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• A novel approach for modelling subsidence induced by fluid extraction from coal seams. • Transient groundwater and subsidence models are linked. • Coal shrinkage induced by gas desorption can significantly impact subsidence. Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management. [ABSTRACT FROM AUTHOR]

• Nationwide streamflow projections for Australia informed by CMIP5 and CMIP6. • Vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. • Lower winter rainfall translates to a significant reduction in annual runoff. • Modelled median projection of runoff shows a 50% reduction in far southwest Australia. • Modelled median projection of runoff shows a 20% reduction in far southeast Australia. Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts. [ABSTRACT FROM AUTHOR]

• A framework to evaluate stochastic rainfall models with a quantitative scale. • 'Good' modelled rainfall does not guarantee 'good' modelled streamflow. • Streamflow-based evaluation is more holistic than rainfall-based evaluation. • Component-wise evaluation can misrepresent ultimate model performance. Stochastic rainfall models are important tools for evaluating hydrological risks such as flooding and drought because of their ability to randomly generate alternative plausible climatic timeseries. The stochastic generation of climatic timeseries is not an end in itself, since they are typically applied to a catchment to determine the performance of water-related infrastructure systems, such as reservoirs or flood-control measures. This methodology typically involves a train of models to determine the end-of-system impact, yet the evaluation of stochastically generated rainfall timeseries is usually a stand-alone procedure focused on metrics directly related to the stochastic generator. This paper demonstrates discrepancies in this approach by evaluating two, daily-timestep, stochastic rainfall models in terms of rainfall metrics and their subsequently generated flow metrics after rainfall-runoff transformation. The two models are a Markov-based model and a latent-variable model, where each model is calibrated and evaluated showing 'overall good' performance. Stochastically generated timeseries, alongside observed rainfall timeseries are inputted to a calibrated catchment model (GR4J) to derive daily flow timeseries. Whereas the rainfall metrics typically showed 'good' performance, streamflow-based metrics are not necessarily 'good'. The procedure is repeated for 277 stations from Australia and 106 stations from the United States of America. Depending on the strictness of the flow-based comparison and region analysed, using the Markov-based model 12–26% of sites were classified as 'poor' performing, and 1%-9% of sites were classified as 'poor' using the latent-variable model. The results demonstrate that catchment-based performance of flow metrics is more holistic since it magnifies features of the rainfall not otherwise visible to rainfall-based evaluation. [ABSTRACT FROM AUTHOR]

Coastal aquifers provide a source of water for more than one billion people, with island freshwater lenses being some of the most vulnerable coastal groundwater systems due to their susceptibility to saltwater intrusion. Basic hydrogeological and hydrochemical knowledge regarding the recharge and salinisation processes of freshwater lenses is important to ensure sustainable utilisation, especially considering possible climate change effects. This paper makes an assessment of the fate of a freshwater lens in a drying climate through a comparison of current and historic hydrochemical data, which to the author’s knowledge is unique to this study. Fresh groundwater stable isotope signatures (δ 18 O, δ 2 H) reflect local amount weighted rainfall signatures (δ 18 O: −3.8‰; δ 2 H: −15.1‰), and confirm rainfall as the origin of fresh groundwater (δ 18 O: −4.47 to −3.82‰; δ 2 H: −20.0 to −16.6‰). Mixing with seawater was identified through enriched groundwater δ 18 O and δ 2 H signatures (maximum values of −0.36‰ and −1.4‰ respectively) compared to local rainfall and higher salinity (maximum 29,267 mg/L Total Dissolved Solids (TDS)) in a number of monitoring wells around the freshwater lens. Enhanced seawater intrusion detected in the northern section of the lens area was identified through significantly increased TDS values over the last 20–40 years, with increases of up to 3000% observed between 1990 and 2014. A reduction in the extent of freshwater by approximately 1 km 2 was identified since 1977, which was found to be primarily caused by a reduction in recharge to the freshwater lens due to a ∼20% decline in winter rainfall in the south-west Western Australian region since the mid 1960s. Groundwater abstraction was found to equate to between 5% and 9% of the estimated recharge for the island, and is not a significant factor in the reduction of the lens extent compared to the observed decline in rainfall recharge. Interestingly, seawater intrusion into the fresh water lens was found to occur by older seawater (0.03–0.09 TU) in regions of the lens that were previously fresh or slightly brackish, while one sample (0.67 TU) suggests either modern seawater intrusion or mixing of older saline groundwaters (>60 years) with rainfall recharge. The use of tritium dating in this island aquifer was essential in identifying ‘older’ seawater that was previously unidentified until now. The isotopic and hydrochemical tools used in this paper quantify the effects of groundwater abstraction and climate variability on the freshwater lens and have implications for the sustainable management of the groundwater resource on Rottnest Island, and elsewhere. [ABSTRACT FROM AUTHOR]

Highlights • Simple lumped surrogate models can emulate the behaviour of high-fidelity models. • States and simulation time is reduced by 3 and 6 orders of magnitude, respectively. • The surrogate models are robust to type of training data and sensitive to model scale. • The surrogate models can be fitted to both warning, RTC, and planning applications. • Uncertainty of surrogate models are within acceptable ranges. Abstract Distributed physically based models (DPMs) have become the standard tool for urban drainage modelling. However, high computational demands limit these in applications where fast or multiple simulations are needed. This paper presents simple fit-for-purpose cheaper-to-run surrogate models (SMs) for pipe network simulations which are validated against a DPM. The SMs are set up by lumping the DPM network into compartments in which the volume of water is governed by mass balances. Outgoing discharges to downstream compartment(s) and surcharging are computed from unambiguous volume-discharge curves. The SMs are applied on a 45 km2 catchment, Elster Creek in Melbourne, Australia. The number of simulated states and simulation times are reduced by approximately 3 and 6 orders of magnitude, respectively. Different SM complexities are examined. The simplest SM using steady state training data performed well with NSE of 0.98 for volume in the most upstream compartment. When emulating the aggregated surcharge from that compartment, the SM captured all surcharge events correctly. NSE improved from 0.35 to 0.84 when subdividing the compartment into 17 subcompartments. Uncertainty of SM parameters was examined using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. Two different sampling methods were applied. Limits of acceptability for real-time control, warning and planning, resulted in many accepted models upstream and few to none in downstream backwater-prone areas. All applications showed SM uncertainty bands within expected uncertainty bands for DPM, supporting the use of a simpler conceptual model in fit-for-purpose modelling in urban water systems when computational demands of DPMs are prohibitive. [ABSTRACT FROM AUTHOR]

• Quantile-quantile rainfall mapping methods are extended to improve streamflow simulations. • Extensions correct rainfall bias at multiple time scales and model rainfall autocorrelation. • Methods reduce bias in rainfall sequencing metrics for downscaled GCM simulations. • Methods reduce bias in streamflow simulations forced by downscaled GCM simulations. Global circulation models (GCMs) provide important insights into future climate change. Bias-correction of downscaled GCM output is integral to any hydrological investigations of climate change due to discrepancies between the statistics of downscaled GCM simulations and observations. Many bias-correction techniques have been developed to support hydrological applications. However, there have been few comparisons of the sensitivity of hydrological simulations to different bias-correction assumptions. This paper investigates the importance of two common assumptions: (i) simultaneously correcting bias at multiple time scales, and (ii) explicitly handling rainfall autocorrelation; in quantile mapping of downscaled GCM rainfall data for hydrological simulation. Four quantile mapping methods are applied to correct bias in dynamically downscaled reanalysis and historical GCM simulations of rainfall for 11 catchments in south-eastern Australia and the performance of bias-corrected rainfall and streamflow simulations is evaluated. All quantile mapping methods investigated can effectively eliminate bias in monthly and annual rainfall totals. Quantile mapping methods that consider differences in the temporal dependence (autocorrelation) structure of downscaled GCM and observed rainfall are most effective in reducing bias in rainfall sequencing statistics, such as probability of consecutive wet or dry days. Streamflow simulations of mean and high streamflow percentiles are underestimated when generated using rainfall that is corrected using quantile mapping methods that do not consider differences in the temporal dependence structure of downscaled GCM and observed rainfall. Future hydrological investigations of climate change should therefore adopt methods that explicitly consider the temporal dependence structures of downscaled GCM and observed rainfall. [ABSTRACT FROM AUTHOR]

• Grassroot collective action can lead to successful groundwater co-management. • The interaction between impacting factors and collective action is dynamic. • Human and social capitals are key to successful collective action. • Collective action needs a 2-way interaction between formal and informal institutions. Resource-user participation in decision-making, through local collective action, likely contributes to effective groundwater management and protection. Despite efforts to construct generalized theoretical frameworks explaining factors influencing successful collective action in groundwater management, this is a highly complex process that remains challenging. This paper analyzes factors facilitating and impeding collective action in groundwater management, using the rare and successful collective-action example of the Angas Bremer irrigation district (South Australia). Drawing on data gathered through semi-structured interviews this paper further explores the context under which collective action arose and changed over time. The findings show a dynamic transient interaction between the following factors responsible for the rise and fall of collective action: (i) perceived crisis, (ii) small, homogenous community (iii) strong leadership from respected community members, (iv) shared norms and values, (v) shared goals, and (vi) trustworthy two-way relationship between resource users and formal institution. Although it is difficult to generalize from one case study, collective action and groundwater management are highly contextual. These findings are useful (i) for other communities with similar characteristics, (ii) to explore conditions for achieving successful groundwater governance including efficient and effective reforms, (iii) to highlight the importance of participatory management processes, (iv) to help government agencies understand what motivates communities to engage in collective action and how that leads to successful groundwater co-management, and (v) to expand on a very small body of literature documenting stakeholders narratives on collective action and groundwater management. [ABSTRACT FROM AUTHOR]

