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Summary Monthly streamflow forecasts with long lead time are being sought by water managers in Australia. In this study, we take a first step towards a monthly streamflow modelling approach by harnessing a coupled ocean–atmosphere general circulation model (CGCM) to produce monthly rainfall forecasts for three catchments across Australia. Bayesian methodologies are employed to produce forecasts based on CGCM raw rainfall forecasts and also CGCM sea surface temperature forecasts. The Schaake Shuffle is used to connect forecast ensemble members of individual months to form ensemble monthly time series forecasts. Monthly forecasts and three-monthly forecasts of rainfall are assessed for lead times of 0–6 months, based on leave-one-year-out cross-validation for 1980–2010. The approach is shown to produce well-calibrated ensemble forecasts that source skill from both the atmospheric and ocean modules of the CGCM. Although skill is generally low, moderate skill scores are observed in some catchments for lead times of up to 6 months. In months and catchments where there is limited skill, the forecasts revert to climatology. Thus the forecasts developed can be considered suitable for continuously forecasting time series of streamflow to long lead times, when coupled with a suitable monthly hydrological model.

Summary Forecasting precipitation at the seasonal time scale remains a formidable challenge. In this study, we evaluate a statistical method for forecasting seasonal precipitation across China for 12 overlapping seasons. We use the Bayesian joint probability modelling approach to establish multiple probabilistic forecast models using eight large-scale oceanic-atmospheric indices at lag times of 1–3 months as predictors. We then merge forecasts from the multiple models with Bayesian model averaging to combine the strengths of the individual models. Forecast skill and reliability are assessed through leave-one-year-out cross validation. The merged forecasts exhibit considerable seasonal and spatial variability in forecast skill. The merged forecasts are most skillful over west China in spring periods and over central-south China in autumn periods. In contrast, forecast skill in most wet summer and dry winter periods is generally low. Positive forecast skill is mostly retained when forecast lead time is increased from 0 to 2 months. Forecast distributions are found to reliably represent forecast uncertainty. Climate indices derived from sea surface temperature in the western Pacific and Indian Ocean tend to contribute more to forecast skill than indices of the El Nino-Southern Oscillation. Large-scale atmospheric circulation patterns, represented by the Arctic Oscillation and North Atlantic Oscillation, appear to contribute little to forecast skill.

Summary This study describes a System for Continuous Hydrological Ensemble Forecasting (SCHEF) designed to forecast streamflows to lead times of 9 days. SCHEF is intended to support optimal management of water resources for consumptive and environmental purposes and ultimately to support the management of impending floods. Deterministic rainfall forecasts from the ACCESS-G numerical weather prediction (NWP) model are post-processed using a Bayesian joint probability model to correct biases and quantify uncertainty. Realistic temporal and spatial characteristics are instilled in the rainfall forecast ensemble with the Schaake shuffle. The ensemble rainfall forecasts are then used as inputs to the GR4H hydrological model to produce streamflow forecasts. A hydrological error correction is applied to ensure forecasts transit smoothly from recent streamflow observations. SCHEF forecasts streamflows skilfully for a range of hydrological and climate conditions. Skill is particularly evident in forecasts of streamflows at lead times of 1–6 days. Forecasts perform best in temperate perennially flowing rivers, while forecasts are poorest in intermittently flowing rivers. The poor streamflow forecasts in intermittent rivers are primarily the result of poor rainfall forecasts, rather than an inadequate representation of hydrological processes. Forecast uncertainty becomes more reliably quantified at longer lead times; however there is considerable scope for improving the reliability of streamflow forecasts at all lead times. Additionally, we show that the choice of forecast time-step can influence forecast accuracy: forecasts generated at a 1-h time-step tend to be more accurate than at longer time-steps (e.g. 1-day). This is largely because at shorter time-steps the hydrological error correction is able to correct streamflow forecasts with more recent information, rather than the ability of GR4H to simulate hydrological processes better at shorter time-steps. SCHEF will form the basis of a streamflow forecast service for Australia to be operated by the Bureau of Meteorology.

