Integrated water resources management is a combination of managing blue and green water resources. Often the main focus is on the blue water resources, as information on spatially distributed evaporative water use is not as readily available as the link to river flows. Physically based, spatially distributed models are often used to generate this kind of information. These models require enormous amounts of data, which can result in equifinality, making them less suitable for scenario analyses. Furthermore, hydrological models often focus on natural processes and fail to account for anthropogenic influences. 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 3 years were incorporated as input data into the Spatial Tools for River basin Environmental Analysis and Management (STREAM) model. To cater for the extensive irrigation water application, an additional blue water component (Qb) was incorporated in the STREAM model to quantify irrigation water use. To enhance model parameter identification and calibration, three hydrological landscapes (wetlands, hillslope and snowmelt) were identified using field data. The model was calibrated against discharge data from five gauging stations and showed good performance, especially in the simulation of low flows, where the Nash–Sutcliffe Efficiency of the natural logarithm (Ens_ln) of discharge were greater than 0.6 in both calibration and validation periods. At the outlet, the Ens_ln coefficient was even higher (0.90). During low flows, Qb consumed nearly 50% of the river flow in the basin. The Qb model result for irrigation 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.