an exampleof how the methodology has been applied to NewZealand rivers.

CONCEPTUAL FRAMEWORK FOR REC

Controlling Factor Classification

Controlling factor classifications are developedusing a priori or top-down approaches. Top-down clas-sifications assume that the classification is more use-ful if classes are defined according to a model of thecauses of ecosystem organization and function ratherthan by direct observation of biological patterns (Bai-l e y, 1995). The guiding principles (s e n s u Z o n n e v e l d ,1994) for controlling factor classification starts withthe development of a simplified hierarchical model ofthe assumed causes of ecosystem pattern. This modelis a pragmatic simplification of reality that allows usto “approach the truth by a series of approximations”(Bailey, 1995). Bailey’s (1995) model for classificationof terrestrial ecosystems is based on an assumptionthat soil and biota are a function of climate, land sur-face shape, and geological substrate. This relationshipexpresses two important assumptions. First, climate,land surface shape, and geological substrate are treat-ed as independent physical components of the land-scape. These independent components are assumedt o control physical regimes (e.g., hydrological, ther-mal, and chemical) at subordinate scales. The secondidea is that the organization and functioning of biotic

communities is largely a direct response to the organi-zation and functioning of the physical environment.Thus, the problem of delineating ecosystem pattern isreduced to understanding the processes that deter-mine the organization and functioning of the physicalenvironment.

The model manages complexity further by viewingecosystems as hierarchies. The hierarchy refers firstto “dominance in spatial scale” (Klijn, 1994). Theapproach delineates patterns on the earth’s surfacethat are homogeneous with respect to physical andbiotic characteristics (Bailey, 1995). This homogeneityis not absolute but depends on the scale of observa-tion (O'Neill et al., 1987; Wiens, 1989). Thus a hierar-chy in space delineates patterns that are spatiallynested with the internal variability of large-scale pat-terns being shown by patterns at smaller scales (seeTable 1).

The problem becomes one of delineating patterns inthe relevant physical regimes at various spatialscales. The complexity of the processes that cause pat-terns in these regimes is reduced by restricting thedetails of the independent components to only thosethat are needed to explain the most important or sig-nificant patterns at specific scales (O'Neill et al.,1987). Thus, the model characterizes specific process-es at a series of system levels. Each system level isdefined by controlling factors, which are landscapecomponents observed at specific spatial scales thatare the cause of patterns at that scale. The systemlevels are linked by the hierarchical organization ofthe classification so that system components and pro-cesses at a particular level are constrained by thebehavior of the level above. Thus, patterns developwithin the constraints set by larger-scale regimes


Multiscale River Environment Classification for Water Resources Management

TABLE 1. Controlling Factor Classification Levels.

Indicative SizeGeneral of Delineated

Scale Regions (km2) Controlling Factors Name of Classification Level

Macro 105 Macroclimate Ecozone* Macro

104 Land surface shape and Ecoprovince*Geological Substrate

Meso 100-1000 Land Surface Shape and Ecoregion* Mesoand Geological Substrate

10-100 Land Surface Shape and Ecodistrict* MesoGeological Substrate

Micro 1-10 Land Surface Shape Ecosection* Microand Lithology

*Klijn and Udo de Haes, 1994.

representing a hierarchy in “dominance of process”(Klijn and Udo de Haes, 1994).

We take the view that a controlling factor classifi-cation will be most useful to management because itis based on a conceptual view of how river ecosystemsare organized in space (Frissell et al., 1986). The ideathat river ecosystems can be understood in terms of aset of hierarchically related physical processes hasbeen conceptualized by Minshall (1988) and as themultiscale filtering framework by Poff (1997) or theriver scaling concept by Habersack (2000). Thus ourclassification codifies these conceptualizations of theprocesses controlling river ecosystems at variousscales.

REC Controlling Factor Hierarchy

The classification procedure (s e n s u Z o n n e v e l d ,1994) comprises three major steps: defining the sys-tem levels in a simplified hierarchical model, charac-terizing the variation at each system level withcategories, and finally mapping the classes.

The REC model assumes that physical regimes inrivers are controlled by four independent landscapecomponents: cl imate, topography (land surfaceshape), geological substrate, and land cover. Weinclude land cover, which superficially deviates fromthe controlling factor approach to landscape classifica-tion in which vegetation is a dependent variable. Forrivers, however, watershed vegetation is a significantindependent landscape component that controlsinstream processes. Vegetation is also generally high-ly modified by human activities and is therefore notnecessarily closely linked to natural physical factors.Watershed vegetation has, accordingly, been identi-fied as a classification variable in many river classifi-cation schemes (e.g., Lotspeich and Platts, 1982;Omernik and Bailey, 1997).

