PubAg

Main content area

The potential natural vegetation of large river floodplains – From dynamic to static equilibrium

Author:
Ochs, Konstantin, Egger, Gregory, Weber, Arnd, Ferreira, Teresa, Householder, John Ethan, Schneider, Matthias
Source:
Journal of hydro-environment research 2020 v.30 pp. 71-81
ISSN:
1570-6443
Subject:
dams (hydrology), ecological models, ecological succession, expert opinion, floodplains, habitats, hydrology, river regulation, rivers, statistical models, vegetation, Rhine River
Abstract:
The potential natural vegetation (PNV) is a useful benchmark for the restoration of large river floodplains because very few natural reference reaches exist. Expert-based approaches and different types of ecological models (static and dynamic) are commonly used for its estimation despite the conceptual differences they imply. For natural floodplains a static concept of PNV is not reasonable, as natural disturbances cause a constant resetting of succession. However, various forms of river regulation have disrupted the natural dynamics of most large European rivers for centuries. Therefore, we asked whether the consideration of succession dynamics and time dependent habitat turnover are still relevant factors for the reconstruction of the PNV.To answer this we compared the results of a simulation of the vegetation succession (1872–2016) of a segment of the upper Rhine river after regulation (damming, straightening and bank protection) to different statistic and expert-based modelling approaches for PNV reconstruction. The validation of the different PNV estimation methods against a set of independent reference plots and the direct comparison of their results revealed very similar performances. We therefore conclude that due to a lack of large disturbances, the vegetation of regulated large rivers has reached a near-equilibrium state with the altered hydrologic regime and that a static perception of its PNV may be justified. Consequently, statistical models seem to be the best option for its reconstruction since they need relatively few resources (data, time, expert knowledge) and are reproducible.
Agid:
6824447