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A canary, a coal mine, and imperfect data: determining the efficacy of open-source climate change models in detecting and predicting extreme weather events in Northern and Western Kenya

Author

Listed:
  • Alvin M. Igobwa

    (@iLabAfrica, Strathmore University, Student Center)

  • Jeremy Gachanja

    (@iLabAfrica, Strathmore University, Student Center)

  • Betsy Muriithi

    (@iLabAfrica, Strathmore University, Student Center)

  • John Olukuru

    (@iLabAfrica, Strathmore University, Student Center)

  • Angeline Wairegi

    (Strathmore University)

  • Isaac Rutenberg

    (Strathmore University)

Abstract

Climate models, by accurately forecasting future weather events, can be a critical tool in developing countermeasures to reduce crop loss and decrease adverse effects on animal husbandry and fishing. In this paper, we investigate the efficacy of various regional versions of the climate models, RCMs, and the commonly available weather datasets in Kenya in predicting extreme weather patterns in northern and western Kenya. We identified two models that may be used to predict flood risks and potential drought events in these regions. The combination of artificial neural networks (ANNs) and weather station data was the most effective in predicting future drought occurrences in Turkana and Wajir with accuracies ranging from 78 to 90%. In the case of flood forecasting, isolation forests models using weather station data had the best overall performance. The above models and datasets may form the basis of an early warning system for use in Kenya’s agricultural sector.

Suggested Citation

  • Alvin M. Igobwa & Jeremy Gachanja & Betsy Muriithi & John Olukuru & Angeline Wairegi & Isaac Rutenberg, 2022. "A canary, a coal mine, and imperfect data: determining the efficacy of open-source climate change models in detecting and predicting extreme weather events in Northern and Western Kenya," Climatic Change, Springer, vol. 174(3), pages 1-24, October.
    • Handle: RePEc:spr:climat:v:174:y:2022:i:3:d:10.1007_s10584-022-03444-6
    DOI: 10.1007/s10584-022-03444-6
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    References listed on IDEAS

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    1. Dercon, Stefan & Hill, Ruth Vargas & Clarke, Daniel & Outes-Leon, Ingo & Seyoum Taffesse, Alemayehu, 2014. "Offering rainfall insurance to informal insurance groups: Evidence from a field experiment in Ethiopia," Journal of Development Economics, Elsevier, vol. 106(C), pages 132-143.
    2. Ringler, Claudia & Zhu, Tingju & Cai, Ximing & Koo, Jawoo & Wang, Dingbao, 2010. "Climate change impacts on food security in Sub-Saharan Africa: Insights from comprehensive climate change scenarios," IFPRI discussion papers 1042, International Food Policy Research Institute (IFPRI).
    3. Enrico Biffis & Erik Chavez, 2017. "Satellite Data and Machine Learning for Weather Risk Management and Food Security," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1508-1521, August.
    4. Anghileri, Daniela & Bozzini, Veronica & Molnar, Peter & Jamali, Andrew A.J. & Sheffield, Justin, 2022. "Comparison of hydrological and vegetation remote sensing datasets as proxies for rainfed maize yield in Malawi," Agricultural Water Management, Elsevier, vol. 262(C).
    5. Adeline Bichet & Arona Diedhiou & Benoit Hingray & Guillaume Evin & N’Datchoh Evelyne Touré & Klutse Nana Ama Browne & Kouakou Kouadio, 2020. "Assessing uncertainties in the regional projections of precipitation in CORDEX-AFRICA," Climatic Change, Springer, vol. 162(2), pages 583-601, September.
    6. Million Tadesse & Bekele Shiferaw & Olaf Erenstein, 2015. "Weather index insurance for managing drought risk in smallholder agriculture: lessons and policy implications for sub-Saharan Africa," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 3(1), pages 1-21, December.
    7. Kenneth W. Sibiko & Matin Qaim, 2020. "Weather index insurance, agricultural input use, and crop productivity in Kenya," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 12(1), pages 151-167, February.
    8. Sommarat Chantarat & Andrew G. Mude & Christopher B. Barrett & Michael R. Carter, 2013. "Designing Index-Based Livestock Insurance for Managing Asset Risk in Northern Kenya," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(1), pages 205-237, March.
    9. Antoine Leblois & Philippe Quirion, 2013. "Agricultural insurances based on meteorological indices: realizations, methods and research challenges," Post-Print hal-00656778, HAL.
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