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6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Where and What Kind of Weather Insurance Index
Can Be Potentially Used
for Main Crops in China on the County-level
Jing Zhang1,2,Zhao Zhang 1,2
1.State Key Laboratory of Earth Surface Processes and Resource
Ecology, Beijing Normal University, Beijing, China;
2.Academy of Disaster Reduction and Emergency Management,
Ministry of Civil Affairs & Ministry of Education, Beijing, China;
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Contents
 Introduction
– Background
– Objective
 Results
– Spatial Distribution
– Probability Distribution
– Reliability Test
 Discussion
– Uncertainty
 Data & Methodology
– Study Area
– Data
– Weather Index
– Method
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Background
• Climate changes do more harm to crops;
• Less cultivated land because of pollution and urbanization;
• Traditional crop loss insurance with limited success;
 The systemic and comprehensive weather index study
Objective
Weather Index Insurance (WII) pays indemnities based on
weather disaster index, which is highly correlated with actual
losses.
• No moral hazard
• No adverse selection
• More suitable for smallholders
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Data
From China Meteorological Administration (CMA) :
Metrological data records (1980-2008)
The Database of Ten Days’Index of Agro-meteorological Disasters (1990-2010)
From the Agricultural Yearbook :
County-level crop yield dataset (1980–2008)
The crop phenology
Chilling injury / Heat damage / Drought / Waterlogging
[Ref]
Ray, D. K., Gerber, J. S., MacDonald, G. K., & West, P. C. (2015). Climate variation explains a third of global crop yield
variability. Nature communications, 6.
Tao, F., Zhang, S., & Zhang, Z. (2013). Changes in rice disasters across China in recent decades and the meteorological and
agronomic causes. Regional Environmental Change, 13(4), 743-759.
Zhang, Z., Wang, P., Chen, Y., Zhang, S., Tao, F., & Liu, X. (2014). Spatial pattern and decadal change of agro-
meteorological disasters in the main wheat production area of China during 1991–2009. Journal of Geographical Sciences,
24(3), 387-396.
Disaster
Crop
Maize / Rice / Winter wheat
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Study Area
ID Name Symbols Wheat rice maize
I Northeast China NE Single cropping rice Spring maize
II North of China NC Winter Wheat Spring maize
III Huang-huai-hai Plain HHH Winter Wheat Summer maize
IV Middle-Lower Yangtze Plain MLYP Winter Wheat Single&double cropping rice
V Sichuang Basin SB Winter Wheat Single cropping rice Spring maize
VI Yunnan-Guizhou Plateau YGP Winter Wheat Single cropping rice Summer maize
VII South China SC Double cropping rice
Figure 2 the main crop areas
Table 1 the subzones for each crop
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Weather Index
AM disaster Crop
Index
categories
Absolute-based Relative-based
Chilling injury
Maize
Threshold-based
CF
CTDRice CGDD−∞,𝑏𝑎𝑠𝑒
Winter wheat CF
Heat
damage
Maize
HT
HTDRice
Winter wheat HGDD 𝑏𝑎𝑠𝑒,∞
Drought
Maize
Percentile-based
DM DPRice
Winter wheat
Waterlogging
Maize
WM WPRice
Winter wheat
Table 2 the index used in the study
• The absolute-based index refers to one particular period in one year.
• The relative-based index is defined as the annual anomaly to the long-time average
annual value.
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Method
Figure 1 the workflow
Black dots: the actual yield, 𝑌;
Red dots: the yield trend, 𝑌𝑡;
Green dots: the meteorological yield, 𝑌𝑤;
𝑌𝑙𝑜𝑠𝑠 =
𝑌𝑤
𝑌𝑡
=
𝑌 − 𝑌𝑡
𝑌𝑡
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Method
 The autocorrelation in time series represents the random
error, which is highly depended on the time.
 The de-autocorrelation is applied to make the correlation
relationship more reliable between two time series.
