Uncertainty Models for Estimates of Physical Characteristics of River Segments Over Large Areas

Spatially comprehensive estimates of the physical characteristics of river segments over large areas are required in many large‐scale analyses of river systems and for the management of multiple basins. Remote sensing and modeling are often used to estimate river characteristics over large areas, but the uncertainties associated with these estimates and their dependence on the physical characteristics of the segments and their catchments are seldom quantified. Using test data with varying degrees of independence, we derived analytical models of the uncertainty associated with estimates of upstream catchment area (CA), segment slope, and mean annual discharge for all river segments of a digital representation of the hydrographic network of France. Although there were strong relationships between our test data and estimates at the scale of France, there were also large relative local uncertainties, which varied with the physical characteristics of the segments and their catchments. Discharge and CA were relatively uncertain where discharge was low and catchments were small. Discharge uncertainty also increased in catchments with large rainfall events and low minimum temperature. The uncertainty of segment slope was strongly related to segment length. Our uncertainty models were consistent across large regions of France, suggesting some degree of generality. Their analytical formulation should facilitate their use in large‐scale ecological studies and simulation models.     

MULTISCALE RIVER ENVIRONMENT CLASSIFICATION FOR WATER RESOURCES MANAGEMENT1

ABSTRACT: 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.     

NUTRIENT CONCENTRATION CRITERIA AND CHARACTERIZATION OF PATTERNS IN TROPHIC STATE FOR RIVERS IN HETEROGENEOUS LANDSCAPES1

ABSTRACT: A method is demonstrated for the development of nutrient concentration criteria and large scale assessment of trophic state in environmentally heterogeneous landscapes. The method uses the River Environment Classification (REC) as a spatial framework to partition rivers according to differences in processes that control the accrual and loss of algae biomass. The method is then applied to gravel bed rivers with natural flow regimes that drain hilly watersheds in New Zealand's South Island. An existing model is used to characterize trophic state (in terms of chlorophyll a as a measure of maximum biomass) using nutrient concentration, which controls the rate of biomass accrual, and flood frequency, which controls biomass loss. Variation in flood frequency was partitioned into three classes, and flow data measured at 68 sites was used to show that the classes differ with respect to flood frequency. Variation in nutrient concentration was partitioned at smaller spatial scales by subdivision of higher level classes into seven classes. The median of flood frequency in each of the three higher level classes was used as a control variable in the model to provide spatially explicit nutrient concentration criteria by setting maximum chlorophyll a to reflect a desired trophic state. The median of mean monthly soluble reactive phosphorus and soluble inorganic nitrogen measured at 68 water quality monitoring sites were then used to characterize the trophic state of each of the seven lower level classes. The method models biomass and therefore allows variation in this response variable to provide options for trophic state and the associated nutrient concentrations to achieve these. Thus it is less deterministic than using reference site water quality. The choice from among these options is a sociopolitical decision, which reflects the management objectives rather than purely technical considerations.     

Estimation of Catchment Nutrient Loads in New Zealand Using Monthly Water Quality Monitoring Data

Causes of variation between loads estimated using alternative calculation methods and their repeatability were investigated using 20 years of daily flow and monthly concentration samples for 77 rivers in New Zealand. Loads of dissolved and total nitrogen and phosphorus were calculated using the Ratio, L5, and L7 methods. Estimates of loads and their precision associated with short‐term records of 5, 10, and 15 years were simulated by subsampling. The representativeness of the short‐term loads was quantified as the standard deviation of the 20 realizations. The L7 method generally produced more realistic loads with the highest precision and representativeness. Differences between load estimates were shown to be associated with poor agreement between the data and the underlying model. The best method was shown to depend on the match between the model and functional and distributional characteristics of the data, rather than on the contaminant. Short‐term load estimates poorly represented the long‐term load estimate, and deviations frequently exceeded estimated imprecision. The results highlight there is no single preferred load calculation method, the inadvisability of “unsupervised” load estimation and the importance of inspecting concentration‐flow, unit load‐flow plots and regression residuals. Regulatory authorities should be aware that the precision of loads estimated from monthly data are likely to be “optimistic” with respect to the actual repeatability of load estimates.     

