Sub-Saharan Africa's international migration constrains its sustainable development under climate change.

Sub-Saharan Africa (SSA) is seen as a region of mass migration and population displacement caused by poverty, violent conflict, and environmental stress. However, empirical evidence is inconclusive regarding how SSA's international migration progressed and reacted during its march to achieving the Sustainable Development Goals (SDGs). This article attempts to study the patterns and determinants of SSA's international migration and the cause and effects on sustainable development by developing a Sustainability Index and regression models. We find that international migration was primarily intra-SSA to low-income but high-population-density countries. Along with increased sustainability scores, international migration declined, but emigration rose. Climate extremes tend to affect migration and emigration but not universally. Dry extremes propelled migration, whereas wet extremes had an adverse effect. Hot extremes had an increasing effect but were insignificant. SSA's international migration was driven by food insecurity, low life expectancy, political instability and violence, high economic growth, unemployment, and urbanisation rates. The probability of emigration was mainly driven by high fertility. SSA's international migration promoted asylum seeking to Europe with the diversification of origin countries and a motive for economic wellbeing. 1% more migration flow or 1% higher probability of emigration led to a 0.2% increase in asylum seekers from SSA to Europe. Large-scale international migration and recurrent emigration constrained SSA's sustainable development in political stability, food security, and health, requiring adequate governance and institutions for better migration management and planning towards the SDGs. Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-022-01116-z.

Engaging Transformation: Using Seasonal Rounds to Anticipate Climate Change.

Seasonal rounds are deliberative articulations of a community's sociocultural relations with their ecological system. The process of visualizing seasonal rounds informs transdisciplinary research. We present a methodological approach for communities of enquiry to engage communities of practice through context-specific sociocultural and ecological relations driven by seasonal change. We first discuss historical précis of the concept of seasonal rounds that we apply to assess the spatial and temporal communal migrations and then describe current international research among Indigenous and rural communities in North America and Central Asia by the creation of a common vocabulary through mutual respect for multiple ways of knowing, validation of co-generated knowledge, and insights into seasonal change. By investigating the relationship between specific biophysical indicators and livelihoods of local communities, we demonstrate that seasonal rounds are an inclusive and participatory methodology that brings together diverse Indigenous and rural voices to anticipate anthropogenic climate change. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10745-021-00269-2.

Site-scale modeling of surface ozone in Northern Bavaria using machine learning algorithms, regional dynamic models, and a hybrid model.

Ozone (O3) is a harmful pollutant when present in the lowermost layer of the atmosphere. Therefore, the European Commission formulated directives to regulate O3 concentrations in near-surface air. However, almost 50% of the 5068 air quality stations in Europe do not monitor O3 concentrations. This study aims to provide a hybrid modeling system that fills these gaps in the hourly surface O3 observations on a site scale with much higher accuracy than existing O3 models. This hybrid model was developed using estimations from multiple linear regression-based eXtreme Gradient Boosting Machines (MLR-XGBM) and O3 reanalysis from European regional air quality models (CAMS-EU). The binary classification of extremely high O3 events and the 1- and 24-h forecasts of hourly O3 were investigated as secondary aims. In this study thirteen stations in Northern Bavaria, out of which six do not monitor O3, were chosen as test sites. Considering the computational complexity of machine learning algorithms (MLAs), we also applied two recent MLA interpretation methods, namely SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME). With SHAP, we showed an increasing effect of temperature on O3 concentrations which intensifies for temperatures exceeding 17 °C. According to LIME, O3 concentration peaks are mainly governed by meteorological factors under dry and warm conditions on a regional scale, whereas local nitrogen oxide concentrations control base O3 concentrations during cold and wet periods. While recently developed MLAs for the spatial estimation of hourly O3 concentrations had a station-based root-mean-square error (RMSE) above 27 µg/m3, our proposed model significantly reduced the estimation errors by about 66% with an RMSE of 9.49 µg/m3. We also found that logistic regression (LR) and MLR-XGBM performed best in the site-scale classification and 24-h forecast of O3 concentrations (with a station-averaged accuracy and RMSE of 0.95 and 19.34 µg/m3, respectively).