Physical regimes at all system levels are the resultof interactions between all four of the independentcomponents of the landscape (climate, topography,geology, and land cover). However, the relative impor-tance of each component differs with scale. The sim-plified model uses the most important, or dominant,component at each scale and declares this to be thecontrolling factor at that system level. Our simplifiedhierarchical model also assumes that physical pat-terns in river environments are essentially responsesto fluvial processes, including hydrological, morpho-dynamic and hydrochemical. These are driven by thedownstream flux of water and constituents derivedfrom watershed sources (Hynes, 1975). It is the con-figuration of the independent landscape components(climate, topography, geological substrate, and landcover) within the watershed that controls physical

patterns within the river network. For example, cli-mate and topography broadly control flood flowregimes as well as erosion and sediment transportprocesses. Another fundamental aspect of our modelis recognition that patterns in physical regimes suchas flow, sediment supply, and water chemistry occurdownstream in the channel network, remote from thegeographic location of the watershed. An example isthe pattern of unstable braiding in the lower reachesof rivers draining mountainous watersheds. Thesechannels may be located in flat areas with low rain-fall, well away from the mountains that generate theflood flows and sediment supply regimes that areresponsible for the braiding.

The REC classification hierarchy comprises sixmain system levels, the names of which reflect thenominated controlling factors: climate, source of flow,geology, land cover, network position, and valley land-form (Figure 1). Each system level represents a set ofprocesses that are observable as patterns in physicaland/or biotic components of rivers at a specific spatialscale. Patterns at each system level are assumed to bethe result of variation in each controlling factor. Thuseach controlling factor is subdivided into categories bydifferentiating differences in each factor at one ofthree general scales: macro, meso, and micro. Weadopt the same ranges for each of these general spa-tial scales that is used by other landscape classifica-tion (e.g., Bailey, 1995) for consistency (see Table 1).Thus in our classification, topography is a factor atmeso scales, differentiating watersheds into moun-tain, hill, and low elevation classes; or at micro scales,differentiating individual sections of the channel net-work according to the landform of the valley the sec-tion traverses.

The first four levels of the model hierarchy charac-terize watershed processes that supply and routew a t e r, sediment, and other constituents of flowthrough and off a landscape (Montgomery, 1999). RECrecognizes that the factors controlling these processesmay be heterogeneous within watersheds. Thus, classmembership for the first four system levels is basedon the integrated effect of controlling factors withinthe watershed. This is evaluated at points along thenetwork, and therefore, classes generally change inthe downstream direction as the product of this inte-gration changes. The fifth and sixth system levelscharacterize local processes (s e n s u M o n t g o m e r y,1999), which are understood as the outcome of water-shed processes (e.g., hydrology and sediment supply)interacting with topographic factors operating at thescale of the local channel network.

Characterizing the watershed processes describedby the first four classification levels has twocomplications. First, the controlling factors at theseclassification levels are rarely homogeneous for large


Snelder and Biggs

watersheds. Therefore, a decision must be made as towhat category to assign to that watershed based onthe intensity of effect of each factor category oninstream processes being described. Second, categorymembership cannot always be determined on thebasis of spatial predominance because certain factorcategories have a disproportionate effect on instreamenvironmental or biotic characteristics. For example,certain rock types can have a disproportionate effecton nutrient supply in watersheds (Close and Davies-C o l l e y, 1990; Biggs, 1995). Another example is pas-toral land use, which has been shown to have adisproportionate influence on suspended solids andnutrient concentrations (Quinn et al., 1997).

Our solution is to apply criteria for watershed pro-cesses in the form of rules. Rules consider the dispro-portionate effect of some factors on fluvial processesand assign categories accordingly. For example, a rulemay recognize the disproportionate effect of soft sedi-mentary geological categories, and membership ofthat category may be prescribed when this rock typeexceeds 25 percent of the watershed. Rules may needto be changed depending on the specific processes andpatterns a classification aims to characterize. For

example, the differentiation of soft and hard sedimen-tary rock classes might use different thresholds if theclassification was to optimize discrimination of eitherhydrological or hydrochemical processes. While therules contain another set of pragmatic decisions thatenable reality to be simplified, they are based onknowledge of the ecosystem effects of differences inthe given factors, which are then objectively appliedto determine class membership.

Climate. The highest REC system level describesclimatic processes that drive the hydrological cycleand river water temperature regimes. Variation inmacroclimate differentiates variation in precipitationand potential evapotranspiration, which drive pat-terns in hydrological cycles including frequency offlooding and low flow periods. Variation in macrocli-mate also differentiates temperature, which modifiesthe watershed response to precipitation and, in tan-dem with solar radiation, sets the potential thermalregime of rivers.

In cool regions precipitation as snow is stored inwinter and is released as snowmelt in spring andsummer (Bailey, 1995). In warm regions snowmelt is



Figure 1. Schematic Diagram Showing the REC Levels Based on the Controlling Factors, Differentiated at Three GeneralScales and the Patterns of Physical Characteristics Discriminated at Each Classification Level.

negligible and runoff regimes follow the temporal dis-tribution of precipitation. Thus, classes at this leveldiscriminate large-scale patterns in hydroclimatologyincluding seasonal thermal regimes and patterns inhigh and low flow, but without the influence of lowersystem levels (see Poff and Ward, 1989).

