Streamflow modelling (also called rainfall-runoff modelling) takes input from meteorological data (primarily rainfall and potential evaporation) and produces to the stream network. Almost all streamflow models contain parameters with unknown values, which must be estimated by calibration. This calibration is usually done by comparing predicted streamflows with those observed at streamflow gauging stations ().
The major challenge in streamflow modelling is to produce credible predictions of streamflow in ungauged parts of the modelling domain. This usually involves the estimation of appropriate model parameters to use in those parts of the landscape. This process of parameter estimation in the absence of direct calibration is called regionalisation ().
There are two broad categories of regionalisation. One involves using parameter values from a gauged catchment that is considered to share similar characteristics (climate, soils, vegetation, geomorphology) to the ungauged area. Often, it is found that the simple expedient of using parameters from the nearest gauged catchment is among the best regionalisation methods. Implicit in this nearest-neighbour approach is the assumption that catchments in proximity are likely to share similar physical and hydrological characteristics and that therefore, optimal models of each of them will also share similar parameter values. However, there is a significant degradation in model performance with this type of regionalisation as regionalisation distance increases ().
The second regionalisation approach involves simultaneous calibration of a model using observations from several nearby gauging stations. In this approach, the calibration procedure uses a single objective function that combines the prediction responses in all gauged catchments and results in a single set of model parameter values that provide best fit to the streamflow observations from all gauges. The key assumption here is that if a single set of parameter values provides good predictions in the gauged catchments it might also be expected to provide good predictions in adjacent ungauged areas. This regionalisation approach is called regional calibration (). Unlike nearest-neighbour calibration, the performance of regionally calibrated models does not degrade with distance from the calibration catchments and is likely to lead to more stable predictions in ungauged parts of the modelling domain.
The temporal and spatial scales of streamflow modelling are dictated largely by the temporal and spatial scales of the available meteorological input data. The (BAs) use meteorological data from the of the Bureau of Meteorology’s Australian Water Availability Project (). These data use a daily time step and are presented on a grid with spacing of 0.05 degrees of latitude and longitude (approximately 5 km). Thus, it follows that the smallest temporal element in the raw streamflow modelling is one day and the smallest spatial element is 0.05 degrees. Note that this raw spatial scale does not preclude modelling at finer spatial scales through interpolation, and nor does it preclude the assessment of the of coal resource developments with sub-pixel extents.
A large number of streamflow models exist in the literature and many of them have been applied widely in Australia. These include:
The first six of these models are typical rainfall-runoff models. Most are relatively parsimonious in their parameterisation. They are amenable to both nearest-neighbour regionalisation and regional calibration. Studies comparing their prediction quality in Australia (e.g. Viney et al., 2014) generally indicate that these models have relatively similar prediction performances. Sacramento is the model usually adopted to provide tributary in state agency river system models (e.g. IQQM and Source Rivers; see Chapter 4).
AWRA-L () is designed for use in a regional calibration setting using gridded input. This regional calibration approach is assisted by the model’s explicit inclusion of vegetation density as a factor controlling streamflow generation. AWRA-L is typically calibrated Australia-wide to yield a single continental parameter set. A recent comparison study by shows that AWRA-L provides streamflow predictions with an improved fit to observations relative to the Sacramento and GR4J models whether the latter are implemented using either nearest-neighbour regionalisation or regional calibration. AWRA-L is part of a suite of models in the AWRA (Australian Water Resources Assessment) system () which also includes a river routing module (AWRA-R). At present, these two components operate together in an uncoupled fashion, but work is underway in CSIRO to develop a fully coupled AWRA model. This fully coupled model is not available for use in the current round of , but may be available for future BAs.
LASCAM has been designed for use at the large catchment scale. Like AWRA-L, LASCAM explicitly includes the effects of vegetation density and has been designed for use in a regionally-calibrated context. LASCAM also includes an embedded routing scheme, thus meaning that it can also replicate many of the functions of a river system model. LASCAM has recently been used in a study in the by but has not been applied in any of the other . This application in the Namoi subregion appears to have been done with limited calibration. Unlike the other candidate models, LASCAM has not yet been implemented using gridded input, although this could be readily done. It also requires more input data and can be difficult and time-consuming to calibrate properly.
