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Third-party rainfall observations could provide an improvement of the current official observation network for rainfall monitoring. Although third-party weather stations can provide large quantities of near-real-time rainfall observations at fine temporal and spatial resolutions, the quality of these data is susceptible due to variations in quality control applied and there is a need to provide greater confidence in them. In this study, we develop an automatic quality evaluation procedure for daily rainfall observations collected from third-party stations in near real time. Australian Gridded Climate Data (AGCD) and radar Rainfields data have been identified as two reliable data sources that can be used for assessing third-party observations in Australia. To achieve better model interpretability and scalability, these reference data sources are used to provide separate tests rather than a complex single test on a third-party data point. Based on the assumption that the error of a data source follows a Gaussian distribution after a log-sinh transformation, each test issues a p-value-based confidence score as a measure of quality and the confidence of the third-party data observation. The maximum of confidence scores from individual tests is used to merge these tests into a single result which provides overall assessment. We validate our method with synthetic datasets based on high-quality rainfall observations from 100 Bureau of Meteorology (BoM) of Australia stations across Australia and apply it to evaluate real third-party rainfall observations owned by the Department of Primary Industries and regional development (DPIRD) of Western Australia. Our method works well with the synthetic datasets and can detect 76.7% erroneous data while keeping the false alarm rate as low as 1.7%. We also discuss the possibility of using other reference datasets, such as numerical weather prediction data and satellite rainfall data.

Third-party rainfall observations could provide an improvement of the current official observation network for rainfall monitoring. Although third-party weather stations can provide large quantities of near-real-time rainfall observations at fine temporal and spatial resolutions, the quality of these data is susceptible because of there is a need to provide greater confidence in them due to variations in quality control applied. In this study, we develop a statistical method for an automated quality control system to evaluate daily rainfall observations collected from third-party stations in near real time. Australian Gridded Climate Data (AGCD) and radar Rainfields data have been identified as two reliable data sources that can be used for assessing third-party observations in Australia. To achieve better model interpretability and scalability, the reference data sources are used to provide three separate tests rather than a complex single test on a third-party data point. Based on the assumption that the error of a data source follows a Gaussian distribution after a log-sinh transformation, each test issues a p-value-based confidence score as a measure of quality and the confidence of the third-party data point. The maximum of confidence scores from individual tests is used to merge these tests into a single result which provides overall assessment. We validate our method with synthetic datasets based on high-quality rainfall observations from 100 Bureau of Meteorology (BoM) of Australia stations across Australia and apply it to evaluate real third-party rainfall observations owned by the Department of Primary Industries and regional development (DPIRD) of Western Australia. Our method works well with the synthetic datasets and can detect 76.7% erroneous data while keeping the false alarm rate as low as 1.7%. We also discuss the possibility of using other reference datasets, such as numerical weather prediction data and satellite rainfall data.

Background Providers often underestimate the influence of patient religious and spiritual (R&S) needs. The current study sought to determine the influence of R&S beliefs on treatment decision making among patients and providers in the context of cancer care. Methods We conducted a systematic review of the literature using web-based search engines and discipline-specific databases. Search terms included a combination of the following Medical Subject Headings and key terms: “cancer,”“spirituality,”“religion,” and “decision making.” We used Covidence to screen relevant studies and extracted data into Microsoft Excel. Results Among 311 screened studies, 32 met inclusion/exclusion criteria. Most studies evaluated the patient perspective ( n = 29), while 2 studies evaluated the provider perspective and 1 study examined both. In assessing patient R&S relative to treatment decision making, we thematically characterized articles according to decision-making contexts, including general ( n = 11), end-of-life/advance care planning ( n = 13), and other: specific ( n = 8). Specific contexts included, but were not limited to, clinical trial participation ( n = 2) and use of complementary and alternative medicine ( n = 4). Within end-of-life/advance care planning, there was a discrepancy regarding how R&S influenced treatment decision making. The influence of R&S on general treatment decision making was both active and passive, with some patients wanting more direct integration of their R&S beliefs in treatment decision making. In contrast, other patients were less aware of indirect R&S influences. Patient perception of the impact of R&S on treatment decision making varied relative to race/ethnicity, being more pronounced among Black patients. Conclusion Most articles focused on R&S relative to treatment decision making at the end of life, even though R&S appeared important across the care continuum. To improve patient-centered cancer care, providers need to be more aware of the impact of R&S on treatment decision making.

