The World Drought Atlas is a new flagship report, produced in collaboration with the U.N. Convention to Combat Desertification (UNCCD), the European Commission, and other partners, which aims to raise awareness of drought risk and resilience. Formally introduced at UNCCD's 16th Conference of Parties in Riyadh in December 2024, the Atlas is aimed at national and regional governments and policymakers, providing a starting point for implementing measures to address drought risks. Using primarily visual materials, the Atlas aims to: i) synthesize, map, and characterize current and future drivers that contribute to drought risks at the global level, ii) illustrate viable risk management and adaptation options, and iii) highlight examples from different systems and regions of the world.
In this presentation, we introduce the Atlas to the research community, briefly covering content and structure before turning to a discussion of the process behind this collaborative effort between scientists and policymakers. We highlight the differences between peer-reviewed research and policy-oriented projects, the value of visual storytelling, and the importance of a globally distributed author list. We also discuss three of the Atlas’ most important messages and how they were addressed: 1) the combined socioecological character of drought, moving away from characterizations of drought as a “natural” hazard; 2) the broad impact of drought geographically, challenging notions that drought is only a problem in the developing world or in arid regions; and 3) the multisectoral and cascading nature of drought impacts, expanding beyond a traditional association of drought with agriculture. We finish with a short discussion of future plans for dissemination of the Atlas and its findings.
Recognizing that the Atlas is itself an example of cross-disciplinary efforts to promote better drought management and adaptation, we see this discussion as an opportunity to share some of the lessons learned in engaging in interdisciplinary, applied work. We hope this work serves as an example of successful multisectoral collaboration that enhances our collective understanding of drought risks and how to manage and respond to them.
How to cite: Maurer, T., Cremonese, E., Rossi, L., Toreti, A., Tsegai, D., Wens, M., de Moel, H., Sabino Siemons, A.-S., Acosta Navarro, J., Hrast Essenfelder, A., Volpi, D., Cotti, D., Sparkes, E., and Hagenlocher, M.: The World Drought Atlas: a wake-up call on drought risks and resilience , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16467, https://doi.org/10.5194/egusphere-egu25-16467, 2025.
Snow droughts are increasingly recognized as an important feature of dry periods in mountain regions worldwide. While the phenomenology of this hazard is becoming clearer, its implications for hydrology, ecosystems, and upstream and downstream communities remain poorly understood. This knowledge gap leaves scientists and decision-makers without the necessary tools to support adaptation in the face of accelerating climate change and declining, increasingly ephemeral snow water resources. Leveraging 13 years of hydrological and multi-sectoral impact data from over 30 headwater catchments across Italy, we demonstrate how snow droughts impose profound and cascading impacts on mountain socio-ecological systems, from seasonal to multi-annual scales, with downstream repercussions. Early findings reveal that snow droughts can increase melt-out events and reduce snow season duration compared to non-snow-drought years. These changes result in significant hydrological consequences, even in the absence of differences in summer precipitation or air temperature between snow-drought and non-snow-drought years. Beyond hydrology, snow droughts impact vegetation productivity and lead to emergency measures in water-resource management for end users, with effects shaped by the spatial and temporal characteristics of water-supply infrastructure. This study highlights the need to frame snow droughts as a socio-ecohydrological risk, with broad implications for water security in mountain regions and downstream areas.
How to cite: Avanzi, F., Terzi, S., Castelli, M., Munerol, F., Andreaggi, M., Galvagno, M., Maurer, T., Massari, C., Carlson, G., Girotto, M., Bertoldi, G., Cremonese, E., Gabellani, S., Altamura, M., Rossi, L., and Notarnicola, C.: Impacts of Mediterranean snow droughts on mountain socio-ecohydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1529, https://doi.org/10.5194/egusphere-egu25-1529, 2025.
Drought events pose significant challenges to ecosystems and human societies, necessitating precise methodologies for their identification and analysis. This study introduces a clustering technique to establish a robust framework for identifying drought objects. The identification process incorporates spatial proximity metrics, Haversine distance calculations, and periodic boundary handling to detect coherent drought-affected regions. Drought objects are further refined by applying a land-sea mask to exclude oceanic areas and merging small-scale clusters to maintain relevance. The study highlights the value of tracking drought objects over time and the critical insights this provides into the spatio-temporal dynamics of droughts.
The methodology enables a dynamic understanding of drought patterns, producing outputs such as high-resolution cluster maps with spatial characteristics, including the severity and area of each cluster. These characteristics are developed using drought events reported in the Geocoded Disasters (GDIS) dataset and are linked to the impact data, such as the number of people affected and economic damage caused by the events. These findings are vital for disaster risk reduction, climate impact studies, and policy-making. By integrating spatial analysis with the clustering, this study provides a comprehensive and reproducible approach to linking the geographical extent and intensity of drought events to their impacts.
How to cite: Samantaray, A. and Messori, G.: Spatiotemporal Mapping of Drought Impacts Across Continents: A Cluster-Based Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4794, https://doi.org/10.5194/egusphere-egu25-4794, 2025.
Drought indices are essential tools for quantifying drought conditions by integrating multiple variables into a single measure that represents its characteristics, such as intensity, duration, and severity. These indices play a key role in real-time monitoring, forecasting, and supporting risk management actions. However, traditional statistical indices often fail to account for the complex interactions between drought precursors and their socio-economic and environmental impacts. Moreover, given the absence of a universally accepted drought definition, no single index is applicable to all drought types, climate conditions, or affected sectors.
In this study, we aim to improve traditional drought detection by defining new impact-based drought indices through Machine Learning algorithms. These indices are designed to better link the observed impacts of extreme droughts across different sectors with their potential drivers, including climatic, meteorological, and hydrological variables, analyzed across multiple spatial and temporal scales. The methodology is applied to the case study of the Adda River basin, focusing on the multisectoral impacts of drought on agricultural production, hydroelectric generation, and recreational and ecosystem services.
