Drainage representation in flood models: Application and analysis of capacity assessment framework

Drainage systems are an integral part of urban infrastructure to help transport and treat wastewater as well as manage flooding during extreme rainfall events. Although there is a significant cost associated with the creation, operation and maintenance of drainage systems, the representation of these systems in flood models is overly simplified. This simplification is due to data protection regulations, and the complexities associated with drainage network modelling. A new framework developed by Water UK in collaboration with the Environmental Agency and sewerage undertakers for UK Drainage Water Management Plans provides data on the capacity and performance of the drainage system. The output from this framework provides a new method of incorporating a more explicit representation of spatially varied drainage capacity in flood models. This study presents the first application of the UK’s capacity assessment framework (CAF) for drainage representation in flood models. We develop a method of using the CAF outputs to represent spatially varied drainage losses across a catchment and assess its impact on flood risk. Three catchments in Leeds are used to quantify the difference generated in flooding when using a national average removal rate (NARR, e.g., 12 mm/hr) and our CAF-derived rainfall removal rates. Although there is variance across catchments, the results show the CAF removal rates increase flood depths, velocities, and flood hazards when compared to the national average due to a more realistic representation of the real system drainage capacity. With the pressures of climate change and continued urban development, a better representation of real drainage systems capacities will become more important and will make local solutions more resilient and relevant to the realities on the ground

Episodic Memory and Appetite Regulation in Humans

Psychological and neurobiological evidence implicates hippocampal-dependent memory processes in the control of hunger and food intake. In humans, these have been revealed in the hyperphagia that is associated with amnesia. However, it remains unclear whether 'memory for recent eating' plays a significant role in neurologically intact humans. In this study we isolated the extent to which memory for a recently consumed meal influences hunger and fullness over a three-hour period. Before lunch, half of our volunteers were shown 300 ml of soup and half were shown 500 ml. Orthogonal to this, half consumed 300 ml and half consumed 500 ml. This process yielded four separate groups (25 volunteers in each). Independent manipulation of the 'actual' and 'perceived' soup portion was achieved using a computer-controlled peristaltic pump. This was designed to either refill or draw soup from a soup bowl in a covert manner. Immediately after lunch, self-reported hunger was influenced by the actual and not the perceived amount of soup consumed. However, two and three hours after meal termination this pattern was reversed - hunger was predicted by the perceived amount and not the actual amount. Participants who thought they had consumed the larger 500-ml portion reported significantly less hunger. This was also associated with an increase in the 'expected satiation' of the soup 24-hours later. For the first time, this manipulation exposes the independent and important contribution of memory processes to satiety. Opportunities exist to capitalise on this finding to reduce energy intake in humans

Keeping Pace with Your Eating: Visual Feedback Affects Eating Rate in Humans

Deliberately eating at a slower pace promotes satiation and eating quickly has been associated with a higher body mass index. Therefore, understanding factors that affect eating rate should be given high priority. Eating rate is affected by the physical/textural properties of a food, by motivational state, and by portion size and palatability. This study explored the prospect that eating rate is also influenced by a hitherto unexplored cognitive process that uses ongoing perceptual estimates of the volume of food remaining in a container to adjust intake during a meal. A 2 (amount seen; 300ml or 500ml) x 2 (amount eaten; 300ml or 500ml) between-subjects design was employed (10 participants in each condition). In two ‘congruent’ conditions, the same amount was seen at the outset and then subsequently consumed (300ml or 500ml). To dissociate visual feedback of portion size and actual amount consumed, food was covertly added or removed from a bowl using a peristaltic pump. This created two additional ‘incongruent’ conditions, in which 300ml was seen but 500ml was eaten or vice versa. We repeated these conditions using a savoury soup and a sweet dessert. Eating rate (ml per second) was assessed during lunch. After lunch we assessed fullness over a 60-minute period. In the congruent conditions, eating rate was unaffected by the actual volume of food that was consumed (300ml or 500ml). By contrast, we observed a marked difference across the incongruent conditions. Specifically, participants who saw 300ml but actually consumed 500ml ate at a faster rate than participants who saw 500ml but actually consumed 300ml. Participants were unaware that their portion size had been manipulated. Nevertheless, when it disappeared faster or slower than anticipated they adjusted their rate of eating accordingly. This suggests that the control of eating rate involves visual feedback and is not a simple reflexive response to orosensory stimulatio

Mean appetite composite scores (100-mm VAS) on the preload and no-preload test days across measurement time points.

<p>Errors bars represent ± 1 <i>SE</i> from the mean. Appetite composite scores were calculated using the following formula: (hunger + (100-fullness))/2. * significant difference between preload day and no-preload day, <i>p</i> <.001.</p

Scatterplot and linear best fit to show the association of pizza variability with COMPX.

<p>Values for pizza variability are standardized residuals adjusted for pizza energy content and loss aversion.</p

Mean COMPX scores where participants are split by high and low loss aversion and high and low expected-satiation (ES) confidence.

<p>Adjusted means were derived from analyses where median splits were taken of the loss aversion and expected-satiation confidence predictors.</p