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Increasing electricity and thermal demand in all sectors, an increasing focus on the reduction in carbon emissions and use of nuclear power, advent of distributed generation and greater use of renewable technologies on an aging electrical and thermal grid system has necessitated the need for modern control and management systems. These new control and management systems need to be able to integrate new technologies, stochasticities and maximise the utilisation of the existing infrastructure while satisfying demands, without requiring complete overhaul of the pre-existing centralised grid system and the transmission and distribution systems. A model predictive control system has been proposed and demonstrated here which is able to create strategies for thermal and electrical systems such that the grid efficiency and security is maintained while minimising resource usage and emissions, while, simultaneously reducing the operating costs in the grid. The model predictive control(MPC) utilises a fully energetic approach for low-voltage microgrids and houses in the residential and commercial sector which comprises of CHP units, heat pumps, storage systems(electric and thermal) and stochastic renewable resources, while accounting for the varying dynamics of the electrical and thermal systems. Finally, validation of the MPC is performed on a testbed with physical units and building emulators which have access to meteorological and resource market data. The capability of the MPC to provide strategies for systems with photovoltaics (PV), heat pumps and CHP units is demonstrated. The MPC implementation developed is input into an optimal system design algorithm based on a multi-objective optimisation genetic algorithm developed for microgrids and urban systems/grids with end-users and polygeneration systems and storage devices. The optimal design of the system is so that the optimal sizes of the polygeneration systems can be identified. This will help in maximising the utilisation of heat pumps, storage devices and other systems in a LV microgrid equipped with an MPC-based thermo-electric energy management system. The work also aims to compare the cost effectiveness versus ability of thermal storage devices compared to electrical storage devices for the same grid in question.

Flash floods and debris flows develop at space and time scales that conventional observation systems for rainfall, streamflow and sediment discharge are not able to monitor. Consequently, the atmospheric, hydrological and geomorphic controls on these hydrogeomorphic processes are poorly understood, leading to highly uncertain warning and risk management. On the other hand, remote sensing of precipitation and numerical weather predictions have become the basis of several flood forecasting systems, enabling increasingly accurate detection of hazardous events. The objective of this paper is to provide a review on current European and international research on early warning systems for flash floods and debris flows. We expand upon these themes by identifying: (a) the state of the art; (b) knowledge gaps; and (c) suggested research directions to advance warning capabilities for extreme hydrogeomorphic processes. We also suggest three areas in which advancements in science will have immediate and important practical consequence, namely development of rainfall estimation and nowcasting schemes suited to the specific space-time scales, consolidating physical, engineering and social datasets of flash floods and debris-flows, integration of methods for multiple hydrogeomorphic hazard warning.

Monitoring the surface composition of CO2 derived from subsurface reservoirs is an important part of the carbon capture and storage (CCS) chain. Most approaches use geochemical or geophysical instrumental approaches but these have the drawback that no long-term time series are available, which depend on a predefined monitoring location. We test a flexible approach using natural archives based on the measurement of radiogenic 14C concentrations in tree rings to detect geogenic CO2 fluxes derived from natural springs in the Latera Caldera, central Italy. The approach can be used as a preliminary check to evaluate natural CO2 leaks from sites designated for CO2 storage, as well as evaluating the extent of leakage in an unforeseen area. An extensive database of soil gas composition and fluxes is available for that site, permitting direct comparison of the tree ring isotopic composition and point sources of CO2 from the subsurface against the mean atmospheric standard. We sampled oak trees (Quercus cerris and Quercus pubescens) directly at the CO2 source (ON), and at short (50 m, NEAR), intermediate (500 m, FAR) and long distances (~3000 m, CONTROL) from CO2, sources, and measured the radioactive 14C concentration in tree rings at ~10 year intervals from 2012 back to 1976. To accurately date the tree rings we constructed a tree-ring chronology using standard dendrochronological methods. We tested whether variation in 14C concentration in tree rings and ring-width are related to distance of trees from CO2 source, as well as climate factors, i.e. precipitation and temperature. Results show that local point sources of CO2 at the location where the tree grows are effectively recorded by the 14C concentration in the cellulose of this tree. The fossil CO2 signal is sharply delineated since already at short distance from the source (~50 m, NEAR) the 14C incorporation is at the detection limit of the tested approach. Tree-ring width of the oaks at Caldera Latera is mainly limited by the amount of precipitation during the growing season, from March to October, while distance to the CO2 point source has no detectable effect on radial growth, likely due to the continuous presence of the enhanced CO2 flux to which the trees adapt their physiology from germination. While the approach is promising and permits data collection at any forested site, a more detailed sampling transect between 0 and 50 m from a point source of CO2 is needed to determine the exact detection limit of the signal in the 14C concentration of the tree ring cellulose.

