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Showing papers by "International Institute for Applied Systems Analysis published in 2022"


Journal ArticleDOI
TL;DR: In this article , the authors analyzed how the experience of climate extremes influences people's environmental attitudes and willingness to vote for Green parties in Europe and found that climate change experiences increase public support for climate action but only under favourable economic conditions.
Abstract: Public support is fundamental in scaling up actions to limit global warming. Here, we analyse how the experience of climate extremes influences people’s environmental attitudes and willingness to vote for Green parties in Europe. To this end, we combined high-resolution climatological data with regionally aggregated, harmonized Eurobarometer data (34 countries) and European Parliamentary electoral data (28 countries). Our findings show a significant and sizeable effect of temperature anomalies, heat episodes and dry spells on environmental concern and voting for Green parties. The magnitude of the climate effect differs substantially across European regions. It is stronger in regions with a cooler Continental or temperate Atlantic climate and weaker in regions with a warmer Mediterranean climate. The relationships are moderated by regional income level suggesting that climate change experiences increase public support for climate action but only under favourable economic conditions. The findings have important implications for the current efforts to promote climate action in line with the Paris Agreement. Exposure to extreme weather events could increase environmental concerns and support for Green parties. With high-resolution data across European countries, the authors demonstrate the existence of such effect, then further discuss the heterogeneity and possible mechanisms.

37 citations


Journal ArticleDOI
15 Jan 2022-Energy
TL;DR: In this paper, the authors show that in humid regions, such as in Brazil, the hydropower storage reservoirs contribute to increase the flow of the river and propose strategies to allow the reservoirs to fill up and to maintain the reservoirs filled in the future.

26 citations


Journal ArticleDOI
TL;DR: In this article, the authors theoretically motivate and apply a system-level theory of change framework that identifies central mechanisms and four distinct pathways, through which bio-based transformation can generate positive or negative outcomes in multiple domains of the Sustainable Development Goals.

26 citations


Journal ArticleDOI
TL;DR: In this article , a simple regression approach was used to map the LSTM state vector to the target stores (soil moisture and snow) of interest, and good correlations between the probe outputs and the target variables of interest provided evidence that LSTMs contain information that reflects known hydrological processes comparable with the concept of variable-capacity soil moisture stores.
Abstract: Abstract. Neural networks have been shown to be extremely effective rainfall-runoff models, where the river discharge is predicted from meteorological inputs. However, the question remains: what have these models learned? Is it possible to extract information about the learned relationships that map inputs to outputs, and do these mappings represent known hydrological concepts? Small-scale experiments have demonstrated that the internal states of long short-term memory networks (LSTMs), a particular neural network architecture predisposed to hydrological modelling, can be interpreted. By extracting the tensors which represent the learned translation from inputs (precipitation, temperature, and potential evapotranspiration) to outputs (discharge), this research seeks to understand what information the LSTM captures about the hydrological system. We assess the hypothesis that the LSTM replicates real-world processes and that we can extract information about these processes from the internal states of the LSTM. We examine the cell-state vector, which represents the memory of the LSTM, and explore the ways in which the LSTM learns to reproduce stores of water, such as soil moisture and snow cover. We use a simple regression approach to map the LSTM state vector to our target stores (soil moisture and snow). Good correlations (R2>0.8) between the probe outputs and the target variables of interest provide evidence that the LSTM contains information that reflects known hydrological processes comparable with the concept of variable-capacity soil moisture stores. The implications of this study are threefold: (1) LSTMs reproduce known hydrological processes. (2) While conceptual models have theoretical assumptions embedded in the model a priori, the LSTM derives these from the data. These learned representations are interpretable by scientists. (3) LSTMs can be used to gain an estimate of intermediate stores of water such as soil moisture. While machine learning interpretability is still a nascent field and our approach reflects a simple technique for exploring what the model has learned, the results are robust to different initial conditions and to a variety of benchmarking experiments. We therefore argue that deep learning approaches can be used to advance our scientific goals as well as our predictive goals.

