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Coal and coal seam gas (CSG) development can potentially affect water-dependent assets (either negatively or positively) through a direct impact on surface water hydrology. This product provides modelled estimates of potential surface water changes due to likely coal resource development in the Galilee subregion. First, the methods are summarised, followed by details regarding the development of the model. The product concludes with predictions of the hydrological response variables, the hydrological characteristics of the system that may change due to coal resource development. The uncertainty and limitations of the models are also reported. The difference in results between baseline and the CRDP is the change that is primarily reported in a BA. This change is due to the additional coal resource development (ACRD) – all coal mines and CSG fields, including expansions of baseline operations that are expected to begin commercial production after December 2012. There are no coal or CSG developments in operation as of the last quarter of 2012 for the Galilee subregion for the baseline coal resource development. There are 17 proposed new developments in the Galilee subregion for ACRD. There is enough available information to include seven of these developments in the numerical modelling for the Galilee subregion.The seven development projects being modelled are the open-cut coal mines Alpha Coal Project and Hyde Park Coal Project, and the combined open-cut and underground coal mines Carmichael Coal Mine, China First Coal Project, China Stone Coal Project, Kevin’s Corner Mine and South Galilee Coal Project. A generic methodology for surface water numerical modelling in the Bioregional Assessment Programme is presented in companion submethodology M06 (as listed in Table 1). This product describes how the methodology has been applied in the Galilee subregion. Numerical simulation of the likely changes in surface water due to coal resource development requires a model or mo

Coal and coal seam gas (CSG) development can potentially affect water-dependent assets (either negatively or positively) through a direct impact on surface water hydrology. This product provides modelled estimates of the potential surface water impacts of likely coal resource developments in the Gloucester subregion. First, the methods are summarised and existing models are reviewed, followed by details regarding the development of the model. The product concludes with predictions of the hydrological characteristics of the system that may change due to coal resource development (referred to as hydrological response variables) also taking into account uncertainty. Surface water modelling in the Gloucester subregion follows the companion submethodology M06 (as listed in Table 1) (Viney, 2016). No river modelling was carried out because the rivers in the subregion are unregulated and their catchments are relatively small. Instead, predicted streamflow is obtained by accumulating output from a spatially-explicit streamflow model (the Australia Water Resource Assessment Landscape model, AWRA-L). The modelling domain comprises the Gloucester and Karuah river basins and includes 34 modelling nodes at which daily streamflow predictions are produced. The model simulation period is from 2013 to 2102. The conceptual model for the Gloucester subregion (in companion product 2.3 for the Gloucester subregion), indicates that CSG and large coal mining development have the potential to directly affect the regional groundwater system and that this direct effect can propagate through to the alluvia of the Gloucester and Karuah river systems. Any impact on the groundwater in the alluvium of those rivers in turn has the potential to affect streamflow and therefore surface water resources in the stream networks of the Gloucester and Karuah rivers. CSG development may impact streamflow if aquifer depressurisation reduces baseflow, while open-cut coal mines, in addition to reducing baseflo

Common streamflow gauging procedures require assumptions about the stage-discharge relationship (the 'rating curve') that can introduce considerable uncertainties in streamflow records. These rating uncertainties are not usually considered fully in hydrological model calibration and evaluation yet can have potentially important impacts. We analysed streamflow gauge data and conducted two modelling experiments to assess rating uncertainty in operational rating curves, its impacts on modelling and possible ways to reduce those impacts. We found clear evidence of variance heterogeneity (heteroscedasticity) in streamflow estimates, with higher residual values at higher stage values. In addition, we confirmed the occurrence of streamflow extrapolation beyond the highest or lowest stage measurement in many operational rating curves, even when these were previously flagged as not extrapolated. The first experiment investigated the impact on regional calibration/evaluation of: (i) using two streamflow data transformations (logarithmic and square-root), compared to using non-transformed streamflow data, in an attempt to reduce heteroscedasticity and; (ii) censoring the extrapolated flows, compared to no censoring. Results of calibration/evaluation showed that using a square-root transformed streamflow (thus, compromising weight on high and low streamflow) performed better than using non-transformed and log-transformed streamflow. Also, surprisingly, censoring extrapolated streamflow reduced rather than improved model performance. The second experiment investigated the impact of rating curve uncertainty on catchment calibration/evaluation and parameter estimation. A Monte-Carlo approach and the nonparametric Weighted Nadaraya-Watson (WNW) estimator were used to derive streamflow uncertainty bounds. These were later used in calibration/evaluation using a standard Nash-Sutcliffe Efficiency (NSE) objective function (OBJ) and a modified NSE OBJ that penalised uncertain flows. Usi