Summary For operational flood forecasting, discharge observations may be assimilated into a hydrologic model to improve forecasts. However, the performance of conventional filtering schemes can be degraded by ignoring the time lag between soil moisture and discharge responses. This has led to ongoing development of more appropriate ways to implement sequential data assimilation. In this paper, an ensemble Kalman smoother (EnKS) with fixed time window is implemented for the GR4H hydrologic model (modele du Genie Rural a 4 parametres Horaire) to update current and antecedent model states. Model and observation error parameters are estimated through the maximum a posteriori method constrained by prior information drawn from flow gauging data. When evaluated in a hypothetical forecasting mode using observed rainfall, the EnKS is found to be more stable and produce more accurate discharge forecasts than a standard ensemble Kalman filter (EnKF) by reducing the mean of the ensemble root mean squared error ( MRMSE ) by 13–17%. The latter tends to over-correct current model states and leads to spurious peaks and oscillations in discharge forecasts. When evaluated in a real-time forecasting mode using rainfall forecasts from a numerical weather prediction model, the benefit of the EnKS is reduced as uncertainty in rainfall forecasts becomes dominant, especially at large forecast lead time.

This study evaluates seasonal precipitation forecasts over China produced by statistically postprocessing multiple-output fields from the European Centre for Medium-Range Weather Forecasts' System4 (SYS4) coupled ocean-atmosphere general circulation model (CGCM). To ameliorate systematic deficiencies in the SYS4 precipitation forecasts, we apply a Bayesian joint probability (BJP) modeling approach to calibrate the raw forecasts. To improve the skill of the calibration forecasts, we use six large-scale climate indices, calculated from SYS4 sea surface temperature forecasts, to establish a set of BJP statistical bridging models to forecast precipitation. The calibration forecasts and bridging forecasts are merged through Bayesian model averaging to combine strengths of the different models. The BJP calibration effectively removes bias and improves statistical reliability of the raw forecasts. The calibration forecasts are skillful at a 0 month lead in most seasons, but skill decreases sharply at a 1 month lead. The skill of the bridging forecasts is more stable at different lead times. Consequently, the merged calibration and bridging forecasts at a 1 month lead are clearly more skillful than the calibration forecasts, and the skill is maintained out to a 4 month lead. The forecast framework used in this study can help to better realize the potential of CGCM ensemble forecasts. The increased reliability as well as improved skill of seasonal precipitation forecasts suggests that the system proposed here could be a useful operational forecasting tool.

Coupled general circulation models (GCMs) are increasingly being used to forecast seasonal rainfall, but forecast skill is still low for many regions. GCM forecasts suffer from systematic biases, and forecast probabilities derived from ensemble members are often statistically unreliable. Hence, it is necessary to postprocess GCM forecasts to improve skill and statistical reliability. In this study, the authors compare three methods of statistically postprocessing GCM output—calibration, bridging, and a combination of calibration and bridging—as ways to treat these problems and make use of multiple GCM outputs to increase the skill of Australian seasonal rainfall forecasts. Three calibration models are established using ensemble mean rainfall from three variants of the Predictive Ocean Atmosphere Model for Australia (POAMA) version M2.4 as predictors. Six bridging models are established using POAMA forecasts of seasonal climate indices as predictors. The calibration and bridging forecasts are merged through Bayesian model averaging. Forecast attributes including skill, sharpness, and reliability are assessed through a rigorous leave-three-years-out cross-validation procedure for forecasts of 1-month lead time. While there are overlaps in skill, there are regions and seasons where the calibration or bridging forecasts are uniquely skillful. The calibration forecasts are more skillful for January–March (JFM) to June–August (JJA). The bridging forecasts are more skillful for July–September (JAS) to December–February (DJF). Merging calibration and bridging forecasts retains, and in some seasons expands, the spatial coverage of positive skill achieved by the better of the calibration forecasts and bridging forecasts individually. The statistically postprocessed forecasts show improved reliability compared to the raw forecasts.

