Results of risk communication for precipitation extremes with positive trend at station Qionghai (ID: 59855).
\r\n\tCongenital hearing loss means hearing loss that is present at birth. I have managed children with hearing loss for many years, and the most touching thing is the light that blooms on the face while the hearing-impaired child heard his mother's voice at first time. The scene of "happy tears" impressed me so much. To hear the voice that has not been heard is so pleasant, as if this ordinary listening experience is a supreme listening enjoyment.
\r\n\r\n\tAge-related hearing loss means a progressive loss of ability to hear high frequencies with aging, also known as presbycusis. Among them are the influence of internal and external factors such as genes, drugs and noise exposure. The studies pointed out that the brain stimulation of the hearing-impaired person is greatly reduced compared with subjects with normal hearing. The connection of auditory cortex and other brain areas has declined a lot, which is probably one of the important causes of dementia or even depression in the elderly.
\r\n\r\n\tNoise-induced hearing loss is hearing impairment resulting from exposure to loud sound. There is actually continuous and endless noise in many workplaces, which may cause chronic and cumulative damage. Some young people often work hard but easily neglect to protect themselves. In addition, in recent years, entertainment noise (such as nightclubs, concerts, and personal listening devices) has caused hearing impairment in young people. These should be avoidable and preventable.
\r\n\r\n\tHearing Science is the study of impaired auditory perception, the technologies and other rehabilitation strategies for persons with hearing loss. Public health has been defined as "the science and art of preventing disease", improving quality of life through organized efforts. To avoid the “epidemic” of hearing loss, it is necessary to promote early screening, use hearing protection, and change public attitudes toward noise.
\r\n\r\n\tBased on these concepts, the book incorporates updated developments as well as future perspectives in the ever-expanding field of hearing loss. Besides, it is also a great reference for audiologists, otolaryngologists, neurologists, specialists in public health, basic and clinical researchers.
",isbn:"978-1-83968-678-8",printIsbn:"978-1-83968-677-1",pdfIsbn:"978-1-83968-679-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"a4b7dbb02ba00e7412422cd5dbffa029",bookSignature:"Dr. Tang-Chuan Wang",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10529.jpg",keywords:"Hidden Hearing Loss, Plasticity, Electrophysiology, Otoacoustic Emission, Newborn Hearing Screening, Genetics, Aging, Hearing Aids, Noise Exposure, Occupational Hearing Loss, Epidemiology, Prevention",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 3rd 2020",dateEndSecondStepPublish:"October 1st 2020",dateEndThirdStepPublish:"November 30th 2020",dateEndFourthStepPublish:"February 18th 2021",dateEndFifthStepPublish:"April 19th 2021",remainingDaysToSecondStep:"3 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Tang-Chuan Wang is an excellent otolaryngologist-head and neck surgeon in Taiwan; a research scholar of Harvard Medical School and University of Iowa Hospitals. He worked in the Hospital of the University of Pennsylvania, Boston Children's Hospital, and Massachusetts Eye and Ear. Due to his contribution to biomedical engineering, he was invited into the executive committee of HIWIN-CMU Joint R & D Center in Taiwan.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"201262",title:"Dr.",name:"Tang-Chuan",middleName:null,surname:"Wang",slug:"tang-chuan-wang",fullName:"Tang-Chuan Wang",profilePictureURL:"https://mts.intechopen.com/storage/users/201262/images/system/201262.gif",biography:'Dr. Tang-Chuan Wang is an excellent otolaryngologist – head and neck surgeon in Taiwan. He is also a research scholar of Harvard Medical School and University of Iowa Hospitals. During his substantial experience, he worked in Hospital of the University of Pennsylvania, Boston Children\'s Hospital and Massachusetts Eye and Ear. Besides, he is not only working hard on clinical & basic medicine but also launching out into public health in Taiwan. In recent years, he devotes himself to innovation. He always says that "in theoretical or practical aspects, no innovation is a step backward". 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"62501",title:"Extreme Value Analysis and Risk Communication for a Changing Climate",doi:"10.5772/intechopen.79301",slug:"extreme-value-analysis-and-risk-communication-for-a-changing-climate",body:'Extreme climate events could, directly or indirectly, impact all sectors of the economy leading to severe losses of life and property [1, 2, 3]. Mitigating natural hazards caused by extreme climate events is crucial to the sustainable development of human society and economy [4]. In IPCC’s report, an extreme climate event is generally defined as the occurrence of a value of a weather or climate variable above (or below) a threshold value near the upper (or lower) ends of the range of observed values of the variable [1]. The fundamental probability theory of extreme values has been well developed for a long time and already applied in resolving the practical problems in engineering design and risk management [5, 6, 7]. For the most part, extreme value theory (EVT) assumes that extreme events are stationary, and these extremes could be successfully characterized by the probability distributions such as the generalized extreme value (GEV) and generalized Pareto (GP) distribution [8, 9]. The occurrences of extreme events are also assumed to be independent or weakly dependent, then, the return levels and return periods could be easily determined [7, 10]. Under stationary condition, there is a simple one-to-one relationship between a return period and a return level, and these two terms can be easily understood [10, 11]. Moreover, risk could be simply communicated using the probability distributions of extremes derived from EVT [11]. In this study, the term “risk” merely refers to the probability of an extreme event with substantial consequence but not the expected loss in general risk analysis.
