The prediction of hydrological dynamics is an important issue in the scientific community of hydrologists and modellers and essential for integrated water resources decision support. However, the utilization of hydrological models in regions with no or a limited amount of measured data is restricted. Remote sensing techniques provide the means to derive such information, e.g. land cover or soil moisture, over various space and time scales. The ERS scatterometers on board the European Remote Sensing Satellite (ERS)- 1 and -2 are able to provide large scale soil moisture content in the top layer (< 5cm) of soil (Wagner et al. 2003) and in combination with an infiltration model, the Soil Water Index (SWI) can be derived. The temporal resolution of the Scatterometer amounts to 3-4 days depending on conceptual formulation. The SWI can be directly derived without any external data sources from ERS Scatterometer data. However, the disadvantage of this dataset is its spatial resolution of 50x50km because a spatial resolution of this size is only useful for global modelling. However, an application of this data set at a regional scale would be a valuable tool for validation and after further investigation as data source, in particular for ungauged basins. Therefore, an assessment of these datasets is necessary to estimate its usefulness for regional hydrological modelling, which is the overall objective of the study. To obtain soil moisture information three approaches can be used: First, field measurements achieve soil moisture information as point measurements and are barely able to show spatially variability representatively over an area of 50x50km. Second, remote sensing techniques offer the possibility to acquire soil moisture information over wide areas. However, currently only one global validated data set is available (Scipal et al. 2005) with the disadvantage of 50x50km spatial resolution. Third, hydrological models predict soil moisture as a part of the hydrological cycle whereas the accuracy of these estimates depends on the model structure as well as model input data. As a result of the inability to represent large spatial distributions by finely resolved ground-based measurements and the deficiency of remote sensing techniques to provide fine-scale soil moisture estimates, the evaluation of the coarse-scale data set will be based on the application of a hydrological model to obtain soil moisture values more finely resolved in terms of time and space than the original Scatterometer data. The applied concept to assess coarse-scale soil moisture data is based on the assumption that the value of one Scatterometer pixel reflects the moisture conditions as an integral response over a representative area and that the remotely measured moisture conditions result from heterogeneous physiographic properties. This spatial variability will be managed by applying the Hydrological Response Units (HRU) approach, which can differentiate the heterogeneity within a grid cell. Additionally two model concepts for soil layer representation are examined: first, the two layer concept dividing the soil layer into horizontal layers and second, the concept of subdividing the soil layer into its pore size storages. Therefore, the Precipitation Runoff Modelling System (PRMS) Model and the J2000 model will be applied in the study area of the Limpopo River in southern Africa. This paper includes preliminary PRMS model results to achieve fine scale soil moisture estimates, which are later used to assess coarse scale soil moisture. The modelling results show a model performance dependent on weather conditions. Whereas the model predicts poorly under dry conditions, it performs satisfactorily under humid conditions. In a next step the application of the J2000 model will show if this model concept is suitable for semiarid catchments and therefore to predict soil moisture generation more accurately then the PRMS model. After completing the hydrological modelling with J2000, the analysis of temporal and spatial time series will follow that will lead to the evaluation of the coarse-scale soil moisture data set.