Skilful forecasts of high streamflows a month or more in advance are likely to be of considerable benefit to emergency services and the broader community. This is particularly true for mesoscale catchments (< 2000 km2) with little or no seasonal snowmelt, where real-time warning systems are only able to give short notice of impending floods. In this study, we generate forecasts of high streamflows for the coming 1-month and coming 3-month periods using large-scale ocean–atmosphere climate indices and catchment wetness as predictors. Forecasts are generated with a combination of Bayesian joint probability modelling and Bayesian model averaging. High streamflows are defined as maximum single-day streamflows and maximum 5-day streamflows that occur during each 1-month or 3-month forecast period. Skill is clearly evident in the 1-month forecasts of high streamflows. Surprisingly, in several catchments positive skill is also evident in forecasts of large threshold events (exceedance probabilities of 25%) over the next month. Little skill is evident in forecasts of high streamflows for the 3-month period. We show that including lagged climate indices as predictors adds little skill to the forecasts, and thus catchment wetness is by far the most important predictor. Accordingly, we recommend that forecasts may be improved by using accurate estimates of catchment wetness.

The majority of international climate modeling centers now produce seasonal rainfall forecasts from coupled general circulation models (GCMs). Seasonal rainfall forecasting is highly challenging, and GCM forecast accuracy is still poor for many regions and seasons. Additionally, forecast uncertainty tends to be underestimated meaning that forecast probabilities are statistically unreliable. A common strategy employed to improve the overall accuracy and reliability of GCM forecasts is to merge forecasts from multiple models into a multimodel ensemble (MME). The most widely used technique is to simply pool all of the forecast ensemble members from multiple GCMs into what is known as a superensemble. In Australia, seasonal rainfall forecasts are produced using the Predictive Ocean–Atmosphere Model for Australia (POAMA). In this paper, the authors demonstrate that mean corrected superensembles formed by merging forecasts from POAMA with those from three international models in the ENSEMBLES dataset remain poorly calibrated in many cases. The authors propose and evaluate a two-step process for producing MMEs. First, forecast calibration of the individual GCMs is carried out by using Bayesian joint probability models that account for parameter uncertainty. The calibration leads to satisfactory forecast reliability. Second, the individually calibrated forecasts of the GCMs are merged through Bayesian model averaging (BMA). The use of multiple GCMs results in better forecast accuracy, while maintaining reliability, than using POAMA only. Compared with using equal-weight averaging, BMA weighting produces sharper and more accurate forecasts.

[1] Seasonal streamflow forecasts are valuable for planning and allocation of water resources. In Australia, the Bureau of Meteorology employs a statistical method to forecast seasonal streamflows. The method uses predictors that are related to catchment wetness at the start of a forecast period and to climate during the forecast period. For the latter, a predictor is selected among a number of lagged climate indices as candidates to give the “best” model in terms of model performance in cross validation. This study investigates two strategies for further improvement in seasonal streamflow forecasts. The first is to combine, through Bayesian model averaging, multiple candidate models with different lagged climate indices as predictors, to take advantage of different predictive strengths of the multiple models. The second strategy is to introduce additional candidate models, using rainfall and sea surface temperature predictions from a global climate model as predictors. This is to take advantage of the direct simulations of various dynamic processes. The results show that combining forecasts from multiple statistical models generally yields more skillful forecasts than using only the best model and appears to moderate the worst forecast errors. The use of rainfall predictions from the dynamical climate model marginally improves the streamflow forecasts when viewed over all the study catchments and seasons, but the use of sea surface temperature predictions provide little additional benefit.

[1] Long lead rainfall forecasts are highly valuable for planning and management of water resources and agriculture. In this study, we establish multiple statistical calibration and bridging models that use general circulation model (GCM) outputs as predictors to produce monthly rainfall forecasts for Australia with lead times up to 8 months. The statistical calibration models make use of raw forecasts of rainfall from a coupled GCM, and the statistical bridging models make use of sea surface temperature (SST) forecasts of the GCM. The forecasts from the multiple models are merged through Bayesian model averaging to take advantage of the strengths of individual models. The skill of monthly rainfall forecasts is generally low. Compared to forecasting seasonal rainfall totals, it is more challenging to forecast monthly rainfall. However, there are regions and months for which forecasts are skillful. In particular, there are months of the year for which forecasts can be skillfully made at long lead times. This is most evident for the period of November and December. Using GCM forecasts of SST through bridging clearly improves monthly rainfall forecasts. For lead time 0, the improvement is particularly evident for February to March, July and October to December. For longer lead times, the benefit of bridging is more apparent. As lead time increases, bridging is able to maintain forecast skill much better than when only calibration is applied.

