Spatial-Temporal Rainfall Variability and Its Relationship With Sea Surface Temperature Over Western Oromia

Table of Contents

1. Introduction

2. Materials and Methods
2.1 Descriptions of the study area
2.2 Methods of data analysis

3. Results and Discussions
3.1 The spatial and temporal distribution and pattern of rainfall climatology
3.2 Variability of Onset, cessation and Length of the Growing Period (LGP)
3.3 Number of rainy days
3.4 Trend Analysis of Annual and Seasonal Rainfall
3.5 Correlation of Rainfall to SST

4. Conclusions and Recommendations

5. References

ABSTRACT

All rural livelihood systems in Ethiopia are highly sensitive to climate. Rainfall is the primary climatic factor that directly affects Agriculture, hydrological dynamics and driver of food insecurity. It varies across space and time. The study aims to analyses spatial-temporal variability of rainfall and its association with SSTs by using statistical method over western Oromia, southwest Ethiopia. Annual and Kiremt rainfall distribution increases toward the central portion of the area from the east and west. Inter-annual and Kiremt rainfall variability varies from low to moderate (CV%=11 to 24%) . The MK-trend test show an increasing change in annual and seasons. Mean onset date is more variable than cessation and late one to two week from previous studies. This may be a signal of climate change impact on the region. Earliest onset in Jimma zone, mean date in the last decade of March and the latest onset in the first decade of May in west Wollega. Belg and kiremt precipitation to SSTs showed statically relationship over different parts of Oceans. Central and tropical eastern Pacific is negatively correlated to kiremt (summer) rains over whole western Oromia zones and Spring (Belg) rainfall anomalies to SSTs in the region show a no significant spacial correlation relationship over most parts of oceans. The study revealed the climatology of rainfall and local variability which is necessary information for users to respond with local relevance in agriculture operation and hydrological management.

Key words: SSTs, climate change, season, Onset, cessation and length of growing period.

1. Introduction

Climate variability/change is the most complex phenomena and one of the major challenging environmental problems facing in the world (Houghton, 2002; Agatsiva, 2010).For instance temperatures raise, rainfall variability and sea level rise, intensification the natural hazards, such as storms, floods, droughts and landslides (IPCC, 2014). Rainfall variability is the primary climatic factor that directly affects agricultural activities, hydrological dynamics and driver of food insecurity. All rural livelihood systems in Ethiopia are highly sensitive to climate. Ethiopia’s agricultural sector makes up the largest share of the country’s economy (close to 50% of GDP), 80% exports and occupies 80% of the labor force and it is almost entirely rainfed (NMA, 2015). Climate variability plays a great role in agricultural production having a direct impact upon operational practices from the start of land preparation to the final harvest (Adefris, 2013). It also influences crop and livestock production, hydrologic balances, input supplies and other components of agricultural systems (IPCC, 2007; Tesso, 2012). Therefore, local specific knowledge of rainfall climatology, distribution pattern and variability in various rainfall properties, including onset date, duration, end of the season and rainfall amount is very important for crop planning and managing the extreme events like floods, droughts and severe storms (Birhanu, 2014). According to Taddesse (2000), rainfall analysis is important hydrologic tool, which bridges the gap between the design need and the availability of design information especially in planning and design of water resource projects.

IPCC estimated the change in precipitation in a warming world. In many tropical wet regions, mean precipitation will likely to increase (IPCC, 2007). In fact, there is signal for increasing/ decreasing trend on rainfall, while significantly increasing of temperature. Recent detection of increasing trends in extreme precipitation and discharge in some catchments implies greater risks of flooding at regional scale (IPCC, 2014). And also due to high rainfall variability, early/late onset and cessation of rainfall is becoming common now. Therefore, evaluating the seasonal variability of rainfall and frequency of occurrence of drought is now very important to be prepared and cope up with (Gebremichael, 2014).

According to the NMA, both seasonal and annual rainfall has exhibited high variability throughout country. In spite of different studies were done in rainfall variability at national level (Abebe, 2006; NMA, 1996, Seleshi, 2004; Wagesho, 2013), regional based detail spatial and temporal variability and SST associations over Western Oromia are still limited. In addition,an onset and cessation of seasonal rainfall vary in Ethiopia, considerably within few kilometers distance due to altitudinal variations, orientation of mountain chains and their physical influence on atmospheric flow (Legese, 2018).Information about the start of the rains is important for appropriate agricultural decision making so that reduction in yield of the crops is avoided by taking appropriate action. Haile, 1988 and Abebe, 2006 did a comprehensive work on the onset and cessation of the kiremt and Belg season respectively for Ethiopia by making use of the statistical packages. Therefore, it is essential to assess the temporal and spatial distribution pattern, trends and variability of rainfall and its correlation with SSTs within the period under study by using statistical method in the area.

