Comparison and Bias-Correction of Satellite-Derived Precipitation Datasets at Local Level in Northern Kenya

Vigna, Ingrid; Bigi, Velia; Pezzoli, Alessandro; Besana, Angelo

doi:10.3390/su12072896

Cited by 6 publications

(9 citation statements)

References 47 publications

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“…mainly found in depressions in the landscape or on soils of impeded drainage [16]. The vegetation is sustained by seasonal precipitation that occur in the long rains season (conventionally from March to May) and the short rain season (conventionally from October to December), which differ for the total amount of precipitation received on average [36][37][38]. Nevertheless, precipitations are subjected to erratic patterns in terms of high variability in length and deviation from the traditional seasons.…”

Section: Area Of Studymentioning

confidence: 99%

Multi-Temporal Evaluation of Quantitative and Phenological Vegetation Dynamics Using Sentinel-2 Images in North Horr (Kenya)

et al. 2021

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According to the Intergovernmental Panel on Climate Change, the Horn of Africa is getting drier. This research aims at assessing browning and/or greening dynamics and the suitability of Sentinel-2 satellite images to map changes in land cover in a semiarid area. Vegetation dynamics are assessed through a remote sensing approach based on densely vegetated areas in a pilot area of North Horr Sub-County, in northern Kenya, between 2016–2020. Four spectral vegetation indices are calculated from Sentinel-2 images to create annual multi-temporal images. Two different supervised classification methods—Minimum Distance and Spectral Angle Mapper—are then applied in order to identify dense vegetated areas. A general greening is found to have occurred in this period with the exception of the year 2020, with an average annual percentage increase of 19%. Results also highlight a latency between climatic conditions and vegetation growth. This approach is for the first time applied in North Horr Sub-County and supports local decision-making processes for sustainable land management strategies.

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Section: Area Of Studymentioning

confidence: 99%

Multi-Temporal Evaluation of Quantitative and Phenological Vegetation Dynamics Using Sentinel-2 Images in North Horr (Kenya)

et al. 2021

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“…Precipitation data are the result of a quantile mapping bias correction applied to the Kenya Meteorological Department precipitation gridded dataset [49]. Precipitation data cover the study area with high resolution (0.0375 × 0.0375 degrees) for the period 1983-2014 with a decadal temporal resolution.…”

Section: Climatic Datamentioning

confidence: 99%

“…The SPI index was calculated at village level and for the three existing stations present in a 250 km radius (Lodwar, Marsabit and Moyale town) using the SPI program (available online at www.drought.unl.edu/droughtmonitoring/SPI/SPIProgram.aspx). The SPI index is calculated using the BCKMD dataset, a bias-corrected satellite-derived precipitation dataset based on the KMD dataset (issued by the official national meteorological service dataset, available at http://kmddl.meteo.go.ke: 8081/SOURCES/.KMD/) corrected with the GPCC [49].…”

Section: Physical Exposure: Standardized Precipitation Index (Spi) Fomentioning

confidence: 99%

A Vulnerability Assessment in Scant Data Context: The Case of North Horr Sub-County

et al. 2020

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In Kenyan rural areas belonging to the Arid and Semi-Arid Lands (ASALs), water quantity and water quality are major issues for the local population. In North Horr Sub-County water quality is threatened by nitrate contamination due to fecal matter pollution. This research, hence, aims at assessing the vulnerability of open shallow water sources to nitrate contamination due to fecal intrusion following flooding events and nitrate percolation in groundwater. The present research, indeed, provides, on one hand, new insights into the analysis of the vulnerability in a scant data context; on the other hand, it assesses the adaptation measures contained in the local development plan. Applying the reference definition of the Intergovernmental Panel on Climate Change (IPCC), the results demonstrate that the open shallow water sources in the northern part of the sub-county are more vulnerable to nitrate contamination. Furthermore, the consistency of the results proves the suitability of the methodology selected. Understanding the vulnerability at the local scale is key to planning risk-reduction strategies as well to increasing the local population’s knowledge about flood-related risks and water quality.

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“…Specifically, these land-based meteorological stations are situated in Lodwar, Moyale and Marsabit. The climatic products were evaluated against the historical observations recorded by the Lodwar, Marsabit and Moyale land-based meteorological stations using a direct, point-to-pixel validation based on a statistical indices approach [22].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multiple Maximum and Minimum Temperature Datasets at Local Level: The Case Study of North Horr Sub-County, Kenya

Siciliano

Bigi

Vigna

et al. 2021

Climate

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Climate analyses at a local scale are an essential tool in the field of sustainable development. The evolution of reanalysis datasets and their greater reliability contribute to overcoming the scarcity of observed data in the southern areas of the world. The purpose of this study is to compute the reference monthly values and ranges of maximum and minimum temperatures for the eight main inhabited villages of North Horr Sub-County, in northern Kenya. The official ten-day dataset derived from the Kenyan Meteorological Department (KMD), the monthly datasets derived from the ERA-Interim reanalysis (ERA), the Observational-Reanalysis Hybrid (ORH) and the Climate Limited Area Mode driven by HadG-EM2-ES (HAD) are assessed on a local scale using the most common statistical indices to determine which is more reliable in representing monthly maximum and minimum temperatures. Overall, ORH datasets showed lower biases and errors in representing local temperatures. Through an innovative methodology, a new set of monthly mean temperature values and ranges derived from ORH datasets are calculated for each location in the study area, in order to guarantee to locals an historical benchmark to compare present observations. The findings of this research provide insights for environmental risk management, supporting local populations in reducing their vulnerability.

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Comparison and Bias-Correction of Satellite-Derived Precipitation Datasets at Local Level in Northern Kenya

Cited by 6 publications

References 47 publications

Multi-Temporal Evaluation of Quantitative and Phenological Vegetation Dynamics Using Sentinel-2 Images in North Horr (Kenya)

Multi-Temporal Evaluation of Quantitative and Phenological Vegetation Dynamics Using Sentinel-2 Images in North Horr (Kenya)

A Vulnerability Assessment in Scant Data Context: The Case of North Horr Sub-County

Comparison of Multiple Maximum and Minimum Temperature Datasets at Local Level: The Case Study of North Horr Sub-County, Kenya

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