The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in Southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The skill of the NWP precipitation forecasts varies considerably between rain gauging stations. In general, high spatial resolution (ACCESS-A and ACCESS-VT) and regional (ACCESS-R) NWP models overestimate precipitation in dry, low elevation areas and underestimate in wet, high elevation areas. The global model (ACCESS-G) consistently underestimates the precipitation at all stations and the bias increases with station elevation. The skill varies with forecast lead time and, in general, it decreases with the increasing lead time. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly), the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. The non-smooth decay of skill with forecast lead time can be attributed to diurnal cycle in the observation and sampling uncertainty. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

[1] General filtering approaches in hydrologic data assimilation, such as the ensemble Kalman filter (EnKF), are based on the assumption that uncertainty of the current background prediction can be reduced by correcting errors in the state variables at the same time step. However, this assumption may not be valid when assimilating stream discharge into hydrological models to correct soil moisture storage due to the time lag between the soil moisture and the discharge. In this paper, we explore the utility of an ensemble Kalman smoother (EnKS) for addressing this time-lag issue. The EnKF and the EnKS are compared for two different updating schemes with the probability distributed model (PDM) via synthetic experiments: (i) updating soil moisture only and (ii) updating soil moisture and routing states simultaneously. The results show that the EnKS is superior to the EnKF when only soil moisture is updated, while the EnKS and the EnKF exhibit similar results when both soil moisture and routing storages are updated. This suggests that the EnKS can better improve the streamflow forecasting for models that do not adopt storage-based routing schemes (e.g., unit-hydrograph-based routing). For models with dynamic routing stores, errors in soil moisture are transferred to the routing stores, which can be corrected effectively by real-time filters. The EnKS-based soil moisture updating scheme is also tested with the GR4H model, for which unit-hydrograph-based routing is used. The result confirms that the EnKS is superior to the EnKF in improving both soil moisture and streamflow forecasting.

Water users along the Murray River, Australia, have traditionally used climatology forecasts of river flows for intra-annual planning of water use and trading. In this paper, we develop and assess the performance of statistical models for forecasting three-month inflow totals for the Murray River. Predictors are selected to represent the influence of initial catchment conditions and future climate on streamflows. These predictors vary with season and location, but are dominated by antecedent streamflows and indices describing the El Nino–Southern Oscillation. For all seasons, the forecasts are skilful with respect to climatology forecasts, and the forecast probability distributions appear to be reliable. Forecast skill is highest for forecasts made between September and December. The forecasts appear to be robust with respect to event size and time, except for the austral autumn seasons for which none of the predictors can forecast the decline in seasonal rainfall over the most recent decade.

Merging forecasts from multiple models has the potential to combine the strengths of individual models and to better represent forecast uncertainty than the use of a single model. This study develops a Bayesian model averaging (BMA) method for merging forecasts from multiple models, giving greater weights to better performing models. The study aims for a BMA method that is capable of producing relatively stable weights in the presence of significant sampling variability, leading to robust forecasts for future events. The BMA method is applied to merge forecasts from multiple statistical models for seasonal rainfall forecasts over Australia using climate indices as predictors. It is shown that the fully merged forecasts effectively combine the best skills of the models to maximize the spatial coverage of positive skill. Overall, the skill is low for the first half of the year but more positive for the second half of the year. Models in the Pacific group contribute the most skill, and models in the Indian and extratropical groups also produce useful and sometimes distinct skills. The fully merged probabilistic forecasts are found to be reliable in representing forecast uncertainty spread. The forecast skill holds well when forecast lead time is increased from 0 to 1 month. The BMA method outperforms the approach of using a model with two fixed predictors chosen a priori and the approach of selecting the best model based on predictive performance.

