A Flash Flood Early Warning System for Rural Kenya: A Pilot Study

  • Authors

    • Prof. Dr. Ir Vinesh Thiruchelvam
    • Mbau Stella Nyambura
    2018-12-03
    https://doi.org/10.14419/ijet.v7i4.38.27812
  • Early warnings, Early warning systems, Floods, Resilience, Rural Kenya
  • The cost of climate change has increased phenomenally in recent years. Therefore, understanding climate change and its impacts, that are likely to get worse and worse into the future, gives us the ability to predict scenarios and plan for them. Flash floods, which are a common result of climate change, follow increased precipitation which then increases risk and associated vulnerability due to the unpredictable rainfall patterns. Developing countries suffer grave consequences in the event that weather disasters strike because they have the least adaptive capacity. At the equator where the hot days are hotter and winds carrying rainfall move faster, Kenya’s Tana River County is noted for its vulnerability towards flash floods. Additionally, this county and others that are classified as rural areas in Kenya do not receive short term early warnings for floods. This county was therefore selected as the study area for its vulnerability. The aim of the study is therefore to propose a flash flood early warning system framework that delivers short term early warnings. Using questionnaires, information about the existing warning system will be collected and analyzed using SPSS. The results will be used to interpret the relationships between variables of the study, with a particular interest in the moderation effect in order to confirm that the existing system can be modified; that is, if the moderation effect is confirmed.  

       

     

     
  • References

    1. [1] Z. Shilenje, and B. Ogwang, International Journal of Atmospheric Sciences, (2015).

      [2] C. Unterberger, Economics of Disasters and Climate Change (2017).

      [3] P. R. Jacobi, R. F. Toledo, and E. Grandisoli, Brazilian Journal of Science and Technology (2016).

      [4] M. K. Dodo, SpringerPlus (2014)

      [5] M. Farnham and P. Kennedy, Environmental and Resource Economics, 61, 3 (2014).

      [6] J. Hoedjes, A. Kooiman, B. Maathuis, M. Said, R. Becht, A. Limo, M. Mumo, J. N. Mathege, A. Shaka, and B. Su, ISPRS International Journal of Geo-Information, 3, 2 (2014).

      [7] C. Tuckwood, and C. Mutisya, Conflict Trends, 3 (2014).

      Winter, and V. Govindarajan, Havard Business Review (2015).

      Mungai, World Economic Forum (2015).

      [8] S. W. M. Weis, V. N. Agostini, L. M. Roth, B. Gilmer, S. R. Schill, J. E. Knowles, and R. Blyther, Climatic Change, (2016).

      [9] Expert Meeting: Improving the Efficiency of Flood Forecasting Services (2013).

      [10] G. Johanson, and G. P. Brooks, Educational and Psychological Measurement (2009).

      [11] U. Sekaran, and R. Bougie, Research Methods for Business. John Wiley and Sons, Inc., West Sussex, United Kingdom, (2016).

      [12] C. Kothari, and G. Garg, Research Methodology. New Age International Publishers, Jaipur, India (2014).

      [13] K. S. Taber, Research in Science Education (2017).

      [14] G. PolanÄiÄ, G. JoÅ¡t, and M. HeriÄko, Empirical Software Engineering, 20, 1 (2013).

      [15] M. Zareen, K. Razzaq, and B. G. Mujtaba, Public Organization Review, 15, 4 (2015).

      [16] G. Arslan, Child Indicators Research (2017).

      [17] Q. Wang, N. A. Bowling, Q. Tian, G. M. Alarcon, and H. K. Kwan, Journal of Business Ethics (2016).

  • Downloads

  • How to Cite

    Ir Vinesh Thiruchelvam, P. D., & Stella Nyambura, M. (2018). A Flash Flood Early Warning System for Rural Kenya: A Pilot Study. International Journal of Engineering & Technology, 7(4.38), 1310-1313. https://doi.org/10.14419/ijet.v7i4.38.27812