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Abstract: Recently, the cities of Yerba Buena and Tucuman in Tucumán Province, Argentina, have been affected by several storm flood events, causing hundreds of people to be evacuated from their homes. Heavy intensity rainfall, new housing developments covering previously permeable grounds, and old drainage systems are the main causes for this situation. This paper presents a simple approach of urban flood hazard assessment in a region where primary data are scarce. The objectives of this study are to develop a GIS-aided urban flood hazard zoning of the two cities applying multicriteria decision analysis and to evaluate it by means of uncertainty and sensitivity analysis. The research methodology focused on the analysis of those variables that control the water routing when high peak flows exceed the drainage-system capacity. The model incorporates five parameters: distance to the drainage channels, topography (heights and slopes), ground water table depths, and urban land use. A final hazard map for each category is obtained using an algorithm that combines factors in weighted linear combinations. The urban flood hazard map shows that the southeast part of San Miguel de Tucumán has the highest flood hazard over an extended area as a consequence of the conjunction of lowlands with slopes under 0.6% and the presence of urban stream channels with poor maintenance plan. In this area, several neighbourhoods have been flooded. The model was evaluated by the error propagation method and global sensitivity analysis to assess the uncertainty and the relative importance of model input factors. Finally, the influence of criterion weights are analyzed ranging from 25% to 75% of their original values showing a robust behaviour. [Copyright &y& Elsevier]

In this paper a method for cloud cover assessment at night-time (when only thermal infrared data are available) is presented. It is based on the analysis of long wave radiation transfer processes in partially cloudy areas, which led to the formulation of a simplified model of the surface-cloud-atmosphere system. The model was implemented in an operational and iterative algorithm to solve the radiative equations. The algorithm was validated using ground data collected at four meteorological stations in Argentina during November 1997 and May-June 1998, which were compared to cloudiness derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer thermal data. Differences between observed and calculated cloudiness were within ±1 okta in 78% of the cases studied, giving a bias of +0.5 oktas and a standard deviation of ±1.0 oktas in cloudiness estimation. [ABSTRACT FROM AUTHOR]