During the past few decades, there were clear and convincing evidences for global warming and climate change [12], which raised fundamental interdisciplinary issues of risk analysis and communication [13, 14]. As climate changes, weather or climate extremes also change [4] and gradually challenge the stationarity assumption in climate and weather extreme value analysis [6, 11, 15, 16, 17]. It has been documented that in some places, climatic and hydrological extremes exhibit some type of nonstationarity in the form of trends, shifts or a combination of them [18, 19, 20]. In a nonstationary world, both the severity and frequency of climate and weather extremes will change [2, 3, 10, 16, 17]. Consequently, extreme value analysis of climate and weather extremes has to consider and account for the nonstationarity [15]. Katz et al. [21] presented a nonstationary GEV distribution by introducing time as a covariate. He further showed that both GEV and GPD distributions could be retained under nonstationarity, and maximum likelihood method was also applicable for parameter estimation [6]. Nonstationary extreme values modeling based on GEV and GPD distributions has been realized in R-package ismev [22] and extRemes [23]. The other R-package GAMLSS (generalized additive model in location, scale, and shape) also allows nonstationary modeling for block maxima, where the parameters are modeled as linear or smooth functions of covariates [24]. Another available R-package for nonstationary extreme value analysis is GEVcdn, in which the parameters in GEV distribution are specified as a function of covariate using a conditional density network [25]. Besides these R-packages, nonstationary extreme value analysis could also been implemented using a MATLAB toolbox NEVA [26]. Although the nonstationary models in these packages performed better than the stationary equivalents in fitting nonstationary climate extreme, the return period (or return level) and risk for nonstationary conditions were not explicitly presented.
In nonstationary extreme value analysis, the concepts of the return period (or return level) and risk needed to be carefully reformulated and extended, because these familiar concepts, strictly speaking, no longer apply in a nonstationary climate [27]. In stationary cases, there exist two parallel interpretations for the return period: expected waiting time to an extreme event and expected number of extreme events in a given return period [7, 11]. In addition, the return level is the same in each year under stationary conditions. In Wigley [16], the return period was defined as the expected waiting time, and the influence of nonstationarity on the risk of extremes was presented using some simple probability arguments. The concept of the return period was further extended to nonstationary condition in Olsen et al. [10], where the return period was defined as the expected waiting time until an exceedance as the measure of risk. The alternative interpretation of the return period (expected number of extreme events in a given return period) for nonstationary conditions was clearly explained in Parey et al. [28, 29]. Recently, Cooley [11] reviewed these two definitions of the return period suggested by Olsen et al. [10] and Parey et al. [28, 29], and proposed that the return period could be used to communicate risk in nonstationary climate. From the perspective of engineering design, Salas and Obeysekera [7] illustrated the estimation of the return period and examined the failure risk of hydrological structures in nonstationary climate. Rootzén and Katz [30] also concerned the failure risk in the design period and proposed a risk-based engineering design concept, Design Life Level, which served as the basis of risk communication in a nonstationary climate. In the above literatures, the concepts of the return period or return level have been extended and adapted to nonstationary condition; however, the interrelations between return period and risk communication, especially for engineering design purpose, was still ambiguous. The major reason causing such ambiguity is the diversified explanations of one terminology for different purposes. Therefore, a comprehensive interpretation of the return period (or return level) and failure risk (or reliability) under either stationary or nonstationary conditions simultaneously are needed.
The aim of this chapter is to present the extension process of return period, return level, and failure risk from stationary condition to nonstationary condition in a different way so that the commonness and difference could be clearly identified. Consistent with the way how a return period is defined and derived in some previous literatures, extreme value analysis will apply to the time series of annual maxima in this study. Accordingly, GEV distribution is used to illustrate the computation of the return period (return level) and failure risk (reliability) in nonstationary climate.
