| Summary: | This data set is a compilation of 1) environmental conditions, 2) biometric- and chamber-based annual CO 2 fluxes, 3) vegetation phenological greenness, and 4) forest-floor environmental conditions, all measured over the Krycklan Catchment Study (KCS, https://www.slu.se/Krycklan ), a multi-scale long-term monitored boreal catchment spanning 68 km 2 in northern Sweden. The environmental measurements cover the period 1991–2020. Specifically, meteorological conditions measured close to the central part of the KCS at the Svartberget reference climate station (64°14′N, 19°46′E, 225 m.a.s.l.) included air temperature at 1.7 m above ground (Ta, °C), global radiation at 1.7 m above ground (Rg, MJ m -2 ), and precipitation (P, mm). Drought conditions were characterized by the Standardized Precipitation Evapotranspiration Index (SPEI) computed at 3-month time scale. SPEI was retrieved from the 0.5° gridded dataset supplied in the Global SPEI Database (SPEIbase v2.8, https://spei.csic.es/database.html ). The data set comprises monthly values obtained during the long-term reference period 1991–2020 (LT 91–20 ), the baseline period 2016–2017 (BL 16–17 ), and the drought year 2018 (D 18 ). The standardized anomaly (ɀ-score) was used to identify extreme environmental measurements during both the BL 16–17 and D 18 periods relative to the LT 91–20 period. Annual CO 2 flux estimates were collected in 50 forest stands located across the KCS during the period 2016–2018 using biometric- and chamber-based methods. However, to prevent confounding effects, one forest stand that was subjected to thinning operations in spring 2018 was excluded from the analysis. The selected forest stands encompassed different landscape attributes such as 1) soil type (i.e., sediment and till), 2) dominant tree species (i.e., pine and spruce), and 3) stand age classes (i.e., initiation, young, middle-aged, mature, and old-growth stands). The annual CO 2 fluxes included the net ecosystem production (NEP) and its component fluxes, i.e., net primary ...
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