Introduction

Intense thunderstorms and an increase in population throughout the Hindu-Kush Himalayan (HKH) region have resulted in an upsurge of lightning-related deaths. Partnering with NASA's Global Hydrology Resource Center Distributed Active Archive Center (GHRC DAAC), NASA's SERVIR Science Coordination Office, Bangladesh Meteorological Department (BMD), Nepal Department of Hydrology and Meteorology (DHM), and the International Centre for Integrated Mountain Development, this study investigated the lightning risks in the HKH region and the correlation between precipitation and lightning. 

Lightning flash point data collected by the Lightning Imaging Sensor (LIS) aboard both the Tropical Rainfall Measuring Mission (TRMM) satellite and the International Space Station (ISS) from January 2001 to December 2017 were plotted to determine the locations where the highest concentrations of lightning strikes occurred. Data from the United Nations Office for Disaster Reduction (UNISDR) Global Assessment Report for 2015 (GAR15), Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) Landscan 2016 global population dataset, and NASA’s Shuttle Radar Topography Mission (SRTM) were used to assess the factors that contribute to a population’s vulnerability to lightning activity. 

Additionally, the team used the TRMM Precipitation Radar (PR) data to identify areas with the highest precipitation rates over Bangladesh and Nepal. A Lightning Risk Map (LRM), created to highlight lightning-prone areas and regions with vulnerable populations, showed that communities in western Nepal and northern Bangladesh are at an increased risk for lightning related injury. A Precipitation and Lightning Correlation was calculated to verify whether areas experiencing heavier precipitation also experienced higher lightning totals. These end products will assist the BMD and the DHM to increase hazard awareness and issue earlier warning times to reduce lightning casualties.  

Science Objectives

• Aid project partners in emergency management
• Identify areas vulnerable to frequent lightning activity
• Provide partners with a resource to use precipitation patterns as a proxy for lightning strikes

Methodology

The team created a Lightning Exposure Map (LEM) to highlight areas with the greatest amount of lightning activity. The team obtained TRMM and ISS LIS lightning data from the GHRC DAAC for the years 2001 to 2017, excluding 2015 and 2016 due to an absence of data during that time period. They then plotted the lightning flash locations in Nepal and Bangladesh using ESRI’s ArcGIS. There, the team calculated the magnitude per square kilometer for lightning flashes using the kernel density tool which fits a surface to each point while accounting for the Earth’s curvature, the number of points, and the standard deviation of the points. This information can be used as a guide by emergency managers on where to focus resources and lightning safety efforts.

The team then created the LRM by overlaying lightning exposure, population density, socio-economic conditions, and elevation to determine areas where the most vulnerable populations were exposed to frequent lightning activity. Using the TRMM LIS climatology product, the team discovered that lightning activity decreased rapidly after an elevation of 1,600 meters. From literature review, the team gathered evidence that rural populations are more exposed to lightning activity than urban populations partly due to the larger number of agriculture workers who are vulnerable to lightning-related injury when working outdoors. They also found that available housing provided insufficient lightning safety protection to the population. The team used fuzzy logic analysis, a suitability mapping technique, in ArcGIS to calculate which areas were at the greatest risk based on these contributing parameters. Data from various sources were obtained to perform this analysis including the socio-economic data from the UNISDR GAR15, the population density data from ORNL Landscan 2016 global population dataset, the elevation data from the SRTM, and the lightning exposure data from the LEM.

Lastly, the team calculated the correlation between precipitation and lightning using Spearman’s Rank Correlation Coefficient which calculates the correlation between two sets of data that are not normally distributed. The team used the lightning flash data from the TRMM LIS along with the convective precipitation rates from the TRMM PR to determine how closely precipitation rates relate to the amount of lightning that occurs. The lightning and precipitation layers were combined to match the number of lightning flashes with the precipitation rates that were occuring in that area. These values were then transferred into Microsoft Excel where the correlation was calculated for each month over the study period.

Results

The LEM indicates that historically lightning occurs most often in the western regions of Nepal and in the Sylhet district of Bangladesh. The team also found that lightning occurrence decreases as elevation increases up the Himalayan Mountains.

The LRM shows high risk areas across northeastern Bangladesh and western Nepal. In Sylhet, a region in northeastern Bangladesh, the large presence of rural agricultural workers, insubstantial rural housing, low elevation, and high lightning density contribute to the high lightning risk in this region. Conversely, despite a high lightning density in Kaski, there is a decrease in risk in western Nepal due to the presence of less rural housing conditions.

The precipitation and lightning correlations were highest March through May, during the pre-monsoon season in Nepal and Bangladesh and were lowest July through September, during the monsoon season. Stronger convective precipitation is present during the pre-monsoon season, which is more conducive to lightning compared to the widespread stratiform rain that is present during the monsoon season. Therefore the seasonal variation in precipitation and lightning correlation values and seasonal variation in precipitation type tend to follow a similar pattern.