CHAPTER 3: STUDY AREA AND METHODOLOGY
3.7 Research methods
3.7.2 Quantitative methods
Creswell (2003:18), defines the quantitative approach as “one in which the investigator primarily uses post positivist claims for developing knowledge (for example, cause and effect thinking, reduction to specific variables and hypotheses and questions, use of measurement and observation, and the test of theories)”. Common approaches to quantitative research include: surveys, correlational research, exploratory research, descriptive research, experimental research and trend analysis (Sukamolson, 2010).
Quantitative research findings may be predictive, confirming or explanatory (Williams, 2007). This study is largely qualitative in approach with quantitative methods used solely to analyze climate trends.
Advantages of using quantitative methods include: objectivity, generalizability of results and repeatability of the process (González López and Ruiz Hernández, 2011). Quantitative data collection is also relatively quick and less time consuming when using statistical software for analysis and the researcher is independent of the results (Johnson and Onwuegbuzie, 2004). Limitations of quantitative methods include inability to explore a phenomenon in-depth (Sukamolson, 2010), the lack of rigour and the disregard of socio-cultural contexts (Kura and Sulaiman, 2012).
The first research question required an investigation of climate trends. Historical meteorological data was used to determine past temperature and rainfall trends in Ndwedwe-Cibane. Women’s perceptions and historical meteorological data were compared for similarities and/or discrepancies. Downscaled GCMs were analyzed to project future temperature and rainfall change. This is essential to develop appropriate adaptation strategies for the future and to see if past and current adaptation strategies were or are appropriate considering the trends from data. This approach comes highly recommended by Lumsden et al. (2009) who assert that meteorological variables should be examined in support of climate change adaptation efforts in Southern Africa.
3.7.2.1 Temperature and rainfall trend analysis
Initially rainfall data was gathered from the Ndwedwe meteorological station (due to proximity to the study area) while temperature data was from Mount Edgecombe, the closest station to Ndwedwe that provides temperature data. There are no temperature recording stations in Ndwedwe. However, after careful analysis of the data, the researcher decided to use rainfall data from the Mount Edgecombe station because the data from Ndwedwe station had too many missing values. Both stations belong to the South African Weather Service - SAWS organization (see table 3.7.2.1 and figure 3.7.2.1 for further details). Monthly minimum and maximum temperatures (°C) and monthly rainfall data (mm) from 1965 to 2015 were used in this study. A period of 50 years for analysis was chosen because “climate is statistical information and is usually based on the weather in one area averaged for at least 30 years”
(Ramamasyet et al., 2007 as cited by Phuong, 2011:3).
75 Table 3.7.2.1: Details of the meteorological station from which temperature and rainfall data was collected (Source: South African Weather Service, 2016 via email)
Station Name
Latitude (S) Longitude (E) Approx.
distance from Cibane (km)
Data years Rainfall Temp
Ndwedwe 29°37'0" 30°56'0" 15 1965-2015 x
Mount Edgecombe
29°42'24.0" 31°02'46.0" 29.8 1965-2015 1965-2015
Figure 3.7.2.1: Location of the meteorological stations in relation to the study area
3.7.2.1.1 Mann-Kendall trend test
The Mann Kendall - MK test can be used to analyze past and future trends of climate variables including temperature and rainfall (Mondal et al., 2012). It is widely used for trend detection in climatological data (Mavromatis and Stathis, 2011). The World Meteorological Organization - WMO (2000) as cited by
76 Lespinas et al. (2010) has dubbed the MK test as the official tool for detection of trends in the context of climate variability and change.
XLSTAT 2013 plug-in of Microsoft excel with 5% significance was downloaded from https://www.xlstat.com/en/download and used to perform the non-parametric MK test to analyze temperature and rainfall data in order to determine the direction of the trend and the significance of the trend. The MK test was chosen on the basis that it robust, simple, can cope with missing data and the data does not need to be normally distributed (Mapurisa and Chikodzi, 2014). To compare participants’
climate change perception with the meteorological data, time series graphs were plotted, trend lines inserted then tested for significance using MK trend test.
The MK test statistic S is calculated using the formula
(1) where xj and xk are the annual values in years j and k, j > k, respectively, and
(2)
When S value is high and positive it implies that the trend is increasing, while a very low negative value indicates a decreasing trend. However, to statistically quantify the significance of the trend it is necessary to calculate the probability associated with S and the sample size n. The variance of S is given as
(3)
Where q is the number of tied groups and tp is the number of data values in the pth group. The values of S and VAR(S) are used to calculate the test statistic Z as given below
77 (4)
The Z value is tested at 95% level of significance (Z0.025=1.96). If the Z value is negative and the absolute value is greater than the level of significance, the trend is decreasing. On the other hand if the Z value is positive and greater than the level of significance, the trend is increasing. There is no trend if the Z value is less than the level of significance (Olofintoye and Sule, 2010).
