Robert Barneveld

Research Scientist

(+47) 968 51 427

Ås F20

Visiting address
Fredrik A. Dahls vei 20, 1430 Ås


The moisture status of the upper 10cm of the soil profile is a key variable for the prediction of a catchment's hydrological response to precipitation, and of pivotal importance to the estimation of trafficability. Prediction, and even mapping, of topsoil water content is complicated, not in the least because of its large spatial heterogeneity. In IRIDA, an EU/JPI project, measurements, models and weather predictions will be applied to estimate the soil moisture status at the sub-field scale in near-real time. The project is in its early stages, during which the relevant parameters will be selected that will allow for soil moisture mapping on agricultural fields at a 10 m resolution.


Knowledge of hydrological processes and water balance elements are important for climate adaptive water management as well as for introducing mitigation measures aiming to improve surface water quality. Mathematical models have the potential to estimate changes in hydrological processes under changing climatic or land use conditions. These models, indeed, need careful calibration and testing before being applied in decision making. The aim of this study was to compare the capability of five different hydrological models to predict the runoff and the soil water balance elements of a small catchment in Norway. The models were harmonised and calibrated against the same data set. In overall, a good agreement between the measured and simulated runoff was obtained for the different models when integrating the results over a week or longer periods. Model simulations indicate that forest appears to be very important for the water balance in the catchment, and that there is a lack of information on land use specific water balance elements. We concluded that joint application of hydrological models serves as a good background for ensemble modelling of water transport processes within a catchment and can highlight the uncertainty of models forecast.