OBSERVED TRENDS IN DAILY PRECIPITATION INDICES IN SOUTH AFRICA:

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 26: (2006) Published online 26 June 2006 in Wiley InterScience ( OBSERVED TRENDS IN DAILY PRECIPITATION INDICES IN SOUTH AFRICA: A. C. KRUGER* South African Weather Service, Private Bag X097, Pretoria 0001, South Africa Received 19 April 2006 Accepted 30 April 2006 ABSTRACT The spatial variations of trends of relevant extreme precipitation indices were examined for 138 rainfall stations in South Africa for the period 1910 to 2004, a follow-up study of various others on precipitation trends in the region. This study aims to inform on trends in daily extreme precipitation indices, over as long a period with instrumental data as possible, while also retaining as sufficient a spatial coverage of results as possible. Data should also be forthcoming from climate stations with the time series spanning the complete study period, avoiding patching of data which can cause inhomogeneities. Some significant changes in indices, averaged over specific areas in South Africa, could be identified. These include areas with significant increases and decreases in annual precipitation, increases in the longest annual dry spell indicating more extreme dry seasons, increases in the longest annual wet spells indicating more extreme wet seasons, and increases in high daily precipitation amounts. The conclusion is that, while in the largest part of South Africa there has been no real evidence of changes in precipitation over the past century, there are however some identifiable areas where significant changes in certain characteristics of precipitation have occurred over the period 1910 to Copyright 2006 Royal Meteorological Society. KEY WORDS: South Africa; precipitation; rainfall; extremes; trends; climate change 1. INTRODUCTION In recent decades many studies have been done on climate trends, especially for precipitation and temperature, compared to the time before the issue of climate change became more prominent during the 1980s. Since then, arguably one of the biggest motivations for such studies is to establish if and how global warming might have influenced the climate on a global as well as regional scales. It was in the Second Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) (Nicholls et al., 1996) that the issue of insufficient studies on observed historical trends in climate extremes was raised. More studies would give a clearer indication of what changes already might have occurred to the global climate, as it was already envisaged at the time that one of the effects of global warming would be an increase in weather and climate extremes. These studies are also useful in the verification of climate model results so that more confidence can be placed in projections of the future climate. Since South Africa is one of a small number of countries in the Southern Hemisphere with a substantial amount of climate data, it is important that effort should be made to inform the broader climate community of the observed historical changes in the climate of this region. Some studies already conducted that incorporate historical trends in precipitation over southern Africa include those of Easterling et al. (2000), Fauchereau et al. (2003), Gill and Kruger (2004), Groisman et al. (2005), Hulme et al. (2001), Mason et al. (1999), Misra (2003), and New et al. (2006). Easterling et al. (2000) studied data from various parts of the world, including parts of South Africa, and found that both southwestern South Africa and the KwaZulu-Natal province in the east experienced significant increases in the * Correspondence to: A. C. Kruger, South African Weather Service, Private Bag X097, Pretoria, 0001, South Africa; andries@weathersa.co.za Copyright 2006 Royal Meteorological Society

