Global Greenhosue Gases Emissions Effect on Extreme Events under an Uncertain Future: A Case Study in Western Cape, South Africa
The growing effect of CO2 and other greenhouse gas (GHG) emissions on the extreme climate risks in the Western Cape, South Africa, calls for the need for better climate adaptation and emissions-reduction strategies to protect the region’s long-term social-economic benefits. This paper presents a comprehensive evaluation of changes in the future extreme events associated with drought and heatwave under three different greenhouse gas (GHG) emissions scenarios: Representative Concentration Pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, from moderate to severe, respectively. Various diagnostic indices were used to determine how future heatwaves and drought will respond to each different RCP climate scenario in Western Cape based on Max Planck Institute-Earth System Model/REMO (MPI-ESM/REMO). The projected simulation results revealed that drought and heatwave extreme climate indices suggest strong relationships between future extreme climate risks and GHG emissions for Western Cape, South Africa. Anthropogenic activities and growing GHG emissions will lead to severer extreme climate stress in terms of drought and the duration, frequency, and magnitude of heatwave stresses. As a result, we believe that reducing the GHG emissions to alleviate future extreme climate stress becomes a practical solution to protect the local’s socio-economic system and further maintain the region’s economic prosperity.
Bowen He, Ke J. Ding, 2023
PDF
Bowen He, Ke J. Ding, 2023
Nashville Wedge Analysis: An Interactive web-based Sustainability Decision Support Tool using Rshiny Framework
A detailed summary of the Nashville city's emissions analysis was published in Rmarkdown, a format that allows for both transparency and reproducibility in the analysis. The report consists of two parts: the analysis of Nashville’s current GHG emissions and a series of potential GHG reduction actions. The first part of this report highlights the Nashville’s current GHG emissions, including the largest sources of emissions and how these emissions were calculated. The second part of this report analyzes the impacts of a variety of possible GHG reduction actions, providing how these actions were calculated to reduce the current GHG emissions and insights into which actions should be prioritized. This allows readers (both policymakers and the public) to understand Nashville’s current GHG emissions as well as different potential behavior wedge options for reaching the city’s 80% reduction target by the year of 2050.
The benefit of the Rmarkdown report format is that it allows for the direct integration of data, analysis, and text in the same document. Therefore, all analysis conducted could be entirely transparent and reproducible. Due to this format, the report is also highly understandable and flexible. Data is integrated into the report, such that any future changes to the data or analysis will propagate into the report. Text serves to help readers understand how the analysis code works such that readers can understand how data is integrated, manipulated and calculated.
Summary: Interactive Application (Interactive web-based Rshiny Framework)
The interactive application was online and open to the public. The application allows users to understand the impacts of different policies on GHG emissions directly, as well as explore interactions between policies. The application was built using the “RShiny” tool which, similar to Rmarkdown, allowing for transparency and flexibility by directly integrating the available data with the application. Users are able to interact with the app to explore the impact of different policies and their combinations on the emissions.
The application was designed in such a way that users may select policies to implement, rates of implementation, and scales of implementation, and directly visualize how GHG emissions change under those different policies combination scenarios. Users may explore interactions between policies by selecting to implement multiple policies at the same time and viewing the impacts on the GHG emissions. This web-based visualization tool is valuable for educating the public on the implications of different policy measures and how the combination of those policies can reduce the emissions. Also, this web-based visualization tool is valuable for decision-makers to make data-driven scientific-based climate resilience policies in a transparent and straightforward way.
Key contributions:
- Understand current GHG sources and future opportunities. The work, both in the form of the detailed Rmarkdown report and the interactive web-based Rshiny application increases policymakers’ and the public’s understanding of the Nashville’s current GHG emissions and future reduction opportunities. The detailed analysis includes recommendations of wedge actions that should be prioritized in order to achieve Nashville’s 80% emissions reduction target by 2050.
- Explore the impacts of various GHG reduction actions. In addition to recommendations from the analysis, the web-based Rshiny application allows anyone to interactively explore pathways towards reducing emissions. The web-based Rshiny application provides the public with a visualization tool to visualize and investigate the implications of proposed actions, including interactions between multiple actions, on their own.
- Increase data transparency and flexibility. As all of the products were built using open-source software and best practices in research reproducibility, the analysis and data are completely transparent. The tools utilized, including Rmarkdown and Rshiny, allow for direct integration of data, analysis, and text, which also ensure flexibility, transparency and adaptability.
