Tropical Pacific sea surface temperature influence on seasonal streamflow variability in Ecuador

Abstract

This study presents a basin‐wide assessment about the spatiotemporal variability of streamflows in Ecuador for the period 1979–2015. The influence of the tropical Pacific sea surface temperature (SST) on streamflow variability from February to April (FMA) period, as the months showing maximum streamflow for the wet season in Ecuador, and from June to August (JJA), corresponding to the dry season, was analysed. First, a long‐term trend analysis was carried out by applying the Sen's slope estimator and the Mann–Kendall test to monthly streamflow data from 45 gaging stations located in different basins across Ecuador. While the coastal region showed the highest generalized positive trends from July to January, the results for the Pacific Andean area suggested a strengthening of the seasonality, presenting an overall increase in the streamflow for all months except August, September and October, which showed negative trends. Second, a singular‐value decomposition (SVD) was applied in order to find the main coupled variability patterns between the FMA streamflow and the quasi‐coetaneous SST (December–February [DJF]) and between the JJA streamflow and the coetaneous SST. The results revealed two main coupled modes for DJF SST/FMA streamflow, the first associated with the canonical El Niño and the second with El Niño Modoki. The latter exerted a major influence on FMA streamflow over most of Ecuador. For JJA streamflow, however, the pattern associated with the traditional El Niño was even more relevant. These results establish the foundations for streamflow modelling in Ecuador based on the Pacific SST, showing the strong response of the streamflows to different types of El Niño events.

The influence of global climate and local hydrological variations over streamflow extremes: The tropical-mountain case

“…According to Vicente-Serrano et al (2017), the ENSO34 index is better related to drought variability in the Andes than ENSO12. More recently, Quishpe-Vásquez et al (2019) also reported that streamflow during the dry season in the Andes is strongly correlated to the ENSO34 index, which is in accordance with our findings.…”

“…Increasing precipitation and streamflow trends have been previously reported in the Andes for the wet season (e.g. Celleri et al 2007;Morán-Tejeda et al 2016;Quishpe-Vásquez et al 2019;Tobar and Wyseure 2018).…”

“…In this way, the CI will exert an influence most likely due to a seasonal persistence inside the 130 natural intra-annual climate cycle. We hypothesized that climate conditions prior to the event-month significantly influence the occurrence of hydrological extremes and are better predictors in comparison to another time window as reported by Zambrano Mera, Rivadeneira Vera, and Pérez-Martín (2018) and Quishpe-Vásquez et al (2019) for several regions of Ecuador.…”

“…For instance, on the one hand, a significant positive correlation between the mesoscale convective systems occurrence and the TNI was found, especially during the rainy season (Campozano et al, 2018;Mendoza et al, 2019). On the other hand, Quishpe-Vásquez et al (2019) reported that ENSO teleconnection indices, which take into account the temperature gradient between the eastern and western tropical Pacific (i.e. TNI), exhibited significant and stable correlations with streamflow in the rainy season in the Andean region of Ecuador.…”

The covariability of sea surface temperature and MAM rainfall on East Africa using singular value decomposition analysis

“…The SVD approach has the advantage of evaluating the cross-covariance matrix of two spatial-temporal fields to identify regions of similar behaviour (Aziz et al, 2010). The same technique was used by (Ogwang et al, 2016;Ogou et al, 2016;Dubache et al 2019;Li & Zhao, 2019;Quishpe-Vásquez et al 2019;Li et al, 2020) to investigate the response of rainfall on SST. This technique is applied to two jointly analysed fields to identify couples of the coupled spatial pattern and their respective temporal variations.…”