This session addresses statistical, remote sensing, physically-based techniques, as well as artificial intelligence and machine learning techniques; aimed at monitoring, modelling and forecasting hydro-meteorological variables relevant to drought and water scarcity. These include, but are not limited to: precipitation, extreme temperatures, snow cover, soil moisture, streamflow, groundwater levels, and the propagation of drought through the hydrological cycle. The development and implementation of drought indicators meaningful to decision-making processes, and ways of presenting and integrating these with the needs and knowledges of water managers, policymakers and other stakeholders, are further issues that are addressed and are invited to submit to this session. Contributions focusing on the interrelationship and feedbacks between drought, low flows, and water scarcity, ; and the impacts these have on socio-economic sectors including agriculture, energy and ecosystems, are welcomed. The session aims to bring together scientists, practitioners and stakeholders in the fields of hydrology and meteorology, as well as in the fields of water resources and drought risk management. Particularly welcome are applications and real-world case studies, both from regions that have long been exposed to significant water stress, as well as regions that are increasingly experiencing water shortages due to drought and where drought warning, supported by state-of-the-art monitoring and forecasting of water resources availability, is likely to become more important in the future.
Snow droughts, that is negative anomalies in snow water equivalent, impact society as well as natural ecosystems in winter and influence the hydrological cycle downstream in spring and summer. Thereby, pronounced snow drought conditions can lead to streamflow droughts, i.e., anomalously low discharges, during the following melt season. Under continued global warming, the frequency and intensity of snow droughts are expected to increase. However, we still know little about the rate at which snow droughts propagate to subsequent streamflow droughts, the spatial patterns of such events, or the influence of snow droughts on the occurrence, intensity or duration of subsequent streamflow droughts. To quantify the link between snow and streamflow drought, we developed a snow drought propagation scheme, which dynamically identifies pairs of snow and streamflow droughts from a high-resolution gridded snow product and streamflow observations, and applied it to 207 catchments in Switzerland and Austria. Between 1961 and 2021, we identified 147 propagating snow droughts, and found that 18 % of the snow droughts propagated to a streamflow drought and that 21 % of streamflow droughts during the melt season were preceded by a snow drought. Propagating snow droughts are most common in high-elevation catchments and among the most extreme snow droughts. Streamflow droughts are characterized by higher deficits, longer durations and earlier occurrences when preceded by a snow drought. We identify snow drought deficit as a good predictor for subsequent streamflow drought deficit and duration when the snow drought is intense and occurs in low-elevation catchments. We show that the presence of water resources management increases the chance of snow drought propagation. Finally, we find that the period 1990–2021 is characterized by an increase in the number of propagating snow droughts compared to 1961–1990. In conclusion, we unveil a non-negligible link between snow and streamflow droughts that could help improve early warning systems for spring and summer droughts.
How to cite: Chartier-Rescan, C., Wood, R., and Brunner, M. I.: Snow drought propagation and its impacts on streamflow drought in the Alps, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3500, https://doi.org/10.5194/egusphere-egu25-3500, 2025.
Drought is one of the costliest natural disasters, causing economic, social and environmental damage worldwide. Many researchers demonstrate that climate change will make extreme weather events more intense in the future. As extreme weather events increase, the frequency and magnitude of drought are likely to increase, requiring a proactive approach to drought management. Reliability, Resilience, and Vulnerability (RRV) are used in drought risk management to assess the management of water resources under drought conditions. The RRV framework provides comprehensive analyses on the probability of success or failure of a system, the rate of recovery (or rebound) of a system from unsatisfactory conditions and quantifying the expected consequences of being in unsatisfactory conditions for extended periods. It is necessary to consider all three criteria as uncertainty increases under climate change. This study proposes a triple drought management index (TDMI) by integrating the RRV indicators. Since the RRV indicators may be dependent on each other in drought situations, a copula model was used to describe the nonlinear dependence structure. The trivariate copulas considered for this study are the Clayton, Frank, and Gumbel copulas of the Archimedean family, which are commonly used in the field of hydrology. According to the TDMI calculation, the Seomjin River basin had a maximum TDMI index value of 2.19 during the period 1992-1994. According to the classification criteria, this corresponds to a severe drought, and indeed, the area was affected by limited water supply during this period. This study proposes a model for more comprehensive drought management by incorporating the RRV indicators. It can not only determine whether a drought is occurring but also comprehensively determine the overall state of the system under drought conditions.
Acknowledgement: This research was supported by Korea Environment Industry & Technology Institute (KEITI) through Water Management Innovation Program for Drought (RS-2022-KE002032) funded by Korea Ministry of Environment.
How to cite: Kim, J., Park, S. M., Yoo, J., and Kim, T.-W.: Development of a Triple Drought Management Index Using Copula-Based Trivariate Frequency Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5548, https://doi.org/10.5194/egusphere-egu25-5548, 2025.
As a complex natural disaster, drought exerts wide-ranging impacts on environmental, hydrological, agricultural, and socioeconomic dimensions. Despite extensive studies on conventional drought types, understanding environmental droughts remains limited, hindering effective assessments. To address this, the present study introduces a novel Environmental Drought Index (EDI) to quantify environmental droughts (Srivastava & Maity, 2023). It evaluates its performance against established indices in India’s Brahmani River basin, specifically the Jaraikela catchment. The EDI was developed by integrating Minimum in-stream Flow Requirements (MFR), calculated by integrating Drought Duration Length (DDL), and Water Shortage Level (WSL). Historical and future streamflow rates (1980–2045) were simulated using the HydroClimatic Conceptual Streamflow (HCCS) model with outputs from three CMIP-6 General Circulation Models (EC-Earth3, MPI-ESM1-2-HR, and MRI-ESM2-0) under SSP245 and SSP585 scenarios. The results indicated a strong agreement between simulated and observed EDI values, particularly for MPI-ESM1-2-HR under SSP585. Severe droughts were found to dominate future scenarios (71–73% of all drought events during FP-2: 2023–2045), especially in non-monsoonal months, contrasting with moderate drought prevalence under SSP245 and the historical period. To further explore drought complexities, the study employed a comprehensive multi-index framework incorporating EDI alongside the 3-month Soil Moisture Anomaly Index (SPAI-3), Vegetation Health Index (VHI), and 3-month Standardized Streamflow Index (SSI-3). This comparative analysis revealed a pronounced upward trend in drought frequency and severity from the late 20th century (1982–2000) to the early 21st century (2001–2023). Severe hydrological droughts increased from 10.5% to 21.7%, while severe environmental droughts rose from 31.6% to 52.2%. Moderate agricultural droughts, in contrast, declined from 100% to 47.8%, and moderate meteorological droughts increased significantly from 57.9% to 87.0%. These findings highlight the evolving drought patterns in the Jaraikela catchment, characterized by more frequent and prolonged droughts. The results underscore the value of EDI in capturing environmental drought dynamics, validated through strong historical correspondence, and its integration within a broader multi-index framework to address gaps in traditional approaches. The study redefines conventional drought classifications by incorporating environmental dimensions and provides adaptive strategies to mitigate the impacts of increasing drought severity under changing climatic conditions.
Keywords: Climate Change Impacts; Water Resource Management; Adaptive Mitigation Strategies; Hydrological Modeling; Drought Vulnerability Assessment; Extreme Climatic Events
Reference: Srivastava, A., & Maity, R. (2023). Unveiling an Environmental Drought Index and its applicability in the perspective of drought recognition amidst climate change. Journal of Hydrology, 627, 130462. https://doi.org/10.1016/j.jhydrol.2023.130462
How to cite: Srivastava, A. and Maity, R.: From Concept to Comparison: Developing and Validating the Environmental Drought Index (EDI) for Holistic Drought Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6357, https://doi.org/10.5194/egusphere-egu25-6357, 2025.
Drought, a recurring extreme climate event caused by prolonged below-average precipitation, results in significant water deficits and poses a substantial threat to India's economy, which is heavily reliant on agriculture. Despite notable monsoon rainfall, drought remains a persistent annual phenomenon, underscoring the need for accurate estimation and continuous monitoring to mitigate its adverse socio-economic impacts. Real-time drought monitoring, including spatial and temporal characterization, is critical for guiding policymakers and water resource managers in revising strategies, facilitating timely drought assistance programs, and distributing relief funds to affected areas and farmers. In India, drought monitoring faces challenges due to limited in-situ data for critical parameters such as evapotranspiration, soil moisture, runoff, and streamflow. Although satellites offer regular surface observations, their data is limited in spatial and temporal coverage due to orbital revisit cycles. Land Surface Models (LSMs), on the other hand, while offering uniform spatiotemporal estimates, are often hindered by uncertainties from atmospheric forcing and initial conditions. To address these limitations, integrating observations (in-situ/satellite) with LSMs through a Land Data Assimilation System (LDAS) has emerged as a promising solution to improve model accuracy, reduce uncertainties, and increase drought monitoring and forecasting skills. This study integrates the Community Land Model version 5.0 (CLM5) with the Data Assimilation Research Testbed (DART) to establish a robust Land Data Assimilation System (LDAS) framework. Specifically, soil moisture data from the European Space Agency’s (ESA) Climate Change Initiative (CCI) were assimilated to enhance soil moisture (SM) estimation. The performance and efficacy of soil moisture (SM) estimates derived from the CLM5-DART LDAS were evaluated across India. Results indicate that CLM5 - DART reanalysis outputs significantly improved the representation of SM compared to standalone CLM5 simulations. These improvements were further analyzed for their impacts on key hydrological components, including evapotranspiration, runoff, and drought monitoring capabilities. The findings demonstrate that data assimilation integration substantially enhances the accuracy and resolution of SM estimates, advancing the reliability of real-time drought monitoring and risk management. This research provides a robust framework for improving drought resilience in India, offering valuable insights to support better-informed water resource management strategies and policy decisions.
How to cite: Naik, D. C., Chadrika Thulaseedharan, D., Raczka, B., and Fiifi Tawia Hagan, D.: Advancing Drought Monitoring in India through Land Data Assimilation with CLM5-DART, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6905, https://doi.org/10.5194/egusphere-egu25-6905, 2025.
Drought has become an increasingly recurrent phenomenon worldwide with far-reaching societal and environmental consequences. To adequately manage the drought, the scientific research is essential. In recent decades numerous indices were developed for drought analysis. The evolution of the geospatial technologies has enabled the design of several indices, based both on terrestrial and satellite data, to analyze the drought characteristics. The most common indices are based on hydroclimatic parameters, simple or combined. In recent years, complex indices (called composite, integrated, multivariate or hybrid) were developed, which incorporate several drought control variables, combined and mapped in GIS environment. They allow a more reliable analysis of drought and the identification of areas susceptible to this hazard. The aim of this paper is to provide an overview of publications on the composite indices for drought assessment developed in GIS environment, based on a scientometric analysis.
The study relies on the Web of Science (WoS) and Scopus databases, from which a total of 345 papers were initially extracted (205 from WoS and 140 from Scopus) by searching for the expressions integrated drought index gis; composite drought gis; multivariate drought gis. Duplicates were removed using the ScientoPy software. Finally, 262 papers were retained from both databases, published between 1994 and 2024. The same software was used for statistical analysis regarding some characteristics of the publications (e.g., the countries and institutions of affiliation of the authors, the scientific fields of the papers, connections between authors, etc.) Furthermore, some of this data was mapped using the ArcGisPro software. For the analysis of author clusters, the VOSviewer software was used.
The results showed that most authors of the identified papers are affiliated in Asian countries, especially in India (64) and China (58), followed by the United States (42). Most authors' affiliation institutions are located in China: the Chinese Academy of Sciences has the highest frequency (10), followed by the Peking University (5), and the University of Chinese Academy of Sciences (5). Iran is also noteworthy with University of Tehran (7), as well as India, represented by the Vidyasagar University (6).
The number of publications per year varied during the analyzed period, with the highest number of 39 in 2024. The major scientific fields to which the papers on composite drought indices belong were: Environmental Sciences and Ecology (76), Water Resources (60), Geology (50), Remote Sensing (34), and Meteorology and Atmospheric Sciences (28).
Out of a total of 1086 authors of the analyzed publications, the highest number of common connections was 35, in general, between Asian researchers. Furthermore, many of the 35 authors with the most connections collaborated between 2006 and 2016, while the other groups published after 2020.
Overall, this scientometric analysis shows that the use in drought research of composite indices developed in GIS environment is still quite limited although in the last 5 years an increase in the number of papers on this topic was noted (mainly in Asian countries). Therefore, more attention should be paid to this more reliable method of drought analysis.
