Different Radial Growth Responses to Climate Change of Three Dominant Conifer Species in Temperate Forest, Northeastern China

We conducted dendroclimatological study on three dominant conifer tree species, Pinus koraiensis, Larix olgensis, and Picea jezoensis, in northeastern China for a better understanding of climate change impacts on temperate forest growth, by discussing the radial growth relationships of these tree species and projecting their radial growth trends under the future climate change scenarios. Based on the tree-ring samples collected from the upper altitude of Changbai Mountain, ring width chronologies were built to examine the growth relationships, and regression equations were established to project the future growth of the species under future climate change projected by the five general circulation models (GCMs) and four representative concentration pathway (RCP) scenarios. Although both temperature and precipitation showed varying degrees of relationships with growth of these three tree species, the limiting climate factors were species-specific. The tree-ring growth of P. koraiensis was limited by the summer temperature and precipitation at the end of growth, namely, significant positive correlations with the current July temperature and the previous September precipitation. Growth of L. olgensis was limited by the temperature before growing season, for its chronology was negatively correlated with the current February and previous December temperature (p < 0.05). The climatic conditions before and after growing season seemed to be the limiting factors of P. jezoensis growth, which was negatively correlated with the current February to April temperature and the current September temperature (p < 0.05), and positively correlated with the current August precipitation (p < 0.05). Under the gradual increasing of temperature predicted by the five GCMs and four RCP scenarios, the radial growth of P. Koraiensis will relatively increase, while that of L. olgensis and P. jezoensis will relatively decrease comparing to the base-line period (1981–2010). The specific growth–climate relationships and the future growth trends are species dependent. P. Koraiensis was the more suitable tree species for the forestation to maintain the sustainable forest in Changbai Mountain.

Spatiotemporal patterns of future meteorological drought in the Yellow River Basin based on SPEI under RCP scenarios

Purpose The purpose of this study is to assess the spatiotemporal patterns of future meteorological drought in the Yellow River Basin under different representative concentration pathway (RCP) scenarios. Design/methodology/approach Delta method is used to process the future climate data of the global climate models, then analyzed the spatiotemporal variation trend of drought in the Yellow River Basin based on standardized precipitation evaporation index (SPEI) under four RCP scenarios. Findings This research was funded by the National Natural Science Foundation of China (41901239), Soft Science Research Project of Henan Province (212400410077, 192400410085), the National Key Research and Development Program of China (2016YFA0602703), China Postdoctoral Science Foundation (2018M640670) and the special fund of top talents in Henan Agricultural University (30501031). Originality/value This study can provide support for future meteorological drought management and prevention in the Yellow River Basin and provide a theoretical basis for water resources management.

Projected Impact of Heat on Mortality and Labour Productivity under Climate Change in Switzerland

Extreme temperatures have reached unprecedented levels in many regions of the globe due to climate change anda further increase is expected. Besides other consequences, high temperatures increase the mortality risk and severely affectthe labour productivity of workers. We perform a high-resolution spatial analysis to assess the impacts of heat on mortality and labour productivity in Switzerland and project their development under different Representative Concentration Pathway (RCP) scenarios, considering that no socio-economic changes takes place. The model is based on the risk framework of the Intergovernmental Panel on Climate Change (IPCC), which combines the three risk components: Hazard, Exposure, and Vulnerability. We model the two impact categories in the same spatially explicit framework and we integrate uncertainties into the analysis through a Monte Carlo simulation. We model, that first, about 670 people die today per year because of heat in Switzerland. Second, the economic costs caused by losses in labour productivity amount to around CHF 413 million (approx. $ 465 million) per year. Should we remain on an RCP8.5 emissions pathway, these values may double (for mortality) or even triple (for labour productivity) by the end of the century. Under an RCP2.6 scenario impacts are expected to slightly increaseand peak around mid-century, when climate is assumed to stop warming. Even though uncertainties in the model are large, theunderlying trend in impacts is unequivocal. The results of the study are valuable information for political discussions and allowfor a better understanding of the cost of inaction.

Relative Risk Prediction of Norovirus Incidence under Climate Change in Korea

As incidences of food poisoning, especially norovirus-induced diarrhea, are associated with climate change, there is a need for an approach that can be used to predict the risks of such illnesses with high accuracy. In this paper, we predict the winter norovirus incidence rate in Korea compared to that of other diarrhea-causing viruses using a model based on B-spline added to logistic regression to estimate the long-term pattern of illness. We also develop a risk index based on the estimated probability of occurrence. Our probabilistic analysis shows that the risk of norovirus-related food poisoning in winter will remain stable or increase in Korea based on various Representative Concentration Pathway (RCP) scenarios. Our approach can be used to obtain an overview of the changes occurring in regional and seasonal norovirus patterns that can help assist in making appropriate policy decisions.