Predicting Periphyton Cover Frequency Distributions across New Zealand's Rivers

Regression models of mean and mean annual maximum (MAM) cover were derived for two categories of periphyton cover (filaments and mats) using 22 years of monthly monitoring data from 78 river sites across New Zealand. Explanatory variables were derived from observations of water quality variables, hydrology, shade, bed sediment grain size, temperature, and solar radiation. The root mean square errors of these models were large (75‐95% of the mean of the estimated values). The at‐site frequency distributions of periphyton cover were approximated by the exponential distribution, which has the mean cover as its single parameter. Independent predictions of cover distributions at all sites were calculated using the mean predicted by the regression model and the theoretical exponential distribution. The probability that cover exceeds specified thresholds and estimates of MAM cover, based on the predicted distributions, had large uncertainties (~80‐100%) at the site scale. However, predictions aggregated by classes of an environmental classification accurately predicted the proportion of sites for which cover exceeded nominated criteria in the classes. The models are useful for assessing broad‐scale patterns in periphyton cover and for estimating changes in cover with changes in nutrients, hydrological regime, and light.     

Effect of Classification Procedure on the Performance of Numerically Defined Ecological Regions

Ecological regionalizations define geographic regions exhibiting relative homogeneity in ecological (i.e., environmental and biotic) characteristics. Multivariate clustering methods have been used to define ecological regions based on subjectively chosen environmental variables. We developed and tested three procedures for defining ecological regions based on spatial modeling of a multivariate target pattern that is represented by compositional dissimilarities between locations (e.g., taxonomic dissimilarities). The procedures use a “training dataset” representing the target pattern and models this as a function of environmental variables. The model is then extrapolated to the entire domain of interest. Environmental data for our analysis were drawn from a 400 m grid covering all of Switzerland and consisted of 12 variables describing climate, topography and lithology. Our target patterns comprised land cover composition of each grid cell that was derived from interpretation of aerial photographs. For Regionalization 1 we used conventional cluster analysis of the environmental variables to define 60 hierarchically organized levels comprising from 5 to 300 regions. Regionalization 1 provided a base-case for comparison with the model-based regionalizations. Regionalization 2, 3 and 4 also comprised 60 hierarchically organized levels and were derived by modeling land cover composition for 4000 randomly selected “training” cells. Regionalization 2 was based on cluster analysis of environmental variables that were transformed based on a Generalized Dissimilarity Model (GDM). Regionalization 3 and 4 were defined by clustering the training cells based on their land cover composition followed by predictive modeling of the distribution of the land cover clusters using Classification and Regression Tree (CART) and Random Forest (RF) models. Independent test data (i.e. not used to train the models) were used to test the discrimination of land cover composition at all hierarchical levels of the regionalizations using the classification strength (CS) statistic. CS for all the model-based regionalizations was significantly higher than for Regionalization 1. Regionalization 3 and 4 performed significantly better than Regionalization 2 at finer hierarchical levels (many regions) and Regionalization 4 performed significantly better than Regionalization 3 for coarse levels of detail (few regions). Compositional modeling can significantly increase the performance of numerically defined ecological regionalizations. CART and RF-based models appear to produce stronger regionalizations because discriminating variables are able to change at each hierarchic level.     

Dam Design can Impede Adaptive Management of Environmental Flows: A Case Study from the Opuha Dam, New Zealand

The Opuha Dam was designed for water storage, hydropower, and to augment summer low flows. Following its commissioning in 1999, algal blooms (dominated first by Phormidium and later Didymosphenia geminata) downstream of the dam were attributed to the reduced frequency and magnitude of high-flow events. In this study, we used a 20-year monitoring dataset to quantify changes associated with the dam. We also studied the effectiveness of flushing flows to remove periphyton from the river bed. Following the completion of the dam, daily maximum flows downstream have exceeded 100 m³ s^?1 only three times; two of these floods exceeded the pre-dam mean annual flood of 203 m³ s^?1 (compared to 19 times >100 m³ s^?1 and 6 times >203 m³ s^?1 in the 8 years of record before the dam). Other changes downstream included increases in water temperature, bed armoring, frequency of algal blooms, and changes to the aquatic invertebrate community. Seven experimental flushing flows resulted in limited periphyton reductions. Flood wave attenuation, bed armoring, and a shortage of surface sand and gravel, likely limited the effectiveness of these moderate floods. Floods similar to pre-dam levels may be effective for control of periphyton downstream; however, flushing flows of that magnitude are not possible with the existing dam infrastructure. These results highlight the need for dams to be planned and built with the capacity to provide the natural range of flows for adaptive management, particularly high flows.     