On farm experimentation (OFE) has been a long-standing method for farmers to assess alternative management at scales relevant to their farming practices. Through the use of spatially distributed designs, whether simple strips or other ‘whole-of-block’ trials, OFE can provide information such as which treatment should be recommended at specific locations, and make important contributions to precision agriculture. However, when treatment response data sets become large, such as with tens of thousands of field observations that are readily collected using on-the-go sensors, existing geostatistical systems for analysing such experiments become computationally intensive, if not impossible. To enable farmers, or their consultants, to generate high-resolution treatment response and recommendation maps on their own computers within a reasonable time, we present a fast and adaptive local cokriging tool for non-colocated and non-stationary OFE data. It uses a spatially-varying neighbourhood radius. It has a graphical user interface accessible via QGIS, a free and open source software. The adaptive local cokriging is demonstrated on three OFE examples. It performs indistinguishably from global cokriging on a small data set, but for large data sets, for which global cokriging is impractical, it predicts significantly more accurately than spatial splines or sampling-based cokriging. It outperforms cokriging base on a fixed number of nearest neighbours when this fixed number is not carefully chosen.

Background: Cancer patients infrequently engage in advance care planning processes. Establishing preferences for future medical treatment without advance care planning may not be patient-centered, as it fails to consider important factors that influence these important decisions. Objective: The purpose of this study was to assess the influence of patient intrapersonal factors including race, religion, level of depression, and cancer stage on overall preferences for future medical treatment, including the presence of a (DNR), power of attorney, and advance directive. Design: A retrospective chart review design was used. Patients were included who were diagnosed with cancer at The Ohio State University James Comprehensive Cancer Center from 01/2015 to 08/2019. Results: A total of 3,463 patients were included. Median age was 59 years (IQR: 49, 67) and the majority of the patients was female (88.7%). Compared with no religious preference, patients who identified as religious had 61% higher odds (95%CI: 1.08-2.40) of having a DNR and approximately 30% higher odds of having a power of attorney (95%CI: 1.08-1.62) or advance directive (95%CI: 1.02-1.64). Patients with clinically relevant depression had more than twice the odds of having a DNR versus patients with no/lower levels of clinical depression (OR: 2.08; 95%CI: 1.40-3.10). White patients had higher odds of having a power of attorney (OR: 1.57; 95%CI: 1.16-2.13) and an advance directive (OR: 3.10; 95% CI: 1.95-4.93) than African-American/Black patients. Conclusions: Understanding the factors that affect preferences for future medical treatment is necessary for medical professionals to provide proper care and support to patients diagnosed with cancer and their families.

This article is a retrospective and a prospective look at SAL on the occasion of its 50th anniversary. The founding editors and various editors reflect on their experiences

Determination of ecological responses to river flows is fundamental to understanding how flow-dependent ecosystems have been altered by regulation, water diversions and climate change, and how to effect river restoration. Knowledge of ecohydrological relationships can support water management and policy, but this is not always the case. Management rules have tended to be developed ahead of scientific knowledge. The lag between practice and knowledge could be addressed by using historical monitoring data on ecological responses to changes in flows to determine significant empirical ecohydrological relationships, as an adjunct to investigating responses prospectively. This possibility was explored in the Murray–Darling Basin, Australia. We assessed 359 data sets collected during monitoring programs across the basin. Of these, only 32 (9%) were considered useful, based on a match between the scale at which sampling was done and ecological responses are likely to occur, and used to test flow–ecology predictions for phytoplankton, macroinvertebrates, fishes, waterbirds, floodplain trees, basin-scale vegetation and estuarine biota. We found relationships between flow and ecological responses were likely to be more strongly supported for large, long-lived, widespread biota (waterbirds, basin-scale vegetation, native fishes), than for more narrowly distributed (e.g. estuarine fishes) or smaller, short-lived organisms (e.g. phytoplankton, macroinvertebrates). This pattern is attributed to a mismatch between the design of monitoring programs and the response time frames of individual biota and processes, and to the use of local river discharge as a primary predictor variable when, for many biotic groups, other predictors need to be considered.

