Based on the comprehensive consideration of the fitting speed and accuracy of the model, the parameters of the random forest model were tested. Finally, we built a total of 100 decision trees. In each decision tree, 90% of the samples were randomly selected to build the sample model, and the remaining 10% were used as test data. The simulation results show that the sample accuracy was 0.94 and the test sample accuracy was 0.81. The overall sample accuracy was relatively high, which can well explain the influence of various factors on the industrial value added, so it can be used for the simulation and prediction of industrial added value.

Through the statistics of the proportion of industrial value added in GDP (from the World Bank) and the GDP data under the Shared Socioeconomic Pathways (SSPs), the industrial added value of each country under different SSP scenarios in the future was obtained. According to the estimated proportion of industrial value added of each country and the proportion of industrial value added of each country under different SSP scenarios in the future, the regression model was constructed. Finally, the distribution of industrial added value under SSP1, SSP2, and SSP3 scenarios in 2030 and 2050 was obtained.

### 3 Results

The industrial value added under different future climate change scenarios was calculated by continent. Then we derived the statistical value of industrial value added of each continent under SSP1, SSP2, and SSP3 scenarios in 2010, 2030, and 2050 (Fig. 2). Overall, in the future, the industrial value added will increase obviously. Compared with other regions, Asia has the largest industrial value added, followed by North America, Europe, Africa, and South America, and Oceania has the smallest industrial value added. The industrial value added of SSP1 was the biggest, followed by SSP2, and the smallest was SSP3.

Fig. 2 Industrial value added under the Shared Socioeconomic Pathway (SSP) 1, SSP2, and SSP3 scenarios in 2030 and 2050

### References

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Mapping Global Road Networks

Wenxiang Wu and Lingyun Hou

### 1 Introduction

Various studies have put forward different methods for reasonably determining the development scale of regional road networks according to the economic development and traffic conditions of each country, and formed some conventional methods for projecting the future scale of road networks, such as land coefficient method, elastic coefficient method, time series method, connectivity analysis method, and generalized cost method. Most of the methods are based on the analysis of the existing data to derive the development pattern so as to determine the road network scale in a future year. The factors related to road network scale are land use type, economic development, and population growth. The object of this study is the global road system. In view of the large spatial scale of the research object, we selected the land coefficient method that is suitable for large-scale projection for future global road network scale.

In order to evaluate the response of the global road system to climate change and accurately explain the current global road spatial distribution characteristics, we spatially processed the current global road length data (2010) and produced the global road length raster data with 0.5° resolution (i.e., roads in kilometers per 0.5° 0.5° grid).

Authors: Wenxiang Wu, Lingyun Hou Map Designers: Qingyuan Ma, Jing'ai Wang, Ying Wang Language Editor: Wenxiang Wu, Lingyun Hou

W. Wu (&) L. Hou

In order to accurately assess the impact of climate change on the road system in the future, we used the collected data of total road length, population, area, and per capita GDP of countries over the years to project the global road lengths under different Shared Socioeconomic Pathways (SSPs) in 2030 and 2050 by using the land coefficient method and finally obtained the global road length data with 0.5° resolution under different SSPs in 2030 and 2050.

The dataset is about the global road network distribution, which is represented by the road length index, that is, the total length of roads in an area. It covers 166 countries/regions, reflecting the detailed spatial distribution of current and future roads in the world and improving the resolution from the national scale of the current projected global road data to 0.5°.

### 2 Data

In this study, the global road spatial distribution vector data were used to spatialize the current global roads; the historical road mileage, population, per capita GDP, and land area of various countries were used to fit the national land coefficient model, and the future global road spatial distribution was simulated and calculated by combining the population and economic data predicted by SSPs.

### 2.1 Global Road Distribution Data

We compared the global road dataset published by the Center for International Earth Science Information Network of Columbia University (https://sedac.ciesin.columbia.edu), DIVA-GIS road data system hosted by Robert J. Hijmans (http://dwww.diva-gis.org), and OpenStreetMap (https:// www.openstreetmap.org). For regions of China, we also

Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China e-mail<EMAIL_ADDRESS>
P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9\_13

considered the data of road distribution provided by the Resource and Environment Sciences and Data Center of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (http://www.resdc. cn). Considering the accuracy, comprehensiveness, and coverage of road data, and combined with online electronic maps such as Google Earth maps, the current global road distribution data were integrated as the basic data of global road data spatialization.

### 2.2 Historical Data of Population, per Capita GDP, and Regional Area and Road Length

The historical data of China's population, per capita GDP, and regional area and road length used in this study are from the provincial data of China Statistical Yearbook published by the National Bureau of statistics. Some of the statistical data are at the prefecture level, published in the provincial statistical yearbooks, and the time span is 1986–2016. The data of population, per capita GDP, and land area of other countries are from the world development indicators released by the World Bank (https://databank.worldbank. org/home.aspx). The total road length data are mainly from the World Bank (https://data.worldbank.org.cn) and the International Road Federation (https://www.irf.global) and supplemented by ASEAN statistical yearbook (https://www. aseansec.org). The time span is 1989–2014.

### 2.3 Shared Socioeconomic Pathways Data

This study projected the global road distribution in grids under SSP1, SSP2, and SSP3 scenarios in 2030 and 2050. The population and GDP forecast data used are derived from the global population and GDP data with 0.5° resolution under the SSP1–SSP5 scenarios produced by Jiang et al. (2017). The original 0.5° resolution population forecast data are from the global and national population data released by the International Institute for Applied Systems Analysis (IIASA), and the economic forecast data are from the GDP data provided by the Potsdam Institute for Climate Impact Research(PIK).

### 3 Method

For the production of the current global road length data, the current global road spatial distribution pattern was taken as the basis, and the global road length data with administrative regions as the basic statistical unit were disaggregated into the grids so as to realize the spatialization of global roads and obtain the global road spatial distribution data of 0.5° grid.

This study mainly estimated the future global road network distribution based on the land coefficient method, which considers that road length is directly proportional to the square root of population and area and the economic index coefficient of the area where the road network is located. The method can be used to calculate the reasonable theoretical road length in an area (Guo 2005). The calculation formula is as follows:

$$L = K \cdot \sqrt{P \cdot A} \tag{1}$$

where K is road network coefficient/economic coefficient; P is population (unit: 10,000 people); A is area (unit: 100 km2 ); and L is road length.

The economic coefficient, population, and area in the model directly reflect the three most important influencing factors of the road network scale, which has strong practicability, and the results can reflect the actual demand of the projected area. Among the three parameters, the economic coefficient K is calculated as follows.

First, the data of road length (Ln), population (Pn), and area (An) over the years were used to calculate the economic coefficient Kn:

$$K\_n = L\_n / \sqrt{P\_n \cdot A\_n} \tag{2}$$

The relationship between per capita GDP (Gn) and the calculated Kn is obtained through regression:

$$K\_n = a + b \cdot G\_n \tag{3}$$

where a and b are regression coefficients.

Using the land coefficient method and the historical data of road length, population, per capita GDP (economic coefficient K), and (regional) land area, we simulated the relationship between road length and socioeconomic factors. Combined with different SSPs scenarios in the future, the global road length data in 2030 and 2050 were obtained (Fig. 1).

### 4 Results

A comparison of the current and future road lengths of the 10 countries with the longest total road mileage in the world in 2010 indicates that the change of road length in the developed countries represented by the United States is very small and that in the developing countries such as India and China is relatively large (Fig. 2).

Fig. 2 Growth of road length of the top 10 countries with the longest total road mileage in 2010 under different Shared Socioeconomic Pathway (SSP) scenarios by 2030 and 2050

A comparison between Western Europe, northeastern United States, and the Bohai Rim of China indicates that the growth rate of road length in the Bohai Rim area is the highest, with clear differences under different SSPs and by different future years; while the growth rate of road length in Western Europe and the northeastern United States is not obvious, especially in the northeastern United States, which is similar to the current (2010) road length. The growth rate and trend of China's roads are similar to that of Asia as a whole, showing a high growth rate, and the growth rate varies significantly under different SSPs and by different years.

The total length of roads in all countries will increase significantly by 2030 and 2050. By 2030, the global total length of road will be about three times of the current (2010) total length, and by 2050, it will be about four to five times of the current level; the growth rate of the United States and Western Europe is the lowest, while that in Asia is the highest, especially in India and China. Under the sustainable development pathway SSP1 scenario, the growth of road network scale is the highest; under the regional competition pathway SSP3 scenario with higher challenges of climate change, the growth of road network scale is the smallest (Table 1).

### References

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to

obtain permission directly from the copyright holder.

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Global Change Risks

# Mapping Global Risk of Heatwave Mortality Under Climate Change

Qinmei Han, Weihang Liu, Wei Xu, and Peijun Shi

### 1 Background

Global warming has become a severe problem worldwide, where the average global temperature has steadily increased over recent decades accompanied by the abnormally hot weather (IPCC 2013). Since the 1950s, heatwave events have increased in frequency, intensity, and duration and their impact on human health will also increase under enhanced global warming (Perkins-Kirkpatrick and Lewis 2020). Heatwaves have become one of the most serious climate events in the world. Exposed to heatwave is associated with increasing mortality—for instance, the European heatwave of 2003 induced more than 70,000 additional deaths in France, Germany, Italy, Spain, and other countries (Robine et al. 2008). For Russia as a whole, the death toll of the 2010 summer heatwave totaled 55,000 people (Barriopedro et al. 2011).

Considering the ever-worsening impact of heatwaves, future projection of heat-related mortality under climate change has been widely studied in recent decades. Heatwave mortality shows an increasing trend under high-emission scenarios (Huang et al. 2011), especially in temperate areas (Gasparrini et al. 2017). Existed studies mainly focus on regional-, country-, or city-level risk assessments, but the distribution and variation of heatwave mortality risk at a

Authors: Qinmei Han, Weihang Liu, Wei Xu, Peijun Shi. Map Designers: Xinli Liao, Jing'ai Wang, Ying Wang. Language Editor: Wei Xu.

