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Influence of Altitude on the Spatiotemporal Variations of Meteorological Droughts in Mountain Regions of the Free State Province, South Africa (1960–2013)

Influence of Altitude on the Spatiotemporal Variations of Meteorological Droughts in Mountain... Hindawi Advances in Meteorology Volume 2018, Article ID 5206151, 11 pages https://doi.org/10.1155/2018/5206151 Research Article Influence of Altitude on the Spatiotemporal Variations of Meteorological Droughts in Mountain Regions of the Free State Province, South Africa (1960–2013) 1,2,3 1,2 1,2,3 M. Mbiriri , G. Mukwada, and D. Manatsa Department of Geography, University of the Free State, QwaQwa Campus, P. Bag X13, Phuthaditjhaba, South Africa Afromontane Research Unit, University of the Free State, QwaQwa Campus, P. Bag X13, Phuthaditjhaba, South Africa Department of Geography, Bindura University of Science Education, P. Bag 1020, Bindura, Zimbabwe Correspondence should be addressed to M. Mbiriri; getrudembiriri@gmail.com Received 3 August 2017; Revised 15 November 2017; Accepted 21 December 2017; Published 22 January 2018 Academic Editor: Anthony R. Lupo Copyright © 2018 M. Mbiriri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Standardized Precipitation Index (SPI) was computed for October to December (OND) and January to March (JFM) summer subseasons for Free State Province, South Africa, to assess the influence of altitude on drought severity and frequency. The observed spatiotemporal heterogeneity in the SPI variability revealed that factors governing drought interannual variability varied markedly within the region for the two subseasons. Strong correlations between𝑟 = 0.76 and 0.93 across the clusters in both subseasons were observed. Significant shift in average SPI, towards the high during the OND subseason, was detected for the far western low- lying and central regions of the province around the 1990s. An ANOVA test revealed a significant relationship between drought severity and altitude during the OND subseason only. The impact of altitude is partly manifested in the strong relationship between meridional winds and SPI extremes. When the winds are largely northerly, Free State lies predominantly in the windward side of the Drakensberg Mountains but lies in the rain shadow when the winds are mostly southerly. The relationship between ENSO and SPI indicates stronger correlations for the early summer subseason than for the late summer subseason while overall presenting a diminishing intensity with height over the province. 1. Introduction severely aeff ct the mountain regions of the world [12, 13]. For example, the frequency of extreme droughts has increased in Climate change impacts have been well recognized by the mountainous regions of Northeast and North China [7]. researchers, environmentalists, and politicians [1–3]. eTh In recognition of this reality, the United Nations Secretary recurrence of extreme climate events has been identified General declared the year 2002 “eTh International Year of as evidence of climatic change [4]. Studies in America [5], Mountains.” In face of climate change, mountain biota will Europe [6], and Asia [7] have been done to examine the suffer the most, as it is adapted to relatively narrow ranges prevalence of extreme climate variables. In Africa, similar of precipitation and temperatures [14]. Studies on mountain studies have been done in the north and central parts of regions are slowly gaining attention in other parts of the world the continent [8, 9] but with little attention given to the such as America [15], Asia [16], and slowly Africa [12, 17]. south. Southern Africa is regarded as semiarid with high Anumberofdrought indiceshavebeendevelopedto annual rainfall variability that has drought as a common understand drought. A drought index is a quantitative mea- feature [10]. eTh impact of drought is ranked top compared to sure that characterizes drought levels by assimilating data other natural disasters [11]. While several studies have been from one or several indicators, for example, precipitation done on climate variability in terms of rainfall, contrasting [18]. Among the most widely used indices are the Palmer resultshavebeenobservedespeciallyfor semiarid regions Drought Severity Index (PDSI), Crop Moisture Index, and the [3]. Research studies that have been done on high elevation Standardized Precipitation Index (SPI) [19]. SPI has become areassincethe1990shaveconcludedthat climatechangewill popular due to simplicity in its calculation, as it only requires 2 Advances in Meteorology ∘ ∘ ∘ rainfall data. It also allows for the detection of various types and 30.7 S and longitudes 24.3 Eand29.8 Eandsprawls of drought that aeff ct different systems and regions while over high plains which stretch over the Maluti-Drakensberg enabling calculation of estimates of the duration, magnitude, Mountains along the border with Lesotho. eTh Drakensberg and intensity of drought [20]. Agricultural drought, also Mountains cover approximately 1,125 km and reach heights known as soil moisture drought, exists when the available of over 3,475 metres above sea level. It is South Africa’s main soil moisture is insucffi ient to sustain the crops and forage watershed and is the source of the Orange River, one of the resulting in a decrease in agricultural productivity [21]. While major sources of water supply for commercial agriculture in SPI is designed for meteorological droughts, it can also be thecountry.Theprovincecoversanareaof129,825km .The used to detect agricultural drought when calculated on a climate of the Free State Province is highly inu fl enced by the short time scale since soil moisture conditions respond to geographic location, which is continental and is characterized precipitation anomalies on a relatively short scale [21]. On by warm to hot wet summers and cool to cold dry winters. longer time scale (say 12 or 24 months), it is more suitable The rainfall season spans from October to March. Greatest for water resources management purposes. The SPI is based amounts are received in January and February, giving the on the probability of precipitation occurrence over a given JFM subseason more average total precipitation than the time period. Since precipitation is not normally distributed, OND subseason, with 56 percent contribution towards the a mathematical transformation is applied so that the trans- seasonal total precipitation. A west to east seasonal monthly formed precipitation values follow a normal distribution. averageprecipitation increase hasbeenobservedoverthe It is aeft r this transformation to normal distribution that province with the extreme west receiving a monthly average the classification of frequencies of the extreme and severe of not more than 50 mm while the extreme east receives an droughts experienced at any location and timescale becomes approximate of over 100 mm during the October to March consistent [20]. eTh SPI, however, like any other index, has period (Figure 1(b)). its limitations; it does not take into account the contri- bution of other variables to drought severity, for example, 2.2. Data. Monthly mean precipitation (mm) covering the atmospheric evaporative demand [22]. In addition, SPI the 1960–2013 period was extracted from Climate does not provide reliable estimations in arid climates [23]. Explorer’s Climate Research Unit (CRU TS4.01) gridded Despite these shortcomings, the SPI remains one of the ∘ ∘ data file at 0.5 × 0.5 spatial resolution (available from most widely used and acceptable indices in climate research, https://climexp.knmi.nl). The wind data was downloaded with its tried and tested effectiveness in decision making from ERA-interim (Resolution T255), which is currently the [24]. largest global atmospheric reanalysis dataset and produced Understanding the climatic conditions in mountain by the European Centre for Medium-Range Weather regions is not only important for agriculture but for bio- Forecasts(ECMWF).Theanalysiswas doneonline using diversity studies also, since life in the mountains is not Climate Explorer. These data products are principally derived driven by elevation per se but by the climatic conditions from observations in line with the World Meteorological associated with elevation [25]. In this study, the only part Organization guidelines and are freely available online. In of the mountain range that lies within the political borders many developing countries observational data with required of Free State Province was considered in order to provide a data lengths and quality are unavailable [29]. It is because comparative analysis between mountainous (to the east) and of this limitation that the use of reanalysis data has gained low-lying areas located to the west of the province. An in- popularity within the climate science field. Several studies depth analysis of mountain related intensity, frequency, and have shown that while the use of these datasets may not be spatial extent of extreme droughts at a higher resolution is as accurate as station data, the difference is insignificant in necessary for a more comprehensive assessment of drought many of the cases [30]. eTh period of 53 years meets the impacts. This work focused on short term meteorological minimum climatic analysis duration requirement outlined droughts (3-month SPIs for December and March) because in the World Meteorological Organization guidelines they can serve as proxies for agricultural droughts which are [31]. important in the Free State Province. Understanding these droughts will be critical for understanding the resilience A total of 107 grid points was used in the analysis covering of theregioninthefuture.Hence,theobjectiveofthis theFreeStateProvince.Thegridpointsintheimmediate study is to analyze severe to extreme drought as well as borders of the province were also included as the influence of severe and extreme wet conditions in mountain regions these points contributes to the overall climate characteristics of southern Africa, with particular emphasis on the Drak- of areas within the province (Figure 2). The province was ensberg Mountains within the borders of the Free State divided into three homogenous subregions or clusters based Province of South Africa for the period between 1960 and on altitude. This was done using Hot Spot Analysis (HSA) 2013. in an ArcGIS (version 10.3) environment. The use of special analyses such as HSA helps in identifying groups of locations with high spatial homogeneity and it has been shown that 2. Materials and Methods such segregation may significantly increase the quality of 2.1. Study Area. The study area is confined to the political interpolation results [32]. Cluster analysis does not require bordersoftheFreeStateProvinceofSouthAfrica (Fig- a priori knowledge on data structure such as normality ure 1(a)). The province is situated between latitudes 26.6 S conditionasrequiredinother statisticaltests [33]. 