Multi-Hazard Risk to Exposed Stock and Critical ... · Disclaimer: The Asia-Pacific Information...
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Multi-Hazard Risk to Exposed Stock and Critical Infrastructure in Central Asia Asia-Pacific Information Superhighway (AP-IS) Working Paper Series
Transcript of Multi-Hazard Risk to Exposed Stock and Critical ... · Disclaimer: The Asia-Pacific Information...
Multi-Hazard Risk to Exposed Stock and
Critical Infrastructure in Central Asia
Asia-Pacific Information Superhighway (AP-IS) Working Paper Series
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The Economic and Social Commission for Asia and the Pacific (ESCAP) serves as the United Nations’ regional hub promoting cooperation among countries to achieve inclusive and sustainable development. The largest regional intergovernmental platform with 53 member States and 9 associate members, ESCAP has emerged as a strong regional think tank offering countries sound analytical products that shed insight into the evolving economic, social and environmental dynamics of the region. The Commission’s strategic focus is to deliver on the 2030 Agenda for Sustainable Development, which it does by reinforcing and deepening regional cooperation and integration to advance connectivity, financial cooperation and market integration. ESCAP’s research and analysis coupled with its policy advisory services, capacity building and technical assistance to governments aim to support countries’ sustainable and inclusive development ambitions.
Disclaimer: The Asia-Pacific Information Superhighway (AP-IS) Working Papers provide policy-relevant analysis on regional trends and challenges in support of the development of the Asia-Pacific Information Superhighway (AP-IS) and inclusive development. The findings should not be reported as representing the views of the United Nations. The views expressed herein are those of the authors. This working paper has been issued without formal editing, and the designations employed and material presented do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries. Correspondence concerning this working paper should be addressed to the email: [email protected]. Contact: ICT and Development Section Information and Communications Technology and Disaster Risk Reduction Division United Nations Economic and Social Commission for Asia and the Pacific United Nations Building Rajadamnern Nok Avenue Bangkok 10200, Thailand Email: [email protected]
The shaded areas of the map indicate ESCAP members and associate members.
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Acknowledgement
This working paper was prepared by Andrew Maskrey, Risk Nexus Initiative, Sanjay Srivastava and Madhurima Sarkar-Swaisgood, Disaster Risk Reduction Section, under the guidance of Tiziana Bonapace, Director, Information and Communications Technology and Disaster Risk Reduction Division of the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP). The working paper benefited from technical development from Ingeniar – Risk Intelligence (alphabetical order) Claudia Villegas R., Diana González C., Gabriel Bernal G., Liliana Carreño T., Mabel Marulanda Fraume, María Alejandra Escovar B., Omar Darío Cardona A., Paula Marulanda Fraume, Atsuko Okuda, Aida Karazhanova, Channarith Meng and Jiwon Seo of ECSAP. Tarnkamon Chantarawat and Sakollerd Limkriangkrai provided administrative support and other necessary assistance for the issuance of this paper.
March 2020
The cover:
Image source: Moehring/Shutterstock.com
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Table of Contents Background ........................................................................................................................................... 6
Methodology ......................................................................................................................................... 7
Overall multi-hazard average annual loss in Asia-Pacific ...................................................................... 8
Modeling future disaster losses .................................................................................................... 8
Multi-hazard risk assessment for Asia Pacific ............................................................................... 9
Multi-hazard average annual loss in Central Asia ............................................................................... 14
Focus countries: Kazakhstan, Kyrgyzstan and Mongolia .................................................................... 15
Kazakhstan .................................................................................................................................. 16
Country overview ................................................................................................................ 17
Agriculture........................................................................................................................... 18
Multi-hazard risk assessment ............................................................................................. 18
............................................................................................................................................................ 22
Risk disaggregation by hazard type..................................................................................... 22
Holistic risk .......................................................................................................................... 28
Drought risk ......................................................................................................................... 29
Exposure to droughts .......................................................................................................... 29
Vulnerability of the agricultural sector ............................................................................... 30
Drought risk estimation ...................................................................................................... 31
Kyrgyzstan ................................................................................................................................... 33
Multi-hazard Risk Assessment ............................................................................................ 33
Indices ................................................................................................................................. 34
Mongolia ..................................................................................................................................... 36
Multi-hazard Risk Assessment ............................................................................................ 36
Indices ................................................................................................................................. 37
Annex 1: Drought Risk (Proxy) ................................................................................................................ 39
Exposure to droughts .......................................................................................................... 39
Vulnerability of the agricultural sector ............................................................................... 39
Annex 2: Drought Risk Assessment of Kazakhstan – A Summary ............................................................... 41
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List of Figures
Figure 3-1 Summary of Multi-hazard losses and Disaggregation by Hazard for the ESCAP Region……………9
Figure 3-2 Absolute Multi-hazard AAL…………………………………………………………………………………………….………11
Figure 3-3 Relative Multi hazard AAL………………………………………………………………………………………….…………..12
Figure 3-4 Multi-hazard Disaster Loss and Infrastructure Hotspots across Asia-Pacific…………………………..13
Figure 4-1 Exposed Infrastructure and Capital Stock……………………………………………………………………………….14
Figure 4-2 Multi-hazard AAL and Infrastructure Risk across Central Asia………………………………………………..15
Figure 5-1 Kazakhstan Data…………………………………………………………………………………………………………………….16
Figure 5-2 Distribution of Exposed Value………………………………………………………………………………………………..19
Figure 5-3 Summary of Multi-hazard Losses and Disaggregation by Hazard for the Kazakhstan……………..20
Figure 5-4 Absolute Multi-hazard AAL……………………………………………………………………………………………….……21
Figure 5-5 Relative Multi-hazard AAL……………………………………………………………………………………………………..21
Figure 5-6 Multi-hazard AAL and Infrastructure Risk within Kazakhstan…………………………………………………22
Figure 5-7 Absolute Earthquake AAL………………………………………………………………………………………………………23
Figure 5-8 Relative Earthquake AAL………………………………………………………………………………………………………..24
Figure 5-9 Earthquake AAL and Infrastructure Risk in Kazakhstan………………………………………………………….24
Figure 5-10 Absolute flood AAL………………………………………………………………………………………………………………25
Figure 5-11 Relative flood AAL………………………………………………………………………………………………………………..26
Figure 5-12 Flood AAL and Infrastructure Risk Hotspots in Kazakhstan…………………………………………………..26
Figure 5-13 DRDIi Values for Kazakhstan………………………………………………………………………………………………..27
Figure 5-14 DRDli Ranking………………………………………………………………………………………………………………………28
Figure 5-15 Holistic Risk Values for Kazakhstan………………………………………………………………………………………28
Figure 5-16 Holistic Risk Ranking…………………………………………………………………………………………………………….29
Figure 5-17 Agriculture GDP for Focus Countries……………………………………………………………………….……………30
Figure 5-18 Vulnerability Index…………………………………………………………………………………………….…………………30
Figure 5-19 Propensity Index………………………………………………………………………………………………………………….31
Figure 5-20 Kazakhstan Drought AAL Ranking………………………………………………………………………………………..32
Figure 5-21 Kyrgyzstan Data…………………………………………………………………………………………………………………..33
Figure 5-22 Summary of Multi-hazard losses and Disaggregation by Hazard for Kyrgyzstan…………………..33
Figure 5-23 Multi-hazard AAL – USD Million (left) ‰ of Capital Stock……………………………………………………..34
Figure 5-24 Earthquake AAL – USD Million (left) ‰ of Capital Stock……………………………………………………….34
