Informations et ressources scientifiques
sur le développement des zones arides et semi-arides

Accueil du site → Doctorat → Pays-Bas → Rainfall variability and estimation for hydrologic modeling : a remote sensing based study at the source basin of the Upper Blue Nile river

University of Twente (2010)

Rainfall variability and estimation for hydrologic modeling : a remote sensing based study at the source basin of the Upper Blue Nile river

Haile, A.T.

Titre : Rainfall variability and estimation for hydrologic modeling : a remote sensing based study at the source basin of the Upper Blue Nile river

Auteur : Haile, A.T.

Etablissement de soutenance : University of Twente

Grade : Doctor University of Twente 2010

Résumé
Rainfall is one of the meteorological forcing terms in hydrologic modelling and therefore its spatial variability in coverage, frequency and intensity affects simulation results. Rainfall variability in particular under the effect of orography adjacent to a large water body is not fully explored. Such study is done for the Gilgel Abbay watershed of the Lake Tana basin (Ethiopia). The study area is the source basin of the Upper Blue Nile River which is one of the major contributors to the River Nile. The livelihood in the Lake Tana basin largely depends on rainfed agriculture and therefore understanding rainfall variability in the basin is required. As part of the study, a set of recording rain gauges have been installed to observe rainfall at high resolution. First, rainfall variability in the Lake Tana basin is evaluated by statistical analysis of rain gauge observations. Furthermore, a convective index is derived from remote sensing observations to infer the pattern of rainfall variability in the basin. Results suggest that orography and the presence of Lake Tana largely affect the diurnal cycle, frequency and intra- and inter-event properties of the rainfall. The rainfall varies significantly at scales much smaller than inter-station distances suggesting that the existing rain gauge network may be inadequate to fully capture the space-time pattern of the rainfall. Such affects the accuracy of spatial rainfall estimation that serves to specify the input to hydrologic models. Second, two remote sensing based approaches have been developed to estimate spatial rainfall : (i) a multi-spectral remote sensing approach, and (ii) a conceptual cloud model approach with inputs from remote sensing and typical ground based observations (pressure and temperature). Results show the potential of remote sensing observations for rainfall estimation although the ground based data still provided some limitations at this point. Third, the effect of the rainfall variability on the accuracy of the simulated stream flows by a physically based rainfall-runoff model is evaluated. The effect of rain gauge density and configuration on rainfall representation and consequently on stream flow simulation is evaluated through a set of performance measures. The large rainfall variability in the study area caused the accuracy of the simulated flow to be significantly affected by both the density and the configuration of the network. The use of rainfall from a single rain gauge resulted in a relative difference of up to 100 % between the simulated and observed stream flows. It is also shown that simulated stream flow largely differs if uniform rainfall input is compared to non-uniform rainfall input. This study is relevant to hydrologic modeling since much research has focused on model development and assessing parameter uncertainty while less attention is given to aspects that relate to effects of rainfall representation.

Présentation

Version intégrale (ITC)

Page publiée le 6 février 2018