The Investigation of 1997 and 2015 El Nino Events in West Sumatera, Indonesia

Abstract

The 1997, 2010, and 2015 El Nino events have been recognized worldwide as a primary factor of decreasing biomass productivity. Its effects occur at farm as well as regional scale. Yet some still think that the effect is only perceived by farmers directly. We proposed a simple method to describe that its effect at catchment scale is not negligible. For this purpose, we analysed an upstream catchment in West Sumatera that normally receives high rainfall up to 5000 mm per year. This catchment is in pristine condition due to its status as national park. We used satellite and ground monitoring systems i.e. rainfall and stream water level. Satellite data such as DEM (Digital Elevation Model) used to trace river networks is acquired from ASTER GDEM in 30 m resolution. To monitor vegetation health, we used NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) on board MODIS (MODerate resolution Imaging Spectroradiometer) Terra (daytime) MOD13Q1 (250 m resolution). Catchment delineation was performed using local land use map. Time series of EVI and NDVI was processes for year 2015, comprising 23 datasets. Rainfall data from year 1980 to 2012 from 9 stations and water level at the main river were analysed. We found the trace of 1997 El Nino very clearly through rainfall anomalies at all stations. As for the 2010 event, the pattern was not consistent across all rainfall stations. The 2015 event was not imminent until late October 2015. Throughout the year, NDVI remains above 0.8. In late October, maximum NDVI dropped below 0.4. This coincides with very low water level in the stream. The same pattern was also found in two neighbouring catchments with similar land use, Kuranji and Air Dingin, and Tampo catchment. This proves that the 2015 El Nino did not only threatens farmers, but also other aspects that depend on vegetation health and stream flow. Both methods are proven to be robust and may be used as an alternative way to monitor vegetation health and the impact of global climate change for ecology and water management such as domestic water use, irrigation and flood control.