Remote Sensing-Based Assessment of the Variability of Winter and Summer Precipitation in the Pamirs and Their Effects on Hydrology and Hazards Using Harmonic Time Series Analysis

Moisture supply in the Pamir Mountains of Central Asia significantly determines the hydrological cycle and, as a result, impacts the local communities via hazards or socioeconomic aspects, such as hydropower, agriculture and infrastructure. Scarce and unreliable in situ data prevent an accurate assessment of moisture supply, as well as its temporal and spatial variability in this strongly-heterogeneous environment. On the other hand, a clear understanding of climatic and surface processes is required in order to assess water resources and natural hazards. We propose to evaluate the potential of remote sensing and regional climate model (RCM) data to overcome such issues. Difficulties arise for the direct analysis of precipitation if the events are sporadic and when the amounts are low. We hence apply a harmonic time series analysis (HANTS) algorithm to derive spatio-temporal precipitation distributions and to determine regional boundaries delimiting areas where winter or summer precipitation dominate moisture supply. We complement the study with remote sensing-based products, such as temperature, snow cover and liquid water equivalent thickness. We find a strong intraand inter-annual variability of meteorological parameters that result in strongly variable water budget and water mobilization. Climatic variability and its effects on floods and droughts are discussed for three outstanding years. The in-house Remote Sens. 2015, 7 9728 developed HANTS toolbox is a promising instrument to unravel periodic signals in remote sensing time series, even in complex areas, such as the Pamir.