Macroclimatic variation can be discerned at a num-ber of scales. Bailey (1995) suggests very large-scalemacroclimate categories are differentiated by latitudi-nal variation at scales of 105 km2 and further differ-entiation associated with continental position andlarge topographic features at scales of 104 to 103 km2.Importantly, however, macroclimate may vary withinwatersheds. Climate categories must be assessed ateach point in a river network by spatial integration ofmacroclimate across the watershed.

Source of Flow. The next system level, source offlow, further describes watershed processes of hydrol-ogy as well as broadly describing sediment supply andtransport processes. Source of flow is defined by thefactor “watershed topography” and is differentiated inspatial units of approximately 100 to 1,000 km2. Atthese scales, watershed topography is the dominantcontrol of climatic variation and therefore hydrologi-cal processes, and it also controls erosion and sedi-ment transport.

Climatic variation at meso scales is a result of theinteraction of broader macroclimate with meso-scaletopographic features (Lotspeich and Platts, 1982; Bai-ley, 1995). Elevation differences result in variation inclimate due to its effect on precipitation and tempera-ture. Variation in watershed elevation is, therefore,the cause of patterns in hydrological regimes within aclimate class including variation in flood frequencyand the seasonality of flow regimes. We distinguishthree main watershed topography categories: moun-tain, hill, and low elevation, which are used to definepatterns in river environments within the higherlevel climate classes.

Mountain source of flow categories are character-ized by greater precipitation and lower temperatures,relative to the climate class mean. These watershedswill therefore store a greater proportion of their totalprecipitation as snow or ice and have significant areasof permanent snow. Seasonal peak flows occur whensolar radiation is highest in spring and mid-summer.Low flows typically occur in winter (Duncan, 1992).Hill source of flow classes are characterized by somesnowpack storage, which increases in the colder cli-mate classes. Snowpack, however, is limited and isusually melted entirely by mid-spring to late spring.Thus, rivers whose source of flow category is “hill” arecharacterized by two low flow periods – summer andwinter (Duncan, 1992). Flood frequency in hill cate-gories is typically high compared to the climate class

mean because of orographic effects of the topography.Precipitation and flood frequency in “low elevation”classes tend to be low compared to the climate classmean. Temperature is higher and runoff processes arenot influenced by storage of precipitation as snow.Thus, low-elevation source of flow categories are char-acterized by flow regimes that follow precipitationand evapotranspiration regimes (Duncan, 1992).

Topography at this system level is also the domi-nant controller of processes that attenuate watershedresponses to rainfall as well as controlling surficialerosion and sediment transport. Runoff, erosion, andsediment transport processes are intense in steepermountain source of flow categories meaning that floodfrequency and potential sediment supply are high.Runoff response is typically more attenuated and ero-sion less intense in less steep watersheds. Sedimentsupply and flood frequency are typically lower in hilland low-elevation source of flow categories wherewatershed slope is generally lower.

Other meso-scale topographic features – lakes, wet-lands, springs, and constructed impoundments – canbe added to the possible source of flow categories. Thepresence of these landscape features in the watershedattenuates runoff response and reduces sedimenttransport.

The hydrological and sediment supply regimes thatare characterized by source of flow categories interactto produce geomorphological process governing chan-nel characteristics. These processes also governinstream disturbance regimes that are due to flowvariability and substrate stability. Thus source of flowis expected to broadly discriminate patterns of specifichabitat attributes that influence biotic communitiesat finer spatial scales (Biggs and Close, 1989; Poff andWard, 1989).

Watersheds by their very nature comprise hetero-geneous topography. Classes at the source of flowlevel, therefore, must be assessed at each point in ariver network by integrating the topographic varia-tion across the watershed and basing a watershedtopography category on the dominant topography ofthe watershed.

G e o l o g y. The next classification level describeswatershed processes and assumes that the factor“watershed geology” is the next dominant cause ofvariation in hydrological, sediment supply, and hydro-chemical processes. Thus, the REC model assumesthat where rivers are homogeneous with respect tothe higher-level classes, patterns in hydrology, sedi-ment supply, and hydrochemical regimes are dominat-ed by watershed geology. The REC defines this systemlevel using rock type in broad groups, differentiatingspatial units of approximately 10 to 100 km2.


Snelder and Biggs

Watershed geology controls ground water storagecapacity and transmissivity and thus is likely to bethe dominant influence on base flow at this scale.Watershed geology is also the dominant controller ofhydrochemical processes, particularly at base flow.Watershed geology strongly influences erosion rates,and therefore, classes at this level provide increaseddiscrimination of sediment supply. Watershed geologycategories are also expected to discriminate patternsin the architecture of material forming channel sub-strates (e.g., platy versus rounded) and sediment par-ticle size.

Watersheds often comprise heterogeneous geology.REC accounts for this at each point in a river networkby integrating the geologic variation across the water-shed and basing a category on the dominant geologyof the watershed.

Land Cover. The final system level dealing withwatershed processes is defined by the factor “water-shed land cover.” The REC defines this system levelusing micro-scale differentiation of vegetation typesinto broad groups in spatial units of approximately 1 to 10 km2.

