Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data

Background and Purpose: Determination of an extreme year from the aspect of the vegetation activity using only meteorological data might be ambiguous and not adequate. Furthermore, in some ecosystems, e.g. forests, the response is not instantly visible, but the effects of the meteorological anomaly can be seen in the following year. The aim of the present paper is to select and characterize typical and anomalous years using satellite-based remote sensing data and meteorological observations during the recent years of 2000-2014 for Central Europe, based on the response of the vegetation. Materials and Methods: In the present study vegetation characteristics were described using remotely sensed official products of the MODerate resolution Imaging Spectroradiometer (MODIS), namely NDVI, EVI, FPAR, LAI, GPP, and NPP, with 8-day temporal and 500 meter spatial resolution for the period of 2000-2014. The corresponding mean temperature and precipitation data (on the same grid) were derived from the Open Database for Climate Change Related Impact Studies in Central Europe (FORESEE) daily meteorological dataset. Land cover specific anomalies of the meteorological and vegetation characteristics were created and averaged on a country-scale, where the distinction between the main land cover types was based on the synergetic use of MODIS land cover and Coordination of Information on the Environment (CORINE) Land Cover 2012 datasets. Results: It has been demonstrated that the anomaly detection based solely on basic meteorological variables is ambiguous since the strength of the anomaly depends on the selected integration time period. In contrast, the effect-based approach exploiting the available, state-of-the-art remote sensing based vegetation indices is a promising tool for the characterization of the anomalous behaviour of the different land cover types. The selection of extreme years was performed in an explicit way using percentile analysis on pixel level. Conclusions: Plant status in terms of both positive and negative anomalies shows strong land cover dependency in Central Europe. This is most likely due to the differences in heat and drought resistance of the vegetation, and species composition. The selection of country-specific extreme years can serve as a basis for forthcoming research

Fine-scale variation in projected climate change presents opportunities for biodiversity conservation in Europe

Climate change is a major threat to global biodiversity, although projected changes show remarkable geographical and temporal variability. Understanding this variability allows for the identification of regions where the present-day conservation objectives may be at risk or where opportunities for biodiversity conservation emerge. We use a multi-model ensemble of regional climate models to identify areas with significantly high and low climate stability persistent throughout the 21st century in Europe. We then confront our predictions with the land coverage of three prominent biodiversity conservation initiatives at two scales. The continental-scale assessment shows that areas with the least stable future climate in Europe are likely to occur at low and high latitudes, with the Iberian Peninsula and the Boreal zones identified as prominent areas of low climatic stability. A follow-up regional scale investigation shows that robust climatic refugia exist even within the highly exposed southern and northern macro-regions. About 23-31 % of assessed biodiversity conservation sites in Europe coincide with areas of high future climate stability, we contend that these sites should be prioritised in the formulation of future conservation priorities as the stability of future climate is one of the key factors determining their conservation prospects. Although such focus on climate refugia cannot halt the ongoing biodiversity loss, along with measures such as resilience-based stewardship, it may improve the effectiveness of biodiversity conservation under climate change

Composition and Elevation of Spruce Forests Affect Susceptibility to Bark Beetle Attacks: Implications for Forest Management

(6) spruce-broadleaf mixed forest

Trends in Winter Warm Spells in the Central England Temperature Record

An important impact of climate change on agriculture and the sustainability of ecosystems is the increase of extended warm spells during winter. We apply crossing theory to the central England temperature time series of winter daily maximum temperatures to quantify how increased occurrence of higher temperatures translates into more frequent, longer-lasting, and more intense winter warm spells. We find since the late 1800s an overall two- to threefold increase in the frequency and duration of winter warm spells. A winter warm spell of 5 days in duration with daytime maxima above 13°C has a return period that was often over 5 years but now is consistently below 4 years. Weeklong warm intervals that return on average every 5 years now consistently exceed ~13°C. The observed changes in the temporal pattern of environmental variability will affect the phenology of ecological processes and the structure and functioning of ecosystems

Natural disturbance regimes as a guide for sustainable forest management in Europe

Dette er den aksepterte versjonen av en artikkel publisert i Ecological Applications. Den blir tilgjengelig fra og med 28.03.2023 etter en embargoperiode på 12 måneder. Du finner den publiserte artikkelen her: https://doi.org/10.1002/eap.2596. / This is the postprint version of the article published in Ecological Applications. It will be available 28.03.2023 after an embargo period of 12 months.You can find the published article here: https://doi.org/10.1002/eap.2596.In Europe, forest management has controlled forest dynamics to sustain commodity production over multiple centuries. Yet over-regulation for growth and yield diminishes resilience to environmental stress as well as threatens biodiversity, leading to increasing forest susceptibility to an array of disturbances. These trends have stimulated interest in alternative management systems, including natural dynamics silviculture (NDS). NDS aims to emulate natural disturbance dynamics at stand and landscape scales through silvicultural manipulations of forest structure and landscape patterns. We adapted a “Comparability Index” (CI) to assess convergence/divergence between natural disturbances and forest management effects. We extended the original CI concept based on disturbance size and frequency by adding the residual structure of canopy trees after a disturbance as a third dimension. We populated the model by compiling data on natural disturbance dynamics and management from 13 countries in Europe, covering four major forest types (i.e., spruce, beech, oak, and pine-dominated forests). We found that natural disturbances are highly variable in size, frequency, and residual structure, but European forest management fails to encompass this complexity. Silviculture in Europe is skewed toward even-aged systems, used predominately (72.9% of management) across the countries assessed. The residual structure proved crucial in the comparison of natural disturbances and silvicultural systems. CI indicated the highest congruence between uneven-aged silvicultural systems and key natural disturbance attributes. Even so, uneven-aged practices emulated only a portion of the complexity associated with natural disturbance effects. The remaining silvicultural systems perform poorly in terms of retention compared to tree survivorship after natural disturbances. We suggest that NDS can enrich Europe’s portfolio of management systems, for example where wood production is not the primary objective. NDS is especially relevant to forests managed for habitat quality, risk reduction, and a variety of ecosystem services. We suggest a holistic approach integrating NDS with more conventional practices.acceptedVersio

