Lightning Estimates of Precipitation Location and Quantity on the Sevilleta Lter, New Mexico

Abstract

Listen

Translate article

Typically, 50‐70% of the total annual precipitation in New Mexico can be produced by convective thunderstorms during the period June through September. These thunderstorms are accompanied by intense lightning and characteristically produce heavy, localized rainfall resulting in high spatial variation in precipitation inputs. During other months precipitation over the entire Sevilleta (105 ha) often occurs from broad‐scale storm systems and is much less spatially variable on a per‐storm basis. Summer precipitation is a primary factor driving plant productivity as well as influencing nutrient cycling, herbivore activity, and detritivore activity. Knowledge of the timing, location, and amounts of precipitation is important in planning or monitoring research activities and spatial modeling of the dynamics in this semiarid region. Technology exists for locating cloud‐to‐ground lightning strikes that has the potential to locate these intense precipitation events, quantify the volume of water associated with them, and document the spatial and temporal variability of this phenomenon over large areas. Near real‐time analysis capability can identify areas receiving precipitation that will experience rapid vegetation growth in this semiarid region. This study developed algorithms relating lightning and precipitation quantity and used lightning location to determine rainfall depth and distribution for areas in New Mexico. There was a significant correlation between rain‐gauge measured precipitation and lightning within a 3‐km radius of the gauge location, with best predictions occurring from regressions that included lightning strikes and relative humidity. Average precipitation volume per cloud‐to‐ground lightning strike averaged 36 190 m3 for the 3 km radius circle, resulting in an average rainfall depth of 1.3 mm per lightning strike. Lightning location technology, combined with a Geographic Information System (GIS), defined the spatial and temporal resolution of these intense, summer precipitation patterns and provided a more detailed estimate of total precipitation and precipitation distribution than was provided by the sparse network of precipitation gauges. Combining this information with satellite sensing of vegetation growth (e.g., greenness index) can identify causal mechanisms for temporal and spatial patterns in short‐term vegetation processes (e.g., primary production) and long‐term vegetation dynamics for this area.