Water demand management in Yemen and Jordan: addressing power and interests

This paper investigates the extent to which entrenched interests of stakeholder groups both maintain water use practice, and may be confronted. The focus is on the agricultural sectors of Yemen and Jordan, where water resource policymakers face resistance in their attempts to reduce water use to environmentally sustainable levels through implementation of water demand management (WDM) activities. Some farmers in both countries that have invested in irrigated production of high-value crops (such as qat and bananas) benefit from a political economy that encourages increased rather than reduced water consumption. The resultant over-exploitation of water resources affects groups in unequal measures. Stakeholder analysis demonstrates that the more ‘powerful’ groups (chiefly the large landowners and the political elites, as well as the ministries of irrigation over which they exert influence) are generally opposed to reform in water use, while the proponents of WDM (e.g. water resource managers, environmental ministries and NGOs, and the international donor community) are found to have minimal influence over water use policy and decisionmaking. Efforts and ideas attempted by this latter group to challenge the status quo are classified here as either (a) influencing or (b) challenging the power asymmetry, and the merits and limits of both approaches are discussed. The interpretation of evidence suggests current practice is likely to endure, but may be more effectively challenged if a long-term approach is taken with an awareness of opportunities generated by windows of opportunity and the participation of ‘overlap groups’.     

A laboratory model of dust devil vortices

The results of a laboratory investigation of the dust devil vortex are presented. Vortex velocity and temperature profiles were measured for various initial conditiions of boundary heat input and outer flow swirl. Three different categories of core structure were observed depending on the amount of axial flow force and circulation strength produced in the experiment and dust devil models are constructed based on the measured mean behavior of each core category.     

An Experimental Performance Evaluation of Spatio-Temporal Join Strategies

Many applications capture, or make use of, spatial data that changes over time. This requirement for effective and efficient spatio‐temporal data management has given rise to a range of research activities relating to spatio‐temporal data management. Such work has sought to understand, for example, the requirements of different categories of application, and the modelling facilities that are most effective for these applications. However, at present, there are few systems with fully integrated support for spatio‐temporal data, and thus developers must often construct custom solutions for their applications. Developers of both bespoke solutions and of generic spatio‐temporal platforms will often need to support the fusion of large spatio‐temporal data sets. Supporting such requests in a database setting involves the use of join operations with both spatial and temporal conditions – spatio‐temporal joins. However, there has been little work to date on spatio‐temporal join algorithms or their evaluation. This paper presents an evaluation of several approaches to the implementation of spatio‐temporal joins that build upon widely available indexing techniques. The evaluation explores how several algorithms perform for databases with different spatial and temporal characteristics, with a view to helping developers of generic infrastructures or custom solutions in the selection and development of appropriate spatio‐temporal join strategies.     

Energy Group optimization for forward and inverse problems in nuclear engineering: application to downwell-logging problems

Simulating radiation transport of neutral particles (neutrons and γ‐ray photons) within subsurface formations has been an area of research in the nuclear well‐logging community since the 1960s, with many researchers exploiting existing computational tools already available within the nuclear reactor community. Deterministic codes became a popular tool, with the radiation transport equation being solved using a discretization of phase‐space of the problem (energy, angle, space and time). The energy discretization in such codes is based on the multigroup approximation, or equivalently the discrete finite‐difference energy approximation. One of the uncertainties, therefore, of simulating radiation transport problems, has become the multigroup energy structure. The nuclear reactor community has tackled the problem by optimizing existing nuclear cross‐sectional libraries using a variety of group‐collapsing codes, whilst the nuclear well‐logging community has relied, until now, on libraries used in the nuclear reactor community. However, although the utilization of such libraries has been extremely useful in the past, it has also become clear that a larger number of energy groups were available than was necessary for the well‐logging problems. It was obvious, therefore, that a multigroup energy structure specific to the needs of the nuclear well‐logging community needed to be established. This would have the benefit of reducing computational time (the ultimate aim of this work) for both the stochastic and deterministic calculations since computational time increases with the number of energy groups. We, therefore, present in this study two methodologies that enable the optimization of any multigroup neutron–γ energy structure. Although we test our theoretical approaches on nuclear well‐logging synthetic data, the methodologies can be applied to other radiation transport problems that use the multigroup energy approximation. The first approach considers the effect of collapsing the neutron groups by solving the forward transport problem directly using the deterministic code EVENT, and obtaining neutron and γ‐ray fluxes deterministically for the different group‐collapsing options. The best collapsing option is chosen as the one which minimizes the effect on the γ‐ray spectrum. During this methodology, parallel processing is implemented to reduce computational times. The second approach uses the uncollapsed output from neural network simulations in order to estimate the new, collapsed fluxes for the different collapsing cases. Subsequently, an inversion technique is used which calculates the properties of the subsurface, based on the collapsed fluxes. The best collapsing option is chosen as the one that predicts the subsurface properties with a minimal error. The fundamental difference between the two methodologies relates to their effect on the generated γ‐rays. The first methodology takes the generation of γ‐rays fully into account by solving the transport equation directly. The second methodology assumes that the reduction of the neutron groups has no effect on the γ‐ray fluxes. It does, however, utilize an inversion scheme to predict the subsurface properties reliably, and it looks at the effect of collapsing the neutron groups on these predictions. Although the second procedure is favoured because of (a) the speed with which a solution can be obtained and (b) the application of an inversion scheme, its results need to be validated against a physically more stringent methodology. A comparison of the two methodologies is therefore given.