PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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197 between schemes allowing it to be understood by both humans and machines; provide digital identification to uniquely identify the work or object to which the metadata refers such as URL (Uniform Resource Locator) or some more persistent identifier like a PURL (Persistent URL) or DOI (Digital Object Identifier); and support archiving and preservation so that resources will survive and continue to be accessible into the future with the growing concern that digital information is fragile by adding special elements to track the lineage of a digital object (where it came from and how it has changed over time), to detail its physical characteristics, and to document its behaviour in order to emulate it on future technologies through schemes such as the National Library of Australia, the British Cedars Project (CURL Exemplars in Digital Archives) and a joint Working Group of Online Computer Library Centre (OCLC) and the Research Library Group (RLG) (NISO, 2004 & Woldai, 2004). There are softwares which could assist in transforming data into Metadata formats such as: DDI Tools (http://www.icpsr.umich.edu/DDI/users/tools.html#a01); Dublin Core tools (http//:dublinecore.org/tools/); GDC Metadata Tools (http://www.nbil.gov/datainfo/metadata/tools/) Metadata Software Tools (http://ukoln.bath.ac.uk/metadata/software-tools/; TEI Software (http://www.tei- c.org/Software/index.html) For harmonizing metadata standards, a minimum mandatory set of metadata items is recommended by International Organization of Standardization (ISOhttp://www.iso.org/iso/en/ISOOnline.frontpage ) (Woldai, 2004). These are: • Metadata Language Code. • Metadata Characterset [default = “ISO 10642-2”. • Hierarchy Level Scope [default = “Dataset”]. • Dataset Language Code, Dataset Characterset [default = “ISO 10646-2”] • Abstract, Category Dataset Citation (Title & Date). • Dataset Contact (Responsible Party Name/ Organization & Responsibility Type) Emmanuel Obeng Bekoe Phd Thesis Chapter 7 Conclusions & Recommendations
198 7.7.1.2 Development of Data Archiving / Back-Up Systems There is an urgency for improved data archiving and back up systems because the demand for information about different measured hydrometric parameters such as rainfall, water level or streamflow is increasing in quantity and quality significantly (Schlaeger et al., 2005). However, for metadata schemes to be put in place, problems associated with data which are listed below should be addressed. � Historical data, mostly from the pre 1990s and some onwards, which are only available in analogue paper media must be electronically input � Inconsistencies in the production of observed/measured/collected data arising from no specific standard being followed must be corrected which is a very serious problem (Woldai, 2004). This could be done for instance by interested groups such as at national level for generic agreement between parties; branch level for agreements between say hydrometeorological institutions of their specific common application and data, internal institutional level, where interval processes are aligned through data and procedure definitions. Standardization is not an easy job (Woldai, 2004) and it requires decisions on how the application sector classifies/aligns measured/collected data of interest. � Security of data holdings because of poor storage must be improved through electronic backups where available or “properly” catalogued in cases of manual paper recordings for ease of access; � The current minimal data exchange/sharing between the other data generating establishments such as the NGOs and the established data collecting agencies like the National Meteorological Services Department must be improved to improve the general data situation for the whole country, since such NGO’s normally work in very remote areas. 7.7.1.3 Ensuring That All Data (new as well as existing) Is Subject To QC/QA Procedures Screening of generated data should be encouraged for data quality assurance and quality control at all times. Software which can assist in quality assurance Emmanuel Obeng Bekoe Phd Thesis Chapter 7 Conclusions & Recommendations
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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
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 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
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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: 196 up facilities/practices/routine
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
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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