Figure 5-25 Flood AAL – USD Million (left) ‰ of Capital Stock…………………………………………………….…………..34
Figure 5-26 Kyrgyzstan Disaster Risk Implications on Development Index……………………………….……………..35
Figure 5-27 Holistic Risk Values for Kyrgyzstan…………………………………………………………………………….…………35
Figure 5-28 Mongolia Data…………………………………………………………………………………………………………….……….36
Figure 5-29 Summary of Multi-hazard Losses and Disaggregation by Hazard for Mongolia……………….……36
Figure 5-30 Multi-hazard AAL – USD Million (left) ‰ of Capital Stock………………………………………….………….37
Figure 5-31 Earthquake AAL – USD Million (left) ‰ of Capital Stock ………………………………………………………37
Figure 5-32 Flood AAL – USD Million (left) ‰ of Capital Stock…………………………………………………………………37
Figure 5-33 Mongolia Disaster Risk Implications on Development Index………………………………………………..38
Figure 5-34 Holistic Risk Values for Mongolia…………………………………………………………………………………………38
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List of tables
Table 1 Multi-hazard AAL for the ESCAP Countries and its Contribution to Total Regional AAL…………………10
Table 2 Main Macroeconomic and Social Indicators for Kazakhstan……………………………………………………….18
Table 3 Exposed Value by Province…………………………………………………………………………………………………………19
Table 4 Multi-hazard AAL for the Kazakhstan and its Contribution to Total AAL…………………….……………….20
Table 5 Earthquake AAL for Kazakhstan and its Contribution to Total Regional AAL……………………………….23
Table 6 Flood AAL for Kazakhstan and its Contribution to Total Regional AAL…………………………………………25
Abbreviations
AAL Annual Average Loss
DRDIi Disaster Risk Implications on the Socio-Economic Development of Countries
EI Exposure Index
ESCAP Economic and Social Commission for Asia and the Pacific
GDP Gross Domestic Product
HDC Human Development Centre
HDI Human Development Index
ICT Information and Communication Technology
PI Propensity Index
RF Physical Risk
RT Total Risk
SDG Sustainable Development Goals
SFDRR Sendai Framework for Disaster Risk Reduction
SIDS Small Island Developing States
UNESCO United Nations Educational, Scientific and Cultural Organization
USDA United States Department of Agriculture
VI Vulnerability Index
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Background
The 2030 Agenda for Sustainable Development recognizes that natural disasters can be an impediment to realizing the development goals. Earthquakes, landslides, floods, and droughts have detrimental impacts on overall country macroeconomic factors and further disproportionately affect the poor and marginalized groups. Therefore, it is imperative to develop multi-hazard assessments that address and map both physical and social vulnerabilities. The Eleventh Tranche of the Development Account Project aims to address the transboundary dimensions of the 2030 Agenda in Central and East Asia through regional economic cooperation and integration in Asia and the Pacific, particularly focusing on transport, connectivity and energy sectors. Recognizing that hazards and disasters present complex, transboundary challenges and vulnerabilities to these sectors, this the project will aim to demonstrate areas of risk hotspots in Central Asia (Kazakhstan and Kyrgyzstan) and East Asia (Mongolia) that may require additional risk informed investments in transboundary infrastructure development. In the sub-region, this will be key to achieving the SDGs and to ensure that poor and vulnerable people are not left behind. In line with the objectives of the Regional Economic Cooperation and Integration in Asia and the Pacific (RECI), this report will give special focus on SDG Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and trans-border infrastructure, to support economic development and human well-being. The report also gives particular focus to Target 11.5- to substantially decrease the direct economic losses relative to global and gross domestic product caused by disasters with a focus on protecting the poor and people in vulnerable situations. The report therefore, develops multi-hazard disaster risk assessment for the three target countries that not only take into account historical but also future damage and loss. For one pilot country (Kazakhstan) the report provides a probabilistic multi-hazard annual average loss to show where disaster risk needs to be built into planning for resilient infrastructure and support stronger connectivity for socioeconomic development. Multi-hazard annual average loss (AAL) which is a probabilistic measure of future disaster losses, currently exist for some natural hazards for Kazakhstan (earthquake, floods). However, it does not take into account slow-onset disasters such as drought, which significantly effects the target countries and will become more severe due to climate change. Kazakhstan is one of the most vulnerable countries to climate change impacts in Central Asia with increasing incidences of aridity and drought. While there are analytical work on seismic risk, landslides and floods in pilot countries, there are no substantive and evidence-based drought specific work available. Piloting drought model in Kazakhstan can be scaled up to Kyrgyzstan and Mongolia. This is an innovative approach for average annual loss modelling and will be used to estimate future losses from disasters in various sectors like water, energy, food and transport infrastructure. Incorporating AAL from drought with losses from other natural disasters will provide a more robust multihazard probabilistic risk assessment which can be used to bolster the resilience in transport, connectivity, and energy sectors.
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Methodology
Towards these goals, first, a Situational Risk Analysis was carried out for all Asia-Pacific countries including Central Asia. The Situational Risk Analysis was based on probabilistic risk metrics produced by partners of Risk Nexus Initiative. The Average Annual Loss (AAL) for earthquakes, tropical cyclones, riverine floods and tsunami was estimated for each country in both absolute and relative terms and expressed for each individual hazard and in multi-hazard terms. Proxy estimates were then calculated to take into account extensive risk and indirect losses, based on extrapolations from available empirical evidence. Disaster risk was then examined as an opportunity cost to the achievement of the Sustainable Development Goals (SDG) and as a threat to financial resilience. Estimates were also made of the impact of underlying risk drivers on existing risk producing metrics for holistic risk in each country. One of the core objectives of the Situational Risk Analysis was to provide indications of the likely contribution to agricultural drought risk in Asia and the Pacific to the total disaster risk. Unlike the risk associated with rapid onset hazards, the risk associated with drought has not yet been estimated probabilistically. A first approximation to agricultural drought risk was carried out using social and economic proxies for exposure and vulnerability of the agricultural sector, enabling the identification of countries with economically important agricultural sectors, and which are characterized by large rural population with high levels of poverty. These are countries, where future investment in probabilistic drought risk assessment would be a priority. A second approximation to agricultural drought risk was carried out extrapolating from the results of probabilistic risk assessments in other contexts. Assuming an agricultural drought AAL of 20% of agricultural GDP it was found that in the region as a whole the multi-hazard AAL would almost triple, and in a number of large economies it would quadruple. Finally, the Situational Risk Analysis highlighted that agricultural drought risk would have the most critical impact in economies where agriculture represents a significant proportion of GDP and where the agricultural drought AAL represents a significant proportion of the total AAL. Secondly, the Situational Risk Analysis was downscaled for three focus countries: Kazakhstan, Kirghizstan and Mongolia. Multi-hazard risk, for earthquake and flood, was estimated at the provincial level in each country, highlighting the concentration of risk in specific areas. Agricultural drought risk was estimated using the same proxy as in the region as a whole indicating that across the three countries, the agricultural drought AAL represented between 64 percent and 87 percent of the total multi-hazard AAL. This downscaled analysis confirmed that estimating agricultural drought risk is therefore critical to a more complete understanding of disaster risk in Central Asian countries. Finally, a detailed probabilistic agricultural drought risk assessment was carried out in one of the three countries, Kazakhstan, in order to demonstrate a robust methodology to calculate the agricultural drought AAL. Unlike most drought risk assessments, based on historical statistics, the probabilistic assessment allows the calculation of all the potential drought scenarios that could occur, including in the context of future climate change. This assessment focused on wheat, the major cereal crop grown in Kazakhstan. It highlighted an AAL that represented approximately 22 percent of the exposed value, which would reduce to 16 percent under a future climate scenario. This is especially noteworthy for both current and future hydropower infrastructure. The single most important conclusion of the study is that unless agricultural drought risk is calculated probabilistically and factored into estimates of multi-hazard risk in countries in the region, it will be difficult to set priorities in disaster risk management, climate change adaptation and sustainable
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development in a way that really addresses the portfolio of risk that most affects economies and livelihoods in the region.