Q. Han W. Liu W. Xu P. Shi (&) Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China e-mail<EMAIL_ADDRESS>
W. Xu P. Shi

global scale need to be further quantitatively evaluated. Mapping heat-related mortality and finding the hotspots will help provide the policy recommendations at both global and national levels.

This study examined future heatwave mortality risk of the world at the grid level (0.25° 0.25°) and country level, respectively, based on the disaster system theory (Shi 1991, 1996, 2002), using the data of temperature and population changes. We also evaluated the decadal mortality risk change in the 2030s (2016–2035) and the 2050s (2046– 2065) as compared to the baseline period (1986–2005) using high spatial resolution climate and population data under different Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP) scenarios, namely RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3. To estimate the regional change, we adopted the regionalization recommended by the Intergovernmental Panel on Climate Change (IPCC), which divides the world into 26 regions.

### 2 Method

In this study, we used daily maximum temperature as the metric to calculate heatwave. The daily maximum temperature data were from the NEX-GDDP dataset, which was released by the National Aeronautics and Space Administration (NASA) in June 2015 (https://dataserver.nccs.nasa. gov/thredds/catalog/bypass/NEX-GDDP/catalog.html). The spatial resolution of the dataset is 0.25° 0.25°. There are 21 general circulation models in this dataset that contain two RCPs—RCP4.5 and RCP8.5—for the period from 1950 to 2100. The temperature data of lower emissions scenario RCP2.6 was computed by sub-project 1. The population projection data used in this study contain SSP1 − 3 computed by sub-project 2. We calculated heatwave intensity and mortality risk for the periods 2016–2035 and 2046–2065 in the future compared to 1986–2005 under the

© The Author(s) 2022

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9\_14

Fig. 1 Technical flowchart for mapping global heatwave mortality risk under climate change

RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios (Fig. 1). The maps shown in section 4 are multi-model ensemble results.

### 2.1 Heatwave Intensity Metrics

To better estimate regional changes of heatwave mortality risk in the world, we chose a relative threshold instead of an absolute threshold. A heatwave event here was defined as at least three consecutive days exceeding the given threshold, which is the 95th percentile value of the daily maximum temperature series over the baseline period 1986–2005 at the grid level, and if the 95th percentile value was lower than 25 °C, we set 25 °C as the threshold in this grid. The annual heatwave duration was defined as the total heatwave days in a given year. The annual heatwave maximum temperature was defined as the maximum temperature of all the heatwave events in a year.

### 2.2 Population Vulnerability

In this study, six mortality vulnerability curves for typical cities in the world were adopted (Gosling et al. 2007). In the IPCC-SREX reports, the world is divided into 26 regions (Seneviratne et al. 2012). We regrouped the 26 regions into 6 groups according to climate types, latitude zones, and terrain elevation. Each vulnerability curve was applied to a group of the IPCC-SREX regions to map heatwave mortality risk of the world.

### 2.3 Mortality Risk Function

Heatwave mortality risk of the world is assessed with formula (1):

$$R = F(T\_{\text{max}}) \times D \times P \tag{1}$$

where R is the annual heatwave mortality risk, F represents the regional vulnerability function, Tmax refers to the annual maximum temperature during heatwaves, P refers to the annual total population of each grid, and D is the annual heatwave duration (days). We computed the annual heatwave mortality risk for the selected periods—1986– 2005, 2016–2035, and 2046–2065. To map the spatial distribution, we then computed the 20-year average value for each period. All calculations, analyses, and the figures of the meteorological metrics and heatwave mortality risk were performed and mapped at the grid level (0.25° 0.25°).

Fig. 2 Heatwave mortality risk of the top 10 countries. The error bar represents the one standard deviation across the 21 (13 for Representative Concentration Pathway (RCP) 2.6) general circulation models and 3 shared socioeconomic pathways (SSPs)

### 3 Results

### 3.1 Heatwave Intensity

The results show that the spatial distribution of heatwave mortality risk for the two periods (2030s and 2050s) under the three scenarios (RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3) are very similar. For the maximum temperature during heatwaves, regions with high values are distributed in North Africa, West Asia, India, and Oceania, mainly near 23°N, and the temperature decreases from the high value areas to the north and south, respectively.

Heatwave duration decreases from the equator to the poles. The highest duration areas include Central Africa, West Asia, Central Asia, Central South America, and Oceania. The longest heatwave days are in Central Africa, Indonesia, and northern South America. The areas with high values in the 2050s are significantly larger than that in the 2030s under different scenarios.

Generally, global averaged heatwave duration during 1986–2005 are under 10 days per year, whereas it increases to 28 days per year in the 2050s under the high-emission scenarios.

### 3.2 Mortality Risk

High mortality risk areas for heatwaves are mainly distributed in the Northern Hemisphere including India peninsula, West Asia, the Mediterranean area, and eastern North America at the grid level. High latitudes in the Northern Hemisphere are mainly of lower risk as compared to the other regions. Overall, global heatwave mortality risk for the baseline period (1986–2005) is about 289,576 persons per year. In comparison, the annual average heatwave mortality risk increases by 8 times, 5 times, and 8 times in the 2050s under the RCP8.5-SSP3, RCP4.5-SSP2, and RCP2.6-SSP1scenarios, respectively.

We performed the zonal statistics analysis of the risk result at the continental level and the results show a substantial increase during the 2050s under the RCP8.5-SSP3 scenario. The heatwave mortality risk increases by 10 times in Africa followed by 8 times in Asia and 6 times in South America. There are 4-times, 4-times, and 3.5-times increase of heatwave mortality risk in Europe, North America, and Oceania, respectively, in the 2050s under the RCP8.5-SSP3 scenario.

The heatwave mortality risk at the country scale was also derived and ranked. The top ten countries with high heatwave mortality are showed in Fig. 2. These countries are mainly located in North Africa and West Asia, distributed around 30°N. Compared to the baseline period, the heatwave mortality risks of all countries increase significantly under different scenario combinations, especially in the 2050s (Fig. 2).

### References

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Mapping Global Risk of River Flood Mortality

Junlin Zhang, Xinli Liao, and Wei Xu

### 1 Background

Globally, river flooding induced by heavy rainfall frequently causes fatalities every year (Jongman et al. 2015; CRED and UNISDR 2018; CRED 2019). Particularly, heavy rainfall will increase in the future with climate warming (Liao et al. 2019). This could lead to greater rain-induced local flooding in some watersheds or regions (IPCC 2012). Besides, exposed populations to floods are increasing with the socioeconomic development (Jongman et al. 2015; Winsemius et al. 2018; Liao et al. 2019).

Generally, river flooding risk assessment has two steps. The first is to simulate river flooding hazard using hydrological or hydrodynamic model and inundation model, and the second is to calculate affected populations by overlaying the population data and flood hazard maps, and the results are used to assess the risk of affected population by floods (Arnell and Gosling 2016; Lim et al. 2018). Projected future precipitation and social-economic datasets provide a basis for these studies. However, when assessing risks in the population, most studies are concerned with the affected population (Alfieri et al. 2015; Dottori et al. 2016; Wing et al. 2018) and few studies concern mortality risks. Shi and Kasperson (2015) and Jongman et al. (2015) assessed the mortality risk using baseline mortality rate. Kinoshita et al. (2018) simulated changing mortality vulnerability with time

Authors: Junlin Zhang, Xinli Liao, Wei Xu.Map Designers: Junlin Zhang, Jing'ai Wang, Ying Wang.Language Editor: Wei Xu.

J. Zhang X. Liao W. Xu (&) Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China e-mail<EMAIL_ADDRESS>
W. Xu

to assess risks, but the vulnerability is measured by an index rather than vulnerability functions that involve certain physical processes. The difficulty of mortality risk research is lacking proven vulnerability functions. In addition, many studies focus on the risk at the country and regional levels, lacking grid level high spatial resolution results.

In order to address the issues, this study assessed the global mortality risks from floods by two main steps. The first was to develop mortality vulnerability functions for all countries by revising an existing vulnerability function. Then future potential death tolls of the 2030s and the 2050s were estimated at the 2.5' grid level under the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP) scenarios of RCP4.5-SSP2 and RCP8.5-SSP3. The results were compiled to produce the risk maps at the 0.25° grid level and country level.

### 2 Method

Figure 1 shows the technical flowchart for mapping flood mortality risk of the world. The study revised the existing vulnerability functions by adjustment coefficient that is calculated based on recorded death tolls. Then future death tolls were estimated using predicted inundation data and population data, and the adjusted vulnerability functions. Future death tolls at the grid level were then aggregated to other geographic units. Finally, the risk size and model uncertainty are analyzed.

### 2.1 Estimation of Risks

### 2.1.1 Estimation of Losses for the Baseline Period

The mortality vulnerability function is represented by Eq. (1) (Jonkman et al. 2008). Using the function, historical death tolls were estimated by Eqs. (2) and (3).

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

© The Author(s) 2022

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9\_15

Fig.1 Technical flowchart for mapping global risk of river flood mortality. AOGCM = Atmosphere–Ocean General Circulation Model; RCP = Representative Concentration Pathway; SSP = Shared Socioeconomic Pathway

$$V(d) = \Phi\left[\frac{\ln(d) - 7.60}{2.75}\right] \tag{1}$$

where U is the cumulative normal distribution. d is the water depth.

$$L\_{\text{his\\_}\\_j} = V(d\_{\text{his\\_}\\_j}) \times S\_{\text{his\\_}} \times f\_{\text{his\\_}\\_j} \tag{2}$$

$$L\_{\rm his} = \frac{1}{11 \times 20} \sum\_{j=1}^{20} \sum\_{i=1}^{11} L\_{\rm his} \\_i \\_{j} \tag{3}$$

where i is the order of the 11 Atmosphere–Ocean General Circulation Models (AOGCMs); j is the sequence of the 20 years; his is the baseline period (1986–2005); Lhis\_i\_j, dhis\_i\_j, and fhis\_i\_j are the estimated death tolls, water depth, and inundation fraction for the ith AOGCM in the jth year for the baseline period, respectively; Shis\_j is the population size in the jth year of the baseline period; Lhis is the annual average death tolls, that is, the multi-model ensemble for the baseline period; V(d) is the vulnerability function.