• Daily flow statistics (indicators) are key to understanding flow-ecology interaction. • This study examined whether monthly data can be used to describe daily flow regimes. • A suite of 173 flow indicators were converted to a monthly timestep where possible. • Around half of monthly indicators had strong correlations with their daily versions. • Using only monthly data, good predictions were achieved for 90% of daily indicators. Flow indicators based on daily flow data are commonly used to characterize the hydrologic regimes of rivers and to investigate links between flow and ecological condition. Discharge data measured at a daily timestep is considered essential for such investigations because there is an underlying premise that longer timesteps (e.g. monthly) are not suitable for capturing the variability in flow behaviour of most relevance to ecologically important processes. This paper investigates this assumption by examining the information content of daily and monthly streamflow data for a diversity of perennial, intermittent and ephemeral flow regimes across Australia, using a novel approach of comparing daily and monthly flow indicators rather than observed flow timeseries. Results show that many flow indicators derived using daily and monthly data are strongly correlated (Spearman ρ greater than 0.9). Partial least squares regression shows that the majority of daily flow indicators can be predicted using monthly flow indicators, and that as few as six monthly flow indicators can be used to estimate a wide range of daily flow characteristics, including those relating to flow magnitudes, and durations of high and low flow events. Daily indicators relating to the frequency and seasonal timing of high and low flows cannot be predicted as accurately (R2 between 0.4 and 0.9), suggesting that information on these facets of the flow regime is partially lost when aggregating to a monthly timestep. Results from this study point to the utility of monthly-based flow indicators in river ecology, opening new opportunities to overcome limitations in gauge network coverage and data resolution in many parts of the world. [ABSTRACT FROM AUTHOR]

• We modify a method to assess yield of carryover reservoir with environmental flow. • The method can be applied at large scales, under historic and future climates. • We quantify yield declines due to climate change and environmental flow provision. Protecting freshwater systems to ensure sustainability requires diagnosing threats over a broad range of scales, from local to global. There is growing acceptance that environmental flows must be protected to sustain river channels and floodplain ecosystems downstream of reservoirs. Meeting environmental water requirements has proven difficult because they reduce water yield available for human use – a particular challenge in regions already experiencing declining water availability with climate change. A further challenge is the difficulty of assessing the impact of environmental water requirements on water availability for other purposes. This paper provides a modification of the Gould-Dincer method to assess the yield of carry-over reservoirs subject to environmental water requirements. The method characterizes reservoir inflows using readily available flow characteristics (annual mean and C v). We develop and test the method using ten examples with well-understood environmental water requirements and a range of annual flow C v s. We then demonstrate this method at continental scale, to investigate the impact on yield of providing high-reliability supply including environmental water requirements, under a changing climate. According to our results, considering climate change and environmental flow provision simultaneously, the decline in water yield is projected to be about 27% and 18% respectively for Australia and US (based on hypothetical reservoirs capacity of one times mean annual inflow). Adapting environmental flow demands to future hydrological conditions results in a 2% and 3% increase in Australia and US-wide yield for human use respectively. While further testing is needed, the method provides a simple and rapid way to estimate the impact of providing environmental flow on yield, applicable at global scale. [ABSTRACT FROM AUTHOR]

• We demonstrate flaws in the CMRSET implementation in Bretreger et al. (2020). • Actual evapotranspiration biases were because they used an incorrect Landsat band. • The calculation of GVMI should have used Landsat SWIR1 band instead of SWIR2. • CMRSET's suitability for irrigation monitoring is demonstrated via new experiments. • CMRSET evapotranspiration using the correct SWIR1 band did not show large biases. In a recent paper published in this journal, Bretreger et al. (2020) estimate irrigation water use from satellite remotely sensed estimates of actual evapotranspiration in five irrigated districts of the Murray-Darling Basin (southeast Australia). They used three models that scale crop reference evapotranspiration with vegetation indices acquired by recent Landsat satellites: (i) IrriSAT, (ii) Kamble and (iii) CMRSET. In their paper, irrigation water use computed with CMRSET generally overestimated observed irrigation water use, sometimes fivefold. Based on these results, Bretreger et al. (2020) discouraged the use of CMRSET for irrigation monitoring. In this comment, we reproduce the experiments in Bretreger et al. (2020), and demonstrate that their overestimation was because they used an incorrect Landsat band in their implementation of CMRSET. CMRSET was originally calibrated using both MODIS-derived Enhanced Vegetation Index (EVI) and Global Vegetation Moisture Index (GVMI). To calculate GVMI, a shortwave infrared (SWIR) band with a wavelength of ∼1.6 μm was used, which for MODIS is named SWIR2, and for Landsat is named SWIR1 (i.e., different sensors have different bands and a different number of bands and SWIR1 in MODIS has a wavelength of ∼1.2 μm). In their Landsat CMRSET implementation, Bretreger et al. (2020) computed GVMI using the Landsat SWIR2 band, which has a wavelength of ∼2.1 μm (see their Table 3). We show that CMRSET implemented with the Landsat SWIR1 band (i.e., with a wavelength of ∼1.6 μm, so being the correct Landsat band to calculate GVMI) yields similar results (both for temporal patterns and magnitude) when compared to the other two remote sensing actual evapotranspiration models in Bretreger et al. (2020). For the irrigation districts, these similar results meant that the mean water year (i.e., July to next June from 2010 to 2017) actual evapotranspiration mean absolute relative difference was 15.3% (with a 5.4%–26.5% range) and the water year irrigation water use mean absolute relative difference was 4.1% (with a 9.8%–18.9% range). Conversely, we show that a Landsat CMRSET implementation using the incorrect Landsat band for the calculation of GVMI (i.e., with a wavelength of ∼2.1 μm) led to a large overestimation agreeing with the CMRSET results reported by Bretreger et al. (2020), both for water year actual evapotranspiration (mean absolute relative difference of 64.6%, with a 51.1%–83.6% range) and irrigation water use (mean absolute relative difference of 44.1%, with a 31.4%–65.1% range). The use of the incorrect Landsat band means that the Bretreger et al. (2020) recommendation specific to their Landsat CMRSET implementation is invalid. A demonstration of Bretreger et al.'s (2020) incorrect Landsat CMRSET implementation and the correct one can be accessed at: https://jorgepena.users.earthengine.app/view/comment-on-bretreger-2020-joh-paper. Despite Bretreger et al.'s (2020) shortcomings in their implementation of the Landsat CMRSET actual evapotranspiration model, timely monitoring of irrigation water use via satellite remote sensing is required at Landsat resolutions (i.e., 30 m) or higher due to the heterogeneous nature of irrigation in most agricultural landscapes, and we applaud them for pursuing this line of research. [ABSTRACT FROM AUTHOR]

• This paper extends the residual mass concept to propose a new method to quantify water availability. • The method automates the identification of deficit periods in the residual mass curve. • The method is validated against SPI and streamflow for Australia. Water supply management for hydroclimatic extremes has commonly been analysed using the cumulative difference between inflow and demand, a concept known as the Residual Mass Curve. This paper extends the residual mass concept to develop a new method to quantify water availability, termed the Residual Mass Severity Index (RMSI). The RMSI improves on the Residual Mass Curve by allowing extreme deficit periods to be easily identified without the need for manual inspection of the Residual Mass Curve. Another advantage of the RMSI is that it does not require a temporal window to be specified to characterise extreme deficit periods, which is needed for many drought indices such as the Standardized Precipitation Index (SPI). This paper develops the method for calculating the RMSI using precipitation time series and compares it to the SPI for different drought durations across Australia. Major hydroclimatic extremes during the instrumental period are assessed using RMSI, SPI, and by comparing the RMSI estimates with streamflow data from 467 catchments with varying sizes and climate across Australia. RMSI is a useful index for water managers interested in assessing water availability with potential applicability in summarising the features of historical hydroclimatic extremes as well as future changes in water availability due to anthropogenic climate change. [ABSTRACT FROM AUTHOR]

