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Identifying drought indices that effectively predict future drought impacts remains a critical challenge in seasonal forecasting, as these indices provide the necessary actionable information that enables stakeholders to better anticipate and respond to drought-related challenges. This study evaluates how drought indices balance forecast skill and relevance for estimating impacts across Europe. Using European Centre for Medium-Range Weather Forecasts SEAS5 seasonal predictions and ERA5 reanalysis as benchmarks, we assessed the predictability skill of drought indices over various accumulation periods and their relevance in estimating drought impacts across Europe, with the aim of enhancing impact-based forecasting. To evaluate these relationships, we built upon the findings from a study that utilized drought impact data from the European Drought Impact Report Inventory and employed random forest models to evaluate the significance of various drought indices in predicting sector-specific impacts. Our findings reveal higher predictability in Northern and Southern Europe, particularly during winter and summer, with some regions showing extended predictability up to six months, depending on the season. Focusing on case studies in the UK and Germany, our results highlight regions and seasons where accurate impact predictions are possible. In both countries, high impact predictability was found up to six months ahead, with sectors such as Agriculture, Water Supply, and Tourism in the UK, and Agriculture and Water Transportation in Germany, depending on the region and season. This analysis represents a significant step forward in identifying the most suitable drought indices for predicting impacts across Europe. Our approach not only introduces a new method for evaluating the relationship between drought indices and impacts, but also addresses the challenge of selecting indices for estimating impacts. This framework advances the development of operational impact-based drought forecasting systems for Europe.

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.

The 2022 European drought has underscored critical deficiencies in European water management. This paper explores these shortcomings and suggests a way forward for European drought risk management. Data for this study was gathered through a continent-wide survey of water managers involved in this event. The survey collected 481 responses from 30 European countries and is comprised of 19 questions concerning sectorial impact in the regions of the responders and drought risk management practices of their organizations. Information from the survey is enriched with climate-related information to offer a comprehensive overview of drought risk management in Europe. Our research focuses on four key aspects: the increasing risk of drought, its spatial and temporal impacts, current drought risk management approaches, and the evolution of drought risk management across the continent. Our findings reveal a consensus on the growing risk of drought, which is confounded by the rising frequency and intensity of droughts. While the 2022 event affected most of the continent, our findings show significant regional disparities in drought risk management capacity among the various countries. Our analysis indicates that current drought risk management measures often rely on short-term operational concerns, particularly in agriculture-dominated economies, leading to potentially maladaptive practices. An overall positive trend in drought risk management, with organizations showing increased awareness and preparedness, indicates how this crisis can be the ideal moment to mainstream European-wide drought risk management. Consequently, we advocate for a European Drought Directive, to harmonize and enforce drought risk management policies across the continent. This directive should promote a systemic, integrated, and long-term risk management perspective. The directive should also set clear guidelines for drought risk management at the national level and for cross-boundary drought collaboration.

Advances in impact modeling and numerical weather forecasting have allowed accurate drought monitoring and skilful forecasts that can drive decisions at the regional scale. State-of-the-art drought early-warning systems are currently based on statistical drought indicators, which do not account for dynamic regional vulnerabilities, and hence neglect the socio-economic impact for initiating actions. The transition from conventional physical forecasts of droughts toward impact-based forecasting (IbF) is a recent paradigm shift in early warning services, to ultimately bridge the gap between science and action. The demand to generate predictions of “what the weather will do” underpins the rising interest in drought IbF across all weather-sensitive sectors. Despite the large expected socio-economic benefits, migrating to this new paradigm presents myriad challenges. In this article, we provide a comprehensive overview of drought IbF, outlining the progress made in the field. Additionally, we present a road map highlighting current challenges and limitations in the science and practice of drought IbF and possible ways forward. We identify seven scientific and practical challenges/limitations: the contextual challenge (inadequate accounting for the spatio-sectoral dynamics of vulnerability and exposure), the human-water feedbacks challenge (neglecting how human activities influence the propagation of drought), the typology challenge (oversimplifying drought typology to meteorological), the model challenge (reliance on mainstream machine learning models), and the data challenge (mainly textual) with the linked sectoral and geographical limitations. Our vision is to facilitate the progress of drought IbF and its use in making informed and timely decisions on mitigation measures, thus minimizing the drought impacts globally.

