PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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147 6.1.1 Dry Season The daily streamflow results of the lumped mode simulation for the entire modelling period were given in Chapter 5 in Figures 5.8 and 5.10 and Table 5.3 for the calibration and validation periods. In Table 6.1, analyses of the simulated results for the dry periods (defined from the beginning of November to the end of February) for the lumped mode are shown for both calibration and validation periods. From Table 6.1 the daily RMS for the dry periods remains constant i.e. 0.64 mm for both calibration and the validation periods, while the weekly RMS differs slightly. However, the daily NSE was unacceptable for both calibration (-1.12) and validation (-10.68) periods. The ACRU model overestimated the daily streamflow during the dry periods by over 50% (PBIAS) during the calibration period and by over 330% (PBIAS) in the validation period. The PME statistic failed during both calibration and validation periods. Table 6.1 Performance Statistics for Daily/Weekly Lumped Simulations at Manhia for the dry period Daily Calibration Daily Validation 11 1968-1970 1970-1972 1971-31/12/72 01/11-28/02 01/11-28/02 01/11-28/02 Daily Weekly Daily Weekly Daily Weekly RMS (mm) 0.64 4.33 0.64 4.15 0.19 1.33 PBIAS (%) -50.19 -48.03 --330.58 -367.17 -164.85 -193.43 NSE -1.12 -1.35 -10.68 -13.85 -3.98 -3.13 PME -ve -ve -ve -ve -ve -ve In considering the lumped mode performance in relation to the PBIAS statistics for the entire period of simulation (i.e. wet and dry seasons), Tables 5.3 and 5.4 showed that there was overestimation of 4% (48 mm) within the calibration period, but nearly 70% (188.7mm) in the validation period. However, this overestimation in the calibration period is entirely accounted for during the dry period where the PBIAS of 50% gave a total simulated streamflow of 144 mm as against 96 mm observed. In the entire validation period, the overestimation 11 For period with observed data Emmanuel Obeng Bekoe PhD Thesis Chapter 6 Discussion of Results
148 in the simulated streamflow of PBIAS of 70% was observed. However unlike in the calibration phase where the entire overestimation is accounted for by the dry period simulation, the overestimation of PBIAS of over 330% (108.6 mm) observed in the dry period validation is less than that observed in the entire validation period. This shows that in terms of the PBIAS in the lumped mode simulation, the overestimation of total flows during calibration is accounted for by the overestimation of baseflow during the dry periods from November to February suggesting that recharge to the groundwater store or the rate of groundwater release may be a contributory cause. However this observation is not so for the validation period suggesting that there might be other factors causing this overestimation some of which are discussed in the proceeding sections. 6.1.2 Water Year Analysis The daily performance statistics of the simulations for the entire validation period (01/03/70-31/12/72) in Figure 5.10 and Table 5.3 are not good because they all fell outside the acceptable values given by Gupta (1999) and Henrikson (2003) but the weekly simulation had better values (Table 5.4) during the calibration period (NSE= 0.83) and the periods 01/03/71-31/12/72 and 01/03/- 31/12/72. However, when the water year of 1970 was omitted from the validation period and validation undertaken for 01/03/1971-31/12/72; there is a significant improvement. Although the improvement in NSE from -0.51 to 0.28 for the daily simulation is still not acceptable according to Henrikson et al., (2003), the weekly simulation NSE is acceptable at 0.54 while the RMS error also improved from 3.20 to 1.88 mm for the weekly and the PBIAS is also better for the weekly than the daily with less overestimation. From the validation simulation in both daily (Table 5.3) and weekly (Table 5.4) timesteps, it is apparent that the year of 1972 has very good statistics with NSE as high as 0.70 for the weekly as against 0.58 for the daily. However, the statistics for the dry periods were poor in all respects for both calibration and Emmanuel Obeng Bekoe PhD Thesis Chapter 6 Discussion of Results
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Cranfield University at Silsoe Inst
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Dedication iii I dedicate this PhD
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v historical data available to the
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vii 3.3.6 Comparison of the ANSWERS
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ix 7.7.2 Guidance On How Best To De
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xi Table 6.1 Performance Statistics
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xiii Figure 4.4 Annual Streamflow d
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xv Abbreviations and Acronyms ABRES
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xvii GWCL Ghana Water Company Limit
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xix SWAT Soil Water Assessment Tool
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xxi = Equals to ft Foot or feet ms
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Emmanuel Obeng Bekoe Phd Thesis Cha
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Figure 1.3 The four principles as b
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2. Hydrology of Tropical West Afric
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18 million inhabitants with an aver
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Figure 2.4 Mean annual rainfalls (m
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Figure 2.7 Mean Evaporation in Marc
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38 computer speed and the developme
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Figure 3.4. Model Components (sourc
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42 input is lumped, and even some o
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44 • Should be able to output dai
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46 (Young et al, 1995, Borah and Be
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48 parameterizing hydrologic models
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50 2. The definition of the catchme
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52 The SWAT model has become widely
