A new snow module improves predictions of isotope-enabled MAIDENiso forest growth model

The representation of snow processes in forest growth models is necessary to accurately predict the hydrological cycle in boreal ecosystems and the isotopic signature of soil water extracted by trees, photosynthates and tree-ring cellulose. Yet, most process-based models do not include a snow module, consequently their simulations may be biased in cold environments. Here, we modified the MAIDENiso model to incorporate a new snow module that simulates snow accumulation, melting and sublimation, as well as thermal exchanges driving freezing and thawing of the snow and the soil. We tested these implementations in two sites in East and West Canada for black spruce (Picea mariana) and white spruce (Picea glauca) forests, respectively. The new snow module improves the skills of the model to predict components of the hydrological cycle. The model is now able to reproduce the spring discharge peak and to simulate stable oxygen isotopes in tree-ring cellulose more realistically than in the original, snow-free version of the model. The new implementation also results in simulations with a higher contribution from the source water on the oxygen isotopic composition of the simulated cellulose, leading to more accurate estimates. Future work may include the development of inverse modelling with the new version of MAIDENiso to produce robust reconstructions of the hydrological cycle and isotope processes in cold environments.

Model-Based Estimation of Amazonian Forests Recovery Time after Drought and Fire Events

In recent decades, droughts, deforestation and wildfires have become recurring phenomena that have heavily affected both human activities and natural ecosystems in Amazonia. The time needed for an ecosystem to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, little is known about the impacts of these disturbances, alone and synergistically, on forest recovery time and the resulting spatiotemporal patterns at the regional scale. In this study, we combined the 3-PG forest growth model, remote sensing and field derived equations, to map the Amazonia-wide (3 km of spatial resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our results indicate that AGB decreases by 4%, 19% and 46% in forests affected by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: (1) the recovery time of the forests takes longer in the southeastern part of the basin, and, (2) as droughts and wildfires become more frequent—since the intervals between the disturbances are getting shorter than the rate of forest regeneration—the long lasting damage they cause potentially results in a permanent and increasing carbon losses from these fragile ecosystems.

Gains or Losses in Forest Productivity under Climate Change? The Uncertainty of CO2 Fertilization and Climate Effects

Global warming poses great challenges for forest managers regarding adaptation strategies and species choices. More frequent drought events and heat spells are expected to reduce growth and increase mortality. Extended growing seasons, warming and elevated CO2 (eCO2) can also positively affect forest productivity. We studied the growth, productivity and mortality of beech (Fagus sylvatica L.) and fir (Abies alba Mill.) in the Black Forest (Germany) under three climate change scenarios (representative concentration pathways (RCP): RCP2.6, RCP4.5, RCP8.5) using the detailed biogeochemical forest growth model GOTILWA+. Averaged over the entire simulation period, both species showed productivity losses in RCP2.6 (16–20%) and in RCP4.5 (6%), but productivity gains in RCP8.5 (11–17%). However, all three scenarios had a tipping point (between 2035–2060) when initial gains in net primary productivity (NPP) (6–29%) eventually turned into losses (1–26%). With eCO2 switched off, the losses in NPP were 26–51% in RCP2.6, 36–45% in RCP4.5 and 33–71% in RCP8.5. Improved water-use efficiency dampened drought effects on NPP between 4 and 5%. Tree mortality increased, but without notably affecting forest productivity. Concluding, cultivation of beech and fir may still be possible in the study region, although severe productivity losses can be expected in the coming decades, which will strongly depend on the dampening CO2 fertilization effect.

