Scenario Database Research Articles

Difference analysis of intended nationally determined contributions pledge and 2°C target

Intended nationally determined contributions (INDCs) are a new strategy for mitigating climate change. Many international organizations and scholars have assessed the possibility of holding the increase in global average temperature to well below 2°C based on INDCs. Although the conclusions of these assessments are consistent, there are still large differences among the assessment results. For example, the global greenhouse gas emissions in 2030 estimated by INDCs are between 47.1–66.5 GtCO2 eq, and the temperature increase at the end of the 21st century estimated by INDCs is between 2.4–4.0°C; the inconsistency represented by these ranges is not conducive to an accurate assessment of the contributions of the current INDCs to global warming mitigation or to the further development of emissions reduction programs. By summarizing the existing studies, we found that the main reasons for the differences in estimates of global greenhouse gas emissions in 2030 made using INDCs are as follows: (1) The studies interpreted INDCs differently, which is attributable to three reasons: The studies (a) made different assumptions for the unquantifiable INDCs; (b) ignored or used different methods to estimate the emissions not covered by INDCs; and (c) used different amounts of INDCs because the studies were performed at different times. (2) The studies used different databases that include different greenhouse gases, accounting methods and data sources to estimate historical greenhouse gas emissions. (3) The studies used different methods for estimating greenhouse gas emissions and removals related to land use, land-use change and forestry (LULUCF). (4) The studies used different values of the global warming potential. Additionally, the main reasons for the differences in the predictions of the temperature increase at the end of the 21st century based on INDCs are as follows: (1) Differences in the estimations of greenhouse gas emissions in 2030 based on INDCs and (2) different methods of extrapolating global greenhouse gas emissions to 2100. There are three main extrapolation methods: one is to maintain the net present value of the carbon price in 2030 and then extrapolate the greenhouse gas emissions to 2100; another is to maintain the decarbonization rate of a certain period of history and then extrapolate the greenhouse gas emissions to 2100; the third is to match the emissions reduction scenario with the current INDC emissions reduction scenario from the IPCC AR5 scenario database and then use the matching emissions reduction scenario as the current INDC emissions reduction scenario. The use of different methods of extrapolating carbon emissions is one of the main reasons for the differences in the prediction results. (3) Differences in the methods for predicting the effects of greenhouse gas emissions on temperature. Statistical methods and simulation methods are the two main prediction methods; they use different calculation methods, which led to the difference in the prediction results. Therefore, the following points are worth noting: (1) Most importantly, to the extent possible, countries should submit absolute emissions reduction targets as much as possible; nonquantifiable INDCs without detailed methods descriptions and data introductions should not be submitted; (2) authorities should recommend certain data sets that are the most suitable for INDC accounting; (3) a global warming potential should be designated to avoid differences in greenhouse gas estimates due to the use of different criteria; and (4) to the extent possible, future research should adopt simulation methods for predicting the impact of global greenhouse gas emissions on temperature.

Stakeholder-designed scenarios for global food security assessments

To guide policymaking, decision makers require a good understanding of the long-term drivers of food security and their interactions. Scenario analysis is widely considered as the appropriate tool to assess ‘wicked problems’, such as ensuring global food security, that are characterized by a high level of complexity and uncertainty. This paper describes the development process, storylines and drivers of four new global scenarios that are specifically designed to explore global food security up to the year 2050. To ensure the relevance, credibility and legitimacy of the scenarios, they have been developed using a participatory process, involving a diverse group of stakeholders. The scenarios consist of storylines and a scenario database that presents projections for key drivers, which can be used as an input into global simulation models.

Application of tsunami scenario database and numerical simulation to tsunami warning and notification process

Over a decade since 2007, Korea Meteorological Administration (KMA) has constructed and modified tsunami scenario database in order to issue tsunami warning soon after a large offshore earthquake and when tsunami is expected to affect Korean coasts. Based on the scenario tsunami database, KMA has improved tsunami issuance criteria and notification process. Moreover, we have developed numerical simulation process using parallelized code of tsunami modeling program. This will be used to provide additional and detailed tsunami information with better accuracy. On the basis of this achievement, it is needed to improve the system and process to be faster and more accurate. Furthermore, researches on non-typical sources of tsunami and complexity and non-linearity near the coast are needed for the coming decade.