The definition of impact-based drought indices relies on the FRamework for Index-based Drought Analysis (FRIDA), which uses a feature extraction algorithm to formulate novel impact-based drought indices that combine all the relevant information about candidate drought drivers (e.g. water levels, snow depth, temperature) to reproduce the observed impacts.
Our findings indicate that FRIDA has produced indices that accurately capture the drought impacts with the Pearson correlation coefficient between observations and model’s outputs that remains consistently above 0.6, with values reaching 0.97 and 0.99 for the hydropower and recreation sectors, respectively. Additionally, it is noteworthy that the inputs selected by the algorithm vary depending on the sector being considered, shedding light on sector-specific connections between drivers and impacts. Ongoing experiments are investigating the potential for further improving our results by adopting a multi-task model for better handling the interdependencies across the impacted sectors with respect single-task models that identify individual indices independently for the different sectors.
How to cite: Merlo, M., Giuliani, M., and Castelletti, A.: Advancing the detection of multi-sector drought impacts via feature extraction and multi-task learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9270, https://doi.org/10.5194/egusphere-egu25-9270, 2025.
Droughts affect diverse sectors, including water resources, agricultural productivity, and ecosystem stability. While indices like the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are widely employed to measure the intensity of droughts, they tend to focus on meteorological and hydrological aspects instead of social and economic dimensions. Notably, droughts of comparable meteorological severity can have vastly different outcomes, influenced by disparities in infrastructure, economic resilience, and community preparedness. Recent drought studies have highlighted the potential of integrating text mining and natural language processing to enhance drought impact assessments. However, many of these studies rely on official reports or newspapers, which often face limitations in temporal and spatial resolution due to the constraints of available data sources. In contrast, social media platforms like Twitter (X) host and disseminate real-time text data from individuals experiencing drought events, providing more granular and dynamic information about drought impacts that traditional methods may struggle to capture.
This study seeks to develop a framework for assessing perceived drought impacts through a set of sectoral impact scores generated from social media data by leveraging text mining techniques. Furthermore, the research compares these social media-derived scores with severity data from the report-based European Drought Impact Database (EDID) and physical drought indices to identify similarities and discrepancies between public perceived impacts, officially reported impacts, and meteorological drought intensity. To our knowledge, this is the first study to convert social media text into indicators of drought impacts across multiple categories, offering an innovative complement to traditional indices and enhancing our understanding of how affected communities perceive drought events.
Focusing on the 2022 Italian drought, we analyzed location-specific tweets using sentence embedding and large language models to identify sector-specific topics. We then examined the spatial and temporal patterns of perceived sectoral impact scores across Italy based on each tweet's relevance to the identified impact sectors. Our analysis revealed that Twitter activity about droughts peaked in the summer, with water availability and societal responses drawing the most attention in Northern Italy. This activity pattern closely aligned with the seasonality identified by SPI metrics, with areas of extreme drought conditions expanded during the summer months. On the other hand, comparisons with the report-based EDID showed inconsistencies, as EDID emphasized more severe impacts on agriculture. This suggests that while social media captures timely public perceived impacts, it may fail to fully reflect the depth or breadth of impacts in certain sectors due to the underrepresentation of specific groups on these platforms.
How to cite: Wang, J., Pernici, B., and Castelletti, A.: Spatial Analysis of Drought Perceived Impacts Using Social Media Text Mining, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14575, https://doi.org/10.5194/egusphere-egu25-14575, 2025.
Drought events significantly challenge communities and ecosystems worldwide, emphasising the urgent need for effective predictive methods to facilitate proactive management and to mitigate their impacts. A clear gap exists between theoretical drought indices, such as SPI, SPEI, and SSMI, and the real-world impacts of droughts. This study aims to address this disparity by leveraging machine learning (ML) techniques to predict reported drought impacts, using data from the European Drought Impact Database (EDID). A variety of ML algorithms, including Random Forest, Quantile Random Forest, Least Absolute Shrinkage and Selection Operator, XGBoost and Linear Regression were assessed. The study also uses likelihood forecasting to quantify the probability of drought impacts. This probabilistic approach and use of lagged indices allows for a deeper understanding of the range of possible outcomes, enabling decision-makers to plan and prepare for varying levels of drought severity.
Unlike location-specific modelling approaches, this study proposes a generalized ML model applicable across the UK. The model's robustness was validated using independent datasets from different regions and periods. The findings indicated that categorising impacts into severity levels, rather than predicting the exact number of impacts and improved the model's accuracy and interpretability. Additionally, the model was applied at a grid scale to generate impact-based drought maps, providing a valuable tool for decision-making in drought risk management. This methodological approach enhances decision-making processes for drought risk management, demonstrating the practical utility of ML techniques that can be applied globally, beyond the UK.
How to cite: Bulut, B., Magee, E., Armitage, R., Tanguy, M., Barker, L., and Hannaford, J.: Bridging Drought Indices and Impacts: Forecasting Future Outcomes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9473, https://doi.org/10.5194/egusphere-egu25-9473, 2025.
The impacts of droughts on the terrestrial ecosystem gross primary production (GPP) are evident with contemporaneous and lagged effects. However, the magnitude of post-drought vegetation GPP loss remains unclear. This study quantitatively assessed the global post-drought GPP loss on the 8-day scale during 2000-2022. Globally, the mean post-drought GPP loss was ~0.74 Pg C yr⁻¹, accounting for ~ 21.45% of the total drought-induced GPP loss. The higher proportions of post-drought GPP loss were evident in humid regions, whereas the higher absolute post-drought GPP loss mainly occurred in regions with higher vegetation cover. Furthermore, the global mean incidence and duration of post-drought GPP loss were 51.23 ± 21.21% and 33.36 ± 13.27 days, respectively. The occurrence and persistence of post-drought GPP loss exhibited a consistent correlation with aridity, but an inverse relationship with vegetation composition. Our findings would contribute to a better understanding of the responses of terrestrial ecosystems to drought.
