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

Accueil du site → Master → Pays Bas → 2018 → Hydrological modelling od sparsely gauged basins using global datasets : a case study in upper Indus basin

UNESCO-IHE Institute for Water Education, Delft (2018)

Hydrological modelling od sparsely gauged basins using global datasets : a case study in upper Indus basin

Ibrahim, Muhammad

Titre : Hydrological modelling od sparsely gauged basins using global datasets : a case study in upper Indus basin

Auteur : Ibrahim, Muhammad

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

Grade : Master of Science (MS) 2018

Résumé partiel
In recent times the use of freely available global data sets has emerged as a potential alternative source to ground hydrological data in sparsely gauged basins. Upper Indus Basin (UIB) with a catchment area of 169495 km2 and a complex hilly terrain ranging from 8269 m to 318 m is distributed in China, Pakistan and India. Pakistan being an agricultural country is totally dependent on freshwater resources of UIB. The catchment is transboundary, has a large area with hilly terrain and limited accessibility. As a result, the catchment is sparsely gauged. In this study for a catchment such as Upper Indus where only limited ground-based hydrological data is available and data sharing issues exist among different organizations in different countries, the use of easily available global data sets in hydrological modelling is tested. Hydrological model of the basin was developed with freely available open source global data sets such as TRMM, PERSIAN-CDR and CMORPH by using SWAT (Soil and Water assessment Tool) tool which is a semi-distributed physically based rainfall-runoff model. PERSIANN-CDR showed less percentage bias as compared to TRMM and CMORPH, and exhibited a rainfall distribution pattern that is comparable to rain gauge data. All three rainfall products under-estimated rainfall on the whole catchment scale. On sub-basins scale, all three products over-estimated rainfall in all subbasins located in China and in the sub-basins near to the outlet of the basin. For the remaining sub-basins, they under-estimated with few exceptions of PERSIANN-CDR. All three satellite products were corrected using ratio bias correction method at seasonal scale and this correction was transferred on the daily scale by multiplying daily values with the seasonal factor. Using this simple method of bias correction limited improvement in the rainfall data on the daily scale was observed as compared to the monthly scale

Sujets  : hydroinformatics hydrological modelling SWAT global data sets rainfall-runoff modelling

Présentation

Page publiée le 1er avril 2021