19 citations


Journal ArticleDOI
01 Jan 2022-Energy
TL;DR: In this paper , the authors show that in humid regions, such as in Brazil, the hydropower storage reservoirs contribute to increase the flow of the river and propose strategies to allow the reservoirs to fill up and to maintain the reservoirs filled in the future.

16 citations


Journal ArticleDOI
27 Oct 2022
TL;DR: In this paper , a trait-based optimality theory was proposed to predict the simultaneous decline in carbon assimilation rate, stomatal conductance and photosynthetic capacity during progressive soil drought.
Abstract: Abstract The global carbon and water cycles are governed by the coupling of CO 2 and water vapour exchanges through the leaves of terrestrial plants, controlled by plant adaptations to balance carbon gains and hydraulic risks. We introduce a trait-based optimality theory that unifies the treatment of stomatal responses and biochemical acclimation of plants to environments changing on multiple timescales. Tested with experimental data from 18 species, our model successfully predicts the simultaneous decline in carbon assimilation rate, stomatal conductance and photosynthetic capacity during progressive soil drought. It also correctly predicts the dependencies of gas exchange on atmospheric vapour pressure deficit, temperature and CO 2 . Model predictions are also consistent with widely observed empirical patterns, such as the distribution of hydraulic strategies. Our unified theory opens new avenues for reliably modelling the interactive effects of drying soil and rising atmospheric CO 2 on global photosynthesis and transpiration.

14 citations


Journal ArticleDOI
TL;DR: In this paper , the authors draw on the Montreal Protocol start-and-strengthen approach to show that accelerated phasedown under the Kigali Amendment could result in additional reductions of 72% in 2050, increasing chances of staying below 1.5 °C throughout this century.
Abstract: Hydrofluorocarbon emissions have increased rapidly and are managed by the Kigali Amendment to the Montreal Protocol. Yet the current ambition is not consistent with the 1.5 °C Paris Agreement goal. Here, we draw on the Montreal Protocol start-and-strengthen approach to show that accelerated phase-down under the Kigali Amendment could result in additional reductions of 72% in 2050, increasing chances of staying below 1.5 °C throughout this century. The current ambition for hydrofluorocarbon emissions reduction by the Kigali Amendment is not sufficient to meet the 1.5 °C Paris Agreement goal. The authors show that a more ambitious Kigali Amendment target could still help in achieving the Paris goal if more countries act early.

12 citations


Journal ArticleDOI
01 Jun 2022-Energy
TL;DR: In this article , the authors proposed an innovative solution that consists of catching water from streams at high altitudes to fill storage containers and transport them down a mountain, converting the potential energy of water into electricity with the regenerative braking systems of electric trucks and storing it in the truck's battery.

10 citations


Posted ContentDOI
28 Mar 2022
TL;DR: In this article , the authors propose a green water boundary within the existing planetary boundaries framework, of which a control variable could be defined as the percentage of ice-free land area on which root-zone soil moisture deviates from Holocene variability for any month of the year.
Abstract: <p>Green water - i.e., land precipitation, evaporation and soil moisture - is fundamental for the functioning of the biosphere and the Earth System, but is increasingly perturbed by continental-to-planetary scale human pressures on land, water and climate systems. The planetary boundaries (PB) framework demarcates a global safe operating space for humanity, but does hitherto not explicitly account for green water. Here, we propose a green-water boundary within the existing PB framework, of which a control variable could be defined as "the percentage of ice-free land area on which root-zone soil moisture deviates from Holocene variability for any month of the year". We provide provisional estimates of baseline departures based on CMIP6 data, and review the literature on soil-moisture induced deterioration in Earth System functioning. The evidences taken together suggest that the green water PB is already transgressed, implying that human modifications of green water need to come to a halt and be reversed. Future research needs to advance our understanding of root-zone water dynamics, including associated large-scale and potentially non-linear interactions with ecohydrology, hydroclimate, biogeochemistry and societies.</p>

