Moldova

This section presents the compounded risk categorization (0-4) of temperature-based heat + population or temperature and humidity-based heat + population, enabling users to understand where and when risks may occur. Heat risk to population is presented spatially and seasonally. Individual elements contributing to the compound risk (i.e., heat conditions and population) are presented separately in the following sections.

01 - Overall Risk Categorization

What you can see in this figure
The map shows the level of risk when combining temperature (hazard) and population (exposure), for different periods into the future and different warming scenarios.  Risk Categorizations are calculated to account for both climate conditions (in this case temperature-based) and high population densities (high exposure to harsh climate conditions). For example, areas with extreme heat but no population are considered less ‘risky’ for a country, than areas with only high to very high heat conditions but with high population density. 

Calculation: If the ensemble climatology number of days for any month was >0.5 days, then the threshold was considered passed. The category was then established relative to the highest threshold (hd: 30, 35, 40 or 45; tr: 20, 23, 26, 29; hi: 35, 37, 39, 41) leading to category (0, 1, 2, 3 or 4). Using the matrix in the color bar: 0-4 (Low-Extreme) Heat in vertical, 0-4 (Low-Extreme) Population in horizontal.

Use the slider on the bottom left of the map to analyze the range in projections using the 10th, 50th (median), or 90th percentile of the multi-model ensemble.

Understanding the Data: Implications and Utility
Spatial presentations of risk categorizations enable users to understand where areas of higher-risk are located, and when the risk is projected to be significant. Notice how heat risk expand into new regions, especially later in the century for higher emission pathways. 

What you can see in this figure
The circle graph presents the risk categorization shown in the map above for each month, for a given region (country or subnational district, as selected above). 

Understanding the Data: Implications and Utility
Understanding the seasonality of risk is key for adaptation and emergency preparation. Notice how extreme heat is usually maximum during summer and during peak humidity season, and how the risk is projected to expand into the historical . 

02 - Extreme Heat Conditions

Capturing ‘heat risk’ in a comprehensive way requires looking across a range of temperature and humidity related conditions that may occur over a 24-hour period, a season, or year. We present multi-threshold metrics for day-time maximum temperatures, nighttime minimum temperatures, and a combined heat index (a measure of air temperature and humidity) as a baseline to evaluate changing and intensifying heat risk conditions for an area. 

What you can see in this figure
This map shows the number of days (Tmax) nights (Tmin) or days with heat index (measuring heat and humidity combined) which surpass different thresholds for the selected SSP and time period. 

Use the slider on the bottom left of the map to analyze the range in projections using the 10th, 50th (median), or 90th percentile of the multi-model ensemble.

Understanding the Data: Implications and Utility
Knowing where extreme heat is likely to occur is important for maximizing resource allocation. Key is to understand where and when extreme heat conditions are more likely to occur.

What you can see in this figure
The graph presents the multi-model ensemble seasonal cycle of the heat variable and its threshold selected from the above dropdown menu (for a given period and scenario). You can ‘turn-on’ additional thresholds to explore the heat magnitude, or analyze how the seasonality of multiple risk thresholds will increase over time and under higher emission scenarios. 

Understanding the Data: Implications and Utility
The seasonal cycle allows you to understand when in the year specific threshold conditions are more likely to be surpassed. The duration of a “heat season” as represented by successive months with high counts of heat above specific thresholds is likely to increase in future decades, and particularly for higher emission pathways. This knowledge is crucial for adaptation preparation.

What you can see in this figure
The map presents a combined annual maximum heat categorization (0-4). Each category reflects the occurrence of days above the four respective thresholds for each of the heat variables (daily maximum temperature: 30C, 35C, 40C, and 45C; nighttime minimum temperature: 20C, 23C, 26C, and 29C; and heat index: 35C, 37C, 39C, and 41C). If at least 0.5 day surpassed the highest threshold, then the highest category is given, reflecting that “at minimum one day of the year experienced the highest heat level”. Risk Factor Categorization: 0-4 represents Low - Extreme Risk.

Use the slider on the bottom left of the map to analyze the range in projections using the 10th, 50th, or 90th percentile of the multi-model ensemble.

Understanding the Data: Implications and Utility
Extreme heat, particularly when combined with high humidity and persistent through the night, significantly impacts various sectors. 

  • Human health faces increased risks of heat-related illnesses and mortality, compounded by disrupted sleep.
  • In agriculture, it damages crops, reduces yields, and stresses livestock.
  • Labor productivity declines across all industries, especially for outdoor work.
  • The energy and utilities sector experiences strained grids and potential outages due to increased demand for cooling and reduced power generation efficiency.
  • Transportation and infrastructure can suffer damage like buckling roads and warped rail lines.
  • Urban heat island effects exacerbate these challenges, creating a cascading negative impact across the economy and environment.

What you can see in this figure
The Heatplot shows the same approach to categorization as the map above, only to offer more detail by forming categories (surpassing up to 4 thresholds) at the monthly level. To include a time perspective, the highest categories in 10-year intervals are shown for each month to reflect evolution of heat periods from 1950 to 2100. 

Understanding the Data: Implications and Utility
Seasons surpassing a particular threshold are projected to expand in time especially for high-emissions scenarios and higher categories might emerge in the core heat season.

03 - Population and Poverty Dynamics

This section explores the socioeconomic backdrop against which one needs to later assess heat risks. Presented are: population (density: persons/ km2 and counts) and poverty classifications. Understanding where populations are located, and what their relative level of poverty is can aid decision-makers in identifying key areas of need. 