Evaluation needs and temporal performance differences of gridded precipitation products in peripheral mountain regions.

Gridded datasets are of paramount importance to globally derive precipitation quantities for a multitude of scientific and practical applications. However, as most studies do not consider the impacts of temporal and spatial variations of included measurements in the utilized datasets, we conducted a quantitative assessment of the ability of several state of the art gridded precipitation products (CRU, GPCC Full Data Product, GPCC Monitoring Product, ERA-interim, ERA5, MERRA-2, MERRA-2 bias corrected, PERSIANN-CDR) to reproduce monthly precipitation values at climate stations in the Pamir mountains during two 15 year periods (1980-1994, 1998-2012) that are characterized by considerable differences in incorporated observation data. Results regarding the GPCC products illustrated a substantial and significant performance decrease with up to four times higher errors during periods with low observation inputs (1998-2012 with 2 stations on average per 124,000 km2) compared to periods with high quantities of regionally incorporated station data (1980-1994 with 14 stations on average per 124,000 km2). If independent stations were considered, the coefficient of efficiency indicated that only three of the gridded datasets (MERRA-2 bias corrected, GPCC, GPCC MP) performed better than the long term station mean for characterizing surface precipitation. Error patterns and magnitudes show that in complex terrain, evaluation of temporal and spatial variations of included observations is a prerequisite for using gridded precipitation products for scientific applications and to avoid overly optimistic performance assessments.

Preserving a Comprehensive Vegetation Knowledge Base--An Evaluation of Four Historical Soviet Vegetation Maps of the Western Pamirs (Tajikistan).

We edited, redrew, and evaluated four unpublished historical vegetation maps of the Western Pamirs (Tajikistan) by the Soviet geobotanist Okmir E. Agakhanjanz. These maps cover an area of 5,188 km2 and date from 1958 to 1960. The purpose of this article is to make the historic vegetation data available to the scientific community and thus preserve a hitherto non available and up to now neglected or forgotten data source with great potential for studies on vegetation and ecosystem response to global change. The original hand-drawn maps were scanned, georeferenced, and digitized and the corresponding land cover class was assigned to each polygon. The partly differing legends were harmonized and plant names updated. Furthermore, a digital elevation model and generalized additive models were used to calculate response curves of the land cover classes and to explore vegetation-topography relationships quantitatively. In total, 2,216 polygons belonging to 13 major land cover classes were included that are characterized by 252 different plant species. As such, the presented maps provide excellent comparison data for studies on vegetation and ecosystem change in an area that is deemed to be an important water tower in Central Asia.

Ground- and satellite-based evidence of the biophysical mechanisms behind the greening Sahel.

After a dry period with prolonged droughts in the 1970s and 1980s, recent scientific outcome suggests that the decades of abnormally dry conditions in the Sahel have been reversed by positive anomalies in rainfall. Various remote sensing studies observed a positive trend in vegetation greenness over the last decades which is known as the re-greening of the Sahel. However, little investment has been made in including long-term ground-based data collections to evaluate and better understand the biophysical mechanisms behind these findings. Thus, deductions on a possible increment in biomass remain speculative. Our aim is to bridge these gaps and give specifics on the biophysical background factors of the re-greening Sahel. Therefore, a trend analysis was applied on long time series (1987-2013) of satellite-based vegetation and rainfall data, as well as on ground-observations of leaf biomass of woody species, herb biomass, and woody species abundance in different ecosystems located in the Sahel zone of Senegal. We found that the positive trend observed in satellite vegetation time series (+36%) is caused by an increment of in situ measured biomass (+34%), which is highly controlled by precipitation (+40%). Whereas herb biomass shows large inter-annual fluctuations rather than a clear trend, leaf biomass of woody species has doubled within 27 years (+103%). This increase in woody biomass did not reflect on biodiversity with 11 of 16 woody species declining in abundance over the period. We conclude that the observed greening in the Senegalese Sahel is primarily related to an increasing tree cover that caused satellite-driven vegetation indices to increase with rainfall reversal.

