2 resultados para river flood model
em Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest
Resumo:
Ecological models have often been used in order to answer questions that are in the limelight of recent researches such as the possible effects of climate change. The methodology of tactical models is a very useful tool comparison to those complex models requiring relatively large set of input parameters. In this study, a theoretical strategic model (TEGM ) was adapted to the field data on the basis of a 24-year long monitoring database of phytoplankton in the Danube River at the station of G¨od, Hungary (at 1669 river kilometer – hereafter referred to as “rkm”). The Danubian Phytoplankton Growth Model (DPGM) is able to describe the seasonal dynamics of phytoplankton biomass (mg L−1) based on daily temperature, but takes the availability of light into consideration as well. In order to improve fitting, the 24-year long database was split in two parts in accordance with environmental sustainability. The period of 1979–1990 has a higher level of nutrient excess compared with that of the 1991–2002. The authors assume that, in the above-mentioned periods, phytoplankton responded to temperature in two different ways, thus two submodels were developed, DPGM-sA and DPGMsB. Observed and simulated data correlated quite well. Findings suggest that linear temperature rise brings drastic change to phytoplankton only in case of high nutrient load and it is mostly realized through the increase of yearly total biomass.
Resumo:
In this paper we present the composition, seasonal dynamics and fluctuations in diversity of the phytoplankton in the Danube River over 24 years. Weekly samplings were conducted at one section of the river at Göd, in the 1669 river kilometer segment. The change in the phytoplankton community structure was analyzed in relation of water temperature and discharge means. Our findings support the opinion that the Danube is very rich in species, although many of the species are rare and could be described only as coloring species. Results indicate trends in the phytoplankton abundance, which are only detectable in long-term studies. By the help of diversity indices we have observed an increase in the phytoplankton community diversity. With the relevant information, an explanation of the significant changes in diversity and richness was formed. Our goals were a construction of a solid database of the phytoplankton, examining the seasonal dynamics of the phytoplankton through a 24 year long study and to see the most important changing factors of the community. The results of this study are to assist and help future model developments to predict the phytoplankton seasonal dynamic patterns.