 The de-autocorrelation model is defined and developed by
Zhang et al
and Wang et al as follows.
𝑌𝑡 = 𝜇 + 𝑇𝑡 + 𝑣 𝑡
𝑇𝑡 = 𝑏𝑡
𝑣 𝑡 = 𝑟=1
𝑞
𝑐 𝑟 𝑣 𝑡−𝑟 + 𝜀𝑡
time lag is examined by matlab and
one year lag are found to be key factor
𝑌𝑡
‘
=
(Yt − cYt−1)
(1 − 𝑐)
= 𝑎 −
𝑏𝑐
1 − 𝑐
+ 𝑏𝑡 +
𝜀𝑡
(1 − 𝑐)
= 𝑎′
+ 𝑏𝑡 + 𝜀𝑡
′
Figure 1 the workflow
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Method
Some sample stations have recorded the agro-
meteorological disasters, where the disaster risk is
calculated as follows.
𝑟𝑖𝑠𝑘 =
𝑖=1
3
𝑝𝑖 ∗ 𝑙𝑜𝑠𝑠𝑖
Where i represents the disaster level, which is 1 for
light loss, 2 for medium loss and 3 for serious loss,
with 𝑝𝑖 as the frequency and valule of 𝑙𝑜𝑠𝑠𝑖 is 1,2,3
corresponding to the disaster level.
Figure 1 the workflow
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
The spatial distribution of insurable counties
MaizeRiceWinter Wheat
 Not all counties are insurable.
 Insurable areas are very different for each disaster and each crop.
 For one specific disaster, spatial distributions of the absolute-based index insurance and the
relative-based index insurance are very similar. But there are more uncalculated counties (grey
color) of former.
 Most WIIs have obvious spatial clusters.
 More spatial clusters, less risk.
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Probability distribution of Pearson R
national->regional
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Probability distribution of Pearson R
national->regional
national->regional
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Probability distribution of Pearson R
national->regional
national->regional
national->regional
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Probability distribution of Pearson R
national->regional
national->regional
national->regional
national->regional
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Probability distribution of Pearson R
 The central line separate the box plot and violin
plot;
 And violin plot is drawn by Kernel density
estimation on the basis of the original data.
 Violin plot describe more details than the boxplot.
 Most violin plots are under skewed-normal
distribution;
 Weak correlation occupy the dominant part;
 For heat damage,
 On the national scale, the overall
correlation for HT insurance for maize is
best, although outliers indicate that HTD
insurance have more high correlated
counties;
 On the regional scale, the absolute WIIs
(HT and HGDD) performs better than
relative WII.
national->regional
Maize Rice
Winter
Wheat
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Reliability Test
Maize
Rice
Winter
Wheat
 For each disaster, the absolute WII map and the relative WII map are combined as the base
map;
 High risk threshold is defined as the 90th percentile; and among weather disaster stations, only
stations with high risk are displayed in maps, for low risk has less reference value.
 For three crops, most high risk sample stations are located in insurable areas. So the insurable
counties we obtained have high reliability.
 Except for lower reliability for the chilling injury insurance and waterlogging insurance of
maize.
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
Uncertainty
 The simple correlation
In fact, the correlation between the yield loss and disaster index can be very
complicated. The Pearson R is one simple measurement. The alternative method like
copula functions and simulated crop models has the potential to find more details.
 Basis risk
The basis risk is the systemic risk for WII, which includes the geographic risk and
production risk.
 Multi-hazards insurance
The single disaster insurance is discussed in the study, but if considering the
interactions between two or more disasters, the spatial distribution of insurable counties
may become very different.