Identification of user requirements for an energy scenario database

Identification of user requirements for an energy scenario database

The Tsunami Scenario Database of the Indonesia Tsunami Early Warning System (InaTEWS): Evolution of the Coverage and the Involved Modeling Approaches

This study reports on recent developments of the Indonesia Tsunami Early Warning System (InaTEWS), specifically the tsunami modeling components used in the system. It is a dual system: firstly, InaTEWS operates a high-resolution scenario database pre-computed with the finite element model TsunAWI; running in parallel, the system also contains a supra real-time modeling component based on the GPU-parallelized linear long-wave model easyWave, capable of dealing with events outside the database coverage. The evolution of the tsunami scenario database over time is covered in the first sections also touching on the involved capacity building efforts. Starting with a coverage of just the Sunda Arc region, the database now includes scenarios for 15 fault zones. The study is augmented by an investigation of warning products used for early warning; the estimated wave height (EWH) and the estimated time of arrival (ETA). These parameters are determined by easyWave and TsunAWI with model specific approaches. Since the numerical setup of the two models is very different, the extent of variations in warning products is investigated for a number of scenarios, where both pure database scenarios and applications to real events are considered. Finally, the performance of the system in past tsunami events is reviewed to point out major system updates.

Study on the Change Characteristics of and Population Exposure to Heatwave Events on the North China Plain

In accordance with the China Meteorological Administration definition, this study considered a weather process with a maximum surface temperature of ≥35°C for more than three consecutive days as a heatwave event. Based on a dataset of daily maximum temperatures from meteorological stations on the North China Plain, including ordinary and national basic/reference surface stations, the intensity-area-duration method was used to analyze the spatiotemporal distribution characteristics of heatwave events on the North China Plain (1961–2017). Moreover, based on demographic data from the Statistical Yearbook and Greenhouse Gas Initiative (GGI) Population Scenario Database of the Austrian Institute for International Applied Systems Analysis (IIASA), population exposure to heatwave events was also studied. The results showed that the frequency, intensity, and area of impact of heatwave events on the North China Plain initially decreased (becoming weaker and less extensive) and then increased (becoming stronger and more extensive). Similarly, the trend of population exposure to heatwave events initially decreased and then increased, and the central position of exposure initially moved southward and then returned northward. Population exposure in the eastern Taihang Mountains was found significantly higher than in the western Taihang Mountains. In relation to the change of population exposure to heatwave events on the North China Plain, the influence of climatic factors was found dominant with an absolute contribution rate of >75%. Except for 2011–2017, increase in population also increased the exposure to heatwaves, particularly in the first half of the study period. Interaction between climatic and population factors generally had less impact on population exposure than either climatic factors or population factors alone. This study demonstrated a method for assessing the impact of heatwave events on population exposure, which could form a scientific basis for the development of government policy regarding adaption to climate change.

Life-Cycle Assessment as a decision-support tool for early phases of urban planning: evaluating applicability through a comparative approach

While ambitious environmental objectives are being set for new constructions in Switzerland, the assessment of urban-scale projects and comparison of their performance to national targets are made possible by a growing number of life-cycle assessment (LCA) tools. However, previous research emphasizes the lack of existing tools to support the decision-making process at the early design stage, characterized by a low level of project details. This paper presents a comparison between three LCA tools. The first, stemming from a research and development project (SETUP), is an exploration tool relying on a database of urban-level scenarios and their environmental performance, able to convert district targets (e.g. 2000-Watt society objectives) into specific sub-targets at the building or component levels. The other two are online LCA tools currently available to practitioners (Sméo and Calculation tool for 2000-Watt-society-sites RH II ), that allow assessing the project and verifying its compliance with a given target. Each tool was applied to a low-carbon case study, the blueFactory district in Fribourg (Switzerland), in two hypothetical contexts corresponding to the schematic and detailed project development phases, characterized by different levels of details. When used for the assessment of a project at a more advanced development stage with a high resolution of detail, findings indicate that Smeo and RH II provide similar environmental performance results. However, in early planning stages, SETUP shows better abilities to support decision-making by providing ranges of results and highlighting uncertainties and the influence of design parameters that have not yet been fixed.

