Thesis Code: T8

Modeling the impacts of drought events on the European energy sector

Short description:

Drought is a slowly developing natural phenomenon that can occur in all climatic zones and propagates through the entire hydrological cycle with long-term socio-economic and environmental impacts. Intensified by anthropogenic climate change, drought has become one of the most significant natural hazards in Europe. 

Although drought monitoring and management are extensively studied in the literature (1), traditional drought indices such as Standardized Precipitation Index (SPI), Standardized Precipitation and Evapotranspiration Index (SPEI) and Standardized Runoff Index (SRI) often fail at yielding precise information on detecting critical events and their associated impacts. This is due to the difficulty of capturing the evolution of drought dynamics impacts across different temporal and spatial scales, including short-term meteorological droughts, medium-term agricultural droughts, and long-term hydrological droughts, as well as the non-physical aspects related to droughts (water management, irrigation, etc.). 

The aim of this thesis is the creation of new impact-based drought indices to better capture and represent drought-related impacts on the European energy sector (e.g. energy production outages due to drought events). The student is expected to carry out the following activities:

  1. Literature review: reviewing the state of the art of existing drought indices, with a specific focus on their use for drought monitoring and/or forecasting at the European scale (e.g. the European Drought Observatory (2)).
  2. Data collection: acquisition of observed/reanalysis data (e.g. ERA5 (3)) of relevant hydroclimatic variables, such as precipitation, temperature, streamflow.
  3. Computational experiments: 
    • computation of alternative drought indices with different spatial and temporal aggregations;
    • comparative analysis of the results, potentially correlating drought indices with drought impact data;
    • spatial analysis, possibly supported by clustering algorithms.

References

  1. Pedro-Monzonìs et al. (2015), A review of water scarcity and drought indexes in water resources planning and management, Journal of Hydrology
  2. https://edo.jrc.ec.europa.eu/edov2/php/index.php?id=1000
  3. https://climate.copernicus.eu/climate-reanalysis 

Relevant courses and knowledge:

Natural Resources Management

Number of students:

1 or 2

Requisites:

The student should be comfortable with data handling and programming skills (Matlab or Python).