Stochastic Analysis of Potential Tsunami Risks with the Contribution of Sea Level Rise in the Sea of Japan through Japanese Coastline

doi:10.21203/rs.3.rs-1600092/v1

Japan encountered one of the most devastating tsunami hazards, resulting in a nuclear accident in 2011. Located in an active seismic zone, Japan's coastline lying through the Sea of Japan are also highly prone to probable inundations in the future. We conducted a probabilistic tsunami risk assessment that emerged with the contribution of sea-level rise on tsunami waves. We present economic and social risk aspects for the selected residential areas, commercial facilities, and industrial zones by considering aleatory variability of tsunami waves and epistemic uncertainty of sea level rises. Economic risk evaluations show that extreme monetary losses are very likely to observe with the positive contribution of projected economic growth rates. However, social risks are comparatively low due to the expected population de-crease in Japan in the following years.

Stochastic analysis

Monte Carlo simulation

tsunami simulation

sea-level rise

GDP projection

population growth rate

potential risk assessment

Global warming due to climate change inevitably causes thermal expansion and glacier melting which are the main reason for sea-level rise (SLR). This phenomenon has been investigated by scientists to make reliable estimations and to define the pathway for the precautions that can be taken against the adverse effects of SLR at the coasts (Kausher et al. 1996; Kasperson and Kasperson 2001; McLean et al. 2001; Wahl et al. 2018; Mucova et al. 2021). Especially, the Intergovernmental Panel on Climate Change (IPCC 2004) is established in 1988 as a United Nations body by the World Meteorological Organization (WMO) to assess the conditions and scientific contributions related to climate change. Assessment Report 6 (IPCC-AR6) is released in 2022 to evaluate the current and probable impacts of climate change, to define the vulnerability of the nations on a global scale, and to shed light on the appropriate worldwide adaptation strategies (IPCC 2022). As pointed out with high confidence in AR6-Chap. 10 (IPCC 2022) prepared by Working Group II, SLR will follow an upward trend through the upcoming years and extreme inundation levels will be observed at the coasts of Asian countries. A sea-level prediction tool that allows regional and global scale mean sea level predictions is prepared by NASA (n.d.) with the contribution of AR6-Working Group I (IPCC 2021). Tsunamis, on the other hand, have been the main concern among scientists due to their devastating economic, social, and environmental consequences. Earthquake-induced tsunamis (EITs) are particularly investigated due to their high level of occurrence probabilities compared to other sources of tsunamis like landslides, volcanos etc. (Fritz et al. 2013; Jaimes et al. 2016; Heller 2019; Cummins et al. 2020).

Laying down on the four active tectonic plates, Japan is surrounded by over 2000 active seismic faults. According to the Cabinet Office of Japan, annually experienced 20% of the earthquakes in the world having a magnitude (${M}_{w}$) greater than 6 takes place in Japan or its surroundings (Cabinet office 2018). Therefore, awareness/preparedness level against earthquakes and tsunamis is really high among Japanese society. Legislative regulations and advanced engineering implementations are able to mitigate the adverse effects of destructive earthquakes and tsunamis. Nonetheless, complete safety has not been established against these natural hazards yet. Especially, after the lessons learned from the 2011 Tohoku earthquake and tsunami, Japanese authorities and scientists have intensively been working on feasible solutions for disaster prevention and mitigation systems, nuclear safety, and risk management means for natural hazards (Itoi and Sekimura 2017).

Although most of the devastating earthquakes have been observed at the Japanese Pacific coastlines, 60 faults evaluated as potential earthquake and tsunami sources were also listed in the Sea of Japan (MLIT 2014). Tsunami propagations and amplifications, inundation characteristics based on the coast structure were comprehensively investigated through the Sea of Japan (Kojima et al. 2017; Suzuki et al. 2018; Wu and Satake 2018; Yamanaka et al. 2019; Mulia et al. 2020; Sato et al. 2020; Yamanaka and Shimozono 2021,2022). Through these recent studies, disaster prevention and mitigation strategies can be developed. However, the literature about economic and social risk evaluations is still limited for the Japanese coastline at the Sea of Japan.