6th
International Disaster and Risk Conference IDRC 2016
‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland
www.grforum.org
THANK YOU
&
QUESTION TIME
hygie@mail.bnu.edu.cn

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Where and What Kind of Weather Insurance Indexes Could be Potentially Used for Main Crops in China on the County-level, Jing ZHANG

  • 1. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Where and What Kind of Weather Insurance Index Can Be Potentially Used for Main Crops in China on the County-level Jing Zhang1,2,Zhao Zhang 1,2 1.State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China; 2.Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing, China;
  • 2. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Contents  Introduction – Background – Objective  Results – Spatial Distribution – Probability Distribution – Reliability Test  Discussion – Uncertainty  Data & Methodology – Study Area – Data – Weather Index – Method
  • 3. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Background • Climate changes do more harm to crops; • Less cultivated land because of pollution and urbanization; • Traditional crop loss insurance with limited success;  The systemic and comprehensive weather index study Objective Weather Index Insurance (WII) pays indemnities based on weather disaster index, which is highly correlated with actual losses. • No moral hazard • No adverse selection • More suitable for smallholders
  • 4. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Data From China Meteorological Administration (CMA) : Metrological data records (1980-2008) The Database of Ten Days’Index of Agro-meteorological Disasters (1990-2010) From the Agricultural Yearbook : County-level crop yield dataset (1980–2008) The crop phenology Chilling injury / Heat damage / Drought / Waterlogging [Ref] Ray, D. K., Gerber, J. S., MacDonald, G. K., & West, P. C. (2015). Climate variation explains a third of global crop yield variability. Nature communications, 6. Tao, F., Zhang, S., & Zhang, Z. (2013). Changes in rice disasters across China in recent decades and the meteorological and agronomic causes. Regional Environmental Change, 13(4), 743-759. Zhang, Z., Wang, P., Chen, Y., Zhang, S., Tao, F., & Liu, X. (2014). Spatial pattern and decadal change of agro- meteorological disasters in the main wheat production area of China during 1991–2009. Journal of Geographical Sciences, 24(3), 387-396. Disaster Crop Maize / Rice / Winter wheat
  • 5. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Study Area ID Name Symbols Wheat rice maize I Northeast China NE Single cropping rice Spring maize II North of China NC Winter Wheat Spring maize III Huang-huai-hai Plain HHH Winter Wheat Summer maize IV Middle-Lower Yangtze Plain MLYP Winter Wheat Single&double cropping rice V Sichuang Basin SB Winter Wheat Single cropping rice Spring maize VI Yunnan-Guizhou Plateau YGP Winter Wheat Single cropping rice Summer maize VII South China SC Double cropping rice Figure 2 the main crop areas Table 1 the subzones for each crop
  • 6. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Weather Index AM disaster Crop Index categories Absolute-based Relative-based Chilling injury Maize Threshold-based CF CTDRice CGDD−∞,𝑏𝑎𝑠𝑒 Winter wheat CF Heat damage Maize HT HTDRice Winter wheat HGDD 𝑏𝑎𝑠𝑒,∞ Drought Maize Percentile-based DM DPRice Winter wheat Waterlogging Maize WM WPRice Winter wheat Table 2 the index used in the study • The absolute-based index refers to one particular period in one year. • The relative-based index is defined as the annual anomaly to the long-time average annual value.
