DOI: 10.5061/dryad.vt4b8gtsf
title: Data from: Potential of typical highland and mountain forests in the Czech Republic for climate-smart forestry: ecosystem-scale drought responses

description: 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.
description: Data were obtained at 3 forest sites in Czech Republic, namely at
 Bílý Kříž (BK), Rajec (RA) and Štítna (ST).<br> Each of the sites is
 equipped with an eddy covariance tower and different kinds of
 meteorological measurements.<br> 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.
description: Submitted data consist of 2 files, one file contains daily
 average (sum) data
 (Jocher_et_al._2021_SI_CLIMO_daily_values.csv),<br> the data in this
 file were used for Figure 3 in Jocher et al. (2021). <br> The second
 file contains 30 minute average data
 (Jocher_et_al._2021_SI_CLIMO_half-hourly_values.csv)<br> [SI =
 Special Issue; CLIMO = CLImate Smart Forestry in MOuntain
 Regions].<br> 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:<br> Tair = air temperature (average)<br>
 VPD = vapour pressure deficit (average)<br> P = precipitation
 (sum)<br> SWC = soil water content (average)<br> NEE = net
 ecosystem exchange (sum)<br> GPP = gross primary productivity
 (sum)<br> Reco = ecosystem respiration (sum)<br> Suffixes BK,
 RA and ST indicate the site were data were obtained.<br> 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:<br> Tair = air temperature (average)<br>
 VPD = vapour pressure deficit (average)<br> NEE = net ecosystem
 exchange (average)<br> qcNEE = quality flag for NEE<br> Ustar
 = friction velocity (average)<br> Rg = global radiation
 (average)<br> Suffixes BK, RA and ST indicate the site were data
 were obtained.<br> The second row in the file gives the unit of the
 corresponding variable. Further information to the
 variables:<br> GPP and Reco in
 Jocher_et_al._2021_SI_CLIMO_daily_values.csv were derived with the daytime
 approach following Lasslop et al. (2010).<br> [Note that NEE in
 Jocher_et_al._2021_SI_CLIMO_daily_values.csv does not necessarily exactly
 equal GPP + Reco due to methodological aspects].<br> VPD in
 Jocher_et_al._2021_SI_CLIMO_half-hourly_values.csv was calculated as
 described in Jocher et al. (2021).<br> NEE in
 Jocher_et_al._2021_SI_CLIMO_half-hourly_values.csv is equal to
 the <br> measured eddy covariance flux and was calculated as
 described in Jocher et al. (2021).<br> qcNEE is a quality flag which
 assesses the flux quality based on a stationarity and<br>
 development of turbulence test (cf. Foken and Wichura, 1996; Mauder and
 Foken, 2004).<br> A value of 0 indicates best quality, a value of 1
 intermediate quality, a value of 2 bad quality.
 Contact, if questions occur:<br> Georg Jocher
 (jocher.g@czechglobe.cz) References:
 Foken, T., Wichura, B., 1996. Tools for quality assessment of
 surface-based flux measurements.<br> 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. <br> Separation of net ecosystem exchange into
 assimilation and respiration using a light response curve approach:
 critical issues and global evaluation. <br> Global Change Biology,
 16(1), 187-208. Mauder, M., Foken, T., 2004.
 Documentation and instruction manual of the eddy-covariance software
 package TK3. <br> Universität Bayreuth, Abt. Mikrometeorologie,
 Arbeitsergebnisse 26, 44 pp.