How to cite: Zhao, Z., Ju, W., and Zhou, Y.: Widespread and Divergent Post-drought Loss of Gross Primary Productivity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14492, https://doi.org/10.5194/egusphere-egu25-14492, 2025.
Tree vitality is a key factor influencing natural hazard-related risks for rail transport, yet it has been little considered in risk models and management concepts. This is primarily due to a lack of reliable tree vitality data along railways. In the project “RailVitaliTree – Tree vitality monitoring and modelling of drought-related risks along railroads with remote sensing and dendroecology”, we are developing a tree vitality monitor for the tree population along Germany’s railway network.
We analyze time-series data – including multispectral satellite images, dendroecological data and climate data – to deepen our understanding of the relationship between climate and tree vitality in the specific microclimate along railways. Based on our findings, we will assess the long-term consequences of drought in a changing climate and its multiplier effects on other natural hazard-related risks. Ultimately, our goal is to enhance the resilience of rail transport to vegetation-related disturbances. Our focus is on the four major tree species in Germany: Scots pine (Pinus sylvestris), european spruce (Picea abies), pedunculate oak (Quercus robur) and common beech (Fagus sylvatica).
In this presentation, we outline our initial steps, study sites and methodology. For our retrospective climate analysis, we examine the spatial distribution and temporal changes in drought stress of these four major tree species from 1961 to the present, using the water balance model LWF-Brook90. We also conduct a correlation analysis to explore the relationship between modelled drought stress and observed changes in tree vitality, as indicated by satellite data (based on the ForestWatch Tool: https://forestwatch.lup-umwelt.de/).
Additionally, we present preliminary dendroecological results from our study sites. We compare growth data from trees along the rail with that of trees in nearby forest stands. This analysis ultimately aims to identify potential forest edge effects and evaluate whether trees along the rail are more susceptible to drought stress.
How to cite: Rutte, D., Billig, L., Braeuning, A., Braun, M., Gey, S., Haeusser, M., Herbst, M., Klinke, R., Kurtz, W., Schmidt-Walther, P., Stöckigt, B., and Szymczak, S.: A Tree Vitality Monitor for the German Railway Network - RailVitaliTree, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16115, https://doi.org/10.5194/egusphere-egu25-16115, 2025.
To explore the interaction and causality between management decision-making and
the evolution of anthropogenic drought, we proposed a comprehensive decision
evaluating framework and analytical method. This framework consists of several key
components: current status description, actions from a virtual agent, the consequences
of these actions, policy objective design, and the identification of an optimal datum
policy. In the context of anthropogenic drought, the modified water accounting and
vulnerability evaluation plus (modified WAVE+) is employed to simulate socio
hydrological interactions, providing a detailed description of the current status. The
consequences of actions are determined using the Monte-Carlo method, serving as
conditional probabilities for anthropogenic drought occurrence. The proposed optimal
objectives, which focus on maximizing supply capacity and minimizing water
shortages, are achieved using a Q-learning mixed strategy integrated with the modified
WAVE+. To further analyze the dynamics of anthropogenic drought, we decomposed
the sources of change in conditional probability into two key factors: anthropogenic
pressure and vis major. This decoupling of socio-hydrological information allows for a
more nuanced causality analysis. By comparing the optimal datum policy with the
quantified evaluations of anthropogenic pressure and vis major, we introduced a
concept to determine whether drought dynamics are resistible or irresistible and
whether there is potential for improvement in decision-making. Applying this
evaluation framework and analytical method to the Shihmen water supply system in
Northern Taiwan, we not only demonstrated how anthropogenic drought co-evolves
with water resource management policies but also conducted an irresistible and
causality analysis of historical drought events.
How to cite: Liu, C.-Y. and Hsu, S.-Y.: From Causes to Consequences: A Novel Aspect for Evaluating Anthropogenic Drought and Water Resource Management Policies , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16920, https://doi.org/10.5194/egusphere-egu25-16920, 2025.
Drought risks are characterized by complex characteristics and processes, which underpin all risk components, i.e. hazard, exposure and vulnerability. The dynamics of drought vulnerability are of particular interest since they can provide important information for adaptive risk management and adaptation practices in the face of growing drought risks, where a static understanding of vulnerability may not be effective or even prove to be maladaptive. For this reason, the scientific and policy-making communities have been increasingly advocating for including vulnerability dynamics in drought risk assessments. However, no overview exists of how scientists approach drought vulnerability dynamics, and there is a lack of conceptual clarity as to which types of changes (e.g. temporal, spatial, or system’s drivers and components) should be the object of dynamic vulnerability assessments.
To fill this gap, we carried out a systematic review of drought vulnerability dynamics to shed light on concepts, approaches, and methodologies available in the scientific literature. The review covered English peer-reviewed publications retrieved from the Scopus database and refined through multiple steps of assessment, using fixed inclusion/exclusion criteria and a “four-eyes” principle. Our review shows that only a minority of the studies considered and assessed vulnerability in its dynamic components. Moreover, within these, most of the applications only considered temporal dynamics, i.e. changes through time, and only a minority investigated drought vulnerability dynamics within a multi-hazard context. This highlights that more research is required to fully account for the complexity of drought risks and to better support risk management. The review results were also instrumental in informing a novel conceptual framework on vulnerability dynamics, which can guide future research advancements and applications, beyond the confines of any single hazards.
How to cite: Schlebusch, M., Cotti, D., Wens, M., Van Loon, A. F., de Brito, M. M., Kchouk, S., and Hagenlocher, M.: Deciphering Vulnerability Dynamics: A Review on Conceptual and Methodological Pluralism in Dynamic Drought Vulnerability Assessments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18424, https://doi.org/10.5194/egusphere-egu25-18424, 2025.
