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    Glacial lakes and GLOFs in a warming Himalaya-Karakoram region: current understanding, challenges, and the way forward

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    AbstractThe cryosphere of the Himalaya–Karakoram is rapidly retreating under climate change, leading to widespread formation and expansion of glacial lakes and increasing the risk of glacial lake outburst floods (GLOFs). To date, more than 388 GLOF events have been documented in the region, primarily from moraine- and ice-dammed lakes, with the highest frequency reported in the Karakoram, followed by the Central and Eastern Himalaya. Ice avalanches and extreme precipitation are the most common triggers. Growing exposure of settlements and infrastructure amplifies impacts, highlighting the need to integrate physical science with social vulnerability, preparedness, and adaptation strategies.

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    IntroductionGlobal warming has heightened environmental concerns globally, with the accelerated melting of the Himalayan cryosphere emerging as one of the most critical challenges in the region1. The geologically young and fragile Himalayas have experienced warming rates of 0.15 °C to 0.60 °C per decade, far exceeding the global average of 0.74 °C per century2,3,4. Covering ~40,800 km2 of glaciers (~22,800 km2 in the Himalayas and ~18,000 km2 in the Karakoram), the region is witnessing unprecedented glacial recession due to this warming trend5,6. Projections under various RCPs indicate a temperature rise of 2.5 °C to 5 °C by the century’s end, particularly in high-altitude zones7.This warming has accelerated glacier retreat, driving the formation and expansion of glacial lakes1,8. Under high-emission scenarios, 65% of the ice mass in High Mountain Asia could disappear by 2100, further increasing the number and size of glacial lakes9. Globally, since 1990, the number, area, and volume of glacial lakes have risen by 53%, 51%, and 48%, respectively10. Glaciers terminating in lakes retreat faster than land-terminating glaciers, amplifying glacial lake expansion and glacial recession rates11. Consequently, Glacial Lake Outburst Floods (GLOFs) have emerged as a major hazard, particularly in the Himalayas. These sudden water discharges from glacier-fed lakes, located at the side, front, beneath, or on the surface of glaciers, pose a severe threat to downstream communities12.GLOFs are the most significant glacier-related hazard in terms of direct damage, having caused thousands of fatalities worldwide, including in the Himalayas13,14,15, the Peruvian Andes16,17, and the European Alps18. Despite their sporadic occurrence, GLOFs are high-impact events that result in extensive economic and human loss. In Nepal, a study estimated potential economic damages ranging from 1.9 to 415 million USD for Imja, Tsho Rolpa, and Thulagi glacial lakes under worst-case scenarios, with hydropower project losses reaching 8.98 billion USD for Imja Tsho, 2.4 billion USD for Tsho Rolpa, and 2.2 billion USD for Thulagi19. Similarly, a potential property damage of 159 million USD was estimated for a GLOF in Limu Chimi Lake, rising to 197 million USD in high flood scenarios20.Research on Himalayan glacial lakes has intensified in recent decades, driven by the increasing occurrence of GLOF disasters in the region21. This growing body of work has examined various aspects of the Himalayan cryosphere, with a focus on glaciers and glacial lakes in the context of climate change. In spite of all the progress so far, efforts to determine the precise number, spatio-temporal dynamics, and hazard levels of glacial lakes remain ongoing. Several datasets with varying spatial and temporal resolutions have been developed to map glacial lakes in the Himalayas22,23,24. Given the dynamic nature of these lakes, regular updates to such inventories are crucial for monitoring their formation, expansion, shrinkage, and shifting over time.The Himalaya-Karakoram (H-K) region is recognized as one of the most vulnerable regions to GLOFs8,13,18,25,26. Continued glacier retreat in the region is projected to increase the frequency of GLOFs in the future12. Approximately one million people live within 10 km of a glacial lake, highlighting the region’s vulnerability27. Historical GLOF events in this region, have demonstrated the significant destruction these sporadic but high-impact events can cause28.This study provides a comprehensive synthesis of existing research on glacial lake expansion and the rising GLOF hazard in the H-K region under climate change. We evaluated (a) current GLOF risks, (b) identified key drivers of GLOFs, and (c) assessed past research, future trends, and the mitigation strategies for GLOF disasters. Furthermore, we identified significant research gaps and limitations that continue to challenge GLOF studies in this vulnerable region.Study areaThe H-K region, stretching over 2400 km from northwest to southeast, is one of the youngest mountain systems in the world. Located along the southern edge of the Tibetan Plateau, it covers an approximate area of 0.65 million square kilometers (Fig. 1). Home to over 19,000 glaciers, the region boasts a total glacier area of around 29,500 square kilometers, including some of the largest glaciers in the world, such as the Siachen Glacier. The Himalayas are renowned for their abundant freshwater resources and serve as the source of major river systems like the Indus, Brahmaputra, and Ganges, which sustain the water needs of over a billion people across India, Pakistan, and Bangladesh. Seasonal precipitation patterns in the region are shaped by the Indian monsoon during the summer and the mid-latitude westerlies in the winter. Temperature and precipitation vary significantly across the region, with mean annual temperatures and precipitation levels ranging from 1 °C and 66.1 mm at Shiquanhe station in the west, to 5.9 °C and 657.3 mm at Nyalam station in the center, and 0.4 °C and 442.6 mm at Yadong station in the east29.Fig. 1: Study area map showing the spatial distribution of glacial lakes and documented past GLOF events across the H–K region, compiled from previously published glacial lake and GLOF datasets94.The accompanying graphs illustrate (left) the number of glacial lakes reported in different inventories12,22,23,76 —note that these datasets are not directly comparable due to differences in minimum mapping area thresholds—and (right) the classification of glacial lake types identified in the respective datasets12,22,23.Full size imageData sources and methodsData sourcesThis study used various datasets to study the different aspects of GLOFs in the H-K region. Key sources include the GLOF inventory from the International Centre for Integrated Mountain Development (ICIMOD), which details historical GLOF episodes and their triggers, as well as many glacial lake inventories that map and monitor glacial lakes throughout the Himalayas. The previous inventories10,22,23,24,30 provided valuable data on the spatial distribution, size, and dynamics of glacial lakes. These datasets, produced from remote sensing and GIS analysis, provide insight into the changing cryosphere and its potential hazards, allowing for comparisons across time periods and geographical regions in the Himalayas. We also relied upon the vast literature regarding Climate Change and GLOF, which includes case studies of previous GLOFs, studies modeling the potential threats and prospects of hazardous glacial lakes, and studies discussing the mitigation strategies for the GLOF disaster.MethodsThe current study uses a comprehensive thematic analysis to investigate the various aspects of GLOFs in the H-K region, focusing on themes such as glacial lake changes, past GLOFs, triggers, and mitigating techniques. The methodology employs a systematic review approach, combining qualitative and quantitative analysis to synthesize findings from scholarly literature and previously produced datasets. The thematic analysis commenced with a detailed review of peer-reviewed journal articles, publications, and scientific databases focused on the H-K region. The study utilized the following keywords to identify relevant research: Climate Change in the Himalayas, Glacier Mass Balance, Glacial Lakes of the Himalayas, Glacial Lake Expansion, GLOFs, GLOF Risk, GLOF Triggers and Mitigation Techniques and Risk Evaluations for GLOFs.Databases such as Web of Science, Scopus, Google Scholar, and GeoRef were employed to capture a broad spectrum of studies. Additionally, conference proceedings from the AGU, EGU, and AAG were examined to incorporate key studies and gray literature, ensuring a comprehensive understanding of research across the H-K region. Datasets on glacial lakes and GLOFs from prior studies were re-analyzed and synthesized to provide insights into their current distribution, dynamics, and historical and future trends. The study employed thematic analysis to organize findings into structured themes, following an inductive approach where themes emerged directly from the literature. This enabled an in-depth exploration of GLOF dynamics, historical patterns, and mitigation strategies specific to the Himalayas. The initial step involved coding articles to identify recurring patterns and topics. Characteristics of glacial lakes, GLOF triggers, climate change impacts, and mitigation strategies were systematically coded using qualitative data analysis software. These codes were then grouped into broader themes such as “GLOF triggers,” “glacial lake inventories,” and “mitigation strategies” (Table S1). Themes were refined and cross-referenced with pivotal studies such as systematic reviews and meta-analyses of glacial lake dynamics, GLOF triggers, mitigation techniques, and case studies of historical GLOFs to ensure consistency and uncover research gaps.The quantitative aspect of the review examined the number and spatial distribution of glacial lakes, historical GLOF occurrences, and mitigation strategies at community, regional, and national levels. GIS-based maps and tables were used to visualize data, highlighting geographical and temporal trends as well as GLOF risk hotspots in the H-K region. The analysis revealed significant gaps in GLOF research, including limited social vulnerability assessments for populations near glacial lakes, insufficient studies on the impacts of climate change on GLOF frequency, and inadequate focus on mitigation measures like early warning systems (EWSs) in the region. These findings underscore the need for more integrated and region-specific research to address the growing risks of GLOFs effectively.Climate change in the H-K regionMountainous regions are among the most sensitive and highly vulnerable zones to climate change31,32 given that temperatures are rising faster than the global average and owing to their heterogeneous nature, both in terms of environmental and human factors33. Since the H-K region encompasses developing countries, depending heavily on the Himalayan “water towers” for their human, agricultural and economic needs, the present and future human-induced climate change concerns are particularly high in the H-K region34. The increase in the frequency of climatic extremes, and erratic patterns in precipitation and temperature35,36,37,38 together with the growing vulnerable population in and downstream of the Himalayan mountains39, enhance the multifaceted threats from anthropogenic climate change40,41. These multifaceted threats include floods (riverine, flash, and glacial), droughts, landslides, and debris flows42 affecting human lives and health, agriculture and food security, ecology and biodiversity, livelihood, and economic development43.Temperature trendsThe H-K region is experiencing increasing temperature trends, as widely acknowledged by the research community44,45,46. The Himalayas and the adjoining Tibetan Plateau have warmed more rapidly in recent decades, with a 0.5 °C increase in annual maximum temperature from 1971–2005, compared to 1901–196047. However, the rates of warming vary across the region (Fig. 2 Table S2), with higher rates observed in elevated areas due to the phenomenon of ‘elevation-dependent warming48,49. In contrast, some sections, such as the Southern Himalayas, exhibit gradual warming or slight cooling trends50. In the Western Himalayas, a 0.9 °C rise in temperature was recorded between 1901 and 2003, with most of the warming occurring post-197251. Similarly, the NW Himalayas have witnessed a 1.6 °C rise (0.16 °C per decade) over the past century3. In the upper Indus basin, warming trends range from 0.07 to 0.55 °C per decade38,52. Notably, nocturnal temperatures are rising faster than daytime temperatures across the region53.Fig. 2: Per annum temperature and precipitation change rates across the Himalaya.Annual rates of temperature and precipitation change in a Western Himalaya, b Central Himalaya, c Eastern Himalaya and d Karakoram Himalaya, as reported in previous studies31,35,189,190,191,192,193,194,195,196,197,198,199.Full size imageRegional Climate Model projections indicate continued warming in the Himalayas, with a median increase of 3.3 °C by 2100 under a moderate (RCP 4.5) scenario. However, the Tibetan Plateau and higher-altitude areas are expected to experience the greatest warming, with a projected rise of 3.8 °C54. Seasonal variations in warming are also expected, with the