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Although traditional physical models have been used to analyse groundwater systems, the emergence of novel machine learning models can improve the accuracy of the predictions. Deep learning has been prominent in environmental and climate change problems. In this paper, we present a framework for utilising deep learning models to predict groundwater levels based on nearby streamflow and rainfall data. We address the missing data problem using a Bayesian linear regression model within the deep learning framework. Our deep learning framework utilises models such as long-short term memory (LSTM) networks and convolutional neural networks (CNN) for multi-step ahead time series prediction. We examine the fluctuations in groundwater levels at various boreholes located near Middle Creek in New South Wales, Australia. We use the National Collaborative Research Infrastructure Strategy (NCRIS) groundwater database and utilise Bayesian linear regression to impute missing data. We investigate the accuracy of the selected models for individual and regional basins and univariate and multivariate strategies. Our results show that the LSTM-based regional model with multivariate strategy using rainfall data provided the best accuracy. • We predict groundwater levels based on stream flow and rainfall data. • Our framework uses Bayesian data imputation and deep learning for multi-step prediction. • We examine the groundwater fluctuations at various boreholes located in Australia. • Results show a fine-tuned regional model with rainfall data yielded the best accuracy. • We find that LSTM models outperformed CNNs using the multivariate approach. [ABSTRACT FROM AUTHOR]

Spatial predictions of wildfire spread are used operationally and in risk estimation. It is important that their outputs are validated to quantify predictive performance and uncertainty. There are numerous loss functions for this simulation validation. Our paper synthesises ten common and five novel loss functions for evaluating the performance of wildfire predictions against observed or simulated wildfires. We describe each loss function; test their sensitivity to scale, rotation and translation; and demonstrate their application with three case study wildfires in south-eastern Australia. Based on their purpose, there are three categories of loss functions: general overlap, partial overlap and distance. Within these categories, there is functional redundancy as many loss functions are appropriate and perform similarly. Using loss functions from across the three different categories is the most comprehensive for evaluating wildfire models, but the choice depends on the simulation purpose and interpretation. • Spatial predictions of wildfire spread are frequently used but not always evaluated. • We synthesised fifteen loss functions for evaluating predicted vs observed wildfire spread. • There is not a 'best' loss function, it depends on simulation use and interpretation. [ABSTRACT FROM AUTHOR]

Accurately predicting estuaries water quality is essential to support immediate intervention to water quality problem management. Deep learning is used to improve forecasting of water quality parameters in many aquatic systems. However, it is frequently constrained by low data frequency and quality. High-frequency, continuous monitoring using integrated in-situ water quality and environmental sensors can be an input to deep learning models resulting in highly accurate water quality and environmental predictions. This paper proposes a novel approach to improve forecasting of water quality and environmental variables. The results of the real-world data from the Swan Canning Estuary sites show how well the suggested model works. With different sizes of training and testing sets, the model can still predict the increased number of hours in high scores. Eliminating highly correlated variables impact the model's performance, emphasising the usefulness of strongly correlated variables in scarce data scenarios. • This article proposes a framework to enhance water quality variable forecasting. • The framework reframes the time series problem as a supervised learning problem. • The results on the set of data from the estuary show how well the framework works. • The approach is envisaged to support management in detecting water quality issues. [ABSTRACT FROM AUTHOR]

Despite the considerable success of deep learning methods in modelling physical processes, they suffer from a variety of issues such as overfitting and lack of interpretability. In hydrology, conceptual rainfall-runoff models are simple yet fast and effective tools to represent the underlying physical processes through lumped storage components. Although conceptual rainfall-runoff models play a vital role in supporting decision-making in water resources management and urban planning, they have limited flexibility to take data into account for the development of robust region-wide models. The combination of deep learning and conceptual models has the potential to address some of the aforementioned limitations. This paper presents a sub-model hybridization of the GR4J rainfall-runoff model with deep learning architectures such as convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The results show that the hybrid models outperform both the base conceptual model as well as the canonical deep neural network architectures in terms of the Nash–Sutcliffe Efficiency (NSE) score across 223 catchments in Australia. We show that our hybrid model provides a significant improvement in predictive performance, particularly in arid catchments, and generalizing better across catchments. • Hybrid modelling features parsimony of data-driven and conceptual hydrologic models. • We present a deep learning-based hybridization for the GR4J rainfall-runoff model. • We present a hierarchical training approach consisting of gradient-based and evolutionary optimization of the respective models. • The results show that hybrid models outperform the canonical (base) hydrologic and deep learning model. • The addition of surface temperature and antecedent streamflow further improves the predictive performance. [ABSTRACT FROM AUTHOR]