A harmonized database of European forest simulations under climate change

Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing. Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations, with the aim to better harness the valuable information contained in local simulations for large-scale policy support, and for fostering a deeper understanding of the effects of climate change on forest ecosystems in Europe

Redistributive impacts of fiscal policies in Mexico: Corrections for top income measurement problems

This study assesses the redistributive impacts of fiscal instruments in a 2014 Mexican household budget survey (ENIGH) correcting for potential top-income measurement problems. We use two correction methods based on within-survey information to re-estimate the redistributive impacts of contributory pensions and cash-like transfers; direct taxes; indirect taxes and subsidies; and in-kind transfers. The two methods are: survey-sample reweighting for households' nonresponse probability, and replacing of top incomes using synthetic values from the Pareto distribution. This replacing is implemented either on all core income concepts, or on net market income from which it is passed onto other incomes through fiscal rules. These corrections yield higher inequality as measured by the Gini (0-9 pc.pt. increase) and the top 1% and 10% income shares (0-5, and 1-5 pc.pt. increases), consistently between the reweighting and replacing methods, and consistently across all income concepts. Moving from pre-fiscal market income to post-fiscal final income, corrections for nonresponse fall slightly, while corrections for mismeasurement rise. Taxable income is subject to the highest inequality, which further undergoes the highest upward correction for top income problems, potentially consistent with evidence of earnings misreporting among the rich. Conversely, nontaxable income has a strong equalizing impact of 3.3-4.5 points of the Gini further accentuated under the top-income corrections. The corrections confirm the inequality-neutral impact of pensions in Mexico, and equalizing impacts of transfers, direct taxes, indirect taxes and subsidies, and in-kind transfers. In-kind transfers, cash-like transfers and direct taxes have the strongest equalizing impacts of 4.7-5.7, 1.6-1.9, and 1.2-2.2 points of the Gini, respectively. Indirect taxes and subsidies are weakly equalizing, by 0.4-0.6 points. Finally, top-income measurement challenges retain their magnitude across the 2010, 2012 and 2014 ENIGH, but household nonresponse becomes more positively selected over time, causing more serious biases

Household Earnings in Putin’s Russia: Distributional Changes across Socioeconomic Groups, 2000–2016

Following Russia's February invasion of Ukraine and the imposition of sanctions by countries worldwide, Russian population faces a crisis with deep but differentiated consequences across socioeconomic groups. We examine the evolution of earnings and societal earnings gaps throughout Vladimir Putin's presidency, including the 2014 oil bust and trade war spurred by Russia's annexation of Crimea. Unconditional quantile regressions are applied to 2000-2016 surveys to estimate the distributional changes across urban/rural, farming/non-farming and gender divides at all earnings quantiles, and growth incidence curves for the respective groups are derived using consistent survey waves around the crisis years of 2014-2015. Urban-rural gaps are found to be pervasive, particularly at lower earnings quantiles, while gender gaps declined over time. Rural and female-headed households receive lower returns on their endowments because they lack employment opportunities. The 2014 shocks affected all groups, particularly the rural poor, export-oriented farmers, and urban rich, not only immediately but over several years

Redistributive impacts of civil war: The case of Côte d'Ivoire

Many of the world's LDCs are plagued by recurring conflict. Conflict impedes sustainable development through various channels, creating conditions conducive to further conflict. Conflict has redistributive impacts, particularly when it erupts in resource-rich countries. Between 2002 and 2011, Côte d'Ivoire faced off two spells of civil war (2002-2007 and 2010-2011) along geographic, religious, ethno-linguistic and economic lines. Poverty and inequality rose throughout the decade. We investigate how the civil war and the associated changes in the political balance impinged on the economic performance of the affected geographic/ religious/ ethnic groups at various income deciles. Growth incidence curves before-after conflict illustrate the income changes experienced by the respective socioeconomic groups. Accounting for distortions due to individual selection and general-equilibrium spillovers, unconditional quantile regressions fitted by the means of a recentered influence function are used to isolate between-group gaps in household incomes attributable to conflict. The results on microdata from three Household Living Standards Surveys (2002, 2008 and 2015) confirm that as the political tide shifted, the economic fortunes of the affected groups turned. Previously marginalized communities - the northern, Gour and Mandé ethnic, and non-Christian groups - have bridged some of their disadvantage in terms of their endowments and the market returns on them. These changes are clearest in the upper half of the income spectrum, leading to profound changes in social order