Stochastic combined risk analysis of EITs with and without the presence of SLR is vital to take required precautions against a probable destructive tsunami event in the future. The combined hazard analysis of SLR and EITs has also been studied among scientists in recent years (Dall'Osso et al. 2014; Li et al. 2018; Nagai et al. 2020). In this study, tsunami inundation levels with and without the presence of SLR are directly obtained from the ongoing project (supported by The Scientific and Technological Research Council of Turkey) conducted by the authors (Yavuz and Itoi 2022).

Contrary to the commonly-focused literature about tsunami generation and inundation mechanisms, economic and social risk aspects are inspected and the results of the risk aspects are revealed as monetary loss and a number of people directly affected by the tsunami, respectively. It should be pointed out that, by doing this, no previous precautions and measurements are taken into account. Economic and social risks are updated for the further years depending on the Gross Domestic Product (GDP) per capita and population growth rate estimations, respectively. Then, comprehensive risk analysis and the rate of change of risk levels are calculated for the projected years (i.e. 2020, 2050, and 2100). The objective of the study is to propose an enforceable approach for Japanese society may decide whether they can take an action or not against the rate of change of economic and social risks for the projected years.

Risk is defined as a function of probability and consequences (Kaplan and Garrick 1981). Risk evaluation conducted in this study is based on stochastic analysis of tsunami inundation levels ( $i$ ) with and without the presence of SLR in Yavuz and Itoi (2022). Annual exceedance probability versus damage levels are calculated by considering the various inundation levels and related damage values. Economic damage ( ${C}_{t,i}^{ML}$ ) values for Japan (in US$) are retrieved from Huizinga et al. (2017) and updated for the projected years according to GDP-per capita growth rates of Japan (OECD n.d.). Similarly, social damage level (i.e. number of people directly affected by tsunami) ( ${C}_{t,i}^{NP}$ ) is also calculated by using the population growth rate predictions by the Statistical Bureau of Japan (n.d.) for the projected years. By means of annual exceedance probabilities and corresponding damage values, economic, social, and environmental risks are calculated for the selected regions. The regions (i.e. Fukuoka city center, Niigata city center, Izumo city center, and Yogano-Sakaiminato city center) shown in Fig. 1 are selected from different parts of the Japanese coastline along the Sea of Japan to reveal a general idea about the inundation levels and topographic conditions of the selected regions.

Same procedure is performed by taking IPCC-SSP scenarios into account for the projected years (i.e. 2020,2050, and 2100). The flowchart of the study is illustrated in Fig. 2.

2.1 Generation Mechanism of Inundation Levels

The number of Monte Carlo simulations is determined in Yavuz and Itoi (2022) based on the Gutenberg-Richter relationship of the assigned probabilistic distributions. ${M}_{w}$ values of the historical earthquake records are compiled from the ISC-GEM ver. 8 earthquake catalog (Di Giacomo et al. 2018). Then, tsunami hazard curves are calculated from 100000 Monte Carlo simulations and illustrated for a sample location in Fig. 3.

The coincidence between the random Monte Carlo simulations shows that the reliability of the analysis is satisfied up to 10^− 4/year. The general framework of the stochastic tsunami hazard analysis retrieved from Yavuz and Itoi (2022) at each selected region is demonstrated in Fig. 4.

In Table 1 and Table 2, EITs + SLR levels are given for each IPCC-SSP scenario of the projected years compared with the 2021 altitudes of the selected regions for 10^− 3/year and maximum probable inundation levels, respectively. The contribution of SLR on inundation levels to the EITs can be seen clearly from the related tables.

Table 1 10^-3/year EITs+SLR levels at some of the selected region

Location	Maximum probable inundation level (m)
	2020	2050			2100
	2020	SSP1-2.6	SSP2-4.5	SSP5-8.5	SSP1-2.6	SSP2-4.5	SSP5-8.5
Fukuoka	0.51	0.71	0.72	0.76	0.89	1.09	1.42
Izumo	2.16	2.34	2.36	2.39	2.53	2.72	2.98
Niigata	2.43	2.65	2.66	2.70	2.83	3.03	3.28

Table 2 Maximum probable EITs+SLR levels at each selected region (corresponding to 10^-4/year)

Location	Maximum probable inundation level (m)
	2020	2050			2100
	2020	SSP1-2.6	SSP2-4.5	SSP5-8.5	SSP1-2.6	SSP2-4.5	SSP5-8.5
Fukuoka	1.93	2.13	2.14	2.18	2.31	2.51	2.84
Matsue	4.01	4.21	4.22	4.26	4.39	4.59	4.96
Niigata	8.03	8.23	8.24	8.28	8.41	8.61	8.98

Risk analyses are conducted for economic, social, and environmental aspects based on the possible inundation levels for the projected years for each IPCC-SSP scenario in the following sections. Thus, the hypothetical economic, social, and environmental risk levels for EITs only, SLR only, and EITs + SLR conditions are calculated separately for each selected region and related SSP scenarios of projected years.