This paper presents an assessment of the hydrological impacts of proposed coal mines and coal seam gas (CSG) developments in the Gloucester, Hunter, Namoi and Galilee subregions of eastern Australia. Surface water and groundwater modelling was carried out to assess the cumulative regional-scale hydrological impacts of multiple coal and coal seam gas developments. The hydrological metrics assessed were groundwater drawdown and changes in low-flow characteristics as measured by the number of low-flow or zero flow days per year. Uncertainty in the modelling was also considered, and the results are presented as the median and plausible range (5th to 95th percentile) from many simulations which attempt to capture a wide range of uncertainty. The results indicate that under the best-case (5th percentile) scenario, the impact of additional/proposed coal resource developments on groundwater drawdown and surface water characteristics can be quite small. However, under the worse-case (95th percentile), groundwater drawdown is very likely for up to 5 km from the coal resource developments, but unlikely beyond 20 km. Altered surface water characteristics can extend for many kilometres downstream, particularly in unregulated systems. The cumulative hydrological impact is enhanced where there are multiple developments in close proximity. The results are being used to inform policy and the approval process for coal resource developments.

This chapter describes a linguistic revitalization model that prioritizes the use of indigenous languages within the delivery of high-quality social programs. Such an approach is complementary to traditional revitalization activities such as language documentation, child education, and publishing. However, the model views improved access to economic, health, and welfare resources as the fundamental terrain for expanding and normalizing the use of indigenous languages. The chapter offers a case study on the implementation of this model in Mayan-speaking communities in Guatemala through collaborative work of Wuqu’ Kawoq | Maya Health Alliance, a non-governmental health care organization.

Rytidosperma species (formerly Austrodanthonia) are native grasses common in temperate grasslands of southern Australia. Nine Rytidosperma species, Lolium perenne and Bromus hordeaceus were grown as microswards in pots in a glasshouse, and their growth response to six levels of applied P was measured. Shoot yield differed up to twofold between the highest- and lowest-yielding Rytidosperma species. Some Rytidosperma species were slow growing with minimal ability to respond to increased soil P availability. However, three species, Rytidosperma duttonianum, Rytidosperma racemosum and Rytidosperma richardsonii, had a similar shoot yield to L. perenne. Species that grew well at high P also grew well at low P, except B. hordeaceus, which was the lowest-yielding species at low P, but had among the highest yields at high P. No species showed evidence of P toxicity. The species exhibited a range in critical external P requirement (i.e. amount of P applied for 90% maximum yield). Among the fast-growing Rytidosperma species, R. richardsonii was notable because it had a low critical external P (16·3 mg P pot−1) and high agronomic P-use efficiency (94·1 g DW g−1 P applied). In contrast, R. duttonianum had a higher critical external P requirement (22·4 mg P pot−1) and lower agronomic P-use efficiency (85 g DW g−1 P applied). It was concluded that it is important to know which Rytidosperma species are present in a grassland to understand how it may respond to P fertilization. The results help to explain the diverse opinions expressed about the productivity of pastures containing Rytidosperma species.

