Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors

Meaningful quantification of data and structural uncertainties in conceptual rainfall-runoff modeling is a major scientific and engineering challenge. This paper focuses on the total predictive uncertainty and its decomposition into input and structural components under different inference scenarios. Several Bayesian inference schemes are investigated, differing in the treatment of rainfall and structural uncertainties, and in the precision of the priors describing rainfall uncertainty. Compared with traditional lumped additive error approaches, the quantification of the total predictive uncertainty in the runoff is improved when rainfall and/or structural errors are characterized explicitly. However, the decomposition of the total uncertainty into individual sources is more challenging. In particular, poor identifiability may arise when the inference scheme represents rainfall and structural errors using separate probabilistic models. The inference becomes ill‐posed unless sufficiently precise prior knowledge of data uncertainty is supplied; this ill‐posedness can often be detected from the behavior of the Monte Carlo sampling algorithm. Moreover, the priors on the data quality must also be sufficiently accurate if the inference is to be reliable and support meaningful uncertainty decomposition. Our findings highlight the inherent limitations of inferring inaccurate hydrologic models using rainfall‐runoff data with large unknown errors. Bayesian total error analysis can overcome these problems using independent prior information. The need for deriving independent descriptions of the uncertainties in the input and output data is clearly demonstrated.Benjamin Renard, Dmitri Kavetski, George Kuczera, Mark Thyer, and Stewart W. Frank

Comparative prebiotic activity of mixtures of cereal grain polysaccharides

The main components of the non-starch polysaccharide (NSP) fraction of wheat flour are arabinoxylan (AX) and β-glucan. These are also present in other cereal grains, but their proportions vary with AX being the major component in wheat and rye and β-glucan in barley and oats. Therefore, it was hypothesised that these NSPs could act synergistically when fermented in vitro at the ratios present in the major foods consumed, resulting in increased prebiotic activity. AX and β-glucan were therefore tested in in vitro fermentation studies to assess their prebiotic activity when used individually and/or in different ratios. Short-chain fatty-acids (SCFAs) produced from in vitro fermentation were measured using HPLC and bacterial populations were measured using flow cytometry with fluorescence in situ hybridisation (Flow-FISH). Fermentation of AX alone resulted in a significant bifidogenic activity and increased concentrations of SCFAs, mainly acetate, after 8-24 h of fermentation, however β-glucan alone did not show prebiotic activity. The greatest prebiotic activity, based on concentration of total SCFAs and increases in total bacteria as well as beneficial Bifidobacterium and Clostridium coccoides/Eubacterium groups, was observed when AX and β-glucan were combined at a 3:1 ratio, which corresponds to their ratios in wheat flour which is major source of cereal fibre in the diet. This indicates that the population of bacteria in the human GI tract may be modulated by the composition of the fibre in the diet, to maximise the prebiotic potential

Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank

Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleo data

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. A case study on two catchments in eastern Australia illustrates this framework. To develop an identifiable model characterizing long-term variability for the first level of the hierarchy, paleoclimate proxies, and instrumental indices describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yr is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run lengths, with 90% between 3 and 33 yr and a mean of 15 yr. The Markov chain model, previously used to simulate oscillating wet/dry climate states, is found to underestimate the probability of wet/dry periods >5 yr, and is rejected in favor of a gamma distribution for simulating the run lengths of the wet/dry IPO-PDO states. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. The model is able to replicate observed statistics such as seasonal and multiyear accumulated rainfall distributions and interannual autocorrelations. Mean seasonal rainfall in the IPO-PDO dry states is found to be 15%-28% lower than the wet state at the case study sites. In comparison, an annual lag-one autoregressive model is unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Copyright © 2011 by the American Geophysical Union.Benjamin J. Henley, Mark A. Thyer, George Kuczera and Stewart W. Frank

Cyber security fear appeals:unexpectedly complicated

Cyber security researchers are starting to experiment with fear appeals, with a wide variety of designs and reported efficaciousness. This makes it hard to derive recommendations for designing and deploying these interventions. We thus reviewed the wider fear appeal literature to arrive at a set of guidelines to assist cyber security researchers. Our review revealed a degree of dissent about whether or not fear appeals are indeed helpful and advisable. Our review also revealed a wide range of fear appeal experimental designs, in both cyber and other domains, which confirms the need for some standardized guidelines to inform practice in this respect. We propose a protocol for carrying out fear appeal experiments, and we review a sample of cyber security fear appeal studies, via this lens, to provide a snapshot of the current state of play. We hope the proposed experimental protocol will prove helpful to those who wish to engage in future cyber security fear appeal research

PCOS remains a diagnosis of exclusion:a concise review of key endocrinopathies to exclude

Polycystic ovarian syndrome (PCOS) is a heterogenous disorder associated with clinical, endocrine and ultrasonographic features that can also be encountered in a number of other diseases. It has traditionally been suggested that prolactin excess, enzymatic steroidogenic abnormalities and thyroid disorders need to be excluded before a diagnosis of PCOS is made. However, there is paucity of data regarding the prevalence of PCOS phenotype in some of these disorders, whereas other endocrine diseases that exhibit PCOS-like features may elude diagnosis and proper management if not considered. This article reviews the data of currently included entities that exhibit a PCOS phenotype and those that potentially need to be looked for, and attempts to identify specific features that distinguish them from idiopathic PCOS