Past to present population and poverty data largely reflect census and survey-based outcomes (roughly up to 2010 in the presentations here). Future projections were crafted in association with the formulation of societal development narratives under the Shared Socioeconomic Pathways (SSPs). The goal of the SSPs is to depict a range of plausible societal futures where different technological, political and environmental trajectories are described. Within each of these storylines, a trajectory of demographic changes is generated, which then, based on an assumption of technologies, lead to likely emissions patterns to reflect that pathway. From these emission lines, a suite of most representative likely radiative forcing levels at the end of the 21st century are then selected to provide the input to climate models. The SSPs reflect the most advanced iteration of socioeconomic narratives offered to date. They consider societal factors such as demographics, human development, economic growth, inequality, governance, technological change and policy orientations. While most factors are given as narratives that sketch broad patterns of change globally and for large world regions, a subset (population1 , GDP, urbanization and educational attainment) are provided as quantitative, country-specific projections. These variables were chosen based on their common use as inputs to emissions or impact models and their relationships to each other. See O’Neill et al. 2017 for more information on scenarios and scenario development. Data presented below depicts population growth, poverty scales, age and sex classifications per each SSP.

What you can see in this figure
Population data as gridded product or at sub-national level is presented as either population count or population density (persons/km2) for “present” (~2010-2015) and as projected in the SSPs for selected time intervals. Note, the population density is calculated based on the grid size and grid contribution to a sub-national polygon. These calculations might suffer from lack of precision for small spatial entities (particularly small islands). 

Thresholds per grid-cell or aggregation area were set by population count or density (count: 1’000, 10’000, 100’000, 1’000’000; density: 1, 10, 100, 1’000).

 

Data for the Historical Reference Period (1995-2014) is derived from:
Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://sedac.ciesin.columbia.edu/data/collection/gpw-v4 

Projection data is derived from: 
Jones, B., and B. C. O'Neill. 2020. Global One-Eighth Degree Population Base Year and Projection Grids Based on the Shared Socioeconomic Pathways, Revision 01. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/m30p-j498. as a advancement from: Jones, B., and B. C. O'Neill. 2016. Spatially Explicit Global Population Scenarios Consistent with the Shared Socioeconomic Pathways. Environmental Research Letters, 11 (2016): 084003.
https://doi.org/10.1088/1748-9326/11/8/084003 

What you can see in this figure
This map presents the current spatial distribution of poverty as a percent of population below a given poverty classification: $1.90, $3.20, $5.50/day, as per World Bank definitions. Use the slider to change the level for poverty classification thresholds.

Data is derived from World Bank Data Catalog: International Poverty Line - Global Subnational Poverty Atlas GSPA https://datacatalog.worldbank.org/search/dataset/0042041 

What you can see in this figure
The figure shows the projected evolution of the given country population throughout the 21st century, for each scenario. The anchor for the scenarios is the “present” level at around 2010. The estimated total population size as well as the age and sex structure (see Age Pyramid graph below) for each country in this year is taken from the UN estimates and projections (the 2010 assessment). Age- and sex- specific proportions are taken from the IIASA database of human capital reconstruction and projections. The method used for carrying out projections by age, sex and educational attainment level is a generalization of the standard cohort-component method of population projections. This standard method is based on the fact that the age group a in year t will be a+x in year t+x (it is the same birth cohort, i.e. group of people born in the same year) after adjusting for the effects of mortality and migration and applying fertility rates to derive the number of births (the three components of population change). 

Understanding the Data: Implications and Utility
Recognizing the assumptions of future population growth along different SSPs is important to judge potential future risks. Note the variations in the rate of change and the timing of peak population, which differ depending on the scenario.

 

For greater detail on the data presented and assumptions of model parameters, see: 
Samir, K. C., & Lutz, W. (2014). The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Global Environmental Change, 378. https://doi.org/10.1016/j.gloenvcha.2014.06.004 issn:09593780 

What you can see in this figure
The figure shows the projected evolution of the given country poverty level throughout the 21st century, for each scenario.

 

Future poverty classifications for each country are aligned with SSP pathways (See O’Neill et al. 2017 for greater detail). The SSPs, apart form SSP3, assume a decline in global poverty conditions. 

Data is derived from:
Rao, ND, P. Sauer, M. Gidden, K. Riahi, Income inequality projections for the Shared Socioeconomic Pathways, Futures. doi https://doi.org/10.1016/j.futures.2018.07.001 

What you can see in this figure
Population pyramid by sex for a given period, country, scenario. 

Understanding the Data: Implications and Utility
Vulnerability to heat and extreme heat conditions can be exacerbated by age: the very young, the very old. However, different age groups – depending stage of life, employment conditions or health challenges can also be at high risk. Examples include: outdoor laborers, i.e. construction, agricultural workers, school age children, or people with cardiovascular issues.

Data is derived from:
Samir, K. C., & Lutz, W. (2014). The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Global Environmental Change, 378. https://doi.org/10.1016/j.gloenvcha.2014.06.004 issn:09593780 

The starting year for these scenario projections is 2010. The estimated total population size as well as the age and sex structure for each country in this year is taken from the UN estimates and projections (the 2010 assessment). Age- and sex- specific proportions are taken from the IIASA data base of human capital reconstruction and projections. The method used for carrying out projections by age, sex and educational attainment level is a generalization of the standard cohort-component method of population projections. This standard method is based on the fact that the age group a in year t will be a+x in year t+x (it is the same birth cohort, i.e. group of people born in the same year) after adjusting for the effects of mortality and migration and applying fertility rates to derive the number of births (the three components of population change).