Identifying decision-relevant uncertainties for dynamic adaptive forest management under climate change

The decision on how to manage a forest under climate change is subject to deep and dynamic uncertainties. The classic approach to analyze this decision adopts a predefined strategy, tests its robustness to uncertainties, but neglects their dynamic nature (i.e., that decision-makers can learn and adjust the strategy). Accounting for learning through dynamic adaptive strategies (DAS) can drastically improve expected performance and robustness to deep uncertainties. The benefits of considering DAS hinge on identifying critical uncertainties and translating them to detectable signposts to signal when to change course. This study advances the DAS approach to forest management as a novel application domain by showcasing methods to identify potential signposts for adaptation on a case study of a classic European beech management strategy in South-West Germany. We analyze the strategy’s robustness to uncertainties about model forcings and parameters. We then identify uncertainties that critically impact its economic and ecological performance by confronting a forest growth model with a large sample of time-varying scenarios. The case study results illustrate the potential of designing DAS for forest management and provide insights on key uncertainties and potential signposts. Specifically, economic uncertainties are the main driver of the strategy’s robustness and impact the strategy’s performance more critically than climate uncertainty. Besides economic metrics, the forest stand’s past volume growth is a promising signpost metric. It mirrors the effect of both climatic and model parameter uncertainty. The regular forest inventory and planning cycle provides an ideal basis for adapting a strategy in response to these signposts.

Conservation of forest based on a fuelwood substitute as well as considering the cultural and spiritual values: an optimal fuelwood harvest model

Excessive fuelwood harvest is a major cause of deforestation in the developing countries. To mitigate this, various preventive measures have been introduced in different countries. Availability of affordable substitute to the community dependent on the forest for domestic energy consumption may prevent further forest degradation. A stock dependent optimal control model of fuelwood harvest from a natural forest is presented here and comparative statics has been used to show that the presence of a fuelwood substitute will reduce its harvest and increase the forest stock. The model indicates that availability of cheaper and high energy content alternative for fuelwood can substantially reduce fuelwood extraction from a forest. Also, a lower discount rate and higher cultural and spiritual values (CSV) will keep the optimal forest stock close to its carrying capacity and reduce fuelwood harvest. The model reveals that the maximum sustainable yield of forest stock and the ratio of energy content per unit mass of fuel play a central role in the fate of forest stock and level of fuelwood harvest. Empirical example of the Southeast Asian forest growth model along with Liquid Petroleum Gas (LPG) as substitute has been used to illustrate the results. The outcomes of this study can incorporated into forest conservation polices.

Forest Productivity Enhancement and Compensatory Growth: A Review and Synthesis

This review and synthesis article attempts to integrate observations from forestry to contemporary development in related biological research fields to explore the issue of forest productivity enhancement and its contributions in mitigating the wood supply shortage now facing the forest sector. Compensatory growth has been clearly demonstrated in the long-term precommercial thinning and fertilization trial near the Shawnigan Lake, British Columbia, Canada. This phenomenon appears similar to many observations from other biological fields. The concept of compensatory growth can be applied to forest productivity enhancement through overcompensation, by taking advantage of theories and methods developed in other compensatory growth research. Modeling technology provides an alternative approach in elucidating the mechanisms of overcompensation, which could reveal whether the Shawnigan Lake case could be generalized to other tree species and regions. A new mitigation strategy for dealing with issues related to wood supply shortage could be formed through searching for and creating conditions promoting overcompensation. A forest growth model that is state dependent could provide a way of investigating the effect of partial harvest on forest growth trajectories and stand dynamics. Results from such a study could provide cost-effective decision support tools to practitioners.

Model-based Estimation of Amazonian Forests Recovery Time After Drought and Fire Events

In the last decades droughts, deforestation and wildfires have become recurring phenomena that have affected both human activities and natural ecosystems in Amazonia. The time an ecosystem requires to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, the factors influencing and controlling the recovery time and its spatiotemporal patterns at the regional scale are still poorly understood. In this study, we combined forest growth model, remote sensing and field plots, to map Amazonia-wide (300-ha resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our simulated results indicate that AGB decreases by 4%, 19% and 46% in forests disturbed by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: 1) the recovery time of the forests takes longer in the southeastern part of the basin, and, 2) as droughts and wildfires become more frequent – since the intervals between the disturbances is getting shorter than forest regeneration – potentially causing a long-lasting damage in these fragile ecosystems and a permanent degradation.