How to cite: Costache, M.-Ș. and Zaharia, L.: GIS-based composite indices for drought assessment: a scientometric analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7662, https://doi.org/10.5194/egusphere-egu25-7662, 2025.
Effective drought management requires precise measurement, but this is challenging due to the variety of drought indices and indicators, each with unique methods and specific uses, and limited ground data availability. This study utilizes remote sensing data from 2001 to 2020 to compute drought indices categorized as meteorological, agricultural, and hydrological. A Gaussian kernel convolves these indices into a denoised, multi-band composite image. Further refinement with a Gaussian kernel enhances a single drought index from each category: Reconnaissance Drought Index (RDI), Soil Moisture Agricultural Drought Index (SMADI), and Streamflow Drought Index (SDI). The enhanced index, encompassing all bands, serves as a predictor for classification and regression tree (CART), support vector machine (SVM), and random forest (RF) machine learning models, further improving the three indices. CART demonstrated the highest accuracy and error minimization across all drought categories, with root mean square error (RMSE) and mean absolute error (MAE) values between 0 and 0.4. RF ranked second, while SVM, though less reliable, achieved values below 0.7. The results show persistent drought in the Sahel, North Africa, and southwestern Africa, with meteorological drought affecting 30% of Africa, agricultural drought affecting 22%, and hydrological drought affecting 21%.
Funding: This work was supported by the Korea Environmental Industry and Technology Institute (KEITI) (Grant number: 2022003460001).
How to cite: Jun, K. S. and Sseguya, F.: Advancing Drought Monitoring and Prediction in Africa Using Remote Sensing, Gaussian Kernel, and Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7989, https://doi.org/10.5194/egusphere-egu25-7989, 2025.
Major impacts associated to hydrological droughts are often neglected in early warning systems. Extensive research in hydrological drought forecasting is crucial to develop an effective early warning strategy. This work aims at quantifying the sub-seasonal to seasonal predictability of these extreme hydroclimatic events globally, by evaluating the skill of the Global Flood Awareness System (GloFAS), as part of the Copernicus Emergency Management System (CEMS). Two river discharge datasets for the period 1991-2020 from the LISFLOOD hydrological model were used, based on: 1) reanalysis (ERA5) forcings, and 2) seasonal forecast (SEAS5). River discharge values were converted into anomalies, namely the Standardized Streamflow Index (SSI), at three-time horizons (1, 3, and 6 months ahead). The skill metrics computed between the SSI reanalysis (reference) and the forecasts were the Pearson correlation coefficient (r), the Gilbert Skill Score (GSS), and the Heidke Skill Score (HSS). Moreover, the signal-to-noise ratio (SNR) of the ensemble forecast was used as a complementary metric to quantify the skill. The study evaluated the overall forecast predictability for the full year, as well as seasonal and spatiotemporal differences in the predictability and the effects of initial conditions. On average, forecast skill is higher for 1 and 3 months ahead (r= 0.81 and r= 0.70, respectively) compared to 6 months ahead (r= 0.61), with similar results in terms of spatial patterns. Seasonal differences in predictability can be well explained by average river discharge seasonality, with highest skill when river discharge is low. The forecast skill spatial patterns indicate a strong dependency on the inter-annual variability of initial conditions and precipitation, especially in summer and spring-summer seasons for the former and in winter and autumn-winter for the latter. Overall, high skill is associated with high SNR, suggesting that SNR could be used as a proxy variable for forecasting skill in operational applications. The results underline the potential of the evaluated sub-seasonal to seasonal forecast for hydrological drought predictions, suggesting a potentially successful implementation as a product as part of the CEMS Global Drought Observatory (GDO) system.
Acknowledgements:
This work is funded by the European Union, under the HORIZON-CL4-2023-SPACE-01 project “Strengthening Extreme Events Detection for Floods and Droughts” (SEED-FD), grant no. 101135110.
How to cite: García-Gamero, V., Cammalleri, C., Ceppi, A., Prudhomme, C., Ramos, A., Acosta Navarro, J. C., and Toreti, A.: Global scale predictability of hydrological drought: evaluating the skill of the GloFAS - Copernicus EMS sub-seasonal to seasonal forecast of river discharge, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8425, https://doi.org/10.5194/egusphere-egu25-8425, 2025.
Droughts in Switzerland have become more frequent and severe in recent years, and this trend is expected to continue. At the same time, increasing water demand and competition between different actors are putting more pressure on existing water resources, leading to drought being rated within the top 10 costliest potential hazards for Switzerland. A comprehensive national monitoring and forecasting system, to be launched in 2025, is being established through the joint efforts of three different government agencies.
We will present the Swiss national drought monitoring system with a particular focus on the web platform and the operational warning system, both of which were developed in close collaboration with local decision-makers and end-users. The information system is a public web platform synthesizing various data streams (i.e. precipitation, streamflow and groundwater, space-based monitoring of vegetation health and land surface temperature) and provides homogeneous forecasts of drought quantities with a horizon of four weeks. Historical observations and sub-seasonal forecasts are merged to provide seamless information on drought that can be easily and interactively compared to action-relevant thresholds as well as historical events. The main drought variables are also summarized into a combined drought index which is used to provide an overall evaluation of the situation and forms the basis for drought warnings. Starting from 2025, drought warnings will be released by national agencies through official channels in the same way as they already are for other natural hazards like floods or heatwaves, over national web platforms and push notifications on the MeteoSwiss mobile App (2.5 million visits per day). The two-tiered warning strategy was designed in collaboration with end-users and authorities to take into account some of the particularly challenging aspects of drought compared to other natural hazards. These include, among other things, the need for sector-specific and impact-oriented information, and the difficulty for a national system to accurately reflect the highly heterogeneous and localized mitigation measures that are of most interest to the end-users during an extreme event.
Analysis of the historical 2018 drought shows that the forecasting system would have correctly triggered a response at the level of regional authorities 1.5 months ahead of the event peak. A higher-level and more broadly visible warning would have been released again a month later, about two weeks ahead of the event peak. We will conclude with an overview of future plans and of the event-based feedback mechanisms through which end-users and regional authorities will contribute to improving the warning system and our ability to track drought impacts at the local scale.
How to cite: Humphrey, V., Huesler, F., Bircher-Adrot, S., Barton, Y., Benelli, L., Buergi, T., Chang, A. Y.-Y., Dobler, F., Imamovic, A., Rempfer, J., von Freyberg, J., Oesch, D., Salvi, H., Sturm, J., Zappa, M., and Scapozza, C.: From the weather forecast to the push notification: Switzerland's new drought warning system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9095, https://doi.org/10.5194/egusphere-egu25-9095, 2025.
Drought is gaining global attention due to its irrefutable and irreparable damages. Aiming at exploiting the great potential of remote sensing platforms to facilitate drought monitoring and characterization, even through multi-sensor-based approaches, this contribution underscores the efficacy of harmonizing Landsat and Sentinel data, driven by high-resolution drone imagery, to monitor drought conditions on a local scale over a large farm located in the Calabria Region, southern Italy.
To accomplish the monitoring, the Normalized Difference Vegetation Index (NDVI) has been exploited, and the cloud coverage has been evaluated at a local level so as to discard the images that are locally cloudy and shadowy and retain instead those locally cloud-free for further process. Machine learning techniques, including Support Vector Machines (SVM), Random Forest (RF), Feedforward Neural Networks (FFNN), and Convolutional Neural Networks (CNN), were employed to develop accurate cloud and shadow masks. The approach was enhanced with special spatial filtering considering seven bands for the cloud masking and the SWIR1 band for shadow masking, leading to remarkable accuracies of 96.9% for Sentinel and 89.4% for Landsat imagery.
Remote sensing data harmonization from different sources was driven by high-resolution drone imagery. Specifically, on July 12, 2024, a drone survey was carried out, and the reflectance in its Red and NIR bands (needed for NDVI calculation) was compared with that provided by satellite data for the same date, highlighting that Sentinel’s reflectance is radiometrically closer to that provided by the drone.
Subsequently, Landsat and Sentinel data were harmonized, and Landsat data were modified to converge to the Sentinel data. In order to do this, over the six months ranging from April 15 to October 15, 2024, a linear relationship between the Landsat and Sentinel Red and NIR spectral bands was determined in the dates when both images were available at most one day of distance. Then, the linear equation coefficients were also estimated for Landsat images acquired at more than one day of distance from Sentinel ones, applying a linear interpolation over time between the closest dates with simultaneous or near-simultaneous (i.e., one-day difference) acquisition between the two platforms.
The procedure was tested by comparing the extracted NDVI values (namely, Sentinel NDVI and harmonized Landsat NDVI) with the local information about agricultural activities and with other four high-resolution drone surveys, implying the effectiveness of the proposed methodology. The proposed integrated approach not only improves the monitoring of drought conditions but can also help agricultural management and disaster response in vulnerable regions.
Acknowledgments: This study was funded by The Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’, Project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009.
How to cite: Nikravesh, G., Persico, R., Evola, B., Senatore, A., and Mendicino, G.: High space- and time-resolution drought monitoring using harmonized Landsat and Sentinel data with Drone imagery, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9445, https://doi.org/10.5194/egusphere-egu25-9445, 2025.
Drought is a natural phenomenon affecting many aspects of human activity, such as water scarcity, food production, agriculture, industry, and ecological conditions. For decades, drought has caused significant financial losses in Europe and worldwide. In the Polish Carpathians, periods with rainwater deficits and an increasing frequency of dry months—especially in the cold half of the year—have been observed. However, there are limited studies on the spatial and temporal variability of meteorological drought in this area.
The aim of this study is to conduct a spatial and temporal analysis of drought, expressed as the Standardized Precipitation Index (SPI), in the heterogeneous region of the Polish Carpathians and the highland areas in East-Central Europe, based on long-term precipitation data. Monthly precipitation data from 30 rainfall stations, collected between 1961 and 2022, were analyzed. The SPI as an indicator of meteorological drought for 3-, 6-, 9-, 12-, 24-, and 48-month periods was calculated. The run theory was applied to identify the different drought events and to evaluate various drought characteristics: the number of drought events (N), the average drought duration (ADD), the average drought severity (ADS), and the average drought intensity (ADI).
As a result, N decreases with the increase of the time scale. In fact, a median of 59 and 15 events have been observed for the 3- and the 48-month SPI, respectively. The statistics of the ADD show an opposite behavior than N, with the lowest values corresponding to the 3-month SPI (median nearly 2 months) and the highest to the 48-month SPI (median of 8.8 months). Moreover, the variability in ADD increases with longer time aggregations. A similar behavior to ADD has been detected for the ADS at different temporal scales, with an average severity of 12.3 that occurred for the 48-month SPI. Finally, the ADI slightly decreases with the increase of the time scale, with the highest values observed for the 3-month SPI (1.48), and the lowest for the 48-month SPI (1.21).
The spatial distribution of the drought characteristics in the Upper Vistula Basin allows us to identify the areas that could also face water stress conditions in the future, and which would thus require drought monitoring and adequate adaptation strategies. In particular, the northwestern part of the region, where soils have lower water-holding capacity and agriculture is more intensive than in the south, is particularly sensitive to drought.
How to cite: Wałęga, A., Wałęga, A., De Marco, A., and Caloiero, T.: Meteorological drought variability in the Upper Vistula Basin in period 1961-2022, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1214, https://doi.org/10.5194/egusphere-egu25-1214, 2025.
Flash droughts are increasingly impacting the Dry Corridor of Central America, particularly in regions dominated by rainfed agriculture, further exacerbating the pressures already faced by agriculture, ecosystems, and water resources management. These phenomena are distinct from the generally accepted concept of droughts due to their rapid intensification, often lasting for three weeks or more. Understanding how flash droughts occur and evolve, along with their impacts, is closely linked to the geographical and socioeconomic contexts of affected areas. This understanding is essential for effective monitoring and represents a critical component of drought management. This study examines the spatial and temporal characteristics of flash droughts in Nicaragua, providing a representative case for understanding regional patterns. The analysis utilizes evaporation and potential evaporation variables derived from remote sensing data. Key metrics—including spatial extent, frequency, duration, and severity of flash drought events—were identified and analyzed. The findings provide valuable insights into the dynamics of flash droughts in dry regions, contributing to efforts aimed at strengthening the resilience of socioeconomically and environmentally vulnerable communities.
How to cite: Ubeda-Trujillo, I., Werner, M., Bertini, C., Coenders-Gerrits, M., and Jewitt, G.: Flash droughts in the Dry Corridor of Central America: A case study in Nicaragua, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1742, https://doi.org/10.5194/egusphere-egu25-1742, 2025.