  • 7. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Method Figure 1 the workflow Black dots: the actual yield, 𝑌; Red dots: the yield trend, 𝑌𝑡; Green dots: the meteorological yield, 𝑌𝑤; 𝑌𝑙𝑜𝑠𝑠 = 𝑌𝑤 𝑌𝑡 = 𝑌 − 𝑌𝑡 𝑌𝑡
  • 8. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Method  The autocorrelation in time series represents the random error, which is highly depended on the time.  The de-autocorrelation is applied to make the correlation relationship more reliable between two time series.  The de-autocorrelation model is defined and developed by Zhang et al and Wang et al as follows. 𝑌𝑡 = 𝜇 + 𝑇𝑡 + 𝑣 𝑡 𝑇𝑡 = 𝑏𝑡 𝑣 𝑡 = 𝑟=1 𝑞 𝑐 𝑟 𝑣 𝑡−𝑟 + 𝜀𝑡 time lag is examined by matlab and one year lag are found to be key factor 𝑌𝑡 ‘ = (Yt − cYt−1) (1 − 𝑐) = 𝑎 − 𝑏𝑐 1 − 𝑐 + 𝑏𝑡 + 𝜀𝑡 (1 − 𝑐) = 𝑎′ + 𝑏𝑡 + 𝜀𝑡 ′ Figure 1 the workflow
  • 9. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Method Some sample stations have recorded the agro- meteorological disasters, where the disaster risk is calculated as follows. 𝑟𝑖𝑠𝑘 = 𝑖=1 3 𝑝𝑖 ∗ 𝑙𝑜𝑠𝑠𝑖 Where i represents the disaster level, which is 1 for light loss, 2 for medium loss and 3 for serious loss, with 𝑝𝑖 as the frequency and valule of 𝑙𝑜𝑠𝑠𝑖 is 1,2,3 corresponding to the disaster level. Figure 1 the workflow
  • 10. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org The spatial distribution of insurable counties MaizeRiceWinter Wheat  Not all counties are insurable.  Insurable areas are very different for each disaster and each crop.  For one specific disaster, spatial distributions of the absolute-based index insurance and the relative-based index insurance are very similar. But there are more uncalculated counties (grey color) of former.  Most WIIs have obvious spatial clusters.  More spatial clusters, less risk.
  • 11. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Probability distribution of Pearson R national->regional
  • 12. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Probability distribution of Pearson R national->regional national->regional
  • 13. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Probability distribution of Pearson R national->regional national->regional national->regional
  • 14. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Probability distribution of Pearson R national->regional national->regional national->regional national->regional
  • 15. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Probability distribution of Pearson R  The central line separate the box plot and violin plot;  And violin plot is drawn by Kernel density estimation on the basis of the original data.  Violin plot describe more details than the boxplot.  Most violin plots are under skewed-normal distribution;  Weak correlation occupy the dominant part;  For heat damage,  On the national scale, the overall correlation for HT insurance for maize is best, although outliers indicate that HTD insurance have more high correlated counties;  On the regional scale, the absolute WIIs (HT and HGDD) performs better than relative WII. national->regional Maize Rice Winter Wheat
  • 16. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Reliability Test Maize Rice Winter Wheat  For each disaster, the absolute WII map and the relative WII map are combined as the base map;  High risk threshold is defined as the 90th percentile; and among weather disaster stations, only stations with high risk are displayed in maps, for low risk has less reference value.  For three crops, most high risk sample stations are located in insurable areas. So the insurable counties we obtained have high reliability.  Except for lower reliability for the chilling injury insurance and waterlogging insurance of maize.
  • 17. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org Uncertainty  The simple correlation In fact, the correlation between the yield loss and disaster index can be very complicated. The Pearson R is one simple measurement. The alternative method like copula functions and simulated crop models has the potential to find more details.  Basis risk The basis risk is the systemic risk for WII, which includes the geographic risk and production risk.  Multi-hazards insurance The single disaster insurance is discussed in the study, but if considering the interactions between two or more disasters, the spatial distribution of insurable counties may become very different.
  • 18. 6th International Disaster and Risk Conference IDRC 2016 ‘Integrative Risk Management – Towards Resilient Cities‘ • 28 Aug – 1 Sept 2016 • Davos • Switzerland www.grforum.org THANK YOU & QUESTION TIME hygie@mail.bnu.edu.cn

Editor's Notes

  1. Case dtudy
  2. At the beginning, it’s necessary to know the background of the weather index insurance. Firstly, the climate change in the past decades do more harm to crops, especially the extreme weather disasters; And then, there is less available land for famers, because of the serious pollution and rapid urbanization in China; So the income of famers decreases. But more unfortunately, the traditional crop insurance do little help to farmers. There are many reasons for the fail of traditional crop insurance, as it’s hard to measure the real loss, the highly dependence on the government, and government spent much more than it received. Above all, one new type crop insurance is developed, called the Weather index insurance, with many advantages. The nature of it is to find the relationship between the weather disaster index and the crop loss according to the historic data, and then in the future, indemnity is directly based on the disaster index rather than crop loss. So the primary work of weather index insurance is to find which disaster index has the potential to be used in insurance, and where could be insured. To achieve this goal, we have collected some historical data.
