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Potential of ecological modelling and smart-drainage development for mitigating adverse effects of future global change-type droughts for the Estonian forest sector

Jan-Peter George
Jan-Peter George
, 
Mait Lang
Mait Lang
, 
Maris Hordo
Maris Hordo
, 
Sandra Metslaid
Sandra Metslaid
, 
Piia Post
Piia Post
 y 
Toomas Tamm
Toomas Tamm
   | 11 mar 2021
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Article Category: Research paper
Publicado en línea: 11 mar 2021
Páginas: 98 - 106
Recibido: 01 sept 2020
Aceptado: 19 oct 2020
DOI: https://doi.org/10.2478/fsmu-2020-0017
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© 2020 Jan-Peter George et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Soil moisture anomaly in Estonia in 2017–2019. Soil moisture anomaly (SMA) in three consecutive years for the months June (upper panel), July (mid panel), and August (lower panel) for Estonia as obtained from the European Drought Observatory (EDO) under https://edo.jrc.ec.europa.eu. Briefly, soil moisture is calculated on a 5 km2 raster over Europe and based on a hydrological rainfall-runoff model, including daily meteorological observations (de Roo et al., 2000; Cammalleri et al., 2020). Subsequently, soil moisture values are transformed into standardized values by comparison to a long-term reference period (i.e. 1995 until the last full-year observation). Negative values (brown colours) indicate a soil moisture deficit and positive values (blue colours) a soil moisture surplus. From the figure below it can be seen that soil moisture conditions in 2017 were well balanced, while in 2018 and 2019 soils reached a severe (light brown) and partly extreme (dark brown) drying status. Like many northern European countries, the entire Estonia was hit by the millennial drought in 2018, while in 2019 mainly the north-eastern part was affected. Such consecutive drought occurrences can boost tree dieback and vulnerability against pathogens, such as the bark beetle, in particular if they appear at higher frequency in the future (see, for instance, Williams et al., 2010; Williams et al., 2013).
Soil moisture anomaly in Estonia in 2017–2019. Soil moisture anomaly (SMA) in three consecutive years for the months June (upper panel), July (mid panel), and August (lower panel) for Estonia as obtained from the European Drought Observatory (EDO) under https://edo.jrc.ec.europa.eu. Briefly, soil moisture is calculated on a 5 km2 raster over Europe and based on a hydrological rainfall-runoff model, including daily meteorological observations (de Roo et al., 2000; Cammalleri et al., 2020). Subsequently, soil moisture values are transformed into standardized values by comparison to a long-term reference period (i.e. 1995 until the last full-year observation). Negative values (brown colours) indicate a soil moisture deficit and positive values (blue colours) a soil moisture surplus. From the figure below it can be seen that soil moisture conditions in 2017 were well balanced, while in 2018 and 2019 soils reached a severe (light brown) and partly extreme (dark brown) drying status. Like many northern European countries, the entire Estonia was hit by the millennial drought in 2018, while in 2019 mainly the north-eastern part was affected. Such consecutive drought occurrences can boost tree dieback and vulnerability against pathogens, such as the bark beetle, in particular if they appear at higher frequency in the future (see, for instance, Williams et al., 2010; Williams et al., 2013).

Figure 2

Project workflow with proposed and already available datasets that could be included. A possible project workflow with already available datasets is demonstrated in the figure below: Temporal and spatial resolution of the datasets are described in detail in the “Explanation” panel. However, the different resolutions will make rescaling and resampling necessary. Once datasets are compiled and rescaled, the vulnerability assessment can be performed. Option 1 would offer a simple and less laborious possibility to assess vulnerability over a huge area without the need for statistical modelling, but it would also be less accurate and solely based on arbitrarily chosen thresholds. In contrast, option 2 is more laborious, more accurate, and would specifically allow for forecasting vulnerability into the future, when high-resolution future climate data is available on a gridded base. Projections (i.e. forecasts) can be compared between optimistic and pessimistic climatic scenarios, such as given in the representative concentration pathways RCP2.6 and RCP8.5. Both of these scenarios make different assumptions about future emission trajectories and comparing vulnerabilities under consideration of one or the other scenario will be a valuable task when combined with a cost-benefit analysis and projected yield loss. However, the accuracy of results obtained by applying option 2 will strongly depend on the response variable which will be used for modelling. While airborne laser scan (LiDAR) is able to provide massive data on tree height in a relatively short time, estimates for biomass or volume still need to be modelled under consideration of species-specific and location-specific allometric functions. Since modelling needs to be performed on relative growth measures (e.g. biomass change from one year to the next) rather than with absolute values, the accuracy of the obtained response variable will be crucial in order to overcome signal-to-noise ratios caused by measurement and/or transformation errors.Finally, when vulnerable areas are identified they can be intersected with the current drainage information obtained from GIS-based information systems. This will help rapidly identify stands and management areas in which transformation of the current drainage system into a smart-drainage system can be realized. As a general note: since installation and maintenance of such smart-drainage systems will be costly, we presume that productive stands or those with high conservation priority should be preferred for case studies in order to achieve economic feasibility of such a system modification.
Project workflow with proposed and already available datasets that could be included. A possible project workflow with already available datasets is demonstrated in the figure below: Temporal and spatial resolution of the datasets are described in detail in the “Explanation” panel. However, the different resolutions will make rescaling and resampling necessary. Once datasets are compiled and rescaled, the vulnerability assessment can be performed. Option 1 would offer a simple and less laborious possibility to assess vulnerability over a huge area without the need for statistical modelling, but it would also be less accurate and solely based on arbitrarily chosen thresholds. In contrast, option 2 is more laborious, more accurate, and would specifically allow for forecasting vulnerability into the future, when high-resolution future climate data is available on a gridded base. Projections (i.e. forecasts) can be compared between optimistic and pessimistic climatic scenarios, such as given in the representative concentration pathways RCP2.6 and RCP8.5. Both of these scenarios make different assumptions about future emission trajectories and comparing vulnerabilities under consideration of one or the other scenario will be a valuable task when combined with a cost-benefit analysis and projected yield loss. However, the accuracy of results obtained by applying option 2 will strongly depend on the response variable which will be used for modelling. While airborne laser scan (LiDAR) is able to provide massive data on tree height in a relatively short time, estimates for biomass or volume still need to be modelled under consideration of species-specific and location-specific allometric functions. Since modelling needs to be performed on relative growth measures (e.g. biomass change from one year to the next) rather than with absolute values, the accuracy of the obtained response variable will be crucial in order to overcome signal-to-noise ratios caused by measurement and/or transformation errors.Finally, when vulnerable areas are identified they can be intersected with the current drainage information obtained from GIS-based information systems. This will help rapidly identify stands and management areas in which transformation of the current drainage system into a smart-drainage system can be realized. As a general note: since installation and maintenance of such smart-drainage systems will be costly, we presume that productive stands or those with high conservation priority should be preferred for case studies in order to achieve economic feasibility of such a system modification.
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