• A spatial mode-based calibration model is developed to post-process short-term forecasts of precipitation fields. • The calibration model directly embeds spatial structure into calibrated ensemble forecasts. • Calibrated ensemble forecasts are shown to be of high quality at both grid-cell and basin scales. Statistical calibration of precipitation forecasts from numerical weather prediction (NWP) models is routinely performed grid-cell by grid-cell, aiming to produce accurate and reliable ensemble forecasts for precipitation fields. Calibrated ensemble members from different grid-cells are then connected using ensemble reordering to form spatially structured ensemble forecasts. However, ensemble reordering approaches, such as the well-known Schaake shuffle method, are often criticized for not considering real physical atmospheric states of precipitation events. In this paper, we propose a spatial mode-based calibration (SMoC) model for post-processing forecast precipitation fields and producing ensemble forecasts with an inbuilt spatial structure, so that ensemble reordering is not required. The SMoC model is developed based on spatial modes derived from empirical orthogonal function (EOF) analyses of precipitation fields and linear regressions of derived EOF expansion coefficients of the first few dominant modes. Unlike conventional calibration models that are applied to forecast grid-cells individually, SMoC is applied to the whole forecast fields, and the spatial structure is inherently present in calibrated ensemble forecasts. There is therefore no need to rely on ensemble reordering for spatial reconstruction. The performance of SMoC is evaluated by applying it to NWP forecasts of substantive precipitation events over the Brisbane drainage basin in eastern Australia. Cross-validated results show that SMoC calibrated forecasts are of high quality at both grid-cell and basin scales. The spatial structure of precipitation is found to be well embedded in the ensemble members of the calibrated forecasts for the basin. [ABSTRACT FROM AUTHOR]

Modelling workflow for BRF-LSTM and BRF-GRU predictive models. [Display omitted] • Deep learning hybrid predictive model (BRF-LSTM) is built for streamflow forecasting. • Climate indices, rainfall and periodicity are factored for accurate predictions. • Adopts feature algorithm (i.e. Boruta) with long short-term memory and gated recurrent unit. • Six gauging sites tested; BRF-LSTM yields > 98% errors in ± 0.015 m, ~1.30% relative error. • The proposed model is useful for hydrological and strategic water resources planning. Long-term forecasting of any hydrologic phenomena is essential for strategic environmental planning, hydrologic and other forms of structural design, agriculture, and water resources management. Climate mode indices, utilising machine learning methods, are frequently considered as predictor variables in order to forecast several different hydrological variables. In this study, a feature selection algorithm based on two different deep learning models, i.e., long short-term memory and a gated recurrent unit, is applied to improve the forecasting capability of streamflow water levels at six gauging stations in the Murray Darling Basin of Australia. This paper therefore aggregates the significant antecedent lag memory of climate mode indices, rainfall, and the monthly factor based on the periodicity as the predictor variables to attain significantly accurate stream water level forecasts. This novel method identifies an improved relationship between the stream water level and climate mode indices through the aggregation of the significant lagged datasets capturing the historical features to predict the future streamflow water level. The boruta feature selection algorithm (BRF) was then applied in a two phase process before and after attaining the significant lagged inputs to screen the optimum predictor variables. The merits of the forecast models were evaluated through different performance evaluation criteria. The results show that the accumulated significant lagged inputs based on climate mode indices, along with the rainfall and periodicity factors are seen to provide improved forecasting of the SWL over the non-BRF deep learning approaches where no prior feature selection was applied. The hybrid LSTM method (i.e., BRF-LSTM model) achieved a unique advantage in terms of SWL forecasting, particularly attaining over 98% of the predictive errors lying within a band of +/-0.015 m with relatively low relative errors (RRMSE ≈1.30% and RMAE ≈ 0.882%), outperforming all of the benchmark models. It is also found that the periodicity factor has a potential influence on the accuracy of the forecast models for the four monitored study stations. This study concludes that the newly developed hybrid deep learning approaches, coupled with the BRF feature selection, provide improved forecasting performance. The hybrid approach developed in this paper can therefore be used to provide a strong provide predictive response algorithm for the hydrological variables that were influenced by the low-frequency variability of the climate model indices in respect to streamflow water level. [ABSTRACT FROM AUTHOR]

Summary: This is the first of three related papers that summarizes streamflow characteristics of a set of 1221 global rivers. The rivers are well distributed world-wide, are un-impacted by upstream reservoirs or diversions for the period of data collection and have at least 10 years of continuous monthly and annual streamflow data. The following key features of annual flows are examined: mean, variability and skewness, distribution type (Gamma or Lognormal), flow percentiles and dependence. High and low frequency persistence is examined through the Empirical Mode Decomposition technique. Low flow run length, magnitude and severity are also explored, where severity is based on Extended Deficit Analysis. It has been observed elsewhere that there are large differences in hydrologic characteristics between Australia and southern Africa in contrast to the rest of the world. This issue is tested further in this paper. The range of analyses and results presented herein also form a suite of empirical evidence that future unified theories of hydrology at the catchment scale must be able to adequately describe. [Copyright &y& Elsevier]

Summary: Calibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by the uncertainty in the observed forcing/response data and the structural error in the model. This study works towards the goal of developing a robust framework for dealing with these sources of error and focuses on model error. The characterisation of model error in CRR modelling has been thwarted by the convenient but indefensible treatment of CRR models as deterministic descriptions of catchment dynamics. This paper argues that the fluxes in CRR models should be treated as stochastic quantities because their estimation involves spatial and temporal averaging. Acceptance that CRR models are intrinsically stochastic paves the way for a more rational characterisation of model error. The hypothesis advanced in this paper is that CRR model error can be characterised by storm-dependent random variation of one or more CRR model parameters. A simple sensitivity analysis is used to identify the parameters most likely to behave stochastically, with variation in these parameters yielding the largest changes in model predictions as measured by the Nash–Sutcliffe criterion. A Bayesian hierarchical model is then formulated to explicitly differentiate between forcing, response and model error. It provides a very general framework for calibration and prediction, as well as for testing hypotheses regarding model structure and data uncertainty. A case study calibrating a six-parameter CRR model to daily data from the Abercrombie catchment (Australia) demonstrates the considerable potential of this approach. Allowing storm-dependent variation in just two model parameters (with one of the parameters characterising model error and the other reflecting input uncertainty) yields a substantially improved model fit raising the Nash–Sutcliffe statistic from 0.74 to 0.94. Of particular significance is the use of posterior diagnostics to test the key assumptions about the data and model errors. The assumption that the storm-dependent parameters are log-normally distributed is only partially supported by the data, which suggests that the parameter hyper-distributions have thicker tails. The results also indicate that in this case study the uncertainty in the rainfall data dominates model uncertainty. [Copyright &y& Elsevier]

Summary Design rainfall statistics are the primary inputs used to assess flood risk across river catchments. These statistics normally take the form of Intensity–Duration–Frequency (IDF) curves that are derived from extreme value probability distributions fitted to observed daily, and sub-daily, rainfall data. The design rainfall relationships are often required for catchments where there are limited rainfall records, particularly catchments in remote areas with high topographic relief and hence some form of interpolation is required to provide estimates in these areas. This paper assesses the topographic dependence of rainfall extremes by using elevation-dependent thin plate smoothing splines to interpolate the mean annual maximum rainfall, for periods from one to seven days, across Australia. The analyses confirm the important impact of topography in explaining the spatial patterns of these extreme rainfall statistics. Continent-wide residual and cross validation statistics are used to demonstrate the 100-fold impact of elevation in relation to horizontal coordinates in explaining the spatial patterns, consistent with previous rainfall scaling studies and observational evidence. The impact of the complexity of the fitted spline surfaces, as defined by the number of knots, and the impact of applying variance stabilising transformations to the data, were also assessed. It was found that a relatively large number of 3570 knots, suitably chosen from 8619 gauge locations, was required to minimise the summary error statistics. Square root and log data transformations were found to deliver marginally superior continent-wide cross validation statistics, in comparison to applying no data transformation, but detailed assessments of residuals in complex high rainfall regions with high topographic relief showed that no data transformation gave superior performance in these regions. These results are consistent with the understanding that in areas with modest topographic relief, as for most of the Australian continent, extreme rainfall is closely aligned with elevation, but in areas with high topographic relief the impacts of topography on rainfall extremes are more complex. The interpolated extreme rainfall statistics, using no data transformation, have been used by the Australian Bureau of Meteorology to produce new IDF data for the Australian continent. The comprehensive methods presented for the evaluation of gridded design rainfall statistics will be useful for similar studies, in particular the importance of balancing the need for a continentally-optimum solution that maintains sufficient definition at the local scale. [ABSTRACT FROM AUTHOR]

Summary One of the fundamental steps in regional rainfall frequency analysis is deciding the method by which rainfall stations are to be grouped together to form regions. This paper compares three methods of forming regions for use in estimating design rainfalls: a fixed region approach where all the available sites are included in a single region, a Region of Influence (ROI) approach based on geographical proximity and a hybrid approach where sites with similar topographic orientations are grouped together. The three region types were implemented in a Bayesian Generalized Least Squares Regression (BGLSR) framework which leads to regionalized regression equations that can be used to predict rainfall L-moments at ungauged sites. A leave-one-out cross validation approach was used to compare the relative accuracy, reliability and uncertainty of the derived rainfall statistics and resulting estimates of the rainfall quantiles. The study used data from two areas of Australia chosen for their highly varied topography and different climatic influences. It was found that all three methods provided good estimates of the L-moment statistics and the rainfall quantiles. The hybrid approach produced the smallest errors in the South-East Queensland region whilst for the Tasmanian region the fixed region approach was best. The results from this study show that although there is a slight benefit in using the proposed hybrid approach for BGLSR, these benefits were minor compared to maximizing the number of stations used to calibrate the BGLSR equations. This conclusion regarding the number of stations could be tested in future work by repeating the analyses in areas with sparser station density. Another test could be to simulate reduced station coverage in the current study areas by leaving stations out of the analyses. Finally it would be interesting to see if similar results are obtained by expanding the study area so that different climatological regimes are assessed. [ABSTRACT FROM AUTHOR]