The aim of the work is to present the analysis of droughts, water resource availability and water stress conditions in Italy obtained based on estimates from ISPRA's BIGBANG national hydrological water budget model. Trends and variations on the availability of water resources and on the occurrence, persistence and magnitude of the drought events that have affected Italy from 1951 to today will also be presented in relation to the current and future impacts of the climate change, with an indication of the impacts on the exposed assets, such as people and cultural assets.
Italy, located in the center of the Mediterranean, one of the hotspots of the climate crisis, can only expect an amplified impact of droughts, which, associated with the increase in temperatures, will lead to an ever-decreasing availability of water resources. In recent decades, Italy has been subject to increasingly frequent drought events affecting not only the southern and insular areas, but also the central-northern and continental areas, which have a generally more humid climate. The ISPRA national analyses show, starting from the 1950s, a statistically increasing trend in the percentages of territory subject to extreme drought on an annual scale. The periods in which the extreme drought conditions affected more than 20% of the national territory were 5, namely 1989-1990, 2002, 2012, 2017 and 2022. The first of these periods is part of the "great drought" that hit Italy in the three-year period 1988-1990, the other 4 are all after that period, while no episode of this magnitude was recorded in the preceding period. This increase in extreme drought events is likely due to climate change.
The increase in water crises is therefore attributable to a lower availability of water resources over the years due to a changing climate, with persistent periods of precipitation deficit and high temperatures, with a negative trend, statically significant observed at the national level by means of BIGBANG estimates from 1951 to today.
The annual national availability of natural water resources in 2022 is estimated at 221.7 mm, equivalent to approximately 67 billion cubic meters, which represents the historical minimum from 1951 to today. This value outlines a reduction of approximately 50% compared to the average annual availability of water resources estimated at 441.9 mm (133.5 billion cubic meters) for the last thirty-year climatological period 1991-2020.
In 2023, the annual value of the renewable water resource is estimated at 372.2 mm, corresponding to 112.4 billion cubic meters, approximately 18% compared to the average annual availability of the long period 1951-2023, resulting from the combined effect of a precipitation deficit and an increase in water volumes of evapotranspiration. The decrease in natural availability of water resources in 2023 was made less severe compared to 2022 by the high volume of precipitation that fell in May, estimated at approximately 49 billion cubic meters, which was, at a national level, more than double the average volume for the same month.
Future projections highlight possible further reductions in water resources.
How to cite: Mariani, S., Braca, G., Lastoria, B., Tropeano, R., Casaioli, M., Piva, F., Marchetti, G., and Bussettini, M.: Drought and water resource assessment at the national level in Italy from 1951 to today, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2062, https://doi.org/10.5194/egusphere-egu25-2062, 2025.
In the context of climate change, increasing competition for freshwater use across various sectors is intensifying pressures on water resources, placing many countries at heightened risk of water scarcity. To mitigate the growing risk of water scarcity, it is imperative to reduce water usage intensity across agriculture, industry, energy production, and domestic sectors. Achieving this requires a comprehensive and detailed understanding of water consumption patterns in each sector, and estimating water storage in groundwater, reservoirs, and snowpack is essential to safeguard water availability for future generations.
The Po River basin in northern Italy has experienced significant hydrological droughts in recent decades (1990-2023), highlighting the need to understand the complex interactions between climate factors and human activities. This study, conducted as part of the INTERROGATION project funded by the Italian Ministry of Universities and Research, presents an integrated approach for water resource management during drought events.
The study employs a flexible conceptual hydrological model (MISDc - Modello Idrologico Semistribuito in Continuo) that incorporates both natural processes and anthropogenic influences. The model is driven by three distinct precipitation datasets: long-term (2000-2023) daily in-situ measurements, high-resolution (1.8km) reanalysis data, and high-resolution (1km) satellite precipitation data. The Bluecat tool (Montanari et al., 2022) is utilized to evaluate the uncertainty in modelled river discharge.
The model's performance is validated using multiple satellite-derived observations including soil moisture, evaporation, groundwater, irrigation, and snow accumulation data developed within the framework of European Space Agency Digital Twin Earth (DTE) Hydrology Next project. The model is capable to reproduce both natural hydrological processes and anthropogenic activities such as irrigation and reservoir operations.
Results demonstrate the effectiveness of combining accurate satellite observations with a well-calibrated hydrological model for capturing spatiotemporal variations in the hydrological cycle within highly anthropized basins. This integrated framework provides valuable insights for developing a decision support system to guide stakeholders in managing water resources during future drought events in the Po River basin.
How to cite: Kalimisetty, S., Ceola, S., Palazzoli, I., Montanari, A., Stocchi, P., Davolio, S., and Camici, S.: Improving the Reconstruction of the Hydrological Cycle through Satellite Observations: The Case Study of the Po River Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7337, https://doi.org/10.5194/egusphere-egu25-7337, 2025.
Abstract
Drought is a natural phenomenon whose likelihood is increasing due to climate change, which is gradually altering temperature and precipitation patterns. While various drought indices exist for monitoring extreme dry conditions, this study employs the Reconnaissance Drought Index (RDI) due to its accuracy and dependency on both precipitation and temperature. The research aims to assess historical droughts in the Lisbon region (Portugal) by applying RDI to a 157-year time series (1864-2021) using monthly precipitation and temperature data from the Lisboa-Geofísico climatological station. The influence of potential evapotranspiration (PET) on drought identification was analysed, alongside temporal drought assessments at short-term (3-month RDI, RDI-3), mid-term (6-month RDI, RDI-6), and long-term (12-month RDI, RDI-12) scales. RDI was computed monthly using the Drought Indices Calculator (DrinC), with three PET methods-Hargreaves, Thornthwaite, and Blaney-Criddle-compared for their performance. The standardized RDI, calculated preferably using the Hargreaves method for the Lisbon region, served as the index for spatial and temporal drought assessment. Results revealed frequent extreme drought events (when RDI values were less than minus two), with the most intense drought occurring in 2005 across all time scales. For meteorological drought (RDI-3 for short-term atmospheric conditions), 39 extreme events occurred, with a total of 51 months under drought conditions, with the longest event (5 months) in 2005. Agricultural drought (RDI-6 for soil moisture deficits) showed 18 extreme events lasting 28 months, with the longest (7 months) in 2005. Hydrological drought (RDI-12 for water resource depletion) exhibited 9 extreme events spanning 25 months, with the longest (9 months) also in 2005. The average return time for extreme drought in Lisbon was estimated at 4, 7, and 8 years for meteorological, agricultural, and hydrological droughts, respectively. This comprehensive regional drought risk assessment based on the standardized RDI index provides valuable insights for effective drought management in the Lisbon region.
Keywords: Drought risk assessment, empirical methods, PET, RDI, Lisbon, Portugal
Acknowledgement
This work was supported by the Slovak Research and Development Agency under the Contract no. APVV-20-0281 a project funded by the Ministry of Education of the Slovak Republic. This work was also supported by the Foundation for Science and Technology (FCT) through funding UIDB/04625/2020 from the research unit CERIS and by the European Union’s Horizon 2020 research and innovation programme SCORE under grant agreement No 101003534.
How to cite: Abd-Elhamid, H., Zelenakova, M., Soľáková, T., Manuela Mortela, M., Angel Espinosa, L., Oskoui, I., Baranczuk, J., and Baranczuk, K.: Analysis of historical drought in the Lisbon region, in the west of Portugal, using Reconnaissance Drought Index, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7449, https://doi.org/10.5194/egusphere-egu25-7449, 2025.
Drought presents significant impacts on water resources, agriculture, and socioeconomic stability, particularly in the Mediterranean region, where climate change intensifies these challenges. This study examines the long-term spatiotemporal trends of drought in Sicily using ERA5-Land reanalysis data from 1950. The Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at 1-, 3-, 6-, 12-, 24-, and 48-month scales were employed to quantify drought conditions across multiple timescales. To detect and quantify trends while accounting for autocorrelation, the Modified Mann-Kendall test and Sen’s slope estimator were applied. Results confirmed that 2002 was the most severe drought year, affecting all timescales. Spatial analysis indicated that western, southern, and southeastern regions, including Trapani, Catania, Syracuse, and Ragusa, experienced the highest severity and frequency of drought events. Conversely, northeastern areas, such as Messina and parts of Palermo, were less affected. SPI exhibited increasing trends in the eastern part of Sicily (Province of Catania); whereas SPEI trends indicated significant drying in western regions. Severe drought episodes (SPI/SPEI ≤ -1.5) were evenly distributed across short-term scales (1- and 3-month scales) but exhibited spatial variability at longer timescales (24- and 48-month scales). Extreme drought episodes (SPI/SPEI ≤ -2) were concentrated in western and northwestern Sicily, with SPI detecting up to 40 extreme events and SPEI identifying up to 25. These findings highlight the critical need for targeted, adaptive strategies to mitigate drought impacts, particularly in western and southern Sicily. Even though ERA5-Land precipitation and temperature data present some limitations, the analysis revealed that they are suitable for identifying the most severe drought episodes, especially at longer aggregation timescales (12 and 24 months). The study thus underscores the importance of continuous drought monitoring and advanced modeling techniques to inform mitigation and adaptation efforts.
How to cite: Peres, D. J., Aschale, T. M., Palazzolo, N., Buonacera, G., and Cancelliere, A.: Spatiotemporal Analysis of Drought Trends in Sicily Using ERA5-Land Data , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8282, https://doi.org/10.5194/egusphere-egu25-8282, 2025.
Large-scale climate oscillations significantly influence regional agricultural droughts and are crucial for understanding their predictability. However, atmospheric teleconnections linked to these droughts under various climate oscillation regimes are complex and not fully understood, especially when considering temporal delays. This study employs Event-based Coincidence Analysis (ECA) to statistically explore the timing and magnitude of relationships between climate oscillation regimes and the onset of agricultural droughts across different agro-ecological zones of India , with time lags (τ) ranging from 1 to 1, 3, 6, 9 and 12 months. ECA is a mathematical framework that quantifies the synchronicity and interdependency between event series such as climate oscillations and agricultural drought events by evaluating the frequency of coinciding occurrences within a defined time window (ΔT) and at specified time lags (τ). We utilize the Standardized Soil Moisture Index (SSMI) to assess agricultural droughts from 1951 to 2014. The SSMI data are aggregated over three months based on GLDAS VIC model observations. Our analysis includes synchronization between drought events and climate indices, such as the Pacific Decadal Oscillation (PDO), Niño 3.4, Atlantic Multidecadal Oscillation (AMO), and the Dipole Mode Index (DMI). Integrating various time lags allows us to capture both immediate and delayed influences of climate on drought prediction and management strategies. Our results identify significant variations in precursor rates across different time lags and regions, clearly delineating how specific climate indices influence agricultural drought dynamics. Notably, in the northern and central zones of India, Niño 3.4 and the AMO are found to strongly drive drought conditions at longer time lags (τ = 6, 9, 12 months), with a peak coincidence rate of 60% during positive Niño 3.4 episodes. Conversely, in the southern and western regions, significant drought mitigation effects are associated with shorter time lags (τ = 1, 3 months), where the DMI and AMO show high precursor rates of 40 to 60 percent during positive phases. This study highlights the distinct temporal dynamics of climate indices and emphasizes the role of atmospheric mechanisms, including wind anomalies and vertical velocity at 850 hPa, in modulating these effects. We observe distinct influences on drought patterns, which vary significantly across regions and time lags, highlighting the necessity for region-specific agricultural and water management strategies based on these dynamics to address both drought occurrence and water scarcity challenges effectively.
How to cite: Venkatesh, K. and Sivakumar, B.: Disentangling Temporally Lagged Synchronization of Climate Oscillations on Agricultural Droughts across India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9018, https://doi.org/10.5194/egusphere-egu25-9018, 2025.
Despite being traditionally regarded as water-rich, alpine regions are increasingly vulnerable to droughts due to the compounding effects of extreme climate events and conflicting water uses. This study focuses on the Upper Adige catchment, where shifts in its traditionally snow-driven hydrological regime are intensifying, calling for systematic adaptation to meet diverse demands across agriculture, ecosystems, and hydropower.
