CNDS congratulates Dr Shyrokaya!

September 26 Anastasiya Shyrokaya successfully defended her thesis: On seasonal predictability of droughts and their impacts: Bridging science and operational applications

Abstract

Droughts are among the most complex and least understood natural hazards, with impacts that are often delayed, diffuse, and deeply context-dependent. Despite advances in hydro-meteorological forecasting, a persistent gap remains between the detection of drought conditions and the anticipation of their societal consequences. This thesis addresses this gap by advancing the science and operational potential of impact-based forecasting for droughts.

This work combined conceptual synthesis, statistical analysis, and machine learning to explore the relationships between drought indicators and sector-specific impacts across Europe and India. First, a structured overview of the current state of the art and practical challenges is provided. Then, drought indicators are related to observed impacts to assess their predictability across Europe using seasonal forecasts. Lastly, a pre-season forecasting framework for crop yield in India is developed and evaluated to explore the feasibility of anticipatory impact prediction at district level.

The findings show that indicator–impact relationships are highly variable across space, time, and sectors, and that even modest improvements in forecast skill can yield meaningful benefits for early action. By integrating seasonal forecasts with impact-relevant indicators, this thesis contributes to the development of more actionable, context-specific early warning systems. It also highlights the need for co-produced, user-centred approaches that bridge the gap between climate signals and real-world decisions.