• An innovative framework to derive intensity-frequency-duration curves from satellite data for remote areas. • A variant of the Bartlett-Lewis model calibrated with sub-daily satellite precipitation data to disaggregate daily gauge data. • This new framework was tested for a wide range of climates in Australia. • IFD curves can be derived with reasonable accuracy for data scarce regions. Intensity-frequency-duration (IFD) curves (also known as intensity-duration-frequency (IDF) curves in some regions) are widely used to take into consideration the probability of occurrence of extreme precipitation for the design of water related infrastructure. Derivation of IFD curves is based on statistical analysis of observed extreme precipitation events and thus the reliability of the curves mostly depends on the record length of observations. At sub-hourly timescales however, ground-based precipitation observations are often absent or very limited in terms of spatial coverage and record length, particularly in remote areas around the world. Given the availability of satellite-derived precipitation products (SPPs) with finer spatiotemporal resolution and near-global coverage, there is great potential to use these products in the planning and design of infrastructure. This paper proposes a novel framework, SIFD (Satellite-derived IFD curves) to combine long-term daily gauge data and sub-hourly SPP to estimate IFD curves for time intervals from 0.5 to 24 h. A recent variant of the Random Parameter Bartlett-Lewis Rectangular Pulse model (RPBLRPX) was used to disaggregate daily gauge data to derive IFD curves. In the absence of sub-daily rain gauge data, the statistics required for the RPBLRPX model were obtained using the Integrated Multi-satellitE Retrievals for GPM (IMERG; 30-min, 0.1° resolution) product derived from the Global Precipitation Measurement (GPM) mission. Performance of the SIFD framework was tested against gauge data from seven Australian sites across a wide range of climatologies. The disaggregation scheme coupled with the RPBLRPX model was capable of reproducing the wet-period mean intensity, wet-period standard deviation, and wet-period fraction with reasonable accuracy across the sub-daily timescales. The coefficient of determination ( r 2) values were 0.39 (mean intensity), 0.38 (standard deviation), and 0.87 (wet-period fraction) at 0.5 h timescale. Furthermore, despite some differences in methodology and record length, the IFD estimates computed using the SIFD framework were fairly close to those of the Bureau of Meteorology (BoM). The skill metrics for Sydney were the best with a bias ratio (BR) of 1.01, root mean square error (RMSE) of 13% of the mean of the BoM estimates, and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.97. The worst case was found at Hobart (BR = 1.43, RMSE = 66%, and NSE = 0.26). Overall, the results indicate that the proposed SIFD framework could provide a viable alternative to estimate unbiased IFD curves at the sub-hourly timescale in the absence of adequate sub-hourly ground-based observations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Wildfires are becoming more severe and frequent and their effects increasingly expose water resources to stress. • Climate, wildfire, streamflow records (1975 to 2018), topography, and landcover data from 92 catchments were combined to quantify the contribution of wildfire to streamflow variability across Australian temperate forests. • On average wildfire regimes relatively results in 8.8% increases in streamflow across Australian temperate forests. • Wildfire effects on regional streamflow varied spatially depending on hydroclimate factors, particularly precipitation. As wildfires become more frequent and severe, there are concerns regarding their impacts on water yield from forested catchments. While there are many studies in Australia about the effects of individual wildfires on streamflow at fine scales (<1 km2) in specific geographic settings, the effects of wildfire regimes on streamflow at broad spatial scales across temperate forests in Australia are not well understood. In this paper, we combined climate, wildfire, streamflow records (1975 to 2018), topographic, and landcover data from 92 catchments (74 – 4740 km2) in the Australian temperate zone to quantify the contribution of wildfire regimes over time to streamflow variability in different hydroclimatic settings (humid, dry sub-humid, and semi-arid) and geographic regions (Southeast Australia (SEA), Southwest Western Australia (SWWA), and Tasmania (TAS)). Wildfire regimes were represented by two metrics: the burnt area to drainage area (BDA) ratio for wildfire events in each year and a spatially averaged metric of Time Since Fire (TSF), which is a spatial average of time since the last wildfire in the catchment. By comparing prefire and postfire runoff ratios our study found that on average there was a short-term increase in runoff ratio (∼3% in year 1 and ∼6% in year 2 post-fire) after wildfires with BDA > 25%. No influence of fire was found in the long-term (15–20 years after the wildfire) runoff ratio. We found that wildfire regime, measured by TSF, explained ∼8.8% of the variation in annual streamflow across the Australian temperate zone, and that with decreasing TSF (i.e., increased wildfire impact), the average streamflow increased. Streamflow variation explained by wildfire regimes varied with hydroclimate. The explained variance of streamflow by wildfire regimes in semi-arid catchments (23%) and dry sub-humid catchments (13%) were higher than humid catchments (5%). Our results provide a broad-scale understanding of how wildfire regimes influence streamflow variability at broad temporal and spatial scales, and provided important context and baseline information for determining the implications of changes in climate and fire regimes for regional water availability. [ABSTRACT FROM AUTHOR]

Summary This paper explores timescales, changing worldviews and the impact of water reform on irrigation communities in Australia whose water sharing arrangements have roots in an earlier era. Through the story of Australian irrigation it describes some subtle shifts and changes in worldviews that have influenced land and water governance reform over time. It shows how reforms can result in tangible adverse effects on communities if they overturn critical features of earlier resource sharing arrangements without consideration of unintended consequences. Where changing worldviews, reforms and the ability of communities to adapt are out of synchronisation then friction ensues, as was seen in the Murray–Darling Basin when proposed reforms have resulted in widespread disputes between reformers and irrigation communities. Failure to understand how perspectives over time have changed leads to a failure to deliver fairness in water governance reforms. If policymakers lose understanding of the rationale for earlier arrangements in land and water governance and introduce reforms that do not take these into account then adaptation to the reform and social acceptance is impeded. Seen in this way, time can be considered a competing element in fair land and water governance. Maintaining an understanding of how and why change takes place over time, and the rationale for key elements of governance developed in an earlier era, is critical for those wishing to overcome the challenges of implementation, deliver fairness, and gain community acceptance of reform. [ABSTRACT FROM AUTHOR]

Summary: This paper examines the use of a controlled fuel reduction burn in native woodland to increase recharge to a shallow aquifer system for water supply in south-western Australia. Soil moisture trends from neutron probe measurements and watertable dynamics from piezometers under adjacent burned and unburned plots were compared to determine the difference in recharge resulting from the fire. Remotely sensed evapotranspiration (ET) subtracted from rainfall was used to independently estimate groundwater and soil moisture recharge and compare against a physically based model of soil–water fluxes. The use of naturally occurring chemicals (n-alkanes and cycloalkanes) produced in the fire as tracers of the recharge pulse in the winter following the fire was tested. It was found that in the winter following the fire (annual rainfall 701mm), water storage in the 9m thick unsaturated zone increased by about 300mm but recharge to the watertable was only about 60mm. In the subsequent year (rainfall 630mm), leaf area index in the burned woodland recovered to be about 10% less than the unburned plot, remotely sensed ET rose above that in the unburned plot and recharge was lower in the burned than unburned plot. This trend continued and, after 3years, increased ET negated any early recharge gains, and there was a net decrease in recharge resulting from the fire. The combination of water balance measurements, modelling and remote sensing provides a robust method to gain hydrological understanding of recharge mechanisms. Further work is required to refine the use of burn residue chemicals as soil water tracers, particularly in improving instrument design for groundwater sample collection in fine textured soils. As climate continues to change and regions with growing water shortages seek innovative solutions, approaches such as controlled burning may be needed; however, in this example, the approach was unproductive and further the environmental consequences need to be evaluated to make an appropriate assessment of trade-offs and viability of this approach. [Copyright &y& Elsevier]