largest increases projected for winter (3.6 °C) and monsoon months (2.7 °C).Precipitation trendsUnlike temperature, there is an absence of spatially consistent long-term trends in the precipitation patterns. This is, in part, due to the impact of local thermodynamic and orographic processes on the synoptic-scale ocean-atmospheric processes55. Seasonal variations further complicate precipitation patterns, with marked differences across regions56. Long-term studies reveal a significant decline in monsoon and annual rainfall in the northwest Indian Himalayas from 1866–200644 and in the western Indian Himalayas from 1960–200657. In the western Indian Himalayas, winter precipitation has generally decreased since 1975, though with regional inconsistencies55,58. Significant declines have been observed in Jammu & Kashmir and Uttarakhand from 1901–200338,56. By contrast, the upper Indus Basin has experienced increasing winter precipitation from 1961–199959. Pre-monsoon precipitation increases have also been noted in the western Indian Himalayas between 1901–200356. The Everest region displays contrasting trends, with the northern areas showing insignificant increases and the southern areas experiencing sharp decreases since the early 1990s60. However, the precipitation simulations from Global Climate Models show greater uncertainty than the temperature simulations.Analysis of some prominent regional studies (Fig. 2, Table S2) highlights consistent warming trends across all sub-regions of the Himalayas, particularly post-1980s, alongside varied precipitation trends. In the Western Himalayas, the mean annual temperature increases range from ⁓0.02 to 0.04 °C/year, with precipitation declines of 3–5.7 mm/year. In the Central Himalayas, the temperatures have increased by ⁓0.04°C/year, while precipitation changes range from +3 to −9.7 mm/year. In the Eastern Himalayas, the temperatures have increased by ⁓0.01 to 0.03 °C/year, with an average precipitation decrease of 3.6 mm/year between 1950 and 2015. In the Karakoram Himalayas, the mean annual temperatures have risen by ⁓0.02 to 0.04 °C/year, though minimum temperatures in the Upper Karakoram show a slight decrease of 0.004 °C/year during 1980–2009. These diverse trends reflect the region’s varied climatic responses to physio-climatic regimes. While temperature trends are relatively homogeneous, precipitation shows more variability, with certain pockets exhibiting increases amidst a general pattern of decline across the H-K region.Climate change impacts on glacier dynamicsThe Himalayas experience extreme temperature fluctuations, variable precipitation, and frequent extreme weather events. Glaciers in the region respond to climate change gradually, with flow rates and lengths adjusting over 10–200 years, while mass balance changes seem to occur more immediately61,62. After meticulously documenting historical glacier changes, it has been determined that the mass balance of Himalayan glaciers has been negative since at least the 1970s5. Since the Little Ice Age, Himalayan glaciers have experienced a total volume loss ranging from 390 km3 to 586 km3 63. During this period, maximum glacier surface lowering occurred several kilometers up the glacier, especially on large valley floor glacier tongues, while minimum surface lowering was observed near the glacier terminus63. Long-term mass balance records from well-studied glaciers across the H-K region reveal a general trend of glacier ice retreat, with notable regional variations (Fig. 3). Numerous studies highlight maximum glacier mass loss in the Western Himalayan region and more stable or less change in the Karakoram region63,64,65,66,67. Both climatic factors (temperature, precipitation, albedo) and non-climatic factors (debris cover, elevation) influence glacier retreat. Black carbon has also been identified as a key factor accelerating glacier melt5,68.Fig. 3Specific mass balance of selected glaciers in different parts of the H-K region as estimated through long-term glaciological mass balance records derived from previous studies (except for the Siachen Glacier, whose mass change record is based on hydrological method)200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215.Full size imageGlacial lakes in the H-K region: distribution and dynamicsResearch on glacial lakes has gained significant attention over the past few decades, largely due to the hazards posed by GLOFs28,69. The use of satellite imagery for monitoring the Himalayan cryosphere began in the 1990s with the advent of remotely sensed data, particularly from the Landsat series70,71. Numerous inventories have since been developed to map and demarcate glacial lakes in the H-K region, with datasets prepared at regional, national, and global scales using remote sensing techniques. While glacial lake datasets now cover nearly all regions of the world, the challenging terrain and harsh climate of the Himalayas remain significant barriers to field-based glacier and glacial lake research. Consequently, most studies in this region rely heavily on remotely-sensed data, which often suffer from limitations such as coarse spatial and temporal resolution, introducing ambiguities in understanding ground-level processes. Furthermore, discrepancies in glacial lake definitions and varying size thresholds have resulted in significant inconsistencies among independently created datasets for the H-K region and the broader Third Pole region22,23,24,72. Despite these challenges, remote sensing continues to be an indispensable tool for glacial lake research, offering valuable insights into this dynamic and critical aspect of the Himalayan cryosphere. Some major glacial lake datasets are discussed below.Review of previous glacial lake inventoriesSeveral inventories have been developed for the H-K region and its associated mountain systems, each differing in scope, methodology, and mapping thresholds (Table S3). E.g., Maharjan et al.73 compiled a comprehensive inventory of glacial lakes in the five major river basins of the Hindu Kush Himalaya using Landsat ETM+ imagery (2005–2007), identifying 25,614 lakes (A ≥ 0.003 km2) with a total area of 1444 km2. Li and Sheng (2012)74 mapped 11,056 and 11,289 lakes (A ≥ 0.0045 km2) in the H-K region for 1990 and 2009, reporting a total area of 2890.38 km2. Fujita et al.75 identified 2276 lakes (233.4 km2) in 2010. Chen et al.22 mapped 8215 glacial lakes (A ≥ 0.008 km2) in High Mountain Asia (HMA) for 2017 with a total area of 832.19 km2, while Wang et al.23 documented 27,205 lakes (1806.5 km2) in 1990 and 30,121 lakes (2080.1 km2) in 2018 using a 0.005 km2 threshold. Zhang et al.12 identified 9208 lakes (779.01 km2) in the H-K region for 2020 (A ≥ 0.003 km2). Zheng et al.76 produced the most extensive glacial lake inventory, mapping 63,727 lakes (2122.01 km2) across the Third Pole using Landsat 8 OLI imagery (A ≥ 0.0009 km2). Local inventories have also been prepared for specific administrative units77,78,79,80 and river catchments81,82,83,84. Regional inventories offer higher accuracy and detail, making them more suitable for GLOF mitigation and regional planning.Despite their value, existing Himalayan glacial lake inventories face several limitations that constrain their direct comparability and application in risk assessments. The lack of standardized mapping thresholds, inconsistent lake definitions (e.g., inclusion or exclusion of supraglacial and ice-contact lakes), and varying classification schemes introduce significant uncertainty when comparing inventories across regions or time periods. Furthermore, most inventories are static snapshots in time and do not adequately capture seasonal variability, rapid lake evolution, or short-lived lakes that may still pose substantial GLOF hazards. Limited integration of lake characteristics critical for hazard assessment—such as dam composition, freeboard, lake volume, and upstream instability—also restricts their usefulness for quantitative risk analyses.Spatial distribution of glacial lakes in the H-K regionInvestigations based on previously published glacial lake inventories reveal pronounced regional patterns in glacial lake distribution across the H–K region (Fig. 4). We analyzed five inventories using GIS to derive the number and total area of glacial lakes reported in different sectors of our study area. While these inventories depict clear spatial patterns, they differ substantially in mapping years and minimum lake area thresholds and therefore are not meant for direct comparisons.Fig. 4: Spatial distribution of glacial lake number and total area across different sectors of the H–K region derived from different existing glacial lake inventories12,22,23,76,216.Although all datasets were clipped to the H–K region, they differ in mapping year and minimum lake area thresholds and therefore are not directly comparable. The year of mapping for each dataset is indicated in brackets in the legends of the embedded charts, while the corresponding mapping thresholds are provided in Supplementary Table S3.Full size imageEarlier inventories consistently identified the Central Himalayas as hosting the highest number of glacial lakes and the largest cumulative lake area, followed by the Eastern and Western Himalayas. In contrast, the most recent inventory by Zheng et al.76, updated to 2023 and employing a much smaller mapping threshold (A > 900 m²), indicates that the Eastern Himalayas now contain the highest number of glacial lakes and the largest total lake area, followed by the Central and Western regions (Fig. 4). The Karakoram region is reported to have the smallest number and area of glacial lakes among the sub-regions. This may reflect underestimation, as numerous small, newly formed moraine-dammed, ice-dammed, and supraglacial lakes in this region, because of the delayed response of this region to climate warming, are often excluded from inventories. With ongoing warming patterns, glacial lake numbers and area in this region are likely to increase significantly in the coming decades. This underscores the importance of regularly updating glacial lake inventories to capture the evolving nature of these dynamic systems.Dynamics of glacial lakesNumerous studies have examined the evolution and spatiotemporal dynamics of glacial lakes in the H-K region, utilizing diverse datasets, lake area thresholds, and mapping approaches, leading to variations in their findings29,74,85,86,87,88. This study examines three widely used glacial lake inventories—Wang et al.23, Chen et al.22, and Zhang et al.12—to assess spatiotemporal changes in glacial lake number and area across the H–K region. All datasets were processed in a GIS environment and clipped to the study area to ensure consistent spatial coverage and facilitate robust comparisons. Despite methodological differences, these inventories generally report an increase in glacial lake numbers and area (Fig. 5, Table S4), which can be linked to a warming climate as reported by earlier studies.Fig. 5: Sector-wise glacial lake changes in the H-K region (derived from three prominent glacial lake inventories mentioned in the figure12,22,23.The graphs in each row (left to right) correspond to Western, Central, Eastern, and Karakoram Himalayas, respectively.Full size imageWang et al.23, using a mapping threshold of A > 0.005 km2, documented an increase of 987 glacial lakes across the H–K region over 28 years, from 7077 lakes in 1990 to 8064 in 2018. This corresponds to an average annual increase of 0.67% in total glacial lake area, with the Karakoram showing the highest annual area change (1.31%), followed by the Central Himalayas (0.89%). In contrast, Chen et al.22, applying a higher mapping threshold of 0.008 km2, reported an increase of 1362 lakes in the same area over a much shorter period of 9 years, from 4136 lakes in 2008 to 5498 in 2017, accompanied by a 1.30% annual increase in total lake area. Within this dataset, the Central Himalayas exhibited the highest areal expansion annually (1.59%), followed by the Western Himalayas (1.41%). The third inventory by Zhang et al.12, used a smaller mapping threshold of 0.003 km2 and observed an increase of 999 lakes in 30 years, from 4297 in 1990 to 5296 in 2020, reflecting a 0.6% annual growth in total lake area. This dataset attributed the greatest area increase to the Central Himalayas (0.76%), followed by the Eastern Himalayas (0.66%), but uniquely reported a decline in the Karakoram region, with 52 fewer lakes and a 0.49% annual reduction in total lake area.These contrasting rates and magnitudes of change highlight that glacial lake evolution varies across time periods and regions, and is also strongly influenced by differences in datasets, mapping thresholds, and methodological approaches. Our analysis shows that glacial lake behavior varies depending on the time period examined and the lake size classes considered. For instance, Zhang et al.12 report a decline in the number of glacial lakes in the Karakoram, whereas other studies document an increase. This apparent contradiction largely stems from the higher mapping threshold adopted in that inventory, which suggests that actually the smaller lakes, which are excluded in that inventory, are increasing as suggested by other inventories, while the larger lakes are probably decreasing in number. Meaningful intercomparison, therefore, requires consistency in how glacial lakes are defined, the minimum mapping area thresholds applied, and the