7 citations


Journal ArticleDOI
TL;DR: In this article , the authors used optical (Landsat) and altimetry remote sensing to reconstruct monthly water storage for 6695 reservoirs worldwide between 1984 and 2015, finding that reservoir storage has diminished substantially for 23 % of reservoirs over the three decades, but increased for 21 %.
Abstract: Abstract. Many thousands of large dam reservoirs have been constructed worldwide during the last 70 years to increase reliable water supplies and support economic growth. Because reservoir storage measurements are generally not publicly available, so far there has been no global assessment of long-term dynamic changes in reservoir water volumes. We overcame this by using optical (Landsat) and altimetry remote sensing to reconstruct monthly water storage for 6695 reservoirs worldwide between 1984 and 2015. We relate reservoir storage to resilience and vulnerability and investigate interactions between precipitation, streamflow, evaporation, and reservoir water storage. This is based on a comprehensive analysis of streamflow from a multi-model ensemble and as observed at ca. 8000 gauging stations, precipitation from a combination of station, satellite and forecast data, and open water evaporation estimates. We find reservoir storage has diminished substantially for 23 % of reservoirs over the three decades, but increased for 21 %. The greatest declines were for dry basins in southeastern Australia (−29 %), southwestern USA (−10 %), and eastern Brazil (−9 %). The greatest gains occurred in the Nile Basin (+67 %), Mediterranean basins (+31 %) and southern Africa (+22 %). Many of the observed reservoir changes could be explained by changes in precipitation and river inflows, emphasizing the importance of multi-decadal precipitation changes for reservoir water storage. Uncertainty in the analysis can come from, among others, the relatively low Landsat imaging frequency for parts of the Earth and the simple geo-statistical bathymetry model used. Our results also show that there is generally little impact from changes in net evaporation on storage trends. Based on the reservoir water balance, we deduce it is unlikely that water release trends dominate global trends in reservoir storage dynamics. This inference is further supported by different spatial patterns in water withdrawal and storage trends globally. A more definitive conclusion about the impact of changes in water releases at the global or local scale would require data that unfortunately are not publicly available for the vast majority of reservoirs globally.

7 citations


Journal ArticleDOI
TL;DR: In this paper , the relativistic modifications of the uncertainty relation derived from the curvature of the background spacetime have been investigated in the non-relativistic limit.
Abstract: The investigations presented in this study are directed at relativistic modifications of the uncertainty relation derived from the curvature of the background spacetime. These findings generalize previous work that is recovered in the nonrelativistic limit. Applying the $3+1$ splitting in accordance with the ADM formalism, we find the relativistic physical momentum operator and compute its standard deviation for wave functions confined to a geodesic ball on a spacelike hypersurface. Its radius can then be understood as a measure of position uncertainty. Under the assumption of small position uncertainties in comparison to background curvature length scales, we obtain the corresponding corrections to the uncertainty relation in flat space. Those depend on the Ricci scalar of the effective spatial metric, the particle is moving on, and, if there are nonvanishing time-space components of the spacetime metric, there are gradients of the shift vector and the lapse function. Interestingly, this result is applicable not only to massive but also to massless particles. Over all, this is not a covariant, yet a consistently general relativistic approach. We further speculate on a possible covariant extension.

Journal ArticleDOI
TL;DR: In this paper , the authors proposed a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input.
Abstract: Background Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms. Purpose Most data-driven denoising techniques are based on deep neural networks, and therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. Methods This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design. Results Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures of 0.7094 and 0.9674 and peak signal-to-noise ratio values of 33.17 and 43.07 on the respective data sets. Conclusions Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architectures.

Journal ArticleDOI
TL;DR: In this paper , seven downward terrestrial gamma-ray flashes (TGFs) were detected by Gamma-ray and broadband low-frequency (LF: 0.8-500 kHz) monitors.
Abstract: Winter thunderstorms in Japan have been recognized as an ideal target to observe high-energy atmospheric phenomena thanks to low-charge-center cloud structures. During four winter seasons in Japan (from 2016 October to 2020 March), seven downward terrestrial gamma-ray flashes (TGFs) were detected by gamma-ray and broadband low-frequency (LF: 0.8–500 kHz) monitors. All the detected TGFs took place at the initial stage of lightning flashes. Based on the LF observation, the seven downward TGFs in the present study can be classified into two types: energetic-bipolar and small-bipolar types. Three of them are energetic-bipolar events, coincident with a high peak-current LF pulse that originates from a negative return stroke with a peak current larger than 100 kA. The others are small-bipolar events, followed by a negative bipolar LF pulse with a moderate peak current. Three of the four small-bipolar events are multi-pulse TGFs, while all of the energetic-bipolar events in this study are single-pulse TGFs.