The recent arrival of the climate crisis has led to a shortage of water for living, industry, and agriculture due to drought. This occurrence has an economic impact on various social sectors, and if it continues for a long period of time, it leads to a decrease in socio-economic activities. Consequently, the socio-economic impact of water shortages has emerged as a pivotal research area. By identifying the socio-economic potential losses due to water use in various industries, we can develop strategies for an effective water distribution system.In light of intensifying climate change, the frequency and intensity of droughts are projected to rise. These droughts are expected to have negative socio-economic impacts in the order of weather, agriculture, life, and industry.In this study, we aim to develop an evaluation technique for socio-economic potential losses due to water shortages in South Korea.Based on the evaluation technique, we intend to assess how much socio-economic potential loss is caused by water shortages in the areas of living, industry, and agriculture. The selected evaluation method is the WIOLP analysis technique of the industry-related analysis, and the analysis was conducted for the years 2015 and 2018, when drought damage occurred in the Republic of Korea. In 2015, it was estimated that a 10% reduction in water usage due to drought would result in damages amounting to approximately 257.9 billion won. A 90% reduction, on the other hand, was predicted to lead to widespread industry-wide damage. In 2018, if the water usage is reduced by 10% due to drought, the estimated loss is projected to be around 318.9 billion won. If usage is reduced by more than 80%, damage is likely to occur across all industries, initially affecting some sectors. The results of this study are expected to contribute to the evaluation of socio-economic potential losses due to water shortages and the assessment of water usage in each sector.
Acknowledgments: This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Water Management Program for Drought Project, funded by Korea Ministry of Environment (MOE). (RS-2023-00230286).
How to cite: Song, Y. and Park, M.: Study on the assessment of socio-economic potential losses due to water shortages , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7559, https://doi.org/10.5194/egusphere-egu25-7559, 2025.
India, being an agriculture-dependent country, experiences recurrent droughts that significantly impact agricultural productivity. Assessing agricultural droughts and accurately identifying their onset is essential for effective planning and mitigation strategies. Soil Moisture (SM)-based drought indices, often paired with the run theory, are commonly used to identify the agricultural drought onsets. However, traditional run theory approaches rely on a single, uniform threshold to detect drought events, which may inadequately represent long-term drought patterns and oversimplify spatial variability in SM conditions. This study addresses these limitations by proposing an enhanced run theory approach that uses multiple dynamic grid-specific thresholds. The southern plateau and hills region of India was chosen as the study area. The thresholds are derived based on the standard deviation of the Standardized Soil Moisture Index (SSI) time series for each grid, ensuring adaptability to spatial heterogeneity of SM conditions. The SSI is calculated using European Space Agency Climate Change Initiative (ESA CCI) SM data. The enhanced run theory is then applied to compute key agricultural drought characteristics including duration, peak, frequency, and intensity.
The results reveal that the computed dynamic SSI thresholds capture subtle but notable spatial variations, reflecting the influence of grid-specific factors such as soil types and land cover. This approach enhances the accuracy of drought detection and characterization. The analysis of drought metrics reveals that drought duration and frequency share similar spatial distributions, suggesting that areas experiencing frequent droughts are also prone to prolonged drought periods. This spatial congruence highlights the consistent vulnerability of certain regions to both drought initiation and sustained impacts. Furthermore, the analysis of drought peak and intensity demonstrates a predominance of moderate drought conditions, with severe droughts occurring less frequently and extreme droughts being rare. The findings underscore the importance of dynamic, location-specific thresholds for improving drought assessment. By capturing spatial variability in SM conditions, the proposed enhanced run theory provides a robust framework for characterizing agricultural droughts.
How to cite: Palagiri, H. and Pal, M.: An Enhanced Run Theory for Agricultural Drought Characterization using Satellite Soil Moisture Data., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8275, https://doi.org/10.5194/egusphere-egu25-8275, 2025.
We present the methodology for near real-time monitoring of emerging drought risk in Sudan, resulting in the release of a national drought risk bulletin every 10-days to inform local stakeholders, humanitarian organizations and policymakers. The bulletin stems from a collaboration between CIMA Research Foundation and Sudanese partners, within the framework of the APIS initiative - Early Warning and Civil Protection for Floods and Droughts in Sudan - funded by the Italian Agency for Development Cooperation. The bulletin is co-created by nine Sudanese institutions with diverse economic, social or scientific expertise under the coordination of the National Council of Civil Defence NCCD, national body in charge of disaster risk reduction operations. Sudan is particularly vulnerable to drought impacts due to its climate, demographic and economy. This vulnerability has been further intensified by the war that erupted in April 2023, creating one of the most severe humanitarian crises in recent history. In this context, this collaboration enhances local resiliency and disaster preparedness while maintaining and supporting local expertise and know-how in a period of crisis.
For the publication of the bulletin, drought risk is evaluated separately for three possible impact categories: croplands, rangelands and population. For each of these, a combination of datasets from publicly available sources and datasets provided by the local partners are used to estimate the hazard, exposure and vulnerability components of risk. For hazard estimation, the combined drought indicator (CDI) is used for hazard to crops and pastures, whereas a 12-month standardised precipitation index (SPI12) is used as a proxy for water availability for the population.
Regarding exposure and vulnerability, a collaborative approach was followed. Several relevant datasets were gathered and discussed with the representatives of the institutions participating in the creation of the bulletin to assess their correctness, validity and relevance. The selected datasets were weighted by the participants based on their expertise to collaboratively estimate the most suitable exposure and vulnerability layers for each of the three impact categories. Finally, a dynamic component was added to these layers considering global phenology data (for croplands and rangelands) as well as the implementation of an innovative approach to capture changes in population vulnerability during the dry season taking into consideration water availability and losses over time.
