Lucas Real

ABSTRACT Accurate and timely flood forecasts are becoming highly essential due to the increased incidence of flood related disasters over the last few years. Such forecasts require a high resolution integrated flood modeling approach. In this paper, we present an integrated flood forecasting system with an automated workflow over the weather modeling, surface runoff estimation and water routing components. We primarily focus on the water routing process which is the most compute intensive phase and present two parallelization strategies to scale it up to large grid sizes. Specifically, we employ nature-inspired decomposition of a simulation domain into watershed basins and propose a master slave model of parallelization for distributed processing of the basins. We also propose an intra-basin shared memory parallelization approach using OpenMP. Empirical evaluation of the proposed parallelization strategies indicates a potential for high speedups for certain types of scenarios (e.g., speedup of 13× with 16 threads using OpenMP parallelization for the large Rio de Janeiro basin).

Remote sensing is a technique which demands a large amount of analysis on data which may have been captured from a variety of sources. Common sources range from aerial vehicles equipped with scanning devices to sensors attached to satellites in space missions. The data acquisition, however, is commonly subject to the interference of external factors, such as particles in the atmosphere and clouds, which may lead to noise in the data. This paper presents a technique to detect the presence of such artifacts, as observed in some digital elevation model data, and an algorithm to patch them. A case study on the second version of the ASTER GDEM shows that the proposed algorithm is effective in the  detection and patching of vertical artifacts and that it can be applied to different data sets in the realm of digital elevation models.