Abstract Computer modelling and design tools can assist in environmental management. In particular, post-mining landscapes with large volumes of materials require shaping for optimal erosional stability and ecological and visual integration into the surrounding undisturbed landscape. This paper evaluates the complementary capabilities of landscape evolution modelling (SIBERIA) and geomorphic design software (Natural Regrade with GeoFluv). An existing 11.5-ha waste rock dump (Hunter Valley, New South Wales, Australia) served as the study site. The SIBERIA modelling demonstrated that geomorphic design reduced erosion by half that of conventional designs while being able to store an extra 7% of mine waste volume. Additionally, the spatial pattern of gullying was able to be predicted by modelling, which allowed management in subsequent geomorphic design, and successively more stable patterns. In conclusion, the joint use of the Natural Regrade with GeoFluv geomorphic design software with the SIBERIA landscape evolution model showed complementary capabilities for enhancing mine rehabilitation. Highlights • Computer models and modelling design greatly improve mine site rehabilitation. • We demonstrate the SIBERIA and Natural Regrade with GeoFluv software. • The complementary tools reduce erosion yet can increase landscape volume. [ABSTRACT FROM AUTHOR]

Abstract: Economic analysis of climate scenarios and alternative water policies is critical for development and implementation of appropriate water policies and programs. Mathematical models have been developed to assess water resources policies due to their ability to explicitly represent the biophysical dynamics of natural systems while integrating these within social and economic constraints. These models have been criticised, however, due to the problems of simplification, overspecialisation, plausibility and lack of empirical validation. This paper introduces a mathematical programming model which uses positive mathematical programming method to calibrate and model agriculture and water use in the Murray-Darling Basin of Australia. This paper reviews the theoretical and technical details of the model development including the key steps taken in collating and scaling the biophysical and economic data, and to address model parameterisation issues. The paper summarises results of an application of the model for assessing climate change impacts in the form of reduced rainfall and water allocations and increased crop water use for agricultural production. The results show the degree of variability in gross values under different climate scenarios compared to the base case scenario, especially in very dry years. The results also show how on-farm adaptation options and water markets can mitigate these losses. [Copyright &y& Elsevier]

Fire behaviour and spread predictions guides suppression strategies and public warnings during wildfires. The scientific understanding of fire behaviour forms the core of these predictions, but is incomplete, and expert judgement and experience are required to augment the evidence based knowledge. Amicus is a new decision support system that implements contemporary, published and operationalised bushfire behaviour models (e.g. rate of spread, flame height, fireline intensity, spotting distance) in the Australian bushfire context. It enables the inclusion of expert judgement and local knowledge, allows users to analyse temporal trends and uncertainty in inputs, and facilitates reliable and practical predictions. This paper provides a comprehensive overview of Amicus, including its operation and functionality, identifies the boundaries of the current understanding of fire science, discusses the major limitations in existing knowledge, and provides a framework for allowing deterministic and anecdotal/local knowledge to be incorporated into formal fire behaviour predictions. [ABSTRACT FROM AUTHOR]