40 Advances in Meteorology 3 ∘   ∘   26 0 0 E 28 0 0 E ∘   ∘   26 0 0 S 26 0 0 S Parys Free State Helbron Frankfort Bothaville Vrede Kroonstad Reitz Warden Welkom ∘   ∘   Virginia 28 0 0 S 28 0 0 S Bethlehem Harrismith Bultfontein Senekal Winburg Boshof Brandfort Ficksburg Bloemfontein Ladybrand Jagersfontein Wepener ∘   ∘   30 0 0 S 30 0 0 S Smithfield Philippolis Zastron Bethulie 0 20 40 80 120 160 (km) ∘   ∘   32 0 0 S 32 0 0 S ∘   ∘   26 0 0 E 28 0 0 E Towns Altitude m.a.s.l. High: 3277 Low: 1041 (a) Location of the Free State Province in South Africa Parys Frankfort Helbron Bothaville Vrede Kroonstad Reitz Warden Welkom Virginia Bethlehem Harrismith Bultfontein Senekal Winburg Boshof Brandfort Ficksburg BLOEMFONTEIN Ladybrand Jagersfontein Wepener Smithfield Philippolis Zastron Bethulie 0 30 60 120 180 240 (km) Average precipitation Elevation m.a.s.l. Seasonal monthly average High: 3277 precipitation Low: 1041 mm High: 140.3 Provincial Boundary Low: 38.9 (b) Distribution of seasonal monthly average precipitation (October–March)for Free StateProvinceinSouth Africa.The data is from 1960 to 2013 Figure 1 HSAisalocalspatialpatternanalysistoolwhich worksby results associated with each feature determine if the differ- considering each feature within the context of neighbouring ence is statistically significant or not. Hence, this variable features and determines if the local pattern (a target feature could potentially improve the results of other interpolation and its neighbours) is statistically different from the global techniques when used instead of elevation as an auxiliary pattern (all features in the dataset). eTh 𝑍-score and 𝑝 value variable.TheclusteringbyHSAenablesthe comparison of 110 4 Advances in Meteorology Table 1: Categorization of dryness/wetness. SPI value occurrence % occurrence Nominal SPI class ≥1.645 ≤5Extremelywet 1.282 to 1.644 6–10 Severely wet 0.842 to 1.281 11–20 Moderately wet 0.524 to 0.841 21–33 Slightly wet −0.523 to 0.523 34–50 Normal −0.841 to−0.524 21–33 Slight drought −1.281 to−0.842 11–20 Moderate drought −1.644 to−1.282 6–10 Severe drought ≤−1.645 ≤5 Extreme drought Adaptedfrom[26]andmodifiedby[27]toenhance suitabilityforapplicat ion in southern Africa. The original classification was developed by [28]. analysis in such a way that OND SPI for 1960 matched JFM SPI for 1961. This is because the rainfall season overlaps two calendar years; thus, the continuous season of October–March is split into October–December of the previous year (1960) and January–March of the following year (1961). To avoid splitting the precipitation season, the meteorological year in southern Africa starts in July and ends in June of the following calendar year and hence is usually referred to as a season 1960/61. In this study, the subseasons were analyzed separately. Trends in the two subseasons were studied using parametric tests for these are regarded as more powerful especially for data that is regarded as independent and normally distributed [36]. (km) 0 30 60 120 2.3. Computing SPI. The SPI was calculated using monthly precipitation data for 53 years (1960–2013) following detailed Altitude procedure described by [28] and expressed using the formula GiZScore (clusters) −2.488–−0.4353 2.191–5.938 𝑥 −𝑥 −0.4352–2.190 𝑖 Provincial Boundary (1) SPI= , Figure 2: Locations of clusters in the Free State Province (Cluster 1 where𝑥 is the monthly rainfall amount,𝑥 is the mean, and𝑠 (green), Cluster 2 (yellow), and Cluster 3 (red)). is the standard deviation of rainfall calculated from the whole time series of monthly values. Using the SPI classification by [27] (Table 1), only severe droughts and wet characteristics of the different areas within to extreme droughts (SPI≤−1.282) were analyzed so as to the province. Further, the Inverse Distance Weighting (IDW) amplify the drought signal. method of interpolation was used in preparing the frequency Frequencies of drought years and the proportion of at maps.TheIDWtechniqueassumesthateachmeasuredpoint least severe drought years in the two subseasons in the has a local influence that diminishes with distance; thus it three individual clusters were calculated. (𝑁)represents gives an output surface that is sensitive to clustering and the thetotal countofdrought yearsfromslightdroughtto presence of outliers. extreme drought. The percentage frequencies were calculated Monthly precipitation values were used to compute as follows: SPIs at 3 months’ scale, for periods January–March and Frequency(%) October–December using Drought Index Calculator (DrinC). The distinct climate controls of the subseasonal Number of severe extreme drought years ( ) rainfall in southern Africa warranted that the analysis be (2) Total number of drought years in53 years done by splitting October–December from January–March. During October to December, usually regarded as early ×100, summer, the atmosphere has a predominant extratropical nature with frequent cut-off lows [34]. In January and wheredroughtisthetotal countofSPIs≤−0.524. February, tropical circulation systems are much more To determine how drought severity is linked to altitudinal prevalent over South Africa with local convection variability we applied the composite analysis technique. We dominating [35]. eTh two subseasons were arranged for extracted the vfi e worst drought years of at least SPI ≤−1.282 Advances in Meteorology 5 2.1 2.0 2.0 1.5 1.8 1.6 1.5 1.5 1.0 1.2 1.2 1.0 0.9 0.8 0.5 0.6 0.5 0.4 0.3 0.0 0.0 0.0 0.0 −0.3 −0.4 −0.5 −0.6 −0.5 −0.8 −0.9 −1.0 −1.2 −1.2 −1.0 −1.5 −1.5 −1.6 −1.8 −2.1 −2.0 −2.0 −1.5 (a) (b) Figure 3: Temporal manifestation of the SPI (bars) for (a) OND and (b) JFM at provincial level. In the insert are the 10-year running variance envelope (solid lines) and mean (dashed line). The SPI is from 1960 to 2013. at provincial scale. eTh years comprised 1965, 1972, 1990, 1994, a shift to a wetter epoch aeft r 1987 though the period and 1997. Averages for each of the clusters were plotted for the is punctuated with more extreme droughts. From the 10- five severe drought years. year running variance (solid line), it can be noted that the interannual variability of SPI for JFM in Figure 3(b) is less variable and the SRSD indicates that the period is devoid 3. Results and Discussion of any significant shift. u Th s, between 1960 and 2013, more severe drought (count) conditions were experienced during 3.1. Temporal Variations of Drought Intensity in the Free State theONDsubseasonthanduringtheJFMsubseason;five Province. Using grid point data, average SPIs were calculated (5) years against three (3) years while more severe wet years forthewholeprovincetogivetheprovincial averageSPI were experienced during the JFM subseason than during the and for individual clusters. eTh inclusion of provincial scale OND subseason. A two-tailed paired samples𝑡-test revealed was done to enable comparison of droughts (wet) years at that there is a significant difference between OND and JFM provincial scale with those at cluster scale. This would assist drought/wet magnitude for the 53 years’ period. eTh JFM in determining the ideal spatial scale to use for drought subseason has more intense droughts/wet conditions than preparedness and response for the region. Figures 3(a) and OND subseason𝑡(49) = 6.727 and 𝑝≤0.05.Itcan be noted 3(b) illustrate the temporal evolution of the SPI at provincial that the manifestation of the SPIs in the two subseasons dieff rs level for OND and JFM, respectively. For the OND subseason, markedly which indicates contrasting processes responsible severe drought years (SPI≤−1.282) were 1994 (−2.023), 1990 for the droughts development within the subseasons. (−1.974), 1965 (−1.653), 1997 (−1.580), and 1972 (−1.393) while The 10-year running mean demonstrates that the decades the wettest year (SPI≥ 1.282) was 2001 (1.575). A shift to higher variance in the ten-year running variance (red line) before the early 1990s had epochs of successive drought and wet years before oscillating around the mean thereaeft r. On is confirmed as a statistically significant shift in variance by the Sequential Regime Shift Detector (SRSD) which has a 𝑝 the overall it is noted that the region is marked by droughts whose magnitudes have intensified in the last three decades. value of 0.002 in the early summer subseason (OND). This Since the JFM SPI patterns appear to be more random, we systematic pattern in variance could represent a coherent influence from an external large-scale process. The 10-year are more inclined to investigate further the OND period in search of less complex systematic drivers of the regional running mean (dashed line in Figure 3) indicates that on extreme events. It appears there is no clear-cut reason for the average the pre-1980s were characterized by rainfall deficits while the post-1980s period had predominantly surplus occurrence of wet or dry extremes in the region during JFM and hence it is likely that more local factors, which