In some previous literatures, the interpretation of the return period usually began with the simple one-to-one relationship between a return period and a return level under stationary condition [7, 10, 28, 29, 30]. Like Cooley [11], we define random variable
In this study, we try to explain the concepts of the return period and failure risk using the time series of annual maxima and the underlying stochastic process but omitting the assumption of stationarity or nonstationarity. In practice, analyzing the extremes and their probability distributions is usually considered as the basis of frequency analysis, engineering design, and risk assessment.
Given a exceedance level
where the second line in Eq. (2) is based on the temporal independence assumption. Then, the expectation of waiting time
The details of the derivations of Eq. (3) were shown in the appendix in [11]. The first definition of the return period is based on the expected waiting time. Specifically, a
Next, we adopt the commonly used definition of failure risk for an engineering structure, which is interpreted as the probability of the failure or the probability of exceedance over its design level in its design life period. We denote the failure risk by
Consequently, the reliability of the focal structure within its design life period is
We define random variable
Then, we get
The expectation of
Now, we say that the
Similarly, the reliability of a focal structure in its design life period
From Eqs. (4) and (8), we find that the two parallel interpretations of failure risk (or reliability) of a focal engineering structure are equivalent irrespective of
Under a stationary assumption,
The
It has been proved that the two interpretations of return period in the stationary case are both correct with this identical exceedance probability under stationarity assumption [11]. Substituting Eq. (9) into Eq. (3), we get the interpretation of the return period based on waiting time of exceedance:
Similarly, substituting Eq. (9) into Eq. (7), we get the alternative interpretation of return period based on expected number of exceedance events:
The simple one-to-one relationship between a return period and a return level in the stationary case has been commonly utilized in frequency analysis and engineering design practice [8, 21]. For example, the frequency or expected waiting time of extreme events exceeding a given exceedance level can be easily determined using Eq. (9) in frequency analysis of climate extremes. In practice, a very important concept for an engineering structure is the design life period. Reversely, given a design life period or exceedance probability, return levels could also be determined easily. Moreover, the failure risk or reliability of a focal structure in its design life period
where
Under nonstationary condition,
To determine
The return level in Eqs. (13) and (14) are the two extensions of the return period in the stationary case; however, these two extensions are not applicable in practical engineering design [7, 30]. For engineering design purpose, Rootzén and Katz [30] presented a new concept, Design Life Level, by keeping the failure risk at a low constant level during the design life period. The relationship between Design Life Level and design life period was expressed by the following equation [30]:
where
A variant of Design Life Level is Minimax Design Life Level [30]. The computation of Minimax Design Life Level is even simpler. During the whole design life period
Similarly, the first step to compute the Design Life Level or Minimax Design Life Level is nonstationary modeling of historical climate extremes. Then, the trends of extremes would be extrapolated over the design life period. Moreover, the statistical uncertainty in the return period and Design Life Level can be described by computing the standard errors using the delta method [11, 30].
In this section, we present two examples of extreme value analysis and risk communication in a changing climate. The cumulative distribution function of the GEV is expressed as [8]:
where
where
The first example of risk communication was for global annual maximum near surface air temperature (1948–2015). The global gridded data were extracted from the reanalysis products with a spatial resolution of 2.5 * 2.5 provided by Earth System Research Laboratory, NOAA (
(a) M-K test for global annual maximum near surface air temperature (1948–2015) (positive trend in white: no significant trend in gray; negative trend in black). (b) Nonstationary 20-year return level of global annual maximum near surface air temperature based on the expected number of events during 1996–2015.
In the second example, we used two time series of annual maximum precipitation (AMP) to illustrate the risk measure and communication under both stationary and nonstationary conditions. The two AMP time series were extracted from observation dataset of daily precipitation, which was provided by the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA). The two AMP time series were selected, because either positive or negative trends were detected. The corresponding weather stations are Qionghai (Station ID: 59855) and Zunhua (Station ID: 54429), which are located at N19
Akaike information criterion (AIC) was also computed for model fitting evaluation [31]. The model that was preferred was having the minimum value of AIC. For AMP time series at station Qionghai, the best fitting model was
Summary of the nonstationary modeling of annual time series of precipitation extremes using GEV distribution models. (a) Station Qionghai (ID:59855); observation period: 1953–2013; the best fitting nonstationary GEV distribution: M1. (b) Station Zunhua (ID:54429); observation period: 1956–2013; the best fitting nonstationary GEV distribution: M3. Symbols: observed values (dots), the estimates of the median (solid lines), and the 5th and 95th percentiles (dashed lines).
