Water level forecasting through fuzzy logic and artificial neural network approaches S. Alvisi, G. Mascellani, M. Franchini, and A. Bárdossy Dipartimento di Ingegneria, Università degli Studi di Ferrara, Italia Institut für Wasserbau, Universität Stuttgart, Deutschland Received: 29 May 2005 – Accepted: 13 June 2005 – Published: 22 June 2005 Correspondence to: S. Alvisi ([email protected]) © ...

Using a global model of continental water balance, forced by interannual variations in precipitation and near-surface atmospheric temperature for the period 1981-1998, we estimate the sea-level changes associated with climate-driven changes in storage of water as snowpack, soil water, and ground water; storage in ice sheets and large lakes is not considered. The 1981-1998 trend is estimated to ...

A system of barrier islands and back-barrier bays occurs along southern Long Island, New York, and in many coastal areas worldwide. Characterizing the bay physical response to water level fluctuations is needed to understand flooding during extreme events and evaluate their relation to geomorphological changes. Offshore sea level is one of the main drivers of water level fluctuations in semienc...

The streamflow and water level complexity of the Poyang Lake basin has been investigated over multiple time-scales using daily observations of the water level and streamflow spanning from 1954 through 2013. The composite multiscale sample entropy was applied to measure the complexity and the Mann-Kendall algorithm was applied to detect the temporal changes in the complexity. The results show th...

Although many hydrologic changes induced by teleseismic waves have been reported, the mechanism(s) responsible for the changes are usually not known. Permeability changes induced by seismic strains are often invoked to explain changes in water level. Using water-level data in Taiwan after the 2008 Wenchuan earthquake, we show here that the observations cannot be properly explained by previously...