Markus Andrews

AbstractRecreational water quality in coastal areas is vital for public health, especially in urban regions. In this study, a recreational water quality index (RWQI) tailored to the Red Sea coastline in Jeddah, Saudi Arabia, was developed to assess water quality at popular beaches. The samples were collected from three different locations. To develop the RWQI, relevant water quality parameters, such as the pH, dissolved oxygen (DO), total nitrogen (TN), total phosphorus (TP), Escherichia coli (E. coli), and enterococci, were identified and weighted on the basis of their significance for recreational water safety. These parameters were rated according to the World Health Organization (WHO) and Saudi Arabia Ambient Water Quality Standards (SAAWQS). The results were then aggregated into a single index score reflecting the overall water quality. The results revealed that Obhur Al-Shamaliya exhibited generally good water quality, particularly in winter, with index scores ranging from 83 to 98, meeting both the WHO and Saudi standards. All three sites recorded poorer water quality during the second sampling period in summer, with nutrient enrichment and E. coli counts exceeding permissible limits (2,300, 3,875, and 2,58.13 CFU/100 mL, respectively), resulting in an index classification as unsuitable for recreational purposes. The urban runoff sites were at high risk, with elevated pollution. Moreover, a one-way ANOVA test confirmed that the seasonal variation in the RWQI was statistically significant (F = 8.29, p = 0.045), emphasizing the influence of environmental factors, such as temperature and runoff, on recreational water quality. These findings underscore the need for season-specific monitoring and management strategies in coastal recreational areas.

AcknowledgementsThe project was funded by the KAU Endowment (WAQF) at King Abdulaziz University, Jeddah, Saudi Arabia. The authors therefore acknowledge with thanks the WAQF and the Deanship of Scientific Research (DSR) for technical and financial support. The authors also express their sincere gratitude to the Department of Environment, Faculty of Environmental Sciences at King Abdulaziz University, for providing the laboratory facilities and resources essential for conducting the experimental work and analysis in this study.Author informationAuthors and AffiliationsDepartment of Environment, Faculty of Environmental Sciences, King Abdulaziz University, Jeddah, 21589, Saudi ArabiaTalal Almeelbi & Fawaz AlghamdiNational Center for Environmental Compliance, Riyadh, Kingdom of Saudi ArabiaFawaz AlghamdiAuthorsTalal AlmeelbiView author publicationsSearch author on:PubMed Google ScholarFawaz AlghamdiView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleAlmeelbi, T., Alghamdi, F. Development and evaluation of a recreational water quality index for the Red Sea Coastline, Saudi Arabia.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-54623-9Download citationReceived: 15 July 2025Accepted: 20 May 2026Published: 24 May 2026DOI: https://doi.org/10.1038/s41598-026-54623-9Share 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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KeywordsCoastal pollutionMicrobiological indicatorsRecreational water quality index (RWQI)Red seaWater quality assessment More

AbstractLake ecosystems are increasingly affected by climate warming, which alters water temperature and seasonal layering, with consequences for water quality and ecosystem functioning. Oxygen in deeper waters is essential for aquatic life and nutrient cycling, but may decline as warming strengthens stratification, reducing vertical mixing while biological activity continues to consume oxygen. Here, we apply a numerical model of oxygen depletion, driven by thermal dynamics from an ensemble of three lake and five climate models, to project deep-water oxygen changes in 73 globally distributed lakes from 2015 to 2099. Under a high-end climate change scenario, oxygen depletion is projected to become more frequent and prolonged, particularly in nutrient-rich lakes, where many are expected to experience extended oxygen-free conditions. Less nutrient-rich lakes show smaller but increasing oxygen declines. These results suggest that nutrient reductions could help sustain oxygen conditions, although continued warming is likely to increase ecological stress.