Book ChapterDOI
01 Jan 2022
TL;DR: In this paper , the authors introduce early warning systems (EWS) in the context of disaster risk reduction, including the main components of an EWS, the roles of the main actors and the need for robust evaluation.
Abstract: Abstract In this chapter, we introduce early warning systems (EWS) in the context of disaster risk reduction, including the main components of an EWS, the roles of the main actors and the need for robust evaluation. Management of disaster risks requires that the nature and distribution of risk are understood, including the hazards, and the exposure, vulnerability and capacity of communities at risk. A variety of policy options can be used to reduce and manage risks, and we emphasise the contribution of early warnings, presenting an eight-component framework of people-centred early warning systems which highlights the importance of an integrated and all-society approach. We identify the need for decisions to be evidence-based, for performance monitoring and for dealing with errors and false information. We conclude by identifying gaps in current early warning systems, including in the social components of warning systems and in dealing with multi-hazards, and obstacles to progress, including issues in funding, data availability, and stakeholder engagement.

Journal ArticleDOI
TL;DR: In this article, the Graz Cycle, a zero emission oxy-combustion power plant, is modelled for both investigations, full load and part load, and control strategies were developed in order to achieve optimum performances and operating efficiencies by means of the assumptions given.

Journal ArticleDOI
01 Jun 2022-Energy
TL;DR: In this article , an innovative proposal for the creation of hydrogen ocean links is presented, which is performed by replacing seawater with pressurized hydrogen and maintaining the pressure in the pipes similar to the outside pressure.

Posted ContentDOI
28 Jun 2022
TL;DR: In this paper , the authors evaluate the global mean temperature projections and effective radiative forcing characteristics (ERF) of climate emulators FaIRv1.6.2, MAGICCv7.5.3, and CICERO-SCM, and look at an interpretation of compatibility with the Paris Agreement.
Abstract: Abstract. While the IPCC’s physical science report usually assesses a handful of future scenarios, the IPCC Sixth Assessment Working Group III report (AR6 WGIII) on climate mitigation assesses hundreds to thousands of future emissions scenarios. A key task is to assess the global-mean temperature outcomes of these scenarios in a consistent manner, given the challenge that the emission scenarios from different integrated assessment models come with different sectoral and gas-to-gas coverage and cannot all be assessed consistently by complex Earth System Models. In this work, we describe the “climate assessment” workflow and its methods, including infilling of missing emissions and emissions harmonisation as applied to 1,202 mitigation scenarios in AR6 WGIII. We evaluate the global-mean temperature projections and effective radiative forcing characteristics (ERF) of climate emulators FaIRv1.6.2, MAGICCv7.5.3, and CICERO-SCM, discuss overshoot severity of the mitigation pathways using overshoot degree years, and look at an interpretation of compatibility with the Paris Agreement. We find that the lowest class of emission scenarios that limit global warming to “1.5 °C (with a probability of greater than 50 %) with no or limited overshoot” includes 90 scenarios for MAGICCv7.5.3, and 196 for FaIRv1.6.2. For the MAGICCv7.5.3 results, “limited overshoot” typically implies exceedance of median temperature projections of up to about 0.1 °C for up to a few decades, before returning to below 1.5 °C by or before the year 2100. For more than half of the scenarios of this category that comply with three criteria for being “Paris-compatible”, including net-zero or net-negative greenhouse gas (GHG) emissions, are projected to see median temperatures decline by about 0.3–0.4 °C after peaking at 1.5–1.6 °C in 2035–2055. We compare the methods applied in AR6 with the methods used for SR1.5 and discuss the implications. This article also introduces a ‘climate-assessment’ Python package which allows for fully reproducing the IPCC AR6 WGIII temperature assessment. This work can be the start of a community tool for assessing the temperature outcomes related to emissions pathways, and potential further work extending the workflow from emissions to global climate by downscaling climate characteristics to a regional level and calculating impacts.

