Effective management of all water uses in a river basin requires spatially distributed information of evaporative water use and the link towards the river flows. Physically based spatially distributed models are often used to generate this kind of information. These models require enormous amounts of data, if not sufficient would result in equifinality. In addition, hydrological models often focus on natural processes and fail to account for water usage. This study presents a spatially distributed hydrological model that has been developed for a heterogeneous, highly utilized and data scarce river basin in Eastern Africa. Using an innovative approach, remote sensing derived evapotranspiration and soil moisture variables for three years were incorporated as input data in the model conceptualization of the STREAM model (Spatial Tools for River basin Environmental Analysis and Management). To cater for the extensive irrigation water application, an additional blue water component was incorporated in the STREAM model to quantify irrigation water use (ETb(I)). To enhance model parameter identification and calibration, three hydrological landscapes (wetlands, hill-slope and snowmelt) were identified using field data. The model was calibrated against discharge data from five gauging stations and showed considerably good performance especially in the simulation of low flows where the Nash–Sutcliffe Efficiency of the natural logarithm (Eln) of discharge were greater than 0.6 in both calibration and validation periods. At the outlet, the Eln coefficient was even higher (0.90). During low flows, ETb(I) consumed nearly 50% of the river flow in the river basin. ETb(I) model result was comparable to the field based net irrigation estimates with less than 20% difference. These results show the great potential of developing spatially distributed models that can account for supplementary water use. Such information is important for water resources planning and management in heavily utilized catchment areas. Model flexibility offers the opportunity for continuous model improvement when more data become available.