Abstract: Regional-scale ecological restoration priorities such as increasing the extent and quality of native vegetation are generally planned at catchment scales, while on-ground restoration actions are generally implemented at paddock or farm scales. This paper describes the use of spatial multi-criteria assessment methodologies to construct maps of regional conservation priorities and assesses how these maps map influence farm-scale actions in Western Victoria, Australia (e.g. farm-scale revegetation for salinity, wind erosion, stock shelter, etc). The study also incorporates agricultural production in the decision analysis through the use of historical yield mapping data obtained from harvest logs from precision agriculture equipment. Via a stakeholder workshop, farmer land use priorities were elicited with and without access to maps of regional conservation priorities. Results highlight that production imperatives drive farmer-led conservation actions and that regional conservation priorities have only limited impact on actions. The paper also identifies limitations of applying MCA methods across multiple decision-making scales such as the need to generalise priorities where domain knowledge is relatively high, and the challenges associated with MCA criteria definition. [ABSTRACT FROM AUTHOR]

Abstract: Agriculture is the highest consumer of water resources in Australia. Soil water models play a vital role in agriculture in terms of estimating water use, water allocation and current water status at a given scale. This paper reviews widely-used soil water models developed in Australia over the last three to four decades that have been used to simulate soil water status at various temporal and spatial scales. These models are categorised in terms of their complexity. This paper provides an overview of soil water models and the basic modelling techniques employed by each model. Considerable emphasis is given to matching existing data availability with input data requirements for each model to identify the limitations of model application in terms of data availability. A comprehensive review of the application of soil water models is also given, supported by assessments of individual model performance. The limitations and assumptions made under various approaches to soil water modelling are subsequently examined. Research and policy agencies are focusing more and more on incorporating temporal models into spatial modelling frameworks for natural resources and water management purposes. These are consequently being used much more in the process of policy development. Complex models, whose wide-range application is often hampered by a lack of specific data, should have their processes simplified in order to be accommodated into spatial frameworks where appropriate. Biophysical processes within simple models should consider new data sources and understanding so as to gain more accurate predictions. [Copyright &y& Elsevier]

Coastal lakes are ecosystems of significant value generating many ecological, social and economic benefits. Increasing demands resulting from urban development and other human activities within coastal lake catchments have the potential to result in their degradation and can lead to conflicts, for example between lake users and upstream communities. There are many techniques that can be used to integrate the variables involved in such conflicts including system dynamics, meta-modelling, and coupled component models, but many of these techniques are too complex for catchment managers to employ on a routine basis. The overall result is the potential to compromise the sustainability of these important ecosystems. This paper describes research to address this problem. It presents the development of an integrated model framework based on a Bayesian network (Bn). Bns are used to assess the sustainability of eight coastal lake-catchment systems, located on the coast of New South Wales (NSW), Australia. The paper describes the potential advantages in the use of Bns and the methods used to develop their frameworks. A case study application for the Cudgen Lake of northern NSW is presented to illustrate the techniques. The case study includes a description of the relevant management issues being considered, the model framework and the techniques used to derive input data. Results for the case study application and their implications for management are presented and discussed. Finally, the directions for future research and a discussion of the applicability of Bn techniques to support management in similar situations are proffered. [Copyright &y& Elsevier]

Abstract: Surface–groundwater (SW–GW) interactions constitute a critical proportion of the surface and groundwater balance especially during dry conditions. Conjunctive management of surface and groundwater requires an explicit account of the exchange flux between surface and groundwater when modelling the two systems. This paper presents a case study in the predominantly gaining Boggabri–Narrabri reach of the Namoi River located in eastern Australia. The first component of the study uses the Upper Namoi numerical groundwater model to demonstrate the importance of incorporating SW–GW interactions into river management models. The second component demonstrates the advantages of incorporating groundwater processes in the Namoi River model. Results of the numerical groundwater modelling component highlighted the contrasting groundwater dynamics close to, and away from the Namoi River where lower declines were noted in a near-field well due to water replenishment sourced from river depletion. The contribution of pumping activities to river depletion was highlighted in the results of the uncertainty analysis, which showed that the SW–GW exchange flux is the most sensitive to pumping rate during dry conditions. The uncertainty analysis also showed that after a drought period, the 95% prediction interval becomes larger than the simulated flux, which implies an increasing probability of losing river conditions. The future prospect of a gaining Boggabri–Narrabri reach turning into losing was confirmed with a hypothetical extended drought scenario during which persistent expansion of groundwater pumping was assumed. The river modelling component showed that accounting for SW–GW interactions improved the predictions of low flows, and resulted in a more realistic calibration of the Namoi River model. Incorporating SW–GW interactions into river models allows explicit representation of groundwater processes that provides a mechanism to account for the impacts of additional aquifer stresses that may be introduced beyond the calibration period of the river model. Conventional river models that neglect the effects of such future stresses suffer from the phenomenon of non-stationarity and hence have inferior low flow predictions past the calibration period of the river model. The collective knowledge acquired from the two modelling exercises conducted in this study leads to a better understanding of SW–GW interactions in the Namoi River thus leading to improved water management especially during low flow conditions. [Copyright &y& Elsevier]

