PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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211 Chaubey, I., Cotter, A. S., Costell, T. A., & Soerens, T. S. (2005) Effect of DEM data resolution on SWAT output uncertainty. Hydrol. Process. 19, 621–628 (2005) Chevallier, P., & Planchon, O. (1993) Hydrological processes in a small humid savanna basin (Ivory Coast). Journal of Hydrology 151: 173-191. Chiew, F. H. S., Pitman, A. J., & McMahon (1996) Conceptual catchment scale rainfall-runoff models and AGCM land-surface T. A parameterisation schemes, Journal of Hydrology, Volume 179, Issues 1-4, 1 May 1996, Pages 137-157 Clark, R. T. (1973) Mathematical models in hydrology, Irrigation and drainage. Paper, Food and Agriculture Organisation of United Nations, Rome. Connolly, R. D., Ciessiola, C. A. A., Silburn, D. M., & Caroll, C. (1997) Distributed parameter hydrology model (ANSWERS) applied to a range of catchment scales using rainfall simulator data. IV Evaluating pasture catchment hydrology. Journal of Hydrology 201 (1997) 311-328. Conway, D., & Mahé, G. (2005). River flow modelling in two large river basins: the Paraná (subtropical) and the Niger (tropical) In Hydrological Processes (in press) Costa, M. H., & Foley, J. A., (2000) Combined Effects of Deforestation and Doubled Atmospheric CO2 Concentrations on the Climate of Amazonia. Journal of Climate. Vol. 13, 18-33. Crawford, N. H., & Linsley, R. K, (1966) Digital Simulation in Hydrology , Stanford Watershed Model IV, Univ. Stanford, CA, USA. Tech. Rep. No. 39. Cunge, J. A. (1998) ANSWERS-2000: Runoff and sediment transport model – Discussion. J Environ Eng-ASCE 124 (1): 77-77 JAN 1998 Dabral, S., & Cohen, M. (2001) ANSWERS (Areal Non-point Source Watershed Evironment Response Simulation) Graphical User Interface. ABE6254-Simulataion of Small Agricultural Watersheds, Report. Nov. 2001. Daily Graphic (2001) Ghana Newspaper; September 14; Farming along Densu banned, a report by Boahene Asamoah and Ceaser Abagali. Daily Graphic Newspaper: (1996) Friday, March 15, 1996 p.16 Daily Graphic, Newspaper (1996) Farming along Densu banned, By Boahene Asamoah &Caesar Abagali (Reporters) Saturday May 15, 1996, p16) Darko, P. (2002) Estimation of natural direct groundwater recharge in southwest Ghana using water balance simulations, J.Hydrol. Hydromech. 50, 2002, 3. 198-212’. Emmanuel Obeng Bekoe PhD Thesis References
212 Davy, E., Mattei, F., & Solomon, S. I. (1976) An Evaluation of Climate and Water Resources for Development of Agriculture in Sudano-Sahelian Sone of West Africa. Special Environmental Report no. 9, WMO Pub. No. 459. Dawdy, D. R., Schaake, J. C., & Alley, W. M., (1978) Users guide for distributed routing rainfall-runoff model. USGS Water Resources In-Vest. Rep. No 78-90, Gulf Coast Hydroscience Center, NSTL, Miss De Klerk A. (1999) Sediment yield and the impacts of land use in the Umkomaas River Valley. Unpublished BSc (Hons) Seminar. School of Biresources Engineering and Environmental Hydrology. University of Natal, Pietermaritzburg. Deliman, P. N,. Glick, R. H., & Ruitz, C. E. (1999) Review of Watershed Water Quality Models. Technical Report W-99-1, U.S. Army Engineer Waterways Experiment Station, Vicsburg, MS. 31 http://el.erdc.usace.army.mil/elpubs/pdf/trw99-1.pdf Dent, M. C. (1988) Estimating crop water requirements for irrigation planning in southern Africa. Agric. Engineering S. Africa. 20, pp. 7–19. De Vos, B. Van Meirvenne, M., Quataert, P., Deckers, J., and Muys, B. (2005). Predictive Quality of Pedotransfer Functions for Estimating Bulk Density of Forest Soils. Soil Science Society of America Journal. 69:500-510 Descroix, L., Nouvelot, J. F., & Vauclin, M. (2002) Evaluation of an antecedent precipitation index to model runoff yield in the western Sierra Madre (North-west Mexico), Journal of Hydrology, Volume 263, Issues 1-4, 10 June 2002, Pages 114 –130 Di Luzio, M. D., Srinivasan, R., Arnold, J. G. (2002) Integration of watershed tools and SWAT modeling into BASINS. Journal of the American Water Resources Association 38(4): 1127–1142. Dickson, K. B., & Benneh, G. (1988) A new geography of Ghana. Longman Group UK Limited. Longman House, Burnt Mill, Harlow, Essex, England. Diem, J. E., & Mote, T. L. (2005) Interepochal changes in summer precipitation in the southeastern United States: Evidence of possible urban effects near Atlanta, Georgia. Jouranl of Applied Meteorology 44 (5): 717-730 MAY 2005 Dolman, A. J; Hall, A. J; Kavvas, M. L.; Oki, T.; Pomeroy, J. W; (Eds) (2001) Soil-Vegetation Atmosphere Transfere Schemes and Large-Scale Hydrological Models,.IAHS Publication vol 270, IAH Press, Center for Ecology and Hydrology, Wallingford, Oxfordshire, ISBN 1-901502-61-9. 372pp. Emmanuel Obeng Bekoe PhD Thesis References
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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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122 as given by performance criteri
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N ∑ t = 1 124 N sim obs 2 obs obs
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126 • Initial best estimate value
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128 5.4 Results of Calibration and
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130 Table 5.4 Performance statistic
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5.5 Water Balance for the lumped si
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134 might be data problems as discu
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136 represented in the model by a c
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Weekkly Flows (mm) 80 70 60 50 40 3
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140 simulated streamflows were deri
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Weekly Flows (mm) 80 70 60 50 40 30
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144 upper limits for the top soils
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146 6. Discussion In Chapter 5, the
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148 in the simulated streamflow of
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150 • The assumptions behind the
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152 data and rainfall distribution
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Monthly cummulative flows Manhia..
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156 response of ACRU at Nsawam in t
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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
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: 210 Brown, C.D., & Hollis, J. M. (1
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
Page 245 and 246: 224 Singh, V. P. & Woolhiser, D. A.
Page 247 and 248: 226 United Nations (1994b) Agenda 2
Page 249 and 250: 228 WRC (2000) Water Resources Comm
Page 251 and 252: Drainage from the topsoil to subsoi
Page 253 and 254: Step 8: Generation of Quickflow fro
Page 255 and 256: horizon and its drainage coefficien
Page 257 and 258: Annex C The Calibrated parameter va
Page 259: Emmanuel Obeng Bekoe PhD Thesis Ann
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