Assessment of water resources from remote mountainous catchments plays a crucial role for the development of rural areas in or in the vicinity of mountain ranges. The scarcity of data, however, prevents the application of standard approaches that are based on data-driven models. The Hindu Kush–Karakoram–Himalaya mountain range is a crucial area in terms of water resources, but our understanding of the response of its high-elevation catchments to a changing climate is hindered by lack of hydro-meteorological and cryospheric data. Hydrological modeling is challenging here because internal inconsistencies—such as an underestimation of precipitation input that can be compensated for by an overestimation of meltwater—might be hidden due to the complexity of feedback mechanisms that govern melt and runoff generation in such basins. Data scarcity adds to this difficulty by preventing the application of systematic calibration procedures that would allow identification of the parameter set that could guarantee internal consistency in the simulation of the single hydrological components. In this work, we use simulations from the Hunza River Basin in the Karakoram region obtained with the hydrological model TOPKAPI to quantify the predictive power of discharge and snow-cover data sets, as well as the combination of both. We also show that short-term measurements of meteorological variables such as radiative fluxes, wind speed, relative humidity, and air temperature from glacio-meteorological experiments are crucial for a correct parameterization of surface melt processes. They enable detailed simulations of the energy fluxes governing glacier–atmosphere interaction and the resulting ablation through energy-balance modeling. These simulations are used to derive calibrated parameters for the simplified snow and glacier routines in TOPKAPI. We demonstrate that such parameters are stable in space and time in similar climatic regions, thus reducing the number of parameters requiring calibration.