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

Accueil du site → Master → Pays Bas → 2013 → A Bayesian approach to merge rainfall from raingauges and TRMM data

UNESCO-IHE Institute for Water Education, Delft (2013)

A Bayesian approach to merge rainfall from raingauges and TRMM data

Tarekegn, T.M

Titre : A Bayesian approach to merge rainfall from raingauges and TRMM data

Auteur : Tarekegn, T.M.

Université de soutenance : UNESCO-IHE Institute for Water Education, Delft

Grade : Master of Science (MS) 2013

Résumé partiel
For hydrological modelling and applications, rainfall is a very important input as it is the main driving force in the physical hydrological process. Hence, for development and better management of water resources a reliable prediction of precipitation data is required. Ground based raingauge stations, satellites and weather radars are nowadays the devices used to estimate rainfall. However, in many parts of the world, especially in developing countries raingauge networks are either sparse in both temporal and spatial dimensions or does not exist at all in some places. Weather radars are expensive and their effective use requires calibration with raingauges. One of the most popular satellite based rainfall product is the Tropical Rainfall Measuring Mission (TRMM), which provides access to near real time 3-hourly rainfall data for a large part of the globe. Both raingauge and satellite based rainfall estimates have each advantages and limitations over one another.A raingauge measures a rain field at a point and that measurement is usually used in inferring rainfall over a much larger area. Usually there is high spatial sampling error in estimation of areal rainfall using different interpolation techniques especially when the network of the raingauges is sparse. On the other hand, though they have higher spatial resolution compared to raingauge measurements, TRMM estimates are at present made at 3 -hour interval. Hence, a snap shot made at 3-hour interval can lead to a higher temporal sampling error associated with TRMM estimates. Fusing of the two rainfall sources is done based on the hypothesis that the errors associated with each measurement will be offset during merging and hence accuracy improves. A method of merging these two rainfall products is explored under the framework of Bayesian Data Fusion (BDF) principle. Besides, the comparison of areal rainfall estimatesbased on the two rainfall data sources is explored in this study, Lake Tana Basin, Upper Blue Nile Basin. Comparison of raingauge based and TRMM areal rainfall estimates were made at different temporal and spatial scales.

Sujets  : rainfall hydrological modelling rainfall measurements lakes Ethiopia

Présentation

Page publiée le 2 avril 2021