are not rainfall. However, it is during the latter period when the worst two droughts occurred in 1990 and 1994. simple to identify in the absence of modeling, play a major role. During the JFM subseason, severe droughts (SPI ≤ −1.282) were experienced in 1992 (−1.990), 2007 (−1.601), and The local relationships to the SPI interannual and spa- 1983 (−1.459). eTh severe wet years (SPI ≥ 1.282) experienced tial variability of the Free State are still largely unknown. However, SPI values are dependent on a combination of during the same subseason were 1974 (1.706), 1976 (1.699), 1988 (1.600), and 1991 (1.366). er Th e appears to have been several unrelated factors, such as number of rainfall events SPI SPI variance SPI SPI variance 6 Advances in Meteorology 30 21 Drought composite 3 Drought composite 2 Drought composite 1 0 0 −1.9 −1.8 −1.7 −1.6 −1.5 −1.4 −1.3 −1.2 −1.1 −1 Cluster 1 Cluster 2 Cluster 3 Drought composite SPI Severe Extreme Frequency (a) (b) Figure 4: (a) Proportion of severe (extreme) droughts to total drought frequencies (SPI ≤ 0.524) and (b) composite SPIs for Clusters 1, 2, and 3 during the OND subseason. eTh composite years are 1965, 1972, 1990, 1994, and 1997. andtheir intensityandtheonsetandcessationoftheseason. at provincial level. The variation of drought intensity with An outstanding characteristic of the Free State region is its cluster indicates that the droughts are not evenly distributed extreme spatial heterogeneity in SPI. It could be assumed in a season and could have their evolution strongly influenced that the heterogeneity is a result of the complex topography by relief. of the region that is largely shaped by the western parts To measure the association of the SPI records in the of the Drakensberg Mountains. It is therefore interesting to three clusters during the OND subseason, linear correlation investigate how this topography influences the drought signal coefficient, 𝑟,was calculated.Pearsoncorrelations of theSPI throughout the region. among the three clusters for the OND subseason show that eTh three clusters obtained from HSA were maintained there is a significant positive relationship between Cluster ∗∗ for the rest of the analysis and were used to assess the 1and Cluster2(𝑟 = .895 ), Cluster 1 and Cluster 3 ∗∗ differences in drought evolution between low-lying areas (𝑟 = .763 ), and between Cluster 2 and Cluster 3 (𝑟= ∗∗ and mountain regions in the southern part of Africa. Severe .894 ). This result implies that dry/wetness variations in all drought years at provincial scale between 1960 and 2013 were the clusters are highly correlated. It clearly shows that the selected only for the OND subseason so as to show the relationship between clusters is weaker and more varied for variation in drought intensity for each of the clusters. This is OND, an indication of weaker spatiotemporal homogeneity. because the JFM subseason is devoid of any signicfi ant shift SRSD found a statistically signicfi ant shift in SPI variance inSPIaswellasawell-denfi edpattern of variability. From in Clusters 1 and 2 around 1990 (𝑝 = 0.000 and𝑝= Figure 4(a), Cluster 3 has the highest drought frequency and 0.003, resp.) during the OND subseason (Figure 5). While the highest percentage of severe droughts while Clusters 1 there is a strong positive correlation between the clusters, and 2 both have equal frequencies and proportion of severe the result of the distribution of the differences between the droughts but lower than those experienced in Cluster 3. paired clusters (paired dieff rences) shows that there is no u Th s, although Cluster 3 recorded the highest frequency of statistically significant difference between the pairs during droughts, more extreme droughts occurred in Cluster 1. eTh the JFM subseason. severe droughts proportion is increasing with cluster while The spatial distribution of drought frequencies in the two the extreme droughts are decreasing. The 𝑡-test results for the subseasonsisshowninFigure6.Thefigureshowshot spots significant difference between droughts of adjacent clusters for drought (SPI≤−1.282) years during the two subseasons. revealed that although there are no signica fi nt dieff rences The highest frequency of droughts is concentrated within the central belt of the province, giving a more horizontal belt between the adjacent clusters’ composites, Clusters 1 and 3 show a significant difference with 𝑝 = 0.1054 (90% confidence of areas most prone to droughts of great intensity during level). Figure 4(b) shows the distribution of drought severity the early summer subseason (Figure 6(a)). This suggests in the three clusters in the composite years summarized in further investigation into the identicfi ation and analysis of the Figure 4(a). The composite years for the three clusters are factors that may be contributing to increased vulnerability of 1965, 1972, 1990, 1994, and 1997. Drought severity decreases these areas to severe droughts during the OND subseason. with increasing altitude, a transition from Cluster 1 to Cluster Common conditions knowntobring aridityare associated 3 during the OND subseason only. None was observed during with anticyclonic conditions but usually their spatial scales of the JFM subseason. However, Cluster 2 experienced the most influence are too large to account for the differing drought intense droughts in 3/5 years of the worst drought years conditions within Free State. During the JFM subseason, Proportion (%) Frequency (N) 5 5.5 5.5 5.5 5.5 Advances in Meteorology 7 1.7 1.8 1.2 1.3 0.7 0.8 0.2 0.3 −0.3 −0.2 −0.8 −0.7 −1.3 −1.2 −1.8 −1.7 −2.2 −2.3 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 (a) (b) Figure 5: Temporal manifestation of SPI with results of the SRSD superimposed to show the shift in the variance (dashed line) for (a) Cluster 1 and (b) Cluster 2 during the OND subseason. eTh data is from 1960 to 2013. Parys Helbron Parys Bothaville Frankfort Vrede Kroonstad Helbron Bothaville Vrede Frankfort Reitz Welkom Kroonstad Warden Reitz Virginia Bethlehem Warden Bultfontein Harrismith Senekal Welkom Winburg Virginia Boshof Bethlehem Bultfontein Harrismith Brandfort Senekal Ficksburg Winburg Boshof Brandfort Ladybrand Ficksburg Bloemfontein Bloemfontein Ladybrand Jagersfontein Wepener Wepener Jagersfontein Smithfield Philippolis Zastron Bethulie Smithfield Philippolis Zastron Bethulie (km) 0 50 100 200 300 400 0 37.5 75 150 225 300 (km) Drought frequency Elevation Towns Elevation value m.a.s.l. Towns Drought frequency High: 3277 High: 3277 Provincial Boundary Provincial Boundary Low: 1041 Low: 1041 JFM drought frequency Drought frequency value value High: 6.13912 High: 7.99957 Low: 3.00101 Low: 4.50257 (a) (b) Figure 6: Frequency of drought years (SPI ≤−1.282) for (a) OND and (b) JFM for the period 1960–2013. however, the areas prone to severe droughts are to the to explain the relationship between droughts and altitude extreme northeastern tip, the central west, and the extreme during the OND period. southeastern areas of the province (Figure 6(b)). Analysis of variance results on the altitudinal influence on drought/wet frequency reveal significance during the The influence of altitude on drought severity was tested JFM subseason only for both severe drought and severe using ANOVA. Results are shown in Table 2. eTh results show wet conditions (in brackets) (Table 3). This means that that altitude has significant influence on drought severity in frequenciesofJFMseveredroughtandwetepisodesvarywith theFreeStateProvince in theOND subseasonbut notduring altitude. the JFM subseason (in brackets). This influence is shown to be signicfi ant ( 𝐹 = 2.54;𝑝 > 0.01). This means that {8,2637} the OND droughts vary significantly with altitude while the 3.2. Wind Patterns Associated with Drought/Wet Events over JFM drought variations are not associated with variations in Free State. Convection is less prominent during the OND altitude, conrm fi ing that the systems that bring precipitation period and hence relief rainfall should dominate the moun- totheregionaredieff rent.Thosefor theONDarealtitude tainous region of Free State. Moreover, since relief has been relatedwhile thosefortheJFM arenot.Thisimpliesthat foundtosignicfi antlyimpactonthesubseasonaldrought relief rainfall, which is wind related, is a possible candidate magnitude, then a more likely factor to affect the rainfall 5.5 5.5 5.5 SPI SPI 5 8 Advances in Meteorology Table 2: Influence of altitude on drought severity for OND and JFM subseasons (in brackets). Altitude Sum of squares df Mean square 𝐹 Sig. 669848.947 83731.118 2.536 .009 Between groups 8 (235716.673) (29464.584) (.888) (.526) 8706611.38 33017.297 Within groups 2637 -- (87500743.65) (33181.928) 87736460.33 Total 2645 - - - (87736460.33) Table 3: Influence of altitude on severe drought (wet) frequencies for JFM subseason. Altitude Sum of squares df Mean square 𝐹 Sig. 964097.644 10 96409.764 5.591 .000 Between groups (1072072.773) (12) (89339.398) (5.856) (.000) 672483.237 39 17243.160 Within groups -- (564508.107) (37) (15256.976) 1636580.880 49 Total --- (1636580.880) (49) received at a particular place is its wind speed and direction. which are northerly over the region that are driven by a This is because these two parameters determine the amount high pressure anomaly area to the southwest of the region of moisture carried by an air mass and the rate of relief as reflected by the geopotential heights. eTh se winds advect forcedadiabaticcoolingwhichissubsequentlytranslatedto relatively warmer and humid tropical air into Free State that rainfall. This implies that a particular place can be in the is forced to rise as they approach the Drakensberg uplands rain shadow/windward side of the mountain depending on to the south. We hypothesize that the low-level northerlies the prevailing wind average characteristics of that season. would enhance the orographic effects of the Drakensberg eTh refore, in this section we analysed the wind characteristics