Rainfall variability over the East African coast

Gamoyo, Majambo; Reason, C. J. C.; Obura, David

doi:10.1007/s00704-014-1171-6

Cited by 34 publications

(33 citation statements)

References 12 publications

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“…In yearly analysis of bias, TARCAT showed the highest underestimation (−123.4 mm month −1 ) in November 2006. During this month, heavy rainfall was experienced over East Africa causing floods [37], and these findings clearly confirm that TARCAT can underestimate heavy rainfall.…”

Section: Discussionsupporting

confidence: 74%

“…Figure 5 shows the bias variations for each product per year during the peak rainfall month (November) of the OND season. During this season, the bias of the satellite products were more uniform, with a general underestimation by TARCAT in 2006, a year in which East Africa received heavy rainfall [37]. It is remarkable that CMAP shows a general uniform low bias throughout the years.…”

Section: Performance Of Monthly Satellite Rainfall Productsmentioning

confidence: 93%

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An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa

2017

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Accurate and consistent rainfall observations are vital for climatological studies in support of better agricultural and water management decision-making and planning. In East Africa, accurate rainfall estimation with an adequate spatial distribution is limited due to sparse rain gauge networks. Satellite rainfall products can potentially play a role in increasing the spatial coverage of rainfall estimates; however, their performance needs to be understood across space-time scales and factors relating to their errors. This study assesses the performance of seven satellite products: Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT), African Rainfall Climatology And Time series (TARCAT), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Centre (CPC) Morphing technique (CMORPH), PrecipitationEstimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP), and Global Precipitation Climatology Project (GPCP), using locally developed gridded (0.05 • ) rainfall data for 15 years (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) over East Africa. The products' assessments were done at monthly and yearly timescales and were remapped to the gridded rain gauge data spatial scale during the March to May (MAM) and October to December (OND) rainy seasons. A grid-based statistical comparison between the two datasets was used, but only pixel values located at the rainfall stations were considered for validation. Additionally, the impact of topography on the performance of the products was assessed by analyzing the pixels in areas of highest negative bias. All the products could substantially replicate rainfall patterns, but their differences are mainly based on retrieving high rainfall amounts, especially of localized orographic types. The products exhibited systematic errors, which decreased with an increase in temporal resolution from a monthly to yearly scale. Challenges in retrieving orographic rainfall, especially during the OND season, were identified as the main cause of high underestimations. Underestimation was observed when elevation was <2500 m and above this threshold; overestimation was evident in mountainous areas. CMORPH, CHIRPS, and TRMM showed consistently high performance during both seasons, and this was attributed to their ability to retrieve rainfall of different rainfall regimes.

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Section: Discussionsupporting

confidence: 74%

Section: Performance Of Monthly Satellite Rainfall Productsmentioning

confidence: 93%

An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa

2017

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“…Two major modes of low‐frequency variability coupled between the ocean and atmosphere are known to have major impacts in tropical and subtropical eastern Africa. First, the IOD impacts climate variability in equatorial eastern Africa (Gamoyo et al , ) and is characterized by a see‐sawing of atmospheric pressures, temperatures, and flow anomalies between the western and eastern equatorial Indian Ocean. With its phases typically beginning in May or June, reaching a peak in August through October, and decaying in November or December (Lim and Hendon, ), the IOD is well‐recognized as an important predictor of eastern African rainfall (Chen and Georgakakos, ).…”