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54 divided according to land use, s
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56 integrated physical conceptual m
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58 water resource assessments in So
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60 For runoff volume computation al
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62 The data situation with regard t
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64 developed in a mainframe environ
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Figure 3.7 The options of ACRU mode
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68 Menubuilder includes a help faci
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70 total evaporation. Evaporative d
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72 potential maximum retention of t
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74 4. Data Availability, Analysis a
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76 (see Table 4.2) had some data ho
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0.5 78 (n - 2) tt = Rsp -----------
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80 means more common than the daily
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82 not been repaired. As a conseque
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Figure 4.5 Land Use map of Densu Ba
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Table 4.4 ∗ * Land use parameters
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a b 88 Figure 4.7: Typical land use
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90 Table 4.8: Soil class percentage
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Figure 4.9. Soils of Southern Afric
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94 • Model 1 displays an increase
Page 117 and 118: 96 sub soil horizons and the redist
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Page 121 and 122: 100 4.3.1 Temperature Daily maximum
Page 123 and 124: Mean Monthly Wind Speed . (knots) 2
Page 125 and 126: 104 network. Precipitation falling
Page 127 and 128: 106 catchment (2) and Manhia sub-ca
Page 129 and 130: 108 5. Hydrological Modelling of th
Page 131 and 132: 110 to this period. Typical output
Page 133 and 134: 112 basin is the streamflow, thus m
Page 135 and 136: 114 ranging from -18% to 34%. This
Page 137 and 138: Change in streamflow (%) 40 30 20 1
Page 139 and 140: Change in streamflow (%) 118 80 60
Page 141 and 142: 120 1. That the two sensitivities h
Page 143 and 144: 122 as given by performance criteri
Page 145 and 146: N ∑ t = 1 124 N sim obs 2 obs obs
Page 147 and 148: 126 • Initial best estimate value
Page 149 and 150: 128 5.4 Results of Calibration and
Page 151 and 152: 130 Table 5.4 Performance statistic
Page 153 and 154: 5.5 Water Balance for the lumped si
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Page 157 and 158: 136 represented in the model by a c
Page 159 and 160: Weekkly Flows (mm) 80 70 60 50 40 3
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Page 163 and 164: Weekly Flows (mm) 80 70 60 50 40 30
Page 165 and 166: 144 upper limits for the top soils
Page 167: 146 6. Discussion In Chapter 5, the
Page 171 and 172: 150 • The assumptions behind the
Page 173 and 174: 152 data and rainfall distribution
Page 175 and 176: Monthly cummulative flows Manhia..
Page 177 and 178: 156 response of ACRU at Nsawam in t
Page 179 and 180: 158 underestimation in the calibrat
Page 181 and 182: 160 the mountainous regions which r
Page 183 and 184: Cummulative Rainfall (mm) 8000 6000
Page 185 and 186: 164 Figure 6.4a Rainfall for subcat
Page 187 and 188: 166 cusecs) on day 26 to 11.9m 3 /s
Page 189 and 190: Monthly Means of Actual Evapotransp
Page 191 and 192: 170 lumped simulation at Manhia (Ta
Page 193 and 194: 172 be corrected by calibration. Ne
Page 195 and 196: 174 The figures show that there is
Page 197 and 198: 176 a simple persistence model and
Page 199 and 200: 178 Based on the above analysis, it
Page 201 and 202: 180 within a year of establishment
Page 203 and 204: 182 corrected soon after collection
Page 205 and 206: 184 One approach to assisting susta
Page 207 and 208: 186 of soil maps and landuse maps w
Page 209 and 210: 188 • the use of daily spot strea
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Page 213 and 214: 192 7.6.1 Density of Observation In
Page 215 and 216: 194 • The long distances between
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198 7.7.1.2 Development of Data Arc
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200 Could there be the need to plac
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202 • Online sources of internati
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204 However, data improvement is re
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206 References Adu, S. V; & Asiama,
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208 Balek, J. (1972) An Application
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210 Brown, C.D., & Hollis, J. M. (1
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212 Davy, E., Mattei, F., & Solomon
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214 FAO_2 (2004) http://www.fao.org
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216 Herpertz, D. (1994) Modellierun
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218 Kouwen, N. (2000) WATFLOOD/SPL:
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220 Mtetwa, S., Kusangaya, S., Schu
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222 Pidwerny, M. (2005) Department
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224 Singh, V. P. & Woolhiser, D. A.
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226 United Nations (1994b) Agenda 2
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228 WRC (2000) Water Resources Comm
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Drainage from the topsoil to subsoi
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Step 8: Generation of Quickflow fro
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horizon and its drainage coefficien
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Annex C The Calibrated parameter va
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Emmanuel Obeng Bekoe PhD Thesis Ann
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PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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