Abstract.A clear understanding of the hydrology is required to capture surface processes and potential inherent hazards in orogens. Complex climatic interactions control hydrological processes in high mountains that in their turn regulate the erosive forces shaping the relief. To unravel the hydrological cycle of a glaciated watershed (Gunt River) considered representative of the Pamir Mountains' hydrologic regime, we developed a remotesensing-based approach. At the boundary between two distinct climatic zones dominated by the Westerlies and Indian summer monsoon, the Pamir Mountains are poorly instrumented and only a few in situ meteorological and hydrological data are available. We adapted a suitable conceptual distributed hydrological model (J2000g). Interpolations of the few available in situ data are inadequate due to strong, relief-induced, spatial heterogeneities. Instead of these we use raster data, preferably from remote sensing sources depending on availability and validation. We evaluate remote-sensing-based precipitation and temperature products. MODIS MOD11 surface temperatures show good agreement with in situ data, perform better than other products, and represent a good proxy for air temperatures. For precipitation we tested remote sensing products as well as the HAR10 climate model data and the interpolation-based APHRODITE data set. All products show substantial differences both in intensity and seasonal distribution with in situ data. Despite low resolutions, the data sets are able to sustain high model efficiencies (NSE ≥ 0.85). In contrast to neighbouring regions in the Himalayas or the Hindu Kush, discharge is dominantly the product of snow and glacier melt, and thus temperature is the essential controlling factor. Eighty percent of annual precipitation is provided as snow in winter and spring contrasting peak discharges during summer. Hence, precipitation and discharge are negatively correlated and display complex hysteresis effects that allow for the effect of interannual climatic variability on river flow to be inferred. We infer the existence of two subsurface reservoirs. The groundwater reservoir (providing 40 % of annual discharge) recharges in spring and summer and releases slowly during autumn and winter, when it provides the only source for river discharge. A not fully constrained shallow reservoir with very rapid retention times buffers meltwaters during spring and summer. The negative glacier mass balance (−0.6 m w.e. yr −1 ) indicates glacier retreat, which will ultimately affect the currently 30 % contribution of glacier melt to annual stream flow. The spatiotemporal dependence of water release from snow and ice during the annual cycle likewise implies spatiotemporally restricted surface processes, which are essentially confined to glaciated catchments in late summer, when glacier runoff is the only source of surface runoff. Only this precise constraint of the hydrologic cycle in this complex region allows for unravelling of the surface processes and natural hazards such as floo...

Newly developed approaches based on satellite altimetry and gravity measurements provide promising results on glacier dynamics in the Pamir‐Himalaya but cannot resolve short‐term natural variability at regional and finer scale. We contribute to the ongoing debate by upscaling a hydrological model that we calibrated for the central Pamir. The model resolves the spatiotemporal variability in runoff over the entire catchment domain with high efficiency. We provide relevant information about individual components of the hydrological cycle and quantify short‐term hydrological variability. For validation, we compare the modeled total water storages (TWS) with GRACE (Gravity Recovery and Climate Experiment) data with a very good agreement where GRACE uncertainties are low. The approach exemplifies the potential of GRACE for validating even regional scale hydrological applications in remote and hard to access mountain regions. We use modeled time series of individual hydrological components to characterize the effect of climate variability on the hydrological cycle. We demonstrate that glaciers play a twofold role by providing roughly 35% of the annual runoff of the Panj River basin and by effectively buffering runoff both during very wet and very dry years. The modeled glacier mass balance (GMB) of −0.52 m w.e. yr−1 (2002–2013) for the entire catchment suggests significant reduction of most Pamiri glaciers by the end of this century. The loss of glaciers and their buffer functionality in wet and dry years could not only result in reduced water availability and increase the regional instability, but also increase flood and drought hazards.

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Glacierized high‐mountainous catchments are often the water towers for downstream region, and modeling these remote areas are often the only available tool for the assessment of water resources availability. Nevertheless, data scarcity affects different aspects of hydrological modeling in such mountainous glacierized basins. On the example of poorly gauged glacierized catchment in Central Asia, we examined the effects of input discretization, model complexity, and calibration strategy on model performance. The study was conducted with the GSM‐Socont model driven with climatic input from the corrected High Asia Reanalysis data set of two different discretizations. We analyze the effects of the use of long‐term glacier volume loss, snow cover images, and interior runoff as an additional calibration data. In glacierized catchments with winter accumulation type, where the transformation of precipitation into runoff is mainly controlled by snow and glacier melt processes, the spatial discretization of precipitation tends to have less impact on simulated runoff than a correct prediction of the integral precipitation volume. Increasing model complexity by using spatially distributed input or semidistributed parameters values does not increase model performance in the Gunt catchment, as the more complex model tends to be more sensitive to errors in the input data set. In our case, better model performance and quantification of the flow components can be achieved by additional calibration data, rather than by using a more distributed model parameters. However, a semidistributed model better predicts the spatial patterns of snow accumulation and provides more plausible runoff predictions at the interior sites.