Summary: This paper describes an algorithm for disaggregating daily rainfall into sub-daily rainfall ‘fragments’ (fine-resolution rainfall sequences) under a future, warmer climate. The algorithm uses a combined generalised additive model (GAM) and method of fragments (MoFs) framework to resample sub-daily rainfall fragments from the historical record conditional on daily rainfall amount and a range of atmospheric covariates. The rationale is that as the atmosphere warms, future rainfall patterns will be more reflective of historical rainfall patterns corresponding to warmer days at the same location, or to locations which have an atmospheric profile more reflective of expected future climate. It was found that the daily to sub-daily scaling relationship varied significantly by season and by location, with rainfall patterns on warmer seasons or at warmer locations typically showing more intense rainfall occurring over shorter periods compared with cooler seasons and stations. Importantly, by regressing against atmospheric covariates such as temperature, this effect was substantially reduced, suggesting that the approach may also be valid when extrapolating to a future climate. The GAM–MoF algorithm was then applied to nine stations around Australia, with the results showing that relative to the daily rainfall amount, the maximum intensity of short duration rainfall increased by between 4.1% and 13.4% per degree change in temperature for the maximum six minute burst, and by between 3.1% and 6.8% for the maximum 1h burst. The fraction of each wet day with no rainfall also increased by between 1.5% and 3.5%. This highlights that a significant proportion of the change to the distribution of rainfall is likely to occur at sub-daily timescales, with important implications for many hydrological systems. [Copyright &y& Elsevier]

Summary: Yield plays a central role in the processes, practices, management and operation of urban water supply systems. Improved accuracy of the yield estimate is important and can be obtained by improving knowledge of important variables. Yield is typically estimated via computational simulation using the entire sequence of available historic climate data. Sensitivity analysis provides a framework and many techniques that can identify important variables in a computational model. Using the Morris method, this paper investigates the importance of input variables used in the estimation of yield of urban water supply systems. The Barwon urban water supply system in Australia is used as the case study. Using a number of climate scenarios of various lengths, sensitivity analysis showed that the security criteria of the Barwon system was the most important to the yield estimate. The upper restriction rule curve also showed notable importance. Significantly the results showed that the use of a single climate sequence for estimating yield would produce misrepresentative results for another climate scenario. [ABSTRACT FROM AUTHOR]

Summary: The effect of future climate scenarios on surface water and groundwater resources has been shown to have a consequent impact on water-dependent ecosystems. A regional-scale analysis of changes in water resources as a result of changing climate was undertaken in south-western Australia; a region with a large number of nationally and internationally recognised water-dependent ecosystems. The paper examines the potential environmental impacts of a substantial reduction in rainfall and an increase in temperature as projected by global climate models (GCMs) on river and groundwater-dependent terrestrial vegetation by 2030. Climate change effects on environmentally significant flow regimes were evaluated by applying the climate projections from 15 GCMs under three global warming ‘scenarios to rainfall–runoff and groundwater models. It was estimated that under a dry future climate scenario, the frequency of river flow rates important for ecological communities is likely to be reduced by up to 2months per year, which is likely to cause a significant level of stress to ecological communities. Additionally, the duration of no-flow periods may increase by more than 120days in some streams. Under a dry future climate scenario, groundwater-dependent vegetation is projected to be at a high risk in over 19% of its current habitat area. Water-dependent ecosystems are also projected to be affected by an increase in groundwater abstraction. The results indicate that the projected impacts of future climate conditions are not likely to be uniform across the region but overall they could cause a continuing threat to water-dependent ecosystems. The implementation of water management plans should place particular emphasis on the ecological water requirements in 6 of the 13 river catchments in south-western Australia and in areas where groundwater abstraction is high. The climate projections and ecological impacts are a continuation of the trends that have taken place in the past three decades. [Copyright &y& Elsevier]

Summary: This paper presents the results of computer simulations of runoff from 13 major fresh and brackish river basins in south-western Australia (SWA) under climate projections obtained from 15 GCMs with three future global warming scenarios equivalent to global temperature rises of 0.7°C, 1.0°C and 1.3°C by 2030. The objective was to apply an efficient methodology, consistent across a large region, to examine the implications of the best available projections in climate trends for future surface water resources. An ensemble of rainfall-runoff models was calibrated on stream flow data from 1975 to 2007 from 106 gauged catchments distributed throughout the basins of the study area. The sensitivity of runoff to projected changes in mean annual rainfall is examined using the climate ‘elasticity’ concept. Averaged across the study area, all 15 GCMs project declines in rainfall under all global warming scenarios with a median decline of 8% resulting in a median decline in runoff of 25%. Such uniformity in projections from GCMs is unusual. Over SWA the average annual runoff under the 5th wettest and 5th driest of the 45 projections of the 2030 climate declines by 10 and 42%, respectively. Under the 5th driest projection the runoff decline ranges from 53% in the northern region to 40% in the southern region. Strong regional variations in climate sensitivity are found with the proportional decline in runoff greatest in the northern region and the greatest volumetric declines in the wetter basins in the south. Since the mid 1970s stream flows into the major water supply reservoirs in SWA have declined by more than 50% following a 16% rainfall reduction. This has already had major implications for water resources planning and for the preservation of aquatic and riparian ecosystems in the region. Our results indicate that this reduction in runoff is likely to continue if future climate projections eventuate. [Copyright &y& Elsevier]

Summary: Determining evaporation rates is essential for efficient management of reservoirs and water resources, particularly in water-scarce countries such as Australia. Today, it is estimated that open water reservoirs in Australia lose around 40% of their total water storage capacity per year to evaporation. While this loss is of significant concern, the threat of a changing climate has been directing greater focus to how much water will be lost from Australia’s reservoirs in the future. This paper analyses evaporation rates from a large water supply reservoir in South-East Queensland (SEQ), Australia, under current climate and predicted climate change conditions using modelling. Daily meteorological projections from nine global climate models were used in the model DYRESM as the driving forces of the thermodynamics of the reservoir under study. Two future 20-year period simulations were undertaken, one from 2030 to 2050, and the other from 2070 to 2090. The modelled future evaporation rates, as well as water temperatures, were then compared with modelled evaporation rates and temperatures obtained using observed meteorological variables for the period of 1990–2010. The results showed that the evaporation rates from the study reservoir will increase in the future. For the period centred in 2040, the annual evaporation will be approximately 8% higher than the 20-year average annual evaporation estimated for the present climate. A more pronounced increase in evaporation is expected in 2070–2090, with annual evaporation predictions being approximately 15% higher than the baseline evaporation. The main agent behind this increase is higher surface air temperatures in the future. According to the modelling results, the mean annual surface air temperature will grow from the present value of 20.4°C to 21.5°C in 2030–2050, and to 23.2°C in 2070–2090. As a consequence, the mean annual surface water temperatures of the reservoir will increase by 0.9°C and 1.7°C in both timeframes, respectively. This will have a significant impact on the evaporation rates, particularly in spring and summer, when the temperature increases will be more significant. [Copyright &y& Elsevier]

Summary: A large part of the world faces the problem of aridity, e.g. in Australia, the semi-arid and arid regions occupy about 50–75% of the country. There is a serious lack of data on many key hydrological processes in the arid regions, which makes it difficult to understand the hydrological processes in these regions. Although arid regions are characterised by a very low mean annual rainfall, it can experience severe flooding causing widespread damage. In hydrological literature, there is a limited presence of the application of regional flood frequency analysis (RFFA) studies for the world arid regions due to limited data availability. This paper presents a RFFA study for the semi-arid and arid regions of Australia using the most up-to-date data set and compares the results with that of the world data. It has been found that arid catchments exhibit a higher loss and lower runoff coefficient values as expected. Based on four model selection criteria and L-moments ratio diagram, it has been found that a Generalised Pareto distribution is preferable to the Exponential distribution to fit the partial duration series flood data in the arid regions of Australia. It has also been found that the arid region exhibits much steeper flood frequency growth curve than that of the humid region and the growth curve gets steeper with the decreasing mean annual rainfall. The developed growth curve for the semi-arid and arid region of Australia is found to be similar to other typical semi-arid and arid regions of the world. The developed prediction equation for the mean annual flood in the semi-arid and arid region of Australia uses two readily obtainable predictor variables (catchment area and design rainfall intensity), which can be used along with the developed growth curve to estimate design floods for the ungauged catchments in these regions with reasonable accuracy. The growth curve developed in this study is primarily applicable to the semi-arid and arid regions of Australia; however, this may be adapted to other similar regions of the world along with the locally developed prediction equation for the scaling factor (i.e. mean annual flood in this case) to derive preliminary design flood estimates. [Copyright &y& Elsevier]

Summary: Indigenous rights, values and interests relating to water have been identified by Australia’s National Water Commission as a national priority area, requiring greater understanding, research attention and government action. Yet Indigenous water values are rarely addressed in water planning, despite objectives in national policy requiring Indigenous participation and the identification of Indigenous social, spiritual and customary values in water plans. Water planners are presently equipped with a very limited number of engagement tools tailored to the water resource management context to redress the historical neglect of Indigenous interests. In an Australian research project focused on water planning, seven participatory planning tools were employed in three Australian case studies with different social and hydrological characteristics to improve the way in which Indigenous values are elicited and incorporated and to enhance the status of Indigenous knowledge in water planning. The results from the two Murray Darling Basin (MDB) case studies reveal the many ways in which Indigenous values have been adversely affected by recent water resource developments and concomitant water scarcity. In the third case on the Tiwi Islands in the Northern Territory, where land title to the entire water planning area is vested in Indigenous traditional owners, methods were refined to ensure engagement and generate capacity to manage the development of a solely Indigenous-owned, first-generation Water Management Strategy, in collaboration with a range of stakeholders. This paper describes the needs and aspirations of Indigenous people, the engagement strategies employed to elicit Indigenous knowledge, assess Indigenous values, and incorporate the results into three developing water plans. In addition, it outlines a set of general principles to guide water planning in other regions and thereby to improve Indigenous access to water. [ABSTRACT FROM AUTHOR]