### 2.1.2 Calculation of Adjustment Coefficients

The study revised the mortality vulnerability function (Eq. 1) for countries to reduce the diversity of vulnerability functions in different areas.

Using Eq. (4), the adjustment coefficients (Kc values) were calculated for the countries with total recorded deaths and estimated deaths both greater than zero during the baseline period. On this basis, the adjustment coefficient is the minimum of above calculated Kc values for the countries with total recorded death tolls equal to zero during the baseline period; and the adjustment coefficient is the average of above calculated Kc values for the countries with total recorded death tolls greater than zero but total estimated deaths equal to zero.

$$K\_c = \frac{\sum\_{j=1}^{20} SL\_{\text{his\\_}c\\_j}}{L\_{\text{his\\_}c}} \tag{4}$$

where Kc is the adjustment coefficient of country c; j represents the sequential number of the 20 years in the baseline period; SLhis\_c\_j is the recorded death tolls of country c.

The adjusted vulnerability function is shown in Eq. (5).

$$AdjV\_c(d) = K\_c \times V(d)\tag{5}$$

where AdjVc(d) is the adjusted vulnerability function of country c.

### 2.1.3 Calculation of Future Losses and Change

Future death tolls of a grid in country c were estimated according to Eq. (6), based on future predicted inundation and population data and adjusted vulnerability function. Next, the study averaged the results of 20 years for all AOGCMs (Eq. 7) to compute uncertainties; then we averaged the results of the 11 AOGCMs as the death tolls of the 2030s or the 2050s to reduce model uncertainties (Eq. 8). Finally, the changes of death tolls from the baseline period to future periods were calculated by Eq. (9).

$$L\_{\text{fut\\_i\\_j}} = A djV\_c(d\_{\text{fut\\_i\\_j}}) \times S\_{\text{fut\\_j}} \times f\_{\text{fut\\_i\\_j}} \qquad (6)$$

where Lfut\_i\_j, dfut\_i\_j, and ffut\_i\_j are the estimated death tolls, water depth, and inundation fraction for the ith AOGCM in the jth year for a future period, respectively; Sfut\_j is the population data in the jth year of a future period.

$$L\_{\text{fit\\_}} = \frac{1}{20} \sum\_{j=1}^{20} L\_{\text{fit\\_}} \\_{i \to j} \tag{7}$$

where Lfut\_i is the average death tolls of 20 years (2016– 2035 or 2046–2065) for the ith AOGCM.

$$L\_{\text{fit}} = \frac{1}{11} \sum\_{i=1}^{11} L\_{\text{fit\\_i}} \tag{8}$$

where Lfut is the death tolls of the 2030s or the 2050s for the multi-model ensemble, which averaged the results from the 11 AOGCMs.

$$
\Delta L = L\_{\text{fat}} - L\_{\text{his}} \tag{9}
$$

where ΔL is the change of death tolls for the 2030s or the 2050s relative to the baseline period.

### 2.2 Model Uncertainty

The result uncertainty of multi-models is measured by standard deviation (Eq. 10) and coefficient of variation CV, the ratio of SD to L in Eq. 10.

$$SD = \sqrt{\frac{\sum\_{i=1}^{11} \left(L\_i - L\right)^2}{11}} \tag{10}$$

where Li is the average death tolls of 20 years for the ith AOGCM (Lhis\_i or Lfut\_i). L is the average death tolls of 20 years for the multi-model ensemble (Lhis or Lfut).

### 3 Results

Globally, the annual average death tolls of the 2030s are approximately 21 thousand persons for the RCP4.5-SSP2 scenario and 23 thousand persons for the RCP8.5-SSP3 scenario; they increase 0.57 and 0.69 times relative to the baseline period, respectively. The annual average death tolls of the 2050s are approximately 26 thousand persons for the RCP4.5-SSP2 scenario and 32 thousand persons for the RCP8.5-SSP3 scenario; they increase 0.88 and 1.31 times relative to the baseline period. The patterns of spatial distribution are similar for different scenarios; high-risk areas are located in East Asia, South Asia, and Southeast Asia, particularly in eastern coastal China and the Ganges River Basin.

Fig.2 Annual average death tolls of the top 10 high-risk countries (in descending order by death toll). The error bar represents the standard deviations across the 11 Atmosphere–Ocean General Circulation Models (AOGCMs)

Using zonal statistics of the death toll results, we calculated the annual average death tolls at the national level. Figure 2 shows the annual average death tolls and errors (measured by standard deviation) of the top ten high-risk countries. The death tolls are higher for India, Bangladesh, China, Haiti, and Indonesia, and lower in Pakistan, Somalia, Algeria, Viet Nam, and the United States. For most countries, the death tolls of the 2050s are higher than that of the 2030s; and the death tolls of the RCP8.5-SSP3 scenario are higher than that of the RCP4.5-SSP2 scenario. The changes of death tolls are higher in India and Bangladesh, increasing 1.22–3.63 times for India and 2.82–5.16 times for Bangladesh. The risk changes of Haiti are the lowest, about 0.03–0.27 times (Fig. 2).

### References

National Academy of Sciences of the United States of America 112 (18): 201414439.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to

obtain permission directly from the copyright holder.

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

# Mapping Global Risk of GDP Loss to River Floods

Junlin Zhang, Xinli Liao, and Wei Xu

### 1 Background

Globally, river flooding induced by heavy rainfall frequently causes serious economic losses every year (Jongman et al. 2015; CRED and UNISDR 2018; CRED 2019). Particularly, heavy rainfall will increase in the future with climate warming (Liao et al. 2019). This could lead to greater rain-induced local flooding in some watersheds or regions (IPCC 2012). Besides, exposed assets to floods are increasing with the socioeconomic development (Jongman et al. 2015; Winsemius et al. 2018; Liao et al. 2019).

Generally, river flooding risk assessment has two steps. The first is to simulate river flooding hazard using hydrological or hydrodynamic model and inundation model, and the second is to calculate economic losses by overlaying the economic data and flood hazard maps, and the results are used to assess the economic loss risks from floods (Arnell and Gosling 2016; Lim et al. 2018). Projected future precipitation and social-economic datasets provide a basis for these studies. Vulnerability functions of economic loss have been developed for the risk assessment (Alfieri et al. 2015; Muis et al. 2015; Dottori et al. 2016; Sarhadi et al. 2016; Wing et al. 2018). But most studies are concerned with asset losses and there are few studies on GDP loss risks. In addition, many studies focus on the risk at the country and

Authors: Junlin Zhang, Xinli Liao, Wei Xu. Map Designers: Junlin Zhang, Jing'ai Wang, Ying Wang. Language Editor: Wei Xu.

J. Zhang X. Liao W. Xu (&) Institute of Disaster Risk Science, Beijing Normal University, Beijing, 100875, China e-mail<EMAIL_ADDRESS>
W. Xu

region levels, lacking grid-level high spatial resolution results.

In order to address the issues, this study assessed the GDP loss risks from floods by two main steps. The first was to develop vulnerability functions of GDP losses for the countries by revising an existing vulnerability function. Then future potential GDP losses of the 2030s and the 2050s were estimated at the 2.5' grid level under the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP) scenarios of RCP4.5-SSP2 and RCP8.5-SSP3. The results were compiled to produce the risk maps at the 0.25° grid level and country level.

### 2 Method

Figure 1 shows the technical flowchart for mapping the risk of GDP loss to floods of the world. This study revised the existing vulnerability functions by adjustment coefficient that is calculated based on recorded GDP losses. Then future losses were estimated using predicted inundation data and GDP data, and the adjusted vulnerability functions. Future losses at the grid level were then aggregated to other geographic units. Finally, the risk size and model uncertainty are analyzed.

### 2.1 Estimation of Risks

### 2.1.1 Estimation of Losses for the Baseline Period

The vulnerability function of asset losses is represented by Eq. (1) (Wing et al. 2018). Using the function, historical asset losses were estimated by Eqs. (2) and (3).

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

© The Author(s) 2022

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9\_16

Fig. 1 Technical flowchart for mapping global risk of GDP loss to river floods. AOGCM = Atmosphere–Ocean General Circulation Model; RCP = Representative Concentration Pathway; SSP = Shared Socioeconomic Pathway

$$V(d) = \begin{cases} -0.0067d^4 + 0.0723d^3 - 0.233d^2 + 0.3953d, d < 5 \\ & 0.0038d + 0.962, d \ge 5 \end{cases} \tag{1}$$

$$L\_{\text{his\\_}\\_j} = V(d\_{\text{his\\_}\\_j}) \times S\_{\text{his\\_}\\_j} \times f\_{\text{his\\_}\\_j} \tag{2}$$

$$L\_{\rm his} = \frac{1}{11 \times 20} \sum\_{j=1}^{20} \sum\_{i=1}^{11} L\_{\rm his} \\_i \\_{j} \tag{3}$$

where i is the order of the 11 Atmosphere–Ocean General Circulation Models (AOGCMs); j is the sequence of the 20 years; his is the baseline period (1986–2005); Lhis\_i\_j, dhis\_i\_j, and fhis\_i\_j are the estimated losses, water depth, and inundation fraction for the ith AOGCM in the jth year for the baseline period, respectively; Shis\_j is the GDP data in the jth year of the baseline period; Lhis is the annual average loss, that is multi-model ensemble for the baseline period; V(d) is the vulnerability function.

