Application of a soil profile model for hydrological estimations in catchment scale

There is a great need to reduce nutrient leaching from arable land into lakes and oceans. By using several different types of models it has previously been possible to describe nutrient losses in a catchment area with a minimum unit of sub-catchment level. At present, it is instead desirable to model a smaller catchment with an opportunity to re-connect the results to the corresponding fields in the catchment. Such models already exist but they are not fully able to properly describe Swedish conditions and land characteristics in our region. With the approach of creating such a model, SLU has developed a project with this work as its first stage. The model is expected to be created under the working name SWE-model which stands for Soil Water Environment and is in this first stage supposed to apply the SOIL model in catchment scale. During the procedure to describe the first step in the process of developing such a model adapted to Swedish conditions and which works in the catchment scale with an area of about 10-30 km2, focus has been set on calculating the transport of water flow from different hydrological response units. Regardless of the processes occurring in the soil after the water has been added, it is assumed that all the water which flows from each simulated unit is drained. In the first step the hydrologic response units were identified based on land use and soil type in the study area. With the help of a script with functions that retrieve and transform data, certain units were chosen for simulation. The script was also created in this project. Finally, the model results were aggregated and summarized for each unique unit, for each sub-catchment, and also for the whole catchment. From the results it is possible to see similarities in the flow dynamics between modeled and measured data. The efficiency coefficient has been calculated to correspond to the mean of the measured values for the whole simulation period. With an automated calibration process the model should be able to perform better. The volume error gives an indication of overestimation from the model