It is desirable – although by no means requisite – that a consistent modelling approach be adopted across all the in the Bioregional Assessment Programme. Since the adopted model will be used for both futures ( and (CRDP)), it is also desirable that a common set of model parameters be used in each or at least in each major river basin in a subregion. This is not just for practical reasons, but also to ensure that the true spatial heterogeneity of generation is represented across the modelling domain, with no significant spatial discontinuities that might arise as artefacts of regionalisation. This rules out the use of nearest-neighbour regionalisation, although all candidate models are capable of being deployed in a regional calibration mode.
Given its adoption for the Bureau of Meteorology’s water accounts and assessments (), its prediction performance relative to other rainfall-runoff models, its ready availability to the modelling team, and the ability to make the code and executables publicly available, it is recommended that AWRA‑L be the streamflow model adopted for BAs. Furthermore, it is recommended that AWRA-L be implemented using regional calibration.
In the main – and modelling of of coal resource development notwithstanding – this approach falls somewhere between the adopt and adapt strategies canvassed in Section 2.2.
There is a requirement that the models used in the BAs, including their code, executables, data and parameters, be made publicly available. All open access data used in the AWRA-L model will be made available through data.gov.au as well as all output data from the model. The metadata for the model will direct users to where the model can be downloaded.
AWRA-L operates on a daily time step using gridded input. It is applied in a modelling domain that includes not just the itself, but also extends upstream of the subregion boundaries to include all upstream tributaries, and downstream of the subregion boundaries to include all of the and all of its tributaries. Raw output is gridded at the same spatial scale as the input data.
Each spatial unit (grid cell) in AWRA-L is divided into a number of hydrological response units (HRUs) representing different landscape components. Hydrological processes are modelled separately for each HRU before the resulting fluxes are combined to give cell outputs. The current version of AWRA-L includes two HRUs which notionally represent (i) tall, deep-rooted vegetation (i.e. forest), and (ii) short, shallow-rooted vegetation (i.e. non-forest). Hydrologically, these two HRUs differ in their aerodynamic control of evaporation, in their interception capacities and in their degree of access to different soil layers.
AWRA‑L requires the following data:
- gridded daily rainfall
- gridded daily potential evaporation (or the raw data from which to estimate it – e.g. gridded daily maximum and minimum temperature, vapour pressure, wind speed, etc.)
- proportion of deep-rooted vegetation in each grid cell
- time series of remotely sensed leaf area index for each grid cell
- daily streamflow at multiple sites
- catchment boundaries for each streamflow measurement site.
The meteorological and vegetation are readily available to modellers in the Bioregional Assessment Programme and are ready to use immediately. Streamflow records are available from the Bureau of Meteorology. However, there are substantial variations in the quality of observed streamflow records (), so there is likely to be a role for programme staff to vet data from individual streamflow gauges before it can be used in model calibration. Catchment boundaries can be extracted from the Australian Hydrological Geospatial Fabric () dataset () using the best available digital elevation and gauge location information.
Because of the nature of the application – in particular that it is mostly focused on the differences between model runs, rather than on absolute predictions, and that its results are presented in an framework – the importance of model calibration is less than it is in most other modelling applications. Nonetheless, model calibration still forms part of the methodology.
The streamflow model is calibrated separately in each using streamflow observations from gauging sites in and near the subregion. Selection criteria for calibration gauges include that the gauges, where possible, should:
- have catchment areas greater than 50 km2
- have at least ten years of observed streamflow data since 1983
- have no significant flow regulation (e.g. upstream reservoirs, irrigation withdrawals, mining)
- be non-nested (i.e. not directly upstream or downstream of another selected gauge).
Since the objective of this calibration is to obtain a single set of model parameters, there should be no impediment to using nearby observations even if they are from catchments outside the modelling domain. Indeed, some subregions contain few, if any, streamflow gauges, so it is necessary to use data from further afield or to relax one or more of the selection criteria. Observations from at least two gauges, and preferably more, should be used in the calibration process. The prediction performance in these calibration catchments should be summarised statistically and combined into a single objective function for optimisation.
