Анализ модели эффективного экономического развития системы "производитель - налоговый центр" на бесконечном интервале на основе принципа нетривиальности решения

На основе принципа нетривиальности решения найдено Парето-оптимальное значение свертки критериев в модели эффективного экономического развития системы "производитель - налоговый центр" на бесконечном временном интервале с двумя экономическими агентами - производителем и налоговым центром. Это позволяет оценивать эффективность инвестиционного проекта, описываемого данной моделью, и разрабатывать инвестиционные стратегии, принимая во внимание интересы каждого из его участников

Landbedeckung in Sachsen, 1961-1979 (Teil 1): Erfassung von Veränderungen in der Landbedeckung in Sachsen 1961-1979: Analyse anhand historischer CORONA- Spionageaufnahmen und tiefer neuronaler Netze (Teil 1)

Inhalt des Berichtes ist es, methodische Grundlagen zu schaffen, welche für die Landbedeckungskartierung und -klassifizierung aus Satellitenbildern verwendet werden können. Das Ziel ist es, trotz wechselnder Datenquellen eine einheitliche und vergleichbare Datengrundlage aufzubauen. Es wurde dabei eine lange Zeitreihe, welche in der Mitte der 1960er Jahre startet, als Geodatensatz erstellt und auf erste Anwendungsfälle getestet. Der Bericht ist nach den beiden grundsätzlich unterschiedlichen methodischen Ansätzen in zwei Teile aufgetrennt: Teil 1 ist die Schriftenreihe des LfULG, Heft 6/2023 „Landbedeckung in Sachsen 1961-1979“; Teil 2 ist die Schriftenreihe des LfULG, Heft 7/2023 „Landbedeckung in Sachsen 1985 – 2020. Die Veröffentlichung richtet sich sowohl an das Fachpublikum als auch an Laien, die sich für die Fernerkundung interessieren. Redaktionsschluss: 30.11.202

Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations

The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. This uncertainty originates from how dynamic global vegetation models (DGVMs) simulate climate impacts on changes in vegetation distribution, productivity, biomass allocation, and carbon turnover. The present-day availability of a multitude of satellite observations can potentially help to constrain DGVM simulations within model-data integration frameworks. Here, we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle and of temporal dynamics in FAPAR, SIF and gross primary production. However, the optimized model reproduces better the observed spatial patterns of biomass, tree cover, and regional forest carbon turnover. Using a machine learning approach, we found that remaining errors in simulated forest carbon turnover can be explained with bioclimatic variables. This demonstrates the need to improve model formulations for climate effects on vegetation turnover and mortality despite the apparent successful constraint of simulated vegetation dynamics with multiple satellite observations

Improving the LPJmL4-SPITFIRE vegetation–fire model for South America using satellite data

Vegetation fires influence global vegetation distribution, ecosystem functioning, and global carbon cycling. Specifically in South America, changes in fire occurrence together with land-use change accelerate ecosystem fragmentation and increase the vulnerability of tropical forests and savannas to climate change. Dynamic global vegetation models (DGVMs) are valuable tools to estimate the effects of fire on ecosystem functioning and carbon cycling under future climate changes. However, most fire-enabled DGVMs have problems in capturing the magnitude, spatial patterns, and temporal dynamics of burned area as observed by satellites. As fire is controlled by the interplay of weather conditions, vegetation properties, and human activities, fire modules in DGVMs can be improved in various aspects. In this study we focus on improving the controls of climate and hence fuel moisture content on fire danger in the LPJmL4-SPITFIRE DGVM in South America, especially for the Brazilian fire-prone biomes of Caatinga and Cerrado. We therefore test two alternative model formulations (standard Nesterov Index and a newly implemented water vapor pressure deficit) for climate effects on fire danger within a formal model–data integration setup where we estimate model parameters against satellite datasets of burned area (GFED4) and aboveground biomass of trees. Our results show that the optimized model improves the representation of spatial patterns and the seasonal to interannual dynamics of burned area especially in the Cerrado and Caatinga regions. In addition, the model improves the simulation of aboveground biomass and the spatial distribution of plant functional types (PFTs). We obtained the best results by using the water vapor pressure deficit (VPD) for the calculation of fire danger. The VPD includes, in comparison to the Nesterov Index, a representation of the air humidity and the vegetation density. This work shows the successful application of a systematic model–data integration setup, as well as the integration of a new fire danger formulation, in order to optimize a process-based fire-enabled DGVM. It further highlights the potential of this approach to achieve a new level of accuracy in comprehensive global fire modeling and prediction

Multimodal anatomy of the human forniceal commissure

Ambiguity surrounds the existence and morphology of the human forniceal commissure. We combine advanced in-vivo tractography, multidirectional ex-vivo fiber dissection, and multiplanar histological analysis to characterize this structure's anatomy. Across all 178 subjects, in-vivo fiber dissection based on the Human Connectome Project 7 T MRI data identifies no interhemispheric connections between the crura fornicis. Multidirectional ex-vivo fiber dissection under the operating microscope demonstrates the psalterium as a thin soft-tissue membrane spanning between the right and left crus fornicis, but exposes no commissural fibers. Multiplanar histological analysis with myelin and Bielchowsky silver staining, however, visualizes delicate cruciform fibers extending between the crura fornicis, enclosed by connective tissue, the psalterium. The human forniceal commissure is therefore much more delicate than previously described and presented in anatomical textbooks. This finding is consistent with the observed phylogenetic trend of a reduction of the forniceal commissure in non-human primates compared to non-primate eutherian mammals