Realities and myths of the Teresken Syndrome--an evaluation of the exploitation of dwarf shrub resources in the Eastern Pamirs of Tajikistan.

In the Eastern Pamirs extensive livestock herding is the predominant land use option and dwarf shrubs - teresken (Krascheninnikovia ceratoides) and to a lesser extent wormwood (Artemisia spp.) - are an important source of thermal energy and are crucial forage plants, particularly in winter. For the post-Soviet period, many papers and reports suggest rapidly increasing exploitation rates with sometimes alarming figures for degradation. Conventional knowledge about the Pamirs pinpoints the frightening and irreversible extraction of teresken as a dwindling resource causing land degradation - termed the 'Teresken Syndrome'. However, these publications are based on limited empirical evidence. This paper discusses the socio-economic role of dwarf shrubs as fuel and forage - today and in the past - and provides an overview of the extent of dwarf shrub distribution and degraded areas. Although our observations confirm extensive dwarf shrub exploitation, general assumptions of severe desertification as a result of dwarf shrub extraction do not yield reliable scenarios. The effects of harvesting on the vegetation cover vary widely depending on extraction practices and yields of different dwarf shrub-dominated formations. Furthermore, considerable and increasing shares of energy needs are satisfied by animal manure and imported coal. On the other hand, for low-income households harvesting dwarf shrubs is an important and reliable option to ensure both their own household energy supply and an income source. We argue that the term 'Teresken Syndrome', which was introduced after the emergency replacement of Soviet coal supplies by dwarf shrubs in the 1990s, is not appropriate anymore to describe the current use of natural resources in the Eastern Pamirs. However, under the current economic circumstances the nexus remains that the Pamir people are caught in the dilemma of using dwarf shrubs as energy and as forage resource. However, the observed negotiation practices concerning competitive use between dwarf shrub extractors and herders could be developed into community-based management schemes of this common pool resource.

Human and climate impact on ¹5N natural abundance of plants and soils in high-mountain ecosystems: a short review and two examples from the Eastern Pamirs and Mt. Kilimanjaro.

Population pressure increasingly endangers high-mountain ecosystems such as the pastures in the Eastern Pamirs and the mountain forests on Mt. Kilimanjaro. At the same time, these ecosystems constitute the economic basis for millions of people living there. In our study, we, therefore, aimed at characterising the land-use effects on soil degradation and N-cycling by determining the natural abundance of (15)N. A short review displays that δ(15)N of plant-soil systems may often serve as an integrated indicator of N-cycles with more positive δ(15)N values pointing towards N-losses. Results for the high-mountain pastures in the Eastern Pamirs show that intensively grazed pastures are significantly enriched in (15)N compared to the less-exploited pastures by 3.5 ‰, on average. This can be attributed to soil organic matter degradation, volatile nitrogen losses, nitrogen leaching and a general opening of the N-cycle. Similarly, the intensively degraded savanna soils, the cultivated soils and the soils under disturbed forests on the foothill of Mt. Kilimanjaro reveal very positive δ(15)N values around 6.5 ‰. In contrast, the undisturbed forest soils in the montane zone are more depleted in (15)N, indicating that here the N-cycle is relatively closed. However, significantly higher δ(15)N values characterise the upper montane forest zone at the transition to the subalpine zone. We suggest that this reflects N-losses by the recently monitored and climate change and antropogenically induced increasing fire frequency pushing the upper montane rainforest boundary rapidly downhill. Overall, we conclude that the analysis of the (15)N natural abundance in high-mountain ecosystems is a purposeful tool for detecting land-use- or climate change-induced soil degradation and N-cycle opening.