An Improved Water Quality Model for the Onkaparinga Catchment
The Mt Lofty Ranges (MLR) watershed is comprised of a number of catchments consisting of the Torrens and Little Para catchments in the north and the Onkaparinga and Myponga catchments in the south. The focus of this study was the Onkaparinga catchment and contains a number of sub-catchment sites that can export very high nutrient loads during periods of intense runoff. The monitoring sites of interest in this report were chosen during a workshop with SA Water, SA EPA and SARDI for their importance in the catchment under study.
Flow data and water quality data is collected at gauges. Because the composite sampling network was established in 1996, historical data for flow date back further than for water quality. Furthermore, flow is measured at regular intervals (every 5 minutes) and is easily aggregated to obtain measurements of daily flow volumes. Composite water quality sampling results in flow-weighted samples of various constituents that are composited for collection every two to four weeks.
Developing the dSedNet plugin
The catchments of the MLR are a primary source of potable water to Adelaide and its environs. Because the catchments are mixed land uses (e.g. horticulture, grazing, hobby farms), the water exiting the catchments is not pristine and many studies have been undertaken to identify and quantify (through measurement and prediction) the sources of constituents, especially total suspended sediment (TSS), total phosphorus (TP) and total nitrogen (TN). Several models that describe the hydrology and related constituent generation and transport through these sub-catchments have been developed, the most recent using the eWater Source® platform. These studies, and models development, have highlighted constituent modelling as one of the issues requiring further work. It was considered that the Source model, as implemented at that time, predicted TN loads in the MLR quite well at longer timescales – however TP and TSS were generally underestimated in high rainfall years and overestimated in low rainfall years. The Event Mean Concentration (EMC) and Dry Weather Concentrations (DWC) approach used in Source was the major factor contributing to the poor representation of inter-annual variability of flow, due to the limited response to flow inherent in this model structure.
An Improved Water Quality Model for the Onkaparinga Catchment
This component of the research focused on three key pollutants, namely total suspended sediment (TSS), total nitrogen (TN) and total phosphorous (TP). Using statistical models, the processes that drive hydrology and water quality in the Onkaparinga catchment were studied and applied to land-use change scenario modelling. Specifically, this research focused on:
Statistical models employed to address the above points, consisted of generalised additive models (GAMs) and generalised additive mixed models (GAMMs) through the Loads Regression Estimator (LRE) package that was developed for the quantification of loads for the Great Barrier Reef catchments (by CSIRO). Site based models for the six sites studied in the Onkaparinga catchment used a variety of hydrological variables as covariates for understanding the variation in the data measured for each site. Specifically, these hydrological variables included flow, decomposed into baseflow and runoff as well as flow discounting terms that took into account past characteristics of the hydrograph. This could consist of a total accumulation of flow from the start of sampling to the short-term flow record prior to the current constituent sampled. Models were fit using the LRE package using the R statistical programming language.
Three scenarios that were explored as part of this research consisted of:
These scenarios were determined at meetings with SA Water and SA EPA and were structured around the statistical modelling approach used to evaluate each scenario. A Random Forests modelling approach was used to develop a spatio-temporal model for each constituent across the six sites of interest in the Onkaparinga catchment. The model is non-parametric and popular in the machine learning and is based on decision tree methodology. The approach can take a large number of potential covariates as predictors to develop an ensemble of decision trees on bootstrap samples of the data. Variable importance rankings can assist in identifying important variables.
Developing the dSedNet plugin
To improve the parameterisation and methods used in Source for modelling constituents, the SedNet dynamic modelling capability was extended. This was primarily a software development exercise, using the Source model already developed for the catchments of the MLR. Three key activities were undertaken:
The development of the dSedNet plugin and spatial parameterisation tool was the main activity within the project. Testing of the plugin was performed by running a series of small modelling trials. In addition to testing the robustness of the code, these trials provided an opportunity to consider the advantages of the dSedNet approach.
The dSedNet software is being developed as a Source plugin; the spatial parameterisation tool has been developed within the core of Source platform. Plugins are easily distributed between modellers and models, while changes to Source core are more difficult to manage as they require quality assurance by eWater and need to fit in with Source development and release cycles. This is advantageous from a maintenance perspective, but does reduce the control that the project team has over its release. At this point, a release date is unknown. The South Australian project team has a version of Source that contains the spatial parameterisation tool, and the dSedNet plugin, and a training workshop was held in May 2015.
The MLR Source model was modified to use the dSedNet plugin, with some parameters changed to match those used in other studies (such as that by Wilkinson et al. in 2014). This is referred as the baseline, noting that it has not been calibrated against observed water quality data.
Additionally, two calibrations (multi- and single-region) of the rainfall-runoff model (SIMHYD) were used, giving two sets of results. The multi-region simulation used separate SIMHYD parameters for each region, according to the calibration of its gauge. The single-region calibration treated all sub-catchments as a single region, i.e. they all had the same SIMHYD parameters. The need to do this emerged during the trials to more closely match the hydrology to other studies and demonstrates the importance of using good hydrological models for constituent modelling.
The Onkaparinga catchment was chosen for the trials, using observations at Houlgraves Weir. Three trials were conducted against previous studies: (i) SedNet sediment budgets Wilkinson et al. (2005), (ii) existing EMC/DWC Source model, and (iii) statistically derived loads.
Simulations were run for the two rainfall-runoff calibrations (multi- and single-region), resulting in predicted (potential) loads of 15.9 and 19.5 kTonnes/year. This compared well with the 15.0 kTonnes/year predicted using SedNet and as reported in the Wilkinson et al. (2005) study, noting that this model was also uncalibrated. The trial against the EMC/DWC approach looked at temporal patterns and the role of parameter settings and the fact that dSedNet has many adjustable parameters that are not available in an EMC/DWC model. The trial against the statistically derived loads highlighted the power of dSedNet being able to incorporate spatial and temporal variation into model inputs. Simulated dSedNet loads tended to follow observed loads.
As a spatially distributed model, dSedNet keeps track of spatial input data so that outputs can be traced back to their source. This is invaluable for targeting catchment remedial and intervention activities, and is not available in the current EMC/DWC model. Moving to a daily (dSedNet) from an annual (SedNet) time-step has been a significant scientific endeavour dSedNet that supports modelling of the temporal dynamics of constituents in a catchment. This supports the ability to anticipate specific events (e.g. impacts of a large flow) at different times within a year and investigate within-year variations.
Operationalising dSedNet within Australia’s national hydrological modelling platform (eWater’s Source) provides researchers, planners and catchment managers with an integrated tool to explore the impacts on the quality of receiving waters of catchment dynamics, such as gully and riparian management, urban and agricultural intensification, and environmental flows.
