Trends and variability in series comprising the mean of fifteen highest daily rainfall intensities in each year were analyzed considering entire Uganda. The data were extracted from high-resolution (0.5° × 0.5°) gridded daily series of the Princeton Global Forcings covering the period 1948–2008. Variability was analyzed using nonparametric anomaly indicator method and empirical orthogonal functions. Possible drivers of the rainfall variability were investigated. Trends were analyzed using the cumulative rank difference approach. Generally, rainfall was above the long-term mean from the mid-1950s to the late 1960s and again in the 1990s. From around 1970 to the late 1980s, rainfall was characterized by a decrease. The first and second dominant modes of variability correspond with the variation in Indian Ocean Dipole and North Atlantic Ocean index, respectively. The influence of Niño 3 on the rainfall variability of some parts of the country was also evident. The southern and northern parts had positive and negative trends, respectively. The null hypothesis
The changes in weather conditions seem to alter the frequency and severity of water disasters from their normal occurrences in different parts of the world. Importantly, extreme rainfall events are directly relevant for planning and management of risk-based hydrometeorological applications. In the same line, several studies have been conducted on extreme rainfall in various parts of the world including Bangladesh [
For disaster preparedness with respect risk-based water management, there is need for the comprehension of historical variation in extreme rainfall and any associated drivers. Some of the recent rainfall-related disasters which claimed lives and property in the study area include the flooding in Kasese region of Uganda in the early May 2013 and mid-May 2016 as well as the deadly landslides in the Mount Elgon which occurred in March 2010 and June 2012. Generally, some of the hotspots for the recent flooding events include Kasese district as well as the Teso region (covering districts of Amuria, Katakwi, Soroti, Kumi, Bukedea, and Kaberamaido). Nonetheless, the flooding occurrences of September 2007 were widespread and caused havoc in the eastern, western, and the central parts of Uganda. The landslides in Uganda especially in the western part are due to seismicity but not rainfall [
It is possible that the occurrences of rainfall-related disasters in the study area may also be exacerbated by the alteration of catchment behavior due to anthropogenic factors such as deforestation, overgrazing, and expansion of urbanized areas. Whereas plans can be put in place to deal with the influence from the anthropogenic factors, for example, by water catchment restoration, local community sensitization, and regulation of the management strategies for river banks, mountainous areas, and so forth, comprehension of the spatiotemporal variation in the rainfall extremes would offer a valuable support for the risk-based planning and management of the applications related to such rainfall-based disasters. Unfortunately, the poor distribution of meteorological stations, short-term data record length, and questionable data quality altogether affect the analyses which would offer an in-depth understanding based on the clarity of the spatiotemporal rainfall variability and trends in Uganda. Furthermore, previous studies on rainfall variability [
Therefore, this study is aimed at (1) investigating the geospatial trends and variability in extreme rainfall intensities and (2) assessing the possible drivers of the rainfall variability.
Uganda (Figure
The study area showing the spatial domain of the rainfall data and locations of some selected meteorological stations (see Table
Correlation between PGF-based series and observed rainfall at selected stations.
S. number | Station name | Coordinate | Data record period | Correlation | |||
---|---|---|---|---|---|---|---|
Long. | Lat. | From | To | Coefficient | Crit. | ||
1 | Masindi | 31.71 | 1.68 | 1965 | 1997 | 0.26 | 0.34 |
2 | Tororo | 34.18 | 0.68 | 1965 | 1997 | −0.15 | 0.34 |
3 | Lira | 32.89 | 2.25 | 1965 | 1997 | −0.33 | 0.34 |
4 | Gulu | 32.28 | 2.77 | 1965 | 1997 | | 0.34 |
5 | Entebbe | 32.46 | 0.05 | 1955 | 1996 | | 0.30 |
6 | Mbarara Met. | 30.60 | 0.56 | 1950 | 2004 | | 0.26 |
7 | Kamenyamigo | 31.67 | −0.30 | 1965 | 1986 | | 0.39 |
8 | Rwoho Forest | 30.55 | −0.85 | 1965 | 1986 | | 0.39 |
Crit.: correlation critical value at the significance level of 5%.
Bold values denote that
The population density of the various districts of Uganda by 2010 (source: Uganda Water Supply Atlas 2010, Ministry of Water and Environment, Uganda).
In gridded (0.5° × 0.5°) form, global daily rainfall data of the Princeton Global Forcings (PGFs) [
Generally, reanalyses/model rainfall datasets are known to be biased in reproducing observed extreme events [
At the location of each station, extreme rainfall events were extracted from both observed and PGF series. One way to obtain extreme events from daily rainfall is to extract the highest intensity in each year. To even out the possible overestimation and/or underestimation of extreme events by the PGF data, average of fifteen highest values in each year was deemed representative of the general condition of extreme rainfall and used for the statistical analyses. Furthermore, for consistency of the validity check with respect to the target of this study, nonparametric anomaly indicator method (NAIM) was applied to extract decadal anomalies from observed and PGF rainfall series. The attractive feature of NAIM is its capacity to eliminate the influence of possible outliers in the series on variability analysis. The cooccurrence of the extracted anomalies from both the observed and PGF-based rainfall is shown graphically (Figure
NAIM decadal changes (%) in the top fifteen rainfall events in each year based on observed and PGF-based series at stations (a) 1, (b) 2, (c) 3, (d) 4, (e) 5, (f) 6, (g) 7, and (h) 8.
For rainfall variability attribution, three climate indices were obtained. These series which were all of monthly temporal resolution included the Indian Ocean Dipole (IOD), the North Atlantic Oscillation (NAO) index [
For trends and variability analyses, monthly series of the climate indices were converted to annual time scale as that for the gridded rainfall.