IntroductionClimate change, the paramount challenge of the 21st century, is affecting virtually every ecosystem across the globe. Lakes, essential aquatic ecosystems, offer critical ecosystem services, including the provision of water for human use and habitats favorable to diverse flora and fauna1,2. Lakes underpin the United Nations’ Sustainable Development Goals (SDGs) by connecting diverse elements of their shared vision for a sustainable, equitable, and prosperous future worldwide3,4. Moreover, lakes are particularly sensitive to climate change5, with climate change-mediated water quality declines, including hypolimnion deoxygenation and potential eutrophication of lakes6,7, underscored at length in literature at varying geographical scales8,9. Oxygen (O2), a vital water quality variable essential in the biological and biogeochemical functioning of lake systems10,11,12, plays a crucial role in lake management efforts13,14, and has been severely negatively affected by climate change8,15. Deoxygenation is especially pronounced in the hypolimnion (deep layers) of stratified lakes, adversely affecting aquatic biodiversity16,17, promoting increased greenhouse gas emissions18,19, accelerating internal nutrient loading11,20, and amplifying the burden and expense of providing safe drinking water21,22. Thus, with the advent of climate-driven changes, there is a need for a concise understanding of future O2 dynamics in lakes to ensure water security globally. However, there is a general lack of global modeling studies examining the impact of future climate-driven changes on lake water quality, particularly regarding their O2 state.Global studies on climate-driven changes to lakes are dominated by characterizations of lake physics. These include the documented loss of ice cover in lakes23, the predicted increase in lakes’ evaporation rates24, the anticipated intensification of lake warming driven by heatwaves25,26, and the expected alterations in lake mixing patterns27. The prolongation of stratification has been identified as one of the main causes of lake ecological degradation, as it adversely affects O2 concentrations in lake hypolimnia by prolonging depletion of O29,27. These detrimental impacts are further amplified by increasing thermal stability of lakes28, further limiting vertical gas fluxes, influenced by the varying degree of warming in the surface and bottom of lakes29,30, and the decreased gas solubility emanating from the increase in temperature8.Furthermore, climate change-mediated increases of surface temperatures simultaneously affect lake habitat conditions, conclusively altering and disrupting their ecological communities31 and metabolism32,33,34. This includes established links between lake metabolism and intensified internal nutrient loading35, where climate warming and eutrophication act synergistically to increase productivity36,37 despite nutrient management efforts33,38,39. Warmer waters lengthen phytoplankton growth periods and accelerate metabolism32,37, leading to increasing phytoplankton productivity and subsequent biomass sinking and decomposition, which depletes hypolimnion O2 during stratification8,40,41. Increased water column stability that promotes anoxia is linked to reduced fish yields in lakes42. Despite advances in understanding the combined effects of climate-mediated changes (increased temperatures and stratification durations) and their interaction with lake O2 dynamics15,43,44, existing research focuses on temperate zone case studies45 while an assessment of future trends in O2 concentrations in lake hypolimnia at a global scale is lacking.In this study, we leverage recent advancements in lake hydrodynamics modeling to project hypolimnetic O2 concentrations of 73 widely-distributed lakes (Table 1). To accomplish this, we employ an ensemble of three process-based lake models, each driven by outputs from five general climate models (GCMs), to simulate future lake thermal regimes and stratification patterns. We subsequently integrate these predictions into a lake O2 depletion model43 that incorporates stratification duration and temperature-driven metabolic processes through a generalized and validated framework. Stratification duration is derived from water temperature profiles and corresponding density gradients for each given year. We incorporate the temperature-dependence of metabolic dynamics by applying temperature scaling based on hypolimnetic temperatures, which we also use to calculate initial O2 concentrations. Empirical data on relationships between O2 consumption rates and observed trophic states are used to estimate volumetric hypolimnetic O2 depletion (VHOD) rates. This validated modeling framework enables projections of hypolimnetic O2 dynamics in each lake until the end of the 21st century.Table 1 Characteristics of the 73 lakes studied (see lake sector details at www.ISIMIP.org)Full size tableImpact of climate warming on lake temperature and oxygen dynamicsClimate scenario-dependent changes in hypolimnion temperature, initial O2 level, volumetric hypolimnetic depletion (VHOD) rate, and stratification duration until 2099 are given in Fig. 1. Consistent warming of hypolimnetic waters was observed across all trophic states (Figs. 1a and S1), with the highest changes projected under pessimistic SSP5-8.5 (+0.14 °C decade−1 in oligotrophic, +0.19 °C decade−1 in mesotrophic, and +0.26 °C decade−1 in eutrophic lakes). Due to their higher-latitude locations, oligotrophic lakes in our dataset experience colder average climate conditions than the lakes with other trophic states, contributing to differences in absolute hypolimnion temperatures and related variables across trophic states. As hypolimnion temperature increases, the initial O2 available in hypolimnia will decrease due to the temperature-dependent gas solubility. Hence, we predicted a similar pattern of decline in initial O2 concentrations in lakes of all three trophic states within each scenario (Fig. 1b). The decline over the simulation period was most pronounced in extreme climate scenarios, with SSP5-8.5 projections showing the strongest decreases (−2.49% in oligotrophic, −3.29% in mesotrophic, and −3.98% in eutrophic lakes), followed by SSP3-7.0 projections (−2.04% in oligotrophic, −2.14% in mesotrophic, and −2.67% in eutrophic lakes), and SSP1-2.6 projections (−0. 39% in oligotrophic, −0.57% in mesotrophic, and −0.55% in eutrophic lakes). The pre-industrial control (picontrol) scenario, representing a stable baseline of the natural climate system, highlights anthropogenic changes relative to natural variability and is included in subsequent figures.Fig. 1: Projected changes in hypolimnetic conditions across lakes with different trophic states.The alternative text for this image may have been generated using AI.Full size imagea Projected changes in hypolimnion temperatures, b initial O2 concentrations at the onset of stratification, c volumetric hypolimnetic O2 depletion (VHOD) rates, d and stratification duration. Trophic state categories are classified as oligotrophic (blue), mesotrophic (green), and eutrophic (orange). Shaded regions denote the standard error of the mean. Oligotrophic lakes are systematically colder due to their higher-latitude locations and larger depths.Additionally, the projected hypolimnetic temperature increases accelerated O2 depletion rates in lakes, with the more severe climate scenarios driving the strongest responses (Fig. 1c and see “Methods”). Higher surface-bottom temperature differences simultaneously extended the duration of stratification, thereby prolonging the period during which O2 depletion occurred. Empirical VHOD measurements used to parameterize O2 depletion rates (Fig. S2) showed a clear increase with trophic state, with eutrophic lakes exhibiting rates ~2.5 times higher than oligotrophic systems. In addition to productivity effects on VHOD, oligotrophic lakes experienced colder average temperatures (Figs. 1a and S1). Accordingly, we predict minimal increases of VHOD rates for oligotrophic lakes under all three scenarios (+0.14% year−1 under SSP5-8.5), whereas in mesotrophic lakes the increases become steeper the more intense climate change becomes (+0.21% year−1 under SSP5-8.5). This pattern is even stronger in eutrophic lakes, where a substantial and consistent increase is realized throughout the simulation period (+0.32% year−1 under SSP5-8.5). Changes in stratification duration vary among trophic states (Fig. 1d; lake-specific changes in Fig. S3) but follow similar relative trends, increasing under SSP5-8.5 by 0.57 days year−1 in oligotrophic lakes, 0.40 days year−1 in mesotrophic lakes, and 0.32 days year−1 in eutrophic lakes. Oligotrophic lakes exhibit longer stratification duration than eutrophic systems by virtue of the respective morphological features of the included selected lakes (e.g., greater average depth in the oligotrophic lakes), thus reflecting regional climatic features of each individual lake.Oxygen depletion and rapid progression towards anoxia in lakesEnsemble simulations revealed a consistent future decline of the time-to-anoxia, defined as the number of days for hypolimnetic O2 to reach anoxic levels (O2 ≤ 0.5 mg L−1) after stratification onset, reflecting accelerated depletion and heightened anoxia risk throughout the 21st century (Fig. 2). Under SSP1-2.6, time-to-anoxia decreased by less than 10 days by 2099, indicating relatively stable O2 conditions compared to other scenarios (Fig. 2a). In contrast, the time-to-anoxia progressively became shorter under both SSP3-7.0 and SSP5-8.5, with accelerated declines during the second half of this century. The most rapid declines occurred under SSP5-8.5, which consistently yielded the shortest time-to-anoxia across nearly all lakes at the end of the century. SSP5-8.5 accelerated anoxia onset by ~30 days, equivalent to a full month, relative to the other climate scenarios (Fig. 2a). Under the strongest warming scenario, stratification duration was projected to increase by ~50 days (Fig. 1d), substantially increasing the risk of anoxia by 2099 relative to present-day conditions. Lakes that currently remain marginally anoxic are particularly vulnerable, with many projected to cross the threshold into persistent anoxia by the end of the century. This trend was also reflected by a systematic increase in the anoxic ratio, defined as the fraction of the stratification period during which the hypolimnion is anoxic, with progressive climate warming (Fig. 2b). Under SSP5-8.5, even more oligotrophic systems exhibited rising anoxic ratios from 2080 onward. In eutrophic lakes, the anoxic ratio increased markedly from ~40 to ~60% by the end of the century. Although oligotrophic lakes reached a time-to-anoxia metric under picontrol and SSP1-2.6 (Fig. 2a), their high initial O2 concentrations and ~180–210 day stratifications (Fig. 1a, d) prevented substantial anoxia development.Fig. 2: Projected changes in time-to-anoxia and anoxic persistence.The alternative text for this image may have been generated using AI.Full size imageTemporal evolution of a time-to-anoxia (days) and b anoxic ratio (%) from 2015 to 2099 under pre-industrial control (picontrol) and three shared socioeconomic pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). Time-to-anoxia represents the average number of days required for hypolimnia O2 to reach anoxic conditions following stratification onset, while anoxic ratio quantifies the proportion of stratification duration and time the hypolimnion is anoxic. Lines represent trophic means, and shading indicates standard error. Colors denote different trophic states: eutrophic (orange), mesotrophic (green), and oligotrophic (blue).To illustrate these patterns, we highlight nine proxy spotlight lakes representing different trophic conditions and climate contexts (Fig. 3; results for all 73 lakes’ time-to-anoxia and time-to-hypoxia (O2 ≤ 2 mg L−1) are shown in Figs. S4 and S5, respectively). The projected magnitude and trajectory of changes to time-to-anoxia were strongly modulated by trophic state, latitude, and the baseline thermal characteristics of a given lake. Despite interannual variability and uncertainty being large for several lakes (Figs. S4 and S5), a coherent directional response emerges across all lake systems, and its consistency with warming-induced stratification indicates an increasing risk of hypolimnetic O2 loss rather than purely stochastic year-to-year fluctuations. Among the oligotrophic spotlight lakes, Kilpisjärvi (Fig. 3a) and Toolik (Fig. 3c) remained highly resilient, with projected time-to-anoxia exceeding 250 days, which far surpassed their future mean projected stratification periods (~78 and ~86 days, respectively), and hence remained oxic. In contrast, despite its oligotrophic state, Tarawera (Fig. 3b) was projected to exhibit anoxia patterns more typical of eutrophic systems, with a loss of over 40 oxic days by the end of the century under both SSP3-7.0 and SSP5-8.5. This stronger response was driven by higher hypolimnetic temperatures, reflecting local climatic conditions, which accelerated VHOD and thereby intensified O2 declines. Hence, Tarawera was projected to display anoxic ratios ranging between ~40 and 48%, markedly higher than the other two oligotrophic systems, underscoring the potentially overriding influence of thermal regime over trophic state in shaping anoxia trajectories and persistence.Fig. 3: Spotlight lakes’ future time-to-anoxia changes across trophic states and climate scenarios.The alternative text for this image may have been generated using AI.Full size imageThe 73 lakes analyzed in this study are depicted as points colored by trophic state; the nine spotlight lakes are highlighted and colored accordingly: eutrophic (orange), mesotrophic (green), and oligotrophic (blue). a–i Projected temporal trends in time during stratification to reach anoxia (2015–2099) for the spotlight lakes under three climate scenarios: SSP1-2.6 (light blue), SSP3-7.0 (orange), and SSP5-8.5 (dark red). The average proportion of anoxic days during stratification (anoxic ratio) (mean anoxic days/mean stratification duration) is color-coded for each scenario and shown in the top-right corner. The anoxic ratio provides a complementary indicator of hypolimnetic O2 dynamics that is distinct from projected changes in time to reach anoxia. Lake latitude (Lat), longitude (Lon), maximum depth (Zmax), and mean temperature (Temp) are provided in the bottom-left corner. a–c Three oligotrophic, d–f three mesotrophic, and g–i three eutrophic lake systems, spanning multiple climate zones (Table 1), are shown. Shaded regions indicate standard errors of predicted mean time-to-anoxia.Conversely, the mesotrophic systems displayed intermediate and progressive declines in time-to-anoxia, with Zlutice (Fig. 3d) and Vendyurskoe (Fig. 3f) projected to lose more than ~20 days by the end of the century under SSP5-8.5, while Mt. Bold (Fig. 3e) exhibited a more pronounced decline in the latter half of the century, with time-to-anoxia falling below ~70 days. Among the spotlight mesotrophic lakes, Mt. Bold exhibited the highest anoxic ratios (~67%), ranking among the most anoxia-prone spotlight lake systems under SSP5-8.5. This exceeded the projected anoxic ratio of Zlutice (~31%) by more than twofold and was markedly higher than that of Vendyurskoe (~14%). Furthermore, under SSP5-8.5, Mt. Bold was projected to experience the longest mean stratification duration (~238 days), exceeding that of Zlutice (~191 days) and more than doubling that of Vendyurskoe (~87 days). Under SSP1-2.6 and SSP3-7.0, Mt. Bold and Zlutice were projected to display persistent (~65–67%) and episodic (~26–29%) anoxia, respectively. In contrast, Vendyurskoe exhibited comparatively low projected anoxic ratios (~6% under SSP1-2.6 and ~11% under SSP3-7.0), reinforcing the marked heterogeneity in anoxia responses across lakes of similar trophic states. Projected stratification durations under SSP1-2.6 and SSP3-7.0 were ~231 and ~236 days for Mt. Bold, ~185 and ~192 days in Zlutice, and ~75 and ~82 days at Vendyurskoe, respectively.Eutrophic systems were predicted to show more pronounced declines in time-to-anoxia and sustained increases in hypolimnetic anoxia prevalence. By the end of the century, Mendota (Fig. 3i) and Hulun (Fig. 3h) were projected to experience a reduction in oxic duration of at least ~10 days under both SSP3-7.0 and SSP5-8.5 during summer stratification. Sau (Fig. 3g) exhibited the most pronounced decline in time-to-anoxia, decreasing from ~95 days at the start of the simulation period to under ~70 days by 2099 under both SSP3-7.0 and SSP5-8.5. Furthermore, Sau is projected to display the highest anoxic ratios among all the spotlight lakes, reaching ~71–74%, depending on the climate scenario. Despite these eutrophic systems being in differing climates with varying morphometries, they converged towards similarly short anoxic onsets and maintained persistent anoxic ratios, exacerbating the detrimental role of high productivity and elevated nutrient levels under warming climates.The magnitude of warming across climate scenarios was a significant determinant of shifting habitat conditions, with SSP5-8.5 inducing the strongest degradation of O2 conditions. As a result, an increasing number of lakes at both ends of the trophic gradient (eutrophic or oligotrophic) were predicted to exhibit end-of-summer-stratification O2 concentrations unsuitable for cold dwelling fish species (<5 mg L−1)46, with some lakes potentially becoming hypoxic (<2 mg L−1), or even anoxic (<0.5 mg L−1) (Fig. 4). About 95–100% of eutrophic lakes were predicted to fall below the 5 mg L−1 threshold under all scenarios (Fig. 4a). Under SSP3-7.0 and SSP5-8.5, hypoxic eutrophic lakes were expected to rise to 80–96% (Fig. 4b), and anoxic eutrophic lakes to 75–90% (Fig. 4c). In contrast, oligotrophic lakes showed greater resilience, with 80–94% of them falling below the 5 mg L−1 threshold across all three scenarios (Fig. 4a), with an increasing trend under SSP3-7.0 and SSP5-8.5. Under SSP5-8.5, the prevalence of hypoxia in oligotrophic lakes was projected to rise markedly, affecting 75% of these systems by the end-of-the-century, more than doubling from 32% at the beginning of the simulation period (Fig. 4b). Concurrently, end-of-summer-stratification anoxia increased from 13% of lakes to 57% of oligotrophic lakes over the same timeframe (Fig. 4c). Under SSP3-7.0, hypoxia in oligotrophic lakes increased from 38% at the start of the simulation to 63% by the end (Fig. 4b), while anoxia increased from 13% in 2015 to 45% in 2099 (Fig. 4c). Overall, these results reinforced that being oligotrophic does not confer full protection against hypoxia or anoxia: elevated hypolimnetic temperatures accelerated O2 depletion even in oligotrophic lakes, although such lakes generally maintain higher O2 levels than those with higher trophic states.Fig. 4: Share of lakes falling below the O2 threshold under varying climate scenarios.The alternative text for this image may have been generated using AI.Full size imageProportion of lakes at the two extreme trophic states projected to reach minimum end-of-summer dissolved O2 concentrations in the hypolimnion during stratification: a below the threshold for cold-water fish survival (<5 mg L−1), b hypoxic conditions (<2 mg L−1), and c anoxia (<0.5 mg L−1). Projections are based on climate scenarios, with picontrol (dashed and black), SSP1-2.6 (light blue), SSP3-7.0 (orange), and SSP5-8.5 (dark red) shown.Translating changes in stratification duration and hypolimnion temperatures into O2 dynamics allows us to provide lake-specific projections. A lake’s trophic state influenced the future development of its hypolimnion O2 concentrations, where more productive lakes (mesotrophic and eutrophic) showed a stronger trajectory towards anoxia (Figs. 4 and 5). Compared with the first decade of our simulations (2015–2020), oligotrophic lakes experienced a milder progression in the direction of anoxia for the last decade of our simulations (2091–2099). In most of the oligotrophic lakes, the predicted changes were along the stratification axis, particularly in SSP1-2.6. Whereas under SSP3-7.0 and SSP5-8.5, an increasing number of the oligotrophic lakes were predicted to also shift along the hypolimnion temperature axis, signifying the impact of warming hypolimnia, and thus the trajectory towards anoxia in the last decade of the century (Fig. 5). A majority of the eutrophic lakes were already predicted to be anoxic in the first decade of the simulation, with their anoxic state predicted to worsen at the end-of-the-century. Eutrophic lakes, similar to mesotrophic lakes, showed sharper increases in anoxia due to prolonged stratification and rising hypolimnion temperatures. These effects were depth-dependent, with shallow lakes more impacted by the combination of temperature increases and extended stratification.Fig. 5: Future hypolimnetic O2 trajectories for individual lakes.The alternative text for this image may have been generated using AI.Full size imageIndividual lake O2 concentration trajectories are shown from the first decade of simulations (2015–2020; downward-pointing unfilled triangles) to the last decade (2090–2099; upward-pointing filled triangles). Triangle color and fill indicate lake depth, ranging from 1.6 m (green) to 304.8 m (dark blue). Contour lines indicate oxygen depletion thresholds of 0.5 mg L−1 (dotted), 2 mg L−1 (dashed), and 5 mg L−1 (solid). Lakes are categorized by trophic state: a–c oligotrophic, d–f mesotrophic, and g–i eutrophic. Panels correspond to different climate scenarios: SSP1-2.6 (a, d, g), SSP3-7.0 (b, e, h), and SSP5-8.5 (c, f, i).DiscussionOur results quantify the widespread threat that a changing climate poses to lake hypolimnetic O2 dynamics across trophic states, stratification patterns, and climatic regions. Hypolimnetic O2 conditions are a fundamental indicator of lake health, with the absence of O2 threatening biodiversity, aquatic habitats, and ecosystem services6,9,47. Observational evidence of widespread hypolimnetic deoxygenation and the progression of hypoxia and anoxia, facilitated by prolonged stratification9,48,49, aligns with our projected O2 declines. For all 73 lakes, end-of-summer-stratification hypolimnetic O2 concentrations are projected to decline, with earlier onset of anoxia and increased anoxic extent, intensifying under stronger warming scenarios. Among modeled oligotrophic lakes, at least 15% are projected to be anoxic in the last decade of this century under SSP1-2.6, increasing to at least 38% under SSP5-8.5 (Fig. 5). Comparable increases occur across the other trophic states, with anoxia in mesotrophic lakes increasing from at least 76% to 96%, and in eutrophic systems from at least 86% to 95% under SSP1-2.6 and SSP5-8.5, respectively. These projections are consistent with empirical evidence from approximately 8000 lakes, where anoxia occurrence has increased from ~39 to 61%9, and are consistent with documented positive feedbacks that intensify and prolong hypolimnetic deoxygenation50, including the effect of stratification prolongation in increasing hypoxia by 0.9–1.7% per decade48. Together, these lines of evidence indicate intensifying climate-driven hypolimnetic O2 declines in both nutrient-rich and historically resilient systems.In agreement with previous studies27,30, we project lengthening stratification durations across all lakes through 2099, with severity dependent on the climate scenario (Fig. 1). Prolonged stratification extends the active period of hypolimnetic O2 depletion48, resulting in increased cumulative O2 loss and expanded anoxic ratios (Fig. 2), and ultimately hypolimnetic O2 concentrations falling below critical thresholds8,11,51,52. As vertical mixing is suppressed during stratification, hypolimnetic O2 cannot be replenished from surface waters.Increasing hypolimnion temperatures further accelerate deoxygenation, rendering lakes that experience hypolimnetic warming particularly vulnerable. In particular, monomictic lakes exhibit climate-induced hypolimnetic warming that drives temperature-dependent alterations in O2 dynamics. Temperature regulates internal biogeochemical processes in lakes by raising hypolimnion temperatures and reducing initial O2 concentrations at stratification onset (Figs. 1 and S1)53,54. Previous studies support these findings, reporting both observed and predicted increases in lake temperatures globally, suggesting deleterious implications on lake water quality15,25,34. We project an increased likelihood of lakes falling below ecologically critical O2 thresholds, with the strongest effects under SSP5-8.5 (Figs. 1 and 4). Hypolimnion temperature emerges as a crucial integrator of climate effects, given the non-linear increase in O2 depletion with temperature. Warmer lakes, particularly those in tropical regions, are more likely in our projections to experience hypoxic and anoxic conditions (Figs. 3 and 5), even under an oligotrophic state, consistent with their heightened vulnerability to climate-driven warming28.Despite these consistent large-scale trends, we project substantial variability in hypolimnetic O2 responses among lakes of the same trophic state. Differences in physical and climatic properties lead to pronounced variability in projected hypolimnetic warming and O2 depletion, with lakes of the same trophic state exhibiting divergent future O2 trajectories depending on their climatic context (Figs. 3, S4, and S5). Baseline thermal structure and stratification regimes, influenced by latitude and regional climate55, further shape these outcomes, where warmer and lower-latitude systems are more prone to higher hypolimnetic temperatures and accelerated O2 loss under extended stratification27,48,50,55. In line with this, we predict wide within-group variability in time-to-anoxia in all trophic states (Figs. 3 and S4), identifying temperature as a dominant control on hypolimnion O2 dynamics within our modeling framework. We also recognize that additional biological and biogeochemical processes not represented in the model further modulate lake-specific vulnerability, as discussed in the subsequent paragraphs.Although our model captures the dominant physical and temperature-driven controls on hypolimnetic O2 dynamics, a comprehensive quantitative assessment of all controlling factors was beyond the scope of our modeling framework, primarily because of data limitations across the full lake data set. Lake morphometry strongly modulates stratification responses50,56 by controlling both hypolimnetic buffering capacity and the coupling between sediment oxygen demand (SOD) and the overlying hypolimnetic water column. While hypolimnetic volume is explicitly represented and benthic demand is included within the VHOD rates, the model does not resolve key lake-specific morphometric shape or hypsometry. Therefore, variations among lakes in the ratio of hypolimnetic volume to sediment surface area are not captured. Because this ratio regulates the balance between benthic O2 consumption relative to the hypolimnetic O2 pool, our model likely understates O2 depletion in lakes with low hypolimnetic volume per unit sediment area, where a given O2 demand produces more rapid drawdown57. The same morphometric characteristics also modulate climate sensitivity, with small and shallow stratifying lakes shown to respond particularly strongly to prolonged stratification and warming30,51, due to reduced hypolimnetic O2 storage and greater sensitivity to hypolimnetic warming. Moreover, explicitly modeling SOD is dependent on other site-specific factors, including organic matter supply58, deep-layer photosynthesis59, and bioturbation activity60, further contributing to uncertainty in hypolimnetic O2 projections41.In addition to these structural limitations, thermal structure and stratification responses are also influenced by optical and regional characteristics that are not explicitly represented in the model. Additional modifiers of thermal structure, such as water color and dissolved organic carbon, further influence lake thermal regimes by enhancing light attenuation and surface heat absorption61,62, thereby potentially suppressing vertical mixing63 and promoting hypolimnetic deoxygenation9. In the underlying physical models, each lake is represented using a lake-specific but static light extinction coefficient that distinguishes clear-water from brown or turbid systems, thereby neglecting both seasonal dynamics and long-term variability in optical conditions. In addition, heat fluxes associated with inflows and outflows are not represented, a limitation widely recognized in multi-lake temperature modeling efforts64. Although morphometric effects on thermal structure are explicitly accounted for, these assumptions limit the ability to capture interannual and long-term variability in stratification and warming among lakes. Consequently, lakes exposed to similar climatic forcing may experience divergent thermal trajectories65, partly driven by unrepresented changes in optical conditions and hydrological connectivity55.Beyond the physical drivers explicitly represented in our modeling framework, future O2 dynamics in lakes will also be shaped by interacting biogeochemical and landscape processes. Lake temperature increases may enhance productivity, potentially disrupting ecosystem functioning36,66,67,68, and worsen O2 conditions synergistically through increased organic matter export to sediments and accelerated microbial remineralization41,69,70. These processes are strongly modulated by watershed inputs and hydrologic change. The lengthening of growing seasons and increased metabolic rates, stemming from rising temperatures enhancing lake productivity, can potentially amplify organic matter production and export to hypolimnia, where accelerated microbial respiration intensifies O2 depletion. Alterations in precipitation regimes71 are likely to further intensify the effects of increasing temperatures, including more frequent extreme rainfall events72 that can increase terrestrial nutrient and organic carbon loading71,73, and potentially alter lake residence times74. Land-use changes within lake catchments71,75, such as urbanization, agricultural intensification, and wetland loss, can further amplify external nutrient and carbon inputs. Collectively, these factors may shift lakes toward higher trophic states (i.e., from mesotrophic to eutrophic conditions), accelerating O2 depletion beyond the rates projected by our model. Although our approach is simple, many of these climatic and watershed influences are partly captured by key model inputs: hypolimnion temperature, stratification duration, and trophic state.In addition to physical and landscape controls, ecosystem structure can substantially modify hypolimnetic O2 demand. Hypolimnetic O2 demand and organic matter export efficiency can differ between diatom-dominated systems and more mobile/buoyant algal communities76,77,78. Diatoms are often dominant in initial spring blooms79,80 and are characterized by quick sinking to lake hypolimnia and sediment, where they cause an uptick in hypolimnetic O2 demand early in the stratified season80, setting the scene for hypoxic and anoxic conditions76. In contrast, buoyant cyanobacteria largely remain in the epilimnion where a significant proportion of their organic matter is decomposed81, with later-season contributions to hypolimnetic O2 demand arising from the fraction that sinks76. Furthermore, algal community composition and the quantity and quality of organic matter exported to the hypolimnia are shaped by trophic interactions, including zooplankton grazing and fish predation, thereby feeding back on hypolimnetic O2 consumption82. The pronounced within-trophic state variability observed in our simulations is consistent with well-established mechanistic controls on lake O2 dynamics, and should be interpreted in the context that additional lake-specific physical57, optical46, and ecological properties50, beyond those represented in our model, are also important determinants of divergent responses to climate warming. Future syntheses may integrate this additional complexity in morphometry, optical properties, and ecosystem structure to better identify lakes at greatest risk. But, following the concept of parsimony, we emphasize the value of our comparatively simple model, which captures key controls on O2 dynamics and enables efficient application at the global scale.Overall, our results indicate that lakes will face intensifying challenges from climate change, particularly with respect to hypolimnion O2 depletion. Rising temperatures will intensify the combined impacts of reduced initial O2 concentrations, prolonged stratification, and enhanced metabolic demand, producing impacts on hypolimnetic O2 dynamics that increasingly resemble those of eutrophication83. Consequently, lake water quality is likely to deteriorate further under continued warming. Hence, actionable strategies to mitigate the impacts of climate warming are necessary to safeguard lake ecosystems and their associated habitats, with a particular focus on reducing the risks of hypolimnetic hypoxia or anoxia. Short-term strategies, including hypolimnetic aeration, may help sustain high-value systems, such as drinking-water reservoirs or conservation-priority lakes. However, such engineered solutions are costly, constraining widespread application.Our findings emphasize that mitigating future warming impacts on hypolimnia O2 conditions could be partially achieved by targeting lake trophic states44. This necessitates reducing the human footprint on nutrient pollution, the main driver of cultural eutrophication35,84. Lake restoration and nutrient load reductions will therefore become increasingly vital, supporting recycling, remediation, and circular management85 while also contributing to progress towards relevant SDGs4. Under more severe warming outcomes, proportionally greater and more stringent nutrient reductions will be required. Proactive and integrated water-quality management3,4,86 will thus be essential to safeguard lake ecosystems amid increasing climatic and anthropogenic pressures.MethodsStudy sitesSimulations from 73 stratifying lakes across the globe from the “local lakes” category of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP) were used in this study. These 73 lakes have water temperature observation data and hypsographic curves, although at varying temporal and vertical resolutions. Using the Carlson’s Trophic State Index, we grouped the lakes by trophic state, based on historical (within 10 years prior to 2015) annual means of either phosphorus, chlorophyll-a, or Secchi depth (26 oligotrophic, 25 mesotrophic, and 22 eutrophic). These lakes also range in mean depths (i.e., 1.7–304.8 m), elevation (i.e., −210 m a.s.l.–4300 m a.s.l.), area (i.e., 0.01–2700 km2), and cover 5 climate gradients (continental, dry, polar, temperate, and tropical) (Table 1 and Fig. 3), with lake climates assigned using the Koppen-Geiger system87. Shallow lakes are included in our dataset since they provide critical insight into how stratification duration and intensity influence hypolimnetic O2 depletion, as they are often overlooked88. Despite their brief stratification, these lakes, representative of the majority globally89, experience strong warming and consequently heightened O2 depletion9.Multi-model projections of temperature and stratification dataLake projections examined in this study consisted of a 15-member ensemble of lake-climate (three lake models driven by five climate models) model simulations. The three 1D physical lake models were applied, using LakeEnsemblR90, to simulate the impacts of climate change on lakes in regards to their vertical temperature and stratification patterns64. Of these models, two are turbulence-based models; the general ocean turbulence model (GOTM – lake-branch version 5.4.0)91 and Simstrat (version 2.4.1)92,93 and the third is an integral energy model, the general lake model (GLM) (version 3.1.0)94. The lake models were driven by bias-corrected climate forcing data from the Coupled Model Intercomparison Project (CMIP6), incorporating five global climate models (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) and four experiments95. The experiments were designed to simulate the projected evolution of the climate over the period 2015–2100 under different greenhouse gas (GHG) emission scenarios. For our analysis, we used data spanning 2015–2099 for lakes in the Northern Hemisphere and 2015–2100 for lakes in the Southern Hemisphere, thereby aligning with the timing of their respective summer seasons (Fig. 3). These scenarios included the picontrol scenario, representing pre-industrial climate forcing based on 1850 GHG levels, and three shared socioeconomic pathway (SSP) scenarios: SSP126 (SSP1-RCP2.6) for low GHG emissions, SSP370 (SSP3-RCP7) for high GHG emissions, and SSP585 (SSP5-RCP8.5) for very high GHG emissions96.The meteorological forcing data from the GCMs, provided at a daily resolution and used to drive each lake model included near-surface air temperature (°C), near-surface wind speed (m s−1), surface downwelling long-wave and short-wave radiation (W m−2), precipitation (kg m−2 s−1), surface air pressure (Pa), and near-surface relative humidity (%) available at a spatial resolution of 0.5°. Additional lake model inputs included lake-specific temperature observations, bathymetries, and lake characteristics (elevation, latitude, longitude, maximum depth and light extinction). All three lake models were calibrated based on wind scaling (0.25–1.5), short-wave radiation scaling (0.7–1.3), and light extinction coefficient scaling (0.7–1.3, ~30% of lake specific default value), as well as three model specific parameters for each individual lake model to locally-observed water temperature profiles, as described by Feldbauer et al.64. In GLM, calibration focused on the mixing efficiencies of hypolimnetic turbulence (0.1–2), convective overturn (0.1–0.3), and unsteady turbulence effects (0.35–0.65). For Simstrat, the calibrated parameters included the fraction of wind energy transferred to Seiche energy (0.0008–0.003), the geothermal heat flux (0.0–0.5, W m−2), and the bottom drag coefficient (0.00075–0.00325). In GOTM, calibration targeted the minimum turbulent kinetic energy (1.5 ⋅ 10−7–1 ⋅ 10−5, m2 s−2), physical bottom roughness (500–3000, m), and constant eddy diffusivity (2.5 ⋅ 10−4–7.5 ⋅ 10−4, m2 s−1).End-of-summer-stratification O2 calculationsWe applied a hypolimnion O2 depletion model, devised by Nkwalale et al.43, to project future hypolimnion O2 concentrations at the end-of-summer-stratification (({{{rm{O}}}}_{2}left({t}_{{end}}right))) for the 73 lakes. The model predicts O2 depletion during lake stratification using three key components; the volume and time-averaged summer hypolimnion temperature (as one whole compartment) (({{boldsymbol{T}}})), summer stratification duration (({t}_{{stratification}})), and the lake’s trophic state via the volumetric hypolimnetic O2 depletion rate, (VHOD). The trophic state dependent standard rates ((VHO{D}_{{trophic}.{state}})) at a reference temperature (({T}_{{REF}})) were independently parameterized from literature43,50,97,98,99 (Fig. S2) using Carlson’s trophic state index (based on Secchi disk depth (m), total phosphorus (µg L−1), or chlorophyll-a (µg L−1)). Furthermore, the rates were temperature corrected (({{boldsymbol{k}}}))100, from the reference temperature to the study lake’s yearly summer stratified hypolimnion temperature:$${VHOD}left({T}_{{trophic}.{state}}right)=,{{VHOD}}_{{trophic}.{state}}{k}^{left(T-{T}_{{REF}}right)}$$
(1)
The model bases O2 depletion on the initial O2 concentration following spring mixing at 100% saturation (({{{rm{O}}}}_{2}left({t}_{{0}}right))). Volumetrically averaged hypolimnion temperatures from the lake models, obtained using rLakeAnalyzer101, were used to calculate initial O2 concentrations, as described in Nkwalale et al.43, following standard approaches in lake O2 modeling102. Stratification duration inputs (({t}_{{stratification}})), obtained through the lake simulations, were defined as the longest continuous summer period of stratification exceeding 5 days28. Following ISIMIP protocols, stratification was defined as a water column density difference greater than 0.1 kg m−3 between the bottom and the surface of a lake27. We re-arranged our model assuming zero-order kinetics:$$frac{{{mathrm{dO}}}_{2}}{{{rm{d}}}t}={VHOD}({T}_{{trophic}.{mathrm{state}}})$$
(2)
to:$${{{rm{O}}}}_{2}left({t}_{{end}}right)=,{{{rm{O}}}}_{2}left({t}_{0}right)-,{t}_{{stratification}}{VHOD}left({T}_{{trophic}.{state}}right)$$
(3)
To enable comparisons among the differing lake systems, we applied two metrics derived from this model. First, we calculated the shifts in time under stratification for a lake to reach anoxic (({t}_{{anoxia}})) (time-to-anoxia) or hypoxic (({t}_{{hypoxia}})) (time-to-hypoxia) conditions by rearranging the model equation as follows:$${t}_{{anoxia}}=({{{rm{O}}}}_{2}left({t}_{0}right)-0.5)/{VHOD}left({T}_{{trophic}.{state}}right)$$
(4)
$${t}_{{hypoxia}}=({{rm{O}}}_{2}({t}_{0})-2)/{VHOD}left({T}_{{trophic}.{state}}right)$$
(5)
The numbers in the numerator represent the values of the O2 thresholds (0.5 mg L−1 for anoxia and 2.0 mg L−1 for hypoxia). The anoxic period is defined as starting at the first onset of anoxia and persisting until the subsequent winter, with no recovery within the same season, and the hypoxia is defined likewise. Additionally, we employed anoxic ratio as an integrative metric to measure lake resilience, which captures the proportion of the stratified period during which O2 concentrations are below the anoxic threshold, calculated as follows:$${anoxic},{ratio}=({t}_{{stratification}}-,{t}_{{anoxia}})/{t}_{{stratification}}$$
(6)
The O2 model performance was previously validated using 5 German lake systems (see Nkwalale et al.43), proving to be good (R2 = 0.6), especially for large-scale and multi-lake systems modeling exercises. The model prioritizes deciphering general trends, simplicity in application, and broad transferability, hence it employs a zero-order kinetics approach through its assumption of linear oxygen depletion over time. Thus, negative O2 concentrations may occur in the model, especially in warm climates or high trophic states, which accelerate O2 depletion. Although the negative O2 concentrations in the results seem unrealistic, a zero-order kinetics model allows for better reflections of a lake’s chemical O2 demand, i.e., the O2 needed to oxidize reduced substances like NH4+, H2S, Fe2+, and Mn2+41,103. In essence, this O2 deficit must be met before stable oxic conditions can be established. Thus, a highly negative O2 concentration indicates the need for intense reaeration before replenishment.Observational O2 data from 11 lakes (Black Oak, Delavan, Green, Kinneret, Mozhaysk, Sammamish, Sunapee, Tarawera, Two Sisters, Washington, and Wingra) was used to validate our modeling framework following that described in Nkwalale, et al.43. Although a moderate R2 of 0.41 is achieved for the framework (Fig. S6), this level of performance is reasonable given its application across highly diverse lake systems spanning the equatorial to polar regions. Furthermore, uncertainty introduced by the O2 model’s VHOD parameterization, along with the broader inherent uncertainties from hindcasting historical thermal and stratification patterns in the 11 lakes using the same approach as described above for future projections (three lake models driven by five climate models), contributes to the overall model uncertainty.Limitations of the studyOur approach relied on several assumptions, notably the complete isolation of hypolimnia conditions during stratification. Although these assumptions may influence individual lake projections, robust conclusions on climate impacts and anoxia risk can be drawn from the modeling framework, which performs relatively well (Fig. S6) considering the global expanse covered in lakes occurring between the equator and the poles. Uncertainties from the climate/lake physics models are explicitly included in our results (Figs. 1 and 2) and further examined in Feldbauer et al.64. Furthermore, our modeling framework neglects explicitly resolving for the seasonal deepening of the thermocline and the associated erosion of the hypolimnion during the course of the summer stratified period. However, hypolimnetic temperatures used to derive the VHODs were calculated as daily, volume-weighted means of the hypolimnion and subsequently averaged over the stratified period, such that late-summer warming and the concurrent reductions in hypolimnetic volume are indirectly incorporated in the empirical O2 demand rates. Accounting for the changing hypolimnetic volume and sediment-water ratios could affect estimates of the timing of anoxia, particularly in shallow lakes48, but is unlikely to alter predicted stratification duration or results for deep systems. This approach is centered on gaining broad applicability, lower uncertainty, and direct linkages to observable, management-relevant outcomes. Thus, it is an efficient and scientifically robust choice in global lake modeling applications.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The databases used in this analysis were publicly available. We gratefully acknowledge the efforts of Lewis et al.104 for compiling and sharing the dataset, which we used for parameterization of the volumetric hypolimnetic depletion rates in the O2 model, which is archived with the Environmental Data Initiative (EDI) under a CC-BY 4.0 license (https://doi.org/10.6073/pasta/2cd6628a942de2a8b12d2b19962712a0). All future climate scenario and physical lake model input data for the O2 model (stratification duration and hypolimnion temperature) across the 73 lakes, including their observed data used in classifying their trophic state, is publicly accessible through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) data portal (https://data.isimip.org/).
Code availability