Watershed land cover controls surface interceptionof rainfall as well as potential evapotranspiration.This classification level therefore discriminates varia-tion in hydrological patterns such as low flow regimes(Rowe et al., 1997). At this level, watershed land coveris also the dominant control on hydrochemical pro-cesses. Land cover classes therefore further discrimi-nate water chemistry (Close and Davies-Colley, 1990).Watershed land cover also influences local variationsin erosion rates and the type of sediment reachingstream channels. Land cover classes therefore provideincreased discrimination of sediment supply and thetype of material forming channel substrates.

Watersheds generally comprise heterogeneous landc o v e r, which REC assesses at each point in a rivernetwork by integrating land cover variation acrossthe watershed and basing a category on the dominantland cover of the watershed.

Network Position. The fifth classification level,“network position,” provides greater discrimination ofecosystem patterns that are caused by local processesoccurring in sections of the river network. This sys-tem level describes the flux of water, sediment, andother constituents of the flow. The characteristics ofthese fluxes change along the network due to increas-ing catchment area. Network position also character-izes changes in river environments that are caused byattenuation of flood flows, homogenization of flow con-stituents, and changes in the relative contribution offlow from ground water storage. Network positiontherefore discriminates patterns in aspects of flow

including the intensity of flood flows, fluxes of sedi-ment and fluxes of chemicals including nutrients.

Variation in the factor network position may bedelineated using categories based on stream order( S t r a h l e r, 1964). Alternatively, other categorizationsmay be useful for subdivision at this level, such as theaverage elevation of network section or distance fromthe river mouth.

Valley Landform. Physical patterns at microscales and smaller are generally attributed to mor-phodynamic processes of local sediment transport,channel erosion, and sediment deposition (e.g., Haber-sack, 2000). The landform characteristics of the valleythe channel occupies, including its shape and geologi-cal features, are generally assumed to control thesemorphodynamic processes at a variety of scales (Fris-sell et al., 1986; Naiman et al., 1992; Habersack,2000). This sixth REC system level is defined bymicro scale (1 to 10 km) differentiation of the land-form of the valley the channel occupies. Patterns thatare discernable at the valley landform level includepatterns in lateral (floodplain) and vertical (hyporhe-ic) connections, hydraulic geometry and bankfull dis-charge (and therefore habitat volume), local floodp o w e r, sediment size range, and the influence ofriparian conditions (Poff, 1997).

A variety of local geological and geomorphic charac-teristics of the valley may be useful for categorizingvariation at the valley landform level, including val-ley slope, side slope gradient, and valley bottom width(Naiman et al., 1992).

Mapping the Classification

The conceptual framework for the REC is opera-tionalized by representing rivers as networks of chan-nels and associated watersheds. A network consists ofadjoining sections, which are the fundamental classi-fication unit. REC classifications for large areas aredeveloped in a Geographic Information System (GIS)environment, which enables automation of the pro-cess described below. In our experience, existing spa-tial data sets are available over large areas atsufficient resolution to classify and map classes downto the valley landform level of the classification.

A channel network and associated watersheds forthe geographic area being classified are required. Wederive the channel networks from a 30 m Digital Ele-vation Model (DEM) after post-processing the data bytechniques described in Nikora et al. (1996) and usingthe tools available within the GRID module ofArc/Info. The network comprises individual sectionsthat link each network junction. A uniquely identified



‘node’ terminates each section. The minimum water-shed area to define a section is 0.02 km2. This pro-duces a network that is commensurate with 1:50,000scale topographic maps, with sections having an aver-age length of approximately 700 m. The network isstored as a set of GIS polylines. Each section is associ-ated with its own section watershed, which is derivedfrom the DEM. All network nodes are linked, allowingthe node watershed to be defined by accumulating allupstream section watersheds.

Landscape classification does not always explicitlyseparate the process of classification, that is, thecharacterization and labeling of classes, from the pro-cess of mapping. Assignment is the process of choos-ing or recognizing the class to which a new objectshould be allotted (Cormack, 1971; Gordon, 1981). Inecosystem classification, the process of mappinginvolves assignment and is distinct from the processof classification (Klijn, 1994). REC uses mappingcharacteristics (s e n s u Klijn and Udo de Haes, 1994)for recognition and mapping of the factor categoriesfor each classification level. Choice of mapping char-acteristics includes subjective judgments and will alsobe constrained by spatial data that are available asGIS coverages. Either continuous or categorical datacan be used to produce mapping characteristics, pro-vided spatial resolution is commensurate with the fac-tors being represented.

The network approach used by the REC meansthat recognition and mapping of classes at each clas-sification level is not as simple as defining polygonswithin which mapping characteristics comply withcertain criteria such as in landscape classifications.For the REC, mapping characteristics are evaluatedfor each node in the network individually, and criteriaare then applied to determine category membership.Factors dominating watershed processes character-ized at the first four classification levels are generallyheterogeneous within watersheds. The mapping char-acteristic is therefore evaluated from maps of cate-gories (e.g. , geological maps) or surfaces of acontinuous variable (e.g., DEMs) that describe thespatial variation of each factor in the watershed.These maps or surfaces are spatially integrated forthe watershed of each node. The product of this inte-gration is the mapping characteristic, and categorymembership is determined by applying criteria tothis. Category membership for the network positionand valley landform classification levels are deter-mined from mapping characteristics that are evaluat-ed for each individual section.