Overall multi-hazard average annual loss in Asia-Pacific
Modeling future disaster losses
The ESCAP region is one of the most disaster prone in the world. Evolving patterns of disaster risk in the region, linked to drivers such as badly planned and managed urban development, environmental degradation weak local governance and climate change manifest in rising levels of disaster loss and damage. Disaster impacts, in turn negatively affect social and economic development and the capacity of countries to achieve the Sustainable Development Goals (SDG) and the Global Targets of the Sendai Framework for Disaster Risk Reduction (SFDRR). Unfortunately, the dominant paradigm for addressing disaster risk in the region remains fundamentally reactive. Few countries have invested in developing the capacities and putting in place the policies and strategies required to manage and reduce risk. A first step towards building a solid political and economic imperative to manage and reduce disaster risk is to estimate probable future disaster losses. Unless governments can measure their levels of risk they are unlikely to find incentives to manage disaster risk. Risk estimations can provide those incentives and in addition allow governments to identify what are the most effective strategies to manage and reduce risks. Effective public policies in disaster risk reduction and sustainable development, ranging from financial protection, risk-informed public investment, resilient infrastructure, territorial planning and impact-based early warning can all benefit from appropriate estimations and layering of risk. The past is not a good guide to the future and models based on limited historical data are likely to underestimate the impact of future high-severity, low-frequency risks. Probabilistic risk models simulate those future disasters which, based on scientific evidence, are likely to occur but which may, as yet, not have occurred. As a result, they resolve the problem posed by the limits of historical data and enable the modelling of low-frequency, high-severity risks, which otherwise are difficult to estimate. This situational risk analysis builds on a global probabilistic risk model, the first of its kind to provide worldwide coverage for multiple hazards, originally developed for the United Nations1. While an increasing number of risk models have been produced for specific hazards and portfolios of exposed assets, these do not provide consistent and comparable risk estimates in a region the size of Asia and the Pacific due to major geographical gaps and the fact that assessments for single hazards use different data sets and methodology. The situational risk analysis presented in this report provides regionally comparable estimations of risk for earthquake, tsunami, flood, tropical cyclone and storm surge and analyses the implications for economic resilience and sustainable development in each country in the region. In addition, the situational risk analysis has taken into account the likely cost of extensive risk, a
1 United Nations, 2017, GAR Atlas, Geneva.
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34%
32%
2%
32%
Contribution hazards to AAL
EARTHQUAKE
TROPICAL CYCLONES
TSUNAMI
FLOODS
risk layer that is often ignored in catastrophic risk assessments and has also estimated the probable cost of indirect disaster risks on the region’s economy. In addition to the rapid-onset hazards mentioned above, the Asia and Pacific region is also subject to slow-onset hazards such as drought. While a probabilistic drought risk model for the region has not yet been developed, this situational risk analysis identifies the countries which have greatest exposure and vulnerability to drought risk in the region. Based on probabilistic assessments of drought risk in other contexts, the potential order of magnitude of agricultural drought risk in the region has been estimated. It highlights that in many countries the risks associated with drought and the agricultural sector represent a very significant proportion of overall multi-hazard risk and in the region as a whole becomes a major opportunity cost to economic and social development. As such the analysis highlights the critical importance of modelling drought risk in the future using probabilistic techniques, thus completing the risk profile of the region and providing a more comprehensive estimation of risk in each country.
Multi-hazard risk assessment for Asia Pacific
The existing multi-hazard risk assessment for the ESCAP region estimates risks to the built environment associated with earthquakes, tropical cyclones, riverine floods and tsunami. The total absolute multi-hazard Average Annual Loss (AAL) for the ESCAP region is USD$ 148,866 million, which represents 53.5% of global multi-hazard risk. Of this total multi-hazard AAL, 33.55 % of the AAL is contributed by earthquakes, 32.47 % by riverine floods, 31.76 % by tropical cyclones, and 2.22 % by tsunami. The multi-hazard AAL is heavily concentrated in a small number of countries with a large concentration of exposed capital stock. Japan represents 40.06 % and China 18.08 % of the total multi-hazard AAL. Other countries with a significant proportion of the region’s multi-hazard AAL include Iran, Philippines, Indonesia, Turkey, India and Bangladesh.
Figure 3-1. Summary of Multi-hazard Losses and Disaggregation by Hazard for the ESCAP Region
When the multi-hazard AAL is expressed as a proportion of a countries capital stock, the metric provides a better indication of the real impact on a country’s economy. In general, most of the countries with the highest relative multi-hazard risk are SIDS. However, large countries like Philippines, Myanmar, Bangladesh, Lao PDR and Cambodia also have both high relative and absolute risk.
Hazard million
US$ [‰]
Contribution to MH-AAL
Earthquake 49,947 0.45 33.55
Tropical cyclones 47,277 0.43 31.76
Tsunami 3,309 0.03 2.22
Floods 48,333 0.44 32.47
MULTI-HAZARD 148,866 1.35 100.00
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Table 1 presents the ranking of ESCAP countries by their absolute AAL, along with the relative multi-hazard AAL. The regional AAL maps are presented in Figure 3-2 and Figure 3-3, in absolute and relative terms, respectively. Table 1. Multi-hazard AAL for the ESCAP Countries and its Contribution to Total Regional AAL
USD million [‰ of capital stock]
JPN Japan 59,637 1.52 40.06%
CHN China 26,908 0.85 18.08%
KOR Republic of Korea 9,863 1.78 6.63%
IDN Indonesia 3,289 1.16 2.21%
IND India 8,584 1.49 5.77%
PHL Philippines 7,854 13.85 5.28%
AUS Australia 4,695 0.71 3.15%
IRN Iran (Islamic Republic of) 4,686 2.27 3.15%
RUS Russian Federation 4,458 0.7 2.99%
BGD Bangladesh 2,964 7.77 1.99%
TUR Turkey 2,433 1.25 1.63%
VNM Viet Nam 2,334 4.79 1.57%
THA Thailand 2,323 1.68 1.56%
MMR Myanmar 2,030 10.39 1.36%
PAK Pakistan 1,253 2.5 0.84%
MYS Malaysia 1,162 0.99 0.78%
NZL New Zealand 746 1.1 0.50%
KAZ Kazakhstan 750 1.02 0.50%
AZE Azerbaijan 311 1.61 0.21%
UZB Uzbekistan 289 1.9 0.19%
KHM Cambodia 242 8.85 0.16%
AFG Afghanistan 221 3.68 0.15%
LAO Lao People's Democratic Republic 213 9.71 0.14%
GEO Georgia 207 3.85 0.14%
PNG Papua New Guinea 162 3.45 0.11%
NPL Nepal 162 2.99 0.11%
LKA Sri Lanka 151 0.72 0.10%
PRK DPR of Korea 138 1.77 0.09%
FJI Fiji 132 11.39 0.09%
TJK Tajikistan 107 5.2 0.07%
KGZ Kyrgyzstan 93 5.02 0.06%
TKM Turkmenistan 91 2.52 0.06%
VUT Vanuatu 67 23.7 0.04%
ARM Armenia 63 2.75 0.04%
BTN Bhutan 54 4.83 0.04%
SLB Solomon Islands 43 11.75 0.03%
MNG Mongolia 35 0.95 0.02%
TON Tonga 33 25.07 0.02%
BRN Brunei Darussalam 32 0.46 0.02%
TLS Timor-Leste 16 1.24 0.01%
WSM Samoa 15 7.61 0.01%
PLW Palau 13 16.67 0.01%
FSM Micronesia (Federated States of) 6 4.79 0.00%
SGP Singapore 2 0 0.00%
MHL Marshall Islands 0 0.33 0.00%
MDV Maldives 0 0.01 0.00%
KIR Kiribati 0 0.02 0.00%
TUV Tuvalu 0 0 0.00%
148,866 1.35 100%Total
ISO Country nameMULTI-HAZARD AAL Contribution
to total value
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Figure 3-2. Absolute Multi-hazard AAL
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Figure 3-3. Relative Multi-hazard AAL
It is possible to visualize the extent to which, countries’ ICT, energy, and road infrastructure in the region can be impacted by economic loss from disasters. Figure 3-4 shows how economic losses from disaster risk cut across borders and critical infrastructure, particularly transportation infrastructure.
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Figure 3-4 Multi-hazard Disaster Loss and Infrastructure Hotspots across Asia-Pacific
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Multi-hazard average annual loss in Central Asia
In Central Asia, Kazakhstan has the highest exposure of capital stock, including infrastructure, followed by Georgia and Azerbaijan. In these countries, infrastructure projects particularly for critical, transboundary infrastructure sectors need to be risk informed.
Figure 4-1 Exposed Infrastructure and Capital Stock
The infrastructure risk hotspots, where economic losses from disasters coincide with infrastructure across Central Asia is shown in figure 3-5. The map geolocates the countries and areas where investments in
Exposed infrastructure and capital stock (million USD)
Kazakhstan Azerbaijan Uzbekistan Georgia Mongolia
Turkmenistan Armenia Tajikistan Kyrgyzstan
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infrastructure needs to be risk informed, particularly in Uzbekistan, Kyrgyzstan and Tajikistan.