highlands over the Free State Province, where the rainfall for the drought and wet events to determine the extent of the has been noted to generally increase with altitude. Therefore wind impacts in defining the drought/wet spatial properties northerly wind anomalies are linked to wet events while for selected seasons for the Free State Province. southwesterlywindanomalies arelinkeddroughtsoverthe The strong linkage between drought and altitude is an Free State region. interesting observation (Table 2). Rain producing systems To illustrate the strong regional connection of the wind in the vicinity of the Drakensberg Region consist of two factor to the SPI, we present in Figures 8(a) and 8(b) types [37]. The predominant sources of rainfall are large- the spatial correlation of the averaged Free State SPI with scale line thunderstorms and orographically induced storms. the surface zonal and meridional winds. The significant eTh former are more prevalent during the solar enhanced association with the two wind elds fi is quite conspicuous convection of the JFM subseason. As such, the latter are where the droughts are related to westerly and southerly wind relatedtothe surfacewindcharacteristics like humidity, anomalies while wet episodes are linked to the reverse wind wind speed, and direction which should account for most of anomalies. The impacts appear to weaken drastically towards the rainfall during OND as this has a large bearing on the the summit of the Drakensberg Mountains. This weakening windward and rain shadow aspect of a particular place. In this demonstrates the visible impacts of the increasing altitude regard, in Figure 7 we present the drought/wet composites from the west and north of the mountains. us, Th despite the in relation to the zonal and meridional anomalous winds at amount of moisture that is carried by the winds to determine the near surface (850 hPa). It is noted that the surface winds the drought/wet episodes, the direction of the wind relative appear tobe themostrobustlinktothe droughtand wet to the uplands also determines which place becomes the rain events during this subseason. Figure 7 indicates that, during shadow and which does not. drought, near surface winds are westerly (a) and northerly (b) with an anomalous high pressure having been built up 3.3. Altitude Modified SPI Relationship with ENSO. The to the west of the region (c). This is further illustrated in ENSO relationship with the JFM SPI for Free State is weak Figure 7(c) where the vector winds are southeasterly over the with an inverse correlation value of −0.29 and a 𝑝 value Free State region that are driven by a low pressure anomaly of 0.032 while that for OND SPI is relatively stronger with situated to the southeast of the subcontinent as indicated by avalue of −0.385 and a 𝑝 value of 0.004. In Figure 9(a) the geopotential heights. This is bound to advect relatively we present the relationship between ENSO and the three cold and relatively drier maritime winds into the region. eTh clusters during OND and JFM. It is interesting to note that wet year composites indicate the opposite where the winds the relationship is strongly determined by the clusters in both are strongly northerly (e) with almost no zonal wind anomaly cases hence signifying the impact of relief on the ENSO’s (d). Figure 7(f) reiterates the corresponding vector winds influence on the extreme rainfall events. It is evident that Advances in Meteorology 9 NCEP/NCAR reanalysis NCEP/NCAR reanalysis NCEP/NCAR reanalysis 850mb zonal wind (m/s) Composite Anomaly 1981–2010 climo 850mb meridional wind (m/s) Composite Anomaly 1981–2010 climo 850mb geopotential height (m) Composite Anomaly 1981–2010 climo ∘ ∘ ∘ NOAA/ESRL Physical Sciences Division NOAA/ESRL Physical Sciences Division NOAA/ESRL Physical Sciences Division 25 S 25 S 25 S ∘ ∘ ∘ 0.6 1.2 26 S 26 S 26 S ∘ ∘ ∘ 27 S 27 S 27 S 0.4 0.8 ∘ ∘ ∘ 28 S 28 S 28 S ∘ ∘ ∘ 0.4 29 S 0.2 29 S 29 S ∘ ∘ ∘ 30 S 30 S 30 S ∘ ∘ ∘ 0 0 31 S 31 S 31 S ∘ ∘ ∘ 32 S 32 S 32 S −0.2 −0.4 ∘ ∘ ∘ 33 S 33 S 33 S ∘ ∘ ∘ −0.4 −0.8 34 S 34 S 34 S ∘ ∘ ∘ 35 S 35 S 35 S −0.6 −1.2 Oct to Dec: 1965, 1990, 1994, 1997 Oct to Dec: 1965, 1990, 1994, 1997 Oct to Dec: 1965, 1990, 1994, 1997 (a) (b) (c) 850mb zonal wind (m/s) Composite Anomaly 1981–2010 climo 850mb meridional wind (m/s) Composite Anomaly 1981–2010 climo 850mb geopotential height (m) Composite Anomaly 1981–2010 climo ∘ ∘ ∘ NOAA/ESRL Physical Sciences Division NOAA/ESRL Physical Sciences Division NOAA/ESRL Physical Sciences Division 25 S 25 S 25 S 0.8 ∘ ∘ 0.7 ∘ 26 S 26 S 26 S ∘ ∘ ∘ 0.6 27 S 27 S 0.5 27 S ∘ ∘ ∘ 28 S 0.4 28 S 28 S 0.3 ∘ ∘ ∘ 29 S 29 S 29 S 0.2 ∘ ∘ ∘ 0.1 30 S 30 S 30 S ∘ 0 ∘ ∘ 31 S 31 S 31 S −0.1 ∘ ∘ ∘ −0.2 32 S 32 S 32 S ∘ ∘ ∘ −0.3 33 S 33 S 33 S −0.4 ∘ ∘ ∘ 34 S 34 S 34 S −0.5 −0.6 ∘ ∘ ∘ 35 S 35 S 35 S −0.8 −0.7 Oct to Dec: 1995, 1996, 2001, 2009 Oct to Dec: 1995, 1996, 2001, 2009 Oct to Dec: 1995, 1996, 2001, 2009 (d) (e) (f ) Figure 7: Zonal, meridional, and geopotential height anomalies for dro ughts (a), (b), and (c) and wet events (d), (e), and (f), respectively. Scalar winds are indicated in (c) and (f) with the years constituting the composites shown in the inserts. Corr Oct–Dec averaged FSP OND SPI 1 index Corr Oct–Dec averaged FSP OND SPI 1 index with Oct–Dec averaged ERA–int taux 1979 : 2012 with Oct–Dec averaged ERA–int taux 1979 : 2012 ∘ ∘ 24 S 24 S ∘ ∘ 25 S 25 S ∘ ∘ 26 S 26 S ∘ ∘ 27 S 27 S ∘ ∘ 28 S 28 S ∘ ∘ 29 S 29 S ∘ ∘ 30 S 30 S ∘ ∘ 31 S 31 S ∘ ∘ 32 S 32 S ∘ ∘ 33 S 33 S ∘ ∘ 34 S 34 S ∘ ∘ 35 S 35 S (a) (b) Figure 8: Spatial correlation of Free State OND SPI with (a) zonal surface winds and (b) meridional surface winds during OND for the period from 1960 to 2013. Tau-𝑥 and tau-𝑦 mean surface winds in the zonal and meridional direction, respectively. ENSO’s impacts are strongest in the lowlands but weakest but also shows a greater influence on Cluster 1. As such the over the highlands. Relief modifies the rainfall received at a altitude for the Free State Province strongly demonstrates a place and the higher the altitude is, the more modification to robust relationship with ENSO. rainfall occurs. Hence, this observation reiterates the impor- tant role played by relief in weakening ENSO’s impacts on the 4. Conclusions rainfall events. It is, however, noted that the correlations are stronger for the OND than for the JFM period for all clusters. The objective of the study was to assess the influence of Figure 9(b) supports the observation that El Nino not only altitude on drought severity and frequency in the Free State does impact more strongly on the OND than the JFM period Province of South Africa. The study revealed the spatial ∘ ∘ 20 E 20 E 20 E ∘ ∘ 21 E 21 E ∘ ∘ 21 E 22 E 22 E ∘ ∘ 23 E 23 E ∘ ∘ ∘ 22 24 E 24 E ∘ ∘ 25 E 25 E ∘ ∘ 23 E 26 E 26 E ∘ ∘ 27 E 27 E ∘ ∘ 24 E 28 E 28 E ∘ ∘ ∘ 29 E 29 E ∘ ∘ 25 E 30 E 30 E ∘ ∘ ∘ 31 E 31 E 26 E ∘ ∘ 32 E 32 E ∘ ∘ 33 E 33 E 27 E ∘ ∘ 34 E 34 E 28 E 29 E 30 E 31 E 32 E ∘ ∘ 20 E 20 E ∘ ∘ 33 E 21 E 21 E ∘ ∘ ∘ 22 E 22 E ∘ ∘ 34 E 23 E 23 E ∘ ∘ 24 E 24 E ∘ ∘ 35 E 25 E 25 E ∘ ∘ 26 E 26 E ∘ ∘ 27 E 27 E ∘ ∘ 28 E 28 E ∘ ∘ 29 E 29 E ∘ ∘ 20 E 30 E 30 E ∘ ∘ 31 E 31 E ∘ ∘ 21 E 32 E 32 E ∘ ∘ 33 E 33 E ∘ ∘ 22 E 34 E 34 E 23 E 24 E 25 E 26 E 27 E ∘ ∘ 20 E 20 E ∘ ∘ 28 E 21 E 21 E ∘ ∘ 22 E 22 E ∘ ∘ 29 E 23 E 23 E ∘ ∘ ∘ 24 E 24 E ∘ ∘ 30 E 25 E 25 E ∘ ∘ ∘ 26 E 26 E 31 E ∘ ∘ 27 E 27 E ∘ ∘ 28 E 28 E 32 E ∘ ∘ 29 E 29 E ∘ ∘ 30 E 30 E 33 E ∘ ∘ 31 E 31 E ∘ ∘ ∘ 32 E 32 E 34 E ∘ ∘ 33 E 33 E ∘ ∘ ∘ 35 E 34 E 34 E 10 Advances in Meteorology Nino 3.4 & Cluster 3 Cluster 3 El Nino comp Nino 3.4 & Cluster 2 Cluster 2 El Nino comp Nino 3.4 & Cluster 1 Cluster 1 El Nino comp −1 −0.8 −0.6 −0.4 −0.2 0 0.2 SPI values SPI values JFM JFM OND OND (a) (b) Figure 9: (a) Relationship between ENSO and the three clusters and (b) the El Nino composite SPI values for the three clusters during OND andJFM.Thedataisfrom1960to2013andtheElNinocompositesarecomprisedofthelowestNino3.4valuesof1965/66,1972/73,1982/83, 1987/1988, 1991/92, 1997/98, and 2002/03. heterogeneity in the SPI over Free State and showed that to more abundant rainfalls,” Geophysical Research Letters,vol. 37, no. 6, Article ID L06704, 2010. the factors governing drought interannual variability varied markedly within the region and from the early part of the [2] A. S. Singh and M. B. Masuku, “Sampling techniques determi- nation of sample size in applied statistics research: an overview,” rainfall season to late subseason. Highland areas (Cluster International Journal of Commerce and Management, vol. II, no. 3) have the highest frequency of droughts although more 11, pp. 1–22, 2014. extreme droughts occurred in the extreme western low-lying [3] N. Batisani and B. Yarnal, “Rainfall variability and trends in regions (Cluster 1). Significant differences among clusters semi-arid Botswana: implications for climate change adaptation during the early summer season, OND, were observed. policy,” Applied Geography,vol.30,no.4,pp. 483–489, 2010. 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Influence of Altitude on the Spatiotemporal Variations of Meteorological Droughts in Mountain Regions of the Free State Province, South Africa (1960–2013)

Advances in Meteorology , Volume 2018: 11 – Jan 22, 2018

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Hindawi Publishing Corporation
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Copyright © 2018 M. Mbiriri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract

Hindawi Advances in Meteorology Volume 2018, Article ID 5206151, 11 pages https://doi.org/10.1155/2018/5206151 Research Article Influence of Altitude on the Spatiotemporal Variations of Meteorological Droughts in Mountain Regions of the Free State Province, South Africa (1960–2013) 1,2,3 1,2 1,2,3 M. Mbiriri , G. Mukwada, and D. Manatsa Department of Geography, University of the Free State, QwaQwa Campus, P. Bag X13, Phuthaditjhaba, South Africa Afromontane Research Unit, University of the Free State, QwaQwa Campus, P. Bag X13, Phuthaditjhaba, South Africa Department of Geography, Bindura University of Science Education, P. Bag 1020, Bindura, Zimbabwe Correspondence should be addressed to M. Mbiriri; getrudembiriri@gmail.com Received 3 August 2017; Revised 15 November 2017; Accepted 21 December 2017; Published 22 January 2018 Academic Editor: Anthony R. Lupo Copyright © 2018 M. Mbiriri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Standardized Precipitation Index (SPI) was computed for October to December (OND) and January to March (JFM) summer subseasons for Free State Province, South Africa, to assess the influence of altitude on drought severity and frequency. The observed spatiotemporal heterogeneity in the SPI variability revealed that factors governing drought interannual variability varied markedly within the region for the two subseasons. Strong correlations between𝑟 = 0.76 and 0.93 across the clusters in both subseasons were observed. Significant shift in average SPI, towards the high during the OND subseason, was detected for the far western low- lying and central regions of the province around the 1990s. An ANOVA test revealed a significant relationship between drought severity and altitude during the OND subseason only. The impact of altitude is partly manifested in the strong relationship between meridional winds and SPI extremes. When the winds are largely northerly, Free State lies predominantly in the windward side of the Drakensberg Mountains but lies in the rain shadow when the winds are mostly southerly. The relationship between ENSO and SPI indicates stronger correlations for the early summer subseason than for the late summer subseason while overall presenting a diminishing intensity with height over the province. 1. Introduction severely aeff ct the mountain regions of the world [12, 13]. For example, the frequency of extreme droughts has increased in Climate change impacts have been well recognized by the mountainous regions of Northeast and North China [7]. researchers, environmentalists, and politicians [1–3]. eTh In recognition of this reality, the United Nations Secretary recurrence of extreme climate events has been identified General declared the year 2002 “eTh International Year of as evidence of climatic change [4]. Studies in America [5], Mountains.” In face of climate change, mountain biota will Europe [6], and Asia [7] have been done to examine the suffer the most, as it is adapted to relatively narrow ranges prevalence of extreme climate variables. In Africa, similar of precipitation and temperatures [14]. Studies on mountain studies have been done in the north and central parts of regions are slowly gaining attention in other parts of the world the continent [8, 9] but with little attention given to the such as America [15], Asia [16], and slowly Africa [12, 17]. south. Southern Africa is regarded as semiarid with high Anumberofdrought indiceshavebeendevelopedto annual rainfall variability that has drought as a common understand drought. A drought index is a quantitative mea- feature [10]. eTh impact of drought is ranked top compared to sure that characterizes drought levels by assimilating data other natural disasters [11]. While several studies have been from one or several indicators, for example, precipitation done on climate variability in terms of rainfall, contrasting [18]. Among the most widely used indices are the Palmer resultshavebeenobservedespeciallyfor semiarid regions Drought Severity Index (PDSI), Crop Moisture Index, and the [3]. Research studies that have been done on high elevation Standardized Precipitation Index (SPI) [19]. SPI has become areassincethe1990shaveconcludedthat climatechangewill popular due to simplicity in its calculation, as it only requires 2 Advances in Meteorology ∘ ∘ ∘ rainfall data. It also allows for the detection of various types and 30.7 S and longitudes 24.3 Eand29.8 Eandsprawls of drought that aeff ct different systems and regions while over high plains which stretch over the Maluti-Drakensberg enabling calculation of estimates of the duration, magnitude, Mountains along the border with Lesotho. eTh Drakensberg and intensity of drought [20]. Agricultural drought, also Mountains cover approximately 1,125 km and reach heights known as soil moisture drought, exists when the available of over 3,475 metres above sea level. It is South Africa’s main soil moisture is insucffi ient to sustain the crops and forage watershed and is the source of the Orange River, one of the resulting in a decrease in agricultural productivity [21]. While major sources of water supply for commercial agriculture in SPI is designed for meteorological droughts, it can also be thecountry.Theprovincecoversanareaof129,825km .The used to detect agricultural drought when calculated on a climate of the Free State Province is highly inu fl enced by the short time scale since soil moisture conditions respond to geographic location, which is continental and is characterized precipitation anomalies on a relatively short scale [21]. On by warm to hot wet summers and cool to cold dry winters. longer time scale (say 12 or 24 months), it is more suitable The rainfall season spans from October to March. Greatest for water resources management purposes. The SPI is based amounts are received in January and February, giving the on the probability of precipitation occurrence over a given JFM subseason more average total precipitation than the time period. Since precipitation is not normally distributed, OND subseason, with 56 percent contribution towards the a mathematical transformation is applied so that the trans- seasonal total precipitation. A west to east seasonal monthly formed precipitation values follow a normal distribution. averageprecipitation increase hasbeenobservedoverthe It is aeft r this transformation to normal distribution that province with the extreme west receiving a monthly average the classification of frequencies of the extreme and severe of not more than 50 mm while the extreme east receives an droughts experienced at any location and timescale becomes approximate of over 100 mm during the October to March consistent [20]. eTh SPI, however, like any other index, has period (Figure 1(b)). its limitations; it does not take into account the contri- bution of other variables to drought severity, for example, 2.2. Data. Monthly mean precipitation (mm) covering the atmospheric evaporative demand [22]. In addition, SPI the 1960–2013 period was extracted from Climate does not provide reliable estimations in arid climates [23]. Explorer’s Climate Research Unit (CRU TS4.01) gridded Despite these shortcomings, the SPI remains one of the ∘ ∘ data file at 0.5 × 0.5 spatial resolution (available from most widely used and acceptable indices in climate research, https://climexp.knmi.nl). The wind data was downloaded with its tried and tested effectiveness in decision making from ERA-interim (Resolution T255), which is currently the [24]. largest global atmospheric reanalysis dataset and produced Understanding the climatic conditions in mountain by the European Centre for Medium-Range Weather regions is not only important for agriculture but for bio- Forecasts(ECMWF).Theanalysiswas doneonline using diversity studies also, since life in the mountains is not Climate Explorer. These data products are principally derived driven by elevation per se but by the climatic conditions from observations in line with the World Meteorological associated with elevation [25]. In this study, the only part Organization guidelines and are freely available online. In of the mountain range that lies within the political borders many developing countries observational data with required of Free State Province was considered in order to provide a data lengths and quality are unavailable [29]. It is because comparative analysis between mountainous (to the east) and of this limitation that the use of reanalysis data has gained low-lying areas located to the west of the province. An in- popularity within the climate science field. Several studies depth analysis of mountain related intensity, frequency, and have shown that while the use of these datasets may not be spatial extent of extreme droughts at a higher resolution is as accurate as station data, the difference is insignificant in necessary for a more comprehensive assessment of drought many of the cases [30]. eTh period of 53 years meets the impacts. This work focused on short term meteorological minimum climatic analysis duration requirement outlined droughts (3-month SPIs for December and March) because in the World Meteorological Organization guidelines they can serve as proxies for agricultural droughts which are [31]. important in the Free State Province. Understanding these droughts will be critical for understanding the resilience A total of 107 grid points was used in the analysis covering of theregioninthefuture.Hence,theobjectiveofthis theFreeStateProvince.Thegridpointsintheimmediate study is to analyze severe to extreme drought as well as borders of the province were also included as the influence of severe and extreme wet conditions in mountain regions these points contributes to the overall climate characteristics of southern Africa, with particular emphasis on the Drak- of areas within the province (Figure 2). The province was ensberg Mountains within the borders of the Free State divided into three homogenous subregions or clusters based Province of South Africa for the period between 1960 and on altitude. This was done using Hot Spot Analysis (HSA) 2013. in an ArcGIS (version 10.3) environment. The use of special analyses such as HSA helps in identifying groups of locations with high spatial homogeneity and it has been shown that 2. Materials and Methods such segregation may significantly increase the quality of 2.1. Study Area. The study area is confined to the political interpolation results [32]. Cluster analysis does not require bordersoftheFreeStateProvinceofSouthAfrica (Fig- a priori knowledge on data structure such as normality ure 1(a)). The province is situated between latitudes 26.6 S conditionasrequiredinother statisticaltests [33]. 