PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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Cummulative mean monthly. AET's (mm) 800 700 600 500 400 300 200 100 0 Mar Apr May Jun Jul Aug Sept Period Oct Nov Ayibotele (1974) ACRU Figure 6.6a. Cumulative mean monthly AETs of ACRU Simulated AET and Ayibotele (1974) calculated AET for Nsawam ACRU Simulated mean monthly AET (mm) 100 80 60 40 20 0 y = 0.8491x + 19.753 R 2 = 0.2004 Dec Jan Emmanuel Obeng Bekoe PhD Thesis Chapter 6 Discussion of Results Feb 0 20 40 60 80 Ayibotele (1974) mean monthly AET (mm) Figure 6.7a Relationship between ACRU simulated and Ayibotele (1974) mean monthly AET for the rainy season at Manhia 169 Cummulative ean monthly AET's (mm) 800 700 600 500 400 300 200 100 0 Mar Apr May Jun Jul Aug Sept Period Oct Nov Ayibotele (1974) ACRU Figure 6.6b Cumulative mean monthly AETs of ACRU Simulated AET and Ayibotele (1974) calculated for Manhia ACRU Simulated mean monthly AET (mm) 100 80 60 40 20 Dec y = 1.7999x - 35.336 R 2 = 0.4471 Jan Feb 0 0 20 40 60 Ayibotele (1974) mean monthly AET (mm) Figure 6.7b Relationship between ACRU simulated and Ayibotele (1974) mean monthly AET for the Dry season at. Manhia 6.4 Streamflow Routing in ACRU In chapter 3 Table 3.2 and Section 3.4.2 the routing mechanisms employed to generate stormflow in a catchment using the ACRU model are the Muskingum and the Muskingum Cunge methods and for reservoir processes the storage indication methods. However the assumption that stormflow generated on a particular day passes the catchment outlet on the same day is valid for small catchments up to 50km 2 in ACRU but is not necessary true of larger catchments (Shulze, 1995). Routing is only simulated in ACRU during semi distributed modelling in large catchments, so that the effects of routing on the flow hydrograph could not be considered during the lumped mode modelling. Considering the size of the Densu basin, this is a weakness within the process description in ACRU. Although a 2-3 day lag between rainfall and streamflow responses was suggested in Section 6.2.3, applying a lag of 1-5 days to the
170 lumped simulation at Manhia (Table 6.3) did not improve the modelling results in terms of the appraisal statistics used, meaning that this is unlikely to be a significant cause of the poor model performance. In the semi-distributed modelling of the Densu, the routing option was not invoked because the required parameters BWIDTH (Bottom width of main channel [m] of the downstream sub-catchment); ZSIDE (Side slope of main channel of the downstream sub-catchment), TWIDTH (Top width of main channel [m] of the downstream sub-catchment) and DELT (Main time interval [minutes] at which all Table 6.3. Results of lagging the simulated streamflow at Manhia in the lumped simulation Daily Calibration Daily Validation Period (1/03/68-28/02/1970) Period (1/03/1970-31/12/1972) Lag period (days) Lag periods (days) 0 1 2 3 4 5 0 1 2 3 4 5 RMS 1.01 1.03 1.05 1.07 1.1 1.14 0.51 0.66 0.68 0.69 0.70 0.72 PBIAS -4.20 -4.28 -4.28 -4.28 -4.28 -4.27 -69.74 -70.70 -72.26 -74.64 -75.41 -76.70 NSE 0.82 0.81 0.81 0.80 0.78 0.77 -0.51 -1.41 -1.66 -1.87 -2.01 -2.27 PME -ve -ve -ve -ve -ve -ve -ve -ve -ve -ve -ve -ve hydrographs are stored internally in the model) were unavailable for this study because profiling data for the river are non existent. These parameters could have been estimated but it was felt that the assumptions required to enable these parameters to be generated would have been too subjective. 6.5 Groundwater simulation in ACRU Many methods exist for groundwater simulation in hydrological models such as use of kinematic equations in SWAT model (Bora and Bera, 2003); by Darcy’s gravity flow method in ANSWERS 2000 model (Bouraoui and Dillaha, 2000), 3- D groundwater flow equations solved using a numerical finite-difference scheme and simulated river-ground-water exchange methods for MIKE SHE model (Bora and Bora, 2003). In ACRU ground water is simulated using two drainage response coefficients i.e. a drainage rate from the bottom subsoil horizon to the 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
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 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: Monthly Means of Actual Evapotransp
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
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:
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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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