Figure 4-2 Multi-hazard AAL and Infrastructure Risk Across Central Asia
Focus countries: Kazakhstan, Kyrgyzstan and Mongolia
This section presents the results of the probabilistic multi-hazard risk assessment for Kazakhstan, Kyrgyzstan and Mongolia. These are the results of the risk assessment performed with the global risk model for 216 countries and territories using robust metrics for five different hazards: earthquakes, tsunami, riverine floods and tropical cyclones (wind and storm surge). A probabilistic drought risk assessment was only performed for Kazakhstan, for the remaining ESCAP countries drought risk has not yet been modelled, thus, in this case the potential order of magnitude of agricultural drought risk was estimated based on probabilistic assessments of drought risk in other contexts which highlight that the drought AAL in the agricultural sector normally represents a maximum of 20% of the agricultural GDP, which was the proxy value used in this report.
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Kazakhstan
Figure 5-1 Kazakhstan Data
The total absolute multi-hazard Average Annual Loss (AAL), associated with earthquakes and riverine floods for Kazakhstan is USD$ 750.46 million, which represents 0.5% of the total multi-hazard risk for the ESCAP region. 51.6% of the AAL is contributed by earthquakes and the remaining by riverine floods. For Kazakhstan there is no losses associated to the other considered hazards. The relative multi-hazard AAL for Kazakhstan is 0.1% of the capital stock. Almaty is the province with the highest multi-hazard AAL (USD$ 272.4 million) which represents 36.3% of the total multi-hazard risk for the country. However, it is Atyrau the province with the highest relative multi-hazard risk (2.01‰) followed by West Kazakhstan (1.90‰) and Almaty (1.41‰). A major part of the losses associated to earthquakes is concentrated in the province of Almaty, which has an earthquake AAL of USD$249.7 million, that represents 64.47% of the total earthquake AAL for the country, followed by South Kazakhstan (18.9%) and East Kazakhstan and Zhambyl with a similar contribution close to 7%. Regarding floods, the losses are more distributed among the different provinces. The province with the highest flood AAL is Akmola with losses of USD$78.7 million, that is, the 21.67% of the total floods AAL, followed by East Kazakhstan (20.3%) and South Kazakhstan (13.06%). The province with the highest relative flood risk is West Kazakhstan (1.8‰) followed by Akmola and Kyzylorda (1.08‰ and 1.07‰, respectively). These results indicate that Kazakhstan experiences a high rate of extensive risk, which manifests as large numbers of high-frequency low-severity disasters that cannot be modelled analytically at the global or regional scale. Evidence from countries where extensive risk has been modelled empirically, highlight that it could add anywhere from 10% to 50% to the total multi-hazard AAL. If a conservative estimate of 30% is assumed, then the total multi-hazard risk for Kazakhstan would rise to USD $975.6 million, which represents 0.61% of the country’s GDP.
Population
18.360 (est. 2018)
million
GDP
159.41 (est. 2017) billion
Agricultural GDP
6,950 (est. 2017)
million
Area: 2,699,700 km2
Population density: 6 people/km2
Median age: 29.6
Rural population (% of total pop): 42.7
Employment in agriculture (% of total emp): 17.7
Human Development Index: 0.80 (2017)
Gender Inequality Index: 0.197 (2017)
Mean years of schooling: 11.8 (2017)
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Multiple assessments following major disasters using the ECLAC methodology indicate that direct losses normally represent only 30 to 40% of total losses (i.e. the sum of direct and indirect losses). Taking into account likely indirect losses, therefore, the total average annual loss in Kazakhstan would rise to USD $1,365 million representing 0.86% of the GDP. Disaster risk also represents an opportunity cost for sustainable development. Where the AAL represents a high proportion of a country’s social expenditure, that country will have difficulties in maintaining or increasing its social expenditure and thus achieving social sector Sustainable Development Goals (SDG). Similarly, countries where the AAL represents a high proportion of capital investment will have difficulty in achieving infrastructure related SDGs. Kazakhstan multi-hazard AAL represents 4.1% of social expenditure and 1.73% of the gross fixed capital formation. Disaster risk can be considered as a contingent liability that increases the fiscal vulnerability of a country’s economy. Kazakhstan has a financing gap of USD $1.21 billion for a loss with a 100-year return period. Holistic risk is a measure that captures not only the physical risk, expressed by the AAL but the underlying risk drivers that generate, configure and magnify risk. It is assumed that Total Risk (RT) can be maximum two times the physical risk of the affected area; this assumption is made with the aim to reflect that socio-economic characteristics can influence the magnitude of a disaster. Therefore, the holistic risk value ranges from 0 to 2. The Total Risk value for Kazakhstan is 0.85, placing it in 14th position among the countries of the ESCAP region.
Country overview
Kazakhstan is the ninth-largest country in the world with an extension of 2,699,700 km2. However, the high percentage of desert or semi-desert dry mix eroded lowlands, grasslands and sandy areas (over 70%)2, prevent it from having a population proportional to its size, with a total population of 18,360,353, Kazakhstan is one of the countries with the lowest population densities globally, just 6 persons per km2. The country’s strategic geographical location links the large and fast-growing markets of China and South Asia and those of Russia and Western Europe by road, rail, and a port on the Caspian Sea.
Kazakhstan transitioned from lower-middle- income to upper-middle income status in less than two decades, the country moved to the upper-middle- income group in 20063. This was mainly due to the high level of foreign investment largely based on its resource riches. However, the global economic crisis hit Kazakhstan in 2007, where real GDP growth fell from 13.5% in 2006 to 1.2% in 2009. After a recovery of the economy from 2010, the country faced again an economic slowdown where the real GDP slowed from 6% in 2013 to 4.2% in 2014 before collapsing to 1.2% in 2015 and 1.1% in 2016, eroding real wages and consumer purchasing power. In 2017, thanks to the ongoing structural reforms aimed at promoting the development and diversification of the economy, focused on non-resource-based aspects, including agriculture and related processing, it started to recover gradually reaching a GDP growth of 4.1% although according to World Bank estimations, it is projected to fall to 2.8% by 2020 if the country doesn’t transition to a new growth model. Concerning the social indicators of the country, Kazakhstan has one of the highest adult literacy rates in the world, which is of 99.8% according to UNESCO data from 2010 and the mean years of schooling is 11.8 (2017). The poverty HDC at national poverty lines presented a significant decrease going from 46.7% in
2 www.worldatlas.com 3 The World Bank
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2002 to 2.7% in 20154. Most recent available HDI value for Kazakhstan is 0.80 (2017) which put the country in the very high human development category, ranking it 58 on the world scale.
Table 2 Main Macroeconomic and Social Indicators for Kazakhstan
Indicator 2010 2015 2016 2017
GDP (US$ billions)5 148.04 184.38 137.28 159.41
Trade in goods and services (% GDP)6 29.7 16.6 19.3 21.5
Current account balance (% GDP)7 0.936 -2.785 -6.464 -3.358
Unemployment (%)8 5.78 4.97 4.95 4.95
Human Development Index9 0.765 0.797 0.797 0.80
Gender Inequality Index10 0.273 0.208 0.2 0.197
Agriculture
Agriculture remains a small-scale sector of the country’s economy, accounting for approximately 5% of GDP, although more than 74% of its territory is suitable for agricultural production, only 25% of the land is arable. Kazakhstan’s major crops are wheat, barley, cotton and rice11. Kazakhstan ranks 12th in the world wheat production and 6th in wheat export, exporting to over 70 countries. Grain production is made on northern country and has the largest share among agricultural sector, with wheat occupying 57% of the total sown area. Three territories in north-central Kazakhstan (Akmola, Kostanai and North Kazakhstan) account for about 80% of the total wheat area. According to USDA estimates, the sown wheat area for 2016/2017 in Kazakhstan totaled 12.4 million hectares, with an estimation in production of 16.5 million metric tons. From the total sown area, a portion usually accounting for 1 to 3% is remained unharvested, but it can increase to as much as 8% due to drought or unfavorable weather during growing season. In the last decade, the government has been working to increase crop diversification, mainly through subsidies, to promote the cultivation of oilseed such as sunflowers, flax, safflowers, rapeseed and soybeans, from 2007 to 2016 the total oilseed area tripled from 0.67 to 2.04 million hectares. Along with the oilseed, the government is promoting the cultivation of feed crops such as corn, barley and forage for the growing livestock industry, backed by recent programs for beef and dairy cattle industries development.