Most discussion surrounding external possessors has centered around dative NPs or clitics in Indo-European or Semitic languages and focused on whether or not such structures are derived via movement. This paper examines external possessors in Chimwiini, a Bantu language that lacks datives. It will be shown that while these constructions share certain structural and semantic restrictions with their counterparts in other languages, these properties do not straightforwardly correlate with arguments for a movement or base-generation analysis. This suggests the derivational mechanics of the constructions involved are independent of the semantic restrictions placed on them.

to examine the present-day knowledge formation and practice of indigenous Kaqchikel-speaking midwives, with special attention to their interactions with the Guatemalan medical community, training models, and allopathic knowledge in general.a qualitative study consisting of participant-observation in lay midwife training programs; in-depth interviews with 44 practicing indigenous midwives; and three focus groups with midwives of a local non-governmental organization.Kaqchikel Maya-speaking communities in the Guatemalan highlands.the cumulative undermining effects of marginalization, cultural and linguistic barriers, and poorly designed training programs contribute to the failure of lay midwife-focused initiatives in Guatemala to improve maternal-child health outcomes. Furthermore, in contrast to prevailing assumptions, Kaqchikel Maya midwives integrate allopathic obstetrical knowledge into their practice at a high level.as indigenous midwives in Guatemala will continue to provide a large fraction of the obstetrical services among rural populations for many years to come, maternal-child policy initiatives must take into account that: (1)Guatemalan midwife training programs can be significantly improved when instruction occurs in local languages, such as Kaqchikel, and (2)indigenous midwives' increasing allopathic repertoire may serve as a productive ground for synergistic collaborations between lay midwives and the allopathic medical community.

We estimate organic carbon (C): total nitrogen (N): total phosphorus (P) ratios in soils under Australia’s major native vegetation groups. We use digital datasets for climate, soils, and vegetation created for the National Land and Water Resources Audit in 2001. Analysis-of-variance is used to investigate differences in nutrient ratios between ecosystems. Linear discriminant analysis and logistic regression are used to investigate the relative importance of climatic variables and soil nutrients in vegetation patterns. We find that the N:P and C:P ratios have a greater range of values than the C:N ratio, although major vegetation groups tend to show similar trends across all three ratios. Some apparently homeostatic groupings emerge: those with very low, low, medium, or high N:P and C:P. Tussock grasslands have very low soil N, N:P, and C:P, probably due to frequent burning. Eucalypt woodlands have low soil N:P and C:P ratios, although their total P level varies. Rainforests and Melaleuca forests have medium soil N:P and C:P ratios, although their total P level is different. Heathlands, tall open eucalypt forests, and shrublands occur on soils with low levels of total P, and high N:P and C:P ratios that reflect foliar nutrient ratios and recalcitrant litter. Certain plant communities have typical soil nutrient stoichiometries but there is no single Redfield-like ratio. Vegetation patterns largely reflect soil moisture but for several plant communities, eucalypt communities in particular, soil N and P (or N:P) also play a significant role. Soil N:P and the presence of Proteaceae appear indicative of nutrient constraints in ecosystems.

[1] The Brune and Churchill curves have long been used to predict sediment trapping efficiencies for reservoirs in the USA which typically experience winter and spring-dominant runoff. Their suitability for reservoirs receiving highly variable summer-dominant inflows has not previously been evaluated. This study compares sediment trapping efficiency (TE) data with the predictions of the two established curves for the Burdekin Falls Dam, a large reservoir in northern tropical Australia which receives highly variable summer-dominant runoff. The measured TE of the reservoir ranged between 50% and 85% and was considerably less than estimates using the Brune and Churchill curves over the 5 year study period. We modified the original equations so that daily trapping can be calculated and weighted based on daily flow volumes. This modification better accounts for shorter residence times experienced by such systems characterized by relatively high intraannual flow variability. The modification to the Churchill equation reasonably predicted sediment TEs for the Burdekin Dam for four of the five monitored years and over the whole monitoring period. We identified four key sediment particle classes: (1) 30 µm which is almost totally (95%) trapped in the dam reservoir. We show that the modification to the Churchill equation has broader application to predict reservoir TE provided that daily flow data are available.

