PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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119 • insensitive (I): output changes by less than the 10% of the input change, i.e. ∆O% ≤ 0.1(∆I%). Considering Figures 5.1-5.5 according to the above scale, it is apparent that for cumulative streamflow and cumulative baseflow, apart from FC1 and CAY which showed moderate sensitivity, the remaining parameters were either slightly sensitive or insensitive, although PO1 is moderately sensitive over some of its range. The 38% increase in rainfall amount gave a corresponding increase in simulated cumulative streamflow of 109.6% which is over 200% that of the input parameter and is therefore extremely sensitive. This is also corroborated by Angus (1989). In Table 5.2 the Densu Basin sensitivities are compared to Angus (1989) who also tested the sensitivity levels of ACRU model parameters on cumulative flows. Table 5.2: Comparison of Densu basin sensitivities to Angus (1989) Parameter FC1 DEPAHO, DEPBHO ABRESP BFRESP CAY ROOTA VEGINT SMDDEP COIAM Sensitivity of Parameter (Angus, 1989) Sensitivity in the Densu Basin Reduced Increased Reduced Increased - - M S H S I I H S I I S S I I S S I I H H M S S S S I S S S I H S I I M S I I From the Table of sensitivities it is apparent that the sensitivities seen in the Densu are lower than those of Angus (1989 in Schulze, 1995). It should be noted here that Angus (1989, in Schulze, 1995) undertook this sensitivity analysis at only one location i.e. Cedera in the KwaZulu Natal Midlands and further emphasised that because it was undertaken at only one location, the result can therefore not be used to represent the response of the output in ACRU in other regions. Some suggested possible reasons for the differences in sensitivities at the two locations could be the following: Emmanuel Obeng Bekoe Phd Thesis Chapter 5 Hydrological modelling of Densu
120 1. That the two sensitivities have been conducted at two different climatic regions i.e Kwazulu Natal in the semi arid climatic region as against the Densu catchment in the tropical region. 2. DEPAHO is a soil parameter given by the depth of the A horizon of soils. Further, the SMDDEP (effective depth of the soil from which stormflow generation takes place) parameter is known to be very shallow (0.1m) in semi arid regions (Schulze 1995) which are characterised by high intensity convective rainfall, so that the response to stormflow could be high (Schulze, 1995). In the ACRU model the default value of SMDDEP is taken to be the depth of DEPAHO if it is not known, since it is always ≤ DEPAHO (Schulze, 1995), meaning that the smaller SMDDEP the higher the stormflow and vice versa. Since the DEPAHO of the soils of the Kwazulu Natal province in the semi arid zone as indicated by Shulze (1995) are very shallow (0.1m) and that of the Densu catchment are around 0.3m, it is apparent that the smaller DEPAHO and SMDDEP requires less rainfall to bring the soil up to field capacity so as to generate stormflows and hence be more sensitive to the soils of semi arid regions than that of the tropical Densu catchment. 3. Comparing the vegetative covers of the Kwazulu Natal province with that of the Densu catchment, the vegetation (land cover) of the semi arid Kwazulu Natal is sparser than the tropical Densu which is nearly covered throughout the year. In its simulations ACRU is sensitive to seasonal above and below-ground vegetation/land cover changes (Schulze, 1995). CAY which is a vegetation dependent parameter is more sensitive when CAY is decreased than when it is increased in the Densu (Figure 5.5). In the ACRU model typical values of the crop coefficient (CAY) have to be specified month-by-month. If there is more than one homogeneous land cover/use zone specified for the catchment, then for each "zone" the percentage area covered by the zone is also given and the land use utility facility in the model will compute an area-weighted monthly CAY. The Densu weighted CAY’s were found to be higher than the Kwazulu Natal monthly values. As CAY’s have a direct relationship with the stage 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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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
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 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: Change in streamflow (%) 118 80 60
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 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
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Page 189 and 190: 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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