Mapping characteristics are evaluated for eachnode, and the data are organized into a database withrows pertaining to nodes and columns pertaining tothe various mapping characteristics. For mapping

characteristics that are derived from continuous vari-able surfaces, a single mapping characteristic – theaverage value of the surface for the watershed – maybe calculated by spatial integration. However, theintegration process for categorical maps results in aset of mapping characteristics that describe the pro-portion of each of the map categories in the water-shed. Depending on how a continuous variable is to beevaluated, a similar set of mapping characteristicscould also by generated for the classification (e.g.,areas of each watershed in various elevation bands).

The mapping procedure applies criteria to thedatabase of mapping characteristics. This produces asimpler database in which each node is assigned acategory for each level of the classification. The classat any level of the REC hierarchy is simply the con-catenation of categories for each level in the classifica-tion hierarchy. The classification for each node isattached, as a set of attributes, to the polyline thatrepresents the upstream network section, and theentire network and classification is saved as a GISmap layer. Classes are mapped by displaying sectionsby class at any hierarchical level required.

EXAMPLE APPLICATION: CLASSIFICATIONOF NEW ZEALAND RIVERS

We present an example of the application of theREC method and discuss the mapping characteristicsand criteria used to classify New Zealand rivers. Wedo not attempt to detail or justify all the criteria butdiscuss them in broad terms to illustrate how themethodology may be applied. We also discuss a prag-matic variation to the conceptual framework in ourapplication of the REC method at the climate andsource of flow levels where classes are mapped usingactual climatic data.

Choice of Categories and Mapping Characteristics

New Zealand is a maritime country, with hills andmountains generally oriented transverse to the pre-vailing westerly winds. Thus, precipitation is stronglyrelated to elevation, with maximum precipitation inthe Southern Alps occurring at 1,200 to 1,700 meters( Tomlinson, 1992). Precipitation extremes generallyreflect the pattern of annual precipitation – the heavi-est and most intense falls tend to occur where annualtotals are highest and dry periods and droughtswhere annual totals are least (Mosley and Pearson,1997). However, rain-shadow areas occur on the east-ern side of the mountains. Because these rain-shadow


Snelder and Biggs

areas are also subject to warm Föhn winds, the rela-tionship between topography and “effective annualprecipitation” (i.e., precipitation minus potentialevapotranspiration) that produces stream flow is notsimple (Tomlinson, 1992). Because of this partialindependence of climate and topography, we usedirect measures of precipitation minus potential evap-otranspiration, and temperature, as mapping charac-teristics to determine categories at the climate level ofour classification.

Surfaces of annual mean precipitation, annualmean evapotranspiration, and annual mean air tem-perature were estimated for points on a 1 km gridacross New Zealand from thin plate splines (Hutchin-son and Gessler, 1994) fitted to meteorological stationdata. The mapping characteristics for the climate

level of the classification are evaluated by integratingthe climate surfaces to determine the watershed aver-age annual effective precipitation and temperaturefor each node. Criteria are applied to determine mem-bership of six climate classes: warm extremely wet,warm wet, warm dry, cool extremely wet, cool wet,and cool dry (see Table 2).

Source of flow categories are assigned primarilyfrom rainfall weighted watershed elevation derivedfrom the mean annual precipitation surface and theDEM. For each node, the mapping characteristics area set of data that tabulates the total annual precipita-tion volume evaluated in 100 m elevation bands.Because source of flow characterizes variation in floodf r e q u e n c y, potential evapotranspiration is not sub-tracted from the annual rainfall surface, in order to



TABLE 2. Summary of Categories, Mapping Characteristics, and CategoryMembership Criteria for Application of REC to New Zealand Rivers.

Classification MappingLevel Classes Notation Characteristics Category Assignment Criteria

1. Climate Warm Extremely Wet WX Mean annual precipitation, Warm: Mean annual temperature ≥ 12˚C.Warm Wet WW mean annual potential Cool: Mean annual temperature < 12˚C.Warm Dry WD evapotranspiration, and Extremely Wet: Mean annual effectiveCool Extremely Wet CX mean annual temperature. precipitation ≥ 1,500 mm.Cool Wet CW Wet: 500 > mean annual effectiveCool Dry CD precipitation < 1,500 mm.

Dry: Mean annual effective precipitation< 500 mm.

2. Source of Flow Mountain M Catchment rainfall volume M: > 50 percent annual rainfall volumeHill H in elevation categories, above 1,000 m ASL.Low Elevation L lake influence index H: 50 percent rainfall volume betweenLake Lk 400 and 100 m ASL.

L: 50 percent rainfall below 400 m ASL.Lk: Lake influence index > 0.033.