Journal ArticleDOI
01 Sep 2022-Energy
TL;DR: Ammonia district cooling could be the missing piece for implementing seawater air-conditioning due to its potential to increase the cooling load of district cooling systems as mentioned in this paper . But the main challenge for this technology is to distribute the cooling service.

Posted ContentDOI
15 Aug 2022
TL;DR: The Geographical, Environmental and Behavioural model (GEB) as discussed by the authors is a coupled agent-based hydrological model that simulates the behaviour and daily bi-directional interaction of up to ~10 million individual farm households with the hydrologogical system on a personal laptop.
Abstract: Abstract. Humans play a large role in the hydrological system; for example, by extracting large amounts of water for irrigation, often resulting in water stress and ecosystem degradation. By implementing large-scale adaptation measures, such as the construction of irrigation reservoirs, water stress and ecosystem degradation can be reduced. Yet we know that many decisions, such as the adoption of more effective irrigation techniques or changing crop types, are made at the farm level by a heterogeneous farmer population. While these decisions are often advantageous for an individual farmer or their community, detrimental effects are frequently experienced downstream. Therefore, to fully comprehend how the human-natural water system evolves over time and space, and to explore which interventions are suitable to reduce water stress, it is important to consider human behaviour and feedbacks to the hydrological system simultaneously at the local household and large basin scales. Therefore, we present the Geographical, Environmental and Behavioural model (GEB), a coupled agent-based hydrological model that simulates the behaviour and daily bi-directional interaction of up to ~10 million individual farm households with the hydrological system on a personal laptop. GEB is dynamically linked with the spatially distributed grid-based hydrological model CWatM at 30’’ resolution (< 1 km at the equator). Because many small-holder farmer fields are much smaller than 1×1 km, CWatM was specifically adapted to implement dynamically sized hydrological response units (HRUs) at the farm level, providing each agent with an independently operated hydrological environment. While the model could be applied globally, we explore its implementation in the heavily managed Krishna basin in India, which encompasses ~8 % of India’s land area and ~11.1 million farmers. Here, we show how six combinations of storylines with endogenous and exogenous drivers of adaptation affect both the hydrological system and the farmer population.

Journal ArticleDOI
TL;DR: In this paper , the authors introduced P dynamics and its interactions with the N and carbon (C) cycles into the Joint UK Land Environment Simulator (JULES), which includes the representation of P stocks in vegetation and soil pools, as well as key processes controlling fluxes between these pools.
Abstract: Abstract. Most land surface models (LSMs), i.e. the land components of Earth system models (ESMs), include representation of nitrogen (N) limitation on ecosystem productivity. However, only a few of these models have incorporated phosphorus (P) cycling. In tropical ecosystems, this is likely to be important as N tends to be abundant, whereas the availability of rock-derived elements, such as P, can be very low. Thus, without a representation of P cycling, tropical forest response in areas such as Amazonia to rising atmospheric CO2 conditions remain highly uncertain. In this study, we introduced P dynamics and its interactions with the N and carbon (C) cycles into the Joint UK Land Environment Simulator (JULES). The new model (JULES-CNP) includes the representation of P stocks in vegetation and soil pools, as well as key processes controlling fluxes between these pools. We develop and evaluate JULES-CNP using in situ data collected at a low-fertility site in the central Amazon, with a soil P content representative of 60 % of soils across the Amazon basin, to parameterize, calibrate, and evaluate JULES-CNP. Novel soil and plant P pool observations are used for parameterization and calibration, and the model is evaluated against C fluxes and stocks and those soil P pools not used for parameterization or calibration. We then evaluate the model at additional P-limited test sites across the Amazon and in Panama and Hawaii, showing a significant improvement over the C- and CN-only versions of the model. The model is then applied under elevated CO2 (600 ppm) at our study site in the central Amazon to quantify the impact of P limitation on CO2 fertilization. We compare our results against the current state-of-the-art CNP models using the same methodology that was used in the AmazonFACE model intercomparison study. The model is able to reproduce the observed plant and soil P pools and fluxes used for evaluation under ambient CO2. We estimate P to limit net primary productivity (NPP) by 24 % under current CO2 and by 46 % under elevated CO2. Under elevated CO2, biomass in simulations accounting for CNP increase by 10 % relative to contemporary CO2 conditions, although it is 5 % lower compared to CN- and C-only simulations. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon rainforest with low-fertility soils.