Summary: The effect of future climate scenarios on surface water and groundwater resources has been shown to have a consequent impact on water-dependent ecosystems. A regional-scale analysis of changes in water resources as a result of changing climate was undertaken in south-western Australia; a region with a large number of nationally and internationally recognised water-dependent ecosystems. The paper examines the potential environmental impacts of a substantial reduction in rainfall and an increase in temperature as projected by global climate models (GCMs) on river and groundwater-dependent terrestrial vegetation by 2030. Climate change effects on environmentally significant flow regimes were evaluated by applying the climate projections from 15 GCMs under three global warming ‘scenarios to rainfall–runoff and groundwater models. It was estimated that under a dry future climate scenario, the frequency of river flow rates important for ecological communities is likely to be reduced by up to 2months per year, which is likely to cause a significant level of stress to ecological communities. Additionally, the duration of no-flow periods may increase by more than 120days in some streams. Under a dry future climate scenario, groundwater-dependent vegetation is projected to be at a high risk in over 19% of its current habitat area. Water-dependent ecosystems are also projected to be affected by an increase in groundwater abstraction. The results indicate that the projected impacts of future climate conditions are not likely to be uniform across the region but overall they could cause a continuing threat to water-dependent ecosystems. The implementation of water management plans should place particular emphasis on the ecological water requirements in 6 of the 13 river catchments in south-western Australia and in areas where groundwater abstraction is high. The climate projections and ecological impacts are a continuation of the trends that have taken place in the past three decades. [Copyright &y& Elsevier]

Summary: In this article, an approach using Bayesian Generalised Least Squares (BGLS) regression in a region-of-influence (ROI) framework is proposed for regional flood frequency analysis (RFFA) for ungauged catchments. Using the data from 399 catchments in eastern Australia, the BGLS-ROI is constructed to regionalise the flood quantiles (Quantile Regression Technique (QRT)) and the first three moments of the log-Pearson type 3 (LP3) distribution (Parameter Regression Technique (PRT)). This scheme firstly develops a fixed region model to select the best set of predictor variables for use in the subsequent regression analyses using an approach that minimises the model error variance while also satisfying a number of statistical selection criteria. The identified optimal regression equation is then used in the ROI experiment where the ROI is chosen for a site in question as the region that minimises the predictive uncertainty. To evaluate the overall performances of the quantiles estimated by the QRT and PRT, a one-at-a-time cross-validation procedure is applied. Results of the proposed method indicate that both the QRT and PRT in a BGLS-ROI framework lead to more accurate and reliable estimates of flood quantiles and moments of the LP3 distribution when compared to a fixed region approach. Also the BGLS-ROI can deal reasonably well with the heterogeneity in Australian catchments as evidenced by the regression diagnostics. Based on the evaluation statistics it was found that both BGLS-QRT and PRT-ROI perform similarly well, which suggests that the PRT is a viable alternative to QRT in RFFA. The RFFA methods developed in this paper is based on the database available in eastern Australia. It is expected that availability of a more comprehensive database (in terms of both quality and quantity) will further improve the predictive performance of both the fixed and ROI based RFFA methods presented in this study, which however needs to be investigated in future when such a database is available. [Copyright &y& Elsevier]

Summary: Most of the plantation impact studies reported in literature normally use either one of the sensitivity-based approach or a hydrological model with few actually comparing the impact results from these different approaches. This paper investigates the impacts of increase or decrease in plantations and climate variability on streamflow using two approaches: the sensitivity-based approach (including a non-parametric model and six Budyko framework based models) and the hydrological modelling approach (using Xinanjiang and SIMHYD models) for three medium sized catchments in Australia. The results from the nine methods show that both plantation expansion/reduction and climatic differences can have major effects on catchment streamflow. There is a small variability in the reduction or increase in streamflow estimated by the nine methods. The results from this study show that the estimates of plantation impacts from the dynamic hydrological models are similar to those from the commonly used sensitivity-based approaches. The sensitivity-based approaches are only applicable where long term datasets are available and they only provide results at a mean annual time scale. The hydrological models simulate plantation impacted streamflow time series and so they can be used to estimate the relative contributions of land cover changes and climate change/variability at a daily, monthly or annual time-step. The outputs from the hydrological models can also be used to investigate the impacts of plantation expansion or reduction and climate change/variability on different runoff characteristics. [ABSTRACT FROM AUTHOR]

Summary: A method to convert daytime evapotranspiration (ET) into daily ET based on variable canopy resistance was investigated in this paper. The study was carried out for maize and canola crops grown in Coleambally Irrigation Area which is located in Murray Darling Basin of Australia. Variable canopy resistance was simulated on the basis of Todorovic method. This study has shown that the method performed well to scale daily ET from daytime ET measurements for both crops. In the case of the maize crop, the coefficient of determination (R 2) was 0.979 and the index of agreement (d) was 0.981. In the case of the canola crop, the coefficient of determination (R 2) was 0.936 and the index of agreement (d) was 0.960. Future work will focus on the study of this method to scale daily ET map from satellite-derived daytime ET map. [ABSTRACT FROM AUTHOR]

Summary: This paper describes a robust methodology for determining current surface and groundwater availability and use, as well as future changes due to climate and landuse changes. It is based on the methodology developed by CSIRO to deliver on four large-scale water availability assessments conducted in the Murray-Darling Basin, Northern Australia, south-west Western Australia, and Tasmania. It will focus on the application of the technique and results from Tasmania, providing a representative example of the approach used. The genesis of this work was the explicit desire by Australian State and Commonwealth governments to use the outputs of these water availability assessments for assisting the formation of state and federal government water policy. For example, the results of the work have already been utilised as a key technical input to decision making on funding for proposed irrigation projects in Tasmania. Outputs from the other three study areas have been used to assist in developing a water resources plan for the Murray-Darling Basin, to guide infrastructure development in northern Australia, and to plan for reductions in water availability due to climate change in south-west Western Australia. The methodology assesses current water availability through the application of rainfall–runoff and river models, and recharge and groundwater models. These were calibrated to streamflow records and groundwater levels, and parameterised using estimates of current surface and groundwater extractions and use. Having derived an estimate of current water availability, the impacts of future climate change on water availability were determined through deriving projected changes in rainfall and potential evaporation from 15 IPCC AR4 global climate models. The changes in rainfall were then dynamically downscaled using the CSIRO-CCAM model over the study area (50,000km2). The future climate sequence was then derived by modifying the historical 84-year climate sequence based on these changes in rainfall and potential evaporation. This future climate sequence was then run through the rainfall–runoff, river, recharge and groundwater models to give an estimate of water availability under future climate. The impacts of landuse changes were derived by modifying the parameters and inputs/outputs of each of the models as appropriate. Results for Tasmania indicate that historical average surface water availability across the study area is 21,815GL/year. Of this, 636GL/year is currently extracted for use. Historical groundwater development in Tasmania has been largely unregulated and unmetered, however current extraction is estimated to be only 38GL/year. In some intensive irrigation areas, the modelling has demonstrated that current and future groundwater extraction will reduce baseflow in connected streams. By 2030, rainfall is projected to decrease by an average of 3% over the study area. This decrease in rainfall and concurrent increase in potential evaporation leads to a decrease in runoff and, to a lesser extent recharge, leading to a reduction in surface water availability of 5% by 2030. Results from this study are being used by the Tasmanian and Australian governments to guide decisions on the development of a sustainable irrigated agriculture industry in Tasmania. [ABSTRACT FROM AUTHOR]

Summary: Dryland salinity threatens many high-value assets in Australia. Assessing the technical feasibility of intervention requires consideration of spatial and temporal dimensions of land management and associated surface–groundwater interactions. This paper presents results regarding the practicability of protecting assets in a 371,000-ha agriculturally dominated catchment in south-eastern Australia using a physically-based catchment model, the Catchment Analysis Tool (CAT). The model links surface land management and hydrology at the paddock scale (daily time step growth and water use, crop rotations and management) with a fully distributed groundwater model enabling the assessment of agronomic versus engineering options for landscape asset protection. Scenarios assessing management options and costs of planting of perennial vegetation or engineering through groundwater pumping are outlined. The impacts of scenarios are reported under future climatic conditions as well as for the ‘steady state’ response. The study demonstrates a linked surface–groundwater modelling approach to estimate groundwater capture zones, response times to equilibrium under current and climate change conditions, as well as groundwater abstraction volumes required to protect landscape features. These factors are important considerations for evidence-based decision making about protecting assets from dryland salinity. [Copyright &y& Elsevier]

