Groundwater recharge and capillary rise in a clayey catchment: modulation by topography and the Arctic Oscillation

International audienceThe signature left by capillary rise in the water balance is investigated for a 16 km2 clayey till catchment in Denmark. Integrated modelling for 1981?99 substantiates a 30% uphill increase in average net recharge, caused by the reduction in capillary rise when the water table declines. Calibration of the groundwater module is constrained by stream flow separation and water table wells. Net recharge and a priori parameterisation has been estimated from those same data, an automatic rain gauge and electrical sounding. Evaluation of snow storage and compensation for a simplified formulation of unsaturated hydraulic conductivity contribute to a modelling of the precipitation-runoff relation that compares well with measurements in other underdrained clayey catchments. The capillary rise is assumed to be responsible for a 30% correlation between annual evapotranspiration and the North Atlantic Oscillation. The observed correlation, and the hypothesis of a hemispherical Arctic Oscillation linking atmospheric pressure with surface temperature, suggests that modelled evapotranspiration from clayey areas is better than precipitation records for identifying the region influenced by oscillation. Keywords: catchment modelling, MIKE SHE, capillary rise, degree-day model, climat

Land-surface modelling in hydrological perspective – a review

The purpose of this paper is to provide a review of the different types of energy-based land-surface models (LSMs) and discuss some of the new possibilities that will arise when energy-based LSMs are combined with distributed hydrological modelling. We choose to focus on energy-based approaches, because in comparison to the traditional potential evapotranspiration models, these approaches allow for a stronger link to remote sensing and atmospheric modelling. New opportunities for evaluation of distributed land-surface models through application of remote sensing are discussed in detail, and the difficulties inherent in various evaluation procedures are presented. Finally, the dynamic coupling of hydrological and atmospheric models is explored, and the perspectives of such efforts are discussed

Artificial neural networks: development and application in groundwater pollution remediation design

Abstract Artificial neural networks (ANNs) are investigated as a tool for the simulation of contaminant loss and recovery in three-dimensional (3-D) heterogeneous groundwater flow and contaminant transport modelling. These methods have useful applications in expert system development, knowledge base development and optimization of groundwater pollution remediation. Conventional numerical model runs are used to develop the ANNs. ANNs have been analysed with the goal of estimating objectives that normally require the use of traditional flow and transport codes such as recovered mass, unrecovered mass and remediation failure. The inputs to the ANNs are variable pumping withdrawal rates at fairly unconstrained 3-D locations. A forwardfeed backwards error propagation ANN architecture is used. The significance of the size of the optimization data set, network architecture and network weight optimization algorithm, with respect to the estimation accuracy and objective are shown to be important. Finally, cross-validation techniques quantify the quality of the weight optimization for strongly under described systems