temporal frameworks used. Despite such differences, most inventories indicate an overall increase in glacial lake numbers and areas across the H–K region, underscoring the strong influence of climate change on the regional cryosphere. However, establishing a more unified and standardized methodological framework would substantially improve the accuracy and comparability of glacial lake datasets, enabling more reliable assessments of glacial lake dynamics and associated hazards. Such a framework should explicitly incorporate observational and model uncertainty, and furnish values with confidence intervals. This can be achieved by developing standardized and transparent mapping protocols, harmonized minimum area thresholds, and consistent lake typologies across studies in the region. Furthermore, coupling inventories with field-based lake volume estimates, dam stability indicators, and process-based hazard information is essential to move beyond descriptive mapping toward inventories that directly support GLOF forecasting, early warning, and risk-informed adaptation planning in the Himalayan region.Recent analytical studies1,21,89 provide strong quantitative evidence that atmospheric warming is the dominant driver of glacier retreat and subsequent glacial lake expansion in High Mountain Asia, including the Himalaya–Karakoram region. Geodetic mass-balance analyses show a marked acceleration of glacier mass loss since the early 2000s, closely correlated with rising air temperatures6. Global and regional trend analyses further demonstrate statistically significant increases in glacial lake number, area, and volume since 1990, with lake expansion strongly linked to enhanced glacier thinning and terminus retreat under warming conditions10,11. Process-based climate–glacier modeling confirms that temperature increases exert a far stronger control on glacier mass balance and lake formation than precipitation variability in the Himalayas9,90. While trends in reported GLOF frequency are influenced by historical under-reporting91, quantitative assessments indicate that expanding lake volumes, unstable dam types, and increasing downstream exposure are collectively driving a rise in GLOF hazard and potential impacts27. Together, these analytical findings substantiate the causal link between warming climate, glacial lake expansion, and escalating GLOF risk, while highlighting the need for future regionally consistent statistical analyses that explicitly integrate climatic drivers, lake evolution, and event occurrence.Historical GLOF events in the regionThe H-K region has witnessed numerous GLOF events in the past. These events have been documented by different previous studies24,92,93,94. Lutzow et al.95 report 516 historic GLOF events in the High Mountain Asia region, out of which 293 events could be traced back to the H-K region in our analysis. Of these 293 events whose exact locations could be identified, 8 were reported from the Western Himalayas, 49 from the Central Himalayas, 35 from the Eastern Himalayas, and 201 events from the Karakorum region. Nie et al.92 compiled an inventory of 62 previous GLOF events. Eleven of these GLOF incidents were classified as unpersuadable, and the other fifty-one as persuadable. Most of the convincing incidents were reported in the eastern (26) and central (25) Himalayas, within the borders of China (28), Nepal (8), Bhutan (13), and India (2). These occurrences varied in elevation from 3669 meters to 5527 meters with an average elevation of 4831 meters.Out of all the previous GLOF inventories, the most comprehensive inventory for the H-K region is the one compiled by Shrestha et al.94 at the ICIMOD. According to our analysis of this inventory, 388 GLOF events have previously occurred in the H-K region, from which 21 have been reported in the Western Himalayas, 99 from the Central Himalayas, 72 from the Eastern Himalayas, and 196 events from the Karakoram region. The highest number of GLOFs were reported from the moraine-dammed lakes (163) followed by the ice-dammed lakes (144) and the supraglacial lakes (50) (Figs. 1, 6). The elevations at which these events were witnessed range between 2562 and 5982. The period between 1900 and 2000 witnessed the largest number of GLOFs (135) in the H-K region followed by the period after 2000 in which 85 GLOF events have been already recorded till date. However, there may be some bias in these figures owing to inadequate reporting and documentation of events in earlier times96. Amongst the Himalayan countries, Pakistan has witnessed the largest number of GLOF events (131) followed by China (123), India (59), and Nepal (54).Fig. 6: Distributional Characteristics of historical GLOF events in the H-K region.The figure depicts (A) Elevationwise distribution of historical GLOFs in the region (B) Country-wise distribution of historical GLOFs in the region (C) Contribution of different lake types to past GLOFs in the region (D) GLOF frequency in different periods in the past (E) GLOF frequency in different months in the past. The figures have been prepared using the previous GLOF databases94,95.Full size imageThe majority of the GLOF events, the dates of which are known, have been reported during the months June, July, and August (Fig. 6). This can be attributed to the fact that these months witness the highest temperatures, leading to peak glacier melting, in addition to the arrival of the rain-laden monsoon winds in the region. Extreme temperature and precipitation events in these months act as major triggers of GLOFs in this region97. There are several glaciers, especially in the Karakoram region, known for recurrent GLOF events. Some of these glaciers have witnessed dozens of GLOF events till now (Fig. 7). The fact that the largest number of GLOF events have been recorded in the Karakoram region despite having the lowest number of glacial lakes is explainable through these ice-dammed lakes that witness recurring GLOF events. E.g. Khurdopin glacier has already witnessed 37 events while Kyagar glacier has witnessed 34 events of GLOF. Other glaciers such as Shisper, Hasanabad, Gulkhin, Batura, etc., have also witnessed multiple GLOF events in the past. These recurrent events are mostly witnessed from ice-dammed and supraglacial lakes that develop on and around these large-sized glaciers. Ice-dammed lakes are capable of causing recurrent GLOFs as the dynamic glacier ice reforms the ice dams once the hydrostatic pressure has been released either through subglacial tunneling98 or dam floatation99.Fig. 7: Glaciers and river basins that have witnessed multiple past GLOF events in the H-K region.A River basins that have witnessed 5 or more GLOF events B Glaciers that have witnessed multiple (5 or more) GLOF events. The data have been compiled from an existing GLOF database94.Full size imageKey triggers of GLOFs and failure mechanismsGLOFs are intricate phenomena resulting from glacier recession and the accumulation of meltwater in depressions, often behind moraine or ice dams. However, the immediate outburst is often triggered by local and short-term factors, with the lake’s intrinsic stability determining its susceptibility and the magnitude of the trigger required100,101,102. Moraine and ice dams, being inherently less stable than bedrock dams, account for the majority of GLOFs in the H-K region. Ice cores within moraine dams, upon melting, create conduits for water flow, which can lead to piping failures. In ice-dammed lakes, subglacial tunneling dominates as the outburst mechanism, a phenomenon frequently observed in regions like Gilgit-Baltistan, where surging glaciers contribute to recurrent GLOFs. Lake geometry also plays a crucial role, with longitudinal or bullet-shaped lakes exerting greater hydrostatic pressure on dams, increasing the likelihood of failure103.Identifying precise GLOF triggers is challenging due to the loss of direct evidence, particularly in overtopping events involving bedrock-dammed or supraglacial lakes, where geomorphological markers or local accounts are often used as proxies96,104. While 84% of triggers in past GLOF events remain unknown, extreme weather events, such as intense precipitation or extreme temperatures, and mass movements, including landslides or ice avalanches, are the most common known triggers, according to the dataset compiled by Shrestha et al.94. Landslides can generate flood waves, causing dam outburst in moraine-dammed lakes or overtopping failure as seen in stable bedrock-dammed lakes105,106. Although underreported, these events could intensify in the future as permafrost degradation increases mass movement activity in deglaciating valleys, as suggested by recent studies105,107,108,109. Ice avalanches from glaciers, which have historically triggered numerous GLOFs28,110, are expected to remain a significant hazard. Glaciers in direct contact with lakes not only recede faster12 but also experience snout calving, increasing the likelihood of ice falls into lakes and subsequent GLOFs. Extreme temperature events exacerbate GLOF risk by inducing subglacial tunneling111, ice core thawing112, slope destabilization through permafrost melting109, and increasing the hydrostatic pressure on dams by rapid meltwater accumulation113. While earthquakes are often considered potential triggers, their role in past GLOFs appears limited, despite their ability to cause mass movements in lake surroundings114, which may exacerbate the GLOF risk in the future.Advancements and gaps in GLOF researchGlacial lake research in the H-K region has seen a boom primarily due to the rise of the Glacial Lake Outburst Flood disaster. The catastrophic GLOF events originating from glacial lakes, such as the Dig Tsho GLOF in Nepal or the Kedarnath GLOF in Uttarakhand, that killed more than 6000 people besides causing huge infrastructural damage in June 2013, caused the research community a serious concern regarding the climate-driven glacial lake expansion in the H-K region. Numerous studies have surfaced from then onwards that have attempted to understand this phenomenon of glacial lake expansion and the rising threat of the GLOF disaster. Such studies have been carried out in almost all parts of the H-K region on varying geographical scales. Here we will summarize some of the most prominent studies related to the GLOF hazard in the H-K region. These studies can be grouped under four major themes as follows:Regional GLOF hazard assessments and risk mappingExtensive research has focused on Potentially Dangerous Glacial Lakes (PDGLs) in the H-K region, either holistically13,30,75,115,116,117 or within subregions such as the Western118,119,120, Central4,121,122,123, and Eastern Himalayas124,125 as well as the Indian Himalayas26,126 and the Karakoram84,118. Common assessment criteria include lake area, dam structure, slope gradients, and mass movement susceptibility. Many studies adopt comprehensive risk assessments, integrating factors such as lake area with vulnerability and downstream exposure, both globally27 and regionally12,15. The encroachment of human settlements into fragile mountain zones further amplifies GLOF risk, establishing the Himalayas as a hotspot for GLOF hazards27. Despite substantial progress, most of such assessments are based on static, snapshot indicators derived from medium-resolution optical imagery, inadequately representing dynamic processes such as rapid lake expansion, glacier–lake interactions, dam degradation, permafrost thaw, and transient triggers18,127. Moreover, field validation is scarce due to extreme terrain12,15. Socio-economic exposure and vulnerability are often simplified or outdated, and uncertainty is rarely quantified27. Future improvements require standardized methodological frameworks, integration of multi-sensor remote sensing, and field-based assessments to analyze the hazard level of the lakes and the regional risk associated with them.GLOF risk assessments for individual glacial lakesStudies estimating the GLOF risk of specific glacial lakes are prevalent in the H-K region. These investigations utilize geospatial techniques and modeling approaches to assess the potential hazards posed by lakes deemed potentially dangerous. They often track lake evolution and surrounding conditions over time128,129,130,131,132 or simulate future GLOF scenarios using hydrodynamic models like HEC-RAS and MIKE 11121,133,134,135,136,137,138. Others analyze past GLOF events to understand local risk dynamics139,140,141,142,143.These studies while needed for identifying local risk scenarios and informing mitigation strategies, such as EWSs for hazardous lakes, are often constrained by limited field data, including bathymetric data, dam composition, and downstream channel conditions144. Hydrodynamic simulations frequently use assumed breach geometries and uniform roughness values, introducing substantial uncertainty in flood magnitude and timing estimates. Moreover, most studies focus on hazard characterization, with limited integration of downstream exposure, vulnerability, and cascading impacts. Focus should be on integration of multi-sensor remote sensing with bathymetric and geotechnical field measurements, uncertainty quantification in breach modeling, and closer coupling of hazard simulations with socio-economic risk assessments to support more robust and actionable GLOF risk management. While some research has addressed these socio-environmental dynamics145,146, significant gaps remain in