### 2.1.2 Calculation of Adjustment Coefficients

The study revised the vulnerability function of asset loss (Eq. 1) for countries to build the vulnerability function of GDP loss. Using Eq. (4), the adjustment coefficients (Kc values) were calculated for the countries with total recorded losses and estimated losses both greater than zero during the baseline period. On this basis, the adjustment coefficient is the minimum of above calculated Kc values for the countries with total recorded losses equal to zero during the baseline period; and the adjustment coefficient is the average of above calculated Kc values for the countries with total recorded losses greater than zero but total estimated losses equal to zero.

$$K\_c = \frac{\sum\_{j=1}^{20} SL\_{\text{his\\_}c\\_j}}{L\_{\text{his\\_}c}} \tag{4}$$

where Kc is the adjustment coefficient of country c; j represents the sequential number of the 20 years in the baseline period; SLhis\_c\_j is the recorded direct economic losses of country c.

The adjusted vulnerability function is shown in Eq. (5).

$$AdjV\_c(d) = K\_c \times V(d)\tag{5}$$

where AdjVc(d) is the adjusted vulnerability function of country c.

### 2.1.3 Calculation of Future Losses and Change

Future losses of a grid in country c were estimated according to Eq. (6), based on future predicted inundation and GDP data and the adjusted vulnerability function. Next, the study averaged the results of 20 years for all AOGCMs (Eq. 7) to compute uncertainties; then we averaged the results of the 11 AOGCMs as the losses of the 2030s or the 2050s to reduce model uncertainties (Eq. 8). Finally, the changes of losses from the baseline period to the future period were calculated by Eq. (9).

$$L\_{\text{fut\\_i\\_j}} = A djV\_c(d\_{\text{fut\\_i\\_j}}) \times S\_{\text{fut\\_i}} \times f\_{\text{fut\\_i\\_j}} \qquad (6)$$

where Lfut\_i\_j, dfut\_i\_j, and ffut\_i\_j are the estimated GDP losses, water depth, and inundation fraction for the ith AOGCM in the jth year for a future period, respectively; Sfut\_j is the GDP data in the jth year of a future period.

$$L\_{\text{fut\\_}i} = \frac{1}{20} \sum\_{j=1}^{20} L\_{\text{fut\\_}i} \tag{7}$$

where Lfut\_i is the average losses of 20 years (2016–2035 or 2046–2065) for the ith AOGCM.

$$L\_{\rm fit} = \frac{1}{11} \sum\_{i=1}^{11} L\_{\rm fit} \\_i \tag{8}$$

where Lfut is the losses of the 2030s or the 2050s for the multi-model ensemble, which averaged the results from the 11 AOGCMs.

$$
\Delta L = L\_{\text{fat}} - L\_{\text{his}} \tag{9}
$$

where ΔL is the change of losses for the 2030s or the 2050s relative to the baseline period.

### 2.2 Model Uncertainty

The result uncertainty of multi-models is measured by standard deviation (Eq. 10) and coefficient of variation CV, the ratio of SD to L in Eq. 10.

$$SD = \sqrt{\frac{\sum\_{i=1}^{11} \left(L\_i - L\right)^2}{11}} \tag{10}$$

where Li is the average losses of 20 years for the ith AOGCM (Lhis\_i or Lfut\_i). L is the average losses of 20 years for the multi-model ensemble (Lhis or Lfut).

### 3 Results

Globally, the annual average GDP losses of the 2030s are approximately USD 223 billion for the RCP4.5-SSP2 scenario and USD 199 billion for the RCP8.5-SSP3 scenario; they increase 4.82 and 4.19 times relative to the baseline period, respectively. The annual average GDP losses of the 2050s are approximately USD 447 billion for the RCP4.5-SSP2 scenario and USD 429 billion for the RCP8.5-SSP3 scenario; they increase 10.69 and 10.21 times relative to the baseline period. The patterns of spatial distribution are similar for different scenarios. High-risk areas are mainly located in East Asia, South Asia, and Southeast Asia. The risks of GDP loss are the highest in the eastern coastal areas of China, the vicinity of the Ganges River Basin, and the coastal areas of the Indo-China Peninsula. Overall, the risk of the 2050s is higher than that of the 2030s. For Africa, the risks are higher in the western, southern, and eastern Africa. For South America, the risks are higher in Liano Orinoco Plain and La Plata Plain. The high-risk areas in Europe are spread out. The high-risk areas in North America are distributed in Atlantic Coastal Plain, along Saint Lawrence River, and in the southern coastal area.

Fig. 2 Annual average GDP losses of the top 10 high-risk countries (in descending order by GDP loss). The error bar represents the standard deviations across 11 Atmosphere–Ocean General Circulation Models (AOGCMs)

The high-risk areas in Australia are mainly in the southeast coastal area.

By zonal statistics of the GDP loss results, we calculated the annual average GDP losses at the national level. Figure 2 shows the annual average GDP losses and errors (measured by standard deviation) of the top ten high-risk countries. The GDP loss risks are the highest for China, higher than USD 100 billion for the 2030s and the 2050s, increasing more than eight times relative to the baseline period. The average annual GDP losses of the remaining countries are all lower than USD 100 billion. The changes are greater for India and Bangladesh relative to the baseline period. In most countries, the risks are higher in the 2050s than in the 2030s.

### References

river floods and the global benefits of adaptation. Proceedings of the National Academy of Sciences of the United States of America 112 (18): 201414439.

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Mapping Global Risk of Crop Yield Under Climate Change

Weihang Liu, Shuo Chen, Qingyang Mu, Tao Ye, and Peijun Shi

### 1 Introduction

Risk of crop yield under climate change refers to the potential changes in crop yield (mean yield, interannual yield variability, and lower extreme yield) caused by climate change. Increases in the interannual variability of yield and the likelihood of lower yield extremes can affect the livelihoods of farmers (Morton 2007), increase pressure on inter-temporal food reserves (Bobenrieth et al. 2013), induce large price changes in the global market or even destabilize regions of the world (Sternberg 2011). The risk of crop yield is one of the primary drivers of food system instability (IPCC 2017), and its response to climate change is critical to understanding the impact of climate change on food security (FAO 2019). Previous studies have mostly focused on the variation in the mean yield in response to climate change (Ray et al. 2015). Climate change impacts on crop yield risk, however, remains a key research gap (IPCC 2017).

Recent studies have provided evidence for changes in the yield risk of major cereal crops and identified significant global impacts of climate change, either at the country level or the 0.5° grid level (Osborne and Wheeler 2013; Iizumi and Ramankutty 2016). These studies have been followed up by regional county-level analysis of the interannual yield variability of maize. Most recently, efforts have been concerned with predicting the impact of future climate change on the interannual yield variability, focusing on wheat and maize at both the global and regional scales, using site-based process-based crop models (Liu et al. 2019) and statistical models (Urban et al. 2012; Ben-Ari et al. 2018; Tigchelaar et al. 2018).

This study evaluated the changes in the global staple crop yield risk from three aspects—mean yield, interannual yield variability, and lower extreme yield—under the RCP2.6-2030s, RCP2.6-2050s, RCP4.5-2030s, RCP4.5- 2050s, RCP8.5-2030s, and RCP8.5-2050s scenarios. The crop yield risk was evaluated by using multi-model ensemble (MME) simulation with global high spatial resolution (0.25°) climate forcing data. Emulators of global gridded crop models (GGCM) were developed to ensure efficient prediction (Lobell andBurke2010;Holzkämper etal.2012;Oyebamijietal.2015; Raimondo et al. 2020). The present results may provide crucial information for climate risk assessment and effective adaptations.

Authors: Weihang Liu, Shuo Chen, Qingyang Mu, Tao Ye, Peijun Shi.

Map Designers:

Wheat: Weihang Liu, Jing'ai Wang, Ying Wang. Maize: Qingyang Mu, Jing'ai Wang, Ying Wang. Rice: Shuo Chen, Jing'ai Wang, Ying Wang. Language Editor: Tao Ye.

W. Liu S. Chen Q. Mu T. Ye (&) P. Shi Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China e-mail<EMAIL_ADDRESS>
© The Author(s) 2022

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9\_17

T. Ye P. Shi

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

Fig. 1 Technical flowchart for mapping crop yield risk of the world under climate change, taking wheat as an example. GGCM = Global Gridded Crop Model; MME = Multi-model ensemble; GCM = General Circulation Model

### 2 Method

The method for detecting changes in the wheat yield variability for future climate scenarios includes the following steps (Fig. 1): (1) Develop emulators for the process-based models in the GGCMs; (2) Conduct MME prediction of the global wheat yield at fine spatial resolution; (3) Correct the bias in the predicted global yield using country-level reported yield to best capture the actual interannual yield variability; (4) Summarize yield risk change. Figure 1 shows the technical flowchart for mapping the risk of crop yield of the world under climate change.

### 2.1 Development of the Global Gridded Crop Model (GGCM) Emulators

### Emulator training via extreme gradient boosting

The development of the emulators consists of training machine learning models on specific GGCM input and output datasets so that the models may replicate the complex process of yield simulation within the crop model. Variables that have been frequently reported to significantly influence wheat yield were prepared as the predicting variables (Folberth et al. 2019), and they cover climate, soil type, length of vegetation growth period, and management practices. All the data for training were computed/adapted from the GGCMs' input and output datasets. For climate data, the monthly variables and the growing season variables were considered. Soil properties, the length of vegetation growth period, and management practices were site-specific variables.

In total, six emulators were trained for the three GGCMs (pDSSAT, pAPSIM, and EPIC-IIASA) each with two cultivation possibilities (rainfed and irrigation). An extreme gradient boosting (XGB) algorithm was used due to its better performance in terms of goodness-of-fit, cross-validation errors, and computation efficiency than the random forest algorithm (Folberth et al. 2019). The predicting variables and the simulated yield in the GGCMs were randomly split into training and validation sets that contained 75% and 25% of the samples (Yue et al. 2019), respectively.

