Understanding risk using quantitative risk assessment
offers critical information for risk-informed reduction actions, investing
in building resilience, and planning for adaptation. This study develops an
event-based probabilistic risk assessment (PRA) model for livestock snow
disasters in the Qinghai–Tibetan Plateau (QTP) region and derives risk
assessment results based on historical climate conditions (1980–2015) and
present-day prevention capacity. In the model, a hazard module was developed
to identify and simulate individual snow disaster events based on boosted
regression trees. By combining a fitted quantitative vulnerability function and
exposure derived from vegetation type and grassland carrying capacity, we
estimated risk metrics based on livestock mortality and mortality rate. In
our results, high-risk regions include the Nyainqêntanglha Range,
Tanggula Range, Bayankhar Mountains and the region between the Kailas Range
and the neighbouring Himalayas. In these regions, annual livestock mortality
rates were estimated as
Livestock snow disasters are serious winter extreme weather events that widely occur in central-to-east Asian temperate steppe and alpine steppes (Li et al., 2018; Tachiiri et al., 2008). In the pastoral areas of these regions, heavy snowfall provides thick and long-lasting snow cover, making forage unavailable or inaccessible (Fernández-Giménez et al., 2015). Together with extremely low temperature and strong wind, this cover severely inhibits natural grazing, claims considerable livestock mortality, and brings devastating impacts to the livelihoods of local herders, even threatening their survival (J. Wang et al., 2013). In response to threats from livestock snow disasters, great efforts have been devoted to understanding their mechanism as a complicated interaction between precipitation, vegetation, livestock, and herding communities (Nandintsetseg et al., 2018; Shang et al., 2012; Sternberg, 2017); the major drivers of (socioeconomic) vulnerability (Fernández-Giménez et al., 2012; Wang et al., 2014; Wei et al., 2017; Yeh et al., 2014); and key factors that could foster adaptive capacity and community resilience (Dong and Sherman, 2015; Fernández-Giménez et al., 2015). Attempts have been made to develop techniques, such as snow disaster monitoring, forecasting, and rapid assessment, to provide critical information for prevention and addressing emergencies (W. Wang et al., 2013; Yin et al., 2017). Quantitative analyses have also been conducted to derive the relationship between livestock loss, snow hazard, and various environmental stressors (Li et al., 2018; Mukund Palat et al., 2015).
Disaster risk is a measure of uncertain consequences. The Sendai Framework
outlines the importance of risk assessment as a critical means of
understanding disaster risk and a prerequisite for other actions, e.g.
risk-based investment for resilience and adaptation (UNISDR,
2015). Following the mainstreaming risk assessment framework of
risk
Researchers have also derived probabilistic risk assessment (PRA) results
for livestock snow disaster. In such a framework, risk measured as a
probability distribution of socioeconomic losses (consequences) are
generally derived with the probability distribution of hazard intensity and
dose-response relationships between hazard intensity and socioeconomic (Carleton
and Hsiang, 2016; Michel-Kerjan and Kousky, 2010; Shi and Kasperson, 2015).
Bai et al. (2011) applied the PRA framework to a
livestock snow disaster risk assessment in Qinghai Province of China. A
function of livestock mortality rate in response to the snow season
(November to April of the preceding year) daily-average snow depth uses
historical disaster records. Historically annual-average snow depths computed
from satellite-retrieved data were used to derive return-period livestock
mortality and mortality rates as the final risk metrics. Based on their
method, quantitative livestock snow disaster risks were mapped nationwide in
China (Shi, 2011). Tachiiri and Shinoda (2012) successfully
extended the framework to future climate change analysis. They trained a
tree-based model to link annual livestock loss rates, the October to April
snow water equivalence, and normalized difference vegetation index. The
projected snow water equivalence values from climate scenarios were then
used to estimate the frequency of anomalous livestock loss rates
These earlier studies primarily developed their PRA models using annual variables. In this study, we develop an event-based PRA method for present and future livestock snow disaster risk assessments for the QTP region. The event-based PRA approach has several important additions compared to earlier studies using annual variables. (1) From the modelling perspective, the event-based framework retains the capability to accommodate multiple events in a year, which is one characteristic of snow disasters. This is important for snow disasters as earlier studies demonstrated that livestock mortality rate exhibits a concave relationship with disaster duration (Li et al., 2018). The losses of one event lasting for 30 days and two events lasting for 15 days each are clearly different. In addition, modelling events provides a mechanism for capturing the change in event frequency and intensity in response to environmental change, such as climate change. (2) From a risk-informed action perspective, the annual evaluation, i.e. potential aggregate duration, is not useful for risk-transfer mechanisms because insurance needs to address the natural event basis, although it might be temporarily acceptable for annual preparedness planning. This is also the critical reason that catastrophe risk models are mostly built on an event basis (Michel-Kerjan et al., 2013).