Speeding up tsunami forecasting to boost tsunami warning in Chile

Abstract. Despite the occurrence of several large earthquakes during the last decade, Chile continues to have a great tsunamigenic potential. This arises as a consequence of the large amount of strain accumulated along a subduction zone that runs parallel to its long coast, and a distance from the trench to the coast of no more than 100 km. These conditions make it difficult to implement real-time tsunami forecasting. Chile issues local tsunami warnings based on preliminary estimations of the hypocenter location and magnitude of the seismic sources, combined with a database of pre-computed tsunami scenarios. Finite fault modeling, however, does not provide an estimation of the slip distribution before the first tsunami wave arrival, so all pre-computed tsunami scenarios assume a uniform slip distribution. We implemented a processing scheme that minimizes this time gap by assuming an elliptical slip distribution, thereby not having to wait for the more time-consuming finite fault model computations.We then solve the linear shallow water equations to obtain a rapid estimation of the run-up distribution in the near field. Our results show that, at a certain water depth, our linear method captures most of the complexity of the run-up heights in terms of shape and amplitude when compared with a fully nonlinear tsunami model. In addition, we can estimate the run-up distribution in quasi-real-time as soon as the results of seismic finite fault modeling become available.

Are interactive web-tools for environmental scenario visualization worth the effort? An experimental study on the Swiss electricity supply scenarios 2035

Interactive web-tools are regarded as powerful methods to present large scenario ensembles from environmental modelling in an understandable and engaging way. Yet, there is little empirical evidence whether they are more effective than traditional approaches such as story-and-simulation. We conducted a between-groups experiment, where two groups of participants (total N = 313) differed in the scenario information received: (1) an interactive web-tool for exploring a large database of Swiss electricity scenarios 2035 and their environmental, health, and economic impacts; (2) a website presenting four of these scenarios with storylines. Results indicate that our interactive web-tool did not lead to benefits in self-reported understanding and engagement because there was no difference between the conditions. In fact, participants using the interactive web-tool performed worse in an understanding quiz than those using the four scenarios. These unexpected findings call for more empirical research whether interactive tools for scenario visualization meet the needs of intended users.

Car-following Security Situation Estimation Based on Multi-source Information Fusion

For intelligent and connected vehicles, analyzing driving scenarios to estimate security situation is the primary premise to ensure vehicles safety，especially in car-following scenario. In this paper, an approach for car-following security situation estimation is proposed based on multi-source information fusion, which uses fuzzy inference system as the estimation framework. By analyzing the driver's diversified driving behaviors in longitudinal direction and dynamic inter-vehicle motion characteristics, the desired acceleration, HWT and TTC-1 are selected to evaluate the security situationunder the current running state. In order to make the security situation evaluation results more accurate and rational, a car-following scenario database is built by PreScan software, and a data-driven analysis method and MOS are employed to determine the membership function of each variable. Finally, through simulation experiment, the RMSE of the approach, proposed in this paper, is 0.0813, compared with MOS, and the security situation evaluation approach is verified.

Data-Driven Upscaling of Orientation Kinematics in Suspensions of Rigid Fibres

Describing the orientation state of the particles is often critical in fibre suspension applications. Macroscopic descriptors, the so-called second-order orientation tensor (or moment) leading the way, are often preferred due to their low computational cost. Closure problems however arise when evolution equations for the moments are derived from the orientation distribution functions and the impact of the chosen closure is often unpredictable. In this work, our aim is to provide macroscopic simulations of orientation that are cheap, accurate and closure-free. To this end, we propose an innovative data-based approach to the upscaling of orientation kinematics in the context of fibre suspensions. Since the physics at the microscopic scale can be modelled reasonably enough, the idea is to conduct accurate offline direct numerical simulations at that scale and to extract the corresponding macroscopic descriptors in order to build a database of scenarios. During the online stage, the macroscopic descriptors can then be updated quickly by combining adequately the items from the database instead of relying on an imprecise macroscopic model. This methodology is presented in the well-known case of dilute fibre suspensions (where it can be compared against closure-based macroscopic models) and in the case of suspensions of confined or electrically-charged fibres, for which state-of-the-art closures proved to be inadequate or simply do not exist.