The Australian Water Resource Assessment (AWRA) modelling system is developed to enable the Australian Bureau of Meteorology to meet its legislated role in providing an annual National Water Account (NWA) and regular Australian Water Resource Assessment Reports. The system uses available observations and an integrated landscape – river water balance model to estimate the stores and fluxes of the water balance required for reporting purposes (Figure 1). The National Landscape model (AWRA-L) provides gridded estimates of landscape runoff, evapotranspiration, soil moisture, and groundwater recharge/storage/lateral flow, and has been calibrated towards reproduction of a nationwide streamflow dataset. The gridded model structure provides an option of incorporating spatial variability of climate, land cover and soil properties. The model can be constrained only against observed streamflow or a combination of streamflow and remotely sensed soil moisture and evapotranspiration. The water balance fluxes from AWRA-L are used as inputs to the regulated river system model (AWRA-R) to undertake basin scale water balance modelling. The AWRA-R model includes river routing, irrigation diversions, reservoir storage, floodplain inundation and river to groundwater interaction components. All the AWRA modelling components are built within a software architecture which allows seamless interactions between the components at the appropriate spatial and temporal scales. The AWRA modelling system has been implemented across Australia and it provides estimates of water balance fluxes and stores which are substantially better than those from continental scale land surface models and similar to or better than those from widely used conceptual rainfall-runoff models. The system is currently being used for hydrological modelling in a number of large scale projects including the Bioregional Assessments and the Northern Australian Central northern rivers and dams. The AWRA modelling system provide

The Australian government is undertaking a program of bioregional assessments (BAs) in order to better understand the potential impacts of coal seam gas (CSG) and large coal mining developments on water resources and water-related assets. The aim of the program is to strengthen the science underpinning decision making on CSG and large coal mining developments. A key component of this work is in providing credible, consistent estimates of the impact of CSG and coal mining developments on river flows. The aim of surface water modelling in bioregional assessments is to provide information on flow characteristics at locations in the stream networks that are relevant for key assets and receptors. In particular, the modelling needs to account for changes in flow regime that relate directly to the impacts of future coal mining and coal seam gas extraction. This paper reviews some of the candidate models for achieving these outcomes and outlines some of the practical considerations behind the resulting choice of modelling tools. AWRA-L and AWRA-R have been chosen as the modelling tools to generate these river flow responses. Fluxes from the landscape (predominantly surface runoff, interflow and baseflow) are modelled using AWRA-L. These fluxes are then accumulated and routed through the river network using AWRA-R.

The Australian Water Resource Assessment (AWRA) modelling system is developed to enable the Australian Bureau of Meteorology to meet its legislated role in providing an annual National Water Account (NWA) and regular Australian Water Resource Assessment Reports. The system uses available observations and an integrated landscape - river water balance model to estimate the stores and fluxes of the water balance required for reporting purposes. The National Landscape model (AWRA-L) provides gridded estimates of landscape runoff, evapotranspiration, soil moisture, and groundwater recharge/storage/lateral flow, and has been calibrated towards reproduction of a nationwide streamflow dataset. The gridded model structure provides an option of incorporating spatial variability of climate, land cover and soil properties. The water balance fluxes from AWRA-L are used as inputs to the regulated river system model (AWRA-R) to undertake basin scale water balance modelling. The AWRA-R model includes river routing, irrigation diversions, reservoir storage, floodplain inundation and river to groundwater interaction components. All the AWRA modelling components are built within a software architecture which allows seamless interactions between the components at the appropriate spatial and temporal scales. The AWRA modelling system has been implemented across Australia and it provides estimates of water balance fluxes and stores which are substantially better than those from continental scale land surface models and similar to or better than those from widely used conceptual rainfall-runoff models. The system is currently being used for hydrological modelling in a number of large scale projects. The AWRA modelling system provides consistent, robust and repeatable water assessments at catchment, regional and continental scale which can be used to guide future water planning and policy development at multiple scales across Australia.