There are three major aims of this study: (1) to develop a hazard module that can identify and capture snow disaster event based on daily weather data. It is the basis for any event-based modelling attempts, and is particularly important for regions where historical records are absent and for future risk assessment where observations and records are not yet available and variabilities from future climate change will exist; (2) to set up an event-based PRA framework for livestock snow disaster risk assessment by integrating snow disaster event (hazards), livestock vulnerability, and exposure together to derive a probabilistic quantification of risk; (3) derive the risk metrics for livestock mortality risk in the QTP and offer risk-informed reduction implications.
Worldwide, the QTP is a region that has suffered most from livestock snow disasters due to its large snow-covered area, long-lasting snow-covered days, and nomadic grazing (Shang et al., 2012). This region is also a hot spot in climate change (Diffenbaugh and Giorgi, 2012; Gu et al., 2014). Quantitative risk assessments for the present day will likely be a significant source of information for disaster risk reduction. In addition, our framework can be adapted for livestock mortality in snow disasters in the context of future climate change analysis and therefore support climate adaptation planning for local government and herding communities.
The QTP contains the world's highest elevation pastoral area
(Wang et al., 2016). It has extremely enriched grassland
resources, with a total alpine grassland cover of CNY 1
The Tibetan Plateau is one of the three primary snowfall regions in China
(Yin et al., 2017; Qin et al., 2015).
On average, the snow cover can attain
In this study, an event-based PRA framework was developed for livestock snow disasters (Fig. 1). We followed the PRA approach proposed by Carleton and Hsiang (2016) and applied the concept of event-based modelling in catastrophic risk models (Michel-Kerjan et al., 2013). The event-based modelling approach was framed using state-of-the-art three-element risk modelling, hazard, exposure, and vulnerability (Kinoshita et al., 2018; Muis et al., 2015) to model losses claimed by individual events. Then PRA was achieved through repetition of individual event modelling, in which a large number of events were drawn from the full distribution of hazards, given the predicted losses and consequences from individual events, from which a full distribution of disaster loss can be obtained.
Event-based probabilistic risk assessment framework for livestock snow disasters in the Qinghai–Tibetan Plateau region.
In our event-based PRA method, the hazard module needs to identify individual snow disaster events to provide event duration (Duration) and wind speed (Wind) during the event, two important inputs to model event loss using the vulnerability function. It requires the exact timing (start and end dates) of each event. However, the timing of each individual event is not straightforward to obtain. For the historical period, there are no ready-to-use snow disaster event data sets at the grid level. The number of meteorological stations capable of observing snowfall in the QTP is limited and are primarily located in the eastern and southern parts of the region. For future risk assessment, no projections of snow disaster events are provided in climate scenario data sets, although models have been developed to simulate daily snow depth (Yuan et al., 2016). Therefore, a snow disaster event identifier–simulator was developed here to identify and simulate snow disasters.
A snow disaster is a weather process with snowfall, low temperature, and
snow cover, with certain duration length, according to the Chinese
national standard for
Technical flow of the snow disaster event identifier–simulator.
For this step, boosted regression tree (BRT) modelling was used to establish
multivariate and non-linear relationships between SDD and various weather
information. The BRT modelling methodology was chosen due to its promising
power for both explanatory and predictor purpose in many ecological and
environmental modelling scenarios (Elith
et al., 2008; Hastie et al., 2009). Other machine-learning methods, such as
random forest, can also be used but are less likely to outperform BRT
according to the literature (Oppel
et al., 2012; Youssef et al., 2016). To fit a BRT model, historical snow
disasters were first turned into SDD flags: if a date was included in a
historical snow disaster it was flagged with “1”, and “0” otherwise.
