A Framework for Quantitative Assessment of Impacts Related to Energy and Mineral Resource Development

Natural resource planning at all scales demands methods for assessing the impacts of resource development and use, and in particular it requires standardized methods that yield robust and unbiased results. Building from existing probabilistic methods for assessing the volumes of energy and mineral resources, we provide an algorithm for consistent, reproducible, quantitative assessment of resource development impacts. The approach combines probabilistic input data with Monte Carlo statistical methods to determine probabilistic outputs that convey the uncertainties inherent in the data. For example, one can utilize our algorithm to combine data from a natural gas resource assessment with maps of sage grouse leks and piñon-juniper woodlands in the same area to estimate possible future habitat impacts due to possible future gas development. As another example: one could combine geochemical data and maps of lynx habitat with data from a mineral deposit assessment in the same area to determine possible future mining impacts on water resources and lynx habitat. The approach can be applied to a broad range of positive and negative resource development impacts, such as water quantity or quality, economic benefits, or air quality, limited only by the availability of necessary input data and quantified relationships among geologic resources, development alternatives, and impacts. The framework enables quantitative evaluation of the trade-offs inherent in resource management decision-making, including cumulative impacts, to address societal concerns and policy aspects of resource development.     

How Frequently Will the Persistent Heavy Rainfall over the Middle and Lower Yangtze River Basin in Summer 2020 Happen under Global Warming?

The middle and lower Yangtze River basin (MLYRB) suffered persistent heavy rainfall in summer 2020, with nearly continuous rainfall for about six consecutive weeks. How the likelihood of persistent heavy rainfall resembling that which occurred over the MLYRB in summer 2020 (hereafter 2020PHR-like event) would change under global warming is investigated. An index that reflects maximum accumulated precipitation during a consecutive five-week period in summer (Rx35day) is introduced. This accumulated precipitation index in summer 2020 is 60% stronger than the climatology, and a statistical analysis further shows that the 2020 event is a 1-in-70-year event. The model projection results derived from the 50-member ensemble of CanESM2 and the multimodel ensemble (MME) of the CMIP5 and CMIP6 models show that the occurrence probability of the 2020PHR-like event will dramatically increase under global warming. Based on the Kolmogorov–Smirnoff test, one-third of the CMIP5 and CMIP6 models that have reasonable performance in reproducing the 2020PHR-like event in their historical simulations are selected for the future projection study. The CMIP5 and CMIP6 MME results show that the occurrence probability of the 2020PHR-like event under the present-day climate will be double under lower-emission scenarios (CMIP5 RCP4.5, CMIP6 SSP1-2.6, and SSP2-4.5) and 3–5 times greater under higher-emission scenarios (3.0 times for CMIP5 RCP8.5, 2.9 times for CMIP6 SSP3-7.0, and 4.8 times for CMIP6 SSP5-8.5). The inter-model spread of the probability change is small, lending confidence to the projection results. The results provide a scientific reference for mitigation of and adaptation to future climate change.     

The Elbe Fault System in North Central Europe—a basement controlled zone of crustal weakness

The Elbe Fault System (EFS) is a WNW-striking zone extending from the southeastern North Sea to southwestern Poland along the present southern margin of the North German Basin and the northern margin of the Sudetes Mountains. Although details are still under debate, geological and geophysical data reveal that upper crustal deformation along the Elbe Fault System has taken place repeatedly since Late Carboniferous times with changing kinematic activity in response to variation in the stress regime. In Late Carboniferous to early Permian times, the Elbe Fault System was part of a post-Variscan wrench fault system and acted as the southern boundary fault during the formation of the Permian Basins along the Trans-European Suture Zone (sensu [Geol. Mag. 134 (5) (1997) 585]). The Teisseyre–Tornquist Zone (TTZ) most probably provided the northern counterpart in a pull-apart scenario at that time. Further strain localisation took place during late Mesozoic transtension, when local shear within the Elbe Fault System caused subsidence and basin formation along and parallel to the fault system. The most intense deformation took place along the system during late Cretaceous–early Cenozoic time, when the Elbe Fault System responded to regional compression with up to 4 km of uplift and formation of internal flexural highs. Compressional deformation continued during early Cenozoic time and actually may be ongoing. The upper crust of the Elbe Fault System, which itself reacted in a more or less ductile fashion, is underlain by a lower crust characterised by low P-wave velocities, low densities and a weak rheology. Structural, seismic and gravimetric data as well as rheology models support the assumption that a weak, stress-sensitive zone in the lower crust is the reason for the high mobility of the area and repeated strain localisation along the Elbe Fault System.     

Evaluating social capital in emergency and disaster management and hazards plans

Natural Hazards - Natural hazards and disasters pose a serious threat to society. Efficient hazard plans are a county’s prerequisite in preparing for potential disasters and serve as primary...     

A new paradigm for the predominance of standing Central Pacific Warming after the late 1990s

Canonical El Niño has a warming center in the eastern Pacific (EP), but in recent decades, El Niño warming center tends to occur more frequently in the central Pacific (CP). The definitions and names of this new type of El Niño, however, have been notoriously diverse, which makes it difficult to understand why the warming center shifts. Here, we show that the new type of El Niño events is characterized by: 1) the maximum warming standing and persisting in the CP and 2) the warming extending to the EP only briefly during its peak phase. For this reason, we refer to it as standing CP warming (CPW). Global warming has been blamed for the westward shift of maximum warming as well as more frequent occurrence of CPW. However, we find that since the late 1990s the standing CPW becomes a dominant mode in the Pacific; meanwhile, the epochal mean trade winds have strengthened and the equatorial thermocline slope has increased, contrary to the global warming-induced weakening trades and flattening thermocline. We propose that the recent predominance of standing CPW arises from a dramatic decadal change characterized by a grand La Niña-like background pattern and strong divergence in the CP atmospheric boundary layer. After the late 1990s, the anomalous mean CP wind divergence tends to weaken the anomalous convection and shift it westward from the underlying SST warming due to the suppressed low-level convergence feedback. This leads to a westward shift of anomalous westerly response and thus a zonally in-phase SST tendency, preventing eastward propagation of the SST anomaly. We anticipate more CPW events will occur in the coming decade provided the grand La Niña-like background state persists.     

Bayesian modelling of environmental risk: example using a small area ecological study of coronary heart disease mortality in relation to modelled outdoor nitrogen oxide levels

Bayesian modelling of health risks in relation to environmental exposures offers advantages over conventional (non-Bayesian) modelling approaches. We report an example using research into whether, after controlling for different confounders, air pollution (NO_x) has a significant effect on coronary heart disease mortality, estimating the relative risk associated with different levels of exposure. We use small area data from Sheffield, England and describe how the data were assembled. We compare the results obtained using a generalized (Poisson) log-linear model with adjustment for overdispersion, with the results obtained using a hierarchical (Poisson) log-linear model with spatial random effects. Both classes of models were fitted using a Bayesian approach. Including spatial random effects models both overdispersion and spatial autocorrelation effects arising as a result of analysing data from small contiguous areas. The first modelling framework has been widely used, while the second provides a more rigorous model for hypothesis testing and risk estimation when data refer to small areas. When the models are fitted controlling only for the age and sex of the populations, the generalized log-linear model shows NO_x effects are significant at all levels, whereas the hierarchical log-linear model with spatial random effects shows significant effects only at higher levels. We then adjust for deprivation and smoking prevalence. Uncertainty in the estimates of smoking prevalence, arising because the data are based on samples, was accounted for through errors-in-variables modelling. NO_x effects apparently are significant at the two highest levels according to both modelling frameworks.