The bulletin has been operational since November 2024 and all the data and results are available to all stakeholders through a tailored access to the online platform myDEWETRA.World.
How to cite: Trotter, L., Isabellon, M., Cremonese, E., Masoero, A., Babiker, S., Ali, S., Khogly, H., Mahmoud, E. M., Sabar, H. S., Abdella, A. I., Abkar, M., Abohassabo, M. I., Musa, A. I. I., Nagi Adam, E. A., Abdelkreem Mohamed, E. E., Rossi, L., and Testa, N.: Drought Risk Assessment in Crisis Context: A Collaborative Approach for Sudan., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15865, https://doi.org/10.5194/egusphere-egu25-15865, 2025.
Research on climate extremes, particularly droughts, is largely limited by the lack of impact data. Current impact data are often sparse if not completely inaccessible or absent. This is the ongoing condition also for mountain areas, which, despite hosting important and interconnected environmental and socio-economic systems, are increasingly impacted by droughts with limited to no-data coverage.
This work explores the use of textual data from online Italian newspaper articles, blogs, and reports to collect information on drought impacts on different socio-economic sectors and regions across the Italian Alps. In particular, we developed a pipeline to create an open database of drought news reporting. We used natural language processing (NLP) methods to automatically (i) extract news articles from Google News using drought-related keywords in Italian language, (ii) filter and clean the retrieved articles extracting text bodies, and (iii) classify them, identifying the impacted sectors (e.g., agriculture, hydropower, tourism) and regions. We evaluated the performance of different state-of-the-art NLP models on the chosen classification tasks (e.g., relevance to the drought topic, extraction of the impacted location) based on both standard NLP metrics and (environmental) resource consumption criteria.
Preliminary results show patterns of correspondence between the frequency of harvested drought impact news and the general trend of drought conditions in the north of Italy (e.g. maximum values of news items in summer 2022 and spring 2023). Around 60% of the collected news items were classified as relevant to the drought topic, 35% were recorded as explicitly covering drought impacts, while 15% were reported to deal with drought damages in detail. Regarding the detection of impacted sectors and locations inside news bodies, due to task complexity, selected models reported varied performance with results highly dependent on the specific news structure and context.
Overall, this study (i) presents a workflow to collect drought impact data for the Italian Alps into an open database, enabling near-real time drought impact monitoring, (ii) enriches the developed database with information on news relevance to the drought topic, documented impacts, and mentioned locations, including reliability estimates for given classifications, (iii) offers methodological guidance for future research by providing information on best performing algorithms and environmental cost criteria, (iv) has the potential for transferability to other areas, languages, or natural hazards to improve the understanding of climate extremes impacts and implement targeted and effective adaptation strategies.
How to cite: Terzi, S., Pomella, A., Frey, J.-C., Piemontese, L., Cremonese, E., and Pittore, M.: Advancing drought impact data collection for the Italian Alps through automatic harvesting and analysis of textual data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10520, https://doi.org/10.5194/egusphere-egu25-10520, 2025.
With approximately 28% of India's geographical area affected by droughts and a significant portion experiencing moderate to severe conditions, it is crucial to analyze these phenomena to assess their socio-economic impacts and develop effective policy responses. This study delves into the complexities of drought characteristics in India, emphasizing the need for advanced analytical methods to understand the evolving nature of droughts under changing climate conditions. From 1902 to 2013, the evolution of four essential drought characteristics: severity, depth, duration, and frequency has been examined across various climate zones. The analysis utilizes gridded precipitation datasets to compare outcomes from conventional Stationary Precipitation Indices (SPI) with a non-stationary, time-varying drought index aimed at offering a more sophisticated comprehension of drought dynamics and their socio-economic consequences. Furthermore, a non-linear trend analysis method has been implemented to identify the intrinsic complexities and non-linear correlations in drought data that conventional techniques tend to overlook.
The results indicate considerable geographical and temporal variations in drought dynamics. Central and southern India experience prolonged drought episodes, while areas like the Indo-Gangetic Plains and western India see shorter yet more severe droughts. The results further underscore the shortcomings of stationarity-based indices, which tend to overestimate drought severity and duration, especially in earlier decades. In contrast, the non-stationary index identifies subtle trends, indicating both gradual and sudden shifts in climatic patterns.
This study reveals critical hotspots of heightened drought risk, illustrating the increasing effects of hydroclimatic extremes in areas predominantly dependent on agriculture and monsoonal precipitation. By enhancing the accuracy of drought assessments and their spatial-temporal variability, the need for region-specific climate adaptation and mitigation strategies has been highlighted. The findings thereby contribute to the broader discourse that underscores the necessity of integrating evolving climate dynamics into future drought projections to tackle the increasing problems posed by hydroclimatic extremes in a rapidly changing environment.
How to cite: Bhattacharjee, D., Rajendran, V., and Dhanya, C. T.: Evolution of meteorological drought characteristics over India using time-varying drought index, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1090, https://doi.org/10.5194/egusphere-egu25-1090, 2025.
Different types of drought, characterized by their distinct temporal scales, often interact in complex ways and pose significant challenges for drought risk assessment and management. This study introduces the innovative concept of "super drought", which refers to the simultaneous occurrence of extreme droughts across multiple time scales, advancing our understanding of compound drought risks. We demonstrate that super drought represents a unique phenomenon where meteorological, agricultural, and hydrological droughts coincide, leading to more severe impacts than when these events occur in isolation.
To quantify super drought, we developed the Comprehensive Multiscalar Index (CMI) based on a vine copula framework. This novel approach overcomes the limitations of traditional drought indices by probabilistically integrating drought conditions across multiple time scales (3-, 6-, 12-, 24-, and 48-month). The CMI was validated against GRACE satellite-based total water storage observations, showing significantly improved performance in capturing overall water deficits compared to conventional indices.