Bowen He, Jonathan Gilligan, 2023
RShiny app
Dasymetric Mapping: A Hierarchical Poisson Spatial Disaggregation Regression Model (HPSDRM) to Incorporate Spatial Autocorrelation
The growing demand for spatially detailed population products in various fields continues to rise. It has resulted in a shift in focus from aggregated areal totals to high-resolution grid estimates. The population data is measured through aggregate-level statistics, which mask fine-scale heterogeneities within areas. This paper develops a geo-spatial model for the disaggregation of the areal population to high-resolution grids while the pycnophylactic property is reserved. We propose a hierarchical Poisson Spatial Disaggregation Regression Model (HPSDRM) with a log link to incorporate land cover covariates and two levels of spatial autocorrelation in the linear predictor. The Bayesian model is fitted using the TMB approach. We demonstrate the predictive ability of the model using simulation studies. The proposed HPSDRM is applied to the Davidson County, Nashville population tract data to disaggregate the tract population to finer grids population with a 150m * 150m resolution. The predicted grid population map successfully reveals the heterogeneity as well as hotspots and cold spots of the population distribution within the tracts. Compared with the other three dasymetric mapping models’ interpolation results, the HPSDRM results obtained indicate obvious improvement regarding the predictive ability, suggesting spatial autocorrelation is indispensable in conducting the spatial disaggregation task. The proposed HPSDRM is expected to be readily applied to various disaggregation schemes, including other socioeconomic indicators.
Bowen He, Jonathan Gilligan, Janey Camp, 2023
PDF
Bowen He, Jonathan Gilligan, Janey Camp, 2023
A Social Fabric Index Model for Disaster Mitigation Policy Assessment
Socio-environmental index models have gained increasing attention as a tool to measure vulnerability, resilience, and sustainability to natural hazards mitigation planning process. Numerous index models have been developed, yet we still lack a design to measure a community’s social fabric. We proposed an indicator-based social fabric index model, aiming to quantitively measure a community’s social fabric status across space. Meanwhile, we also investigate the methods of social fabric index construction design, examining uncertainty and sensitivity analysis regarding the model decisions related to each model construction stage, including indicator transformation, normalization, PCA selection and rotation, and weighting scheme. The coefficient of variance and confidence interval are computed to indicate the uncertainty for the inductive social fabric index, suggesting that index precision increases with social fabric status. Global sensitivity analysis results indicate that for an inductive SoFI model, transformation and weighting scheme play a critical role in contributing to the total output uncertainty. Finally, we argue the importance of the emotional and psychological effects of the social fabric concept and provide recommendations and future work direction so that the next generation of social fabric index model can be developed with greater integration, transparency, confidence, and robustness.
Bowen He, Jonathan Gilligan, Janey Camp, 2023
PDF
Bowen He, Jonathan Gilligan, Janey Camp, 2023
Investigating Extreme Weather Events under an Uncertain Future: a Case Study in Western Cape, South Africa
The growing effect of CO2 and other greenhouse-gas (GHG) emissions on the extreme climate risks in the Western Cape, South Africa, calls for the need for better climate adaptation and emissions-reduction strategies to protect region’s long-term social-economic benefits. This paper presents a comprehensive evaluation of changes in the future extreme events associated with drought and heatwave under three different greenhouse gases (GHG) emissions scenarios: Representative Concentration Pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, from moderate to severe, respectively. A variety of diagnostic indices are used to determine how future heatwaves and drought will respond to each different RCP climate scenario in Western Cape based on Max Planck Institute-Earth System Model/REMO (MPI-ESM/REMO). The projected simulation results reveal that both drought-related and heatwave-related extreme climate indices suggest very strong relationships between the future extreme climate risks and the GHG emissions for Western Cape, South Africa. Anthropogenic activities and growing GHG emissions will lead to severer extreme climate stress in terms of drought as well as duration, frequency, and magnitude of heatwave stresses. As a result, we believe that reducing the GHG emissions to alleviate future extreme climate stress becomes a practical solution to protect the local’s socio-economic system and further maintain the region’s economic prosperity.
Bowen He, Ke J. Ding, 2023
PDF
Bowen He, Ke J. Ding, 2023
A Risk and Decision Analysis Framework for Evaluating PM2.5 Emissions: a Case Study in Long Beach, LA.
