Future Meteorological Droughts in Ecuador: Decreasing Trends and Associated Spatio-Temporal Features Derived From CMIP5 Models

Abstract

Droughts are one of the most spatially extensive disasters that are faced by societies. Despite the urgency to define mitigation strategies, little research has been done regarding droughts related to climate change. The challenges are due to the complexity of droughts and to future precipitation uncertainty from Global Climate Models (GCMs). It is well-known that climate change will have more impact on developing countries. This is the case for Ecuador, which also has the additional challenges of lacking meteorological drought studies covering its three main regions: Coast, Highlands, and Amazon, and of having an intricate orography. Thus, this study assesses the spatio-temporal characteristics of present and future droughts in Ecuador under Representative Concentrations Pathways (RCP) 4.5 and 8.5. The 10 km dynamically downscaled products (DGCMs) from Coupled Model Intercomparison Project 5 (CMIP5) was used. The Standardized Precipitation Index (SPI) for droughts was calculated pixel-wise for present time 1981-2005 and for future time 2041-2070. The results showed a slightly decreasing trend for future droughts for the whole country, with a larger reduction for moderate droughts, followed by severe and extreme drought events. In the Coast and Highland regions, the intra-annual analysis showed a reduction of moderate and severe future droughts for RCP 4.5 and for RCP 8.5 throughout the year. Extreme droughts showed small and statistically non-significant decreases. In the Amazon region, moderate droughts showed increases from May to October, and decreases for the rest of the year. Additionally, severe drought increases are expected Thus, in the Amazon, the rainy period showed a decreasing trend of droughts, following the wetter in wet-and drier in dry paradigm. Climate change causes decision-making process and calls for adaptation strategies being more challenging. In this context, our study has contributed to better mapping the space-time evolution of future drought risk in Ecuador, thus providing valuable information for water management and decision making as Ecuador faces climate change.

Spatial–Temporal Assessment of Historical and Future Meteorological Droughts in China

“…As for the future drought projections, Spinoni et al [10] investigated the global meteorological drought hot spots by using the Coordinated Regional Climate Downscaling Experiment data and concluded that southern South America, the Mediterranean region, southern Africa, southeastern China, Japan, and southern Australia were projected to experience more frequent and severe droughts in the future, especially under the high-concentration emission scenario, while for high latitudes in the Northern Hemisphere and Southeast Asia, a decrease in drought was projected. Campozano et al [11] evaluated meteorological droughts in Ecuador during 2041-2070 and found that a slightly decreasing trend was projected for future droughts for the whole country. Jincy Rose and Chithra [12] concluded that the Bharathapuzha river basin of Kerala in India was projected to become severely drought-prone during 2026-2040.…”

Impacts of Climate Change on the Precipitation and Streamflow Regimes in Equatorial Regions: Guayas River Basin

“…Nonetheless, it highlights the large uncertainty of future temperature and precipitation in GCMs under a high emissions scenario (RCP8.5) [93,94]. There are additional challenges due to the complexity of droughts [95,96] and GR2M model performance associated with input data uncertainty [62]. Ecuador's environment should also be considered a special case because of the country's location, as it is influenced by the Humboldt current, the El Niño phenomenon, trade wind dynamics, the position of the intertropical convergence zone and the presence of the Andes mountain range [36,[97][98][99][100], which together give this area great climatic variability.…”

Parsimonious Models of Precipitation Phase Derived from Random Forest Knowledge: Intercomparing Logistic Models, Neural Networks, and Random Forest Models

“…Considering similar thresholds under CC, e.g., −1 and 3, for snow or rain PP, this study showed that temperature and specific humidity are much more important than other variables such as incoming and outgoing radiation or wind. Although it is uncertain, the amount of precipitation in Ecuador is expected to increase due to CC [1,54], but climate models have less uncertainty with temperature, which will raise 4.5-5 • C by the end of this century under "business as usual" type emissions scenarios [55]. Additionally, more moisture is expected to affect the tropical Andes due to enhanced easterlies in CC.…”