A Set of Biogeochemical Model Approaches for Integrated Modeling of Climate Change Impacts. Biospheric Carbon and Nitrogen Cycles

The biospheric research component of the Forestry and Climate Change Project has had as one of its objectives to develop a concept for a coupled carbon, nitrogen and water model which can be part of integrated models for analyses of climate change. This Working Paper describes a set of such model concepts. These models are now being tested and modified for implementation for regional analyses in the assessment phase of Siberian Forest Study

Dynamics of Fully Stocked Stands in the Territory of the Former Soviet Union

IIASA, the Russian Academy of Sciences, and Russian governmental organizations initiated the Siberian Forest Study in 1992, with the following objectives: Identification of possible future sustainable development options for the Siberian forest sector (assess the biosphere role of Siberian forests, and identify suitable strategies for sustainable development of forest resources, industry, infrastructure, and society); Identification of policies for various options to be implemented by Russian and international agencies. The first phase of the study built relevant and consistent databases for the upcoming analyses of the Siberian forest sector (Phase II). Nine cornerstone areas have been identified for the assessment analyses, namely, further development of the databases, greenhouse gas balances, forest resources and forest utilization, biodiversity and landscapes, non-wood functions, environmental status, forest industry and markets, transportation infrastructure, and socioeconomics. The existing increment estimations in the former USSR and Russia are limited to net increment calculations for periods between inventories which are aggregated for groups of species. Thus, the so called average increment as presented in the Forest State Account is an accumulative characteristic of the growing stock. The work in this paper presents a system developed to estimate the gross and net increment as well as the natural mortality for major forest species by ecoregion. In this report, the work dealing with evenaged fully stocked stands is presented. In the future, analyses for different stocking densities and different types of age structures will be carried out. This work on increment and mortality is a crucial step for the further analyses of the greenhouse gas balances, forest resources and forest utilization, and biodiversity and landscapes in cornerstones in phase 11

Modelling for integrated fire management

Integrated Fire Management assumes predictive tools which enable spatial and temporal location of possible wildfires in accordance with climate conditions, socio-demographic situation and vegetation structure. Such predictive tool was suggested recently for a global scale. The model SEVER-FIRE consists from four parts: 1) estimate of fire danger risk based on Nesterov Index applied by Russian Forest Service; 2) estimate of potential number of lightning and human ignitions; 3) estimate of spread of fire and 4) estimate of fire duration due to rain termination or fire suppression. Model operates at daily time step with spatial resolution 10 by 10 km. Model is validated using historic fire statistics (for numbers of lightning against human fires) and remote sensing data (for areas burnt). Model was used to predict future dynamics of areas burnt under four anthropogenic greenhouse emissions scenario providing different climate and socio-economic conditions. It was shown that future areas burnt may decrease, stay almost unchanged or increase to the end of this century according to greenhouse emissions scenarios. Integrated Fire Management assumes predictive tools which enable spatial and temporal location of possible wildfires in accordance with climate conditions, socio-demographic situation and vegetation structure. Such predictive tool was suggested recently for a global scale. The model SEVER-FIRE consists from four parts: 1) estimate of fire danger risk based on Nesterov Index applied by Russian Forest Service; 2) estimate of potential number of lightning and human ignitions; 3) estimate of spread of fire and 4) estimate of fire duration due to rain termination or fire suppression. Model operates at daily time step with spatial resolution 10 by 10 km. Model is validated using historic fire statistics (for numbers of lightning against human fires) and remote sensing data (for areas burnt). Model was used to predict future dynamics of areas burnt under four anthropogenic greenhouse emissions scenario providing different climate and socio-economic conditions. It was shown that future areas burnt may decrease, stay almost unchanged or increase to the end of this century according to greenhouse emissions scenarios.Â

Evolution of seaports of the Russian Far East in relation to changes in the energy structure in Pacific Asia

We estimated the current state of seaports Vanino and Sovetskaya Gavan, situated in the Russian Far East, as beneficiaries to themselves (at local scale), to Khabarovsk Krai (regional or provincial scale) and to the Russian Federation (inter-regional or federal scale). Further, we make projections for the near future (until the year 2030) for conditions of current export fuel supply demands in China and for conditions of climate-friendly energy restructuring in China. It is shown that the coal specialization of Vanino and Sovetskaya Gavan seaports will not be profitable in the near future (year 2030) for conditions of climate-friendly energy restructuring in China. There will be considerable economic losses at the national level if coal specialization is persistent. Thus, environmental policies regarding energy structure in a country with a large economy may sufficiently influence the development of transport, industry and urban infrastructure on an inter-regional, regional or local level for a country importing fuel resources.</p