40 Advances in Meteorology 3 ∘   ∘   26 0 0 E 28 0 0 E ∘   ∘   26 0 0 S 26 0 0 S Parys Free State Helbron Frankfort Bothaville Vrede Kroonstad Reitz Warden Welkom ∘   ∘   Virginia 28 0 0 S 28 0 0 S Bethlehem Harrismith Bultfontein Senekal Winburg Boshof Brandfort Ficksburg Bloemfontein Ladybrand Jagersfontein Wepener ∘   ∘   30 0 0 S 30 0 0 S Smithfield Philippolis Zastron Bethulie 0 20 40 80 120 160 (km) ∘   ∘   32 0 0 S 32 0 0 S ∘   ∘   26 0 0 E 28 0 0 E Towns Altitude m.a.s.l. High: 3277 Low: 1041 (a) Location of the Free State Province in South Africa Parys Frankfort Helbron Bothaville Vrede Kroonstad Reitz Warden Welkom Virginia Bethlehem Harrismith Bultfontein Senekal Winburg Boshof Brandfort Ficksburg BLOEMFONTEIN Ladybrand Jagersfontein Wepener Smithfield Philippolis Zastron Bethulie 0 30 60 120 180 240 (km) Average precipitation Elevation m.a.s.l. Seasonal monthly average High: 3277 precipitation Low: 1041 mm High: 140.3 Provincial Boundary Low: 38.9 (b) Distribution of seasonal monthly average precipitation (October–March)for Free StateProvinceinSouth Africa.The data is from 1960 to 2013 Figure 1 HSAisalocalspatialpatternanalysistoolwhich worksby results associated with each feature determine if the differ- considering each feature within the context of neighbouring ence is statistically significant or not. Hence, this variable features and determines if the local pattern (a target feature could potentially improve the results of other interpolation and its neighbours) is statistically different from the global techniques when used instead of elevation as an auxiliary pattern (all features in the dataset). eTh 𝑍-score and 𝑝 value variable.TheclusteringbyHSAenablesthe comparison of 110 4 Advances in Meteorology Table 1: Categorization of dryness/wetness. SPI value occurrence % occurrence Nominal SPI class ≥1.645 ≤5Extremelywet 1.282 to 1.644 6–10 Severely wet 0.842 to 1.281 11–20 Moderately wet 0.524 to 0.841 21–33 Slightly wet −0.523 to 0.523 34–50 Normal −0.841 to−0.524 21–33 Slight drought −1.281 to−0.842 11–20 Moderate drought −1.644 to−1.282 6–10 Severe drought ≤−1.645 ≤5 Extreme drought Adaptedfrom[26]andmodifiedby[27]toenhance suitabilityforapplicat ion in southern Africa. The original classification was developed by [28]. analysis in such a way that OND SPI for 1960 matched JFM SPI for 1961. This is because the rainfall season overlaps two calendar years; thus, the continuous season of October–March is split into October–December of the previous year (1960) and January–March of the following year (1961). To avoid splitting the precipitation season, the meteorological year in southern Africa starts in July and ends in June of the following calendar year and hence is usually referred to as a season 1960/61. In this study, the subseasons were analyzed separately. Trends in the two subseasons were studied using parametric tests for these are regarded as more powerful especially for data that is regarded as independent and normally distributed [36]. (km) 0 30 60 120 2.3. Computing SPI. The SPI was calculated using monthly precipitation data for 53 years (1960–2013) following detailed Altitude procedure described by [28] and expressed using the formula GiZScore (clusters) −2.488–−0.4353 2.191–5.938 𝑥 −𝑥 −0.4352–2.190 𝑖 Provincial Boundary (1) SPI= , Figure 2: Locations of clusters in the Free State Province (Cluster 1 where𝑥 is the monthly rainfall amount,𝑥 is the mean, and𝑠 (green), Cluster 2 (yellow), and Cluster 3 (red)). is the standard deviation of rainfall calculated from the whole time series of monthly values. Using the SPI classification by [27] (Table 1), only severe droughts and wet characteristics of the different areas within to extreme droughts (SPI≤−1.282) were analyzed so as to the province. Further, the Inverse Distance Weighting (IDW) amplify the drought signal. method of interpolation was used in preparing the frequency Frequencies of drought years and the proportion of at maps.TheIDWtechniqueassumesthateachmeasuredpoint least severe drought years in the two subseasons in the has a local influence that diminishes with distance; thus it three individual clusters were calculated. (𝑁)represents gives an output surface that is sensitive to clustering and the thetotal countofdrought yearsfromslightdroughtto presence of outliers. extreme drought. The percentage frequencies were calculated Monthly precipitation values were used to compute as follows: SPIs at 3 months’ scale, for periods January–March and Frequency(%) October–December using Drought Index Calculator (DrinC). The distinct climate controls of the subseasonal Number of severe extreme drought years ( ) rainfall in southern Africa warranted that the analysis be (2) Total number of drought years in53 years done by splitting October–December from January–March. During October to December, usually regarded as early ×100, summer, the atmosphere has a predominant extratropical nature with frequent cut-off lows [34]. In January and wheredroughtisthetotal countofSPIs≤−0.524. February, tropical circulation systems are much more To determine how drought severity is linked to altitudinal prevalent over South Africa with local convection variability we applied the composite analysis technique. We dominating [35]. eTh two subseasons were arranged for extracted the vfi e worst drought years of at least SPI ≤−1.282 Advances in Meteorology 5 2.1 2.0 2.0 1.5 1.8 1.6 1.5 1.5 1.0 1.2 1.2 1.0 0.9 0.8 0.5 0.6 0.5 0.4 0.3 0.0 0.0 0.0 0.0 −0.3 −0.4 −0.5 −0.6 −0.5 −0.8 −0.9 −1.0 −1.2 −1.2 −1.0 −1.5 −1.5 −1.6 −1.8 −2.1 −2.0 −2.0 −1.5 (a) (b) Figure 3: Temporal manifestation of the SPI (bars) for (a) OND and (b) JFM at provincial level. In the insert are the 10-year running variance envelope (solid lines) and mean (dashed line). The SPI is from 1960 to 2013. at provincial scale. eTh years comprised 1965, 1972, 1990, 1994, a shift to a wetter epoch aeft r 1987 though the period and 1997. Averages for each of the clusters were plotted for the is punctuated with more extreme droughts. From the 10- five severe drought years. year running variance (solid line), it can be noted that the interannual variability of SPI for JFM in Figure 3(b) is less variable and the SRSD indicates that the period is devoid 3. Results and Discussion of any significant shift. u Th s, between 1960 and 2013, more severe drought (count) conditions were experienced during 3.1. Temporal Variations of Drought Intensity in the Free State theONDsubseasonthanduringtheJFMsubseason;five Province. Using grid point data, average SPIs were calculated (5) years against three (3) years while more severe wet years forthewholeprovincetogivetheprovincial averageSPI were experienced during the JFM subseason than during the and for individual clusters. eTh inclusion of provincial scale OND subseason. A two-tailed paired samples𝑡-test revealed was done to enable comparison of droughts (wet) years at that there is a significant difference between OND and JFM provincial scale with those at cluster scale. This would assist drought/wet magnitude for the 53 years’ period. eTh JFM in determining the ideal spatial scale to use for drought subseason has more intense droughts/wet conditions than preparedness and response for the region. Figures 3(a) and OND subseason𝑡(49) = 6.727 and 𝑝≤0.05.Itcan be noted 3(b) illustrate the temporal evolution of the SPI at provincial that the manifestation of the SPIs in the two subseasons dieff rs level for OND and JFM, respectively. For the OND subseason, markedly which indicates contrasting processes responsible severe drought years (SPI≤−1.282) were 1994 (−2.023), 1990 for the droughts development within the subseasons. (−1.974), 1965 (−1.653), 1997 (−1.580), and 1972 (−1.393) while The 10-year running mean demonstrates that the decades the wettest year (SPI≥ 1.282) was 2001 (1.575). A shift to higher variance in the ten-year running variance (red line) before the early 1990s had epochs of successive drought and wet years before oscillating around the mean thereaeft r. On is confirmed as a statistically significant shift in variance by the Sequential Regime Shift Detector (SRSD) which has a 𝑝 the overall it is noted that the region is marked by droughts whose magnitudes have intensified in the last three decades. value of 0.002 in the early summer subseason (OND). This Since the JFM SPI patterns appear to be more random, we systematic pattern in variance could represent a coherent influence from an external large-scale process. The 10-year are more inclined to investigate further the OND period in search of less complex systematic drivers of the regional running mean (dashed line in Figure 3) indicates that on extreme events. It appears there is no clear-cut reason for the average the pre-1980s were characterized by rainfall deficits while the post-1980s period had predominantly surplus occurrence of wet or dry extremes in the region during JFM and hence it is likely that more local factors, which are not rainfall. However, it is during the latter period when the worst two droughts occurred in 1990 and 1994. simple to identify in the absence of modeling, play a major role. During the JFM subseason, severe droughts (SPI ≤ −1.282) were experienced in 1992 (−1.990), 2007 (−1.601), and The local relationships to the SPI interannual and spa- 1983 (−1.459). eTh severe wet years (SPI ≥ 1.282) experienced tial variability of the Free State are still largely unknown. However, SPI values are dependent on a combination of during the same subseason were 1974 (1.706), 1976 (1.699), 1988 (1.600), and 1991 (1.366). er Th e appears to have been several unrelated factors, such as number of rainfall events SPI SPI variance SPI SPI variance 6 Advances in Meteorology 30 21 Drought composite 3 Drought composite 2 Drought composite 1 0 0 −1.9 −1.8 −1.7 −1.6 −1.5 −1.4 −1.3 −1.2 −1.1 −1 Cluster 1 Cluster 2 Cluster 3 Drought composite SPI Severe Extreme Frequency (a) (b) Figure 4: (a) Proportion of severe (extreme) droughts to total drought frequencies (SPI ≤ 0.524) and (b) composite SPIs for Clusters 1, 2, and 3 during the OND subseason. eTh composite years are 1965, 1972, 1990, 1994, and 1997. andtheir intensityandtheonsetandcessationoftheseason. at provincial level. The variation of drought intensity with An outstanding characteristic of the Free State region is its cluster indicates that the droughts are not evenly distributed extreme spatial heterogeneity in SPI. It could be assumed in a season and could have their evolution strongly influenced that the heterogeneity is a result of the complex topography by relief. of the region that is largely shaped by the western parts To measure the association of the SPI records in the of the Drakensberg Mountains. It is therefore interesting to three clusters during the OND subseason, linear correlation investigate how this topography influences the drought signal coefficient, 𝑟,was calculated.Pearsoncorrelations of theSPI throughout the region. among the three clusters for the OND subseason show that eTh three clusters obtained from HSA were maintained there is a significant positive relationship between Cluster ∗∗ for the rest of the analysis and were used to assess the 1and Cluster2(𝑟 = .895 ), Cluster 1 and Cluster 3 ∗∗ differences in drought evolution between low-lying areas (𝑟 = .763 ), and between Cluster 2 and Cluster 3 (𝑟= ∗∗ and mountain regions in the southern part of Africa. Severe .894 ). This result implies that dry/wetness variations in all drought years at provincial scale between 1960 and 2013 were the clusters are highly correlated. It clearly shows that the selected only for the OND subseason so as to show the relationship between clusters is weaker and more varied for variation in drought intensity for each of the clusters. This is OND, an indication of weaker spatiotemporal homogeneity. because the JFM subseason is devoid of any signicfi ant shift SRSD found a statistically signicfi ant shift in SPI variance inSPIaswellasawell-denfi edpattern of variability. From in Clusters 1 and 2 around 1990 (𝑝 = 0.000 and𝑝= Figure 4(a), Cluster 3 has the highest drought frequency and 0.003, resp.) during the OND subseason (Figure 5). While the highest percentage of severe droughts while Clusters 1 there is a strong positive correlation between the clusters, and 2 both have equal frequencies and proportion of severe the result of the distribution of the differences between the droughts but lower than those experienced in Cluster 3. paired clusters (paired dieff rences) shows that there is no u Th s, although Cluster 3 recorded the highest frequency of statistically significant difference between the pairs during droughts, more extreme droughts occurred in Cluster 1. eTh the JFM subseason. severe droughts proportion is increasing with cluster while The spatial distribution of drought frequencies in the two the extreme droughts are decreasing. The 𝑡-test results for the subseasonsisshowninFigure6.Thefigureshowshot spots significant difference between droughts of adjacent clusters for drought (SPI≤−1.282) years during the two subseasons. revealed that although there are no signica fi nt dieff rences The highest frequency of droughts is concentrated within the central belt of the province, giving a more horizontal belt between the adjacent clusters’ composites, Clusters 1 and 3 show a significant difference with 𝑝 = 0.1054 (90% confidence of areas most prone to droughts of great intensity during level). Figure 4(b) shows the distribution of drought severity the early summer subseason (Figure 6(a)). This suggests in the three clusters in the composite years summarized in further investigation into the identicfi ation and analysis of the Figure 4(a). The composite years for the three clusters are factors that may be contributing to increased vulnerability of 1965, 1972, 1990, 1994, and 1997. Drought severity decreases these areas to severe droughts during the OND subseason. with increasing altitude, a transition from Cluster 1 to Cluster Common conditions knowntobring aridityare associated 3 during the OND subseason only. None was observed during with anticyclonic conditions but usually their spatial scales of the JFM subseason. However, Cluster 2 experienced the most influence are too large to account for the differing drought intense droughts in 3/5 years of the worst drought years conditions within Free State. During the JFM subseason, Proportion (%) Frequency (N) 5 5.5 5.5 5.5 5.5 Advances in Meteorology 7 1.7 1.8 1.2 1.3 0.7 0.8 0.2 0.3 −0.3 −0.2 −0.8 −0.7 −1.3 −1.2 −1.8 −1.7 −2.2 −2.3 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 (a) (b) Figure 5: Temporal manifestation of SPI with results of the SRSD superimposed to show the shift in the variance (dashed line) for (a) Cluster 1 and (b) Cluster 2 during the OND subseason. eTh data is from 1960 to 2013. Parys Helbron Parys Bothaville Frankfort Vrede Kroonstad Helbron Bothaville Vrede Frankfort Reitz Welkom Kroonstad Warden Reitz Virginia Bethlehem Warden Bultfontein Harrismith Senekal Welkom Winburg Virginia Boshof Bethlehem Bultfontein Harrismith Brandfort Senekal Ficksburg Winburg Boshof Brandfort Ladybrand Ficksburg Bloemfontein Bloemfontein Ladybrand Jagersfontein Wepener Wepener Jagersfontein Smithfield Philippolis Zastron Bethulie Smithfield Philippolis Zastron Bethulie (km) 0 50 100 200 300 400 0 37.5 75 150 225 300 (km) Drought frequency Elevation Towns Elevation value m.a.s.l. Towns Drought frequency High: 3277 High: 3277 Provincial Boundary Provincial Boundary Low: 1041 Low: 1041 JFM drought frequency Drought frequency value value High: 6.13912 High: 7.99957 Low: 3.00101 Low: 4.50257 (a) (b) Figure 6: Frequency of drought years (SPI ≤−1.282) for (a) OND and (b) JFM for the period 1960–2013. however, the areas prone to severe droughts are to the to explain the relationship between droughts and altitude extreme northeastern tip, the central west, and the extreme during the OND period. southeastern areas of the province (Figure 6(b)). Analysis of variance results on the altitudinal influence on drought/wet frequency reveal significance during the The influence of altitude on drought severity was tested JFM subseason only for both severe drought and severe using ANOVA. Results are shown in Table 2. eTh results show wet conditions (in brackets) (Table 3). This means that that altitude has significant influence on drought severity in frequenciesofJFMseveredroughtandwetepisodesvarywith theFreeStateProvince in theOND subseasonbut notduring altitude. the JFM subseason (in brackets). This influence is shown to be signicfi ant ( 𝐹 = 2.54;𝑝 > 0.01). This means that {8,2637} the OND droughts vary significantly with altitude while the 3.2. Wind Patterns Associated with Drought/Wet Events over JFM drought variations are not associated with variations in Free State. Convection is less prominent during the OND altitude, conrm fi ing that the systems that bring precipitation period and hence relief rainfall should dominate the moun- totheregionaredieff rent.Thosefor theONDarealtitude tainous region of Free State. Moreover, since relief has been relatedwhile thosefortheJFM arenot.Thisimpliesthat foundtosignicfi antlyimpactonthesubseasonaldrought relief rainfall, which is wind related, is a possible candidate magnitude, then a more likely factor to affect the rainfall 5.5 5.5 5.5 SPI SPI 5 8 Advances in Meteorology Table 2: Influence of altitude on drought severity for OND and JFM subseasons (in brackets). Altitude Sum of squares df Mean square 𝐹 Sig. 669848.947 83731.118 2.536 .009 Between groups 8 (235716.673) (29464.584) (.888) (.526) 8706611.38 33017.297 Within groups 2637 -- (87500743.65) (33181.928) 87736460.33 Total 2645 - - - (87736460.33) Table 3: Influence of altitude on severe drought (wet) frequencies for JFM subseason. Altitude Sum of squares df Mean square 𝐹 Sig. 964097.644 10 96409.764 5.591 .000 Between groups (1072072.773) (12) (89339.398) (5.856) (.000) 672483.237 39 17243.160 Within groups -- (564508.107) (37) (15256.976) 1636580.880 49 Total --- (1636580.880) (49) received at a particular place is its wind speed and direction. which are northerly over the region that are driven by a This is because these two parameters determine the amount high pressure anomaly area to the southwest of the region of moisture carried by an air mass and the rate of relief as reflected by the geopotential heights. eTh se winds advect forcedadiabaticcoolingwhichissubsequentlytranslatedto relatively warmer and humid tropical air into Free State that rainfall. This implies that a particular place can be in the is forced to rise as they approach the Drakensberg uplands rain shadow/windward side of the mountain depending on to the south. We hypothesize that the low-level northerlies the prevailing wind average characteristics of that season. would enhance the orographic effects of the Drakensberg eTh refore, in this section we analysed the wind characteristics highlands over the Free State Province, where the rainfall for the drought and wet events to determine the extent of the has been noted to generally increase with altitude. Therefore wind impacts in defining the drought/wet spatial properties northerly wind anomalies are linked to wet events while for selected seasons for the Free State Province. southwesterlywindanomalies arelinkeddroughtsoverthe The strong linkage between drought and altitude is an Free State region. interesting observation (Table 2). Rain producing systems To illustrate the strong regional connection of the wind in the vicinity of the Drakensberg Region consist of two factor to the SPI, we present in Figures 8(a) and 8(b) types [37]. The predominant sources of rainfall are large- the spatial correlation of the averaged Free State SPI with scale line thunderstorms