Assessing Spatial and Temporal Precipitation Dynamics in Upper East Region of Ghana Using Chirps Data from 1981 to 2016

Thomas Moore Okrah (), J. A Quaye-Ballard (), S. A Andam-Akorful () and Ibrahim Abdul Sulemana ()

International Journal of Geography and Geology, 2019, vol. 8, issue 4, 110-127

Abstract: Precipitation variability evaluation assumes a vital part in water asset administration and rainfed agribusiness. In Ghana, rain gauge stations are poorly distributed and also, obtaining long time span of data is difficult due to data inconsistency, as a result remotely sensed precipitation products are largely used to complement ground gauge stations data for assessing climatic variability and water resource managements. In this study, Climate Hazards Group Infrared Precipitation with Station (CHIRPS) precipitation data from 1981-2016 and also utilizing Cumulative Residual Analysis (CRA), Mann-Kendall, Sen's Slope, Wavelet Transform (WT) and Principal Component Analysis (PCA). Results show that 1981, 1999, 2002 and 2013 were four noteworthy years with changes in precipitation. The Mann-Kendal and Sen's Slope demonstrate that 55.71% of UER indicated diminishing month to month precipitation with 27.34% critical patterns. Precipitation in April, May, and June demonstrated a diminishing pattern while July, August, and September showed an increasing trend during the 35 years’ study period. Further investigation using Continuous Wavelet Transform indicated an annual variation significant. PCA uncovered that the spatial variability of precipitation in UER is extremely assorted, however greater part (33.76%) of the fluctuation is situated in the north-eastern part. Thus, the results from remotely sensed precipitation products an effective, efficient and cheap way of assessing top to the bottom comprehension of precipitation variation at regional level.

Keywords: Cumulative residual analysis (CRA); Mann-Kendall and Sen’s Slope tests; Wavelet transform (WT); Principal component analysis (PCA); Remote sensing; Ghana; CHIRPS data (search for similar items in EconPapers)
Date: 2019
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