2.2 Economic and Population Growth Rate Projections

Projection of socioeconomic conditions is significant to reliably estimate the future probable economic and social damages for the selected regions in Japan. There are several future scenarios are revealed about the population sizes and economic development of the countries in the literature (Li et al. 2019; Vollset et al. 2020; Gu et al. 2021).

The economic growth rate (GDP-per capita) at a global scale is estimated as ~ 2.03% per year between 2010 and 2100 (Christensen et al. 2018). The population growth rate of Japan is expected to decrease drastically in the future years even though all the estimations show a substantial increase in global population size (Vollset et al. 2020; Chen et al. 2020). Nevertheless, most of the future economic development and growth scenarios for Japan shows that Japan will remain the fourth-largest economy in 2100 (Vollset et al. 2020; Ikeda and Managi 2019). Honjo et al. (2021) conducted a comprehensive economic growth rate projection at the prefecture-level in Japan until 2100, depending on Japan shared socioeconomic pathways (JPNSSPs) developed by Japan National Institute of Population and National Security Research. Ten different scenarios based on economic productivities of the past years are used to estimate economic projections for the years 2050 and 2100. Honjo et al., (2021) pointed out that GDP per capita is estimated to increase 237% between 2010 and 2100. However, the GDP growth rate is estimated to be adversely affected due to the aging and decline in the population. In this study, OECD long term GDP volume growth (%) forecast is used to generate ${GDP}_{t}^{GR}$ for 2021 and 2050 (Yavuz and Itoi 2022). 2100 ${GDP}_{t}^{GR}$ value is determined depending on the most optimistic scenario released by Honjo et al., (2021) .

Statistics Bureau of Japan (n.d.) released a statistical handbook that includes present-day Japan statistics in October 2021. Chapter 2 of the handbook includes 2020 population census results and ${P}_{t}^{GR}$ projections up to 2060 (see Table 3).

Table 3

2020 population census results of Japan and ${\text{P}}_{\text{t}}^{\text{G}\text{R}}$ projections
Year	Population (1000)	Rate of population change (%)	Population Density (per/km²)
2020	125708	-0.36	337
2030	119125	-0.54	319
2040	110919	-0.71	297
2050	101923	-0.84	273
2060	92840	-0.93	249

The ${GDP}_{t}^{GR}$ of Japan is estimated to become lower than approximately 1% around 2025 and remain around 0% until 2100 (see Fig. 5a). Depending on the information and projection values provided by the Statistics Bureau of Japan (n.d.), ${P}_{t}^{GR}$ projections are made for the related years (see Fig. 5b).

Combined risk analyses at each selected region (i.e. EITs in the presence of SLR) are conducted for the projected years. Depending on the inundation levels, social and economic risks are calculated by using the number of people directly affected by tsunami (${C}_{t,i}^{NP}$) and monetary loss (${C}_{t,i}^{ML}$) as consequences of risk aspects, respectively. It is obvious that economic and social risks are going to change for the projected years due to an increase or decrease in the growth rates in the future. To reveal a realistic risk estimation for the selected regions, monetary values and/or population rates are projected for the determined years depending on the recorded Gross Domestic Product (${GDP}_{t}^{GR}$) and Population Growth (${P}_{t}^{GR}$) rates.