3. Geology Alluvium Al Proportions of each Class = The spatially dominant geologyHard Sedimentary HS geological category category unless combined softSoft Sedimentary SS in section catchment sedimentary geological categoriesVolcanic Basic Vb exceed 25 percent of catchment area,Volcanic Acidic Va in which case class = SS..Plutonic Pl

4. Land Cover Bare B Proportions of each land Class = The spatially dominant land coverIndigenous Forest IF cover category in section category unless P exceeds 25 percent ofPasture P catchment catchment area, in which case class = P,Tussock T or unless U exceed 15 percent ofScrub S catchment area, in which case classExotic Forest EF = U.Wetland WUrban B

5. Network Position Low Order LO Stream order of network Stream order 1 and 2.Middle Order MO section Stream order 3 and 4.High Order HO Stream order ≥ 5.

6. Valley Landform High Gradient HG Valley slope of section Valley slope > 0.04.Medium Gradient MG based on Euclidian length 0.02 ≥ Valley slope ≤ 0.04.Low Gradient LG Valley slope < 0.02.

maximize discrimination of patterns in flood frequen-cy. New Zealand is a narrow country, and watershedslope is therefore highly correlated with watershedelevation. We therefore consider that it is unneces-sary to include measures of watershed slope as a spe-cific mapping characteristic.

Rules assign the four major source of flow cate-gories (mountain, hill, low elevation, and lake) at eachnode (Table 2). The mountain class is defined bywatershed nodes that receive more than 50 percent oftheir total annual rainfall above a nominated eleva-tion threshold of 1,000 m. Nodes receiving greaterthan 50 percent of their total annual rainfall belowthe mountain threshold but above a nominal eleva-tion of 400 m are categorized as hill. Watersheds thatreceive greater than 50 percent of their annual aver-age rainfall below the lower hill threshold of 400 mare categorized as low elevation.

The degree to which upstream lakes control flowand sediment regimes at any node is a function of therunoff volume within each lake’s watershed, its storage-discharge relationship, and the contributionof runoff to that node from any “non-lake” watershed.Because storage-discharge relationships are unavail-able for most lakes, we use an index as a mappingcharacteristic. The lake index (LI) includes: VLW, thevolume of annual rainfall in the watershed of eachlake; AL, the area of each lake; ALW, the area of eachlake’s watershed; and VW, the volume of annual rain-fall in the node watershed, as has the form:

A rule assigns a lake class to all nodes where thevalue of LI exceeds a specified value (see Table 2).Our classification also recognizes wetlands, springs,and regulated rivers (downstream of controlledimpoundments) as source of flow categories because ofthe dominant effect of these topographic features onfluvial processes. Because these infrequently occur-ring categories cannot be mapped from availabletopographic data, they are classified by hand.

For the geology level of REC, we use the NewZealand Land Resources Inventory (LRI), which clas-sifies geology into 55 categories at a scale of 1:50,000.This resolution of rock type is higher than necessaryto differentiate the processes being considered at thisscale. The rock types defined by the LRI are thereforeaggregated into six summary categories that occur incharacteristic spatial units of 10 to 100 km2 (Table 2).For example, metamorphic rocks (e.g., argillite,schist, and greywacke) are merged into a categorytermed “hard sedimentary rocks.”

Even with the aggregation of geological categories,watershed geology is rarely homogeneous whenstream order exceeds approximately 3. Rules areapplied to data listing the summarized rock type byproportion of node watershed area in order to assign ageological category for each node. Rules recognize thenonproportional effect of different geologies on sedi-mentary and hydrochemical processes. For example,our rules assign a “soft sedimentary” geology categoryif the area of soft sedimentary rock types in the nodewatershed exceeds 25 percent. This recognizes theproportionally greater contribution of this geology tosediment (Hicks et al., 1996) and nutrient supply(Biggs, 1995; Biggs and Gerbeaux, 1993).

We use a national Land Cover Database (LCDB) toevaluate the proportion of watershed in various vege-tation categories at each node of the network. TheLCDB defines 17 land cover categories, derived fromdigitizing satellite images, at a mapping scale of1:50,000. We group the LCDB categories to nine broadcategories that occur in characteristic spatial units of1 to 10 km2 (Table 2).

Land cover is rarely homogeneous in watersheds ofstream order exceeding 2. Our rules, once again, rec-ognize the nonproportional contribution of differentland cover to water chemistry and sediment supply.For example, if the area of pasture is greater than 25percent, land cover is classified as “pasture” recogniz-ing the proportionally greater contribution of nutri-ents associated with pastoral runoff (e.g., Biggs, 1995)and effects of this on higher trophic levels (e.g., Quinnand Hickey, 1990).

Network position categories (see Table 2) areassigned from the stream order (Strahler, 1964),which is evaluated for each network section duringthe development of the river network. Valley landformcategories (see Table 2) are assigned using a singlecharacteristic, the slope of the valley that the sectionoccupies. This is determined from the DEM using theEuclidian distance and elevation difference betweendownstream and upstream ends of each network sec-tion.

