Long-term Water Regime Studies of a Degraded Floating Fen in Hungary

Abstract

Historical trends in water management and recent climatic variations put wetlands in the Carpathian basin under strong pressure and led to their degradation. The lack of extended site specific environmental data series inhibits the understanding of long-term eco-hydrological processes. This undermines the success of restoration and/or management efforts. As a precedent we analyzed a recently degraded Hungarian lowland wetland, the Nyárjas fen in order to identify the main cause of its drying. Our method is based on one-dimensional simulations of a variably saturated soil column representing the dominant hydrological conditions of the wetland. To properly define the necessary soil hydraulic parameters, soil sampling, laboratory measurements and inverse modelling were carried out. The hydrological simulations for the 1961-2010 period clearly suggest that (i) the wetland degraded due to a temporal unfavorable combination of regional groundwater depletion and decreased precipitation, (ii) and could not recover afterwards despite the improvement of hydrological conditions. The ecological water demand of the Nyárjas fen can be explicitly expressed in terms of groundwater level. However, water availability is a necessary, but not sufficient criteria of good habitat status. The elaborated methodology provides the basis of bottom-up type environmental water demand estimation on a regional/national scale.

Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative

“…Digital soil mapping (DSM), which has become a successful sub-discipline of soil science with an active research output [10], aims to provide spatial soil information for a wide range of studies, such as precision agriculture [11,12], hydrology [13][14][15], environmental sciences [16,17], conservation biology [18,19] or spatial planning [20,21]. For this purpose, geostatistical techniques are widely used, which have been complemented by machine learning algorithms in the past decade.…”