understanding the social aspects of GLOF risk in the Himalayas104.Historical GLOF Events and future trend predictionsRegional studies on the historical accounts of GLOF events24,79,80,81,95,147,148 and analyses of GLOF occurrence patterns14,21,78 also form a significant part of Himalayan GLOF research. The most comprehensive GLOF inventory to date, prepared by Shrestha et al.94 for ICIMOD, identifies 388 GLOF events in the region. While studies show contrasting GLOF trends, with Veh et al.91 reporting no significant trend in the Himalayas and Zhang et al.12 reporting an increase in the frequency of GLOFs globally, biases in historical reporting are evident. Research indicates that 2–4 out of 5 GLOFs in the early to mid-20th century likely went unnoticed or unreported96,149. Nonetheless, these studies suggest an increase in GLOF frequency in the coming decades. Studies have also explored the future glacial lake development through modeling approaches150,151,152. Furian et al.152 projected the formation of 2700 new glacial lakes (A > 0.1 km2) in the Third Pole region by the end of the 21st century, primarily in over-deepened glacial valleys likely to retain meltwater. Such forward-looking studies are crucial for anticipating and managing future GLOF risks effectively.Studies on mitigation strategies for GLOF risk reductionNumerous studies have examined GLOF mitigation and management strategies in various parts of the H-K region (Hanisch et al., Wang et al.)129,153,154,155,156,157,158. These studies highlight that GLOF risks can be effectively managed or at least reduced to a large extent with the timely implementation of appropriate strategies, encompassing both structural and non-structural measures. Notable examples can be given from mitigation approaches adopted in Nepal on lakes such as Tsho Rolpa and Imja128. Research also emphasizes the need for comprehensive strategies that coordinate actions across community sectors to mitigate GLOF impacts. Socio-economic vulnerability assessments of downstream populations, and identification of marginalized mountain communities are integral to GLOF risk mitigation159,160,161. In the context of high population exposure to GLOFs in the region, non-structural and community-based measures, which are less technically and economically demanding, are pivotal. These approaches not only address social vulnerabilities but also offer sustainable and inclusive solutions for disaster mitigation in the developing Himalayan region.GLOF risk management strategies in the H-K regionThe H-K region faces a serious threat of GLOF in the upcoming decades which underpins the need to devise robust mitigation and management strategies145,149. Glacial Lake Outburst Flood risk management strategies involve a combination of different elements of disaster management such as EWSs, structural measures, and community preparedness158,162. An integrated framework of GLOF risk management strategies in the Himalaya–Karakoram region, encompassing EWSs, structural and non-structural measures, policy integration, and international collaboration, is illustrated in Fig. 8.Fig. 8: Conceptual framework illustrating integrated GLOF risk management strategies in the Himalaya–Karakoram (H-K) region.The diagram highlights five complementary components: (1) Early Warning Systems based on satellite monitoring, automatic weather stations, and real-time surveillance for rapid alert dissemination; (2) Structural measures including spillways, controlled drainage, siphoning, and moraine-dam strengthening to reduce lake-outburst hazard; (3) Non-structural measures and community preparedness emphasizing education, evacuation planning, simulation drills, and emergency response capacity; (4) International collaboration through data sharing, joint research, and regional cooperation for transboundary GLOF risk reduction; and (5) Policy and planning integration incorporating GLOF risk assessment, land-use zoning, and resilient infrastructure planning.Full size imageEarly warning systemsEWSs play a pivotal role in detecting potential GLOF events and issuing timely alerts to vulnerable communities158. Comprehensive EWSs leverage high-resolution satellite imagery for monitoring hazardous glacial lakes, along with automatic weather stations and hydrological devices to track extreme weather conditions and water level fluctuations163. The integration of robust communication networks ensures rapid dissemination of warnings to authorities and local residents. Examples of EWSs in the H-K region include manual systems, such as those implemented at Raphstreng Tsho and Luggye Tsho in Bhutan, and automatic systems like the ICIMOD-designed EWS at Imja Glacial Lake in Nepal’s Dudh-Koshi basin164. Despite notable progress in Nepal, other nations, including Pakistan, Afghanistan, and India, require significant advancements to establish effective EWSs, highlighting a critical gap in regional preparedness. Mobile phones have proven effective in saving lives during several GLOF events and represent one of the most practical and affordable tools for disseminating early warnings in vulnerable communities, at least during daytime165. This potential can be realized by strengthening and expanding mobile network coverage in high-mountain settlements at risk.Structural measuresStructural measures aim to reduce GLOF risks by enhancing the stability and managing the water levels of glacial lakes166. These measures often include constructing spillways, engineered channels designed to release water in a controlled manner to prevent overflow167. For instance, artificial spillways and drainage systems have been implemented in Bhutan to mitigate GLOF risks, notably at Thorthormi Tsho, where a spillway lowered the lake level by 5 m, releasing ~14 million m³ of water over 4 years13. Controlled breaching of moraine dams is another effective method for reducing lake levels safely128. Siphoning has also been widely used, as demonstrated at Lake Palcacocha in the Peruvian Andes and Tsho Rolpa Lake in Nepal, to lower water levels to reduce the immediate risk129,155. Dam reinforcement, which involves strengthening natural moraine dams using engineering techniques, is another key structural intervention. For example, reinforcing the outlet channel and dam of Jialong Co in the central Himalayas significantly reduced the likelihood of outbursts caused by ice avalanches or landslides166. However, the vulnerability of glacial lakes is dynamic, influenced by changes in their surroundings over time168,169. Thus, structural measures must be tailored to specific lake conditions at specific times. While these measures do not fully eliminate the risk of GLOFs, they can reduce the severity of potential disasters or delay them to buy time for comprehensive risk management programs170. However, a major limitation of structural interventions in the H-K region lies in their high financial and technical requirements, which are often beyond the capacities of developing countries and especially the local communities directly threatened by such floods. These constraints frequently undermine the effectiveness of such measures, as demonstrated by cases in Nepal where inadequate funding impeded glacial lake level–lowering projects, thereby reducing their overall effectiveness171.Non-structural measures and community preparednessIn addition to hazard mitigation, vulnerability mitigation forms a critical pillar of risk management in the H-K region159,172,173. Strengthening the capacity of local communities to respond effectively to disasters through education and training is particularly vital in developing nations like India, Pakistan, Nepal, and Bhutan (Heath et al.)174. Structural measures often face financial and technical challenges in these regions, but community-based non-structural approaches offer a feasible pathway to enhance resilience against cryospheric disasters such as GLOFs175,176,177. Community-based approaches emphasize capacity building by educating vulnerable communities about GLOF risks, warning signs, and safety measures. For example, Bhutan’s Department of Disaster Management has developed integrated disaster management plans, enabling communities to identify hazards and vulnerabilities effectively178. In Nepal a 1-year Local Adaptation Plan of Action facilitated by HiMAP (High Mountains Adaptation Partnership) in the Khumbu region to support community-led climate adaptation is another example. Key components of community approach include well-formulated evacuation plans, regular simulation drills, and the provision of emergency supplies, first aid kits, and communication tools during GLOF events. In Nepal and Bhutan, local communities have actively participated in evacuation planning and drills as part of the Community-Based Flood and Glacial Lake Outburst Risk Reduction Project, significantly improving disaster preparedness179.However, cognitive biases and heuristics can undermine community preparedness efforts. For instance, at Tsho Rolpa Lake, repeated evacuations without subsequent events led to diminished trust in mitigation measures and monitoring institutions173. This highlights a key limitation of community-centric programs, where unmet expectations can erode confidence and cooperation. Sustained engagement, transparent communication, and continuous capacity-building efforts are essential to ensuring long-term community participation in risk reduction initiatives.Policy and planning integrationThere is a need to incorporate GLOF risk assessments into regional and national development plans to ensure resilient infrastructure and land use180. Comprehensive risk assessment studies are important to identify vulnerable areas and assess potential GLOF impacts in the future. However, risk assessment should be augmented with proper land use planning with zoning regulations to avoid construction in high-risk zones181. Environmental management in ecologically fragile mountain ecosystems should be given more attention to developing sustainable practices to reduce contributing factors such as deforestation and improper land use patterns in high-risk zones182. To encourage the building of robust infrastructure in glacial zones, the National Disaster Management Authority of India has included GLOF risk assessments in its national disaster management framework. More such efforts need to be incorporated into national and state disaster management plans.International collaborationsCross-border cooperation and knowledge-sharing among the countries of the H-K region that exhibit almost similar glacial environments are critical in enhancing the GLOF risk management practices183,184,185. This includes the sharing and exchange of hydro-meteorological data among countries and collaborative research projects to study the glacial lake dynamics in the region186,187. Capacity-building projects and workshops among the Himalayan countries, such as the HiMAP project in Nepal, which emphasized the need for interdisciplinary collaboration in building climate adaptation and resilience188, can also prove beneficial in developing a region-wide disaster resilience framework. Such initiatives should be well-funded by National and International organizations dealing with the cryospheric hazards and the development of the mountainous areas. A pertinent example of such collaboration is through the ICIMOD, where member countries share data and expertise to develop regional GLOF risk reduction strategies.ConclusionThis study provides a comprehensive review of past glacial lake research in the H-K region, with a specific focus on the GLOF hazard. By examining previous studies and analyzing various datasets, the research highlights trends in glacial lake dynamics under changing climatic conditions. We assessed trends in glacial lake dynamics across different Himalayan zones using datasets compiled previously by researchers. Our findings highlight a steady increase in both the number and size of glacial lakes across the region, driven by warming temperatures. Coupled with expanding human populations in fragile mountainous areas, this trend makes the Himalayas a hotspot for heightened GLOF risk. The region has experienced 388 GLOF events, primarily from moraine and ice-dammed lakes. The Central and Eastern Himalayas contain the largest number of glacial lakes, but the highest GLOF risk is found in the Eastern Himalayas, where large, potentially dangerous lakes exist. Climatic trends suggest a general rise in GLOF hazards, with high-risk zones extending from the Eastern to the Western Himalayas, including the Karakoram, which has exhibited a delayed response to climate change compared to other areas.While research in the region has focused on identifying PDGLs, assessing GLOF risks, reconstructing past events, projecting future lake growth, and studying trends in GLOF frequency, social aspects of GLOF risk have been less explored. Flood risk management solutions should include EWSs using hydrological monitoring and remote sensing, structural measures like spillways and dam reinforcement, and community preparedness through building evacuation plans and robust public communication networks. Effective GLOF mitigation also requires integrating risk assessments into national planning and fostering international cooperation. This review highlights key gaps in glacial lake research in the Himalayas, contributing to a better understanding of the GLOF hazard and its mitigation in the region.

