IPCC's current conceptualization of 'vulnerability' needs more clarification for climate change vulnerability assessments

Vulnerability assessment of heat waves within a risk framework using artificial intelligence

Current efforts to adapt to climate change are not sufficient to reduce projected impacts. Vulnerability assessments are essential to allocate resources where they are needed most. However, current assessments that use principal component analysis suffer from multiple shortcomings and are hard to translate into concrete actions. To address these issues, this article proposes a novel data-driven vulnerability assessment within a risk framework. The framework is based on the definitions from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, but some definitions, such as sensitivity and adaptive capacity, are clarified. Heat waves that occurred between 2001 and 2018 in Quebec (Canada) are used to validate the framework. The studied impact is the daily mortality rates per cooling degree-days (CDD) region. A vulnerability map is produced to identify the distributions of summer mortality rates in aggregate dissemination areas within each CDD region. Socioeconomic and environmental variables are used to calculate impact and vulnerability. We compared abilities of AutoGluon (an AutoML framework), Gaussian process, and deep Gaussian process to model the impact and vulnerability. We offer advice on how to avoid common pitfalls with artificial intelligence and machine-learning algorithms. Gaussian process is a promising approach for supporting the proposed framework. SHAP values provide an explanation for the model results and are consistent with current knowledge of vulnerability. Recommendations are made to implement the proposed framework quantitatively or qualitatively.

A novel climate and health decision support platform: Approach, outputs, and policy considerations

BACKGROUND

The adverse health impacts of climate change are increasingly apparent and the need for adaptation activities is pressing. Risks, drivers, and decision contexts vary significantly by location, and high-resolution, place-based information is needed to support decision analysis and risk reduction efforts at scale.

METHODS

Using the Intergovernmental Panel on Climate Change (IPCC) risk framework, we developed a causal pathway linking heat with a composite outcome of heat-related morbidity and mortality. We used an existing systematic literature review to identify variables for inclusion and the authors' expert judgment to determine variable combinations in a hierarchical model. We parameterized the model for Washington state using observational (1991-2020 and June 2021 extreme heat event) and scenario-driven temperature projections (2036-2065), compared outputs against relevant existing indices, and analyzed sensitivity to model structure and variable parameterization. We used descriptive statistics, maps, visualizations and correlation analyses to present results.

RESULTS

The Climate and Health Risk Tool (CHaRT) heat risk model contains 25 primary hazard, exposure, and vulnerability variables and multiple levels of variable combinations. The model estimates population-weighted and unweighted heat health risk for selected periods and displays estimates on an online visualization platform. Population-weighted risk is historically moderate and primarily limited by hazard, increasing significantly during extreme heat events. Unweighted risk is helpful in identifying lower population areas that have high vulnerability and hazard. Model vulnerability correlate well with existing vulnerability and environmental justice indices.

DISCUSSION

The tool provides location-specific insights into risk drivers and prioritization of risk reduction interventions including population-specific behavioral interventions and built environment modifications. Insights from causal pathways linking climate-sensitive hazards and adverse health impacts can be used to generate hazard-specific models to support adaptation planning.

Has the IPCC's revised vulnerability concept been well adopted?

In the Third and Fourth Assessment Reports (TAR and AR4, respectively) by the Intergovernmental Panel on Climate Change (IPCC), vulnerability is conceived as a function of exposure, sensitivity, and adaptive capacity. However, in its Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) and Fifth Assessment Report (AR5), the IPCC redefined and separated exposure, and it reconceptualized vulnerability to be a function of sensitivity and capacity to cope and adapt. In this review, we found that the IPCC's revised vulnerability concept has not been well adopted and that researchers' preference, possible misinterpretation, possible confusion, and possible unawareness are among the possible technical and practical reasons. Among the issues that need further clarification from the IPCC is whether or not such a reconceptualization of vulnerability in the SREX/AR5 necessarily implies nullification of the TAR/AR4 vulnerability concept as far as the IPCC is concerned.