The export of pollutant loads from coastal catchments is of primary interest to natural resource management. For example, Reef Plan, a joint initiative by the Australian Government and the Queensland Government, has indicated that a 20% reduction in sediment is required by 2020. There is an obvious need to consider our ability to detect any trend if we are to set realistic targets or to reliably identify changes to catchment loads. We investigate the number of years of monitoring aquatic pollutant loads necessary to detect trends. Instead of modelling the trend in the annual loads directly, given their strong relationship to flow, we consider trends through the reduction in concentration for a given flow. Our simulations show very low power (

Degradation of coastal ecosystems in the Great Barrier Reef (GBR) lagoon, Australia, has been linked with increased land-based runoff of suspended solids, nutrients and pesticides since European settlement. This study estimated the increase in river loads for all 35 GBR basins, using the best available estimates of pre-European and current loads derived from catchment modelling and monitoring. The mean-annual load to the GBR lagoon for (i) total suspended solids has increased by 5.5 times to 17,000ktonnes/year, (ii) total nitrogen by 5.7 times to 80,000tonnes/year, (iii) total phosphorus by 8.9 times to 16,000tonnes/year, and (iv) PSII herbicides is 30,000kg/year. The increases in river loads differ across the 10 pollutants and 35 basins examined, reflecting differences in surface runoff, urbanisation, deforestation, agricultural practices, mining and retention by reservoirs. These estimates will facilitate target setting for water quality and desired ecosystem states, and enable prioritisation of critical sources for management.

Despite exhibiting a high degree of uniformity, Bantu languages display a surprising amount of variation in extraction contexts, particularly with regard to agreement patterns. This paper examines this variation, focusing first on Bantu OVS relatives and topicalization structures in which the raised operator triggers morphological subject agreement on the verb. After critiquing accounts by Henderson (2006) and others, this paper proposes an analysis of OVS structures in which the establishment of agreement relations is delayed until the end of the derivation. I then take this account of OVS as the basis for discussing variation in the agreement properties of operator constructions across Bantu languages.

We consider the use of generalized additive models with correlated errors for analysing trends in time series. The trend is represented as a smoothing spline so that it can be extrapolated. A method is proposed for choosing the smoothing parameter. It is based on the ability to predict a short term into the future. The choice not only addresses the purpose in hand, but also performs very well, and avoids the tendency to under-smooth or to interpolate the data that can occur with other data-driven methods used to choose the smoothing parameter. The method is applied to data from a chemical process and to stream salinity measurements. Copyright © 2008 John Wiley & Sons, Ltd.

Like linguistic perception, visual perception is an active process in which the mind makes use of innate structural principles in the computational process. It is therefore useful to ask whether the visual and linguistic computational systems make use of the same or similar principles. This article describes the role played by principles of symmetry in visual perception as suggested by researchers in that field, and suggests that a subset of those principles play a strong role in the perception of linguistic structure. It is claimed that a distinction should be made between the construction of linguistic structure and its perception in the computational system, and that principles of sym-metry apply in subtly different ways in each. It is argued that movement’s inherent locality, successive-cyclicity, has a bipartite nature, being sensitive only to certain barrier nodes in the construction of structure while adjoining to every intermediate projection in the perception of structure.

For nearly 30 years it has been assumed that some English relatives require a movement relationship between the external head noun phrase and its clause-internal position (the raising analysis) whereas other relatives do not allow such a relationship (the matching analysis). This division has largely been motivated by reconstruction facts. This paper argues that the matching analysis is eliminable if one assumes vehicle change is a general property of A-bar movement, and if one accepts that sentence construction and movement are derivational while chain formation is representational. This allows a uniform raising analysis for English relatives that eliminates difficulties with previous raising analyses, allows relative clauses to be uniformly treated as adjuncts, and accounts for otherwise mysterious extraposition facts. The conclusions also have implications for the idea that adjuncts may be merged acyclically in a derivation.

Carstens (2001) argues that multiple agreement constructions in Bantu arise through raising of the subject through each verb's specifier. This paper argues against this account, providing evidence from relative inversion that subjects move directly from their base position to their final position with no intermediate stops. It is argued that these facts are consistent with a Multiple Agree analysis in which agreement on participle verbs is parasitic on the φ-features of their selecting auxiliary verbs. Carstens's arguments against Chomsky's (2000, 2001) system of φ-complete Case checking are also discussed and a new argument against Chomsky's system is presented that demands φ and Case feature checking relations be divorced. Data come from Swahili and Kirundi.

