PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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RMS (mm) 1.5 1 0.5 173 0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Annual Mean Daily flow (mm) Figure 6.8a PBIAS (%) 50 0 0.0 -50 0.5 1.0 1.5 2.0 2.5 3.0 -100 -150 -200 Figure 6.8b NSE 1 0.5 Annual Mean Daily Flow (mm) 0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Annual Mean Daily Flow (mm) Figure 6.8c Figures 6.8a-c, Evaluation of the calibrated ACRU model on an annual basis for five years for annual mean flows at Manhia. Emmanuel Obeng Bekoe Phd Thesis Chapter 6 Discussion
174 The figures show that there is no apparent trend between the appraisal statistics RMS, PBIAS and NSE and the annual mean daily flow in this study for the years studied. However, this is not conclusive because of their relatively short duration of observations (only five years) and more data would be required before the variability can be properly explained. 6.7 Soil Parameters on ET As discussed in Section 3.4.2.3, there are several relationships between soils and evapotranspiration. In ACRU evapotranspiration is considered as soil water evaporation (from the topsoil horizon only) and plant transpiration (from all horizons in the root zone). This shows that soil parameters required for evapotranspiration computation such as soil depth, field capacities, and redistribution rates should be computed accurately for correct evapotranspiration to be attained. However, as described in Section 4.2.5, soil parameters were generated because direct data were not available. As such if the generated data is not accurate the outcome of computed ET may be different from actual ET computed. Therefore problems with soil parameters could not be ruled out from the poor results seen in some of the simulations. 6.8 Manual Calibration Hydrologic models are intended to be applicable to different watersheds and for that reason they typically have parameters that must be adjusted to make the model behaviour match the behaviour of the catchment of interest (Gupta et al., 1999, Masden, 2000, Ajami et al., 2004). No mater how sophisticated the model structure is if the parameters are poorly specified, the model simulated fluxes (e.g. streamflows) can be quite different from those actually observed (Gupta et al., 1999). Therefore the calibration procedure must be conducted carefully to maximise the reliability of the model. ACRU model is described as a multi parameter model (Schulze, 1995) and therefore has many parameters such as COFRU, COAIM, BFRESP, CONST, STOIMP and FOREST which are not directly observable and yet have to be estimated for the Densu catchment. Emmanuel Obeng Bekoe Phd Thesis Chapter 6 Discussion
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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
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96 sub soil horizons and the redist
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98 from the topsoil (A horizon) to
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100 4.3.1 Temperature Daily maximum
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Mean Monthly Wind Speed . (knots) 2
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104 network. Precipitation falling
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106 catchment (2) and Manhia sub-ca
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108 5. Hydrological Modelling of th
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110 to this period. Typical output
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112 basin is the streamflow, thus m
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114 ranging from -18% to 34%. This
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Change in streamflow (%) 40 30 20 1
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Change in streamflow (%) 118 80 60
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120 1. That the two sensitivities h
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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 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 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: 172 be corrected by calibration. Ne
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
Page 217 and 218: 196 up facilities/practices/routine
Page 219 and 220: 198 7.7.1.2 Development of Data Arc
Page 221 and 222: 200 Could there be the need to plac
Page 223 and 224: 202 • Online sources of internati
Page 225 and 226: 204 However, data improvement is re
Page 227 and 228: 206 References Adu, S. V; & Asiama,
Page 229 and 230: 208 Balek, J. (1972) An Application
Page 231 and 232: 210 Brown, C.D., & Hollis, J. M. (1
Page 233 and 234: 212 Davy, E., Mattei, F., & Solomon
Page 235 and 236: 214 FAO_2 (2004) http://www.fao.org
Page 237 and 238: 216 Herpertz, D. (1994) Modellierun
Page 239 and 240: 218 Kouwen, N. (2000) WATFLOOD/SPL:
Page 241 and 242: 220 Mtetwa, S., Kusangaya, S., Schu
Page 243 and 244: 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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