In this study, we investigate the formation mechanisms and leading causes of hydrological drought in this area analyzing 27 historical drought events related to the 1997-2022 time window. We apply the conceptual hydrological model ICHYMOD to assess key drought formation mechanisms in the region. The model is initially validated against observed streamflow time series and demonstrates reliable performance in capturing both dry and wet day patterns and in identifying severe drought events, with accuracy exceeding 75% across several validation sites. The analysis then focuses on a model-based evaluation of hydrological drought formation with reference to the entire Upper Adige basin, assessing how drought propagates through the hydrological cycle and identifying recurrent patterns. A tree-based classification framework aimed at classifying the droughts according to their driving mechanism is developed, deriving threshold and classification criteria informed by expert knowledge of the region.
The automated classification subdivides the historical events into six categories, and the results closely mirror the outcomes of visual classification, affirming the robustness of the approach and its alignment with domain expertise. 25% of droughts originating from two or more leading mechanisms are classified as composite, constituting one additional category. Our results reveal that the longest droughts are typically driven by early snowmelt, which depletes summer water reserves, or by precipitation deficits heading into winter, leading to prolonged recessions of water resources. These drought categories also record the highest deficits in terms of streamflow volume, partially due to their extended durations. The lowest streamflows typically occur in spring, driven by either rainfall deficits or delayed snowmelt at the end of the winter recession. Temperature emerges as a key driver with contrasting effects: while high temperatures accelerate snowmelt and exacerbate summer droughts, excessively low temperatures prolong winter recessions, intensifying spring water conflicts when demands are most critical.
This framework provides a systematic approach to understanding drought formation in alpine regions and can be leveraged in conjunction with hydrometeorological monitoring to support the development of an operational drought warning system. Integrating real-time observations with the classification logic enables actionable early warnings, enhancing preparedness and guiding response strategies for future drought events.
How to cite: Galletti, A., Shrestha, S., Terzi, S., and Bertoldi, G.: Driver-based classification of hydrological droughts in a large alpine catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9383, https://doi.org/10.5194/egusphere-egu25-9383, 2025.
Drought can be considered the most severe and the most complex weather-related natural hazard. With impacts that may extend to large areas and log time spans, and the capability to occur in all climatic zones, drought is the first hazard for the number of people affected. Due to the transboundary nature of drought events, an effective monitoring of their evolution must properly account for the full spatio-temporal structure. This characterization is a key step for a proper attribution of the related impacts. In addition, understanding common features in major droughts is of utmost importance for both monitoring and forecasting activities. In this work, we introduce a set of tools used to summarize the main properties of major droughts in Europe, with the goal of subdividing the events in groups characterized by similar properties. We used a European dataset of meteorological droughts (from 1981 to 2020) that detects events based on the Standardized Precipitation Index using an event-oriented spatio-temporal clustering algorithm. Spatio-temporal characteristics of major droughts were summarized using Normalized Area - Time Accumulation curves to follow their expansion/contraction as a function of time and analyzing the main direction of expansion of the events. A clustering algorithm was applied to classify events. We identified three groups: a first group comprised of warm-season events, characterized by a longer duration, a shorter early growing phase, and a longer exhaustion phase; a second group, less numerous, comprised by droughts occurring during the cold season, that tend to have a shorter duration, a longer early growing phase and a shorter exhaustion phase; and a third group comprised of droughts occurring across the two periods. This last class is characterized by a longer duration and a high variability in most of the other characteristics.
How to cite: Banfi, F., De Michele, C., and Cammalleri, C.: A joint spatio-temporal characterization of the major meteorological droughts in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13570, https://doi.org/10.5194/egusphere-egu25-13570, 2025.
A broad variety of research indicates that climate change has intensified and extended meteorological droughts across parts of Europe, with southern European regions experiencing particularly severe impacts (IPCC, 2012). The majority of SEE countries are experiencing an increase in drought severity and frequency according to a broad plethora of research. The most commonly used drought indicators by regional experts are meteorological drought indexes such as SPI, SPEI, and PDSI, as well as more specific parameters like the maximum seasonal dry spell (DS), SGI, specific discharge and SRI indexes, vegetation stress parameters. Impact-based assessments, including yield reduction, crop damage, and total economic loss, are also employed. In general, most results are coherent in their conclusions and indicate negative trends, showing an increase in aridity associated with both temperature increases and a lack of precipitation, except in some subregions in Croatia and Bulgaria.
The number of publications devoted to droughts varies greatly by year and country. The maximum publication activity on drought index dynamics was reached in the late 2010s. Over the last five years, there has been a shift to impact-based approaches by major crop types. Serbia, Slovenia, and Romania have had the highest number of publications focused on droughts across the SEE region during the last 15 years, covering all three types of droughts (meteorological, agricultural, and hydrological) not only by calculated indexes but also by impacts. The most underrepresented countries are Albania, North Macedonia, and Montenegro. In this overview, an average area under the “alert” class of CDI was calculated for each SEE country for 2012-2024 to illustrate the general picture. The country-scale signatures show major familiarity in drought-prone areas over the period. After the catastrophic drought in 2012, followed by a drop and plateau (until 2018), steady growth in the area under “alert” is observed, reaching 8-25% in 2023-2024.
To enhance the development and support of drought risk management tools and policies, DMCSEE was launched in 2009. Since 2010, regional bulletins have been issued on a monthly basis. To mitigate drought impacts and increase awareness, national drought monitors are urgently needed in the region due to the major role of agriculture and significant vulnerability. However, dynamically updated Drought Monitors and national Drought Early Warning Systems (EWS) are currently under development in Slovenia, Croatia, Serbia, and Romania. The operational stage has been achieved at the national level only in Croatia and partly in Romania and Slovenia. An AI-driven and impact-based EWS with medium-range lag time is a promising solution for dynamically updated platforms at the regional scale.
The study was supported by the HE project SONATA (GA 101159546).
How to cite: Radulović, M., Mimić, G., Kharlamov, M., and Kireeva, M.: Droughts in South East Europe (SEE): recent tendencies, existing tools and regional initiatives, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15105, https://doi.org/10.5194/egusphere-egu25-15105, 2025.
Climate change, characterized by rising temperatures and increased weather extremes, poses risks to food security and water supply. Warmer temperatures allow northern regions to extend agricultural activities and cultivate alternative crops that necessitate longer growing seasons. However, the increase in hydrological extremes, such as droughts and heatwaves, poses a significant risk to agricultural productivity in northern Europe, especially in regions with no access to irrigation networks. This highlights the urgent need for implementing climate-resilient agricultural water management strategies such as controlled drainage and sub-irrigation, which offer potential benefits for productivity and nutrient runoff reduction. This study aims to assess hydrological deficits and excesses in the growing season across a sub-Arctic region by analyzing daily precipitation and evapotranspiration data. The model leverages gridded precipitation and evapotranspiration datasets with 1km resolution and crop coefficients to simulate daily water storage dynamics. Developing a computational model, we analyze the spatiotemporal pattern of maximum deficit and excess water from 1981 to 2023. Findings from the study provide valuable insights and a basis for calculating the water reservoir capacity to overcome the summer drought posed by climate change in agriculture. The model's results will be applied to developing flexible operation system support to manage (automate) tank-drainage systems during flash drought or heavy precipitation conditions.
How to cite: Gohari, A., Panchanathan, A., Naghdyzadegan Jahromi, M., and Torabi Haghighi, A.: Climate-Resilient Water Management for Sub-Arctic Agriculture: Insights from Spatiotemporal modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1779, https://doi.org/10.5194/egusphere-egu25-1779, 2025.
The Alpine water towers are essential for sustaining life and driving the economy across central and southern Europe. This vital resource faces growing pressure from global warming, which is changing precipitation patterns, reducing snow availability and accelerating glacier melt, and from economic growth, which is driving an ever-increasing demand for water. Consequently, significant shifts in water’s spatial and temporal availability are observed, accompanied by a rising frequency and intensity of drought events. In this context, the Interreg Alpine Space project, Alpine DROught Prediction (A-DROP, 2024-2027), aims to enhance the preparedness of the Alpine regions for droughts and foster a sustainable use of water. The project partners, from research to public administrations, collaboratively develop and implement solutions for water management based on science. Embedding the drought monitoring methods and platforms set up in previous EU projects, like the Alpine Drought Observatory (https://ado.eurac.edu/), the ambition of A-DROP is to create 1) an innovative hydrological drought early warning and forecasting tool, not yet available for alpine river basins, that complements the instruments adopted by the regional water authorities, paving the way for a pan-Alpine prediction system, and 2) an open, spatially consistent database of climate and hydrological variables, drought indices, and impacts at an unprecedented level of detail, integrable with local water management systems. In pilot areas, decision-makers and stakeholders in agriculture, hydropower production, and winter tourism exploit the new dataset and the A-DROP prediction tool in real situations. Specifically, pilot 1 focuses on optimizing farm water consumption in Slovenia, pilot 2 develops a climate for ski resorts in France, Italy and Germany, pilot 3 generates an optimized hydropower management tool for a plant in Germany, and pilot 4 creates a drought public dashboard and, concurrently with pilot 5, tests a seasonal hydrological forecast system over two Italian regions. In parallel, A-DROP employs multi-faceted regional hydroclimatic model ensemble simulations to estimate climate change effects on droughts, thus informing decision-making processes, and facilitating risk reduction and adaptation pathways. Tailored information and training sessions support the transition process at the policy and operational levels towards science-based water governance. The active involvement of actors from macro-regional strategies, like EUSALP, and observers from public administrations facilitates the translation of A-DROP outputs into co-designed guidelines for water governance policies.
How to cite: Castelli, M., Avanzi, F., Carmagnola, C., Cvejić, R., Disse, M., Ferrario, I., François, H., Isabellon, M., Jacob, A., Korošec, T., Ludwig, R., Massart, S., Notarnicola, C., Schneider, S., Stevenin, H., Terzi, S., Tuo, Y., and Wagner, W.: Enhancing the readiness for drought events in the European Alps bridging research and practice, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4231, https://doi.org/10.5194/egusphere-egu25-4231, 2025.
Climate change is disrupting atmospheric patterns, which, in turn, alters precipitation regimes worldwide. Droughts are becoming more frequent, intense, prolonged, and spatially distributed, posing a threat to water security for millions of people. Drought monitoring is particularly critical in Brazil, a country that encompasses diverse climate regimes and biomes, and where rainfall variability greatly impacts social vulnerabilities, biodiversity, and the economy. To better understand disruptions in rainfall patterns leading to drier conditions in Brazil, we evaluated the correlation between the occurrence of atmospheric blockings and episodes of the South Atlantic Convergence Zone (SACZ) with rainfall variability, particularly for droughts, in various biomes. The Standardized Precipitation Index (SPI) was used to characterize precipitation variability, presenting simple yet robust statistical insights into the distribution, duration and frequency of rainfalls surpluses (positive values) and droughts (negative values). The SPI values for 1, 6 and 12 months were calculated using observed rainfall data from the Brazilian Daily Weather Gridded Data (BR-DWGD) database, from 1961 to 2024. SACZ episodes and atmospheric blocking events were identified using indices developed by LAMMOC/UFF research group, which effectively describe the behaviour of these systems across various regions of the country. The atmospheric blocking index was calculated using ERA5 reanalysis data, while NCEP reanalysis data was the input to the SACZ index. All data were normalized prior to statistical analyses, which included Pearson’s correlation coefficient, Principal Component Analysis (PCA), K-means clustering, Mann-Kendall test, and trend analysis to identify and quantify trends. The results demonstrate that atmospheric blocking events are increasing in all regions of Brazil. Conversely, the SACZ occurrences did not demonstrate a significant trend. The correlation between atmospheric blockings and SPI values exhibit a strong pattern in all evaluated time scales and regions, demonstrating significant positive influence in the Pampa biome within all evaluated time scales, suggesting that blockings, regardless of their position, incur in rainfall surpluses in South Brazil. In the other biomes, blockings show a consistent negative influence, particularly in Cerrado, Pantanal and Amazonia (Central and Northern regions). Cerrado shows correlations of up to -0.5, the highest values observed in the analysis - suggesting atmospheric blockings have an inhibiting effect in precipitation, creating drier conditions that are concerning for wildfire hazard in central Brazil, and also in Southern Amazonia. SACZ and SPI correlation is not as clear, with small to no trend in most biomes, except for the slight negative influence on the Pampa, region where precipitation decreases as active SACZs concentrate rainfall northward. Understanding the correlation between these important atmospheric systems and the precipitation variability observed in Brazil is valuable to drought monitoring and prediction, and may help to identify early warning signals for major droughts, providing insights that can guide mitigation and adaptation strategies to address the impacts of climate change, which affects differently the regions of the country due to the complexity of its diverse climate regimes and biomes, and therefore, water availability and wildfire hazard.