Multi-hazard risk assessment
The probabilistic risk assessment presented herein consists in evaluate the losses in a group of exposed assets during each of the scenarios that collectively describe the hazard. For this assessment, exposed values were obtained from the GAR Atlas (UNISDR, 2017). The exposed value for Kazakhstan is USD $743,310 million. Table 2 presents the exposed values by province. The distribution of the values among the different provinces is presented in Figure 1.
4 Most recent available data. World Bank Data. 5 World Economic Outlook. International Monetary Fund [Last consulted 13 March 2019] 6 World Bank Open Data. https://data.worldbank.org/ [Last consulted 12 March 2019] 7 World Economic Outlook. International Monetary Fund [Last consulted 13 March 2019] 8 Ibid 9 UNDP – Human Development Reports. [Last consulted 13 March 2019] 10 Ibid 11 https://www.export.gov/article?id=Kazakhstan-Agricultural-Sector
19
Table 3 Exposed Value by Province
Province Multi-Hazard AAL Contribution to total
value USD million
Almaty $ 192,531.08 26.2
South Kazakhstan $ 111,619.34 15.2
East Kazakhstan $ 86,239.44 11.7
Akmola $ 72,809.64 9.9
Karagandy $ 59,284.95 8.1
Pavlodar $ 38,677.95 5.3
North Kazakhstan $ 35,424.92 4.8
Kostanay $ 34,206.10 4.7
Zhambyl $ 33,096.11 4.5
West Kazakhstan $ 21,988.69 3.0
Kyzylorda $ 13,140.26 1.8
Aktobe $ 12,621.93 1.7
Atyrau $ 11,798.65 1.6
Mangystau $ 10,870.93 1.5
Total $ 734,310.00 100.0
Figure 5-2 Distribution of Exposed Value
The existing multi-hazard risk assessment for Kyrgyzstan estimates risks to the built environment associated with earthquakes and riverine floods. The total absolute multi-hazard Average Annual Loss (AAL) for Kazakhstan is USD$ 750.46 million, which represents 0.5% of the regional multi-hazard risk. Of this total multi-hazard AAL, 51.61 % is contributed by earthquakes and 48.39 % by riverine floods. The major part of the multi-hazard AAL is concentrated in the province of Almaty, which contributes 36.3% to the total multi-hazard AAL, followed by South Kazakhstan (16.07%) and East Kazakhstan (13.49%).
20
For this report, drought was included using a proxy value of 20% of the agricultural GDP, which for Kazakhstan means a drought AAL of USD$ 1,390 million, thus increasing the total multi-hazard AAL to $2,140 million, which represents 1.34% of the country’s GDP. In these terms, drought accounts for almost 65% of the total multi-hazard AAL, 18% is contributed by earthquakes and 17% by floods.
Figure 5-3 Summary of Multi-hazard Losses and Disaggregation by Hazard for the Kazakhstan
Table 1 presents the ranking of Kazakhstan’s regions by their absolute AAL, along with the relative multi-hazard AAL. The national AAL maps are presented in Figure 3-2 and Figure 3-3, in absolute and relative terms, respectively.
Table 4 Multi hazard AAL for the Kazakhstan and its Contribution to Total AAL
18.09%
16.97%
64.94%
Earthquake
Flood
Drought (Proxy)
USD mill ion [‰ of capital stock]Almaty 272.4 1.4 36.3
South Kazakhstan 120.6 1.1 16.1East Kazakhstan 101.3 1.2 13.5
Akmola 79.0 1.1 10.5West Kazakhstan 41.9 1.9 5.6
Zhambyl 39.6 1.2 5.3Atyrau 23.8 2.0 3.2
Pavlodar 15.1 0.4 2.0Karagandy 14.7 0.2 2.0Kyzylorda 14.5 1.1 1.9
North Kazakhstan 14.5 0.4 1.9Kostanay 10.3 0.3 1.4
Aktobe 1.7 0.1 0.2Mangystau 1.2 0.1 0.2
Total 750.5 1.02 100.0
ProvinceMulti-Hazard AAL Contribution to
total value
USD$
Million ‰
Contribution to Mh AAL
Earthquake 387.30 0.53 18.09
Flood 363.16 0.49 16.97
Tropical cyclones - 0.00 0.00
Tsunami - 0.00 0.00
Drought 1,390 1.89 64.94
TOTAL 2,593.04 3.53 100.00
21
Figure 5-4 Absolute Multi-hazard AAL
Figure 5-5 Relative Multi-hazard AAL
The exposed infrastructure projects within the country is given in Figure 5-5. West Kazakhstan, Almaty, and Akhmora have the highest concentration of disaster risk and infrastructure. Therefore, in these provinces, infrastructure that is risk-informed is crucial.
22
Figure 5-6 Multi-hazard AAL and Infrastructure Risk within Kazakhstan
Risk disaggregation by hazard type
In terms of earthquake hazard, AAL is heavily concentrated in the province of Almaty that represents 64.5% of the total earthquake AAL, followed by South Kazakhstan that accounts for the 18.9% of the total. Almaty has also the highest relative earthquake risk (1.3‰), followed by Zhambyl with a relative AAL of 0.8%. In terms of flood hazard Akmola represents 21.7% and East Kazakhstan 20.3% of the total flood AAL, followed by South Kazakhstan and West Kazakhstan that represent 13.1% and 11.1% respectively. However, Atyrau and West Kazakhstan have the highest relative flood risk.
23
Table 5 Earthquake AAL for Kazakhstan and its Contribution to Total Regional AAL
Figure 5-7 Absolute Earthquake AAL
USD mill ion [‰ of capital stock]Almaty 249.7 1.3 64.5
South Kazakhstan 73.2 0.7 18.9East Kazakhstan 27.5 0.3 7.1
Zhambyl 26.9 0.8 6.9Pavlodar 4.3 0.1 1.1
Karagandy 2.4 0.0 0.6West Kazakhstan 1.5 0.1 0.4
Mangystau 0.8 0.1 0.2Kyzylorda 0.5 0.0 0.1
Akmola 0.3 0.0 0.1Atyrau 0.2 0.0 0.0
Kostanay 0.0 0.0 0.0Aktobe 0.0 0.0 0.0
North Kazakhstan 0.0 0.0 0.0Total 387.3 0.53 100.0
ProvinceEarthquake Contribution to
total value
24
Figure 5-8 Relative Earthquake AAL
The exposed infrastructure projects within the country for earthquakes is given in Figure 5-9.
Figure 5-9 Earthquake AAL and Infrastructure Risk in Kazakhstan
25
Table 6 Flood AAL for Kazakhstan and its Contribution to Total Regional AAL
Figure 5-10 Absolute Flood AAL
USD mill ion ‰ of capital stockAkmola 78.7 1.08 21.7
East Kazakhstan 73.7 0.86 20.3South Kazakhstan 47.4 0.42 13.1West Kazakhstan 40.4 1.84 11.1
Atyrau 23.6 2.00 6.5Almaty 22.7 0.12 6.3
North Kazakhstan 14.5 0.41 4.0Kyzylorda 14.0 1.07 3.9Zhambyl 12.8 0.39 3.5
Karagandy 12.2 0.21 3.4Pavlodar 10.8 0.28 3.0Kostanay 10.3 0.30 2.8
Aktobe 1.7 0.13 0.5Mangystau 0.4 0.03 0.1
Total 363.2 0.49 100.0
ProvinceFlood Contribution to
total value
26
Figure 5-11 Relative Flood AAL
The exposed infrastructure projects within the country for floods is given in Figure 5-12.
