The influence of El Niño–Southern Oscillation regimes on eastern African vegetation and its future implications under the RCP8.5 warming scenario

Fer, Istem; Tietjen, Britta; Jeltsch, Florian; Wolff, Christian

doi:10.5194/bg-14-4355-2017

Cited by 38 publications

(31 citation statements)

References 60 publications

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“…Likewise, CCSM4 has improved El Niño-Southern Oscillation (ENSO) variability with more reasonable frequency distribution compared to earlier versions of CCSMs [32]. ENSO is a major driver of interannual rainfall variability in eastern Africa [33].…”

Section: Environmental Modelling Datamentioning

confidence: 99%

Modelling Climate Suitability for Rainfed Maize Cultivation in Kenya Using a Maximum Entropy (MaxENT) Approach

et al. 2019

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Climate change and variability are projected to alter the geographic suitability of lands for crop cultivation. In many developing countries, such as Kenya, information on the mean changes in climate is limited. Therefore, in this study, we model the current and future changes in areas suitable for rainfed maize production in the country using a maximum entropy (MaxENT) model. Maize is by far a major staple food crop in Kenya. We used maize occurrence location data and bioclimatic variables for two climatic scenarios-Representative Concentration Pathways (RCP) 4.5 and 8.5 from two general circulation models (HadGEM2-ES and CCSM4) for 2070. The study identified the annual mean temperature, annual precipitation and the mean temperature of the wettest quarter as the major variables that affect the distribution of maize. Simulation results indicate an average increase of unsuitable areas of between 1.9–3.9% and a decrease of moderately suitable areas of 14.6–17.5%. The change in the suitable areas is an increase of between 17–20% and in highly suitable areas of 9.6% under the climatic scenarios. The findings of this study are of utmost importance to the country as they present an opportunity for policy makers to develop appropriate adaptation and mitigation strategies required to sustain maize production under future climates.

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Section: Environmental Modelling Datamentioning

confidence: 99%

Modelling Climate Suitability for Rainfed Maize Cultivation in Kenya Using a Maximum Entropy (MaxENT) Approach

et al. 2019

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“…In this paper, we demonstrated the ability of TropWet to identify significant changes in the extent of green vegetation over the Southern African region. In this manner, TropWet is able to support on-going efforts to quantify the specific effects of variations in El Niño or La Niña events (e.g., [44]). In doing so, policy makers have a tool to examine the implications of these events and help mitigate against them.…”

Section: Discussionmentioning

confidence: 99%

“…TropWet was also applied to the Southern Africa region including Angola, Namibia, South Africa, Botswana, Zimbabwe, Zambia, Swaziland, Lesotho, Malawi, and Mozambique ( Figure 1). This region is vulnerable to the effects of global atmospheric circulation patterns such as the El Niño-Southern Oscillation (ENSO) and the Subtropical Indian Ocean Dipole (SIOD) [44,45]. Evidence shows that La Niña events coinciding with a positive SIOD can result in wetter than average conditions, whereas El Niño events coinciding with a negative SIOD phase can result in drier conditions [69].…”

Section: Southern African Countriesmentioning

confidence: 99%

“…The combination of the El Niño-Southern Oscillation and the SIOP is known to affect precipitation patterns across Southern Africa [44,45,84]. TropWet was applied to this region for two periods: (1) La Niña and positive SIOD phase (December 2018 to March 2019) producing wetter than average conditions and (2) El Niño and negative SIOD phase (December 2015 to March 2016) representing drier than average conditions.…”

Section: Southern Africamentioning

confidence: 99%

“…In this instance, the Southern African region experienced a greatly reduced extent of green vegetation between these two events (Figure 7), equivalent to 183.2 million ha in 2015-2016 and 261.6 million ha in 2018-2019. This is representative of a vast reduction in crops or grasses for domestic herbivores during El Niño events with serious consequences for food and social security [44,[85][86][87].…”

Section: Southern Africamentioning

confidence: 99%

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Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

