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

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  • Ingrid Vigna

    (Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino & Università di Torino, 10125 Torino, Italy
    Both authors contributed equally to this work.)

  • Velia Bigi

    (Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino & Università di Torino, 10125 Torino, Italy
    Both authors contributed equally to this work.)

  • Alessandro Pezzoli

    (Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino & Università di Torino, 10125 Torino, Italy)

  • Angelo Besana

    (Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino & Università di Torino, 10125 Torino, Italy)

Abstract

Understanding ongoing trends at local level is fundamental in research on climate change. However, in the Global South it is hampered by a lack of data. The scarcity of land-based observed data can be overcome through satellite-derived datasets, although performance varies according to the region. The purpose of this study is to compute the normal monthly values of precipitation for the eight main inhabited areas of North Horr Sub-County, in northern Kenya. The official decadal precipitation dataset from the Kenyan Meteorological Department (KMD), the Global Precipitation Climatology Centre (GPCC) monthly dataset and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) monthly dataset are compared with the historical observed data by means of the most common statistical indices. The GPCC showed the best fit for the study area. The Quantile Mapping correction is applied to combine the high resolution of the KMD dataset with the high performance of the GPCC set. A new and more reliable bias-corrected monthly precipitation time series for 1983–2014 results for each location. This dataset allows a detailed description of the precipitation distribution through the year, which can be applied in the climate change adaptation and tailored territorial planning.

Suggested Citation

  • Ingrid Vigna & Velia Bigi & Alessandro Pezzoli & Angelo Besana, 2020. "Comparison and Bias-Correction of Satellite-Derived Precipitation Datasets at Local Level in Northern Kenya," Sustainability, MDPI, vol. 12(7), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2896-:d:341745
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    References listed on IDEAS

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    1. Ochieng, Justus & Kirimi, Lilian & Mathenge, Mary, 2016. "Effects of Climate Variability and Change on Agricultural Production: The Case of Small-Scale Farmers in Kenya," Working Papers 229711, Egerton University, Tegemeo Institute of Agricultural Policy and Development.
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    Cited by:

    1. Velia Bigi & Ingrid Vigna & Alessandro Pezzoli & Elena Comino, 2021. "Multi-Temporal Evaluation of Quantitative and Phenological Vegetation Dynamics Using Sentinel-2 Images in North Horr (Kenya)," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    2. Velia Bigi & Alessandro Pezzoli & Elena Comino & Maurizio Rosso, 2020. "A Vulnerability Assessment in Scant Data Context: The Case of North Horr Sub-County," Sustainability, MDPI, vol. 12(15), pages 1-23, July.

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