CM2Mc-LPJmL v1.0: biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model

The terrestrial biosphere is exposed to land-use and climate change, which not only affects vegetation dynamics but also changes land–atmosphere feedbacks. Specifically, changes in land cover affect biophysical feedbacks of water and energy, thereby contributing to climate change. In this study, we couple the well-established and comprehensively validated dynamic global vegetation model LPJmL5 (Lund–Potsdam–Jena managed Land) to the coupled climate model CM2Mc, the latter of which is based on the atmosphere model AM2 and the ocean model MOM5 (Modular Ocean Model 5), and name it CM2Mc-LPJmL. In CM2Mc, we replace the simple land-surface model LaD (Land Dynamics; where vegetation is static and prescribed) with LPJmL5, and we fully couple the water and energy cycles using the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. These include a sub-daily cycle for calculating energy and water fluxes, conductance of the soil evaporation and plant interception, canopy-layer humidity, and the surface energy balance in order to calculate the surface and canopy-layer temperature within LPJmL5. Exchanging LaD with LPJmL5 and, therefore, switching from a static and prescribed vegetation to a dynamic vegetation allows us to model important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the impacts of managed land (crop growth and irrigation). Our results show that CM2Mc-LPJmL has similar temperature and precipitation biases to the original CM2Mc model with LaD. The performance of LPJmL5 in the coupled system compared to Earth observation data and to LPJmL offline simulation results is within acceptable error margins. The historical global mean temperature evolution of our model setup is within the range of CMIP5 (Coupled Model Intercomparison Project Phase 5) models. The comparison of model runs with and without land-use change shows a partially warmer and drier climate state across the global land surface. CM2Mc-LPJmL opens new opportunities to investigate important biophysical vegetation–climate feedbacks with a state-of-the-art and process-based dynamic vegetation model

Recent global and regional trends in burned area and their compensating environmental controls

The apparent decline in the global incidence of fire between 1996 and 2015, as measured by satellite-observations of burned area, has been related to socioeconomic and land use changes. However, recent decades have also seen changes in climate and vegetation that influence fire and fire-enabled vegetation models do not reproduce the apparent decline. Given that the satellite-derived burned area datasets are still relatively short (<20 years), this raises questions both about the robustness of the apparent decline and what causes it. We use two global satellite-derived burned area datasets and a data-driven fire model to (1) assess the spatio-temporal robustness of the burned area trends and (2) to relate the trends to underlying changes in temperature, precipitation, human population density and vegetation conditions. Although the satellite datasets and simulation all show a decline in global burned area over ~20 years, the trend is not significant and is strongly affected by the start and end year chosen for trend analysis and the year-to-year variability in burned area. The global and regional trends shown by the two satellite datasets are poorly correlated for the common overlapping period (2001–2015) and the fire model simulates changes in global and regional burned area that lie within the uncertainties of the satellite datasets. The model simulations show that recent increases in temperature would lead to increased burned area but this effect is compensated by increasing wetness or increases in population, both of which lead to declining burned area. Increases in vegetation cover and density associated with recent greening trends lead to increased burned area in fuel-limited regions. Our analyses show that global and regional burned area trends result from the interaction of compensating trends in controls of wildfire at regional scales

Development of an atmospheric chemistry model coupled to the PALM model system 6.0 : implementation and first applications

In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence-resolving PALM model system 6.0 (formerly an abbreviation for Parallelized Large-eddy Simulation Model and now an independent name). The new chemistry model is implemented in the PALM model as part of the PALM-4U (PALM for urban applications) components, which are designed for application of the PALM model in the urban environment (Maronga et al., 2020). The latest version of the Kinetic PreProcessor (KPP, 2.2.3) has been utilized for the numerical integration of gas-phase chemical reactions. A number of tropospheric gas-phase chemistry mechanisms of different complexity have been implemented ranging from the photostationary state (PHSTAT) to mechanisms with a strongly simplified volatile organic compound (VOC) chemistry (e.g. the SMOG mechanism from KPP) and the Carbon Bond Mechanism 4 (CBM4; Gery et al., 1989), which includes a more comprehensive, but still simplified VOC chemistry. Further mechanisms can also be easily added by the user. In this work, we provide a detailed description of the chemistry model, its structure and input requirements along with its various features and limitations. A case study is presented to demonstrate the application of the new chemistry model in the urban environment. The computation domain of the case study comprises part of Berlin, Germany. Emissions are considered using street-type-dependent emission factors from traffic sources. Three chemical mechanisms of varying complexity and one no-reaction (passive) case have been applied, and results are compared with observations from two permanent air quality stations in Berlin that fall within the computation domain. Even though the feedback of the model's aerosol concentrations on meteorology is not yet considered in the current version of the model, the results show the importance of online photochemistry and dispersion of air pollutants in the urban boundary layer for high spatial and temporal resolutions. The simulated NOx and O-3 species show reasonable agreement with observations. The agreement is better during midday and poorest during the evening transition hours and at night. The CBM4 and SMOG mechanisms show better agreement with observations than the steady-state PHSTAT mechanism.Peer reviewe