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.

Is night-time wind direction important to best practice wild dog trapping and baiting?

We discovered a significant bias for wild dog scent station spoor (scats and scratches) to be positioned on the north-easterly side of roads and intersections. Counts of this spoor, 50 metres in each direction of north-south and east-west intersections were made in state forests near Roma in southwest Queensland, Cecil Plains on the Darling Downs and Maryborough on the coast during mating season in April/May 2007. While 51% of 190 and 83% of 120 scent station spoor were located on the north-eastern sector of the intersections at Cecil Plains and Roma respectively, spoor were more evenly distributed across all four sectors at Maryborough (n=47).

Grazing impacts on the susceptibility of rangelands to wind erosion: The effects of stocking rate, stocking strategy and land condition

An estimated 110 Mt of dust is eroded by wind from the Australian land surface each year, most of which originates from the arid and semi-arid rangelands. Livestock production is thought to increase the susceptibility of the rangelands to wind erosion by reducing vegetation cover and modifying surface soil stability. However, research is yet to quantify the impacts of grazing land management on the erodibility of the Australian rangelands, or determine how these impacts vary among land types and over time. We present a simulation analysis that links a pasture growth and animal production model (GRASP) to the Australian Land Erodibility Model (AUSLEM) to evaluate the impacts of stocking rate, stocking strategy and land condition on the erodibility of four land types in western Queensland, Australia. Our results show that declining land condition, over stocking, and using inflexible stocking strategies have potential to increase land erodibility and amplify accelerated soil erosion. However, land erodibility responses to grazing are complex and influenced by land type sensitivities to different grazing strategies and local climate characteristics. Our simulations show that land types which are more resilient to livestock grazing tend to be least susceptible to accelerated wind erosion. Increases in land erodibility are found to occur most often during climatic transitions when vegetation cover is most sensitive to grazing pressure. However, grazing effects are limited during extreme wet and dry periods when the influence of climate on vegetation cover is strongest. Our research provides the opportunity to estimate the effects of different land management practices across a range of land types, and provides a better understanding of the mechanisms of accelerated erosion resulting from pastoral activities. The approach could help further assessment of land erodibility at a broader scale notably if combined with wind erosion models.