Importance of climate,forest fires and human population size in the Holocene boreal forest composition change in northern Europe

The relative importance of climate, forest fires and human population size on long‐term boreal forest composition were statistically investigated at regional and local scales in Fennoscandia. We employ pollen data from lakes, reflecting regional vegetation, and small forest hollows, reflecting local vegetation, from Russia, Finland and Sweden to reconstruct the long‐term forest composition. As potential drivers of the Holocene forest dynamics we consider climate, generated from a climate model and oxygen isotope data, past forest fires generated from sedimentary charcoal data and human population size derived from radiocarbon dated archaeological findings. We apply the statistical method of variation partitioning to assess the relative importance of these environmental variables on long‐term boreal forest composition. The results show that climate is the main driver of the changes in Holocene boreal forest composition at the regional scale. However, at the local scale the role of climate is relatively small. In general, the importance of forest fires is low both at regional and local scales. The fact that both climate and forest fires explain relatively small proportions of variation in long‐term boreal vegetation in small forest hollow records demonstrates the complexity of factors affecting stand‐scale forest dynamics. The relative importance of human population size was low in both the prehistorical and the historical time periods. However, this is the first time that this type of data has been used to statistically assess the importance of human population size on boreal vegetation and the spatial representativeness of the data may cause bias to the analysis.     

Analysis of the interannual variability of annual daily extreme water levels in the St Lawrence River and Lake Ontario from 1918 to 2010

We compared the interannual variability of annual daily maximum and minimum extreme water levels in Lake Ontario and the St Lawrence River (Sorel station) from 1918 to 2010, using several statistical tests. The interannual variability of annual daily maximum extreme water levels in Lake Ontario is characterized by a positive long‐term trend showing two shifts in mean (1929–1930 and 1942–1943) and a single shift in variance (in 1958–1959). In contrast, for the St Lawrence River, this interannual variability is characterized by a negative long‐term trend with a single shift in mean, which occurred in 1955–1956. As for annual daily minimum extreme water levels, their interannual variability shows no significant long‐term change in trend. However, for Lake Ontario, the interannual variability of these water levels shows two shifts in mean, which are synchronous with those for maximum water levels, and a single shift in variance, which occurred in 1965–1966. These changes in trend and stationarity (mean and variance) are thought to be due to factors both climatic (the Great Drought of the 1930s) and human (digging of the Seaway and construction of several dams and locks during the 1950s). Despite this change in means and variance, the four series are clearly described by the generalized extreme value distribution. Finally, annual daily maximum and minimum extreme water levels in the St Lawrence and Lake Ontario are negatively correlated with Atlantic multidecadal oscillation over the period from 1918 to 2010. Copyright © 2013 John Wiley & Sons, Ltd.     

Identifying influence areas with connectivity analysis – application to the local perturbation of heterogeneity distribution for history matching

Numerical representations of a target reservoir can help to assess the potential of different development plans. To be as predictive as possible, these representations or models must reproduce the data (static, dynamic) collected on the field. However, constraining reservoir models to dynamic data – the history-matching process – can be very time consuming. Many uncertain parameters need to be taken into account, such as the spatial distribution of petrophysical properties. This distribution is mostly unknown and usually represented by millions of values populating the reservoir grid. Dedicated parameterization techniques make it possible to investigate many spatial distributions from a small number of parameters. The efficiency of the matching process can be improved from the perturbation of specific regions of the reservoir. Distinct approaches can be considered to define such regions. For instance, one can refer to streamlines. The leading idea is to identify areas that influence the production behavior where the data are poorly reproduced. Here, we propose alternative methods based on connectivity analysis to easily provide approximate influence areas for any fluid-flow simulation. The reservoir is viewed as a set of nodes connected by weighted links that characterize the distance between two nodes. The path between nodes (or grid blocks) with the lowest cumulative weight yields an approximate flow path used to define influence areas. The potential of the approach is demonstrated on the basis of 2D synthetic cases for the joint integration of production and 4D saturation data, considering several formulations for the weights attributed to the links.     

Hot Spots of Nitrification in the Elbe Estuary and Their Impact on Nitrate Regeneration

Estuaries act as an organic matter and nutrient filter in the transition between the land, rivers and the ocean. In the past, high nutrient and organic carbon load and low oxygen concentration made the Elbe River estuary (NW Europe) a sink for dissolved inorganic nitrogen. A recent reduction in loads and subsequent recovery of the estuary changed its biogeochemical function, so that nitrate is no longer removed on its transition towards the coastal North Sea. Nowadays in the estuary, nitrification appears to be a significant nitrate source. To quantify nitrification and determine actively nitrifying regions in the estuary, we measured the concentrations of ammonium, nitrite and nitrate, the dual stable isotopes of nitrate and net nitrification rates in the estuary on five cruises from August 2012 to August 2013. The nitrate concentration increased markedly downstream of the port of Hamburg in summer and spring, accompanied by a decrease of nitrate isotope values that was clearest in summer exactly at the location where nitrate concentration started to increase. Ammonium and nitrite peaked in the Hamburg port region (up to 18 and 8 μmol L^?1, respectively), and nitrification rates in this region were up to 7 μmol L^?1 day^?1. Our data show that coupled re-mineralization and nitrification are significant internal nitrate sources that almost double the estuary’s summer nitrate concentration. Furthermore, we find that the port of Hamburg is a hot spot of nitrification, whereas the maximum turbidity zone (MTZ) only plays a subordinate role in turnover of nitrate.     

Accessing effects and signals of leakage from a CO<Subscript>2</Subscript> reservoir to a shallow freshwater aquifer by reactive transport modelling

CO₂ geological storage is a transitional technology for the mitigation of climate change. In the vicinity of potential CO₂ reservoirs in Hungary, protected freshwater aquifers used for drinking water supplies exist. Effects of disaster events of CO₂ escape and brine displacement to one of these aquifers have been studied by kinetic 1D reactive transport modelling in PHREEQC. Besides verifying that ion concentrations in the freshwater may increase up to drinking water limit values in both scenarios (CO₂ or brine leakage), total porosity of the rock is estimated. Pore volume is expected to increase at the entry point of CO₂ and to decrease at further distances, whereas it shows minor increase along the flow path for the effect of brine inflow. Additionally, electrical conductivity of water is estimated and suggested to be the best parameter to measure for cost-effective monitoring of both worst-case leakage scenarios.