Data and R-scripts (version 4.4.1) used for the main analysis and plotting are available at https://doi.org/10.5281/zenodo.19543982.
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Lewis, A. S. et al. Dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes from 1921-2022. (Environmental Data Initiative, 2023).Download referencesAcknowledgementsThis work was made possible through international team efforts facilitated by the Global Lake Ecological Observatory Network (GLEON) and the inter-Sectoral Impact Model Intercomparison Project (ISIMIP).FundingL.G.T.N., K.R., and D.M.B. disclose support for the research of this work from the European Union’s Horizon Europe Marie Sklodowska-Curie [grant no. 956623] (inventWater). J.F. discloses support for publication of this work from the German Federal Ministry of Research, Technology and Space [project FKZ 01LR2005A1-G1]. J.P.M. discloses support for the research of this work from the European Union’s Horizon 2020 [grant no. 101017861] (SMARTLAGOON), FORMAS, and the European Union’s Horizon Europe Programme [grant no. 101060874] (MEWS). T.B., R.L. disclose support for the research of this work from the Aarhus University Research Foundation [grant no. AUFF-E-2023-7-11]. T.M. declares no relevant funding. Open Access funding enabled and organized by Projekt DEAL.Author informationAuthor notesThese authors jointly supervised this work: Karsten Rinke, Robert Ladwig.Authors and AffiliationsDepartment of Lake Research, Helmholtz Center for Environmental Research-UFZ, Magdeburg, GermanyLipa G. T. Nkwalale & Karsten RinkeInstitute of Hydrobiology, Technische Universität Dresden, Dresden, GermanyJohannes FeldbauerDepartment of Ecology and Genetics, Uppsala University, Uppsala, SwedenJorrit P. MesmanDepartment of Ecoscience, Aarhus University, Aarhus, DenmarkTuba Bucak & Robert LadwigDepartment of Community and Ecosystem Ecology, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, GermanyTom ShatwellCentre for Advanced Studies of Blanes (CEAB), Spanish National Research Council (CSIC), Blanes, SpainDaniel Mercado-BettinAuthorsLipa G. T. NkwalaleView author publicationsSearch author on:PubMed Google ScholarKarsten RinkeView author publicationsSearch author on:PubMed Google ScholarJohannes FeldbauerView author publicationsSearch author on:PubMed Google ScholarJorrit P. MesmanView author publicationsSearch author on:PubMed Google ScholarTuba BucakView author publicationsSearch author on:PubMed Google ScholarTom ShatwellView author publicationsSearch author on:PubMed Google ScholarDaniel Mercado-BettinView author publicationsSearch author on:PubMed Google ScholarRobert LadwigView author publicationsSearch author on:PubMed Google ScholarContributionsLipa G. T. Nkwalale—conceptualization, methodology, coding, visualization, project administration, writing-original draft. Karsten Rinke—conceptualization, methodology, visualization, supervision, funding, writing-original draft. Johannes Feldbauer—methodology, coding, visualization, writing-review and editing. Jorrit P. Mesman—methodology, writing-review and editing. Tuba Bucak—coding, visualization, writing-review and editing. Tom Shatwell—visualization, writing-review and editing. Daniel Mercado-Bettin—coding, visualization, writing-review and editing. Robert Ladwig—conceptualization, methodology, coding, visualization, supervision, writing-original draft.Corresponding authorCorrespondence to
Lipa G. T. Nkwalale.Ethics declarations

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Communications Sustainability thanks the anonymous reviewers for their contribution to the peer review of this work. Primary handling editors: Somaparna Ghosh. A peer review file is available.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationTransparent Peer Review File (download PDF )Global Lake Anoxia is Projected to Intensify Under Climate Change: Supplementary Material (download PDF )Reporting Summary (download PDF )Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/.
Reprints and permissionsAbout this articleCite this articleNkwalale, L.G.T., Rinke, K., Feldbauer, J. et al. Global lake anoxia is projected to intensify under climate change.
Commun. Sustain. 1, 86 (2026). https://doi.org/10.1038/s44458-026-00093-zDownload citationReceived: 13 November 2025Accepted: 12 May 2026Published: 23 May 2026Version of record: 23 May 2026DOI: https://doi.org/10.1038/s44458-026-00093-zShare 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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AbstractLack of regular monitoring of water sources may lead to undetected contamination, posing serious health risks and necessitating regular water quality assessments. Sampling for physicochemical, microbial analyses, and online surveys across three higher education institutions was done to evaluate water quality. Spatiotemporal variations among physicochemical parameters showed that the pH, EC, and TDS decreased during the wet season, reflecting the dilution effect of rain. However, DO increased from 0.67 to 4.83 ppm, indicating better aeration. PCA showed seasonal variability, whereas the correlation matrix highlighted both positive and negative interrelationships between temperature-pH (− 0.25), DO-ORP (0.11), and TDS-EC (1.00). Potentially toxic metals were either negligible or not detected. Metagenomics revealed the presence of 29 bacterial phyla, 61 classes, 124 orders, 241 families, and 457 genera. Canonical correspondence analysis showed the influence of Mo, EC, salinity, and TDS on Bacteroidota, Chloroflexota, Cyanobacteriota, and Planctomycetota, whereas Verrucomicrobiota, Acidobacteriota, Chlamydiota, Candidatus Melainabacteria, Bdellovibrionota, and Deinococcota were affected by Ni, pH, and COD. Pathogen mapping revealed the presence of Vibrio, Pseudomonas, Enterobacter spp., etc., responsible for diseases such as cholera, diarrhea, and typhoid. Also, occupants’ perception about the water quality emphasizes the need for better management of drinking water in HEIs.

AcknowledgementsWe thankfully acknowledge the Principal, Miranda House, University of Delhi, Delhi, India, for providing financial and infrastructural support to carry out the study. Deepika Tandon, Associate Professor, Department of English, Miranda House, was consulted for the same and gratefully acknowledged for the inputs regarding the language and grammar of the paper. Prafullit Bisht, Assistant Professor, Department of Geography, Miranda House is also acknowledged for rendering help in preparation of study area map.FundingThis work was supported by Miranda House R&D project Grant MH-32/2024.Author informationAuthors and AffiliationsMolecular Biotechnology and Bioinformatics Laboratory, Department of Zoology, Miranda House, University of Delhi, Delhi, IndiaRekha Kumari, Ritesh Kumar & Shailender KumarDepartment of Environmental Studies, University of Delhi, Delhi, IndiaChirashree GhoshDepartment of Life Science, School of Earth, Environmental and Biological Sciences, Central University of South Bihar, Gaya, Bihar, IndiaRitesh KumarDepartment of Botany, Miranda House, University of Delhi, Delhi, IndiaRashmi ShakyaDepartment of Bio-Sciences and Technology, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana, 133207, IndiaAdesh K. SainiCentre for Environmental Studies and Disaster Management, Miranda House, University of Delhi, Delhi, IndiaShailender KumarAuthorsRekha KumariView author publicationsSearch author on:PubMed Google ScholarChirashree GhoshView author publicationsSearch author on:PubMed Google ScholarRitesh KumarView author publicationsSearch author on:PubMed Google ScholarRashmi ShakyaView author publicationsSearch author on:PubMed Google ScholarShailender KumarView author publicationsSearch author on:PubMed Google ScholarAdesh K. SainiView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Rekha Kumari.Ethics declarations

Competing interests
The authors have no conflict of interest.

Ethical approval
All the experimental protocols, especially the online survey questionnaire, were approved by the institutional review committee (IRC) under the R&D cell, Miranda House, University of Delhi, and an ethical waiver was given via reference no. MH-32/2024/IRC-3/10-02-2024.

Informed consent
Informed consent is a moral principle that respects the autonomy and personhood of individuals. It allows individuals to make independent decisions about whether to participate in research.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1. (download DOCX )Supplementary Material 2. (download TIF )Supplementary Material 3. (download TIFF )Supplementary Material 4. (download TIFF )Supplementary Material 5. (download TIFF )Supplementary Material 6. (download DOCX )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 articleKumari, R., Ghosh, C., Kumar, R. et al. Assessment of water quality and microbial contamination in institutional water resources: a necessity to understand health risks.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-53672-4Download citationReceived: 26 September 2025Accepted: 13 May 2026Published: 22 May 2026DOI: https://doi.org/10.1038/s41598-026-53672-4Share 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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KeywordsPhysicochemical parametersWater qualityMonitoringMetagenomicsPathogen mapping More

AbstractStreamflow droughts threaten water, food, and energy security, often triggering socioeconomic crises. Compound streamflow droughts arise when low streamflow coincides with or is preceded by precipitation deficits and followed by elevated evaporative demand within ±2 months of onset. Yet the role of onset seasonality in shaping drought hazards remains largely unexplored. Using 374 global daily streamflow records (1901 − 2023), we demonstrate that over 70% of catchments show significant links between onset seasonality and drought severity. Since the 1900s, modest-to-extreme drought frequency has markedly increased (0.17 ± 0.14 to 5.2 ± 0.52 events/decade), with more than half beginning during March–May. Compound droughts display wider uncertainty ranges than uncompounded extremes, indicating more variable and less predictable evolution, and show pronounced clustering in arid (~44%) and sub-humid (23%) regimes, with hotspots in northwestern North America, central Europe, eastern South America, and northeastern Australia. Drought severity is amplified two-fold or more in ~56% of catchments under compound conditions when conditioned on onset seasonality, likely driven by land-atmosphere coupling. These patterns reveal hemispheric contrasts and pronounced seasonal clustering: ~37% of Northern Hemisphere sites experience drought onsets during June–August (boreal summer), whereas in the Southern Hemisphere, onsets are strongly concentrated during the extended austral summer (December–March). Additionally, 45% of Northern Hemisphere sites portray a southeastward clustering of elevated drought hazard, while 60% of Southern Hemisphere sites show a northwestward synchronization. Ignoring onset seasonality substantially underestimate compound drought risk, its propagation and associated impacts.