  3. Data collections in this study are from the China Meteorological Administration and the Agricultural Yearbook. Some are free open, while some are paid service. Three most common crops are selected. They are maize, rice and winter wheat. And disasters are defined as chilling injury, heat damage, drought and waterlogging. I won’t make more introduction about how the weather disaster hurt the crop, but there some references could be helpful if you are interested in.
  4. The planting area for three crops are divided into 7 regions. And for three crops, the extend of each region is defined according to the land use map of China and Agricultural Yearbook. Seven regions are called the Northeast China, the North China, Huang-huai-hai Plain, the middle-lower Yangtze Plain, the Sichuan Basin, the Yunnan-Guizhou Plateau, and the South China. Their names may be very difficult for you to understand. But don’t worry, I will use the ID number to represent each region in next slides.
  5. There have been many previous studies concentrating on the weather disaster index in China. And in my study, for each disaster and each crop, we have two different disaster indices, so that we could compare them to decide which one is better when they are applied into the insurance. The relative-based index is defined as the annual anomaly to the long-time average annual value. The absolute-based index refer to one particular period in one year.
  6. The flow chart is shown on the left. We have three spatial scales to do analysis. For each county, we detect if it’s insurable or not by using the historical data. Firstly, it is believed that the crop yield is the combination of the trend yield and weather yield. The trend yield is owe to the development of society and technology, which can be calculated as the average value in one short-term period. And then we obtain the relative weather yield loss. Before to calculate the correlation coefficient, the de-autocorrelation process are applied to remove the autocorrelation effect in the yield loss time series and disaster index time series. Among China, about 1000 counties for maize, 1300 counties for rice,1200 counties for winter wheat are selected and divided into 7 regions. So we can compare the regional difference and probability distribution on the regional scale. Finally on the country scale, the reliability of insurable counties is examined by overlap the high risk weather station on the insurable counties. More details will be discussed in the result. And here, I just introduce the method how to calculate the disaster risk. It’s widely known that the disaster risk represents the probability and degree of damages.
  7. The flow chart is shown on the left. We have three spatial scales to do analysis. For each county, we detect if it’s insurable or not by using the historical data. Firstly, it is believed that the crop yield is the combination of the trend yield and weather yield. The trend yield is owe to the development of society and technology, which can be calculated as the average value in one short-term period. And then we obtain the relative weather yield loss. Before to calculate the correlation coefficient, the de-autocorrelation process are applied to remove the autocorrelation effect in the yield loss time series and disaster index time series. Among China, about 1000 counties for maize, 1300 counties for rice,1200 counties for winter wheat are selected and divided into 7 regions. So we can compare the regional difference and probability distribution on the regional scale. Finally on the country scale, the reliability of insurable counties is examined by overlap the high risk weather station on the insurable counties. More details will be discussed in the result. And here, I just introduce the method how to calculate the disaster risk. It’s widely known that the disaster risk represents the probability and degree of damages.
  8. The flow chart is shown on the left. We have three spatial scales to do analysis. For each county, we detect if it’s insurable or not by using the historical data. Firstly, it is believed that the crop yield is the combination of the trend yield and weather yield. The trend yield is owe to the development of society and technology, which can be calculated as the average value in one short-term period. And then we obtain the relative weather yield loss. Before to calculate the correlation coefficient, the de-autocorrelation process are applied to remove the autocorrelation effect in the yield loss time series and disaster index time series. Among China, about 1000 counties for maize, 1300 counties for rice,1200 counties for winter wheat are selected and divided into 7 regions. So we can compare the regional difference and probability distribution on the regional scale. Finally on the country scale, the reliability of insurable counties is examined by overlap the high risk weather station on the insurable counties. More details will be discussed in the result. And here, I just introduce the method how to calculate the disaster risk. It’s widely known that the disaster risk represents the probability and degree of damages.
