Floods are often defined as events with magnitudes exceeding a threshold, e.g. a statistical quantile 1. Flood samples derived using such thresholds are useful in frequency analyses aimed at defining design floods for hydraulic design. However, it is unclear how these practical statistical thresholds relate to actual flood impacts in terms of inundation. Therefore, this thesis aims to explore (1) to which extent statistical flood thresholds identify events that trigger actual impacts and (2) how the relationship between statistical and impact-related flood thresholds varies in space and by hydro-climate.

The analysis builds on a flood stage database provided by the National Weather Service in the United States (https://www.weather.gov/), which consists of impact-related flood stages for river basins in the United States, streamflow data from the US. Geological Survey (USGS) 2, and on catchment characteristics data from the GAUGES II database 3,4. It relates the streamflow values corresponding to these impact-related flood stages to flood thresholds derived from streamflow observations using typical statistical approaches (e.g. quantile-based threshold) and tries to explain the resulting relationships using catchment and climate characteristics. You will (1) use the R-programming language to process streamflow and flood stage data, (2) evaluate the relationship between statistical and impact-related flood thresholds, and (3) assess whether and how the relationship between statistical and impact-related thresholds can be explained by hydro-climate and physiography.

You will improve your data management, visualization, statistical and R-programming skills by organizing a unique database, producing informative maps of threshold relationships, performing statistical analyses, and adapting existing and developing new R-code.

Dr. Manuela Brunner (University of Freiburg, manuela.brunner@hydrology.uni-freiburg.de)

Manuela Brunner manuela.brunner@hydrology.uni-freiburg.de

English or German

1. Meylan P, Favre A-C, Musy A. Predictive Hydrology. A Frequency Analysis Approach. Science Publishers. St. Helier, Jersey, British Channel Islands; 2012.

2. USGS. USGS Water Data for the Nation. National Water Information System: Web Interface. Published online 2019. https://waterdata.usgs.gov/nwis

3. Falcone JA. GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow. Digital spatial data set 2011. Published online 2011:http://water.usgs.gov/GIS/metadata/usgswrd/XML/ ga.

4. Falcone JA, Carlisle DM, Wolock DM, Meador MR. GAGES: A stream gage database for evaluating natural and altered flow conditions in the conterminous United States. Ecology. 2010;91(2):621. doi:10.1890/09-0889.1