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PhD Thesis Emmanuel Obeng Bekoe - Cranfield University

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174<br />

The figures show that there is no apparent trend between the appraisal<br />

statistics RMS, PBIAS and NSE and the annual mean daily flow in this study for<br />

the years studied. However, this is not conclusive because of their relatively<br />

short duration of observations (only five years) and more data would be<br />

required before the variability can be properly explained.<br />

6.7 Soil Parameters on ET<br />

As discussed in Section 3.4.2.3, there are several relationships between soils<br />

and evapotranspiration. In ACRU evapotranspiration is considered as soil<br />

water evaporation (from the topsoil horizon only) and plant transpiration (from<br />

all horizons in the root zone). This shows that soil parameters required for<br />

evapotranspiration computation such as soil depth, field capacities, and<br />

redistribution rates should be computed accurately for correct<br />

evapotranspiration to be attained. However, as described in Section 4.2.5, soil<br />

parameters were generated because direct data were not available. As such if<br />

the generated data is not accurate the outcome of computed ET may be<br />

different from actual ET computed. Therefore problems with soil parameters<br />

could not be ruled out from the poor results seen in some of the simulations.<br />

6.8 Manual Calibration<br />

Hydrologic models are intended to be applicable to different watersheds and for<br />

that reason they typically have parameters that must be adjusted to make the<br />

model behaviour match the behaviour of the catchment of interest (Gupta et al.,<br />

1999, Masden, 2000, Ajami et al., 2004). No mater how sophisticated the<br />

model structure is if the parameters are poorly specified, the model simulated<br />

fluxes (e.g. streamflows) can be quite different from those actually observed<br />

(Gupta et al., 1999). Therefore the calibration procedure must be conducted<br />

carefully to maximise the reliability of the model. ACRU model is described as a<br />

multi parameter model (Schulze, 1995) and therefore has many parameters<br />

such as COFRU, COAIM, BFRESP, CONST, STOIMP and FOREST which are<br />

not directly observable and yet have to be estimated for the Densu catchment.<br />

<strong>Emmanuel</strong> <strong>Obeng</strong> <strong>Bekoe</strong> Phd <strong>Thesis</strong> Chapter 6 Discussion

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