2. THE INFLUENCE OF DECLINING HYDROMETEOROLOGICAL
3.2 Methodology
3.2.2 Model selection and configuration
The multi-purpose, multi-soil-layered, daily time-step, physical-conceptual ACRU Agrohydrological Model (Schulze, 1995; Smithers and Schulze, 2004) was selected. The ACRU model has been applied in several land use change studies within South Africa (Tarboton and Schulze, 1990, 1991; Schulze et al., 1997; Kienzle et al., 1997; De Wiennaar and Jewitt, 2010; Warburton et al., 2012; Le Maitre et al., 2014). The model has also been applied in other countries such as Zimbabwe (Butterworth et al., 1999), Germany (Herpertz, 2001), Canada (Forbes et al., 2010), New Zealand (Kienzle and Schmidt, 2008; Schmidt et al., 2009) and the USA (Martinez et al., 2008). A full description of the model physical processes is given in the companion paper on the model confirmation by Shabalala and Toucher (2018; Chapter 2).
The Department of Water Affairs and Forestry (DWAF, 2003) divided the country’s Primary Catchments into Quaternary Catchments (QCs) for water management purposes. Schulze and Horan (2011) divided each QC into three fifth order quinary catchments based on altitude.
This study was divided into two phases. Following confirmation of the ACRU model’s ability to simulate streamflow for QCs V11K (26 km2) and V31F (156 km2) by Shabalala and Toucher (2018), these QC catchments (Figure 3.1), with the configuration used in the confirmation study, were used to simulate the impacts of potential land use change scenarios on runoff components (accumulated baseflow, quickflow and streamflow). The impacts of
land uses that resulted in significant changes in the runoff components were then investigated at a larger catchment scale. Quaternary Catchment V11K was not responsive to changes in land cover, due to the small size of the catchment. Therefore, results from this catchment were excluded. Secondary Catchment V1 in the upper uThukela (Figure 3.1), which is composed of 29 QCs was selected for the upscaling of the selected land management scenarios. Modelling of V1 was performed at a quinary scale, i.e. 87 quinary catchments.
Each quinary catchment was divided into homogenous hydrological response units (HRUs) based on land cover.
Figure 3.1: Location of the study catchments Quaternary Catchments V31F and V11K and Secondary Catchment V1 with altitude, rivers and towns displayed.
Due to unavailability of good quality rainfall data extending to present day (Shabalala and Toucher, 2017), daily rainfall for 1960 - 2000 for a representative station for each quinary catchment was obtained from a national database (Lynch, 2004). Daily minimum and maximum temperature estimates for this period were obtained from a 1’ by 1’
latitude/longitude gridded database (Schulze and Maharaj, 2004). Reference evaporation was estimated using the Hargreaves and Samani (1985) equation.
The ACRU model requires A and B horizon soil variables as input. These include soil depth (m), field capacity (m/m), porosity (m/m), permanent wilting point (m/m) and the fraction of water that is drained from the A to the B horizon, and from the B horizon to groundwater when the soil is saturated. Estimates for these variables were obtained from a gridded database by Schulze and Horan (2007). The model also accounts for the amount of groundwater that contributes to daily runoff; this was set at 0.9%, as recommended by Schulze et al. (1995) for South African catchments.
Configuration of the ACRU model includes consideration of stormflow generation variables, viz. the fraction of stormflow that is converted into runoff on the same day as the rainfall event (QFRESP) and the critical stormflow generation depth (SMDDEP). QFRESP was exctracted from the Schulze and Horan (2007) gridded database, while SMDDEP was set at the depth of the A horizon, as recommended by Schulze et al. (1995).
The primary source of land cover information was the Ezemvelo KZN Wildlife (EKZN, 2011), supplemented with information from the National Land Cover (NLC, 2000). The general proportions of the land uses in the study catchments are shown in Table 3.1 below.
Table 3.1: Study catchment details and land use percentages.
Catchment uThukela V31F V1
Area (km2) 29036 148 7647
MAP (mm p.a) 810 811 864
Average Altitude (m.a.s.l) 1234 1323 787
Land use (%)
Natural vegetation 57 63 65
Degraded areas 9 2 7
Water bodies 2 1 2
Wetlands - 4 1
Commercial forests 4 7 1
Commercial agriculture
- Dryland 6 18 7
- Irrigated
Subsistence agriculture
3 8
3 -
3 5 Residential & Urban areas 6 2 9
For modelling purposes, the Acocks (1988) Veld Type was determined for where areas of natural grasslands and forest remained. The dominant veld types in the study area were the southern tall grassveld and the Natal sour sandveld. The mode of irrigation used in the study was based on expert consultation. Irrigation occurred once the plant available water (PAW) had been depleted by 20%. Based on field visitation, the irrigated crop was set as pastures growing between March and October, with irrigation water drawn from dams. Commercial dryland agriculture was chosen to be sugarcane for QC V31F and maize, planted on 15th November, growing over 140 days, was selected for V1. For each vegetation type, the ACRU model requires above (crop coefficients, coefficient of initial abstraction and interception storage) and below-ground (root distribution between the A and B horizons) physiological variables to estimate daily water use. These were obtained from a database by Schulze (2004). The onset of plant stress was set to 40% of plant available water, as suggested by Schulze (1995). Subsistence farming in the uThukela catchment is associated with land degradation due poor management (Schulze and Horan, 2007; Dlamini et al., 2011, 2014;
Elledbout, 2012; Chaplot et al., 2012, 2016). To account for management practices such as overgrazing and erosion, disjunct impervious areas were assumed to flow into subsistence agricultural areas. There was no information on the specific volumes and water movement from the dams within the study area; therefore, the total volume of all small farm dams was assumed to be one dam at the bottom of each quinary catchment. Daily seepage was assumed to be 0.0006 of the total dam volume, as suggested by Schulze et al. (1995). No environmental flows were released from the dams, and inter-basin transfers were not included in the modelling.