Section: Atmospheric and Oceanic Circulation Variabilitymentioning

confidence: 99%

Inter‐annual hydroclimatic variability in coastal Tanzania

Rohli

Ates

Rivera‐Monroy

et al. 2019

Intl Journal of Climatology

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Climatic controls regulate the coupled natural and human systems in coastal Tanzania, where mangrove wetlands provide a wealth of ecosystem services to coastal communities. Previous research has explained the precipitation seasonality of eastern Africa in terms of the local monsoons. This research examines a wider range of hydroclimatic variables, including water vapour flux, evapotranspiration, runoff, and ocean salinity, and the sources of low‐frequency atmosphere–ocean variability that support mangrove productivity and associated ecosystem services. Results confirm previous work suggesting that the northeast monsoon (kaskazi) largely corresponds to the “short rains” of October–December and extends through February, while the southeast monsoon (kusi) corresponds to the “long rains” of March–May and the drier June–September. The Indian Ocean Dipole (IOD) and, to a lesser extent, El Niño–Southern Oscillation (ENSO) are important modulators not only of precipitation (as has been shown previously) but also of water vapour flux, evapotranspiration, runoff, and salinity variability. During kaskazi, positive (negative) hydroclimatic anomalies occur during positive (negative) IOD, with a stronger IOD influence occurring during its positive phase, when seasonal anomalies of precipitation, evapotranspiration, and runoff exceed +50, 25, and 100%, and nearby salinity decreases by 0.5 practical salinity units. During kusi, the contrast between the positive and negative IOD modes is subtler, and the pattern is dictated more by variability in “long rains” months than in the dry months. The coincidence of the positive IOD and El Niño amplify this hydroclimatic signal. Because previous work suggests the likelihood of increased tendency for positive IOD and increased moisture variability associated with El Niño events in the future, wetter conditions may accompany the kaskazi, with less change expected during the kusi. These results advance understanding of the key environmental drivers controlling mangrove productivity and wetland spatial distribution that provide ecosystem services essential to the well‐being of the human population.

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“…From any station, there is no month where rainfall was not recorded (Figure 2). According to Gamoyo et al [19], there is a statistically-significant correlation between stations and variability of rainfall except for Mtwara, which is not significantly correlated with the others. This observation is in agreement with [6] in that the Mtwara region exhibits different rainfall variability to elsewhere in coastal Tanzania.…”

Section: Rainfall Datamentioning

confidence: 99%

“…This paper uses rainfall data from the Tanga Tanga, Dar es Salaam and Mtwara meteorological stations (Figure 1). Tanga and Dar es Salaam are found in a bimodal rainfall zone, with 'long' rains centred on March to May (MAM) known in Tanzania as Masika rains, and 'short' rains in October to December (OND) known as Vuli rains [2,19]. Mtwara is in a unimodal rainfall zone, with a single maximum in October-April [20].…”

Section: Description Of the Study Areamentioning

confidence: 99%

Long-Term Rainfall Trends over the Tanzania Coast

Kabanda

2018

Atmosphere

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Spatial and temporal rainfall trends over the Tanzanian coast are analysed and trends for over 50 years are investigated. This type of study is crucial at this time because the area under study is now one of the world's economic hotspots, as major gas fields have been discovered and the area also has high potential for oil field discoveries. Methods applied in this study include the Mann-Kendall test for rainfall data to detect the long-term trends, while Sen's slope estimator test was used for finding the magnitude of change over time. The results exhibited rainfall trend patterns with substantial variations between the stations. The Z value of the Mann-Kendall test showed various months with negative trend at a significance level ≥95%. The few months that showed a positive trend were not statistically significant. Generally, rainfall trends varied in different months for different stations. However, the most outstanding observation on individual months is July, which showed a highly statistically significant (99.9%) reduction in rainfall for the whole coastal area, including the regions of Mtwara, Dar es Salaam and Tanga. The last part of this paper describes the relationship between July rainfall and the horizontal winds from the National Centre for Environmental Prediction/National Centre for Atmospheric Research (NCEP/NCAR) re-analysis. It is observed that the strength of the anticyclonic flow over the southwest Indian Ocean, which is associated with the westward fluxes of moisture, is responsible for rainfall over the whole coastal area of Tanzania during July.

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Rainfall variability over the East African coast

Cited by 34 publications

References 12 publications

An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa

An Assessment of Satellite-Derived Rainfall Products Relative to Ground Observations over East Africa

Inter‐annual hydroclimatic variability in coastal Tanzania

Long-Term Rainfall Trends over the Tanzania Coast

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