Infrastructure Engineering - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 2 of 2
  • Item
    No Preview Available
    Testing uncertainty in a model of stream bank erosion
    Jha, S ; Western, AW ; Rutherfurd, ID ; Grayson, RB ( 2020-01-01)
    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.
  • Item
    Thumbnail Image
    A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments
    Guo, D ; Lintern, A ; Webb, A ; Ryu, D ; Bende-Michl, U ; Liu, S ; Western, A (Copernicus GmbH, 2020)
    Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years, across 102 catchments, which span over 130,000 km2. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NOx), and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which had been identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explainable (19.9%), the model explains 38.2% (NOx) to 88.6% (EC) of total spatio-temporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; temporal variability remains largely unexplained across all catchments, while long-term trends are well captured. The model is best used to predict proportional changes in water quality in a Box-Cox transformed scale, but can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot-spots and hot moments for waterway pollution; (2) predicting effects of catchment changes on water quality e.g. urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on: (1) alternative statistical model structures to improve fitting for truncated data, for constituents where a large amount of data below the detection-limit; and (2) better representation of non-conservative constituents (e.g. FRP) by accounting for important biogeochemical processes.