The results were shown in Tables 1 and 2, respectively. The first three rows in Tables 1 and 2 mainly illustrated the relationship between the return period and the return level under stationary and nonstationary conditions. The last two rows in Tables 1 and 2 showed the two concepts, Design Life Level and Minimax Design Life Level, for the purpose of engineering design under nonstationary conditions. For AMP time series at Qionghai station, there was a significant positive trend in precipitation extremes. Given a 50-year return period, the associated return level under stationary assumption was much lower than those under nonstationary assumption. From the perspective of engineering design, the return levels shown in the first three rows were unacceptable, because the failure risks in the following 50 years (design life period) were all larger than 0.55. To ensure a low failure risk, a higher design level is needed. For AMP time series at Zunhua station, there was a significant negative trend in precipitation extremes. When nonstationarity was considered, the return level became lower due to the decreasing trend in precipitation extremes. Furthermore, due to the same reason, the Design Life Level and Minimax Design Life Level in 50-year design life period (2014–2063) were lower than the 50-year return level under stationary assumption.
Risk communication | ||||||
---|---|---|---|---|---|---|
Model | Equation | Return level | Standard error | Return period (or EWT)a | Risk | Reliability |
Eq. (9) | 398.31 | 46.15 | 50 | 0.6358 | 0.3642 | |
Eq. (13) | 485.00 | 65.67 | 50 | 0.5594 | 0.4406 | |
Eq. (14) | 466.61 | 61.77 | 50 | 0.6359 | 0.3641 | |
Eq. (16) | 818.87 | 13.4 | 177.03 | 0.0407 | 0.9593 | |
Eq. (17) | 790.58 | 15.0 | 171.96 | 0.05 | 0.95 |
Results of risk communication for precipitation extremes with positive trend at station Qionghai (ID: 59855).
EWT stands for expected waiting time.
Risk communication | ||||||
---|---|---|---|---|---|---|
Model | Equation | Return level | Standard error | Return period (or EWT)a | Risk | Reliability |
Eq. (9) | 282.3 | 50.59 | 50 | 0.6358 | 0.3642 | |
Eq. (13) | 81.44 | 20.07 | 50 | 0.7647 | 02353 | |
Eq. (14) | 83.72 | 18.75 | 50 | 0.6548 | 0.3452 | |
Eq. (16) | 105.62 | 11 | 199.78 | 0.001 | 0.9989 | |
Eq. (17) | 98.41 | 19.59 | 189.98 | 0.05 | 0.95 |
Results of risk communication for precipitation extremes with negative trend at station Zunhua (ID: 54429).
EWT stands for expected waiting time.
Due to the climate change, the stationary assumption that was commonly used in statistical analysis of climate extremes gradually became unacceptable [6, 15, 17]. How to quantify and communicate risk of climate extremes in nonstationary climate is essential for engineering design and risk assessment [7, 11, 30]. There were many attempts to quantify and communicate risk in a changing climate such as extending the concepts of the return period from stationary condition to nonstationary condition [10, 28, 29] or developing a new concept of the return level [30]. In stationary climate, frequency analysis, engineering design, and risk assessment were all based on the stationary extreme value distribution model [8]. It was assumed that the fitted extreme value distribution model on historical records also applied for future observations. Also due to stationarity, the concepts of risk measure for different purposes had not been strictly distinguished. Unlike the simple one-to-one relationship between a return level and a return period under stationary condition, risk measure and communication were more complicated under nonstationary condition, especially due to the time-varying essence of climate extremes. Therefore, a clear interpretation and illustration of the methods for risk measure and communication in a changing climate are of great importance.
In this study, climate extremes were presented in the form of annual maxima of extreme climate events. This chapter began with the two parallel interpretations of the return period, in which, the implicit relationship between a return level and a return period was included, but the stationary or nonstationary assumptions were omitted. This implicit relationship was also considered as the basis for frequency analysis and engineering design. In the stationary case, the two interpretations of the return period were equivalent. Although they were no more equivalent in the nonstationary case, they both provided independent methods for determining the associated return level for a given return period. Risk assessment usually aims to a focal engineering structure with a given design level. We showed that the concept of failure risk (or reliability) also had two parallel interpretations, and these two interpretations were consistent irrespective of stationary or nonstationary assumptions. In order to illustrate how risk was quantified and communicated in a changing climate, two examples of nonstationary climate extremes were used. Totally, we have reviewed two methods for estimating the return level for a given return period under nonstationary condition [10, 28, 29] and two newly refined concepts of the return level in a given design life period for engineering design purpose [30]. In the first example, we detected the trend of annual maximum of global near surface air temperature during 1948–2015. Nonstationary GEV distribution with a time-varying location parameter was used to fit near surface air temperature extremes with significant trends, and the expected number-based return levels in 1996–2015 were computed. In the second example, time series of observed annual precipitation extremes at two weather stations in China with significant trends was analyzed. Both stationary and nonstationary GEV distribution models were used to fit the precipitation extremes. For each station, one best fitting GEV distribution was identified. Then, the linear trends in the parameters of GEV distribution were extrapolated into the following 50 years (also considered as the design life period). Return level, Design Live Level, and Minimax Design Live Level were all computed for the design life period (2014–2063), respectively. It was concluded that the communication of risk in a changing climate was obviously different from that in a stationary climate. For frequency analysis purpose, general return level/return period might be quite capable of communicating risk of climate extremes. While for the engineering design purpose, Design Live Level or Minimax Design Live Level were recommended, because the failure risk of a focal structure would be very low if Design Live Level or Minimax Design Live Level were chosen as the design level.