AcknowledgementsFunding for this work is supported by IIT Kharagpur’s open access agreements with Springer Nature. Indo-German Science and Technology Centre (IGSTC/WISER 2022/PG/47/2022-23/514) provided partial funding.FundingOpen access funding provided by Indian Institute of Technology Kharagpur.Author informationAuthors and AffiliationsAgricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, IndiaAparna Raut & Poulomi GanguliAuthorsAparna RautView author publicationsSearch author on:PubMed Google ScholarPoulomi GanguliView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Poulomi Ganguli.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 information (download PDF )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 articleRaut, A., Ganguli, P. Onset seasonality controls compound streamflow drought risk at a global scale.
npj Nat. Hazards (2026). https://doi.org/10.1038/s44304-026-00221-8Download citationReceived: 24 January 2026Accepted: 06 May 2026Published: 21 May 2026DOI: https://doi.org/10.1038/s44304-026-00221-8Share 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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AbstractAchieving advanced nitrogen removal in municipal wastewater treatment plants remains challenging under low-strength influent and seasonal temperature fluctuations. Here, we optimized a full-scale anaerobic/aerobic/anoxic process (40,000 m3/d) and evaluated its year-round performance for influent with biochemical oxygen demand of 49.1 ± 9.9 mg/L and total nitrogen of 18.2 ± 1.4 mg/L. By reducing the air-water ratio to 0.39 and shortening the anaerobic hydraulic retention time to 1.8 h, the system achieves effluent total nitrogen as low as 3.3 mg/L and maintains 4.1 mg/L even as the temperature declines from 24.6 °C to 11.3 °C. The combined measures preserve intracellular carbon in the anaerobic zone and limit aerobic over-oxidation, enabling sustained endogenous denitrification in the downstream anoxic zone and clarifier. Endogenous denitrifiers and hydrolytic bacteria are selectively enriched, and functional genes associated with intracellular carbon metabolism and denitrification show higher abundance. Compared to the parallel anaerobic/anoxic/oxic process treating the same influent, chemical use decreases by 40%, sludge production by 8%, and aeration energy by 73%. This work demonstrates that full-scale implementation of this process provides an energy-efficient solution for low-strength municipal wastewater treatment.

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AcknowledgementsThe authors would like to express their sincere gratitude to Xinkai Environment Investment Co. Ltd. for providing the full-scale facility and their extensive support throughout the study. We also thank the on-site operational staff for their invaluable assistance with daily monitoring and maintenance.FundingY.P. and L.Z. disclose support for the research of this work from the National Science and Technology Major project for Comprehensive Environmental Management in the Beijing-Tianjin-Hebei Region [grant number 2025ZD1203701]. X.G. discloses support for publication of this work from Beijing Municipal Postdoctoral Work Program [grant number 2025-ZZ-50]. Y.P. discloses support for the research of this work from the Beijing International Base for Smart Technology Innovation and Cooperation. Z.A., J.D. and X.C. declare no relevant funding.Author informationAuthors and AffiliationsNational Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing, PR ChinaZeming An, Xinjie Gao, Liang Zhang & Yongzhen PengXinkai Environment Investment Co. Ltd., Beijing, ChinaJing Ding & Xiaoxin CaoAuthorsZeming AnView author publicationsSearch author on:PubMed Google ScholarXinjie GaoView author publicationsSearch author on:PubMed Google ScholarJing DingView author publicationsSearch author on:PubMed Google ScholarXiaoxin CaoView author publicationsSearch author on:PubMed Google ScholarLiang ZhangView author publicationsSearch author on:PubMed Google ScholarYongzhen PengView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Yongzhen Peng.Ethics declarations

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Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary Information (download PDF )Transparent Peer Review file (download PDF )Source dataSource data (download ZIP )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 articleAn, Z., Gao, X., Ding, J. et al. Full scale optimization of an anaerobic/aerobic/anoxic process for nitrogen removal from low-strength municipal wastewater.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-73481-7Download citationReceived: 14 October 2025Accepted: 08 May 2026Published: 21 May 2026DOI: https://doi.org/10.1038/s41467-026-73481-7Share 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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AbstractThe Surface Water and Ocean Topography (SWOT) mission provides unprecedented spatial coverage and temporal resolution of inland water surface elevation (WSE) and extent. This study examines SWOT high-rate raster product during a drought-to-flood transition in the Paraná River floodplain. SWOT observations are evaluated using in situ and satellite datasets, including multispectral imagery and nadir radar altimetry. These multi-sensor comparisons are used to characterize the spatial and temporal consistency of SWOT observations in floodplains under contrasting hydrological conditions, from disconnected low-water stages to widespread inundation. Results show that SWOT captures water level variability in channels with Pearson correlation coefficients exceeding 0.87 and mean absolute errors between 0.09 m and 0.21 m. Comparisons with Sentinel-6 A and Sentinel-3B reveal coherent WSE profiles (correlations up to 0.78) for transects covering channels and vegetated wetlands. Floodplain inundation extents derived from SWOT show high Precision (> 0.96) relative to multispectral products but moderate Recall (0.38–0.50 for GLAD), indicating inundated areas not detected by multispectral sensors. The inundated fraction detected only by SWOT increases with the flood pulse, reaching 53% of total water extent at peak flood in vegetated wetlands. These findings demonstrate SWOT’s capability to monitor river-floodplain hydrodynamics and to improve large-scale water storage and budget assessments.

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AcknowledgementsThe authors thank the SWOT mission teams and data providers for their support.FundingThis work was supported by the French Space Agency CNES (Centre National d’Études Spatiales) under the TOSCA program “Wetland Hydrology and GreenHouse Gases using SWOT (WHYGHGS)” (Grant No. T34–4500082211), and by the MELICERTES project (ANR-22-PEAE-0010) funded by the French National Research Agency (ANR) under the France2030 program and the national PEPR “agroécologie et numérique” program.Author informationAuthors and AffiliationsUniv Rouen Normandie, Univ Caen Normandie, CNRS, M2C, UMR 6143, Rouen, 76000, FranceEdward Salameh & Lucas SengsourinhoISPA, UMR1391 INRAE Bordeaux Science Agro, Villenave d’Ornon, 33140, FranceCassandra Normandin, Valentine Sollier & Frédéric FrappartInstitut National de Recherche en Sciences et Technologies du Numérique, Bordeaux Sud-Ouest, Talence, FranceValentine SollierAuthorsEdward SalamehView author publicationsSearch author on:PubMed Google ScholarLucas SengsourinhoView author publicationsSearch author on:PubMed Google ScholarCassandra NormandinView author publicationsSearch author on:PubMed Google ScholarValentine SollierView author publicationsSearch author on:PubMed Google ScholarFrédéric FrappartView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Edward Salameh.Ethics declarations

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The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1 (download DOCX )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 articleSalameh, E., Sengsourinho, L., Normandin, C. et al. Assessing SWOT capabilities for monitoring floodplain water dynamics in the Paraná Basin.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-53807-7Download citationReceived: 26 February 2026Accepted: 14 May 2026Published: 20 May 2026DOI: https://doi.org/10.1038/s41598-026-53807-7Share 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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KeywordsSWOTParaná RiverFloodplainsWater surface elevationInundationVegetated wetlands More

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IntroductionAll forms of life on Earth are sustained by the provision of water, which is abundant and essential. However, the quality of water is increasingly under threat of microbial contamination, which is a significant global public health problem. Waterborne diseases, including diarrheal disorders, cholera, typhoid fever, hepatitis A, and poliomyelitis, have a strong association with contaminated water sources and poor sanitation facilities1. Surface waters, including rivers, are very vulnerable to microbial contamination by the continuous influx of untreated wastewater generated from human settlements, industries, and agriculture. The microbial flora of wastewater is a complex and dynamic ecosystem that includes a variety of pathogenic and non-pathogenic microorganisms. In addition, wastewater is considered a major reservoir for enteric bacteria, including Salmonella typhimurium and Escherichia coli (E. coli)3,4,5. The presence of microorganisms in surface waters significantly increases the risk of disease transmission. Therefore, the disinfection of water is a significant aspect that needs to be addressed. Among the pathogenic microorganisms found in contaminated waters, E. coli is commonly used to indicate the presence of fecal contamination in water sources due to their prevalence, persistence, and strong association with the presence of pathogenic microorganisms. In contrast, Gram-positive bacteria, including Bacillus sp., have a high resistance to conventional disinfection methods owing to the formation of endospores and the presence of a thick peptidoglycan cell wall.Traditional disinfection techniques like ultraviolet light, ozonation, and chlorination have commonly been employed for water disinfection purposes. Nevertheless, these techniques are also accompanied by certain limitations. Chlorination is the most commonly used disinfection method but is known to react with natural organic matter, resulting in the formation of toxic disinfection by-products that are harmful to the environment as well as human health10. Furthermore, long-term use of chlorinated disinfectants also promotes the survival of chlorine-resistant bacteria strains11,12. Similarly, ozonation is an effective disinfection method but is limited by high operational and maintenance costs, as well as the potential for microbial regrowth in water systems13. All these limitations have led to a search for alternative disinfection techniques that are not only effective but also environmentally friendly and chemical-free.Plasma disinfection is a new disinfection technology that is being explored for application purposes in the environmental as well as biomedical sciences. Initial experiments have shown that bacteria like Gram-positive and Gram-negative bacteria have successfully been inactivated using different types of plasma discharges14,15,16,17,18. Cold atmospheric plasma (CAP) has gained substantial interest as a non-thermal method of sterilization. It does not require any harmful chemical reagents and does not involve high temperatures. In addition, using air instead of noble gases makes this method a more convenient and advantageous approach in practice19. As air plasma systems eliminate the need for external gas supply and enhance in situ generation of reactive oxygen and nitrogen species (RONS) directly without additives or gas cost, enhancing their applicability for large-scale treatment51. However, the effectiveness of liquid-plasma interactions depends critically on the properties of bacteria, including Gram type, strain, and cell density, as well as their physiological states20.A complex of energetic electrons, ions, ultraviolet (UV) photons, electric fields, and RONS in a partially ionized gas is formed in a CAP-treated solution. In this context, reactive species, such as hydrogen peroxide (H₂O₂), superoxide anion (O₂⁻), hydroxyl radical (•OH), and singlet oxygen (¹O₂), are known to play a crucial role in inactivating bacteria by causing oxidative damage to DNA, RNA, and phospholipid membranes in bacteria22. Many publications have confirmed the effectiveness of using plasma in removing bacteria in aquatic environments23,24,25,26,27,28,29, while further studies have investigated the influence of corona discharge plasma on bacterial inactivation30,31,32,33.Despite the growing body of knowledge regarding the potential of plasma technology for the inhibition of bacteria, there are knowledge gaps to be addressed. There is a lack of information regarding the influence of plasma technology on the growth dynamics of bacteria, as well as the inactivation mechanism and plasma–water interaction in natural waters. In addition, there is a lack of scientific data regarding the potential use of corona discharge plasma technology for the treatment of Nile water and wastewater.Previous studies focused mainly on model water or single bacterial strains; research on complex natural matrices like Nile water and raw wastewater is limited12. This study examines CAP-induced bacterial inactivation kinetics in those systems by combining microbiological analysis, plasma diagnostics, and physicochemical characterization to clarify the underlying mechanisms.This research aims to bridge the knowledge gap regarding the application of corona discharge cold atmospheric plasma (CAP) for bacterial inactivation in Nile water and wastewater. Specifically, it evaluates the effectiveness of CAP against both Gram-positive and Gram-negative bacteria in these complex water matrices. It also investigates plasma–water interactions, bacterial growth dynamics following plasma treatment, and plasma-induced morphological changes in bacterial cells.MethodsExperimental corona plasma systemThe experimental setup for the corona discharge device is depicted in Fig. 1. The corona discharge apparatus was assembled from a coaxial electrode assembly connected to a high-voltage alternating current power supply (NeonPro NP-7500-30, 7.5 kV, 30 mA). The silver tape anode was wrapped around the outside of the glass tube, while the 0.3 mm stainless-steel wire cathode was situated in the middle of the tube (15 mm inner diameter). The distance between the cathode tip and the water surface was fixed at 5 mm. Plasma treatment was performed at 2.5 kV to ensure stable non-thermal corona discharge and avoid transition to arc discharge at higher voltages, thereby maintaining the desired non-equilibrium plasma conditions16.Fig. 1The alternative text for this image may have been generated using AI.Full size imageExperimental CAP set-up.Under these conditions, plasma appeared as filamentary streamers above the water surface, with charged species drifting toward the anode34. Different plasma exposure times were selected according to the experimental objectives. Exposure durations ranging from 2 to 8 min were applied to determine the optimal time required for complete bacterial inactivation in 10 mL water samples. For optical density experiments, a 24-minute exposure was used with 50 mL samples, based on preliminary optimization tests. Regarding agitation, the experiments were conducted under controlled conditions without external stirring to isolate the effect of corona discharge plasma on bacterial inactivation.Atmospheric plasma diagnosticTo characterize the plasma discharge, waveforms of voltage and current were recorded. The experimental setup employed in this study was adapted from the design previously reported by Abd El-Reda et al.35. The high voltage and current waveforms of the corona plasma were examined using high-voltage probes and current sensors built into the electrical circuit of the plasma system by a two-channel digital oscilloscope (XDS4504). The discharge current was measured using a CP-07 + current probe operated in the 400 mA range, providing a sensitivity of 1 mA/mV. Consequently, the recorded signal from channel 1 was directly converted to discharge current without additional scaling. The applied high voltage was simultaneously measured using a high-voltage probe (1000:1) connected in parallel to a plasma electrode and channel 2. Both voltage and current waveforms were recorded with a temporal resolution of 0.02 µs.A spectrometer that was attached to an optical cable and placed one centimeter away from the plasma was used to collect optical emission data. Species identification and spectral feature recording in the 300–900 nm region was made possible by this configuration.Collection of water samplesTwo different natural water sources were investigated; the water samples were gathered in clean, autoclaved bottles. The first sample source was Nile water, collected from the Nile River in Sohag Governorate, Egypt. The second sample source consisted of raw sewage water collected from the Sohag Governorate sewage treatment plant.Pour plate methodologyFor both water samples, serial dilutions were prepared to determine the optimal dilution factor by the pour plate method. All samples were prepared under strictly controlled and standardized conditions. Specifically, each experiment was conducted using the same volume and concentration of 0.9% saline solution (9 mL), followed by the addition of 1 mL from the same raw water sample to ensure consistency across all tests and minimize variability between experiments.Five test tubes, each with 10 mL of diluted water from a specific source, were exposed to corona discharge plasma at room temperature for 2, 4, 6, and 8 min, plus an untreated control (0 min). For bacterial enumeration, 1 mL of water was aseptically transferred, before and after plasma treatment, into sterile Petri dishes using the pour plate technique. Nutrient agar and MacConkey agar media were used for Nile water and wastewater samples, respectively. After solidification, the plates were incubated for 24 h at 37 °C. Using the conventional plate count method, the colony-forming units per milliliter (CFU/mL) were used to calculate the number of surviving bacteria. The treated volume was monitored before and after plasma exposure, and no significant volume loss was observed during the treatment time.Identification of Gram-positive and Gram-negative bacteriaGram staining was performed to differentiate bacterial species. Bacillus sp. (Gram-positive) appeared as purple rod-shaped bacilli, while E. coli (Gram-negative) appeared as pink rod-shaped cells under oil immersion microscopy (100×).Bacterial growth curve analysisIn this experiment, plasma was applied to bacterial cells at two distinct growth stages: the early exponential phase and the mid-exponential phase. The aim here is to investigate the impact of plasma treatment on bacterial growth curves and to determine whether plasma exposure leads to complete bacterial lysis, thereby preventing regrowth following incubation. The study also examined whether bacteria in the mid-exponential phase are more or less susceptible to plasma-induced cell lysis compared to bacteria in the early exponential phase. Using a spectrophotometer, turbidity was measured as optical density at 600 nm (OD₆₀₀) to track bacterial growth. Initially, a single Escherichia coli colony was inoculated into 5 mL of nutrient broth (NB) medium (3 g yeast extract, 5 g sodium chloride, and 5 g peptone per liter of demineralized water). The colony was then incubated for the entire night to reach the stationary phase. Subsequently, 1 mL aliquots of the overnight culture were transferred into three separate conical flasks, each containing 50 mL of sterile broth medium, as described below:

Control flask: Cultures were incubated in a shaking incubator without plasma treatment, and optical density (OD) was measured at hourly intervals.

Early exponential phase treatment: At 0 h, cells were exposed to plasma for 24 min, corresponding to the previously optimized exposure time required for complete inactivation in 50 mL suspensions. Optical density was measured immediately after treatment and subsequently monitored hourly during incubation.

Mid-exponential phase treatment: Cultures were first incubated for 4 h to reach the mid-exponential phase and then exposed to plasma for 24 min. Optical density was recorded immediately after treatment and at hourly intervals during subsequent incubation.