Risk-based methods promise improved decision-making for managing of contaminants, such as salinity, sediments, nutrients, and toxicants, that can adversely affect the ecological condition of aquatic ecosystems. Two aspects of ecological risk assessment (ERA) and management—stakeholder involvement and more quantitative approaches to risk analysis—are particularly challenging. Stakeholder involvement is crucial both in the risk assessment process and the development, acceptance, and implementation of a risk management plan. Additionally, a number of quantitative approaches (particularly Bayesian approaches and multi-criteria decision-making) have been identified as having the potential to include expert-based inputs into risk-based decision-making. These offer promise for better inclusion of stakeholder knowledge and preferences into the decision-making process, and for improving the links between stakeholder inputs and potential risks to the ecological condition of the system. A major challenge fo...

We modelled the distribution of soil properties across the agricultural zone on the Australian continent using data mining and knowledge discovery from databases (DM&KDD) tools. Piecewise linear tree models were built choosing from 19 climate variables, digital elevation model (DEM) and derived terrain attributes, four Landsat multi-spectral scanner (MSS) bands, land use and lithology maps as predictors of topsoil and subsoil pH, organic C, % clay, and total N and P. The actual geographic location of the sampled soil data points was not used as a predictor. Classification trees were used to estimate topsoil and subsoil horizon thickness and texture class using similar predictors. That maps could be made using the decision tree models attests to the occurrence of knowledge discovery from the soil point databases used as training data. The decision tree models were evaluated and interpreted in a spatial context by: (1) tabulation of variables selected by the tree models; (2) mapping of the extent over which individual predictors were used and their thresholds; and (3) mapping of the extent over which combinations of predictors were used. The evaluation and interpretation process indicates that the models are consistent with general principles of soil genesis and that detailed investigation of the models¿ structure in a spatial context may have other uses in biogeographical studies and geo-ecological process modelling.

This paper describes the construction of Australia-wide soil property predictions from a compiled national soils point database. Those properties considered include pH, organic carbon, total phosphorus, total nitrogen, thickness, texture, and clay content. Many of these soil properties are used directly in environmental process modelling including global climate change models. Models are constructed at the 250-m resolution using decision trees. These relate the soil property to the environment through a suite of environmental predictors at the locations where measurements are observed. These models are then used to extend predictions to the continental extent by applying the rules derived to the exhaustively available environmental predictors. The methodology and performance is described in detail for pH and summarized for other properties. Environmental variables are found to be important predictors, even at the 250-m resolution at which they are available here as they can describe the broad changes in soil property.

Trends in ecologically meaningful water quality variates are important for establishing changes in water quality. Linear assessments of the trend at different monitoring sites are relatively easy to compare but may be misleading as they can poorly represent the nature of the trend. Non-linear trends, on the other hand, more faithfully capture the trend but have been much more difficult to compare between sites. In this article we investigate the ability of functional data analysis to explore between site differences in non-linear water quality trends. Functional data analysis is briefly reviewed, concentrating in particular on the techniques of functional principal component analysis, functional linear modelling and functional cluster analysis. These techniques are then applied to some nutrient and sediment trend data obtained from a number of monitoring sites in three dams in South East Queensland, Australia. Functional data analysis is found to be a valuable exploratory tool for providing insight into the between-site, depth and dam differences. Copyright © 2005 John Wiley & Sons, Ltd.

Summary Measurement error and misclassification models feature prominently in the literature. This paper describes misreporting error, which can be considered to fall somewhere between these two broad types of model. Misreporting is concerned with situations where a continuous random variable X is measured with error and only reported as the discrete random variable Z. Data grouping or rounding are the simplest examples of this, but more generally X may be reported as a value z of Z which refers to a different interval from the one in which X lies. The paper discusses a method for handling misreported data and draws links with measurement error and misclassification models. A motivating example is considered from a prenatal Down's syndrome screening, where the gestational age at which mothers present for screening is a true continuous variable but is misreported because it is only ever observed as a discrete whole number of weeks which may in fact be in error. The implications this misreporting might have for the screening are investigated.