Performance evaluation of relationa embedded databases: an empirical study

Introduction: With the rapid deployment of embedded databases across a wide range of embedded devices such as mobile devices, Internet of Things (IoT) devices, etc., the amount of data generated by such devices is also growing increasingly. For this reason, the performance is considered as a crucial criterion in the process of selecting the most suitable embedded database management system to be used to store/retrieve data of these devices. Currently, many embedded databases are available to be utilized in this context. Materials and Methods: In this paper, four popular open-source relational embedded databases; namely, H2, HSQLDB, Apache Derby, and SQLite have been compared experimentally with each other to evaluate their operational performance in terms of creating database tables, retrieving data, inserting data, updating data, deleting data. Results and Discussion: The experimental results of this paper have been illustrated in Table 4. Conclusions: The experimental results and analysis showed that HSQLDB outperformed other databases in most evaluation scenarios.

Real-time estimation of break sizes during LOCA in nuclear power plants using NARX neural network

Abstract This paper deals with break size estimation of loss of coolant accidents (LOCA) using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Previous studies used static approaches, requiring time-integrated parameters and independent firing algorithms. NARX neural network is able to directly deal with time-dependent signals for dynamic estimation of break sizes in real-time. The case studied is a LOCA in the primary system of Bushehr nuclear power plant (NPP). In this study, number of hidden layers, neurons, feedbacks, inputs, and training duration of transients are selected by performing parametric studies to determine the network architecture with minimum error. The developed NARX neural network is trained by error back propagation algorithm with different break sizes, covering 5%–100% of main coolant pipeline area. This database of LOCA scenarios is developed using RELAP5 thermal-hydraulic code. The results are satisfactory and indicate feasibility of implementing NARX neural network for break size estimation in NPPs. It is able to find a general solution for break size estimation problem in real-time, using a limited number of training data sets. This study has been performed in the framework of a research project, aiming to develop an appropriate accident management support tool for Bushehr NPP.

An Evaluation of Modelled Tsunami Arrival Times

The Joint Australian Tsunami Warning Centre provides forecasts of tsunami arrival times based on the Tsunami Travel Times (TTT) software. In this work, travel times from a pre-computed scenario database and TTT are evaluated through comparison with observed arrival times at tide gauges and tsunameters. It is found that travel times from the scenario database are at least as accurate as those from TTT and in most cases, more accurate. The ‘best’ scenario-based method is Tp20—the time of arrival of the first peak that is at least 20% of the maximum amplitude at that point.

Framework for Offline Flood Inundation Forecasts for Two-Dimensional Hydrodynamic Models

The paper presents a new methodology for hydrodynamic-based flood forecast that focuses on scenario generation and database queries to select appropriate flood inundation maps in real-time. In operational flood forecasting, only discharges are forecasted at specific gauges using hydrological models. Hydrodynamic models, which are required to produce inundation maps, are computationally expensive, hence not feasible for real-time inundation forecasting. In this study, we have used a substantial number of pre-calculated inundation maps that are stored in a database and a methodology to extract the most likely maps in real-time. The method uses real-time discharge forecast at upstream gauge as an input and compares it with the pre-recorded scenarios. The results show satisfactory agreements between offline inundation maps that are retrieved from a pre-recorded database and online maps, which are hindcasted using historical events. Furthermore, this allows an efficient early warning system, thanks to the fast run-time of the proposed offline selection of inundation maps. The framework is validated in the city of Kulmbach in Germany.

Pathways toward zero-carbon electricity required for climate stabilization

This paper provides pathways of the carbon content of electricity extracted from the Intergovernmental Panel on Climate Change’s fifth Assessment Report scenarios database. It demonstrates three policy-relevant aspects of the carbon content of electricity that are implicit in most integrated assessment model results but under-discussed in academia and the policy debate. First, climate stabilization at any level from 1.5 °C to 3 °C requires the carbon content of electricity to decrease quickly and become almost carbon-free before the end of the century. As such, the question for policy makers is not whether to decarbonize electricity but when and how to do so. Second, decarbonization of electricity is still possible and required if some of the key zero-carbon technologies—such as nuclear power or carbon capture and storage—turn out to be unavailable. Third, progressive decarbonization of electricity is part of every country’s cost-effective means of contributing to climate stabilization. The pathways of the carbon content of electricity reported here can be used to benchmark existing decarbonization targets, such as those set by the European Energy Roadmap or inform new policies in other countries. They can also be used to assess the desirable uptake rates of electric and plug-in hybrid vehicles, electric stoves and heat pumps, industrial electric furnaces, or other electrification technologies.