Summary: One of the main concerns regarding water storage in Australia, and other semi-arid countries, is the high rate of evaporation that inevitably leads to significant water loss. In this paper, the use of air-bubble plume systems to reduce evaporation from large reservoirs is assessed. A destratification system was designed for a large dam based on its depth and stratification strength with the intention of destratifying the reservoir in a short time period. The model DYRESM was then used to simulate the water dynamics under destratification conditions. Different strategies for the operation of the aeration system were assessed, from 10-days operation periods at times of high evaporation rates to continuous operation over longer time spans. The modelled water column temperatures and evaporation rates were analysed and it was found that artificial destratification was only effective in reducing evaporation in spring. In summer, heat is added to the water at a rapid rate, and artificial destratification only helps reduce evaporation in the initial days of operation. The effect of artificial destratification in reducing evaporation in autumn depends on the operation of the system during summer. If operated in summer, the rates of evaporation in autumn will increase due to the additional heat added to the water during the summer. In winter, overturn takes place and artificial destratification has no influence on water temperatures and evaporation. It was concluded that aeration by air-bubble plumes would only be effective in reducing evaporation if the hypolimnetic water does not become warm when mixing takes place. This is an ideal situation, but is unlikely to happen in practice. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a tool for documenting and monitoring water use benefits in the Great Barrier Reef catchments that allows temporal and spatial comparison along the region. Water, water use benefits and water allocations are currently receiving much attention from Australian policy makers and conservation practitioners. Because of the inherent complexity and variability in water quality, it is essential that scientific information is presented in a meaningful way to policy makers, managers and ultimately, to the general public who have to live with the consequences of the decisions. We developed an inexpensively populated and easily understandable water use benefit index as a tool for community-based monitoring of water related trends in the Great Barrier Reef region. The index is developed based on a comparative list of selected water-related indices integrating attributes across physico-chemical, economic, social, and ecological domains currently used in the assessment of water quality, water quantity and water use benefits in Australia. Our findings indicate that the proposed index allows the identification of water performance indicators by temporal and spatial comparisons. Benefits for decision makers and conservation practitioners include a flexible way of prioritization towards the domain with highest concern. The broader community benefits from a comprehensive and user-friendly tool, communicating changes in water quality trends more effectively. [ABSTRACT FROM AUTHOR]

Summary: This paper presents results from a modelling study carried out to investigate whether the calibrated parameter values for rainfall–runoff models based on historical observed data can be used to reliably predict runoff responses to changes in future climate inputs. The modelling experiments are carried out for four rainfall–runoff models using long records of historical daily climate and streamflow data from 61 catchments in southeast Australia. The results indicate that the models, when calibrated using more than 20years of data, can generally be used for climate impact studies where the future mean annual rainfall is not more than 15% drier or 20% wetter than the mean annual rainfall in the model calibration period. It is generally more difficult for a model calibrated over a wet period to predict runoff over a dry period compared to a model calibrated over a dry period to predict runoff over a wet period. For southeast Australia, there is a good reason to use the recent records to calibrate rainfall–runoff models to represent the current prolonged drought over the region and for climate change impact studies where the large majority of climate models predict a drier future across this region. [ABSTRACT FROM AUTHOR]

Summary: Australia is typical of many countries in that it has an extensive network of rainfall recording stations which record rainfall data in various forms ranging from a daily time step down to 6-min resolution. However, while the length of historical daily records is often large, there are very few 6-min (pluviograph) records available of significant length. Not only does this lack of significant short time scale data impose a major obstacle in the application of a Monte Carlo approach to risk estimation, it also inhibits the application of rainfall simulation models that use this data for direct calibration. While the advent of numerous stochastic rainfall models provides methods for extending historical rainfall records, without adequate historical rainfall data available for calibration their accuracy is questionable. This paper describes the development of a new technique which significantly extends the applicability of stochastic point rainfall models that require historical data for calibration. The technique uses a new ‘master–target’ scaling relationship. A model calibration is undertaken at a ‘master’ site with a long pluviograph record, which is then scaled to the ‘target’ site using the information from the target site in form of either a short pluviograph or a daily rainfall record. This approach removes the need for significant pluviograph data at the ‘target’ site and enables the stochastic rainfall model to be applied at sites with either short pluviograph or daily rainfall records. The master–target scaling technique is demonstrated using an existing high-resolution point rainfall model based on wet-dry alternating storm events. Extensive testing using numerous pairs of Australian sites demonstrates its validity. [ABSTRACT FROM AUTHOR]

Summary: Transpiration is a major component of the water balance of tropical rainforests and it is important to know how it might change in response to climate and/or land use change. Substantial amounts of transpiration data are now available for Australian rainforests in northern Queensland and this paper presents an analysis of these data for four different rainforest types. Measured transpiration rates are much lower than potential transpiration rates, largely because the rainforest canopy conductances are much lower than that associated with the potential rate. Canopy conductance was found to be strongly related to atmospheric vapour pressure deficit and only weakly to solar radiation; because the forests are highly coupled to the atmosphere, due to their very high aerodynamic conductance. The best predictive model of rainforest transpiration is one based directly on daily average vapour pressure deficit and solar radiation. Models based on canopy conductance can give daytime transpiration rates, but they do not account for the significant amounts (20–50%) of nocturnal transpiration that are observed. We also found little/no evidence for any direct soil moisture influence on canopy conductance or transpiration. [Copyright &y& Elsevier]

Summary: Melbourne is facing a severe drought having its 12th consecutive below average rainfall year. Water authorities have been forced to impose rigorous water restrictions including voluntary per capita water use targets after more than 20years of unrestricted water supply. The current severe drought and dwindling water resources have accelerated the use of alternative water sources including domestic rainwater. There is a large variation in average annual rainfall in the Greater Melbourne area ranging from 1050mm in the east to 450mm in the west. Hence, there is a significant difference in the tank size required in the west and the east of Melbourne to meet a similar demand and to provide the same supply reliability. The paper presents a novel methodology and a relationship for optimal sizing of rainwater tanks considering the annual rainfall at the geographic location, the demand for rainwater, the roof area (catchment area) and the desired supply reliability. The characteristic of the developed dimensionless curve reflects these variables and paves the way for developing a web based interactive tool for selecting the optimum rainwater tank size. [Copyright &y& Elsevier]

Summary: The paper conceptualises new environmental derivatives, water options, which can be used as an important leverage to facilitate the growing Australian water market. A model of water option pricing has been developed. This model is based on the theory of stochastic processes with jumps. The developed model is justified by analysis of real price dynamics in water market in northern Victoria, Australia. The price of European call options is modelled as a Brownian motion and compound Poisson process. The numerical simulation for modelled water and option prices is obtained for a set of predefined jumps and hypothetical water price. Potential impacts of the introduction of water options on irrigators and the water market are discussed. [Copyright &y& Elsevier]

Summary: Functional-coefficient models with a periodic component are proposed for short-term streamflow forecasting. Traditionally, analyses are conducted for anomaly data after removing an annual pattern or detrending the data after data differencing. Alternatively, periodic models establish separate models for individual seasons. However, the setting of periodic models cannot guarantee the smoothness in model coefficients which is necessary when the time scale is small (for example, daily). In this paper we consider the use of functional-coefficient models with a periodic component, which extend the periodic regression for short-term forecasting. Unlike the traditional functional-coefficient models which extend the threshold regression model, our functional-coefficient model with a periodic component enjoys an invariance property under data differencing. As case studies, the models are applied to Australian streamflows in three typical climate conditions and Ying Luo Gorge (YLX) in Hei River of North-Western China. [Copyright &y& Elsevier]

• Storage-discharge relation of groundwater was determined by a tracer-aided approach. • Dynamics of groundwater discharge in response to precipitation were assessed. • Residence time of groundwater was quantified based on storage-discharge relations. Sustainable groundwater management is becoming increasingly important due to intensifying water deficits around the world, which highlights the necessities and challenges to explore the dynamics of groundwater at the catchment-scale. In this paper, a tracer-aided approach has been developed and applied to quantify the relationships between groundwater storage and discharge, upon which the dynamics of groundwater storage and its residence time are assessed. Robust nonlinear storage-discharge relationships are found for the eight studied catchments located in a headwater region of the Murray-Darling Basin, Australia. The dynamics of groundwater storage for all the studied catchments largely show synchronization with regional climate. The estimated highest and active groundwater storages in a year are more responsive to precipitation than the lowest storages. For all the studied catchments, during dry seasons without recharge, the depletion rate of groundwater is found to decrease with the decline in groundwater storage, which can be ascribed to the vertical heterogeneity of their unconfined aquifers. The mean residence time of groundwater represented by MRT 75 (i.e. time taken for groundwater storage to diminish from its mean level to the 75th percentile) varies between 57 and 111 days across the studied catchments, suggesting considerable hydrogeological differences among the studied catchments. The groundwater dynamics observed in this research could contribute to improving hydrogeological model performance in simulating the connection between groundwater and surface water at the catchment-scale. Moreover, the findings could serve to develop guidelines for groundwater extraction when considering the constraints of environmental flow requirements. [ABSTRACT FROM AUTHOR]