A reliable statistical modeling on long-term data was the basis of risk communication in a changing climate [26]. In nonstationary extreme value modeling, there are usually many candidate models. The choice of extreme value distribution models might influence the risk measure substantially in nonstationary and changing climate, because the trend captured by the extreme value distribution should be extrapolated into the future design life period. In this chapter, we only chose time as the covariate in the nonstationary extreme value analysis, and the parameters in GEV distribution model were expressed as the linear function of time. Perhaps, there might be more suitable trends such as quadratic or exponential trends leading to more candidate models. Evaluating all these candidate models was not an easy task. In practice, only a few of commonly used models was evaluated and compared. Moreover, the model selection process is not simply by using tools such as AIC or BIC [11]. Sometimes, additional expert knowledge is needed. In nonstationary extreme value modeling, besides time, some other climate indictors representing the variability of the climate system were also chosen as covariates. Although the historical data could be successfully fitted with these additional climate indictors, it was difficult to extrapolate the historical climate variability. That is because the climate variability itself is difficult to predict due to the complicity of the climate system. To reduce the uncertainty in statistical extrapolation, the output of numerical climate models was also used. However, the reasonability of simulation results was constrained by the parameters setting and initial values [30]. Additionally, the standard error of return levels and Design Live Level was all estimated using the delta method. Although standard error is a simple measure to quantify the uncertainty of nonstationary extreme value modeling, the uncertainty cannot be properly reflected using the symmetric confidence interval. Lastly, as pointed in [30], neither Design Life Level nor Minimax Design Life Level could be used as the criteria for realistic engineering design, because more economic and political factors should be considered besides the failure risk and reliability. All the above mentioned things are outside the scope of this chapter; here, our primary objective was to discriminate the concepts and their interpretations of the return period/return level, failure risk/reliability under stationary and nonstationary conditions, and to illustrate the computations using realistic climate extremes. With these examples, we believed that the concepts those were related to risk measure and communication in a changing climate could be easily understood and applied.
This work was partly supported by the Youth Innovation Promotion Association of CAS (2016195), CAS Knowledge Innovation Project (KZCX2-EW-QN209), and National Natural Science Foundation of China (31570423).
\nWe do not see our hand in what happens, so we call certain events melancholy accidents when they are the inevitabilities of our projects (Stanley Cavell)1.
\nIndia, a country of rivers and is a big centre of the flood. It is a regular and the most destructive disaster in India. The earliest evidence of devastative flood dates back to the flood of the Indus Valley Civilization that sabotaged the great civilization. In independent India, the first major flood was recorded in 1953, and following the flood’s impact first national policy on the disaster was set up in the year of 1954. Notably, India accounts for 1/5th of the global lives’ loss, and around 30 million people are evacuated every year. The area vulnerable to flood is more than 40 million hectares, and the average area affected is 8 million hectares2. Whatever the disastrous flood has done vandalisation should not be measured only in economic terms; however, from 2011 to 2016, INR 144665.79 crore has been estimated as the total damage in the country. In 1953, total damage around INR 52.40 crore had been reported, while in the year of 2016 it reached to INR 57291.098 crore. The most vulnerable states to flood are Uttar Pradesh, Bihar, Assam, West Bengal, Gujrat, Odisha, Andhra Pradesh, Maharashtra, Punjab and Jammu & Kashmir. Uttar Pradesh, Bihar, West Bengal, and Assam account for 17 percent of India’s geographical area but disproportionately account for 43–52 percent of all flood-prone areas of the country [2].
\nIwasaki [3] has reported that, these floods or cyclones severely squeezed people’s livelihood. It affects the traditional economic base of the family that changes the family’s main source of income [4]. Sina et al. [5] mentioned that, displacement due to flood is a major challenge of restoring the livelihood which needs timely assessment in building resilience in livelihoods. The aid packages are usually short-term fulfill of the needs of the victims but rarely focus the long-term revival of their livelihoods. The current institutional mechanisms are lagging behind the large-scale post-disaster reconstruction [6]. These frequent occurrence of floods have caused a blow to the livelihood resilience of the poor and marginalized sections [7]. The severity of the disaster can be reduced with the help of local and national commitments [3] along with the intervention of livelihood diversification programs in the flood affected area [3]. Along with this, social cohesion is playing a pivotal role for restoring the livelihood of disaster-displaced people [8].