Two calibration runs are performed, one with an objective function biased towards high flows and one with an objective function biased towards low flows. This is because streamflow at both ends of the hydrograph spectrum are likely to be important for receptor impact modelling and for water balance estimation.
The objective functions used in calibration should seek to optimise the joint prediction of temporal variability in the streamflow hydrographs and the overall bias in model prediction. This can be achieved by basing calibration on the methodology of . In the case of the high flow calibration, a function F, which characterises prediction quality, is evaluated for each catchment. This function is given by:
where Ed(1.0) is the daily Nash-Sutcliffe efficiency with a Box-Cox lambda value of 1.0, Em is the monthly Nash-Sutcliffe efficiency and B is the bias (prediction error divided by sum of observations). The optimiser then maximises an objective function that is given by:
In the case of the low flow calibration, F is given by:
where Ed(0.1) is the Nash-Sutcliffe efficiency with a Box-Cox lambda value of 0.1. These F values are used along with the same functional form for the objective function as for the high flow calibration.
Although the two resulting deterministic model predictions are not used directly in reporting BA outcomes, they are used in BAs to:
The calibration period used will depend in part on the temporal coverage of the available streamflow observations. Ideally, the calibration period should cover at least 20 years – preferably in recent decades – and should be preceded by at least 10 years of spin-up to allow water stores to equilibrate. In subregions or river basins where 20 years of observational data are not available, consideration should be given to including streamflow observations from farther afield into the response .
Previous applications and assessments of AWRA‑L () have indicated that there is little difference in model performance between the catchments used in calibration and those used in independent validation. For this reason, it is recommended that no independent validation be done on AWRA‑L modelling in the Bioregional Assessment Programme. This frees up all the available streamflow data to be used in calibration to better constrain model parameters. It also means that the quality of the model’s performance in the calibration catchments will provide a strong indication of its performance in other parts of the modelling domain. There will, however, be validation of model performance during the uncertainty analysis against observations of several metrics of streamflow.
Coal resource development is defined with two potential futures:
- baseline coal resource development (), a future that includes all coal mines and coal seam gas (CSG) fields that are commercially producing as of December 2012
- coal resource development pathway (), a future that includes all coal mines and coal seam gas (CSG) fields that are in the baseline as well as those that are expected to begin commercial production after December 2012.
The difference in development between CRDP and baseline is defined as the additional coal resource development, all coal mines and coal seam gas (CSG) fields, including expansions of baseline operations, that are expected to begin commercial production after December 2012.
In order to assess the impacts due to the additional coal resource development, the modelling undertaken in the BAs must produce and compare outputs from two simulations: a baseline simulation without the additional coal resource development and a CRDP simulation with the additional coal resource development.
The starting date for the two simulations is January 2013.
Any pre-existing coal resource developments (i.e. those that were commercially producing before 2013) are included in both simulations. The modelling outputs report the changes in surface water availability between the baseline and CRDP.
Some proposals for coal resource developments contain insufficient information to allow meaningful modelling. For example, they may be lacking in groundwater pumping rate information or detailed development footprint information. Such proposals will be dealt with through commentary only. Only those proposals that do have sufficient information will be modelled and it is those developments that are considered here and are of relevance to the modelling outcomes in product 2.5 (water balance assessment) and product 2.6.1 (surface water numerical modelling).
The key outcome of the is in determining how the leads to changes in flow regime and to . In reality, this can be achieved using any (consistent) climate input signal for the two simulation runs. Nonetheless, it is possible that the magnitudes of these changes could be different when the coal resource developments are superimposed over different climates. It is therefore ideal – though not crucial – that the projections into the future for both simulations use climate input that reflects likely climate change trends. To be consistent with the philosophy of the , a single ‘mid-range’ future climate time series will be constructed.
It is important to recognise that the BA is not a climate change study. The main focus is on the of coal resource development activities on water resources and water-dependent assets. Both the baseline and CRDP simulations will use the same climate input; BA is interested in the differences between the two simulations caused by the coal resource development, not the impact of climate change on streamflow characteristics.
