Simulating the connections of ENSO and the rainfall regime of East Africa and the upper Blue Nile region using a climate model of the Tropics

Zaroug, Modathir; Giorgi, Filippo; Coppola, Erika; Abdo, Gamal; Eltahir, Elfatih A. B.

doi:10.5194/hess-18-4311-2014

Cited by 15 publications

(7 citation statements)

References 50 publications

(64 reference statements)

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“…This is the same ENSO-streamflow relationship found in Eltahir [55] and Dettinger and Diaz [8], and may result from integrating less significant but anomalous precipitation from upstream reaches through hydrological processes (e.g. evapotranspiration, infiltration, and discharge) over large catchment areas [56,57]. Additionally, in the upstream regions of the Nile river, anomalous soil moisture conditions affect the corresponding streamflow patterns more than precipitation in DJF (figures 3(e)-(g), DJF).…”

Section: Africasupporting

confidence: 61%

Identification of symmetric and asymmetric responses in seasonal streamflow globally to ENSO phase

Lee

Ward

Block

2018

Environ. Res. Lett.

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The phase of the El Niño Southern Oscillation (ENSO) has large-ranging effects on streamflow and hydrologic conditions globally. While many studies have evaluated this relationship through correlation analysis between annual streamflow and ENSO indices, an assessment of potential asymmetric relationships between ENSO and streamflow is lacking. Here, we evaluate seasonal variations in streamflow by ENSO phase to identify asymmetric (AR) and symmetric (SR) spatial pattern responses globally and further corroborate with local precipitation and hydrological condition. The AR and SR patterns between seasonal precipitation and streamflow are identified at many locations for the first time. Our results identify strong SR patterns in particular regions including northwestern and southern US, northeastern and southeastern South America, northeastern and southern Africa, southwestern Europe, and central-south Russia. The seasonally lagged anomalous streamflow patterns are also identified and attributed to snowmelt, soil moisture, and/or cumulative hydrological processes across river basins. These findings may be useful in water resources management and natural hazards planning by better characterizing the propensity of flood or drought conditions by ENSO phase.

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Section: Africasupporting

confidence: 61%

Identification of symmetric and asymmetric responses in seasonal streamflow globally to ENSO phase

Lee

Ward

Block

2018

Environ. Res. Lett.

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“…The data range covered 65 years of summer rainfall (1951-2015) and 50 years of spring rains (1951-2000) over Ethiopia. There are some previous studies [52][53][54] that also showed the availability of using CRU data.…”

Section: Rainfall Datamentioning

confidence: 99%

Seasonal Rainfall Variability in Ethiopia and Its Long-Term Link to Global Sea Surface Temperatures

Alhamshry

Fenta

Yasuda

et al. 2019

Water

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Investigating the influence of sea surface temperatures (SSTs) on seasonal rainfall is a crucial factor for managing Ethiopian water resources. For this purpose, SST and rainfall data were used to study a wide range of inhomogeneous areas in Ethiopia with uneven distribution of rainfall for both summer and spring seasons. Firstly, a preliminary subdivision of rainfall grid points into zones was applied depending on spatial homogeneity and seasonality of rainfall. This introduced new clusters, including nine zones for summer rainfall peak (July/August) and five zones for spring rainfall peak (April/May). Afterward, the time series for each zone was derived by calculating the rainfall averaged over grid points within the zone. Secondly, the oceanic regions that significantly correlated with the Ethiopian rainfall were identified through cross-correlations between rainfalls averaged over every homogeneous zone and the monthly averaged SST. For summer rainfall as a main rainy season, the results indicated that the Gulf of Guinea and southern Pacific Ocean had a significant influence on rainfall zones at a lag time of 5-6 and 6-7 months. Besides, for summer rainfall zones 8 and 9 at lag time 5-6 months, the common SST regions of the southern Pacific Ocean showed the opposite sense of positive and negative correlations. Thus, the difference in SSTs between the two regions was more strongly correlated (r ≥ 0.46) with summer rainfall in both zones than others. For the spring season, the results indicated that SST of the northern Atlantic Ocean had a strong influence on spring rainfall zones (3 and 5) at a lag time 6-7 months, as indicated by a significant correlation (r ≥ −0.40). Therefore, this study suggests that SSTs of southern Pacific and northern Atlantic oceans can be used as effective inputs for prediction models of Ethiopian summer and spring rainfalls, respectively.

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“…The teleconnections known to act over East Africa are IOD [Saji et al, 1999] and ENSO [Ropelewski and Halpert, 1987;Trenberth, 1997]. In particular, most of the interannual variability during the short rains as well as other seasons has been linked to IOD [Behera et al, 2005;Black et al, 2003;Conway et al, 2007;Lanckriet et al, 2015;Marchant et al, 2007;Omondi et al, 2013;Williams and Funk, 2011;Zaroug et al, 2014]. Several studies have also found the imprint of ENSO on the variability of the short rains over parts of East Africa [Giannini et al, 2008;Indeje et al, 2001;Plisnier et al, 2000;Schreck and Semazzi, 2004;Segele et al, 2009;Smith and Semazzi, 2014;Sun et al, 1999].…”

Section: Introductionmentioning

confidence: 99%

Vegetation response to precipitation variability in East Africa controlled by biogeographical factors

et al. 2016

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Ecosystem sensitivity to climate variability varies across East Africa, and identifying the determinant factors of this sensitivity is crucial to assessing region‐wide vulnerability to climate change and variability. Such assessment critically relies on spatiotemporal data sets with inherent uncertainty, on new processing techniques to extract interannual variability at a priori unknown time scales and on adequate statistical models to test for biogeographical effects on vegetation‐precipitation relationships. In this study, interannual variability in long‐term records of normalized difference vegetation index and satellite‐based precipitation estimates was detected using ensemble empirical mode decomposition and standardized precipitation index with varying accumulation periods. Environmental effect modeling using additive models with spatially correlated effects showed that ecosystem sensitivity is primarily predicted by biogeographical factors such as annual precipitation distribution (reaching maximum sensitivity at 500 mm yr−1), vegetation type and structure, ocean‐climate coupling, and elevation. The threat of increasing climate variability and extremes impacting productivity and stability of ecosystems is most imminent in semiarid grassland and mixed cropland ecosystems. The influence of oceanic phenomena such as El Niño–Southern Oscillation and Indian Ocean Dipole is foremost reflected in precipitation variability, but prolonged episodes also pose risks for long‐term degradation of tree‐rich ecosystems in the East African Great Lakes region.

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Simulating the connections of ENSO and the rainfall regime of East Africa and the upper Blue Nile region using a climate model of the Tropics

Cited by 15 publications

References 50 publications

Identification of symmetric and asymmetric responses in seasonal streamflow globally to ENSO phase

Identification of symmetric and asymmetric responses in seasonal streamflow globally to ENSO phase

Seasonal Rainfall Variability in Ethiopia and Its Long-Term Link to Global Sea Surface Temperatures

Vegetation response to precipitation variability in East Africa controlled by biogeographical factors

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