This study examines the L.A.-Long Beach Metro area concerning the future risk of the PM2.5 concentration increase. Population expansion, economic growth, and temperature increase are incorporated to estimate the probability of the magnitude of PM2.5 emission increase. Three possible sectors for the reduction of PM2.5 emissions are considered: ocean-going vessels, refineries, and electricity-generating units. The decision of how best to allocate emissions-reduction efforts among these three sectors is analyzed using a quantitative and qualitative decision-analysis framework. For quantitative analysis, Expected Monetary Value (EMV) and Expected Utility (EU) methods are used to select the optimal sector to invest in. Based on the EMV calculation, the refineries sector is 3.5 times and 6.4 times more worthy of investment compared to the electricity-generating units and the ocean-going vessels sector, respectively. For the qualitative analysis, three criteria (investment efficiency, implementation difficulty, time to become effective) are considered in the decision-making process and sensitivity analysis is conducted to inform the ocean-going vessel sector is the optimal alternative for all possible scenarios. The refineries sector is more preferred than the electricity-generating units sector when the implementation difficulty’s weight is smaller than 50%. This study provides a valuable risk and decision analysis framework for analyzing the air pollution problem associated with the future PM2.5 concentration increase caused by three risk factors: population growth, economic growth, and climate change.
Bowen He, Qun Guan, IJERPH, 2021
PDF
Bowen He, Qun Guan, IJERPH, 2021
Localize GHG Emissions Effects under an Uncertain Future: a Case Study in Western Cape, South Africa
The growing impact of CO2 and other greenhouse-gas (GHG) emissions on the socio-climate system in the Western Cape, South Africa, urgently calls for the need for better climate adaptation and emissions-reduction strategies. While the consensus has been that there is a strong correlation between CO2 emissions and the global climate system, few studies on climate change in the Western Cape have quantified the impact of climate change on local climate metrics such as precipitation and evaporation under different future climate scenarios. The present study investigates three different CO2 emissions scenarios: Representative Concentration Pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, from moderate to severe, respectively. Specifically, we used climate metrics including precipitation, daily mean and maximum near-surface air temperature, and evaporation to evaluate the future climate in Western Cape under each different RCP climate scenario. The projected simulation results reveal that temperature-related metrics are more sensitive to CO2 emissions than water-related metrics. Districts closer to the south coast are more resilient to severer GHG emissions scenarios compared to inland areas regarding temperature and rainfall; however, coastal regions are more likely to suffer from severe droughts such as the “Day-Zero” water crisis. As a result, a robust drying signal across the Western Cape region is likely to be seen in the second half of the 21st century, especially under the scenario of RCP 8.5 (business as usual) without efficient emissions reduction policies.
Bowen He, Ke J. Ding, Earth, 2021
PDF
Bowen He, Ke J. Ding, Earth, 2021
A Mathematical Approach to Improving the Representation of SW-GW Interactions: Spectral Analysis Incorporated FFT
It is well known that land surface topography governs surface–groundwater interactions under some circumstances and can be separated in a Fourier-series spectrum that provides an exact analytical solution of both the surface and the underlying three-dimensional groundwater flows. We evaluate the performance of the current Fourier fitting process by testing on different scenarios of synthetic surfaces. We identify a technical gap and propose a new version of the approach which incorporates the spectral analysis method to help identify the statistically significant frequencies of the surface to guide the refinement and mesh. Our results show that spectral analysis is the method that can help improve the accuracy of representing the surface, thus further improving the accuracy of predicting the bedform-driven hyporheic exchange flows.
Bowen He, Qun Guan, JWCC, 2020
PDF
Bowen He, Qun Guan, JWCC, 2020
The Statistical Analysis and Prediction Associated with Nuclear Meltdown Accidents Risk Evaluation
The relevant safety property associated with nuclear meltdown is evaluated from both reactors’ internal and external factors using 3 statistical models: logistic regression model, linear discriminant model, and support vector machines (SVM). For each statistical model, the relevant factors that affect the nuclear reactors and probability of nuclear meltdown are evaluated by mathematical statistical analytics. Through the study, the phenomena are found that external factors have the trend to overwhelm inner factors and play a dominate role in the accident. The model analysis and their prediction results presented here could potentially provide nuclear engineers and relevant decision-makers with suggestions on selecting appropriate locations, designs and relevant construction and operation strategy for nuclear reactors from a statistical perspective.
Bowen He, Qun Guan, IJNSS, 2022
PDF
Bowen He, Qun Guan, IJNSS, 2022
Geant4 Simulation of Proton Displacement Damage in GaN
The transport of proton in GaN was simulated with the Geant4. The information of the type and the energy of the PKA(Primary Knock-on Atoms) created in GaN and the number of displacement damage were calculated with protons with energy of 1MeV, 10MeV, 100MeV, 500MeV. The distribution of displacement damage of 10 MeV proton was calculated. The NIEL(Non-Ionization Energy Loss) was studied and calculated of those 4 energy proton irradiating GaN and the factors impacting the production of displacement damage were deliberated.The study of radiation resistant of GaN was achieved.
Bowen He, Chaohui He, Shuaishuai Shen, Yuanmiaoliang Chen, AST, 2017