### Predicting global crop yield by MME simulation

The MME yield prediction was performed at the global level for the 0.25° grids for the years 1986–2005 (baseline period), 2016–2035 (the 2030s), and 2046–2065 (the 2050s) under the RCP2.6, RCP4.5, and RCP8.5 scenarios. The MME approach has been proven to be a reliable method in reproducing the main effects anticipated for climate change when simulations are compared with observation (Asseng et al. 2015; Frieler et al. 2017). The large climate model-crop model emulator setup in our framework enabled a robust MME estimate as well as analysis of spatial heterogeneity and inter-model uncertainty (Martre et al. 2015). There were 432 treatments (13 or 21 general circulation models (GCMs) 3 emulators for the baseline period, RCP2.6-2030s, RCP2.6-2050s, RCP4.5-2030s, RCP4.5-2050s, RCP8.5-2030s, and RCP8.5-2050s) each simulated for 20-year periods. For future yield predictions, planting dates, soil properties, and management practices were assumed to remain constant through time. The MME median across the 3 emulators and the 13 or 21 GCMs was taken as the final estimate.

### Bias correction of the predicted yield in the baseline period and in future applications

The emulator-predicted global wheat yields were corrected for bias using the national yield reports of the FAO. Rainfed and irrigated yield were first aggregated to the grid and national levels using an area-weighted average (Müller et al. 2017).

### 2.2 Measuring the Yield Risk

To fully describe the yield risk, the conventional approach was to derive the probability distribution or the cumulative distribution of the yield (Coble et al. 2010; Ye et al. 2015). In this study, we computed the mean yield (multi-year average), yield interannual variability (multi-year standard deviation), and the lower yield extremes (multi-year 10th percentile) across different 20-year periods. To account for the uncertainty associated with the projected input climate data, the computation was conducted for simulated yield series derived independently with each GCM and emulator, and the results of the MME median were reported.

### 3 Results

### 3.1 Wheat Yield Risk

Globally, countries with higher mean wheat yield are mainly located in Europe, while those with lower yield are mainly located in East Africa, the Middle East, and the northern United States. The change of yield risk shows a complex connection with warming. For higher emissions and warming scenarios, although countries at higher latitudes would be more likely to experience higher interannual variability of yield, their mean yield and lower extreme yield tend to increase, compared with the baseline period. Countries at low latitudes, e.g., India, would suffer more, that is, its lower extreme yield will be much worse in the future while its yield interannual variability slightly declines.

From the perspective of mean wheat yield, among the top 10 wheat-producing countries in the world (Fig. 2), the mean yields of France and Germany stand out, exceeding 6 t/ha. The mean yields of the other wheat-producing countries are lower than 3 t/ha. There are two patterns of variation of the mean yields under warming conditions. On the one hand, the rising temperature will bring a favorable effect on mean wheat yield. For example, the mean yields in China, the United States, Russia, France, Canada, Germany, and Turkey will gradually climb up with increasing warming. On the

Fig. 2 Mean wheat yield of the top 10 major wheat-producing countries (in descending order by total production). The error bar represents the one standard deviation across the 21 general circulation models (GCMs) and three emulators

Fig. 3 Wheat yield interannual variability of the top 10 major wheat-producing countries (in descending order by total production). The error bar represents the one standard deviation across the 21 general circulation models (GCMs) and three emulators

other hand, warming will impose an adverse influence on the other wheat-producing countries such as India, Australia, and Pakistan, whose mean yields will fall during the future periods.

From the perspective of wheat yield interannual variability, among the top ten wheat-producing countries in the world (Fig. 3), Russia, France, and Germany have higher wheat yield interannual variability. China, India, and the United States—the top three countries in wheat production —are at the middle level, while Pakistan has the lowest interannual variability. With increasing warming (e.g., higher emissions scenario or further into the future), the interannual variability will rise in all countries, but the degree will vary. In China, the interannual variability of yield will grow insignificantly under the medium emissions scenario (RCP4.5) but will increase slightly (less than 0.4 t/ha) under the high emissions scenario (RCP8.5). The interannual variability in France is flat under the three scenarios of RCP4.5-2030s, RCP4.5-2050s, and RCP8.5-2030s. However, when the temperature rises to a considerable degree (RCP8.5-2050s), the interannual variability will increase sharply.

From the perspective of lower extreme wheat yield, among the top ten wheat-producing countries in the world (Fig. 4), the countries with the highest lower extreme wheat yield are France and Germany, where the yields are as high

Fig. 4 Lower extreme wheat yield of the top 10 major wheat-producing countries (in descending order by total production). The error bar represents the one standard deviation across the 21 general circulation models (GCMs) and three emulators

as 4–6 t/ha, while the countries with the lowest wheat yield are Australia, Canada, and Pakistan, where the yields can be as low as 1 t/ha. The relationship between changes in lower extreme wheat yields and warming is not uniform across countries: with the increase of warming, most countries show an overall upward trend, but India and Australia show a downward trend. For example, a warmer world will cause lower extreme wheat yield in India to become even lower. However, it will have a favorable impact in high latitude areas, especially in the United States, where the lower extreme wheat yield will increase steadily under the four scenarios of RCP4.5-2030s, RCP4.5-2050s, RCP8.5-2030s, and RCP8.5-2050s.

### 3.2 Maize Yield Risk

Globally, countries with higher mean maize yield are mainly located in Europe, northern China, and the United States while those with lower yield are mainly located in central Africa and southern India. Climate change will have a two-sided effect on maize production in most of the regions. Warming will reduce the maize yield interannual variability risk (lower variability) but increase the lower extreme wheat yield risk (lower yield) in the regions between south of the Sahara and north of the equator in Africa and northern India. Similarly, it will increase the variability risk (higher variability) but reduce the extreme yield risk (higher yield) in

Fig. 5 Mean maize yield of the top 10 major maize-producing countries (in descending order by total production). The error bar represents the one standard deviation across the 21 general circulation models (GCMs) and three emulators

Fig. 6 Maize yield interannual variability of the top 10 major maize-producing countries (in descending order by total production). The error bar represents the one standard deviation across the 21 general circulation models (GCMs) and three emulators

Europe and southeast Africa. On the contrary, the North China Plain and the central United States will be surely harmed from warming, while the Great Lakes region of the United States and central South America will benefit from it.

From the perspective of mean maize yield, among the top ten maize-producing countries in the world (Fig. 5), the United States, Chile, and Canada have the highest mean maize yields, with values higher than 7 t/ha. The mean maize yields of China and Argentina are moderate (around 4.5 t/ha), while that of Brazil, Mexico, Ukraine, and India are lower, ranging from 1.5 t/ha to 3.5 t/ha. Few maize-producing countries will benefit from warming excluding Ukraine and Canada, whose mean maize yield will increase during the future periods. The mean maize yield of the United States, China, Brazil, Chile, and Russia in the future periods tend to maintain a relatively consistent level compared to that in the baseline period. In comparison, the mean maize yields of Argentina, Mexico, and India will decline slightly in the future.

From the perspective of maize yield interannual variability, among the top ten maize-producing countries in the world (Fig. 6), in Chile and Canada, the interannual variability is higher than 0.6 t/ha. Conversely, in Mexico, India, and Russia, it is low and about 0.2 t/ha. With the increase of warming, the interannual variability of the United States, China, Brazil, and Mexico will show a steady increasing

Fig. 7 Lower extreme maize yield of the top 10 major maize-producing countries (in descending order by total production). The error bar represents the one standard deviation across the 21 general circulation models (GCMs) and three emulators

trend—for example, under the RCP4.5 and RCP8.5 scenarios, the interannual variability will become higher over time in Canada and Russia. However, the interannual variability under the RCP4.5-2050s scenario will be slightly higher than that under the RCP8.5-2030s scenario. In contrast, the interannual variability in Argentina, Chile, and India will show a descending trend in general.

From the perspective of lower extreme maize yield, among the top ten maize-producing countries in the world (Fig. 7), lower extreme yields are higher in the United States, Chile, and Canada, with values greater than 5 t/ha during all the periods and under all the scenarios. There are large differences in the relationship between the changes of warming and lower extreme maize yield among the top ten maize-producing countries. The lower extreme maize yield in Canada tends to grow as temperature rise, but in most of the countries (such as the United States, China, Brazil, Chile, Ukraine, and Russia), it will fall after rising. The rising lower extreme maize yield in short term may be a result of the increasing precipitation. However, the lower extreme maize yields in Argentina, Mexico, and India will be generally on the decline.

### 3.3 Rice Yield Risk

Globally, countries with higher mean rice yield are mainly located in the Mediterranean region, China, the southeastern United States, and Peru while those with lower yield are mainly located in Africa. There is uncertainty about the risks in some areas, such as central Africa and coastal Asia, because they will have increasing yield interannual variability but higher lower extreme rice yield. However, in general, climate change will reduce the rice yield risk at mid-latitude regions, especially Ukraine, northeastern China, and southeastern South America, where yield interannual variability will reduce along with the increasing lower extreme rice yield. Conversely, most of the regions in the low-latitude area, namely those in eastern Africa, southern Asia, and northern South America, would experience higher risk in terms of yield interannual variability and lower extreme yield.

From the perspective of mean rice yield, among the top ten rice-producing countries in the world (Fig. 8), China, the United States, and Spain have the highest mean rice yields (more than 6 t/ha) and Peru and Iran have lower but still considerable mean yields (around 5.5 t/ha). Indonesia and Ukraine have the mean rice yields of 4 t/ha, while Brazil, India, and Nigeria have the lowest ones (less than 2.5 t/ha). Warming has little effect on mean rice yields, especially for Brazil, India, Peru, Spain, Nigeria, and Iran, where yields will remain largely unchanged under the RCP4.5-2030s, RCP4.5-2050s, and RCP8.5-2030s scenarios, although they will decrease under the RCP8.5-2050s scenario compared with that in the baseline period. However, the mean rice yields of China and Ukraine will increase slightly while those of Indonesia and the United States will decrease marginally.