    Data availability

    No datasets were generated or analyzed during the current study.
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    Omar Wani.Ethics declarations

    Competing interests
    The authors declare no competing interests.

    Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary informationRights and permissions
    Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
    Reprints and permissionsAbout this articleCite this articleRather, A.F., Ahmed, R., Shamim, T. et al. Glacial lakes and GLOFs in a warming Himalaya-Karakoram region: current understanding, challenges, and the way forward.
    npj Nat. Hazards 3, 7 (2026). https://doi.org/10.1038/s44304-026-00168-wDownload citationReceived: 02 October 2025Accepted: 12 January 2026Published: 22 January 2026Version of record: 22 January 2026DOI: https://doi.org/10.1038/s44304-026-00168-wShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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      Assessment of hydrological drought vulnerability using SPI and SWI indices in Tiruttani block Tamilnadu

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      AbstractDrought is a natural phenomenon that manifests in multiple forms, each characterized by distinct indicators and impacts on both environmental systems and human society. Recent meteorological observations have highlighted a significant decline in rainfall, which has directly contributed to reduced groundwater storage in aquifers. This study presents an integrated approach to assess hydrological drought vulnerability by combining 30 years of rainfall data (1995–2024) from three rain-gauge stations and Climate Hazards Group InfraRed Precipitation with Station gridded precipitation, along with groundwater level time series and PET data for SPEI computation. The research outlines the methodology for deriving SPI, SPEI, and SWI, integrating them into a single hydrological drought severity index (HDSI), generating spatial and temporal drought maps in GIS, and developing a hydrological drought vulnerability map through weighted overlay analysis incorporating soil, aquifer depth, land use, slope, and HDSI for the Tiruttani block in the Thiruvallur district of Tamil Nadu, India. DrinC and ArcGIS Pro 3.1 software’s were utilized for the analyses. Rainfall drought years were identified using the 3-month, 6-month and 12-month standardized precipitation index (SPI) and SPEI, while groundwater drought years were determined using the standardized water level index (SWI). SPI results showed 14 years Near Normal droughts, 7 years moderately wet, and 7 years drought (1 severe). Also distinct drought clustering across two major temporal phases 1995–2007 and 2008–2024 corresponding to shifts in rainfall distribution and hydroclimatic behavior over the semi-arid region. SWI results indicated moderate drought was observed in the years 1995, 2000, 2001, 2007 and extreme droughts in the years 2005, 2006, 2009, 2010, 2012, 2013, and 2014. The integrated SPI, SPEI, and SWI values were ranked and used in a weighted overlay analysis with soil, aquifer depth, land use, slope, and HDSI to evaluate the spatial variability of hydrological drought vulnerability across the study area. The findings revealed that 72% of the area falls under severe to extreme hydrological drought conditions, 12% under moderate drought, 3.5% under mild drought, while only 0.05% remains relatively drought-free. The study concludes that the region is facing escalating hydrological stress driven by inadequate rainfall conservation and groundwater mismanagement. Improved groundwater recharge practices and effective rainwater conservation strategies are essential to prevent further intensification of drought conditions.

      Data availability

      The datasets generated and analysed during the study are available from the corresponding author upon reasonable request.
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      Deepa Krishnan.Ethics declarations

      Competing interests
      The authors declare no competing interests.

      Ethics approval
      In this study, animal experiments were not applicable.

      Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
      Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
      Reprints and permissionsAbout this articleCite this articleKrishnan, D., Partheeban, P., Ramadoss, M. et al. Assessment of hydrological drought vulnerability using SPI and SWI indices in Tiruttani block Tamilnadu.
      Sci Rep (2026). https://doi.org/10.1038/s41598-026-36100-5Download citationReceived: 10 September 2025Accepted: 09 January 2026Published: 20 January 2026DOI: https://doi.org/10.1038/s41598-026-36100-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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      KeywordsPrecipitationGISSPISPEISWIHDSIHydrological drought More

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      Surface water quality prediction via an MLA-Mamba hybrid neural network with GRPO optimization

      by

      AbstractSurface water quality forecasting is crucial for pollution early warning and sustainable water resource management. However, accurate prediction of key water quality indicators remains challenging due to the highly nonlinear spatio-temporal dynamics and complex inter-variable relationships. Traditional statistical models and conventional machine learning approaches often struggle to effectively capture these couplings, leading to limited predictive performance. In this study, we propose a novel hybrid deep learning framework, termed MLA-Mamba, which integrates an improved Mamba-based sequence modeling network with a Multi-Head Local Attention (MLA) mechanism, optimized through a Gradient Reparameterization Optimization (GRPO) strategy. The Mamba module is designed to extract long-range temporal dependencies from water quality time series via a state-space modeling paradigm, while the MLA mechanism captures localized spatial correlations among multiple monitoring stations. To the best of our knowledge, this study represents one of the first explorations of applying Gradient Reparameterization Optimization (GRPO) to water quality prediction tasks. Furthermore, a multi-task learning scheme is incorporated to jointly predict multiple key indicators, including permanganate index (CODMn), ammonia nitrogen (NH3–N), total phosphorus (TP), and total nitrogen (TN), thereby exploiting inter-variable dependencies to enhance overall forecasting accuracy. The proposed GRPO strategy dynamically adjusts learning rates during training to accelerate convergence and improve model stability. Experimental evaluations on two real-world surface water datasets demonstrate that the proposed MLA-Mamba model achieves consistent performance improvements over the evaluated baseline methods across multiple error metrics. In addition, predictive uncertainty is quantified via Monte Carlo dropout, enabling the estimation of confidence intervals to support risk-aware water quality assessment. These results highlight the effectiveness of integrating advanced sequence modeling, attention-driven spatial feature extraction, and adaptive optimization for robust environmental time series forecasting.

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      Data availability

      The datasets used and analysed during the current study are not publicly available, but are available from the corresponding author upon reasonable request. Interested researchers may contact Dr. Wang at [email protected].
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      Download referencesFundingThis work is supported by the Guizhou Provincial Science and Technology Support Program (2023): “Research on Water Quality Prediction and Early Warning Technology Integrating AI in Big Data Environment” (Grant No. Qiankehe Support [2023] General 108).Author informationAuthors and AffiliationsSchool of Computer Science and Technology, Guizhou University, Guiyang, 550025, ChinaRonghao Wei & Hang ChenGuizhou Ecological Environment Monitoring Center, Guiyang, 550081, ChinaHaihe WangSchool of Mathematical Sciences, Guizhou Normal University, Guiyang, 550014, ChinaHaihe WangAuthorsRonghao WeiView author publicationsSearch author on:PubMed Google ScholarHang ChenView author publicationsSearch author on:PubMed Google ScholarHaihe WangView author publicationsSearch author on:PubMed Google ScholarContributionsRonghao Wei: Methodology, Writing–original draft, Software, Formal analysis, Project administration, Resources. Hang Chen: Methodology, Formal analysis, Project administration, Resources. Haihe Wang: Supervision, Methodology, Resources, Funding acquisition, Writing–review & editing.Corresponding authorCorrespondence to
      Haihe Wang.Ethics declarations

      Competing interests
      The authors declare no competing interests.

      Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions
      Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
      Reprints and permissionsAbout this articleCite this articleWei, R., Chen, H. & Wang, H. Surface water quality prediction via an MLA-Mamba hybrid neural network with GRPO optimization.
      Sci Rep (2026). https://doi.org/10.1038/s41598-026-36229-3Download citationReceived: 18 August 2025Accepted: 10 January 2026Published: 20 January 2026DOI: https://doi.org/10.1038/s41598-026-36229-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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      KeywordsSurface water quality predictionSpatio-temporal deep learningMLA-Mamba networkMulti-head local attentionGradient reparameterization optimization More

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      Global water security threatened by rising inequality

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      AbstractThe global water-scarcity crisis is fundamentally driven by inequality, yet most forecasts overlook equity as a causal factor, leading to misdiagnosed problems and ineffective solutions. Here we develop a machine-learning-based global water-use forecasting model to project future water use and scarcity under distinct Shared Socioeconomic Pathways representing alternative development trajectories. Drawing on decades of historical data on human adaptation and resource use, the model predicts that by 2050, 6.5 billion people—equivalent to 65.5% of the global population—will face severe water scarcity under a high-challenge fragmentation scenario. By 2100, this figure is projected to rise to 8.0 billion, or 63% of the global population, far exceeding most previous estimates. Our analysis shows that a high inequality pathway directly amplifies water-scarcity risk. Critically, a technology-driven pathway improves aggregate water-use efficiency but concurrently deepens social and spatial inequalities. These findings underscore the need to move beyond purely technological fixes towards integrated, equitable water management, demonstrating that greater justice is inseparable from greater water security.

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      Fig. 1: Regional distribution of water-scarce population and proportion under SSP scenarios.Fig. 2: Forecasted global water use in 2100.Fig. 3: Global water scarcity in 2100.Fig. 4: Inequality in global water use.

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      Data availability

      All datasets generated and analysed during this study are available via Zenodo at https://doi.org/10.5281/zenodo.17445879 (ref. 67). Source data are provided with this paper.
      Code availability

      The source code for the ML-GWF model is available via Zenodo at https://doi.org/10.5281/zenodo.17445879 (ref. 67).
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      Sheng, J. Data and code for: global water security threatened by rising inequality. Zenodo https://doi.org/10.5281/zenodo.17445879 (2025).Download referencesAcknowledgementsThis research received joint financial support from the National Natural Science Foundation of China (grants 72474065, 72074119), the National Social Science Fund of China (grant 23&ZD103), the Fundamental Research Funds for the Central Universities (grant B240207007) and the Jiangsu Provincial Water Conservancy Science and Technology Project (grants 205032, 205047). We gratefully acknowledge P. D’Odorico (University of California Berkeley), H. Jiang (The Nature Conservancy) and Y. Yang (Chongqing University) for their insightful comments.Author informationAuthors and AffiliationsBusiness School, Hohai University, Nanjing, ChinaJichuan Sheng 
      (盛济川) & Qian Cheng 
      (成茜)College of Economics and Management, Nanjing Forestry University, Nanjing, ChinaJichuan Sheng 
      (盛济川) & Hongqiang Yang 
      (杨红强)AuthorsJichuan Sheng 
      (盛济川)View author publicationsSearch author on:PubMed Google ScholarQian Cheng 
      (成茜)View author publicationsSearch author on:PubMed Google ScholarHongqiang Yang 
      (杨红强)View author publicationsSearch author on:PubMed Google ScholarContributionsJ.S. and Q.C. contributed equally to this work. J.S. conceived the study, designed the model and wrote the manuscript. Q.C. performed the data analysis, ran the model and assisted in writing. H.Y. contributed to the initial draft and manuscript revision. All authors discussed the results and approved the final manuscript.Corresponding authorCorrespondence to
      Jichuan Sheng 
      (盛济川).Ethics declarations

      Competing interests
      The authors declare no competing interests.

      Peer review

      Peer review information
      Nature Geoscience thanks the anonymous reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Aliénor Lavergne and Tom Richardson, in collaboration with the Nature Geoscience team.

      Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Actual water-scarce population (WSI > 2) in 2019 and forecasted water-scarce population (WSI > 2) in 2100 under multiple scenarios (the units are people per square kilometers).a, Baseline scenario (2019). b–f, Forecasted water-scarce population in 2100 under the SSP1-SSP5 scenarios, respectively. These global maps are generated through a hybrid downscaling of national-level forecasts and serve to illustrate the potential spatial distribution of water stress, rather than providing locally validated predictions. Basemaps from Natural Earth.Extended Data Fig. 2 The dominant factors of water use forecasting at the national scale.a, Dominant factors identified from the Training set. b–f, Dominant forecasting factors under the SSP1-SSP5 scenarios, respectively. Note: Variable abbreviations in the figure are as below – GDP: Gross Domestic Product, POP: Population, IWE: Irrigation Water Efficiency, IWU: Industrial Water Use, MWE: Municipal Water Efficiency, URB: Urbanization Rate, IA: Irrigation Area, HFC: High-frequency sequence components of water consumption, LFC: Low-frequency sequence components of water consumption. Basemaps from Natural Earth.Source dataExtended Data Fig. 3 Ranking of global water intensity and water inequality.(a) and (c). Forecasted global water intensity in 2050 and 2100 (Water use per 10,000 USD of GDP). (b) and (d). Ranking of global water intensity and water inequality under different scenarios in 2050 and 2100.Source dataExtended Data Fig. 4 Forecasted water-saving potential in 2100 relative to SSP1 (the units are cubic meters per square kilometer).a–d, Forecasted water-saving potential in 2100 relative to SSP1 under the SSP2-SSP5 scenarios, respectively. These global maps are generated through a hybrid downscaling of national-level forecasts and serve to illustrate the potential spatial distribution of water stress, rather than providing locally validated predictions. Basemaps from Natural Earth.Supplementary informationSupplementary InformationSupplementary Results, Supplementary Figures 1-24, and Supplementary Tables 1-16.Source dataSource Data Fig. 1Statistical source data.Source Data Fig. 2Statistical source data.Source Data Fig. 4Statistical source data.Source Data Extended Data Fig. 2Statistical source data.Source Data Extended Data Fig. 3Statistical source data.Rights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this articleCite this articleSheng, J., Cheng, Q. & Yang, H. Global water security threatened by rising inequality.
      Nat. Geosci. (2026). https://doi.org/10.1038/s41561-025-01905-yDownload citationReceived: 16 October 2023Accepted: 12 December 2025Published: 20 January 2026Version of record: 20 January 2026DOI: https://doi.org/10.1038/s41561-025-01905-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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      Discrepancies in Arctic-boreal lake area trends driven by sensitivity to dry conditions

      by

      Abstract

      Northern lakes provide habitat for wildlife, regulate biogeochemical cycles, and supply subsistence resources for local communities. Monitoring long-term changes in these lakes is crucial to understanding how human activity and climate change affect these ecosystems. However, multidecadal trends in northern lake area are highly uncertain, with different studies reporting directionally opposite trends over the same region. Here, we examine the sources of differences between lake area estimates and short-term lake area trends derived from one Sentinel-2-based and two Landsat-based surface water products across five northern study regions. We show that differences in the magnitude and direction of regional lake area trends are related to systematic between-product differences in surface water detection in dry vs. wet years, with larger discrepancies in dry years. In some regions, these between-product differences were substantial enough to result in directionally opposite short-term trends (2016–2021), providing an explanation for disagreements in long-term (decadal-scale) studies. These between-product differences in wet vs. dry years reflect how the products classify mixed shoreline and other ambiguous pixels, which are more prevalent in regions with small, shallow lakes and aquatic vegetation. Resolving discrepancies in long-term trends will require new technologies and methods designed to differentiate between water, land, and inundated vegetation.

      Data availability

      Data developed for this analysis is freely and publicly available through the ORNL-DAAC. [https://doi.org/10.3334/ORNLDAAC/2430].
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      Download referencesAcknowledgementsThis work was supported by NSF OPP award 2317873 to EW and NASA New Investigators Award 80NSSC21K0920 to SC.Author informationAuthors and AffiliationsDivision of Earth and Climate Sciences, Nicholas School of the Environment, Duke University, Durham, NC, USAElizabeth E. Webb & Sarah W. CooleyDepartment of Geography, University of Oregon, Eugene, OR, USAEric Levenson & James MazeAuthorsElizabeth E. WebbView author publicationsSearch author on:PubMed Google ScholarSarah W. CooleyView author publicationsSearch author on:PubMed Google ScholarEric LevensonView author publicationsSearch author on:PubMed Google ScholarJames MazeView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization was carried out by EW and SC; data curation by EW and EL; investigation by EW and JM; formal analysis by EW; methodology by EW, SC, and EL; and supervision by SC.Corresponding authorCorrespondence to
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      Reprints and permissionsAbout this articleCite this articleWebb, E.E., Cooley, S.W., Levenson, E. et al. Discrepancies in Arctic-boreal lake area trends driven by sensitivity to dry conditions.
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      Long term water quality improvements associated with the restored Prairie Creek wetlands in Ohio’s Grand Lake St. Marys Watershed

      by

      Abstract

      Wetland restoration has emerged in recent years as an essential strategy for improving water quality to mitigate harmful algal blooms and the associated decline in water quality, especially across the Midwest. As a consequence, there is a need for more long-term monitoring datasets to better understand the nutrient and sediment processing potential of these systems over time. In this study, surface water samples were collected and analyzed, and water volumes were tracked weekly from the inlet and outlet of a pump driven, flow through wetland along Prairie Creek in the Grand Lake St. Marys watershed over an 8-year period (2017–2024) in order to estimate yearly and seasonal nutrient load reductions. During this time, the wetland processed 4.44 million m3, roughly 5.7%, of the annual Prairie Creek flows, wherein it showed promising overall concentration reductions between stream and wetland for TP (56%), SRP (81%), NO3–N (60%), and TSS (10%). A Bayesian nonlinear model was used to describe within dataset variation among seasons and years highlighting the kind of ranges these systems can exhibit in water quality improvements. The results from this study show significant nutrient and sediment load reductions can be achieved using restored wetlands as a mitigation tool. Furthermore, these data contribute to our understanding of long-term efficiency, within year seasonal changes, as well as how management strategies can help restored wetlands realize their potential as tools to improve water quality.

      Data availability

      Datasets generated during the current study are available from the corresponding author upon reasonable request.
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      Download referencesAcknowledgementsWe wish to thank the Lake Restoration Commission and their community donors (particularly G.A. Wintzer & Son, Auglaize County, Mercer County, City of St. Marys, City of Celina, and the Lake Improvement Association) for providing funding to Wright State University Lake Campus (WSU # 670536) to support the monitoring of this project. We recognize the Clean Ohio Greenspace Conservation Program for funding property acquisition as well as the Ohio EPA (OEPA Sect. 319 Program) in addition to many community partners for facilitating wetland design (Access Engineering and KCI) and construction (VTF Excavation) on the property. We appreciate the Ohio Department of Natural Resources for their role in site maintenance, vegetation management, and logistics required for pump operations (particularly Sean Finke). We recognize the many Lake Campus undergraduate students involved in the project over the years: P Poore, N Mazzone, T Ricketts, N Gnau, G McDonald, C Cobb, C Ewing, B Strang, B Axe, M Zehringer, M Morden, J Birt, M Gels, S Wendel, J Dodds, A Selby, M Wheeler, and K Kline, without which monitoring would not have been possible. Lastly, we acknowledge all those whose dedication to conservation has improved the watershed, especially the late Mr. Milton Miller and Dr. Thomas Knapke whose early involvement and enthusiasm for this project helped to see it become a model for future conservation wetlands in the region and beyond.FundingThis work was supported by a Grant from the Lake Restoration Commission (WSU Grant #670536).Author informationAuthors and AffiliationsWright State University, Lake Campus, Celina, OH, 45822, USAStephen J. Jacquemin, Morgan C. Grunden & Haley N. HoehnFrancis Marion University, Florence, SC, 29502, USAJason C. DollMercer County Community and Economic Development Office, Celina, OH, 45822, USATheresa A. DirksenAuthorsStephen J. JacqueminView author publicationsSearch author on:PubMed Google ScholarJason C. DollView author publicationsSearch author on:PubMed Google ScholarMorgan C. GrundenView author publicationsSearch author on:PubMed Google ScholarHaley N. HoehnView author publicationsSearch author on:PubMed Google ScholarTheresa A. DirksenView author publicationsSearch author on:PubMed Google ScholarContributionsAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Stephen J. Jacquemin, Jason Doll, Morgan Grunden, Haley Hoehn, and Theresa Dirksen. The first draft of the manuscript was written by Stephen J. Jacquemin, Jason Doll, Morgan Grunden, and Haley Hoehn, and all authors commented on the manuscript. All authors read and approved the submitted manuscript.Corresponding authorCorrespondence to
      Stephen J. Jacquemin.Ethics declarations

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      Reprints and permissionsAbout this articleCite this articleJacquemin, S.J., Doll, J.C., Grunden, M.C. et al. Long term water quality improvements associated with the restored Prairie Creek wetlands in Ohio’s Grand Lake St. Marys Watershed.
      Sci Rep (2026). https://doi.org/10.1038/s41598-025-34212-yDownload citationReceived: 11 August 2025Accepted: 26 December 2025Published: 16 January 2026DOI: https://doi.org/10.1038/s41598-025-34212-yShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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      KeywordsWetland restorationNutrient load reductionsBayesian wetland modelingWatershed conservationEutrophication More

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      Download referencesAcknowledgementsKV thanks NHMRC for Fellowship GNT1194466 and ARC for grants DP220103543 and DP250101028.Author informationAuthors and AffiliationsCollege of Medicine and Public Health, Flinders University, Bedford Park, SA, AustraliaWenshao Li, Ngoc Huu Nguyen, Vi Khanh Truong & Krasimir VasilevState Key Laboratory of Electrical Insulation and Power Equipment, Centre for Plasma Biomedicine, School of Electrical Engineering, Xi’an Jiaotong University, Xi’an, Shanxi, People’s Republic of ChinaRenwu ZhouIntegration Center for Medical Innovations, Xi’an Jiaotong University, Xi’an, Shanxi, ChinaRenwu ZhouSchool of Chemical and Biomolecular Engineering, The University of Sydney, Sydney, NSW, AustraliaPatrick J. CullenAuthorsWenshao LiView author publicationsSearch author on:PubMed Google ScholarNgoc Huu NguyenView author publicationsSearch author on:PubMed Google ScholarRenwu ZhouView author publicationsSearch author on:PubMed Google ScholarVi Khanh TruongView author publicationsSearch author on:PubMed Google ScholarPatrick J. CullenView author publicationsSearch author on:PubMed Google ScholarKrasimir VasilevView author publicationsSearch author on:PubMed Google ScholarContributionsW.L., N.H.N., V.K.T., R. Z., P.J.C. and K.V. jointly developed and contributed to the writing of this paper.Corresponding authorCorrespondence to
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      Global risk pooling mitigates financial risk from drought in hydropower-dependent countries

      by

      AbstractMore than 50 countries rely on hydropower for over 25% of their electricity generation, making them vulnerable to drought and resulting revenue losses. Governments can offset financial losses for publicly-owned hydropower generators, but this can create fiscal pressures and lead to negative consequences, such as lower bond ratings. Index-based financial instruments, used to manage weather-related risk, offer an alternative, though data collection and index design are challenging. Using remotely sensed hydrometeorological data, we develop index insurance contracts to manage drought-related financial risk for hydropower-dependent countries. Low correlations in drought across these countries allow cost reductions when risks are pooled. Pooling the contracts yields average savings of 54% compared to individual risk management via reserves. These findings indicate that pooled index insurance can strengthen financial resilience in countries dependent on hydropower and support governments in mitigating drought-related economic risks.