22.214.171.124 Construction of future climate input
As the future climate is unlikely to be stationary, it is desirable to incorporate a trajectory of likely climate change in the future climate time series from January 2013 to December 2102. To avoid a climate change signal being present in the donor time series used for creating a future climate time series, and to ensure the donor series has the highest notional data quality, a shorter 30-year period will be used as the basis for generating the future climate time series. A recent 30-year period (January 1983 to December 2012) will be assumed short enough that a changing climate trend is not significant and assumed to be long enough to be representative of the climate variability (i.e. contains the millennium drought in southern Australia and the floods of 2011 in some ). The 30-year historical climate time series will be repeated three times to create a 90-year time series.
Global climate model (GCM) outputs will be downscaled separately for each 30-year period using the ‘seasonal scaling’ approach described by . This is neither the most sophisticated nor the simplest method of downscaling, but has been proven to be effective and the method will require little development to be adapted for use in . The three 30-year periods will be modelled as step changes in climate, nominally representing 2030, 2060 and 2090. The seasonal scaling method modifies the historical time series using seasonal scaling factors and then modifies the daily rainfall according to the projected change in temperature and the GCM-predicted change in rainfall per degree of climate change.
The very simplistic representation of the future climate time series and ignoring of the in the future climate are justified as the BA projects are not investigating the impact of climate change upon the and . The only two forward modelling runs that will be conducted are the and the . The future climate time series will be used for both runs and so it will not be possible to disentangle the impact of the future climate from the impact of the future coal resource development upon the assets and receptors.
The landscape modelling will be conducted using the 90-year future climate time series on a daily basis to create the input time series for the river and modelling ( and , respectively). The river modelling will be conducted using the entire 90-year future climate time series. The groundwater modelling will be conducted on a monthly time step for the 90-year period until 2102 to enable the – groundwater interactions to be accounted for in the river modelling.
126.96.36.199 Choice of climate change signal
In each , the future climate series will be based on the projections of a single global climate model (GCM) and a single emissions scenario. This choice of GCM and emissions scenario must be transparent and defensible. There is considerable in future climate projections so a desktop study will be conducted of previous comparison studies to determine an appropriate GCM for each subregion.
In all , climate projections from 15 GCMs are available. Associated with each GCM are local scaling factors which give the change in rainfall expected per degree of global warming. We will use scaling factors for the AR4 emissions scenario A1B (). Depending on GCM, the scaling factors may be seasonal or monthly. Together with seasonal or monthly trends in historical rainfall, it is possible to use these scaling factors to assess the change in mean annual rainfall associated with each GCM. In each subregion, we will choose the scaling factors from the GCM that produces the median change in mean annual rainfall.
It is expected that in all s where new modelling is being undertaken, the application of the landscape model will closely follow the methodology outlined in Section 3.4.
The most likely scope for variation is in the selection of a suitable objective function for calibration of the low flow parameter set. This choice might be dictated at the local level by two factors: the nature of the flow characteristics and the required . An objective function for a low flow calibration, for example, might include a metric describing the degree of intermittency in streamflows. Such a metric, however, might be redundant in a subregion where streams are typically permanent.
In subregions where analysis will be based on existing model results – which are likely to come from models other than AWRA-L – it is likely that these results will have been generated from a single set of model parameters, most likely one that is predicated largely on high flows. This means that projected impacts on low flow characteristics may be more uncertain in these subregions.
METHODOLOGY FINALISATION DATE
- 1 Background and context
- 2 Components of surface water modelling
- 3 Streamflow modelling
- 4 River system modelling
- 5 Constituent modelling
- 6 Modelling the impacts of coal resource development
- 7 Linkages with other modelling components
- 8 Outputs from surface water modelling
- Appendix A Modifications to AWRA-R
- Appendix B Proposed structure of product 2.6.1 (surface water numerical modelling) and product 2.5 (water balance assessment)
- Contributors to the Technical Programme
- About this submethodology