2 2276 A. C. KRUGER average number of days per year with heavy rain, for the period 1926 to 1997 and 1901 to 1997, respectively, for the two regions. However, the climate data utilized for this study is not clearly mentioned. Fauchereau et al. (2003) found no significant trend in December to April synthetic rainfall index of southern Africa described by Richard et al. (2000) for the period 1901 to However, greater interannual variability of rainfall was found to exist from the late 1960s onwards. Hulme et al. (2001) also found no significant trend in annual rainfall over South Africa for the period 1901 to 1995, by using an artificial dataset developed by New et al. (2000). However, Mason et al. (1999) found that there is evidence of significant increases in the annual extremes between the periods 1931 to 1960 and 1961 to Focusing on the eastern part of South Africa, Groisman et al. (2005) found no significant change in the annual and summer precipitation totals for the region during the period 1906 to 1997, but a significant increase in the annual frequency of very heavy precipitation. The latest study is that by New et al. (2006) on trends in daily extremes over southern and West Africa for the period 1961 to Only eight South African stations were used, possibly causing the result that no spatial coherent trends were visible from the trends in rainfall indices studied. From the above studies one can make the deduction that, although there were no significant changes in annual rainfall during the past century, there is evidence that there were some significant increases in extremes and interannual variability of precipitation over specific areas in South Africa. Gill and Kruger (2004) included most of the instrumental period from real station data for a study on trends in annual precipitation over South Africa from 1910 to 2000, but did not include trends in precipitation extremes. By using more or less the same data set, this paper attempts to fill the gap by informing mainly on changes in observed daily precipitation extremes, using as many climate stations with continuous data series as possible. 2. DATA The particular period of study was chosen to obtain the longest period possible while still retaining a sufficient number of climate stations with rainfall records spanning the whole period, to obtain a good spatial coverage of results over South Africa. The indices that were calculated are based on daily precipitation amounts, and because all rainfall stations with this long period of data would have some number of days with either no observations or accumulated data in their records, the concession was made that rainfall stations with no more than 5% missing or accumulated daily data could be included for analysis. Precipitation data is much more difficult to quality control than data for other climatic parameters, e.g. temperature, mainly because of the relatively poor spatial correlation of daily precipitation amounts. This problem is aggravated for precipitation data in regions that are arid, where precipitation is mostly of convective origin, or that are sparse in data. Unfortunately there are areas in South Africa, especially in the central and western interior, that have all three of the mentioned problems. Because of the long period that is studied, the spatial coverage of usable climate stations is compromising in the sense that relative quality control of data is for the most part impossible. Therefore, data were mostly accepted at face value, relying on quality control procedures that were in place at the South African Weather Service when the data was initially digitized. However, after initial selection of climate stations with sufficient record length and amount of data, the indices that are reported on in this study were calculated for intercomparison of results. Stations that showed consistently different results in trends from those stations in its wide vicinity were identified, and if no physical explanation, e.g. that the particular station was situated in mountainous terrain where different precipitation characteristics exist over relatively short distances, could be found to justify the differences in trends, those stations were omitted from the results. One of the data quality problems identified in this way was the underreporting of precipitation, but missing data at times of high precipitation episodes also occurred. Eventually a total of 138 rainfall stations remained for the study, as shown in Figure 1 in which the provinces of South Africa are also depicted. For the most part, the stations are spatially well distributed throughout South Africa. Arguable exceptions are in the provinces of KwaZulu-Natal, Northern Cape, and parts of Limpopo. The small number of stations in the Northern Cape is mainly due to the sparse population and the aridity of the region (see Kruger, 2006).

3 TRENDS IN DAILY PRECIPITATION INDICES IN SOUTH AFRICA 2277 Limpopo North-West Gauteng Mpumalanga Free State KwaZulu-Natal Northern Cape Eastern Cape Western Cape Figure 1. Map of South Africa which depicts the 138 rainfall stations selected for the study. Province names are shown 3. ANALYSES AND RESULTS The analysis of precipitation extremes was based on the precipitation indices developed under the auspices of the World Climate Research Programme (WCRP) project on Climate Variability and Predictability (CLIVAR) Working Group on Climate Change Detection (WGCCD) (Peterson et al., 2001). These indices were initially used in an analysis by Frich et al. (2002) of the trends in the global climate, as well as in subsequent regional workshops that endeavored to fill in the large areas where no digital daily data was available for the initial analysis. So far workshops have been held for the Caribbean (Peterson et al., 2002), North Africa (Easterling et al., 2003), South America (Haylock et al., 2006), South West and South Asia (Peterson, 2005), Central and South Asia (Klein Tank et al., 2005), Central America and northern South America (Aguilar et al., 2005), and southern Africa (New et al., 2006), coordinated by the WMO/CLIVAR Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). Alexander et al. (2006) give a global overview of changes in daily climate extremes, and include the results of the above-mentioned papers as well. At these workshops, including the one in South Africa, the period of analysis was limited to the latter half of the twentieth century. RClimDex ( and its predecessor ClimDex are software packages that were developed for use at these regional workshops. RClimDex has features for quality control and calculation of indices of precipitation as well as temperature. However, while the quality control of temperature involves the identification of outliers of which the validity should be verified by the user, the quality control of precipitation is limited only to the identification of negative data values. It is therefore expected of the user to make sure that especially precipitation data is of sufficient quality before utilizing RClimDex to calculate indices. In this study, which can be considered as a follow-up study of that by New et al. (2006), the data was also analyzed with the RClimDex software package. Reporting on the same precipitation indices as those published on the results of the above-mentioned workshops is beneficial for comparative purposes, and also to fill gaps in regional and global analyses. The indices reported on are listed in Table I. Linear trends were calculated for each precipitation index and the results plotted on maps, together with an indication of significance at the 5% level. A nonparametric trend statistic to determine significance of trend would have been more suitable, but the test employed by the RClimDex software package was retained for