Journal ArticleDOI
TL;DR: In this article , the authors assess the impact of different shared socioeconomic pathways (SSPs) and climate futures on the extent of population lacking access to cooling where needed and energy requirements for basic comfort for a set of 22 megacities in the Global South.
Abstract: Abstract As urban areas are increasingly exposed to high temperatures, lack of access to residential thermal comfort is a challenge with dramatic consequences for human health and well-being. Air-conditioning (AC) can provide relief against heat stress, but a massive AC uptake could entail stark energy demand growth and mitigation challenges. Slums pose additional risks due to poor building quality, failing to provide adequate shelter from severe climatic conditions. Thus, it is unclear how many people in the Global South will still lack access to basic cooling under different future climate and socioeconomic developments. We assess the impact of different shared socioeconomic pathways (SSPs) and climate futures on the extent of population lacking access to cooling where needed—the cooling gap—and energy requirements for basic comfort for a set of 22 megacities in the Global South. We find that different SSPs greatly influence the extent of future cooling gaps, generally larger in SSP3 due low income levels, and consequent limited access to AC and durable housing. Megacities in Sub-Saharan Africa and South Asia have the largest share of population affected, ranging from 33% (SSP1) to 86% (SSP3) by mid-century. Energy requirements to provide basic cooling for all are higher in SSP1 for most megacities, driven by urbanization, and can increase by 7 to 23% moving from 2.0 to 3.0 °C temperature rise levels. Strategies combining improved building design and efficient cooling systems can improve adaptation to heat stress in cities while reducing energy and emission requirements to reach climate and sustainability goals.

Journal ArticleDOI
TL;DR: In this paper , the authors present a method for statistically estimating the true number of cases with confidence intervals from the reported number of deaths and estimates of the infection fatality ratio; assuming that the time from infection to death follows a known distribution.
Abstract: At the outset of an epidemic, available case data typically underestimate the total number of infections due to insufficient testing, potentially hampering public responses. Here, we present a method for statistically estimating the true number of cases with confidence intervals from the reported number of deaths and estimates of the infection fatality ratio; assuming that the time from infection to death follows a known distribution. While the method is applicable to any epidemic with a significant mortality rate, we exemplify the method by applying it to COVID-19. Our findings indicate that the number of unreported COVID-19 infections in March 2020 was likely to be at least one order of magnitude higher than the reported cases, with the degree of underestimation among the countries considered being particularly high in the United Kingdom.


Journal ArticleDOI
TL;DR: In this article , a search for neutrinos in coincidence with solar flares from the GOES flare database was performed on a 10.8 kton-year exposure of KamLAND collected from 2002 to 2019.
Abstract: We report the result of a search for neutrinos in coincidence with solar flares from the GOES flare database. The search was performed on a 10.8 kton-year exposure of KamLAND collected from 2002 to 2019. This large exposure allows us to explore previously unconstrained parameter space for solar flare neutrinos. We found no statistical excess of neutrinos and established 90% confidence level upper limits of $8.4 \times 10^7$ cm$^{-2}$ ($3.0 \times 10^{9}$ cm$^{-2}$) on electron anti-neutrino (electron neutrino) fluence at 20 MeV normalized to the X12 flare, assuming that the neutrino fluence is proportional to the X-ray intensity.