How to cite: Guida Barroso, A., Sancho, L., da Fonseca Aguiar, L., Esposte Coutinho, P., Victalino Galves, V. L., Michel, G. P., Zanandrea, F., and Cataldi, M.: Assessing the impacts of South Atlantic Convergence Zone (SACZ) and atmospheric blockings on rainfall variability in Brazilian biomes using Standard Precipitation Index (SPI), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12692, https://doi.org/10.5194/egusphere-egu25-12692, 2025.
During drought periods, reservoirs are intended to ensure water availability to meet specific demands within a river basin. However, the increasing frequency and duration of droughts that may be caused by climate change and rising population demands for water may prevent reservoirs from replenishing the necessary volumes for subsequent drought events, potentially prolonging their effects. This study aims to investigate how reservoirs can influence the time of propagation of droughts in the context of climate change, in Brazilian river basins located across different biomes. To achieve this, i) the time of propagation from meteorological to hydrological droughts was calculated using standardized indices for the period 1990 to 2024; additionally, ii) in each basin, drought events in a main reservoir was evaluated using the Standardized Reservoir Drought Index over the same period; and finally, iii) indicators representing the effects of climate change — such as the temporal evolution of evapotranspiration — and increased water demands driven by human activities — such as changes in land use and occupation in agricultural and urban areas— was also be assessed for the same period. This analysis seeks to discuss potential relationships among the time of propagation time to hydrological droughts, reservoir droughts, population demands, and climate change.
How to cite: Leite, C., Cunha, A. P., Figueredo Costa, V. A., and Mendiondo, E. M.: Propagation of droughts with Standardized Indexes associated with storage services under changes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13033, https://doi.org/10.5194/egusphere-egu25-13033, 2025.
Urban growth is one of the main drivers of global change, with urban population expected to grow from 56% of the world total (2020) to 70% by 2050, mainly in less developed regions. Over the past two decades, more than 80 major metropolises have faced extreme drought and water shortages, with future projections outlining an increasing risk of water crises. In this context, the sustainable management of urban water resources emerges as a critical challenge. While studies on water scarcity have traditionally focused on agriculture, given its significant impact, urban systems—despite being resource-intensive—receive comparatively less attention. Moreover, most intra-urban studies are limited to specific case studies, lacking a comprehensive and scalable framework for cross-city comparisons.
This work aims to fill this gap, integrating a socio-economic framework with an engineering one to explore the sustainability of water use in urban green spaces. We perform the analysis on 20 cities with populations exceeding one million, located in developing countries, characterized by socio-economic disparities and different climatic conditions (aridity, temperatures, rainfall). Using the "Degree of Urbanization" approach, we define urban system boundaries to ensure comparability across cities. Within these boundaries, we map urban green spaces, using the Normalized Difference Vegetation Index (NDVI) to assess their extent and condition and quantify their green and blue water demand. We combine these data with those relating to water demand for domestic use and assess their overall impact on urban water scarcity. Our domestic water demand data is derived from a global raster dataset (50 km resolution) for the period 2015–2019. We apply a statistical downscaling technique to achieve a finer 2 km resolution, enabling intra-urban analyses. The downscaling process models the relationship between domestic water demand and city-specific indicators, such as population density, relative wealth indices, and monthly climate parameters.
The ultimate goal is to develop an adaptable model to assess the spatial distribution of water sustainability in urban environments. By integrating socio-economic and environmental factors, this research provides new insights into the role of urban green spaces in shaping water demand and urban water scarcity. In a context where climate change and urbanization are intensifying pressures on water resources, this research contributes to a more informed and equitable management of urban water systems.
How to cite: Tolazzi, A., Galli, N., Rulli, M. C., and Corbari, C.: Sustainability of water use in urban green spaces: a multi-city analysis in developing countries, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18443, https://doi.org/10.5194/egusphere-egu25-18443, 2025.
Reservoirs play an important role in mitigating ill effects of drought. There could however be both desirable and undesirable effects of reservoirs on the water cycle. Many studies have explored the temporal aspects such as propagation rate and response time of drought propagation, yet not much has been revealed about the spatial characteristics of drought propagation. The present study aims to quantify the effects of reservoir networks on drought propagation from meteorological to hydrological drought via agricultural and reservoir drought, considering 7 major reservoirs in the semi-arid Krishna River Basin of India using 19 years of data from 2000 to 2019. The Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSMI), Standardized Reservoir Storage Index (SRSI) and Standardized Streamflow Index (SSI) representing meteorological, agricultural, reservoir and hydrological drought, respectively, were estimated at 1 and 3-months and at a threshold value of 0. The spatial water distribution is described using the ‘downstreamness concept’, and the upstream-downstream drought propagation were closely investigated. The results indicate that the meteorological drought propagates to agricultural and reservoir drought with drought lengthening. Whereas the hydrological drought propagation from upstream to downstream is attributed mainly to drought severity. Usually, the mild and moderate upstream reservoir droughts do not propagate to the downstream reservoirs, but severe drought propagates to downstream reservoirs with prolongation of duration and increase in severity. During drought propagation from upstream to downstream, the downstreamness of stored volume (Dsv) decreases from above the downstreamness of storage capacity (Dsc) at the start, indicating more water in the downstream reservoir, to below Dsc at the end, indicating more water in the upstream reservoir. Importantly, the findings from the study provides essential insights for implications for policymakers for river-basin scale water resource management and drought mitigation considering upstream–downstream drought propagation dynamics.
Keywords: Drought Propagation, Meteorological to Hydrological Drought, Downstreamness, Upstream-Downstream.
How to cite: Gupta, A., Jain, M. K., Pandey, R. P., and Hannah, D. M.: Impact of reservoir network on propagation from meteorological to hydrological drought in a semi-arid basin of India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18766, https://doi.org/10.5194/egusphere-egu25-18766, 2025.
Globally, droughts are becoming longer, more frequent, and more severe, and their impacts are multidimensional. These impacts typically extend beyond the water balance, as long-term, cumulative changes in the water balance can lead to regime shifts in land cover. Here, we assess the effects of temporal changes in water supply and demand over multiple time scales on vegetation productivity and land cover changes in continental Chile, which has experienced a severe drought since 2010. Across most of continental Chile, we observed a persistent negative trend in water supply and a positive trend in atmospheric water demand since 2000. However, in water-limited ecoregions, we have observed a negative temporal trend in the water demand of vegetation, which intensified over longer time scales. This long-term decrease in water availability and the shift in water demand have led to a decrease in vegetation productivity, especially for the Chilean Matorral and the Valdivian temperate forest ecoregions. We found that this decrease is primarily associated with drought indices associated with soil moisture and actual evapotranspiration at time scales of up to 12 months. Further, our results indicate that drought intensity explains up to 78% of temporal changes in the area of shrublands and 40% of the area of forests across all ecoregions, while the burned area explained 70% of the temporal changes in the area of croplands. Our results suggest that the impacts of long-term climate change on ecosystems will extend to drought-tolerant vegetation types, necessitating the development of context-specific adaptation strategies for agriculture, biodiversity conservation and natural resource management.
How to cite: Zambrano, F., Vrieling, A., Meza, F., Duran-Llacer, I., Fernández, F., Venegas-González, A., Raab, N., and Craven, D.: Shifts in water supply and demand shape land cover change across Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20588, https://doi.org/10.5194/egusphere-egu25-20588, 2025.
Drought is a natural disaster causing the greatest global losses and having the most significant impacts across various sectors. In the Mediterranean region, particularly in the Tensift River Basin, Morocco, drought severely affects water availability, agriculture, and local economies. Despite its importance, traditional monitoring systems often fail to provide timely warnings or accurately quantify and report drought impacts. This study evaluates the performance of the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) in detecting drought events, focusing on optimizing thresholds and timescales to enhance monitoring accuracy. Using Receiver Operating Characteristic (ROC) analysis, we assessed the correspondence between estimated drought events and reported impacts, achieving AUC values of 78.34% for SPI and 68.32% for SPEI. These results highlight the strengths of both indices in detecting drought onset and duration while addressing limitations such as sensitivity to PET methods. The findings emphasize the importance of tailoring thresholds, timescales, PET models, and probability distributions to local climatic conditions. The proposed framework is crucial for mitigating drought impacts and supporting decision-makers in sustainable water resource management in the Tensift Basin. Additionally, this research underscores the need for systematic reporting of drought impacts to inform the development of comprehensive drought atlases and regional management strategies.
Keywords: Drought Impact, ROC Analysis, Threshold Optimization, Drought Risk, Climate Change
How to cite: Naim, M. and Bonaccorso, B.: Linking Drought Index-Based Metrics to Real-World Impacts for Enhanced Monitoring in the Tensift River Basin, Morocco, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5243, https://doi.org/10.5194/egusphere-egu25-5243, 2025.
Posters on site: Fri, 2 May, 14:00–15:45 | Hall A
Droughts pose a major global challenge, particularly in Taiwan, where critical industries such as semiconductor manufacturing are significantly impacted. The mountainous terrain, which constitutes 70% of Taiwan, complicates the estimation of Land Surface Temperature (LST) due to surface heterogeneity. Accurate drought estimations necessitate consistent LST retrieval methods. This study employs a Machine Learning (ML)-based normalization method linked to surface variables to enhance LST accuracy. We introduce the Surface Water Availability and Temperature (SWAT), integrating the improved LST, Normalized Difference Latent Heat Index (NDLI), and Normalized Difference Vegetation Index (NDVI). The SWAT, along with existing indices, was used to assess drought conditions in Taiwan from 2001 to 2023. These results were validated against satellite indicators such as the Crop Water Stress Index (CWSI) and Net Primary Productivity (NPP). Our findings reveal that the SWAT correlates strongly with the CWSI and NPP, indicating significantly higher sensitivity to drought status compared to existing indices. Additionally, the SWAT demonstrated high temporal consistency with the CWSI and NPP across most regions of Taiwan. Generally, the SWAT, supported by the ML-based LST normalization method, proves to be a robust index for monitoring drought conditions in mountainous regions.
How to cite: Liou, Y.-A. and Thai, M.-T.: Enhancing Drought Monitoring in Taiwan’s Mountainous Terrain Using the Surface Water Availability and Temperature (SWAT), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3056, https://doi.org/10.5194/egusphere-egu25-3056, 2025.
With increasing frequency and severity of drought hazards worldwide, reliable monitoring and forecasting of drought conditions becomes more and more relevant for efficient drought management. In this context, the OUTLAST project provides global monitoring and seasonal forecasting of drought hazard indicators (DHIs) across three sectors, ranging from meteorological and agricultural to hydrological DHIs. In OUTLAST, a consistent framework is developed in which ERA5 (for monitoring) and bias-corrected SEAS5 data (for seasonal forecasts) are used to calculate meteorological DHIs. The same climate data forces the Global Crop Water Model1 and the global hydrological model WaterGAP2 in order to derive agricultural and hydrological DHIs respectively. The global OUTLAST DHIs will be freely available via the WMO’s HydroSOS web portal.
To adequately support drought management and decision-making, it is essential to identify and evaluate the accuracy of OUTLAST DHIs. Therefore, we apply a twofold evaluation procedure: 1) a global evaluation against various observation-based datasets with (nearly) global coverage, and 2) a regional evaluation in collaboration with experts who will potentially use OUTLAST products in their daily work. While the first provides a general assessment of the overall performance, the latter allows evaluation whether actual drought conditions are sufficiently monitored by the global OUTLAST system.
Here, we focus on the global evaluation of DHIs for the historical period 1981-2020 by comprehensively comparing the performance of model-based DHIs from multiple sectors, including (1) the standard precipitation index, (2) the rainfed crop drought hazard indicator, and (3) the empirical percentiles of streamflow, against observation-based data, such as (a) remote sensing-based precipitation, (b) global evapotranspiration data, and (c) observed streamflow of large river basins. By analyzing DHIs from multiple sectors simultaneously, we show the effect of drought - and error- propagation in the hydrological cycle on the ability to capture observed drought conditions by model-based DHIs. Besides, the capability to accurately reproduce historic drought conditions represents the accuracy that users can expect when employing the OUTLAST near-real time monitoring and seasonal forecasts for drought management decisions.