From the perspective of rice yield interannual variability, among the top ten rice-producing countries in the world (Fig. 9), Ukraine has the greatest interannual variability (>0.8 t/ha), followed by Indonesia (>0.6 t/ha). However, as the top three rice-producing countries, China, Brazil, and India have relatively little interannual variability. In several major rice-producing countries, the increase of interannual variability of rice yield has a weak positive relationship with the increase of warming. In China, Brazil, India, Indonesia,

Fig. 8 Mean rice yield of the top 10 major rice-producing countries (in descending order by total production). The error bar represents the one standard deviation across the 21 general circulation models (GCMs) and three emulators

Fig. 9 Rice yield interannual variability of the top 10 major rice-producing countries (in descending order by total production). The error bar represents the one standard deviation across the 21 general circulation models (GCMs) and three emulators

Fig. 10 Lower extreme rice yield of the top 10 major rice-producing countries (in descending order by total production). The error bar represents the one standard deviation across the 21 general circulation models (GCMs) and three emulators

Peru, the United States, and Nigeria, the interannual variability of rice yield will increase as the climate gets warmer, but the trend is not significant. On the other hand, the interannual variability of rice yield in Ukraine will show a fluctuating upward trend, that is, under the high emissions scenario (RCP8.5), interannual variability will be higher than that in the medium emissions scenario (RCP4.5), but the interannual variability in the near future (the 2030s) is higher than that in the far future (the 2050s). In Spain and Iran, the yield interannual variability will decrease first, but increase slightly when it is much warmer.

From the perspective of lower extreme rice yield, among the top ten rice-producing countries in the world (Fig. 10), the lower extreme rice yield is high in China, the United States, and Spain (>5 t/ha), followed by Brazil and India (2– 3 t/ha) and Nigeria (1–2 t/ha). Among the top ten rice-producing countries, the lower -extreme rice yields in China, Brazil, Peru, and Spain will increase first and then decrease under higher emissions scenario and further into the future, but in Ukraine the lower extreme rice yield will keep increasing. The lower extreme rice yields in India, Indonesia, the United States, Nigeria, and Iran will generally decrease.

### References

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Appendix A Projection of Future Climate Change

Lianlian Xu and Aihui Wang

### Introduction

The Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles under different emissions scenarios (Taylor et al. 2012; Dai 2013) are widely used to understand the past and predicit the future climate changes. However, the horizontal resolution of the general circulation models (GCMs) in the CMIP5 project are relatively coarse, which has limited the ability of the models to capture the spatially detailed information of (extreme) climate events at the regional or local scales (Ines and Hansen 2006; Xue et al. 2014). To overcome this limitation, statistical downscaling methods are widely used (Maurer and Hidalgo 2008; Li et al. 2010; Xue et al. 2014; Cannon et al. 2015; Chen et al. 2017).

### Models and Method

The bias correction and spatial downscaling (BCSD) algorithm (Xu and Wang 2019) is used to correct and downscale the daily maximum temperature, daily minimum temperature, and daily precipitation from 13 GCMs in the CMIP5 project under the RCP2.6 scenario. Historical simulations (1961–005) and future projections (2006–065) under the RCP2.6 scenario in the CMIP5 project (Taylor et al. 2012) are adopted to be statistical downscaled in this study. Table A.1 provides the basic information about these GCMs.

In order to use the BCSD method, the Global Meteorological Forcing Dataset (GMFD, 0.25° 0.25°) is taken as the observations. The GMFD is derived from merged reanalysis products of remote sensing and in situ observations (Sheffield et al. 2006), which was originally developed as the atmospheric forcing dataset for offline land surface models. After the three steps of BCSD, we obtain the global daily maximum temperature, daily minimum temperature, and daily precipitation at a spatial resolution of 0.25° 0.25° from 13 GCMs under the RCP2.6 scenario.

### References

Cannon, A.J., S.R. Sobie, and T.Q. Murdock. 2015. Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes? Journal of Climate 28 (17): 6938–6959.

Chen, H.P., J.Q. Sun, and H.X. Li. 2017. Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set. Atmospheric and Oceanic Science Letters 10 (6): 403–410.

Dai, A.G. 2013. Increasing drought under global warming in observations and models. Nature Climate Change 3: 52–58.

Ines, A.V.M., and J.W. Hansen. 2006. Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology 138 (1): 44–53.

L. Xu A. Wang

Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China

L. Xu

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9

School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 519082, China

© The Editor(s) (if applicable) and The Author(s) 2022

Table A.1 List of the 13 general circulation models (GCMs) from the CMIP5 archive

Li, H.B., J. Sheffield, and E.F. Wood. 2010. Bias correction of monthly precipitation and temperature fields from the IPCC AR4 models using equidistant quantile matching. Journal of Geophysical Research: Atmospheres 115(D10).

Maurer, E.P., and H.G. Hidalgo. 2008. Utility of daily vs. monthly large-scale climate data: An intercomparison of two statistical downscaling methods. Hydrology and Earth System Science. Discussions 12(2): 551–563.

Sheffield, J., G. Goteti, and E.F. Wood. 2006. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. Journal of Climate 19 (13): 3088–3111.

Taylor, K.E., R.J. Stouffer, and G.A. Meehl. 2012. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93 (4): 485–498.

Xu, L., and A. Wang. 2019. Application of the bias correction and spatial downscaling algorithm on the temperature extremes from CMIP5 multimodel ensembles in China. Earth and Space Science 6: 2508–2524.

Xue, Y.K., Z. Janjic, J. Dudhia, R. Vasic, and F. De Sales. 2014. A review on regional dynamical downscaling in intraseasonl to seasonal simulation/prediction and major factors that affect downscaling ability. Atmospheric Research 147–148: 68–85.

# Appendix B Projection of Future Population and Economic System Changes

Fubao Sun, Jing'ai Wang, Yujie Liu, Wenxiang Wu, Huiyi Zhu, and Yaojie Yue

### Introduction

The shared socioeconomic pathways (SSPs), which qualitatively and quantitatively describe broad patterns of possible global socioeconomic development with assumptions about climate change and policy responses under different challenges to mitigation and adaptation (O'Neill et al. 2014), are one of the core contents in the Intergovernmental Panel on Climate Change (IPCC) scientific assessment reports (IPCC 2014) and in the current literature (O'Neill et al. 2016). The increasing demand of the Scenario Model Intercomparison Project (ScenarioMIP) is calling for spatially explicit population, GDP, crop distribution, industrial value added, and road networks projections of high resolution for the future SSPs in both socioeconomic development and climate change adaption and mitigation research.

### F. Sun

### J. Wang Y. Yue

© The Editor(s) (if applicable) and The Author(s) 2022

To date the global population, GDP, industrial values add, road networks projections for the five SSPs are mainly provided at the national and super-national scales from several global institutions, which have depicted a wide range of uncertainty within different organizations (Riahi et al. 2017; Xue et al. 2018) and limited the usage of integration with data from other disciplines. And the projections of gridded datasets at the global scale, to the best of our knowledge, are very limited so far.

This atlas has presented a set of spatially explicit global socioeconomic factors: population, GDP, crop distribution, industrial value added, and road networks projections that reflect substantial long-term changes of socioeconomic activities for both the historical period and future projections under different SSPs.

### Data Availability

Some detailed information regarding this atlas is shown in F. Sun (&) the table below.

(continued)

Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China e-mail<EMAIL_ADDRESS>
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China

Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

Y. Liu W. Wu H. Zhu

Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9

The global GDP disaggregation results for 2005 as historical period and for 2030–2100 as future projections for SSP1–5 at 10-year interval are provided (purchasing power parity in 2005 USD), acknowledging the two-children policy in China, with spatial resolutions of 30 arc-seconds (approximately 1 km at the equator) and 0.25 degrees. The global gridded GDP are provided in geotif format at https:// doi.org/10.5281/zenodo.4350027. The GDP values are disaggregated within its administrative boundaries, and the Antarctica, oceans, as well as some desert or wilderness areas are filled with value 0.

The global industrial added value disaggregation results for 2010 as historical period and for 2030 and 2050 as future projections for SSP1–3 are provided (purchasing power parity in 2010 USD), with spatial resolutions of 0.5 degrees. The global and China 1 km gridded industrial added value is provided in geotif format at http://www.geodoi.ac.cn/ WebCn/doi.aspx?Id=805 and https://www.scidb.cn/en/ detail?dataSetId=633694460995174400&dataSetType= journal, respectively.

Further detailed information regarding the atlas is available from "Global dataset of gridded GDP scenarios", which is provided by the Global Change Risk of Population and Economic Systems (GCR-PES): Mechanisms and Assessments Project, Beijing Normal University, Beijing, China (http://gcr.bnu.edu.cn/).

### References

IPCC. 2014. Climate change 2013: The physical science basis: Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge: U. K., Cambridge University Press.

O'Neill, B.C., E. Kriegler, K. Riahi, K.L. Ebi, S. Hallegatte, T.R. Carter, R. Mathur, and D.P. Van Vuuren. 2014. A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Climatic Change 122: 387–400.

O'Neill, B.C., C. Tebaldi, D.P. Van Vuuren, V. Eyring, P. Friedlingstein, G. Hurtt, R. Knutti, E. Kriegler, et al. 2016. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geoscientific Model Development 9: 3461–3482.

Riahi, K., D.P. Van Vuuren, E. Kriegler, J. Edmonds, and B. C. O'neill, S. Fujimori, N. Bauer, K. Calvin, R. Dellink and O. Fricko. . 2017. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change 42: 153–168.

Xue, Q., W. Song, and H. Zhu. 2018. Global industrial added value 1 km square grid dataset. Journal of Global Change Data & Discovery 2 (1): 9–17.