The possibility that plants could serve as indicators of underlying soil salinity in northern Queensland, Australia, was investigated using 2197 site observations recorded during a land resources inventory of the Dalrymple Shire. The area surveyed intersects three biogeographical regions: the desert uplands, the Einasleigh uplands and the northern brigalow belt. The three dominant plant species in the lower, middle, and upper vegetation strata were recorded, along with laboratory measurements of electrical conductivity down each soil profile. Correspondence analysis, generalized additive models and clustering were used to investigate the relationship between plant occurrence, climatic and edaphic factors. The results of these statistical analyses strongly suggest a relationship between salinity and brigalow plant communities. A generalized additive model to predict soil salinity from environmental variables including vegetation data is presented.

The Great Barrier Reef (GBR) is chronically exposed to agricultural run-off containing pesticides, many of which are known endocrine disrupting chemicals (EDCs). Here, we measure mRNA transcript abundance of two EDC biomarkers in wild populations of barramundi (Lates calcarifer) and coral trout (Plectropomus leopardus and Plectropomus maculatus). Transcription levels of liver vitellogenin (vtg) differed significantly in both species amongst sites with different exposures to agricultural run-off; brain aromatase (cyp19a1b) revealed some differences for barramundi only. Exposure to run-off from sugarcane that contains pesticides is a likely pathway given (i) significant associations between barramundi vtg transcription levels, catchment sugarcane land use, and river pesticide concentrations, and (ii) consistency between patterns of coral trout vtg transcription levels and pesticide distribution in the GBR lagoon. Given the potential consequences of such exposure for reproductive fitness and population dynamics, these results are cause for concern for the sustainability of fisheries resources downstream from agricultural land uses.

Summary A framework was developed for the continental assimilation of satellite soil moisture (SM) into an operational water balance modelling system. The ensemble Kalman filter (EnKF) was implemented to assimilate AMSR-E and ASCAT-derived SM products into the landscape model of the Australian Water Resources Assessment system (AWRA-L) and generate ensembles of daily top-layer and shallow root-zone soil moisture analyses for the continent at 0.05° resolution. We evaluated the AWRA-L SM estimates with and without assimilation against in situ moisture measurements in southeast Australia (OzNet), as well as against a new network of cosmic-ray moisture probes (CosmOz) spread across the country. Results show that AWRA-L root-zone moisture estimates are improved though the assimilation of satellite SM: model estimates of 0–30 cm moisture content improved for more than 90% of OzNet sites, with an increase in average correlation from 0.68 (before assimilation) to 0.73 (after assimilation); while estimates 0–90 cm moisture improved for 60% of sites with increased average correlation from 0.56 to 0.65. The assimilation of AMSR-E and ASCAT appeared to yield similar performance gains for the top-layer, however ASCAT data assimilation improved root-zone estimation for more sites. Poor performance of one data set was compensated by the other through joint assimilation. The most significant improvements in AWRA-L root-zone moisture estimation (with increases in correlation as high as 90%) occurred for sites where both the assimilation of satellite soil moisture improved top-layer SM accuracy and the open-loop deep-layer storage estimates were reasonably good. CosmOz SM measurements exhibited highest correlation with AWRA-L estimates for modelled root-zones layer thicknesses ranging from 20 cm to 1 m. Slight improvements through satellite data assimilation were observed for only 2 of 7 CosmOz sites, but the comparison was affected by a short data overlap period. The location of some of the CosmOz probes was not optimal for evaluation of satellite SM assimilation, but their utility is demonstrated and the observations may become suitable for assimilation themselves in future.

