The global long-term sea level trend is obtained from the analysis of tide gauge data and TOPEX/Poseidon data. The linear trend of global mean sea level is highly non-umiform spatially, with an average rate of 2.2 mm year-1 in T/P sea-level rise from October 1992 to September 2002. Sea level change duc to temperature vanation (the thermosteric sea level) is discussed. The results are compared with TOPEX/Poseidon altimeter data in the same temporal span at different spatial scales. It is indicated that the ther-mal effect accounts for 86% and 73% of the observed seasonal variability in the northern and southern hemispheres, respectively. The TOPEX/Poseidon observed sea level lags behind the TSI, by 2 months in the zonal band of 40°-60° in both the northern and southern hemispheres. Systematic differences of about 1-2cm between TOPEX/Poseidon observations and thermosteric sea level data are obtained. The potential causes for these differences include water exchange among the atmosphere, land, and oceans, and some pos-sible deviations in thermosteric contribution estimates and geophysical corrections to the TOPEX/Poseidon data. 相似文献
FluBiDi is a two-dimensional model created to simulate real events that can take days and months, as well as short events (minutes or hours) and inclusive laboratory tests. To verify the robustness of FluBiDi, it was tested using a previous study with both designed and real digital elevation models. The results highlight good agreement between the models (i.e. Mike Flood, SOBEK, ISIS 2D, and others) tested and FluBiDi (around 90% for a specific instant and 95% for the complete time simulation). In the simulated hydrographs, the discharge peak value, time to peak, and water level results were accurate, reproducing them with an error of less than 5%. The velocity differences observed in a couple of tests in FluBiDi were associated with very short periods of time (seconds). However, FluBiDi is highly accurate for simulating floods under real topographical conditions with differences of around 2 cm when water depth is around 150 cm. The average water depth and velocities are precise, and the model describes with high accuracy the pattern and extent of floods. FluBiDi has the capability to be adjusted to different types of events and only requires limited input data. 相似文献