2020

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Knowledge of the location and extent of surface water and inundated vegetation is vital for a range of applications including flood risk management, biodiversity monitoring, quantifying greenhouse gas emissions, and mapping water-borne disease risk. Here, we present a new tool, TropWet, which enables users of all abilities to map wetlands in herbaceous dominated regions based on simple unmixing of optical Landsat satellite imagery in the Google Earth Engine. The results demonstrate transferability throughout the African continent with a high degree of accuracy (mean 91% accuracy, st. dev 2.6%, n = 10,800). TropWet demonstrated considerable improvements over existing globally available surface water datasets for mapping the extent of important wetlands like the Okavango, Botswana. TropWet was able to provide frequency inundation maps as an indicator of malarial mosquito aquatic habitat extent and persistence in Barotseland, Zambia. TropWet was able to map flood extent comparable to operational flood risk mapping products in the Zambezi Region, Namibia. Finally, TropWet was able to quantify the effects of the El Niño/Southern Oscillation (ENSO) events on the extent of photosynthetic vegetation and wetland extent across Southern Africa. These examples demonstrate the potential for TropWet to provide policy makers with crucial information to help make national, regional, or continental scale decisions regarding wetland conservation, flood/disease hazard mapping, or mitigation against the impacts of ENSO.Landsat) with microwave systems (e.g., AMSR: Advanced Microwave Scanning Radiometer, SMAP: Soil Moisture Active Passive) [17,20]. Similarly, there is growing evidence that information from global navigation systems (GNSS-R: Global Navigation Satellite System Reflectometry) can provide timely and reliable classifications of wetlands by exploiting signals over both open water and vegetated water surfaces [21][22][23][24]. These approaches provide valuable tools for quantifying wetland dynamics at continental scales with important applications such as characterising greenhouse gas flux [17,18,24,25]. However, assessments of wetland extent and dynamics using these approaches tend to be generated at relatively coarse spatial resolutions (e.g., 25-36 km) and have limited applicability for informing decisions at a national or sub-national level, particularly related to more fine-scale environmental challenges such as those related to biodiversity, public health, and flood hazard.In terms of inundation mapping, perhaps the most mature area of research is the use of EO satellite imagery for mapping flood water hazards [2][3][4][5]7,[26][27][28][29][30][31][32]. Radar imagery provides one of the most reliable means of detecting flood water mainly due to the fact that this imagery is: (i) independent of cloud cover, (ii) relatively high resolution (e.g., Sentinel-1: 10 m), (iii) relatively high revisit times (e.g., Sentinel-1: 6-12 days), and (iv) there is a strong signal (low backscatter) over relatively smooth water surfaces. ...

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Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Tanzania

Gebrechorkos

Hülsmann

Bernhofer

2018

Intl Journal of Climatology

153

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East Africa is one of the most vulnerable regions of Africa to extreme weather and climate events. Regional and local information on climate extremes is critical for monitoring and managing the impacts and developing sustainable adaptation measures. However, this type of information is not readily available at the necessary spatial resolution. Therefore, here we test trends and variability of temperature (1979-2010) and precipitation (1981-2016) extremes in East Africa, particularly Ethiopia, Kenya, and Tanzania, at a spatial resolution of 0.1 and 0.05 , respectively, using the indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We use gridded data sets with high accuracy and resolution from the Terrestrial Hydrology Research Group, University of Princeton and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Trends of 19 indices are computed by fitting a linear model and using the nonparametric Mann-Kendall test and the magnitude of change is computed using the Sen's slope method. The results show an increasing trend in monthly maximum and minimum values of daily maximum and minimum temperature in large parts of the region. This is accompanied by significant increasing trends in warm nights (TN90p), warm days (TX90p), warm spell duration index (WSDI), and summer days index (SU). In addition, cold days (TX10p) and cold nights (TN10p) showed a significant decreasing trend. In general, the results show an increasing tendency in temperatures extremes, which is in line with rising global mean temperature. In addition, most of the temperature extremes observed after 2000 are warmer than the longterm mean . Precipitation indices, on the other hand, showed increasing and decreasing trends in Ethiopia, Kenya, and Tanzania, but no general pattern. The outcomes enable identifying hot spot areas and planning of adaptation and mitigation measures at much finer spatial scale than previously possible.

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The influence of El Niño–Southern Oscillation regimes on eastern African vegetation and its future implications under the RCP8.5 warming scenario

Cited by 38 publications

References 60 publications

Modelling Climate Suitability for Rainfed Maize Cultivation in Kenya Using a Maximum Entropy (MaxENT) Approach

Modelling Climate Suitability for Rainfed Maize Cultivation in Kenya Using a Maximum Entropy (MaxENT) Approach

Tropical Wetland (TropWet) Mapping Tool: The Automatic Detection of Open and Vegetated Waterbodies in Google Earth Engine for Tropical Wetlands

Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Tanzania

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