Anomalies characterizing variability in the series were derived using the NAIM [
To examine the structures which explain the maximum amount of variance in the NAIM anomalies at the various grid points, the EOF analysis was conducted. The structure and sampling dimensions were taken in terms of space and time, respectively. The EOF was obtained as a set of structures produced in the first (i.e., the structure) dimension. The complementary set of structures referred to as the Principal Components (PCs) was produced in the sampling (i.e., time) dimension. Of course, the EOFs and PCs are orthogonal in their own dimension. What makes the PCs so valuable in variability analyses is the orthogonality property, that is, lack of correlation in time. To isolate regions with similar temporal variation so as to enable the identification of areas with maximum correlation between the variables and their components, rotation of the eigenvectors was required [
Under the null hypothesis
To test null hypothesis
Whereas the statistic CRD test gives information about the direction of the increase or decrease in the variable, the magnitude of the change is given in terms of the trend slope
Figure
(a) Percentage of variability explained in the rainfall and (b) the first (EOF1) and second (EOF2) dominant EOF factor loadings.
In the temporal EOF factor loadings, the zero horizontal line, that is, the reference for the variability corresponds to the mean of the long-term series. Considering the entire spatial domain where Uganda is located, the mean of the first two dominant modes of variability,
Figure
Spatial variation in (a) EOF1, (b) EOF2, and (c)
Figure
Observed changes (Obs) in the rainfall from (a) to (h) different parts of Uganda based on the spatial variation in the sites for the EOF factor loadings.
It is noticeable that the rainfall variability tended to differ (though to varying extents) from one part of the country to another. As briefly mentioned before, this could be probably due to the dissimilarity in the microclimate or the influence from regional features such as water bodies, topography, or transition in land cover and/or use on the rainfall variation.
The null hypothesis
Spatial differences in the significance of rainfall variability.
Figure
Correlation between NAIM anomalies from rainfall and those of (a) IOD, (b) NAO, and (c) Niño 3.
The rainfall variability in the northern part seems to be more linked with the variation in the Niño 3 than that of the IOD. Although the strength of the linkage between rainfall and Niño 3 is lower than that with IOD, the correlation between the rainfall temporal anomalies and the variation in the Niño 3 is also visible in the south-western region. In a previous study on the El-Niño Southern Oscillation (ENSO) and interannual rainfall variability in Uganda [
The second dominant mode of variability (EOF2) (Figure
Figure
Trends in terms of (a) slope
This study assessed the spatiotemporal variability and trends in extreme rainfall intensities based on high-resolution (0.5° × 0.5°) gridded daily series of the Princeton Global Forcings (PGFs) covering the entire Uganda in East Africa for the period 1948–2008. The variability analyses were based on the empirical orthogonal function and nonparametric anomaly indicator method. The cooccurrence of the rainfall variability with the large-scale Ocean-Atmosphere interactions was investigated. Statistical analyses of trends were conducted using the recently introduced method which relies on the cumulative rank difference in the data.
Generally, rainfall was above the long-term mean from the mid-1950s to the late 1960s and again in the 1990s as well as the early 2000s. However, from around 1970 to the late 1980s, rainfall was characterized by a decrease. The most dominant mode of variability (EOF1) resonates well with variation in the sea surface temperature of the Indian Ocean. The second dominant mode of variability (EOF2) corresponds to the variation of the sea level pressure in the North Atlantic Ocean. The influence of Niño 3 on the rainfall variability of some parts of the country was also evident.
Whereas generally the southern part (especially around Lake Victoria) as well as the south-western districts of Tororo, Bududa, and so forth had a long-term increase in the rainfall, the northern region was characterized by a decrease in rainfall. For the data extracted at a total of 168 grid points, the null hypothesis
Based on the rainfall changes assessed using the PGF data, the temporal rainfall variation over the data period was characterized by both oscillatory and long-term increase or decrease. It is known that when short-term data are used to conduct frequency analyses, the derived rainfall quantiles might be biased from those that would be obtained from long-term series. Given that the variation in rainfall may be explained by the anomalies in suitable climate indices, such biases in the rainfall quantiles for short-term data can be estimated using suitable long-term rainfall variability drivers. Besides, the rainfall variability drivers can be used to predict an upcoming period of decrease or increase rainfall. Generally, the variation and changes in the climate system are tending to alter the frequency and severity of rainfall-based or water-based disasters in many parts of the world. For the case of Uganda, these results demonstrate the need to embrace the context of stationarity in hydrometeorology for planning, designing, operation, and management of risk-based water resources applications.
It is recommended that the insights from the findings as in this study be updated in the future, especially when long-term observed data become available or if the biases of the rainfall reanalyses datasets in reproducing historical extreme rainfall events reduce tremendously. Eventually, it would also be vital to conduct another detailed research to examine the geospatial differences in climate change impacts on rainfall extremes across the country. This could be done based on statistical downscaling of high-resolution global climate models to data series at grid cells covering the entire country as considered in this study.
Before investigating the cyclical variation, the data should be detrended if it is characterized by monotonic trend. If the series has seasonal component, it should be first deseasonalized. On the contrary, if the data is dominated by cyclical fluctuations (i.e., when there is neither monotonic trend nor seasonal component) the series is used without detrending. According to [
To assess decadal anomalies,
NAIM results for (a) the decomposition of synthetic series, (b) detrending of the series, and (c) significance of the decadal anomalies in the series.
Rescaled data and decadal anomaly
Detrended series
Extracted variability and 95% confidence interval
Under the null hypothesis
To test for monotonic trend in the series, (
The standardized test statistic Estimate the linear trend slope Using Approximate where If where
The author declares that there is no competing interests regarding the publication of this paper.
The gridded rainfall data used were based on the Princeton Global Forcings obtained online from