PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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109 In the semi distributed modelling mode, the weightings were similarly established for sub-catchment 1 (Koforidua rainfall station had a Thiessen weight of 1 with Nsawam having no influence), catchment 2 (areal rainfall was computed with the weights 0.57 for Nsawam and 0.43 for Koforidua) and for sub-catchment 3 (the weighting of Nsawam was 1 with no influence from Koforidua) Out of the various evapotranspiration methods available within ACRU, ACRU was run using the pan evaporation and the Hargreaves (1985) daily methods. These methods were chosen because (1) the ACRU model has been developed using the pan evaporation method (see section 3.4 in chapter 3) and (2) because Hargreaves (1994) cited by Droogers & Allen (2002) showed that in estimating reference evapotranspiration under poor data conditions the Hargreaves (1985) temperature method (Hargreaves & Samani, 1985), which requires only mean daily maximum and minimum temperature data, performed nearly (R 2 =0.97) as accurately as Penman Monteith equation (defined in Allen et al., 1998) in estimating evapotranspiration ETo on a weekly or longer time step where good data were lacking. According to Jensen et al. (1997), in Hargreaves & Allen (2003), when the Hargreaves (1985) and the Penman Monteith methods were compared to the lysimeter method, Hargreaves (1985) gave a Standard Error of Estimate (SEE) of 0.34mm day -1 with R 2 =0.94 for monthly estimates. The SEE for the Penman Monteith was 0.32 mmday -1 with R 2 =0.96 for the monthly data. Similarly, Droogers & Allen (2002) also showed that the Hargreaves (1985) method for computing monthly ETo calculation is “remarkable” in comparison to the Penman Monteith method when inaccuracy in weather measurements is common and may perform even “better” than Penman Monteith under conditions of substantial data error (Root Mean Square Error (RMSE) = 0.72 mm. d -1 ). 5.1.1 Output Simulation The period of consideration for this study was 1968 to 1972 because flow data are only available and reliable in this period because rating curve is applicable Emmanuel Obeng Bekoe Phd Thesis Chapter 5 Hydrological modelling of Densu
110 to this period. Typical output of the ACRU model for streamflow simulation includes simulated streamflow, baseflow, stormflow, total actual evaporation, baseflow store and soil moisture contents for the A and B soil horizons. 5.2 Sensitivity Analysis Atkinson et al. (2002) identified sensitivity analysis as an effective means of determining the dominant physical controls on simulated streamflow variability, as it provides insight into the operation of the model and explores its response to parameter variations. McCuen (1973) defines sensitivity “as a measure of the effect of change of one factor on another factor” and noted that in terms of hydrological modelling this definition refers to a measure of the effect of changes in model input, or model structure, on model output. Bergstrom (1991) noted that an analysis of the sensitivity of a model is a very useful tool for building confidence in its structure. McCuen (1973) observed that sensitivity analysis is useful in all phases of the modelling process because it enables the hydrological modeller to gain a better understanding of the correspondence between the model output and the physical processes being modelled whilst according to Bergstrom (1991) sensitivity analysis; • helps keep the model simple; • identifies variables and parameters with little/insignificant effect on results; • identifies interactions between model components and parameters and, • identifies the stability of results in relation to the uncertainty of the assumptions made in the model. The following model parameters were considered for the sensitivity analysis, as Angus (1989) found the ACRU output of cumulative streamflow to be sensitive to these parameters in studies in Southern Africa: DEPAHO: Depth (m) of the topsoil of the soil profile. DEPBHO: Thickness (m) of the subsoil of the soil profile. ABRESP: Fraction of “saturated” soil water to be redistributed daily from the topsoil (A horizon) into the subsoil (B horizon) when the topsoil is above its drained upper limit. 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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Page 81 and 82: 60 For runoff volume computation al
Page 83 and 84: 62 The data situation with regard t
Page 85 and 86: 64 developed in a mainframe environ
Page 87 and 88: Figure 3.7 The options of ACRU mode
Page 89 and 90: 68 Menubuilder includes a help faci
Page 91 and 92: 70 total evaporation. Evaporative d
Page 93 and 94: 72 potential maximum retention of t
Page 95 and 96: 74 4. Data Availability, Analysis a
Page 97 and 98: 76 (see Table 4.2) had some data ho
Page 99 and 100: 0.5 78 (n - 2) tt = Rsp -----------
Page 101 and 102: 80 means more common than the daily
Page 103 and 104: 82 not been repaired. As a conseque
Page 105 and 106: 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: 108 5. Hydrological Modelling of th
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 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
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160 the mountainous regions which r
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Cummulative Rainfall (mm) 8000 6000
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164 Figure 6.4a Rainfall for subcat
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166 cusecs) on day 26 to 11.9m 3 /s
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Monthly Means of Actual Evapotransp
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170 lumped simulation at Manhia (Ta
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172 be corrected by calibration. Ne
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174 The figures show that there is
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176 a simple persistence model and
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178 Based on the above analysis, it
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180 within a year of establishment
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182 corrected soon after collection
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184 One approach to assisting susta
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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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