\nBahraich (part of eastern Uttar Pradesh) is one of the districts which have been under the regular influence of flood for decades, but in recent years, an alarming increase in the devastation can be visited in the region. It has serious repercussions, such as displacement, migration, poverty, hunger, unemployment, diseases, and many more issues. People are compelled to accept the tragedy of their life and trying to compromise themselves with the unfortunate socio-economic condition.
\nLivelihood: the term ‘livelihood’ as a systematic study developed from basically the rural studies, since then it has been used in various dimensions in academia. The conventional meaning of the term is an economic activity, while in fact, apart from the economic aspect it comprises a wide range of issues such as ecological, political, cultural, environmental and so forth. The formal analysis of the term can be given as “a livelihood comprises the capabilities, assets (both natural & social) and activities required for a means of living; a livelihood is sustainable which can cope with the recovery from and shocks, maintain or enhance its capabilities and assets, both now and in the future, while not undermining the natural resource base” [9]. This analysis primarily consists of three concepts: capability, equity and sustainability; and these are interconnected and interdependent in its proper implication. Scholars emphasize the relationship as “each is also both and means…. linked together, capabilities, equity and sustainability present a framework or paradigm for development thinking which is both normative and practical” [9]. Here, through separate descriptions, the core meaning of the terms can be understood.
\nCapability: in the developmental studies this was firstly used and systematically studied by Amartya Sen as a theoretical foundation to understand human development, he maintains that income, utilities, resources, and wealth act as means towards an end not an end in themselves, further he states that development should be understood as the removal of major barriers to our freedom like poverty, illiteracy, poor economic opportunities and so on. [10]).
\nEquality: the traditional method of measuring the equity has been concerned with only relative distribution of income, while it keeps a vast meaning like property, opportunity and capability; and avoids discrimination on any ground [9].
\nSustainability: there is a prosperous series of intellectual criticism on the developmental model in which production, employment, and poverty reduction are a key concern. It claims that this is mainly made for the welfare of the capitalism and market, and lesser responsible towards the people of the rural and marginalized communities. In recent decades, the developmental model moved to the diverse framework of the society and inclined towards the social, cultural and political field of the communities [9].
\nHence, the term ‘livelihood’ does not merely denote the issue of employment or source of income, but the entire construction of a community viz., polity, society, culture, economy, wherein people live for centuries, comes under the periphery of ‘livelihood’.
\nDisaster: The disaster is a natural phenomenon or just a deleterious consequence of human’s unnecessary intervention in the natural world, which is a controversial debate across the world. Difficult to conclude that this is an ‘act of God’ or the human’s self-made tragedy, but cannot be denied the fact that modern technological advancement has multiplied the intensity and consistent occurrence of the disaster. The term ‘disaster’ is derived from astrological science and has been understudies for more than one century, but in terms of its specific academic studies, it has expanded since the 1950s [11]. To formulate the term, the World Health Organization on Emergency and Humanitarian Action (EHA) states that a disaster is an occurrence a level of suffering that exceeds the capacity of adjustment of the affected community3. The Centre on the Epidemiology of Disaster, Brussels holds the view as “a disaster is a situation or event which overwhelms local capacity, necessitating a request to a national or international level for external assistance, an unforeseen and often sudden event that causes great damages, destruction and human suffering” [11]. Similarly, “disaster is seen as a process leading to an event that involves a combination of a potentially destructive agent from the natural or technological sphere and a population in a socially produced condition of vulnerability” [12]. There is no major dispute on the character of disaster as ‘massive human suffering’, but has big dissension over it is either ‘man-made’ or the ‘revenge of god’. Of course, nature naturally generates a number of disasters like cyclones, earthquakes, floods, famine and many more, but the role of modern science and technology should not be overlooked that has escalated the pace of disaster [1]. For instance, the flood of Uttarakhand in 2013, and the Kosi flood of Bihar in 2008 are basically the consequences of unnecessary human encroachment in the domain of nature [13].
