Long-term (33 years) rainfall and runoff dynamics in a tropical dry forest ecosystem in western Mexico: Management implications under extreme hydrometeorological events

Rainfall, runoff and sediment yield have been measured over three decades (1983–2015) in five contiguous small watersheds (12–28 ha) covered by mature tropical dry forest (TDF) at the Chamela Biological Field Station, UNAM, in the southern Pacific Coast of Jalisco, Mexico. Hydrological dynamics strongly drive the seasonal and inter-annual variability of TDF primary productivity, litter decomposition and nutrient cycling. Although the study region is constantly influenced by storms related to hurricane activity, they rarely make landfall. However, hurricanes have landed twice over a four-year period in the reserve: category 2 Hurricane Jova, in October 2011, and category 4 Hurricane Patricia, in October 2015. The long-term data has allowed to evaluate ecosystem’s response to these two major hydrometeorological events, in the context of the historic rainfall/runoff pattern. The results showed that antecedent precipitation and rainfall intensity were the major factors controlling rainfall-runoff and soil erosion processes. The analysis also showed the timing of the onset of the rainy season was very regular but the length of the rainy season was quite variable. The highly variable rainfall pattern and high probability of intense storms at the onset of the wet season were linked with the vulnerability of TDF to soil erosion. Also, two types of recurrent and intense drought periods have been identified: the high inter-annual droughts; and the frequent within-wet season droughts (“canículas”). There is a clear influence of the “El Niño”- Southern Oscillation phenomenon (ENSO) on the study area, with lower annual rainfall during its hot phase, and higher annual rainfall during its cold phase (“La Niña”). The relevance of these findings is discussed in terms of forest ecosystem management practices in the region and its implications concerning the current climate-change forecast for the study area.