PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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143 Table. 5.8 Daily performance statistics for Manhia (sub basin 3) in the semi distributed mode Daily Calibration Daily Validation Period (1/03/68-28/02/1970) Period (1/03/1970-31/12/1972) All All Water Water Dry Dry Months Dry months Months year year Months Months 01/11- 1971- 1972- 1970- 1971- Period 28/02 Period 31/12/72 31/12/72 1972 31/12/72 RMS (mm) 1.24 0.42 0.57 0.41 0.32 0.70 0.30 PBIAS (%) 15.26 23.99 -65.35 -14.91 -31.16 -279.76 -156.17 NSE 0.73 0.11 -1.17 0.10 0.33 -19.89 -10.60 PME -ve -ve -ve -ve -ve -ve -ve Table. 5.9 Weekly performance statistics for Manhia (sub basin 3) in the semi distributed mode Daily Calibration Daily Validation Period (1/03/68-28/02/1970) Period (1/03/1970-31/12/1972) All All Water Water Dry Dry Months Dry months Months year year Months Months 01/11- 1971- 1972- 1970- 1971- Period 28/02 Period 31/12/72 31/12/72 1972 31/12/72 RMS (mm) 5.68 1.84 2.33 1.43 1.01 2.92 1.10 PBIAS (%) 15.19 23.31 -63.16 -31.58 -28.79 -290.52 -181.15 NSE 0.77 0.25 -0.96 0.39 0.67 -13.73 -2.67 PME -ve -ve -ve -ve 0.14 -ve -ve For the monthly calibration the NSE was 0.80 but for the validation period, it was -1.49 failing the Henrikson et al. (2003) criteria of NSE ≥ 0.5. The PBIAS was 15.26% meaning it was underestimating as compared to the lumped mode at Manhia which was overestimating (4.27%) for the same monthly simulation. 5.7 Conclusion The modelling of the Densu Basin using the ACRU model has been undertaken and the results shown by means of sensitivity analysis, calibration and validation. The sensitivity analysis demonstrated that cumulative total streamflow and baseflow respond to changes in ACRU parameters within the Densu Basin. The most sensitive parameter values identified in this basin are the monthly crop coefficients (CAY), Vegetation interception loss (VEGINT), Fraction of the effective root system in top soils (ROOTA) and the Drained Emmanuel Obeng Bekoe Phd Thesis Chapter 5 Hydrological modelling of Densu
144 upper limits for the top soils (FC1). The calibrated values of these parameters are shown in Annex C for the lumped and semi distributed mode modelling. The sensitivities observed in the Densu-Basin were found to be lower than those reported by Angus (1989) which were undertaken in only one location and therefore the result of this sensitivity analysis compared to Angus (1989) is not conclusive. However, the results of this sensitivity analysis will complement the knowledge of sensitivity analysis for the tropics as Angus (1989) was undertaken for semi arid catchments. During the modelling, the performance criteria used for appraising of the model performance were undertaken with the NSE, PBIAS, DRMS and the PME objective functions. The criteria of Henrikson et al. (2003) for NSE which was to be ≥ 0.5 to be accepted was followed and also the PME which was to be ≥ 0 (Gupta et al., 1999) was used. There was variability in the results with regard to the NSE which fared well > 0.5 for the calibration period but poorly in the validation period. The PME statistic failed for both daily and weekly simulations improving (0.4-0.65, ref. Table 5.7) for only the aggregated daily into monthly simulations for the various modes in the calibration period. On the whole the semi distributed mode simulation statistics compared to the lumped mode did not show any significant improvement. However, from the daily, daily aggregated into weekly (weekly) and also daily aggregated into monthly (monthly) simulated flows, it is seen from the results that the latter had very good statistics comparatively with very high NSE and improved PME for the calibration period but poor NSE and failed PME for the validation periods. To conclude, modelling the streamflows from the Densu Basin with the ACRU model from the appraisal statistics shows that the model performed better in the calibration period than the validation period. Again the statistics show that the lumped mode simulation performance was better than the semi distributed mode performance with the aggregated daily flows into monthly flows giving the ‘best’ results suggesting that ACRU would be more suited for long term Emmanuel Obeng Bekoe Phd Thesis Chapter 5 Hydrological modelling of Densu
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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
Page 113 and 114: Figure 4.9. Soils of Southern Afric
Page 115 and 116: 94 • Model 1 displays an increase
Page 117 and 118: 96 sub soil horizons and the redist
Page 119 and 120: 98 from the topsoil (A horizon) to
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
Page 155 and 156: 134 might be data problems as discu
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
Page 161 and 162: 140 simulated streamflows were deri
Page 163: Weekly Flows (mm) 80 70 60 50 40 30
Page 167 and 168: 146 6. Discussion In Chapter 5, the
Page 169 and 170: 148 in the simulated streamflow of
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
Page 211 and 212: 190 statistics suggest that the cur
Page 213 and 214: 192 7.6.1 Density of Observation In
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194 • The long distances between
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196 up facilities/practices/routine
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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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