The geomorphology of the Anthropocene: emergence,status and implications

The Anthropocene is proposed as a new interval of geological time in which human influence on Earth and its geological record dominates over natural processes. A major challenge in demarcating the Anthropocene is that the balance between human‐influenced and natural processes varies over spatial and temporal scales owing to the inherent variability of both human activities (as associated with culture and modes of development) and natural drivers (e.g. tectonic activity and sea level variation). Against this backdrop, we consider how geomorphology might contribute towards the Anthropocene debate by focusing on human impact on aeolian, fluvial, cryospheric and coastal process domains, and how evidence of this impact is preserved in landforms and sedimentary records. We also consider the evidence for an explicitly anthropogenic geomorphology that includes artificial slopes and other human‐created landforms. This provides the basis for discussing the theoretical and practical contributions that geomorphology can make to defining an Anthropocene stratigraphy. It is clear that the relevance of the Anthropocene concept varies considerably amongst different branches of geomorphology, depending on the history of human actions in different process domains. For example, evidence of human dominance is more widespread in fluvial and coastal records than in aeolian and cryospheric records, so geomorphologically the Anthropocene would inevitably comprise a highly diachronous lower boundary. Even to identify this lower boundary, research would need to focus on the disambiguation of human effects on geomorphological and sedimentological signatures. This would require robust data, derived from a combination of modelling and new empirical work rather than an arbitrary ‘war of possible boundaries' associated with convenient, but disputed, ‘golden’ spikes. Rather than being drawn into stratigraphical debates, the primary concern of geomorphology should be with the investigation of processes and landform development, so providing the underpinning science for the study of this time of critical geological transition. Copyright © 2016 John Wiley & Sons, Ltd.     

Micro-computed tomography imaging and probabilistic modelling of rock fracture by freeze–thaw

A major problem in studies of rock fracture by frost is the paucity of direct observations in space and time of the initiation and growth of microcracks and their transition to macrocracks. Such observations are essential to understand the location, timing and controls of rock fracture by freeze–thaw. The aim of the present work is to image and elucidate the early stages of rock fracture by applying imaging and statistical methods to a frost-weathering experiment using intact specimens of a limestone (chalk) and sandstone. First, micro-computed tomography (μ-CT) is used to visualize rock fracture in three dimensions over the course of 20 freeze–thaw cycles and to estimate transverse strain using a pixel-based approach. Second, probabilistic correlation functions are applied to quantify the progressive expansion of the fracture phase and associated damage to rock specimens. The method of μ-CT is demonstrated for visualizing the growth and coalescence of microcracks and their transition to macrocracks. Fracture proceeded faster and to a greater extent in chalk relative to sandstone, and the macrocracks in chalk were mostly concentric and vertical. Both fracture development and positive transverse strain (dilation) accelerated after cycle 15, suggesting that a threshold has been exceeded, after which macrocracks were evident. Of three probabilistic correlation functions applied to the μ-CT results, the modified lineal-path function – which measures the continuous connectivity of the fracture phase in a specific direction – reveals that damage was more extensive in the chalk than the sandstone. It also allows a novel approach to define and quantify three zones of microcracking during freeze–thaw cycling of anisotropic rock: (1) the zone of inherent flaws; (2) the zone of active microcracking; and (3) the zone of weak influence during microcracking. The broader significance of this work is that it provides a new approach to investigate mechanistically how frost action damages rock. © 2019 John Wiley & Sons, Ltd.     

Submarine groundwater springs are characterized by distinct fish communities

The inflow of terrestrial groundwater into the ocean is increasingly recognized as an important local source of nutrients and pollutants to coastal ecosystems. Although there is evidence of a link between fresh submarine groundwater discharge (SGD)-derived nutrients and primary producer and primary consumer abundances, the effects of fresh SGD on the productivity of higher trophic levels such as ichthyofaunal communities remain unclear. To further investigate this relationship, we sampled three sites inside a coral reef lagoon in Mauritius: One site entailing six distinct groundwater springs, a site highly influenced by freshwater influx through the springs, and a strictly marine control site. Using remote underwater video surveys, we found that fish abundances were significantly higher at the groundwater springs than at the other two sampling sites.Principal component analyses showed that the springs and the spring-influenced part of the lagoon were best described by elevated water nutrient loadings, whereas the control site was characterized by higher water salinity and pH. Macroalgae cover was highest at the control site and the springs. Herbivores and invertivores dominated the fish community at the springs, in contrast to generalists at the control site. At the spring-influenced site, we mainly encountered high coral/turf algae cover and high abundances of associated fish feeding groups (territorial farmers, corallivores). Our results provide evidence of a fresh SGD-driven relationship between altered hydrography and distinct fish communities with elevated abundances at groundwater springs in a coral reef lagoon. These findings suggest that the management and assessment of secondary consumer productivity in tropical lagoons should take into account the effects of groundwater springs.     

