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Data from: Potential of typical highland and mountain forests in the Czech Republic for climate-smart forestry: ecosystem-scale drought responses

Citation

Jocher, Georg et al. (2021), Data from: Potential of typical highland and mountain forests in the Czech Republic for climate-smart forestry: ecosystem-scale drought responses, Dryad, Dataset, https://doi.org/10.5061/dryad.vt4b8gtsf

Abstract

Climate-smart forestry (CSF) consists of an extensive framework of actions directed to mitigating and adapting to global climate change impacts on the resilience and productivity of forest ecosystems. The study connected to this data set investigates the impact of the pan-European 2018 drought on carbon exchange dynamics in typical highland and mountain forests in the Czech Republic, including two coniferous (Norway spruce at Bílý Kříž and Rajec) and one deciduous (European beech at Štítná) stand. Our results show annual net ecosystem CO2 uptake at Rajec to be reduced by 50% during the drought year in comparison to a reference year with normal climatic conditions. Bílý Kříž is less affected by drought, as the local microclimate ensures sufficient water supply. The European beech forest at Štítná is most resilient against drought and its negative impacts: there we detect no differences in carbon exchange dynamics between the drought year and the reference year. Our results are demonstrated on the basis of monthly and annual carbon exchange values and corresponding environmental variables. This data set consists of two files, one containing daily average (sum) data, the second one containing 30 minute average data. The 30 minute average data were the basis of all daily, monthly and annual average (sum) data shown in the study connected to this data set.

Methods

Data were obtained at 3 forest sites in Czech Republic, namely at Bílý Kříž (BK), Rajec (RA) and Štítna (ST).
Each of the sites is equipped with an eddy covariance tower and different kinds of meteorological measurements.
Table 1 in Jocher et al. (2021) gives an overview over the site characteristics, table 2 an overview over the instrumentation at the measurement sites.

Usage Notes

Submitted data consist of 2 files, one file contains daily average (sum) data (Jocher_et_al._2021_SI_CLIMO_daily_values.csv),
the data in this file were used for Figure 3 in Jocher et al. (2021). 
The second file contains 30 minute average data (Jocher_et_al._2021_SI_CLIMO_half-hourly_values.csv)
[SI = Special Issue; CLIMO = CLImate Smart Forestry in MOuntain Regions].
Data of both files cover the period from January 1, 2016 until December 31, 2018.

Jocher_et_al._2021_SI_CLIMO_daily_values.csv contains the following variables:
Tair = air temperature (average)
VPD = vapour pressure deficit (average)
P = precipitation (sum)
SWC = soil water content (average)
NEE = net ecosystem exchange (sum)
GPP = gross primary productivity (sum)
Reco = ecosystem respiration (sum)
Suffixes BK, RA and ST indicate the site were data were obtained.
The second row in the file gives the unit of the corresponding variable.

Jocher_et_al._2021_SI_CLIMO_half-hourly_values.csv contains the following variables:
Tair = air temperature (average)
VPD = vapour pressure deficit (average)
NEE = net ecosystem exchange (average)
qcNEE = quality flag for NEE
Ustar = friction velocity (average)
Rg = global radiation (average)
Suffixes BK, RA and ST indicate the site were data were obtained.
The second row in the file gives the unit of the corresponding variable.

Further information to the variables:
GPP and Reco in Jocher_et_al._2021_SI_CLIMO_daily_values.csv were derived with the daytime approach following Lasslop et al. (2010).
[Note that NEE in Jocher_et_al._2021_SI_CLIMO_daily_values.csv does not necessarily exactly equal GPP + Reco due to methodological aspects].
VPD in Jocher_et_al._2021_SI_CLIMO_half-hourly_values.csv was calculated as described in Jocher et al. (2021).
NEE in Jocher_et_al._2021_SI_CLIMO_half-hourly_values.csv is equal to the 
measured eddy covariance flux and was calculated as described in Jocher et al. (2021).
qcNEE is a quality flag which assesses the flux quality based on a stationarity and
development of turbulence test (cf. Foken and Wichura, 1996; Mauder and Foken, 2004).
A value of 0 indicates best quality, a value of 1 intermediate quality, a value of 2 bad quality.

Contact, if questions occur:
Georg Jocher (jocher.g@czechglobe.cz)

References:

Foken, T., Wichura, B., 1996. Tools for quality assessment of surface-based flux measurements.
Agricultural and Forest Meteorology, 78, 83-105.

Lasslop, G., Reichstein, M., Papale, D., Richardson, A.D., Arneth, A., Barr, A., Stoy, P., Wohlfahrt, G., 2010. 
Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. 
Global Change Biology, 16(1), 187-208.

Mauder, M., Foken, T., 2004. Documentation and instruction manual of the eddy-covariance software package TK3. 
Universität Bayreuth, Abt. Mikrometeorologie, Arbeitsergebnisse 26, 44 pp.