1. INTRODUCTION AND BACKGROUND
5.2 Methodology
5.2.1 Observed Rainfall Data Acquisition and Treatment
Daily observed rainfall data were obtained from two rainfall stations located in and around the Mkabela Catchment, located approximately 1km east of the town of Wartburg, near Pietermaritzburg, South Africa (29.34’ 57.00" S, 30.36’ 48" E) (Figure 5.1). The two stations, Windy Hill No. 2 and Noodsberg-Jaagbaan, (marked as “Union Mill Jaagbaan”) are also indicated in Figure 5.1. The records from these stations had data available over different periods and were combined to form a single composite record for the period 1950 to 2011.
An adjustment of daily rainfall data from the Windy Hill Number 2 station was carried out in order to ensure that it represented the Mkabela Catchment in its entirety, more realistically.
Rainfall data from the Noodsberg-Jaagbaan station was not adjusted since this station was considered to be close enough to the Mkabela Catchment to permit the use of its data without the necessity for adjustment. The adjustment of the Windy Hill Number 2 station rainfall data was carried out using the ACRU model rainfall adjustment or CORPPT function. As mentioned in Chapter 4, median monthly rainfall data were used to determine month-by- month adjustment factors, which were applied to the record to adjust daily rainfall data (Smithers and Schulze, 2004). Essentially, the aim of the adjustment was to best estimate the rainfall in the Mkabela Catchment using available data over the period of interest (i.e. 1950- 1970). In adjusting the records from the Windy Number 2 station to better represent the catchment rainfall, the data from the two selected stations were, consequently, made to be more comparable.
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Figure 5.1 Location of the Mkabela Catchment within the Nagle Water Management Unit (WMU). Shown in (b) are the selected rainfall gauging stations (circled) and other rainfall stations and towns in and around the Nagle WMU.
5.2.2 Climate Change Projections Considered
As already noted in the introduction, the climate change projections used in this study were derived from 7 downscaled GCMs. The downscaled climate change projections were obtained from the CSIR and from CSAG based at the University of Cape Town (UCT). Table 5.1 provides a summary of the relevant information regarding the selected downscaled GCMs. The selection of the downscaled GCMs was based on projected changes in mean annual precipitation (MAP) described by each downscaled GCM. The GFDL2.1 and MIROC GCMs downscaled by the CSIR represented wet and dry climate change projections respectively. The GCMs downscaled by CSAG all represented wet climate change projections, with variations in the degrees of change in MAP.
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All the downscaled GCMs used in this study were originally derived from coupled GCM (CGCM) projections that contributed to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) (IPCC, 2007).
Table 5.1 Information on selected downscaled GCMs including downscaling institutions and record lengths.
The GFDL2.1 and MIROC GCMs were dynamically downscaled by the CSIR using the high- resolution conformal-cubic atmospheric model (CCAM) developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Australia. This was performed by forcing the CCAM model using bias-corrected sea-surface temperatures and sea-ice fields of coupled GCMs to produce regional scale climate change projections (Engelbrecht et al., 2009). The CSAG GCMs were empirically downscaled by using observed data to derive relationships between synoptic scale and local climates and applying the resulting relationships to GCM output to generate higher resolution local climate change projections (Hewitson et al., 2005).
Downscaled GCM Downscaling Institute
Time Periods Considered
GDFL2.1 CSIR 1971-1990
2046-2065
MIROC CSIR 1971-1990
2046-2065
CSIRO CSAG-UCT 1971-1990
2046-2065
ECHO CSAG-UCT 1971-1990
2046-2065
IPSL CSAG-UCT 1971-1990
2046-2065
ECH5 CSAG-UCT 1971-1990
2046-2065
MRI CSAG-UCT 1971-1990
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As mentioned in Chapter 4, these projections represent a range in global models (including their various sensitivities, parameterizations, etc.), downscaling methodologies and institutions and, therefore, reflect the uncertainty that is inherent in climate change projections at present. This was considered critical since using projections from a single combination of GCM-downscaling institution runs the risk of obtaining a biased view of the future.
Changes in mean annual precipitation (MAP), changes in the number of raindays (days with rainfall above 0 mm), number of days between defined rainfall depth thresholds and the variation in daily rainfall amounts were determined in order to characterise changes in rainfall distribution between the present (1971-1990) and the future (2046-2065). These criteria give an indication of whether individual rainfall events are increasing or declining going into the future and thus provide an indication of the probable changes in the behaviour of runoff and NPS pollutants between the present and the future (Lumsden et al., 2009). In summary, the rainfall statistics assessed include:
a) mean annual precipitation (MAP),
b) standard deviation of annual precipitation,
c) coefficient of variation (CV) of annual precipitation, d) number of days in the time series with zero rainfall, e) total number of raindays,
f) number of days in the time series with rainfall events between 1 and 5mm, g) number of days in the time series with rainfall events between 5 and 10 mm, h) number of days in the time series with rainfall events between 10 and 15mm, i) number of days in the time series with rainfall events between 15 mm and 20mm, j) number of days in the time series with rainfall events between 20 mm and 30mm, k) number of days in the time series with rainfall events between 30 mm and 40mm, l) number of days in the time series with rainfall events between 40 mm and 50mm, m) number of days in the time series with rainfall events between 50 mm and 100mm and
finally,
n) number of days in the time series with rainfall events above 100mm.
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The disaggregation of rainfall into these event ranges was considered important in the isolation and analysis of individual rainfall events that trigger important hydrological responses such as stormflow, runoff and, consequently, NPS pollutant generation and transport. For instance, the 10mm rainfall depth threshold is considered a typical amount required for generating stormflow and, thus, runoff (Lumsden et al., 2009). Similarly, events above the 20mm threshold are associated with heavy rainfall events coupled with resultant high stormflow.
Detailing rainfall patterns of change under conditions of climate change is, therefore, critical in understanding the potential generation and transport characteristics of agricultural NPS pollutants from hydrological response units (HRUs). This also facilitates the potential improvement of the management of these pollutants under future conditions of change. In addition to assessing changes in the frequency of runoff-producing rainfall events, this study also includes the less extreme rainfall events represented by the 1-5mm depth threshold and the more extreme events above the 20-30mm depth threshold. Event ranges above the 20- 30mm range were included to ensure completeness of the analyses. This was done in order to fully detail the rainfall time series of each downscaled GCM. The following section outlines the results of these analyses.