ORIGINAL PAPER Precipitation–altitude relationships on different timescales and at different precipitation magnitudes in the Qilian Mountains Lei Wang 1,2 & Rensheng Chen 1 & Yaoxuan Song 1 & Yong Yang 1 & Junfeng Liu 1 & Chuntan Han 1 & Zhangwen Liu 1 Received: 28 November 2016 /Accepted: 30 October 2017 # Springer-Verlag GmbH Austria, part of Springer Nature 2017 Abstract It is generally accepted that altitude is the main var- iable governing the spatial distribution of precipitation in the mountains. This study mainly discusses the precipitation–alti- tude relationships on different timescales and at different indi- vidual precipitation magnitudes in the entire study period (April 2012 to September 2015), wet season (May to September), and dry season (October to April), and tries to find a threshold to determine whether the correlation between precipitation and altitude is significant. In this study, the half-hourly data, includ- ing precipitation, wind speed, and air temperature, from April 2012 to September 2015 are obtained by six automatic meteo- rological stations located on the north slope of Qilian Mountains, which range from 2980 to 4484 m a.s.l., and hori- zontal distance is approximately 7000 m. Results indicate that (i) if all samples in the entire study period are to be investigated, the individual precipitation had to reach about 30 or 40 mm, then the sample may pass the significance test at p < 0.05 or at p < 0.01, respectively. The thresholds in wet season are same as that during entire study period. The thresholds in dry season are about 10 and 15 mm (ii) with increasing timescale, the percent- age of samples that pass the test increases. However, it is until the monthly scale whether it is wet or dry season or the entire study period, the precipitation–altitude relationships have sta- tistical significance and using the monthly or yearly scale as the time unit can be better applied to the research, which is based on the precipitation–altitude relationships. 1 Introduction At least half the global fresh water resources originate from mountains (Celleri et al. 2007). Thus, mountains play a critical role in the global water cycle and are the main source for all uses (Jansky et al. 2002). Precipitation is the critical input data to a variety of ecological and hydrological models, is the main source of water in the alpine mountainous area, and is an important part of water cycle processes (Running et al. 1987; Daly et al. 1994). Watershed hydrology is mainly based on accurate precipitation data, which are key to water balance in the alpine mountainous area. However, accurate precipitation data are scarce in alpine mountainous areas mainly restricted by complex topography and sparse recording stations (Sevruk 1994; Daly et al. 2007). Clearly, such biased positioning of precipitation gauges, as practiced generally by meteorological services, cannot sufficiently express the complex distribution processes of precipitation in the mountains, particularly in the alpine mountainous areas (Bhatt and Nakamura 2005). Moreover, the gauges are distributed in such a way as to meet the practical requirements of the national meteorological ser- vices rather than the scientific requirements (Sevruk 1997). For example, Anduo Station, the highest of China’ s routine meteorological stations, is located in the flat area of the Tibetan Plateau at an elevation of 4800 m, whereas the mean elevation of the Tibetan Plateau is approximately 4325 m, and many of the surrounding mountains are higher than Anduo Station (Chen et al. 2014a, b). The shortage of data has re- stricted research on the quantity of alpine precipitation and distribution of precipitation (Walser et al. 2004). The valida- tion of satellite remote sensing precipitation data and model * Rensheng Chen crs2008@lzb.ac.cn 1 Qilian Alpine Ecology & Hydrology Research Station, Key Laboratory of Inland River Basin Ecohydrology, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, No. 320, Donggang West Road, Chengguan District, Lanzhou 730000, China 2 University of Chinese Academy of Sciences, Beijing 100049, China Theor Appl Climatol https://doi.org/10.1007/s00704-017-2316-1