To support operational drought monitoring and research, we developed superdrought.com as the first online platform dedicated to global super drought assessment. The platform provides: (1) near-real-time global monitoring at 0.5° resolution, (2) interactive visualization tools with customizable temporal and spatial analysis capabilities, and (3) free access to historical CMI datasets from 1961 to present. This comprehensive system enables users to track the evolution of compound drought events and assess their spatial patterns and temporal dynamics.
This integrated framework of concept, methodology, and operational platform represents a significant advancement in drought risk assessment. By highlighting that the most devastating droughts often result from the synchronization of water deficits across multiple components of the hydrological cycle, our approach provides new insights for drought risk assessment and early warning systems, emphasizing the need for integrated approaches in drought monitoring and management.
How to cite: Wang, L., Huang, G., Chen, W., and Wang, T.: Super Drought: An Innovative Framework for Understanding Compound Drought Risk with Online Monitoring Platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2379, https://doi.org/10.5194/egusphere-egu25-2379, 2025.
Flash agricultural droughts (FDs) are defined based on the quick depletion of the crop root zone soil moisture (RZSM) which can have wide negative implications on the agricultural yield loss and associated sectors. FDs can be the sub-set of the traditional slow-developing agricultural droughts. The current study has investigated this intricate underlying interconnection over different HRRs in India for the period of 40 years (1981-2020). Traditional agricultural droughts are characterized using the monthly Standardized Soil Moisture Index (SSMI-1) and FDs using the Standardized Anomaly of the Pentad Root Zone Soil Moisture (SASM). Further, the long-term and short-term persistence is analyzed using the MF-DFA (Multifractal Detrended Fluctuation Analysis) based Hurst index approach in both time series data of flash and traditional droughts indices which has discovered the persistence information in the flash and traditional droughts. The results of the current study have inferred that FDs have long-term persistence (LTP) in humid regions, whereas short-term persistence (STP) is characteristic of traditional droughts in the same region. For the arid and semi-arid climate, the case is reversed with FDs having the STP and traditional droughts having the LTP during the studied period of 40 years. The results of the current analysis show that the persistence in the flash and traditional droughts has a synchrony with the background climate of different HRRs of India, which highlights the varying vulnerability for both types (flash and seasonal) of droughts.
How to cite: Pachore, A. and Remesan, R.: Dynamics of Persistence in Flash and Traditional Droughts across Homogeneous Rainfall Regions of India , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9084, https://doi.org/10.5194/egusphere-egu25-9084, 2025.
Flash droughts can develop suddenly, often within just a few weeks, and are marked by rapidly depleting soil moisture and intense heat stress. These conditions can have devastating effects on crop growth and disrupt entire ecosystems. What makes flash droughts especially challenging is their tendency to occur during the peak growing season, leaving little time for the agricultural and ecological sectors to prepare or mitigate losses. While a lack of precipitation is the primary trigger, other factors like high evaporative demand, low humidity, increased solar radiation, and clear skies can intensify their onset. Since flash droughts are driven by a combination of factors, it is crucial to rely on diverse and accurate data sources to effectively monitor their development and spread.
Previous studies have largely focused on analyzing the evolution of flash droughts using reanalysis data. However, there has been no comprehensive examination of their development at large scales incorporating a wide range of satellite observations. In this study, we characterized flash droughts over the Contiguous United States (CONUS) using remote sensing data from 2003 to 2020. We employed a unique combination of satellite climatic, agricultural, and ecological variables, including Atmospheric Infrared Sounder (AIRS) Vapor Pressure Deficit (VPD), Relative Humidity (RH), Temperature, ERA5 Soil Moisture, Global Precipitation Measurement (GPM) Precipitation, MODIS (Moderate Resolution Imaging Spectroradiometer) Evapotranspiration (ET), Potential Evapotranspiration (PET), Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI), land cover map, and Orbiting Carbon Observatory-2 (OCO-2) Contiguous Solar-Induced Chlorophyll Fluorescence (CSIF). Flash drought events were identified based on root zone soil moisture (RZSM), with all variables aggregated into 8-day (octad) averages to analyze their temporal evolution and lead-lag correlations with RZSM.
Furthermore, the deteriorating impact of flash droughts associated with background aridity needs to be considered when monitoring their agricultural and ecological impacts. To address this, we investigated ecosystem responses to flash droughts across five climate regimes defined using the Aridity Index (AI) within the CONUS. We separated agricultural lands from natural vegetation to differentiate the development of flash droughts across these distinct ecosystems. Finally, we examined the propagation timeline of flash droughts from meteorological to agricultural and ecological droughts using cross-correlation and Cross Wavelet Transform methods.
How to cite: Farahmand, A., Zeraati, M., Seager, R., Madani, N., AghaKouchak, A., Wen, Y., Fowler, H., Mehran, A., and Parazoo, N.: Utilizing Satellite Data for Large-Scale Monitoring and Analysis of Flash Droughts Across the Contiguous United States , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13779, https://doi.org/10.5194/egusphere-egu25-13779, 2025.
Knowledge on the impact of droughts represents a pivotal milestone for the assessment of drought risk and the improvement of water management. While drought as a hazard has a non-boundary spatial pattern, different countries, with different socio-economic backgrounds can be characterized by various levels of vulnerability and follow different paths to cope with its. Deciphering the impacts of the past drought events can considerably improve societal complex responses and inform the choice of adaptive measures and water supply management in the face of the future similar events. Building-up a database of past droughts along the Prut Valley represents the first work package of the project: “Exploring the paths to cope with hydro-climatic risks in transboundary rural areas along the Prut Valley. A multi-criteria analysis”. A comprehensive database was created regarding the events recorded between 1860 and 2024 on both banks of the Prut River. The data were gathered from scientific literature and by exploring the digital and printed newspapers from both countries (written in Romanian, in Romanian with Cyrillic characters and in Russian). The information about droughts has been recorded and presented differently, mainly because of particular political, economic, and social conditions from the two countries (we mention that during the period 1918-1940 both territories were within the same borders). The supervised collection of the impact of droughts made possible a rigorous selection of events, eliminating duplicates, irrelevant news, and an in depth analysis of cascading impacts. The value of this database is multiplied by the geoscientific expertise of the authors as well as by the investigation of all the available documents.
