Inferential, non-parametric statistics to assess quality of probabilistic forecast systems

Many statistical forecast systems are available to interested users. In order to be useful for decision-making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and their statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of `quality’. However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what ‘quality’ entails and how to measure it, leading to confusion and misinformation. Here we present a generic framework to quantify aspects of forecast quality using an inferential approach to calculate nominal significance levels (p-values) that can be obtained either by directly applying non-parametric statistical tests such as Kruskal-Wallis (KW) or Kolmogorov-Smirnov (KS) or by using Monte-Carlo methods (in the case of forecast skill scores). Once converted to p-values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. Our analysis demonstrates the importance of providing p-values rather than adopting some arbitrarily chosen significance levels such as p < 0.05 or p < 0.01, which is still common practice. This is illustrated by applying non-parametric tests (such as KW and KS) and skill scoring methods (LEPS and RPSS) to the 5-phase Southern Oscillation Index classification system using historical rainfall data from Australia, The Republic of South Africa and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. We found that non-parametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system or quality measure. Eventually such inferential evidence should be complimented by descriptive statistical methods in order to fully assist in operational risk management.

Duckweed-based Wastewater Treatment Systems: Design Aspects and Integrated Reuse Options for Queensland Conditions

Point sources of wastewater pollution, including effluent from municipal sewage treatment plants and intensive livestock and processing industries, can contribute significantly to the degradation of receiving waters (Chambers et al. 1997; Productivity Commission 2004). This has led to increasingly stringent local wastewater discharge quotas (particularly regarding Nitrogen, Phosphorous and suspended solids), and many municipal authorities and industry managers are now faced with upgrading their existing treatment facilities in order to comply. However, with high construction, energy and maintenance expenses and increasing labour costs, traditional wastewater treatment systems are becoming an escalating financial burden for the communities and industries that operate them. This report was generated, in the first instance, for the Burdekin Shire Council to provide information on design aspects and parameters critical for developing duckweed-based wastewater treatment (DWT) in the Burdekin region. However, the information will be relevant to a range of wastewater sources throughout Queensland. This information has been collated from published literature and both overseas and local studies of pilot and full-scale DWT systems. This report also considers options to generate revenue from duckweed production (a significant feature of DWT), and provides specifications and component cost information (current at the time of publication) for a large-scale demonstration of an integrated DWT and fish production system.

Economic Analysis of Sugarcane Farming Systems for Water Quality Improvement in the Great Barrier Reef Catchments.

In this report we analyse the private financial-economic impacts of transitioning to improved sugarcane management in the National Resource Management regions of the Wet Tropics, Burdekin Dry Tropics and Mackay Whitsundays. In order to do so, we: 1) compare farm GMs; 2) present information on capital investment associated with the transition; 3) perform a net present value analysis of the investments and; 4) undertake a risk analysis for cane and legume yields and prices. It must be noted that transaction costs are not captured within this project.

An overview on the role of Hexanchiformes in marine ecosystems: biology, ecology and conservation status of a primitive order of modern sharks

The large size, high trophic level and wide distribution of Hexanchiformes (cow and frilled sharks) should position this order as important apex predators in coastal and deep-water ecosystems. This review synthesizes available information on Hexanchiformes, including information not yet published, with the purpose of evaluating their conservation status and assessing their ecological roles in the dynamics of marine ecosystems. Comprising six species, this group has a wide global distribution, with members occurring from shallow coastal areas to depths of c. 2500 m. The limited information available on their reproductive biology suggests that they could be vulnerable to overexploitation (e.g. small litter sizes for most species and suspected long gestation periods). Most of the fishing pressure exerted on Hexanchiformes is in the form of commercial by-catch or recreational fishing. Comprehensive stock and impact assessments are unavailable for most species in most regions due to limited information on life history and catch and abundance time series. When hexanchiform species have been commercially harvested, however, they have been unable to sustain targeted fisheries for long periods. The potentially high vulnerability to intense fishing pressure warrants a conservative exploitation of this order until thorough quantitative assessments are conducted. At least some species have been shown to be significant apex predators in the systems they inhabit. Should Hexanchiformes be removed from coastal and deep-water systems, the lack of sympatric shark species that share the same resources suggests no other species would be capable of fulfilling their apex predator role in the short term. This has potential ecosystem consequences such as meso-predator release or trophic cascades. This review proposes some hypotheses on the ecology of Hexanchiformes and their role in ecosystem dynamics, highlighting the areas where critical information is required to stimulate research directions.

Automating Crop Damage Assessments with Unmanned Aerial Systems and Machine Learning

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.