River Environment Classification (REC) is a new system for classifying river environments that is based on climate, topography, geology, and land cover factors that control spatial patterns in river ecosystems. REC builds on existing principles for environmental regionalization and introduces three specific additions to the “ecoregion” approach. First, the REC assumes that ecological patterns are dependent on a range of factors and associated landscape scale processes, some of which may show significant variation within an ecoregion. REC arranges the controlling factors in a hierarchy with each level defining the cause of ecological variation at a given characteristic scale. Second, REC assumes that ecological characteristics of rivers are responses to fluvial (i.e., hydrological and hydraulic) processes. Thus, REC uses a network of channels and associated watersheds to classify specific sections of river. When mapped, REC has the form of a linear mosaic in which classes change in the downstream direction as the integrated characteristics of the watershed change, producing longitudinal spatial patterns that are typical of river ecosystems. Third, REC assigns individual river sections to a class independently and objectively according to criteria that result in a geographically independent framework in which classes may show wide geographic dispersion rather than the geographically dependent schemes that result from the ecoregion approach. REC has been developed to provide a multiscale spatial framework for river management and has been used to map the rivers of New Zealand at a 1:50,000 mapping scale.

Obtaining a better knowledge of how flow variability affects lotic biota is of considerable importance to stream and river management. We contend that processes at different hierarchical levels of organization in lotic ecosystems are sensitive to variation in flow at related hierarchical temporal scales. Ecosystem disturbance caused by large-scale events (i.e. infrequent, but high magnitude flow events with a recurrence interval of years to many days) tend to determine high-level characteristics of ecosystem structure (e.g. determining species pools, periphyton versus macrophyte dominance) and function (e.g. balance between auto-and heterotrophy). The high-level ecosystem characteristics then set the stage for processes that are influenced by flow variation that occurs at smaller temporal scale (i.e. minutes to milliseconds) such as colonization, biotic interactions and mass transfer enhancement of production. We contend that large-scale temporal events predominantly affect lotic ecosystems through physical drag processes ('drag-disturbance'), whereas small-scale flow variations affect ecosystems through mass-transfer processes (including invertebrate and fish food-uptake). Drag-disturbance and mass-transfer related processes mark the opposite ends of a continuum of flow variability controlled processes, with moderate temporal scale flow variability events affecting ecosystems through both drag-disturbance and mass-transfer processes in similar proportions. Flow variability, and associated effects on ecosystems, across these scales is discussed with reference to New Zealand rivers. We suggest that these concepts can be integrated across the full range of temporal scales based on a spectrum of velocity variations. This may provide a unifying conceptual model for how the structure and functioning of lotic ecosystems are linked to flow variability.

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Abstract. Understanding large-scale patterns in flow intermittence is important for effective river management. The duration and frequency of zero-flow periods are associated with the ecological characteristics of rivers and have important implications for water resources management. We used daily flow records from 628 gauging stations on rivers with minimally modified flows distributed throughout France to predict regional patterns of flow intermittence. For each station we calculated two annual times series describing flow intermittence; the frequency of zero-flow periods (consecutive days of zero flow) in each year of record (FREQ; yr −1 ), and the total number of zero-flow days in each year of record (DUR; days). These time series were used to calculate two indices for each station, the mean annual frequency of zero-flow periods (mFREQ; yr −1 ), and the mean duration of zero-flow periods (mDUR; days). Approximately 20 % of stations had recorded at least one zero-flow period in their record. Dissimilarities between pairs of gauges calculated from the annual times series (FREQ and DUR) and geographic distances were weakly correlated, indicating that there was little spatial synchronization of zero flow. A flow-regime classification for the gauging stations discriminated intermittent and perennial stations, and an intermittence classification grouped intermittent stations into three classes based on the values of mFREQ and mDUR. We used random forest (RF) models to relate the flow-regime and intermittence classifications to several environmental characteristics of the gauging station catchments. The RF model of the flow-regime classification had a cross-validated Cohen's kappa of 0.47, indicating fair performance and the intermittence classification had poor performance (cross-validated Cohen's kappa of 0.35). Both classification models identified significant environment-intermittence associations, in particular with regional-scale climate patterns and also catchment area, shape and slope. However, we suggest that the fair-to-poor performance of the classification models is because intermittence is also controlled by processes operating at scales smaller catchments, such as groundwater-table fluctuations and seepage through permeable channels. We suggest that high spatial heterogeneity in these small-scale processes partly explains the low spatial synchronization of zero flows. While 20 % of gauges were classified as intermittent, the flow-regime model predicted 39 % of all river segments to be intermittent, indicating that the gauging station network under-represents intermittent river segments in France. Predictions of regional patterns in flow intermittence provide useful information for applications including environmental flow setting, estimating assimilative capacity for contaminants, designing bio-monitoring programs and making preliminary predictions of the effects of climate change on flow intermittence.

