2022/45/N/ST10/03551

PI: mgr. Nelson Venegas Cordero

Project overview

River floods have been an extreme event that has affected Poland with consequences of economic losses and even fatalities in recent decades (e.g. 1997 – Odra river basin; 2010 – Vistula river basin). Their occurrence depends on various factors, such as snowmelt or intense rainfall, whereas their long-term variability may depend on climate change and anthropogenic changes (e.g., urbanization). Although the topic of detection of flood trends at a global, pan-European, and national scale is quite widely discussed, the attribution of flood changes has been marginally addressed and still raises many concerns in the scientific community. Research on flood attribution in Poland is still insufficient, especially for analyses covering different drivers, e.g. climate vs. land-use change. The research will capitalize on the recently published study on flood trend detection of the Principal Investigator (Venegas-Cordero et al., 2022, DOI j.ejrh.2022.101098) as well as on the SWAT model set up and calibrated for the entire country.

The main objective of this project is to determine the attribution of drivers such as climate change (extreme precipitation, snowmelt, soil moisture) and urbanization, on river floods in Poland.

More specifically, the proposed project will attempt to verify two following general hypotheses:

H1) Extreme rainfall and imperviousness changes are two key factors explaining historical river flood changes in urbanized areas;

H2) Factors such as extreme rainfall, snowmelt, and soil moisture change are the principal drivers of river flood changes over the country.

To achieve these objectives and verify hypotheses, this project will employ two principal, scale-specific approaches: 1) study focusing on selected catchments with the highest percentage of imperviousness change in the last two decades aiming to assess the role of extreme precipitation and increased soil sealing; 2) Attribution analysis for the country scale using various climatological variables and machine learning techniques.

The project will consist of three principal tasks. Task 1 includes data collection and integration, which will allow for the handling of spatial, and hydrometeorological data in different formats. Task 2 is oriented on the attribution analysis, responses to precipitation change, and urbanization based on a paired-catchments approach. Task 3 will be focused on attribution analysis for the country scale. The project will be carried out in collaboration with the Escuela de Ingenieros de Caminos, Canales y Puertos of the Universidad Politécnica de Madrid (UPM, Spain), within the research group of Hydroinformatics and Water Resources.