Abstract: Fire danger indices are used by fire management agencies to assess fire weather conditions and issue public warnings. The most widely used fire danger indices in Australia are the McArthur Fire Forest Danger Index and the Grassland Fire Danger Index. These indices are calculated at weather stations using measurements of weather variables and fuel information. For a vast country like Australia when assessing the risk of severe fire weather events, it is also important to calculate the spatial distribution of these indices considering the extreme tail of the distribution. The spatial distribution of one of the fire weather danger indices regularly used in Australia is presented in this paper. In particular, we present the spatial distribution of the long-term tendency of extreme values of the McArthur Forest Fire Danger Index (FFDI). This indicator of fire weather conditions was assessed by calculating the return period of its extreme values by fitting extreme value distributions to data sets of FFDI at 78 recording stations around Australia. The spatial distribution of these return periods was obtained by using spatial interpolation algorithms with the recording stations measurements. Two conventional and two new algorithms based on machine-learning techniques were tested. This study shows that the best interpolation results for the FFDI can be obtained by using a combination of random forest and inverse distance weighting interpolation algorithms. The spatial distribution of the seasonal FFDI return period shows that the highest FFDI over large parts of southern Australia occurs during the summer months whilst in northern Australia it occurs in spring. The results also show that the FFDI in eastern Australia, the most populated region of the country, is higher inland than in the coastal areas particularly during spring and summer. [Copyright &y& Elsevier]

Abstract: Approximately 1.3 million ha of forested and agricultural land in south-eastern Australia was burnt by wildfires in early 2003. This paper describes a modelling process to assess the impacts of the fires on the quality of receiving waters and river systems in the fire-affected catchments. First, we construct and parameterise the E2 catchment modelling framework to represent the flow, and sediment and nutrient loads for the water storages and river systems in fire-affected sub-catchments and second, we assess likely impacts of the fires on loads of total suspended sediments (TSS), total nitrogen (TN) and total phosphorus (TP). Very good calibration (with coefficient of efficiency values generally greater than 0.8) of the rainfall-runoff models to observed flow at several gauging stations within each catchment was achieved. Digitised land use layers were reclassified to form functional units representing unburnt and burnt land uses. Pre- and post-fire loads of TSS, TN and TP predicted by the model at end of catchment outlets and water storages were then compared relative to pre-fire loads. Compared to pre-fire conditions, the models predicted that the Ovens, Kiewa, Upper Murray and Snowy catchments would deliver, on average, approximately 30 times greater TSS, 5 times greater TN, and 8 times the amount of TP. Proportional increases in predicted loads at the catchment outlets were generally smaller than increases observed at water quality monitoring sites. These differences reflect the proximity of the monitoring stations to the burnt areas, the total percentage of catchment burnt, and the amount of rainfall. The predictions of load increases carry important assumptions and limitations, and such an approach can only be used for making relative assessments of the impacts of the fires on average suspended sediments and nutrient loads. [Copyright &y& Elsevier]