Physicochemical characterization of treated waterA mercury thermometer (Testo-T1) was used to monitor the temperatures of the plasma-treated water at various points in time. The thermometer is submerged in the water tube, and the temperature is recorded at three separate points within the tube: the center of the water, 1 cm below the surface, and on the surface of the water exposed to plasma. The same technique is used to test the temperature of the untreated control sample. To examine and track the change, the recorded temperature readings were tabulated.To look into the impact of atmospheric corona discharge treatment on pH value of the treated water, a calibrated pH meter (model: Adwa AD1030, Romania) with a glass electrode to detect acidity or alkalinity was used to test the pH values of the untreated and treated each water type, Nile water and wastewater, at intervals of 2, 4, 6, and 8 min. Before measurement, the pH meter is calibrated using standard buffer solutions for accuracy purposes. The samples are mixed by gentle stirring to ensure homogeneity. The pH probe is inserted into the water, and a measurement is taken after the pH meter stabilizes. An untreated water control sample is also taken for measurement. The pH probe is cleaned with distilled water before measurement to avoid cross-contamination. The effect of plasma therapy on the chemical properties of the water is evaluated by comparing the pH readings.A conductivity/TDS meter for measurement is a tabletop conductivity/TDS meter (Model: Adwa AD3000, Romania). Electrical conductivity (EC) is measured for samples of Nile water and effluent before and after treatment using this meter. The samples are not immediately measured after plasma treatment, as this effect might alter conductivity readings. All samples are left to cool to room temperature before measurement, as a precautionary measure against potential effects of plasma-induced heating on conductivity readings. The temperature of each sample is recorded before measurement, and it is noticed that all samples reach approximately the same temperature. The probe is cleaned with distilled water before measurement to avoid cross-contamination. The EC readings are recorded after stable readings are obtained.Morphological analysisTreated and untreated bacterial suspensions were centrifuged using a centrifuge operating at 4,000 rpm for 15 min and then washed with a 0.9% saline solution. The collected cells, also known as pellets, were chemically fixed using a 90% ethanol solution, dried with air, and then coated with a thin gold film before examination. Scanning Electron Microscopy analysis was carried out using a JSM-5400 LV microscope for Bacillus sp. (15 kV accelerating voltage, 15,000–20,000x magnification) and a Sigma 500 VP microscope for Escherichia coli (5 kV accelerating voltage, 7,500x magnification). Multiple regions from each sample were examined to ensure representative morphological observations.Statistical analysisThe statistical analysis was carried out using IBM SPSS Statistics program (version 24). All experiments were conducted using independent measurements. Data is expressed as mean ± standard error (SE). The standard deviation (SD) was calculated, and the SE was determined as SD/√n, where n represents the number of independent measurements. Error bars in all graphical representations indicate SE. Prior to analysis, data were examined for consistency and suitability for parametric testing. Statistical comparisons were carried out using one way analysis of variance (ANOVA) and paired-samples t-test, as appropriate to the experimental design. At p < 0.05, differences were deemed statistically significant, and at p < 0.01 or p < 0.001, they were deemed extremely significant.ResultsVoltage/current waveforms and OES analysisTime-resolved voltage and current waveforms of the corona discharge generated above the water’s surface at an applied voltage of 2.5 kV are shown in Fig. 2. The discharge current (blue curve) is marked by impulsive current spikes connected to brief streamer events that happen close to the voltage peaks, whilst the applied voltage (black curve) shows stable sinusoidal behavior. These characteristics can be linked to variations in electron and ion transport pathways.Fig. 2The alternative text for this image may have been generated using AI.Full size imageHigh voltage and current waveforms of the CAP system at 2.5 kV applied voltage.Under plasma exposure, the water surface serves as a conductive, polarizable interface capable of sustaining significant interfacial charge accumulation. The origin, propagation, and quenching of successive streamer micro-discharges are known to be affected by this surface charge’s distortion of the local electric field near the interface36. Reactive oxygen and nitrogen species (RONS) are encouraged to form because the discharge, being a non-thermal (non-equilibrium) plasma, usually keeps the gas temperature low while maintaining energetic electrons37. Plasma-based water treatment can be supported by repeating streamer activity above the water’s surface, which can improve plasma–liquid interaction and make it easier for reactive species to enter the liquid phase38.The optical emission spectrum (OES) of the air plasma produced inside the plasma reactor, obtained throughout the wavelength range of 200–900 nm, is displayed in Fig. 3. One well-known characteristic of air plasma discharges is that strong bands in the 300–400 nm range dominate the OES39,40. The second positive system (SPS) of molecular nitrogen N2 is primarily responsible for these bands41,42. At approximately 317, 337.8, 358.3, 376.8, and 381.5 nm, distinctive N₂ emission bands are seen. Molecular nitrogen excitation is the predominant radiative mechanism in plasma, as seen by the maximum intensity band at 337.8 nm. The first negative system (FNS) of ionized nitrogen N2+ is shown by a clear emission line at 392.4 nm, which indicates the presence of high-energy electrons and effective ionization processes within the discharge39,40. The OH radical, which is frequently linked to water vapor or ambient humidity in air plasmas, is responsible for the feeble emission observed close to 296.7 nm40,43. The emission intensity significantly drops at wavelengths greater than 400 nm, which is in line with the restricted number of permitted electronic transitions of air plasma species in the visible range39,41.Fig. 3The alternative text for this image may have been generated using AI.Full size imageOptical emission spectrum of CAP generated in the tube at 2.5 kV. The main figure shows the emission spectrum in the wavelength range of 200–900 nm. The inset presents a magnified view of the UV region (250–450 nm).Effect of CAP on viable bacterial countThe plate count method was employed to quantify the number of surviving colony-forming units per milliliter (CFU/mL) following plasma treatment. Figure 4 illustrates a clear decrease in the number of viable bacteria in Nile water with an increase in the time of plasma treatment, with no colonies being observed after 8 min of treatment. Similarly, Fig. 5 indicates a clear decrease in the number of viable bacteria in wastewater samples with an increase in the time of treatment, with complete inactivation being observed after 6 min of treatment. Gram-negative bacteria were more susceptible to treatment with plasma, which could be attributed to the susceptibility of the outer membrane of Gram-negative bacteria to damage by reactive species, thereby resulting in rapid disruption of their envelopes. Gram-positive bacteria were more resistant to treatment with plasma, which could be attributed to the thicker peptidoglycan layer in Gram-positive bacteria, although complete structural disruption was observed with prolonged treatment.Fig. 4The alternative text for this image may have been generated using AI.Full size imageBacterial colonies number in Nile water with time of CAP treatment (0–8 min).Fig. 5The alternative text for this image may have been generated using AI.Full size imageBacterial colonies number in wastewater with time of CAP treatment (0–8 min).Scanning electron microscopy analysisThe shape changes are visible under SEM for Bacillus cells that are exposed to CAP. These changes are quite distinct from those of untreated controls. While the untreated cells retain their normal rod-like shape and smooth surface integuments (Fig. 6a). In contrast, CAP-treated cells (Fig. 6b–e) exhibited pronounced structural damage. After 2 min of treatment (Fig. 6b), cells appeared visibly deformed, with irregular shapes and roughened surfaces, indicating membrane damage.Fig. 6The alternative text for this image may have been generated using AI.Full size imageBacillus sp. cell morphology: a untreated control; b 2 min; c 4 min; d 6 min; e 8 min; f higher magnification of 8 min. Red arrows indicate deformed or damaged cells.As the exposure time increases (see Fig. 6c, d, and e), however, the cells lose their uniformity in size and shape, manifesting surface dimples and small pores as the cell envelopes start to break down gradually. A closer view of the cells exposed to the radiation for 8 min (see Fig. 6f), which is of high magnification, shows that very few badly shrunk and damaged cells are left, which indicates high levels of inactivation.E. coli cells treated with CAP have a similar effect on the cells, showing significant damage with the SEM images (Fig. 7). The walls of the cells are damaged, with the surface becoming rough and having an irregular shape. When the treatment time increases to 4 and 6 min (Fig. 7b–d), the cells take an irregular shape with the formation of dimples and the appearance of pores, indicating significant damage to the cells due to the plasma action.Fig. 7The alternative text for this image may have been generated using AI.Full size imageSEM images of E. coli: a before plasma; b after 2 min; c after 4 min; d after 6 min. Red arrows indicate deformed or damaged cells.Effect of plasma treatment on bacterial growth curveThe bacterial growth curve typically has four phases: lag, exponential, stationary, and death phases, which represent the dynamic balance between anabolic and catabolic processes in the bacterial culture44. During the lag phase, the bacterial culture adjusts to the new environment and accumulates the necessary enzymes for the growth process to continue. When the bacterial culture is transferred from one medium to another identical medium, the lag phase can be eliminated, and the exponential phase can be reached immediately.In the exponential phase, the bacterial culture divides rapidly and exponentially with a constant generation time. During this phase, the logarithm of the bacterial number is directly proportional to the time, i.e., the logarithm of the bacterial number is linearly related to the time45. After the exponential phase, the stationary phase is reached, where the nutrients in the medium become depleted, and the metabolic wastes accumulate, leading to a balance between the death and growth rates in the bacterial culture. Finally, in the death phase, the nutrient depletion in the medium causes the death rate to exceed the growth rate, leading to a decline in the bacterial population46.Optical density at 600 nm (OD600) is an indirect method of determining the concentration of bacteria in a liquid culture by measuring the amount of light scattered by the suspended bacteria, as opposed to the amount of light absorbed by the bacteria. An increase in OD600 indicates an increase in the concentration of suspended bacteria, while a corresponding decrease in OD600 following the application of the plasma treatment indicates a corresponding decrease in suspended bacterial matter.As shown in Fig. 8, the untreated control sample exhibited the typical growth curve of a bacterial culture, with the OD600 increasing as the culture entered the exponential phase of growth and levelling off as the culture entered the stationary phase of growth. As shown in the second sample, the application of plasma treatment at the beginning of the exponential phase of growth resulted in an immediate decrease in OD600, indicating the lysis and subsequent inactivation of a portion of the bacterial culture. As the culture grew, the surviving bacteria began to divide, causing a corresponding increase in OD600. As the effects of the plasma treatment accumulated, the lysis of the bacterial culture dominated the culture, causing the OD600 to subsequently decrease.Fig. 8The alternative text for this image may have been generated using AI.Full size imageBacterial growth curve (OD600) with distinct phases over 24 h.In the third sample, initial incubation without plasma exposure allowed normal exponential growth. When plasma treatment was applied during the mid-exponential phase, partial lysis and cellular damage were observed, as reflected by a decrease in OD. After approximately one hour, limited regrowth of surviving cells produced a brief OD increase, followed by a progressive decline as damaged and lysed cells became dominant.Temperature, pH, and EC of treated waterFigure 9 demonstrates that the water temperature increased with plasma treatment time, reaching a maximum of approximately 54 °C at the plasma-exposed surface and gradually decreasing with increasing water depth. Since bacterial inactivation typically requires temperatures exceeding 65 °C, and many pathogenic microorganisms require temperatures above 74 °C49, the recorded temperature values indicate that the applied CAP treatment operated under non-thermal conditions. Therefore, thermal effects were unlikely to be the primary mechanism responsible for bacterial inactivation observed in this study.Fig. 9The alternative text for this image may have been generated using AI.Full size imageThe relationship between the water temperature at various places and the plasma exposure duration.The initial pH of Nile water and wastewater samples was 6.44 and 6.21, respectively, as measured using a calibrated pH meter before plasma treatment. The pH gradually decreased with increasing plasma treatment time, reaching a value of 3.11 after 8 min of treatment, as shown in Fig. 10. This data agrees with the reported by35 which employed the same method. The low pH level of the water has been attributed to the effect of the plasma treatment process, which promotes the formation of compounds that enhance the solution’s acidity levels. These include nitric acid, which results from the interaction of nitrate radicals with hydrogen produced by the dissociation of water due to the effect of the plasma treatment process. Solutions with low pH are effective in controlling the activity of disease-causing organisms50. However, the rate of pH reduction was higher in Nile water than in wastewater after the same time of exposure to the plasma treatment process.Fig. 10The alternative text for this image may have been generated using AI.Full size imageVariation of pH of Nile water and wastewater samples as a function of plasma treatment time.The higher rate of pH reduction in Nile water can be attributed to the lower buffering capacity of the water. The effect of the plasma treatment process was found to produce acidic compounds such as nitric and nitrous acids in the water due to the interaction of the water with the atmospheric air during the treatment process51,52. On the other hand, the wastewater contains higher concentrations of bicarbonates, organic matter, and ammonia that counteract the effect of the formation of the acidic compounds by the plasma treatment process53.Figure 11 shows that the electrical conductivity of Nile water and wastewater increases due to the effect of the plasma treatment process with the longer period of exposure to the treatment process. However, the rate of increase of the electrical conductivity of the wastewater was higher than that of the Nile water due to the higher concentration of organic matter and ions in the wastewater. The effect of the plasma treatment process promotes the formation of various ions such as NO₃⁻, NO₂⁻, H⁺, and H₂O₂ that significantly enhance the electrical conductivity of the water.Fig. 11The alternative text for this image may have been generated using AI.Full size imageEC variation in Nile water and wastewater samples as a function of plasma treatment duration.Statistical evaluationAs shown in Tables 1 and 2, a statistically significant negative correlation is seen for the duration of plasma treatment and bacterial colony counts. The one-way analysis of variance for this experiment showed that p = 0.0001 (p < 0.001), indicating a highly significant effect for plasma treatment on bacterial colony counts for both water samples. The paired samples t-test showed that there is a statistically significant reduction in bacterial colony counts for all durations of plasma treatment when compared to the untreated control group (p < 0.05).Table 1 Statistical analysis of Nile water samples using one-way ANOVA and paired-samples t-test.Full size tableTable 2 One-way ANOVA and paired-samples t-test in a statistical study of wastewater samples.Full size tableDiscussionThe results suggest that CAP, which was generated using a corona discharge, is very effective in reducing the population of both Gram-positive bacteria, Bacillus sp., and Gram-negative bacteria, E. coli, in Nile River water and wastewater. The significant reduction in the number of colony-forming units per milliliter (CFU/mL) confirms that exposure to non-thermal plasma plays an essential role in bacterial inactivation. The antibacterial effects can be attributed to the interaction of the CAP with water, which leads to the formation of reactive oxygen and nitrogen species (RONS). Although the interaction of the CAP with water and the formation of reactive species have been observed during the degradation of organic compounds using a corona discharge and DBD plasma, such as atrazine degradation in water, the study by Papalexopoulou et al. showed the importance of the role of plasma-induced chemistry in liquid phase treatment processes54, our study expands these ideas to include biologically complex systems, where bacterial inactivation depends on bacterial growth, bacterial responses, and structural damage, thereby emphasizing the complexity of bacterial control using CAP. Moreover, the results of electrical characterization and optical emission spectroscopy (OES) analysis also support the generation of reactive species such as hydroxyl radicals (•OH), nitrite (NO₂⁻), nitrate (NO₃⁻), and hydrogen peroxide (H₂O₂), which are known to cause oxidative stress and damage to the essential components of the bacterial cell, such as DNA, proteins, and lipid membranes55,56. The dominance of nitrogen-containing species in the plasma emission spectra is consistent with the results of earlier studies on the performance of APS systems, in which nitrogen-containing species play a role in the generation of long-lived reactive species in water46.The results also show that Gram-negative bacteria such as E. coli are more susceptible to CAP treatment than Gram-positive bacteria such as Bacillus sp. This difference in susceptibility can be attributed to the differences in the composition of the bacterial envelope. Gram-positive bacteria have a thick peptidoglycan layer in their envelope, which provides mechanical stiffness and some degree of protection to the bacteria. Gram-negative bacteria, on the other hand, have a thin peptidoglycan layer and an outer lipid layer in their envelope, which can be subject to lipid peroxidation and oxidative damage47.The difference in inactivation efficiency, ≥ 6-log reduction in Nile water and ≥ 2.6-log reduction in wastewater, can be attributed to matrix effects, including higher organic load and buffering capacity that reduce reactive species activity26,52, as well as the use of selective MacConkey agar, which may underestimate the initial bacterial load compared to the general nutrient agar used for Nile water.The results of the scanning electron microscopy (SEM) analysis provide direct evidence of the progression of the morphological damage to the bacterial cells, as shown by the roughening of the surface, depressions, perforations, and collapse of the cell wall. These effects are characteristic of the oxidative degradation of the bacterial envelope. These results are consistent with earlier findings suggesting that reactive oxygen species are capable of oxidizing the side chain groups of amino acids, thereby causing the denaturation of proteins57.The timing of the application of the CAP treatment with respect to the phase of the bacterial culture growth cycle was also seen to influence the efficiency of the treatment. More efficient inactivation was observed with the mid-exponential phase of the culture cycle as opposed to the early exponential phase. Cells are more susceptible to oxidative stress and electrophysical damage during the mid-exponential phase of the culture cycle as a result of increased metabolic activity, as well as the suppression of the activation of the stress response system46. The transient increase observed in the OD600 after the CAP treatment indicates the partial survival of the treated culture, as well as the ability of the damaged cells to regenerate to a certain extent. However, the subsequent decline of the OD600 indicates irreversible damage to the bacterial culture.Significant physicochemical changes were observed to the treated water as a result of the application of the CAP treatment. A significant reduction of the pH of the treated water to a value of 3.1 was observed, as well as an increase in the electrical conductivity of the treated water. Previous studies have shown that plasma-treated water retains its antimicrobial activity even after pH neutralization, confirming that reactive species, rather than acidity alone, play the dominant role in bacterial inactivation59.These results are consistent with the production of PAW with high levels of ROS. The greater extent of the reduction of the pH of the treated Nile water with respect to the wastewater is a result of the relatively lower buffering capacity of the former. These physicochemical effects are significant as they contribute to the inactivation of the bacterial culture. Importantly, the results of the measurements of the temperature of the treated culture confirmed that the inactivation of the culture was not the result of the heat effects of the CAP treatment.The findings obtained in this research correlate with existing studies on plasma-mediated microbial inactivation in aqueous environments. In these environments, the bactericidal effect of plasmas is explained by the action of reactive species, UV radiation, and temporary electric fields. This research extends existing knowledge by investigating bacterial growth patterns under plasma treatment and determining that bacteria are more susceptible to this treatment during the mid-exponential phase of growth. The application of cold atmospheric plasma (CAP) to Nile River water and wastewater samples fills a significant gap in existing knowledge. Only a limited number of studies have examined CAP application to aqueous environments with complicated chemical compositions. The findings obtained in this research support CAP as a promising and eco-friendly substitute for traditional disinfection methods such as chlorination and ozonization. Unlike traditional disinfectants used for water treatment, CAP operates at normal pressure and near room temperature. It does not produce harmful disinfection by products. CAP’s effectiveness against resistant bacterial species such as Bacillus sp. indicates its potential for future application in sustainable water and wastewater treatment systems. This is especially true for regions that face persistent challenges with microbial contaminants.ConclusionThe purpose of this study is to assess the application of cold atmospheric plasma (CAP) created by corona discharge for bacterial inactivation in Nile River water and wastewater. The electrophysical characteristics and optical emission spectroscopy (OES) indicate that there are significant interactions between plasma and water. This allows for the generation of reactive oxygen and nitrogen species. This study indicates that CAP application causes a significant reduction in bacterial populations. The study found that Gram-negative bacteria are more susceptible to CAP applications compared to Gram-positive bacteria. This effect is due to differences in membrane structure. The growth curve indicates that bacterial lysis plays a major role in CAP application. This effect is pronounced when CAP application occurs at the exponential phase. The scanning electron microscopy (SEM) images indicate that CAP applications cause significant morphological changes. The evaluation of physicochemical parameters further confirmed the non-thermal nature of the applied CAP, as evidenced by moderate temperature increases accompanied by significant reductions in pH and increases in electrical conductivity, reflecting plasma-induced chemical modifications of the treated water. Overall, the findings highlight corona discharge CAP as a promising, environmentally friendly, and effective strategy for mitigating microbial contamination in natural water bodies and wastewater treatment applications.