\nThe history of flooding in the region goes back in the deep past. In the known history the region has been gravely affected by flooding from 1922 to 1925. 1946, 1954, 1955, 1960, 1961 and 1963 were also the year of flooding, but the flooding of 1969 was much more devastative than previous years. 1978, 1980, 1981, 1982 and 1983 were also the year of tragedy. In the year of 1983, the sudden release of water from Girija Bairaj of Nepal caused severe damage to the region, while this was not the year of heavy rainfall. Hundreds of people died due to the sudden release of water without any prior information. 1986, 87 and 1988 were not untouched by the flood. In the year 1990 flood-affected a few pockets of the region. In the year 1991, there was no flood, but the Ghaghara river induced soil erosion. Again, in the year 1992 floods made a catastrophe. In the year 1993, it broke the previous records. The year 1998 was also the year of flood due to the sudden release of water by Nepal. The year of 2000 multiplied the pain of the region. Gopiya Bairaj of Nepal released water and hundreds of people died and thousands of displaced. In the last few decades, dozens of villages have been physically disappeared and the population is displaced by the flood and many more are under threat of disaster. A few of the examples of the villages that are already collapsed in recent decades or under the process are given below:
\nCollapsed village | \nYear of displacement | \nNumber of displaced people | \n
---|---|---|
Khargapur | \n1968 | \n10,000 | \n
Gangapur | \n1994 | \n4000 | \n
Panchadupur | \n1998 | \n4000 | \n
Baharpur | \n1998 | \n5000 | \n
Silauta | \n2001 | \n7000 | \n
Umaraiya | \n2001 | \n3000 | \n
Bhauri Sipahiya | \n2002 | \n8000 | \n
Munsari | \n2003 | \n4000 | \n
Maikapurva | \n2003 | \n6000 | \n
Sansari | \n2004 | \n2500 | \n
Kapraul | \n2004 | \n4000 | \n
Golaganj | \n2006 | \n8000 | \n
Magraul | \n2007 | \n7000 | \n
Pipri | \n2007 | \n4000 | \n
Bansgadhi | \n2011–2012 | \n6000 | \n
Tarapurva | \n2012 | \n3000 | \n
Jarwal | \nTill the date | \n4000 | \n
Kayampur | \nTill the date | \n3000 | \n
Baundi | \nTil the date | \n3000 | \n
Jogapurva | \nTill the date | \n3000 | \n
Source: Super Idea, Sept.20124.
Tracing the reasons for the flood in the region, we find a few major human activities that induce disaster. If we blame nature for this disaster it would be an injustice, not only to nature but also to the people of the area. A sudden release of water by the government of Nepal through dams like Gopiya & Girija is one of the main reasons. The people claim if river water comes slowly in a natural way, it is lesser devastative compared to sudden releases from dams. The rapid flow of water comes only after the collapse of dams or highways or such kinds of big constructions, not by the natural processes of the flood. These unplanned and unmapped developmental projects have made hindrances in the ways of floodwater, therefore the water stuck up to 20–30 feet for many days. Apart from these, sand mining is also a major cause of soil erosion and changes in the direction of the flow of water. Illegal sand mining in the basin of the Ghaghara is rampant. And astonishingly, the nexus of the local public representatives, contractors and bureaucrats are involved in this activity as villagers claimed and were also deeply observed during the field study.
\nThe loss of the community of the region can be best understood through the theoretical lens of Piero Bourdieu’s ‘capital’ that he describes as “accumulated, human labor, which can potentially produce different forms of profit” [14]. Further, he classifies it in three sections as cultural, social and economic capital.
\nSocial capital: “Social capital is the aggregate of the actual or potential resources which are linked to possession of a durable network of more or less institutionalized relationships of mutual acquaintance and recognition or in other words, to membership in a group” [14]. The displacement caused by flood displaces and damages not only the physical body of a community but also crushes the social institution and relationships like kinship, lineage that has been a product of centuries-old social interactions and behaviors. One villager Kamlesh Kashyap (Baundi, 35 years old man) asked “earlier we used to marry in our own communities, but now it is not possible, since most of the people of our community have migrated towards other states for their survival. We are left here and staying on the dams or roadside, so how is it possible to get a suitable bride or groom?”. The structure of kinship and marriage were such kinds of capital they could face and challenge the problems of daily life through.
\nCultural capital: cultural capital can exist in three forms as ‘embodied state’ (mind & body), ‘objectified state’ such as books, instrument, etc., and ‘institutionalized state’ like academic qualification etc. [14]. An around seventy years old man Shankar Kashyap of Jogapurva showed me the pity of the loss of cultural capital. “Till ten years ago we used to make our house by the wild bushes, unfortunately, it is not flourishing due to the annual occurrence of flood, so now we purchase it from other areas to build the houses. Similarly, there were several herbal plants that were useful in curing many diseases, and we also used to make domestic products like toys, pots etc. New generations have neither the knowledge of wild herbs-shrubs, plants and bushes nor have the knowledge of making all that thing”. Here, we see flood excludes the community along with their traditional knowledge.