Abstract: Influencing the management of private landholders is a key element of improving the condition of Australia’s natural resources. Despite substantial investment by governments, effecting behavioural change on a scale likely to stem biodiversity losses has proven difficult. Understanding landholder decision-making is now acknowledged as fundamental to achieving better policy outcomes. There is a large body of research examining landholder adoption of conservation practices. Social researchers are able to employ a suite of conventional techniques to analyse their survey data and assist in identifying significant and causal relationships between variables. However, these techniques can be limited by the type of data available, the breadth of issues that can be represented and the extent that causality can be explored. In this paper we discuss the findings of a unique study exploring the benefits of combining Bayesian Networks (BNs) with conventional statistical analysis to examine landholder adoption. Our research examined the landholder fencing of native bushland in the Wimmera region in south east Australia. Findings from this study suggest that BNs provided enhanced understanding of the presence and strength of causal relationships. There was also the additional benefit that a BN could be quickly developed and that this process helped the research team clarify and understand relationships between variables. However, a key finding was that the interpretation of the results of the BNs was complemented by the conventional data analysis and expert review. An additional benefit of the BNs is their capacity to present key findings in a format that is more easily interpreted by researchers and enables researchers to more easily communicate their findings to natural resource practitioners and policy makers. [ABSTRACT FROM AUTHOR]

Abstract: Concerns about the difficulties in securing water have led the Australian coal mining industry to seek innovative ways to improve its water management and to adopt novel strategies that will lead to less water being used and more water being reused. Simulation tools are essential to assess current water management performance and to predict the efficiency of potential strategies. As water systems on coal mines are complex and consist of various inter-connected elements, a systems approach was selected, which views mine site water management as a system that obtains water from various sources (surface, groundwater), provides sufficient water of suitable quality to the mining tasks (coal beneficiation, dust suppression, underground operations) and maintains environmental performance. In this paper, the model is described and its calibration is illustrated. The results of applying the model for the comparison of the water balances of 7 coal mines in the northern Bowen Basin (Queensland, Australia) are presented. The model is used to assess the impact of applying specific water management strategies. Results show that a simple systems model is an appropriate tool for assessing site performance, for providing guidance to improve performance through strategic planning, and for guiding adoption of site objectives. [Copyright &y& Elsevier]

Abstract: Disturbance of forested catchments by fire, logging, or other natural or human induced events that alter the evapotranspiration regime may be a substantial threat to domestic, environmental and industrial water supplies. This paper describes the physically-based modelling of the long term changes in water yield from two wildfire affected catchments in north-eastern Victoria, Australia, and of fire and climate change scenarios in Melbourne''s principal water supply catchment. The effect of scale, data availability and quality, and of forest species parameterisation are explored. The modelling demonstrates the importance of precipitation inputs, with Nash and Sutcliffe Coefficients of Efficiency of predicted versus observed monthly flows increasing from 0.5 to 0.8 with a higher density of rainfall stations, and where forest types are well parameterised. Total predicted flow volumes for the calibrations were within 1% of the observed for the Mitta Mitta River catchment and <4% for the Thomson River, but almost −10% for the less well parameterised Tambo River. Despite the issues of data availability simulations demonstrated the potential for significant impacts to water supply in SE Australia from wildfire and climate change. For example, for the catchments modelled the moderate climate change impact on water yield was more pronounced than the worst fire scenario. Both modelled cases resulted in long term water yield declines exceeding 20%, with the climate change impact nearing 30%. A simulation using observed data for the first four post-fire years at the Mitta Mitta River catchment showed Macaque was able to accurately predict total flow. [Copyright &y& Elsevier]

Abstract: Marxan is the most widely used conservation planning software in the world and is designed for solving complex conservation planning problems in landscapes and seascapes. In this paper we describe a substantial extension of Marxan called Marxan with Zones, a decision support tool that provides land-use zoning options in geographical regions for biodiversity conservation. We describe new functions designed to enhance the original Marxan software and expand on its utility as a decision support tool. The major new element in the decision problem is allowing any parcel of land or sea to be allocated to a specific zone, not just reserved or unreserved. Each zone then has the option of its own actions, objectives and constraints, with the flexibility to define the contribution of each zone to achieve targets for pre-specified features (e.g. species or habitats). The objective is to minimize the total cost of implementing the zoning plan while ensuring a variety of conservation and land-use objectives are achieved. We outline the capabilities, limitations and additional data requirements of this new software and perform a comparison with the original version of Marxan. We feature a number of case studies to demonstrate the functionality of the software and highlight its flexibility to address a range of complex spatial planning problems. These studies demonstrate the design of multiple-use marine parks in both Western Australia and California, and the zoning of forest use in East Kalimantan. [Copyright &y& Elsevier]