The loads regression estimator (LRE) was introduced by Wang et al. (2011) as an improved approach for quantifying the export of loads and the corresponding uncertainty from river systems, where data are limited. We extend this methodology and show how LRE can be used to analyze a 24 year record of total suspended sediment concentrations for the Burdekin River. For large catchments with highly variable discharge such as that of the Burdekin River, it is important to quantify loads and their uncertainties accurately to determine the current load and to monitor the effect of changes in catchment management. The extended methodology incorporates (1) multiple discounted flow terms to represent the effect of flow history on concentration, (2) a term that captures sediment trapping and spatial sources of flow in terms of the ratio of flow from above the Burdekin Falls Dam, and (3) catchment vegetation cover. Furthermore, we validated model structure and performance in relation to the application tested. We also considered errors in gauged flow rates of 10% that were consistent with the literature. The results for the Burdekin site indicate substantial variability in loads across years. The inclusion of vegetation cover as a predictor had a significant impact on total suspended sediment (TSS) concentration, with values up to 2.1% lower noted per increasing percentage of vegetation cover. TSS concentration was up to 38% lower in years with greater proportions of flow from above the dam. The extended LRE methodology resulted in improved model performance. The results suggest that management of vegetation cover in dry years can reduce TSS loads from the Burdekin catchment, and this is the focus of future work.

[1] We present a piecewise linear decision tree model for predicting percent of soil organic C (SOC) in the agricultural zones of Australia generated using a machine learning approach. The inputs for the model are a national database of soil data, national digital surfaces of climate, elevation, and terrain variables, Landsat multispectral scanner data, lithology, land use, and soil maps. The model and resulting map are evaluated, and insights into biogeological surficial processes are discussed. The decision tree splits the overall data set into more homogenous subsets, thus in this case, it identifies areas where SOC responds closely to climatic and other environmental variables. The spatial pattern of SOC corresponds well to maps of estimated primary productivity and bioclimatic zones. Topsoil organic C levels are highest in the high rainfall, temperate regions of Tasmania, Victoria, and Western Australia, along the coast of New South Wales and in the wet tropics of Queensland; and lowest in arid and semiarid inland regions. While this pattern broadly follows continental vegetation, soil moisture, and temperature patterns, it is governed by a spatially variable hierarchy of different climatic and other variables across bioregions of Australia. At the continental scale, soil moisture level, rather than temperature, seems most important in controlling SOC.

[1] This paper advocates the use of Generalized Additive Models (GAMs) for the estimation of nonlinear trends in water quality in the presence of serially correlated errors. The GAM methodology is applicable to a range of physical, chemical and biological water quality variates. Comparison with the estimate based on Seasonal Kendall's Slope and robust regression is discussed. An example is given concerning log-transformed stream electrical conductivity, which is adjusted for flow. The monthly data have first order autocorrelation exceeding 0.5 and the trend is markedly nonlinear. Seasonal effects are shown to have been changing over time.

Rytidosperma species are perennial grasses found in cool temperate grasslands of Australia. The species differ in their intrinsic growth rates, response to phosphorus (P) fertiliser application and critical external P requirements (P required for 90% maximum growth). The present study examined whether internal P-utilisation efficiency (PUE) by Rytidosperma species influenced these differences. The PUE of nine Rytidosperma species and two grasses of Mediterranean origin, Bromus hordeaceus L. and Lolium perenne L., was assessed using alternative measures of shoot P concentration or its reciprocal. No measure of PUE was correlated with the critical external P requirements of the species. One measure of PUE, shoot dry matter per unit P, when assessed at a common shoot P content was correlated with potential growth rate (P

Slip-tendency analysis is a new technique that permits rapid assessment of stress states and related potential fault activity. The tendency of a surface to undergo slip in a given stress field depends on its frictional characteristics (primarily controlled by rock type) and the ratio of shear to normal stress acting on the surface, here defined as slip tendency (determined by orientation of the surface within the stress field). An interactive computer tool displays the stress tensor in terms of its associated slip-tendency distribution and the relative likelihood and direction of slip on surfaces of all orientations. The technique provides easy visualization and rapid evaluation of stress in terms of its potential for causing slip on individual faults or fault populations for use in seismic-risk and fault-rupture–risk assessment, exploration for high-risk and earthquake-prone blind faults, selection of likely earthquake focal mechanism solutions, and for use in analysis of compatibility of geologic structures.