PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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137 observed streamflow hydrograph was obtained. The parameters that gave the best fit to the observed hydrograph were then taken as the calibrated values for this sub-catchment. To calibrate the subsequent sub-catchments, the parameters of upstream sub-catchments were fixed to their calibrated values and runs undertaken until optimized values gave satisfactory performance indices. Nsawam Initial observations in simulating in the semi distributive mode (Fig 5.13) for the daily simulation were that the shape of the initial hydrograph produced from simulating sub-catchments (1+2) was good visually in terms of patterns of peaks, onset of the rainy season and dry seasons, but that the timing of these peaks and rainy seasons were not coinciding with the observed streamflow. The daily calibrated and validation simulations are shown in Figures 5.13 and performance appraisal statistics are given in Table 5.5. Daily Flows (mm) 18 16 14 12 10 8 6 4 2 0 01/01/1968 30/05/1968 Calibration period Validation period 27/10/1968 26/03/1969 23/08/1969 20/01/1970 19/06/1970 Emmanuel Obeng Bekoe Phd Thesis Chapter 5 Hydrological modelling of Densu 16/11/1970 Period (days) 15/04/1971 Rainfall Simulated Observed Figure 5.13 Comparison of observed and simulated daily flows for the calibration and validation periods of the semi distributed model at Nsawam. 12/09/1971 09/02/1972 08/07/1972 05/12/1972 0 50 100 150 200 250 Daily Rainfall (mm)
Weekkly Flows (mm) 80 70 60 50 40 30 20 10 0 04/01/1968 27/06/1968 19/12/1968 12/06/1969 04/12/1969 138 Calibration period Validation period 28/05/1970 Emmanuel Obeng Bekoe Phd Thesis Chapter 5 Hydrological modelling of Densu 19/11/1970 Period (Weeks) 13/05/1971 Rainfall Simulated Observed Figure 5.14 Comparison of observed and simulated weekly flows for the calibration and validation periods of the semi distributed model at Nsawam. Table 5.5. Daily performance statistics for semi distributed modelling at Nsawam Daily Calibration Daily Validation Period (1/03/68-28/02/1970) Period (1/03/1970-31/12/1972) All Dry All Period Period Dry Dry Months months Months Months Months 01/11- 01/03/1971- 01/03/1972- 01/031971- Period 28/02 Period 31/12/72 31/12/72 1970-1972 31/12/72 RMS (mm) 0.99 0.44 0.44 0.42 0.31 0.39 0.16 PBIAS (%) -6.38 44.89 -14.1 10.44 14.67 -121.11 -59.36 NSE 0.47 0.16 -0.30 0.07 0.35 -7.65 -10.26 PME -ve -ve -ve -ve -ve -ve -ve From the appraisal statistics, the daily NSE for the calibration was 0.47, with an overestimation of 6.38% given by the PBAIS. The performance model efficiency fell below zero. In the validation period, the daily NSE deteriorated to -0.30 but improved when the year 1970 water year was not included. The NSE for only 1972 was further improved to 0.35. 04/11/1971 27/04/1972 19/10/1972 0 50 100 150 200 250 300 350 Weekly Rainfall (mm)
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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
Page 107 and 108: Table 4.4 ∗ * Land use parameters
Page 109 and 110: a b 88 Figure 4.7: Typical land use
Page 111 and 112: 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
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Page 157: 136 represented in the model by a c
Page 161 and 162: 140 simulated streamflows were deri
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 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
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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
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188 • the use of daily spot strea
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190 statistics suggest that the cur
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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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