# Appendix C Risk Assessment: Framework and Models

Tao Ye, Wei Xu, and Peijun Shi

### Introduction

Global climate change featured by warming has created serious challenges to sustainable development and human security (IPCC 2014). It has become an important consensus of the international society to assess global change risk at the global scale and carry out tailored governance and risk-based adaptation (Aalst et al. 2014). Research initiatives from international organizations (World Economic Forum 2017), academic institutions, and state governments (DEFRA 2012) have covered global change risk assessment in some key fields, including people's health (Stephenson et al. 2013), population exposure and mortality (Hirabayashi et al. 2013), economic loss and assets exposures (Burke et al. 2015; Dietz et al. 2016), and food security (Rosenzweig et al. 2014). The assessment of global change risk is generally regarded as the evaluation of a co-evolutionary system consisting of changing climate and socioeconomic subsystems and their interactions (Winsemius et al. 2016). Correspondingly, such assessment predicts potential future losses using predefined quantitative models to integrate the combined dynamics of future climate change and socioeconomic development (Bouwer 2013; Liu et al. 2018; Wing et al. 2018). In the past decades, great efforts have been devoted to build climate projection (Eyring et al. 2016) and shared socioeconomic pathways (O'Neill et al. 2014) to produce datasets for common uses. Models used to describe the interactions, either statistical or process-based, are mostly problem-specific, and require substantially more efforts to catch up.

### Conceptual Framework

The general conceptual framework to assess global change risk is to develop model systems that can integrate the combined dynamics of future climate change and socioeconomic development. Overall, it succeeded the hazard, vulnerability, and exposure framework of classic risk assessment models, but needs to accommodate the dynamics of climate, population, and economic systems (Fig. C.1).

Two types of major driving forces are considered in the models: climate change and socioeconomic system change. For climate change, three factors were considered: near-surface air temperature, precipitation, and wind speed. As suggested by the Intergovernmental Panel on Climate Change (IPCC 2012), climate change contains the changes in its mean, variability, and skewness. When focusing on the risk, the changes in variability and skewness alters climate extremes. In light of this, the changes in the mean, interannual variability, and extremes of each climate factor were considered as the driving forces from the hazard side of risk.

The changes of population and economic systems indicate the changes in both exposure and vulnerability. Exposed units considered in this atlas included population, staple grain production of paddy rice, wheat, and maize, and gross domestic product (GDP).

Risk assessment models were employed to describe the interactions between climate change hazards and the population and economic systems. The interaction-focused approach determines the models used correspondingly. For population affected and mortality and GDP losses, only

T. Ye

Institute of Disaster Risk Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

W. Xu

State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

P. Shi

Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining, 810008, China e-mail<EMAIL_ADDRESS>
© The Editor(s) (if applicable) and The Author(s) 2022

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9

Fig. C.1 Conceptual framework of the global change risk assessment

climate extremes were considered, including extremely high temperature and rainstorms (and subsequent flood events). For these parts, classical natural disaster risk assessment models were employed by using a loss function (also know as quantitative vulnerability function) fitted from historical losses to link hazard and loss. For staple grain production, the impacts of changes in climate mean, variability, and extremes were all considered. Correspondingly, crop emulators derived from global gridded crop models were used to emulate crop yields under different climate change scenarios, from which the changes in mean yield, yield interannual variability, and lower extremes of yield were obtained (Table C.1).

### Technical Specifications

### Forcing and Scenarios

For climate change, Representative Concentration Pathways (RCPs) employed in the IPCC Coupled Model Intercomparison Project 5 (CMIP5) were used. To fully accommodate potential climate change, RCP2.6, RCP4.5, and RCP8.5 were actually used. Unless specifically indicated, climate forcing of NEX-GDDP released by the National Aeronautics and Space Administration (NASA) with 21 general circulation models (GCMs) and 0.25 degrees was used for RCP4.5 and RCP8.5. Thirteen GCMs in the CMIP5 project were downscaled using the bias correction and spatial downscaling (BCSD) method to supplement the NEX-GDDP dataset under the RCP2.6 scenario. More details about the downscaled data can be found in Appendix 1 (Projection of Future Climate Change).

For socioeconomic change, the Shared Socioeconomic Pathways (SSPs) setting the fundamental population and GDP growth paths in the future were considered. The SSP1, SSP2, and SSP3 were used in accordance with the RCP2.6, RCP4.5, and RCP8.5 scenarios. More details about the projection of global distribution of population, crop, GDP, industrial value added, and road networks are found in Appendix 2 (Projection of Future Population and Economic System Changes), and earlier chapters in Part II.

### Risk Metrics

Annual average loss (AAL) was used as the key risk metric indicator. Simulated losses were averaged over each 20-year period, namely the baseline period (1986 − 2005), the 2030s (2016 − 2035), and the 2050s (2046 − 2065), following the CMIP5 framework. In the atlas, multi-model ensemble means and spreads of AALs were reported. The difference in cross-period AALs is the final measure of global change risk, the change in risk jointly driven by climate change and population and economic system changes.

### References

Aalst, Maarten Van, Neil Adger, Douglas Arent, Jonathon Barnett, Richard Betts, Eren Bilir, Joern Birkmann, et al. 2014. "Climate Change 2014: Impacts, Adaptation, and Vulnerability." Assessment Report 5, no. October 2013: 1– 76.

Bouwer, Laurens M. 2013. Projections of Future Extreme Weather Losses under Changes in Climate and Exposure. Risk Analysis : An Official Publication of the Society for Risk Analysis 33 (5): 915–930. https://doi.org/10.1111/j.1539- 6924.2012.01880.x.

Burke, Marshall, Solomon M. Hsiang, and Edward Miguel. 2015. Global Non-Linear Effect of Temperature on Economic Production. Nature 527 (1): 235–239. https://doi. org/10.1038/nature15725.

DEFRA. 2012. UK Climate Change Risk Assessment: Government Report. https://www.gov.uk/government/ uploads/system/uploads/attachment\_data/file/69487/ pb13698-climate-risk-assessment.pdf.

Dietz, Simon, Alex Bowen, Charlie Dixon, and Philip Gradwell. 2016. 'Climate Value at Risk' of Global Financial Assets. Nature Climate Change 6 (April): 1–5. https://doi. org/10.1038/nclimate2972.

Eyring, Veronika, Mattia Righi, Axel Lauer, Martin Evaldsson, Sabrina Wenzel, Colin Jones, Alessandro Anav, et al. 2016. ESMValTool (v1.0) – a Community Diagnostic and Performance Metrics Tool for Routine Evaluation of Earth System Models in CMIP. Geoscientific Model Development 9 (5): 1747–1802. https://doi.org/10.5194/gmd-9- 1747-2016.

IPCC. 2012. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Edited by Christopher B. Field, Vicente Barros, Thomas F. Stocker, and Qin Dahe. Intergovernmental Panel on Climate Change Spacial Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. https://doi.org/10. 1017/CBO9781139177245.

IPCC. 2014. "Climate Change 2014: Synthesis Report." Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In: Core Writing Team, Pachauri RK, Meyer LA (Eds) IPCC, Geneva, Switzerland, 151 P. Vol.<PHONE_NUMBER>. https://doi.org/10.1017/CBO9781107415324.

O'Neill, Brian C., Elmar Kriegler, Kristie L. Ebi, Eric Kemp-Benedict, Keywan Riahi, Dale S. Rothman, Bas J. van Ruijven, et al. 2014. The Roads Ahead: Narratives for Shared Socioeconomic Pathways Describing World Futures in the 21st Century. Global Environmental Change 42 (January): 169–180. https://doi.org/10.1016/j.gloenvcha. 2015.01.004.

Rosenzweig, Cynthia, Joshua Elliott, Delphine Deryng, Alex C. Ruane, Christoph Müller, Almut Arneth, Kenneth J. Boote, et al. 2014. Assessing Agricultural Risks of Climate Change in the 21st Century in a Global Gridded Crop Model Intercomparison. Proceedings of the National Academy of Sciences of the United States of America 111 (9): 3268–3273. https://doi.org/10.1073/pnas.1222463110.

Stephenson, Judith, Susan F. Crane, Caren Levy, and Mark Maslin. 2013. Population, Development, and Climate Change: Links and Effects on Human Health. Lancet 382 (9905): 1665–1673. https://doi.org/10.1016/S0140-6736(13) 61460-9.

Wing, Oliver E J., Paul D. Bates, Andrew M. Smith, Christopher C. Sampson, Kris A. Johnson, Joseph Fargione, and Philip Morefield. 2018. Estimates of Present and Future Flood Risk in the Conterminous United States. Environmental Research Letters 13 (3). https://doi.org/10.1088/ 1748-9326/AAAC65.

Winsemius, Hessel C., Jeroen C. J. H. Aerts, Ludovicus P. H. van Beek, Marc F. P. Bierkens, Arno Bouwman, Brenden Jongman, Jaap C. J. Kwadijk, et al. 2016. Global Drivers of Future River Flood Risk. Nature Climate Change 6 (4): 381–385. https://doi.org/10.1038/nclimate2893.

World Economic Forum. 2017. The Global Risks Report 2017 12th Edition. Insight Report. https://doi.org/10.1017/ CBO9781107415324.004.

# Appendix D Atlas Architecture and Design

Jing'ai Wang, Ying Wang, and Tian Liu

### Design Concept

The maps of global change risk of population and economic systems are designed to express the spatial distribution of various elements. The main contents are the climate change, changes of exposures, and risks, and the core contents are the regional differences and changes of risks in different time periods and under different emissions and socioeconomic scenarios in the future. The maps visualize where the areas with the greatest changes in the extremes of basic meteorological elements will be, where or during what time period the risk will change the most, and where the highest risk zones will be found, which help the readers and users of the atlas to understand the spatial patterns of change in the future and make informed decisions. Every type of maps contains information on indicator (including mean, variability, and extremes), time period and scenario (including different time periods and emissions and socioeconomic scenarios), and attribute (different grades). The atlas is based on the three-dimensional structure (Fig. D.1) to present the contents, with the selected methods of presentation and color scheme and layout.

J. Wang Y. Wang T. Liu School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing, 100,875, China

### Cartographic Units

Grid is the fundamental unit for the assessment of basic climate factors, exposure, and risk of the population and economic systems as well as their cartographic presentation, which is of 0.25° 0.25°.

### Technical Flowchart

The mapping and compilation of this atlas include the following steps: preparation and design, map drafting, map plotting, and map generalization (Fig. D.2).

### Cartographic Presentation

A variety of conventional cartographic presentation methods are used in this atlas to describe the change of basic climate factors, exposure, and risk (Table D.1), such as the quality-based method, line method, quantity-based method, etc.