Classification of Tsunami Hazard Area along the Eastern Coast of South Korea based on Improved Tsunami Scenario Database

We classified the areas in the eastern coast of South Korea which are highly likely to be affected by the tsunami that occurred along the northwestern coast of Japan using the improved tsunami scenario database of the Korea Meteorological Administration. To improve the accuracy of database, we used the latest data on bathymetry, coastline, and land information and increased grid resolution for numerical simulation. When tsunami occurred along the northwestern coast of Japan, the maximum wave height appeared in the around Imwon. And the wave height of Gangwon coastal area was higher than those of Gyeongsang. The tsunami height increased as the magnitude of the earthquake increased and decreased as it moved southward along the eastern coast. In conclusion, attention should be paid when an earthquake of magnitude 7.0 or greater occurs in the plate boundary region of the East Sea. And when a tsunami occurs in this region, we need to focus on the area near Imwon. Keywords: Tsunami, Improved Scenario Database, Tsunami Hazard Area, East Sea Coast

Bioenergy in the IPCC Assessments

Estimates of total global climate change mitigation potential of bioenergy have varied between IPCC AR1 to AR5 (including SRREN). Ranges are shown where given (for different carbon prices from AR3 onwards), and the point in the range for AR5 corresponds to the mean potential for BECCS in 2100 for scenarios consistent with a 2°C target from the IPCC AR5 WGIII scenario database

A simulation framework for assessing the market and grid driven value of flexibility options in distribution grids

The increasing share of renewable distributed energy resources (DERs) entails a rising number of congestions in the German distribution grids, such as voltage band violations and overloading of network assets. Consequently the need for grid expansion increases [1]. A politically promoted alternative to grid expansion is managing congestions by the preventive use of flexibility options, such as flexible generation, load management and storage systems [2]. However, preventive congestion management is not yet established in distribution grids. Thus, various measures are under discussion to stimulate the provision of flexibility options by its owners for concerns of congestion management. Assessing the suitability of solutions among these congestion management measures (CMMs) requires a realistic modeling of grid operation, energy system structures and the behavior of the owners of DER and flexibility options.The intended application of the presented simulation framework is consequently the assessment of the macro- and micro-economic effects of preventative CMMs in distribution grids. Supporting that goal, the design of an agent-based simulation framework, which incorporates detailed optimization models for simulating the behavior of grid users (owners of DER and flexibility options) and Distribution System Operators (DSO) is presented. Exemplarily it is applied on a realistic distribution grid use case. The simulation framework enables analyzing the consequences of the grid users’ interaction with the DSO following defined CMMs. Within the framework, the grid users are modeled as individually or market driven oriented users, who both aim at improving their micro-economic situation. The DSO’s operational target is the minimization of congestion management costs under application of several CMMs.The exemplary application focuses on four congestion management concepts, such as flexible tariffs and a market-based flexibility provision. The chosen use case comprises a congested distribution grid with high PV penetration and grid users at the medium and low voltage level. In the use case, the alternatively necessary curtailment of renewable energy can be reduced from 15% to 35% depending on the considered CMM. The most promising CMM (a market-based flexibility provision) reduces the DSO’s congestion management costs by 33%, without degrading the grid users’ economic situation. Future work will advance the analyses based on a comprehensive database of realistic distribution grid scenarios.

Operational Loss Data Collection: A Literature Review

This paper is the first to provide a comprehensive overview of the worldwide operational loss data collection exercises (LDCEs) of internal loss, external loss, scenario analysis and business environment and internal control factors (BEICFs). Based on analyzing operational risk-related articles from 2002 to March 2017 and surveying a large amount of other information, various sources of operational risk data are classified into five types, i.e. individual banks, regulatory authorities, consortia of financial institutions, commercial vendors and researchers. Then by reviewing operational risk databases from these five data sources, we summarized and described 32 internal databases, 26 external databases, 7 scenario databases and 1 BEICFs database. We also find that compared with developing countries, developed countries have performed relatively better in operational risk LDCEs. Besides, the two subjective data elements of scenario analysis and BEICFs are less used than the two objective data elements of internal and external loss data in operational risk estimation.