CHAPTER 5 ASSESSING SUITABILITY OF THE ACRU HYDROLOGICAL MODEL
5.4 Model Parameterization and Data
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112 Figure 5.5: Example of subcatchment (a) and HRU streamflow configuration (b), for Bonsa catchment, showing the cascading of water flow from HRUβs to subcatchments
5.4.2 Hydrometeorological Data
Daily streamflow records between 1970 and 2010 for the Bonsaso gauging station (Figure 5.1), were obtained from the Hydrological Services Department of Ghana. The streamflows records had missing data, ranging from a few days upto six months for many years. However, since there were no nearby gauging stations within the catchment, streamflow infilling was not done. For climate data, daily rainfall, daily maximum and minimum temperature records for four stations inside the Bonsa catchment and six neighbouring stations (Figure 5.1), were obtained from the Ghana Meteorological Agency (GMA) and Goldfields Ghana Ltd at Damang, Western region. Missing temperature data were estimated with the: (1) within- station technique (Allen and DeGaetano, 2001), which uses average values in the preceding or subsequent time steps of the same station and (2) linear regression (Allen and DeGaetano, 2001), using data from nearby stations. Erroneous rainfall values were identified by plotting graphs of the rainfall records and comparing the patterns of near-by stations (CWC, 1999).
113 The days with rainfall records significantly higher than those of near-by stations were suspected to be erroneous, since storm cells in the region are noted to be fairly large, with a mean area of more than 500 km2(Jackson et al., 2009). The suspected erroneous records were further investigated and confirmed by identifying them using the outlier identification procedure in the IBM SPSS software. Since daily rainfall data is positively skewed, a Box- Cox transformation (Makhuvha et al., 1997), was used to transform the records into approximately normally distributed data. Overall, only a few daysβ rainfall records per year were identified as erroneous, which were recorded mainly during the rainy seasons. Outliers were removed because they are observations that do not follow the pattern of the majority of the observations and they exert disproportionate influence on any model (Schulze, 1995), which uses such observations.
For both the erroneous and the missing rainfall records, linear regression, using records from the highest correlated climate station and the station under consideration, was used for infilling. Further, in order to convert point rainfall to areal rainfall, which maintains the spatial variability of the sub-catchments/HRUs rainfall, monthly rainfall correction factors were generated and applied in the ACRU model, using the driver station method (Schulze, 1995Forbes et al., 2011).The driver station method generates monthly rainfall correction factors using ratio of median monthly rainfall surfaces to the gauged median monthly rainfall.
In this study the median monthly rainfall surfaces were created using a spline interpolation of 20 year median monthly rainfall records for climate stations in and around the study area.
Additionally, the Hargreaves and the Samani method (Hargreaves et al., 1985) was used to compute reference evapotranspiration by the ACRU model, as this method required only monthly temperatures.
5.4.3 Soil data
A digital soil map for the Bonsa catchment was obtained from the Soil Research Institute (SRI) of Ghana. According to the SRI map, the Bonsa catchment is made up of three FAO soil classes, namely Ferric Acrisols (91.5%), Dystric Fluvisols (2.9%) and Plinthic Ferralsols (5.6%). According to Dwomo and Dedzoe (2010), the soils are also grouped under the forest oxysols, following the generalised soil classification system for Ghana. The forest oxysol
114 parameters (Table 5.1) used in this study were obtained from Adjei-Gyapong and Asiamah (2002) and Dwomo and Dedzoe (2010). The ACRU model requires the depths of the topsoil (A horizon; DEPAHO) and the subsoil (B horizon; DEPBHO), the soil texture and permanent wilting points, field capacities and porosities for both topsoil and the subsoil. To obtain the soil water content at permanent wilting point (WP) and the soil water content at drained upper limit (field capacity) for the top- and subsoil horizons, the pedotransfer functions developed by Schulze et al. (1995), shown in Equation (5.1), were applied.
ππππ΄ = 0.0572 + 0.00322 β πΆπ%
π·ππΏπ΄ = 0.1506 + 0.00365 β πΆπ%
ππππ΅ = 0.0520 + 0.00322 β πΆπ%
π·ππΏπ΅ = 0.1567 + 0.00365 β πΆπ%(5.1)
Where PWPA and DULA are the soil water content at permanent wilting point and drained upper limit, respectively, for the topsoil, while PWPB and DULB are the soil water contents for the subsoil. The Cl% is the percentage clay content in the soil.