Analysis of fire patterns and drivers with global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations

Biomass burning is an important environmental process with a strong influence on vegetation and on the atmospheric composition. It competes with microbes and herbivores to convert biomass to CO2 and it is a major contributor of gases and aerosols to the atmosphere. To better understand and predict global fire occurrence, fire models have been developed and coupled to dynamic global vegetation models (DGVMs) and Earth system models (ESMs). We present SEVER-FIRE v1.0 (Socio-Economic and natural Vegetation ExpeRimental global fire model version 1.0), which is incorporated into the SEVER DGVM. One of the major focuses of SEVER-FIRE is an implementation of pyrogenic behavior of humans (timing of their activities and their willingness and necessity to ignite or suppress fire), related to socioeconomic and demographic conditions in a geographical domain of the model application. Burned areas and emissions from the SEVER model are compared to the Global Fire Emission Database version 2 (GFED), derived from satellite observations, while number of fires is compared with regional historical fire statistics.We focus on both the model output accuracy and its assumptions regarding fire drivers and perform (1) an evaluation of the predicted spatial and temporal patterns, focusing on fire incidence, seasonality and interannual variability; (2) analysis to evaluate the assumptions concerning the etiology, or causation, of fire, including climatic and anthropogenic drivers, as well as the type and amount of vegetation. SEVER reproduces the main features of climate-driven interannual fire variability at a regional scale, for example the large fires associated with the 1997–1998 El Niño event in Indonesia and Central and South America, which had critical ecological and atmospheric impacts. Spatial and seasonal patterns of fire incidence reveal some model inaccuracies, and we discuss the implications of the distribution of vegetation types inferred by the DGVM and of assumed proxies of human fire practices.We further suggest possible development directions to enable such models to better project future fire activityinfo:eu-repo/semantics/publishedVersio

Historical and future global burned area with changing climate and human demography

Wildfires influence terrestrial carbon cycling and represent a safety risk, and yet a process-based understanding of their frequency and spatial distributions remains elusive. We combine satellite-based observations with an enhanced dynamic global vegetation model to make regionally resolved global assessments of burned area (BA) responses to changing climate, derived from 34 Earth system models and human demographics for 1860–2100. Limited by climate and socioeconomics, recent BA has decreased, especially in central South America and mesic African savannas. However, future simulations predict increasing BA due to changing climate, rapid population density growth, and urbanization. BA increases are especially notable at high latitudes, due to accelerated warming, and over the tropics and subtropics, due to drying and human ignitions. Conversely, rapid urbanization also limits BA via enhanced fire suppression in the immediate vicinity of settlements, offsetting the potential for dramatic future increases, depending on warming extent. Our analysis provides further insight into regional and global BA trends, highlighting the importance of including human demographic change in models for wildfire under changing climate

Scenarios of demographic distributional aspects of health co-benefits from decarbonising urban transport

Background There is limited knowledge on the distribution of the health co-benefits of reduced air pollutants and carbon emissions in the transport sector across populations. Methods This Article describes a health impact assessment used to estimate the health co-benefits of alternative land passenger transport scenarios for the city of Beijing, China, testing the effect of five transport-based scenarios from 2020 to 2050 on health outcomes. New potential scenarios range from implementing a green transport infrastructure, to scenarios primarily based on the electrification of vehicle fleets and a deep decarbonisation scenario with near zero carbon emissions by 2050. The health co-benefits are disaggregated by age and sex and estimated in monetary terms. Findings The results show that all the alternative mitigation scenarios result in reduced PM2·5 and CO2 emissions compared to a business-as-usual scenario during 2020–50. The near zero scenario achieves the largest health co-benefits and economic benefits annually relative to the sole mitigation strategy, preventing 300 (95% CI 229–450) deaths, with health co-benefits and CO2 cost-saving an equivalent of 0·01% (0·00–0·03%) of Beijing's Gross domestic product in 2015 by 2050. Given Beijing's ageing population and higher mortality rate, individuals aged 50 years and older experience the greatest benefit from the mitigation scenarios. Regarding sex, the greatest health benefits occur in men. Interpretation This assessment provides estimates of the demographic distribution of benefits from the effects of combinations of green transport and decarbonising vehicles in transport futures. The results show that there are substantial positive health outcomes from decarbonising transport in Beijing. Policies aimed at encouraging active travel and use of public transport, increasing the safety of active travel, improving public transport infrastructure, and decarbonising vehicles lead to differential benefits. In addition, disaggregation by age and sex shows that the health impacts related to transport pollution disproportionately influence different age cohorts and genders