Abstract: Human activities related to urbanization and industrialization have established a vast territory of urban soil worldwide. On traditional soil maps, urban and industrial areas usually appear as blind spots as they were beyond the interest of national soil survey campaigns. Furthermore, these soil maps are likely already outdated with respect to urban soil due to rapid urban expansion in recent decades. This research aims to evaluate the use of land cover data to estimate the urban soil information gap considering the highly urbanized region of Flanders, Belgium, as a case study. The current extent and spatial distribution of anthropogenic urban soil (1) was estimated through reclassification of recently acquired (2012) exhaustive land cover data, discriminating three qualitative likelihood levels (high-intermediate-low) of anthropogenic influence by urbanization, and (2) compared with its occurrence as represented by the 'Technosols/Not Surveyed area' in the legacy soil map of Belgium, as this map unit best matches with the likelihood for anthropogenic urban soil at the time of the National Soil Survey conducted between end 1940s and mid 1970s. The proposed reclassification of the land cover map resulted in 16.3% and 16.7% of Flanders' total area that corresponds with a high and intermediate likelihood for anthropogenic urban soil, which highlights the underestimation of the anthropogenic urban soil extent as represented by the 'Technosol/Not Surveyed' unit in the legacy soil map (only 13.7%). Moreover, a more realistic spatial pattern of anthropogenic urban soil occurrence was obtained, providing an improved basis for urban soil spatial analysis studies. The produced anthropogenic urban soil likelihood map therefore presents a useful supporting tool for coordinating future soil surveys in urban environments.

Abstract: In environmental assessments, the characterization of urban soils relies heavily on invasive investigation, which is often insufficient to capture their full spatial heterogeneity. Non-invasive geophysical techniques enable rapid collection of high-resolution data and provide a cost-effective alternative to investigate soil in a spatially comprehensive way. This paper presents the results of combining multi-receiver electromagnetic induction and stepped-frequency ground penetrating radar to characterize a former garage site contaminated with petroleum hydrocarbons. The sensor combination showed the ability to identify and accurately locate building remains and a high-density soil layer, thus demonstrating the high potential to investigate anthropogenic disturbances of physical nature. In addition, a correspondence was found between an area of lower electrical conductivity and elevated concentrations of petroleum hydrocarbons, suggesting the potential to detect specific chemical disturbances. We conclude that the sensor combination provides valuable information for preliminary assessment of urban soils.

Abstract: The selection and identification of a suitable hydrological model structure is a more challenging task than fitting parameters of a fixed model structure to reproduce a measured hydrograph. The suitable model structure is highly dependent on various criteria, i.e. the modeling objective, the characteristics and the scale of the system under investigation and the available data. Flexible environments for model building are available, but need to be assisted by proper diagnostic tools for model structure selection. This paper introduces a qualitative method for model component sensitivity analysis. Traditionally, model sensitivity is evaluated for model parameters. In this paper, the concept is translated into an evaluation of model structure sensitivity. Similarly to the one-factor-at-a-time (OAT) methods for parameter sensitivity, this method varies the model structure components one at a time and evaluates the change in sensitivity towards the output variables. As such, the effect of model component variations can be evaluated towards different objective functions or output variables. The methodology is presented for a simple lumped hydrological model environment, introducing different possible model building variations. By comparing the effect of changes in model structure for different model objectives, model selection can be better evaluated. Based on the presented component sensitivity analysis of a case study, some suggestions with regard to model selection are formulated for the system under study: (1) a non-linear storage component is recommended, since it ensures more sensitive (identifiable) parameters for this component and less parameter interaction; (2) interflow is mainly important for the low flow criteria; (3) excess infiltration process is most influencing when focussing on the lower flows; (4) a more simple routing component is advisable; and (5) baseflow parameters have in general low sensitivity values, except for the low flow criteria. (C) 2014 Elsevier B.V. All rights reserved.

Abstract: The use of temperature-time series measured in streambed sediments as input to coupled water flow and heat transport models has become standard when quantifying vertical groundwater-surface water exchange fluxes. We develop a novel methodology, called LPML, to estimate the parameters for 1-D water flow and heat transport by combining a local polynomial (LP) signal processing technique with a maximum likelihood (ML) estimator. The LP method is used to estimate the frequency response functions (FRFs) and their uncertainties between the streambed top and several locations within the streambed from measured temperature-time series data. Additionally, we obtain the analytical expression of the FRFs assuming a pure sinusoidal input. The estimated and analytical FRFs are used in an ML estimator to deduce vertical groundwater-surface water exchange flux and its uncertainty as well as information regarding model quality. The LPML method is tested and verified with the heat transport models STRIVE and VFLUX. We demonstrate that the LPML method can correctly reproduce a priori known fluxes and thermal conductivities and also show that the LPML method can estimate averaged and time-variable fluxes from periodic and nonperiodic temperature records. The LPML method allows for a fast computation of exchange fluxes as well as model and parameter uncertainties from many temperature sensors. Moreover, it can utilize a broad frequency spectrum beyond the diel signal commonly used for flux calculations.