Water balance in a poorly gauged basin in West Africa using atmospheric modelling and remote sensing information [Elektronische Ressource] / von Sven Wagner

Hammer

Informations

• Publié par: universitat_stuttgart
• Publié le: 01 janvier 2008
• Nombre de lectures: 14
• Langue: English
• Poids de l'ouvrage: 6 Mo

Extrait

Heft 173 Sven Wagner

Water Balance in a Poorly Gauged
Basin in West Africa Using Atmospheric
Modelling and Remote Sensing
Information



Water Balance in a Poorly Gauged Basin in West Africa
Using Atmospheric Modelling and Remote Sensing
Information





Von der Fakultät Bau- und Umweltingenieurwissenschaften der
Universität Stuttgart zur Erlangung der Würde eines
Doktor-Ingenieurs (Dr.-Ing.) genehmigte Abhandlung



Vorgelegt von
Sven Wagner
aus Weingarten




Hauptberichter: Prof. Dr. rer. nat. Dr.-Ing. habil. András Bárdossy
Mitberichter: Prof. Dr. rer. nat. habil. Wolfgang Seiler


Tag der mündlichen Prüfung: 30. April 2008









Institut für Wasserbau der Universität Stuttgart
2008





Heft 173 Water Balance in a Poorly
Gauged Basin in West Africa
Using Atmospheric Modelling
and Remote Sensing
Information


von
Dr.-Ing.
Sven Wagner













Eigenverlag des Instituts für Wasserbau der Universität Stuttgart D93 Water Balance in a Poorly Gauged Basin in West Africa Using
Atmospheric Modelling and Remote Sensing Information























Titelaufnahme der Deutschen Bibliothek


Wagner, Sven:
Water Balance in a Poorly Gauged Basin in West Africa Using Atmospheric
Modelling and Remote Sensing Information / von Sven Wagner. Institut für
Wasserbau, Universität Stuttgart. - Stuttgart: Inst. für Wasserbau, 2008

(Mitteilungen / Institut für Wasserbau, Universität Stuttgart: H. 173)
Zugl.: Stuttgart, Univ., Diss., 2008)
ISBN 3-933761-77-8
NE: Institut für Wasserbau <Stuttgart>: Mitteilungen


Gegen Vervielfältigung und Übersetzung bestehen keine Einwände, es wird lediglich
um Quellenangabe gebeten.




