Impact of Short-Term Flooding on Livelihoods in the Kenya Rift Valley Lakes

Obando, Joy Apiyo; Onywere, S. M.; Shisanya, C.A.; Ndubi, Antony Oduya; Masiga, Daniel K.; Irura, Zephania; Mariita, Nicholas; Maragia, Haron

doi:10.1007/978-4-431-56000-5_12

Cited by 21 publications

(44 citation statements)

References 14 publications

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“…Observed differences in the Water class are mainly caused by the slowly retreating water level, a consequence of the sudden increase of the water level of Lake Baringo in 2010 [107].…”

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confidence: 99%

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Rima

Einzmann

et al. 2017

Remote Sensing

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Abstract:Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis-competing for nutrients and water-has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pléiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the "Woody Weeds" project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pléiades data (OOB accuracy 0.83 and independent reference accuracy 0.87-0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80-0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pléiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures.

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“…Observed differences in the Water class are mainly caused by the slowly retreating water level, a consequence of the sudden increase of the water level of Lake Baringo in 2010 [107].…”

mentioning

confidence: 99%

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Rima

Einzmann

et al. 2017

Remote Sensing

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“…This proxy‐based inference is consistent with sporadic observations going back to the early 20th Century (Tiercelin et al ., ; Fig. C), which place the highest known historical levels in the early 1900s and during the 2011 to 2013 transgression (Obando et al ., ) and never saw the north‐central sill fall dry.…”

Section: Discussionmentioning

confidence: 99%

“…). During the then prevailing highstand conditions following a major transgression from 2011 to 2013 (Obando et al ., ), the surface level of Lake Bogoria was situated at ca 996 m above sea level (a.s.l. ), only ca 3 m below the lake's assumed overflow level set by the elevation of the water divide near Loboi village immediately to the north‐west (Young & Renaut, ).…”

Section: Study Sitementioning

confidence: 99%

Multi‐basin depositional framework for moisture‐balance reconstruction during the last 1300 years at Lake Bogoria, central Kenya Rift Valley

et al. 2018

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Multi-proxy analysis of sediment cores from five key locations in hypersaline, alkaline Lake Bogoria (central Kenya Rift Valley) has allowed reconstruction of its history of depositional and hydrological change during the past 1300 years. Analyses including organic matter and carbonate content, granulometry, mineralogical composition, charcoal counting and high-resolution scanning of magnetic susceptibility and elemental geochemistry resulted in a detailed sedimentological and compositional characterization of lacustrine deposits in the three lake basins and on the two sills separating them. These palaeolimnological data were supplemented with information on present-day sedimentation conditions based on seasonal sampling of settling particles and on measurement of physicochemical profiles through the water column. A new age model based on 210 Pb, 137 Cs and 14 C dating captures the sediment chronology of this hydrochemically complex and geothermally fed lake. An extensive set of chronological tie points between the equivalent high-resolution proxy time series of the five sediment sequences allowed transfer of radiometric dates between the basins, enabling interbasin comparison of sedimentation dynamics through time. The resulting reconstruction demonstrates considerable moisture-balance variability through time, reflecting regional hydroclimate dynamics over the past 1300 years. Between ca 690 and 950 AD, the central and southern basins of Lake Bogoria were reduced to shallow and separated brine pools. In the former, occasional near-complete desiccation triggered massive trona precipitation. Between ca 950 and 1100 AD, slightly higher water levels allowed the build-up of high pCO 2 leading to precipitation of nahcolite still under strongly evaporative conditions. Lake Bogoria experienced a pronounced highstand between ca 1100 and 1350 AD, only to recede again afterwards. For a substantial part of the time between ca 1350 and 1800 AD, the northern basin was probably disconnected from the united central and southern basins. Throughout the last 1667 two centuries, lake level has been relatively high compared to the rest of the past millennium. Evidence for increased terrestrial sediment supply in recent decades, due to anthropogenic soil erosion in the wider Bogoria catchment, is a reason for concern about possible adverse impacts on the unique ecosystem of Lake Bogoria.

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“…The basin experiences a bimodal rainfall pattern with a major peak in April-May and a minor peak in October-November coupled with recent climatic changes that lead to heavy rains and prolonged drought patterns [41,42]. Lake Naivasha is a closed lake system vulnerable to changes in the catchment and climatic changes; since 2012, the lake flooded parts of the terrestrial land around it increasing in size from 107.6 km 2 to 168.87 km 2 between 2010 and 2013 [43,44]. The lake is a Ramsar site, a wetland of international importance since 1995 [45] and a biodiversity hotspot with several species of birds and animals that depend on it.…”

Section: Study Areamentioning

confidence: 99%

Heavy metals in surface sediments of Lake Naivasha, Kenya: spatial distribution, source identification and ecological risk assessment

et al. 2020

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Heavy metals retained in sediments pose a threat to aquatic ecosystem and society. Knowledge of the status and sustainability of lake sediment in areas undergoing rapid economic development is scarce, and yet it is required in order to achieve the United Nations Sustainable Development goals. We investigated heavy metal concentration in Lake Naivasha sediments using enrichment factor, geoaccumulation index, pollution load index (PLI), risk index (RI) and multivariate statistics to determine metal accumulation, distribution and the lake's pollution status. Sediments sampling followed a grid approach, and seven heavy metals: Co, Cr, Cu, Mn, Pb, Zn and Fe, were analyzed in total; concentrations were determined using portable X-ray fluorescence. The distribution maps were interpolated in a GIS platform, and the toxicity risk of sediments was determined. The concentrations of elements in the sediments are in the following decreasing order: Fe > Mn > Zn > Cu > Cr > Co > Pb. High concentration of heavy metals is found in the southern shoreline and east of the lake due to shoreline discharge and agricultural effluents drained into the lake and shoreline transgression. Pb and Zn are of anthropogenic origin, while Cu and Mn originate from both natural and anthropogenic sources and Fe and Co are from natural sources. In addition, the mean toxicity decreases in the order: Cu > Fe > Co > Pb > Zn > Mn > Cr. The PLI values were above one (> 1), indicating progressive deterioration of the sediment quality. Though the overall ecological risk of the lake is low to moderate (RI 36.73-60.51), hotspots of pollution should be marked for intervention. This assessment also helps to fill the knowledge gap required to formulate the right policies in implementing sustainable development goals in similar lakes.

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Impact of Short-Term Flooding on Livelihoods in the Kenya Rift Valley Lakes

Cited by 21 publications

References 14 publications

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Multi‐basin depositional framework for moisture‐balance reconstruction during the last 1300 years at Lake Bogoria, central Kenya Rift Valley

Heavy metals in surface sediments of Lake Naivasha, Kenya: spatial distribution, source identification and ecological risk assessment

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