Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains
- 1Remote Sensing Group, Geology Institute, Technische Universität Bergakademie Freiberg, B.-von-Cotta-Str. 2, 09599 Freiberg, Germany
- 2Helmholtz Centre for Environmental Research (UFZ), Halle, Germany
- 3Remote Sensing Group, Helmholtz Institute Freiberg of Resource Technology, Freiberg, Germany
- 4Helmholtz Centre Potsdam, German Research Centre for Geosciences (GFZ), Potsdam, Germany
- 5Gewässerkundlicher Landesdienst, Hochwassernachrichtenzentrale, Thüringer Landesamt für Umwelt und Geologie (TLUG), Jena, Germany
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 remote-sensing-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) 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 floods and landslides as well as water availability in the downstream areas. The proposed conceptual model has a tremendous importance for the understanding of the denudation processes in the region. In the Pamirs, large releases of running water that control erosion intensity are primarily controlled by temperature and the availability of snow and glaciers, thus making the region particularly sensitive to climatic variations.