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Baseline and future (2036-2065) river water levels and discharges at 4 gauging stations along the Cambodian Mekong (Kratie, Kampong Cham, Chrouy Changva, and Neak Loeung) under different scenarios of climate change (RCP 4.5 and 8.5) and infrastructural developments. Average depth and duration flood maps are also included for each scenario. A full description of the methods and results can be found in the article: Alexander J. Horton, Nguyen V. K. Triet, Long P. Hoang, Sokchhay Heng, Panha Hok, Sarit Chung, Jorma Koponen, and Matti Kummu. (2022). The Cambodian Mekong floodplain under future development plans and climate change. Nat. Hazards Earth Syst. Sci.

Water resources index W quantifies the potential dependence of the world's lowland areas on water resources originating in mountain areas upstream. The data cover the timeframe from the 1960s (1961–1970) to the 2040s (2041–2050) in decadal steps. Data for projections from the 2010s onwards are available for three scenario pathways (SSP1-RCP4.5, SSP2-RCP6.0, SSP3-RCP6.0) and show median results from 5 CMIP5 GCMs (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1‑M). The files are GeoTIFF formatted and in a regular raster of 5’×5’ (arc minutes in WGS 1984 coordinate system) The values of W can be classified using the following ranges: W ≤ -2 → Essential but vastly insufficient -2 < W < -1 → Essential but insufficient -1 ≤ W < 0 → Essential and sufficient W = 0 → No surplus from mountains 0 < W ≤ 1 → Supportive 1 < W < 2 → Minor W ≥ 2 → Negligible The values of W are rounded to four decimal places and limited to a range of -1110 to 9998. Values falling outside of that range are set to the nearest limit. he following flag values apply to W: -5555 indicates that there is no water balance surplus from the mountain area upstream, but a lowland water balance surplus; -6666 indicates that there is no water balance surplus from the mountain area upstream, and a lowland water balance deficit. Mountain areas and oceans are NODATA, large ice shields are omitted (Greenland: NODATA, Antarctica: not covered in extent). Mountain areas provide disproportionally high runoff in many parts of the world, and here we quantify for the first time their importance for water resources and food production from the viewpoint of the lowland areas downstream. The dataset maps the degree to which lowland areas potentially depend on runoff contributions from mountain areas (39% of land mass) between the 1960s and the 2040s.

Erosion, transport and deposition of wall impurities are major concerns in future magnetic fusion devices, both from the perspective of the fusion plasma and the machine wall. An extensive study on molybdenum transport and deposition performed in the TEXTOR tokamak yielded a detailed deposition map that is ideal for benchmark deposition studies. A qualitative benchmark is attempted in this article with the ASCOT code. We set up a full 3D model of the TEXTOR tokamak and studied the influence of different physical mechanisms and their strengths on molybdenum deposition patterns on the simulated plasma-facing components: atomic processes, Coulomb collisions, scrape-off layer (SOL) profiles, source distribution, marker starting energy, radial electric field strength, SOL flow and toroidal plasma rotation. The outcome comprises 13 simulations, each with 100,000 markers. The findings are: • Toroidal plasma movement, either within the LCFS or as SOL flow, is negligible. • SOL profile and marker starting energy have modest impact on deposition. • Source distribution has a large impact in combination with radial electric field profiles. • The E⇀×B⇀ drift has the highest impact on the deposition profiles.

Climate change has brought a compelling need for cooling living spaces to the attention of researchers as well as construction professionals. The problem of overheating enclosures is now exacerbated in traditionally affected areas and is also affecting countries that were previously less prone to the issue. In this paper, we address measurements of thermal comfort and cooling emission efficiency parameters for different devices: ceiling panels, underfloor cooling, fan-assisted radiators, and fan coil. These devices were tested in low and high cooling capacities of up to 40 W/m2 while also featuring heating dummies to imitate internal heat gains. Air temperatures were measured at different heights, allowing to evaluate the thermal stratification with high accuracy. Thermal comfort differences of the tested systems were quantified by measuring both air velocities and operative temperatures at points of occupancy. In summary, the best-performing cooling devices for the studied cooling applications were the ceiling panels and fan radiators, followed by underfloor cooling, with a limitation of stratification. Because of the strong jet, fan coil units did not achieve thermal comfort within the whole occupied zone. The results can be utilized in future studies for cooling emission efficiency and energy consumption analyses of the different cooling devices.

Abstract It is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to maximize the battery availability for forklift operations. As the use of data-driven methods for battery prognostics is increasing, we demonstrate the capabilities of ARIMA and under circumstances when there is little prior information available about the batteries. For this work, we had a unique data set of 31 lithium-ion battery packs from forklifts in commercial operations. On the one hand, results indicate that the developed ARIMA model provided relevant tools to analyze the data from several batteries. On the other hand, BAG model results suggest that the developed supervised learning model using decision trees as base estimator yields better forecast accuracy in the presence of large variation in data for one battery.

AbstractAlthough the role of livestock in future food systems is debated, animal proteins are unlikely to completely disappear from our diet. Grasslands are a key source of primary productivity for livestock, and feed‐food competition is often limited on such land. Previous research on the potential for sustainable grazing has focused on restricted geographical areas or does not consider inter‐annual changes in grazing opportunities. Here, we developed a robust method to estimate trends and interannual variability (IV) in global livestock carrying capacity (number of grazing animals a piece of land can support) over 2001–2015, as well as relative stocking density (the reported livestock distribution relative to the estimated carrying capacity [CC]) in 2010. We first estimated the aboveground biomass that is available for grazers on global grasslands based on the MODIS Net Primary Production product. This was then used to calculate livestock carrying capacities using slopes, forest cover, and animal forage requirements as restrictions. We found that globally, CC decreased on 27% of total grasslands area, mostly in Europe and southeastern Brazil, while it increased on 15% of grasslands, particularly in Sudano‐Sahel and some parts of South America. In 2010, livestock forage requirements exceeded forage availability in northwestern Europe, and southern and eastern Asia. Although our findings imply some opportunities to increase grazing pressures in cold regions, Central Africa, and Australia, the high IV or low biomass supply might prevent considerable increases in stocking densities. The approach and derived open access data sets can feed into global food system modelling, support conservation efforts to reduce land degradation associated with overgrazing, and help identify undergrazed areas for targeted sustainable intensification efforts or rewilding purposes.

AbstractIntegrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of ‘IAMs’. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.

This paper presents a smart Transactive energy (TE) framework in which home microgrids (H-MGs) can collaborate with each other in a multiple H-MG system by forming coalitions for gaining competitiveness in the market. Profit allocation due to coalition between H-MGs is an important issue for ensuring the optimal use of installed resources in the whole multiple H-MG system. In addition, considering demand fluctuations, energy production based on renewable resources in the multiple H-MG can be accomplished by demand-side management strategies that try to establish mechanisms to allow for a flatter demand curve. In this regard, demand shifting potential can be tapped through shifting certain amounts of energy demand from some time periods to others with lower expected demand, typically to match price values and to ensure that existing generation will be economically sufficient. It is also possible to obtain the maximum profit with the coalition formation. In essence the impact of the consumption shifting in the multiple H-MG schedule can be considered while conducting both individual and coalition operations. A comprehensive simulation study is carried out to reveal the effectiveness of the proposed method in lowering the market clearing price (MCP) for about 15% of the time intervals, increasing H-MG responsive load consumption by a factor of 30%, and promoting local generation by a factor of three. The numerical results also show the capability of the proposed algorithm to encourage market participation and improve profit for all participants.