2. Materials and Methods

2.1 Descriptions of the study area

Western Oromia is found in the Southwestern part of Ethiopia. It is located between 7.2 o to 10.5o North latitudes and 38.2o to 34.2o East longitudes (Figure1). The study area is characterized by a tropical high land climate with heavy rain fall, warm temperature and long wet period. It is the parts of the country experience a unimodal rainfall pattern and annual mean rainfall exceeds 1500mm.

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Fig.1 Shows map of study area Western Oromia over southwest Ethiopia. 2.2 Data

The gauge data of 13 stations and rainfall Chirps data were obtained by from NMA of Ethiopia (Table 1). The Hadley Centre SST dataset used to obtain SST (Rayner,et al., 2003) from: https://www.metoffice.gov.uk/hadobs/hadisst/.

2.2 Methods of data analysis

The inverse distance weighting (IDW) method estimates the values of an attribute at unsampled points using a linear combination of values at sampled points weighted by an inverse function of the distance from the point of interest by using ArcGIS 10.3.1. It expressed as:

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Where di = distance b/n x0 & xi, p = power parameter, and n = number of sampled points.

Coefficient of Variability (CV) is calculated to evaluate the variability of rainfall, by dividing the standard deviation to mean (µ).

Where: standard deviation ((1/n-1) ∑ (Xi-µ) 2), and Xi: rainfall of the year i. CV<20% indicates low, CV b/n 20% and 30% moderate, CV>30% high, CV>40% very high and CV>70% extremely high variability of rainfall.

Trend Analysis

Non-parametric methods (Mann-Kendall test and Sen's slope estimator) were used to detect trends using XLSTAT software. The statistics is given as:

, where, S is the Mann-Kendall’s test; xi and xj are the sequential rainfall values in the year i and j (j>i), and n is the length of the time series. The Sgn (xj −xi) is an indicator function, where j>I, calculated as:

The variance (Var(s)) of S-statistics in the x values, is given by: . Where m is the number of tied groups in the data set and it is the number of data points in the ith tied group. For the sample size n≥10, the standard test Zmk is calculated as:

Zmk is used to evaluate the trend. Positive ZMK indicate increasing trends; negative ZMK values reflect decreasing trends. The presence of a statistically significant trend is evaluated using the ZMK value. Z1-α/2 is the critical value of ZMK. For 5% and 10% significance level, the value of Z1-α/2 is 1.96 and 1.64 respectively. It is not affected by missing data, insensitivity to extreme values and better performance even for normally distributed data. Then, the slope (change per unit time) was calculated as:

Where, Xi and Xj are data values at times i and j (j>i), respectively. The median of these n values of Q is Sen’s estimator of slope (Wagesho, 2013).

Method of determination of Onset, Cessation Rainy Season and length of growing period (LGP)

The onset date of the rain for individual years is: the one with 20mm of total rainfall received over three consecutive days that were not followed by greater than ten days of dry spell length within 30 days. The date of cessation occurs when the first day of dry spell with duration of at least 20 days occurred after seasonal rainfall activity. In addition, a fixed average 5 mm of evapo-transpiration per day, and 100 mm/meter of the maximum soil water holding capacity of the area were considered. The LGP was determined by subtracting onset date from end date. (Legese, 2018).

Rainfall and SST Correlation

The monthly rainfall anomalies and the SSTs were used for identifying relationships. The correlation method using in the study was Pearson’s correlation coefficient (r) which was calculated by equation:

Where r = Pearson’s correlation coefficient; xi and yi were the values of SSTs and rainfall respectively at the time i; (xµ) and (yµ) was the average of the SSTs and rainfall; and n was the total number of year. The t-test for the statistical significant calculated as the equation: .The t (n-1), 0.5 was considered for determination significant of r values by comparison with the critical value at t (n-1), 0.5.

3. Results and Discussions

3.1 The spatial and temporal distribution and pattern of rainfall climatology

Mean annual and kiremt rainfall distribution over Western Oromia increases toward the central portion of the area from the east and west because of the local topography. The annual rainfall climatology is characterized by large spatial variations which range from less than 1277mm/year over West Wollega to 2070mm/year over East Wollega zone. Seasonally, 291.8 to 532.9 mm, 729.3 to 1325.6 mm and 144.2 to 317.7 mm contribution come from Belg, kiremit and bega respectively (Table 3). During the Kiremt season (JJAS), the central parts of the study area received high rainfall up to 78% (Nekemt) of the annual rainfall and its distributions decreases to 53% southward both from the east and west (Illubabor & jimma zones. Inversely, Belg (FMAM) &Bega (ONDJ) seasonal rainfall increases southward from east and west (Illubabor&jimma zones) upto 30% and 17% of the annual rainfall respectively and this is due to ITCZ position. (Figures 2). The mean Belg and Bega rainfall varies from 320mm (at WW & EW) to 530mm (at Jimma & Illubabor) and from140mm to 317mm over the Wollega and Jimma and illubabor areas, respectively. The Belg and Bega rainfall over Jimma & Illubabor zones is higher than the main rainy season fall in the lowland areas of Ethiopia as studies by Wagesho (2013) and Misganaw (2014).

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