4 2278 A. C. KRUGER Table I. Precipitation indices used in this study (RR indicates daily precipitation amount) Index Description Units PRCPTOT Annual total precipitation from wet days (RR >= 1mm) mm CDD Annual maximum number of consecutive dry days (RR < 1 mm) Days CWD Annual maximum number of consecutive wet days (RR >= 1mm) Days R95p Annual total precipitation from RR > 95th percentile (based on the period 1961 to 1990) mm R99p Annual total precipitation from RR > 99th percentile (based on the period 1961 to 1990) mm R30 mm Annual number of days when RR >= 30 mm Days RX1day Annual maximum precipitation in 1 day mm RX5day Annual maximum precipitation in 5 consecutive days mm this study. It was found that for samples of the data series used, the test for significance based on linear trend compared well with the Man Kendall rank statistic for about 90 95% of cases, for all indices. Regionally averaged series were calculated for areas with stations showing similar trends as follows, similar to New et al. (2006): where x r,t x i,t n t ( xi,t x i x r,t = i=1 s i )/ n t is the regionally average index at year t is the index for station i at year t x i is the index mean at station i over the period s i is the index standard deviation at station i over the period and n t is the number of stations with indices in year t Standardization was performed to avoid the average series being dominated by stations with relatively high or low index values, as the index units are expressed in either days or millimeters. In the calculation of the average index series, it was found that for all indices, means and standard deviations of index values can vary a lot between stations, even over relatively short distances. This is the main motivation to use stations with continuous data over the whole study period, making standardization possible. Where an area was identified as showing a statistically significant average trend, it is depicted on the map of the relevant index Annual total precipitation The trends in total annual precipitation are shown in the map of PRCPT (annual total precipitation from wet days, i.e. days with precipitation > = 1 mm) in Figure 2. A daily precipitation amount of 1 mm or less is a high threshold for values to be omitted, but previous studies have found that with lower thresholds problems can arise with underreporting of small precipitation amounts and changes in units of measurement (Hennessy et al., 1999). Blocks A to F on the map indicate areas for which significance of regionally averaged trends was determined. The most apparent feature from the results is that trends in annual precipitation over the last almost a century is not consistent throughout South Africa. Also, most stations showed no significance in trends, which corresponds inter alia to the trend analysis done by Hulme et al. (2001) for the period However, there are some spatial coherencies in significant trends, from which the following tendencies in annual precipitation are identified:

5 TRENDS IN DAILY PRECIPITATION INDICES IN SOUTH AFRICA 2279 PRCPTOT ( ) A E B F C D Trend: Annual total precipitation in wet days per year (RR > = 1mm) < to to to 1.5 > 1.5 Figure 2. Trends in PRCPTOT for the period Trends significant at the 5% level are circled Areas with significant decreases in annual precipitation are the northern part of Limpopo, an area comprising the southern part of Mpumalanga, northeastern Free State and western KwaZulu-Natal, an area in southeastern Eastern Cape, as well as the south coast of South Africa. Areas with significant increases in annual precipitation include the northern part of North West and an area covering parts of the Western Cape, Northern Cape, and Eastern Cape Longest annual dry spell CDD, the index for the annual maximum number of consecutive dry days, i.e. days with precipitation less than 1 mm, usually occurs in the driest season of the year. The driest season is winter for the biggest part of South Africa, with the exception of the Western Cape where the longest dry period per year can be expected in summer. The trends in CDD, shown in Figure 3, therefore give an indication whether possible increases in water stress are experienced by mainly natural vegetation, but also to a lesser extent by crops that are cultivated during the drier period of the year. An area covering the Free State and Eastern Cape provinces, as indicated on the map, experienced, on average, significant increases in the annual maximum period of consecutive dry days. However, it was determined that winter precipitation over the Free State and Eastern Cape showed no significant trends. Therefore, one can conclude that the increase in CDD in those areas is not due to decreases in winter precipitation but that winter precipitation has become more extreme with longer dry periods. Stations in other parts of the country show mixed results for trends in CDD, of which the significant results show little or no spatial coherence Longest annual wet spell Trends in CWD, which is the longest annual number of consecutive wet days, i.e. days with precipitation greater than 1 mm, are depicted in Figure 4. The longest annual wet spell will usually occur during the wet

6 2280 A. C. KRUGER CDD ( ) Trend: Max consecutive dry days per year < to to to 0.3 > 0.3 Figure 3. Trends in CDD for the period Trends significant at the 5% level are circled CWD ( ) B A C Trend: Max consecutive wet days per year < to to to > Figure 4. Trends in CWD for the period Trends significant at the 5% level are circled

7 TRENDS IN DAILY PRECIPITATION INDICES IN SOUTH AFRICA 2281 season and therefore trends in CWD can give an indication whether precipitation has become more extreme during summer for the largest part of South Africa, but winter for the Western Cape. Trends in CWD is therefore of more concern than trends in CDD, as it can give an idea whether an area has come to experience more extremes in precipitation during the rainy season, when most agricultural activity takes place. The spatial pattern of significant trends in CWD indicates only a relatively small spatially coherent area (indicated by the area A on the map) overlapping the Free State and North West provinces, which has been verified to have an average positive trend that is significant. An area (B) roughly overlapping Limpopo, Gauteng, Mpumalanga, and the eastern Free State, as well as an area (C) in the Eastern Cape, shows significant negative trends, which can be a positive indication that precipitations are becoming less extreme in those areas. However, most of the areas showing negative trends also show negative trends in PRCPTOT, where a decline in the annual precipitation experienced causally leads to a decline in CWD Heavy precipitation The remaining indices not yet discussed give a more direct measure of heavy precipitation days or events. It should be noted that, as for the previous indices discussed, trends in these indices should be considered together with trends in total annual precipitation (PRCPTOT), as a decline in PRCPTOT will cause a decline in extreme, high-rainfall events and vice versa, disregarding other factors. The indices R95p and R99p, shown in Figures 5 and 6, indicate the amount of annual precipitation coming from extreme daily events. Stations in the same enclosed areas in both figures, covering mainly southern Free State and most of the Eastern Cape provinces, as well as a part of KwaZulu-Natal in the case of R99p, show on average a significantly positive trend. Annual precipitation has not increased significantly, and therefore it is concluded that daily rainfall has become more extreme in these areas. The same reasoning as above applies to trends in the index indicating annual number of days with very heavy precipitation, i.e. R30 mm, shown in Figure 7. One should note that the wetter east and extreme southern and southwestern parts of South Africa will on average experience more days with heavy precipitation. The most apparent feature of trends in R30 mm is that most stations show no significant results, while there are many more stations with positive trends compared to only nine stations with negative trends. The only area that shows an average significantly positive result is the area shown in the southern Free State and the north of the Eastern Cape. In general, relatively few days with more than 30 mm occur in the southern and western parts of South Africa, where most of the precipitation is from frontal origin, compared to more convective precipitation in the remainder of the country. Trends in the RX1day and RX5day indices, shown in Figures 8 and 9 respectively, indicate whether there are changes in the amount of precipitation received in the day with the highest precipitation and 5-day episode per year with the highest precipitation. These indices give an indication of trends of precipitation amounts usually coming from extreme weather occurrences such as very heavy thunderstorms or cut-off lows in the upper-air or tropical cyclones, but the latter influencing only the eastern parts of South Africa on a very irregular basis. Trends in RX1day reveal almost the same coherent area with an average significantly positive result as with R30 mm, but only extending southwards to cover most of the Eastern Cape province. This shows that while the number of days with extremely high precipitation has increased, the amount of precipitation during the day of year with highest precipitation has also increased over this area. In the case of RX5day, the southern Free State but also a large part of KwaZulu-Natal, as indicated, showed an average significantly positive result. 4. SUMMARY AND CONCLUSIONS New et al. (2006), in their study of trends in daily extremes over mainly southern Africa for the period 1961 to 2000, concluded that there are few consistent and statistically significant trends in the precipitation indices that they calculated. In this study, a longer period of data and a much larger number of rainfall stations were used, and thereby it was possible to identify trends on a more regional scale to a large degree. In this way,

