Exploring the use of bivariate mapping for objective classification of hydrological regimes including variability and climate induced changes

The characterization of river runoff regime, and of hydrological regimes in general is generally based the seasonal distribution of discharge in a river basin.

Background

The characterization of river runoff regime, and of hydrological regimes in general is generally based the seasonal distribution of discharge in a river basin. The attribution of a discharge regime is finally rather subjective and no sound approach is available to state if discharge regime is really changing. In the framework of a National Research effort (CCHydro) we realized spatially distributed maps of water balance elements and want now to explore if it is possible to assign objective classification of discharge regimes and/or climate regime by adapting the concept of bivariate mapping (e.g. Teuling, 2011, see link below).

Tasks

Goal of this master thesis is exploring the potential of bivariate mapping for classifying hydrological regimes. Data on water balance for the whole of Switzerland (Picture) are available for the period 1980 to 2009. Numerous Scenarios for future climate impact of water resources including different periods in the future and emission scenarios are also available. The candidate should compare a classification based on bivariate mapping with classic evaluation based on the observed discharge. Visualization techniques to highlight the variability of the regimes and the impact of climate change need to be realized and discussed. The thesis should be written in the style of a scientific paper.

Literature

www.hydrol-earth-syst-sci-discuss.net/8/5733/2011/hessd-8-5733-2011.html

Profile of the candidate

The candidate has to be motivated in completing a successful Master Thesis. He/she should be able to work independently. He/she brings sound knowledge on climatology and catchment hydrology. Basic knowledge in Statistics (e.g. R) is welcome. Candidates with knowledge of a programming language (C, FORTRAN, Tcl, and IDL) or the motivation to learning a scripting language are particularly adequate.

The Research Group

Contact

Massimiliano Zappa
Swiss Federal Institute for Forest, Snow and Landscape Research WLS
Zürcherstrasse 111, CH-8903 Birmensdorf
Phone +41 44 739 24 33

Email: zappa@wsl.ch