Natural and imposed injustices: the challenges in implementing 'fair' flood risk management policy in England - Johnson, Clare L.; Penning-Rowsell, Edmund C.; Parker, Dennis J. (The Geographical Journal, 2007)
This paper examines the challenges facing English flood risk management (FRM) policy and practice when considering fair decision-making processes and outcomes at a range of spatial scales. It is recognised that flooding is not fair per se : the inherent natural spatial inequality of flood frequency and extent, plus the legacy of differential system interventions, being the cause. But, drawing on the three social justice models – procedural equality, Rawls’ maximin rule and maximum utility – the authors examine the fairness principles currently employed in FRM decision-making. This is achieved, firstly, in relation to the distribution of taxpayer’s money for FRM at the national, regional and local levels and, secondly, for non-structural strategies – most notably those of insurance, flood warnings and awareness raising, land use control, home owner adaptation and emergency management. A case study of the Lower Thames catchment illustrates the challenges facing decision-makers in ‘real life’: how those strategies which appear to be most technically and economically effective fall far short of being fair from either a vulnerability or equality perspective. The paper concludes that if we are to manage flood risk somewhat more fairly then a move in the direction of government funding of nationally consistent non-structural strategies, in conjunction with lower investment decision thresholds for other local-level FRM options, appears to offer a greater contribution to equality and vulnerability-based social justice principles than the status quo.

Extension of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada - Gauthier, Pierre; Tanguay, Monique; Laroche, Stéphane; Pellerin, Simon; Morneau, Josée (Monthly Weather Review, 2007)
On 15 March 2005, the Meteorological Service of Canada (MSC) proceeded to the implementation of a four-dimensional variational data assimilation (4DVAR) system, which led to significant improvements in the quality of global forecasts. This paper describes the different elements of MSC’s 4DVAR assimilation system, discusses some issues encountered during the development, and reports on the overall results from the 4DVAR implementation tests. The 4DVAR system adopted an incremental approach with two outer iterations. The simplified model used in the minimization has a horizontal resolution of 170 km and its simplified physics includes vertical diffusion, surface drag, orographic blocking, stratiform condensation, and convection. One important element of the design is its modularity, which has permitted continued progress on the three-dimensional variational data assimilation (3DVAR) component (e.g., addition of new observation types) and the model (e.g., computational and numerical changes). This paper discusses some numerical problems that occur in the vicinity of the Poles where the semi-Lagrangian scheme becomes unstable when there is a simultaneous occurrence of converging meridians and strong wind gradients. These could be removed by filtering the winds in the zonal direction before they are used to estimate the upstream position in the semi-Lagrangian scheme. The results show improvements in all aspects of the forecasts over all regions. The impact is particularly significant in the Southern Hemisphere where 4DVAR is able to extract more information from satellite data. In the Northern Hemisphere, 4DVAR accepts more asynoptic data, in particular coming from profilers and aircrafts. The impact noted is also positive and the short-term forecasts are particularly improved over the west coast of North America. Finally, the dynamical consistency of the 4DVAR global analyses leads to a significant impact on regional forecasts. Experimentation has shown that regional forecasts initiated directly from a 4DVAR global analysis are improved with respect to the regional forecasts resulting from the regional 3DVAR analysis.