Droughts are often long-lasting phenomena, without a distinct start or end and with impacts cascading across sectors and systems, creating long-term legacies. Nevertheless, our current perceptions and management of droughts and their impacts are often event-based, which can limit the effective assessment of drought risks and reduction of drought impacts. Here, we advocate for changing this perspective and viewing drought as a hydrological–ecological–social continuum. We take a systems theory perspective and focus on how “memory” causes feedback and interactions between parts of the interconnected systems at different timescales. We first discuss the characteristics of the drought continuum with a focus on the hydrological, ecological, and social systems separately, and then we study the system of systems. Our analysis is based on a review of the literature and a study of five cases: Chile, the Colorado River basin in the USA, northeast Brazil, Kenya, and the Rhine River basin in northwest Europe. We find that the memories of past dry and wet periods, carried by both bio-physical (e.g. groundwater, vegetation) and social systems (e.g. people, governance), influence how future drought risk manifests. We identify four archetypes of drought dynamics: impact and recovery, slow resilience building, gradual collapse, and high resilience–big shock. The interactions between the hydrological, ecological, and social systems result in systems shifting between these types, which plays out differently in the five case studies. We call for more research on drought preconditions and recovery in different systems, on dynamics cascading between systems and triggering system changes, and on dynamic vulnerability and maladaptation. Additionally, we advocate for more continuous monitoring of drought hazards and impacts, modelling tools that better incorporate memories and adaptation responses, and management strategies that increase societal and institutional memory. This will help us to better deal with the complex hydrological–ecological–social drought continuum and identify effective pathways to adaptation and mitigation.

Despite the scientific progress in drought detection and forecasting, it remains challenging to accurately predict the corresponding impact of a drought event. This is due to the complex relationships between (multiple) drought indicators and adverse impacts across different places/hydroclimatic conditions, sectors, and spatiotemporal scales. In this study, we explored these relationships by analyzing the impacts of the severe 2018-2019 central European drought event in Germany. We first computed the standardized precipitation index (SPI), the standardized precipitation evaporation index (SPEI), the standardized soil moisture index (SSMI) and the standardized streamflow index (SSFI) over various accumulation periods, and then related these indicators to sectorial losses from the European drought impact report inventory (EDII) and media sources. To cope with the uncertainty associated with both drought indicators and impact data, we developed a fuzzy method to categorize them. Lastly, we applied the method at the region level (EU NUTS1) by correlating monthly time series. Our findings revealed strong and significant relationships between drought indicators and impacts over different accumulation periods, albeit in some cases region-specific and time-variant. Furthermore, our analysis established the interconnectedness between various sectors, which displayed systematically co-occurring impacts. As such, our work provides a new framework to explore drought indicators-impacts dependencies across space, time, sectors, and scales. In addition, it emphasizes the need to leverage available impact data to better forecast drought impacts.

Droughts in Europe are becoming increasingly frequent and severe, with the 2022 drought surpassing previous records and causing widespread socio-economic impacts. This study employs a Europe-wide survey that integrates data from 481 respondents from 30 European countries, involved in the management of the 2022 European drought, together with hydroclimatic data (i.e., Standardized Precipitation Evapotranspiration Index; SPEI), to provide a holistic assessment of the effect of drought preparedness on response effectiveness and timeliness during the 2022 drought through statistical methods. It specifically assesses the role of forecasting systems and Drought Management Plans (DMPs) in improving preparedness and in facilitating more effective and timely responses. Additionally, the study investigates how drought management practices and awareness have evolved as a consequence of the 2018 European drought and how recent experiences shape water managers’ perceptions. The findings emphasize the urgent need for a standardized, continent-wide drought risk management coordination to address the multifaceted nature of drought risk by integrating climatic and societal factors, and advocates for a Drought Directive as a means to achieve it. This research aims to inform policy development towards sustainable and holistic drought risk management, highlighting the crucial roles of preparedness, awareness, and adaptive strategies in mitigating future drought impacts.

Accurate drought impact-based forecasting of crop yield in India remains challenging due to the country’s hydro-climatic diversity and complex interactions between climate variability, ecosystem vulnerability, and agriculture. This study develops a framework integrating observed and forecasted drought indices across multiple accumulation periods to predict standardised crop yields at seasonal lead time before planting. Using district-level and cluster-based approaches, we apply Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks to establish indicator–impact relationships for paddy rice (wet season) and wheat (dry season), leveraging historical yield data and seasonal forecasts.

District-level models outperform cluster-based ones, with Random Forest showing the best performance. Over 80% of wheat districts and 70% of rice districts achieve strong predictive accuracy, defined as RMSE below 0.2 in the test set. Incorporating ECMWF’s SEAS5 forecasts enables reliable rice yield predictions up to six months before the season—covering over 80% of wheat districts and 60–70% of rice districts. Forecast skill assessed using Continuous Ranked Probability Score (CRPS) confirms robustness across space and time, especially in districts with moderate yield variability. Weighted CRPS shows forecasts for extremely low yields (below the 10th percentile) are accurate and reliable—crucial for early warning and preparedness.

This work advances operational impact-based drought forecasting in India, offering a tool to inform anticipatory action among farmers, water managers, and supply chains. By linking drought observations and seasonal forecasts to crop yield outcomes, the study provides a replicable early warning approach to support targeted mitigation and enhance climate resilience in agriculture.