3.7.2.1.2 Sen’s Slope Estimator
Sens slope estimator was used to measure the slope of the trend. Sens slope estimator is a non-parametric procedure developed by Sen (1968) as cited in Drápela and Drápelová (2011). It is the true slope (change per unit time) of a linear trend (Drápela and Drápelová, 2011; Olofintoye and Sule, 2010). Advantages of this procedure include: the data need not conform to any distribution, missing values are allowed and it is not affected by single data errors or outliers (Olofintoye and Sule, 2010). The linear model f (t) can be described as
(5) where Q is the slope, B is a constant and t is time.
To derive the slope estimate Q in equation the slopes of all data value pairs are calculated using the equation
(6) Where: Q = slope between data points Xj and Xk
Xj = data measurement at time j Xk = data measurement at time k
78 j = time after time k, Xj and Xk constitute the pairs of observations identified by place in the series. The median of these estimates is Sen’s estimator of slope, where j>k.
Additionally, to compare results obtained from the MK test and Sen’s slope estimator, linear trend lines were plotted for each state using Microsoft Excel 2011.
3.7.2.2 Temperature and rainfall change projections
GCMs are used to project future climate change (Aksornsingchai and Srinilta, 2011). However GCM spatial resolution is low (Aksornsingchai and Srinilta, 2011). Since it represents an area of about 300km and therefore has limited value in local studies (Ngcobo, 2013; Tadross et al., 2008). The climate of a particular area is influenced by local topographic features which are not considered in global projections due to the low resolution (Barichievy, 2009; Sarhadi et al., 2016), hence the importance of downscaling GCMs to improve spatial detail. Downscaled GCMs assume there is a constant relationship between the large scale (predictor) and local climate (predictands) (Aksornsingchai and Srinilta, 2011). GCMs are downscaled to facilitate adaptation strategies (Marengo et al., 2009). It is important to note that projections regardless of scale should always be treated with caution, they show possible scenarios for the future which are not definite (Daron, 2014).
Statistical downscaling of Coupled Model Inter-comparison Project Phase 5- CMIP5 projections (across 10 different GCMs) to station locations in South Africa have been undertaken by CSAG for different periods. In this study the 2015 to 2035 (relative to 1980-2000) period was selected, an arbitrary period of twenty years to account for inter-annual variability while projecting for the Representative Concentration Pathway - RCP 4.5. These projections are available on the CSAG Climate Information Platform - CIP website (http://cip.csag.uct.ac.za/webclient2/datasets/africa-merged-cmip5/) and are presented under section 4.3 in this study.
Statistical downscaled CMIP3 projections are also available but were not used because firstly, they only present total monthly rainfall anomalies under A2 and B1 SRES scenarios while the researcher is interested in rainfall and temperature changes. CMIP5 projections included total monthly rainfall;
average minimum temperature and average maximum temperature which are important variables in this study. Secondly, according to Kumar et al. (2014) CMIP5 models are more advanced than CMIP3.
CMIP5 models typically have finer resolution processes, and are well-integrated into earth system components (Kumar et al., 2014). Moreover, some of the CMIP5 models include carbon cycle feedback, atmospheric chemistry, aerosol, biogeochemistry, and dynamic vegetation components (Kumar et al., 2014). Thirdly, Sun et al. (2015a) add that CMIP5 projections encompass a larger range of possible future greenhouse gas concentrations, which translates into more climate outcomes. Kusunoki and
79 Arakawa (2015), Sun et al. (2015b) and several other authors (Kug et al. 2012, Liu et al. 2012, and Monerie et al. 2012) as cited in Kumar et al. (2014) have observed CMIP5 simulation improvements in their studies.
The focus of this study is exclusively on RCP which is an improvement from the SRES approach (Terink et al., 2013). The CIP projections are available under RCP 4.5 and RCP 8.5. RCP 4.5 is a stabilization scenario, it assumptions include reduction of greenhouse gas emissions due to the introduction of climate policies, for example setting prices for greenhouse gas emissions (Thomson et al., 2011). Under this scenario the future is consistent with increasing reforestation programmes, decreasing use of crop and grasslands due to increased productivity and dietary changes (Bjørnæs, 2013). Contrary to RCP 4.5, RCP 8.5 is a scenario where there are no policy changes and greenhouse gases continue unabated in the absence of mitigation (Bjørnæs, 2013). Additionally assumptions include increasing human population, slow income growth, lowest rate of technology development and high- energy intensity developments (Bjørnæs, 2013; Riahi et al., 2011). RCP 4.5 was adopted in this study since the researcher understands that it is the most applicable in the South African context because there are climate policies and reforestation projects, for example the Bufflesdraai and eMdloti reforestation projects. Within the CIP the closest station to Ndwedwe-Cibane is the old Durban international airport station (29°58'12.0"S 30°57'00.0"E) which is approximately 59.8 km from the study area.