Local scale interventions dominate over catchment scale controls to accelerate the recovery of a degraded stream.

A premise of stream restoration theory and practice is that it is often futile to attempt to restore a stream at the reach scale (101-103 metres) until catchment scale problems have been addressed. This study considers reach scale restoration actions undertaken in Bryan Creek, a sand bed river in south east Australia impacted by a sediment pulse, after catchment sediment sources have been addressed. Local scale interventions, which were in-stream sand extraction, fencing to exclude stock and riparian revegetation, were evaluated by quantifying cross-section and thalweg variability, mapping in-stream and riparian vegetation and by classifying the morphology that emerged following each intervention. Following intervention channel reaches moved to one of three distinct states: simple clay bed, eroding reaches dominated by Juncus acutus, and reaches with deep pools and Phragmites australis. Boundaries between the intervention reaches were sharp, suggesting local scale interventions dominate over catchment scale processes. The magnitude and spread of variability metrics were similar between all reaches and differences in variability bore no relation to intervention type, despite the stark difference in post-intervention morphology. These findings suggest that cross-section and thalweg variability metrics are an inadequate proxy for the effectiveness of local scale interventions in accelerating the recovery of sand bed reaches from a bedload pulse. The most important implications for river managers is that local scale interventions can lead to substantial and rapid improvements in condition, and the change in condition of these reaches is almost independent of other reaches. In this case, the key to the pattern of reach scale geomorphic recovery is excluding stock from waterways so that a specific macrophyte can establish, trap sediment and develop pools

The Case for "Environment in All Policies" : Lessons from the "Health in All Policies" Approach in Public Health

BACKGROUND: Both public health, and the health of the natural environment, are affected by policy decisions made across portfolios as diverse as finance, planning, transport, housing, education, and agriculture. A response to the interdependent character of public health has been the "health in all policies" (HiAP) approach. OBJECTIVES: With reference to parallels between health and environment, this paper argues that lessons from HiAP are useful for creating a new integrated environmental management approach termed "environment in all polices" (EiAP). DISCUSSION: This paper covers the theoretical foundations of HiAP, which is based on an understanding that health is strongly socially determined. The paper then highlights how lessons learned from HiAP's implementation in Finland, California, and South Australia might be applied to EiAP. It is too early to learn from evaluations of HiAP, but it is apparent that there is no single tool kit for its application. The properties that are likely to be necessary for an effective EiAP approach include a jurisdiction-specific approach, ongoing and strong leadership from a central agency, independent analysis, and a champion. We then apply these properties to Victoria (Australia) to demonstrate how EiAP might work. CONCLUSIONS: We encourage further exploration of the feasibility of EiAP as an approach that could make explicit the sometimes surprising environmental implications of a whole range of strategic policies. Citation: Browne GR, Rutherfurd ID. 2017. The case for "environment in all policies": lessons from the "health in all policies" approach in public health. Environ Health Perspect 125:149-154; http://dx.doi.org/10.1289/EHP294

Testing uncertainty in a model of stream bank erosion

Sediment and nutrient loads in Australian rivers are a significant management concern. The National Land and Water Audit (2002) identified bank erosion as a major source of sediment, particularly in southern Australian systems. This paper tests a method of incorporating uncertainty into and the up-scaling of a cross-section scale stream bank erosion model. The cross-section scale model is based on an understanding of fluvial erosion and mass failure processes in which fluvial erosion is estimated using an excess shear stress approach while mass failure is estimated using a limit equilibrium analysis at the cross-section scale. Figure 1 shows a schematic of the model. A Monte-Carlo framework is used to propagate input uncertainty to output uncertainty in the model and to scale up to the reach scale. Widely available databases are used to estimate variables for the two model components. A range of spatial information (GIS layers) is used to describe spatial variations in general properties such as soil type and catchment area. These are considered to be relatively well known (compared with cross-section geometry, geotechnical properties of the bank materials, riparian tree density, and hydrologic variables), although spatially coarse. A variety of empirical models and assumptions are used to transform the spatial information into model parameters, which are considered to be relatively poorly known. Two major challenges, which are related, involve incorporating the effects of natural variability along a river reach and estimating the uncertainty in the model inputs and the effect that this has on uncertainty in the model prediction. A Monte Carlo framework is used to achieve this. This involves developing a series of statistical models to predict the erosion model inputs and their (co)variability. A hierarchical approach is used to develop these input models. An attempt is first made to construct a statistical model that predicts each model parameter from available spatial information using multiple regressions. Uncertainty in these parameters is incorporated using the regression error statistics. Where cross-correlations were found to be important, these were incorporated in the generation models. Where it was not possible to develop empirical relationships with available spatial data sets, a suitable parametric distribution is fitted for those input variables for which some data is available. Where no data were available for fitting a distribution, a distribution was assumed with a shape and parameters based on heuristic consideration of the relevant processes. Once both the erosion model and the various input models were established, the Monte Carlo technique was applied. This involves generating sets of the input variables of the model from the respective stochastic input models and the running the erosion model. This allows the probability distribution for the model output to be estimated for a location in the stream network. The model is tested using historical records of plan form change from a 40km reach of the Goulburn River downstream of Eildon Dam in Victoria, Australia. The results obtained from the model are promising; with bank erosion rates being predicted within a factor of two without calibration. A series of sensitivity analyses (detail sensitivity analysis, scenario analysis, and advance sensitivity analysis) were conducted to identify key variables for predicting bank erosion rates using this particular bank erosion model. This suggested that bank angle, bank material physical characteristics, stream bed slope, and the high-flow flow regime (bankfull duration) control the behaviour of the model for loam bank materials

Passive Recovery of Wood Loads in Rivers

A growing worldwide body of literature is demonstrating the geomorphic and ecological roles played by wood in rivers. After more than a century of removing wood from rivers in many parts of the world, researchers and managers are now interested in returning the load of wood back to a more natural condition. The mechanical placement of wood in rivers is expensive, and so it is useful to know how long it will take for in‐stream wood loads to passively recover a target load by recruitment from riparian forests. Of fundamental interest to managers and researchers alike are the questions: (1) can a river passively recover to a preremoval load of wood, and (2) if so, how long will recovery take? We address these questions using the example of the anabranching King River, Northeast Victoria, Australia, which was desnagged twice: once in 1957 and again in 1980. We predict a recovery time of 255 ± 23 years using a complete census of recovering wood loads to develop and parameterize a mass balance delivery model run in a Monte Carlo simulation. Our results indicate that with a healthy supply of riparian vegetation and minimal interference from managers, rivers are likely to passively recover natural wood loads at least two and a half centuries after desnagging. Using the data and methods described in this paper, we develop a theory of recovery, conceptually describing the recovery process as a sequence of five stages that can be used to monitor and track wood loads through time.Full Tex

Human impacts on suspended sediment and turbidity in the River Murray, South Eastern Australia: Multiple lines of evidence

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