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The new edition of Australian Rainfall and Runoff (hereafter designated ARR 2015) contains completely revised design rainfall Intensity-Frequency-Duration (IFD) curves prepared by the Bureau of Meteorology. These curves are estimated from current climate observations. The Fifth Assessment Report of the Intergovernmental Panel on Climate Change states that "extreme precipitation events over most of the mid-latitude land masses and over wet tropical regions will very likely become more intense and more frequent by the end of this century, as global mean surface temperature increases." This suggests that the current-climate IFD curves may become unsuitable for infrastructure design in future decades. While ARR 2015 includes an interim guideline on incorporating climate change into design flood estimation, the guidance is based on a 'broad brush' approach due to the paucity of published regionally specific results. As a first step towards bridging this gap, a pilot project titled 'Rainfall Intensity-Frequency-Duration (IFD) Relationships under Climate Change' was commissioned in June 2013 and administered by Engineers Australia. Its principal aim was to provide insight into how the new design rainfall IFD curves might be affected by anthropogenic climate change. This paper describes the background for and components of the investigation, the challenges involved, the major research findings and recommendations for further action.

The concept of phytoplankton functional types has emerged as a useful approach to classifying phytoplankton. It finds many applications in addressing some serious contemporary issues facing science and society. Its use is not without challenges, however. As noted earlier, there is no universally-accepted set of functional types, and the types used have to be carefully selected to suit the particular problem being addressed. It is important that the sum total of all functional types matches all phytoplankton under consideration. For example, if in a biogeochemical study, we classify phytoplankton as silicifiers, calcifiers, DMS-producers and nitrogen fix- ers, then there is danger that the study may neglect phytoplankton that do not contribute in any significant way to those functions, but may nevertheless be a significant contributor to, say primary production. Such considerations often lead to the adoption of a category of “other phytoplankton” in models, with no clear defining traits assigned them, but that are nevertheless necessary to close budgets on phytoplankton processes. Since this group is a collection of all phytoplankton that defy classification according to a set of traits, it is difficult to model their physi- ological processes. Our understanding of the diverse functions of phytoplankton is still growing, and as we recognize more functions, there will be a need to balance the desire to incorporate the increasing number of functional types in models against observational challenges of identifying and mapping them adequately. Modelling approaches to dealing with increasing functional diversity have been proposed, for example, using the complex adaptive systems theory and system of infinite diversity, as in the work of Bruggemann and Kooijman (2007). But it is unlikely that remote-sensing approaches might be able to deal with anything but a few prominent functional types. As long as these challenges are explicitly addressed, the functional- type concept

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensembe inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future ensemble applications.

We characterize near-surface ocean diurnal warm-layer events, using satellite observations and fields from numerical weather forecasting. The study covers April to September, 2006, over the area 11 degrees W to 17 degrees E and 35 degrees N to 57 degrees N, with 0.1 degrees cells. We use hourly satellite SSTs from which peak amplitudes of diurnal cycles in SST (dSSTs) can be estimated with error similar to 0.3 K. The diurnal excursions of SST observed are spatially and temporally coherent. The largest dSSTs exceed 6 K, affect 0.01% of the surface, and are seen in the Mediterranean, North and Irish Seas. There is an anti-correlation between the magnitude and the horizontal length scale of dSST events. Events wherein dSST exceeds 4 K have length scales of <= 40 km. From the frequency distribution of different measures of wind-speed minima, we infer that extreme dSST maxima arise where conditions of low wind speed are sustained from early morning to mid afternoon.

This paper deals with three concerns about the evaluative framework that is currently dominant within health economics. These concerns are: that the evaluative framework is concerned entirely with health; that the evaluative framework has an individualistic focus on patients alone; and that the methods used to estimate 'health' within the current evaluative framework could be improved both in terms of the generation of descriptive systems and in using valuation methods that rely less on people's ability to express their preferences on a cardinal scale. In exploring these issues the Investigating Choice Experiments for Preferences of Older People (ICEPOP) programme has explicitly focused on both the topic of older people and the methods of discrete choice experiments. A capability index has been developed and attributes for an economic measure of end-of-life care are currently being generated, providing the possibility of extending the evaluative framework beyond health alone. A measure of carer's experience and a framework for extending measurement in end-of-life care to loved ones are both also in development, thus extending the evaluative framework beyond the patient alone. Rigorous qualitative methods employing an iterative approach have been developed for use in constructing attributes, and best-worst scaling has been utilized to reduce task complexity and provide insights into heterogeneity. There are a number of avenues for further research in all these areas, but in particular there is need for greater attention to be paid to the theory underlying the evaluative framework within health economics. © The Royal Society of Medicine Press Ltd 2008.

Gelbstoff is the main absorber in natural waters of UV and visible light, and has an increasing absorption with decreasing wavelength in the range between 700 and 350 nm. Gelbstoff is often an important source of interference in the determination of plant pigments (e.g., Chlorophyll a) in coastal waters using remotely- sensed ocean color data. The existence of gelbstoff will affect the transfer of radiance in the water column, and then affect the depth of euphotic layer and photosynthesis. Many biogeochemical processes in the ocean, including trace metal complexation, are influenced by the presence of gelbstoff. Accurate estimates of gelbstoff concentrations in the ocean are critical not only to sea-land interaction studies, but also to biogeochemical and ecological studies. In this study in-situ data of the absorption coefficient of gelbstoff (Ag) and remote sensing reflectance (Rrs) in the Pearl River estuary were collected at 36 stations from 2 cruises conducted in 25-26 January 2003 and 5-6 January 2004 respectively. The Pearl River Estuary is the largest estuarine system in the South China Sea (SCS) region with eight discharge mouths of the river system and dynamic water mixing processes. The in-situ remote sensing reflectance data were calculated into 6 bands responding to the wavelength ranges of SeaWiFS band 1 to band 6 (412 to 670 nm). The relationships of the measured Ag to 240 band combinations to measured Rrs were analyzed, and the band combination with highest correlation coefficients with Ag was used for algorithm development. A local algorithm for retrieval of gelbstoff absorption coefficient was developed and applied to estimate gelbstoff absorption coefficient from atmospheric corrected SeaWiFS data. The accuracy of remotely-sensed retrieval of gelbstoff was evaluated by comparison to the measured data. The spatial distribution of gelbstoff absorption coefficient in the Pearl River estuary waters was revealed by thematic images retrieved from SeaWiF