Variables used to explain and predict days that would be considered SDD were
inspired by the two standards, GB/T20482-2017 and QX/T 178-2013. Both
standards agree well for the important indicators that define a snow
disaster. We included daily snow depth (SD, cm), daily maximum (maxWind),
mean (meanWind) and minimum wind (minWind) speed (m s
Historical snow disaster event data with the time of each event for each
meteorological station were used to train the BRT model. These data were
obtained from two sources. Records for 1980–2007 were a collection of snow
disaster records published in six provincial meteorological yearbooks for
the Tibetan Plateau (W. Wang et al., 2013).
Records from 2008 to 2015 were obtained from the China Meteorological Science
Data Sharing Service System (CMSDS,
BRT model fitting was conducted using the package
A fitted BRT model can help predict the probability of a single day being
judged as a SDD. To predict/rebuild snow disaster events, these single-day
probabilities must be deemed snow disaster events, an ensemble of multiple
SDDs. Because the explicit output from the BRT suffered from prediction
errors, simply using a threshold to turn probabilities into
Finally, the fitted BRT model together with the tuned parameters of
smoothing and filtering was applied to generate all snow disaster events
during 1980–2015 by grid. The China meteorological forcing data set
(He and Yang, 2011) obtained from the Scientific Data Centre
of Cold and Arid Regions
Vulnerability is a critical function that links dose (hazard inputs) and
response (loss estimates) (Carleton and Hsiang,
2016). For livestock snow disasters, a set of vulnerability functions have
been estimated linking livestock mortality (rate) to snow disaster duration,
within-disaster environmental stress, summer season vegetation productivity,
and disaster prevention capacity (Fang et al., 2016;
Wang et al., 2016). To fulfil the goal of event-based modelling, the
vulnerability relationship must be built on an event basis. Using
generalized additive models, Li et al. (2018) derived
the quantitative relationship between livestock mortality (rate), snow
disaster event duration, within-disaster wind speed, pre-winter vegetation
condition, and time index. Using the identical data set, we included disaster
prevention capacity in the analysis, using socioeconomic indicators as a
proxy, and followed Li et al. (2018)'s approach to derive the model with
the best predictive power. We tried different socioeconomic indicators,
including gross domestic production, value added of animal husbandry, fiscal
revenue, fiscal expenditure, and gross domestic production per capita,
following suggestions from the literature (Wei et al., 2017). We found that the
model using value added from animal husbandry yielded the best fitting
result, having a deviance-based
Given such a relationship, the vulnerability is a truly dose-response function between livestock mortality rate (mortality/herd size) and snow hazard intensity together with other environmental stressors and prevention capacity, as proposed by (Carleton and Hsiang, 2016). In contrast to simply defining vulnerability as the loss rate (Jongman et al., 2015; Kinoshita et al., 2018), the potential influence from socioeconomic development is embedded in the vulnerability function.
Exposure measures the distribution of assets or population exposed to hazards (Kinoshita et al., 2018). In our framework, it provides the spatial distribution of herd size and density exposed to snow disaster and converts outputs from event loss modelling and livestock mortality rate (the response variable in the modelled vulnerability function) into mortality (death toll). By definition (Fernández-Giménez et al., 2012), livestock in nomadic grazing are most prone to snow disaster because they obtain food mostly from grassland. Livestock raised in ranches or industrial livestock farms in agricultural regions, by contrast, are much less exposed because they have steady food supplies from crop by-products and are well protected by infrastructure. Therefore, the estimated number of livestock grazing on grassland was used to denote livestock exposure to snow disasters.
A full gridded distribution map of herd size/density grazing on grassland in
the QTP is not directly available, but it can be derived according to the
rule of thumb for forage–livestock balance. According to the
There are several factors that determine the carrying capacity of a given
region, but the most important is grassland type, according the
Snow disaster event losses measured with livestock mortality rate (death
toll divided by herd size) were modelled by taking requested inputs into the
vulnerability function. Duration and Wind were outputs from the hazard module. Growing
season aggregate precipitation
Loss based on historical prevention capacity. To model actual historical loss for model calibration and validation purposes, we used the actual county-level Value_Add of the study area for 1980–2015. Because Value_Add increases with time, it indicates the increasing prevention capacity and therefore declining livestock mortality (rate) with time.