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1Siebert, S., & Döll, P. (2010). Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. Journal of Hydrology, 384(3-4), 198-217. https://doi.org/10.1016/j.jhydrol.2009.07.031
2Müller Schmied, H., Trautmann, T., Ackermann, S., Cáceres, D., Flörke, M., Gerdener, H., Kynast, E., Peiris, T. A., Schiebener, L., Schumacher, M. & Döll, P. (2024). The global water resources and use model WaterGAP v2. 2e: description and evaluation of modifications and new features. Geoscientific Model Development, 17(23), 8817-8852. https://doi.org/10.5194/gmd-17-8817-2024
How to cite: Trautmann, T., Abbasi, N., Weber, J., Vu, T., Dietrich, S., Doell, P., Kunstmann, H., Lorenz, C., and Siebert, S.: Evaluation of a global multi-sectoral drought hazard monitoring and forecasting system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3754, https://doi.org/10.5194/egusphere-egu25-3754, 2025.
Climatic drought, characterized by a decrease in precipitation and an increase in temperatures, significantly influences groundwater resources by reducing recharge and increasing abstraction. Interactions between climatic droughts and groundwater systems are complex, because of the varying hydrodynamic properties of aquifers, which influence their responses to surface stresses. Understanding these relationships is crucial for optimizing groundwater resource management and mitigating drought-induced crises. This study investigated the relationships between climatic droughts and groundwater level fluctuations in two climatically different basins in Iran: the semi-arid Mashad Basin (Khorasan Razavi province) and the arid Gowharkuh Basin (Sistan and Baluchestan province). We employed the Standardized Precipitation Index (SPI) to represent climatic conditions and the Standardized Water Table Index (SWTI) to show groundwater level fluctuations. Time series analyses were conducted in both time and frequency domains to assess the measure and quality of relationships between climatic conditions and water level variations. In the time domain, we calculated correlation coefficients and lag times between SPI and SWTI, using a modified cross-correlation function (MCCF). This innovative approach allowed for cross-correlation calculations between time series of unequal lengths. Using the Blackman-Tucky method, we computed spectral density, cross-spectrum amplitude, coherency, and phase functions in the frequency domain. Time domain results showed that the correlation coefficient and lag time between climatic variations and groundwater levels were higher in the Gowharkuh Plain (0.9 and 7 years) compared to the Mashhad Plain (0.7 and 5 years), highlighting the influence of interacting factors, including climatic, hydrological, and hydrogeological conditions, as well as human interventions, in shaping these relationships. Frequency domain analysis indicated that low-frequency fluctuations in SPI (long-term droughts) exert the most significant impact on groundwater resources.
How to cite: Naderi, R.: Effects of Climatic Drought on Groundwater Level Based on Time Series Analysis in Time and Frequency Domains, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4870, https://doi.org/10.5194/egusphere-egu25-4870, 2025.
The last twenty years have shown the most extreme drought events in Europe on record. In the Rhine River basin, these droughts have severely impacted the shipping and industry sectors due to low water levels limiting the transport of goods. Drought prediction, therefore, is crucial but difficult to achieve due to the complexities of the propagation from meteorological to hydrological droughts. In this study, we analyzed the relation between several meteorological drought indices and the occurrence of hydrological droughts. We found that the Standardized Precipitation Evapotranspiration Index (SPEI) shows the highest correlation. SPEI was then used to single out extreme meteorological droughts from the LAERTES-EU data set, which contains about 12.500 years of meteorological variables simulated under current climate conditions by several setups of the regional COSMO-CLM model. These most extreme meteorological droughts were then propagated through the hydrological model WRF-Hydro to produce streamflow at the Rhine, which was then evaluated in terms of hydrological drought severity by comparison with observed hydrological droughts. Overall, this approach reveals insights into the magnitude of extremely rare hydrological droughts, and their predictability from the corresponding meteorological drought indices.
How to cite: Campoverde, A., Ehret, U., Ludwig, P., and Pinto, J. G.: Meteorological to hydrological drought propagation using the large ensemble of regional climate model simulations for Europe (LAERTES-EU). A case study for the Rhine River Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5611, https://doi.org/10.5194/egusphere-egu25-5611, 2025.
In recent years, Europe has experienced severe droughts (2018-2020) due to reduced summer precipitation and high temperatures, leading to reduced runoff and groundwater levels. According to climate change projections, these conditions will become more frequent. These droughts have significant impacts on ecosystems, drinking water supplies and navigation, for example.
During such dry periods, rivers are mainly fed by groundwater. The aim of this study is to statistically analyse the interaction between surface water discharge, especially during dry periods, and groundwater levels. For 128 catchments in Lower Saxony, Germany, correlations between selected low flow characterising indices and groundwater level indices are calculated. Therefore, groundwater levels from spatial interpolation of shallow, unconfined aquifers were aggregated at the catchment level. The study focuses on mean and minimum groundwater levels over different monthly time periods as well as the standardised groundwater level index (SGI) to reveal possible patterns and relationships with low flow indices. We expect to find non-linear correlations particularly between the SGI and specific low flow indicators such as lowest 7-day average flow (NM7Q), deficit volume and low flow duration. A further aim is to investigate whether these relationships can be used to improve statistical models, such as multiple linear regression, to provide a predictive framework for low flow conditions based on groundwater levels. Such relationships and correlations may improve our understanding of how groundwater levels can act as an additional predictor of low flow conditions.
How to cite: Iffland, R. and Haberlandt, U.: Relationships between low-flow-indices and groundwater levels in Lower Saxony, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6173, https://doi.org/10.5194/egusphere-egu25-6173, 2025.
Groundwater droughts in temperate regions are typically considered rare phenomena and consequently neglected in research despite their significant socio-economic and ecological impacts. In light of increasing water demands and climate change intensity, understanding and predicting groundwater droughts are essential for sustainable water resource management.
This study aims to define, identify and predict groundwater drought events across the Irish groundwater network by integrating multiple drought identification indices with machine learning (ML) techniques. Groundwater level (GWL) time series from 100 monitoring stations, methods: (i) the Threshold Level Method (TLM), which identifies drought when GWLs fall below predefined thresholds (ii) the Percentage of Normal (PON), which quantifies deviations in mean GWL relative to a baseline reference period; and (iii) the Standardised Groundwater Index (SGI), which normalises GWLs to classify drought severity. Subsequently, these approaches were evaluated and compared based on their ability to characterise drought events, using the 2018 drought for validation. This process enabled the selection of the most suitable indicator for predictive modelling.
An ensemble of ML binary classifiers including Logistic Regression (LR), Generalized Linear Models (GLM), Decision Trees (DT), Random Forest (RF), and XGBoost (XGB) were trained using meteorological inputs such as precipitation and temperature, to predict groundwater drought occurrences. However, the imbalanced class problem (rare drought events) was found to reduce classifier accuracy therefore, datasets were resampled using the Synthetic Minority Over-sampling Technique (SMOTE) technique, using several balance conditions of 50%, 40%, 30%, 20% minority class distribution.
Analyses indicate that the TLM and PON exhibit low sensitivity for drought detection, whereas the SGI was significantly more effective in characterising drought events within the Irish hydrogeological environment. Results show that the SMOTE technique enhanced performance of LR, GLM, and DT models, demonstrated by higher area under the receiver operating characteristic curve (AUC), and area under the precision/recall curve (AUCPR) values. However, XGB showed superior stability and accuracy across all sampling conditions. Notably, with a 40% minority class, XGB achieved the highest Recall and Precision values of 91.6% and 95.2%, respectively. As expected, model interpretations highlighted precipitation as a key precursor to drought propagation, with stations showing variable vulnerability linked to cumulative precipitation lags.
Future research directions will involve developing multi-scale early-warning models for groundwater drought using machine learning and deep learning. These models will be upscaled to a national level to map spatiotemporal impacts and inform groundwater management planning under changing climatic conditions.
How to cite: Mohamed, T., Nasr, A., and Hynds, P.: Predicting Groundwater Drought in Ireland Using a Machine Learning Ensemble , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9278, https://doi.org/10.5194/egusphere-egu25-9278, 2025.
Like those recently experienced in 2018 and 2022, European droughts significantly alter ecosystem processes, such as photosynthesis and evapotranspiration. Quantifying these large-scale alterations and understanding their drivers is essential to studying the drought impacts on ecosystem performance, water resource management, and carbon emission budgeting. However, to this date, because of differing definitions of drought events and complex interactions among eco-hydrological variables across multiple time scales, research has only painted a blurry picture of the impacts of droughts on ecosystems.
In this work, based on pan-European simulations of the land surface model CLM5-BGC, we identified drought events with a generalized clustering algorithm considering water deficits in multiple compartments of the hydrological cycle (groundwater, soil moisture, evapotranspiration, and vapor pressure deficit). Further, we distinguished these droughts' direct and lagged effects by aggregating water deficits across various time scales and their impacts on ecosystem processes by accounting for the absolute anomalies at the event locations.
We highlight statistics and trends of the identified drought events, their drivers, and their impact on photosynthesis and evapotranspiration, with increasingly severe soil moisture and vapor pressure deficits. In the shorter time scales, atmospheric droughts are the primary driver of photosynthesis and evapotranspiration anomalies. This study presents a novel multi-scale and multivariate approach to droughts, paving the way for holistic and more precise considerations of their impacts on ecosystems.
How to cite: Poppe Teran, C., S. Naz, B., Belleflamme, A., Vereecken, H., and Hendricks Franssen, H.-J.: The drought response of European ecosystem processes via multiple components of the hydrological cycle, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10881, https://doi.org/10.5194/egusphere-egu25-10881, 2025.
Seasonal hydrological forecasts have become an essential tool for water resources management, especially in the context of increasing droughts in the 21st century. As part of the CIPRHES project, the purpose here is to assess the capacity of a hydrological forecast modelling chain to simulate low-water flows over France, in order to extract relevant indicators of hydrological droughts for decision-makers, such as the anticipation, i.e., the start date of a drought event, and the precision, i.e., the lowest observed flow for 10 consecutive days (VCN10). Seamless meteorological forecasts, combining 10-days ECMWF forecasts with 134-days forecasts simulated by the ARPEGE model using the Ensemble Copula Coupling method, are used to force the SURFEX land surface model coupled with the CTRIP river routing model to simulate 144-days river hydrological forecasts. To bring this study into real-time conditions, data assimilation is performed on a 7-days simulation prior to each forecast using the observed discharges at the gauged stations from the CAMELS database, to correct the internal states of the CTRIP model. The results show that data assimilation significantly improves the simulations over the assimilated period, and its persistence (i.e., the duration of the effect of the data assimilation) is over 30 days for the largest rivers but close to 0 days on the smaller ones. This last point leads to a poor effect of data assimilation on the CAMELS database catchments, most of them having a surface lower than 1000 km2. However, the modelling chain simulates a good anticipation for 70% of the used stations from the CAMELS database, and a precision deviation closed to 0 for the large majority of the stations. A post-bias correction procedure based on the Empirical Quantile Mapping (EQM) method at each station allows to improve the estimations of these indicators, e.g., good anticipation for 86% of the stations.
How to cite: Jeantet, A., Munier, S., and Rousset, F.: Using a seamless forecast ensemble to force the CTRIP river routing model in order to simulate hydrological drought indicators useful to decision-makers in France., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11671, https://doi.org/10.5194/egusphere-egu25-11671, 2025.
Hydrological research conducted in the province of Cuneo, located in southern Piedmont, Italy, highlights significant trends in meteorological droughts, showing increasing duration and intensity over recent decades. Prolonged dry periods caused by low precipitation, often combined with high temperatures and elevated evapotranspiration, lead to severe impacts on agriculture, surface water resources, and socio-economic systems. This study identifies major drought events affecting the Cuneo area using standardized meteorological and hydrological indices: the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Streamflow Index (SSI). The propagation of drought from meteorological to hydrological conditions is analysed by correlating basin-wide precipitation indices at various temporal scales with streamflow indices at the basin outlet. Spearman’s correlation coefficient, adjusted for autocorrelation, is used to determine the temporal scale with the highest correlation, providing an indication on the basin’s drought response time. Spatial variability in response times is further explored in relation to basin characteristics such as gauge elevation and drainage area. Beyond characterizing drought propagation, the study integrates the quantitative analysis with qualitative insights obtained collaborating with water utility managers. Their direct experience of droughts periods in the water supply system represents an invaluable source of information. We aim at combining the quantitative and qualitative pieces of information to link drought causes to their real consequences and impacts on the study area, addressing both physical and socio-economic dimensions.
How to cite: Rivella, B., Mombrini, E., Tamea, S., and Viglione, A.: Droughts and Changes in Water Resource Availability in the Cuneo Province , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12748, https://doi.org/10.5194/egusphere-egu25-12748, 2025.
