Accurate representation of flow sources in process-based hydrologic models remains challenging for remote, data-scarce regions. This study applies stable isotope tracers (18 O and 2 H) in water as auxiliary data for the calibration of the isoWATFLOOD ™ model. The most efficient method of those evaluated for introducing isotope data into model calibration was the PA-DDS multiobjective search algorithm. The compromise solutions incorporating isotope data performed slightly inferior in terms of streamflow simulation compared to the calibrated solution using streamflow data only. However, the former solution outperformed the latter one in terms of isotope simulation. Approximation of the model parameter uncertainty into internal flow path partitioning was explored. Inclusion of isotope error facilitated a broader examination of the total parameter space, resulting in significant differences in internal storage and flow paths, most significantly for soil storage and evapotranspiration loss. Isotope-optimized calibration reduced evaporation rates and increased soil moisture content within the model, impacting soil water velocity but not streamflow celerity. Flow-only calibration resulted in artificially narrow model prediction bounds, significantly underestimating the propagation of parameter uncertainty, while isotope-informed calibrations yielded more reliable and robust bound on model predictions. Our findings demonstrate that the accuracy of a complex, spatially distributed, and process-based model cannot be judged from one summative flow-based model performance evaluation metric alone. Plain Language Summary To predict how water moves through watersheds and adequately anticipate hydrologic response to a changing climate, we depend on numerical models to estimate transport and storage of water on and below Earth's surface. These models are only as good as the observation data we have to test them against, which can be insufficient for many remote parts of the world. Our study introduces a new source of data (i.e., isotope tracers, a fingerprint of water's origin) into a model to improve parameter estimation and overall reliability. The largest benefit of adding these data is that it nudges the model into a more realistic distribution of water storage and flow paths to the rivers, particularly soil storage and evaporation loss. This is important for improving the reliability of model projections under changing climates and to better understand how the distribution of water on Earth is changing.