Screening the Flemish (Belgian) soil map for its currency

The currency of the Flanders drainage class map was evaluated using data from two monitoring networks: one with good spatial coverage but poor temporal coverage and another with better temporal but poor spatial coverage. We combine both networks to obtain point expressions for mean highest (MHW) and mean lowest water tables (MLW) by applying time series analysis and total least squares regression. The resulting MHW and MLW point data set was used to evaluate the currency of the existing map and to identify regional differences

Combined conduction and natural convection cooling of offshore power cables in porous sea soil

The power that can be carried by offshore power cables is often restricted by the temperature limit of the materials inside the cable. It is therefore essential to predict the heat transfer behavior of the dissipated power from the cable to the environment. Offshore cables are buried in the seabed, which is a porous structure of sea soil saturated with water. Both conduction of heat through the soil, as well as natural convection due to the flow of water through the porous soil, are possible ways of heat transfer. Most cases are best described as a combination of these heat transfer effects. In this paper, a numerical model is made to predict the heat transfer from the cable to the environment by modeling the surrounding soil as a porous medium. The influence of soil parameters such as conductivity, heat capacity and permeability, as well as geometrical parameters, such as burial depth and cable diameter, are tested. An analytical expression, which can estimate the heat transfer rate for conduction dominated heat flows, is used. For convection dominated heat flows, a correlation in function of the Darcy-modified Rayleigh number is used. For heat flows which are a combination of conduction and convection effects, an algebraic summation of the thermal conductance due to convection and conduction is found not to give adequate agreement with the simulations. It is shown that an asymptotic expansion of the limiting equations for conductive and convective heat transfer rate can be used to determine the total heat flow effectively. Several soil samples in the North Sea are analyzed, and the thermal properties are used as inputs for the model. These calculations show that conduction is the main heat transfer effect and that convection has a limited effect on the heat transfer

Spatial prediction of water table dynamics in Flanders

Shallow water tables are one of the most important land characteristics. They determine the potential land use: the depth is essential in the evaluation of suitability for agriculture and in nature development projects, it is also crucial to identify possible problems with other land uses. Although it is easy to derive the water table on a specific location at one point in time, the depth is highly variable in time (due to its dependence on precipitation surplus) and in space (as is easily observed when strolling through the countryside). The common method for estimating the water table depth in Flanders was by using estimates based on the existing natural drainage class map of Flanders. This natural drainage class map is based on data collected during the national soil survey. It was derived from the depth of gley mottles and the reduction horizon and the position in the landscape and is often used to predict the depth and variability of the water table. Rather than duplicating this methodology, a different method was applied based on actual measurements of the water table. The evaluation was done in three case studies in different part of Flanders: The Dijle Valley, Kluizen and Damme. These encompass some of the major regions where the water table is of great importance: sandy Flanders, river valleys in central Flanders, the coastal region and areas with temporary water tables. The method consisted of three steps: (1) Locations with sufficient water table measurements were used as reference time series. Their data were combined with daily precipitation surplus data using time series analysis to derive climate representative water table statistics. The most important statistics derived were the mean highest water table (MHW) defined as the mean value of the three highest water table levels measured biweekly (or semi-monthly) and the mean lowest water table (MLW) by using the three lowest water table levels. A continuous time series model (PIRFICT) was chosen and implemented, using the precipitation and potential evapotranspiration as driver variables. (2) The data derived from these reference time series were combined with data from shorter time series. In the case of Dijle, additional series were already existing. In contrast for Kluizen and Damme, 500 locations were visited twice, once during winter and once during summer, when the water table is supposed to be on its highest/lowest respectively. This was used to estimate the MHW and MLW in these locations. (3) Finally these locations were used to create areawide predictions. Two major methods were used: remapping and relabeling methods. Relabeling methods use the old soil drainage class map and apply new attributes to the existing polygons without changing their borders. Remapping uses ancillary maps as a basis: from the digital elevation model relevant properties like slope and wetness index were derived and combined with other properties such as the horizontal and vertical distance to channels. To map MHW, MLW and drainage class, point measurements and these ancillary maps were used in a regression kriging approach. All methods proved to be superior to estimates using only the old soil drainage class maps. Nevertheless, the timing of measurements was important, and a large number of summer measurements taken in Damme during an unusually dry summmer could not be used in the further analysis, leading to much higher uncertainties associated with these maps. Apart from testing the updating methodologies in different study areas, a second subject was an evaluation of the predictive quality of the current drainage class map in different parts of Flanders. This predictive quality was evaluated by combining data from networks with good spatial but poor temporal coverage with data from networks with better temporal but poor spatial coverage. Point predictions for MHW and MLW were derived by applying time series modelling (in networks with a good temporal coverage) and by using total least squares regression (to expand to the other sites). The resulting MHW and MLW point data set was used to evaluate the currency of the existing map and to identify regional differences. The quality of the current map is moderate, and large differences occur between regions. Especially the Campine region shows large and systematic differences, whereas the southeastern hills and chalk-loam region are relatively accurate. If more weight is given to errors in the wetter drainage classes, about 50% of the area of Flanders would benefit from remapping

Methods for updating the drainage class map in Flanders, Belgium

Phreatic groundwater dynamics are one of the most important land characteristics for agriculture, nature development and other land uses. In Belgium, these dynamics are usually estimated from the natural drainage classes, indicated on the Belgian soil map. This information is however partly outdated, due to human intervention (artificial drainage, levelling, groundwater extraction) and –possibly- climate change. Moreover, these morphological classes were not based on actual groundwater measurements. Two groups of methods to update the old map using measured groundwater levels were applied at two locations in Flanders. A first group are 'relabeling' methods. These methods preserve the spatial structure of the old map, but assign new classes to it based on the new groundwater level observations. A second method 'remapping' uses areawide high-resolution digital auxiliary information to remap the area and create new mapping boundaries. These methods were applied to two different locations in Flanders: the valley of river Dijle (800 ha, south of Leuven) and an area close to the village of Kluizen (300 ha, east of Ghent). Validation shows that remapping provides better results than relabeling methods, although both groups of methods improve the quality of the original map