Map groups can organize the maps of complex disaster processes in an intuitive way. Within a map group, each series contains seven combinations of time period and scenario—the baseline (2000s), 2030s-RCP2.6 (SSP1), 2030s-RCP4.5 (SSP2), 2030s-RCP8.5 (SSP3), 2050s-RCP2.6 (SSP1), 2050s-RCP4.5 (SSP2), and 2050s-RCP8.5 (SSP3). Examples of the map groups are provided in Table D.2 (Tables D.3 and D.4).

### Map Color Design

The final color system of the maps in the atlas is presented in Table D.5.

J. Wang Y. Wang (&) T. Liu

Institute of Disaster Risk Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100,875, China e-mail<EMAIL_ADDRESS>
© The Editor(s) (if applicable) and The Author(s) 2022

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9

Fig. D.2 Technical flowchart for the mapping and compilation of the atlas

### Table D.1 Map presentation methods

Table D.2 Climate change map group

Table D.3 Change of exposure map group

In this atlas, the color design is demanding. In the part of climate change, the presentation of attributes adopts a ten-grade classification system. The color design principles are (1) emphasizing the areas with extreme values since these areas have the top level of risk for warning, and using light gray for areas with no data and dark gray for extremely low values in some maps (i.e., annual mean temperature variability); (2) highlighting the main colors (red, blue, brown) of the three basic climate factors (temperature, precipitation, wind); and (3) highlighting coastal boundaries by using thicker lines and darker color, for maps related to temperature and wind speed. In the rainfall maps, the coastal boundaries are depicted in black to distinguish them from the dominant color (blue).

In the part of exposures, the presentation of attributes also adopts a ten-grade classification system. The color design

Table D.4 Crop yield risk map group

principles are (1) highlighting the main colors (orange, purple, green, brown) of each element (population, GDP, crops, and roads) in the distribution map of population and economic system elements; (2) emphasizing the colors of both the element itself and the hazard factor (rainstorm-blue, drought-brown, high temperature-red/purple); and (3) representing the 0 or no data areas with white or light gray, except for crop exposure to extremely high temperature (emphasized in black), and high values are emphasized in red or purple for warning in the exposure maps.

In the part of risks, the presentation of attributes adopts a 5/6-grade classification system. The color design principles are (1) using red/purple in all maps to emphasize the areas with high risk; (2) using white to represent areas with no data in the heatwave mortality risk, flood mortality risk, and flood GDP loss risk maps, and the color of the ocean is changed from dark blue to light blue to indicate different periods; (3) in the crop risk maps the color of areas with no data is changed from dark gray to light gray to indicate different periods.

All maps in the atlas follow the principle of applying bright and distinct colors to clearly show the spatial distribution of the attributes.

### Table D.5 Color system of the maps

### Cartographic Specifications

The national/regional boundary map of this atlas was same as the one in the World Atlas of Natural Disaster Risk. The designations employed and the presentation of material on the maps do not imply the expression of any opinion concerning the legal status of any country, territory, city, or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. It uses the Equivalent Difference Latitude Parallel Polyconic Projection with Central Meridian of 150°E. We transformed the projection from the Equivalent Difference Latitude Parallel Polyconic Projection into the Robinson Projection and performed registration before using the boundaries in the maps of this atlas.

All maps in the atlas adopt the Robinson Project with Central Median of 160°E. According to the tasks and purposes of the atlas, we used the following scales for the full map of the world: 1:140,000,000 (Part I and baseline, single page) and 1:200,000,000 (Parts II, III, and IV, 1/2 page).

# Appendix E Integrated Assessment Model of Global Change Risk of Population and Economic Systems

### Ning Li, Zhengtao Zhang, Jidong Wu, Saini Yang, and Hongjian Zhou

To integrate the population risk (injured and casualties, affected population) and the economic risk (production reduction of major crops, damage to road networks, GDP losses) due to global change, the integrated assessment model of population and economic system risks of global change is constructed and released.

This model integrates the theories and methods of global change risk assessment, and can quantitatively assess the risks of the population and economic systems caused by changes in the mean, variability, and extremes of climate elements (temperature, precipitation, wind). This model can meet the requirements of population and economic system risk assessment under different emissions scenarios (RCP2.6, RCP4.5, and RCP8.5, and SSP1, SSP2, and SSP3) at different spatial resolutions (50 km 50 km for the global scale, and high resolution of 30 km 30 km for hotspot areas), and in different time periods (the 2030s and 2050s). The global multi-regional assessment model and multi-scale assessment model are systematically integrated, and finally a global population and economic system risk assessment model is formed.

The model is mathematically reliable and produces useful results. It can effectively identify the quantitative relationships between the characteristics of different types of hazards and their intensity and the impact on the populations, the function of the transportation networks, the capital stock,

N. Li (&) Z. Zhang J. Wu S. Yang

and the industrial systems. It can also integrate different emissions scenarios and future near-term (the 2030s) and medium-term (the 2050s) risk changes, and help to understand the mechanism of climate change that may lead to risks.

Each module in this model describes the key steps in model construction and is based on a set of reasonable assumptions. Its results are replicable, and each model has been exemplified by actual cases (Fig. E.1). The platform is shown in Fig. E.2.

The three key modules and their component models are described as follows:

	- A model for evaluating the impact of future average temperature on China's population mortality. This model assesses the risk of population mortality under the RCP4.5 and RCP8.5 scenarios at a regional scale.
	- A model for evaluating the impact of future drought on the exposure of the affected population in the Beijing, Tianjin, and Hebei region. The model evaluates the exposure of the population to droughts under different future scenarios.
	- A model for evaluating the impact of future heatwaves on the global exposure of the affected population. The model considers the thermal stress of extreme temperature and humidity, and quantifies the risk of heatwaves affecting the population at the global and regional scales under different future scenarios.

School of National Safety and Emergency Management, Beijing Normal University, Beijing, 100875, China e-mail<EMAIL_ADDRESS>
H. Zhou

National Disaster Reduction Center of China, Ministry of Emergency Management of the People's Republic of China, Beijing, 100124, China

© The Editor(s) (if applicable) and The Author(s) 2022

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9

Fig. E.1 Composition of the integrated assessment model of global change risk of population and economic systems

Fig. E.2 Platform of the integrated assessment model of global change risk of population and economic systems

	- A comprehensive disaster impact assessment model of floods on the function of China's highway network system. The model evaluates the differences in the impact of floods on the function of highway network systems, changes in regional traffic flow, and different adaptation measures.
	- An exposure assessment model of China's highways under the influence of extremely high temperature and extreme precipitation events. The model identifies the time and location at which the highways may suffer destructive damages, and assesses the degree of exposure to high temperature and precipitation events of highways in China in the future.
	- A direct loss assessment model of typhoon events. This model uses the changes in the intensity and frequency of future typhoon hazards predicted by climate models to estimate the future spatiotemporal development trends of

GDP and asset stock exposure of China to typhoons (based on SSPs data).

	- A direct loss assessment model of rainstorm and flood events. This model uses the nonlinear relationship between the direct economic losses of historical disasters and the three climate elements of risk to evaluate the direct losses of heavy rains and floods in China.
	- An indirect economic impact assessment model of the disaster-affected labor forces. This model assesses the indirect economic impact of the decline in labor supply and its recovery due to a heavy rain in Wuhan City on Hubei Province and China.
	- A functional impact assessment model of highway network system. This model evaluates the differences in regional traffic flow due to the floods in China, and changes and different adaptation measures.

# Appendix F Global Change Risk Map Platform of Population and Economic Systems

Peijun Shi, Wei Xu, Tao Ye, and Bo Chen

To further explore the spatial distribution and changes of various elements, the digital atlas platform—the Global Change Risk Map Platform of Population and Economic Systems—is designed.

The platform is the first to systematically present the global change risks of population and economic systems at a global scale. It is multi-period, multi-scenario, high-resolution, and interactive. The platform systematically presents the global changes in environmental elements, changes in climate elements (temperature, precipitation, wind), changes in population and economic systems (GDP, crops, industrial added value, transportation infrastructure), and global change risks in population and economic systems. The map data cover three periods—the baseline period (1985 − 2005), the 2030s (2016 − 2035), and the 2050s (2046 − 2065). For data in the 2030s and the 2050s, three Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), three Shared Socioeconomic Pathway (SSP) scenarios (SSP1, SSP2, and SSP3) and their combination scenarios are included. The platform includes about 1150 maps in total (Table F.1) and most maps have a spatial resolution of

P. Shi (&) W. Xu T. Ye B. Chen

Institute of Disaster Risk Science, Faculty of Geographical Science, Beijing Normal University, Beijing, 100,875, China e-mail<EMAIL_ADDRESS>
P. Shi W. Xu T. Ye B. Chen

State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100,875, China

Academy of Plateau Science and Sustainability of the People's Government of Qinghai Province and Beijing Normal University, Xining, 810,008, China

© The Editor(s) (if applicable) and The Author(s) 2022

P. Shi

P. Shi, Atlas of Global Change Risk of Population and Economic Systems, IHDP/Future Earth-Integrated Risk Governance Project Series, https://doi.org/10.1007/978-981-16-6691-9

Table F.1 Maps on the digital atlas planform of global change risk of population and economic systems

(continued)

Table F.1 (continued)

Note Value = the value for the current period (baseline, 2030s, or 2050s); change = the change between 2030s/2050s and the baseline period; MME = multi-model mean; MMS = uncertain of the multi-model; RCP = Representative Concentration Pathway; SSP = Shared Socioeconomic Pathway.

Fig. F.1 Global change risk map platform of population and economic systems

0.25° 0.25°. All maps on the platform use the project of EPSG 4326 (the World Geodetic System 1984).

The platform enables map query, browsing and roaming, zoom in and out, map download, data display, data query, data download, and layer color manipulation (Fig. F.1). Compared with traditional paper maps, the platform is dynamic and interactive. More information about the platform can be found at http://www.grisk.info.