Data availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request.
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Download referencesAcknowledgementsThe authors extend their sincere gratitude to Sohag University for its essential support of the present study.FundingOpen access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). This research received no external funding.Author informationAuthors and AffiliationsPhysics Department, Faculty of Science, Sohag University, Sohag, 82524, EgyptF. M. El-Hossary, E. A. Noureldein & M. Abo El-KassemPhysics department, Faculty of Education and Arts, Sohar University, Sohar 311, OmanM. Abo El-KassemDepartment of Botany and Microbiology, Faculty of Science, Sohag University, Sohag, 82524, EgyptAly E. Abo-AmerAuthorsF. M. El-HossaryView author publicationsSearch author on:PubMed Google ScholarE. A. NoureldeinView author publicationsSearch author on:PubMed Google ScholarM. Abo El-KassemView author publicationsSearch author on:PubMed Google ScholarAly E. Abo-AmerView author publicationsSearch author on:PubMed Google ScholarContributionsF.M.E. and A.E.A. conceptualized and designed the study. M.A. contributed to the study design. E.A.N. performed the experiments. F.M.E., A.E.A., M.A., and E.A.N. contributed to writing the manuscript. All authors reviewed and approved the final version of the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleEl-Hossary, F.M., Noureldein, E.A., El-Kassem, M.A. et al. Cold atmospheric plasma for bacterial inactivation in Nile water and wastewater.
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KeywordsCold atmospheric plasma (CAP)Corona dischargeBacterial inactivationNile waterWastewater treatment More

AbstractIran’s long history of climate-related crises, primarily driven by droughts, has been intensified by ongoing climate change, placing forest ecosystems under increasing hydroclimatic stress. In recent decades, prolonged droughts combined with elevated atmospheric moisture deficits have reduced ecosystem resilience and increased vulnerability to degradation. To better understand long-term drought dynamics and their ecological impacts, we developed two 200-year chronologies (1821–2020) of tree-ring width (TRW) and stable oxygen isotope variations (δ1⁸O) from Juniperus polycarpos in the Hezar Masjed Mountains, northeastern Iran. The δ1⁸O record served as a proxy for atmospheric moisture conditions and was used to reconstruct growing-season (March–September) vapor pressure deficit (VPD). When combined with TRW in a multiple regression framework, this dual-parameter approach enabled reconstruction of the Standardized Precipitation-Evapotranspiration Index (SPEI07), representing cumulative growing-season hydroclimatic conditions related to soil moisture availability. This allows the differentiation of atmospheric and soil drought impacts on tree growth over two centuries. By classifying drought years into VPD-only, SPEI-only, and combined drought events, we found that drought conditions associated with reduced soil moisture availability (SPEI) exerted the strongest constraint on radial growth. Tree growth declined most strongly during severe SPEI droughts, followed by severe combined drought (COMB-D) years, whereas atmospheric drought alone (VPD-D) had a weaker and more transient effect. Growth typically recovered within two years following drought events. Analysis of long-term drought classifications (1821–2020) revealed a shift towards more intense droughts in recent decades, particularly in the frequency of severe VPD and combined drought years. Our findings highlight that tree growth in semi-arid mountain ecosystems is primarily limited by soil moisture availability, with atmospheric drought acting as an additional stressor when coinciding with soil moisture deficits. This study demonstrates the value of combining multiple tree-ring proxies to disentangle drought mechanisms and improve understanding of forest responses to climate change.