Figure 5-12 Flood AAL and Infrastructure Risk Hotspots in Kazakstan
27
Implications for sustainable development (DRDIi)
The Disaster Risk Implications on the Socio-Economic Development of Countries (DRDIi) index examines the opportunity cost of disaster risk on sustainable development. For example, where the AAL represents a high proportion of a countries social expenditure, that country will have difficulties in maintaining or increasing its social expenditure and thus achieving social sector Sustainable Development Goals (SDG). Similarly, countries where the AAL represents a high proportion of capital investment will have difficulty in achieving infrastructure related SDGs. This index attempts to reveal the weight of the AAL on the social expenditure, capital investment and savings (domestic investment), reserves (financial capacity) and the produced capital or capital stock (assets at risk) of each country. It reflects the constraints to sustainable development posed by disaster risk. As seen in Error! Reference source not found. 9, values of economic, financial and social implications are similar for Kazakhstan. Considering the average annual loss, investment, savings and expenditures would be severely affected if the potential losses were paid every year. A financial strategy for risk reduction and mitigation, as well as reserves or catastrophe funds for emergency response, recovery and rehabilitation are needed to avoid an increase in AAL in the future. In relation with the other countries of the region, in the DRDIi Kazakhstan is ranked 13 of 4612
Figure 5-13 DRDIi Values for Kazakhstan
12 The DRDIi was not calculated for the PDR of Korea and Tuvalu due to lack of data
0 100
Low implications
0 100
52.87
High implications
Disaster Risk Implications on Development Index DRDIi
DRGI (Gross Savings)
DRSIDisaster Risk Social
Implications
54.17
46.95
55.95
37.94
57.5
DREIDisaster Risk Economic
Implications
DRGIDisaster Risk Growth and
Financial Implications
DRGI (Gross Fixed Capital
Formation)
28
Figure 5-14 DRDli Ranking
Holistic risk
Disaster risk is not exogenous to the economy and society of a country but is configured and accumulates over time through a range of underlying risk drivers, in the social, environmental, territorial and governance spheres. In addition, these underlying risk drivers can also be considered as aggravating factors that magnify the impact of physical risk in a given society and economy, representing in other words the resilience of a country to physical risk. In the ESCAP region, holistic risk (Cardona, 2001) has been calculated for each country by capturing how underlying risk drivers or social, economic, environmental factors– (using an aggravating factor, F), which, worsen the current existing physical risk (RF). Fourteen variables were chosen to represent the underlying risk drivers or aggravating factors13. The total risk value for Kazakhstan is 0.85, it is ranked 14 in the region.
Figure 5-15 Holistic Risk Values for Kazakhstan
13 Indicators of Social Fragility are: Internet access, Access to sanitation, Access to improved drinking water, GINI Index, Unemployment, Inflation and Urban growth rate. Indicators of Lack of Resilience are: Gross National Savings, Social expenditure, Governance, Human Development Index, Ecosystems vitality, Paved roads and Infant mortality
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52.87
0.850 2
0 1
Holistic Risk
RFPhysical
Risk AAL
FAggravating
factor
0.41
0.60
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Figure 5-16 Holistic Risk Ranking
Drought risk
Droughts differ from other natural hazards because their effects often accumulate slowly over an extended period, taking several years in some cases, with impacts that are less measurable spreading over large geographical areas. Drought impacts range from losses to agricultural production (water stress to crops and farm animals), to a generalized reduction of water availability for hydropower generation and human consumption. During droughts, risk may increase to levels that exceed those represented by rapid-onset hazards. A comprehensive framework for the probabilistic modelling of drought hazard based on climate simulation has now been developed (Bernal et.al. 2017) and applied in a growing number of countries, including Kazakhstan in the ESCAP region. In the absence of probabilistic drought hazard models in the countries of the region, it is only possible to assess the potential importance of drought risk in the agriculture sector using proxy values derived from economic and social variables.
Exposure to droughts
A proxy of the exposure of the agricultural sector to drought hazard in a given country can be expressed by the ratio of the agricultural GDP to total GDP. Evidently the greater the participation of agriculture in a country’s economy, the greater the potential exposure of that economy to drought. The ratio of agricultural to total GDP is referred to as the Exposure Index (EI):
𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝐼𝑛𝑑𝑒𝑥 (𝐸𝐼) =𝐴𝑔. 𝐺𝐷𝑃
𝑇𝑜𝑡𝑎𝑙 𝐺𝐷𝑃
Error! Reference source not found. presents the distribution of agricultural GDP in both absolute terms and as a proportion of total GDP in the focus countries. Agricultural GDP of Kazakhstan accounts for almost the 5% of the total GDP. Although the contribution of the agricultural sector to the total GDP is lower than that from the other two countries, it still is significative and thus, an impact in the sector would hit the national economy.
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0.846
30
Figure 5-17 Agriculture GDP for Focus Countries
Vulnerability of the agricultural sector
An agricultural sector, based on labor intensive, low-productive agriculture, is likely to be more vulnerable to droughts than sectors based on highly-productive, mechanized agriculture based on a greater employment of technology. Kazakhstan is on its way to transform the agricultural sector in a new driver for the countries economy, increasing the investments and promoting the modernization of production assets, as well as subsidizing the diversification of crops. Given the high percentage of rural population in the country (42.7%), an impact to the agricultural sector would be important for the country’s economy. To account for vulnerability, we propose a Vulnerability Index, which is an index composed of the proportion of rural population, proportion of rural poverty and proportion of employment in the agricultural sector.
𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝐼𝑛𝑑𝑒𝑥 (𝑉𝐼) =𝑅𝑢𝑟𝑎𝑙 𝑃𝑜𝑝. +𝑅𝑢𝑟𝑎𝑙 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 + 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡. 𝑖𝑛 𝐴𝑔.
3
Figure 5-18 Vulnerability Index
0
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Kazakhstan Mongolia Kyrgyzstan
GDP Agriculture
Million USD % Total GDP
21.60
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liaVulnerability Index (VI)
31
Even though it is a rough approximation, the Vulnerability Index alone provides an important insight of the propensity to droughts in the countries. Furthermore, and following the same rationale, we propose a Propensity Index defined as a function of both exposure and vulnerability indices.
𝑃𝑟𝑜𝑝𝑒𝑛𝑠𝑖𝑡𝑦 𝐼𝑛𝑑𝑒𝑥 (𝑃𝐼) =𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝐼𝑛𝑑𝑒𝑥 + 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝐼𝑛𝑑𝑒𝑥
2
Kazakhstan is ranked 38nd in the propensity index.
Figure 5-19 Propensity Index
Drought risk estimation
As mentioned above, drought risk was estimated using a proxy value of 20% of the agricultural GDP. This percentage was selected based on probabilistic drought risk assessments from other regions where the drought AAL normally represents a maximum of 20% of the agricultural GDP. It is worth noting that this is a very coarse approximation, which should only be used for comparison purposes between countries and not for quantifying risk or decision making14. Using this approach, drought AAL for Kazakhstan is USD $1,390 million which represents 0.87% of total GDP. The country is ranked 34th in the region.
14 Comparative results can be found at: Maskrey, A., Cardona, O. D., Marulanda, M., Marulanda, P., Bernal, G. Situational Risk Analysis: ESCAP Asia and the Pacific. Report for the UN Economic and Social Commission for Asia and the Pacific – ESCAP. 2019
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Propensity Index (PI)
32
Figure 5-20 Kazakhstan Drought AAL Ranking
The limits of this approach can be seen when the results are compared with those of a fully probabilistic agricultural drought risk assessment for wheat, the major cereal crop of Kazakhstan, which revealed an AAL of USD 361 million or approximately 5% of the agricultural GDP. While the agricultural drought risk associated with other crops would increase this AAL, it is unlikely that the combined AAL would exceed 10% of the agricultural GDP.
3.19
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Kyrgyzstan
Figure 5-21 Kyrgyzstan Data
Multi-hazard Risk Assessment
The existing multi-hazard risk assessment for Kyrgyzstan estimates risks to the built environment associated with earthquakes and riverine floods. The total absolute multi-hazard Average Annual Loss (AAL) for Kyrgyzstan is USD$ 92.68 million, which represents 0.06% of the regional multi-hazard risk. Of this total multi-hazard AAL, 67.54 % is contributed by earthquakes and 32.46 % by riverine floods. Drought was included using a proxy value of 20% of the agricultural GDP, which for Kyrgyzstan means a drought AAL of USD$ 186.52 million, thus increasing the total multi-hazard AAL to $279.2 million, which represents 3.69% of the country’s GDP. In these terms, drought accounts for almost 67% of the total multi-hazard AAL, 22% is contributed by earthquakes and 11% by floods.