  9. Ok, now here we go to results. The first result is about the spatial distribution of insurable counties. The insurable counties of three crops are displayed one by one. The uncalculated reason is that few years are judged to be hurt by weather disaster. So we don’t have enough data to calculate the correlation coefficient.
  10. Then we draw the boxplot and violin plot to display the probability distribution of correlation coefficient R. So that we can find more difference and details about the insurable counties. The distribution is both on the national scale and regional scale, for chilling injury, heat damage, drought and waterlogging in turn. Here, we only take the heat damage as an example to explain the figure. Look at the left figure. From the boxplot, we can know the value of R at quartiles and median. And from the violin plot, we can know the probability of any R. And then look at the right figure. We have 7 regions for each crop. The region ID are listed. And in these figures, the first two plots are for maize, the next two for rice, and the rest two for winter wheat. The plots with colored background are for relative-based index insurance. So how about my results
  11. Then we draw the boxplot and violin plot to display the probability distribution of correlation coefficient R. So that we can find more difference and details about the insurable counties. The distribution is both on the national scale and regional scale, for chilling injury, heat damage, drought and waterlogging in turn. Here, we only take the heat damage as an example to explain the figure. Look at the left figure. From the boxplot, we can know the value of R at quartiles and median. And from the violin plot, we can know the probability of any R. And then look at the right figure. We have 7 regions for each crop. The region ID are listed. And in these figures, the first two plots are for maize, the next two for rice, and the rest two for winter wheat. The plots with colored background are for relative-based index insurance. So how about my results
  12. Then we draw the boxplot and violin plot to display the probability distribution of correlation coefficient R. So that we can find more difference and details about the insurable counties. The distribution is both on the national scale and regional scale, for chilling injury, heat damage, drought and waterlogging in turn. Here, we only take the heat damage as an example to explain the figure. Look at the left figure. From the boxplot, we can know the value of R at quartiles and median. And from the violin plot, we can know the probability of any R. And then look at the right figure. We have 7 regions for each crop. The region ID are listed. And in these figures, the first two plots are for maize, the next two for rice, and the rest two for winter wheat. The plots with colored background are for relative-based index insurance. So how about my results
  13. Then we draw the boxplot and violin plot to display the probability distribution of correlation coefficient R. So that we can find more difference and details about the insurable counties. The distribution is both on the national scale and regional scale, for chilling injury, heat damage, drought and waterlogging in turn. Here, we only take the heat damage as an example to explain the figure. Look at the left figure. From the boxplot, we can know the value of R at quartiles and median. And from the violin plot, we can know the probability of any R. And then look at the right figure. We have 7 regions for each crop. The region ID are listed. And in these figures, the first two plots are for maize, the next two for rice, and the rest two for winter wheat. The plots with colored background are for relative-based index insurance. So how about my results
  14. Then we draw the boxplot and violin plot to display the probability distribution of correlation coefficient R. So that we can find more difference and details about the insurable counties. The distribution is both on the national scale and regional scale, for chilling injury, heat damage, drought and waterlogging in turn. Here, we only take the heat damage as an example to explain the figure. Look at the left figure. From the boxplot, we can know the value of R at quartiles and median. And from the violin plot, we can know the probability of any R. And then look at the right figure. We have 7 regions for each crop. The region ID are listed. And in these figures, the first two plots are for maize, the next two for rice, and the rest two for winter wheat. The plots with colored background are for relative-based index insurance. So how about my results
  15. After the above analysis, we use disaster risk to exam the reliability of the insurable counties. You can see that there are some small bars on the map. The height of each bar represents the value of disaster risk. Our conclusion is that So we still need to do more work to study them.