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IntroductionUnderstanding how ecosystems respond to long-term climate variability is crucial, particularly in regions increasingly affected by drought and water scarcity. Iran, dominated by arid and semi-arid climates typical of large areas of southwest Asia, is highly vulnerable to climate change and its compounding impacts on ecosystems and human societies1,2. Nearly 65% of Iran’s land area is classified as arid and further 25% as semi-arid, underscoring the country’s general high susceptibility to warming and drying trends3,4. In recent decades, climate change has exacerbated Iran’s already serious hydrological challenges, driving higher temperatures, altered precipitation regimes, and prolonged drought episodes. Meteorological observations recorded widespread increases in temperature extremes, including more frequent heat waves and warm nights and days, coupled with a decline in cold days5,6. In line with rising temperatures, Iran experiences a significant rise in aridity and evaporation7,8, but also in extreme hydroclimatic events such as floods and droughts9,10. Chaparinia et al.11 reported significant reductions in heavy precipitation indices and total wet-day precipitation across many regions of Iran—particularly in the drier areas—accompanied by a marked increase in consecutive dry days. These spatially differentiated trends point to an overall shift toward greater water scarcity, especially in already arid parts of the country. Most climate projection studies conclude that large parts of Iran are experiencing a trajectory toward warmer and drier climate conditions, with the most pronounced impacts expected in already fragile semi-arid regions6,12. Drought duration, intensity, and frequency are expected to increase significantly13,14, especially in the eastern and northeastern parts of the country15,16. However, long-term instrumental climate records in Iran are limited in both duration and spatial coverage, particularly in high-elevation and remote regions, which constrains the assessment of long-term hydroclimatic variability and highlights the need for proxy-based reconstructions.Although numerous investigations have assessed drought trends in Iran using different indicators—including Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Palmer Drought Severity Index (PDSI)—most focus on general water deficits without distinguishing the underlying drivers or their different impacts on ecosystems. To date, no study has examined long-term changes in vapor pressure deficit (VPD), a key atmospheric metric of moisture demand, despite its critical role in shaping drought intensity under warming conditions. Drought itself can manifest in different forms depending on the variable of interest and spatial or temporal scale, such as meteorological, hydrological, agricultural, and socioeconomic droughts17,18. From an eco-physiological perspective, drought can arise from soil moisture–related drought conditions (as approximated by SPEI, which integrates precipitation and atmospheric demand) or from atmospheric moisture deficits (as reflected by VPD), each imposing stress on vegetation19.With ongoing climate change, mountain forests and woodlands in Iran are increasingly exposed to severe droughts and elevated atmospheric moisture deficits, reducing resilience and increasing vulnerability of these ecosystems. According to projections of the future drought vulnerability index, current areas classified as having very low vulnerability are projected to shift toward very high vulnerability, particularly in eastern and northeastern Iran20, underscoring the importance of studying the impacts of drought on regional ecosystems. Trees may be affected by soil drought, limiting water uptake, and by atmospheric drought, which drives evaporative demand. Due to expected increases in drought intensity, frequency, and duration, understanding which type of drought most strongly constrains tree growth and vitality is essential for anticipating regional forest responses and managing ecosystem risks. However, our current understanding of how soil versus atmospheric drought shapes tree physiology and growth is still limited. Long-term records that separately capture these different aspects of drought are therefore urgently needed.Dendroclimatology offers a valuable approach to investigating historical tree responses to drought by using annually resolved records preserved in tree rings21,22,23. In Iran, tree-ring studies have provided insights into past hydroclimatic variability24,25,26,27. While some studies have assessed general drought variability using tree rings28,29, no work has yet distinguished between soil and atmospheric drought parameters using tree-ring parameters, nor evaluated their respective impacts on tree growth in Iran. Long-lived Juniperus polycarpos are particularly suitable for disentangling drought impacts, given their multi-centennial lifespans and pronounced sensitivity to moisture fluctuations25,30. This distinction is particularly important given that tree-ring width (TRW) predominantly reflects growth processes tied to carbon assimilation and water availability in soils31,32, while stable oxygen isotopes (δ1⁸O) in cellulose primarily record the δ1⁸O signal of source water adjusted during evaporative enrichment at the leaf level, which is linked to atmospheric demand and stomatal regulation33,34. Under arid climate conditions, δ1⁸O in tree rings has proven to be a promising proxy for recording variations of precipitation, relative humidity, or VPD35,36, rather than primarily reflecting the isotopic signal of the source water37,38,39. This divergence in the δ1⁸O signal preserved in tree rings is determined mainly by the extent of isotopic exchange between source water and phloem sugars during cellulose synthesis—a physiological process that is modulated by site conditions40,41,42. Accordingly, tree-ring δ1⁸O has been widely used as a tool for reconstructing hydroclimate variability21,43,44. Foroozan et al.26 found that δ1⁸O in junipers in northern Iran is a robust proxy for atmospheric moisture deficit and reconstructed precipitation changes and drought episodes for the past 500 years25.In this study, we utilize multi-centennial TRW and δ1⁸O chronologies from long-lived J. polycarpos in northeastern Iran to reconstruct both soil drought (SPEI) and atmospheric drought (VPD) over the past two centuries using multi-proxy regression models. This dual-reconstruction approach enables us to assess the relative contributions of soil versus atmospheric moisture deficits to tree growth reductions—a distinction not previously explored in Iranian dendroclimatology. Our results provide novel insights into how different types of drought stress constrain semi-arid forest ecosystems under a changing climate.Materials and methodsStudy area and regional climateThe Hezar Masjed Mountains (HM) are located in northeastern Iran and stretch for approximate.ly 500 km in a northwest–southeast (NW–SE) direction forming part of the northern highlands of Khorasan Razavi Province. The HM range acts as a natural barrier for moist air masses, separating the arid Qara Qum Desert to the north from the Mashhad plains to the south.The highest peak, Mount Hezar Masjed (36°58′12″N, 59°21′36″E; 36.97311°N, 59.35612°E) (Fig. 1), rises to approximately 3,130 m above sea level (m a.s.l.) and is located about 70 km northwest of Mashhad. Phytogeographically, the region belongs to the Irano-Turanian zone and is characterized by open juniper woodlands typical for mountain forest-steppe ecosystems on north-facing as well as on south-facing slopes45. Juniper trees grow from elevations as low as 1,340 m but form denser stands between 2,000 and 2,700 m a.s.l46. The soils in these habitats are shallow, calcareous, and alkaline, with pH values ranging from 7.7 to 8.7, with limited horizon development and low organic matter46. According to the FAO national soil map of Iran47,48, the dominant soil types in the region are calcareous lithosols, brown soils, and chestnut soils, with surrounding areas classified as brown soils–lithosols. These classifications indicate predominantly shallow, stony soils over limestone bedrock, with limited profile development and low organic matter. Based on the World Reference Base (WRB), these soils correspond primarily to Leptosols and Regosols, with possible Calcisols in calcareous zones. These soil types are typical of arid and semi-arid mountain ecosystems and are characterized by low water retention capacity.Fig. 1The alternative text for this image may have been generated using AI.Full size image(a) Location of the study site in the Hezar Masjed Mountains, northeastern Iran. The map was created using QGIS (version 3.44.9; https://www.qgis.org). Elevation data were derived from a digital elevation model (DEM) obtained from the USGS EarthExplorer platform (https://earthexplorer.usgs.gov). (b) Ombrothermic diagram illustrating the monthly mean temperature (red line) and precipitation (blue line) recorded at the Hezar Masjed station (2400 m a.s.l.) for the period 1984–2020. (c) Landscape view of the Hezar Masjed Mountains showing a typical high-elevation juniper forest (photograph taken by the authors).The regional bioclimate is Mediterranean xeric-continental,49, characterized by pronounced seasonality, high continentality, and dry, warm summers. The wet season lasts 5 to 6 months, spanning mid-autumn (mid-October) through spring (May), with its peak precipitation occurring in winter (Fig. 1b). In contrast, the summer months (July to September) are dry, with elevated VPD and high evapotranspiration, creating stressful conditions under which only cold- and drought-tolerant species such as juniper can persist. The thermal growing season likely spans from March to September, based on seasonal temperature trends, although no studies have yet documented the precise timing of cambial activity in Juniperus polycarpos for the region. Our sampling site is situated in the southern part of the Hezar Masjed Mountains in Razavi Khorasan Province, NE Iran, at elevations between 2000 and 2700, corresponding to the core zone of juniper forest distribution (Fig. 1).Tree species and samplingThe tree-ring samples were collected from an open J. polycarpos forest stand located at 37°02′–03′ N and 59°18′–24′ E. Our sampling strategy focused on selecting mature trees of dominant size and healthy appearance to maximize the length of the tree-ring chronology and minimize the occurrence of false or missing rings. Species identification of J. polycarpos was verified by experts from the Faculty of Natural Resources, University of Tehran, during field sampling. Reference voucher material from the study site is deposited in the plant and wood collection of the Faculty of Natural Resources, University of Tehran. Sampling permissions and licenses for collecting tree cores were obtained prior to the field campaigns in accordance with national regulations governing scientific field studies.Due to the site’s popularity among local hikers and campers, it was challenging to find old-growth trees undisturbed by human activity. Therefore, we collected samples from trees growing across a range of elevations, between 2,000 and 2,656 m a.s.l from locations where human impact was minimal. This elevation variability among sampled trees was later considered in the isotope analysis to account for potential altitudinal effects on δ1⁸O values. We also ensured that the sampled trees were not accessing groundwater, to avoid interference with the climate signal recorded in δ1⁸O values. We extracted at least two increment cores at breast height (ca. 1.3 m), approximately 180° apart around the stem, from each of 47 living J. polycarpos trees, using a 5 mm increment borer.Tree-ring width measurement and chronology developmentAll increment cores (n = 98) were air-dried and prepared by sanding the surface with progressively finer sandpaper to clearly reveal annual ring boundaries, following standard procedures50. Tree-ring widths were measured under a stereomicroscope using the LINTAB 6 measuring system51, a precision of 0.001 mm. Visual and statistical cross-dating was conducted within and between trees to assign each annual ring to the exact calendar year. Cross-dating accuracy was verified using TSAP-Win™ software52, based on statistical parameters such as Gleichläufigkeit (GLK) and t-values53,54. This process also helped identify and correct false or missing rings, as well as minimize measurement errors. To develop the site chronology, individual tree-ring series were detrended to remove biological age-related growth trends using a signal-free age-dependent spline detrending approach55, implemented in the RCSigFree_45v2b software (www.ldeo.columbia.edu). This approach removes biological growth trends while minimizing distortion of any long‐term climatic variance in the final chronology. The chronology was developed by calculating a bi-weight robust mean of all detrended and cross-dated individual series.The chronology quality and strength of the common signal between the individual tree-ring series were assessed by computing expressed population signal (EPS), sub-sample signal strength (SSS), mean inter-series correlation (Rbar), signal-to-noise ratio (SNR), and first-order autocorrelation. These were calculated using the dplR package56 in R57. Although the full tree-ring width chronology spans 499 years (1523–2021), the present analysis focuses on the common period from 1821 to 2020, which overlaps with the stable isotope record and allows for integrated multi-proxy analysis.Stable oxygen isotope analysisFor stable isotope analysis, we selected trees that exhibited strong growth coherence with the mean local tree-ring width chronology and showed no evidence of missing or false rings. In line with standard recommendations33,58, we ensured a minimum replication of five individual trees throughout the entire period of investigation (1821–2020), allowing the construction of a robust mean isotope chronology representative of regional climate signals. Individual annual rings for the selected trees were carefully separated using a scalpel, then split into smaller fragments and stored in Eppendorf tubes for α-cellulose purification. The α-cellulose component was isolated using a multi-stage chemical procedure described by59, which included pretreatment, bleaching, and purification steps. The purified α-cellulose was homogenized using an ultrasonic device to ensure thorough mixing of earlywood and latewood components from each annual ring. This homogenization step is critical because it integrates the full climate signal captured within a given year. The homogenized samples were then freeze-dried prior to isotope analysis.Dried α-cellulose samples were weighed into silver capsules and analyzed for stable oxygen isotope ratios (δ1⁸O) using a high-temperature pyrolysis system (HT Oxygen Analyzer) coupled to a continuous-flow isotope ratio mass spectrometer (IRMS; Delta V Advantage, Thermo Fisher Scientific). All analyses were performed at the Stable Isotope Laboratory of the Institute of Geography, Friedrich-Alexander-Universität Erlangen-Nürnberg. Analytical precision was typically better than ± 0.25‰ for δ1⁸O measurements. The oxygen isotope values are expressed as δ1⁸O in per mil (‰) relative to the international VSMOW standard (Vienna Standard Mean Ocean Water).Climate data and drought index acquisitionDue to the remote and mountainous location of the study area, no meteorological station is located directly within the sampling site. This limitation is common in remote mountainous regions of northeastern Iran, where long-term high-elevation climate records are largely unavailable. While absolute climatic conditions may differ with elevation, nearby stations are assumed to capture regional variability in temperature and moisture conditions. Therefore, we collected and reviewed all available instrumental data from surrounding areas. Among these, two synoptic stations, Mashhad and Sarakhs, provided the most consistent records and strongest correlations with both tree-ring parameter variables. Therefore, the arithmetic mean of their data was used to approximate regional-scale climate variability despite elevation-related differences in absolute values and to serve as input for the calculation of VPD.For the calibration period (1984–2020), the mean annual precipitation was 213 mm, and the mean annual temperature was 16.93 °C (Fig. 1). The driest period during the year extends from June to September, with August receiving the lowest monthly precipitation (0.19 mm). The wettest months occur during winter and early spring, with a maximum in March (48.7 mm). Mean monthly temperatures range from 3.8 °C in January to 28.4 °C in August (Fig. 1). To quantify atmospheric drought, we calculated vapor pressure deficit (VPD) using monthly maximum temperature and relative humidity data averaged from the Mashhad and Sarakhs synoptic stations. VPD was computed using standard equations that calculate the difference between saturation vapor pressure and actual vapor pressure60. Monthly VPD values were averaged to characterize seasonal atmospheric dryness and were used as an indicator of the evaporative demand imposed on trees.Besides instrumental climate data, we used the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales (SPEI01–SPEI07), which integrates both precipitation and potential evapotranspiration (PET) to assess hydroclimatic drought conditions related to soil moisture availability. We focused on short-term accumulation periods (SPEI01–SPEI07), as these are most relevant to growing-season physiological processes and showed the strongest and most consistent relationships with the tree-ring proxies.Monthly SPEI data were obtained from the Global SPEI Database (https://spei.csic.es/spei_database), which provides global-scale drought information at 0.5° spatial and monthly temporal resolution, based on precipitation and PET data from the Climatic Research Unit (CRU) of the University of East Anglia. Data were extracted for the grid cell that best covers the coordinates of our study area. Given the coarse spatial resolution of the CRU-based SPEI product and the mountainous terrain of the study area, these data are interpreted as representing regional hydroclimatic variability rather than exact local site conditions.Statistical analysesAll statistical analyses were conducted in R (version 4.5.3)57. Specific packages used for individual analyses are described below, where relevant.Altitude correction of δ1⁸O valuesThe sampled J. polycarpos trees were distributed across a wide elevational gradient (approximately 2000 to 2656 m a.s.l.). To assess whether tree-ring δ1⁸O values were influenced by elevation, we tested for a significant altitude effect61,62. Such an effect has been reported in previous studies e.g.,63,64,65, typically reflecting changes in temperature, precipitation isotopic composition, and VPD along elevation gradients. In particular, the δ1⁸O of source water generally decreases with increasing elevation due to isotopic fractionation during precipitation, while changes in temperature and evaporative demand can further influence δ1⁸O enrichment in leaf water during cellulose synthesis66,67,68. A linear regression model was applied to the mean δ1⁸O value of each tree over the study period against its corresponding sampling elevation:$$delta^{18} {text{O}} = {upbeta}_{0} + {upbeta}_{1} cdot {text{Elevation}}$$
(1)
The regression revealed a statistically significant negative relationship between elevation and δ1⁸O (r = 0.75, p < 0.01), corresponding to a decrease of 0.339‰ per 100 m of elevation gain. The model explained 56.4% of the variance in δ1⁸O values (R2 = 0.564), supporting a moderate but significant altitudinal effect, which is illustrated in Supplementary Fig. S1. Based on this relationship, δ1⁸O values were normalized to a reference elevation of 2000 m a.s.l. for all trees using the equation:$$delta^{18} {text{O}}_{corrected} = delta^{18} {text{observed}} + {upbeta}_{1} cdot left( {2000 – {text{Elevation}}} right)$$
(2)
This correction was applied to improve comparability among trees sampled across different elevations and to reduce potential altitudinal bias in the isotope–climate signal, while preserving the temporal variability of the individual series (Fig. S2). Because this approach assumes that the elevation effect remains temporally stable, it is acknowledged as a potential limitation.The corrected δ1⁸O values were used in all subsequent statistical analyses, including climate response analysis and reconstruction.Correlation analyses and climate reconstruction using tree-ring parametersTo reconstruct seasonal drought indices, we evaluated the relationships between TRW, corrected δ1⁸O values, and the soil and atmospheric drought indicators. Specifically, we assessed correlations between tree-ring parameters and SPEI indices across accumulation periods ranging from one to seven months (SPEI01–SPEI07), as well as across individual calendar months. All correlation analyses using Spearman rank correlations with bootstrapped significance testing were performed using the dendroTools package69. Monthly and seasonal VPD values were examined similarly using both tree ring proxies. To determine the best predictor(s) for reconstructing seasonal drought variability, we used linear regression models. Separate models were built for (1) VPD using δ1⁸O as the sole predictor, and (2) SPEI using both TRW and δ1⁸O as predictors. In the latter case, we employed multiple linear regression to test whether combining both proxies, which capture partly distinct drought-related processes, improved reconstruction skill relative to single-proxy models. The target variable for the soil drought reconstruction was SPEI07 for September, selected based on its strong and significant correlations with both proxies and its ability to represent cumulative growing-season moisture conditions. Longer accumulation periods may better reflect long-term hydrological drought persistence, but they are less directly linked to growing-season physiological processes governing annual ring formation and were therefore not the focus of this study. The VPD reconstruction target was the mean March–September VPD, which correlated most strongly with δ1⁸O.To identify the most effective predictor or combination of predictors for reconstructing seasonal drought variability, we performed both simple and multiple linear regression analyses. Prior to model fitting, we assessed the underlying assumptions of linear regression, including linearity, homoscedasticity, and normality of residuals. Predictor collinearity for the multiple regression model was tested using the variance inflation factor (VIF), and found to be below the commonly accepted threshold (VIF < 5), indicating acceptable levels of multicollinearity.Given the relatively short instrumental period (1984–2020), leave-one-out cross-validation (LOOCV) was employed to evaluate the temporal robustness and predictive accuracy of each model. For each iteration, one year was withheld from the training dataset, the model was re-fit using the remaining years, and the withheld year was predicted. This process was repeated across all years to calculate cross-validated performance metrics, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and the reduction of error (RE).The final models were applied to the full tree-ring dataset (1821–2020) to generate annually resolved reconstructions of SPEI and VPD.Classification of drought years and growth responseTo classify drought years in a way that reflects the specific climatic conditions and tree growth responses in our study region, we applied K-means clustering to the reconstructed time series of vapor pressure deficit (VPD), Standardized Precipitation-Evapotranspiration Index (SPEI), and a combined matrix of both indices, spanning the period 1821–2020. All classifications are based on reconstructed growing-season drought conditions (March–September), consistent with the temporal scale of the climate reconstructions. Thus, references to “drought years” throughout the manuscript refer to years characterized by drought conditions during the growing season rather than annual means.These classifications were used to distinguish between different types of droughts: VPD-only drought, SPEI-only drought, and combined drought years (COMB) to reflect years with either co-occurring or non-coinciding atmospheric and soil moisture deficits. For each input (VPD, SPEI, and combined), the number of clusters was set to three (k = 3), corresponding to years of severe drought, moderate drought, and non-drought conditions. This unsupervised, data-driven approach identifies natural groupings in the dataset by minimizing within-cluster variance and offers an ecologically meaningful classification that avoids the limitations of fixed percentile thresholds (e.g., using SPEI thresholds such as SPEI <  − 2 for extreme drought). As a result, the drought categories better capture the site-specific hydroclimatic regimes that trees have experienced historically.Years assigned to the lowest, intermediate, and highest value clusters were labeled as severe drought (D), moderate drought (M), and non-drought (ND), respectively, for each index. This resulted in three classes per index (e.g. VPD-D, VPD-M, VPD-ND). The table below summarizes the classification scheme (Table 1):Table 1 Classification scheme used for drought year (based on growing-season conditions) identification based on cluster analysis of reconstructed drought indices.Full size tableThis clustering-based classification scheme was then used to test growth sensitivity to different drought types.We applied Superposed Epoch Analysis (SEA) using the dplR package56 to assess how tree growth responded to these distinct drought types. SEA was conducted using the tree-ring width index chronology (RWI), with severe drought years identified from each cluster (VPD-D, SPEI-D, COMB-D). For each drought type, SEA was performed using a ± 3-year lag window around the drought event (lag 0), and 1000 Monte Carlo resampling iterations were used to generate confidence intervals and assess statistical significance. This approach allowed us to test whether tree growth significantly deviated from the long-term mean in the drought year and surrounding years, offering insights into both the immediate and legacy effects of drought on growth.ResultsClimate sensitivity of tree-ring parametersFigure 2 indicates the TRW and δ1⁸O chronologies developed from J. polycarpos cover the period 1821–2020. The TRW chronology exhibited higher temporal autocorrelation (0.354) than δ1⁸O (0.144) (Table 2), reflecting a strong biological memory. Inter-series correlations were also high for both proxies (r̄ = 0.322 for RWI; 0.490 for δ1⁸O), indicating a strong common signal among trees. Both chronologies were well replicated and exceeded the widely accepted thresholds of climate sensitivity, with EPS values > 0.88, SSS > 0.89, and high signal-to-noise ratios (7.70 for δ1⁸O, 8.60 for RWI). Both chronologies display clear inter-annual variability as well as multi-decadal fluctuations over the common period (1821–2020), reflecting coherent yet contrasting responses of growth and isotopic composition to long-term environmental and climatic variability in the region (r =  − 0.28, p < 0.001).Fig. 2The alternative text for this image may have been generated using AI.Full size imageTree-ring width index (RWI) and stable oxygen isotope (δ1⁸O) chronologies of J. polycarpos from the Hezar Masjed Mountains, NE Iran, from 1821 to 2020. (a) Standardized ring width index (RWI) with 10-year low-pass FFT filter (purple) and sample depth (number of increment cores, red). (b) δ1⁸O chronology with 10-year FFT filter and 95% confidence interval (green shading).Table 2 Statistical characteristics of the tree-ring width index (RWI) and stable oxygen isotope (δ1⁸O) chronologies of J. polycarpos.Full size tableWe assessed the correlations between δ1⁸O, RWI, and both VPD and SPEI indices across monthly and seasonal scales, including different SPEI accumulation periods (SPEI01–SPEI07), during the calibration period (1984–2020).The analyses revealed a clear difference in climate sensitivity between the two parameters. As shown in Fig. 3, δ1⁸O exhibited positive and significant correlations with monthly VPD from March through September, peaking in May (ρ = 0.575). Seasonal correlations further confirmed its sensitivity to atmospheric moisture demand, with the highest correlation for the March–September mean VPD (ρ = 0.72). In contrast, TRW showed weaker and generally negative correlations with VPD from April to June, with the strongest negative correlation in April–May (ρ = − 0.64). Based on these results and the seasonal dynamics of tree physiology in the study region, we selected VPD (March–September) to represent atmospheric moisture conditions during the full growing season for reconstructing atmospheric drought.Fig. 3The alternative text for this image may have been generated using AI.Full size imageBootstrapped Spearman rank correlations (ρ) between various monthly and seasonal vapor pressure deficit (VPD) values and the two tree-ring parameter series (RWI, δ1⁸O) during the calibration period (1984–2020).Results of the correlation analyses with SPEI are shown in Fig. 4. Correlation strengths varied between proxies, accumulation periods, and months. δ1⁸O showed moderate to strong negative correlations with SPEI, especially for accumulation periods of 5 to 7 months during late spring and summer (Fig. 4b). The strongest monthly relationship was observed with SPEI05 in September (ρ = − 0.653), whereas the whole growing season (March-September) target variable, SPEI07 in September, also showed a significant correlation with δ1⁸O(ρ = − 0.617).Fig. 4The alternative text for this image may have been generated using AI.Full size imageCorrelation heatmaps of monthly Standardized Precipitation Evapotranspiration Index (SPEI) with tree-ring proxies and combined models. (a) TRW-SPEI correlations, (b) δ1⁸O-SPEI correlations, and (c) combined model (TRW + δ1⁸O) correlations with SPEI. Each panel shows correlations for SPEI accumulation periods (SPEI01–SPEI07) across months.In contrast, RWI response to shorter accumulation windows earlier in the growing season, with the highest correlation for SPEI04 in May (ρ = 0.652; Fig. 4a), while the correlation with SPEI07 in September remained significant but lower (ρ = 0.565).Combining both proxies in a multiple linear regression model further improved the correlation with seasonal SPEI, confirming their complementary value. the strongest multiple correlation was found for SPEI05 in July (r = 0.73), while SPEI07 in September also showed a strong relationship (r = 0.715; Fig. 4c).Although some shorter accumulation periods and narrower monthly windows showed slightly stronger correlations with individual proxies (Table S1), SPEI07 in September was selected as the reconstruction target because it best represents the cumulative March–September moisture balance over the full growing season and therefore aligns most closely with the physiological period relevant to annual ring formation at the study site.Accordingly, SPEI07 September was chosen as the soil drought target for reconstruction using the combined TRW and δ1⁸O predictors, while growing-season VPD (March–September) was used to represent atmospheric drought, reconstructed using δ1⁸O alone.Tree-ring based drought reconstructionsVPD reconstructionTo reconstruct atmospheric drought, we developed a simple linear regression model using elevation-corrected δ1⁸O values as the predictor of growing-season VPD (March–September mean). The final model was defined as:$${text{VPD }} = , {-}{ 4}.{643 } + , 0.{218}cdotdelta^{18} {text{O}}$$
