Flood inundation dynamics and socioeconomic vulnerability under environmental change

Floods are a major threat to human existence and historically have both caused the collapse of civilizations and forced the emergence of new cultures. The physical processes of flooding are complex. Increased population, climate variability, change in catchment and channel management, modified landuse and land cover, and natural change of floodplains and river channels all lead to changes in flood dynamics, and as a direct or indirect consequence, social welfare of humans. Section 5.16.1 explores the risks and benefits brought about by floods and reviews the responses of floods and floodplains to climate and landuse change. Section 5.08.2 reviews the existing modeling tools, and the top–down and bottom–up modeling frameworks that are used to assess impacts on future floods. Section 5.08.3 discusses changing flood risk and socioeconomic vulnerability based on current trends in emerging or developing countries and presents an alternative paradigm as a pathway to resilience. Section 5.08.4 concludes the chapter by stating a portfolio of integrated concepts, measures, and avant-garde thinking that would be required to sustainably manage future flood risk.

Does convective aggregation need to be represented in cumulus parameterizations?

Tropical deep convection exhibits a variety of levels of aggregation over a wide range of scales. Based on a multisatellite analysis, the present study shows at mesoscale that different levels of aggregation are statistically associated with differing large-scale atmospheric states, despite similar convective intensity and large-scale forcings. The more aggregated the convection, the dryer and less cloudy the atmosphere, the stronger the outgoing longwave radiation, and the lower the planetary albedo. This suggests that mesoscale convective aggregation has the potential to affect couplings between moisture and convection and between convection, radiation, and large-scale ascent. In so doing, aggregation may play a role in phenomena such as “hot spots” or the Madden-Julian Oscillation. These findings support the need for the representation of mesoscale organization in cumulus parameterizations; most parameterizations used in current climate models lack any such representation. The ability of a cloud system-resolving model to reproduce observed relationships suggests that such models may be useful to guide attempts at parameterizations of convective aggregation.

Chaos of Wolbachia Sequences Inside the Compact Fig Syconia of Ficus benjamina (Ficus: Moraceae)

Figs and fig wasps form a peculiar closed community in which the Ficus tree provides a compact syconium (inflorescence) habitat for the lives of a complex assemblage of Chalcidoid insects. These diverse fig wasp species have intimate ecological relationships within the closed world of the fig syconia. Previous surveys of Wolbachia, maternally inherited endosymbiotic bacteria that infect vast numbers of arthropod hosts, showed that fig wasps have some of the highest known incidences of Wolbachia amongst all insects. We ask whether the evolutionary patterns of Wolbachia sequences in this closed syconium community are different from those in the outside world. In the present study, we sampled all 17 fig wasp species living on Ficus benjamina, covering 4 families, 6 subfamilies, and 8 genera of wasps. We made a thorough survey of Wolbachia infection patterns and studied evolutionary patterns in wsp (Wolbachia Surface Protein) sequences. We find evidence for high infection incidences, frequent recombination between Wolbachia strains, and considerable horizontal transfer, suggesting rapid evolution of Wolbachia sequences within the syconium community. Though the fig wasps have relatively limited contact with outside world, Wolbachia may be introduced to the syconium community via horizontal transmission by fig wasps species that have winged males and visit the syconia earlier.

The ocean's role in polar climate change: asymmetric Arctic and Antarctic responses to greenhouse gas and ozone forcing

