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. 2020 Nov 1;6(10):e05375.
doi: 10.1016/j.heliyon.2020.e05375. eCollection 2020 Oct.

Suitability of different data sources in rainfall pattern characterization in the tropical central highlands of Kenya

Oduor O Nathan  1 , Ngetich K Felix  1 , Kiboi N Milka  1 , Muriuki Anne  2 , Adamtey Noah  3 , Mugendi N Daniel  4
Affiliations

Suitability of different data sources in rainfall pattern characterization in the tropical central highlands of Kenya

Oduor O Nathan et al. Heliyon. .

Abstract

Uncertainty in rainfall pattern has put rain-fed agriculture in jeopardy, even for the regions considered high rainfall potential like the Central Highlands of Kenya (CHK). The rainfall pattern in the CHK is spatially and temporally variable in terms of onset and cessation dates, frequency and occurrence of dry spells, and seasonal distribution. Appraisal of the variability is further confounded by the lack of sufficient observational data that can enable accurate characterisation of the rainfall pattern in the region. We, therefore, explored the utilisation of satellite daily rainfall estimates from the National Aeronautics and Space Administration (NASA) for rainfall pattern characterisation in the CHK. Observed daily rainfall data sourced from Kenya meteorological department were used as a reference point. The observation period was from 1997 to 2015. Rainfall in the CHK was highly variable, fairly distributed and with low intensity in all the seasons. Onset dates ranged between mid-February to mid-March and mid-August to mid-October for long rains (LR) and short rains (SR) seasons, respectively. Cessation dates ranged from late May to mid-June and mid-December to late December for the LR and SR, respectively. There was a high probability (93%) of dry spell occurrence. More research needs to be done on efficient use of the available soil moisture and on drought tolerant crop varieties to reduce the impact of drought on crop productivity. Comparison between satellite and observed rain gauge data showed close agreement at monthly scale than at daily scale, with general agreement between the two datasets. Hence, we concluded that, given the availability, accessibility, frequency of estimation and spatial resolution, satellite estimates can complement observed rain gauge data. Stakeholders in the fields of agriculture, natural resource management, environment among others, can utilise the findings of this study in planning to reduce rainfall-related risks and enhance food security.

Keywords: Agricultural sciences; Hydrology; Rainfall cessation; Rainfall characterization; Rainfall onset; Rainfall pattern; Satellite estimates.

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Figures

Figure 1
Figure 1
Map showing the counties under study (Source of the base map: Esri, HERE, Garmin FAO OpenStreet contributors).
Figure 2
Figure 2
Map showing onset dates for long rains and short rains.
Figure 3
Figure 3
Map showing cessation dates for long rains and short rains.
Figure 4
Figure 4
Times series of Cumulative departure index (CDI) for annuals, long rains and short rains in (a) Embu, (b) Kiambu, (c) Murang'a, (d) Meru, (e) Kirinyaga, (f) Nyeri and (g) Tharaka-Nithi counties.
Figure 5
Figure 5
Time series of rainfall anomaly index (RAI) for long rains and short rains in (a) Embu, (b) Kiambu, (c) Murang'a, (d) Meru, (e) Kirinyaga, (f) Nyeri and (g) Tharaka-Nithi.
Figure 6
Figure 6
Temporal distribution of average daily rainfall as a percentage of the total rainfall received over the long rains (a) and short rains (b) seasons across the counties.
Figure 7
Figure 7
Times series of Cumulative departure index for observed and satellite estimates in Embu (a), Meru (b), and Tharaka-Nithi (c) counties across the years.
Figure 8
Figure 8
Scatter plots comparing satellite estimates and rain gauge based data sets at daily (a), (c) and (e) for Embu, Meru and Tharaka-Nithi respectively and monthly (b), (d) and (f) for Embu, Meru and Tharaka-Nithi respectively from 1999 to 2008.
See this image and copyright information in PMC

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