This manuscript presents a simple yet effective method to account for uncertainty in hydrologic ensemble forecasting applications. Most operational hydrological ensemble forecasting systems only account for uncertainty in future climate (e.g. precipitation) forcings, ignoring other sources of uncertainty (e.g. model error). The result can be under-dispersive and overconfident forecasts. Ensemble dressing is a form of statistical post-processing to include information about the uncertainty of individual ensemble members. First, the historical simulations can be corrected to remove systematic biases. Next, the simulated and observed flow data are transformed to ensure that model residuals can be fitted to a distribution (e.g. normal); this study uses a 2-parameter log-sinh transformation. Model residuals in transformed space determine the width of the error distribution. Ensemble forecasts are then generated and transformed. Each ensemble is dressed with the error distribution, the results are untransformed, and finally, a probabilistic forecast is derived from the collective distribution of the dressed ensembles. The method is applied to 128 catchments in southeast Australia, demonstrating that the raw ensemble can be made reliable through the use of this dressing method. Copyright © 2012 John Wiley & Sons, Ltd.

Lagged oceanic and atmospheric climate indices are potentially useful predictors of seasonal rainfall totals. A rigorous Bayesian joint probability modeling approach is applied to find the cross-validation predictive densities of gridded Australian seasonal rainfall totals using lagged climate indices as predictors over the period of 1950–2009. The evidence supporting the use of each climate index as a predictor of seasonal rainfall is quantified by the pseudo-Bayes factor based on cross-validation predictive densities. The evidence strongly supports the use of climate indices from the Pacific region with weaker, but positive, evidence for the use of climate indices from the Indian region and the extratropical region. The spatial structure and seasonal variation of the evidence for each climate index is mapped and compared. Spatially, the strongest supporting evidence is found for forecasting in northern and eastern Australia. Seasonally, the strongest evidence is found from August–October to November–January and the weakest evidence is found from March–May to May–July. In some regions and seasons, there is little evidence supporting the use of climate indices for forecasting seasonal rainfall. Climate indices derived from sea surface temperature anomalies in the Pacific region show stronger persistence in the relationship with Australian seasonal rainfall totals than climate indices derived from sea surface temperature anomalies in the Indian region. Climate indices derived from atmospheric variables are also strongly supported, provided they represent the large-scale circulation. Many climate indices are found to show similar supporting evidence for forecasting Australian seasonal rainfall, leading to the prospect of combining climate indices in multiple predictor models and/or model averaging.

Statistical methods commonly used for forecasting climate and streamflows require the selection of appropriate predictors. Poorly designed predictor selection procedures can result in poor forecasts for independent events. This paper introduces a predictor selection method for the Bayesian joint probability modeling approach to seasonal streamflow forecasting at multiple sites. The method compares forecasting models using a pseudo-Bayes factor (PsBF). A stepwise expansion of a base model is carried out by including the candidate predictor with the highest PsBF that exceeds a selection threshold. Predictors representing the initial catchment conditions are selected on their ability to forecast streamflows and predictors representing future climate influences are selected on their ability to forecast rainfall. The final forecasting model combines selected predictors representing both initial catchment conditions and future climate influences to jointly forecast seasonal streamflows and rainfall. Applications of the predictor selection method to two catchments in eastern Australia show that the best predictors representing initial catchment conditions and future climate influences vary with location and forecast date. Antecedent streamflows are the best indicator of the initial catchment conditions. Predictors representing future climate influences are only selected for forecasts made between July and January. Indicators of El Niño dominate the selected predictors representing future climate influences. The skill of streamflow forecasts varies considerably between locations and throughout the year. Skill scores for the perennial streams of the Goulburn River catchment exceed 40% for several seasons, while for the intermittent streams in the Burdekin River catchment, the skill scores are lower.