Abstract: The waste intermodal station of Clyde, in the city of Sydney, Australia, is in the heart of a complex network of terminals connected by road and rail to transport urban waste from its first collection to its final disposal. The amount of waste the network is projected to handle in 2015 will increase from about 340,000tonnes/year in 2006 up to about 1.5million tonnes/year, following population and consumption raise. The paper proposes a discrete-event model to represent Clyde Transfer Station (TS) and its relations with the other terminals. Such a model allows one to evaluate the effects of different expansion plans of the station structures as well as different policies of the collection service. The results show that, as often happens, a careful consideration of management alternatives can decrease the necessity of structural enlargement, which is normally much more expensive, if at all possible. [Copyright &y& Elsevier]

Fire danger rating systems are used to assess the potential for bushfire occurrence, fire spread and difficulty of fire suppression. Typically, fire danger rating systems combine meteorological information with estimates of the moisture content of the fuel to produce a fire danger index. Fire danger indices are used to declare fire bans and to schedule prescribed burns, among other applications. In this paper a simple fire danger index F that is intuitive and easy to calculate is introduced and compared to a number of fire danger indices pertaining to different fuel types that are used in an operational setting in Australia and the United States. The comparisons suggest that F provides a plausible measure of fire danger rating and that it may be a useful pedagogical tool in the context of fire danger and fire weather. [Copyright &y& Elsevier]

Abstract: Water availability in outback Australia is defined by the occurrence of large rainfall events, and changes often from a situation of scarcity to temporary abundance. Informal institutions are often able to translate such dynamics into sustainable water-use rules. Policy interventions are mainly focused on changing access rules to avoid over-use or inefficiencies. Such formal institutional changes can lead to unexpected unsustainable outcomes; outcomes that are often captured in the ‘story’ behind informal arrangements. This paper analyses one case study on water access in outback Australia and translates field work results into an agent-based model. The agent-based model is calibrated based on data from experiments conducted with actual farmers from the case study region. In order to project unintended outcomes of institutional changes, interventions in water access is explored in an applied context. The core focus of the modelling exercise is the treatment of newcomers on a newly created trading scheme for water access rights. Simulation results show that total water extraction is significantly lower if the burden of water restrictions is limited to newcomers while the regional economic performance is not statistically different from a case in which the burden is carried by all irrigators. Interview data documents that currently extraction levels are regulated by informal processes as community members communicate observations on river health to irrigators who adjust accordingly, which indicates perceived personal responsibility. Overall, the environmental and economic performance of a water trading scheme that will replace this informal self-regulating system depends on how water restrictions are implemented and how valid the overall cap of 20% outtake is, which is not widely accepted throughout the community. [Copyright &y& Elsevier]

Abstract: Jakeman et al. (Jakeman, A.J., Letcher, R.A., Norton, J.P., 2006. Ten iterative steps in development and evaluation of environmental models. Environmental Modelling & Software 21, 602–614) outline ten steps in the pursuit of good practice in model development and application to increase the credibility and impact of results from environmental models. This paper shows how the ten steps of model development are relevant to numerical groundwater modelling, using a model of a data-rich coastal groundwater system near Bowen in Queensland, Australia as an example. The model is Geographic Information System-based and estimates the dynamical water balance using Darcy''s Law. The method, which is generally applicable to data-rich aquifers, proved cost and time effective and provided important insights to the groundwater dynamics of the area. [Copyright &y& Elsevier]