Loss based on present prevention capacity. For risk assessment purposes, we used the constant value of Value_Add from 2015 for two reasons. First, we needed to fit probability distributions over the modelled loss to derive final risk metrics, and the process required that the underlying loss samples were at least stationary in their means and variances. Using a constant Value_Add value for 2015 avoided introducing trends inherent within Value_Add into the modelled loss, as the Value_Add has been growing. Second, because we assumed that Value_Add is a proxy for prevention capacity, using the Value_Add value for 2015 in loss modelling helped to estimate the potential loss given a very recent prevention capacity (year 2015) rather than those of the 1980s or 1990s. Therefore, the derived risk metrics are more helpful for prevention planning and insurance implications in the near future.
The searching of snow disaster event and modelling of loss starts every
August and ends in June of the next year. Event mortality rates were then
aggregated into annual mortality rates, considering the possibility of
multiple events per location annually, although unlikely. In aggregation, we
assumed that the second snow disaster event can only have an impact on
livestock surviving from the first event and so on. Therefore, the annual
aggregate loss rate in a given grid is
Event/annual mortality (death toll) can then be derived by multiplying event/annual loss rate in any given location by its herd size. For each grid, 35 annual loss records were modelled (there are 35 winters in 36 years), including both mortality and mortality rate figures. The number of event loss records differ by location, depending on the identified number of events for each grid.
In the risk metrics, modelled losses of discrete event/annual losses were turned into a probability distribution of losses. We followed standard risk metrics by deriving the average and return-period values (Michel-Kerjan et al., 2013; Shi and Kasperson, 2015) of annual mortality rate and death toll for each grid. Model-derived annual mortality rates, based on constant Value_Add for 2015, were used to derive risk metrics. Due to our limited time span for repetition, return periods of 10 years (the 90th percentile of the distribution), 20 years, and 50 years were considered, while the 100 years usually used in flood and earthquake studies (Kinoshita et al., 2018) was not considered. The kernel density method was employed to fit non-parametric distributions to derive the return-period values by grid. We used the Gaussian kernel function and its corresponding optimal window width in the fitting process according to the rule of thumb for optimality (Deng et al., 2007; Silverman, 1986). In addition, aggregate mortality rate and death tolls at the municipal level were derived using zonal statistics to better validate the result with historical losses, and provide policy implications.
The trained BRT model retained six variables but excluded SD, minWind, and Pre as the
result of the predictor selection process. In the final model, we used
lr In BRT, the relative importance is calculated based
on the number of times a variable is selected for splitting, weighted by the
squared improvement to the model as a result of each split, and averaged
over all trees. The relative importance for each variable is scaled so that
the sum is 100 (Elith et al., 2008).
Relative influence of variables predicting a snow disaster day. Blue bars are relative importance of each factor and the sum of all relative importance fractions is 100 %.
After tuning the window size with the moving average and threshold, we found
the best results with a window size of 21 and threshold of 0.18. The derived
results captured the timing of occurrence of historical events (Fig. S1) and
matched the empirical cumulative density functions (ECDFs) for historical
durations (Fig. 4), for both event and annual
aggregate durations. In historical records, two or more events in a single
year at a single location are rare. Therefore, ECDFs for historical single
event duration and annual aggregation duration were quite close to each other
in Fig. 3. Two-sample Kolmogorov–Smirnov tests were also conducted to
verify the degree of agreement between ECDFs. For single-event duration
(observed vs. predicted), the test statistic was 0.138, and its
corresponding
Empirical cumulative density functions for historical and model-predicted snow disaster duration.
With the tuned model, the timing of snow disaster events were identified in the historical period 1980–2015. Correspondingly, the annual occurrence frequency and duration of snow disaster events were derived (Fig. 5). In the figure, non-grassland areas, including permanent snow areas, were masked using the vegetation map. Across the entire plateau, the annual-average frequency was below 0.2 in most regions; i.e. on average, snow disasters occur every 5 years in these regions. Higher-frequency regions were primarily located in major mountains, including the Tanggula Range and Nyainqêntanglha Range in the central part of the plateau, and the Kailas Range and the neighbouring Himalayas. These regions are higher elevation and spatially close to permanent snow-covered areas. For major pastoral production regions, i.e. the Naqu prefecture in the central QTP, the annual-average frequency was 0.2 to 1, echoing the local proverb, “small disaster once in 3 years, and a major disaster once in 5 years” (Ye et al., 2018).