Unknown future precipitation is the dominant source of uncertainty for many streamflow forecasts. Numerical weather prediction (NWP) models can be used to generate quantitative precipitation forecasts (QPF) to reduce this uncertainty. The usability and usefulness of NWP model outputs depend on the application time and space scales as well as forecast lead time. For streamflow nowcasting (very short lead times; e.g., 12 h), many applications are based on measured in situ or radar-based real-time precipitation and/or the extrapolation of recent precipitation patterns. QPF based on NWP model output may be more useful in extending forecast lead time, particularly in the range of a few days to a week, although low NWP model skill remains a major obstacle. Ensemble outputs from NWP models are used to articulate QPF uncertainty, improve forecast skill, and extend forecast lead times. Hydrologic prediction driven by these ensembles has been an active research field, although operational adoption has lagged behind. Conversely, relatively little study has been done on the hydrologic component (i.e., model, parameter, and initial condition) of uncertainty in the streamflow prediction system. Four domains of research are identified: selection and evaluation of NWP model–based QPF products, improved QPF products, appropriate hydrologic modeling, and integrated applications.

Summary This study reports the development and testing of a “dual-pass” method for correcting slowly varying errors in simulations of streamflow. Error correction is a form of data assimilation used to improve streamflow forecasts. The dual-pass method is ideally suited for catchments with long-lasting shifts in runoff efficiency that the hydrologic model poorly simulates. In a first pass, the simulation is rescaled (i.e. multiplicative correction is applied), based on the cumulative error over the prior 365 days. In a second pass, a correction is added to the adjusted series, based on the error from the most recent timestep. When tested on 330 Australian and 183 United States catchments, the dual-pass approach improved the median four-measure validation skill score from 0.83 to 0.89 for the GR4J model and from 0.56 to 0.79 for a naive coefficient model. The majority of improvement comes from the second pass (the short-memory component). The magnitudes of correction at each pass are controlled by two tuneable parameters; a global sensitivity analysis determined that satisfactory performance could be had without tuning of the long-memory (first pass) error-correction parameter. For most catchments, the use of the long-memory error-correction neither degrades nor significantly improves performance. International hydrological modeling datasets have relatively fewer catchments with slowly varying errors than might be encountered in operational forecasting environments, therefore, there is a need for better identification and study of such problem catchments.

Summary A monthly water partition and balance model, Wapaba, based on the Budyko framework model is introduced and applied to over three hundred Australian catchments. The model uses consumption curves to partition water to a number of components according to supply and demand and to represent spatial and temporal heterogeneity of catchment processes in a parsimonious formulation. The Wapaba model was benchmarked against the well known abcd monthly water balance model as well as the original monthly Budyko framework model and found to outperform both on the Australian catchments. The Wapaba model was also compared with two daily water balance models widely used in Australia. It had validation performance comparable to or better than the daily water balance models in simulating monthly runoff on the Australian catchments. This indicates that the monthly model has not been disadvantaged for not using the within-month temporal sequences of the forcing variables. The finding supports the use of monthly water balance models for applications where one is primarily interested in monthly, seasonal and annual streamflow volumes or where daily input data are not readily available.

Flash flooding is one of the most hazardous natural events, and it is frequently responsible for loss of life and severe damage to infrastructure and the environment. Research into the use of new modelling techniques and data types in flash flood forecasting has increased over the past decade, and this paper presents a review of recent advances that have emerged from this research. In particular, we focus on the use of quantitative precipitation estimates and forecasts, the use of remotely sensed data in hydrological modelling, developments in forecasting models and techniques, and uncertainty estimates. Over the past decade flash flood forecast lead-time has expanded up to six hours due to improved rainfall forecasts. However the largest source of uncertainty of flash flood forecasts remains unknown future precipitation. An increased number of physically based hydrological models have been developed and used for flash flood forecasting and they have been found to give more plausible results when compared with the results of conceptual, statistical, and neural network models. Among the three methods for deciding flash flood occurrence discussed in this review, the rainfall comparison method (flash flood guidance) is most commonly used for flash flood forecasting as it is easily understood by the general public. Unfortunately, no existing model is capable of making reliable flash flood forecasts in urban watersheds even though the incidence of urban flash flooding is increasing due to increasing urbanisation. Copyright © 2011 John Wiley & Sons, Ltd.