Figure 5-22 Summary of multi-hazard losses and disaggregation by hazard for Kyrgyzstan
22%
11%
67%
Earthquake
Flood
Drought (Proxy)
USD$
Million ‰
Contribution to Mh AAL
Earthquake 62.60 3.39 22
Flood 30.08 1.63 11%
Tropical cyclones - 0.00 0.00
Tsunami - 0.00 0.00
Drought (Proxy) 186.52 11.35 67%
TOTAL 279.2 15.12 100.00
Population
6.2 (est. 2018) million
GDP
7.565 (est. 2017) billion
Agricultural GDP
932.6 (est. 2017)
million
Area: 199.951 km2
Population density: 29 people/km2
Median age: 25.5
Rural population (% of total pop): 63.9
Employment in agriculture (% of total emp): 26.1
Human Development Index: 0.672 (2017)
Gender Inequality Index: 0.392 (2017)
Mean years of schooling: 10.9 (2017)
34
Figure 5-23 Multi-hazard AAL - USD Million (left) ‰ of Capital Stock (right)
Figure 5-24 Earthquake AAL - USD Million (left) ‰ of Capital Stock (right)
Figure 5-25 Flood AAL - USD Million (left) ‰ of Capital Stock (right)
Indices
Implications for Sustainable Development (DRDIi)
This index attempts to reveal the weight of the AAL on the social expenditure, capital investment and savings (domestic investment), reserves (financial capacity) and the produced capital or capital stock (assets at risk) of each country. It reflects the constraints to sustainable development posed by disaster risk. The value of the DRDIi indicates that the country would be significantly affected if the potential losses
35
were paid every year. Therefore, a financial strategy for risk reduction and mitigation, as well as reserves or catastrophe funds for emergency response, recovery and rehabilitation are needed to avoid an increase in AAL in the future. In relation with the other countries of the region, in the DRDIi Kyrgyzstan is ranked 24 of 4615.
Figure 5-26 Kyrgyzstan Disaster Risk Implications on Development Index
Holistic Risk
In the ESCAP region, holistic risk (Cardona, 2001) has been calculated for each country by capturing how underlying risk drivers or social, economic, environmental factors– (using an aggravating factor, F), which, worsen the current existing physical risk (RF). Fourteen variables were chosen to represent the underlying risk drivers or aggravating factors16. The total risk value for Kyrgyzstan 1.17, it is ranked 32 in the region.
Figure 5-27 Holistic Risk Values for Kyrgyzstan
15 The DRDIi was not calculated for the PDR of Korea and Tuvalu due to lack of data 16 Indicators of Social Fragility are: Internet access, Access to sanitation, Access to improved drinking water, GINI Index, Unemployment, Inflation and Urban growth rate. Indicators of Lack of Resilience are: Gross National Savings, Social expenditure, Governance, Human Development Index, Ecosystems vitality, Paved roads and Infant mortality
0 100
Low implications
0 100
Disaster Risk Implications on Development Index DRDIi
63.02
High implications
DREIDisaster Risk Economic
Implications60.16
DRGIDisaster Risk Growth and
Financial Implications64.77
DRGI (Gross Fixed Capital
Formation)64.77
DRGI (Gross Savings) 0
DRSIDisaster Risk Social
Implications64.13
1.170 2
0 1
Holistic Risk
RFPhysical
Risk AAL 0.74
FAggravating
factor0.59
36
Mongolia
Figure 5-28 Mongolia Data
Multi-hazard Risk Assessment
The existing multi-hazard risk assessment for Mongolia estimates risks to the built environment associated with earthquakes and riverine floods. The total absolute multi-hazard Average Annual Loss (AAL) for Mongolia is USD$ 34.87 million, which represents 0.02% of the regional multi-hazard risk. Of this total multi-hazard AAL, 10.98 % is contributed by earthquakes and 89.02 % by riverine floods. Drought was included using a proxy value of 20% of the agricultural GDP, which for Mongolia means a drought AAL of USD$ 236.59 million, thus increasing the total multi-hazard AAL to $271.46 million, which represents 2.37% of the country’s GDP. In these terms, drought accounts for 87% of the total multi-hazard AAL, 11.43% is contributed by floods and 1.42% by earthquakes.
Figure 5-29 Summary of multi-hazard losses and disaggregation by hazard for Mongolia
1.42%
11.43%
87.15%
Earthquake
Flood
Drought (Proxy)
USD$
Million ‰
Contribution to Mh AAL
Earthquake 3.83 0.10 1.42
Flood 30.08 0.85 11.43
Tropical cyclones - 0.00 0.00
Tsunami - 0.00 0.00
Drought (Proxy) 236.59 6.47 87.15
TOTAL 279.2 15.12 100.00
Population
2.8 (est. 2018) million
GDP
11.135 (est. 2017) billion
Agricultural GDP
1,182.9 (est. 2017) million
Area: 1,560,500 km2
Population density: 2 people/km2
Median age: 27.4
Rural population (% of total pop): 31.6
Employment in agriculture (% of total emp): 29.8
Human Development Index: 0.741 (2017)
Gender Inequality Index: 0.301 (2017)
Mean years of schooling: 10.1 (2017)
37
Figure 5-30 Multi-hazard AAL - USD Million (left) ‰ of Capital Stock (right)
Figure 5-31 Earthquake AAL - USD Million (left) ‰ of Capital Stock (right)
Figure 5-32 Flood AAL - USD Million (left) ‰ of Capital Stock (right)
Indices
Implications for Sustainable Development (DRDIi)
This index attempts to reveal the weight of the AAL on the social expenditure, capital investment and savings (domestic investment), reserves (financial capacity) and the produced capital or capital stock (assets at risk) of each country. It reflects the constraints to sustainable development posed by disaster risk. The value of the DRDIi indicates that the country would be significantly affected if the potential losses
38
were paid every year. Therefore, a financial strategy for risk management is needed to avoid an increase in AAL in the future and a serious blow to the economy in case of a disaster. In relation with the other countries of the region, in the DRDIi Mongolia is ranked 7 of 4617.
Figure 5-33 Mongolia Disaster Risk Implications on Development Index
Holistic Risk
In the ESCAP region, holistic risk (Cardona, 2001) has been calculated for each country by capturing how underlying risk drivers or social, economic, environmental factors– (using an aggravating factor, F), which, worsen the current existing physical risk (RF). Fourteen variables were chosen to represent the underlying risk drivers or aggravating factors18. The total risk value for Mongolia 0.99, it is ranked 19 in the region.
Figure 5-34 Holistic Risk Values for Mongolia
17 The DRDIi was not calculated for the PDR of Korea and Tuvalu due to lack of data 18 Indicators of Social Fragility are: Internet access, Access to sanitation, Access to improved drinking water, GINI Index, Unemployment, Inflation and Urban growth rate. Indicators of Lack of Resilience are: Gross National Savings, Social expenditure, Governance, Human Development Index, Ecosystems vitality, Paved roads and Infant mortality
0 100
Low implications
0 100
Disaster Risk Implications on Development Index DRDIi
49.71
High implications
DREIDisaster Risk Economic
Implications52.27
DRGIDisaster Risk Growth and
Financial Implications45.24
DRGI (Gross Fixed Capital
Formation)51.99
DRGI (Gross Savings) 38.49
DRSIDisaster Risk Social
Implications51.64
1.000 2
0 1
Holistic Risk
RFPhysical
Risk AAL 0.60
FAggravating
factor0.67
39
Annex 1: Drought Risk (Proxy)
A comprehensive framework for the probabilistic modelling of drought hazard based on climate simulation has now been developed (Bernal et.al. 2017) and applied in a growing number of countries, including Kazakhstan in the ESCAP region. In the absence of probabilistic drought hazard models in the countries of the region, it is only possible to assess the potential importance of drought risk in the agriculture sector using proxy values derived from economic and social variables.