The main result consists in the identification of the main temporal benchmarks (such as those from 1904, 1907, 1928, 1946, 1965) and spatial hotspots (especially in the southern part of the study area) in the manifestation of droughts. Coupling the database with GIS techniques that allow us a large type of assigned attributes, the cartographical outputs of this work will clearly contribute to an accurate configuration of past drought events. This constitutes a scientific starting point for drought risk assessment, better choices of adaptive measures and the improvement of water management targeting citizens, farmers, and decision-makers.
Some conclusions can be addressed regarding future approaches of the mitigation of droughts in rural agricultural areas such as our study area: (i) droughts are not only a farmers major problem but they affect entire rural communities; (ii) solving local capacity to develop alternative water supply during the summer must represent not only a local/regional priority but a national and European Union one; (iii) increasing resilience to droughts must include a participatory locally-adapted approach based on the experience of citizens; (iv) there is a pressing need to acknowledge the importance of transboundary network and projects, especially in the case of droughts monitoring and proactive water management.
How to cite: Margarint, M. C., Bunduc, T., Niculita, M., Bejan, I., Albulescu, A.-C., Chiriac, I., Botnari, A., Chelariu, E.-O., Fedor, A.-D., and Enea, A.: Completing the drought impact database for the transboundary region of the Prut Valley (Romania/Republic of Moldova), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14905, https://doi.org/10.5194/egusphere-egu25-14905, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 3
EGU25-1146 | ECS | Posters virtual | VPS12
Advancing Drought Resilience in Mediterranean Drylands: Insights from Vegetation-Soil Moisture Interactions and Remote Sensing in Keri Forest, CreteMon, 28 Apr, 14:00–15:45 (CEST) vPoster spot 3 | vP3.22
EGU25-20010 | ECS | Posters virtual | VPS12
Drought vulnerability assessment in SwedenMon, 28 Apr, 14:00–15:45 (CEST) vPoster spot 3 | vP3.23
Sweden, known for its abundant water resources, has recently experienced drought events with significant socio-economic and environmental impacts, revealing existing vulnerabilities in the society. Future climatic projections indicate changes in precipitation and temperature patterns, stressing the need for improved drought risk management. The vulnerability component of risk is often less studied than the hazard component, primarily due to its inherent complexity. Drought vulnerability is highly context-dependent, shaped by the interplay of social, ecological, and hydroclimatic factors. In the context of a changing climate, assessing drought vulnerability is becoming increasingly important. However, such assessments are scarce in Nordic regions.
To address this gap, this study quantifies vulnerability factors related to coping capacity, adaptive capacity, and susceptibility, and integrates them to map drought vulnerability hotspots across Sweden. Based on a stakeholder-validated set of vulnerability factors for water-dependent sectors (including agriculture, forestry, energy, water supply, and environmental management), municipal-level data sources were screened to identify and quantify relevant vulnerability indicators. A probabilistic approach was employed to assess the sensitivity of regional vulnerability patterns to the weighting of vulnerability factors. The resulting spatial distribution of relative vulnerability reflects the heterogeneous socio-hydrological systems across municipalities and highlights the importance of sustainable local economic adaptation to water availability in reducing sensitivity and mitigating drought impacts. Our vulnerability assessment provides valuable insights for local and regional planners, supporting the effective allocating of resources and the development of targeted drought mitigation strategies at municipal level. The findings underscoring the need for context-specific assessments to account for regional and sectoral differences in drought vulnerability. Furthermore, the results emphasize the complexity of drought risk and the challenges of integrating diverse vulnerability factors in diverse socio-hydrological contexts.
How to cite: Canedo Rosso, C., Stenfors, E., Teutschbein, C., and Nyberg, L.: Drought vulnerability assessment in Sweden, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20010, https://doi.org/10.5194/egusphere-egu25-20010, 2025.
EGU25-13798 | ECS | Posters virtual | VPS12
Unveiling environmental dimensions of hydrological drought in Southern Spain using open-source data and machine learning techniquesMon, 28 Apr, 14:00–15:45 (CEST) vPoster spot 3 | vP3.24
Drought is an increasing hydroclimatic threat in the Mediterranean, profoundly impacting water resources and ecosystems. Andalusia (Spain) is highly vulnerable due to climatic variability and prolonged dry periods. Effective drought management requires methods to assess impacts on groundwater and surface water systems, which in turn threaten ecological and socio-economic resilience. While socio-economic impacts are more analysed, environmental effects are overlooked due to delayed onset or unclear links to drought. However, drought-induced degradation of natural resources and hydrology-linked ecosystem services can exacerbate challenges in agroforestry, livestock, and tourism. Examining the environmental dimensions of hydrological drought risk is therefore essential.
This research takes a first step in analysing the impacts of drought on water-related ecosystem services. It specifically investigates hydrological and hydrogeological anomalies and examines their spatial and temporal dynamics across varying levels of drought severity. This study defines hydrological anomalies by leveraging high-resolution, open-access data from Copernicus and other datasets available on Google Earth Engine. These include estimates of soil moisture, groundwater storage, terrestrial water storage, flows and evapotranspiration that can be obtained from GLDAS 2.2, FLDAS, CERRA-Land, etc. In situ measurements, such as piezometric and streamflow records, are also integrated to validate findings and provide a robust basis for analysis of the impacts on water systems. Machine learning algorithms are then used to model the complex linkages between the identified hydrological anomalies and the climatic conditions, measured with well-known drought indices like the Standardized Precipitation-Evapotranspiration Index (SPEI) at different scales.