The Bureau of Meteorology requires a water balance modelling system developed using state-of-theart hydrological science and computing technology that quantifies retrospective water flux and storage terms. The Australian Water Resources Assessment (AWRA)

The "Millennium Drought" (2001-2009) can be described as the worst drought on record for southeast Australia. Adaptation to future severe droughts requires insight into the drivers of the drought and its impacts. These were analyzed using climate, water

The Australian Water Resources Assessment system Landscape model (AWRA-L) aims to produce interpretable water balance component estimates covering all of Australia, and as much as possible agree with water balance observations, including point gauging data and satellite observations. The opportunities to evaluate AWRA-L water balance predictions in Australia are severely limited by the limited amount of field data (e.g. flux tower observations, soil moisture measurements) and the limited range of environments and conditions for which observations are available. Opportunities exist to further evaluate and improve AWRA-L model predictions by using global collations of in situ soil moisture, flux tower, and streamflow data available from the broader scientific community. To evaluate AWRA-L against these observations, global input data are required. We reviewed and compared results of published studies about meteorological data that could be used to parameterise AWRA-L globally. Review findings include: • Satellite-based rainfall performs better during warm seasons and in the tropics, although overestimating total rainfall. Reanalysis data outperforms satellite-based rainfall during winter and in higher latitudes. Gauge bias-corrected TRMM 3B42V6 reduces observed bias in many areas globally. A blending approach may enhance rainfall quality estimates on a global scale, using rainfall from reanalysis in higher latitudes and satellite estimates such as TRMM 3B42V6 in mid-latitudes. •Global monthly, annual and climatological surface temperature anomalies from reanalysis had very similar values. At the daily scale, compared daily maximum and minimum temperature probability density functions from ERA-40, JRA-25 and NCEP-DOE were dissimilar with large regional differences, but overall no reanalysis showed more skill than the other two when compared against regional observational temperature data. •Surface shortwave radiation derived from satellite data generally has smaller bi

Remotely sensed data are widely used for estimating hydrological variables, such as land surface soil moisture, land surface evapotranspiration and catchment runoff because they provide temporally dynamic and spatially explicit information on land surface characteristics. Passive microwave observations have been used to infer surface soil moisture information because they are not affected by cloud cover and there is a physical relationship relating emissions to soil water. Remote sensing vegetation cover types and leaf area index time series data have been used as inputs into distributed, semi-distributed and lumped hydrological models (Liu et al., 2007). This paper investigates the potential to improve runoff, soil moisture and vegetation dynamics predictions in ungauged catchments using a land surface hydrological model, AWRA-L, together with remotely sensed leaf area index measurements from NOAA-AVHRR and surface soil moisture measurements from TRMM-TMI. The study is conducted in 579 unregulated catchments across Australia. The AWRA-L model was regionally calibrated (i.e. a single set of parameters optimised) for half the catchments in four experiments: (1) against daily recorded streamflow data (Exp1); (2) against daily recorded streamflow together with monthly NOAAAVHRR leaf area index data (Exp2); (3) against daily recorded streamflow together with daily TRMM-TMI soil moisture data (Exp3); and (4) against all three data sets (Exp4). Next, the four optimised parameter sets obtained from the four regional calibration schemes were applied to the remaining half of the catchments for validation to evaluate the modelling skills for daily runoff and soil moisture predictions in independent catchments. This validation gives an indication of the abilities of the different calibration schemes to provide predictions in ungauged or poorly gauged catchments. The results here show that (1) it is technically feasible (i.e. use of advanced scientific computing, such as CSIRO

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in mult

An ensemble of 10 hydrological models was applied to the same set of land use change scenarios. There was general agreement about the direction of changes in the mean annual discharge and 90% discharge percentile predicted by the ensemble members, although a considerable range in the magnitude of predictions for the scenarios and catchments under consideration was obvious. Differences in the magnitude of the increase were attributed to the different mean annual actual evapotranspiration rates for each land use type. The ensemble of model runs was further analyzed with deterministic and probabilistic ensemble methods. The deterministic ensemble method based on a trimmed mean resulted in a single somewhat more reliable scenario prediction. The probabilistic reliability ensemble averaging (REA) method allowed a quantification of the model structure uncertainty in the scenario predictions. It was concluded that the use of a model ensemble has greatly increased our confidence in the reliability of the model predictions.