Summary 1. Classifications that group rivers and streams with similar ecological characteristics are used increasingly to underpin conservation and resource management planning. Uses include identifying systems that may respond similarly to human activities or management actions, setting guidelines and standards to manage human impacts, interpreting data from inventory (survey) and monitoring, and identifying priority sites for conservation management. 2. Traditional approaches to river classification have been based mostly on delineating landscape units (ecoregions), often by grouping adjacent catchments having similar ecological character. However, use of this approach can be complicated by marked local heterogeneity of river systems. Instead, classifications may be more ecologically relevant if individual river or stream segments having similar environmental conditions are grouped together, independent of their geographic locations. The latter approach also allows the use of more rigorous classification procedures, including newly emerging techniques that optimise the ability of a classification to discriminate patterns in parallel sets of biological data. 3. Here, we explore the use of one of these newer techniques, generalised dissimilarity modelling (GDM), an extension of generalised regression techniques, that defines an optimal set of transformations of candidate environmental predictors to maximise explanation of species turnover in site‐based biological data. 4. Using two biological data sets describing the distributions of freshwater fish and macroinvertebrates and a candidate set of functionally relevant environmental variables, we used GDM to identify the variables, weightings and transformations that best explain biological dissimilarities across sites. We then used these as input to a multivariate classification of 567 000 river and stream segments throughout New Zealand. Weightings and transformations of these variables were also specified from the GDM analysis. The matrix of transformed environmental predictors was classified in a two‐stage process, using non‐hierarchical mediod clustering to define an initial set of 400 groups, with relationships between these groups then defined using hierarchical clustering. 5. The resulting classification better discriminates sites with similar biological character than previous classifications, particularly at higher levels of classification detail. Key factors contributing to this success include the use of detailed, segment‐specific environmental variables, coherently accounting for the longitudinal connectivity inherent in rivers including its implications for the construction of biologically relevant predictors, and the use of a modelling technique (GDM) designed to specifically analyse biological turnover and its relationships with environment.

River water quality in New Zealand is at great risk of impairment in low elevation catchments because of pervasive land-use changes, yet there has been no nationwide assessment of the state of these rivers. Data from the surface-water monitoring programmes of 15 regional councils and unitary authorities, and the National River Water Quality Network were used to assess the recent state (1998-2002) and trends (1996-2002) in water quality in low-elevation rivers across New Zealand. Assessments were made at the national level, and within four land-cover classes (native forest, plantation forest, pastoral, and urban). Finer-scaled assessments were made by subdividing the large number of pastoral sites into six climate classes, and seven stream orders. At the national level, median concentrations of the faecal indicator bacterium Escherichia coli, and dissolved inorganic nitrogen and dissolved reactive phosphorus exceeded guidelines recommended for the protection of aquatic M03047; Online publication date

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The flow regime is recognized as a key factor determining biological and physical processes and characteristics in rivers. Because of this, there is interest in classification and regionalization of rivers in order to delineate patterns in flow regime character at landscape scales. The River Environment Classification (REC) is an a priori mapped classification of rivers. The REC is based on a hierarchical model of 'controlling factors', which are assumed to be the dominant causes of variation in physical and biological characteristics of rivers at a variety of spatial scales. The first and second levels of the REC are based on climate and topography and are expected to discriminate rivers according to differences in their flow regimes. Classes are assigned to individual 'sections' of the river network based on categorical description of the climate and topography of each section's unique watershed. This paper describes a test of the REC's ability to explain variation in hydrological character of rivers. Flows that were measured continuously at 335 sites distributed throughout New Zealand were summarized by 13 flow variables and were classified using the REC. Principal components analysis was used to show that the REC classes have distinctive flow regime characteristics. We quantified the classification strength (i.e. the extent to which the mean between-class inter-site dissimilarity exceeds the mean within-class inter-site hydrological dissimilarity) of the REC based on the 13 flow variables. The classification strength of the REC was greater than for two existing regionalizations and a classification that is based on climate, but which does not account for the river network. We attribute the increased classification strength of the REC to its explicit consideration of the causes of spatial variation in flow regimes among rivers and its representation of the network spatial structure of rivers.