8 2282 A. C. KRUGER R95p ( ) Trend: Annual total precipitation per year when RR > 95th percentile < to to to 0.9 > 0.9 Figure 5. Trends in R95p for the period Trends significant at the 5% level are circled R99p ( ) Trend: Annual total precipitation per year when RR > 99th percentile < to to to 0.45 > 0.45 Figure 6. Trends in R99p for the period Trends significant at the 5% level are circled

9 TRENDS IN DAILY PRECIPITATION INDICES IN SOUTH AFRICA 2283 R30mm ( ) Trend: Annual count of days per year with RR > 30mm < to to to 0.03 > 0.03 Figure 7. Trends in R30 mm for the period Trends significant at the 5% level are circled RX1day ( ) Trend: Daily maximum precipitation per year (mm) < to to to 0.15 > 0.15 Figure 8. Trends in RX1day for the period Trends significant at the 5% level are circled

10 2284 A. C. KRUGER RX5day ( ) Trend: 5-day maximum precipitation per yer (mm) < to to to 0.3 > 0.3 Figure 9. Trends in RX5day for the period Trends significant at the 5% level are circled areas with some noteworthy exceptions could be identified from the large number of results, which show no significant trend. The study period of almost a century also largely eliminate the effect of the decadal-scale cyclical behavior that precipitation in South Africa exhibit (Kruger, 1999), which can cause apparent trends to be identified as real trends when trend studies are done with much shorter time series. Some significant changes in South African precipitation that could be identified include, areas that show increases and decreases in annual precipitation, areas with apparently more variable intraseasonal winter and summer precipitation, and areas that show an increase in days per year with relatively high or extreme precipitation amounts. REFERENCES Aguilar E, Peterson TC, Ramírez Pbando P, Frutos R, Retana JA, Solera M, Soley J, González García I, Araujo RM, Rosa Santos A, Valle VE, Brunet M, Aguilar L, Álvarez L, Bautista M, Castañón C, Herrera L, Ruano E, Sinay JJ, Sánchez E, Hernández Oviedo GI, Obed F, Salgado JE, Vázquez JL, Baca M, Gutiérrez M, Centella C, Espinosa J, Martínez D, Olmedo B, Ojeda Espinoza CE, Núñez R, Haylock M, Benavides H, Mayorga R Changes in precipitation and temperature extremes in Central America and northern South America, Journal of Geophysical Research 110: D Alexander LV, Zhang X, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Rupa Kumar K, Revadekar J, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai P, Rusticucci M, Vazquez-Aguirre JL Global observed changes in daily climate extremes of temperature and precipitation. Journal of Geophysical Research 111: D Easterling DR, Alexander LA, Mokssit A, Detemmerman V CCI/CLIVAR Workshop to develop priority climate indices. Bulletin of the American Meteorological Society 8: Easterling DR, Evans JL, Groisman PY, Karl TR, Kunkel KE, Ambenje P Observed variability and trends in extreme climate events: a brief review. Bulletin of the American Meteorological Society 81: Fauchereau N, Trzaska S, Rouault M, Richard Y Rainfall variability and changes in Southern Africa during the 20th century in the global warming context. Natural Hazards 29: Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Klein Tank A, Peterson T Observed coherent changes in climatic extremes during the second half of the twentieth century. Climate Research 19:

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