Result TO01000345-V2
Within the following text, you’ll find an in-depth exposition of the findings from TO01000345-V2 – Maps of ecophysiology variables related to forest regulation functions for selected tree species in the Czech Republic and Norway in past and present. This result consists of multiple maps of forest regulation function derived using two approaches. First, analysing a 20 years series of satellite observations from MODIS sensor using an energy balance diagnostic model DisALEXI (Hain and Anderson, 2017). Second, interpolating climatic and soil in situ observations to drive a water balance model SoilClim (Hlavinka et al., 2011, Fischer et al., 2018). The maps are produced for the Czech Republic and are attached as separate files.
List of attached specialised maps:
Map_V2-1.pdf Actual evapotranspiration for conifers in the Czech Republic derived from DisALEXI time series 2001–2020
Map_V2-2.pdf Actual evapotranspiration for broadleaf tree species in the Czech Republic derived from DisALEXI time series 2001–2020
Map_V2-3.pdf Trends of normalized actual evapotranspiration for conifers in the Czech Republic derived from DisALEXI during 2001–2020
Map_V2-4.pdf Trends of normalized actual evapotranspiration for broadleaf tree species in the Czech Republic derived from DisALEXI during 2001–2020
Map_V2-5.pdf Actual evapotranspiration for conifers in the Czech Republic derived from in situ data driven water balance model SoilClim time series 2001–2020
Map_V2-6.pdf Actual evapotranspiration for broadleaf tree species in the Czech Republic derived from in situ data driven water balance model SoilClim time series 2001–2020
Map_V2-7.pdf Trends of relative soil moisture (in soil depth of 0-40 cm) for conifers in the Czech Republic derived from in situ data driven water balance model SoilClim time series 2001–2020
Map_V2-8.pdf Trends of relative soil moisture (in soil depth of 0-40 cm) for broadleaf tree species in the Czech Republic derived from in situ data driven water balance model SoilClim time series 2001–2020
Map_V2-9.pdf Shift in actual evapotranspiration for conifers in the Czech Republic derived from in situ data driven water balance model SoilClim between the period 1981–2010 and 2001–2020
Map_V2-10.pdf Shift in actual evapotranspiration for broadleaf tree species in the Czech Republic derived from in situ data driven water balance model SoilClim between the period 1981–2010 and 2001–2020
Map_V2-11.pdf Shift in actual evapotranspiration normalized by reference evapotranspiration for conifers in the Czech Republic derived from in situ data driven water balance model SoilClim between the period 1981–2010 and 2001–2020
Map_V2-12.pdf Shift in actual evapotranspiration normalized by reference evapotranspiration for broadleaf tree species in the Czech Republic derived from in situ data driven water balance model SoilClim between the period 1981–2010 and 2001–2020
Map_V2-13.pdf Shift in relative soil moisture (in soil depth of 0-40 cm) for conifers in the Czech Republic derived from in situ data driven water balance model SoilClim between the period 1981–2010 and 2001–2020
Map_V2-14.pdf Shift in relative soil moisture (in soil depth of 0-40 cm) for broadleaf tree species in the Czech Republic derived from in situ data driven water balance model SoilClim between the period 1981–2010 and 2001–2020
References
Fischer M, Zenone T, Trnka M, Orság M, Montagnani L, Ward EJ, Tripathi AM, Hlavinka P, Seufert G, Žalud Z, King JS, Ceulemans R (2018) Water requirements of short rotation poplar coppice: Experimental and modelling analyses across Europe. Agricultural and Forest Meteorology 250–251: 343–360.
Hain CR, Anderson MC (2017) Estimating morning change in land surface temperature from MODIS day/night observations: Applications for surface energy balance modeling. Geophysical Research Letters 44: 9723–9733.
Hlavinka, P., Trnka, M., Balek, J., Semeradova, D., Hayes, M., Svoboda, M., Eitzinger, J., Mozny, M., Fischer, M., Hunt, E., Zalud, Z., 2011. Development and evaluation of the SoilClim model for water balance and soil climate estimates. Agricultural Water Management 98, 1249–1261. https://doi.org/10.1016/j.agwat.2011.03.011