Catchment managers face considerable challenges in managing ecological assets. This task is made difficult by the variable and complex nature of ecological assets, and the considerable uncertainty involved in quantifying how various threats and hazards impact upon them. Bayesian approaches have the potential to address the modelling needs of environmental management. However, to date many Bayesian networks (Bn) developed for environmental management have been parameterised using knowledge elicitation only. Not only are these models highly qualitative, but the time and effort involved in elicitation of a complex Bn can often be overwhelming. Unfortunately in environmental applications, data alone are often too limited for parameterising a Bn. Consequently, there is growing interest in how to parameterise Bns using both data and elicited information. At present, there is little formal guidance on how to combine what can be learned from the data with what can be elicited. In a previous publication we proposed a detailed methodology for this process, focussing on parameterising and evaluating a Bn. In this paper, we further develop this methodology using a risk assessment case study, with the focus being on native fish communities in the Goulburn Catchment (Victoria, Australia). [Copyright &y& Elsevier]

Abstract: This paper uses a scenario analysis system – Range-ASSESS – to examine the potential for gains and losses of soil carbon in the Australian rangelands as affected by grazing and climate. The analysis involves a factorial examination of the effect of stocking rates and all possible 5-year historical climates between 1889 and 1999. The analysis also looks at the sensitivity of results to the method of calculation of safe carrying capacity, and to the thresholds used to calculate grazing and dryness indices that drive transitions in state and transition models. The analysis showed that different vegetation zones produced different responses to changes in stocking depending upon the spatial distribution of dryness index, nature of carbon state and transition model, rules governing transitions, and relative significance of soil carbon. At a stocking density equivalent to 100% of 1997 levels, the soil carbon loss from rangelands was about 400 Mt C in 40% of the 5-year periods using a sensitive growth deviation threshold to determine dryness index. If a less sensitive threshold was used, potential loss was reduced to about 200 Mt C. If the grazing pressure threshold for a grazing index of four is adjusted to a more generous level, then potential losses in the dry periods are substantially reduced. The analysis is intended to be indicative of a likely approximate outcome rather than a quantitative measure of system response. The results indicate that the interpretation of the effect of the drought-grazing pressure interaction on perennial plant survival, and consequent organic carbon input to soils, is a major source of uncertainty and a critical area for more experimental measurement. [Copyright &y& Elsevier]

Climate models use broad definitions of vegetation-based biophysical parameters that may not represent the continentally-specific nature of Australian vegetation well. This paper explores the impact on the January simulation of climate over Australia of this common simplification. First, we map the Australian distribution of vegetation types onto the default classification used in one high-resolution climate model. Second, through a search of the literature we chose replacement values for the biophysical parameters to better reflect the properties of eucalypts. Third, we choose several sets of biophysical parameters and allow these to vary regionally. We assess the impact of these changes on simulated rainfall, temperature and latent heat flux over an ensemble of six different Januaries. We find that the model simulates rainfall and temperature reasonably well over Australia in January and that replacing the default parameter set with a single set of more appropriate values degrades model performance slightly. Allowing the biophysical parameters to vary regionally leads to some small improvements in the simulation of temperature and precipitation. We find large impacts on the simulated latent heat flux. Overall, the model is not substantially improved by careful selection of eucalypt parameters. We comment that given the shortage of observed data on eucalypts and the almost total absence of biophysical parameters for other Australian vegetation types, this lack of sensitivity to the biophysical parameters is reassuring since it implies that the lack of data is not presently seriously limiting to our particular modelling capability. [Copyright &y& Elsevier]

Viral epidemics killed 60–75% of Australia''s pilchards in 1995 and again in 1998/1999. The spread and impact of these epidemics depended upon features with relevant spatial scales of centimetres to continental and time scales of minutes to years or decades. A suite of models was developed to analyse different aspects of the spread and impact of the epidemics. No single model had to describe all scales, but models of epidemic spread required resolution of features on scales of minutes to months and kilometers to continental scale. This paper details the approaches used to solve the numerical problems presented by attempts to model the epidemics and their impacts and the computer programs used to solve the numerical methods. A phase-duration model of infection required the development of numerically efficient ‘conveyer-belt’ implementation and a novel analytical solution of epidemic wave velocity. An existing age-structure model was adapted to simulate post-epidemic recovery. The potential for management of the epidemics, in the light of modelling results, is discussed. [Copyright &y& Elsevier]

