Search

The main objective of this Austria Fire Futures study is to develop a unique and innovative concept containing new sets of fire risk hotspot maps at highest spatial resolution under various climate change scenarios and integrate novel insights on local fuel types into forest and forest fire risk models, including new variables such as morphology and recreational activities. To generate such maps on a local scale, fire hazard modeling is necessary to identify endangered forest types in combination with topographic effects. Furthermore, recent fire events in the Austrian Alps show that social aspects, particularly the hiking tourism, are paid too little attention to.Based on the above motivation, we believe that an innovative and improved fire risk hotspot mapping is the fundament for all further forest- and wildfire prevention and hence needs to be seen as an indispensable tool for an integrated fire management (prevention, suppression, post fire measures) while substantially contributing to mitigating climate change as well as minimizing damage to ecosystems, their services, and people.The study will improve our understanding of fire-vulnerable forest areas that may shift over time and space given the underlying climate and fuel assumptions. This will allow experts, practitioners, and the interested public to take a look into the future in order to comprehend and derive solid short-/medium-/and long-term recommendations for fire resilient and sustainable forest management and fire emergency planning.

We will present approaches to modeling wildfire dynamics using the IIASA’s wildFire cLimate impacts and Adaptation Model (FLAM). FLAM operates with a daily time step and uses mechanistic algorithms to parametrize the impacts of climate, human activities, and fuel availability on wildfire probabilities, frequencies, and burned areas. Validation on historical data and future projections under climate change scenarios will be discussed at various scales and resolutions. We will present results for the following case-studies: (i) projections of global burned areas driven by climate change scenarios until 2100; (ii) modeling burned areas and adaptation options in Europe; (iii) modeling burned areas and their feedback to land-use change in Indonesia with a particular emphasis on extreme fires due the impacts of El Niño southern oscillation using historical data and the delta approach for future scenarios; (iv) regional variability and driving forces behind forest fires in Sweden. Our results support international analyses that, irrespective of changes in management, it is evident that climate change is very likely to increase the frequency and impact of wildland fires in the coming decades.

Wildfire events are driven by complex interactions of the climate and anthropogenic interventions. Predictions of future wildfire events, their extremity, and their impact on the environment and economy must account for the interactions between these drivers. Economic policy and land use decisions influence the susceptibility of an area to climate extremes, the probability of burning, and future decision making. To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia’s 2015 fire events, which saw significant production losses of palm oil – a product imported by the EU chiefly as a biofuel – surpassing 7%. We explored analogous events under three warming conditions and two palm oil sector adaptation scenarios using two storylines: ensemble mean climate and high aridity conditions. We employed a model chain consisting of IIASA's wildfire climate impacts and adaptation model (FLAM) and the partial equilibrium global biosphere management model (GLOBIOM) to predict burned area and assess resultant production losses in the oil palm sector in Indonesia. To quantify the changes in burned area, we applied a delta approach based on the different degrees of global warming that can be expected. To define fire-induced oil palm losses and associated economic impacts, we combined the burned areas from FLAM with land-use change and productivity estimates from GLOBIOM. We found that the total burned area and production loss increased across the projections and climate warming by up to 25%, with only minor differences between storylines. By varying characteristics of regional climate change features, we found that these results are spatially explicit and robust across projections. Our results highlight the importance of including future warming and drought conditions in predicting oil palm losses and land use decision making. They leave room to explore how climatic and economic impacts could be mitigated through economic and land use management policies affecting Indonesia and the EU.

Рассматривается задача оптимального управления с дифференциальной и интегральной связями. Начальное условие в рассматриваемой управляемой системе обыкновенных дифференциальных уравнений имеет нелокальный вид; оно определяется решением системы. Разрабатывается алгоритм поиска оптимального управления, максимизирующего функционал прибыли. Работа посвящена обоснованию элементов алгоритма, который позволяет свести решение исходной задачи к решению более простых задач оптимального управления, связь с которыми осуществляется через один из параметров модели. Доказана возможность и описан способ вычисления такого значения параметра, которое определяет решение исходной задачи. Предложенный подход позволяет эффективно решать оптимизационные задачи, возникающие в моделях управления структурированными популяциями, взаимодействующими на стационарном состоянии.

Changes in climate, land use, and land management impact the occurrence and severity of wildland fires in many parts of the world. This is particularly evident in Europe, where ongoing changes in land use have strongly modified fire patterns over the last decades. Although satellite data by the European Forest Fire Information System provide large-scale wildland fire statistics across European countries, there is still a crucial need to collect and summarize in-depth local analysis and understanding of the wildland fire condition and associated challenges across Europe. This article aims to provide a general overview of the current wildland fire patterns and challenges as perceived by national representatives, supplemented by national fire statistics (2009–2018) across Europe. For each of the 31 countries included, we pres ent a perspective authored by scientists or practitioners from each respective country, representing a wide range of disciplines and cultural backgrounds. The authors were selected from members of the COST Action “Fire and the Earth System: Science & Society” funded by the European Commission with the aim to share knowledge and improve communication about wildland fire. Where relevant, a brief overview of key studies, particular wildland fire challenges a country is facing, and an overview of notable recent fire events are also presented. Key perceived challenges included (1) the lack of consistent and detailed records for wildland fire events, within and across countries, (2) an increase in wild land fires that pose a risk to properties and human life due to high population densities and sprawl into forested regions, and (3) the view that, irrespective of changes in management, climate change is likely to increase the frequency and impact of wildland fires in the coming decades. Addressing challenge (1) will not only be valuable in advancing national and pan-European wildland fire management strategies, but also in evaluating perceptions (2) and (3) against more robust quantitative evidence.