2.3 Economic Risk Analysis

Economic risk levels of each selected region are calculated by multiplying the exceedance probabilities of inundations obtained from tsunami simulations and its corresponding monetary losses ($US\$/{m}^{2}$) (i.e. damage level) released by the EU Joint Research Center (Huizinga et al. 2017). To conduct a combined risk evaluation due to SLR and EITs, SLR levels are initially considered for the selected regions and additional inundation levels due to EITs are added to determine the overall inundated areas. By doing so, risk evaluations are conducted for each SSP scenario at each related year. For the year $t$ and for inundation level $i$, the monetary loss ($US\$/{m}^{2}$) is estimated using the following equation:

${C}_{t,i}^{ML}={C}_{i}{{GDP}_{t}^{GR}C}_{max}^{ML} {A}_{t,i}$ (1)

where ${C}_{t,i}^{ML}$ is the monetary loss ($\text{U}\text{S}\text{\$}$) of the related year $t$ and inundation level $i$, ${C}_{max}^{ML}$ is the maximum loss value ($US\$/{m}^{2}$) depending on the land-use category released by Huizinga et al. (2017), ${GDP}_{t}^{GR}$ is the projected economic growth rate for the year of $t$, ${A}_{t,i}$ is the inundated area depending on $i$ and $t$,${C}_{i}$ is the inundation level-monetary loss function specifying ${C}_{max}^{ML}$ at continental level (Huizinga et al. 2017).

Determination of the economic risk for both the projected year$t$ and the inundation level $i$ at all stages can be possible by creating inundation depth-monetary loss curves for each selected region. Then, economic risk can be computed by integrating the area under the inundation depth-monetary loss curve for each region. Economic risk can be calculated by using the following equation:

${Risk}_{t}^{Economic}=\sum {\varDelta \text{E}\text{P}}_{t}\stackrel{-}{{C}_{t,i}^{ML}}$ (2)

where ${\varDelta \text{E}\text{P}}_{t}$is the interval between two annual exceedance probabilities for year $t$, $\stackrel{-}{{C}_{t,i}^{ML}}$ is the mean projected monetary losses belongs to ${\varDelta \text{E}\text{P}}_{t}$ for the year $t$. By doing so, the economic risk level is determined for each inundation level interval. The overall economic risk level for a selected region is determined just by summing up the risk levels at each inundation level.

2.4. Social Risk Analysis

Social risk for each selected region is evaluated using a similar approach as in economic risk evaluation. In this study, number of people ${C}_{t,i}^{NP}$ in the inundated area for year $t$ is expressed as a function of current population density ($pd$), population growth rate ratios (${P}_{t}^{GR})$, and the inundated area (${m}^{2}$) depending on $i$ and $t$ (${A}_{t,i}$). $pd$ value for each region can easily be obtained from the literature. The number of people in the affected region is calculated as:

${NP}_{t,i} ={PGR}_{t}{p}_{d}{A}_{t,i}$ (3)

Although ${C}_{t,i}^{NP}$ can be the consequence parameter of the social risk calculation alone, some other parameters (i.e. vulnerability, fragility) can also be added to calculate the social risk. In this study, vulnerability coefficient $({C}_{vul}$) proposed by Yavuz et al., (2020) is considered to calculate the social risk in detail. ${C}_{vul}$ is identified depending on age class, tsunami awareness and preparedness, literacy rate, and annual income level of the related population in the selected region (Yavuz et al. 2020). The calculation of the social risk is possible just by multiplying the calculated consequence with the exceedance probability intervals for each inundation level. However, a precise number of physically damaged people cannot be easily calculated. Inundation level and wave velocity up to some certain level may not physically affect the people. Kurisu et al. (2018) conducted an experimental study on improving the survival ratios during tsunamis. It is concluded that the tsunami waves with 0.59 ± 0.13 m high can easily drown an adult standing on s concrete block. Nakamura et al. (2017) also performed a three-dimensional numerical simulation on drowning prevention of a human and resulted that tsunami wave velocities above 2.5 m/s can physically affect an ordinary human. In this study, these two criteria are also considered to calculate the social damage level as shown in the following equation:

${C}_{t,i}^{NP}=\left\{\begin{array}{c} 0 for i<0.5 m, {V}_{i}<2.5 m/s\\ \sum {P}_{t}^{GR}{p}_{d}{A}_{t,i}{C}_{vul} for i\ge 0.5 m, {V}_{i}\ge 2.5 m/s\end{array}\right.$ (4)

Calculation of the social risk is possible for both the projected year$t$ and the inundation level $i$ at all stages by multiplying the annual exceedance probability of the calculated inundation levels with the ${C}_{t,i}^{NP}$. In this study, social risk is defined as:

${Risk}_{t}^{Social}=\left\{\begin{array}{c} 0 for i<0.5 m, {V}_{i}<2.5 m/s\\ \sum {\varDelta \text{E}P}_{t}\stackrel{-}{{C}_{t,i}^{NP}} for i\ge 0.5 m, {V}_{i}\ge 2.5 m/s\end{array}\right.$ (5)

where ${\varDelta \text{E}\text{P}}_{t}$is the interval between two annual exceedance probabilities for year $t$, $\stackrel{-}{{C}_{t,i}^{NP}}$ is the mean projected social damage level belongs to ${\varDelta \text{E}\text{P}}_{t}$ for the year $t$. For $i<0.5 m and/or {V}_{i}<2.5 \text{m}/\text{s}$, social risk is assumed to be zero. Casio (n.d.) provides an online tsunami speed calculator based on the simple equation of $V=\sqrt{gh}$ which is often used to calculate tsunami speed triggered by underwater earthquake. The calculator has a simple structure as just execute the tsunami speed from manually defined tsunami wave height. According to this reference, for $i\ge 1.0 m$, tsunami speed is executed as 3.13 m/s and for $i\ge 0.5 m$, the speed of the tsunami is given as 2.21 m/s. Depending on the results provided by Casio (n.d.), the social risk is calculated just based on the tsunami wave height at the coast.

In this study, economic, social, and environmental risk aspects are empirically examined for the selected regions along the Japanese Coastline at the Sea of Japan. Statistically investigated EITs with and without the presence of climate change-related SLR are retrieved from Yavuz and Itoi (2022) and potential risk circumstances are deterministically produced for each IPCC-SSP scenario at the projected years. The results of each analysis are shown in the following sections and required discussions are made accordingly.

3.1. Economic Risk Evaluation

${Risk}_{t}^{Economic}$ is calculated for residential, commercial and industrial elements at potential risk considering EITs, SLR and EITs + SLR circumstances for each IPCC-SSP scenario of the projected years. Up-to-date maximum damage values ${C}_{max}^{ML}$ (in US$) for residential, commercial and industrial construction costs are calculated based on the method given by Huizinga et al. (2017). Then, the projections of the damage values are computed based on the ${GDP}_{t}^{GR}$ -per capita of Japan for 2050 and 2100. The ${GDP}_{t}^{GR}$ -per capita and the ${C}_{max}^{ML}$ values of the elements at potential risk are illustrated at each projected years in Table 4.

Table 4 -per capita of Japan and the values of residential, commercial and industrial elements (in US$)

As demonstrated in Table 8, ${\text{G}\text{D}\text{P}}_{\text{t}}^{\text{G}\text{R}}$-per capita values are estimated to increase for the projected years, even though the purchasing power parity is estimated to be almost the same in the following years. The increase in ${\text{G}\text{D}\text{P}}_{\text{t}}^{\text{G}\text{R}}$-per capita in the following years is resulted due to the drastic decrease in estimation in population of Japan in the future (see Fig. 5 (a) and (b)).

The change of the damage values with respect to inundation levels for the projected years ${C}_{t,i}^{ML}$ are also calculated based on the information released by the EU Joint Research Center (Huizinga et al. 2017) and shown in Table 5.

Table 5 values (in US$) for residential, commercial and industrial elements for the projected years

Parallel to the information given in the report prepared by Huizinga et al. (2017), ${C}_{t,i}^{ML}$ values are increased up to 6 m inundation depth. Then, ${\text{C}}_{\text{m}\text{a}\text{x}}^{\text{M}\text{L}}$ values are assumed to be valid for $i\ge 6.0 m$.

Here, economic risks assessment is illustrated for some elements at potential risk in Niigata region (i.e. Niigata city center, Niigata Port, Gas Chemical Co., Ltd. Niigata Factory, and Niigata Airport). As an example, densely populated areas and altitude values in Niigata city center and its suburbs are shown in Fig. 6.

