Authors :
Ahmed Abshir Ahmed
Volume/Issue :
Volume 11 - 2026, Issue 10 - October
Google Scholar :
https://tinyurl.com/ysjw3edh
DOI :
https://doi.org/10.38124/ijisrt/26Oct957
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working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and
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Abstract :
Rainfall variability strongly influences water availability, runoff generation, drought risk, and urban stormwater conditions in arid and semi-arid environments. Hargeisa, Somaliland, experiences pronounced seasonality and substantial year-to-year rainfall variability, but an integrated assessment across temporal scales, anomalies, spatial patterns, trends, and daily extremes remains limited. This study evaluates the recent rainfall regime of Hargeisa using daily Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS) and Google Earth Engine (GEE) for 2010–2025. Rainfall was processed within the study boundary and aggregated to monthly, seasonal, and annual scales.
Keywords :
CHIRPS; Google Earth Engine; Hargeisa; Somaliland; Rainfall Variability; Rainfall Anomalies; Mann–Kendall; Sen’s Slope; Rainfall Extremes.
References :
- Funk, C., Peterson, P., Landsfeld, M., et al. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2, 150066. https://doi.org/10.1038/sdata.2015.66
- Climate Hazards Center. CHIRPS rainfall dataset. University of California, Santa Barbara.
- Gorelick, N., Hancher, M., Dixon, M., et al. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
- Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1509.
- Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245–259. https://doi.org/10.2307/1907187
- Kendall, M. G. (1975). Rank Correlation Methods (4th ed.). Charles Griffin, London.
- Hirsch, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. Water Resources Research, 18(1), 107–121.
- Hamed, K. H., & Rao, A. R. (1998). A modified Mann–Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1–4), 182–196.
- Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes, 16(9), 1807–1829.
- Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379–1389. https://doi.org/10.1080/01621459.1968.10480934
- Helsel, D. R., Hirsch, R. M., Ryberg, K. R., Archfield, S. A., & Gilroy, E. J. (2020). Statistical Methods in Water Resources. U.S. Geological Survey.
- Hirsch, R. M., Alexander, R. B., & Smith, R. A. (1991). Selection of methods for the detection and estimation of trends in water quality. Water Resources Research, 27(5), 803–813.
- Yue, S., & Wang, C. Y. (2004). The Mann–Kendall test, modified by the effective sample size, to detect trends in serially correlated hydrological series. Water Resources Management, 18, 201–218.
- Burn, D. H., & Hag Elnur, M. A. (2002). Detection of hydrologic trends and variability. Journal of Hydrology, 255(1–4), 107–122.
- Nicholson, S. E. (2017). Climate and rainfall variability over eastern Africa. Reviews of Geophysics, 55(3), 590–635.
- Nicholson, S. E. (2014). A detailed look at the recent drought situation in the Greater Horn of Africa. Journal of Arid Environments, 103, 1–14.
- Lyon, B., & DeWitt, D. G. (2012). A recent and abrupt decline in the East African long rains. Geophysical Research Letters, 39, L02702.
- Funk, C., Nicholson, S. E., Landsfeld, M., et al. (2018). The centennial trends Greater Horn of Africa precipitation dataset. Scientific Data, 5, 180068.
- Alexander, L. V., Zhang, X., Peterson, T. C., et al. (2006). Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research: Atmospheres, 111, D05109.
- Donat, M. G., Alexander, L. V., Yang, H., et al. (2013). Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. Journal of Geophysical Research: Atmospheres, 118(5), 2098–2118.
- Zhang, X., Alexander, L., Hegerl, G. C., et al. (2011). Indices for monitoring changes in extremes based on daily temperature and precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2(6), 851–870.
Rainfall variability strongly influences water availability, runoff generation, drought risk, and urban stormwater conditions in arid and semi-arid environments. Hargeisa, Somaliland, experiences pronounced seasonality and substantial year-to-year rainfall variability, but an integrated assessment across temporal scales, anomalies, spatial patterns, trends, and daily extremes remains limited. This study evaluates the recent rainfall regime of Hargeisa using daily Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS) and Google Earth Engine (GEE) for 2010–2025. Rainfall was processed within the study boundary and aggregated to monthly, seasonal, and annual scales.
Keywords :
CHIRPS; Google Earth Engine; Hargeisa; Somaliland; Rainfall Variability; Rainfall Anomalies; Mann–Kendall; Sen’s Slope; Rainfall Extremes.