Quantifying and managing diffuse P losses from small catchments or at the farm scale requires detailed knowledge of farming practices and their interaction with catchment processes. However, detailed knowledge may not be available and hence modeling is required. This paper demonstrates two approaches to developing tools that assist P losses from New Zealand or Australian dairy farms. The first is largely empirical and separates sources of P within a paddock into soil, fertilizer, dung, and treading impacts (including damage to grazed pasture). This information is combined with expert knowledge of hydrological processes and potential point sources (e.g., stream crossings) to create a deterministic model that can be used to evaluate the most cost and labor efficient method of mitigating P losses. For instance, in one example, 45% of annual P lost was attributed to the application of superphosphate just before a runoff event for which a mitigation strategy could be to use a less water soluble P fertilizer. The second approach uses a combination of interviews, expert knowledge and relationships to develop a Bayesian Network that describes P exports. The knowledge integration process helped stakeholders develop a comprehensive understanding of the problem. The Network, presented in the form of a "cause and effect", diagram provided a simple, visual representation of current knowledge that could be easily applied to individual circumstances and isolate factors having the greatest influence on P loss. Both approaches demonstrate that modeling P losses and mitigation strategies does not have to cover every process or permutation and that a degree of uncertainty can be handled to create a working model of P losses at a farm or small catchment scale.

Stochastically generated stream flow and climatic data may be used as input to water resources simulation models for planning purposes. Dealing with non-concurrent and missing data, variable transformation and parameter uncertainty presents a significant challenge in the development of methods for stochastic data generation. In this paper, a Bayesian method is introduced for multi-site stochastic generation of annual stream flow and climatic data. A contemporaneous autoregressive lag-one model CAR(1) with the Box-Cox transformation is used to capture key statistical structure of multiple annual stream flow and climatic time series while keeping the number of model parameters to a minimum. The posterior joint distribution of the model parameters is formulated, allowing for inputs of historical data series that are not continuous or concurrent, thus avoiding the need to infill or truncate data records and maximising the value of available data. Parameter and uncertainty inference are solved numerically by using Markov Chain Monte Carlo simulations. Subsequent stochastic generation of data fully accounts for parameter uncertainty. In addition, a re-parameterization scheme is used to handle the problem of strong inter-parameter dependence from the Box-Cox transformation. The method was applied to the Melbourne Water supply system to demonstrate its computational feasibility.

Stochastic generation of hydrological time series is a useful tool for the design and management of water resources systems. One of the shortcomings of many available models for stochastic generation of daily rainfall data is that they are unable to preserve satisfactorily key statistical properties simultaneously at daily, monthly and annual time scales. In this paper, a method for coupling two different time scales of stochastic hydrological time series models is introduced. The key feature of the method is to first generate two resembling time series, one preserving key statistical properties at a finer time scale and the other at a coarser time scale. Adjustment is then made to the finer time scale series so that this series becomes consistent with the coarser time scale series. Because the initial two time series resemble each other, the adjustment is kept small. In the paper, the technique for generating two resembling time series is described. The implementation of the method for coupling daily and monthly rainfall series is demonstrated. Test results of the method using rainfall data from a number of sites around Australia showed that the coupling method was able to generate daily rainfall time series that preserved satisfactorily some key statistical properties at daily, monthly and even annual time scales.

The dairy industry in the Murray Dairy Region, Australia, is reliant on irrigation, as evapotranspiration greatly exceeds rainfall during the main growing season. Border-check irrigation is the predominant method of irrigation used in the region, but centre pivot irrigation has emerged as a potential alternative in response to the increasing scarcity and value of irrigation water. This paper presents an economic evaluation of conversion from border-check to centre pivot irrigation for dairy pasture production in the Murray Dairy Region. The evaluation was based on a hypothetical farm with available land to expand the area of irrigated pasture production to utilise water saving generated by converting from border-check to centre pivot irrigation. Income and cost related to the conversion were identified. A pre-tax discounted cash flow analysis was performed. Under the assumption that the conversion resulted in 20% less water use and 10% more pasture production per hectare, an investment in the conversion of border-check to centre pivot irrigation would produce a net present value of $218,200 and a benefit cost ratio of 1.36 at a discount rate of 10%, and an internal rate of return of 23.1%.