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      Data availability

      Data on index insurance contract formulation and costs of risk management generated in this study have been deposited in a Zenodo repository (https://doi.org/10.5281/zenodo.17329375). Input data used in this analysis is publicly available and can be found on the Earthdata database: land surface temperature (10.5067/modis/mod11c3.061), precipitation (10.5067/gpm/imerg/3b-month/06), vegetation indices (10.5067/modis/mod13c2.061), and snow cover extent (10.5067/modis/mod10cm.061). Basin boundary data is taken from the HYBAS database and the Global Reservoir and Dam database provided coordinates and uses for dams (10.1890/100125). Additional data used in this study can be found in the Zenodo repository (10.5281/zenodo.17329375).
      Code availability

      Code used for analysis is publicly available at https://github.com/rcuppari/Hydro_Risk_Pooling62.
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      Atmospheric rivers are associated with nine out of every 10 floods in major global river basins

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      AbstractThe role of atmospheric rivers (ARs) in 119 major flood events across 50 global river basins was investigated. The ARs were linked to 74% of the flood areas and 89% of the full basins. Their contributions were evident during and before floods, with influences spanning mid-latitudes to tropical regions, causing human fatalities and population displacements. These findings underscore ARs as dominant and widespread drivers of extreme flood events worldwide.

      Atmospheric rivers (ARs) are narrow corridors of intense water vapour moving poleward in the lower troposphere1,2 typically associated with a low-level jet stream ahead of the cold front of an extratropical cyclone3. However, they can also be linked to other meteorological configurations, including low-level jets, tropical plumes, and related large-scale circulation features involving tropical, extratropical, and tropical-extratropical interactions4,5.These phenomena frequently drive extreme precipitation and winds over short periods6, which often result in natural disasters7, overwhelming river systems and triggering widespread flooding8. While ARs contribute 22–50% of total runoff in global river basins and can increase flood occurrence by up to 80% in AR-affected regions9, their hydrological influence extends beyond these well-documented direct impacts, including drought busters10. The role of ARs in preconditioning catchments through antecedent precipitation and their spatial influence beyond flood zones remains poorly quantified.Floods are among the most frequent and devastating natural disasters worldwide and are responsible for the greatest number of fatalities among all natural hazards, as well as long-term displacement, infrastructure damage, and economic disruption that affect vulnerable populations worldwide7,11. As climate change intensifies the hydrological cycle12, floods have increased substantially across diverse geographic regions13,14, representing an increasing threat that demands improved understanding of the atmospheric drivers responsible for extreme precipitation and flood generation.Atmospheric rivers have emerged as a critical driver of flood events globally, yet their role in flood generation across diverse climatic regions remains incompletely characterised. Conventional analyses of ARs have focused largely on their structure, the associated precipitation and impacts along the western coastal areas in midlatitude continents, particularly North America and Europe15,16. Nevertheless, their global significance remains incompletely described, as flood generation mechanisms vary across latitudes and continents, with tropical and subtropical basins home to billions remaining understudied. This study focuses on global river basins as they represent fundamental hydrological units that capture the integrated response to atmospheric inputs like precipitation associated with ARs.To quantify the global relationship between ARs and flooding, the spatiotemporal coincidence of ARs with flood events was analysed. We examined entire river basins, HydroBASINS level 4 and 5 sub-basins17, representing successive, hierarchically nested subdivisions of the major basins, and flood-affected areas to capture direct impacts and upstream influences, respectively. In addition, analysed ARs activity during flood periods and up to 3 days before, to assess both immediate triggers and antecedent conditioning effects. Using the Global Active Archive of Large Flood Events18, we identified 119 major flood events across 24 of 50 global river basins between 1999 and 2018 (Fig. 1), with the spatial intersections between flood extents and the corresponding HydroBASINS level 4 and 5 sub-basins shown in Fig. S2. Our analysis reveals striking patterns; 88 of the 119 flood events (74%) were influenced by ARs that directly intersected the inundated region, spanning 18 distinct basins across multiple continents, including the Amazon, Niger, Congo, and Yangtze River basins, where AR impacts on flooding have been largely unquantified (Table S1). When the impact of the AR outside the flood area but within the basin was also considered, this influence expanded to 106 events (89%) across 22 basins, also demonstrating the widespread role of ARs in flood generation beyond direct precipitation zones. The analysis and results, however, are conditional on major floods as documented by the Dartmouth Flood Observatory (DFO). Regions experiencing frequent AR landfalls, such as the U.S. West Coast and Western Europe, do not exhibit strong AR-flood associations in our study, as basin-scale floods meeting DFO criteria were relatively rare during the study period. Furthermore, while our methodology was necessary for consistent global AR-flood attribution analysis within defined hydrological units, it may underrepresent the flood burden in regions with frequent, extensive, transboundary, or seasonally recurring flooding (e.g. Southeast Asia). This limitation reflects the constraints of our basin-focused analytical framework rather than a diminished importance of flooding in these regions, or the potential role of AR-like features within monsoon systems in generating these floods.Fig. 1: Geographical distribution of studied river basins and flood events.Map showing the location of 24 river basins (black contours) affected by 119 flood events, with coloured regions indicating the corresponding flood-affected areas. River basins boundaries from HydroBASINS project at https://www.hydrosheds.org. Arrows represent the major mechanisms of moisture transport at the global scale: Atmospheric Rivers (ARs; dark red arrows), Low-Level Jets (LLJs; blue arrows), and monsoon systems (cyan arrows). The main LLJs are indicated: The Great Plains Low-Level Jet (GPLLJ), the Chocó Low-Level Jet (ChocoLLJ), the South American Low-Level Jet (SALLJ), the Caribbean Low-Level Jet (CLLJ), the West African Westerly Jet (WAWJ), and the Somali Low-Level Jet (Somali LLJ). The two-phase nature of the SALLJ is highlighted, distinguishing between the Chaco Jet Event (CJE) and the No-Chaco Event (NCJE). The major monsoon regimes are also indicated: The West African Monsoon (WAM), Western and Southern India (WSI), and the Australian Monsoon (AM).Full size imageWhen the river basin was considered to be the reference area, ARs coincided with nearly 90% of the flood events during the core flood period and ~30% of all 3 days prior to the events (Fig. 2a). To assess the robustness of these associations across spatial scales, we also analysed hydrobasins sub-basin levels 4 and 5 areas, which represent more hydrologically constrained domains than entire river basins. AR coincidences during flood events decreased systematically to 79% (level 4) and 77% (level 5), demonstrating spatial scale dependence while maintaining high association rates. The coincidences for days –1, –2, and –3 individually were similar, with ARs affecting ~55–57% of the events on each of those days at the river basin scale. At sub-basin scales, individual daily percentages ranged from 34 to 49% (level 4) and 27 to 38% (level 5), with percentages increasing from day −1 to day −3. Collectively, ARs during the three preceding days coincided with 30% (river basin), 29% (level 4), and 24% (level 5) of flood events. When the flood-affected area was used as the reference area, the percentage of flood events in which ARs were present decreased but remained above 70%. In contrast, the coincidence of ARs during the preceding days decreased markedly to about half, and even less when assessed collectively for all 3 days. These findings suggest that ARs frequently precede flood onset and impact broader upstream regions, even without directly overlapping with flooded zones, highlighting the potential key role of the river basin in modulating surface runoff and flood generation.Fig. 2: Association between atmospheric rivers and flood events.a Percentage of flood events and the three days prior to flood onset coincident with ARs considering either the river basin area, sub-basin level 4, sub-basin level 5, and flood area. b Number of flood events with total precipitation percentages attributed to ARs in various ranges over the basin, sub-basin level 4, sub-basin level 5, and flood areas. Number of flood events with total precipitation percentages attributed to ARs for c river basin areas, d sub-basin level 4 areas, e sub-basin level 5 areas, and f flood areas.Full size imageAt the river basin scale, the majority of floods events (60 events), showed AR-related precipitation representing less than 10% (Fig. 2b). Lower distributions in the 0–10% range were observed at sub-basin scales, with 45 and 44 events at levels 4 and 5, respectively, whereas ~40 events fell into this range when the flood area was considered. However, for the remaining precipitation percentage ranges, the number of events across spatial scales does not differ substantially. Notably, more events were recorded in flood-affected areas, where AR-related precipitation accounted for more than 80% of the total precipitation. Overall, these results highlight a potential spatial mismatch between the precipitation footprint of ARs and the actual extent of the resulting floods. This mismatch arises because flood generation depends not only on precipitation location but also on basin-scale hydrological processes, including upstream–downstream water routing, antecedent soil moisture conditions, and drainage network configuration. Statistical analysis confirmed that ~72% of events in which ARs intersected the flood area, and about 67.0% of events with AR presence in the river basin showed precipitation significantly above climatological values (p < 0.10). When constrained to sub-basin scales, 72.3% (level 4) and 73.0% (level 5) of events showed significant AR associations, confirming that approximately three-quarters of major floods remain robustly associated with ARs even under spatially refined hydrological assessment.Next, we analysed the AR-driven precipitation during the 3 days preceding flood onset, determining the number of flood events categorised by the precipitation percentage attributable to ARs across different spatial scales. For most events, AR-associated precipitation accounted for a relatively low percentage of the total rainfall over the entire river basin during each of the 3 days preceding flood onset (Fig. 2c). At sub-basin scales (Fig. 2d, e) days −3 and −2 show the highest number of events in low contribution bins (f_AR < 20%). This pattern reverses dramatically at high contribution bins (f_AR > 70%), where day −1 dominates, particularly at level 5. This pattern aligns with the distribution observed during the flood period itself (Fig. 2f), suggesting that upstream AR-driven precipitation, although not always intense, play a critical role in increasing antecedent soil moisture, as confirmed in Fig. S1, and thus increasing the basin’s vulnerability to flooding from subsequent rainfall, as stated in previous findings (e.g. Webb et al.19).In contrast, a considerable number of events fall in the highest precipitation bins (80–90% and 90–100%), particularly on the day immediately preceding flood onset (Fig. 2d–f). This result indicates that, in many cases, nearly all the rainfall triggering the flood originated from a single AR event occurring just 1 day before the flood, supporting the idea that ARs often serve not only as contributing factors but also as the principal hydrometeorological drivers of flood events, which have been demonstrated to affect water availability9. Conversely, days –2 and –3 are associated with markedly fewer events associated with higher precipitation, indicating that while ARs may influence atmospheric and soil conditions up to 72 h in advance, their peak hydrological impact is concentrated within the 24 h immediately preceding the flood. This temporal clustering suggests that ARs play dual roles as both immediate flood triggers and antecedent preconditioners that progressively saturate catchments, thereby lowering the threshold for runoff generation and amplifying the flood response when subsequent rainfall occurs, whether from continued AR activity or other weather systems. This preconditioning effect has significant implications for flood forecasting and early warning. In addition, climate warming is expected to intensify water vapour transport in ARs by 6.3–9.7% per degree Celsius of warming20, altering their frequency, duration, and geographical distribution, implying a significant contribution to future flood risk21.These results are based on the tARget v4 AR detection algorithm using the 85th percentile threshold of the Integrated Vapour Transport (IVT). Although this threshold is widely accepted and validated across multiple regions, the different detection methods or thresholds could yield different absolute percentages of AR-associated floods. Indeed, the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) has shown that tropical regions exhibit the largest variance in AR frequency among different catalogues, with disagreement concentrated primarily due to high humidity that confounds certain detection methodologies, and different approaches to marginal detections22. Consequently, our conclusions remain robust across different methodologies, as they rely on the physical AR-flood association instead of being tied to specific AR intensity thresholds.To assess the broader societal implications of AR-driven floods, we analysed the human impacts of the 106 AR-associated flood events across affected river basins from 1999 to 2018, examining spatial patterns of inundation, mortality, and population displacement. Indeed, since 2000, flood-related disasters have increased by 134% compared with the previous two decades23, with ARs contributing substantially to this trend. The spatial analysis reveals that AR-induced floods show annual recurrent impacts and larger inundation areas across tropical and subtropical basins. It is well observed in the Amazon and Mississippi River basins in South and North America, respectively, the basins across the Sahel region in Africa, and the Yangtze River in China (Fig. 3a). The annual total affected area exhibits marked interannual variability, with peak years such as 2004 and 2014 showing the highest cumulative flood extent (Fig. 3b). These findings demonstrate that AR-flood impacts affect regions with high population density and economic activity, where flood-prone river basins coincide with centres of human settlement.Fig. 3: Global distribution and impacts of floods during 1999–2018.a Locations of floods with colour scale representing the total geographical area affected (million km²). b Annual total area affected by floods. c Reported deaths and their locations. d Annual total deaths. e People displaced due to floods and their corresponding locations (millions). f Annual total displaced population.Full size imageFlood-related mortality displays substantial spatial heterogeneity, with the highest death tolls reported in the Niger River and Nile River basins in Africa and the Yangtze River Basin in China (Fig. 3c). The temporal evolution of this indicator reveals pronounced peaks in 2006, 2012, 2014, and 2016 (Fig. 3d). These areas are highly vulnerable to floods and make up nearly three-quarters of the total modelled displacement, averaging almost 10 million globally each year24. The displacement of populations due to AR-associated floods during the study period was most pronounced in the Nile River basin, the Andean region southwest of the Amazon River basin, and the southeast of the Mississippi River Basin (Fig. 3e). The temporal profile of displaced persons reveals a notable maximum in 1999, followed by a secondary peak in 2006, which is consistent with years of elevated mortality (Fig. 3f).This study reveals that ARs function as a near-universal driver of major flood events in river basins across diverse climate zones and hydrological regimes. However, further research is needed to develop improved methods for quantifying how antecedent AR-related precipitation drives flooding preconditions in catchments, for example, the land use changes, or infrastructure modifications that influence flood generation. These limitations do not fundamentally undermine our core findings, as a large sample size and robust statistical associations provide strong evidence. In addition, the potential for using AR characteristics (intensity, duration, orientation) as predictors of flood magnitude and extent, beyond simple precipitation forecasts, should be explored. This would enable more targeted adaptation strategies in future AR scenarios, to avoid major impacts on society, agriculture and finally the economy.MethodsFlood event databaseThe flood event records were obtained from the DFO17, which is accessible at https://floodobservatory.colorado.edu. This database was used to identify flood occurrences across 50 representative river basins worldwide, covering a broad range of climatic regions, hydrological regimes, and socio-environmental contexts (Table S2) over 20 years (1999–2018), which we set as the study period. The information compiled in this archive is derived from a combination of sources, including news reports, governmental records, instrumental measurements, and satellite-based remote sensing. The archive provides detailed records of flood events, encompassing key attributes that were used in this study, such as spatial outlines of affected areas, onset and end dates, event durations, and associated impacts, including reported fatalities and numbers of displaced persons.Atmospheric river detectionThe global atmospheric river database developed by Guang and Waliser25 was also utilised. These authors implemented the tARget v4.0 (Tracking Atmospheric Rivers Globally as Elongated Targets) algorithm to provide daily, objectively detected AR footprints based on the IVT magnitude (areas exceeding the 85th percentile threshold) and direction derived from ERA5 reanalysis at 6 h intervals and a 0.25° × 0.25° horizontal resolution. An object is retained if its length exceeds 2000 km and has a length-to-width ratio greater than 2. This criterion ensures that only long and narrow objects, characteristic of ARs, are kept. They also implemented an algorithm to better handle ARs in tropical and polar areas and zonal ARs, which is crucial to identify AR-like structures in tropical regions where the background moisture content is naturally relatively high and where traditional fixed-threshold approaches might miss AR events. Thus, the term AR in this study refers to structures objectively detected by this algorithm, which in tropical regions may differ dynamically (e.g. monsoon moisture surges, tropical plumes, cross-equatorial flows) from classical extratropical ARs. However, these features share the fundamental characteristic of transporting anomalously high amounts of water vapour in coherent, elongated structures that justify the use of a unified detection framework4.Flood selection criteriaFrom the DFO archive, we selected flood events that (1) occurred within any of the 50 pre-selected river basins and (2) had their entire flood-affected area completely contained within a single basin boundary. This spatial containment criterion ensured unambiguous AR-flood attribution and avoided complications from floods spanning multiple basins or extending beyond basin limits. Given the very large spatial extent of some of the major river basins considered, flood attribution was further refined by identifying the HydroBASINS sub-basins (levels 4 and 5) intersecting each flood-affected area. These sub-basins represent hierarchically nested subdivisions of the original basins that preserve upstream–downstream connectivity, allowing the spatial extent of each flood event to be characterised within smaller, hydrologically coherent units.Attribution of precipitation to ARsTo quantify the fraction of precipitation attributable to ARs, we used daily precipitation data from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset version 2.8 at 0.1° spatial resolution26. For each flood event, we identified the spatial overlap between the AR footprint from the tARget database and the flood-affected area from DFO for each day during the flood period and the three preceding days. We then calculated the total precipitation over the entire flood-affected area (P_total) and the precipitation within the AR-flood intersection area (P_AR). The fraction of precipitation attributable to the AR was computed as f_AR = (P_AR / P_total) × 100, representing the percentage of total flood-area precipitation that occurred within pixels where an AR was present. This fraction was calculated separately for each flood event and for each of the 3 days preceding the flood onset, which allow to assess both immediate triggers and antecedent conditioning effects. When the AR footprint completely covered the flood-affected area, f_AR = 100%, indicating all precipitation was AR-related, whereas f_AR = 0% when no AR was present over the flood area. This analysis was conducted at multiple spatial supports, including the flood-affected area, the intersecting HydroBASINS level 4 and 5 sub-basins, and the corresponding major river basin, in order to assess the sensitivity of AR–precipitation attribution to spatial aggregation and to better approximate hydrological connectivity.As an example, representative for all cases, we present the spatial extent of a flood event (blue line) that affected the La Plata River basin from 4 to 15 July 2013 (Fig. 4). The shape of the AR is delineated by the magenta line, covering the whole flood area and intersecting part of the river basin. In this case, we considered this AR to be associated with the flood area and the river basin. The vectors show the IVT arriving at the flood area from the northeast, with the maximum values coinciding with the flood area, and the rainfall observed over subbasin 4, primarily in the southern half. This approach provides a consistent and objective metric for assessing AR contributions across our global dataset, under the assumption that precipitation falling within the AR footprint can be attributed to AR-related moisture transport.Fig. 4: Example of coincidence between an atmospheric river and a flood-affected area.a The La Plata River basin (black contour) and the flood-affected area (blue contour) during 4–15 July 2013 are shown together with the footprint of an atmospheric river (magenta contour). Coloured shading depicts the vertically integrated water vapour transport (IVT, kg m⁻¹ s⁻¹), and arrows indicate the direction and magnitude of the moisture flux. b Accumulated precipitation during 4–15 July 2013 in the selected sub-basin.Full size imageAssessment of precipitation anomaliesTo assess whether the accumulated precipitation during each event was anomalous with respect to the long-term climatology, we first calculated the total precipitation of the event over its duration in the year of occurrence. For comparison, we then computed the accumulated totals for the same calendar days in all other years of the record, thereby constructing a reference distribution. Statistical significance was evaluated by contrasting the event total against the distribution mean, while accounting for temporal autocorrelation through an effective sample size adjustment. A confidence level of 0.90 was adopted to identify those events with total precipitation considered statistically significant.