Furthermore, the ACRU model requires porosities of the top- and subsoil horizons, as well as the surface properties of soils (e.g. crusting and cracking) and soil redistribution rates. In this study the soil surface properties were inferred from the soil textural classes, but for the soil porosities and the redistribution rates, values published by Schulze et al. (1995) were used, since no published records existed for the study area.
115 Soil series Horizon Depth (cm) Sand Silt Clay Texture
Ah1 0-5 66 14 20 Sandy loam
Ah2 5-12 64 14 22 Sandy clay loam
BA 12-36 59 13 28 Sandy clay loam
Bts1 36-72 39 14 47 Clay
Bts2 72-110 28 21 51 Clay
Bt 110-150 30 23 47 Clay
Proportion (%)
Ankasa
Table 5.1: Forest oxysols of the high rain forest zone, southwest Ghana (Dwomo and Dedzoe, 2010)
5.4.4 Land Use Information
The land cover information required by the ACRU hydrological model are monthly values of the average crop coefficient for the pervious land cover (CAY), the fraction of effective root system in the soil (ROOTA), the mean value of leaf area index (ELAIM) and the interception loss by vegetation (VEGINT; mm.rain day-1). The ACRU model converts the monthly vegetation parameter values into daily values, using Fourier analysis. The model also allows the use of either ELAIM or VEGINT for computing interception. The values of the initial land use parameters applied in this study are shown in Table 5.2. The initial parameter values were obtained from previous studies (Schulze, 1995; Bekoe, 2005; Warburton et al., 2010).
For water bodies, since field observations showed that they were mainly water contained in mining tailings dams, they were considered as closed storages, where there is no inflow from upstream or outflow to the downstream catchment. The only change was through increase in volume of water by discharge of mining waste and rainfall and loss of water by evaporation.
The parameter value of the coefficient of initial abstraction (COIAM) for evergreen forest was obtained from a study by Poncea and Shetty (1995), who collated data on water abstraction by various vegetation types including those in the tropics. Maximum evaporation from soil surface can be suppressed by land surface cover such as leaf litter and mulch and their values vary with time. For the Bonsa catchment, the percentage surface covers (PCSUCO) for evergreen, secondary and shrubs/farms were estimated, based on field observations. The values for settlements and mining areas were, however, set to zero, since they are mostly bare lands.
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Land use Variable Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec VEGINT(mm.rain/day) 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0
ROOTA 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8
CAY 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7
COIAM 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
*PCSUCO 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 VEGINT(mm.rain/day) 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0 4.0
ROOTA 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7
Evergreen and CAY 0.8 0.8 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 secondary forests COIAM 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4
*PCSUCO (Evergreen) 80.0 80.0 80.0 80.0 80.0 80.0 80.0 80.0 80.0 80.0 80.0 80.0
*PCSUCO (secondary) f.)70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 70.0 VEGINT(mm.rain/day) 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.8 Settlements and ROOTA 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 mining areas CAY 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7
COIAM 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
*PCSUCO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Shrubs/farms
Table 5.2: Initial land use parameters**
*Values obtained from field observations, **adapted from Schulze (1995), Bekoe (2005) and Warburton (2012).
5.4.5 Streamflow Simulation Control Variables
The ACRU model uses streamflow simulation control variables to determine the amount of rainfall that becomes part of streamflow and groundwater store. Typical values of streamflow simulation control variables obtained from Schulze (1995) were used for the simulation. The fraction of saturated soil water to be redistributed daily from the topsoil into the subsoil (ABRESP) and the fraction of saturated soil water to be redistributed daily from the subsoil into ground water store (BFRESP) were set to 0.1, which is a typical value for clay soils, while the soil depth from which stormflow takes place (SMDDEP) was set at 0.4 m, which is also a typical value for high rainfall catchments. A baseflow response fraction of 0.009, which is also typical value, was set to control amount of groundwater that becomes part of streamflow in a day. Quickflow response fraction (QFRESP), the fraction of rainfall that becomes streamflow during a rain day for each land use type was input in the ACRU model.
For the evergreen, secondary and shrubs/farms, QFRESP of 0.1, 0.2 and 0.36 were used, respectively. The values were selected based on an assumption arising out of a study by Asomaning (1992), who obtained 0.36 as total runoff coefficient for a degraded forested catchment in southern Ghana. Furthermore, QFRESP of 0.7 was used for the settlements
117 (California EPA, 2011), which is a typical value, while a value of 0.5 was used for the mining areas, with the assumption that they are more pervious than settlement areas.