Analysis of rainfall extremes in the Ngong River Basin of Kenya: Towards integrated urban flood risk management

Juma, Benard; Olang, Luke O.; Hassan, Mohammed; Chasia, Stanley; Bukachi, Vera; Shiundu, Paul M.; Mulligan, Joe

doi:10.1016/j.pce.2020.102929

Cited by 19 publications

(10 citation statements)

References 41 publications

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“…The destructions witnessed over recent years are threatening the economy and ecosystem (IPCC, 2014). For instance, recurrent droughts (Nicholson, 2014; Haile et al ., 2020; Ayugi et al ., 2020a, 2020b) and floods (Kilavi et al ., 2018; Juma et al ., 2020) remain the signature features affecting millions of people and have a negative impact on the gross domestic product in the region that is mainly dependent on the agrarian economy (World Bank, 2012; FAO, 2019). Studies characterizing observed events have been based on various reanalysis or satellite‐derived data sets with clear patterns observed and hotspots identified (Liebmann et al ., 2014; Lyon, 2014; Gebremeskel et al ., 2019).…”

Section: Introductionmentioning

confidence: 99%

Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa

Ayugi

Jiang

Zhu

et al. 2021

Intl Journal of Climatology

145

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This study examines the improvement in Coupled Model Intercomparison Project Phase Six (CMIP6) models against the predecessor CMIP5 in simulating mean and extreme precipitation over the East Africa region. The study compares the climatology of the precipitation indices simulated by the CMIP models with the CHIRPS data set using robust statistical techniques for 1981-2005. The results display the varying performance of the general circulation models (GCMs) in the simulation of annual and seasonal precipitation climatology over the study domain. CMIP6 multi-model ensemble mean (hereafter MME) shows improved performance in the local annual mean cycle simulation with a better representation of the rainfall within the two peaks, especially the MAM rainfall relative to their predecessor. Moreover, simulation of extreme indices is well captured in CMIP6 models relative to CMIP5. The CMIP6-MME performed better than the CMIP5-MME with lesser biases in simulating Simple Daily Intensity Index (SDII), consecutive dry days (CDD), and very heavy precipitation days >20 mm (R20mm) over East Africa. Remarkably, most CMIP6 models are unable to simulate extremely wet days (R95p). Some CMIP6 models (e.g., NorESM2-MM and CNRM-CM6-1) depict robust performance in reproducing the observed indices across all analyses. OND season shows wet biases for some indices (i.e., R95p, PRCPTOT), except for SDII, CDD, and R20mm in CMIP6 models. Consistent with other studies, the mean ensemble performance for both CMIP5/6 shows better performance as compared with individual models due to the cancellation of some systematic errors in the

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Section: Introductionmentioning

confidence: 99%

Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa

Ayugi

Jiang

Zhu

et al. 2021

Intl Journal of Climatology

145

View full text Add to dashboard Cite

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“…The damages witnessed over recent years is threatening the economy and ecosystem (IPCC, 2014). For instance, recurrent droughts (Nicholson, 2014;Haile et al, 2019;Tan et al, 2020) and floods (Kilavi et al, 2018;Juma et al, 2020) remain the signature features affecting millions of people and has a negative impact on the gross domestic product in the region that is mainly dependent on the agrarian economy (World Bank, 2012;FAO, 2019). Characterizing observed events has been based on various reanalysis or gauge-based estimates that have pointed clear historical patterns and vulnerable regions identified (Liebman et al, 2014;Lyon, 2014;Gebremeskel et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Evaluation and projection of mean surface temperature using CMIP6 models over East Africa

Ayugi

Ngoma

Babaousmail

et al. 2021

Journal of African Earth Sciences

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This study evaluates the historical mean surface temperature (hereafter T2m) and examines how T2m changes over East Africa (EA) in the 21 st century using CMIP6 models. An evaluation was conducted based on mean state, trends, and statistical metrics (Bias, Correlation Coefficient, Root Mean Square Difference, and Taylor skill score). For future projections over EA, five best performing CMIP6 models (based on their performance ranking in historical mean temperature

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“…East Africa consists of Burundi, Kenya, Rwanda, Tanzania, and Uganda. The population of East Africa depends on rain‐fed agriculture (Camberlin and Philippon, 2002; Verdin et al ., 2005); therefore, floods and droughts caused by precipitation anomalies have great impacts on the livelihood of people and the gross domestic product of the countries (IPCC, 2012; World Bank, 2012; Nicholson, 2014; Kilavi et al ., 2018; Gebremeskel et al ., 2019; Juma et al ., 2020). Precipitation over East Africa exhibits both spatial and temporal variability due to complex topography, local, and global influences (Omondi et al ., 2014; Yang et al ., 2015a; Didier et al ., 2016).…”

Section: Introductionmentioning

confidence: 99%

Coupled Model Intercomparison Project phase 6 evaluation and projection of East African precipitation

Makula

Zhou

2021

Intl Journal of Climatology

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This study evaluates the performance of 43 Coupled Model Intercomparison Project phase 6 (CMIP6) models in reproducing annual cycle, climatology, and trend of observed precipitation over East Africa during 1950–2014. The results show that the CMIP6 models display good performance in simulating the annual cycle (i.e., bimodal pattern) of East African precipitation; however, most models and the multimodel ensemble mean (ENSEM) underestimate the March to May (MAM) precipitation while overestimating the October to December (OND) precipitation. Spatially, most models seem to perform relatively better in simulating OND precipitation compared to MAM season, despite wet (dry) biases over most parts of East Africa with the wettest (driest) biases over the Lake Victoria basin (southeastern Tanzania) during OND (MAM). The metrics have shown that nine models (i.e., MPI‐ESM1‐2‐HR, AWI‐CM‐1‐MR, MPI‐ESM1‐2‐LR, IPSL‐CM6A‐LR, CAS‐ESM2‐0, BCC‐CSM2‐MR, NorESM2‐LM, CESM2‐WACCM, and CMCC‐CM2‐SR5) outperform the other individual models during both OND and MAM. Compared with individual models, the ENSEM better agrees with the observation. Besides, the ENSEM and most models can reproduce the increasing trend in OND precipitation as observed over East Africa. However, for the secular change of MAM precipitation, given that half of the models show increasing trends, the ENSEM simulation produces an inconsistent trend with the observation. The ensemble of the above nine models with better performance projects an increase of precipitation averaged over East Africa in both MAM and OND by the end of the 21st century, with larger changes under SSP5‐8.5 than under SSP2‐4.5. Spatially, the largest percentage increase in MAM precipitation is projected to occur in central Tanzania and northwestern Kenya, while southeastern Tanzania shows the greatest percentage decrease. The projected percentage increases in OND precipitation are larger in the northeastern highlands of Tanzania and northwest Kenya.

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Analysis of rainfall extremes in the Ngong River Basin of Kenya: Towards integrated urban flood risk management

Cited by 19 publications

References 41 publications

Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa

Comparison of CMIP6 and CMIP5 models in simulating mean and extreme precipitation over East Africa

Evaluation and projection of mean surface temperature using CMIP6 models over East Africa

Coupled Model Intercomparison Project phase 6 evaluation and projection of East African precipitation

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