(3)
The model explained 53.4% of the variance in observed VPD (R2 = 0.534, Adj. R2 = 0.520, p < 0.001), with a residual standard error of 0.22 kPa (Fig. 5, right panel). The Durbin–Watson test indicated some degree of autocorrelation in residuals (DW = 1.38, p = 0.022), a typical feature in dendroclimatological time series70,71. Leave-One-Out Cross-Validation (LOOCV) confirmed the robustness of the model, with RMSE = 0.22, MAE = 0.17, and cross-validated R2 = 0.48. In addition, the Reduction of Error (RE) was calculated as 0.48, indicating that the model performs substantially better than the climatological mean and provides skillful year-to-year predictions across the calibration period (1984–2020). The final model was used to estimate VPD from 1821 to 2020, resulting in a high-resolution reconstruction of growing-season atmospheric moisture demand over two centuries (Fig. 5, left panel). The reconstruction reveals both short-term peaks and multi-decadal fluctuations in VPD, with notable dry periods occurring in the mid-1800s, the early 1900s, and the post-1980s period. A 10-year FFT low-pass filter highlights long-term atmospheric drying trends in the twentieth century.Fig. 5The alternative text for this image may have been generated using AI.Full size imageReconstruction of growing-season atmospheric drought (VPD March–September) based on δ1⁸O from J. polycarpos. Left: Reconstructed VPD (black) with 10-year low-pass FFT filter (purple) and calibration error as grey shading. Top right: Calibration scatter plot of observed vs predicted VPD with regression line, 95% confidence band, and prediction interval. Bottom right: Time series comparison of observed and predicted VPD during the calibration period (1984–2020).Before proceeding to the soil drought reconstruction, we evaluated the effect of the applied altitude correction on δ1⁸O. We compared model performance and correlations using the uncorrected and corrected δ1⁸O chronologies. The corrected δ1⁸O series showed stronger correlations with both VPD (r = 0.73 vs. 0.70) and SPEI (r = –0.63 vs. –0.60), and slightly improved regression model performance (Adj. R2 = 0.52 for VPD; 0.48 for SPEI) compared to the uncorrected series (Adj. R2 = 0.47 for both). These results confirm that elevation correction enhances the climatic signal preserved in the δ1⁸O chronology and improves the predictive skill of the drought reconstructions, while preserving the temporal variability and coherence among individual series (Figure S2), indicating that the correction does not distort the underlying climatic signal.SPEI reconstructionFurther, we developed a multiple linear regression model using elevation-corrected δ1⁸O values and tree-ring width index (RWI) as predictors of SPEI07 for September to reconstruct growing-season soil moisture drought.The final model was defined as:$${text{SPEI }} = { 14}.{371 }{-} , 0.{491}cdotdelta^{18} {text{O }} + { 2}.{537}cdot{text{RWI}}$$
(4)
Both predictors were statistically significant (p < 0.01), with δ1⁸O showing a negative relationship and TRW a positive one, consistent with their known physiological responses to water availability. The model explained 51.1% of the variance in observed SPEI values (R2 = 0.511, Adj. R2 = 0.483) and had a residual standard error of 0.78 (Fig. 7, right panel). Model assumptions were met, although the Durbin-Watson test indicated potential autocorrelation in residuals (DW = 1.37, p = 0.021), a common feature in climate time series. Model validation using Leave-One-Out Cross-Validation (LOOCV) demonstrated robust predictive skill, yielding an average RE (Reduction of Error) of 0.98, with an RMSE = 0.81 and MAE = 0.67. The LOOCV R2 was 0.43, indicating good model stability across the calibration period (1984–2020).The final model was applied to the full proxy record to produce an annually resolved reconstruction of SPEI07 for September spanning the period 1821–2020 (Fig. 6, left panel). The reconstruction captures high interannual and decadal variability in soil moisture availability, with extended droughts evident in the late nineteenth century and 20th-century multi-year dry phases. A 10-year low-pass FFT filter highlights longer-term hydroclimatic trends. The 10-year low-pass FFT–filtered reconstructions of VPD and SPEI are shown together in Supplementary Figure S3, highlighting their coherent decadal-scale variability over the past two centuries. During the calibration period (1984–2020), observed VPD and SPEI exhibited a significant negative correlation (r = − 0.58, p < 0.001), consistent with the expected inverse relationship between atmospheric demand and soil moisture availability. At decadal timescales, the smoothed reconstructed series also showed a significant negative correlation (r = − 0.69, p < 0.001), although some deviations between the series were evident.Fig. 6The alternative text for this image may have been generated using AI.Full size imageReconstruction of the growing-season SPEI based on δ1⁸O and TRW parameters from J. polycarpos. Left Panel: Reconstructed (black) and observed (pink dashed) SPEI07 September values with a 10-year low-pass FFT filter (blue). The shaded area shows ± 1 standard error of model residuals during calibration. Right: Calibration plot comparing observed versus estimated SPEI values for the period 1984–2020. The solid red line indicates the best-fit linear regression between observed and predicted SPEI values (R2 = 0.51, p < 0.001), while the dashed gray line represents the 1:1 line (y = x), indicating perfect agreement.Classification of drought eventsBoxplots of each drought classification based on growing-season conditions (Fig. 7) confirmed a consistent separation of the climatic conditions across the categories. VPD-D years were characterized by significantly higher vapor pressure deficit values (mean VPD = 3.29 kPa), while SPEI-D years showed strongly negative soil moisture anomalies (mean SPEI = − 1.55). The COMB clusters showed similar distinctions, with COMB-D years averaging -1.53 for SPEI and 3.28 kPa for VPD, accompanied by notably reduced tree growth (mean RWI = 0.77) compared to COMB-ND years (mean RWI = 1.12) (Table 3). Notably, across all three drought classification frameworks (SPEI, VPD, and combined), approximately two-thirds of the years in the 200 years were categorized as drought years (severe or moderate, based on growing-season conditions), indicating a high baseline frequency of water stress conditions in this semi-arid region.Fig. 7The alternative text for this image may have been generated using AI.Full size imageBoxplots displaying the distribution of reconstructed VPD and SPEI values across drought severity categories. Left panelt: VPD-based drought classification (VPD-D, VPD-M, VPD-ND). Center: SPEI-based drought classification (SPEI-D, SPEI-M, SPEI-ND). Right panel: Combined drought clusters (COMB-D, COMB-M, COMB-ND) showing both, VPD (right axis) and SPEI (left axis) distributions. Boxes depict the interquartile range (IQR), with black dots representing outliers and black squares indicating the mean. Horizontal lines show medians. A box with stripes marks VPD.Table 3 Summary statistics of drought clusters.Full size tableEvaluation of drought severity showed a clear increase in the proportion of severe drought years (D), particularly in the VPD classification across the last two centuries. Severe atmospheric droughts comprised 46.4% of drought years in the most recent period (1971–2020), compared to 31.4% in 1821–1870 and 25.7% in 1921–1970. Similar trends, though less pronounced, were also observed in SPEI and combined classifications, COMB-D (from 18.8 to 34.5%) (Table S2).The full chronology of individual drought years and their corresponding tree-ring width anomalies is provided in Supplementary Fig. S4. Despite the general agreement between individual and combined drought classifications, some discrepancies were observed. For instance, a number of years classified as VPD-D did not appear in the COMB-D cluster (Table 3 and Fig. S4), suggesting that atmospheric drought alone did not always result in combined drought conditions from a tree-growth perspective.To further explore this pattern, we analyzed the compositional makeup of the combined drought clusters (Fig. 8). The results showed years classified as SPEI-D + VPD-M were more frequently grouped into the COMB-D cluster, while the reverse combination (SPEI-M + VPD-D) was often classified as COMB-M category, suggesting a stronger influence of soil moisture deficit on combined drought classification (Fig. 9).Fig. 8The alternative text for this image may have been generated using AI.Full size imageComposition of combined drought categories (COMB-D, COMB-M, COMB-ND) based on the combinations of VPD and SPEI classifications. The large central pie chart displays the relative frequency of each combined drought class across the full 200-year period. Smaller surrounding pie charts illustrate the composition of each COMB cluster by its constituent VPD/SPEI combinations.Fig. 9The alternative text for this image may have been generated using AI.Full size imageTree growth responses to drought events classified by drought type. (a) Mean ring-width index (RWI) during a five-year superposed epoch window (lag –2 to + 2) for severe drought years identified as SPEI-dominated (SPEI-D), VPD-dominated (VPD-D), and combined droughts (COMB-D). Stripes indicate statistically significant anomalies at lag 0 (growing-season based drought year). (b) Standardized growth anomalies (SEA) showing growth departures relative to the 95% confidence interval (shaded area) derived from 1000 Monte Carlo simulations.Tree growth responses to drought eventsAccording to the Superposed Epoch Analysis (SEA), tree growth exhibited distinct responses to different types of droughts. The most substantial and persistent growth reductions occurred during SPEI-dominated drought years (SPEI-D), with a significant decline in the drought year (lag 0) of − 1.46 standardized units (p < 0.001), and a mean ring-width index (RWI) of 0.76. Compound drought years (COMB-D) showed similarly strong but slightly less severe reductions (− 1.41, p < 0.001, RWI = 0.77), while VPD-only drought years resulted in a more moderate anomaly of –0.50 (p < 0.001, RWI = 0.92).Post-drought recovery was observed in all drought categories by lag + 1 and + 2, although growth remained below average (RWI < 1) for both SPEI-D and COMB-D years. In contrast, VPD-D years exhibited the mildest and shortest-lived growth reduction, with RWI values returning to above-average levels (RWI > 1) more quickly.DiscussionIntegrating multiple tree-ring parameters for an improved climate interpretationBoth proxies recorded signals of atmospheric and soil moisture deficits, but with distinct seasonal timing and response strength. δ1⁸O showed strong correlations with VPD and SPEI, revealing a peak correlation with VPD during the March–September season (r = 0.723), consistent with previous findings that oxygen isotopes in tree rings are strongly controlled by evaporative demand and atmospheric drought conditions during the growing season35,36,72, particularly in arid conditions34,42The positive coefficient reflects the mechanistic relationship between higher δ1⁸O values in tree rings and increased evaporative demand on leaf water enrichment, linking δ1⁸O closely to atmospheric drought conditions34,73. This enhanced atmospheric signal may be further explained by two key mechanisms: the increasing vapor pressure deficit during the growing season, and the relatively low rate of oxygen exchange between xylem water (source water) and phloem sugars during cellulose synthesis. Together, these processes reduce the influence of source water and instead amplify the isotopic signature of leaf water, which is directly shaped by evaporative demand and atmospheric aridity38,40Conversely, the observed negative correlation with SPEI, consistent with other studies74, suggests that during years with low soil moisture availability (i.e., years with negative SPEI values), evaporative demand was higher, resulting in stronger δ1⁸O enrichment. In addition to leaf-level evaporative enrichment, drought conditions may also lead to enriched δ1⁸O in soil water before uptake, particularly in shallow soils subject to intense evaporation. This secondary enrichment can further elevate δ1⁸O values in tree rings, especially in semi-arid environments75.In contrast, TRW showed the highest sensitivity to soil moisture conditions during the early growing season, with a peak correlation with SPEI04-May (January–May; r = 0.65). This underscores the importance of cumulative soil water availability in the months leading up to and during the onset of radial growth, a pattern also reported in other tree-ring studies from semi-arid regions76,77,78. Juniper trees in the western Tien Shan exhibited the strongest growth sensitivity to SPEI79. The importance of soil moisture as a limiting factor for tree growth has been emphasized across a wide range of species, especially under current trends of increasing aridity and drying conditions80,81.Overall, our findings suggest that δ1⁸O responds more strongly to mid- to late-season atmospheric drought, whereas antecedent soil moisture conditions exert a greater influence on TRW during the early growing season. These complementary seasonal sensitivities highlight the benefit of using both proxies to capture the full range of climate–growth interactions. Li et al.82 found that TRW and δ1⁸O in two conifer species with different rooting depths at the southern edge of the Tengger Desert were predominantly influenced by growing-season SPEI and summer relative humidity, respectively. It should be noted that longer accumulation periods may better reflect long-term hydrological drought persistence, but they showed weaker relationships with the proxy data in our study.By applying a multiple regression framework combining δ1⁸O and TRW, we captured complementary aspects of drought variability and improved the representation of seasonal SPEI relative to the individual predictors. The improved model fit and validation statistics underscore the utility of dual-proxy approaches for enhancing climate signal detection, particularly in environments where univariate TRW–climate relationships are weak or non-stationary. Our findings support previous studies that have demonstrated the value of multi-parameter tree-ring approaches83,84,85,86.A general limitation of proxy-based climate reconstructions is the assumption that the proxy–climate relationship remains sufficiently stable through time. In our case, the relatively short instrumental period does not allow moving-window analyses or split-period calibration/verification with longer subperiods. However, the identified relationships are consistent with known tree physiological mechanisms and were supported by LOOCV statistics, which increases confidence in the reconstruction. In addition, the use of coarse-resolution gridded climate data (0.5° CRU data) may introduce spatial mismatches in mountainous terrain; therefore, our reconstructions should be interpreted as reflecting regional hydroclimatic variability rather than exact site-specific conditions.Distinct influence of atmospheric and soil drought on tree growthThe diverging correlations between tree-ring parameters and growing-season drought indicators highlight distinct physiological responses of trees to soil versus atmospheric drought. The consistent negative correlation between δ1⁸O and VPD reflects increased evaporative enrichment of leaf water under high atmospheric demand, confirming that δ1⁸O is a reliable indicator of short-term vapor pressure deficit dynamics/ variations and reflects evaporative conditions in the study area. In contrast, TRW showed strong positive correlations with SPEI, indicating that tree growth is more closely tied to the temporal accumulation of soil moisture availability. Drier conditions reduce water availability for uptake and turgor maintenance, thereby limiting cambial activity and triggering growth decline.Our drought classification and growth response analyses further support this differentiation. Years classified as severe drought conditions based on SPEI (SPEI-D), reflecting reduced growing-season moisture availability, produced the strongest and most prolonged growth reductions, nearly matching the declines observed in compound drought years (COMB-D). By contrast, years dominated by atmospheric drought alone (VPD-D) had milder and more transient effects, with faster recovery of growth. Additionally, the cluster composition analysis revealed that most COMB-D years were composed of SPEI-D + VPD-M combinations rather than VPD-D years. This asymmetry underscores that soil moisture deficits are the primary driver of compound drought years and of overall growth suppression in this semi-arid mountain ecosystem.Stomatal closure is a key plant strategy to maintain hydraulic safety during drought87, but the degree and timing of closure are determined by the complex interplay between atmospheric demand and soil water availability. As VPD rises, stomata begin to close to limit water loss. However, under sustained high VPD and concurrent soil drying, stomatal closure becomes more complete, limiting gas exchange and further reducing cambial activity. Thus, although the initial response to atmospheric dryness may be modest, prolonged or combined stress from soil moisture deficits leads to sharper physiological constraints88,89,90.Soil water availability and VPD are often tightly coupled, especially in semi-arid regions, where evapotranspiration is highly constrained by soil moisture91,92. In ecosystems with shallow soils, sparse vegetation, or high radiation loads—such as our open juniper forests in NE Iran—this coupling becomes even more pronounced. Root access to deeper soil layers, groundwater, or rock water can decouple this relationship and buffer drought impacts93, but J. polycarpos has a relatively shallow root system and relies primarily on superficial or stored water reserves94. Therefore, reductions in soil moisture can have a more immediate and pronounced impact on growth than rising VPD alone. In our study site, which receives low annual precipitation and features exposed soils with high evaporative losses, atmospheric dryness acts primarily to exacerbate soil moisture depletion95. This suggests that VPD acts as a more complementary or secondary role in growth limitation, consistent with recent findings showing strong soil moisture–VPD coupling under semi-arid conditions90,96.SPEI integrates multi-month water deficits, capturing the cumulative nature of drought and the water budget required to sustain growth, whereas VPD reflects short-term atmospheric dryness and can vary substantially within a season19,97. While SPEI is widely used to represent hydroclimatic drought conditions related to soil moisture availability, it does not directly measure soil water content and should therefore be interpreted as an integrated climatic proxy rather than a direct soil moisture variable. Our findings are consistent with98, who also found that tree-ring δ13C in J. polycarpos was influenced by soil moisture availability, highlighting the species’ sensitivity to soil drought.Altogether, these results suggest that soil drought is the dominant control on growth in J. polycarpos at our site. While VPD can amplify drought stress, especially in combination with low soil moisture, its standalone effect on growth is comparatively weaker. This underscores the importance of recognizing and managing compound drought risks, particularly in arid mountain ecosystems where growth sensitivity to soil moisture dominates99,100. Atmospheric drought plays a critical but reinforcing role and should not be viewed in isolation89,101,102.Implications for long-term forest resilience and climate changeUnderstanding how forest ecosystems respond to drought is essential for anticipating climate impacts and guiding forest management. In the arid and semi-arid mountain regions of West Asia, our findings confirm that long-term deficits in soil moisture, as captured by SPEI, leave more persistent and severe imprints on tree growth than short-term fluctuations in atmospheric demand (VPD). While atmospheric drought can intensify stress when combined with depleted soil water, it is rarely the sole driver of major growth suppression.In all three classification schemes (SPEI, VPD, and combined), approximately two-thirds (64–66%) of years were categorized as drought years (years with a dry growing season). This high prevalence of drought years underscores the persistent pressure of both soil and atmospheric moisture deficits on forest productivity and resilience. These results align with emerging evidence from other drought-prone ecosystems103,104, where increasing drought frequency and severity threaten long-term forest productivity and recovery99,103,104,105,106. Although the total number of drought years has remained relatively stable over time, we observed a clear increase in the proportion of severe drought years in the most recent period (1971–2020). This was particularly evident for VPD, where severe events rose from 31.4% (1821–1870) to 46.4% (1971–2020), and for combined droughts (COMB-D), which increased from 18.8 to 34.5% in the same timeframe. Similar increases are also seen in SPEI-D proportions. These results align with broader regional and global trends showing increased drought intensity, even in the absence of a rise in drought frequency21,43,107. Based on tree-ring-based reconstructions of June–August VPD, 43) also found an increasing trend in summer VPD during recent centuries in Central Europe and the Mediterranean region over the past four centuries, supporting the signal of intensifying atmospheric drought across the World. This emerging drought intensification trend, driven by both soil and atmospheric aridity, poses particular risks to the resilience of semi-arid mountain forests such as those in northeastern Iran2,3. Species growing in these regions may face growing limitations on (biomass) productivity, regeneration, and long-term persistence.To sustain ecosystem functioning under future drought scenarios, adaptive management strategies must prioritize soil moisture retention through measures that enhance infiltration, reduce surface runoff, and minimize evaporative losses. In parallel, identifying or selecting drought-resilient species capable of tolerating both edaphic and atmospheric stress will be key to climate-smart reforestation efforts.ConclusionsOur study highlights the value of integrating multiple tree-ring parameters —tree-ring width (TRW) and stable oxygen isotopes (δ1⁸O) —to disentangle the impacts of soil and atmospheric drought on tree growth in a semi-arid mountain ecosystem. While TRW primarily captured the influence of cumulative soil moisture during the early growing season, δ1⁸O proved highly sensitive to mid-to-late season atmospheric dryness, particularly vapor pressure deficit (VPD). Together, these proxies offered a seasonally complementary view of tree-climate interactions.Our drought classification framework highlights the differentiated roles of soil and atmospheric drought in shaping tree growth trajectories. While soil moisture availability emerged as the primary constraint on growth in this semi-arid mountain ecosystem, atmospheric dryness—particularly when co-occurring with soil drought—acted as a compounding stressor. These insights demonstrate the importance of disentangling distinct drought mechanisms to understand tree vulnerability and resilience under changing climatic conditions.Crucially, our analysis of long-term drought classifications across four 50-year periods (1821–2020) revealed a notable shift toward more intense droughts in recent decades. This emerging trend of drought intensification, even in the absence of more frequent drought events, underscores the growing stress on mountain forests under a warming climate.These findings underscore the vulnerability of semi-arid mountain ecosystems in West Asia to intensifying climate stress, particularly from compound drought events. Effective management strategies must therefore prioritize soil moisture retention and the selection of drought-resilient species that can tolerate both atmospheric and edaphic moisture limitations. Long-term, multi-proxy reconstructions such as those presented here provide critical insights into the historical dynamics of drought impacts and offer valuable guidance for anticipating and mitigating future risks under climate change.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Download referencesAcknowledgementsThe authors are grateful to the laboratory staff of the Institute of Geography at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) for their valuable assistance during the tree-ring width measurements and stable isotope analyses.FundingOpen Access funding enabled and organized by Projekt DEAL. This study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project number 517781499 (BR 1895/32-1 and FO 1500/2-1).Author informationAuthors and AffiliationsInstitute of Geography, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 91058, Erlangen, GermanyZeynab Parisa Foroozan, Mohammad Hossein Mazaherifar, Sugam Aryal & Achim BräuningDepartment of Environment and Biodiversity, Paris-Lodron-Universität Salzburg, 5020, Salzburg, AustriaJussi GrießingerDepartment of Wood and Paper Science & Technology, Faculty of Natural Resources, University of Tehran, Karaj, 31587-77871, IranKambiz PourtahmasiAuthorsZeynab Parisa ForoozanView author publicationsSearch author on:PubMed Google ScholarMohammad Hossein MazaherifarView author publicationsSearch author on:PubMed Google ScholarSugam AryalView author publicationsSearch author on:PubMed Google ScholarJussi GrießingerView author publicationsSearch author on:PubMed Google ScholarKambiz PourtahmasiView author publicationsSearch author on:PubMed Google ScholarAchim BräuningView author publicationsSearch author on:PubMed Google ScholarContributionsZ. P. F. Writing—original draft, Visualization, Validation, Writing—review & editing, Supervision, Methodology, Investigation, Formal analysis, Data acquisition, Conceptualization, Project administration, Funding acquisition; M.H.M. Data acquisition, Writing—review & editing; S.A. Methodology, Validation, Writing—review & editing; J.G. Validation, Writing—review & editing; K.P. Investigation, Validation, Writing—review & editing; A. B. Conceptualization, Methodology, Supervision, Writing—review & editing, Project administration, Funding acquisition.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleForoozan, Z.P., Mazaherifar, M.H., Aryal, S. et al. Tree-ring width and δ18O-derived hydroclimatic reconstructions allow a distinction between soil and atmospheric drought in the Mountain Forests of Northeastern Iran.
Sci Rep 16, 15601 (2026). https://doi.org/10.1038/s41598-026-52364-3Download citationReceived: 16 January 2026Accepted: 05 May 2026Published: 19 May 2026Version of record: 19 May 2026DOI: https://doi.org/10.1038/s41598-026-52364-3Share 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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KeywordsMulti-proxy reconstructionDrought reconstructionAltitude correctionSemi-arid mountain forestsForest drought resilience More