Gridded annual frequency and annual-average occurrence
The distribution of mean annual aggregate duration of snow disasters was consistent with annual frequency, indicating strong controls from elevation and topology. For most regions, mean annual aggregate duration was below 3 days. For typical pastoral regions, i.e. Naqu, a snow disaster can last for more than 14 days on average. In comparison, the annual aggregate duration can last for more than 21 days in high-elevation mountainous areas, including the Himalayas in the southwest and alpine meadows to the east of the Bayankhar Mountains, which is nearly 10 % of the total grids with valid values.
Model-derived annual snow disaster losses (1980–2015) are provided in Fig. 6. The orange time series show losses modelled using Value_Add from historical values (dynamic), assuming historical prevention capacity. The blue time series show losses modelled using constant Value_Add from 2015, assuming present-day prevention capacity. All losses are for a specific snow disaster season from August to the following June, rather than a civil year.
Model-derived annual livestock loss from snow disasters in the
QTP (1980–2015). The unit of modelled loss has been converted from sheep units
to heads (units) by them dividing by 2.2 (total sheep units
To measure model performance, historical losses over the QTP for 1980–2015
were collected from the China Meteorological Disaster Catalog (Wen, 2008)
(for 1980–2000) and China Meteorological Disaster Yearbook (2004–2016)
(China Meteorological Administration, 2004–2016). The model result did
capture the interannual variation of losses: the correlation coefficient of
the modelled loss and recorded historical loss was 0.688 and the
root-mean-square error was 250 841. Our model also captured most years that
experienced severe snow disaster loss, i.e. major loss years with annual
aggregate loss of over 500 000 heads (units). These years include 1981
(referring to 1981 snow season, August to June of the next year), 1982,
1985, 1986, 1988, 1989, 1992–1995, 1997, 2007, and 2012. The correlation
coefficient of the modelled loss and recorded historical loss of these years
was 0.779, the root-mean-square error was 400 671, and
mean-absolute-percentage error was 37 %. For the peak loss years (annual
aggregate
The modelled historical loss also exhibited a clear decreasing trend compared to the modelled loss associated with present-day prevention capacity (the blue series). The difference indicates that an improved prevention capacity, using value added of animal husbandry as a proxy, played an important role in reducing livestock loss in snow disasters. In addition, it also confirmed that the modelled historical loss cannot be used for directly fitting the probability distribution of loss due to its pronounced trend. Instead, the modelled loss associated with present-day prevention capacity is appropriate.
The assessed livestock snow disaster risk measured using the annual mortality rate is presented in Fig. 7. Because it is not viable to present the full probability distribution of livestock mortality rate by grid, these figures include the annual average and three return-period mortality rate maps (10, 20, and 50 years), upon which the non-pasture areas were masked.
Gridded livestock snow disaster risk in terms of mortality rate (%)
in annual-average values, and 10-, 20-, and 50-year return-period values. The
grid size is 0.1
Spatial distributions of mortality rate for different return periods are highly consistent (Fig. 7). The pattern is very similar to the pattern of annual aggregate snow disaster duration (Fig. 5), confirming the dominant influence of snow disaster duration. High-mortality rate regions are primarily located in the major mountainous areas, including the Tanggula Range and Nyainqêntanglha Range in the central QTP, the Kailas Range and the neighbouring Himalayas in the southwest QTP, Bayankhar mountains in the east QTP, and southern part of the Kalakoram Range and west end of the Kunlun Mountains in the northwest corner of the QTP. Classified by administrative districts, high mortality rate regions include the Yushu and Guoluo prefectures in Qinghai Province and Naqu, southwest Ngari, and the northwest Shigatse prefecture in the Tibet Autonomous Region. In these regions, the annual-average mortality rate reaches 10 % and in some parts of Guoluo and Shigatse, the 50-year mortality rate can reach more than 10 %.
Risk metrics in terms of livestock mortality were derived by multiplying the mortality rate by exposure (Fig. 8). Again, annual-average mortality and the mortality at 10-, 20-, and 50-year return periods are all reported.
Gridded livestock snow disaster risk in terms of mortality (sheep
units km
Mortality appears small in Fig. 8, generally with several sheep
units km
Livestock snow disaster risk in terms of mortality (1000 sheep units) by prefecture.
Note: only prefectures with a majority of land mass within the QTP are listed. Statistics reported in the table only refer to areas within the QTP.