Temporal trends in δ18O composition of precipitation in Germany: insights from time series modelling and trend analysis

Temporal and spatial variations of stable oxygen (¹⁸O) and hydrogen (²H) isotope measurements in precipitation act as important proxies for changing hydro‐meteorological and regional and global climate patterns. Temporal trends in time series of the stable isotope composition in precipitation were rarely observed, and they are poorly understood. These might be a result of a lack of proper trend detection tools and effort for exploring trend processes. Here, we investigate temporal trends of δ¹⁸O in precipitation at 17 observation stations in Germany between 1978 and 2009. We test if significant trends in the isotope time series from different models can be observed. Mann–Kendall trend tests are applied on the isotope series, using general multiplicative seasonal autoregressive integrate moving average (ARIMA) models, which account for first and higher order serial correlations. Effects of temperature, precipitation, and geographic parameters on isotope trends are also investigated in the proposed models. To benchmark our proposed approach, the ARIMA results are compared with a trend‐free pre‐whitening procedure, the state of the art method for removing the first order autocorrelation in environmental trend studies. Moreover, we further explore whether higher order serial correlations in isotope series affects our trend results. Overall, three out of the 17 stations show significant changes when higher order autocorrelation are adjusted, and four show a significant trend when temperature and precipitation effects are considered. The significant trends in the isotope time series generally occur only at low elevation stations. Higher order autoregressive processes are shown to be important in the isotope time series analysis. Results suggest that the widely used trend analysis with only the first order autocorrelation adjustment may not adequately take account of the high order autocorrelated processes in the stable isotope series. The investigated time series analysis method including higher autocorrelation and external climate variable adjustments is shown to be a better alternative. Copyright © 2014 John Wiley & Sons, Ltd.     

Attribution of long-term changes in peak river flows in Great Britain

ABSTRACT

We investigate the evidence for changes in the magnitude of peak river flows in Great Britain. We focus on a set of 117 near-natural “benchmark” catchments to detect trends not driven by land use and other human impacts, and aim to attribute trends in peak river flows to some climate indices such as the North Atlantic Oscillation (NAO) and the East Atlantic (EA) index. We propose modelling all stations together in a Bayesian multilevel framework to be better able to detect any signal that is present in the data by pooling information across several stations. This approach leads to the detection of a clear countrywide time trend. Additionally, in a univariate approach, both the EA and NAO indices appear to have a considerable association with peak river flows. When a multivariate approach is taken to unmask the collinearity between climate indices and time, the association between NAO and peak flows disappears, while the association with EA remains clear. This demonstrates the usefulness of a multivariate and multilevel approach when it comes to accurately attributing trends in peak river flows.     

GCM-related uncertainty for river flows and inundation under climate change: the Inner Niger Delta

ABSTRACT

A semi-distributed hydrological model of the Niger River above and including the Inner Delta is developed. GCM-related uncertainty in climate change impacts are investigated using seven GCMs for a 2°C increase in global mean temperature, the hypothesised threshold of “dangerous” climate change. Declines in precipitation predominate, although some GCMs project increases for some sub-catchments, whilst PET increases for all scenarios. Inter-GCM uncertainty in projected precipitation is three to five times that of PET. With the exception of one GCM (HadGEM1), which projects a very small increase (3.9%), river inflows to the Delta decline. There is considerable uncertainty in the magnitude of these reductions, ranging from 0.8% (HadCM3) to 52.7% (IPSL). Whilst flood extent for HadGEM1 increases (mean annual peak +1405 km²/+10.2%), for other GCMs it declines. These declines range from almost negligible changes to a 7903 km² (57.3%) reduction in the mean annual peak.

Editor Z.W. Kundzewicz; Associate editor not assigned     

Assessing hazardous waste transport risks using a GIS

Abstract

The transport phase is an often neglected element in the risk assessment of non-nuclear hazardous waste life cycles. Data on special and hazardous waste movements are difficult to acquire, but information collected by the London Waste Regulation Authority during the 1980s gives details of waste consignments from cradle to grave, including U.K. grid references for waste producer and disposal sites. A GIS was used to model the routing of aqueous waste cargoes and assess the potential impacts of such movements. Deficiencies in the consignment records required many assumptions to be made and various scenarios were explored. Roads predicted to see passage of these wastes, together with the estimated levels of tanker traffic, were integrated with the distribution of population, groundwater vulnerability and accident probabilities to evaluate the transportation risks for different localities. Comparisons and evaluations of the implications of different routing scenarios across the study region were made.     

Contextual neural gas for spatial clustering and analysis

This study aims to introduce contextual Neural Gas (CNG), a variant of the Neural Gas algorithm, which explicitly accounts for spatial dependencies within spatial data. The main idea of the CNG is to map spatially close observations to neurons, which are close with respect to their rank distance. Thus, spatial dependency is incorporated independently from the attribute values of the data. To discuss and compare the performance of the CNG and GeoSOM, this study draws from a series of experiments, which are based on two artificial and one real-world dataset. The experimental results of the artificial datasets show that the CNG produces more homogenous clusters, a better ratio of positional accuracy, and a lower quantization error than the GeoSOM. The results of the real-world dataset illustrate that the resulting patterns of the CNG are theoretically more sound and coherent than that of the GeoSOM, which emphasizes its applicability for geographic analysis tasks.