Summary: This paper assesses the impact and quantifies the relative contribution of stormwater runoff (diffuse pollution sources) and point pollution sources on the quality of receiving water in the urban catchment of Orange. The study results were employed to develop management strategies to control stormwater pollution at the catchment level. The Orange urban catchment has experienced moderate to high levels of pollution in terms of nutrients (P and N), suspended solids (SS), heavy metals, fecal coliforms, and, to a lesser extent, salinity (TDS). Treated sewage effluent (point source) contributed, on average, 5% SS, 29% total nitrogen (TN) and 41% total phosphorous (TP). The nutrient yield per unit area was 2–31-folds higher than those reported for other Australian urban catchments. The overall contribution of the urban sources accounted for 93% and 94% of the TP and TN mass loads, respectively. In contrast, stormwater pollution in coastal urban catchments, that have similar population and land use, is dominated by rural diffuse sources contributing 81–99% of nutrient mass loads. This striking difference is attributed largely to the position of the catchment with respect to the hydrological system. Orange urban catchment is situated at the headwater of its river system and as such rural runoff into the urban part of the catchment was limited. The coastal catchments, on the other hand, are located at the end of their river systems and thus rural inflow into the urban area is substantially higher than those in upland catchments. This comparative assessment may suggest that the relative impact, per capita, of urban stormwater pollution on the receiving water is more significant in the upland catchments than in the coastal catchments. A stormwater management plan (SMP), consisting of structural and non-structural strategies, was developed to control stormwater pollution and enhance the quality of receiving water. [Copyright &y& Elsevier]

Summary: This paper reports a comparison of measured and modelled interception for three different forest types at six rainforest locations in northern Queensland. The Gash interception model was able to reproduce cumulative interception at the sites accurately, provided an appropriate value of canopy storage capacity (S) was used, 2.0–3.6mm. These values are significantly higher than S values generally reported in other rainforest studies (∼1mm) and the reason may be that Australian rainforests contain many epiphytes and mosses, which can trap significant quantities of water within the canopy. There is also some evidence of a seasonal variation in S and wet canopy evaporation rate (E), both being lower in the dry season than the wet season. However, although the rainfall rate (R), S and E all affect the seasonal value of interception, the changes in these three parameters tend to compensate and so the biggest factor affecting seasonal variations in interception is the number of small storms. The consequence of this is that it is still possible to get good estimates of seasonal and annual interception using R, S and E values that are fixed for the entire year. Values of E fell in the range 0.35–0.81mmh−1, which are 1.4–9 times the concurrent rates estimated using the Penman–Monteith equation. This implies that either our rainforests received very large amounts of advected energy during rain storms, or the Penman–Monteith E values are too low. Some advection of energy to our sites is quite feasible given their proximity to the ocean and generally well exposed locations. However, most of the above discrepancy is probably due to underestimation of the Penman–Monteith values of E, because of errors in the estimation of the above canopy relative humidity, due to the use of weather data adjacent to rather than above the forests and inherent difficulties of measuring the very high humidity’s that occur during rainfall. [Copyright &y& Elsevier]

Summary: The extreme value distributions of hydrologic variables such as rainfall and streamflow play a key role in the design of water-related infrastructure. The relationship between the intensity, frequency and duration of extreme values is commonly evaluated using observed annual maximum values. This paper considers maximum values that occur within a given season and the relationship between seasonal maxima and annual maxima. Using a climatic index, the seasonal and annual maxima are also partitioned into multiple climatic states so that comparisons of seasonal maxima both within and between climatic states can be made. The distributions of seasonal and annual rainfall maxima are investigated for numerous locations around Australia and a detailed analysis, involving both seasonal and climatic partitions, is given for the daily streamflows of the Murray–Darling Basin, Australia. The results demonstrate significant differences between seasonal distributions within and between climatic states. The application of seasonal extremes to an urban design scenario is illustrated for Scott Creek in the Adelaide Hills, South Australia. The methodology presented can be used in a general setting to gain insight into the relationship between extremes from seasonal and climatic periods to their annual counterparts. [Copyright &y& Elsevier]

Summary: Soil moisture patterns and dynamics are important for numerous applications such as flood forecasting, climate modeling, and management of agricultural lands. Unfortunately, widespread observations of soil moisture are not currently available at the spatial scale of most of these applications. Given these data limitations and the complexity of soil moisture dynamics, there is a need to gain a better understanding of soil moisture patterns and to develop methods that can efficiently estimate these patterns from limited observations. In this paper, we use Empirical Orthogonal Function (EOF) analysis to study the Tarrawarra soil moisture dataset from Australia. EOF analysis partitions the observed variation into a series of time-invariant spatial patterns (EOFs) that can be multiplied by temporal varying (but spatially constant) coefficients and summed to reconstruct observed soil moisture patterns. Using this approach, we identify two spatial patterns underlying soil moisture variability at Tarrawarra, which supports previous contentions that the spatial patterns are controlled by local soil properties in wet and dry conditions and topographic characteristics during intermediate conditions. We also use the EOF analysis to identify points whose variability is most representative of each of the underlying spatial patterns and thus can be used to monitor these distinct modes of variability. Finally, we show that the EOF approach can be used to estimate soil moisture patterns for unobserved times if a field campaign has collected detailed soil moisture observations for a limited time period in the past. [Copyright &y& Elsevier]

Summary: This paper addresses the issue of hydrological connectivity between discrete units of the landscape, notably, upland and riparian zones, and its implication for runoff generation and chemical transport. It presents results based on a field experiment carried in Susannah Brook catchment in Western Australia, during which measurements of relevant physical and chemical parameters were carried out using a sampling strategy that enabled us to capture the complete cycle of hydrological connection and disconnection over the entire hillslope. The results show that the upland and riparian zones respond to rainfall events almost independently and differently, and remain disconnected from each other for much of the year. During a 2–3 month period in mid-winter, however, a shallow groundwater system becomes established all the way across the hillslope, providing a direct hydrological connection between the two zones, enabling not only down-slope transport of fresh water but also nitrates that had previously accumulated in the upland zone. Associated with the establishment of connectivity is a sharp increase in the hydraulic gradient that drives shallow subsurface flow to the stream. These results have important implications for the modelling of runoff generation and nutrient export. The lack of connectivity for much of the year precludes the use of models that assume that the shallow subsurface flow system is connected all the way up the slope, and that hydraulic gradient is equal to local topographic gradient. The findings relating to hydrological connectivity also have important ramifications for Cl− and transport and export. The complex internal dynamics of flow, transport and reaction, and their dependence on hydraulic connectivity, must be explicitly captured if we are to develop predictive models that remain accurate as well as internally consistent. [Copyright &y& Elsevier]

Summary: This paper presents a comparison of three multi-site stochastic weather generators for simulation of point rainfall occurrences at a network of 30 raingauge stations around Sydney, Australia. The approaches considered include a parametric hidden Markov model (HMM), a multi-site stochastic precipitation generation model (proposed by [Wilks, D.S., 1998. Multi-site generalization of a daily stochastic precipitation model, J. Hydrol. 210, 178–191.]) and a non-parametric K-nearest neighbour (KNN) model. The HMM generates the precipitation distribution conditional on a discrete weather state representing certain identified spatial rainfall distribution patterns. The spatial dependence is maintained by assumption of a common weather state across all stations while the temporal dependence is simulated by assuming the weather state to be Markovian in nature. The Wilks model preserves serial dependence through the assumption of an order one Markov dependence at each location. The spatial dependence is simulated by prescribing a dependence pattern on the uniform random variates used to generate the rainfall occurrence at each location from the associated conditional probability distribution. The K-nearest neighbour approach simulates spatial dependence by simultaneously generating precipitation occurrence at all locations. Temporal persistence is simulated through Markovian assumptions on the rainfall occurrence process. The three methods are evaluated for their ability to model spatial and temporal dependence in the rainfall occurrence field and also the relative ease with which the assumptions of spatial and temporal dependence can be accommodated. Our results indicate that all the approaches are successful in reproducing spatial dependence in the multi-site rainfall occurrence field. However, the different orders of assumed Markovian dependence in the observed data limit their ability in representing temporal dependence at time scales longer than a few days. While each approach comes with its own advantages and disadvantages, the alternative proposed by Wilks has an overall advantage in offering a mechanism for modelling varying orders of serial dependence at each point location, while still maintaining the observed spatial dependence with sufficient accuracy. [Copyright &y& Elsevier]

Abstract: The influence of irrigation and dryland land-use development has increased groundwater recharge, leading to increased discharge of existing saline groundwater and increased the salt load to adjacent rivers. This paper describes further development of a unit response approach to assess the effect and timing of changes to recharge on groundwater discharge to a river. The unit response approach uses a linearised Boussinesq equation to develop an effectively one-dimensional unit response function for a change in recharge to an aquifer some distance from the river. The Glover and Balmer solution for stream depletion is extended from a point sink to an areal source case, and the effects of both a sloping and a bounded aquifer are considered. This approach can provide approximate lead-in and lag times for the impacts of recharge change on groundwater discharge and salt load to rivers. This approach has been applied to estimate the effects of the development of the Bookpurnong Irrigation Area (Loxton, South Australia), and compared with the existing estimates. [Copyright &y& Elsevier]