Exposure to droughts
A proxy of the exposure of the agricultural sector to drought hazard in a given country can be expressed by the ratio of the agricultural GDP to total GDP. Evidently the greater the participation of agriculture in a country’s economy, the greater the potential exposure of that economy to drought. The ratio of agricultural to total GDP is referred to as the Exposure Index (EI):
𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝐼𝑛𝑑𝑒𝑥 (𝐸𝐼) =𝐴𝑔. 𝐺𝐷𝑃
𝑇𝑜𝑡𝑎𝑙 𝐺𝐷𝑃
Error! Reference source not found. presents the distribution of agricultural GDP in both absolute terms and as a proportion of total GDP in the focus countries.
Agriculture GDP for focus countries
Vulnerability of the agricultural sector
An agricultural sector, based on labor intensive, low-productive agriculture, is likely to be more vulnerable to droughts than sectors based on highly productive, mechanized agriculture based on a greater employment of technology. To account for vulnerability, we propose a Vulnerability Index, which is an index composed of the proportion of rural population, proportion of rural poverty and proportion of employment in the agricultural sector.
𝑉𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑖𝑙𝑖𝑡𝑦 𝐼𝑛𝑑𝑒𝑥 (𝑉𝐼) =𝑅𝑢𝑟𝑎𝑙 𝑃𝑜𝑝. +𝑅𝑢𝑟𝑎𝑙 𝑃𝑜𝑣𝑒𝑟𝑡𝑦 + 𝐸𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡. 𝑖𝑛 𝐴𝑔.
3
0
20
40
60
80
100
0
1,000
2,000
3,000
4,000
5,000
Kazakhstan Mongolia Kyrgyzstan
GDP Agriculture
Million USD % Total GDP
40
Even though it is a rough approximation, the Vulnerability Index alone provides an important insight of the propensity to droughts risk in the countries. Furthermore, and following the same rationale, we propose a Propensity Index defined as a function of both exposure and vulnerability indices.
41.18
29.28 21.60
0
20
40
60
80
Mic
ron
esia
(Fe
der
ate
d…
Solo
mo
n Is
lan
ds
Van
uatu
Nep
al
Mya
nm
ar
Afg
han
ista
n
Tong
a
Tajik
ista
n
DPR
of
Kore
a
Lao
Peo
ple
's D
emoc
rati
c…
Pap
ua
Ne
w G
uin
ea
Tim
or-
Lest
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Pak
ista
n
Kir
ibat
i
Ban
glad
esh
Ind
ia
Bhu
tan
Sam
oa Fiji
Aze
rbai
jan
Cam
bo
dia
Kyr
gyzs
tan
Vie
t N
am
Geo
rgia
Phi
lipp
ines
Sri L
anka
Uzb
ekis
tan
Arm
eni
a
Tuva
lu
Thai
lan
d
Ind
one
sia
Mo
ngo
lia
Turk
men
ista
n
Mal
dive
s
Mar
shal
l Isl
and
s
Chi
na
Kaz
akhs
tan
Ira
n (Is
lam
ic R
epub
lic o
f)
Pal
au
Turk
ey
Mal
aysi
a
Rep
ublic
of
Kor
ea
Bru
nei
Da
russ
alam
New
Ze
alan
d
Japa
n
Sin
gap
ore
Rus
sian
Fe
dera
tio
n
Au
stra
lia
Vulnerability Index (VI)
26.75
19.81
12.98
0
10
20
30
40
50
60
Mic
ron
esia
(Fe
der
ate
d St
ates
…
Van
uatu
Nep
al
Mya
nm
ar
Afg
han
ista
n
Kir
ibat
i
Tajik
ista
n
Solo
mo
n Is
lan
ds
Tong
a
Pak
ista
n
Pap
ua
Ne
w G
uin
ea
Lao
Peo
ple
's D
emoc
rati
c…
Cam
bo
dia
Bhu
tan
Ind
ia
Ban
glad
esh
Tim
or-
Lest
e
Vie
t N
am
Sam
oa
Kyr
gyzs
tan
Fiji
DPR
of
Kore
a
Uzb
ekis
tan
Tuva
lu
Phi
lipp
ines
Arm
eni
a
Geo
rgia
Aze
rbai
jan
Sri L
anka
Ind
one
sia
Thai
lan
d
Mo
ngo
lia
Mar
shal
l Isl
and
s
Turk
men
ista
n
Mal
dive
s
Ira
n (Is
lam
ic R
epub
lic o
f)
Chi
na
Kaz
akhs
tan
Pal
au
Turk
ey
Mal
aysi
a
New
Ze
alan
d
Rep
ublic
of
Kor
ea
Bru
nei
Da
russ
alam
Japa
n
Rus
sian
Fe
dera
tio
n
Au
stra
lia
Sin
gap
ore
Propensity Index (PI)
41
Annex 2: Drought Risk Assessment of Kazakhstan – A Summary
A fully probabilistic drought risk assessment was performed to estimate the economic losses over vulnerable and exposed cultivated land units of rainfed spring wheat crop in Kazakhstan. This study considered both the current weather conditions (normal weather 1981-2010) and the future projections of climate change following the Global Circulation Model HadGEM2-AO_Run 1 (Bellouin et al., 2011), considered in this study as the most appropriate GCM for the region. The probabilistic drought risk approach represents a major step forward, as it moves the focus of the analysis from looking at individual and historical drought events, and instead widens the scope of how drought hazard is commonly viewed by considering all possible events that could occur (and that have not necessarily occurred yet) and integrating them probabilistically. In addition to the hazard model, a vulnerability model for crops facing droughts is also used, where the response of the wheat yield to water is evaluated considering the total yield production under optimal conditions and under water stress events. Finally, drought risk assessment is modeled in terms of economic losses associated with yield reduction due to water scarcity. Risk is expressed in terms of the Average Annual Loss (AAL), a risk metric analogous to that used to assess other risks. The drought risk assessment results are presented as follows.
Probable maximum loss curve (left) and summary of annual expected losses (right) for the normal weather and climate change scenarios.
The spatial distribution of the AAL (total and relative to its exposed value) shows how in total economic terms, production losses are concentrated in the arid and dry steppe, mainly in Akmola and Kostanay provinces. In relative terms, AAL is higher near the arid and semi-arid regions of the country. It is interesting to notice how the relative AAL (in percentage) gradually decreases from south to north, as the ecoregions changes from desertic to forest steppe in the northern area of the country (North Kazakhstan province). The relative AAL values in South Kazakhstan province are related to annual mean precipitation values near 400 mm but 1400 mm mean annual evapotranspiration. In other words, the high moisture demand of the atmosphere affects crop growth. That is why in this area of the country irrigation is much needed.
Normal
Weather
Climate
Change
Exposed
valueUSD Millions
USD Millions $361 $260
% 22.3% 16.0%
Annual
Average
Loss
$1,621
Results
42
a. AAL for normal weather scenario
b. AAL for climate change scenario
c. AALR for normal weather scenario
d. AALR for climate change scenario
Average Annual Loss results, total in US dollars and relative to its exposed value, for the normal weather and climate change scenarios.
Agriculture is vulnerable and climate-dependent. Crop yield is strongly related to extreme weather conditions and climate variability. The results presented in this document, for the case of rainfed spring wheat in Kazakhstan, shows how under current conditions, drought risk has a strong impact on crop production that might affect economic and food security conditions in the short term, but which could be reduced if vulnerability decreases. The current conditions will change on a future climate change scenario, although the real effects are difficult to predict because the dispersion of the projections of global circulation models, the uncertainty on agriculture practices in the future or the success of adaptation initiatives currently applied to reduce vulnerability. Due to the complexity of droughts as hazardous phenomena and the interactions between the socio-ecological systems during these events, research on drought risk is limited worldwide. However, quantifying risk and its hazard, exposure, and vulnerability components brings tools to make decisions which aims to reduce the social vulnerability when droughts happen. Some applications of the drought risk model could include land use planning, agriculture insurance schemes to transfer risk, cost-benefit analysis for crop management initiatives (irrigation, reservoir construction, fertilizers, crop rotation) and climate adaptation strategies. These applications could be developed at the local, subnational or national scale by the public or private sector.