Results

A full classification follows the form climate/sourceof flow/geology/land cover/network position/valleylandform, with potential classes at each classificationlevel and shortened notation being summarized onTable 2. Figure 2 shows an abbreviated classificationdiagram for the first four levels of the classification.This “tree” diagram illustrates the formation of lowerlevel classes by subdivision of the preceding leveland the hierarchical nature of the classification. The


Snelder and Biggs

LI =VLW ⋅ AL

ALW∑∑

VW

(1)

number of potential classes at any level in the classifi-cation is dependent on the number of categoriesdefined at that level, multiplied by the number ofclasses at the subsequent level. The number of poten-tial classes therefore rapidly increases moving downthe classification. For example, the geology level ofour classification has 6 x 4 x 6 = 144 potential classes.However, not all will occur in any given region of NewZealand. In the Southland region (Figure 3), whichcovers an area of 30,000 km2, 61 of these potentialclasses occur, of which 22 classes make up 90 percentof the total network length.

Figure 3 shows classes mapped for the climate levelof the classification for the South Island of NewZealand and additionally, source of flow, geology, andvalley landform levels within the Southland region.Indicative mapping scales for landscape classifica-tions, such as those suggested by Klijn (1994), areinappropriate due to the dendritic nature of the chan-nel network. Larger scales than those used for land-scape classification are thus preferred to map RECclasses across equivalent geographic areas.

The mapped classes appear as linear mosaics.Classes at the first three system levels are generallyhomogeneous over large areas of the network (Figure3). This occurs because qualitative change in the fac-tors causing patterns at large spatial scales occur rel-atively infrequently within watersheds. Main stemsin the channel network tend to appear as linearfeatures, which are formed by network sections that share the same classification. These linear fea-tures may pass through areas where influent tribu-taries have different classifications. For example,

Figure 3 shows hill source of flow rivers traversingalluvial out-wash plains where local streams are clas-sified as low elevation. The mapped classificationss h o w, however, that changes in high level classesoften occur within different parts of the network inlarge watersheds. For example, Figure 3 shows thatsource of flow may be categorized “mountain” high inthe watershed but may eventually change to “hill” asthe lower elevation parts of the watershed are inte-grated and become the dominant watershed topogra-p h y. Changes in class often occur at discontinuitieswhere large tributaries meet main stems that havesignificantly different controlling factors. This reflectsthe observation that tributary junctions can result indiscontinuities in morphology (Knighton, 1982), waterquality (Vannote et al., 1980), and biological commu-nities (Bruns et al., 1984). This means the criteriaused to define classes may not be critical to the pat-terns that are delineated. This is because change inclasses tends to be associated with the addition oftributaries where major changes in characteristicsoccur relative to the criteria that determine classmembership.

The size of classes (i.e., total length of channel ineach class) is easily retrieved from the GIS database,as section length is an attribute that is derived indeveloping the network, allowing the relative frequen-cy of occurrence of each class to be calculated. Theclassification of each section at every level, plus theflexibility of GIS for handling the classification asa multiple attribute GIS layer, allows the classifica-tion to be mapped at any level. The classification istherefore not a single map or series of maps such as



Figure 2. An Abbreviated Classification Diagram Showing the Formation of Selected Classesto the Land Cover Level of the REC Classification. The large font is the factor category

and the small font is the full class at each level (see Table 2 for the notation key).

ecoregions. Classifications can be made to suit indi-vidual applications and show patterns of processesand properties of interest.

DISCUSSION

The REC codifies knowledge of the factors control-ling river ecosystems to enable patterns to be delin-eated and help elucidate the causative processes. A

major advantage of the REC is the ability to treatrivers as networks and through this the increasedability to change the “grain” of the classification. Theability to detect patterns is a function of both theextent and the grain of the investigation (Wi e n s ,1989). The REC may be carried out over any spatialextent; however, the range in grain size that is achiev-able is much greater than classifications such asecoregions. REC can assist in pattern detection inmany characteristics of river ecosystems provided thegraining of the dependent ecosystem variables (e.g.,


Snelder and Biggs

Figure 3. A Classification of the South Island of New Zealand Showing Classes at the Climate Level of the RECClassification, and Zooming to Three Scales in the “Southland Region” Showing Source of Flow, Geology, and

Valley Landform Levels of the REC Classification (see Table 2 for the notation key). The colors for eachpanel have been chosen to maximum class discrimination and there is no relationship between class

colors in each panel. Line thickness has been scaled by the order of the each network section.

biotic community or water quality characteristics) iscommensurate with the level of the classification usedto differentiate the area being investigated.

REC classes are geographically independent andcharacterize similar environments that occur at manylocations. The REC classification procedure and use ofGIS methods has the advantage that, once deter-mined, classification criteria are consistently appliedto map classes. In comparison, gestalt classificationprocedures such as ecoregions “hide” the reasons forclass membership. Ecoregions are geographicallydependent and unrelated to each other because classi-fication at any level is not performed according to anymodel of ecological processes or criteria. Using theREC classification similarities and differences can beinvestigated between large rivers whose watershedsare heterogeneous with respect to controlling factors,or between different locations on the main stem of asingle large river. Thus, the classification providesmanagers with a more powerful spatial framework fordetecting and mapping patterns and stratifying riversfor management purposes.