Carbon–nitrogen interactions in European forests and semi-natural vegetation – Part 2: Untangling climatic, edaphic, management and nitrogen deposition effects on carbon sequestration potentials

The effects of atmospheric nitrogen deposition (Ndep) on carbon (C) sequestration in forests have often been assessed by relating differences in productivity to spatial variations of Ndep across a large geographic domain. These correlations generally suffer from covariation of other confounding variables related to climate and other growth-limiting factors, as well as large uncertainties in total (dry + wet) reactive nitrogen (Nr) deposition. We propose a methodology for untangling the effects of Ndep from those of meteorological variables, soil water retention capacity and stand age, using a mechanistic forest growth model in combination with eddy covariance CO2 exchange fluxes from a Europe-wide network of 22 forest flux towers. Total Nr deposition rates were estimated from local measurements as far as possible. The forest data were compared with data from natural or semi-natural, non-woody vegetation sites. The response of forest net ecosystem productivity to nitrogen deposition (dNEP ∕ dNdep) was estimated after accounting for the effects on gross primary productivity (GPP) of the co-correlates by means of a meta-modelling standardization procedure, which resulted in a reduction by a factor of about 2 of the uncorrected, apparent dGPP ∕ dNdep value. This model-enhanced analysis of the C and Ndep flux observations at the scale of the European network suggests a mean overall dNEP ∕ dNdep response of forest lifetime C sequestration to Ndep of the order of 40–50 g C per g N, which is slightly larger but not significantly different from the range of estimates published in the most recent reviews. Importantly, patterns of gross primary and net ecosystem productivity versus Ndep were non-linear, with no further growth responses at high Ndep levels (Ndep > 2.5–3 g N m−2 yr−1) but accompanied by increasingly large ecosystem N losses by leaching and gaseous emissions. The reduced increase in productivity per unit N deposited at high Ndep levels implies that the forecast increased Nr emissions and increased Ndep levels in large areas of Asia may not positively impact the continent's forest CO2 sink. The large level of unexplained variability in observed carbon sequestration efficiency (CSE) across sites further adds to the uncertainty in the dC∕dN response.

A method for the assessment of forest regrowth site index based on Earth observations and modelling

<p>Presented is a method for the estimation of a productivity class/site index of the forest regrowth after stand replacement natural and human induced disturbances. The method uses Global Forest Change project data on spatial distribution of forest loss sites (including the information about the date of the disturbance) with a 30 m resolution based on Landsat data. Joint analysis of this data resampled to 100 m spatial resolution together with a Russian Land Cover map for 2016 developed based on 100 m PROBA-V data is used to identify reforestation sites and to determine the forest type. Based on this information an appropriate forest growth model is chosen to simulate forest characteristics' dynamics for different site indexes. Finally information on forest characteristics from satellite data-based products is compared to the modeling results for the forest age, computed as a difference between the date of the disturbance and the date of the satellite data product. Reforestation site is assigned a productivity class that yields the best consistency between modeling results and existing satellite data products information.</p><p>Application of the presented method was tested over the European part of Russia using a 100 m global growing stock volume (GSV) map developed within Globbiomass project and lidar vegetation canopy height measurements from ICESat-2/ATLAS system (ATL08 data product). It was found that ICESat-2/ATLAS data is better suited for the proposed approach.</p><p>Presented method is aimed at the development of a reference dataset on forest parameters since obtained information on forest type, age and site index together can be used to estimate other crucial characteristics, including GSV, mean height, mean stem diameter, basal area, productivity, growth and mortality parameters, using the appropriate model. It is also worth mentioning that proposed approach allows estimation of characteristics of young forests which are rarely represented in the field survey-based reference datasets.</p><p>This work was supported by the Russian Science Foundation [grant number 19-77-30015]. Data processing and analysis was carried out using resources of the Centre for collective use ‘IKI-Monitoring’ developed by the Space Research Institute of the Russian Academy of Sciences.</p>