Soil data at station Samoylov (2019)

• Main Authors: Boike, Julia, Bolshiyanov, Dimitry Yu, Bornemann, Niko, Grigoriev, Mikhail N, Grünberg, Inge, Lange, Stephan, Miesner, Frederieke
• Format: Dataset
• Language: English
• Published: PANGAEA 2022
• Subjects: AWI_Perma; dataset; LTO; Permafrost Research; Samoylov; Samoylov_WST; Samoylov Island; Lena Delta; Siberia; Soil; Weather station/meteorological observation; WST; Arctic; lena delta; lena river; permafrost; Tundra
• Online Access: https://doi.pangaea.de/10.1594/PANGAEA.947034; https://doi.org/10.1594/PANGAEA.947034

Understanding permafrost processes and changes requires long-term observational datasets. This dataset is a continuation of the dataset available from the long-term observational site Samoylov, located in the Lena River Delta, Siberia (72.37°N, 126.48°E). The location is characterized by a cold, dry tundra climate with mean annual air temperature of -11.7°C (using years with complete data between 1998 and 2017). The monthly mean temperatures over this period varied between 9.4°C in the warmest month (July) and -31.7°C in the coldest month (February). The average summer rainfall (June-October) was 145.2 mm. This dataset adds recent years to the observations of meteorological parameters, energy balance, and subsurface observations which have been recorded since 1998. The instrumentation, calibration, processing and data quality control is explained in Boike et al. (2019). The data provide observations of temporally variable parameters that mitigate energy fluxes between permafrost and atmosphere, such as ground heat flux, active layer and permafrost temperature, soil volumetric water content, relative permittivity, and soil bulk electrical conductivity. Those variables were measured at various depths and beneath different microtopographic features (a polygon center, a rim, a slope, and a trough), representing landscape heterogeneity. The observations are suitable for use in integrating, calibrating and testing permafrost as a component in Earth System Models. The resulting quality-controlled dataset is unique in the Arctic and serves as a baseline for future studies.