Strong Influence of Variable Treatment on the Performance of Numerically Defined Ecological Regions

Numerical clustering has frequently been used to define hierarchically organized ecological regionalizations, but there has been little robust evaluation of their performance (i.e., the degree to which regions discriminate areas with similar ecological character). In this study we investigated the effect of the weighting and treatment of input variables on the performance of regionalizations defined by agglomerative clustering across a range of hierarchical levels. For this purpose, we developed three ecological regionalizations of Switzerland of increasing complexity using agglomerative clustering. Environmental data for our analysis were drawn from a 400 m grid and consisted of estimates of 11 environmental variables for each grid cell describing climate, topography and lithology. Regionalization 1 was defined from the environmental variables which were given equal weights. We used the same variables in Regionalization 2 but weighted and transformed them on the basis of a dissimilarity model that was fitted to land cover composition data derived for a random sample of cells from interpretation of aerial photographs. Regionalization 3 was a further two-stage development of Regionalization 2 where specific classifications, also weighted and transformed using dissimilarity models, were applied to 25 small scale “sub-domains” defined by Regionalization 2. Performance was assessed in terms of the discrimination of land cover composition for an independent set of sites using classification strength (CS), which measured the similarity of land cover composition within classes and the dissimilarity between classes. Regionalization 2 performed significantly better than Regionalization 1, but the largest gains in performance, compared to Regionalization 1, occurred at coarse hierarchical levels (i.e., CS did not increase significantly beyond the 25-region level). Regionalization 3 performed better than Regionalization 2 beyond the 25-region level and CS values continued to increase to the 95-region level. The results show that the performance of regionalizations defined by agglomerative clustering are sensitive to variable weighting and transformation. We conclude that large gains in performance can be achieved by training classifications using dissimilarity models. However, these gains are restricted to a narrow range of hierarchical levels because agglomerative clustering is unable to represent the variation in importance of variables at different spatial scales. We suggest that further advances in the numerical definition of hierarchically organized ecological regionalizations will be possible with techniques developed in the field of statistical modeling of the distribution of community composition.     

PM2.5 and volatile organic compounds (VOCs) in ambient air: a focus on the effect of meteorology

PM(2.5) and VOCs (benzene, toluene, m-p-o-xylenes) concentrations were measured in an urban and a suburban site in Athens, Greece, during the period between April and November 2004. This period, which is considered to be the warmer period in Greece, is characterized by the development of sea-breeze over the Attica Basin. Additionally strong Northern, North-eastern winds called "The Etesians", predominate during the summer months (July-August), acting positively to the dispersion of pollutants. In this campaign, 24 days with sea-breeze development were observed, 15 days with northern winds, 6 days with southern winds while the rest of the days presented no specific wind profile. Maximum concentrations of PM(2.5), VOCs and nitrogen oxides, were detected during the days with sea-breeze, while minimum concentrations during the days with northern winds. Ozone was the only pollutant that appeared to have higher concentrations in the background site and not in the city centre, where benzene presented strong negative correlation with ozone, indicating the photochemical reaction of hydrocarbons that lead to the ozone formation. The BTX ratios were similar for both sites and wind profiles, indicating common sources for those pollutants. T/B ratio ranged in low levels, between 3-5 for site A and 2-5 for site B, suggesting vehicles emissions as the main sources of volatile compounds. Finally, the strong correlations of PM(2.5) and benzene concentrations, between the two sampling sites, indicate that both the city centre and the background site, are affected by the same sources, under common meteorological conditions (sea-breeze, northern winds).     

A Method to Assess and Define Environmental Flow Rules for Large Jurisdictional Regions1

Snelder, Ton, Doug Booker, and Nicolas Lamouroux, 2011. A Method to Assess and Define Environmental Flow Rules for Large Jurisdictional Regions. Journal of the American Water Resources Association (JAWRA) 47(4):828‐840. DOI: 10.1111/j.1752‐1688.2011.00556.x Abstract: Hydrological rules of thumb are used across jurisdictional regions to set minimum flows and allocation limits that apply by default (i.e., when more detailed site‐scale studies have not been carried out). Uniform rules do not account for spatial variation in environmental characteristics, resulting in inconsistent consequences for the protection of ecosystems, and the reliability of water resources. We developed a method for assessing hydrological rules of thumb that describes their consequences for protection of the ecosystem (in terms of retention of physical habitat) and the reliability of the water resource. The method links regionalized flow duration curves, at‐station hydraulic geometry, and generalized physical habitat models to make assessments at many locations across a region. The method estimates, for a given set of rules, the retained physical habitat for specified taxa/life stages and the proportion of the time abstractions are restricted. We applied the method to assess a set of rules that are proposed as default minimum flows and allocation limits for New Zealand rivers. The assessment showed that the minimum flow rules had variable consequences. The method could be used to quantify the tradeoff between environmental protection and water resources availability and reliability.