

@mastersthesis {6987,
	title = {Remote sensing of energy and water fluxes over {Volta} {Savannah} catchments in {West} {Africa}},
	year = {2007},
	note = {U231370},
	school = {University of Durham (United Kingdom)},
	type = {phdPh.{D}.},
	address = {Ann Arbor},
	abstract = {The deterioration of the West African savannah in the last three decades is believed to be closely linked with about 0.50C rise in temperature leading to evaporation losses and declining levels of the Volta Lake in Ghana. Although hydrological models can be used to predict climate change impacts on the regional hydrology, spatially-observed ground data needed for this purpose are largely unavailable. This thesis seeks to address this problem by developing improved methods for estimating energy and water fluxes (e.g. latent heat [ET]) from remotely sensed data and to demonstrate how these may be used to parameterize hydrological models. The first part of the thesis examines the potential of the Penman-Monteith method to estimate local-scale ET using ground-based hydrometereological observations, vegetation coefficients and environmental data. To improve ET estimation at the regional-scale, the second part of the thesis develops spatial models through energy balance modelling and data up-scaling methods, driven by radiometric measurements from recent satellite sensors such as the Landsat ETM+ MODIS and ENVISAT-AATSR. The results were validated using estimates from the Penman-Monteith method, field observations, detailed satellite measurements and published data. It was realised that the MODIS sensor is a more useful source of energy and water balance parameters than AAATSR. The final part of the thesis demonstrates the application of the ET model for predicting runoff (Q) using a simplified version of the regional water balance equation. This is followed by analysis of flow sensitivity to declining scenarios of biomass volume. The results showed the absence of Q for {\textgreater}90\% of the study area during the dry season due largely to crude model approximation and lack of rainfall data, which makes model testing during the wet season important. Runoff prediction may be improved if spatial estimates of rainfall, ET and geographical data (e.g. land-use/cover maps, soil \& geology maps and DEM) could be routinely derived from satellite imagery.},
	keywords = {0388:Hydrologic sciences, Bibliographies, DXN115955, Earth sciences, Hydrologic sciences, Psychology{\textendash}Abstracting, Statistics},
	url = {http://search.proquest.com/docview/301705656?accountid=14270},
	author = {Opoku-Duah, S}
}
