Predicting monthly precipitation along coastal Ecuador: ENSO and transfer function models

Guenni, Lelys Bravo de; García, Mariangel; Muñoz, Ángel G.; Santos, José Luís; Cedeño, Alexandra; Perugachi, Carlos; Castillo, José E.

doi:10.1007/s00704-016-1828-4

Cited by 18 publications

(15 citation statements)

References 20 publications

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“…However, there is no agreement among the scientific community about which index best captures the ENSO events (Hanley et al , ) or which has the greatest effect over the entire territory (Morán‐Tejeda et al , ; Tobar and Wyseure, ). Most studies either analysed the precipitation in certain areas of Ecuador (Rossel and Cadier, ; Pineda et al , 2013; Pineda and Willems, 2016; De Guenni et al , ) or focused on correlations with specific El Niño indices. The most used teleconnection indices are El Niño1+2, El Niño3.4, El Niño3 and El Niño4 (Morán‐Tejeda et al , ; Vicente‐Serrano et al , ; Tobar and Wyseure, ), although the Southern Oscillation Index (SOI) and the Multivariate ENSO Index (MEI) have also been considered (Villar et al , ).…”

Section: Introductionmentioning

confidence: 99%

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

Quishpe-Vásquez

Gámiz‐Fortis

Ojeda

et al. 2019

Intl Journal of Climatology

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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.

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Section: Introductionmentioning

confidence: 99%

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

Quishpe-Vásquez

Gámiz‐Fortis

Ojeda

et al. 2019

Intl Journal of Climatology

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“…Further work is in progress to explore different sources of predictability of local meteorological conditions in coastal Ecuador, to improve the skill of seasonal climate forecasts in this region. 37 Despite these limitations, this work advances the stateoftheart of climate services for the health sector in Ecuador, by transitioning from proof of concept to application. The successful implementation of climate services for health depends on availability of relevant, highquality climate data, as well as the institutional and human capacity to transform the data into reliable and tailored climate products and services.…”

Section: Discussionmentioning

confidence: 99%

Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador

Lowe

Stewart-Ibarra

Petrova

et al. 2017

The Lancet Planetary Health

109

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“…One or more predictors can be considered as input variables to the model. On the other hand, predictors can have a lagged effect on the predictant variables, and one must decide how many past values of the predictor variable would make an impact on the predictant variable [16]. Following the transfer function models with the time varying approach used by [17], one might consider the following model:…”

Section: Wavelet Transfer Function Modelmentioning

confidence: 99%

“…In this equation, we assume that W t andη t are independent, where W t is the pre-whitened input series x t andỹ t andη t are the filtered output series of y t and the random noise η t , respectively, using the operator of the ARMA (p, q) model as a filter. It can be shown that the cross-correlation between the filtered series and the pre-whitened series W t is γỹ tW t (h) = σ 2 W α h ; therefore, their sample values allow obtaining an approximate estimate of the coefficients of the transfer function α 0 , α 1 , · · · [16].…”

Section: Polynomial Transfer Function Modelmentioning

confidence: 99%

Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

Vivas

Allende‐Cid

Salas

et al. 2019

Entropy

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It is well known that environmental fluctuations and fishing efforts modify fishing patterns in various parts of the world. One of the most affected areas is northern Chile. The reduction of the gaps in the implementation of national fisheries’ management policies and the basic knowledge that supports the making of such decisions are crucial. That is why in this research, a transfer function method with variable coefficients is proposed to forecast monthly disembarkation of anchovies and sardines in northern Chile, taking into account the incidence of large-scale climatic variables on landings. The method uses a least squares procedure and wavelets to expand the coefficients of the transfer function. Linear estimators of the time varying coefficients are proposed, followed by a truncation of the wavelet expansion up to an appropriate scale. Finally, the estimators for the transfer function coefficients are obtained by using the inverse wavelet transformation. Research results suggest that the transfer function models with variable coefficients fit the behavior of the anchovies’ landing with great accuracy, while the use of transfer function models with constant coefficients fits sardines’ landings better. Both fisheries’ landings could be explained to a large extent from the large scale climatic variables.

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Predicting monthly precipitation along coastal Ecuador: ENSO and transfer function models

Cited by 18 publications

References 20 publications

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

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

Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador

Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

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