Distribution Matters: Sensitivity of Gridded Data of Population and Economic Conditions to Global Water Scarcity Assessment

Availability of water per capita is among the most fundamental water-scarcity indicators and has been used extensively in global grid-based water resources assessments. Recently, it has been extended to include the economic aspect, a proxy of the capability for water management. We applied the extended index globally under SSP–RCP scenarios using gridded population and economic conditions from two independent sources and unexpectedly found that the gridded data were significantly sensitive to global water-scarcity assessment. One projection assumed urban concentration of population and assets, whereas the other assumed dispersion. In analyses using multiple SSP–RCP scenarios representing a world of sustainability (SSP1–RCP2.6), regional rivalry (SSP3–RCP7.0), and fossil fuel development (SSP5–RCP8.5) in the future, multiple GCMs, and two gridded datasets showed that the water-scarce population ranges from 0.32–665 million. Uncertainties in the SSP–RCP and GCM scenarios were 6.58–489 million and 0.68–315 million, respectively. The population distribution assumption had a similar impact, with an uncertainty of 169–338 million. These results highlight the importance of the subregional distribution of socioeconomic factors for predicting the future global environment.

Projecting armed conflict risk in Africa towards 2050 along the SSP-RCP scenarios: a machine learning approach

In the past decade, several efforts have been made to project armed conflict risk into the future. This study broadens current approaches by presenting a first-of-its-kind application of machine learning (ML) methods to project sub-national armed conflict risk over the African continent along three Shared Socioeconomic Pathway (SSP) scenarios and three Representative Concentration Pathways (RCPs) towards 2050. Results of the open-source ML framework CoPro are consistent with the underlying socioeconomic storylines of the SSPs, and the resulting out-of-sample armed conflict projections obtained with Random Forest classifiers agree with the patterns observed in comparable studies. In SSP1-RCP2.6, conflict risk is low in most regions although the Horn of Africa and parts of East Africa continue to be conflict-prone. Conflict risk increases in the more adverse SSP3-RCP6.0 scenario, especially in Central Africa and large parts of Western Africa. We specifically assessed the role of hydro-climatic indicators as drivers of armed conflict. Overall, their importance is limited compared to main conflict predictors but results suggest that changing climatic conditions may both increase and decrease conflict risk, depending on the location: in Northern Africa and large parts of Eastern Africa climate change increases projected conflict risk whereas for areas in the West and northern part of the Sahel shifting climatic conditions may reduce conflict risk. With our study being at the forefront of machine learning (ML) applications for conflict risk projections, we identify various challenges for this arising scientific field. A major concern is the limited selection of relevant quantified indicators for the SSPs at present. Nevertheless, ML models such as the one presented here are a viable and scalable way forward in the field of armed conflict risk projections, and can help to inform the policy-making process with respect to climate security.

The Potential for Future Shifts in Tree Species Distribution Provided by Dispersal and Ecological Niches: A Comparison between Beech and Oak in Europe

(1) Due to global warming, distribution ranges of temperate tree species are shifting northwards and upslope to cooler areas. Shifts in distribution first become visible through changes in regeneration dynamics. However, the future distribution of tree species in the face of rapid climate change depends not only on the climatic suitability of the tree species, but also on its ability to disperse into new habitats. The aim of the study was therefore to examine how the distribution of European beech and European oak shifts and how species can spread from fragmented seed trees. (2) In order to investigate the shift in distribution of beech and oak, the bioclimatic envelopes of the old trees and different size classes of the natural regeneration were compared. Subsequently, a simulation of the potential distribution for the present climate, as well as for the climate for the reference period 2091–2100, for three different representative concentration pathways (RCP) scenarios was determined. In order to determine which of these areas can actually be colonised, a dispersal potential for the species was determined using a quantile regression, taking habitat fragmentation into account. (3) The results of the present study demonstrate range shifts of the tree species regeneration distribution (B0, B1 and B2) compared to the overstorey distribution (OST). While oak regeneration shows an expansion of its distribution in the cold-wet range, beech regeneration shows a reduction of its distribution in the dry-warm range. As the dispersal potential of oak exceeds that of beech, it is expected that oak will be better able to spread from fragmented seed trees. However, the results also indicate that many areas, despite climatic suitability, cannot be colonised due to too large dispersal distances. (4) For the forest management, this results in an important planning tool for future tree species composition, as climatic suitability, habitat connectivity and dispersal ability are taken into account.