Advanced VH-Based Long-Term Drought and Food Security Prediction

Abstract

This chapter describes drought prediction based on the interrelationship between climate forcing phenomena and vegetation health. Among climate events such as El Nino Southern Oscillation (ENSO), inter-decadal variability of sea surface temperature (SST), sea level pressure (SLP), and others, vegetation health was found to have strong relationship with ENSO events and might be used as an advanced indicator of drought of appearance and its impact on crop losses. It has been known that ENSO is changing global and regional temperature and precipitation patterns cyclically, every 3–7 years. El Nino, a warmer cycle of ENSO, is leading to a warmer world, while La Nina’s cooler cycle is triggering a cooler world. Some regions during these cycles experience dry and warm conditions but others moist and cool. ENSO signal was found to correlate strongly with weather parameters and vegetation health. Since ENSO events can be predicted a few months ahead of their start, they provide an advanced warning on drought several-month ahead of potential crop losses. ENSO-based VH assessments can be used for 2–3 month advanced predictions of crop losses and food security situation. This chapter discusses inter-decadal variability of SST and SLP, their correlation with VH indices, and how to use these indices for advanced assessment of losses in crop production. The most recent ENSO case was recorded in 2015, when sea surface temperature (SST) in the central tropical Pacific (TP) was warmer than normal, what indicated about the potential for the development of El Nino Southern Oscillation (warm phase). By December 2015, El Nino intensified when SST anomaly in the Nino-3.4 tropical Pacific area exceeded +2.0 °C, which indicated about the strongest event of the past 36 years. El Nino normally impacts weather, ecosystems, and socioeconomics (agriculture, fisheries, energy, human health, water resource, etc.) on all continents. However, the current El Nino was stronger than another recent strong 1997–1998 event. Therefore, this chapter is showing how ENSO impacts world ecosystems, which areas are affected, how intensive might be El Nino or La Nino (cold phase), and, what is the most important, how to predict droughts from satellite data in crop areas several months ahead of crop losses. Satellite-based vegetation health (VH) method and 36-year of its data have been used as the criteria of the impact following sea surface temperature (SST) changes in tropical Pacific. Specifically, the chapter shows VH-SST teleconnection during ENSO years, focusing on estimation of vegetation response to the strongest El Nino, an intensity of the response and transition of the impact from boreal winter to spring and summer. Two types of ecosystem response were identified. In boreal winter, ecosystems of northern South America, southern Africa, eastern Australia, and Southeast Asia experienced strong vegetation stress during El Nino, which will negatively affect agriculture, energy, and water resources. In Argentina, south-eastern USA and the Horn of Africa ecosystem response is opposite. One of the worst disasters associated with ENSO is drought. The advantages of this study are in derivation of vegetation response to moisture, thermal, and combined conditions including an early detection of drought-related vegetation stress. For the first time, ENSO impact was evaluated based on all events with |SSTa| > 0.5 °C and strong events with |SSTa| > 2.0 °C. The 2015 strong El Nino has triggered drought in Brazil, southern Africa, south-eastern Asia, and eastern Australia during December–February. Such conditions have transitioned from boreal winter to spring 2016 and even to summer in northern Brazil and south-eastern Asia.