Probable inundated areas are determined for four different infrastructures at risk and calculated ${Risk}_{t}^{Economic}$ results are given as ${C}_{t,i}^{ML}$ (monetary losses in US$) and shown in Fig. 7 and Fig. 8, respectively. In these fıgures, economic risk due to only SLR and only EITs -demonstrated by gray and blue cylinders- represent the positive contribution of ${GDP}_{t}^{GR}$ on economic risk evaluation for all determined scenarios. When ${C}_{t,i}^{ML}$ only considering both SLR&EIT increase and ${C}_{t,i}^{ML}$ only considering SLR also increase and is dominant, it means selected residential, commercial and industrial elements at potential risk will be inundated frequently due to SLR when no measure against SLR will be introduced. When ${C}_{t,i}^{ML}$ considering both SLR and EITs increase and ${C}_{t,i}^{ML}$ only considering SLR does not increase and is not dominant, it means selected residential, commercial and industrial elements at potential risk will not be affected by SRL itself but will be affected by higher tsunami height due to SLR in case of rare earthquakes. Combination of SLR and EITs –demonstrated by orange cylinder- shows that the economic risk with the positive contribution of ${GDP}_{t}^{GR}$ will definitely increase for future years and for all IPCC-SSP scenarios.

In Fig. 7 (a), both SLR and EITs are adversely affect the residential areas in Niigata city center and in the suburbs. In the year 2050, EIT is dominated the economic risk for all scenarios comparing with the SLR effect. In 2100, the adverse effect of SLR is drastically increased due to the low-lying areas in the selected region. The amount of monetary loss is estimated to reach up to 80 billion US$ due to the combined economic risk analysis for the residential areas of Niigata. Similarly, in Fig. 7 (b), the change in economic risk is estimated to dominate by SLR in 2100 SSP4.5 and SSP8.5 scenarios for Niigata Airport. Since Niigata Airport has a slight high altitude from the sea level, there is no SLR contribution observed for the SSP scenarios in 2050. Due to the small area and the slight high altitude of the Niigata Airport, the amount of monetary loss is estimated to reach up to 180 to 750 million US$ in 2100 for all SSP scenarios.

Economic risk evaluation results of Niigata port and Niigata gas chemical factory are also shown in Fig. 8 (a) and (b), respectively. Since both of the selected regions have reasonably higher altitudes, there is no monetary loss estimated due to SLR for none of the future years and corresponding SSP scenarios. Therefore, economic risk values are relatively low compared with the low-lying lands exposed to the SLR effect. Depending on the worst case scenario (i.e. 2100_SSP8.5), monetary loss reaching slightly over 200 million US$ loss is estimated for Niigata Port and over 20 million US$ loss is estimated for Gas Chemical-Niigata Factory (see Fig. 8 (a) and (b)).

Considering the revealed results in this study, it is remarkably no doubt that SLR has a positive contribution to the adverse effects of EITs on the selected elements at potential risk for all future scenarios. Even if the selected regions having enough altitude due not affected by SLR only, EITs may have a greater effect due to the positive contribution of SLR.

3.2. Social Risk Evaluation

${Risk}_{t}^{Social}$ is calculated at each selected densely populated areas and calculated as the number of people affected due to the inundation (${C}_{t,i}^{NP}$) considering EITs, SLR and EITs + SLR circumstances for each IPCC-SSP scenario of the projected years. The results calculated for the selected densely populated areas are illustrated in Fig. 9 and Fig. 10, respectively at the coastline of the Sea of Japan (i.e. Fukuoka City-Fukuoka Prefecture, Izumo City-Shimane Prefecture, Yogano&Sakaiminato City-Tottori Prefecture and Niigata City-Niigata Prefecture).

As illustrated in Fig. 9 (a), Fukuoka city center is socially affected by the SLR –shown as a gray cylinder- and EITs + SLR -shown as an orange cylinder- circumstances only for 2100_SSP8.5 scenario. The social risk is dominated by SLR and the adverse effect of SLR is also increased the combined risk for this region. Even for the worst SLR and EITs + SLR scenarios, the Fukuoka city center is not importantly affected by the hazards and the social risk level stays at a minor level. There is no social risk for Fukuoka city center considering the EITs only, by any of the scenarios. High altitudes starting just from the coastline, distance from the major earthquake faults majorly located in the northeast of the Sea of Japan, and the sheltered topography of the Hakata Bay splendidly protect the Fukuoka city center against the possible adverse effects of SLR and EITs.