Our results illustrate the spatial distribution and offer quantitative metrics of risk in terms of livestock mortality and mortality rate due to snow disasters in the QTP. The spatial pattern of risk agrees with earlier studies covering this region quite well. From an empirical perspective, the literature frequently mentions Eastern Inner Mongolia, the northern Tianshan Mountains in Xinjiang, and northeastern QTP as centres of snow disaster around China (Gao, 2016; Hao et al., 2002). Within the QTP, high-frequency snow disaster regions that are mentioned repeatedly in the literature include Yushu, Guoluo, Naqu, Shigates, and Nagri (Bai et al., 2011), which have all been identified in our study. As for risk assessment, our results also agree well with earlier studies. For instance, regions between the Kailas Range and the neighbouring Himalayas, southern Qinghai Province (mainly Yushu and Guoluo), and the northwestern corner of the QTP are all considered as higher risk regions in both qualitative (Liu et al., 2014) and quantitative (Shi, 2011, 106–107) risk assessment results. In northern and western Naqu and the central-to-western end of the Nyainqêntanglha Range, our results are consistent with the national snow disaster risk map (Shi, 2011; hereafter termed “risk maps”), which are from higher risk to the highest risk. Nevertheless, these regions are considered the lowest of lower risks in the results presented by Liu et al. (2014).
Our results for the magnitude of annual-average mortality rate were smaller
than those in the risk maps of China (Shi, 2011, 104–107);
in general, our values were about half of those previously reported. For the
high-risk regions, annual-average mortality rates were generally
Our results rebuilt a complete list of annual livestock snow disaster losses for the 1980–2015 period (Fig. 5). The modelled loss shows a clear declining trend. Major and peak loss years occurred frequently before year 2000 but rarely after that. Using a different historical data set, Wei et al. (2017) suggested that based on trends from 1960 to 2015, the loss would increase in the long run. However, focusing on the later part of their data set, i.e. 1980–2015, similar downward trending results would have been derived.
Our results indicate that both climate change and improved prevention
capacity have contributed to the declining trend in annual livestock loss.
The effect of climate change is revealed by the model-derived historical
loss using a constant value added from animal husbandry (the blue series in
Fig. 6). It has a very modest declining trend, at
Improved prevention capacity plays a much more significant role in declining
annual livestock losses. This conclusion is supported by the difference
between the two model-derived annual loss series in
Fig. 6 because they share an identical historical
snow disaster event set and differ only in prevention capacity. For
model-derived historical losses (orange series in Fig. 6),
the exponential trend indicates that annual livestock losses decrease
7.9 % per year or 57 349 heads (units) per year if a linear trend is applied.
Therefore, improving prevention capacity accounted for a reduction of approximately
6.1 % in annual livestock loss per year if exponential trends
are assumed or
Our study differs from the existing literature largely in its event-based
PRA framework. Such a framework derives unique information, which was not
obtained in earlier methods based on annual analyses and is important for
preparedness decisions and insurance solutions. With the event-based PRA
framework, the following information is derived for better risk reduction.
The event-based framework provides an estimate of the frequency
distribution of single-disaster events. Overall, our analysis indicates that
snow disasters are frequent in terms of annual occurrence, but more than one
snow disaster a year is unlikely (Fig. 5). Given this finding, counter measures can be implemented to build
prevention capacity to handle one event annually (Mechler et
al., 2010). In addition, the framework can be further applied to climate
change analysis. Our snow disaster event identifier can help reveal the
changes in frequency and intensity (mainly Duration) of snow disasters in response
to climate change and therefore provide information for adaptation. Our results for single-event duration provide important quantitative
references for hay and fodder storage, which were not achieved by earlier
annual basis analyses. For the majority of higher-risk regions, once a snow
disaster occurred, it lasted for an average of 12 days
(Fig. 5). At return periods of 10 and 20 years,
the durations of single events were up to 21 and 28 days, respectively
(Fig. S3). At return periods of 50 years, single events could last for more
than 40–50 days. The regional-average durations of a 20-year event in Naqu,
Yushu, Guoluo, and south Ngari, were estimated to be 24, 22, 26, and 26 days,
respectively. From a preparedness perspective, the amount of hay and fodder
storage needed from herder households and local government reserves
combined can be readily estimated from our results once their goal of
preparedness capacity is set; i.e. they are capable of managing a 10-year event.