Droughts are among the most severe hydro-meteorological hazards. IPCC (2022) reports that the global area affected by droughts is expected to increase in the context of climate change. Their impact on the agriculture, economy, and ecosystems of a region is significant. Flash droughts represent a particularly challenging phenomenon characterized by their rapid onset. They are primarily driven by a sudden increase in evapotranspiration coupled with significant deficits in precipitation. The duration of flash droughts is relatively shorter as compared to traditional droughts. They are difficult to predict and often lack adequate mitigation measures. High-resolution indices such as the pentad-scale (5-day) SPEI (Standardized Precipitation Evapotranspiration Index) have emerged as essential tools to detect and evaluate the flash droughts.
The present study investigates the flash droughts across India during the period 1979 to 2020. It utilizes the IMDAA dataset (0.12°×0.12°) to develop a pentad-scale SPEI dataset throughout India. The analysis reveals that northern and central states, including Punjab, Haryana, Madhya Pradesh, and eastern Maharashtra, experience comparatively prolonged and severe flash droughts. The spatial evaluation of drought progression is also conducted across multiple agro-climatic zones. We assessed the predictability of flash droughts at a lead time of 7, 14, and 21 days utilizing data-driven frameworks such as LSTM, Transformers, and Informers. The temporal evaluation of prediction performance is done across both monthly and seasonal scales. The findings of the study underscore the need for improving the prediction performance of flash droughts, particularly across regions with high elevation variability. This approach aims to strengthen the nation’s resilience to flash droughts in the face of a changing climate.
How to cite: Pathania, A. and Gupta, V.: Deciphering Flash Droughts in India: Trends, Dynamics, and Prediction Insights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13154, https://doi.org/10.5194/egusphere-egu25-13154, 2025.
Meteorological drought is a natural phenomenon caused mainly by a prolonged precipitation deficiency, that may propagate to the surface and groundwater systems leading to the manifestation of hydrological drought events. The impacts of drought are often less visible in the subsurface due to sparse observational records while the response of groundwater to weather variability depends on antecedent groundwater levels and hydraulic and storage properties of the aquifer system. Although groundwater is often the only resilient water resource in arid and semi-arid areas, a notable decline in groundwater levels can be difficult to manage.
There is increasing evidence that coastal groundwater, which serves as the main water source for various needs (urban water supply, agriculture, etc.), is at even greater risk in semi-arid areas where the quality and quantity of fresh water stored in aquifers is threatened by seawater intrusion. It is important to note that, in these islands, periods of low recharge coincide with peak water consumption, which in turn leads to overexploitation of the aquifers to meet the increased water demands.
To that end, the present study focuses on the assessment of the complex relationship between drought conditions and coastal groundwater, emphasizing on its multidimensional nature which involves the consideration of several factors, such as pumping regimes, land use, water demands, subsurface heterogeneity, geomorphology of the study area and hydraulic connection to the sea. The principal goal is to identify critical features through a comprehensive modeling approach using distributed numerical modelling and easily accessible data and tools, providing the means for informed water management, especially in ungauged coastal aquifers.
The study analysed the case of a coastal aquifer located in the Greek island Kalymnos in the Aegean Sea for a period of 73 years (1950-2022). The primary source of groundwater in the study area is a calcareous unconfined coastal aquifer. A transient three-dimensional variable-density flow and salt transport numerical model was developed using SEAWAT code. Time-varying recharge input, was simulated with the ZOODRM model, a distributed recharge model. The pumping regimes were calculated based on both urban and agricultural water demands. Three drought indices for various timescales were employed for assessing drought evolution throughout the study period. That is, the Reconnaissance Drought Index (RDI) indicating the meteorological conditions, the Effective RDI (eRDI) and the Agricultural Standardized Precipitation Index (aSPI). The last two were utilised for identifying the agricultural drought conditions. The MH-data software was used for managing the meteorological input data (precipitation and potential evapotranspiration) that were obtained from the ERA5-Land database and the DrinC software was used for the drought analysis.
The outcomes of the study identified significant correlations between the freshwater volume and the drought indices, indicating the response of the aquifer to meteorological and agricultural drought. The time-varying pumping and recharge, along with the corresponding meteorological and agricultural drought conditions, also provide insights on water availability and potential water depletion during drought episodes. The proposed workflow may serve as an effective and cost-efficient strategy that may be utilized in areas with limited field data.
How to cite: Vangelis, H., Kopsiaftis, G., Tigkas, D., Kourtis, I. M., and Christelis, V.: Investigating groundwater response to meteorological and agricultural drought under increased water demand: insights from a Mediterranean coastal aquifer using numerical modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13898, https://doi.org/10.5194/egusphere-egu25-13898, 2025.
Rapa Nui Island, is the most isolated inhabited place in the world and a popular tourist destination, like other island communities located in the Pacific Ocean. This unique system, which lacks rivers or permanent surface watercourses, is particularly vulnerable to climatic variations that could affect groundwater recharge, which is their main source of freshwater. The increase in water consumption, along with predictions of less precipitation and higher temperatures due to climate change, underscores the need to better understand future drought conditions on Rapa Nui Island.
Here, we selected and statistically downscale and bias-corrected 11 CMIP5 and 3 CMIP6 Global Circulation Models (GCMs) under the scenarios RCP8.5 and SSP5-8.5, respectively, to study the projections of droughts events in Rapa Nui until the end of the century. To do so, we analyze severe and extreme droughts using the SPI(12) and SPEI(12) indexes estimating potential evapotranspiration (PET) with the Thornthwaite and Hargreaves methods.
Our results indicate a sustained decrease in precipitation, an increase in temperature, and a higher frequency of drought events with longer durations and greater intensities compared to historical climatological periods (1970-2014). Specifically, by the end of the century, average annual precipitation is projected to decrease by more than 20% (29% under the SSP 5-8.5 scenario compared to 24% under RCP 8.5), while the mean temperature is expected to increase by approximately 2°C for each scenario. Regarding extreme droughts, projections based on the SSP 5-8.5 result in more adverse outcomes, particularly in the far future (2065–2100). For the SPI index, extreme drought frequencies under this scenario are projected to exceed historical frequencies by 61% in the distant future, and by 23% compared to those projected under the RCP 8.5 scenario.
We conclude that the analysis of drought is highly dependent on the method used to estimate PET. For instance, the projected results using the Thornthwaite method show differences exceeding 17% in the frequencies of extreme droughts by the end of the century compared to the Hargreaves method. Both scenarios project more intense and prolonged droughts than those experienced in the past, emphasizing the urgency of investigating and implementing measures to ensure the population's water supply security and the preservation of the island's biodiversity, always integrating the opinions and respecting the culture of the Rapa Nui people.
Finally, these results highlight the importance of studying representative values of this variable during the historical period and underscore the relevance of adopting measures to mitigate climate risks associated with drought events in fragile systems such as that of Rapa Nui.
How to cite: Sheldon, D., Aliaga, J., Muñoz, E., Toro, I., and Vargas, X.: Future Drought projections in a fragile island system: The case of Rapa Nui., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14058, https://doi.org/10.5194/egusphere-egu25-14058, 2025.
Drought assessment is a critical component of water resource management, ensuring the stability of water supplies and minimizing the impacts of droughts. Focusing on percentile-based criteria and available water supply duration, the United States Drought Monitor (USDM) employs a five-tiered drought assessment ranging from abnormally dry conditions (D0) to exceptional drought (D4), with percentiles delineating each stage. Camrose City in Canada monitors drought conditions in four stages: watch, warning, critical, and emergency based on the number of days water can be supplied to the population. These monitoring schemes highlight the importance of hydrological and statistical data in identifying drought conditions and guiding proactive responses.
Considering the practices of drought monitoring in Building on these international practices, this study proposes a unified guideline for drought condition monitoring schemes for dams and rivers in South Korea. The guideline incorporates percentile thresholds (30%, 20%, 10%, 5%) for indicators such as reservoir storage rates and river levels. For reservoir management, thresholds are set based on water availability durations (90, 60, 30, 20 days).
The drought monitoring guideline is further validated using two methods for a testbed, the Dongbok Dam; the supply-based criteria defined thresholds as 25.6-17.1-8.5-5.7 million m³ for reservoir volume and 28-19-9-6% for reservoir rates. Alternatively, the percentile-based method yielded thresholds of 52.8-44.2-32.3-25.4%. The Pyeongchang River was selected as a representative case for rivers where supply-based criteria are inapplicable. The 10-day percentile-based criteria showed higher thresholds during the flood season (April–September) and lower thresholds during the non-flood season (October–February).
This research emphasizes integrating global best practices into localized drought monitoring systems. By adopting standardized and scientifically robust methods, water resource managers can improve resilience against droughts and ensure sustainable water availability for future generations.
Acknowledgment: This work was supported by the 2023-2024 K-water through research on improving dam operation strategies to respond to drought, funded by the Korea Ministry of Environment(MOE)(grant number).
How to cite: Kim, T.-W., Kim, M. J., Lee, J. H., and Kwon, H. H.: Unified Guidelines for Drought Condition Monitoring in Local Dams and Rivers in South Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14097, https://doi.org/10.5194/egusphere-egu25-14097, 2025.
The intensification of climate change has exacerbated the frequency and severity of extreme hydrological events, particularly droughts, posing critical challenges to global water resource management. The Zhuoshui River Basin, as a vital water supply region in Taiwan, has recently faced increasing extremes in rainfall and drought, highlighting the urgent need for effective management strategies. To address these challenges, this study develops a deep learning-based model for long-term monthly river flow prediction, emphasizing its significance in supporting water resource management and decision-making under worsening drought conditions.
Using historical hydrological data, the model was trained and optimized with input variables such as rainfall, evapotranspiration, and groundwater levels to explore their interactions with river flow and assess their influence on predictive performance. Future climate scenarios provided by the IPCC AR6 (Sixth Assessment Report) were employed to project river flow and groundwater levels over the next 80 years, offering insights into potential drought risks.
By combining the predicted river flow and groundwater levels with established drought assessment indices, the study quantifies drought severity and provides a scientific foundation for developing sustainable water resource management strategies in the Zhuoshui River Basin under the impact of climate change.
Keywords: Long-term streamflow forecasting, Deep learning, Drought Risk, Climate Change
How to cite: Chen, Z. and Chang, L.-C.: Enhancing Long-Term River Flow Prediction for Effective Water Resource Management under Intensifying Drought Risks and Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14667, https://doi.org/10.5194/egusphere-egu25-14667, 2025.
Understanding how climate change affects the soil moisture drought recovery process is a priority to guide adaptation planning in drought management and to promote climate-resilient agriculture. A future climate scenario analysis framework was developed to project the spatiotemporal trends of global soil moisture drought and assess future changes in extreme drought recovery probabilities relative to the baseline period. Additionally, the two-factor analysis of variance approach was conducted to quantify the contributions of different uncertainty sources in climate change projections. The latest Inter-Sectoral Impact Model Intercomparison Project (ISIMIP 3b) simulations indicate that global soil moisture droughts will increase in frequency, extent, and intensity in the future. The strongest, most robust increases were projected in Amazon, central and southern Europe, southern Africa, southern China, southeastern Asia, and Oceania. Although a reduction in drought magnitude was projected in the northern high-latitudes, the recovery time and the precipitation required to terminate a drought were anticipated to increase compared to the baseline period. Compared to the baseline period, approximately 57.5% of global regions are projected to experience a decline in drought recovery probability during crop growing seasons under SSP1-2.6 scenario, particularly in northern North America, northern Europe, northwestern Asia, western Central Africa, the central Amazon basin, and southern Australia. Under SSP3-7.0 and SSP5-8.5 scenarios, this proportion will rise to 61.3% and 60.3%, respectively. The ANOVA-based assessment reveals that climate model is the dominant uncertainty source, accounting for approximately 59.5%–66.8% of the total variance. Additionally, the contributions of emission scenarios and their interactions increase as drought recovery time lengthens, particularly in Southern Northern America, Central Africa, Southern Asia, Southern South America, Southern Africa and Oceania. Although future drought recovery probability projections are associated with non-negligible uncertainties, the increasingly difficult to recover from extreme droughts at the global scale highlights the importance of taking certain measures to mitigate drought risks.
How to cite: Yuan, F. and Zhang, L.: Future Evolution and Sources of Uncertainty in Global Drought Recovery Probabilities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14683, https://doi.org/10.5194/egusphere-egu25-14683, 2025.