and orographically induced storms. the surface zonal and meridional winds. The significant eTh former are more prevalent during the solar enhanced association with the two wind elds fi is quite conspicuous convection of the JFM subseason. As such, the latter are where the droughts are related to westerly and southerly wind relatedtothe surfacewindcharacteristics like humidity, anomalies while wet episodes are linked to the reverse wind wind speed, and direction which should account for most of anomalies. The impacts appear to weaken drastically towards the rainfall during OND as this has a large bearing on the the summit of the Drakensberg Mountains. This weakening windward and rain shadow aspect of a particular place. In this demonstrates the visible impacts of the increasing altitude regard, in Figure 7 we present the drought/wet composites from the west and north of the mountains. us, Th despite the in relation to the zonal and meridional anomalous winds at amount of moisture that is carried by the winds to determine the near surface (850 hPa). It is noted that the surface winds the drought/wet episodes, the direction of the wind relative appear tobe themostrobustlinktothe droughtand wet to the uplands also determines which place becomes the rain events during this subseason. Figure 7 indicates that, during shadow and which does not. drought, near surface winds are westerly (a) and northerly (b) with an anomalous high pressure having been built up 3.3. Altitude Modified SPI Relationship with ENSO. The to the west of the region (c). This is further illustrated in ENSO relationship with the JFM SPI for Free State is weak Figure 7(c) where the vector winds are southeasterly over the with an inverse correlation value of −0.29 and a 𝑝 value Free State region that are driven by a low pressure anomaly of 0.032 while that for OND SPI is relatively stronger with situated to the southeast of the subcontinent as indicated by avalue of −0.385 and a 𝑝 value of 0.004. In Figure 9(a) the geopotential heights. This is bound to advect relatively we present the relationship between ENSO and the three cold and relatively drier maritime winds into the region. eTh clusters during OND and JFM. It is interesting to note that wet year composites indicate the opposite where the winds the relationship is strongly determined by the clusters in both are strongly northerly (e) with almost no zonal wind anomaly cases hence signifying the impact of relief on the ENSO’s (d). Figure 7(f) reiterates the corresponding vector winds influence on the extreme rainfall events. It is evident that Advances in Meteorology 9 NCEP/NCAR reanalysis NCEP/NCAR reanalysis NCEP/NCAR reanalysis 850mb zonal wind (m/s) Composite Anomaly 1981–2010 climo 850mb meridional wind (m/s) Composite Anomaly 1981–2010 climo 850mb geopotential height (m) Composite Anomaly 1981–2010 climo ∘ ∘ ∘ NOAA/ESRL Physical Sciences Division NOAA/ESRL Physical Sciences Division NOAA/ESRL Physical Sciences Division 25 S 25 S 25 S ∘ ∘ ∘ 0.6 1.2 26 S 26 S 26 S ∘ ∘ ∘ 27 S 27 S 27 S 0.4 0.8 ∘ ∘ ∘ 28 S 28 S 28 S ∘ ∘ ∘ 0.4 29 S 0.2 29 S 29 S ∘ ∘ ∘ 30 S 30 S 30 S ∘ ∘ ∘ 0 0 31 S 31 S 31 S ∘ ∘ ∘ 32 S 32 S 32 S −0.2 −0.4 ∘ ∘ ∘ 33 S 33 S 33 S ∘ ∘ ∘ −0.4 −0.8 34 S 34 S 34 S ∘ ∘ ∘ 35 S 35 S 35 S −0.6 −1.2 Oct to Dec: 1965, 1990, 1994, 1997 Oct to Dec: 1965, 1990, 1994, 1997 Oct to Dec: 1965, 1990, 1994, 1997 (a) (b) (c) 850mb zonal wind (m/s) Composite Anomaly 1981–2010 climo 850mb meridional wind (m/s) Composite Anomaly 1981–2010 climo 850mb geopotential height (m) Composite Anomaly 1981–2010 climo ∘ ∘ ∘ NOAA/ESRL Physical Sciences Division NOAA/ESRL Physical Sciences Division NOAA/ESRL Physical Sciences Division 25 S 25 S 25 S 0.8 ∘ ∘ 0.7 ∘ 26 S 26 S 26 S ∘ ∘ ∘ 0.6 27 S 27 S 0.5 27 S ∘ ∘ ∘ 28 S 0.4 28 S 28 S 0.3 ∘ ∘ ∘ 29 S 29 S 29 S 0.2 ∘ ∘ ∘ 0.1 30 S 30 S 30 S ∘ 0 ∘ ∘ 31 S 31 S 31 S −0.1 ∘ ∘ ∘ −0.2 32 S 32 S 32 S ∘ ∘ ∘ −0.3 33 S 33 S 33 S −0.4 ∘ ∘ ∘ 34 S 34 S 34 S −0.5 −0.6 ∘ ∘ ∘ 35 S 35 S 35 S −0.8 −0.7 Oct to Dec: 1995, 1996, 2001, 2009 Oct to Dec: 1995, 1996, 2001, 2009 Oct to Dec: 1995, 1996, 2001, 2009 (d) (e) (f ) Figure 7: Zonal, meridional, and geopotential height anomalies for dro ughts (a), (b), and (c) and wet events (d), (e), and (f), respectively. Scalar winds are indicated in (c) and (f) with the years constituting the composites shown in the inserts. Corr Oct–Dec averaged FSP OND SPI 1 index Corr Oct–Dec averaged FSP OND SPI 1 index with Oct–Dec averaged ERA–int taux 1979 : 2012 with Oct–Dec averaged ERA–int taux 1979 : 2012 ∘ ∘ 24 S 24 S ∘ ∘ 25 S 25 S ∘ ∘ 26 S 26 S ∘ ∘ 27 S 27 S ∘ ∘ 28 S 28 S ∘ ∘ 29 S 29 S ∘ ∘ 30 S 30 S ∘ ∘ 31 S 31 S ∘ ∘ 32 S 32 S ∘ ∘ 33 S 33 S ∘ ∘ 34 S 34 S ∘ ∘ 35 S 35 S (a) (b) Figure 8: Spatial correlation of Free State OND SPI with (a) zonal surface winds and (b) meridional surface winds during OND for the period from 1960 to 2013. Tau-𝑥 and tau-𝑦 mean surface winds in the zonal and meridional direction, respectively. ENSO’s impacts are strongest in the lowlands but weakest but also shows a greater influence on Cluster 1. As such the over the highlands. Relief modifies the rainfall received at a altitude for the Free State Province strongly demonstrates a place and the higher the altitude is, the more modification to robust relationship with ENSO. rainfall occurs. Hence, this observation reiterates the impor- tant role played by relief in weakening ENSO’s impacts on the 4. Conclusions rainfall events. It is, however, noted that the correlations are stronger for the OND than for the JFM period for all clusters. The objective of the study was to assess the influence of Figure 9(b) supports the observation that El Nino not only altitude on drought severity and frequency in the Free State does impact more strongly on the OND than the JFM period Province of South Africa. The study revealed the spatial ∘ ∘ 20 E 20 E 20 E ∘ ∘ 21 E 21 E ∘ ∘ 21 E 22 E 22 E ∘ ∘ 23 E 23 E ∘ ∘ ∘ 22 24 E 24 E ∘ ∘ 25 E 25 E ∘ ∘ 23 E 26 E 26 E ∘ ∘ 27 E 27 E ∘ ∘ 24 E 28 E 28 E ∘ ∘ ∘ 29 E 29 E ∘ ∘ 25 E 30 E 30 E ∘ ∘ ∘ 31 E 31 E 26 E ∘ ∘ 32 E 32 E ∘ ∘ 33 E 33 E 27 E ∘ ∘ 34 E 34 E 28 E 29 E 30 E 31 E 32 E ∘ ∘ 20 E 20 E ∘ ∘ 33 E 21 E 21 E ∘ ∘ ∘ 22 E 22 E ∘ ∘ 34 E 23 E 23 E ∘ ∘ 24 E 24 E ∘ ∘ 35 E 25 E 25 E ∘ ∘ 26 E 26 E ∘ ∘ 27 E 27 E ∘ ∘ 28 E 28 E ∘ ∘ 29 E 29 E ∘ ∘ 20 E 30 E 30 E ∘ ∘ 31 E 31 E ∘ ∘ 21 E 32 E 32 E ∘ ∘ 33 E 33 E ∘ ∘ 22 E 34 E 34 E 23 E 24 E 25 E 26 E 27 E ∘ ∘ 20 E 20 E ∘ ∘ 28 E 21 E 21 E ∘ ∘ 22 E 22 E ∘ ∘ 29 E 23 E 23 E ∘ ∘ ∘ 24 E 24 E ∘ ∘ 30 E 25 E 25 E ∘ ∘ ∘ 26 E 26 E 31 E ∘ ∘ 27 E 27 E ∘ ∘ 28 E 28 E 32 E ∘ ∘ 29 E 29 E ∘ ∘ 30 E 30 E 33 E ∘ ∘ 31 E 31 E ∘ ∘ ∘ 32 E 32 E 34 E ∘ ∘ 33 E 33 E ∘ ∘ ∘ 35 E 34 E 34 E 10 Advances in Meteorology Nino 3.4 & Cluster 3 Cluster 3 El Nino comp Nino 3.4 & Cluster 2 Cluster 2 El Nino comp Nino 3.4 & Cluster 1 Cluster 1 El Nino comp −1 −0.8 −0.6 −0.4 −0.2 0 0.2 SPI values SPI values JFM JFM OND OND (a) (b) Figure 9: (a) Relationship between ENSO and the three clusters and (b) the El Nino composite SPI values for the three clusters during OND andJFM.Thedataisfrom1960to2013andtheElNinocompositesarecomprisedofthelowestNino3.4valuesof1965/66,1972/73,1982/83, 1987/1988, 1991/92, 1997/98, and 2002/03. heterogeneity in the SPI over Free State and showed that to more abundant rainfalls,” Geophysical Research Letters,vol. 37, no. 6, Article ID L06704, 2010. the factors governing drought interannual variability varied markedly within the region and from the early part of the [2] A. S. Singh and M. B. Masuku, “Sampling techniques determi- nation of sample size in applied statistics research: an overview,” rainfall season to late subseason. Highland areas (Cluster International Journal of Commerce and Management, vol. II, no. 3) have the highest frequency of droughts although more 11, pp. 1–22, 2014. extreme droughts occurred in the extreme western low-lying [3] N. Batisani and B. Yarnal, “Rainfall variability and trends in regions (Cluster 1). Significant differences among clusters semi-arid Botswana: implications for climate change adaptation during the early summer season, OND, were observed. policy,” Applied Geography,vol.30,no.4,pp. 483–489, 2010. These results are evidence that altitudinal variations have [4] IPCC, Climate Change 2014 Synthesis Report.Contribution of asignicfi antimpactondroughtseverityduring theearly Working Groups I, II and III to the Fifth Assessment Report of summerseasonbutnot inthelatesummerseason.The the Intergovernmental Panel on Climate Change,IPCC, Geneva, study targeted droughts of SPI≤−1.282. The relationship Switzerland, 2014. between ENSO and SPI indicates stronger correlations for the [5] C. A. Costa and D. Santos, “Trends in indices for extremes in early summer subseason than for the late summer subseason. daily air temperature over Utah, USA,” Revista Brasileira de The local impacts of ENSO are strongly impacted on by Meteorologia,vol.261,pp. 19–28,2011. altitude where the lower regions are stronger but weaker [6] A. Moberg and P. D. Jones, “Trends in indices for extremes at higher altitudes. Mountains are an important ecosystem in daily temperature and precipitation in central and western resource that warrants effective conservation strategies for Europe, 1901–99,” International Journal of Climatology,vol.25, what transpires in the region govern numerous economic no.9,pp. 1149–1171, 2005. activities oen ft well beyond the boundaries of the mountain [7] M.J.Zhang,J.Y.He,B.L.Wangetal.,“Extremedroughtchanges areas themselves. It is from this perspective that Disaster in Southwest China from 1960 to 2009,” Journal of Geographical Management agencies need to consider the vulnerability of Sciences,vol.23, no.1,pp. 3–16,2013. the province to drought conditions. [8] J.Esper,D.Frank,U.Bun ¨ tgen, A. Verstege, J. Luterbacher, and E. Xoplaki, “Long-term drought severity variations in Morocco,” Geophysical Research Letters,vol.34, no.17,ArticleIDL17702, Conflicts of Interest [9] S. M. Vicente-Serrano, S. 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