Integrating Multiple Sources of Information for Improving Hydrological Modelling: an Ensemble Approach

The availability of Earth observation (EO) and numerical weather prediction data for hydrological modelling and water management has increased significantly, creating a situation that today, for the same variable, estimates may be available from two or more sources of information. Precipitation data, for example, can be obtained from rain gauges, weather radar, satellites, or outputs from numerical weather models. Land use data can be obtained from land survey, satellite imagery, or a combination of the two. Each of these data sources provides an estimate of a catchment characteristic and related hydrological model parameters, or of a hydrometeorological variable. Estimates from each data source vary in magnitude or temporal and spatial variability. It is not always
possible to judge which data source is the most accurate. One data source may perform poorly in one situation but give an accurate estimate for another. Yet, in hydrological modelling, usually, a particular set of catchment characteristics and input data is selected, possibly ignoring other relevant data sources. One of the reasons may be that despite vast research and development efforts in integration methods for sub-sets of the available data sources, there is no comprehensive data-model integration framework assuming existence and enabling effective use of multiple data sources in hydrological modelling.
The main objective of this thesis, therefore, is to develop such a data-model integration framework, and test it on a case study.