A procedure for incorporating spatial variability in ecological risk assessment of Dutch river floodplains

Floodplain soils along the river Rhine in the Netherlands show a large spatial variability in pollutant concentrations. For an accurate ecological risk characterization of the river floodplains, this heterogeneity has to be included into the ecological risk assessment. In this paper a procedure is presented that incorporates spatial components of exposure into the risk assessment by linking geographical information systems (GIS) with models that estimate exposure for the most sensitive species of a floodplain. The procedure uses readily available site-specific data and is applicable to a wide range of locations and floodplain management scenarios. The procedure is applied to estimate exposure risks to metals for a typical foodweb in the Afferdensche and Deestsche Waarden floodplain along the river Waal, the main branch of the Rhine in the Netherlands. Spatial variability of pollutants is quantified by overlaying appropriate topographic and soil maps resulting in the definition of homogeneous pollution units. Next to that, GIS is used to include foraging behavior of the exposed terrestrial organisms. Risk estimates from a probabilistic exposure model were used to construct site-specific risk maps for the floodplain. Based on these maps, recommendations for future management of the floodplain can be made that aim at both ecological rehabilitation and an optimal flood defense.     

Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains

This study investigated the relation between vegetation reflectance and elevated concentrations of the metals Ni, Cd, Cu, Zn and Pb in river floodplain soils. High-resolution vegetation reflectance spectra in the visible to near-infrared (400-1350 nm) were obtained using a field radiometer. The relations were evaluated using simple linear regression in combination with two spectral vegetation indices: the Difference Vegetation Index (DVI) and the Red-Edge Position (REP). In addition, a multivariate regression approach using partial least squares (PLS) regression was adopted. The three methods achieved comparable results. The best R(2) values for the relation between metals concentrations and vegetation reflectance were obtained for grass vegetation and ranged from 0.50 to 0.73. Herbaceous species displayed a larger deviation from the established relationships, resulting in lower R(2) values and larger cross-validation errors. The results corroborate the potential of hyperspectral remote sensing to contribute to the survey of elevated metal concentrations in floodplain soils under grassland using the spectral response of the vegetation as an indicator. Additional constraints will, however, have to be taken into account, as results are resolution- and location-dependent.