      Data availability

      The AR database used in this study is publicly available at https://doi.org/10.6084/m9.figshare.c.6953288.v1. The flood inventory data are available at [https://floodobservatory.colorado.edu]. The daily MSWEP reanalysis data are available at https://www.gloh2o.org/mswep/. The GLEAM data are available at [https://www.gleam.eu/]. The Python scripts used for processing the data are available from the corresponding author upon reasonable request.
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      Download referencesAcknowledgementsM.S. and A.P-A. are thankful for the support from Xunta de Galicia (Consellería de Cultura, Educación, Formación Profesional e Universidades) under Postdoctoral Grant Nos. ED481D−2024/017 and ED481B−2023/016, respectively. R.S. acknowledges grant RYC2021−034044-I funded by the Ministerio de Ciencia, Innovación y Universidades, Spain (MICIU/AEI/https://doi.org/10.13039/501100011033) and the European Union Next Generation EU/PRTR. A.P-A., M.S. and R.S. are also supported by the project Excelencia-ED431F-2024/03 funded by the Xunta de Galicia. EPhysLab members are supported by the SETESTRELO project (grant no. PID2021−122314OB-I00) funded by the Ministerio de Ciencia, Innovación y Universidades, Spain (MICIU/AEI/https://doi.org/10.13039/501100011033), Xunta de Galicia under the Project ED431C2021/44 (Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas (Grupos de Referencia Competitiva) and Consellería de Cultura, Educación e Universidade). This work was also made possible by the computing resources and technical support provided by Climate System Research Unit, UVigo-CESGA.Author informationAuthors and AffiliationsCentro de Investigación Mariña, Environmental Physics Laboratory (EPhysLab), Universidade de Vigo, Ourense, SpainMilica Stojanovic, Albenis Pérez-Alarcón, Rogert Sorí, Raquel Nieto & Luis GimenoInstituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, Lisboa, PortugalAlbenis Pérez-AlarcónClimate System Research Unit, UVigo-CESGA, Santiago de Compostela, SpainRaquel Nieto & Luis GimenoAuthorsMilica StojanovicView author publicationsSearch author on:PubMed Google ScholarAlbenis Pérez-AlarcónView author publicationsSearch author on:PubMed Google ScholarRogert SoríView author publicationsSearch author on:PubMed Google ScholarRaquel NietoView author publicationsSearch author on:PubMed Google ScholarLuis GimenoView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization: M.S., A.P.-A., R.S, R.N., L.G. Methodology: M.S., R.S, L.G. Investigation: M.S., A.P.-A., R.S. Software: M.S., A.P.-A., R.S. Data Curation: M.S., A.P.-A., R.S. Visualization: M.S., R.S. Supervision: R.N., L.G. Writing—original draft: M.S., R.S. Writing – review and editing: M.S., A.P.-A., R.S, R.N., L.G.Corresponding authorCorrespondence to
      Milica Stojanovic.Ethics declarations

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      Reprints and permissionsAbout this articleCite this articleStojanovic, M., Pérez-Alarcón, A., Sorí, R. et al. Atmospheric rivers are associated with nine out of every 10 floods in major global river basins.
      npj Nat. Hazards 3, 6 (2026). https://doi.org/10.1038/s44304-026-00167-xDownload citationReceived: 05 September 2025Accepted: 09 January 2026Published: 15 January 2026Version of record: 15 January 2026DOI: https://doi.org/10.1038/s44304-026-00167-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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