1. Introduction
Natural disasters are widespread in the Nepal Himalayas due to the Asian monsoon, a large orographic barrier, and the seismically active Himalayas. Geophysical hazards such as earthquakes and landslides along with water-related hazards such as floods are common in Nepal, claiming loss of life and property every year. Therefore, the government of Nepal (GoN) and other organizations are focusing on preparedness and mitigative measures for these kinds of hazards. However, meteorological hazards such as a rainstorms, strong wind, thunderstorm/lightning, and snowstorms occur continuously in this region, claiming lives and property. Among meteorological hazards, lightning continues to be one of the deadliest disasters in Nepal, claiming many lives every year. These lightning strikes can create a forest fire, which then starts cascading hazards affecting the rural population in mountainous areas.
Lightning dominates in tropical latitudes, including Latin America, Africa, and other maritime regions (Christian et al. 2003); however, it causes injuries and deaths nearly all around the world (Cooper and Holle 2019). This phenomenon can also be affected by local topography, wind, relative humidity, aerosol, and surface temperature (Goswami et al. 2010; IPCC 2007; Williams 2005); however, it has not been confirmed which lightning regime is sensitive to climate change (Harel and Price 2020). Lightning can be an important marker to investigate changes in extreme events (Brooks 2013; Price et al. 2011) and climate forecasting (Banerjee et al. 2014; Myhre et al. 2013). Lightning events are very common in the Himalayan region due to a large amount of atmospheric water vapor coming from the Indian Ocean (Malla 2009) along with the orographic lifting of this moist air (Saha et al. 2019). Lightning activities are pronounced in the premonsoon season in the Nepal Himalayas due to the clockwise circulation of humid air transport to the mountains and valleys from the Bay of Bengal (Aryal 2018). This lightning activity is maximal in the premonsoon region of the arc-shaped Himalayan foothill area and gradually weakens and shifted to the northwest corner of Pakistan (Kumar and Karma 2012). Lightning affects infrastructure, power interruptions, structural damage, and data losses in developing countries that can result significant economic loss (Cooper and Holle 2019; Mills et al. 2010). Moreover, the person who survives fatality events may have lifelong injuries, disability, and physiological trauma (Gomes and Ab Kadir 2011).
There has been extensive literature available on lightning hazard around the globe (Cardoso et al. 2014; Holle et al. 2005; López and Holle 1995 1996, 1998; Nag et al. 2015; Navarrete-Aldana et al. 2014; Salerno et al. 2012; Zhang et al. 2011) and some in South Asia (Ali et al. 2018; Biswas et al. 2016; Das et al. 2007; Dewan et al. 2017; Gadge and Shrigiriwar 2013; Gomes et al. 2006; Illiyas et al. 2014; Murali Das et al. 2009; Siddiqui and Rashid 2008; Singh and Singh 2015). Only a few previous studies (Aryal 2018; Baral and Mackerras 1992; Mäkelä et al. 2014; Saha et al. 2019) have focused on lightning occurrences and characteristics in Nepal, and none of them focused on the spatiotemporal variation of lightning fatalities and economic impacts despite the high death toll.
Lightning killed 1792 people from 1990 to 2019 and ranks third among major natural hazards in Nepal (Fig. 1). According to the Disaster Risk Reduction Portal, GoN, lightning claimed the highest number fatalities (94) and affected 2884 people with a loss of USD 110,982 in 2019 (GoN 2020). The historical record of lightning activity and its effects is very limited due to the limited communication and recording system in Nepal. Moreover, these fatality events are localized, and therefore the concerned organizations are not paying much attention. However, the death toll and destruction of property have been increasing due to these lightning fatality events in recent years. Therefore, this research aims to analyze the spatiotemporal variation of lightning data available from 1971 to 2019 and possible early warning systems.
Number of deaths caused by different hazards from 1990 to 2019 (UNDRR 2020; GoN 2020).
Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0106.1
2. Data and methods
The data for the study come from DesInventar [United Nations Disaster Risk Reduction (UNDRR) 2020] from 1971 to 2016 and the Disaster Risk Reduction portal of the government of Nepal (http://drrportal.gov.np/) from 2017 to 2019. The DesInventar dataset (https://www.desinventar.net/) is a web-based platform started by UNDRR to store the data collected based on media reporting, that is, daily national newspapers, periodicals, relevant reports, government records, journals, and research in different countries. In addition to the Disaster Risk Reduction Portal, GoN collects the information from the government organizations including Nepal police, Nepal armed police force, regional emergency operating centers, and district administration offices covering all Nepal. The data consist of deaths, affected population, and economic loss of all 75 districts of Nepal from 1971 to 2019.
The downloaded data were analyzed using Statistical Package for Social Sciences (SPSS, version 23) and geographic information system (ArcGIS) tools. The descriptive analysis on different attributes such as fatality number, affected number, economic losses, fatality rate, and fatality density was conducted using SPSS. Further, a two-tailed hypothesis test at 95% confidence interval (CI) was performed with one sample t test for testing a statistical significance of mean difference for each district with a mean of national average (1.77 fatality rate per million per year). A GIS was used for encoding the lightning fatality data as per district boundary. GIS is helpful for the integration of district locations with attribute data. The total population of each district was obtained from 5 national census years of 1971, 1981, 1991, 2001, and 2011 and average population was computed of the respective districts (Central Bureau of Statistics 2011). The total number of recorded fatalities from 1971 to 2019 was divided by the average total population, and the resulting number was divided by one million for each district to calculate the fatality rate per million population of that district. Similarly, fatality density was calculated by dividing the total number of fatalities by the area of each district (Table 1). Then, each district was ranked on the basis of fatality rate and fatality density. The government of Nepal divided the country into 77 districts in 2015; however, this study used the previous administrative division of Nepal (75 districts) to match the data before and after 2017. The data from a newly formed districts were combined for uniformity of data analysis.
District name, area of district in square kilometers, average population in millions, number and rank of lightning-related deaths and injuries, fatality rate per million people per year and its rank, and fatality density per square kilometer and its rank by district for Nepal from 1971 to 2019. An asterisk indicates P < 0.05.
3. Study area
Nepal lies between China and India, covering 147 516 km2 in surface area (Fig. 2). The altitude varies from 70 m (Terai plain) to the top of the world, Mount Everest (8848.86 m). Nepal has three major ecological regions, namely, Terai, hill, and mountain. The population is mainly concentrated in the Terai plain with a high population density (Central Bureau of Statistics 2011). The climate is mostly dominated by the Asian monsoon originated from the Indian Ocean and controlled by the orographic barrier of the Himalayas. Rainfall distribution varies from place to place within a single monsoon season. The annual precipitation (1971–2014) pattern shows the districts of central and eastern Nepal show a decreasing trend, whereas districts in far-western and western Nepal show an increasing trend. Similarly, the temperature trend (1974–2014) shows the normal annual minimum temperature is low (<0°C) in the mountain districts while the districts of hills and Terai have the highest (15°–20°C) (Department of Hydrology and Meteorology 2017).
Map of Nepal showing different ecological regions with district boundaries. The inset shows the location of Nepal in South Asia.
Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0106.1
4. Results and discussions
a. Spatiotemporal distribution
The fatality rate and fatality density are plotted in Fig. 3. The overall fatality rate of the entire period is 1.77 per million per year (Table 1). The fatality rate varies from country to country; however, this rate is lower than most of the less developed countries. For example, Malawi has a fatality rate of 84 deaths per million people per year (Mulder et al. 2012), which is remarkably higher than other countries. The previous studies show that highly developed countries such as Austria (>0), the United Kingdom (>0), Australia (0.1), the United States (0.1), and France (0.2) have a low fatality rate compared to less developed countries such as Swaziland (15.5), Zimbabwe (14–21), South Africa (6.3), Mexico (2.7), India (2.0), and Colombia (1.8) (Holle 2016a). There are few examples because most countries do not have information on national statistics on lightning fatalities or published data in their national reports and scientific journals.