Journal ArticleDOI
TL;DR: In this article , the authors conducted an exploratory evaluation of the relationship between environmental flow violation and freshwater biodiversity at globally aggregated scales and for freshwater ecoregions, and found no statistically significant negative relationship between EF violation and watershed biodiversity.
Abstract: Abstract. The freshwater ecosystems around the world are degrading, such that maintaining environmental flow1 (EF) in river networks is critical to their preservation. The relationship between streamflow alterations (subsequent EF violations2) and the freshwater biodiversity response is well established at the scale of stream reaches or small basins (∼<100 km2). However, it is unclear if this relationship is robust at larger scales, even though there are large-scale initiatives to legalize the EF requirement. Moreover, EFs have been used in assessing a planetary boundary3 for freshwater. Therefore, this study intends to conduct an exploratory evaluation of the relationship between EF violation and freshwater biodiversity at globally aggregated scales and for freshwater ecoregions. Four EF violation indices (severity, frequency, probability of shifting to a violated state, and probability of staying violated) and seven independent freshwater biodiversity indicators (calculated from observed biota data) were used for correlation analysis. No statistically significant negative relationship between EF violation and freshwater biodiversity was found at global or ecoregion scales. These findings imply the need for a holistic bio-geo-hydro-physical approach in determining the environmental flows. While our results thus suggest that streamflow and EF may not be the only determinant of freshwater biodiversity at large scales, they do not preclude the existence of relationships at smaller scales or with more holistic EF methods (e.g., including water temperature, water quality, intermittency, connectivity, etc.) or with other biodiversity data or metrics.

Posted ContentDOI
28 Mar 2022
TL;DR: In this article , the authors assess the potential of a carbon capture and utilization pathway to increase the fuel production of the sugarcane ethanol industry in a land-neutral way and develop a spatio-temporal model to determine the cost-optimal system configuration, the resulting land effciency, and consequently the land sparing potential.
Abstract: &lt;p&gt;Brazil is the global frontrunner in the production of sugarcane ethanol. Strong national biofuels policies, a consolidated internal demand for ethanol for transportation purposes, and a global growing demand for sugar and ethanol have supported this development. The sugarcane ethanol industry has contributed to economic growth, technological progress, job creation and is among the key strategies for mitigating CO&lt;sub&gt;2&lt;/sub&gt; emissions in Brazil. However, the industry is also responsible for a wide range of undesirable impacts on land. Biodiversity loss, structural soil degradation, pollution, and depletion of water sources can result from the associated direct and indirect land-use change. We therefore assess the potential of a carbon capture and utilization pathway to increase the fuel production of this industry in a land-neutral way.&amp;#160;&lt;/p&gt;&lt;p&gt;The pathway combines the almost clear surplus CO&lt;sub&gt;2&lt;/sub&gt;-stream from the ethanol fermentation process with H&lt;sub&gt;2&lt;/sub&gt; produced using wind and solar power to synthesize methanol. The change of use of land from sugarcane production to renewable electricity generation is an intensification step which allows to spare significant amounts of land.&lt;/p&gt;&lt;p&gt;To understand the implications of this pathway in terms of land-use and cost, we develop a spatio-temporal model to determine the cost-optimal system configuration, the resulting land effciency, and consequently the land sparing potential. The core of the model consists of a techno-economic optimization model that minimizes cost for a system that includes variable renewable electricity generation (wind and solar power), storage (electricity, CO&lt;sub&gt;2&lt;/sub&gt; and H&lt;sub&gt;2&lt;/sub&gt;), electrolyzers and methanol synthesis installations for each one of the sugarcane ethanol production plants in the country. The optimization model relies crucially on two time-series which we derived specifically for each Brazilian ethanol plant based on a consolidated spatially explicit data set of sugarcane ethanol installations: first, individual time series of the CO&lt;sub&gt;2&lt;/sub&gt;-streams from ethanol fermentation, and second multi-year time series of wind and solar power in hourly temporal resolution using ERA5 and ERA5-land reanalysis data. Furthermore, we extensively review costs of individual system components and derive footprints of Brazilian solar and wind power plants from satellite imagery.&lt;/p&gt;&lt;p&gt;The proposed pathway leads to a combined amount of ethanol and methanol that represents an increase of&amp;#160; 43%-49% compared to the current output of the ethanol industry in energetic terms. This amounts to around 100 TWh of methanol that would be sufficient to cover the projected growth in Brazil biofuel demand until 2030. In contrast, if the same amount of energy would be provided by sugarcane ethanol, produced at the current average Brazilian sugarcane-to-ethanol land-use efficiency, an additional 23,000 km&lt;sup&gt;2&lt;/sup&gt; - 27,000 km&lt;sup&gt;2&lt;/sup&gt; of land would be required. This underlines the significant land sparing potential of the proposed pathway.&amp;#160;&lt;/p&gt;

