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| Titel |
Impact of dam-induced hydrological changes on riparian vegetation |
| VerfasserIn |
Stefano Tealdi, Carlo Camporeale, Luca Ridolfi |
| Konferenz |
EGU General Assembly 2010
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 12 (2010) |
| Datensatznummer |
250033921
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| Zusammenfassung |
| Hydrological disturbances are a key factor for the riparian vegetation, which is a highly
dynamic ecosystem prone to external forcing. Random fluctuations of water stages drive in
fact the alternation of periods of floods and exposure of the vegetated plots. During
flooding, the plots are submerged and vegetation is damaged by burial, uprooting
and anoxia, while during exposure periods vegetation grows according to the soil
moisture content and the phreatic water table depth. The distribution of vegetation
along the riparian transect is then directly connected to the stochasticity of river
discharges.
River damming can have remarkable impacts on the hydrology of a river and,
consequently, on the riparian vegetation. Several field studies show how the river regulation
induced by artificial reservoirs can greatly modify the statistical moments and the
autocorrelation of the discharge time series. The vegetation responds to these changes
reducing its overall heterogeneity, declining - substituted by exotic species - and shifting its
starting position nearer or far away from the channel center. These latter processes are known
as narrowing and widening, respectively.
In our work we explore the effects of dam-induced hydrological changes on the
narrowing/widening process and on the total biomass along the transect. To this aim we use
an eco-hydrological stochastic model developed by Camporeale and Ridolfi [2006], which is
able to give a realistic distribution of the biomass along the transect as a function of a few
hydrologic, hydraulic and vegetation parameters. We apply the model to an exemplifying
case, by investigating the vegetation response to a set of changes in mean discharge and
coefficient of variation. The range of these changes is deduced from the analysis of field data
in pre- and post-dam conditions.
Firstly, we analyze the narrowing/widening process. In particular, we analyze two
percentage differences of the starting transversal position with respect to the pre-dam
condition: in the first one the mean discharge is kept constant and the coefficient of variation
is changed, in the second one the opposite is made. In the first case, we find non negligible
values of the percentage differences – of the order of 10-40% - and we note that they depend
on the ratio Tg/Td, where Tg and Td are the typical timescales of growth and decay of the
vegetation, respectively. The values collapse on different non monotone curves, depending
on the Tg/Td ratio. In general, when the coefficient of variation increases, there
is a widening, while when it decreases there is a narrowing. The non monotony
makes possible the widening even in some situations with decreasing coefficient of
variation.
In the second case, i.e. maintaining constant the coefficient of variation and changing
the mean discharge, the obtained values are again non negligible - up to 15% -
and collapse on a nonlinear curve, for each Tg/Td ratio. The decrease in the mean
discharge always brings to a narrowing. Finally, we note that the sum of the two
percentage differences just explained gives a good approximation of the overall
narrowing/widening consequent to a change of both the mean discharge and the coefficient of
variation.
Similar analyses are made for the total biomass along the transect and its temporal
variability, and also for these variables we find nonlinear curves and great changes, of the
order of 100-1000%. In conclusion, we propose a method to assess the impact of river
regulation on the riparian vegetation and we quantify some of the changes promoted by a
reservoir on the vegetation, highlighting how great they can be.
Camporeale, C., Ridolfi, L., 2006. Riparian vegetation distribution induced by river flow
variability: a stochastic approach. Water Resour. Res. 42. |
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