An entropy-based approach for the optimization of rain gauge network using satellite and ground-based data

Abstract Accurate and precise rainfall records are crucial for hydrological applications water resources management. The accuracy continuity of ground-based time series rely on the density distribution rain gauges over territories. In context a decline gauge distribution, how to optimize design optimal networks is still an unsolved issue. this work, we present method network using satellite-based observations, maximizing information content network. We combine Climate Prediction Center MORPhing technique (CMORPH) observations at ungauged locations with existing in Rio das Velhas catchment, Brazil. use greedy ranking algorithm rank potential place new sensors, based their contribution joint entropy Results show that most informative catchment correspond those areas highest variability satellite can be successfully employed monitoring networks.