Herausgegeben 2008 vom Eigenverlag des Instituts für Wasserbau
Druck: Document Center S. Kästl, OstfildernAcknowledgements
IsincerelythankDr. HaraldKunstmann(IMK-IFU)forproposingthetopic,
his guidance and continuous support throughout this work.
I am very grateful to Prof. Andr´as B´ardossy (University Stuttgart) for the
excellent supervision, invaluable suggestions and guidance.
My sincere gratitude to Prof. Wolfgang Seiler (IMK-IFU) for giving me
the opportunity to work at the Institute for Meteorology and Climate Re-
search (IMK-IFU) in Garmisch-Partenkirchen, his support and accepting to
co-supervise my thesis.
This work is part of the research project GLOWA-Volta funded by the
BMBF (German Ministry of Education and Research). Many thanks to all
co-workers and partners of the GLOWA-Volta project for the good atmosphere
and cooperation; especially to Dr. Marc Andreini and Dr. Bouboucar Barry,
theprojectcoordinatorsinGhana,fortheirgreatsupportduringthefieldcam-
paigns. The collaboration with the Hydrological- and Meteorological Services
Department in Ghana is gratefully acknowledged.
Many thanks to Dr. Christopher Conrad and Dr. Rene R. Colditz (Uni-
versity Wuerzburg) for the good cooperation on the assimilation of satellite
derived land surface properties in hydrological modelling.
A big thank you to Dr. Andreas Marx (IMK-IFU), Sabine Pakosch (UniBw
Munich) and Claudia Mende for proofreading the manuscript, giving valuable
suggestions and the help with the English language.
I wish to express special thanks to all colleagues at IMK-IFU for the overall
good working atmosphere, indispensable coffee breaks and their manifold help
during this work; especially to the co-workers Dr. Gerlinde Jung (now at IIA-
CNR) and Patrick Laux for their support within the GLOWA-Volta project.
Finally, I am extremely grateful to my family and my partner Sabine for
their understanding and support/encouragement at all time.
iiContents
Acknowledgements ii
List of Figures vii
List of Tables xi
List of Abbreviations xiii
Abstract xv
Zusammenfassung xvii
1 Introduction 1
1.1 Motivation and objectives . . . . . . . . . . . . . . . . . . . . . 1
1.2 Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 The GLOWA-Volta project. . . . . . . . . . . . . . . . . . . . . 5
2 The study area 7
2.1 The Volta basin . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Climate of the White Volta basin . . . . . . . . . . . . . . . . . 8
2.3 Hydrology of the White Volta basin . . . . . . . . . . . . . . . . 11
3 Joint atmospheric-hydrological modelling 15
3.1 Regional atmospheric modelling . . . . . . . . . . . . . . . . . . 15
3.1.1 Mesoscale meteorological model MM5 . . . . . . . . . . . 17
3.1.2 Setup of MM5 for the White Volta basin . . . . . . . . . 20
3.2 Hydrological modelling . . . . . . . . . . . . . . . . . . . . . . . 22
3.2.1 Concepts of hydrological modelling . . . . . . . . . . . . 22
3.2.2 Water balance simulation model WaSiM-ETH . . . . . . 24
3.2.3 Setup of WaSiM for the White Volta basin . . . . . . . . 27
3.3 Joint atmospheric-hydrological modelling for the White Volta
basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4 Data basis and field campaign 35
4.1 Historical hydro-meteorological observations . . . . . . . . . . . 35
4.2 Current hydro-meteorological observations . . . . . . . . . . . . 36
4.3 The TRMM product 3B42 . . . . . . . . . . . . . . . . . . . . . 37
iiiContents
4.4 Hydro-meteorological field campaign . . . . . . . . . . . . . . . 37
5 Performance of meteorological, hydrological and joint modelling 41
5.1 Performance of meteorological simulations . . . . . . . . . . . . 41
5.1.1 Performance of the real time and scaled TRMM product
3B42 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.1.2 MM5 results scaled with GPCC . . . . . . . . . . . . . . 51
5.2 Performance of hydrological simulations. . . . . . . . . . . . . . 55
5.2.1 Calibration and validation . . . . . . . . . . . . . . . . . 55
5.2.2 Long-term hydrological simulations . . . . . . . . . . . . 59
5.3 Performance of joint atmospheric-hydrological simulations . . . 71
5.3.1 MM5-WaSiM simulations. . . . . . . . . . . . . . . . . . 72
5.3.2 Scaled MM5-WaSiM simulations . . . . . . . . . . . . . . 72
5.3.3 Scaled TRMM-WaSiM simulations . . . . . . . . . . . . 75
5.3.4 Station data based WaSiM simulations . . . . . . . . . . 75
5.3.5 WaSiM simulations using MM5/TRMM input data at
observation sites . . . . . . . . . . . . . . . . . . . . . . 76
5.3.6 Performance comparison of hydrological modelling using
different meteorological data sources . . . . . . . . . . . 77
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6 Assimilation of satellite derived land surface properties 81
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.2 Potential evapotranspiration after Penman-Monteith . . . . . . 82
6.3 Assimilation of satellite derived land surface properties in WaSiM 84
6.4 MODIS-Products for the White Volta basin . . . . . . . . . . . 86
6.4.1 MODIS instrument . . . . . . . . . . . . . . . . . . . . . 86
6.4.2 MODIS LAI and albedo for the White Volta basin . . . . 86
6.4.3 MODIS time series generation . . . . . . . . . . . . . . . 87
6.5 Relationship between albedo, LAI and precipitation . . . . . . . 90
6.6 Impact of satellite derived land surface properties on hydro-
logical simulations . . . . . . . . . . . . . . . . . . . . . . . . . 92
6.6.1 Albedo comparison . . . . . . . . . . . . . . . . . . . . . 92
6.6.2 LAI comparison . . . . . . . . . . . . . . . . . . . . . . . 94
6.6.3 Impact of MODIS albedo and LAI on water balance es-
timation . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7 Propagation of precipitation uncertainties in water balance estima-
tions 111
7.1 Geostatistical interpolation techniques . . . . . . . . . . . . . . 111
7.1.1 Inverse distance weighting . . . . . . . . . . . . . . . . . 112
7.1.2 Ordinary kriging . . . . . . . . . . . . . . . . . . . . . . 112
7.1.3 External drift kriging . . . . . . . . . . . . . . . . . . . . 114
ivContents
7.2 Turning band simulations . . . . . . . . . . . . . . . . . . . . . 116
7.2.1 Turning band method . . . . . . . . . . . . . . . . . . . 117
7.2.2 Conditional simulations . . . . . . . . . . . . . . . . . . 118
7.2.3 Normal score transformation . . . . . . . . . . . . . . . . 119
7.3 Areal precipitation results . . . . . . . . . . . . . . . . . . . . . 120
7.3.1 Variogram analysis . . . . . . . . . . . . . . . . . . . . . 120
7.3.2 Areal precipitation fields . . . . . . . . . . . . . . . . . . 122
7.3.3 Cross validation results . . . . . . . . . . . . . . . . . . . 130
7.3.4 Turning bands. . . . . . . . . . . . . . . . . . . . 133
7.4 Impactofarealprecipitationestimationsonhydrologicalmodelling135
7.4.1 Impa