Alternatively, our results can also help local regions measure their
preparedness capacity given their amount of hay and fodder storage. For
instance, according to the authors' survey (Ye et al.,
2018), the total amount of hay purchased can only support supplementary
feeding of county-wide livestock for at most 3–5 days in some counties in
central Naqu. Such a level of preparedness can only endure a snow
disaster with a less than 5-year return period. Our event-based PRA results can also provide solid technical support for
insurance solutions. Earlier studies that assessed risk on an annual basis
using annual aggregate snow-covered days or snow depth variables were
incapable of doing so because insurance indemnities are clearly triggered by
specific events. The frequency distribution of event occurrence and event
duration provides necessary information to help the design insurance trigger
schemes. These insurance products can be conventional (indemnity-based),
where the post-disaster loss-adjustment is conducted based on herder
households. In addition, our results can readily support calculating
actuarially fair premium rates and at-risk loadings by applying deductible
conditions, which can turn event loss records into event-based insurance
losses (Wang and Zhang, 2003; Ye et al., 2017).
Several limitations in our risk assessment model must be mentioned. First,
our hazard module, which rebuilds or predicts snow disasters, still suffers from
uncertainty. We obtained a good AUC score from the BRT model for identifying
snow disaster days and also a good agreement in the timing of occurrence and
distribution of duration for longer-duration events. However, the
performance in capturing small disasters of short duration, i.e.
Because the exact spatial distribution of sheep units is unavailable, exposure data were derived according to the computed carrying capacity by grassland type. The total herd sizes computed differ within 20 % of those officially released. For the historical period, prior to the implementation of the forage–livestock balance policy, the actual herd size exposed would be larger than carrying capacity due to overgrazing. In addition, having a larger herd size than the carrying capacity would exacerbate the pressure on grassland, lead to larger hay and fodder deficit in harsh winters, and increase herd vulnerability to snow disasters. Therefore, our model-derived historical loss was conservative. For risk assessment purposes, using present-day exposure is reasonable to estimate livestock loss distribution in the next couple of years. For short and mid-range future risk assessment, a projection of exposure will be needed, which will require projected future grassland structure and productivity changes (Gao et al., 2016).
Finally, our risk metrics were derived from events rebuilt from historical climate data but not from stochastic simulations. Consequently, we have a limited number of events and annual loss records. We are only confident in risk metrics less than once in 35 years. Metrics for any higher return periods were derived from extrapolation and must be used with caution. This limitation can be resolved by inputting stochastic climate data sets using a stochastic weather simulator.
Quantitative risk metrics derived under a probabilistic risk assessment
framework are critical for understanding disaster risks and providing
quantitative evidence for risk-informed decision-making and
resilience-building. In this study, we developed an event-based PRA approach
for livestock snow disaster in the QTP region and derived risk metrics for
livestock mortality and mortality rate. Our assessment results show that the
spatial distributions for mortality rate and mortality size are quite
similar. Hazard intensity, in terms of disaster duration, was the major
driver of spatial differences in livestock mortality, while the influence
from exposure in terms of herd size was quite modest. High-risk regions
include the Nyainqêntanglha Range, Tanggula Range, Bayankhar mountains,
and the region between the Kailas Range and the neighbouring Himalayas. At a
return period of 20 years, the annual livestock mortality rate was estimated
to be
Compared to earlier results, our approach relies on the prediction and simulation of snow disaster events, and correspondingly the modelled livestock losses are on an event basis. In addition, our quantitative results for the return-period disaster duration are valuable for preparing hay and fodder reserves and designing insurance protection. The methodology developed here can be further adapted to future climate change risk analysis and providing risk-informed adaption suggestions for the QTP region.
Historical snow disaster event data used to train the
vulnerability function (Eq. 1) as well as the BRT model for SDD probability can
be found at
The supplement related to this article is available online at:
TY and YL designed the research. YL and WL developed the model code and performed the simulations. TY and JW conducted all the art works. TY prepared the manuscript with contributions from all co-authors.
The authors declare that they have no conflict of interest.
This study was supported by the National Key R & D Program of China (grant number 2016YFA0602404), Fund for Creative Research Groups of the National Natural Science Foundation of China (grant number 41621061), and State Key Laboratory of Earth Surface Processes and Resource Ecology. We also thank two anonymous referees and the editor for their comments and suggestions on an earlier draft.
This paper was edited by Margreth Keiler and reviewed by two anonymous referees.