The longitudinal zonation or position of a site inthe hydrographic network has long been recognized asa fundamental predictor of physical and biotic pattern(e.g., Hawkes, 1975). This led Hawkins et al. (2000) toconclude that explicit recognition of the stream con-tinuum may improve the strength of landscape classi-f ications. The REC approach deals with thisfundamental problem by explicitly representing thecontinuum caused by the changing product of integra-tion of watershed factors in the downstream direction.Patterns, however, are not due to this longitudinalchange in watershed characteristics alone. The inter-action between watershed scale processes such ashydrologic and sediment regimes, with local con-straints imposed by more rapidly changing factors atthe scale of individual network sections, producessmaller scale patterns in hydraulic and substrate con-ditions that are major determinants of plant (e.g.,Biggs, 1996), invertebrate (e.g., Quinn and Hickey,1990), and fish (e.g., Jowett and Duncan, 1990)assemblages. In a synthesis of tests of many land-scape classifications, Hawkins et al. (2000) concludedthat weak explanation of biotic variation was due toan absence of these local habitat features. Classifica-tion down to the valley landform level by the RECapproach incorporates factors that operate at thisscale but emphasizes that these patterns are con-strained by larger watershed processes. Lower classi-fication levels such as the reach, pool/riffle, andmicrohabitat levels of classification suggested by Fris-sell et al. (1986), would nest coherently below the val-ley landform level. These classification levels arebased on factors that are generally too small to be

adequately defined from spatial data that are avail-able for large areas but could be implemented, whennecessary, from site data collected in the field.

REC’s classification levels are well justified by thetheories of hierarchic control of physical and bioticpattern in rivers (e.g., Poff, 1997). The most subjectiveaspect of the classification is the division of classes ateach level of the classification. Many applications arebased on analyses that use information on the rela-tive frequency of occurrence of classes, for example,determination of representativeness of protectedareas. Care is therefore needed to ensure that eachclass discriminates “real” ecological variation at thatlevel of the classification. In addition, discriminationmust be balanced against the number of classes thatare manageable when using the classification.

The REC method is more technically demandingand involves a more difficult classification procedurethan ecoregion approaches. REC, however, is not nec-essarily more data intensive especially if carried outat lower spatial resolution. Computer based tools,particularly GIS, are necessary for operationalizingthe classification in an efficient and consistent man-n e r. Attention to how the large quantity of data isassembled and handled ensures maximum flexibility.Careful database management allows the classifica-tion to be “dynamic” enabling the number of classes atany level of the classification to be changed, criteria tobe altered, and even other factors to be incorporatedat a later date without losing the integrity of the clas-sification. This allows prototypes of the classificationto be produced, for example, to examine the effect ofchanging criteria on the boundaries between classes.

The dynamic form of the REC classificationextends its use into other applications such as spatialmodeling. For example, the first four levels of theclassification stratify rivers into a number of classesthat can be expected to exhibit within class similari-ties (e.g., geology and thus flood flows and sedimentinput rates controlling morphological patterns). Lon-gitudinal evolution could be expected to vary consis-tently within a class (e.g., downstream hydraulicgeometry). Thus, each high-level class allows certainmodel parameters to be stabilized and allows a limit-ed set of parameters, related to longitudinal variation(slope, drainage area), to be used as control variables.

The choice of mapping characteristics in our classi-fication of New Zealand’s rivers includes judgmentsthat are not necessarily universally applicable. Forexample, in continental areas, where elevation andslope are less well correlated than in New Zealand,source of flow may need to incorporate measures ofwatershed slope to adequately differentiate erosionand sediment transport processes.



Finally, we emphasize that as an a priori classifica-tion, the results remain a hypothesis about ecosystemorganization and function until tested. Complete test-ing of such a multiscale classification is unlikely to be performed as a single study. This means the classifi-cation must become part of the assumptions andhypotheses underlying policy development that arelater tested by monitoring (e.g., Lee, 1993). Compre-hensive testing as well as use of the REC for broadscale environmental assessment are now under wayin New Zealand with promising results.

ACKNOWLEDGMENTS

We are grateful for the financial support of the New ZealandMinistry for the Environment and the Environmental Hydrologyand Hydraulics Program (Contract C01813) of the Foundation forResearch Science and Technology (New Zealand). We thank our col-leagues for their contributions – Ian Jowett, John Leathwick, JohnQuinn, Keri Niven, Kit Rutherford, Mark Weatherhead, MurrayHicks, Paul Mosley, Rachel O’Brien, Randy Cambell, Richard Jon-gens, Ross Woods, Theo Stephens, and Ude Shankar. We appreciatethe hospitality from and discussions with Nicolas Lamouroux andJean-Gabriel Wasson of Cemagref, Lyon, France. We are gratefulfor reviews from Brian Sorrell and two anonymous reviewers.

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