Journal ArticleDOI
TL;DR: In this paper , the authors describe electricity supply, use and preferences among micro-and small-enterprises (MSEs), which are important providers of livelihoods in emerging economies, using primary surveys of firm owners (N = 696) in Bihar, India.

Peer ReviewDOI
08 Jul 2022
TL;DR: In this paper , the authors diagnose the OSCAR model using observations and results from ESMs from the current Coupled Model Intercomparison Project 6.1 in a probabilistic framework, reaching a total of 567,700,000 simulated years.
Abstract: While Earth system models (ESMs) are process-based and can be run at high resolutions, they are only limited by computational costs. Reduced complexity models, also called simple climate models or compact models, provide a much cheaper alternative, although at a loss of spatial information. Their structure relies on the sciences of the Earth system, but with a calibration against the most complex models. Therefore it remains important to evaluate and validate reduced complexity models. Here, we diagnose such a model the newest version of OSCAR (v3.1) using observations and results from ESMs from the current Coupled Model Intercomparison Project 6. A total of 99 experiments are selected for simulation with OSCAR v3.1 in a probabilistic framework, reaching a total of 567,700,000 simulated years. A first highlight of this exercise that the ocean carbon cycle of the model may diverge under some parametrizations and for high-warming scenarios. The diverging runs caused by this unstability were discarded in the post-processing. Then, each physical parametrization is weighted based on its performance against a set of observations, providing us with constrained results. Overall, OSCAR v3.1 shows good agreement with observations, ESMs and emerging properties. It qualitively reproduces the responses of complex ESMs, for all aspects of the Earth system. We observe some quantitative differences with these models, most of them being due to the observational constraints. Some specific features of OSCAR also contribute to these differences, such as its fully interactive atmospheric chemistry and endogenous calculations of biomass burning, wetlands CH4 and permafrost CH4 and CO2 emissions. The main points of improvements are a low sensitivity of the land carbon cycle to climate change, an unstability of the ocean carbon cycle, the seemingly too simple climate module, and the too strong climate feedback involving short-lived species. Beyond providing a key diagnosis of the OSCAR model in the context of the reduced-complexity models intercomparison project (RCMIP), this work is also meant to help with the upcoming calibration of OSCAR on CMIP6 results, and to provide a large group of CMIP6 simulations run consistently within a probabilistic framework.


Posted ContentDOI
05 Aug 2022
TL;DR: In this article , an Intersection over Union ratio (IOU) approach is proposed to select station locations on a coarser grid-scale, reducing the errors in assigning stations to the correct upstream basin.
Abstract: Abstract. The Global Runoff Data Centre provides time series of observed discharges that are valuable for calibrating and validating the results of hydrological models. We address a common issue in large-scale hydrology that has not been satisfactorily solved, though investigated several times. To compare simulated and observed discharge, grid-based hydrological models must fit reported station locations to the resolution-dependent gridded river network. We introduce an Intersection over Union ratio approach to selected station locations on a coarser grid-scale, reducing the errors in assigning stations to the correct upstream basin. We update the 10-year-old database of watershed boundaries with additional stations based on a high-resolution (3 arc seconds) river network and provide source codes and high- and low-resolution watershed boundaries. The dataset is stored on Zenodo with the associated DOI https://doi.org/10.5281/zenodo.6906577.