In recent decades, the Arctic has been warming and sea ice disappearing. By contrast, the Southern Ocean around Antarctica has been (mainly) cooling and sea-ice extent growing. We argue here that interhemispheric asymmetries in the mean ocean circulation, with sinking in the northern North Atlantic and upwelling around Antarctica, strongly influence the sea-surface temperature (SST) response to anthropogenic greenhouse gas (GHG) forcing, accelerating warming in the Arctic while delaying it in the Antarctic. Furthermore, while the amplitude of GHG forcing has been similar at the poles, significant ozone depletion only occurs over Antarctica. We suggest that the initial response of SST around Antarctica to ozone depletion is one of cooling and only later adds to the GHG-induced warming trend as upwelling of sub-surface warm water associated with stronger surface westerlies impacts surface properties. We organize our discussion around ‘climate response functions’ (CRFs), i.e. the response of the climate to ‘step’ changes in anthropogenic forcing in which GHG and/or ozone-hole forcing is abruptly turned on and the transient response of the climate revealed and studied. Convolutions of known or postulated GHG and ozone-hole forcing functions with their respective CRFs then yield the transient forced SST response (implied by linear response theory), providing a context for discussion of the differing warming/cooling trends in the Arctic and Antarctic. We speculate that the period through which we are now passing may be one in which the delayed warming of SST associated with GHG forcing around Antarctica is largely cancelled by the cooling effects associated with the ozone hole. By mid-century, however, ozone-hole effects may instead be adding to GHG warming around Antarctica but with diminished amplitude as the ozone hole heals. The Arctic, meanwhile, responding to GHG forcing but in a manner amplified by ocean heat transport, may continue to warm at an accelerating rate.

Effects of organic and conventional fertilizer treatments on host selection by the aphid parasitoid Diaeretiella rapae

The type and quantity of fertilizer supplied to a crop will differ between organic and conventional farming practices. Altering the type of fertilizer a plant is provided with can influence a plant’s foliar nitrogen levels, as well as the composition and concentration of defence compounds, such as glucosinolates. Many natural enemies of insect herbivores can respond to headspace volatiles emitted by the herbivores’ host plant in response to herbivory. We propose that manipulating fertilizer type may also influence the headspace volatile profiles of plants, and as a result, the tritrophic interactions that occur between plants, their insect pests and those pests’ natural enemies. Here, we investigate a tritrophic system consisting of cabbage plants, Brassica oleracea, a parasitoid, Diaeretiella rapae, and one of its hosts, the specialist cabbage aphid Brevicoryne brassicae. Brassica oleracea plants were provided with either no additional fertilization or one of three types of fertilizer: Nitram (ammonium nitrate), John Innes base or organic chicken manure. We investigated whether these changes would alter the rate of parasitism of aphids on those plants and whether any differences in parasitism could be explained by differences in attractivity of the plants to D. rapae or attack rate of aphids by D. rapae. In free-choice experiments, there were significant differences in the percentage of B. brassicae parasitized by D. rapae between B. oleracea plants grown in different fertilizer treatments. In a series of dual-choice Y-tube olfactometry experiments, D. rapae females discriminated between B. brassicae-infested and undamaged plants, but parasitoids did not discriminate between similarly infested plants grown in different fertilizer treatments. Correspondingly, in attack rate experiments, there were no differences in the rate that D. rapae attacked B. brassicae on B. oleracea plants grown in different fertilizer treatments. These findings are of direct relevance to sustainable and conventional farming practices.

Minimal regulation of platelet activity by PECAM-1.

PECAM-1 is a member of the superfamily of immunoglobulins (Ig) and is expressed on platelets at moderate level. PECAM-1 has been reported to have contrasting effects on platelet activation by the collagen receptor GPVI and the integrin, alphaIIbbeta3, even though both receptors signal through Src-kinase regulation of PLCgamma2. The present study compares the role of PECAM-1 on platelet activation by these two receptors and by the lectin receptor, CLEC-2, which also signals via PLCgamma2. Studies using PECAM-1 knockout-mice and cross-linking of PECAM-1 using specific antibodies demonstrated a minor inhibitory role on platelet responses to the above three receptors and also under some conditions to the G-protein agonist thrombin. The degree of inhibition was considerably less than that produced by PGI2, which elevates cAMP. There was no significant difference in thrombus formation on collagen in PECAM-1-/- platelets relative to litter-matched controls. The very weak inhibitory effect of PECAM-1 on platelet activation relative to that of PGI2 indicate that the Ig-receptor is not a major regulator of platelet activation. PECAM-1 has been reported to have contrasting effects on platelet activation. The present study demonstrates a very mild or negligible effect on platelet activation in response to stimulation by a variety of agonists, thereby questioning the physiological role of the immunoglobulin receptor as a major regulator of platelet activation.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.