A pilot study in an Andalusian sub-basin with minimal anthropogenic influence serves as a testbed for developing a scalable methodology to evaluate the impacts of short and long-term drought conditions on groundwater and surface water. In line with related relevant research, correlation analyses run for this pilot highlight strong associations between hydrological variables and drought indices. A rapid response of surface water systems to short-term droughts is observed, while groundwater displays delayed, yet significant changes linked to drought, reflecting its buffering capacity and resilience.
This research highlights the potential of tested datasets for assessing drought impacts on water systems and demonstrates the value of open-source hydrological data for improving drought risk assessment and predictive tools. However, the study also reveals limitations regarding spatial resolution, which constrain detailed-scale assessments. On the one hand, the follow-up research will expand the performed analysis to additional sub-basins across Andalusia to compare results. On the other hand, similar modelling methodologies will be applied to understand how the identified droughts and associated anomalies in surface and groundwater systems propagate, leading to a reduction in the provision of ecosystem services. This will include exploring ecological impacts such as failures to maintain ecological flows, declines in extension of wetlands, or anomalies in primary productivity and ecosystem functioning in natural areas.
How to cite: Serrano Acebedo, P., Limones Rodríguez, N., and Aguilar Alba, M.: Unveiling environmental dimensions of hydrological drought in Southern Spain using open-source data and machine learning techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13798, https://doi.org/10.5194/egusphere-egu25-13798, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 3
EGU25-2768 | ECS | Posters virtual | VPS13
Estimating drought impacts on crop yield using AI and EOWed, 30 Apr, 14:00–15:45 (CEST) vPoster spot 3 | vP3.23
Climate change has intensified droughts in many parts of the world, severely impacting different sectors. In particular, the agricultural sector is highly sensitive to precipitation deficits and the resulting soil moisture deficit, leading to a drastic reduction in crop productivity. There is an urgent need to ensure access to food for a growing population in future, making it essential to address agricultural drought induced crop yield losses. Multimodal satellite and reanalysis climate data archives, coupled with advancements in machine learning, offer a promising avenue to address this issue, but studies are often limited to the calculation of drought indices. In order to produce actionable insights and allow for time to prepare for drought-related food production deficits, specific information on crop losses is needed. Therefore, this study demonstrates the potential of the machine learning algorithm Random Forest (RF) for annual crop yield forecasting using multimodal datasets, for two agriculturally important drought-prone regions in India and Germany. Using 11 climate variables from ERA5 data and PKU GIMMS NDVI (version 1.2) from 1990 to 2021, an RF model was trained to predict crop yields for two common crops across the study sites. The model was evaluated at different spatial scales and the spatial transferability of the model was also tested, using Root Mean Square Error (RMSE; absolute error metric) and Mean Absolute Percentage Error (MAPE; relative error metric). Feature importance was also assessed across scales and across different study sites, using the mean decrease in impurity as a post-hoc explainability tool. Results show that different features are important for accurate crop yield predictions in different regions, for different crops, and across different space-time scales. Spatial transferability requires retraining the model with local data, due to the strong influence of local climatic and agricultural conditions as well as data availability. Findings pave the way for long lead time predictions of drought impacts on agricultural productivity purely open source data, contributing directly to improving global food security equitably, as the methods are equally applicable in data-rich and data-poor contexts.
How to cite: Sahu, H., Garg, P. K., Vijay, S., and Dasgupta, A.: Estimating drought impacts on crop yield using AI and EO, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2768, https://doi.org/10.5194/egusphere-egu25-2768, 2025.
EGU25-14311 | ECS | Posters virtual | VPS13
Ranking of extreme drought events in the Amazon Basin between 1980 and 2024Wed, 30 Apr, 14:00–15:45 (CEST) vPoster spot 3 | vP3.24
The Amazon Basin (AB), the largest hydrographic basin in the world, spans across seven countries in South America. It constitutes a highly intricate system, rich in natural resources, and is marked by substantial biological heterogeneity. The AB plays a pivotal role in the regulation of environmental processes, serving a key component of the global hydrological cycle and climate systems. Understanding the increasing frequency, intensity and spatial extent of extreme drought events in this region is vital for safeguarding the regional ecosystem. This study aims to classify extreme drought events in the AB using the Standardized Precipitation-Evapotranspiration Index (SPEI), derived from ERA5 reanalysis data, covering the period from 1980 to 2024. To assess both agricultural and hydrological droughts, this research incorporates the accumulation periods of 6 and 12 months (SPEI-6 and SPEI-12). The ranking methodology accounts for various SPEI time scales, the extent of the affected area, and the average SPEI intensity within that area. The results highlight that the 2023/24 drought episode was the most intense ever recorded in the AB, with over 90% (80%) of the region affected for the month of January for SPEI-6 (SPEI-12), surpassing known past mega-events, such as the 2005, 2010 and 2015/16 episodes. These extreme conditions were observed across all timespans. Specifically, for January 2024 under the SPEI-6 and for September 2024 under the SPEI-12, more than half of the AB was categorized as experiencing exceptional drought, as established by the 1st percentile of the SPEI distribution. Furthermore, the results underscore the persistence of consecutive periods of drought, especially since the beginning of 2020. With the climate projections indicating continued warming in the region, increased evapotranspiration and lower rates of rainfall are expected, potentially leading to even drier periods. This marks the significance of studies focused on understanding the development and impacts of droughts, as they play a critical role in the mitigation of future environmental risks.
How to cite: Albuquerque, R., Monteiro dos Santos, D., Miranda, V., Gouveia, C., Liberato, M., Trigo, R., Peres, L., and Libonati, R.: Ranking of extreme drought events in the Amazon Basin between 1980 and 2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14311, https://doi.org/10.5194/egusphere-egu25-14311, 2025.