Sustainable forest management (SFM) practices can potentially reverse loss of forest cover due to deforestation, while concomitantly preserving and maintaining biodiversity, and stimulating jobs, income, and forest services. Recent studies found that significant logging residues (LR) (i.e. leaves, branches, and buttress roots) suitable for bioenergy production were often left in the felling area, triggering risks of forest fires and increased CO2 emissions due to wildfires or decomposition processes. For impact assessment of forest management practices, we collected primary harvesting data and estimated net primary productivity (NPP) and net ecosystem exchange (NEE) for 13 forest plots in the Brazilian Amazon. We applied a process-based forestry growth model (BGC-Man) to analyze the impacts on forest dynamics of selective logging and removal of LR, subject to landscape, soil texture, and daily weather. We explored the following selective logging scenarios: the Legal Reserve (i.e. reference) scenario, a scenario with one cutting cycle over the whole period, and a scenario with three timber rotation periods of 30 yr. Two of the later scenarios were complemented with harvesting of the woody LR (Ø ⩾ 10 cm) for charcoal production. For each scenario, we computed forest NPP and NEE over a 120 yr time horizon. Results suggest that using woody LR (i.e. 77% of total LR) for charcoal production would result in an economic gain equivalent to 24%–46% of the timber price. Our findings indicate that under scenarios where LR were removed, forest NPP recovered to the reference level and even higher, while income and jobs from harvesting LR for charcoal production were generated. We conclude that SFM could enhance forest productivity and deliver economic benefit from otherwise unexploited LR.

Sweden has the world’s highest density of moose (Alces alces). Moose is not only a valuable game species; it also causes forest damages and traffic accidents. To avoid moose browsing, foresters respond by planting spruce (Picea abies) to an extent that reshapes the forest landscape with impacts on both production and biodiversity. To address this problem and maintain a healthy moose population in balance with the other interests, an adaptive management based on the knowledge and experiences of local hunters and landowners is advocated. However, the different stakeholders do not agree on what is an appropriate moose population, which leads to conflicts that are hard to resolve. A key problem is that it is very difficult to encompass and foresee long-term consequences of different options for moose hunting and forest management. This makes it challenging to form coherent strategies that integrate different sectorial interests at a national level. To address this issue, we have developed a systems analysis framework for integrated modeling of the moose population, forestry, and their interactions and consequences for biodiversity. We analyze the short and long-term consequences for multiple scenarios of moose hunting and forest management. Based on the results we elucidate and quantify the trade-offs and possible synergies between moose hunting and forest production. This analysis can be used to support better informed and more constructive discussions among the stakeholders in the Swedish forest sectors, and to support policies for long term sustainable forest and moose management.

Chronic wasting disease (CWD) is a contagious neural prion-disease affecting deer populations in North America with severe ecological and societal consequences. CWD is fatal and infectious prions spread and remain in the environment for many years even without animals present. The recent appearance of CWD in reindeers in Norway called for a drastic culling operation to prevent further spreading of the disease. This appears to have stopped the spreading of CWD among reindeers, but due to the persistence of CWD prions in the environment a reappearance of new cases among reindeer or other species in the future cannot be excluded. To evaluate the risks and the effectiveness of alternative management (monitoring and culling) options, we developed a model of CWD dynamics and management. The model includes stochastic population and spatial dynamics of the four relevant deer species in northern Sweden and Norway: reindeer (Rangifer tarandus), roe deer (Capreolus capreolus), red deer (Cervus elaphus) and moose (Alces alces). Transmission of CWD is modelled via direct contacts and via the environment. The model was parameterized and calibrated based on CWD studies from USA, data from the Norwegian CWD cases, and local deer population and vegetation data. The results indicate that without management a CWD epidemic can be initiated by a single infected reindeer and would spread to other deer species. It would lead to dramatic population declines of reindeer and red deer and would also reduce the populations of roe deer and moose. The disease prevalence would stabilise at a about 50% after 50 years, as observed in some areas in the USA. A management strategy to cull only visibly sick animals, even with very efficient detection, cannot prevent a catastrophic development but merely slow the outbreak. To prevent an outbreak and the establishment of CWD it is necessary to cull all individuals, not only visibly sick ones, of an affected species in a relatively large area (30x30 km in our model) once a case is detected. Further, to prevent a slow buildup of CWD in the environment and eliminate the risk of outbreaks in the future it is necessary to expand this area of culling even further. Although the model has not yet been thoroughly validated due to scarcity of data, the results suggest that the drastic culling done in Norway was appropriate and necessary to prevent establishment of CWD and that further monitoring and potential culling is required to prevent outbreaks in the future.

Forests provide multiple ecosystem services, including woody and non-woody biomass products, contribution to carbon budget, and provision of public goods. Therefore, forest management problems are important in the context of optimizing those services. In this study, we formalize the spatial dynamic forest management model as the discrete-time optimal control problem with discrete time and age dynamics. The objective of the forest owner is to maximize the sum of discounted profits over time. Control variables stand for ratios of the forest harvested at every time period, every cell (forest type) and for each age class. In this setup, we deal with a high-dimensional bilinear control problem. The problem is solved using the discrete Pontryagin’s maximum principle. This approach allows us to derive an optimal solution in a constructive manner and reduce computational costs which may arise in the linear programming and recursive dynamics optimization methods. Results are illustrated by the model example with sample age-dependent cost functions, biomass factors, and price projection. Future research will deal with including mortality factors and disturbances into the model dynamics.