Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China

摘要

The alpine region is important in riverine and watershed ecosystems as a contributor of freshwater, providing and stimulating specific habitats for biodiversity. In parallel, recent climate change, human activities and other perturbations may disturb hydrological processes and eco-functions, creating the need for next-generation observational and modeling approaches to advance a predictive understanding of such processes in the alpine region. However, several formidable challenges, including the cold and harsh climate, high altitude and complex topography, inhibit complete and consistent data collection where and when it is needed, which hinders the development of remote-sensing technologies and alpine hydrological models. The current study presents a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen-ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin (HRB) in China. Meteorological and hydrological data were monitored from an observation network connecting a group of automatic meteorological stations (AMSs). In addition, to capture snow accumulation and ablation processes, snow cover properties were collected from a snow observation superstation using state-of-the-art techniques and instruments. High-resolution soil physics datasets were also obtained to capture the freeze–thaw processes from a frozen-ground observation superstation. The updated datasets were released to scientists with multidisciplinary backgrounds (i.e., cryospheric science, hydrology and meteorology), and they are expected to serve as a testing platform to provide accurate forcing data and validate and evaluate remote-sensing products and hydrological models for a broader community. The datasets are available from the Cold and Arid Regions Science Data Center at Lanzhou ( https://doi.org/10.3972/hiwater.001.2019.db , Li, 2019).