South Africa is likely to experience higher temperatures and less rainfall as a result of climate change. Resulting changes in regional water endowments and soil moisture will affect the productivity of cropland, leading to changes in food production and international trade patterns. High population growth elsewhere in Africa and Asia will put further pressure on natural resources and food security in South Africa. Based on four climate change scenarios from two general circulation models (CSIRO and MIROC) and two IPCC SRES emission scenarios (A1B, B1), this study assesses the potential impacts of climate change on global agriculture and explores two alternative adaptation scenarios for South Africa. The analysis uses an updated GTAP-W model, which distinguishes between rainfed and irrigated agriculture and implements water as an explicit factor of production for irrigated agriculture. For South Africa to adapt to the adverse consequences of global climate change, it would require yield improvements of more than 20 percent over baseline investments in agricultural research and development. A doubling of irrigation development, on the other hand, will not be sufficient to reverse adverse impacts from climate change in the country.

Climate change may alter mycorrhizal communities, which impact ecosystem characteristics such as carbon sequestration processes. These impacts occur at a greater magnitude in Arctic ecosystems, where the climate is warming faster than in lower latitudes. Cassiope tetragona (L.) D. Don is an Arctic plant species in the Ericaceae family with a circumpolar range. C. tetragona has been reported to form ericoid mycorrhizal (ErM) as well as ectomycorrhizal (ECM) symbioses. In this study, the fungal taxa present within roots of C. tetragona plants collected from Svalbard were investigated using DNA metabarcoding. In light of ongoing climate change in the Arctic, the effects of artificial warming by open-top chambers (OTCs) on the fungal root community of C. tetragona were evaluated. We detected only a weak effect of warming by OTCs on the root-associated fungal communities that was masked by the spatial variation between sampling sites. The root fungal community of C. tetragona was dominated by fungal groups in the Basidiomycota traditionally classified as either saprotrophic or ECM symbionts, including the orders Sebacinales and Agaricales and the genera Clavaria, Cortinarius, and Mycena. Only a minor proportion of the operational taxonomic units (OTUs) could be annotated as ErM-forming fungi. This indicates that C. tetragona may be forming mycorrhizal symbioses with typically ECM-forming fungi, although no characteristic ECM root tips were observed. Previous studies have indicated that some saprophytic fungi may also be involved in biotrophic associations, but whether the saprotrophic fungi in the roots of C. tetragona are involved in biotrophic associations remains unclear. The need for more experimental and microscopy-based studies to reveal the nature of the fungal associations in C. tetragona roots is emphasized.

The beneficial results of the exponential expansion of photovoltaic installations in Germany and Italy are discussed. Remarkable falls in the peak price of electricity have been observed in both countries. The reasons are discussed in the light of the data from the Kombikraftwerk project. This has demonstrated, in a scaled, real-time experiment, how the demand on the German grid can be met by photovoltaics and wind with back-up from biogas and (pumped hydro) storage. We discuss the implications of the fall in price of photovoltaic cells particularly for 3rd generation technology. Using the specific example of the UK, we demonstrate the advantages of the complementary nature of wind and photovoltaic resources. We demonstrate that the wind and photovoltaic capacity targets for an all renewably powered UK are likely to be significantly lower than in Germany. We conclude by summarising the evidence in favour of a moratorium on all new electricity generation other than by the renewables.

AbstractOngoing rapid climate change is predicted to cause local extinction of plant species in mountain regions. However, some plant species could have persisted during Quaternary climate oscillations without shifting their range, despite the limited evidence from fossils. Here, we tested two candidate mechanisms of persistence by comparing the macrorefugia and microrefugia (MR) hypotheses. We used the rare and endemic Saxifraga florulenta as a model taxon and combined ensembles of species distribution models (SDMs) with a high‐resolution paleoclimatic and topographic dataset to reconstruct its potential current and past distribution since the last glacial maximum. To test the macrorefugia hypothesis, we verified whether the species could have persisted in or shifted to geographic areas defined by its realized niche. We then identified potential MR based on climatic and topographic properties of the landscape and applied refined scenarios of MR dynamics and functions over time. Last, we quantified the number of known occurrences that could be explained by either the macrorefugia or MR model. A consensus of two or three SDM techniques predicted absence between 14–10, 3–4 and 1 ka bp, which did not support the macrorefugia model. In contrast, we showed that S. florulenta could have contracted into MR during periods of absence predicted by the SDMs and later re‐colonized suitable areas according to the macrorefugia model. Assuming a limited and realistic seed dispersal distance for our species, we explained a large number of the current occurrences (61–96%). Additionally, we showed that MR could have facilitated range expansions or shifts of S. florulenta. Finally, we found that the most recent and the most stable MR were the ones closest to current occurrences. Hence, we propose a novel paradigm to explain plant persistence by highlighting the importance of supporting functions of MR when forecasting the fate of plant species under climate change.

AbstractThe recent rise in atmospheric methane (CH4) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year−1) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.