Niigata region is evaluated to prone EITs due to its topographic structure. Therefore, Niigata city center and its suburbs with their extremely wide low-lying lands and densely populated areas are heavily affected by EITs and EITs + SLR hazard conditions with respect to all scenarios (see Fig. 9 (b)). Whereas, considering the SLR conditions according to all SSP scenarios released by IPCC that the results are evaluated not to be socially destructive until 2050 in this study. In 2100, the adverse effect of EITs for the region is slightly decreased due to the drastic decrease in the population according to ${P}_{t}^{GR}$ estimations. However, SLR estimations depending on SSP scenarios are heavily affected the selected region in 2100 and the number of adversely affected people is increased starting from 20000 to 40000 according to the scenarios even though the significant reduction in the population in this area. The additional contribution of the EITs to the SLR scenarios is estimated to increase the number of affected people by more than double.

Yogano and Sakaiminato cities are adjacent to each other and have the same coastline through the Sea of Japan. As illustrated in Fig. 10 (a), SLR scenarios are not importantly affected these cities except the worst-case scenario (i.e. 2100_SSP8.5). Contrary to the SLR estimations, around 100 people going to be affected from EIT evaluations and it is getting a decrease in the projected years due to the decreased population numbers depending on ${P}_{t}^{GR}$ estimations. A sudden increase in the number of adversely affected people is observed in 2100 depending on the SSP8.5 scenario and the number of physically affected people is evaluated to be increased up to 1500 even though the negative effect of ${P}_{t}^{GR}$.

Izumo city, on the other hand, has a considerable social risk level mainly driven by the SLR (see Fig. 10 (b)). Especially for the scenarios of 2100_SSP4.5 and 2100_SSP8.5, the number of physically affected people increased to 300 and 3000, respectively. Although Izumo city has low-lying lands, the location of the city with respect to major earthquake faults is assessed to be the major reason for the effect of EIT remained negligible.

The investigations have been performed by IPCC and by scientists indicate that the SLR will inevitably be experienced throughout the world’s oceans and seas. Hence, it will not be a surprise that especially the marine hazards will also be getting worse with the contribution of SLR in the future. In this study, a comprehensive deterministic analysis of economic, social, and environmental risk aspects depending on the EITs with and without SLR contribution obtained from Yavuz abd Itoi (2022) for the Japanese coastline lying through the Sea of Japan.

Economic risk analysis is conducted for residential, commercial, and industrial elements at risk in the study area. Depending on the analysis, economic risk levels are unavoidably increased for most of the selected elements regardless of the topographic conditions due to the positive contribution of SLR to the EITs. Additionally, GDP growth rates are also increased the amount of monetary losses in the projected years.

Social risk evaluation is conducted for densely populated areas. Contrary to economic risk values, social risk levels are slightly decreased for the projected years due to the decrease of population of Japan. As from 2100, a sudden increase in social risk levels is estimated for all IPCC-SSP scenarios due to the adverse effect of SLR on EITs. It can be concluded that especially low-lying residential areas are extremely prone to the effects of climate change-related SLR and EITs. The results of EITs and SLR with respect to social risks might be higher in the future due to the aging society of Japan.

It is remarkably no doubt that SLR has a positive contribution to the adverse effects of EITs on the selected elements at potential risk for all future scenarios. Even if some of the selected regions having enough altitude due not affected by SLR only, EITs may have a greater effect due to the positive contribution of SLR. Therefore, required actions should be taken not only for natural hazards one by one, but also for combined risk circumstances.

Conflict of Interest:

The authors have no competing interests to declare that are relevant to the content of this article.

Funding:

This study was funded by the Scientific and Technological Research Council of Turkey (TUBITAK) within 2219-International Postdoctoral Research Fellowship Program for Turkish Citizens. Grant No: 1059B192001058.

Author Contributions:

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Cuneyt Yavuz. The first draft of the manuscript was written by Cuneyt Yavuz. Review and supervision of the manuscript were conducted by Tatsuya Itoi. All authors read and approved the final manuscript.

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Stochastic Analysis of Potential Tsunami Risks with the Contribution of Sea Level Rise in the Sea of Japan through Japanese Coastline

Status:

Version 1

Abstract

Figures

1 Introduction

2 Materials And Methods