Distribution of lightning fatalities from 1971 to 2019: (a) fatality rate per million people per year by district and (b) fatalities per square kilometer by district.
Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0106.1
The districtwide lightning fatality rate ranges from 0.10 to 4.83 per million people per year (Fig. 3). The fatality rate is high in the Dolakha district (4.83) followed by Okhaldhunga (4.72), Ramechhap (4.69), Nuwakot (4.62), and Makwanpur (4.62). Similarly, the fatality rates are low in Baitadi (0.10), Dang (0.16), Kathmandu (0.24), and Surkhet (0.51) (Fig. 3). The fatality rate in Dolakha district ranks the highest where the population is relatively small (0.2 million). In contrast, the Makwanpur district has the largest number of deaths (94) but still ranks fifth in the population-weighted fatality rate. Moreover, the null hypothesis of equal means with national average is rejected for all the districts except Humla (significance P = 0.098), Manang (P = 0.053), Morang (P = 0.964), and Mustang (P = 0.669). This implies the statistically significant mean difference in fatality rate in all the districts except the above four districts. The highest fatality rate shift from higher- to lower-population regions also matches previous research from other countries (Dewan et al. 2017; Navarrete-Aldana et al. 2014; Zhang et al. 2011).
The fatality density is highest in Bhaktpur district (0.067) followed by Nuwakot (0.056), Jhapa (0.051), Lalitpur (0.049), and Morang (0.047). The fatality density is lowest in Dolpa (0.0001), Bajhang (0.0003), Humla (0.0004), Darchula (0.0004), and Mustang (0.0008). The low number of fatality events and death in the midwestern and far-western region might be due to low population density (Central Bureau of Statistics 2011). The distribution of lightning fatality events is scattered across the country (Fig. 3); however, the fatality density is mostly concentrated in central and eastern Nepal. A high concentration of lightning fatality events exists in central Nepal (Makwanpur, Nuwakot, Sindhupalchowk, Kathmandu, and Dhading) followed by midwestern and far-western development regions. The rural mountainous districts such as Makawanpur (94), Morang (88), Jhapa (83), and Nuwakot (63) have the highest number of deaths compared to urban districts such as Kathmandu (12), Kaski (36), Kailali (21), and Chitwan (27). This finding is consistent with previous findings (Dewan et al. 2017; Gomes and Ab Kadir 2011; Holle 2016b) where higher number of fatalities in developing countries are located in the rural areas. The contributing factors include minimal or no lightning protection in public buildings and households, extensive labor-intensive agriculture, and lack of public awareness about lightning hazards and its protection (Gomes and Ab Kadir 2011; Holle 2016b; Holle and Cooper 2016; Raga et al. 2014).
The economic loss due to lightning varies from district to district. Some districts such as Dolakha, Sindhuli, Khotang, Dang, Kavre, and Kapilbastu have an economic loss greater than USD 20,000, whereas Mustang, Dadeldhura, and Manang do not report any loss. The lightning fatality events were widespread in Nepal. There were altogether 2501 fatality events (1971–2019), where 1927 people lost their lives and 20 569 people were affected (injured and lost properties) (Fig. 4a) (Table 1). The number of fatality events and deaths increased significantly after 1990 (Fig. 4b). This is related to the major change in the political system widely reported as the “end of an era” where 30 years of the autocratic “panchayat” system was eliminated, and multiparty democracy was established (Koirala 1991). This political reform provided opportunities for migration within the country, and the fatality data reporting system has been improved. The highest number of deaths (131) occurred in 2013, when 127 lightning fatality events were reported. The total economic loss during this period was USD 459,631, which has remarkably increased since 2013 (Fig. 4c).
Temporal variation of lightning effects in Nepal from 1971 to 2019: (a) number of lightning events vs total deaths and (b) number of affected people, and (c) economic loss in U.S. dollars.
Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0106.1
The distribution of lightning fatality events, deaths, affected numbers, and economic loss varies in different ecological regions (Fig. 5). The fatality events, deaths, and affected population increased dramatically after 1990, showing an increasing trend (Fig. 5a). The number of fatality events and the death toll was higher in the hill region than in other regions. The highest number of deaths was found to be 18 (2013), 77 (2013), and 37 (2003) in mountain, hill, and Terai, respectively (Fig. 5b). High numbers of fatality events in the hill and Terai are frequent in the foothills of the Himalayas and are partially due to the conversion of copious amounts of moisture into rainfall (Kumar and Karma 2012). The total number of affected people was highest in 1997 (3005) in the Terai; however, the exact reason is still unknown (Fig. 5c).
Distribution by year of (a) number of events, (b) number of deaths, (c) number of affected people, and (d) total economic loss (USD) in three ecological regions of Nepal.
Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0106.1
In the past few decades, Nepal has made remarkable progress in information technology, and many people have been using mobile phones and the internet in all parts of the country. The analysis showed higher reported lightning fatality events in those districts where there is better availability of communication facilities, that is, phones and the internet (Fig. 6). For example, Morang, Jhapa, and Makanwanpur districts have both a high communication facility density and fatality density. Therefore, the huge increase in reported lightning fatalities in recent years could be due to internet penetration and other measures of information gathering that results in lightning fatality reports reaching the agencies collecting the information. This has been found to be the case in Bangladesh (Dewan et al. 2017), Turkey (Tilev-Tanriover et al. 2015), and Malawi (Kalindekafe et al. 2018). In addition, another reason might be the increasing population as a result of internal migration from existing urban areas to headquarters of rural and urban municipalities after the reclassification of rural and urban municipalities in 2015. Most of the concentrated settlements and district headquarters lie in the ridge of the mountain region of Nepal (Fig. 2).
Distribution of lightning fatalities from 1971 to 2019 vs communication facility density.
Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0106.1
b. Lightning injury and damage prevention
A lightning early warning system (LEWS) is a very important tool to save many lives and property in the mountainous region. There are mainly two components of LEWS, that is, a device detecting lightning and an application that uses the detection data system to provide a lightning early warning (Murphy et al. 2006). Moreover, two types of lightning, namely, “cloud to ground” and “in cloud,” are typically detected for early warning (Nag et al. 2015). The history of LEWS is not long in the context of Nepal. The Department of Hydrology and Meteorology (DHM) of the government of Nepal has installed lightning detection network sensors in nine places: Tribhuvan International Airport, Tumglintar Airport, Biratnagar Airport, Simara Airport, Bhairahawa Airport, Pokhara Airport, Nepalgunj Airport, Surkhet Airport, and Attariya, Kailali district in 2018 (DHM 2019) (Fig. 7). This network collects lightning data, location, and time, which are very useful for the analysis of past events and future prediction. This network detects the direction of thunderstorms for one hour and is therefore useful for lightning forecasting. Similarly, DHM has been using a nowcasting technique with information from the weather radar station at Rata Nangala of Surkhet, the lightning detection network, one sounding station at Kirtipur, and 88 automated weather stations for forecasting from present situation up to 3–6 h. Moreover, numerical weather prediction models are being used for nowcasting supported by high-power computing facilities in the newly established advanced hydrometeorological work station on the premises of DHM (Department of Hydrology and Meteorology 2019). Currently, the GoN is installing two additional weather radars at Ribdikot of Palpa in the central region and Rametar of Udaypur in the eastern region of Nepal for better coverage (Fig. 7). The forecasting will be more accurate after the installation of these two radars, however, in situ power shortages, insufficient technically trained human resources for data processing, maintenance, data storage and interpretations are some key challenges in the present scenario.
Distribution of lightning detection network, sounding stations, and weather radar stations in Nepal.