The Box–Cox transformation is widely used to transform hydrological data to make it approximately Gaussian. Bayesian evaluation of parameter uncertainty in stochastic models using the Box–Cox transformation is hindered by the fact that there is no analytical solution for the posterior distribution. However, the Markov chain Monte Carlo method known as the Metropolis algorithm can be used to simulate the posterior distribution. This method properly accounts for the nonnegativity constraint implicit in the Box–Cox transformation. Nonetheless, a case study using the AR(1) model uncovered a practical problem with the implementation of the Metropolis algorithm. The use of a multivariate Gaussian jump distribution resulted in unacceptable convergence behaviour. This was rectified by developing suitable parameter transformations for the mean and variance of the AR(1) process to remove the strong nonlinear dependencies with the Box–Cox transformation parameter. Applying this methodology to the Sydney annual rainfall data and the Burdekin River annual runoff data illustrates the efficacy of these parameter transformations and demonstrate the value of quantifying parameter uncertainty.

Immune escape is a fundamental trait of cancer. Dendritic cells (DC) that interact with T cells represent a crucial site for the development of tolerance to tumor antigens, but there remains incomplete knowledge about how DC-tolerizing signals evolve during tumorigenesis. In this study, we show that DCs isolated from patients with metastatic or locally advanced breast cancer express high levels of the adiponectin receptors AdipoR1 and AdipoR2, which are sufficient to blunt antitumor immunity. Mechanistic investigations of ligand-receptor interactions on DCs revealed novel signaling pathways for each receptor. AdipoR1 stimulated IL10 production by activating the AMPK and MAPKp38 pathways, whereas AdipoR2 modified inflammatory processes by activating the COX-2 and PPARγ pathways. Stimulation of these pathways was sufficient to block activation of NF-κB in DC, thereby attenuating their ability to stimulate antigen-specific T-cell responses. Together, our findings reveal novel insights into how DC-tolerizing signals evolve in cancer to promote immune escape. Furthermore, by defining a critical role for adiponectin signaling in this process, our work suggests new and broadly applicable strategies for immunometabolic therapy in patients with cancer., link_to_OA_fulltext

The vulnerability of African agriculture to climate change creates key tasks of promoting agricultural adaptations and adaptive capacity in agriculture, and embracing opportunities for low-carbon development in agriculture. Agricultural research services, and agricultural advisory services, will be key actors in this work. This report synthesises findings from the project Climate Learning for African Agriculture, assessing how African agricultural research and advisory services are taking account of climate issues in their policies and practices, and how they can better do so in future. The project worked both at a regional level and through case studies in Sierra Leone, Benin, Uganda and Mozambique, with a strong emphasis on shared learning through national- and local-level workshops.

The greatest risk factor for Alzheimer's disease (AD) is age, and changes in the ageing nervous system are likely contributors to AD pathology. Amyloid beta (Abeta) accumulation, which occurs as a result of the amyloidogenic processing of amyloid precursor protein (APP), is thought to initiate the pathogenesis of AD, eventually leading to neuronal cell death. Previously, we developed an adult-onset Drosophila model of AD. Mutant Abeta42 accumulation led to increased mortality and neuronal dysfunction in the adult flies. Furthermore, we showed that lithium reduced Abeta42 protein, but not mRNA, and was able to rescue Abeta42-induced toxicity. In the current study, we investigated the mechanism/s by which lithium modulates Abeta42 protein levels and Abeta42 induced toxicity in the fly model. We found that lithium caused a reduction in protein synthesis in Drosophila and hence the level of Abeta42. At both the low and high doses tested, lithium rescued the locomotory defects induced by Abeta42, but it rescued lifespan only at lower doses, suggesting that long-term, high-dose lithium treatment may have induced toxicity. Lithium also down-regulated translation in the fission yeast Schizosaccharomyces pombe associated with increased chronological lifespan. Our data highlight a role for lithium and reduced protein synthesis as potential therapeutic targets for AD pathogenesis.