A suite of gridded daily satellite (CMORPH, IMERG) and in-situ (NClimGrid) precipitation datasets are used to compute a near-real time standardized precipitation index (SPI) over various time scales (from 1-month to 36-month). Over CONUS, the Standardized Precipitation Evapotranspiration Index (SPEI) is also computed using daily potential evapotranspiration (PET) derived from NClimGrid daily temperature estimates. The drought indices: CMORPH-SPI (global; 1998-present; 0.25x0.25deg.), IMERG-SPI (global; 2000-present; 0.1x0.1deg.), NClimGrid-SPI and NClimGrid-SPEI (CONUS; 1951-present; 0.05x0.05deg.) are used to perform a historical analysis of drought events and derive long-term statistics on drought occurrences, duration, and severity at the local, national, regional, and global scales. The impact of precipitation and temperature (i.e., PET) changes is assessed by considering several reference periods such as different durations (i.e., from a decade to the full period of record) and different time frames (i.e., 1961-1990, 1971-2000, etc.). The evolution of the distribution parameters (Gamma, Pearson III) computed for an ensemble of reference periods allows to account for long-term change in temperature and precipitation patterns. In addition to the drought indices (SPI, SPEI), the year-to-date rainfall deficit is estimated with respect to drought classification (abnormally dry, moderate, severe, extreme, exceptional) and the impact of isolated or multi-day rainfall events on drought conditions is evaluated. This work provides a better understanding of drought propagation across a continuum of accumulation scales and allows to estimate the likelihood of any deviations from normal rainfall conditions to evolve into meteorological drought.
How to cite: Prat, O., Coates, D., Eldho, I., Wilkins, S., Willett, D., Leeper, R., Nelson, B., Shaw, M., and Ansari, S.: Spatial and Temporal Drought Patterns Derived from High-Resolution Daily SPI and SPEI Datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14752, https://doi.org/10.5194/egusphere-egu25-14752, 2025.
Drought is one of the most significant natural hazards, impacting ecosystems, water resources, and human livelihoods worldwide. Traditional drought analysis often focuses on specific types or limited geographical regions, leaving a critical gap in understanding the global evolution and interconnection of drought events across different timescales and dimensions.
This study aims to address this gap by employing DBSCAN (Density-Based Spatial Clustering of Applications with Noise, e.g., Camalieri and Toreti, 2023) algorithm to identify, and quantify diverse characteristics of meteorological, hydrological, and agricultural droughts on a global scale. Specifically, we focus on the sensitivity of the DBCAN parameters, which are crucial for distinguishing meaningful drought clusters from noise in large, complex datasets. Our objective is to develop and validate a robust framework for detecting and assessing the spatiotemporal evolution of drought in different compartments of hydrological cycle, enabling a more comprehensive evaluation of entire drought dynamics.
Using a global hydrological dataset forced with ERA5 meteorologic dataset (Řehoř et al, 2024), we implement a 3D DBSCAN method, integrating spatial and temporal dimensions. The dataset provides key outputs of a hydrological model, including soil moisture, precipitation, potential evapotranspiration, and discharge, which are used to calculate drought metrics and identify large clusters with a total area exceeding 150,000 km² and lasting at least 30 days. At this stage, we work with historical data from 1980 to 2022, providing a robust platform to assess spatiotemporal drought patterns. This historical dataset will serve as a foundation for a future comparison with projected climate scenarios from 2025 to the end of the 21st century, enabling insights into potential changes in drought characteristics.
Our findings reveal that 3D DBSCAN is highly effective in capturing the spatiotemporal evolution of drought events, with parameter sensitivity playing a pivotal role in cluster detection. Small adjustments of algorithm’s inputs significantly influence the size, shape, and distribution of clusters, highlighting the need for careful calibration. This framework provides new insights into the relationships between drought events across regions and temporal scales, highlighting their potential to inform water resource management and climate adaptation strategies.
Cammalleri, C. and Toreti, A., 2023. A generalized density-based algorithm for the spatiotemporal tracking of drought events. Journal of Hydrometeorology, 24(3), pp.537-548.
Řehoř, J., Brázdil, R., Rakovec, O., Hanel, M., Fischer, M., Kumar, R., Balek, J., Poděbradská, M., Moravec, V., Samaniego, L. and Trnka, M., 2024. Global catalog of soil moisture droughts over the past four decades. EGUsphere, 2024, pp.1-34.
We acknowledge the Czech Science Foundation grant 23-08056S.
How to cite: Šťovíček, V., Hanel, M., Kumar, R., Moravec, V., Markonis, Y., Cammalleri, C., Řehoř, J., Trnka, M., and Rakovec, O.: Global-scale spatiotemporal clustering of multivariate drought events using 3D DBSCAN, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16877, https://doi.org/10.5194/egusphere-egu25-16877, 2025.
Drought is a multifaceted natural hazard characterized by complex mechanisms, diverse contributing factors, and slow onset, affecting food, water, energy, and ecosystem security. Brazil, like many regions worldwide, has faced significant drought challenges over the past decade, impacting basins that play a critical role in water supply, hydropower generation, and agriculture. This study explores the application of Machine Learning (ML) algorithms and Two-variate Standardized Index (TSI) to forecast drought conditions at 3- and 6-month time scales.
In this study we employ Support Vector Regression (SVR) and Multilayer Perceptron Artificial Neural Networks (ANNs), using as predictors univariate indices and climate indices representing climate modes of variability that influence Brazil's precipitation and drought regimes. Our methodology includes feature selection through Recursive Feature Elimination, lagged correlations, and statistical evaluation using the Mean Absolute Error (MAE), Mean Square Error (MSE) and Coefficient of Determination (R²).
Results demonstrate that both SVR and ANN models effectively predict drought conditions, with R² varying between 0.71 and 0.91, MRS less than 0.2 and MAE not exceeding 0.35, for key indices at 3- and 6-months lags. The strong predictive performance underscores the potential of ML to address challenges in drought forecasting, enabling proactive water resource management and mitigation in regions vulnerable to hydrometeorological extremes.
How to cite: Cuartas, L. A., Naghabi, A., Nikravesh, G., Campos, J. A., Taheri Dehkordi, A., Ahmadi, K., Fujita, T., Senatore, A., Mendicino, G., and Uvo, C. B.: Machine Learning Framework for Hydrological Drought Forecasting in Brazilian Basins with Diverse Climates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17593, https://doi.org/10.5194/egusphere-egu25-17593, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot A
EGU25-7150 | ECS | Posters virtual | VPS9
Fusion of Stacked Generalization and Predictor Selection Technique for Downscaling in Drought Monitoring: A Case Study in a Semi-Arid AreaTue, 29 Apr, 14:00–15:45 (CEST) vPoster spot A | vPA.13
Drought is one of the most severe climate-induced phenomena; with significant impacts on agriculture, water resources, and ecosystems. Drought monitoring under climate change scenarios becomes crucial, particularly in regions vulnerable to water scarcity, such as semi-arid areas in Iran. Although Global Climate Models (GCMs) contain coarse spatial resolutions, they provide valuable insights in better assessing the variability of drought characteristics—such as duration, severity, and intensity in the future. To achieve this aim, downscaling of climate variables as triggers of droughts is required to monitor drought in local scale. Latyan region in Iran, as an important area to supply water, is a critical place based on its climate, drought event occurrences, and water demand and supply stress. This study tried to accurately downscale and bias-correct the climate variables utilizing the latest CMIP6 models (ACCESS-CM2, BCC-ESM1, CanESM5, HadGEM3-GC31-LL, and MIROC6) and AI techniques in the case study. This research employs a predictor selection technique in conjunction with a stack generalization model to improve the accuracy of the downscaling process. After careful examination of predictors, surface temperature, precipitation, and surface air pressure have been used along with annual cycles for training four machine learning models including Multilayer Perceptron (MLP), Support Vector Regression (SVR), Random Forest and Stack Generalization (SG) models for the sake of downscaling. Results showed that MIROC6 model is the best model according to all downscaling methods. In addition, among MLs, stacked generalization model improved the statistical metrics considerably with a Nash-Sutcliffe Efficiency (NSE) of 0.64, Mean Squared Error (MSE) of 1051.3, and Kling-Gupta Efficiency (KGE) of 0.68 for MIROC6 model. Selection of the proper GCM and downscaling method can help decision-makers take proper measures against drought to reduce drought impacts.
How to cite: Mirdarsoltany, A., Rahimi, L., Anderson, C., and Graf, T.: Fusion of Stacked Generalization and Predictor Selection Technique for Downscaling in Drought Monitoring: A Case Study in a Semi-Arid Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7150, https://doi.org/10.5194/egusphere-egu25-7150, 2025.
EGU25-7201 | ECS | Posters virtual | VPS9
Risk Mapping and Adaptation Strategies: Enhancing SWE Predictions with an LSTM Model for Snowmelt-Dependent RegionsTue, 29 Apr, 14:00–15:45 (CEST) | vPA.14
Accurately predicting Snow Water Equivalent (SWE) has become increasingly crucial. It holds particular significance for managing water resources in regions heavily reliant on snowmelt. The present study introduces an integrated Long Short-Term Memory (LSTM) model that incorporates extreme heat events and diverse climate change projections to generate detailed SWE distribution maps and long-term trend analyses. By including lagged SWE observations and climate indicators, the model captures the intricate temporal dynamics of snowfall accumulation and melt processes, thereby improving forecast accuracy and stability.
Previous studies indicate that areas dependent on seasonal snowpack face accelerated snowmelt timing and reduced water availability under rising temperatures. These shifts can exert critical impacts on agricultural irrigation, ecosystem habitats, and water allocation strategies, highlighting the importance of robust forecasting tools for proactive resource management. Furthermore, the development of comprehensive risk maps pinpoints high-risk hotspots where anticipated temperature increases coincide with substantial changes in SWE and snowmelt patterns. These zones are prime candidates for early adaptation measures, including infrastructure upgrades and policy interventions aimed at mitigating potential water shortages.
As global warming persists, this modeling framework provides stakeholders, policymakers, and local communities with valuable insights into emerging water resource risks. The integration of climate change scenarios into the LSTM model underscores the necessity of forward-looking research that can inform both short-term operations and long-term planning. Ultimately, this approach lays the groundwork for crafting sustainable adaptation strategies, preserving agricultural output, protecting ecosystems, and ensuring water security in regions where snowmelt is pivotal to resource availability.
How to cite: Tsang, P. J. and Tsai, W. P.: Risk Mapping and Adaptation Strategies: Enhancing SWE Predictions with an LSTM Model for Snowmelt-Dependent Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7201, https://doi.org/10.5194/egusphere-egu25-7201, 2025.
EGU25-7192 | ECS | Posters virtual | VPS9
Drivers of Soil Moisture Dynamics over Continental United StatesTue, 29 Apr, 14:00–15:45 (CEST) | vPA.27
Soil moisture dynamics play a crucial role in hydrological processes, influencing runoff generation, drought stress, and water management. To better understand the complex drivers of soil moisture dynamics, we present a novel hybrid architecture integrating Vision Transformers (ViT), spatial attention mechanisms, and Long Short-Term Memory (LSTM) networks. This architecture enables investigation of controlling factors across diverse landscapes in the Continental United States (CONUS) by incorporating spatial awareness at two levels: through ViT's ability to capture spatial patterns and through explicit spatial attention between neighboring stations. We leverage a comprehensive set of environmental data sources, including in-situ measurements from the International Soil Moisture Network (ISMN), ERA5 climate reanalysis, USGS elevation products, MODIS land cover, and SoilGrids soil characteristics. Initial results from a one-year training period and three-month testing period (R² = 0.73, 0.72, 0.73 for 24h, 48h, and 72h predictions) reveal important insights about the hierarchical importance of different drivers across prediction windows. Our preliminary analysis shows that static physical properties (particularly slope and soil structure) and hydraulic characteristics maintain high importance across temporal scales, while the influence of dynamic weather features varies with prediction horizon. The model's dual spatial attention mechanisms and temporal components enable discovery of both local and regional controls on soil moisture dynamics. The identified feature importance hierarchies provide initial insights into the spatiotemporal controls on soil moisture dynamics across CONUS. Ongoing work extends the training to the full temporal extent of available data to develop a more comprehensive understanding of these driving factors. This approach advances our fundamental understanding of soil moisture processes at continental scales, with implications for a future tool for land characterization and ecological site classification.
How to cite: Rahman, M., Meles, M., Bradford, S., and Nearing, G.: Drivers of Soil Moisture Dynamics over Continental United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7192, https://doi.org/10.5194/egusphere-egu25-7192, 2025.