Evaluation of Drought Vulnerability of Maize and Influencing Factors in Songliao Plain Based on the SE-DEA-Tobit Model

Rain-fed agriculture is easily affected by meteorological disasters, especially drought. As an important factor of risk formation, actively carrying out agricultural drought vulnerability assessments is conducive to improving food security and reducing economic losses. In this study, an SE-DEA model with regional exposure and drought risk as input factors and the maize yield reduction rate and drought-affected area as output factors is established. The aim is to evaluate and zone the drought vulnerability of the maize belt in the Songliao Plain. The results show the following: (1) From 2000 to 2019, the drought vulnerability of maize showed a fluctuating increasing trend. The vulnerability in Harbin and central Jilin Province is high, which is extremely unfavorable for maize production. (2) Comparing the historical disaster data with the drought vulnerability map generated using the SE-DEA model, it could be found that the results obtained using the SE-DEA model are reliable. (3) The Tobit model shows that the proportion of the effective irrigated area is more important to alleviate vulnerability. For drought vulnerability zoning using a cluster analysis, we suggest that regulated deficit irrigation should be actively developed in high-vulnerability areas to ensure maize yield while improving water efficiency. The results of this study can provide a basis for the development of drought mitigation and loss reduction strategies, and they provide new ideas for future research.

Land Surface Snow Phenology Based on an Improved Downscaling Method in the Southern Gansu Plateau, China

Snow is involved in and influences water–energy processes at multiple scales. Studies on land surface snow phenology are an important part of cryosphere science and are a hot spot in the hydrological community. In this study, we improved a statistical downscaling method by introducing a spatial probability distribution function to obtain regional snow depth data with higher spatial resolution. Based on this, the southern Gansu Plateau (SGP), an important water source region in the upper reaches of the Yellow River, was taken as a study area to quantify regional land surface snow phenology variation, together with a discussion of their responses to land surface terrain and local climate, during the period from 2003 to 2018. The results revealed that the improved downscaling method was satisfactory for snow depth data reprocessing according to comparisons with gauge-based data. The downscaled snow depth data were used to conduct spatial analysis and it was found that snow depth was on average larger and maintained longer in areas with higher altitudes, varying and decreasing with a shortened persistence time. Snow was also found more on steeper terrain, although it was indistinguishable among various aspects. The former is mostly located at high altitudes in the SGP, where lower temperatures and higher precipitation provide favorable conditions for snow accumulation. Climatically, factors such as precipitation, solar radiation, and air temperature had significantly singular effectiveness on land surface snow phenology. Precipitation was positively correlated with snow accumulation and maintenance, while solar radiation and air temperature functioned negatively. Comparatively, the quantity of snow was more sensitive to solar radiation, while its persistence was more sensitive to air temperature, especially extremely low temperatures. This study presents an example of data and methods to analyze regional land surface snow phenology dynamics, and the results may provide references for better understanding water formation, distribution, and evolution in alpine water source areas.

Understanding Household Vulnerability and Relative Poverty in Forestry Transition: A Study on Forestry-Worker Families in China’s Greater Khingan Mountains State-Owned Forest Region

China’s logging ban policy has profoundly transformed the forestry industry, creating substantial impacts for forestry-worker households. Empirical evidence is needed to examine whether and how severe the transition policy produces vulnerability and relative poverty for the affected households. This paper samples forestry-worker households from the Greater Khingan Mountains state-owned forest region as the study subjects to evaluate their household vulnerabilities with the LVI approach proposed by IPCC. This study computes the relative poverty scores of the families and further identifies the relationships between the selected household’s socio-economic factors and the estimated vulnerability and poverty scores with OLS regression. This study derives a sleuth of valuable points. (1) The majority of the forestry-worker households have had to suffer downsized, vulnerable livelihoods with escalated relative poverty due to the policy’s impact. (2) Poverty closely connects with vulnerability, where the more impoverished the household, the more vulnerable it is. Poverty is, however, not the only factor affecting household vulnerability. Other factors, particularly of public and social-financial factors, are interconnected with poverty and, thus, compound the vulnerability issue. (3) Factors, e.g., family size, age and gender of household head, bank deposits, and life and job satisfaction, which are typically very beneficial for improving family vulnerability and poverty. (4) Regular financial assistance programs, particularly the commonly-trusted one-off household compensation program, might not provide solid support for addressing long-term household vulnerability and poverty. This study contributes to a broader understanding of household vulnerability and relative poverty for improved policy and program development addressing forestry workers and household vulnerabilities.