\nEconomic capital: “economic capital is at the root of all the other types of capital and that these transformed, disguised forms of economic capital, never entirely reducible to that definition, produce their most specific effects only to the extent that they conceal the fact that economic capital is at their root” [14]. Lakshmi Devi (Jogapurva, 50 years old woman) told the pain of her life how she lost her ornaments while flood water entered into the house. “I had some gold & silver made ornaments that I used to keep within the soil of the room so that no one could steal. I had purchased it by savings from my rigorously earned income. It was kept for the days of crisis or ceremony like the daughter’s marriage or disease of the family members, but the spate of water entirely wiped out my house along with my ornaments. When floodwater came in, I was in my farmland that is why I could not save all that”.
\nThe vulnerability of livelihood: the term vulnerability is widely used but is basically a vague term and its meaning varies across the disciplines. However, it may be considered as “vulnerability is an individual or group’s reduced capacity to cope with, resist, and recover from the impacts of a natural or human-made hazard”5. From the lens of the given definition we just look at the story of Sunil (Baundi, 40 years old man) who asked “I had ten bigha farmland in which I used to cultivate crops affluently. Now every year water is stuck in the land so now we are neither able to cultivate in that nor sell since purchasers will give a very little amount of money. Even we are in confusion we should start a business or migrate towards the cities or should continue in risky agriculture. Going to other cities is difficult since I have four small children. Due to the dearth of money, I am not able to provide quality education. We are just trying to alive ourselves…nothing more”. The present and the future of the communities of the region is in dark. Several farmers have changed their production of crops. Sukhan (Jogapurva, 45 years old man) told “earlier we used to produce wheat and rice like commercial crops, that was helpful in fulfilling my livelihood issues, but now I am compelled to change my agricultural production, in which there is no benefit and cannot properly fulfill the need of my livelihood. Flood has converted my fertile land into desert, so now I just plant watermelon in my farmland”.
\nLosing all forms of capital and displacing from its native places, the communities of the area are in the process of just being a gathering of the people or crowds in different cities or nearby places. They are uprooting from its age-old livelihood sources that were sustainable in its nature. Their vulnerability arises grave questions to the policymakers and academicians. What would be the future of such communities in terms of health, education, employment and etc.? What can we expect from such communities in the domain of creativity and productivity, which are the core value of human civilization?
\nA public representative of the district, Rajesh Tripathi (Bahraich, 50 years old, man) told: “There is no strong long-term planning of administration, they just work during the flood, and once the flood is over, they get rid of their responsibilities”. Here, the response and action of the local administration can be understood as in the three periods, before the flood, during the flood and after the flood. There is a serious dearth of long-term planning and preparedness before the state, so cannot be denied the fact that it is the very reason for the ravage of the flood. The eyes of the administration open just after the coming of the disaster. During the disaster, local administration works only as an agency of relief distribution like biscuits, plastics, rice, gram, matchbox and etc., unfortunately, discrimination in terms of caste, class, gender and so on, in this allocation is also clearly visible. Astonishingly, the role of the administration in the post-flood period is much more deleterious than the flood. Corruption in rehabilitation processes, social-conflict induced by the official’s work-culture, unemployment, diseases and so many socio-economic problems rapidly emerge in this period. For instance, massive soil erosion takes place during the disaster, which erases the demarcations of farmlands of the people. They go to the district’s land department offices for the re-demarcation of the farmlands, but officials demand a heavy amount in bribe, which is very difficult for the people, who had recently been ruined by the flood. Therefore, they try to manage it on its own community-based understating, but several times it turns into social mayhem. Sluggish and irresponsible practices of the administration can be noticed in the settlement of sources of survival, like fishing, agriculture and so forth. Apart from the rampant corruption and irregularities in the rehabilitation and relief process, another important feature is, which is totally absent from the public policies; government is ignorant of the loss of social and cultural capital, their central policies move around merely economic capital.
\nWe should not hide our face from the fact that our unmapped and uneven developmental policies have multiplied the pace and intensity of natural disasters that subsequently sabotage the rural livelihood setup, which is sustainable in its form from the time immemorial. The need of the hour is to come out from such public policies that overlook the interest of the agrarian communities since India’s around seventy percent population live in the rural areas only. Apart from policy-making, one of the major problems is in the implementation of existing policies, since the bureaucracy is indulged in extreme corruption and money-making through illegal sources that finally hampers the livelihood issues of the communities.
\nWe would like to acknowledge the contribution of Harry Raj, who helped us in shaping the chapter.
\nThe researcher has changed the original name of the respondents to protect their identity and to maintain confidentiality.
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