LIGHTNING ESTIMATES OF PRECIPITATION LOCATION AND QUANTITY ON THE SEVILLETA LTER, NEW-MEXICO

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 a nd characteristically produce heavy, localized rainfall resulting in h igh spatial variation in precipitation inputs. During other months pre cipitation over the entire Sevilleta (10(5) ha) often occurs from broa d-scare storm systems and is much less spatially variable on a per-sto rm basis. Summer precipitation is a primary factor driving plant produ ctivity as well as influencing nutrient cycling, herbivore activity, a nd detritivore activity. Knowledge of the timing, location, and amount s of precipitation is important in planning or monitoring research act ivities 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, quanti fy the volume of water associated with them, and document the spatial and temporal variability of this phenomenon over large areas. Near rea l-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 q uantity and used lightning location to determine rainfall depth and di stribution for areas in New Mexico. There was a significant correlatio n between rain-gauge measured precipitation and lightning within a 3-k m radius of the gauge location, with best predictions occurring from r egressions that included lightning strikes and relative humidity. Aver age precipitation volume per cloud-to-ground lightning strike averaged 36190 m(3) for the 3 km radius circle, resulting in an average rainfa ll depth of 1.3 mm per lightning strike. Lightning location technology , combined with a Geographic Information System (GIS), defined the spa tial and temporal resolution of these intense, summer precipitation pa tterns and provided a more detailed estimate of total precipitation an d precipitation distribution than was provided by the sparse network o f precipitation gauges. Combining this information with satellite sens ing of vegetation growth (e.g., greenness index) can identify causal m echanisms for temporal and spatial patterns in short-term vegetation p rocesses (e.g., primary production) and long-term vegetation dynamics for this area.