Citation: Weather, Climate, and Society 13, 3; 10.1175/WCAS-D-20-0106.1
These radar stations will provide accurate information about lightning events in the future; however, the GoN should construct lightning shelters in different parts of Nepal. The GoN has introduced a lightning protection code within the National Building Code 1994 (Ministry of Physical Planning and Works 1994), but this rule is provisional. Some government organizations such as the Nepal Academy of Science and Technology (NAST), Ministry of Home Affairs (MoHA), and Department of Urban Development and Building Construction (DUDBC) have installed LEWS (Pangeni 2015). The lightning protection consists of three basic parts: (i) air terminals or arrestors that divert the lightning from damaging the structure, (ii) down conductors that connect the air terminals to channel lightning energy to the ground, and (iii) ground terminal or electrodes that dissipate the lightning energy into the ground (Cooper and Holle 2019). However, the practice of this code is not effective in Nepal because most people are unaware of the code, and some people do not install lightning arrestors despite awareness because it is not mandatory. Hence, it is recommended that the GoN develop standard guidelines on lightning safety measures including a 30–30 rule for safety precautions guidelines for outdoors people. The 30–30 rule explains that an individual needs to start counting to 30 after seeing lightning, and if the sound of thunder is heard before counting to 30, then the individual should immediately go indoors and suspend activities for at least 30 min after the last clap of thunder.
The impact of lightning can result in lifelong injuries, and therefore public education on lightning safety is very important. However, there is no safe place outside in a thunderstorm. It is difficult to detect the “first strike” and to predict lightning strikes (NOAA 2016). However, people can save their lives if they go to a safe place at the first sign of a thunderstorm. This can be encouraged by introducing lightning safety activities in schools and public institutions. The integration of lightning safety education in school curricula will help children to understand lightning impact and safety measures. Moreover, activities in school also help to educate children’s parents and communities. The hoarding board (billboard)/community map with popular fictional or religious characters showing lightning events and safety procedures may help to create a public awareness. There should be public awareness about how to understand the weather forecast and what to do after the warning sign has been issued. This should be supported by a series to raise awareness on how to recognize a lightning hazard and actions to take even if there is no lightning early warning. The low-cost, easy to implement, stand-alone lightning protection systems for outdoor activities can be applied in regions with high lightning occurrence density (Gomes 2016).
5. Future work
The lightning fatality density map presented in this study is new in the context of Nepal. It would be useful to produce a fatality risk map combining the population density map and a cloud-to-ground lightning flash density map. The flash density map can be derived from the satellite data (Roeder et al. 2015) and the empirical relationship of flash events recorded by meteorological stations (Ali et al. 2018). The population and lightning fatality densities discussed here are static; however, the migration rate is very high in Nepal. Therefore, dynamic fatality risk maps should be produced engaging different stakeholders such as social scientists, economists, and physical scientists in the future for accurate prediction and lightning risk reduction.
6. Conclusions
Lightning hazard is one of the most commonly reoccurring phenomena in the Nepal Himalayas because of the strong monsoon and the orographic barrier. The analysis from 1971 to 2019 reported 2501 lightning events in which 1927 people lost their lives and 20 569 people were affected (injured and lost properties). District fatality rates range from 0.10 to 4.83 per million people per year, and the Bhaktapur district has highest fatality density (0.067). Moreover, an increasing number of lightning fatalities have been reported in recent years; however, an uneven relationship between number of lightning fatality events versus total fatalities and economic loss exists. Lightning fatality events and economic loss are mostly concentrated in central and eastern Nepal. The high and low concentration of loss and damages are mainly due to geographical distribution, population density, and economic activities. The provision of the LEWS implementation in the national building code should be compulsory along with some other campaigns such as training and awareness raising. Moreover, the government, as well as other development organizations, should focus more on LEWS and start a web-based lightning deaths and injuries inventory recording system.
Acknowledgments
The author acknowledges Sasmita Poudel and Suraj Gautam for the constructive review and suggestions.
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