Long-term spatial-temporal trends and variability of rainfall over Eastern and Southern Africa

Muthoni, Francis; Odongo, V.O.; Ochieng, Justus; Mugalavai, Edward M.; Mourice, Sixbert K.; Hoesche-Zeledon, Irmgard; Mwila, Mulundu; Bekunda, Mateete

doi:10.1007/s00704-018-2712-1

Cited by 116 publications

(81 citation statements)

References 60 publications

(100 reference statements)

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“…Studies on the CHIRPS PCP product shows high correlations, at monthly and regional scales, in Eastern Africa ( r = 0.7–0.93) (Dinku et al, 2018; Gebrechorkos, Hülsmann, & Bernhofer, 2018) and Burkino Faso ( r = 0.95) (Dembélé & Zwart, 2016) with little to no bias. Muthoni et al (2019) reported that CHIRPS v2 slightly overestimated low‐intensity rainfall below 100 mm and slightly underestimated high‐intensity rainfall above 100 mm compared in Eastern and Southern Africa. On an annual, basin‐scale, the CHIRPS PCP product does not show significant bias, except for in largely ungauged tropical basins (e.g.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of WaPOR V2 evapotranspiration products across Africa

Blatchford

Mannaerts

Njuki

et al. 2020

Hydrological Processes

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The Food and Agricultural Organization of the United Nations (FAO) portal to monitor water productivity through open‐access of remotely sensed derived data (WaPOR) offers continuous actual evapotranspiration and interception (ETIa‐WPR) data at a 10‐day basis across Africa and the Middle East from 2009 onwards at three spatial resolutions. The continental level (250 m) covers Africa and the Middle East (L1). The national level (100 m) covers 21 countries and 4 river basins (L2). The third level (30 m) covers eight irrigation areas (L3). To quantify the uncertainty of WaPOR version 2 (V2.0) ETIa‐WPR in Africa, we used a number of validation methods. We checked the physical consistency against water availability and the long‐term water balance and then verify the continental spatial and temporal trends for the major climates in Africa. We directly validated ETIa‐WPR against in situ data of 14 eddy covariance stations (EC). Finally, we checked the level consistency between the different spatial resolutions. Our findings indicate that ETIa‐WPR is performing well, but with some noticeable overestimation. The ETIa‐WPR is showing expected spatial and temporal consistency with respect to climate classes. ETIa‐WPR shows mixed results at point scale as compared to EC flux towers with an overall correlation of 0.71, and a root mean square error of 1.2 mm/day. The level consistency is very high between L1 and L2. However, the consistency between L1 and L3 varies significantly between irrigation areas. In rainfed areas, the ETIa‐WPR is overestimating at low ETIa‐WPR and underestimating when ETIa is high. In irrigated areas, ETIa‐WPR values appear to be consistently overestimating ETa. The relative soil moisture content (SMC), the input of quality layers and local advection effects were some of the identified causes. The quality assessment of ETIa‐WPR product is enhanced by combining multiple evaluation methods. Based on the results, the ETIa‐WPR dataset is of enough quality to contribute to the understanding and monitoring of local and continental water processes and water management.

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Section: Discussionmentioning

confidence: 99%

Evaluation of WaPOR V2 evapotranspiration products across Africa

Blatchford

Mannaerts

Njuki

et al. 2020

Hydrological Processes

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“…Although P. fijiensis was not detected in Kilimanjaro region, the pathogen is present in other areas close to this region. Kilimanjaro region has experienced significant decline in precipitation over the last 37 years (Muthoni et al , ), which could have rendered the area less conducive for P. fijiensis proliferation. Furthermore, soils in Kilimanjaro region are volcanic and known to be rich in silicon (Zech et al , ), which is reported to modulate development and establishment of P. fijiensis (Kablan et al , ).…”

Section: Discussionmentioning

confidence: 99%

Distribution of Pseudocercospora species causing Sigatoka leaf diseases of banana in Uganda and Tanzania

et al. 2019

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Sigatoka leaf diseases are a major constraint to banana production. A survey was conducted in Tanzania and Uganda to assess the distribution of Pseudocercospora species and severity of Sigatoka leaf diseases. Pseudocercospora species were identified using species‐specific primers. Sigatoka‐like leaf diseases were observed in all farms and on all cultivars, but disease severity varied significantly (P < 0.001) between countries, districts/regions within countries, altitudinal ranges and banana cultivars. In all regions except Kilimanjaro, P. fijiensis, the causal agent of black Sigatoka, was the only pathogen associated with Sigatoka disease. Mycosphaerella musae was associated with Sigatoka‐like symptoms in Kilimanjaro region. Black Sigatoka disease was more severe in Uganda, with a mean disease severity index (DSI) of 37.5%, than in Tanzania (DSI = 19.9%). In Uganda, black Sigatoka disease was equally severe in Luwero district (mean DSI = 40.4%) and Mbarara district (mean DSI = 37.9%). In Tanzania, black Sigatoka was most severe in Kagera region (mean DSI = 29.2%) and least in Mbeya region (mean DSI = 11.5%). Pseudocercospora fijiensis, the most devastating sigatoka pathogen, was detected at altitudes of up to 1877 m a.s.l. This range expansion of P. fijiensis, previously confined to altitudes lower than 1350 m a.s.l. in East Africa, is of concern, especially for smallholder banana farmers growing the susceptible East African Highland bananas (EAHB). Among the banana varieties sampled, the EAHB, FHIA hybrids and Mchare were the most susceptible. Here, the loss of resistance in Yangambi KM5, a banana variety previously resistant to P. fijiensis, is reported for the first time.

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“…The dataset is available in a 0.05 • and a 0.25 • spatial and a daily temporal resolution from 1981 until today. CHIRPS has been shown to perform well over Eastern Africa [10,31,49].…”

Section: Observational Data For Evaluationmentioning

confidence: 99%

Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

2020

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Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources.

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Long-term spatial-temporal trends and variability of rainfall over Eastern and Southern Africa

Cited by 116 publications

References 60 publications

Evaluation of WaPOR V2 evapotranspiration products across Africa

Evaluation of WaPOR V2 evapotranspiration products across Africa

Distribution of Pseudocercospora species causing Sigatoka leaf diseases of banana in Uganda and Tanzania

Did ERA5 Improve Temperature and Precipitation Reanalysis over East Africa?

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