Many catchments in Australia have experienced increased sediment and nutrient loadings and concomitant declines in water quality and increases in problematic blue–green algal blooms over the past several decades. This paper describes an integrated hydrologic, sediment and nutrient export modelling framework known as catchment-scale management of diffuse sources (CatchMODS). The modelling framework is designed to simulate and assess catchment-scale land and water management activities designed to reduce nutrient and sediment delivery to receiving waters. The Ben Chifley Dam Catchment in New South Wales, Australia is used as a case study for describing the development of the framework. Improving water quality through reduction of sediment and nutrient loads will reduce the probability of algal bloom occurrence in the dam. The innovation of the system is the integration of otherwise separate modelling approaches to enable biophysical and economic assessment of different management options. The outputs from the research are used to improve and focus on-ground remediation, targeted to specific stream reaches or subcatchment areas, as well as to encourage sustainable management practices more broadly in the catchment. With minimal modification, the methods developed are applicable elsewhere to address water quality decline. [Copyright &y& Elsevier]

The Australian water balance model (AWBM) is a catchment water balance model that calculates runoff from rainfall at daily or hourly time increments. The daily version iss used for water yield and water management studies; the hourly version is used for design flood estimation. This paper describes the origin and development of the AWBM beginning with elementary modelling components of saturation overland flow. A particular feature of the AWBM is the development of model-specific calibration procedures based on the model structure, including a graphical analysis of rainfall and runoff data, multiple linear regression and an automatic self-calibrating procedure. Application of the model for daily streamflow simulation is illustrated using data from 19 catchments located across Australia. Application at hourly time steps for design flood estimation is demonstrated on three catchments in Victoria. A procedure for use of the model to estimate daily streamflows on ungauged catchments is illustrated using the 19 catchments from the water yield study. Applications of the model in several research programs are described. [Copyright &y& Elsevier]

In this paper, we undertake an empirical investigation into the possibility of climate change in Australian centres using average monthly air temperatures for six sites around the country over the period 1901:1–1998:12. By estimating a multivariate structural time series model and carrying out the appropriate tests, it is concluded that the temperature series is I(1). A graphical inspection of the extracted trends reveals that temperature has an upward trend in many centres. Given that the data may involve measurement errors, the results should be treated with caution. [Copyright &y& Elsevier]

A new computer-based tool, the Rainfall Reliability Wizard, is described for rapid spatial assessments of rainfall reliability. It employs conditional statistical methods to analyse rainfall reliability using over 100 years of gridded monthly rainfall data across Australia. Features of the Wizard include analyses of long term inter-seasonal and intra-seasonal reliability, discrete events, and the ability to analyse in absolute and relative terms. The Wizard is an easy-to-use program that runs on personal computers and a simplified version of the software is available on the Internet. Selected functions of the Wizard are illustrated in this paper which includes an analysis of rainfall reliability and the drought event of 1994 across the Australian wheat belt. [Copyright &y& Elsevier]

Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes that determine vertical reef accretion and assemblage changes in fossil reef drill cores. We present a Bayesian framework called Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core that represent environmental conditions affecting the growth of coral assemblages in geological timescales. Weencounter multimodal posterior distributions and investigate the challenges of sampling using Markov chain Monte-Carlo (MCMC) methods, which includes parallel tempering MCMC. We use a synthetic reef-core to investigate fundamental issues and then apply the methodology to a selected reef-core from the Great Barrier Reef in Australia. The results show that Bayesreef accurately estimates and provides uncertainty quantification of the selected parameters that represent environment and ecological conditions in pyReef-Core. Bayesreef provides insights into the complex posterior distributions of the parameters in pyReef-Core , which provides the groundwork for future research in this area. • Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. • pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes. • This paper presents Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core. • The results show that Bayesreef accurately estimates and provides uncertainty quantification for parameters in pyReef-Core. • Bayesreef provides insights in of pyReef-Core and establishes the groundwork for future research. [ABSTRACT FROM AUTHOR]